Global Burden of Periodontitis and Causal Links with Sleep Disorders: A Mendelian Randomization and Multi-Omics Analysis with Focus on Lactylation-Mediated Mechanisms

Global Burden of Periodontitis and Causal Links with Sleep Disorders: A Mendelian Randomization and Multi-Omics Analysis with Focus on Lactylation-Mediated Mechanisms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Global Burden of Periodontitis and Causal Links with Sleep Disorders: A Mendelian Randomization and Multi-Omics Analysis with Focus on Lactylation-Mediated Mechanisms Shucan Zheng, Haibin Shao, Weilu Wang, Xiaoying Chen, Minghui Zhu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7547880/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Periodontitis and insomnia represent two prevalent chronic conditions with substantial global health impact. Emerging evidence suggests potential bidirectional relationships between these conditions, yet causal mechanisms remain poorly understood. Methods We conducted a comprehensive three-pronged analysis using Global Burden of Disease (GBD) 2021 data to quantify periodontitis burden across 204 countries and territories from 1990–2021. Two-sample Mendelian randomization (MR) analyses were performed to investigate causal relationships between sleep-related phenotypes and periodontitis risk using large-scale GWAS summary statistics. Multi-omics bioinformatics approaches integrated periodontitis transcriptomic data with insomnia-associated and lactylation-related gene sets to elucidate shared molecular mechanisms. Results Global analysis revealed a 44% increase in absolute periodontitis burden (12.8 to 18.4 million DALYs) despite modest improvements in age-standardized rates (-0.34% annually). Pronounced regional disparities persisted, with Sub-Saharan Africa and South Asia bearing disproportionate burden. MR analysis identified insomnia as a primary causal risk factor for periodontitis (OR = 1.245, 95% CI: 1.016–1.526, P = 0.034 in multivariable analysis), with effect sizes comparable to established risk factors. Educational attainment demonstrated robust protective effects (15.7% risk reduction per additional year). Transcriptomic analysis identified 25 cross-talk genes predominantly enriched in inflammatory pathways, including IL1B, CXCL8, and AGE-RAGE signaling. Novel lactylation-mediated epigenetic regulation was revealed through correlations between transcriptional regulators XBP1/MEF2C and lactylation enzymes HDAC1/SIRT1. Five core biomarkers showed excellent diagnostic performance (AUC > 0.7), with CD93 demonstrating superior discriminatory capacity (AUC = 0.879). Conclusions This study establishes sleep disorders as major modifiable risk factors for periodontitis, with robust causal evidence and molecular validation. The identification of lactylation-mediated epigenetic mechanisms provides novel therapeutic targets. Integration of sleep assessment into periodontal care represents a paradigm shift toward precision oral health interventions addressing upstream determinants of disease. Periodontitis Sleep disorders Insomnia Global burden of disease Mendelian randomization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Periodontitis and insomnia are two prevalent chronic conditions that not only threaten individual health but also impose a substantial burden on global public health systems. The Global Burden of Disease Study (GBD) 2010 reported that severe periodontitis affects approximately 11.2% of the global population, making it the sixth most common disease worldwide. This condition leads to an estimated global productivity loss of $ 54 billion annually [ 1 , 2 ]. In response, the 75th World Health Assembly, in 2022, adopted the Global Strategy for Oral Health, aiming to end the global oral health crisis by 2030 [ 3 ].Insomnia, another widespread health issue, affects approximately one-third of the adult population over their lifetime, with higher prevalence among women, the elderly, and socio-economically disadvantaged groups [ 4 – 6 ]. Adequate sleep and sleep quality are essential for overall health, as inadequate sleep and sleep disorders have been linked to numerous adverse health outcomes [ 7 ]. Increasing evidence suggests a significant overlap between periodontitis and insomnia. Both conditions are strongly associated with chronic low-grade inflammation, immunometabolic disorders, and share multiple risk factors and health consequences [ 6 , 8 ]. Previous studies have shown that sleep disorders lead to elevated levels of vascular endothelial markers (e.g., E-selectin and s-ICAM-1), increased production of pro-inflammatory cytokines (e.g., IL-1, IL-6, and TNF-α), and heightened expression of inflammation-related genes [ 9 , 10 ]. These changes significantly overlap with the inflammatory profile observed in patients with periodontitis [ 11 – 14 ].A novel epigenetic mechanism—lactylation—is receiving growing attention in the context of various inflammatory diseases. Lactylation involves the covalent attachment of lactic acid-derived acyl groups to lysine residues and was first identified as a regulator of inflammation-related gene expression in macrophages [ 15 ]. Rather than being a mere metabolic byproduct, lactate may act as a signaling molecule that links cellular metabolism with gene regulation. Specifically, lactylation influences the transcriptional activity of inflammatory genes, modulates immune cell polarization, and affects cell fate decisions by altering chromatin structure and transcriptional regulation [ 16 , 17 ].Recent research has confirmed significant lactic acid accumulation in the periodontal tissues of mice with periodontitis. This accumulation appears to contribute to disease progression by regulating the release of inflammatory cytokines—such as IL-1β, IL-6, and TNF-α—and by modulating macrophage polarization [ 18 ]. Recent studies have indicated that reduced sleep duration and insomnia are significantly associated with an increased risk of periodontitis. The underlying mechanisms are thought to involve immune dysfunction, disruption of inflammatory homeostasis, and changes in the oral microbiome [ 6 , 8 , 19 , 20 ]. Supporting this, an experimental animal study found that sleep-deprived rats exhibited more severe gingivitis and accelerated alveolar bone loss compared to control rats [ 21 ]. However, two recent Mendelian randomization analyses did not establish a definitive causal relationship between sleep disorders and periodontitis [ 22 , 23 ].Given the limited evidence and lack of clarity regarding the causal links between these conditions, the role of lactate modification-related genes in the co-morbidity of periodontitis and insomnia remains poorly understood.The potential cross-regulatory networks involving lactylation in the context of both periodontitis and sleep disorders remain largely unexplored. To address this knowledge gap, our study undertakes a comprehensive evaluation of the global burden of periodontitis using data from the Global Burden of Disease (GBD) 2021 project. Building on this foundation, we employ Mendelian randomization to explore the genetic relationship between periodontitis and sleep disturbances. Furthermore, we integrate periodontitis-related data with gene sets linked to lactate modification in insomnia through advanced bioinformatics strategies. By doing so, we aim to uncover common lactylation-associated pathways and pinpoint critical regulatory genes that may influence the coexistence of these conditions. Ultimately, this research aims to contribute fresh theoretical perspectives and methodological tools for clarifying the role of epigenetic regulation in the shared pathology of periodontitis and insomnia. Methods Data Sources and Study Design This study undertook a multi-layered analytical approach to evaluate the worldwide impact of periodontitis, probe its genetic links with sleep-related disorders, and examine shared molecular mechanisms, with particular emphasis on lactylation-associated genes. To achieve this, we applied a three-step strategy: first, analysis of population-level disease burden using the Global Burden of Disease (GBD) dataset; second, Mendelian randomization to infer causal relationships; and third, integrative multi-omics bioinformatics to investigate underlying regulatory processes. Global Burden of Periodontitis Data Analysis Periodontitis data were sourced from the Global Burden of Disease (GBD) 2021 study, which provides standardized estimates for 371 diseases and injuries across 204 countries and territories during the period 1990–2021. These estimates integrate diverse epidemiological sources and apply harmonized diagnostic definitions through the DisMod-MR Bayesian meta-regression framework (GBD Collaborators, 2022). Key epidemiological metrics included incidence, prevalence, mortality, years of life lost (YLL), years lived with disability (YLD), and disability-adjusted life years (DALYs). To facilitate comparability, results were age-standardized using the GBD World Standard Population and were further disaggregated by age, sex, geographic region, and Socio-demographic Index (SDI) quintile. The SDI is a composite indicator that incorporates national levels of education, income, and fertility. We calculated age-standardized rates (ASR) and estimated annual percentage changes (EAPC) to quantify temporal trends over three decades[ 24 , 26 ]. Health inequalities were assessed via the slope index of inequality (SII) and concentration index (CI), both standard measures for global disease disparity[ 27 ]. Population growth, aging, and epidemiological transition influences were disentangled using decomposition analysis, and temporal disease trends were projected using AutoRegressive Integrated Moving Average (ARIMA) models[ 28 ]. All GBD data are aggregated and publicly available and thus exempt from additional ethics board review. Mendelian Randomization Analysis To elucidate potential causal relationships between periodontitis and sleep-related phenotypes, we performed two-sample MR analyses using publicly available summary statistics from large-scale genome-wide association studies (GWAS)[ 29 , 30 ]. The GWAS sample sizes for the analyzed traits were as follows: moderate-to-vigorous physical activity (n = 377,234), vigorous physical activity (n = 261,055), years of schooling (n = 766,345), alcohol consumption (n = 112,117), body mass index (n = 681,275), smoking initiation (n = 607,291), nap during day (n = 462,400), sleeplessness/insomnia (n = 462,341), alcohol intake frequency (n = 462,346), coffee intake (n = 428,860), tea intake (n = 447,485), birth weight (n = 261,932), snoring (n = 430,438), and overall health rating (n = 460,844). Exposure (periodontitis) and outcome (insomnia, sleep duration, or chronotype) datasets were harmonized on the European-ancestry subset to minimize confounding due to population stratification. Only autosomal biallelic SNPs with a genome-wide significant association with the exposure phenotype (P < 5×10⁻⁶) were selected as instrumental variables (IVs), with additional clumping based on linkage disequilibrium (LD, r² 10 to prevent weak instrument bias[ 31 ]. MR analyses were primarily conducted via Inverse Variance Weighting (IVW), with supplementary evaluation using MR-Egger regression, weighted median, and weighted mode methods to account for potential violations of the MR assumptions (such as horizontal pleiotropy). Robustness and validity were checked with Cochran’s Q for heterogeneity, MR-Egger intercept for directional pleiotropy, MR-PRESSO for outlier detection, and leave-one-out sensitivity analysis[ 32 , 33 ]. All summary statistics were harmonized for effect allele alignment, with ambiguous or palindromic SNPs handled using minor allele frequency data. Where multiple correlated outcomes were examined, statistical significance was Bonferroni-corrected; otherwise, a two-sided P < 0.05 was applied. Data Collection and Gene Set Construction Transcriptome datasets for periodontitis were retrieved from the Gene Expression Omnibus (GEO). All available datasets with clear diagnostic definitions and high-quality profiles of diseased and healthy gingival/periodontal tissues were included. Genes associated with insomnia were extracted from GeneCards using “insomnia” as the search term, and lactylation-related genes were curated from protein modification databases (e.g., PhosphoSitePlus, UniProt), literature, and hallmark pathway lists (including key enzymes such as EP300, HDACs, SIRT family)[ 34 ]. Data Processing and Differential Gene Expression Transcriptome data were processed (background correction, normalization, batch effect removal using ComBat in R ‘sva’ package), and principal component analysis (PCA) was performed to confirm correction. Differentially expressed genes (DEGs) were detected using limma (microarray) or DESeq2 (RNA-seq), filtered by |log₂ fold change| ≥ 1 and Benjamini–Hochberg-adjusted P < 0.05[ 35 ]. “Cross-talk” genes were defined as DEGs overlapping between periodontitis datasets and insomnia/lactylation signature gene sets. Functional and Pathway Enrichment Analysis Enrichment analyses for Gene Ontology (GO) biological process (BP), cellular component (CC), molecular function (MF), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed using clusterProfiler in R, with P.adjust < 0.05 and q-value < 0.05 considered significant[ 36 ]. Network and Lactylation Correlation Analysis Protein-protein interaction (PPI) networks were constructed using the STRING database and visualized in Cytoscape; higher-order modules were detected using MCODE. Correlations between cross-talk and lactylation-related genes were evaluated using Pearson correlation analysis across normalized data; |r| >0.5 and P < 0.05 defined significant pairs. Enriched and correlated genes were further used for biological pathway analysis. Diagnostic and Predictive Evaluation Receiver operating characteristic (ROC) curve analysis was performed (using the pROC package) to evaluate the discriminatory value of candidate genes, with an area under the curve (AUC) > 0.7 indicating practical relevance. Statistical Analysis All analyses were conducted in R (v4.3.3) with best-practice packages for transcriptomics (limma, DESeq2, sva, clusterProfiler), GWAS/MR (TwoSampleMR, MRPRESSO), and visualization (ggplot2, Cytoscape, pROC). Missing data were handled with multiple imputation as appropriate. The study adhered to the STROBE and GRAMMS guidelines for transparent reporting of epidemiological and genetic studies. Ethical Considerations This study relied exclusively on publicly available, de-identified data, and was thus exempt from institutional review board oversight. Results 1.Global and Regional Burden of Periodontitis The comprehensive analysis of Global Burden of Disease (GBD) 2021 data revealed substantial global disparities in periodontitis burden, with marked heterogeneity in disease distribution across geographical regions and sociodemographic strata [1,2]. Globally, periodontitis demonstrated a complex temporal evolution, with age-standardized DALY rates showing a modest declining trend from 186.42 per 100,000 (95% UI: 142.67-241.38) in 1990 to 167.89 per 100,000 (95% UI: 128.45-218.73) in 2021, representing an estimated annual percentage change of -0.34% (95% CI: -0.42 to -0.26). However, this apparent improvement in age-standardized burden was substantially offset by dramatic increases in absolute disease burden attributed to population growth and global demographic aging, with total worldwide DALYs rising from approximately 12.8 million in 1990 to 18.4 million in 2021—representing a 44% increase in absolute periodontitis burden (Figure 1,TableS1-4) . Table.1 The incidence cases and age-standardized incidence rate of periodontitis in 1990 and 2021, along with their temporal trend. Rate per 100 000(95%UI) 2021 1990-2021 1990 Incidence cases The age-standardized Incidence rate Incidence cases The age-standardized Incidence rate EAPC Global 50823934 (39615908-59174250) 1069.38 (852.99-1239.59) 89613534 (79069091-101005642) 1069.44 (942.71-1204.58) 0.32 (0.23-0.41) SDI region High-middle SDI 10535594 (8268653-12506988) 985.52 (771.35-1170.18) 16372283 (14237282-18707352) 970.97 (832.75-1128.41) 0.12 (-0.02-0.26) High SDI 9407307 (7522992-11034311) 932.72 (744.76-1092.57) 13683899 (11977317-15679772) 927.32 (793.31-1084.81) -0.1 (-0.24-0.04) Low-middle SDI 10856413 (8316650-12736447) 1193.01 (946.99-1367.83) 21624748 (18713530-24468525) 1174.58 (1030.71-1314.31) -0.08 (-0.1--0.07) Low SDI 4736383 (3610010-5560975) 1285.03 (1013.31-1477.37) 8817758 (7466036-10258848) 1032.5 (884.68-1174.38) -0.95 (-1.05--0.85) Middle SDI 15240268 (11775330-17960032) 1064.46 (857.67-1231.66) 29049440 (25504028-32385126) 1064.11 (933.75-1186.6) 0.12 (0.04-0.21) GBD region Advanced Health System 14037486 (11202848-16524364) 939.61 (744.99-1107.46) 18898772 (16372340-21860234) 931.81 (783.84-1099.97) 0.17 (-0.13-0.47) Africa 5763278 (4441146-6735149) 1229.53 (976.99-1402.34) 10203076 (8493678-12150804) 957.92 (820.04-1112.98) -1.05 (-1.44--0.66) African Region 4889583 (3772621-5725790) 1287.63 (1025.43-1465.11) 8201816 (6831133-9738692) 937.16 (801.05-1092.19) -1.36 (-1.76--0.95) America 7205937 (5721719-8408010) 1081.48 (871.62-1251.26) 12726090 (11257499-14297491) 1088.31 (955.4-1230.52) 0.3 (0.18-0.43) Andean Latin America 335714 (280692-389115) 1149.24 (1000-1286.59) 763116 (634616-918340) 1143.29 (960.68-1349.21) 0.54 (0.34-0.75) Asia 28983533 (22415835-34199911) 1066.45 (848.57-1241.68) 56031592 (49289650-62566637) 1105.46 (970.39-1236.42) 0.65 (0.53-0.77) Australasia 191773 (143849-237127) 856.66 (639.99-1051) 359593 (295930-435523) 931.83 (744.96-1143.69) 1.07 (0.78-1.36) Basic Health System 19942630 (15517849-23654772) 1024.04 (814.71-1196.9) 37065198 (32385757-41754482) 1010.34 (881.14-1140.54) 0.7 (0.53-0.86) Caribbean 396095 (310705-467206) 1239.02 (996.6-1424.35) 581042 (483068-678815) 1138.31 (943.48-1335.36) 0.05 (-0.03-0.13) Central Africa 664165 (507699-781876) 1350.42 (1068.87-1554.44) 1185866 (972756-1438302) 970.84 (814.3-1158.56) -1.54 (-2--1.08) Central Asia 540130 (414989-648594) 981.56 (762.83-1167.76) 915222 (715484-1129682) 951.75 (760.91-1159.99) 0.35 (0.15-0.56) Central Europe 1288006 (1014058-1525609) 915.1 (711.94-1086.89) 1519605 (1288462-1787330) 962.94 (786.58-1160.5) 0.55 (0.23-0.87) Central Latin America 1561298 (1204972-1840805) 1215.36 (984.68-1385.95) 3180018 (2737643-3633620) 1196.97 (1033.36-1365.93) 0.48 (0.3-0.66) Central Sub-Saharan Africa 525993 (393297-634980) 1318.33 (1037.15-1526.28) 785630 (571748-1042683) 845.51 (644.79-1080.53) -2.17 (-2.72--1.63) Commonwealth High Income 1050874 (815386-1275525) 814.03 (629.68-984.82) 1598045 (1354377-1891387) 832.64 (694.35-1004.22) 0.5 (0.19-0.8) Commonwealth Low Income 2030959 (1510320-2454921) 1321.82 (1035.49-1528.4) 3829127 (3119347-4555413) 1146.3 (960.85-1335.69) -0.42 (-0.76--0.08) Commonwealth Middle Income 12179540 (9369811-14307537) 1267.31 (1011.3-1456.62) 24213220 (21091566-27041241) 1185.97 (1049.84-1317.47) -0.02 (-0.2-0.15) East Asia 10910643 (8525980-13025176) 991.88 (785.23-1170.57) 18834272 (16163330-21356912) 960.22 (823.18-1094.59) 0.94 (0.65-1.23) East Asia & Pacific - WB 16126126 (12498140-19343189) 965.32 (756.78-1144.76) 28099867 (24345108-31886769) 936.71 (809.08-1066.59) 0.7 (0.49-0.92) Eastern Africa 1555621 (1166196-1869756) 1268.15 (994.49-1460.3) 2919002 (2429092-3419452) 1016.74 (865.53-1160.62) -0.87 (-1.35--0.38) Eastern Europe 2799238 (2267561-3255493) 1083.31 (869.62-1264.9) 2841483 (2397274-3332064) 1031.36 (849.56-1229.99) 0.08 (-0.22-0.39) Europe & Central Asia - WB 9123635 (7390963-10653332) 957.11 (766.02-1121.03) 11184933 (9498366-13074713) 943.19 (776.54-1126.72) 0.19 (-0.09-0.46) European Region 9186327 (7438486-10733187) 956.57 (765.18-1120.89) 11301451 (9591526-13213812) 941.94 (775.39-1125.59) 0.18 (-0.09-0.45) High-income Asia Pacific 1637363 (1234715-1989052) 809.97 (613.64-983.61) 2419327 (2006157-2883589) 839.84 (665.34-1023.86) 0.45 (-0.01-0.91) High-income North America 3120188 (2527787-3664930) 979.86 (785.52-1147.54) 4451645 (3883258-5053106) 922.28 (792.49-1058.1) -0.21 (-0.58-0.15) Latin America & Caribbean - WB 4136606 (3212629-4850284) 1145.49 (928.04-1312.73) 8329113 (7331217-9437335) 1165.91 (1026.1-1322.81) 0.56 (0.42-0.7) Limited Health System 15510580 (11844837-18252198) 1251.3 (996.44-1438.29) 31329635 (27027812-35288448) 1168.34 (1029.95-1305.73) -0.04 (-0.27-0.18) Middle East & North Africa - WB 1797642 (1380236-2139855) 1003.65 (808.15-1174.13) 4955942 (4068453-5924849) 1053.28 (889.64-1238.73) 0.76 (0.48-1.05) Minimal Health System 1285269 (980632-1513204) 1308.12 (1026.1-1505.09) 2254523 (1845389-2740352) 960.01 (803.93-1141.3) -1.43 (-1.91--0.95) North Africa and Middle East 2214458 (1705166-2669482) 931.78 (735.48-1109.36) 6402774 (5334264-7593001) 1056.6 (902.98-1238.97) 0.91 (0.62-1.2) North America 3120514 (2528131-3665235) 979.94 (785.6-1147.62) 4451983 (3883497-5053387) 922.33 (792.54-1058.12) -0.21 (-0.58-0.15) Northern Africa 904688 (704358-1074241) 1023.61 (829.09-1193.66) 2165301 (1735105-2636134) 1092.74 (903.44-1300.28) 0.62 (0.37-0.87) Oceania 41008 (30696-50831) 926.21 (705.96-1116.84) 25514 (18485-35150) 261.57 (196.46-359.87) -5.36 (-6.13--4.59) Region of the Americas 7205937 (5721719-8408010) 1081.48 (871.62-1251.26) 12726090 (11257499-14297491) 1088.31 (955.4-1230.52) 0.3 (0.18-0.43) South-East Asia Region 12780106 (9779679-15091891) 1196.64 (945.65-1376.88) 26224993 (22904827-29298810) 1223.37 (1077.67-1354.79) 0.42 (0.27-0.57) South Asia 11302568 (8663618-13369169) 1271.29 (1011.83-1467.01) 24065975 (20949359-26844234) 1288.55 (1138.93-1428.39) 0.37 (0.21-0.54) South Asia - WB 11575151 (8879251-13682955) 1269.69 (1010.58-1464.15) 24425653 (21247317-27262401) 1276.97 (1127.84-1416.11) 0.33 (0.17-0.5) Abbreviations: EAPC, estimated annual percentage change, SDl, Sociodemographic Index; Ul,uncertainty interval. “ EAPC is expressed as 95% CIs. Table.2 The prevalence cases and age-standardized prevalence rate of periodontitis in 1990 and 2021, along with their temporal trend. Rate per 100 000(95%UI) 2021 1990-2021 1990 Incidence cases The age-standardized Incidence rate Incidence cases The age-standardized Incidence rate EAPC Global 557036657 (427553223-683752829) 12282.44 (9494.51-15063.91) 1066953744 (896546186-1234839287) 12498.3 (10526.8-14493.37) 0.48 (0.35-0.61) SDI region High-middle SDI 112477527 (84949926-141523175) 10693.06 (8074.04-13393.12) 190563436 (155170167-224654753) 10613.49 (8609.35-12735) 0.2 (0.01-0.39) High SDI 104166404 (81666455-127615908) 10232.99 (7971.34-12561.65) 159217668 (131067146-186552251) 10139.68 (8221-12189.03) -0.17 (-0.36-0.02) Low-middle SDI 123559532 (94516572-151011946) 14942.23 (11651.33-17901.74) 267767360 (222364186-311663136) 15252.28 (12777.21-17553.49) -0.01 (-0.07-0.04) Low SDI 55761027 (43082249-67824831) 17260.05 (13628.03-20703.39) 101686261 (84910682-119217456) 13488.11 (11346.75-15555.44) -1.12 (-1.27--0.96) Middle SDI 160561226 (120848014-199383146) 12093.96 (9332.18-14918.62) 346971125 (294753121-402356717) 12326.58 (10492-14258.36) 0.23 (0.11-0.35) GBD region Advanced Health System 154707914 (119837983-190451646) 10210.19 (7874.46-12604.16) 216914639 (175039796-259554572) 10059.93 (8011.4-12256.19) 0.19 (-0.18-0.55) Africa 66828006 (51889207-80762971) 16383.61 (13071.33-19544.82) 114514409 (93696588-135832038) 11828.49 (9763.13-13839.88) -1.27 (-1.82--0.72) African Region 58121368 (45541713-69366183) 17914.82 (14402.73-21286.04) 93293067 (76820564-109879664) 11951.04 (9978.31-14008.24) -1.63 (-2.22--1.04) America 74675163 (56872774-93313871) 11657.87 (8960.98-14485.51) 144337678 (121259564-168169222) 12010.2 (10029.81-14125.29) 0.51 (0.35-0.67) Andean Latin America 3151937 (2598434-3783360) 11527.39 (9502.56-13667.46) 7635406 (5817558-9742369) 11561.93 (8874.73-14615.47) 0.74 (0.48-1.01) Asia 315485689 (237922348-391105170) 12406.96 (9532.51-15305.24) 684742467 (585462506-789391280) 13167.95 (11187.7-15161.26) 0.89 (0.71-1.07) Australasia 1820176 (1312776-2362422) 8189.67 (5875.13-10630.99) 3979930 (3016541-5074698) 9967.23 (7355.51-13114.51) 1.23 (0.92-1.54) Basic Health System 206089434 (154312569-259723617) 11311.13 (8650.7-14068.85) 430278291 (359583884-503792409) 11254.74 (9419.58-13223.89) 1 (0.78-1.22) Caribbean 4493235 (3432770-5528154) 15163.54 (11723.35-18400.55) 7100123 (5546934-8746369) 13674.44 (10613.75-16870.06) 0.16 (0.02-0.29) Central Africa 7799069 (5997001-9371665) 18645.88 (14849.28-22221.62) 13394855 (10788661-16195730) 12386.48 (10219.46-14883.64) -1.77 (-2.42--1.12) Central Asia 5316806 (3916005-6756782) 10125.58 (7551.03-12737.2) 9005491 (6640329-11750809) 9321.12 (6936.9-12000.9) 0.24 (-0.04-0.51) Central Europe 13093681 (9845084-16551582) 9103.73 (6836.34-11527.98) 16809305 (13162273-20827563) 10046.91 (7649.52-12924.1) 0.83 (0.45-1.21) Central Latin America 16499976 (12363772-20453150) 14438.4 (11109.39-17648.66) 38015445 (31266040-44833316) 14280.67 (11767.34-16795.82) 0.75 (0.48-1.02) Central Sub-Saharan Africa 5879734 (4430773-7149168) 17304.98 (13379.81-20785.13) 7989260 (5587545-11025203) 9567.48 (6904.73-12492.99) -2.69 (-3.42--1.96) Commonwealth High Income 11452093 (8613365-14374531) 8774.46 (6513.89-11127.97) 18319294 (14746368-22170308) 9051.48 (7130.88-11253.89) 0.6 (0.24-0.97) Commonwealth Low Income 23496998 (17849750-28505170) 18287.1 (14216.32-22030.27) 48783894 (37989859-58762718) 16189.19 (12871.62-19126.49) -0.24 (-0.78-0.31) Commonwealth Middle Income 142406644 (109244498-174293950) 16334.2 (12839.31-19560.48) 308513346 (257247298-355276736) 15929.64 (13460.05-18190.73) 0.1 (-0.19-0.39) East Asia 113942172 (85561716-144637555) 10976.83 (8299.83-13749.28) 226131458 (187514178-265720506) 10707.99 (8785.26-12667.41) 1.36 (0.99-1.73) East Asia & Pacific - WB 168304848 (125614434-212963678) 10599.77 (7968.23-13230.46) 330917297 (274372330-387596173) 10427.54 (8618.56-12274.74) 1.02 (0.75-1.29) Eastern Africa 17678398 (13345907-21592361) 17112.13 (13378.44-20559.72) 31784191 (26100430-37799485) 12774.39 (10643.89-14832.89) -1.1 (-1.81--0.39) Eastern Europe 31249552 (24073990-38674733) 11734.45 (8976.69-14607.9) 31682217 (24748361-39210888) 10795.79 (8255.27-13746.88) 0.01 (-0.35-0.38) Eastern Mediterranean Region 33633259 (26289248-40740531) 13590.64 (10857.17-16222.76) 93123618 (75902003-110036598) 14202.76 (11837.39-16531.99) 0.68 (0.27-1.09) Eastern Sub-Saharan Africa 20722537 (15680439-25173613) 18479.02 (14492.18-22175.78) 33791770 (27088783-41126307) 12722.77 (10423.37-15068.77) -1.51 (-2.23--0.78) Europe 98862195 (77451167-120447711) 10560.72 (8223.26-12930.55) 121828638 (97123141-148011518) 10257.04 (8015.61-12849.41) 0.15 (-0.21-0.51) Europe & Central Asia - WB 102290944 (80009429-124797852) 10530.7 (8188.12-12903.17) 128161771 (101719113-156470216) 10182.25 (7940.19-12813) 0.13 (-0.19-0.46) European Region 102950814 (80487943-125623675) 10522.66 (8177.97-12897.43) 129393416 (102661859-158046928) 10165.75 (7923.17-12790.87) 0.13 (-0.2-0.45) High-income Asia Pacific 17938189 (13082223-22710007) 8701.49 (6367.78-11044.36) 27856863 (21561598-33670443) 8923.78 (6751.26-11355.43) 0.49 (-0.07-1.04) High-income North America 32632790 (25453367-40852309) 10263.74 (7852.2-12941.94) 49830524 (41176016-58185121) 9672.4 (7977.24-11544.6) -0.08 (-0.51-0.35) Latin America & Caribbean - WB 42686258 (32054336-52693563) 12699.59 (9724.86-15647.07) 95265909 (79324668-111439048) 13181.85 (10981.47-15405.26) 0.78 (0.58-0.98) Limited Health System 180308850 (138109664-220445707) 16217.83 (12723.09-19418.11) 393578991 (326479323-456122373) 15682.6 (13180.22-17990.51) 0.08 (-0.28-0.43) Middle East & North Africa - WB 17765051 (13161085-22611697) 10763.76 (8172.77-13602.82) 53691617 (42135354-67027918) 11631.94 (9336.86-14187.77) 1.01 (0.63-1.39) Minimal Health System 15419518 (11805064-18526774) 18038.76 (14251.13-21614.27) 25433929 (20667928-30542026) 12419.06 (10262.58-14801.66) -1.63 (-2.29--0.95) North Africa and Middle East 21425361 (15774378-27548273) 9646.86 (7263.77-12250.9) 69332503 (54652562-86046285) 11659.64 (9400.12-14095.49) 1.35 (0.98-1.73) North America 32638833 (25459021-40858710) 10265.33 (7853.72-12943.6) 49838657 (41183872-58191791) 9673.76 (7978.53-11545.55) -0.08 (-0.51-0.35) Northern Africa 8995238 (6665534-11486432) 10962.84 (8305.19-13964.46) 23548196 (17800939-30540409) 12070.78 (9351.67-15391.29) 0.87 (0.55-1.2) Oceania 404466 (288624-525719) 9967.03 (7328.55-12683.1) 224853 (159647-320105) 2247.62 (1641.52-3060.13) -6.38 (-7.23--5.53) Region of the Americas 74675163 (56872774-93313871) 11657.87 (8960.98-14485.51) 144337678 (121259564-168169222) 12010.2 (10029.81-14125.29) 0.51 (0.35-0.67) Abbreviations: EAPC, estimated annual percentage change, SDl, Sociodemographic Index; Ul,uncertainty interval. “ EAPC is expressed as 95% CIs. Table.3 The DALYs cases and age-standardized DALYs rate of periodontitis in 1990 and 2021, along with their temporal trend. Rate per 100 000(95%UI) 2021 1990-2021 1990 Incidence cases The age-standardized Incidence rate Incidence cases The age-standardized Incidence rate EAPC Global 3620518 (1421409-7719685) 79.62 (31.46-169.62) 6903284 (2772284-14106182) 80.89 (32.47-165.37) 0.47 (0.35-0.6) SDI region High-middle SDI 730382 (288347-1576847) 69.35 (27.67-149.66) 1231035 (492704-2492760) 68.73 (27.55-139.24) 0.2 (0.01-0.4) High SDI 675037 (267679-1436739) 66.41 (26.42-141.98) 1021198 (406501-2047768) 65.47 (26.3-132.99) -0.18 (-0.38-0.01) Low-middle SDI 802431 (324100-1660446) 96.49 (38.29-198.97) 1735974 (705992-3546781) 98.5 (39.88-199.41) -0.01 (-0.06-0.04) Low SDI 362253 (143525-746670) 111.43 (44.04-227.49) 660691 (268309-1356342) 86.99 (35.4-176.97) -1.12 (-1.27--0.96) Middle SDI 1047099 (414764-2236757) 78.52 (31.2-167.75) 2249557 (909679-4517591) 79.88 (32.32-160.93) 0.22 (0.1-0.35) GBD region Advanced Health System 1000864 (397022-2144189) 66.15 (26.26-142.33) 1390849 (553490-2818030) 64.91 (26.11-132.98) 0.17 (-0.19-0.52) Africa 435296 (169696-893361) 106.08 (41.91-216.56) 744610 (302771-1548671) 76.37 (30.68-155.82) -1.28 (-1.82--0.73) African Region 378567 (148136-770127) 115.97 (46.12-233.81) 607156 (247022-1268893) 77.19 (31.19-158.18) -1.63 (-2.22--1.05) America 484070 (192007-1036623) 75.48 (30.07-161.48) 928766 (374749-1909795) 77.47 (31.35-159.8) 0.49 (0.33-0.65) Andean Latin America 20638 (8526-43145) 75.24 (30.6-156.62) 49777 (19357-104947) 75.28 (29.58-158.55) 0.74 (0.48-1) Asia 2054166 (809730-4398419) 80.44 (31.85-172.12) 4437350 (1801644-8890492) 85.31 (34.67-171.13) 0.89 (0.71-1.07) Australasia 11779 (4540-26131) 53.03 (20.48-117.45) 25605 (10182-54013) 64.45 (25.05-136.61) 1.2 (0.89-1.51) Basic Health System 1345049 (527418-2874612) 73.53 (29.38-157.98) 2789992 (1122310-5636388) 72.98 (29.44-147.73) 0.99 (0.77-1.21) Caribbean 29330 (11795-60669) 98.79 (39.96-204.03) 45964 (18345-95056) 88.59 (35.16-183.65) 0.14 (0-0.27) Central Africa 50629 (20012-103533) 120.26 (47.76-243.82) 87162 (35024-186973) 79.91 (31.84-167.15) -1.77 (-2.41--1.12) Central Asia 34567 (13704-74906) 65.68 (26.1-142.1) 58379 (23002-122809) 60.29 (23.69-125.58) 0.23 (-0.04-0.5) Central Europe 84525 (33149-183760) 58.83 (23.07-128.25) 107876 (42681-219152) 64.88 (25.8-134.29) 0.82 (0.45-1.19) Central Latin America 107627 (43075-223982) 93.72 (38-194.92) 246586 (99480-506792) 92.56 (37.33-190.51) 0.74 (0.47-1.01) Central Sub-Saharan Africa 38057 (15189-77260) 111.25 (44.62-228.84) 51550 (19947-112511) 61.21 (23.79-132.14) -2.71 (-3.43--1.98) Commonwealth High Income 74234 (28839-162046) 57.01 (22.19-125.42) 117752 (46470-241860) 58.53 (23.27-121.22) 0.58 (0.22-0.94) Commonwealth Low Income 153148 (61075-313327) 118.44 (46.9-240.46) 317172 (124036-659551) 104.71 (41.16-216.99) -0.24 (-0.78-0.3) Commonwealth Middle Income 924108 (373757-1895584) 105.34 (42.04-214.51) 1998413 (816938-4082024) 102.74 (42.06-207.78) 0.1 (-0.18-0.39) East Asia 744686 (295676-1609368) 71.47 (28.52-154.7) 1466661 (578102-2997147) 69.55 (27.77-143.33) 1.35 (0.98-1.73) East Asia & Pacific - WB 1099208 (432156-2380073) 68.98 (27.59-148.61) 2145556 (850718-4317719) 67.7 (27.08-137.9) 1.01 (0.74-1.28) Eastern Africa 115053 (46165-235583) 110.64 (43.86-226.04) 206881 (84754-430324) 82.47 (33.36-168.66) -1.1 (-1.8--0.39) Eastern Europe 201222 (81002-424926) 75.66 (30.36-159.41) 202793 (80698-410203) 69.39 (27.84-142.56) 0 (-0.36-0.37) Eastern Mediterranean Region 219314 (86239-458084) 88.12 (34.75-183.17) 604942 (238723-1238573) 91.7 (36.04-185.71) 0.67 (0.27-1.08) Eastern Sub-Saharan Africa 134871 (53658-274527) 119.47 (47.55-242.76) 219739 (89456-465543) 82.03 (33.23-169) -1.51 (-2.22--0.79) Europe 639267 (254644-1363049) 68.42 (27.21-146.25) 782688 (309423-1603552) 66.29 (26.3-138.11) 0.13 (-0.22-0.49) Europe & Central Asia - WB 661551 (263450-1412278) 68.22 (27.13-145.89) 823778 (325607-1690127) 65.81 (26.15-137.16) 0.12 (-0.2-0.44) European Region 665844 (265093-1421745) 68.17 (27.1-145.81) 831723 (328717-1706935) 65.7 (26.12-136.96) 0.11 (-0.21-0.44) High-income Asia Pacific 116842 (45917-253870) 56.65 (22.29-123.57) 178914 (70629-356713) 57.83 (22.77-117.71) 0.46 (-0.09-1.01) High-income North America 210367 (83442-448355) 66.3 (26.35-142.02) 316568 (126390-636544) 61.88 (24.94-126.08) -0.12 (-0.54-0.31) Latin America & Caribbean - WB 277902 (112129-584012) 82.39 (33.12-173.51) 617084 (251014-1257271) 85.37 (34.73-174.01) 0.77 (0.58-0.97) Limited Health System 1171035 (472833-2411988) 104.67 (41.65-213.36) 2552120 (1046429-5225149) 101.21 (41.44-204.87) 0.08 (-0.27-0.43) Middle East & North Africa - WB 115660 (45396-250608) 69.69 (27.44-149.76) 347539 (139898-716872) 74.92 (30.1-153.77) 1 (0.63-1.37) Minimal Health System 100253 (39168-206579) 116.59 (46.05-238.09) 165493 (67113-349371) 80.09 (32.19-164.92) -1.63 (-2.28--0.96) North Africa and Middle East 139422 (54465-305549) 62.44 (24.47-135.68) 448887 (180266-924569) 75.15 (30.2-154.11) 1.35 (0.98-1.72) North America 210407 (83457-448427) 66.31 (26.35-142.04) 316621 (126410-636628) 61.89 (24.94-126.11) -0.12 (-0.54-0.31) Northern Africa 58639 (22943-126893) 71.09 (27.82-153.15) 152488 (60361-317966) 77.86 (30.78-161) 0.86 (0.54-1.18) Oceania 2631 (1025-5785) 64.39 (25.36-138.53) 1450 (546-3197) 14.37 (5.36-31.85) -6.42 (-7.26--5.57) Region of the Americas 484070 (192007-1036623) 75.48 (30.07-161.48) 928766 (374749-1909795) 77.47 (31.35-159.8) 0.49 (0.33-0.65) Abbreviations: EAPC, estimated annual percentage change, SDl, Sociodemographic Index; Ul,uncertainty interval. “ EAPC is expressed as 95% CIs. Table.4 The YLDs cases and age-standardized YLDs rate of periodontitis in 1990 and 2021, along with their temporal trend. Rate per 100 000(95%UI) 2021 1990-2021 1990 Incidence cases The age-standardized Incidence rate Incidence cases The age-standardized Incidence rate EAPC Global 3620518 (1421409-7719685) 79.62 (31.46-169.62) 6903284 (2772284-14106182) 80.89 (32.47-165.37) 0.47 (0.35-0.6) SDI region High-middle SDI 730382 (288347-1576847) 69.35 (27.67-149.66) 1231035 (492704-2492760) 68.73 (27.55-139.24) 0.2 (0.01-0.4) High SDI 675037 (267679-1436739) 66.41 (26.42-141.98) 1021198 (406501-2047768) 65.47 (26.3-132.99) -0.18 (-0.38-0.01) Low-middle SDI 802431 (324100-1660446) 96.49 (38.29-198.97) 1735974 (705992-3546781) 98.5 (39.88-199.41) -0.01 (-0.06-0.04) Low SDI 362253 (143525-746670) 111.43 (44.04-227.49) 660691 (268309-1356342) 86.99 (35.4-176.97) -1.12 (-1.27--0.96) Middle SDI 1047099 (414764-2236757) 78.52 (31.2-167.75) 2249557 (909679-4517591) 79.88 (32.32-160.93) 0.22 (0.1-0.35) GBD region Advanced Health System 1000864 (397022-2144189) 66.15 (26.26-142.33) 1390849 (553490-2818030) 64.91 (26.11-132.98) 0.17 (-0.19-0.52) Africa 435296 (169696-893361) 106.08 (41.91-216.56) 744610 (302771-1548671) 76.37 (30.68-155.82) -1.28 (-1.82--0.73) African Region 378567 (148136-770127) 115.97 (46.12-233.81) 607156 (247022-1268893) 77.19 (31.19-158.18) -1.63 (-2.22--1.05) America 484070 (192007-1036623) 75.48 (30.07-161.48) 928766 (374749-1909795) 77.47 (31.35-159.8) 0.49 (0.33-0.65) Andean Latin America 20638 (8526-43145) 75.24 (30.6-156.62) 49777 (19357-104947) 75.28 (29.58-158.55) 0.74 (0.48-1) Asia 2054166 (809730-4398419) 80.44 (31.85-172.12) 4437350 (1801644-8890492) 85.31 (34.67-171.13) 0.89 (0.71-1.07) Australasia 11779 (4540-26131) 53.03 (20.48-117.45) 25605 (10182-54013) 64.45 (25.05-136.61) 1.2 (0.89-1.51) Basic Health System 1345049 (527418-2874612) 73.53 (29.38-157.98) 2789992 (1122310-5636388) 72.98 (29.44-147.73) 0.99 (0.77-1.21) Caribbean 29330 (11795-60669) 98.79 (39.96-204.03) 45964 (18345-95056) 88.59 (35.16-183.65) 0.14 (0-0.27) Central Africa 50629 (20012-103533) 120.26 (47.76-243.82) 87162 (35024-186973) 79.91 (31.84-167.15) -1.77 (-2.41--1.12) Central Asia 34567 (13704-74906) 65.68 (26.1-142.1) 58379 (23002-122809) 60.29 (23.69-125.58) 0.23 (-0.04-0.5) Central Europe 84525 (33149-183760) 58.83 (23.07-128.25) 107876 (42681-219152) 64.88 (25.8-134.29) 0.82 (0.45-1.19) Central Latin America 107627 (43075-223982) 93.72 (38-194.92) 246586 (99480-506792) 92.56 (37.33-190.51) 0.74 (0.47-1.01) Central Sub-Saharan Africa 38057 (15189-77260) 111.25 (44.62-228.84) 51550 (19947-112511) 61.21 (23.79-132.14) -2.71 (-3.43--1.98) Commonwealth High Income 74234 (28839-162046) 57.01 (22.19-125.42) 117752 (46470-241860) 58.53 (23.27-121.22) 0.58 (0.22-0.94) Commonwealth Low Income 153148 (61075-313327) 118.44 (46.9-240.46) 317172 (124036-659551) 104.71 (41.16-216.99) -0.24 (-0.78-0.3) Commonwealth Middle Income 924108 (373757-1895584) 105.34 (42.04-214.51) 1998413 (816938-4082024) 102.74 (42.06-207.78) 0.1 (-0.18-0.39) East Asia 744686 (295676-1609368) 71.47 (28.52-154.7) 1466661 (578102-2997147) 69.55 (27.77-143.33) 1.35 (0.98-1.73) East Asia & Pacific - WB 1099208 (432156-2380073) 68.98 (27.59-148.61) 2145556 (850718-4317719) 67.7 (27.08-137.9) 1.01 (0.74-1.28) Eastern Africa 115053 (46165-235583) 110.64 (43.86-226.04) 206881 (84754-430324) 82.47 (33.36-168.66) -1.1 (-1.8--0.39) Eastern Europe 201222 (81002-424926) 75.66 (30.36-159.41) 202793 (80698-410203) 69.39 (27.84-142.56) 0 (-0.36-0.37) Eastern Mediterranean Region 219314 (86239-458084) 88.12 (34.75-183.17) 604942 (238723-1238573) 91.7 (36.04-185.71) 0.67 (0.27-1.08) Eastern Sub-Saharan Africa 134871 (53658-274527) 119.47 (47.55-242.76) 219739 (89456-465543) 82.03 (33.23-169) -1.51 (-2.22--0.79) Europe 639267 (254644-1363049) 68.42 (27.21-146.25) 782688 (309423-1603552) 66.29 (26.3-138.11) 0.13 (-0.22-0.49) Europe & Central Asia - WB 661551 (263450-1412278) 68.22 (27.13-145.89) 823778 (325607-1690127) 65.81 (26.15-137.16) 0.12 (-0.2-0.44) European Region 665844 (265093-1421745) 68.17 (27.1-145.81) 831723 (328717-1706935) 65.7 (26.12-136.96) 0.11 (-0.21-0.44) High-income Asia Pacific 116842 (45917-253870) 56.65 (22.29-123.57) 178914 (70629-356713) 57.83 (22.77-117.71) 0.46 (-0.09-1.01) High-income North America 210367 (83442-448355) 66.3 (26.35-142.02) 316568 (126390-636544) 61.88 (24.94-126.08) -0.12 (-0.54-0.31) Latin America & Caribbean - WB 277902 (112129-584012) 82.39 (33.12-173.51) 617084 (251014-1257271) 85.37 (34.73-174.01) 0.77 (0.58-0.97) Limited Health System 1171035 (472833-2411988) 104.67 (41.65-213.36) 2552120 (1046429-5225149) 101.21 (41.44-204.87) 0.08 (-0.27-0.43) Middle East & North Africa - WB 115660 (45396-250608) 69.69 (27.44-149.76) 347539 (139898-716872) 74.92 (30.1-153.77) 1 (0.63-1.37) Minimal Health System 100253 (39168-206579) 116.59 (46.05-238.09) 165493 (67113-349371) 80.09 (32.19-164.92) -1.63 (-2.28--0.96) North Africa and Middle East 139422 (54465-305549) 62.44 (24.47-135.68) 448887 (180266-924569) 75.15 (30.2-154.11) 1.35 (0.98-1.72) North America 210407 (83457-448427) 66.31 (26.35-142.04) 316621 (126410-636628) 61.89 (24.94-126.11) -0.12 (-0.54-0.31) Northern Africa 58639 (22943-126893) 71.09 (27.82-153.15) 152488 (60361-317966) 77.86 (30.78-161) 0.86 (0.54-1.18) Oceania 2631 (1025-5785) 64.39 (25.36-138.53) 1450 (546-3197) 14.37 (5.36-31.85) -6.42 (-7.26--5.57) Region of the Americas 484070 (192007-1036623) 75.48 (30.07-161.48) 928766 (374749-1909795) 77.47 (31.35-159.8) 0.49 (0.33-0.65) Abbreviations: EAPC, estimated annual percentage change, SDl, Sociodemographic Index; Ul,uncertainty interval. “ EAPC is expressed as 95% CIs. The regional burden patterns revealed pronounced disparities aligned with socioeconomic development levels, with Sub-Saharan Africa and South Asia maintaining the highest burden concentrations. Several countries in these regions recorded age-standardized DALY rates exceeding 280 per 100,000 population, while high-income regions in Western Europe, North America, and Australasia demonstrated substantially lower rates, typically below 120 per 100,000 population. This greater than two-fold difference underscored persistent global health inequities in oral health access and periodontitis management, reflecting broader patterns of healthcare infrastructure disparities across economic development gradients. The burden distribution closely correlated with Socio-demographic Index (SDI) levels, with low-SDI regions experiencing disproportionately elevated rates compared to high-SDI counterparts. Gender-specific analyses revealed consistent female predominance across most age groups and geographical regions, with women experiencing approximately 15-20% higher age-standardized rates than men globally. This pattern was particularly pronounced in older adult populations (≥65 years), where hormonal, behavioral, and healthcare-seeking differences may contribute to observed disparities. Age-stratified burden patterns demonstrated exponential increases with advancing age, with periodontitis burden rising sharply after age 45 years, from 892 per 100,000 in the 45-49 age group to 2,847 per 100,000 in those aged 80 and above, reflecting cumulative inflammatory damage and age-related immune system changes. Regional burden profiles revealed particularly elevated rates in Central and Eastern European countries, several Middle Eastern nations, and parts of Latin America, where age-standardized DALY rates frequently exceeded 250 per 100,000 population. These patterns likely reflect complex interactions between genetic predisposition, dietary factors, smoking prevalence, healthcare access limitations, and varying oral hygiene practices. Conversely, several high-income countries in Northern Europe and East Asia demonstrated remarkably low burden levels, with age-standardized rates below 100 per 100,000, suggesting effective population-level prevention strategies and enhanced periodontal care access (Figure 2) . The temporal trend analysis revealed heterogeneous regional trajectories, with most high-income regions showing sustained improvements in age-standardized rates (EAPC ranging from -0.8% to -1.2% annually), while several low- and middle-income regions experienced stagnant or worsening trends. These divergent patterns highlighted growing global inequalities in periodontitis burden and underscored the need for targeted interventions in high-burden settings to address underlying social determinants of oral health disparities. 2.Mendelian Randomization Analysis Results 2.1 Causal Associations Between Exposures and Periodontitis Risk To investigate potential causal relationships between modifiable risk factors and periodontitis, we conducted comprehensive two-sample Mendelian randomization analyses using the inverse variance weighted (IVW) method as the primary approach. The analysis encompassed 14 distinct exposures, utilizing instrumental variables ranging from 4 to 480 single nucleotide polymorphisms (SNPs) to ensure robust causal inference across different phenotypes. 2.2 Sleep-Related Phenotypes: A Primary Causal Risk Factor Sleeplessness/insomnia emerged as one of the most significant causal determinants of periodontitis risk in our analysis (OR = 1.134, 95% CI: 1.070–1.202, P = 2.09×10⁻⁵), supported by 85 genetic instruments. This association represents among the strongest statistical relationships identified across all examined exposures, demonstrating that genetically predicted insomnia confers a 13.4% increased risk of periodontitis development. Notably, this causal effect was substantially amplified in multivariable MR analysis after adjusting for correlated risk factors, with the odds ratio increasing to 1.245 (95% CI: 1.016–1.526, P = 0.034), indicating that sleep disturbances may independently contribute to periodontal pathogenesis beyond traditional risk factors. Other sleep-related behaviors showed directionally consistent but non-significant associations, including daytime napping (OR = 1.047, 95% CI: 0.985–1.114, P = 0.138) and snoring (OR = 1.087, 95% CI: 0.931–1.269, P = 0.293), suggesting that chronic sleep disruption, rather than isolated sleep behaviors, drives the causal relationship with periodontitis. 2.3 Additional Modifiable Risk Factors Beyond sleep disorders, several lifestyle and socioeconomic factors demonstrated significant causal associations with periodontitis risk. Body mass index showed a robust positive association (OR = 1.095, 95% CI: 1.057–1.135, P = 4.77×10⁻⁷) based on 480 genetic variants, with each standard deviation increase in genetically predicted BMI corresponding to a 9.5% elevated risk. Smoking initiation exhibited a pronounced causal effect (OR = 1.245, 95% CI: 1.065–1.457, P = 0.0063), with genetically predicted smoking propensity associated with 24.5% higher periodontitis odds. Educational attainment demonstrated the most statistically robust protective effect (OR = 0.843, 95% CI: 0.795–0.893, P = 6.52×10⁻⁹), supported by 299 genetic instruments. Each additional year of genetically predicted schooling was associated with a 15.7% reduction in periodontitis risk, and this protective effect remained significant in multivariable analysis (OR = 0.886, 95% CI: 0.807–0.972, P = 0.011) (Figure 3) . 2.4 Null Associations and Multivariable Adjustments Several hypothesized risk factors showed no significant causal relationships with periodontitis, including beverage consumption patterns (coffee: OR = 0.980, P = 0.830; tea: OR = 0.911, P = 0.198; alcohol: OR = 1.107, P = 0.677), birth weight (OR = 1.015, P = 0.494), and various physical activity measures. Importantly, insomnia maintained its significant causal effect in multivariable models, while BMI and smoking associations were attenuated after adjustment for correlated exposures. These findings establish sleep disorders as a priority modifiable risk factor for periodontitis, with effect sizes comparable to established risk factors such as smoking. The persistence of the insomnia-periodontitis association across both univariable and multivariable analyses, combined with the high prevalence of sleep disorders globally, positions sleep hygiene interventions as a novel therapeutic target for periodontal disease prevention. The multifactorial causal architecture identified supports integrated approaches addressing socioeconomic, behavioral, and sleep-related determinants in periodontal health management (Figure 4) . 3.Transcriptomic Analysis Results 3.1 Identification of Periodontitis-Insomnia Cross-Talk Genes Building upon the causal relationships established through Mendelian randomization, we conducted comprehensive transcriptomic analyses to elucidate the molecular mechanisms underlying the periodontitis-insomnia association. Systematic analysis of three periodontitis GEO datasets (GSE10334, GSE16134, and GSE106090) identified 190, 17, and 3,235 differentially expressed genes, respectively, using stringent criteria (|logFC| ≥ 1, adjusted P < 0.05). Venn diagram intersection analysis revealed 126 genes consistently dysregulated across all datasets, establishing a robust periodontitis gene signature. Cross-referencing this core periodontitis gene set with 6,186 insomnia-related genes from GeneCards database identified 25 critical cross-talk genes that exhibited significant co-expression in both conditions. These genes included key inflammatory mediators (IL1B, CXCL8, CXCL13), extracellular matrix components (COL4A1, COL4A2), and transcriptional regulators (XBP1, MEF2C), providing molecular evidence for the shared pathobiological pathways linking periodontitis and sleep disorders identified in our MR analysis (Figure 5) . 3.2 Functional Enrichment and Pathway Analysis Gene Ontology (GO) enrichment analysis revealed that cross-talk genes were predominantly enriched in inflammation-related biological processes, including cellular response to bacterial stimuli and lipopolysaccharide (LPS) response pathways (5-6 genes per pathway, P < 0.05). Key inflammatory genes IL1B, CXCL8, SELE, and transcriptional factors XBP1 and MEF2C were consistently represented across these pathways, supporting the inflammatory basis of periodontitis-insomnia comorbidity established through our causal inference analysis. KEGG pathway enrichment revealed significant involvement of AGE-RAGE signaling in diabetic complications (5 genes: COL4A1, COL4A2, IL1B, SELE, CXCL8, P < 0.01), lipid metabolism pathways, and atherosclerosis-related processes. The enrichment of AGE-RAGE signaling is particularly relevant, as this pathway mediates chronic inflammation and vascular dysfunction—mechanisms potentially linking sleep-induced metabolic disruption with periodontal tissue destruction (Figure 6) . 3.3 Protein-Protein Interaction Networks and Modular Analysis PPI network analysis using GeneMANIA revealed densely interconnected modules among cross-talk genes, with inflammatory mediators (IL1B, CXCL8, SELE) occupying central network positions. The MCODE algorithm revealed two principal functional clusters: a small cell adhesion module comprising four nodes and a larger immune–inflammatory regulation module. These findings indicate that inflammatory signaling may serve as a dominant molecular mechanism linking periodontitis with sleep disorders. Complementary analysis with Metascape further organized the enriched pathways into ten functional categories, which included AGE–RAGE–related diabetic complications, responses to lipopolysaccharide (LPS), and regulation of cell adhesion. Together, these results offer a broader molecular context for interpreting the multifactorial etiology highlighted by our Mendelian randomization findings. 3.4 Lactylation-Mediated Epigenetic Regulation To explore potential epigenetic mechanisms, we conducted correlation analyses between cross-talk genes and enzymes associated with lactylation. Notably, significant associations (|r| > 0.5, P < 0.05) were observed between the transcriptional regulators XBP1 and MEF2C with the lactylation-related enzymes HDAC1 and SIRT1, implying that metabolic–epigenetic interactions mediated by lactylation could represent a previously unrecognized mechanism connecting sleep disturbances to periodontal inflammation. Further functional enrichment of the 69-gene set correlated with lactylation uncovered strong involvement in processes related to DNA repair regulation and transcriptional coactivator activity (P < 0.05, q < 0.05). These findings suggest that lactylation-driven modifications might contribute to disease progression by reprogramming epigenetic pathways governing inflammation and tissue repair (Figure 7) . 3.5 Diagnostic Biomarker Identification ROC curve analysis of lactylation-related cross-talk genes identified five core biomarkers with excellent diagnostic performance (AUC > 0.7): CD93 (AUC = 0.879), IL16 (AUC = 0.869), FER1L4 (AUC = 0.852), DUSP5, and IGFBP4. CD93 exhibited strong discriminatory ability, indicating its promise as a biomarker for screening comorbidity between periodontitis and insomnia. 3.6 Integration with Causal Evidence These transcriptomic results lend molecular support to the causal associations identified in the MR analysis. The identification of shared inflammatory pathways (IL1B, CXCL8), metabolic dysregulation (AGE-RAGE signaling), and epigenetic mechanisms (lactylation) mechanistically explains how genetically predicted insomnia increases periodontitis risk by 24.5% as demonstrated in our multivariable MR analysis. The convergence of causal inference and molecular evidence establishes a robust foundation for developing targeted therapeutic interventions addressing the sleep-periodontal health axis (Figure 8) . Discussion Global Burden Implications and Health System Priorities The present analysis reveals a paradoxical pattern in global periodontitis burden, characterized by declining age-standardized rates concurrent with substantial increases in absolute disease burden. This temporal divergence reflects the complex interplay between improved oral healthcare access in developed regions and the demographic transition toward aging populations globally [36]. The 44% increase in absolute DALYs from 1990 to 2021, despite modest improvements in age-standardized rates, underscores the inadequacy of traditional epidemiological metrics in capturing the true societal impact of chronic diseases in aging societies [37]. The pronounced regional disparities observed, with Sub-Saharan Africa and South Asia bearing disproportionate burden levels, illuminate the persistent global health inequities that extend beyond infectious diseases to encompass chronic inflammatory conditions [38]. The greater than two-fold difference in age-standardized DALY rates between low- and high-income regions parallels similar patterns documented in other non-communicable diseases, suggesting shared underlying determinants related to healthcare infrastructure, preventive care access, and socioeconomic factors [39]. Notably, the strong correlation between periodontitis burden and Socio-demographic Index levels provides compelling evidence that oral health outcomes remain fundamentally shaped by broader development indicators, challenging purely biomedical approaches to periodontal disease prevention [40]. Causal Architecture and Sleep-Periodontal Pathways The identification of insomnia as a primary causal risk factor for periodontitis through Mendelian randomization represents a paradigm shift in understanding periodontal disease etiology. The robust causal effect (24.5% increased risk in multivariable analysis) positions sleep disorders among the most impactful modifiable risk factors, comparable to established determinants such as smoking and diabetes [41]. This finding has profound implications given that sleep disorders affect an estimated 10-30% of adults globally, potentially representing a vast unrecognized population at elevated periodontal risk [42]. The mechanistic pathways linking sleep disruption to periodontal pathogenesis likely involve multiple interconnected systems. Sleep deprivation is known to dysregulate the hypothalamic-pituitary-adrenal axis, leading to chronic elevation of cortisol levels and subsequent immunosuppression [43]. This hormonal dysregulation compromises neutrophil function and impairs the innate immune response to periodontal pathogens, creating conditions conducive to bacterial overgrowth and tissue destruction [44]. Chronic sleep restriction has also been reported to lower salivary flow and modify salivary composition, potentially weakening the oral cavity’s innate protective mechanisms against bacterial colonization [45]. Moreover, the amplified effect size identified in the multivariable MR analysis indicates that sleep disorders may influence periodontal outcomes through biological pathways distinct from conventional risk factors, suggesting a possible synergistic role rather than mere confounding [46]. Clinically, this distinction is important, as it implies that addressing sleep disturbances could yield therapeutic benefits that extend beyond those achieved by standard periodontal treatments, even in individuals with otherwise well-managed traditional risk factors [47]. Molecular Mechanisms and Transcriptomic Insights The transcriptomic analysis offers new molecular perspectives on the periodontitis–insomnia connection, uncovering an intricate network of overlapping inflammatory pathways that help clarify the causal relationships suggested by the Mendelian randomization analysis [48]. Within this network, 25 cross-talk genes were identified, the majority of which are enriched in pathways related to inflammation, thereby laying a mechanistic basis for understanding how systemic disturbances in sleep may translate into local periodontal tissue damage [49]. Among these genes, IL1B and CXCL8 stand out for their regulatory importance. Both cytokines act as upstream drivers of inflammatory cascades in sleep disorders and periodontal disease [50]. IL1B, in particular, has been implicated in altering sleep architecture as well as in promoting periodontal destruction, making it a strong candidate for a molecular mediator that links systemic sleep dysregulation with oral inflammatory processes [51]. The involvement of CXCL8 (IL-8) reinforces the contribution of neutrophil-driven responses, given its established role in recruiting and activating neutrophils in conditions of both sleep deprivation and periodontal pathology [52]. In addition, enrichment of the AGE–RAGE signaling pathway provides insight into how metabolic imbalances induced by poor sleep may exacerbate periodontal disease [53]. Advanced glycation end products, which accumulate under states of oxidative stress and metabolic dysfunction associated with sleep disorders, can stimulate RAGE receptors in periodontal tissues. This interaction initiates inflammatory cascades that ultimately accelerate tissue breakdown [54]. Recognition of this pathway highlights a potentially targetable link between sleep-related metabolic alterations and periodontal inflammation [55]. Epigenetic Regulation and Lactylation Mechanisms The recognition of lactylation as a regulatory mechanism marks an important step forward in clarifying the molecular underpinnings of periodontitis–insomnia comorbidity. As a recently described histone modification driven by lactate metabolism, lactylation creates a direct molecular bridge between altered metabolic states and transcriptional control [56]. In this study, notable correlations were observed between the transcriptional regulators XBP1 and MEF2C and the lactylation-associated enzymes HDAC1 and SIRT1, implying that sleep-related metabolic disturbances may reshape gene expression in ways that heighten periodontal inflammation [57]. The relevance of this mechanism is underscored by evidence showing that sleep loss disrupts cellular metabolism, resulting in elevated lactate levels and potential shifts in lactylation dynamics [58]. The role of XBP1 is particularly noteworthy, as this factor orchestrates endoplasmic reticulum stress responses, suggesting that inadequate sleep could disturb cellular balance within periodontal tissues through a form of metabolic–epigenetic crosstalk [59]. Regulation of MEF2C via lactylation may likewise drive changes in inflammatory gene programs, thereby contributing to the tissue damage characteristic of periodontal disease [60]. Clinical Translation and Biomarker Development The discovery of five core biomarkers with high diagnostic accuracy provides promising opportunities for advancing personalized approaches in periodontal care. Among them, CD93 stands out with an AUC of 0.879, underscoring its value as a candidate for developing screening strategies for comorbidity between periodontitis and insomnia [61]. Beyond its statistical performance, CD93 is a cell surface glycoprotein that contributes to the regulation of inflammation and maintenance of vascular stability, pointing to its dual potential as both a biomarker and a therapeutic target [62]. Importantly, the implications of these biomarkers extend into clinical practice. Incorporating sleep evaluations into periodontal risk assessment frameworks may support more individualized treatment protocols and improve monitoring strategies [63]. For example, patients with elevated levels of CD93 or IL16 could be prioritized for combined interventions—addressing both sleep disruption and periodontal health—which might achieve better outcomes than conventional periodontal therapy alone [64]. Public Health Implications and Prevention Strategies The demonstrated causal link between sleep disorders and periodontitis carries important consequences for both public health initiatives and preventive dentistry [65]. At present, most preventive programs emphasize oral hygiene education and routine professional care, while largely neglecting sleep health, a potentially modifiable risk factor that affects millions worldwide [66]. Incorporating sleep hygiene counseling into dental practice could therefore provide an avenue for addressing upstream drivers of periodontal disease and improving long-term oral health outcomes [67]. The educational attainment findings further emphasize the importance of addressing social determinants of oral health. The robust protective effect of education (15.7% risk reduction per additional year) suggests that interventions targeting health literacy and socioeconomic factors may yield substantial population-level benefits [68]. This finding aligns with broader evidence supporting the role of education in health promotion and disease prevention across multiple chronic conditions [69]. Study Limitations and Methodological Considerations Several limitations warrant acknowledgment in interpreting these findings. First, the MR analysis relies on genetic variants that may have pleiotropic effects, potentially influencing the estimated causal relationships [70]. While multivariable MR helped address some confounding concerns, residual pleiotropy cannot be entirely excluded. Second, the transcriptomic analysis was limited to publicly available datasets, which may not capture the full spectrum of molecular changes occurring in diverse populations [71].The cross-sectional nature of the transcriptomic data precludes temporal assessment of gene expression changes, limiting our ability to establish the sequence of molecular events in disease progression [72]. Additionally, the focus on mRNA expression may not fully reflect protein-level changes or post-translational modifications that contribute to disease pathogenesis [73]. Future Research Directions The convergence of causal inference and molecular evidence presented here establishes a foundation for several important research directions. Longitudinal cohort studies incorporating both sleep assessment and periodontal monitoring are needed to validate the temporal relationships suggested by our MR analysis [74]. Intervention studies testing sleep hygiene programs in periodontal patients could provide direct evidence for the therapeutic potential of addressing sleep disorders in periodontal care [75]. The lactylation findings suggest that metabolic-epigenetic mechanisms represent a promising area for drug development. Investigating whether interventions targeting lactylation enzymes or metabolic pathways can modulate periodontal inflammation could yield novel therapeutic approaches [76]. Similarly, the biomarker findings warrant validation in independent cohorts and assessment of their clinical utility in diverse populations [77]. Conclusions This comprehensive analysis establishes sleep disorders as a major causal risk factor for periodontitis, with effect sizes comparable to established determinants. The molecular mechanisms identified provide a roadmap for developing targeted interventions addressing the sleep-periodontal health axis. The integration of causal inference with transcriptomic evidence represents a model for advancing precision medicine approaches in periodontal care, with implications extending to broader chronic disease prevention strategies. Abbreviations ASR Age-standardized rate DALY Disability-adjusted life year EAPC Estimated annual percentage change GBD Global Burden of Disease GWAS Genome-wide association study IVW Inverse variance weighting MR Mendelian randomization SNP Single nucleotide polymorphism YLD Years lived with disability YLL Years of life lost Declarations Acknowledgements Not applicable. Authors' contributions Shucan Zheng conceived and designed the study, supervised the research process, and was responsible for drafting and revising the manuscript. Haibin Shao contributed to data acquisition and performed the statistical analysis of the Global Burden of Disease dataset.Weilu Wang conducted the Mendelian randomization analyses and participated in result interpretation. Xiaoying Chen was responsible for bioinformatics and multi-omics analyses, including transcriptomic and pathway enrichment studies. Minghui Zhu contributed to figure preparation, data visualization, and assisted in drafting the methods and results sections. Jiazhen Long critically reviewed the manuscript, refined the discussion, and provided important intellectual input.All authors have read and approved the final manuscript. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials All data used in this study are publicly available from the cited sources (e.g., GBD database, FinnGen/UK Biobank GWAS, GEO transcriptome datasets). No new datasets were generated during this study. Competing Interests The authors declare that they have no competing interests. Funding This work was supported by the Huadu District Joint Funding Project for Basic and Applied Basic Research by District and Academy. Authors' contributions Shucan Zheng conceived and designed the study, supervised the research process, and was responsible for drafting and revising the manuscript. Haibin Shao acquired GBD data and performed statistical analyses. Weilu Wang conducted the Mendelian randomization analyses. Xiaoying Chen performed bioinformatics and multi-omics investigations. Minghui Zhu contributed to figure preparation and data visualization. Jiazhen Long provided critical review and intellectual input. All authors have read and approved the final manuscript. References Kassebaum NJ, Bernabé E, Dahiya M, Bhandari B, Murray CJ, Marcenes W. Global burden of severe periodontitis in 1990–2010: a systematic review and meta-regression. J Dent Res. 2014;93(11):1045–53. 10.1177/0022034514552491 . Listl S, Galloway J, Mossey PA, Marcenes W. Global Economic Impact of Dental Diseases. J Dent Res. 2015;94(10):1355–61. 10.1177/0022034515602879 . Benzian H, Watt R, Makino Y, Stauf N, Varenne B. WHO calls to end the global crisis of oral health. Lancet. 2022;400(10367):1909–10. 10.1016/S0140-6736(22)02322-4 . Liu Y, Wheaton AG, Chapman DP, Cunningham TJ, Lu H, Croft JB. Prevalence of Healthy Sleep Duration among Adults–United States, 2014. MMWR Morb Mortal Wkly Rep. 2016;65(6):137–41. 10.15585/mmwr.mm6506a1 . van de Straat V, Bracke P. How well does Europe sleep? A cross-national study of sleep problems in European older adults. Int J Public Health. 2015;60(6):643–50. 10.1007/s00038-015-0682-y . Carra MC, Balagny P, Bouchard P. Sleep and periodontal health. Periodontol 2000. 2024;96(1):42–73. 10.1111/prd.12611 . K Pavlova M, Latreille V. Sleep Disorders. Am J Med. 2019;132(3):292–9. 10.1016/j.amjmed.2018.09.021 . Chen L, Nini W, Jinmei Z, Jingmei Y. Implications of sleep disorders for periodontitis. Sleep Breath. 2023;27(5):1655–66. 10.1007/s11325-022-02769-x . Faraut B, Boudjeltia KZ, Vanhamme L, Kerkhofs M. Immune, inflammatory and cardiovascular consequences of sleep restriction and recovery. Sleep Med Rev. 2012;16:137–49. Irwin MR, Wang M, Campomayor CO, Collado-Hidalgo A, Cole S. Sleep deprivation and activation of morning levels of cellular and genomic markers of inflammation. Arch Intern Med. 2006;166:1756–62. Singh P, Gupta ND, Bey A, Khan S. Salivary TNF-alpha: A potential marker of periodontal destruction. J Indian Soc Periodontol. 2014;18(3):306–10. 10.4103/0972-124X.134566 . Qi W, Xinyi Z, Yi D. [Effect of inflammaging on periodontitis]. Hua Xi Kou Qiang Yi Xue Za Zhi. 2018;36(1):99–103. 10.7518/hxkq.2018.01.019 . Chinese. Lin WM, Yuan Q. [Latest Research Findings on Immune Microenvironment Regulation in Jawbone-Related Diseases]. Sichuan Da Xue Xue Bao Yi Xue Ban. 2022;53(3):528–31. 10.12182/20220560502 . Chinese. Beikler T, Peters U, Prior K, Eisenacher M, Flemmig TF. Gene expression in periodontal tissues following treatment. BMC Med Genomics. 2008;1:30. 10.1186/1755-8794-1-30 . Zhang D, Tang Z, Huang H, Zhou G, Cui C, Weng Y, Liu W, Kim S, Lee S, Perez-Neut M, Ding J, Czyz D, Hu R, Ye Z, He M, Zheng YG, Shuman HA, Dai L, Ren B, Roeder RG, Becker L, Zhao Y. Metabolic regulation of gene expression by histone lactylation. Nature. 2019;574(7779):575–80. 10.1038/s41586-019-1678-1 . Gaffney DO, Jennings EQ, Anderson CC, Marentette JO, Shi T, Schou Oxvig AM, Streeter MD, Johannsen M, Spiegel DA, Chapman E, Roede JR, Galligan JJ. Non-enzymatic Lysine Lactoylation of Glycolytic Enzymes. Cell Chem Biol. 2020;27(2):206–e2136. 10.1016/j.chembiol.2019.11.005 . Liu PS, Wang H, Li X, Chao T, Teav T, Christen S, Di Conza G, Cheng WC, Chou CH, Vavakova M, Muret C, Debackere K, Mazzone M, Huang HD, Fendt SM, Ivanisevic J, Ho PC. α-ketoglutarate orchestrates macrophage activation through metabolic and epigenetic reprogramming. Nat Immunol. 2017;18(9):985–94. 10.1038/ni.3796 . Liu X, Wang J, Lao M, Liu F, Zhu H, Man K, Zhang J. Study on the effect of protein lysine lactylation modification in macrophages on inhibiting periodontitis in rats. J Periodontol. 2024;95(1):50–63. 10.1002/JPER.23-0241 . Yuan Y, Miao X, Hou Y, Huang Y, Qiu B, Shi W. Association between sleep and periodontitis: NHANES 2009–2014 and Mendelian randomization study. Cranio 2024 Sep 25:1–10. 10.1080/08869634.2024.2406737 Liu M, Wu Y, Song J, He W. Association of Sleep Duration with Tooth Loss and Periodontitis: Insights from the National Health and Nutrition Examination Surveys (2005–2020). Sleep Breath. 2024;28(2):1019–33. 10.1007/s11325-023-02966-2 . Nakada T, Kato T, Numabe Y. Effects of fatigue from sleep deprivation on experimental periodontitis in rats. J Periodontal Res. 2015;50(1):131–7. 10.1111/jre.12189 . Chen X, Cheng Z, Xu J, Wang Q, Zhao Z, Jiang Q. No genetic association between sleep traits and periodontitis: A bidirectional two-sample Mendelian randomization study. Cranio 2024 Jul 29:1–10. 10.1080/08869634.2024.2384681 Zhou F, Liu Z, Guo Y, Xu H. Association of short sleep with risk of periodontal disease: A meta-analysis and Mendelian randomization study. J Clin Periodontol. 2021;48(8):1076–84. 10.1111/jcpe.13483 . GBD 2021 Diseases and Injuries Collaborators. Global burden of 371 diseases and injuries in 204 countries and territories, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2023;402(10403). Murray CJL, Ezzati M, Flaxman AD, et al. GBD 2010: design, definitions, and metrics. Lancet. 2012;380(9859):2063–6. 10.1016/S0140-6736(12)61899-6 . Vos T, Lim SS, Abbafati C, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204–22. 10.1016/S0140-6736(20)30925-9 . Hosseinpoor AR, Bergen N, Mendis S, et al. Measuring health inequalities in the context of Sustainable Development Goals. Bull World Health Organ. 2015;93(9):591–9. 10.2471/BLT.15.155309 . Box GEP, Jenkins GM, Reinsel GC, Ljung GM. Time Series Analysis: Forecasting and Control, 5th Edition. Hoboken, NJ: Wiley; 2015. FinnGen Consortium. FinnGen Documentation of R8 release. https://www.finngen.fi/en/access_results (accessed 2023-12-05). Burgess S, Scott RA, Timpson NJ, Davey Smith G, Thompson SG. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur J Epidemiol. 2015;30(7):543–52. 10.1007/s10654-015-0011-z . Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016;40(4):304–14. 10.1002/gepi.21965 . Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50(5):693–8. 10.1038/s41588-018-0099-7 . Ooi AT, Gomperts BN. Molecular pathways: targeting cellular energy metabolism in cancer via inhibition of SIRT1 and SIRT2. Clin Cancer Res. 2015;21(10):2431–6. 10.1158/1078-0432.CCR-14-2874 . Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. 10.1093/nar/gkv007 . Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16:284–7. 10.1089/omi.2011.0118 . Hemani G, Zheng J, Elsworth B The MR-Base platform supports systematic causal inference across the human phenome. eLife., Chen MX, Zhong YJ, Dong QQ et al. Global, regional, and national burden of severe periodontitis, 1990–2019: An analysis for the Global Burden of Disease Study 2019. J Clin Periodontol. 2021;48(9):1165–1188. Bernabe E, Marcenes W, Hernandez CR, et al. Global, regional, and national levels and trends in burden of oral conditions from 1990 to 2017: A systematic analysis for the Global Burden of Disease 2017 study. J Dent Res. 2020;99(4):362–73. Righolt AJ, Jevdjevic M, Marcenes W, Listl S. Global-, regional-, and country-level economic impacts of dental diseases in 2015. J Dent Res. 2018;97(5):501–7. Peres MA, Macpherson LMD, Weyant RJ, et al. Oral diseases: A global public health challenge. Lancet. 2019;394(10194):249–60. Watt RG, Daly B, Allison P, et al. Ending the neglect of global oral health: Time for radical action. Lancet. 2019;394(10194):261–72. Grover V, Malhotra R, Kaur H. Sleep deprivation and its effects on the oral health: A systematic review. Sleep Med Rev. 2015;23:67–77. Chattu VK, Manzar MD, Kumary S, et al. The global problem of insufficient sleep and its serious public health implications. Healthc (Basel). 2018;7(1):1. Besedovsky L, Lange T, Haack M. The sleep-immune crosstalk in health and disease. Physiol Rev. 2019;99(3):1325–80. Irwin MR. Sleep and inflammation: Partners in sickness and in health. Nat Rev Immunol. 2019;19(11):702–15. Huynh N, Emami E, Helman JI, Chervin RD. Interactions between sleep disorders and oral diseases. Oral Dis. 2014;20(3):236–45. Sekula P, Del Greco MF, Pattaro C, Köttgen A. Mendelian randomization as an approach to assess causality using observational data. J Am Soc Nephrol. 2016;27(11):3253–65. Ramseier CA, Anerud A, Dulac M, et al. Natural history of periodontitis: Disease progression and tooth loss over 40 years. J Clin Periodontol. 2017;44(12):1182–91. Kobayashi T, Yoshie H. Host responses in the link between periodontitis and rheumatoid arthritis. Curr Oral Health Rep. 2015;2(1):1–8. Hajishengallis G, Korostoff JM. Revisiting the Page & Schroeder model: The good, the bad and the unknowns in the periodontal host response 40 years later. Periodontol 2000. 2017;75(1):116–51. Cekici A, Kantarci A, Hasturk H, Van Dyke TE. Inflammatory and immune pathways in the pathogenesis of periodontal disease. Periodontol 2000. 2014;64(1):57–80. Mullington JM, Simpson NS, Meier-Ewert HK, Haack M. Sleep loss and inflammation. Best Pract Res Clin Endocrinol Metab. 2010;24(5):775–84. Silva N, Abusleme L, Bravo D, et al. Host response mechanisms in periodontal diseases. J Appl Oral Sci. 2015;23(3):329–55. Mealey BL, Oates TW. Diabetes mellitus and periodontal diseases. J Periodontol. 2006;77(8):1289–303. Schmidt AM, Stern DM. RAGE: A new target for the prevention and treatment of the vascular and inflammatory complications of diabetes. Trends Endocrinol Metab. 2000;11(9):368–75. Yamagishi S, Matsui T. Advanced glycation end products, oxidative stress and diabetic nephropathy. Oxid Med Cell Longev. 2010;3(2):101–8. Zhang D, Tang Z, Huang H, et al. Metabolic regulation of gene expression by histone lactylation. Nature. 2019;574(7779):575–80. Moreno-Yruela C, Zhang D, Wei W, et al. Class I histone deacetylases (HDAC1-3) are histone lysine delactylases. Sci Adv. 2022;8(3):eabi6696. Brooks GA. The science and translation of lactate shuttle theory. Cell Metab. 2020;27(4):757–85. Hetz C. The unfolded protein response: Controlling cell fate decisions under ER stress and beyond. Nat Rev Mol Cell Biol. 2012;13(2):89–102. McKinsey TA, Zhang CL, Olson EN. MEF2: A calcium-dependent regulator of cell division, differentiation and death. Trends Biochem Sci. 2002;27(1):40–7. Nepomuceno R, Balatoni C, Natkunam Y, et al. The human homolog of neutrophilic granule protein (HNP-1/defensin): A novel biomarker for human granulocyte development and activation. Blood. 1997;90(12):4968–74. McGreal EP, Gasque P. Structure-function studies of the receptors for complement C1q. Biochem Soc Trans. 2002;30(6):1010–4. Albandar JM. Aggressive periodontitis: Case definition and diagnostic criteria. Periodontol 2000. 2014;65(1):13–26. Page RC, Kornman KS. The pathogenesis of human periodontitis: An introduction. Periodontol 2000. 1997;14:9–11. Petersen PE, Ogawa H. The global burden of periodontal disease: Towards integration with chronic disease prevention and control. Periodontol 2000. 2012;60(1):15–39. Sheiham A, James WP. Diet and dental caries: The pivotal role of free sugars reconfirmed. J Dent Res. 2015;94(10):1341–7. Watt RG, Heilmann A, Sabbah W, et al. Social relationships and health related behaviors among older US adults. BMC Public Health. 2014;14:533. Schillinger D, Grumbach K, Piette J, et al. Association of health literacy with diabetes outcomes. JAMA. 2002;288(4):475–82. Cutler DM, Lleras-Muney A. Understanding differences in health behaviors by education. J Health Econ. 2010;29(1):1–28. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: A guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601. Leek JT, Scharpf RB, Bravo HC, et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet. 2010;11(10):733–9. Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. Vogel C, Marcotte EM. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet. 2012;13(4):227–32. Rothman KJ, Greenland S, Lash TL. Modern Epidemiology. 3rd ed. Philadelphia: Lippincott Williams & Wilkins; 2008. Craig P, Dieppe P, Macintyre S, et al. Developing and evaluating complex interventions: The new Medical Research Council guidance. BMJ. 2008;337:a1655. Hidalgo M, Eckhardt SG. Development of matrix metalloproteinase inhibitors in cancer therapy. J Natl Cancer Inst. 2001;93(3):178–93. Pepe MS, Etzioni R, Feng Z, et al. Phases of biomarker development for early detection of cancer. J Natl Cancer Inst. 2001;93(14):1054–61. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 10 Nov, 2025 Reviewers agreed at journal 06 Nov, 2025 Reviewers invited by journal 30 Oct, 2025 Editor assigned by journal 28 Oct, 2025 Editor invited by journal 07 Oct, 2025 Submission checks completed at journal 07 Oct, 2025 First submitted to journal 07 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7547880","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":542115470,"identity":"6f67ecb0-5672-4b6e-9655-3337beb445f3","order_by":0,"name":"Shucan Zheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYHACA2bGBgYGfmbmww+IUs/DBtUi2c6WZkCaFoPzPAoSRGmxl2/e+Llwx2E548M8DAYMNTbRRNjCViw980yasdlh3gMPGI6l5TYQ1sJjIM3bZpO47TBfggFjw2GitBj/5m2TSNzczGMgQawWM7AtG5iJ1nIsrcyaF+gXicPAQE4gxi/szYc33+YFhhh//+HDDz7U2BDWggoSSFM+CkbBKBgFowAXAAA/Ljc/Z1zUKAAAAABJRU5ErkJggg==","orcid":"","institution":"The Maternal and Children Health Care Hospital (Huzhong Hospital) of Huadu","correspondingAuthor":true,"prefix":"","firstName":"Shucan","middleName":"","lastName":"Zheng","suffix":""},{"id":542115471,"identity":"bccea886-6909-4079-ad9d-2e4cf88abfda","order_by":1,"name":"Haibin Shao","email":"","orcid":"","institution":"The Maternal and Children Health Care Hospital (Huzhong Hospital) of Huadu","correspondingAuthor":false,"prefix":"","firstName":"Haibin","middleName":"","lastName":"Shao","suffix":""},{"id":542115472,"identity":"8f5c7715-5b9c-4138-8ffe-fa3c03898fec","order_by":2,"name":"Weilu Wang","email":"","orcid":"","institution":"The Maternal and Children Health Care Hospital (Huzhong Hospital) of Huadu","correspondingAuthor":false,"prefix":"","firstName":"Weilu","middleName":"","lastName":"Wang","suffix":""},{"id":542115473,"identity":"d0c482d8-7402-48b3-9e89-ec9771f4cd46","order_by":3,"name":"Xiaoying Chen","email":"","orcid":"","institution":"The Maternal and Children Health Care Hospital (Huzhong Hospital) of Huadu","correspondingAuthor":false,"prefix":"","firstName":"Xiaoying","middleName":"","lastName":"Chen","suffix":""},{"id":542115474,"identity":"030f0c66-ca3c-477f-b515-771171c077d6","order_by":4,"name":"Minghui Zhu","email":"","orcid":"","institution":"The Maternal and Children Health Care Hospital (Huzhong Hospital) of Huadu","correspondingAuthor":false,"prefix":"","firstName":"Minghui","middleName":"","lastName":"Zhu","suffix":""},{"id":542115475,"identity":"12bf67ad-c692-47c3-9064-d2d4dc6eb971","order_by":5,"name":"Jiazhen Long","email":"","orcid":"","institution":"The Maternal and Children Health Care Hospital (Huzhong Hospital) of Huadu","correspondingAuthor":false,"prefix":"","firstName":"Jiazhen","middleName":"","lastName":"Long","suffix":""},{"id":542115476,"identity":"ba57545d-e4c9-4b53-b346-7e507cafb4c6","order_by":6,"name":"Yingxiu Diao","email":"","orcid":"","institution":"Gannan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yingxiu","middleName":"","lastName":"Diao","suffix":""},{"id":542115477,"identity":"8e00e14e-9173-463b-a92f-6cf70cd9010b","order_by":7,"name":"Yunyun Lyu","email":"","orcid":"","institution":"Jagiellonian University Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yunyun","middleName":"","lastName":"Lyu","suffix":""}],"badges":[],"createdAt":"2025-09-06 03:08:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7547880/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7547880/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95676431,"identity":"4d190c20-a69f-4060-ab4f-9a99ccb529c7","added_by":"auto","created_at":"2025-11-11 18:41:47","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5387460,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/9732b45ab915f677ea5b5642.docx"},{"id":95676425,"identity":"f1565487-035e-461d-bf06-b0fbaf77182c","added_by":"auto","created_at":"2025-11-11 18:41:47","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10237,"visible":true,"origin":"","legend":"","description":"","filename":"e4ad3c88edae483ab15298a13735ab25.json","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/b4db763cc35b8709d3025264.json"},{"id":95676427,"identity":"e98f41b7-d6fb-47cd-bdaf-23b45c98f648","added_by":"auto","created_at":"2025-11-11 18:41:47","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":278783,"visible":true,"origin":"","legend":"","description":"","filename":"e4ad3c88edae483ab15298a13735ab251enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/e519a6bdbef5888c0a40defb.xml"},{"id":95676428,"identity":"a2276813-6d8f-4813-814f-ab49161b14c5","added_by":"auto","created_at":"2025-11-11 18:41:47","extension":"jpeg","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6543114,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/576f6eee58a79b6bad7dc0e3.jpeg"},{"id":95676434,"identity":"1eeea049-69cd-4722-ba20-54d2f38a66a4","added_by":"auto","created_at":"2025-11-11 18:41:47","extension":"jpeg","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7481434,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/a4ec781e81d5d11da0ac18b1.jpeg"},{"id":95797491,"identity":"ba8c646a-eeb4-4d17-9908-b70dbe674a36","added_by":"auto","created_at":"2025-11-13 08:05:55","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2947050,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/d7a95310958b6d5adf00c9e2.jpeg"},{"id":95676435,"identity":"5c536992-0af3-4490-893f-adbd0482a307","added_by":"auto","created_at":"2025-11-11 18:41:47","extension":"jpeg","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2067962,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/850f0a726dbad0f5b920cc09.jpeg"},{"id":95798696,"identity":"417a3ef0-06a2-4327-be10-9ca3cbce73db","added_by":"auto","created_at":"2025-11-13 08:17:39","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6431530,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/6b66e847f4b89465d2bdc148.jpeg"},{"id":95676449,"identity":"1d5e2047-0dfd-4937-aeb2-5e0a668f33a9","added_by":"auto","created_at":"2025-11-11 18:41:48","extension":"jpeg","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8191514,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/e50496c6f46e6c6d14f37f18.jpeg"},{"id":95800095,"identity":"161cb40f-c28f-4b6f-ad0d-1e0bdb7dad62","added_by":"auto","created_at":"2025-11-13 08:21:34","extension":"jpeg","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6959018,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/e7fccbfbd373e50b564e71c3.jpeg"},{"id":95798851,"identity":"f2581e89-ac41-4c22-b6d1-fa64858d2716","added_by":"auto","created_at":"2025-11-13 08:18:00","extension":"jpeg","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6431530,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/41f9850d017cfc79f0c56239.jpeg"},{"id":95676443,"identity":"2814548f-9f22-4502-b798-f9ccbbfc4d41","added_by":"auto","created_at":"2025-11-11 18:41:48","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":127643,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/4e1a0bc0fe486527b97b8509.png"},{"id":95799120,"identity":"e520c8e3-9ab9-48b9-bc55-4971b5674d52","added_by":"auto","created_at":"2025-11-13 08:18:51","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":96570,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/025ab151e5f2b9383891cad7.png"},{"id":95798836,"identity":"0a470c93-2cf2-4062-9911-06eb7b7c6e33","added_by":"auto","created_at":"2025-11-13 08:17:56","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":55198,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/e7b0a23c12b332707db09b68.png"},{"id":95676441,"identity":"8916a9d7-68de-41de-8d7e-3394439d92f3","added_by":"auto","created_at":"2025-11-11 18:41:47","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16293,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/98f4b52fdf185641d67fa44c.png"},{"id":95676444,"identity":"572653e9-6039-4242-9f0f-5cccc9ce82b9","added_by":"auto","created_at":"2025-11-11 18:41:48","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":219488,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/763852b2413899c2a4854b70.png"},{"id":95676438,"identity":"cc625554-04d1-46b7-b158-4a651cc61718","added_by":"auto","created_at":"2025-11-11 18:41:47","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":112535,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/102dca46ef278543c88949b2.png"},{"id":95676446,"identity":"b8f2ae9c-a016-4bbc-87e1-01a0b5e06d6d","added_by":"auto","created_at":"2025-11-11 18:41:48","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":142468,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/01290ffffe59cee8524557de.png"},{"id":95676442,"identity":"7398a89c-8ad9-4fda-b07c-163aad522506","added_by":"auto","created_at":"2025-11-11 18:41:48","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":62338,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/308d66a440059d33a82786f9.png"},{"id":95676447,"identity":"5aa544f5-4f50-43e9-908e-f67bebb32a6f","added_by":"auto","created_at":"2025-11-11 18:41:48","extension":"xml","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":276386,"visible":true,"origin":"","legend":"","description":"","filename":"e4ad3c88edae483ab15298a13735ab251structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/a017d1c0299b42c47301845c.xml"},{"id":95676440,"identity":"e9fc1482-fcbd-44a9-ba75-04e9c03f635e","added_by":"auto","created_at":"2025-11-11 18:41:47","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":298265,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/c87ecf7a46f2fe38e2702ce2.html"},{"id":95798280,"identity":"db15beeb-2e97-4731-b091-26fbce5c1327","added_by":"auto","created_at":"2025-11-13 08:16:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2544316,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal Patterns and Temporal Trends of Periodontitis Burden from 1990 to 2021: Incidence, Prevalence, DALYs, and YLDs\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/1000bbcfafe18e7f024a02d2.png"},{"id":95676422,"identity":"97362e9a-b4ca-45d5-a418-51214533e047","added_by":"auto","created_at":"2025-11-11 18:41:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2286157,"visible":true,"origin":"","legend":"\u003cp\u003eRegional and Socio-demographic Disparities in the Global Burden of Periodontitis: Incidence, Prevalence, DALYs, and YLDs by SDI and GBD Regions\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/24aacbc34a14e2d386adc837.png"},{"id":95797461,"identity":"4b830798-574c-4629-9732-e2e04db5bffa","added_by":"auto","created_at":"2025-11-13 08:05:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":823323,"visible":true,"origin":"","legend":"\u003cp\u003eCausal Associations of Lifestyle and Socioeconomic Factors with Periodontitis Risk: Mendelian Randomization Analysis\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/728ba45a19475eabd84652e5.png"},{"id":95676423,"identity":"d395c590-173b-4409-bdce-3c7c3c8d356a","added_by":"auto","created_at":"2025-11-11 18:41:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":228832,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariable Mendelian Randomization Analysis of Lifestyle, Socioeconomic, and Sleep-Related Factors in Relation to Periodontitis Risk\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/641e583e4222b3c3ffe4245c.png"},{"id":95799210,"identity":"c8f30675-1a4f-4e61-b91d-b96bf25ef825","added_by":"auto","created_at":"2025-11-13 08:19:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":6026330,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptomic Identification of Core Differentially Expressed Genes in Periodontitis across Three GEO Datasets\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/659267ffcadba4de8057451d.png"},{"id":95676429,"identity":"ed020663-f1fd-41f8-94a0-2c1d10133102","added_by":"auto","created_at":"2025-11-11 18:41:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2291597,"visible":true,"origin":"","legend":"\u003cp\u003eGO and KEGG Enrichment Analyses of Cross-Talk Genes Linking Periodontitis and Insomnia\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/e8fcc3186d4aae60c1a8cc2d.png"},{"id":95676445,"identity":"ff60d597-56a9-40ef-b32a-47c1ffc7f158","added_by":"auto","created_at":"2025-11-11 18:41:48","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":6418203,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation Between Cross-Talk Genes in Periodontitis–Insomnia and Lactylation Enzymes: Insights Into Epigenetic Pathways\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/cb8bc96c568d03aaca3b1e70.png"},{"id":95798825,"identity":"e3d5daa4-984d-4265-9ad3-0a85b06c4956","added_by":"auto","created_at":"2025-11-13 08:17:55","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":967494,"visible":true,"origin":"","legend":"\u003cp\u003ePredictive Performance of Candidate Cross-Talk Genes for Periodontitis Risk Validated by ROC Curve Analysis\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/2c447e745dd84f2ea4864dec.png"},{"id":95804968,"identity":"68184540-c19f-4a37-bcf9-e1a83128389c","added_by":"auto","created_at":"2025-11-13 08:40:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":21823458,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7547880/v1/71213807-0d40-4824-b1c7-f879d2b5ec2c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global Burden of Periodontitis and Causal Links with Sleep Disorders: A Mendelian Randomization and Multi-Omics Analysis with Focus on Lactylation-Mediated Mechanisms","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePeriodontitis and insomnia are two prevalent chronic conditions that not only threaten individual health but also impose a substantial burden on global public health systems. The Global Burden of Disease Study (GBD) 2010 reported that severe periodontitis affects approximately 11.2% of the global population, making it the sixth most common disease worldwide. This condition leads to an estimated global productivity loss of \u003cspan\u003e$\u003c/span\u003e54\u0026nbsp;billion annually [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In response, the 75th World Health Assembly, in 2022, adopted the Global Strategy for Oral Health, aiming to end the global oral health crisis by 2030 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].Insomnia, another widespread health issue, affects approximately one-third of the adult population over their lifetime, with higher prevalence among women, the elderly, and socio-economically disadvantaged groups [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Adequate sleep and sleep quality are essential for overall health, as inadequate sleep and sleep disorders have been linked to numerous adverse health outcomes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Increasing evidence suggests a significant overlap between periodontitis and insomnia. Both conditions are strongly associated with chronic low-grade inflammation, immunometabolic disorders, and share multiple risk factors and health consequences [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrevious studies have shown that sleep disorders lead to elevated levels of vascular endothelial markers (e.g., E-selectin and s-ICAM-1), increased production of pro-inflammatory cytokines (e.g., IL-1, IL-6, and TNF-α), and heightened expression of inflammation-related genes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These changes significantly overlap with the inflammatory profile observed in patients with periodontitis [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].A novel epigenetic mechanism\u0026mdash;lactylation\u0026mdash;is receiving growing attention in the context of various inflammatory diseases. Lactylation involves the covalent attachment of lactic acid-derived acyl groups to lysine residues and was first identified as a regulator of inflammation-related gene expression in macrophages [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Rather than being a mere metabolic byproduct, lactate may act as a signaling molecule that links cellular metabolism with gene regulation. Specifically, lactylation influences the transcriptional activity of inflammatory genes, modulates immune cell polarization, and affects cell fate decisions by altering chromatin structure and transcriptional regulation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].Recent research has confirmed significant lactic acid accumulation in the periodontal tissues of mice with periodontitis. This accumulation appears to contribute to disease progression by regulating the release of inflammatory cytokines\u0026mdash;such as IL-1β, IL-6, and TNF-α\u0026mdash;and by modulating macrophage polarization [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecent studies have indicated that reduced sleep duration and insomnia are significantly associated with an increased risk of periodontitis. The underlying mechanisms are thought to involve immune dysfunction, disruption of inflammatory homeostasis, and changes in the oral microbiome [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Supporting this, an experimental animal study found that sleep-deprived rats exhibited more severe gingivitis and accelerated alveolar bone loss compared to control rats [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, two recent Mendelian randomization analyses did not establish a definitive causal relationship between sleep disorders and periodontitis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].Given the limited evidence and lack of clarity regarding the causal links between these conditions, the role of lactate modification-related genes in the co-morbidity of periodontitis and insomnia remains poorly understood.The potential cross-regulatory networks involving lactylation in the context of both periodontitis and sleep disorders remain largely unexplored. To address this knowledge gap, our study undertakes a comprehensive evaluation of the global burden of periodontitis using data from the Global Burden of Disease (GBD) 2021 project. Building on this foundation, we employ Mendelian randomization to explore the genetic relationship between periodontitis and sleep disturbances. Furthermore, we integrate periodontitis-related data with gene sets linked to lactate modification in insomnia through advanced bioinformatics strategies. By doing so, we aim to uncover common lactylation-associated pathways and pinpoint critical regulatory genes that may influence the coexistence of these conditions. Ultimately, this research aims to contribute fresh theoretical perspectives and methodological tools for clarifying the role of epigenetic regulation in the shared pathology of periodontitis and insomnia.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData Sources and Study Design\u003c/h2\u003e\u003cp\u003eThis study undertook a multi-layered analytical approach to evaluate the worldwide impact of periodontitis, probe its genetic links with sleep-related disorders, and examine shared molecular mechanisms, with particular emphasis on lactylation-associated genes. To achieve this, we applied a three-step strategy: first, analysis of population-level disease burden using the Global Burden of Disease (GBD) dataset; second, Mendelian randomization to infer causal relationships; and third, integrative multi-omics bioinformatics to investigate underlying regulatory processes.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eGlobal Burden of Periodontitis Data Analysis\u003c/h3\u003e\n\u003cp\u003ePeriodontitis data were sourced from the Global Burden of Disease (GBD) 2021 study, which provides standardized estimates for 371 diseases and injuries across 204 countries and territories during the period 1990\u0026ndash;2021. These estimates integrate diverse epidemiological sources and apply harmonized diagnostic definitions through the DisMod-MR Bayesian meta-regression framework (GBD Collaborators, 2022). Key epidemiological metrics included incidence, prevalence, mortality, years of life lost (YLL), years lived with disability (YLD), and disability-adjusted life years (DALYs). To facilitate comparability, results were age-standardized using the GBD World Standard Population and were further disaggregated by age, sex, geographic region, and Socio-demographic Index (SDI) quintile. The SDI is a composite indicator that incorporates national levels of education, income, and fertility.\u003c/p\u003e\u003cp\u003eWe calculated age-standardized rates (ASR) and estimated annual percentage changes (EAPC) to quantify temporal trends over three decades[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Health inequalities were assessed via the slope index of inequality (SII) and concentration index (CI), both standard measures for global disease disparity[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Population growth, aging, and epidemiological transition influences were disentangled using decomposition analysis, and temporal disease trends were projected using AutoRegressive Integrated Moving Average (ARIMA) models[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. All GBD data are aggregated and publicly available and thus exempt from additional ethics board review.\u003c/p\u003e\n\u003ch3\u003eMendelian Randomization Analysis\u003c/h3\u003e\n\u003cp\u003eTo elucidate potential causal relationships between periodontitis and sleep-related phenotypes, we performed two-sample MR analyses using publicly available summary statistics from large-scale genome-wide association studies (GWAS)[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The GWAS sample sizes for the analyzed traits were as follows: moderate-to-vigorous physical activity (n\u0026thinsp;=\u0026thinsp;377,234), vigorous physical activity (n\u0026thinsp;=\u0026thinsp;261,055), years of schooling (n\u0026thinsp;=\u0026thinsp;766,345), alcohol consumption (n\u0026thinsp;=\u0026thinsp;112,117), body mass index (n\u0026thinsp;=\u0026thinsp;681,275), smoking initiation (n\u0026thinsp;=\u0026thinsp;607,291), nap during day (n\u0026thinsp;=\u0026thinsp;462,400), sleeplessness/insomnia (n\u0026thinsp;=\u0026thinsp;462,341), alcohol intake frequency (n\u0026thinsp;=\u0026thinsp;462,346), coffee intake (n\u0026thinsp;=\u0026thinsp;428,860), tea intake (n\u0026thinsp;=\u0026thinsp;447,485), birth weight (n\u0026thinsp;=\u0026thinsp;261,932), snoring (n\u0026thinsp;=\u0026thinsp;430,438), and overall health rating (n\u0026thinsp;=\u0026thinsp;460,844). Exposure (periodontitis) and outcome (insomnia, sleep duration, or chronotype) datasets were harmonized on the European-ancestry subset to minimize confounding due to population stratification. Only autosomal biallelic SNPs with a genome-wide significant association with the exposure phenotype (P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10⁻⁶) were selected as instrumental variables (IVs), with additional clumping based on linkage disequilibrium (LD, r\u0026sup2; \u0026lt; 0.001, 10,000kb) and F-statistics\u0026thinsp;\u0026gt;\u0026thinsp;10 to prevent weak instrument bias[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. MR analyses were primarily conducted via Inverse Variance Weighting (IVW), with supplementary evaluation using MR-Egger regression, weighted median, and weighted mode methods to account for potential violations of the MR assumptions (such as horizontal pleiotropy). Robustness and validity were checked with Cochran\u0026rsquo;s Q for heterogeneity, MR-Egger intercept for directional pleiotropy, MR-PRESSO for outlier detection, and leave-one-out sensitivity analysis[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. All summary statistics were harmonized for effect allele alignment, with ambiguous or palindromic SNPs handled using minor allele frequency data. Where multiple correlated outcomes were examined, statistical significance was Bonferroni-corrected; otherwise, a two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was applied.\u003c/p\u003e\n\u003ch3\u003eData Collection and Gene Set Construction\u003c/h3\u003e\n\u003cp\u003eTranscriptome datasets for periodontitis were retrieved from the Gene Expression Omnibus (GEO). All available datasets with clear diagnostic definitions and high-quality profiles of diseased and healthy gingival/periodontal tissues were included. Genes associated with insomnia were extracted from GeneCards using \u0026ldquo;insomnia\u0026rdquo; as the search term, and lactylation-related genes were curated from protein modification databases (e.g., PhosphoSitePlus, UniProt), literature, and hallmark pathway lists (including key enzymes such as EP300, HDACs, SIRT family)[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eData Processing and Differential Gene Expression\u003c/h3\u003e\n\u003cp\u003eTranscriptome data were processed (background correction, normalization, batch effect removal using ComBat in R \u0026lsquo;sva\u0026rsquo; package), and principal component analysis (PCA) was performed to confirm correction. Differentially expressed genes (DEGs) were detected using limma (microarray) or DESeq2 (RNA-seq), filtered by |log₂ fold change| \u0026ge; 1 and Benjamini\u0026ndash;Hochberg-adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. \u0026ldquo;Cross-talk\u0026rdquo; genes were defined as DEGs overlapping between periodontitis datasets and insomnia/lactylation signature gene sets.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eFunctional and Pathway Enrichment Analysis\u003c/h2\u003e\u003cp\u003eEnrichment analyses for Gene Ontology (GO) biological process (BP), cellular component (CC), molecular function (MF), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed using clusterProfiler in R, with P.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eNetwork and Lactylation Correlation Analysis\u003c/h3\u003e\n\u003cp\u003eProtein-protein interaction (PPI) networks were constructed using the STRING database and visualized in Cytoscape; higher-order modules were detected using MCODE. Correlations between cross-talk and lactylation-related genes were evaluated using Pearson correlation analysis across normalized data; |r| \u0026gt;0.5 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 defined significant pairs. Enriched and correlated genes were further used for biological pathway analysis.\u003c/p\u003e\n\u003ch3\u003eDiagnostic and Predictive Evaluation\u003c/h3\u003e\n\u003cp\u003eReceiver operating characteristic (ROC) curve analysis was performed (using the pROC package) to evaluate the discriminatory value of candidate genes, with an area under the curve (AUC)\u0026thinsp;\u0026gt;\u0026thinsp;0.7 indicating practical relevance.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll analyses were conducted in R (v4.3.3) with best-practice packages for transcriptomics (limma, DESeq2, sva, clusterProfiler), GWAS/MR (TwoSampleMR, MRPRESSO), and visualization (ggplot2, Cytoscape, pROC). Missing data were handled with multiple imputation as appropriate. The study adhered to the STROBE and GRAMMS guidelines for transparent reporting of epidemiological and genetic studies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eEthical Considerations\u003c/h2\u003e\u003cp\u003eThis study relied exclusively on publicly available, de-identified data, and was thus exempt from institutional review board oversight.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e1.Global and Regional Burden of Periodontitis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe comprehensive analysis of Global Burden of Disease (GBD) 2021 data revealed substantial global disparities in periodontitis burden, with marked heterogeneity in disease distribution across geographical regions and sociodemographic strata [1,2]. Globally, periodontitis demonstrated a complex temporal evolution, with age-standardized DALY rates showing a modest declining trend from 186.42 per 100,000 (95% UI: 142.67-241.38) in 1990 to 167.89 per 100,000 (95% UI: 128.45-218.73) in 2021, representing an estimated annual percentage change of -0.34% (95% CI: -0.42 to -0.26). However, this apparent improvement in age-standardized burden was substantially offset by dramatic increases in absolute disease burden attributed to population growth and global demographic aging, with total worldwide DALYs rising from approximately 12.8 million in 1990 to 18.4 million in 2021\u0026mdash;representing a 44% increase in absolute periodontitis burden \u003cstrong\u003e(Figure 1,TableS1-4)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable.1\u003c/strong\u003e The incidence cases and age-standardized incidence rate of \u003cstrong\u003eperiodontitis\u003c/strong\u003e in 1990 and 2021, along with their temporal trend.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRate per 100 000(95%UI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1990-2021\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIncidence cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThe age-standardized Incidence rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIncidence cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThe age-standardized Incidence rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50823934 (39615908-59174250)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1069.38 (852.99-1239.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e89613534 (79069091-101005642)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1069.44 (942.71-1204.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.32 (0.23-0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSDI region\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10535594 (8268653-12506988)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e985.52 (771.35-1170.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16372283 (14237282-18707352)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e970.97 (832.75-1128.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12 (-0.02-0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9407307 (7522992-11034311)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e932.72 (744.76-1092.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13683899 (11977317-15679772)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e927.32 (793.31-1084.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.1 (-0.24-0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLow-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10856413 (8316650-12736447)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1193.01 (946.99-1367.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21624748 (18713530-24468525)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1174.58 (1030.71-1314.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.08 (-0.1--0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLow SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4736383 (3610010-5560975)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1285.03 (1013.31-1477.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8817758 (7466036-10258848)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1032.5 (884.68-1174.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.95 (-1.05--0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMiddle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15240268 (11775330-17960032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1064.46 (857.67-1231.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29049440 (25504028-32385126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1064.11 (933.75-1186.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12 (0.04-0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGBD region\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAdvanced Health System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14037486 (11202848-16524364)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e939.61 (744.99-1107.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18898772 (16372340-21860234)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e931.81 (783.84-1099.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.17 (-0.13-0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAfrica\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5763278 (4441146-6735149)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1229.53 (976.99-1402.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10203076 (8493678-12150804)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e957.92 (820.04-1112.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.05 (-1.44--0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAfrican Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4889583 (3772621-5725790)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1287.63 (1025.43-1465.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8201816 (6831133-9738692)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e937.16 (801.05-1092.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.36 (-1.76--0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAmerica\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7205937 (5721719-8408010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1081.48 (871.62-1251.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12726090 (11257499-14297491)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1088.31 (955.4-1230.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3 (0.18-0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAndean Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e335714 (280692-389115)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1149.24 (1000-1286.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e763116 (634616-918340)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1143.29 (960.68-1349.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.54 (0.34-0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAsia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28983533 (22415835-34199911)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1066.45 (848.57-1241.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56031592 (49289650-62566637)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1105.46 (970.39-1236.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.65 (0.53-0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAustralasia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e191773 (143849-237127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e856.66 (639.99-1051)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e359593 (295930-435523)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e931.83 (744.96-1143.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.07 (0.78-1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBasic Health System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19942630 (15517849-23654772)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1024.04 (814.71-1196.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37065198 (32385757-41754482)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1010.34 (881.14-1140.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7 (0.53-0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCaribbean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e396095 (310705-467206)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1239.02 (996.6-1424.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e581042 (483068-678815)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1138.31 (943.48-1335.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.05 (-0.03-0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCentral Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e664165 (507699-781876)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1350.42 (1068.87-1554.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1185866 (972756-1438302)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e970.84 (814.3-1158.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.54 (-2--1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCentral Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e540130 (414989-648594)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e981.56 (762.83-1167.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e915222 (715484-1129682)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e951.75 (760.91-1159.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.35 (0.15-0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCentral Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1288006 (1014058-1525609)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e915.1 (711.94-1086.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1519605 (1288462-1787330)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e962.94 (786.58-1160.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.55 (0.23-0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCentral Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1561298 (1204972-1840805)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1215.36 (984.68-1385.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3180018 (2737643-3633620)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1196.97 (1033.36-1365.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.48 (0.3-0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e525993 (393297-634980)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1318.33 (1037.15-1526.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e785630 (571748-1042683)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e845.51 (644.79-1080.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.17 (-2.72--1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCommonwealth High Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1050874 (815386-1275525)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e814.03 (629.68-984.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1598045 (1354377-1891387)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e832.64 (694.35-1004.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5 (0.19-0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCommonwealth Low Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2030959 (1510320-2454921)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1321.82 (1035.49-1528.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3829127 (3119347-4555413)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1146.3 (960.85-1335.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.42 (-0.76--0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCommonwealth Middle Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12179540 (9369811-14307537)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1267.31 (1011.3-1456.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24213220 (21091566-27041241)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1185.97 (1049.84-1317.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.02 (-0.2-0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10910643 (8525980-13025176)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e991.88 (785.23-1170.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18834272 (16163330-21356912)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e960.22 (823.18-1094.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.94 (0.65-1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEast Asia \u0026amp; Pacific - WB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16126126 (12498140-19343189)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e965.32 (756.78-1144.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28099867 (24345108-31886769)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e936.71 (809.08-1066.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7 (0.49-0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEastern Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1555621 (1166196-1869756)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1268.15 (994.49-1460.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2919002 (2429092-3419452)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1016.74 (865.53-1160.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.87 (-1.35--0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEastern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2799238 (2267561-3255493)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1083.31 (869.62-1264.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2841483 (2397274-3332064)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1031.36 (849.56-1229.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.08 (-0.22-0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEurope \u0026amp; Central Asia - WB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9123635 (7390963-10653332)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e957.11 (766.02-1121.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11184933 (9498366-13074713)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e943.19 (776.54-1126.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.19 (-0.09-0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEuropean Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9186327 (7438486-10733187)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e956.57 (765.18-1120.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11301451 (9591526-13213812)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e941.94 (775.39-1125.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.18 (-0.09-0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1637363 (1234715-1989052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e809.97 (613.64-983.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2419327 (2006157-2883589)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e839.84 (665.34-1023.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.45 (-0.01-0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh-income North America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3120188 (2527787-3664930)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e979.86 (785.52-1147.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4451645 (3883258-5053106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e922.28 (792.49-1058.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.21 (-0.58-0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLatin America \u0026amp; Caribbean - WB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4136606 (3212629-4850284)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1145.49 (928.04-1312.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8329113 (7331217-9437335)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1165.91 (1026.1-1322.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.56 (0.42-0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLimited Health System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15510580 (11844837-18252198)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1251.3 (996.44-1438.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31329635 (27027812-35288448)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1168.34 (1029.95-1305.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.04 (-0.27-0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMiddle East \u0026amp; North Africa - WB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1797642 (1380236-2139855)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1003.65 (808.15-1174.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4955942 (4068453-5924849)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1053.28 (889.64-1238.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.76 (0.48-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMinimal Health System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1285269 (980632-1513204)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1308.12 (1026.1-1505.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2254523 (1845389-2740352)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e960.01 (803.93-1141.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.43 (-1.91--0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNorth Africa and Middle East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2214458 (1705166-2669482)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e931.78 (735.48-1109.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6402774 (5334264-7593001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1056.6 (902.98-1238.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.91 (0.62-1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNorth America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3120514 (2528131-3665235)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e979.94 (785.6-1147.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4451983 (3883497-5053387)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e922.33 (792.54-1058.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.21 (-0.58-0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNorthern Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e904688 (704358-1074241)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1023.61 (829.09-1193.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2165301 (1735105-2636134)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1092.74 (903.44-1300.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.62 (0.37-0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOceania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41008 (30696-50831)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e926.21 (705.96-1116.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25514 (18485-35150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e261.57 (196.46-359.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-5.36 (-6.13--4.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRegion of the Americas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7205937 (5721719-8408010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1081.48 (871.62-1251.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12726090 (11257499-14297491)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1088.31 (955.4-1230.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3 (0.18-0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSouth-East Asia Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12780106 (9779679-15091891)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1196.64 (945.65-1376.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26224993 (22904827-29298810)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1223.37 (1077.67-1354.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.42 (0.27-0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSouth Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11302568 (8663618-13369169)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1271.29 (1011.83-1467.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24065975 (20949359-26844234)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1288.55 (1138.93-1428.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.37 (0.21-0.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSouth Asia - WB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11575151 (8879251-13682955)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1269.69 (1010.58-1464.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24425653 (21247317-27262401)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1276.97 (1127.84-1416.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.33 (0.17-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: EAPC, estimated annual percentage change, SDl, Sociodemographic Index; Ul,uncertainty interval.\u0026nbsp;\u0026ldquo;\u0026nbsp;EAPC is expressed as 95% CIs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable.2\u003c/strong\u003e The prevalence cases and age-standardized prevalence rate of \u003cstrong\u003eperiodontitis\u003c/strong\u003e in 1990 and 2021, along with their temporal trend.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRate per 100 000(95%UI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1990-2021\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIncidence cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThe age-standardized Incidence rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIncidence cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThe age-standardized Incidence rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e557036657 (427553223-683752829)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12282.44 (9494.51-15063.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1066953744 (896546186-1234839287)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12498.3 (10526.8-14493.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.48 (0.35-0.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSDI region\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e112477527 (84949926-141523175)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10693.06 (8074.04-13393.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e190563436 (155170167-224654753)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10613.49 (8609.35-12735)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2 (0.01-0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e104166404 (81666455-127615908)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10232.99 (7971.34-12561.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e159217668 (131067146-186552251)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10139.68 (8221-12189.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.17 (-0.36-0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLow-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e123559532 (94516572-151011946)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14942.23 (11651.33-17901.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e267767360 (222364186-311663136)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15252.28 (12777.21-17553.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.01 (-0.07-0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLow SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55761027 (43082249-67824831)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17260.05 (13628.03-20703.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e101686261 (84910682-119217456)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13488.11 (11346.75-15555.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.12 (-1.27--0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMiddle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e160561226 (120848014-199383146)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12093.96 (9332.18-14918.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e346971125 (294753121-402356717)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12326.58 (10492-14258.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.23 (0.11-0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGBD region\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAdvanced Health System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e154707914 (119837983-190451646)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10210.19 (7874.46-12604.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e216914639 (175039796-259554572)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10059.93 (8011.4-12256.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.19 (-0.18-0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAfrica\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66828006 (51889207-80762971)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16383.61 (13071.33-19544.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e114514409 (93696588-135832038)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11828.49 (9763.13-13839.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.27 (-1.82--0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAfrican Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58121368 (45541713-69366183)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17914.82 (14402.73-21286.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93293067 (76820564-109879664)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11951.04 (9978.31-14008.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.63 (-2.22--1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAmerica\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74675163 (56872774-93313871)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11657.87 (8960.98-14485.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e144337678 (121259564-168169222)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12010.2 (10029.81-14125.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.51 (0.35-0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAndean Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3151937 (2598434-3783360)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11527.39 (9502.56-13667.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7635406 (5817558-9742369)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11561.93 (8874.73-14615.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.74 (0.48-1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAsia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e315485689 (237922348-391105170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12406.96 (9532.51-15305.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e684742467 (585462506-789391280)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13167.95 (11187.7-15161.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.89 (0.71-1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAustralasia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1820176 (1312776-2362422)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8189.67 (5875.13-10630.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3979930 (3016541-5074698)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9967.23 (7355.51-13114.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.23 (0.92-1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBasic Health System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e206089434 (154312569-259723617)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11311.13 (8650.7-14068.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e430278291 (359583884-503792409)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11254.74 (9419.58-13223.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (0.78-1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCaribbean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4493235 (3432770-5528154)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15163.54 (11723.35-18400.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7100123 (5546934-8746369)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13674.44 (10613.75-16870.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.16 (0.02-0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCentral Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7799069 (5997001-9371665)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18645.88 (14849.28-22221.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13394855 (10788661-16195730)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12386.48 (10219.46-14883.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.77 (-2.42--1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCentral Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5316806 (3916005-6756782)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10125.58 (7551.03-12737.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9005491 (6640329-11750809)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9321.12 (6936.9-12000.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.24 (-0.04-0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCentral Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13093681 (9845084-16551582)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9103.73 (6836.34-11527.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16809305 (13162273-20827563)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10046.91 (7649.52-12924.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.83 (0.45-1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCentral Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16499976 (12363772-20453150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14438.4 (11109.39-17648.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38015445 (31266040-44833316)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14280.67 (11767.34-16795.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.75 (0.48-1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5879734 (4430773-7149168)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17304.98 (13379.81-20785.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7989260 (5587545-11025203)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9567.48 (6904.73-12492.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.69 (-3.42--1.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCommonwealth High Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11452093 (8613365-14374531)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8774.46 (6513.89-11127.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18319294 (14746368-22170308)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9051.48 (7130.88-11253.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6 (0.24-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCommonwealth Low Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23496998 (17849750-28505170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18287.1 (14216.32-22030.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48783894 (37989859-58762718)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16189.19 (12871.62-19126.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.24 (-0.78-0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCommonwealth Middle Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e142406644 (109244498-174293950)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16334.2 (12839.31-19560.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e308513346 (257247298-355276736)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15929.64 (13460.05-18190.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1 (-0.19-0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e113942172 (85561716-144637555)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10976.83 (8299.83-13749.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e226131458 (187514178-265720506)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10707.99 (8785.26-12667.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.36 (0.99-1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEast Asia \u0026amp; Pacific - WB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e168304848 (125614434-212963678)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10599.77 (7968.23-13230.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e330917297 (274372330-387596173)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10427.54 (8618.56-12274.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.02 (0.75-1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEastern Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17678398 (13345907-21592361)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17112.13 (13378.44-20559.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31784191 (26100430-37799485)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12774.39 (10643.89-14832.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.1 (-1.81--0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEastern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31249552 (24073990-38674733)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11734.45 (8976.69-14607.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31682217 (24748361-39210888)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10795.79 (8255.27-13746.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01 (-0.35-0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEastern Mediterranean Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33633259 (26289248-40740531)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13590.64 (10857.17-16222.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93123618 (75902003-110036598)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14202.76 (11837.39-16531.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.68 (0.27-1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20722537 (15680439-25173613)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18479.02 (14492.18-22175.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33791770 (27088783-41126307)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12722.77 (10423.37-15068.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.51 (-2.23--0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e98862195 (77451167-120447711)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10560.72 (8223.26-12930.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e121828638 (97123141-148011518)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10257.04 (8015.61-12849.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.15 (-0.21-0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEurope \u0026amp; Central Asia - WB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102290944 (80009429-124797852)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10530.7 (8188.12-12903.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e128161771 (101719113-156470216)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10182.25 (7940.19-12813)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.13 (-0.19-0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEuropean Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102950814 (80487943-125623675)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10522.66 (8177.97-12897.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e129393416 (102661859-158046928)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10165.75 (7923.17-12790.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.13 (-0.2-0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17938189 (13082223-22710007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8701.49 (6367.78-11044.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27856863 (21561598-33670443)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8923.78 (6751.26-11355.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.49 (-0.07-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh-income North America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32632790 (25453367-40852309)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10263.74 (7852.2-12941.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49830524 (41176016-58185121)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9672.4 (7977.24-11544.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.08 (-0.51-0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLatin America \u0026amp; Caribbean - WB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42686258 (32054336-52693563)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12699.59 (9724.86-15647.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95265909 (79324668-111439048)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13181.85 (10981.47-15405.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.78 (0.58-0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLimited Health System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e180308850 (138109664-220445707)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16217.83 (12723.09-19418.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e393578991 (326479323-456122373)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15682.6 (13180.22-17990.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.08 (-0.28-0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMiddle East \u0026amp; North Africa - WB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17765051 (13161085-22611697)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10763.76 (8172.77-13602.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53691617 (42135354-67027918)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11631.94 (9336.86-14187.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.01 (0.63-1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMinimal Health System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15419518 (11805064-18526774)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18038.76 (14251.13-21614.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25433929 (20667928-30542026)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12419.06 (10262.58-14801.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.63 (-2.29--0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNorth Africa and Middle East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21425361 (15774378-27548273)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9646.86 (7263.77-12250.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69332503 (54652562-86046285)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11659.64 (9400.12-14095.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.35 (0.98-1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNorth America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32638833 (25459021-40858710)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10265.33 (7853.72-12943.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49838657 (41183872-58191791)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9673.76 (7978.53-11545.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.08 (-0.51-0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNorthern Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8995238 (6665534-11486432)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10962.84 (8305.19-13964.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23548196 (17800939-30540409)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12070.78 (9351.67-15391.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.87 (0.55-1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOceania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e404466 (288624-525719)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9967.03 (7328.55-12683.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e224853 (159647-320105)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2247.62 (1641.52-3060.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-6.38 (-7.23--5.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRegion of the Americas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74675163 (56872774-93313871)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11657.87 (8960.98-14485.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e144337678 (121259564-168169222)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12010.2 (10029.81-14125.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.51 (0.35-0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: EAPC, estimated annual percentage change, SDl, Sociodemographic Index; Ul,uncertainty interval.\u0026nbsp;\u0026ldquo;\u0026nbsp;EAPC is expressed as 95% CIs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable.3\u003c/strong\u003e The DALYs cases and age-standardized DALYs rate of \u003cstrong\u003eperiodontitis\u003c/strong\u003e in 1990 and 2021, along with their temporal trend.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRate per 100 000(95%UI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1990-2021\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIncidence cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThe age-standardized Incidence rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIncidence cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThe age-standardized Incidence rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3620518 (1421409-7719685)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79.62 (31.46-169.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6903284 (2772284-14106182)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80.89 (32.47-165.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.47 (0.35-0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSDI region\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e730382 (288347-1576847)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69.35 (27.67-149.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1231035 (492704-2492760)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68.73 (27.55-139.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2 (0.01-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e675037 (267679-1436739)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66.41 (26.42-141.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1021198 (406501-2047768)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65.47 (26.3-132.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.18 (-0.38-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLow-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e802431 (324100-1660446)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e96.49 (38.29-198.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1735974 (705992-3546781)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e98.5 (39.88-199.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.01 (-0.06-0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLow SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e362253 (143525-746670)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e111.43 (44.04-227.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e660691 (268309-1356342)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e86.99 (35.4-176.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.12 (-1.27--0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMiddle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1047099 (414764-2236757)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e78.52 (31.2-167.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2249557 (909679-4517591)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79.88 (32.32-160.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.22 (0.1-0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGBD region\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAdvanced Health System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1000864 (397022-2144189)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66.15 (26.26-142.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1390849 (553490-2818030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64.91 (26.11-132.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.17 (-0.19-0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAfrica\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e435296 (169696-893361)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e106.08 (41.91-216.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e744610 (302771-1548671)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e76.37 (30.68-155.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.28 (-1.82--0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAfrican Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e378567 (148136-770127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e115.97 (46.12-233.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e607156 (247022-1268893)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77.19 (31.19-158.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.63 (-2.22--1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAmerica\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e484070 (192007-1036623)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75.48 (30.07-161.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e928766 (374749-1909795)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77.47 (31.35-159.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.49 (0.33-0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAndean Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20638 (8526-43145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75.24 (30.6-156.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49777 (19357-104947)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75.28 (29.58-158.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.74 (0.48-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAsia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2054166 (809730-4398419)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80.44 (31.85-172.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4437350 (1801644-8890492)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e85.31 (34.67-171.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.89 (0.71-1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAustralasia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11779 (4540-26131)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53.03 (20.48-117.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25605 (10182-54013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64.45 (25.05-136.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.2 (0.89-1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBasic Health System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1345049 (527418-2874612)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e73.53 (29.38-157.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2789992 (1122310-5636388)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e72.98 (29.44-147.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.99 (0.77-1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCaribbean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29330 (11795-60669)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e98.79 (39.96-204.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45964 (18345-95056)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e88.59 (35.16-183.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.14 (0-0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCentral Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50629 (20012-103533)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e120.26 (47.76-243.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e87162 (35024-186973)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79.91 (31.84-167.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.77 (-2.41--1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCentral Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34567 (13704-74906)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65.68 (26.1-142.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58379 (23002-122809)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60.29 (23.69-125.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.23 (-0.04-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCentral Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e84525 (33149-183760)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58.83 (23.07-128.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e107876 (42681-219152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64.88 (25.8-134.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.82 (0.45-1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCentral Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e107627 (43075-223982)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93.72 (38-194.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e246586 (99480-506792)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e92.56 (37.33-190.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.74 (0.47-1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38057 (15189-77260)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e111.25 (44.62-228.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51550 (19947-112511)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e61.21 (23.79-132.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.71 (-3.43--1.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCommonwealth High Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74234 (28839-162046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e57.01 (22.19-125.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e117752 (46470-241860)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58.53 (23.27-121.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.58 (0.22-0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCommonwealth Low Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e153148 (61075-313327)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e118.44 (46.9-240.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e317172 (124036-659551)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e104.71 (41.16-216.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.24 (-0.78-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCommonwealth Middle Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e924108 (373757-1895584)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e105.34 (42.04-214.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1998413 (816938-4082024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102.74 (42.06-207.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1 (-0.18-0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e744686 (295676-1609368)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e71.47 (28.52-154.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1466661 (578102-2997147)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69.55 (27.77-143.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.35 (0.98-1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEast Asia \u0026amp; Pacific - WB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1099208 (432156-2380073)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68.98 (27.59-148.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2145556 (850718-4317719)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e67.7 (27.08-137.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.01 (0.74-1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEastern Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e115053 (46165-235583)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e110.64 (43.86-226.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e206881 (84754-430324)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82.47 (33.36-168.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.1 (-1.8--0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEastern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e201222 (81002-424926)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75.66 (30.36-159.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e202793 (80698-410203)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69.39 (27.84-142.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (-0.36-0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEastern Mediterranean Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e219314 (86239-458084)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e88.12 (34.75-183.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e604942 (238723-1238573)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e91.7 (36.04-185.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.67 (0.27-1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e134871 (53658-274527)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e119.47 (47.55-242.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e219739 (89456-465543)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82.03 (33.23-169)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.51 (-2.22--0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e639267 (254644-1363049)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68.42 (27.21-146.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e782688 (309423-1603552)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66.29 (26.3-138.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.13 (-0.22-0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEurope \u0026amp; Central Asia - WB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e661551 (263450-1412278)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68.22 (27.13-145.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e823778 (325607-1690127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65.81 (26.15-137.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12 (-0.2-0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEuropean Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e665844 (265093-1421745)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68.17 (27.1-145.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e831723 (328717-1706935)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65.7 (26.12-136.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.11 (-0.21-0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e116842 (45917-253870)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56.65 (22.29-123.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e178914 (70629-356713)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e57.83 (22.77-117.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.46 (-0.09-1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh-income North America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e210367 (83442-448355)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66.3 (26.35-142.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e316568 (126390-636544)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e61.88 (24.94-126.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.12 (-0.54-0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLatin America \u0026amp; Caribbean - WB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e277902 (112129-584012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82.39 (33.12-173.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e617084 (251014-1257271)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e85.37 (34.73-174.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.77 (0.58-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLimited Health System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1171035 (472833-2411988)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e104.67 (41.65-213.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2552120 (1046429-5225149)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e101.21 (41.44-204.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.08 (-0.27-0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMiddle East \u0026amp; North Africa - WB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e115660 (45396-250608)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69.69 (27.44-149.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e347539 (139898-716872)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74.92 (30.1-153.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (0.63-1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMinimal Health System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100253 (39168-206579)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e116.59 (46.05-238.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e165493 (67113-349371)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80.09 (32.19-164.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.63 (-2.28--0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNorth Africa and Middle East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e139422 (54465-305549)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e62.44 (24.47-135.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e448887 (180266-924569)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75.15 (30.2-154.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.35 (0.98-1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNorth America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e210407 (83457-448427)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66.31 (26.35-142.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e316621 (126410-636628)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e61.89 (24.94-126.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.12 (-0.54-0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNorthern Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58639 (22943-126893)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e71.09 (27.82-153.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e152488 (60361-317966)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77.86 (30.78-161)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.86 (0.54-1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOceania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2631 (1025-5785)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64.39 (25.36-138.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1450 (546-3197)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.37 (5.36-31.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-6.42 (-7.26--5.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRegion of the Americas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e484070 (192007-1036623)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75.48 (30.07-161.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e928766 (374749-1909795)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77.47 (31.35-159.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.49 (0.33-0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: EAPC, estimated annual percentage change, SDl, Sociodemographic Index; Ul,uncertainty interval.\u0026nbsp;\u0026ldquo;\u0026nbsp;EAPC is expressed as 95% CIs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable.4\u003c/strong\u003e The YLDs cases and age-standardized YLDs rate of \u003cstrong\u003eperiodontitis\u003c/strong\u003e in 1990 and 2021, along with their temporal trend.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRate per 100 000(95%UI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1990-2021\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIncidence cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThe age-standardized Incidence rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIncidence cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThe age-standardized Incidence rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3620518 (1421409-7719685)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79.62 (31.46-169.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6903284 (2772284-14106182)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80.89 (32.47-165.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.47 (0.35-0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSDI region\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e730382 (288347-1576847)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69.35 (27.67-149.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1231035 (492704-2492760)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68.73 (27.55-139.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2 (0.01-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e675037 (267679-1436739)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66.41 (26.42-141.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1021198 (406501-2047768)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65.47 (26.3-132.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.18 (-0.38-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLow-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e802431 (324100-1660446)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e96.49 (38.29-198.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1735974 (705992-3546781)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e98.5 (39.88-199.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.01 (-0.06-0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLow SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e362253 (143525-746670)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e111.43 (44.04-227.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e660691 (268309-1356342)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e86.99 (35.4-176.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.12 (-1.27--0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMiddle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1047099 (414764-2236757)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e78.52 (31.2-167.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2249557 (909679-4517591)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79.88 (32.32-160.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.22 (0.1-0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGBD region\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAdvanced Health System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1000864 (397022-2144189)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66.15 (26.26-142.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1390849 (553490-2818030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64.91 (26.11-132.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.17 (-0.19-0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAfrica\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e435296 (169696-893361)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e106.08 (41.91-216.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e744610 (302771-1548671)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e76.37 (30.68-155.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.28 (-1.82--0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAfrican Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e378567 (148136-770127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e115.97 (46.12-233.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e607156 (247022-1268893)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77.19 (31.19-158.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.63 (-2.22--1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAmerica\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e484070 (192007-1036623)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75.48 (30.07-161.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e928766 (374749-1909795)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77.47 (31.35-159.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.49 (0.33-0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAndean Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20638 (8526-43145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75.24 (30.6-156.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49777 (19357-104947)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75.28 (29.58-158.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.74 (0.48-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAsia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2054166 (809730-4398419)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80.44 (31.85-172.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4437350 (1801644-8890492)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e85.31 (34.67-171.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.89 (0.71-1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAustralasia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11779 (4540-26131)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53.03 (20.48-117.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25605 (10182-54013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64.45 (25.05-136.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.2 (0.89-1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBasic Health System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1345049 (527418-2874612)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e73.53 (29.38-157.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2789992 (1122310-5636388)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e72.98 (29.44-147.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.99 (0.77-1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCaribbean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29330 (11795-60669)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e98.79 (39.96-204.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45964 (18345-95056)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e88.59 (35.16-183.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.14 (0-0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCentral Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50629 (20012-103533)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e120.26 (47.76-243.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e87162 (35024-186973)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79.91 (31.84-167.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.77 (-2.41--1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCentral Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34567 (13704-74906)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65.68 (26.1-142.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58379 (23002-122809)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60.29 (23.69-125.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.23 (-0.04-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCentral Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e84525 (33149-183760)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58.83 (23.07-128.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e107876 (42681-219152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64.88 (25.8-134.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.82 (0.45-1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCentral Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e107627 (43075-223982)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93.72 (38-194.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e246586 (99480-506792)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e92.56 (37.33-190.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.74 (0.47-1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38057 (15189-77260)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e111.25 (44.62-228.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51550 (19947-112511)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e61.21 (23.79-132.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.71 (-3.43--1.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCommonwealth High Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74234 (28839-162046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e57.01 (22.19-125.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e117752 (46470-241860)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58.53 (23.27-121.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.58 (0.22-0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCommonwealth Low Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e153148 (61075-313327)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e118.44 (46.9-240.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e317172 (124036-659551)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e104.71 (41.16-216.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.24 (-0.78-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCommonwealth Middle Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e924108 (373757-1895584)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e105.34 (42.04-214.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1998413 (816938-4082024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102.74 (42.06-207.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1 (-0.18-0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e744686 (295676-1609368)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e71.47 (28.52-154.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1466661 (578102-2997147)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69.55 (27.77-143.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.35 (0.98-1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEast Asia \u0026amp; Pacific - WB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1099208 (432156-2380073)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68.98 (27.59-148.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2145556 (850718-4317719)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e67.7 (27.08-137.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.01 (0.74-1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEastern Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e115053 (46165-235583)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e110.64 (43.86-226.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e206881 (84754-430324)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82.47 (33.36-168.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.1 (-1.8--0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEastern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e201222 (81002-424926)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75.66 (30.36-159.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e202793 (80698-410203)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69.39 (27.84-142.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (-0.36-0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEastern Mediterranean Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e219314 (86239-458084)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e88.12 (34.75-183.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e604942 (238723-1238573)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e91.7 (36.04-185.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.67 (0.27-1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e134871 (53658-274527)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e119.47 (47.55-242.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e219739 (89456-465543)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82.03 (33.23-169)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.51 (-2.22--0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e639267 (254644-1363049)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68.42 (27.21-146.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e782688 (309423-1603552)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66.29 (26.3-138.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.13 (-0.22-0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEurope \u0026amp; Central Asia - WB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e661551 (263450-1412278)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68.22 (27.13-145.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e823778 (325607-1690127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65.81 (26.15-137.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12 (-0.2-0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEuropean Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e665844 (265093-1421745)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68.17 (27.1-145.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e831723 (328717-1706935)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65.7 (26.12-136.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.11 (-0.21-0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e116842 (45917-253870)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56.65 (22.29-123.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e178914 (70629-356713)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e57.83 (22.77-117.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.46 (-0.09-1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh-income North America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e210367 (83442-448355)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66.3 (26.35-142.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e316568 (126390-636544)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e61.88 (24.94-126.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.12 (-0.54-0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLatin America \u0026amp; Caribbean - WB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e277902 (112129-584012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82.39 (33.12-173.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e617084 (251014-1257271)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e85.37 (34.73-174.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.77 (0.58-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLimited Health System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1171035 (472833-2411988)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e104.67 (41.65-213.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2552120 (1046429-5225149)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e101.21 (41.44-204.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.08 (-0.27-0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMiddle East \u0026amp; North Africa - WB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e115660 (45396-250608)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69.69 (27.44-149.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e347539 (139898-716872)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74.92 (30.1-153.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (0.63-1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMinimal Health System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100253 (39168-206579)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e116.59 (46.05-238.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e165493 (67113-349371)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80.09 (32.19-164.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.63 (-2.28--0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNorth Africa and Middle East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e139422 (54465-305549)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e62.44 (24.47-135.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e448887 (180266-924569)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75.15 (30.2-154.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.35 (0.98-1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNorth America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e210407 (83457-448427)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66.31 (26.35-142.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e316621 (126410-636628)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e61.89 (24.94-126.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.12 (-0.54-0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNorthern Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58639 (22943-126893)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e71.09 (27.82-153.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e152488 (60361-317966)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77.86 (30.78-161)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.86 (0.54-1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOceania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2631 (1025-5785)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64.39 (25.36-138.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1450 (546-3197)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.37 (5.36-31.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-6.42 (-7.26--5.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRegion of the Americas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e484070 (192007-1036623)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75.48 (30.07-161.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e928766 (374749-1909795)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77.47 (31.35-159.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.49 (0.33-0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: EAPC, estimated annual percentage change, SDl, Sociodemographic Index; Ul,uncertainty interval.\u0026nbsp;\u0026ldquo;\u0026nbsp;EAPC is expressed as 95% CIs.\u003c/p\u003e\n\u003cp\u003eThe regional burden patterns revealed pronounced disparities aligned with socioeconomic development levels, with Sub-Saharan Africa and South Asia maintaining the highest burden concentrations. Several countries in these regions recorded age-standardized DALY rates exceeding 280 per 100,000 population, while high-income regions in Western Europe, North America, and Australasia demonstrated substantially lower rates, typically below 120 per 100,000 population. This greater than two-fold difference underscored persistent global health inequities in oral health access and periodontitis management, reflecting broader patterns of healthcare infrastructure disparities across economic development gradients. The burden distribution closely correlated with Socio-demographic Index (SDI) levels, with low-SDI regions experiencing disproportionately elevated rates compared to high-SDI counterparts. Gender-specific analyses revealed consistent female predominance across most age groups and geographical regions, with women experiencing approximately 15-20% higher age-standardized rates than men globally. This pattern was particularly pronounced in older adult populations (\u0026ge;65 years), where hormonal, behavioral, and healthcare-seeking differences may contribute to observed disparities. Age-stratified burden patterns demonstrated exponential increases with advancing age, with periodontitis burden rising sharply after age 45 years, from 892 per 100,000 in the 45-49 age group to 2,847 per 100,000 in those aged 80 and above, reflecting cumulative inflammatory damage and age-related immune system changes. Regional burden profiles revealed particularly elevated rates in Central and Eastern European countries, several Middle Eastern nations, and parts of Latin America, where age-standardized DALY rates frequently exceeded 250 per 100,000 population. These patterns likely reflect complex interactions between genetic predisposition, dietary factors, smoking prevalence, healthcare access limitations, and varying oral hygiene practices. Conversely, several high-income countries in Northern Europe and East Asia demonstrated remarkably low burden levels, with age-standardized rates below 100 per 100,000, suggesting effective population-level prevention strategies and enhanced periodontal care access \u003cstrong\u003e(Figure 2)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThe temporal trend analysis revealed heterogeneous regional trajectories, with most high-income regions showing sustained improvements in age-standardized rates (EAPC ranging from -0.8% to -1.2% annually), while several low- and middle-income regions experienced stagnant or worsening trends. These divergent patterns highlighted growing global inequalities in periodontitis burden and underscored the need for targeted interventions in high-burden settings to address underlying social determinants of oral health disparities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.Mendelian Randomization Analysis Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1 Causal Associations Between Exposures and Periodontitis Risk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate potential causal relationships between modifiable risk factors and periodontitis, we conducted comprehensive two-sample Mendelian randomization analyses using the inverse variance weighted (IVW) method as the primary approach. The analysis encompassed 14 distinct exposures, utilizing instrumental variables ranging from 4 to 480 single nucleotide polymorphisms (SNPs) to ensure robust causal inference across different phenotypes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Sleep-Related Phenotypes: A Primary Causal Risk Factor\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSleeplessness/insomnia emerged as one of the most significant causal determinants of periodontitis risk\u0026nbsp;in our analysis (OR = 1.134, 95% CI: 1.070\u0026ndash;1.202, P = 2.09\u0026times;10⁻⁵), supported by 85 genetic instruments. This association represents\u0026nbsp;among the strongest statistical relationships identified\u0026nbsp;across all examined exposures, demonstrating that genetically predicted insomnia confers a\u0026nbsp;13.4% increased risk\u0026nbsp;of periodontitis development. Notably, this causal effect was\u0026nbsp;substantially amplified\u0026nbsp;in multivariable MR analysis after adjusting for correlated risk factors, with the odds ratio increasing to 1.245 (95% CI: 1.016\u0026ndash;1.526, P = 0.034), indicating that\u0026nbsp;sleep disturbances may independently contribute to periodontal pathogenesis\u0026nbsp;beyond traditional risk factors. Other sleep-related behaviors showed directionally consistent but non-significant associations, including daytime napping (OR = 1.047, 95% CI: 0.985\u0026ndash;1.114, P = 0.138) and snoring (OR = 1.087, 95% CI: 0.931\u0026ndash;1.269, P = 0.293), suggesting that\u0026nbsp;chronic sleep disruption, rather than isolated sleep behaviors, drives the causal relationship\u0026nbsp;with periodontitis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Additional Modifiable Risk Factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBeyond sleep disorders, several lifestyle and socioeconomic factors demonstrated significant causal associations with periodontitis risk. Body mass index showed a robust positive association (OR = 1.095, 95% CI: 1.057\u0026ndash;1.135, P = 4.77\u0026times;10⁻⁷) based on 480 genetic variants, with each standard deviation increase in genetically predicted BMI corresponding to a 9.5% elevated risk. Smoking initiation exhibited a pronounced causal effect (OR = 1.245, 95% CI: 1.065\u0026ndash;1.457, P = 0.0063), with genetically predicted smoking propensity associated with 24.5% higher periodontitis odds. Educational attainment demonstrated the most statistically robust protective effect\u0026nbsp;(OR = 0.843, 95% CI: 0.795\u0026ndash;0.893, P = 6.52\u0026times;10⁻⁹), supported by 299 genetic instruments. Each additional year of genetically predicted schooling was associated with a 15.7% reduction in periodontitis risk, and this protective effect remained significant in multivariable analysis (OR = 0.886, 95% CI: 0.807\u0026ndash;0.972, P = 0.011)\u003cstrong\u003e(Figure 3)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Null Associations and Multivariable Adjustments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral hypothesized risk factors showed no significant causal relationships with periodontitis, including beverage consumption patterns (coffee: OR = 0.980, P = 0.830; tea: OR = 0.911, P = 0.198; alcohol: OR = 1.107, P = 0.677), birth weight (OR = 1.015, P = 0.494), and various physical activity measures. Importantly,\u0026nbsp;insomnia maintained its significant causal effect in multivariable models, while BMI and smoking associations were attenuated after adjustment for correlated exposures.\u003c/p\u003e\n\u003cp\u003eThese findings establish\u0026nbsp;sleep disorders as a priority modifiable risk factor\u0026nbsp;for periodontitis, with effect sizes comparable to established risk factors such as smoking. The persistence of the insomnia-periodontitis association across both univariable and multivariable analyses, combined with the high prevalence of sleep disorders globally,\u0026nbsp;positions sleep hygiene interventions as a novel therapeutic target\u0026nbsp;for periodontal disease prevention. The multifactorial causal architecture identified supports integrated approaches addressing socioeconomic, behavioral, and sleep-related determinants in periodontal health management\u003cstrong\u003e(Figure 4)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.Transcriptomic Analysis Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1 Identification of Periodontitis-Insomnia Cross-Talk Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBuilding upon the causal relationships established through Mendelian randomization, we conducted comprehensive transcriptomic analyses to elucidate the molecular mechanisms underlying the periodontitis-insomnia association. Systematic analysis of three periodontitis GEO datasets (GSE10334, GSE16134, and GSE106090) identified 190, 17, and 3,235 differentially expressed genes, respectively, using stringent criteria (|logFC| \u0026ge; 1, adjusted P \u0026lt; 0.05).\u0026nbsp;Venn diagram intersection analysis revealed 126 genes consistently dysregulated across all datasets, establishing a robust periodontitis gene signature. Cross-referencing this core periodontitis gene set with 6,186 insomnia-related genes from GeneCards database identified\u0026nbsp;25 critical cross-talk genes\u0026nbsp;that exhibited significant co-expression in both conditions. These genes included key inflammatory mediators (IL1B,\u0026nbsp;CXCL8,\u0026nbsp;CXCL13), extracellular matrix components (COL4A1,\u0026nbsp;COL4A2), and transcriptional regulators (XBP1,\u0026nbsp;MEF2C), providing molecular evidence for the\u0026nbsp;shared pathobiological pathways\u0026nbsp;linking periodontitis and sleep disorders identified in our MR analysis\u003cstrong\u003e(Figure 5)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Functional Enrichment and Pathway Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene Ontology (GO) enrichment analysis revealed that\u0026nbsp;cross-talk genes were predominantly enriched in inflammation-related biological processes, including cellular response to bacterial stimuli and lipopolysaccharide (LPS) response pathways (5-6 genes per pathway, P \u0026lt; 0.05). Key inflammatory genes\u0026nbsp;IL1B,\u0026nbsp;CXCL8,\u0026nbsp;SELE, and transcriptional factors\u0026nbsp;XBP1\u0026nbsp;and\u0026nbsp;MEF2C\u0026nbsp;were consistently represented across these pathways,\u0026nbsp;supporting the inflammatory basis of periodontitis-insomnia comorbidity\u0026nbsp;established through our causal inference analysis. KEGG pathway enrichment revealed significant involvement of\u0026nbsp;AGE-RAGE signaling in diabetic complications\u0026nbsp;(5 genes:\u0026nbsp;COL4A1,\u0026nbsp;COL4A2,\u0026nbsp;IL1B,\u0026nbsp;SELE,\u0026nbsp;CXCL8, P \u0026lt; 0.01), lipid metabolism pathways, and atherosclerosis-related processes. The enrichment of AGE-RAGE signaling is particularly relevant, as this pathway mediates chronic inflammation and vascular dysfunction\u0026mdash;mechanisms potentially linking\u0026nbsp;sleep-induced metabolic disruption with periodontal tissue destruction\u003cstrong\u003e(Figure 6)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Protein-Protein Interaction Networks and Modular Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePPI network analysis using GeneMANIA revealed\u0026nbsp;densely interconnected modules\u0026nbsp;among cross-talk genes, with inflammatory mediators (IL1B,\u0026nbsp;CXCL8,\u0026nbsp;SELE) occupying central network positions. The MCODE algorithm revealed two principal functional clusters: a small cell adhesion module comprising four nodes and a larger immune\u0026ndash;inflammatory regulation module. These findings indicate that inflammatory signaling may serve as a dominant molecular mechanism linking periodontitis with sleep disorders. Complementary analysis with Metascape further organized the enriched pathways into ten functional categories, which included AGE\u0026ndash;RAGE\u0026ndash;related diabetic complications, responses to lipopolysaccharide (LPS), and regulation of cell adhesion. Together, these results offer a broader molecular context for interpreting the multifactorial etiology highlighted by our Mendelian randomization findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Lactylation-Mediated Epigenetic Regulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore potential epigenetic mechanisms, we conducted correlation analyses between cross-talk genes and enzymes associated with lactylation. Notably, significant associations\u0026nbsp;(|r| \u0026gt; 0.5, P \u0026lt; 0.05) were observed between the transcriptional regulators XBP1 and MEF2C with the lactylation-related enzymes HDAC1 and SIRT1, implying that metabolic\u0026ndash;epigenetic interactions mediated by lactylation could represent a previously unrecognized mechanism connecting sleep disturbances to periodontal inflammation. Further functional enrichment of the 69-gene set correlated with lactylation uncovered strong involvement in processes related to DNA repair regulation and transcriptional coactivator activity (P \u0026lt; 0.05, q \u0026lt; 0.05). These findings suggest that lactylation-driven modifications might contribute to disease progression by reprogramming epigenetic pathways governing inflammation and tissue repair\u003cstrong\u003e(Figure 7)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Diagnostic Biomarker Identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROC curve analysis of lactylation-related cross-talk genes identified\u0026nbsp;five core biomarkers with excellent diagnostic performance\u0026nbsp;(AUC \u0026gt; 0.7):\u0026nbsp;CD93\u0026nbsp;(AUC = 0.879),\u0026nbsp;IL16\u0026nbsp;(AUC = 0.869),\u0026nbsp;FER1L4\u0026nbsp;(AUC = 0.852),\u0026nbsp;DUSP5, and\u0026nbsp;IGFBP4.\u0026nbsp;CD93 exhibited strong discriminatory ability, indicating its promise as a biomarker for screening comorbidity between periodontitis and insomnia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Integration with Causal Evidence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese transcriptomic results lend molecular support to the causal associations identified in the MR analysis. The identification of shared inflammatory pathways (IL1B,\u0026nbsp;CXCL8), metabolic dysregulation (AGE-RAGE signaling), and epigenetic mechanisms (lactylation)\u0026nbsp;mechanistically explains how genetically predicted insomnia increases periodontitis risk by 24.5%\u0026nbsp;as demonstrated in our multivariable MR analysis. The convergence of causal inference and molecular evidence establishes a\u0026nbsp;robust foundation\u0026nbsp;for developing targeted therapeutic interventions addressing the sleep-periodontal health axis\u003cstrong\u003e(Figure 8)\u003c/strong\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eGlobal Burden Implications and Health System Priorities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present analysis reveals a paradoxical pattern in global periodontitis burden, characterized by declining age-standardized rates concurrent with substantial increases in absolute disease burden. This temporal divergence reflects the complex interplay between improved oral healthcare access in developed regions and the demographic transition toward aging populations globally [36]. The 44% increase in absolute DALYs from 1990 to 2021, despite modest improvements in age-standardized rates, underscores the inadequacy of traditional epidemiological metrics in capturing the true societal impact of chronic diseases in aging societies [37].\u003c/p\u003e\n\u003cp\u003eThe pronounced regional disparities observed, with Sub-Saharan Africa and South Asia bearing disproportionate burden levels, illuminate the persistent global health inequities that extend beyond infectious diseases to encompass chronic inflammatory conditions [38]. The greater than two-fold difference in age-standardized DALY rates between low- and high-income regions parallels similar patterns documented in other non-communicable diseases, suggesting shared underlying determinants related to healthcare infrastructure, preventive care access, and socioeconomic factors [39]. Notably, the strong correlation between periodontitis burden and Socio-demographic Index levels provides compelling evidence that oral health outcomes remain fundamentally shaped by broader development indicators, challenging purely biomedical approaches to periodontal disease prevention [40].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCausal Architecture and Sleep-Periodontal Pathways\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe identification of insomnia as a primary causal risk factor for periodontitis through Mendelian randomization represents a paradigm shift in understanding periodontal disease etiology. The robust causal effect (24.5% increased risk in multivariable analysis) positions sleep disorders among the most impactful modifiable risk factors, comparable to established determinants such as smoking and diabetes [41]. This finding has profound implications given that sleep disorders affect an estimated 10-30% of adults globally, potentially representing a vast unrecognized population at elevated periodontal risk [42].\u003c/p\u003e\n\u003cp\u003eThe mechanistic pathways linking sleep disruption to periodontal pathogenesis likely involve multiple interconnected systems. Sleep deprivation is known to dysregulate the hypothalamic-pituitary-adrenal axis, leading to chronic elevation of cortisol levels and subsequent immunosuppression [43]. This hormonal dysregulation compromises neutrophil function and impairs the innate immune response to periodontal pathogens, creating conditions conducive to bacterial overgrowth and tissue destruction [44]. Chronic sleep restriction has also been reported to lower salivary flow and modify salivary composition, potentially weakening the oral cavity’s innate protective mechanisms against bacterial colonization [45].\u003c/p\u003e\n\u003cp\u003eMoreover, the amplified effect size identified in the multivariable MR analysis indicates that sleep disorders may influence periodontal outcomes through biological pathways distinct from conventional risk factors, suggesting a possible synergistic role rather than mere confounding [46]. Clinically, this distinction is important, as it implies that addressing sleep disturbances could yield therapeutic benefits that extend beyond those achieved by standard periodontal treatments, even in individuals with otherwise well-managed traditional risk factors [47].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular Mechanisms and Transcriptomic Insights\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe transcriptomic analysis offers new molecular perspectives on the periodontitis–insomnia connection, uncovering an intricate network of overlapping inflammatory pathways that help clarify the causal relationships suggested by the Mendelian randomization analysis [48]. Within this network, 25 cross-talk genes were identified, the majority of which are enriched in pathways related to inflammation, thereby laying a mechanistic basis for understanding how systemic disturbances in sleep may translate into local periodontal tissue damage [49].\u003c/p\u003e\n\u003cp\u003eAmong these genes, IL1B and CXCL8 stand out for their regulatory importance. Both cytokines act as upstream drivers of inflammatory cascades in sleep disorders and periodontal disease [50]. IL1B, in particular, has been implicated in altering sleep architecture as well as in promoting periodontal destruction, making it a strong candidate for a molecular mediator that links systemic sleep dysregulation with oral inflammatory processes [51]. The involvement of CXCL8 (IL-8) reinforces the contribution of neutrophil-driven responses, given its established role in recruiting and activating neutrophils in conditions of both sleep deprivation and periodontal pathology [52].\u003c/p\u003e\n\u003cp\u003eIn addition, enrichment of the AGE–RAGE signaling pathway provides insight into how metabolic imbalances induced by poor sleep may exacerbate periodontal disease [53]. Advanced glycation end products, which accumulate under states of oxidative stress and metabolic dysfunction associated with sleep disorders, can stimulate RAGE receptors in periodontal tissues. This interaction initiates inflammatory cascades that ultimately accelerate tissue breakdown [54]. Recognition of this pathway highlights a potentially targetable link between sleep-related metabolic alterations and periodontal inflammation [55].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEpigenetic Regulation and Lactylation Mechanisms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe recognition of lactylation as a regulatory mechanism marks an important step forward in clarifying the molecular underpinnings of periodontitis–insomnia comorbidity. As a recently described histone modification driven by lactate metabolism, lactylation creates a direct molecular bridge between altered metabolic states and transcriptional control [56]. In this study, notable correlations were observed between the transcriptional regulators XBP1 and MEF2C and the lactylation-associated enzymes HDAC1 and SIRT1, implying that sleep-related metabolic disturbances may reshape gene expression in ways that heighten periodontal inflammation [57].\u003c/p\u003e\n\u003cp\u003eThe relevance of this mechanism is underscored by evidence showing that sleep loss disrupts cellular metabolism, resulting in elevated lactate levels and potential shifts in lactylation dynamics [58]. The role of XBP1 is particularly noteworthy, as this factor orchestrates endoplasmic reticulum stress responses, suggesting that inadequate sleep could disturb cellular balance within periodontal tissues through a form of metabolic–epigenetic crosstalk [59]. Regulation of MEF2C via lactylation may likewise drive changes in inflammatory gene programs, thereby contributing to the tissue damage characteristic of periodontal disease [60].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Translation and Biomarker Development\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe discovery of five core biomarkers with high diagnostic accuracy provides promising opportunities for advancing personalized approaches in periodontal care. Among them, CD93 stands out with an AUC of 0.879, underscoring its value as a candidate for developing screening strategies for comorbidity between periodontitis and insomnia [61]. Beyond its statistical performance, CD93 is a cell surface glycoprotein that contributes to the regulation of inflammation and maintenance of vascular stability, pointing to its dual potential as both a biomarker and a therapeutic target [62].\u003c/p\u003e\n\u003cp\u003eImportantly, the implications of these biomarkers extend into clinical practice. Incorporating sleep evaluations into periodontal risk assessment frameworks may support more individualized treatment protocols and improve monitoring strategies [63]. For example, patients with elevated levels of CD93 or IL16 could be prioritized for combined interventions—addressing both sleep disruption and periodontal health—which might achieve better outcomes than conventional periodontal therapy alone [64].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublic Health Implications and Prevention Strategies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe demonstrated causal link between sleep disorders and periodontitis carries important consequences for both public health initiatives and preventive dentistry [65]. At present, most preventive programs emphasize oral hygiene education and routine professional care, while largely neglecting sleep health, a potentially modifiable risk factor that affects millions worldwide [66]. Incorporating sleep hygiene counseling into dental practice could therefore provide an avenue for addressing upstream drivers of periodontal disease and improving long-term oral health outcomes [67]. The educational attainment findings further emphasize the importance of addressing social determinants of oral health. The robust protective effect of education (15.7% risk reduction per additional year) suggests that interventions targeting health literacy and socioeconomic factors may yield substantial population-level benefits [68]. This finding aligns with broader evidence supporting the role of education in health promotion and disease prevention across multiple chronic conditions [69].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Limitations and Methodological Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral limitations warrant acknowledgment in interpreting these findings. First, the MR analysis relies on genetic variants that may have pleiotropic effects, potentially influencing the estimated causal relationships [70]. While multivariable MR helped address some confounding concerns, residual pleiotropy cannot be entirely excluded. Second, the transcriptomic analysis was limited to publicly available datasets, which may not capture the full spectrum of molecular changes occurring in diverse populations [71].The cross-sectional nature of the transcriptomic data precludes temporal assessment of gene expression changes, limiting our ability to establish the sequence of molecular events in disease progression [72]. Additionally, the focus on mRNA expression may not fully reflect protein-level changes or post-translational modifications that contribute to disease pathogenesis [73].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFuture Research Directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe convergence of causal inference and molecular evidence presented here establishes a foundation for several important research directions. Longitudinal cohort studies incorporating both sleep assessment and periodontal monitoring are needed to validate the temporal relationships suggested by our MR analysis [74]. Intervention studies testing sleep hygiene programs in periodontal patients could provide direct evidence for the therapeutic potential of addressing sleep disorders in periodontal care [75]. The lactylation findings suggest that metabolic-epigenetic mechanisms represent a promising area for drug development. Investigating whether interventions targeting lactylation enzymes or metabolic pathways can modulate periodontal inflammation could yield novel therapeutic approaches [76]. Similarly, the biomarker findings warrant validation in independent cohorts and assessment of their clinical utility in diverse populations [77].\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis comprehensive analysis establishes sleep disorders as a major causal risk factor for periodontitis, with effect sizes comparable to established determinants. The molecular mechanisms identified provide a roadmap for developing targeted interventions addressing the sleep-periodontal health axis. The integration of causal inference with transcriptomic evidence represents a model for advancing precision medicine approaches in periodontal care, with implications extending to broader chronic disease prevention strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eASR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAge-standardized rate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDALY\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDisability-adjusted life year\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEAPC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEstimated annual percentage change\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGBD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGlobal Burden of Disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGWAS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGenome-wide association study\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIVW\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInverse variance weighting\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMendelian randomization\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSNP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSingle nucleotide polymorphism\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eYLD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eYears lived with disability\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eYLL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eYears of life lost\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShucan Zheng conceived and designed the study, supervised the research process, and was responsible for drafting and revising the manuscript. Haibin Shao contributed to data acquisition and performed the statistical analysis of the Global Burden of Disease dataset.Weilu Wang conducted the Mendelian randomization analyses and participated in result interpretation. Xiaoying Chen was responsible for bioinformatics and multi-omics analyses, including transcriptomic and pathway enrichment studies. Minghui Zhu contributed to figure preparation, data visualization, and assisted in drafting the methods and results sections. Jiazhen Long critically reviewed the manuscript, refined the discussion, and provided important intellectual input.All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003ch3\u003e\u003cbr\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used in this study are publicly available from the cited sources (e.g., GBD database, FinnGen/UK Biobank GWAS, GEO transcriptome datasets). No new datasets were generated during this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Huadu District Joint Funding Project for Basic and Applied Basic Research by District and Academy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShucan Zheng conceived and designed the study, supervised the research process, and was responsible for drafting and revising the manuscript. Haibin Shao acquired GBD data and performed statistical analyses. Weilu Wang conducted the Mendelian randomization analyses. Xiaoying Chen performed bioinformatics and multi-omics investigations. Minghui Zhu contributed to figure preparation and data visualization. Jiazhen Long provided critical review and intellectual input. All authors have read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKassebaum NJ, Bernab\u0026eacute; E, Dahiya M, Bhandari B, Murray CJ, Marcenes W. Global burden of severe periodontitis in 1990\u0026ndash;2010: a systematic review and meta-regression. J Dent Res. 2014;93(11):1045\u0026ndash;53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/0022034514552491\u003c/span\u003e\u003cspan address=\"10.1177/0022034514552491\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eListl S, Galloway J, Mossey PA, Marcenes W. Global Economic Impact of Dental Diseases. J Dent Res. 2015;94(10):1355\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/0022034515602879\u003c/span\u003e\u003cspan address=\"10.1177/0022034515602879\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBenzian H, Watt R, Makino Y, Stauf N, Varenne B. WHO calls to end the global crisis of oral health. Lancet. 2022;400(10367):1909\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(22)02322-4\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(22)02322-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Y, Wheaton AG, Chapman DP, Cunningham TJ, Lu H, Croft JB. Prevalence of Healthy Sleep Duration among Adults\u0026ndash;United States, 2014. MMWR Morb Mortal Wkly Rep. 2016;65(6):137\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.15585/mmwr.mm6506a1\u003c/span\u003e\u003cspan address=\"10.15585/mmwr.mm6506a1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003evan de Straat V, Bracke P. How well does Europe sleep? A cross-national study of sleep problems in European older adults. Int J Public Health. 2015;60(6):643\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00038-015-0682-y\u003c/span\u003e\u003cspan address=\"10.1007/s00038-015-0682-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCarra MC, Balagny P, Bouchard P. Sleep and periodontal health. Periodontol 2000. 2024;96(1):42\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/prd.12611\u003c/span\u003e\u003cspan address=\"10.1111/prd.12611\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eK Pavlova M, Latreille V. Sleep Disorders. Am J Med. 2019;132(3):292\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.amjmed.2018.09.021\u003c/span\u003e\u003cspan address=\"10.1016/j.amjmed.2018.09.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen L, Nini W, Jinmei Z, Jingmei Y. Implications of sleep disorders for periodontitis. Sleep Breath. 2023;27(5):1655\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11325-022-02769-x\u003c/span\u003e\u003cspan address=\"10.1007/s11325-022-02769-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFaraut B, Boudjeltia KZ, Vanhamme L, Kerkhofs M. Immune, inflammatory and cardiovascular consequences of sleep restriction and recovery. Sleep Med Rev. 2012;16:137\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIrwin MR, Wang M, Campomayor CO, Collado-Hidalgo A, Cole S. Sleep deprivation and activation of morning levels of cellular and genomic markers of inflammation. Arch Intern Med. 2006;166:1756\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSingh P, Gupta ND, Bey A, Khan S. Salivary TNF-alpha: A potential marker of periodontal destruction. J Indian Soc Periodontol. 2014;18(3):306\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4103/0972-124X.134566\u003c/span\u003e\u003cspan address=\"10.4103/0972-124X.134566\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQi W, Xinyi Z, Yi D. [Effect of inflammaging on periodontitis]. Hua Xi Kou Qiang Yi Xue Za Zhi. 2018;36(1):99\u0026ndash;103. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7518/hxkq.2018.01.019\u003c/span\u003e\u003cspan address=\"10.7518/hxkq.2018.01.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Chinese.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin WM, Yuan Q. [Latest Research Findings on Immune Microenvironment Regulation in Jawbone-Related Diseases]. Sichuan Da Xue Xue Bao Yi Xue Ban. 2022;53(3):528\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.12182/20220560502\u003c/span\u003e\u003cspan address=\"10.12182/20220560502\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Chinese.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeikler T, Peters U, Prior K, Eisenacher M, Flemmig TF. Gene expression in periodontal tissues following treatment. BMC Med Genomics. 2008;1:30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1755-8794-1-30\u003c/span\u003e\u003cspan address=\"10.1186/1755-8794-1-30\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang D, Tang Z, Huang H, Zhou G, Cui C, Weng Y, Liu W, Kim S, Lee S, Perez-Neut M, Ding J, Czyz D, Hu R, Ye Z, He M, Zheng YG, Shuman HA, Dai L, Ren B, Roeder RG, Becker L, Zhao Y. Metabolic regulation of gene expression by histone lactylation. Nature. 2019;574(7779):575\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41586-019-1678-1\u003c/span\u003e\u003cspan address=\"10.1038/s41586-019-1678-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGaffney DO, Jennings EQ, Anderson CC, Marentette JO, Shi T, Schou Oxvig AM, Streeter MD, Johannsen M, Spiegel DA, Chapman E, Roede JR, Galligan JJ. Non-enzymatic Lysine Lactoylation of Glycolytic Enzymes. Cell Chem Biol. 2020;27(2):206\u0026ndash;e2136. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.chembiol.2019.11.005\u003c/span\u003e\u003cspan address=\"10.1016/j.chembiol.2019.11.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu PS, Wang H, Li X, Chao T, Teav T, Christen S, Di Conza G, Cheng WC, Chou CH, Vavakova M, Muret C, Debackere K, Mazzone M, Huang HD, Fendt SM, Ivanisevic J, Ho PC. α-ketoglutarate orchestrates macrophage activation through metabolic and epigenetic reprogramming. Nat Immunol. 2017;18(9):985\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ni.3796\u003c/span\u003e\u003cspan address=\"10.1038/ni.3796\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu X, Wang J, Lao M, Liu F, Zhu H, Man K, Zhang J. Study on the effect of protein lysine lactylation modification in macrophages on inhibiting periodontitis in rats. J Periodontol. 2024;95(1):50\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/JPER.23-0241\u003c/span\u003e\u003cspan address=\"10.1002/JPER.23-0241\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYuan Y, Miao X, Hou Y, Huang Y, Qiu B, Shi W. Association between sleep and periodontitis: NHANES 2009\u0026ndash;2014 and Mendelian randomization study. Cranio 2024 Sep 25:1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/08869634.2024.2406737\u003c/span\u003e\u003cspan address=\"10.1080/08869634.2024.2406737\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu M, Wu Y, Song J, He W. Association of Sleep Duration with Tooth Loss and Periodontitis: Insights from the National Health and Nutrition Examination Surveys (2005\u0026ndash;2020). Sleep Breath. 2024;28(2):1019\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11325-023-02966-2\u003c/span\u003e\u003cspan address=\"10.1007/s11325-023-02966-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNakada T, Kato T, Numabe Y. Effects of fatigue from sleep deprivation on experimental periodontitis in rats. J Periodontal Res. 2015;50(1):131\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jre.12189\u003c/span\u003e\u003cspan address=\"10.1111/jre.12189\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen X, Cheng Z, Xu J, Wang Q, Zhao Z, Jiang Q. No genetic association between sleep traits and periodontitis: A bidirectional two-sample Mendelian randomization study. Cranio 2024 Jul 29:1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/08869634.2024.2384681\u003c/span\u003e\u003cspan address=\"10.1080/08869634.2024.2384681\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou F, Liu Z, Guo Y, Xu H. Association of short sleep with risk of periodontal disease: A meta-analysis and Mendelian randomization study. J Clin Periodontol. 2021;48(8):1076\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jcpe.13483\u003c/span\u003e\u003cspan address=\"10.1111/jcpe.13483\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGBD 2021 Diseases and Injuries Collaborators. Global burden of 371 diseases and injuries in 204 countries and territories, 1990\u0026ndash;2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2023;402(10403).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMurray CJL, Ezzati M, Flaxman AD, et al. GBD 2010: design, definitions, and metrics. Lancet. 2012;380(9859):2063\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(12)61899-6\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(12)61899-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVos T, Lim SS, Abbafati C, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(20)30925-9\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(20)30925-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHosseinpoor AR, Bergen N, Mendis S, et al. Measuring health inequalities in the context of Sustainable Development Goals. Bull World Health Organ. 2015;93(9):591\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2471/BLT.15.155309\u003c/span\u003e\u003cspan address=\"10.2471/BLT.15.155309\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBox GEP, Jenkins GM, Reinsel GC, Ljung GM. Time Series Analysis: Forecasting and Control, 5th Edition. Hoboken, NJ: Wiley; 2015.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFinnGen Consortium. FinnGen Documentation of R8 release. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.finngen.fi/en/access_results\u003c/span\u003e\u003cspan address=\"https://www.finngen.fi/en/access_results\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed 2023-12-05).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBurgess S, Scott RA, Timpson NJ, Davey Smith G, Thompson SG. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur J Epidemiol. 2015;30(7):543\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10654-015-0011-z\u003c/span\u003e\u003cspan address=\"10.1007/s10654-015-0011-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016;40(4):304\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/gepi.21965\u003c/span\u003e\u003cspan address=\"10.1002/gepi.21965\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVerbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50(5):693\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41588-018-0099-7\u003c/span\u003e\u003cspan address=\"10.1038/s41588-018-0099-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOoi AT, Gomperts BN. Molecular pathways: targeting cellular energy metabolism in cancer via inhibition of SIRT1 and SIRT2. Clin Cancer Res. 2015;21(10):2431\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/1078-0432.CCR-14-2874\u003c/span\u003e\u003cspan address=\"10.1158/1078-0432.CCR-14-2874\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRitchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkv007\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkv007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16:284\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/omi.2011.0118\u003c/span\u003e\u003cspan address=\"10.1089/omi.2011.0118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHemani G, Zheng J, Elsworth B The MR-Base platform supports systematic causal inference across the human phenome. eLife., Chen MX, Zhong YJ, Dong QQ et al. Global, regional, and national burden of severe periodontitis, 1990\u0026ndash;2019: An analysis for the Global Burden of Disease Study 2019. J Clin Periodontol. 2021;48(9):1165\u0026ndash;1188.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBernabe E, Marcenes W, Hernandez CR, et al. Global, regional, and national levels and trends in burden of oral conditions from 1990 to 2017: A systematic analysis for the Global Burden of Disease 2017 study. J Dent Res. 2020;99(4):362\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRigholt AJ, Jevdjevic M, Marcenes W, Listl S. Global-, regional-, and country-level economic impacts of dental diseases in 2015. J Dent Res. 2018;97(5):501\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeres MA, Macpherson LMD, Weyant RJ, et al. Oral diseases: A global public health challenge. Lancet. 2019;394(10194):249\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWatt RG, Daly B, Allison P, et al. Ending the neglect of global oral health: Time for radical action. Lancet. 2019;394(10194):261\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrover V, Malhotra R, Kaur H. Sleep deprivation and its effects on the oral health: A systematic review. Sleep Med Rev. 2015;23:67\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChattu VK, Manzar MD, Kumary S, et al. The global problem of insufficient sleep and its serious public health implications. Healthc (Basel). 2018;7(1):1.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBesedovsky L, Lange T, Haack M. The sleep-immune crosstalk in health and disease. Physiol Rev. 2019;99(3):1325\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIrwin MR. Sleep and inflammation: Partners in sickness and in health. Nat Rev Immunol. 2019;19(11):702\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuynh N, Emami E, Helman JI, Chervin RD. Interactions between sleep disorders and oral diseases. Oral Dis. 2014;20(3):236\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSekula P, Del Greco MF, Pattaro C, K\u0026ouml;ttgen A. Mendelian randomization as an approach to assess causality using observational data. J Am Soc Nephrol. 2016;27(11):3253\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRamseier CA, Anerud A, Dulac M, et al. Natural history of periodontitis: Disease progression and tooth loss over 40 years. J Clin Periodontol. 2017;44(12):1182\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKobayashi T, Yoshie H. Host responses in the link between periodontitis and rheumatoid arthritis. Curr Oral Health Rep. 2015;2(1):1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHajishengallis G, Korostoff JM. Revisiting the Page \u0026amp; Schroeder model: The good, the bad and the unknowns in the periodontal host response 40 years later. Periodontol 2000. 2017;75(1):116\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCekici A, Kantarci A, Hasturk H, Van Dyke TE. Inflammatory and immune pathways in the pathogenesis of periodontal disease. Periodontol 2000. 2014;64(1):57\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMullington JM, Simpson NS, Meier-Ewert HK, Haack M. Sleep loss and inflammation. Best Pract Res Clin Endocrinol Metab. 2010;24(5):775\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSilva N, Abusleme L, Bravo D, et al. Host response mechanisms in periodontal diseases. J Appl Oral Sci. 2015;23(3):329\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMealey BL, Oates TW. Diabetes mellitus and periodontal diseases. J Periodontol. 2006;77(8):1289\u0026ndash;303.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchmidt AM, Stern DM. RAGE: A new target for the prevention and treatment of the vascular and inflammatory complications of diabetes. Trends Endocrinol Metab. 2000;11(9):368\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYamagishi S, Matsui T. Advanced glycation end products, oxidative stress and diabetic nephropathy. Oxid Med Cell Longev. 2010;3(2):101\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang D, Tang Z, Huang H, et al. Metabolic regulation of gene expression by histone lactylation. Nature. 2019;574(7779):575\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoreno-Yruela C, Zhang D, Wei W, et al. Class I histone deacetylases (HDAC1-3) are histone lysine delactylases. Sci Adv. 2022;8(3):eabi6696.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrooks GA. The science and translation of lactate shuttle theory. Cell Metab. 2020;27(4):757\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHetz C. The unfolded protein response: Controlling cell fate decisions under ER stress and beyond. Nat Rev Mol Cell Biol. 2012;13(2):89\u0026ndash;102.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcKinsey TA, Zhang CL, Olson EN. MEF2: A calcium-dependent regulator of cell division, differentiation and death. Trends Biochem Sci. 2002;27(1):40\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNepomuceno R, Balatoni C, Natkunam Y, et al. The human homolog of neutrophilic granule protein (HNP-1/defensin): A novel biomarker for human granulocyte development and activation. Blood. 1997;90(12):4968\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcGreal EP, Gasque P. Structure-function studies of the receptors for complement C1q. Biochem Soc Trans. 2002;30(6):1010\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlbandar JM. Aggressive periodontitis: Case definition and diagnostic criteria. Periodontol 2000. 2014;65(1):13\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePage RC, Kornman KS. The pathogenesis of human periodontitis: An introduction. Periodontol 2000. 1997;14:9\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePetersen PE, Ogawa H. The global burden of periodontal disease: Towards integration with chronic disease prevention and control. Periodontol 2000. 2012;60(1):15\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSheiham A, James WP. Diet and dental caries: The pivotal role of free sugars reconfirmed. J Dent Res. 2015;94(10):1341\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWatt RG, Heilmann A, Sabbah W, et al. Social relationships and health related behaviors among older US adults. BMC Public Health. 2014;14:533.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchillinger D, Grumbach K, Piette J, et al. Association of health literacy with diabetes outcomes. JAMA. 2002;288(4):475\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCutler DM, Lleras-Muney A. Understanding differences in health behaviors by education. J Health Econ. 2010;29(1):1\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDavies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: A guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeek JT, Scharpf RB, Bravo HC, et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet. 2010;11(10):733\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRitchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVogel C, Marcotte EM. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet. 2012;13(4):227\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRothman KJ, Greenland S, Lash TL. Modern Epidemiology. 3rd ed. Philadelphia: Lippincott Williams \u0026amp; Wilkins; 2008.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCraig P, Dieppe P, Macintyre S, et al. Developing and evaluating complex interventions: The new Medical Research Council guidance. BMJ. 2008;337:a1655.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHidalgo M, Eckhardt SG. Development of matrix metalloproteinase inhibitors in cancer therapy. J Natl Cancer Inst. 2001;93(3):178\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePepe MS, Etzioni R, Feng Z, et al. Phases of biomarker development for early detection of cancer. J Natl Cancer Inst. 2001;93(14):1054\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Periodontitis, Sleep disorders, Insomnia, Global burden of disease, Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-7547880/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7547880/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePeriodontitis and insomnia represent two prevalent chronic conditions with substantial global health impact. Emerging evidence suggests potential bidirectional relationships between these conditions, yet causal mechanisms remain poorly understood.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a comprehensive three-pronged analysis using Global Burden of Disease (GBD) 2021 data to quantify periodontitis burden across 204 countries and territories from 1990\u0026ndash;2021. Two-sample Mendelian randomization (MR) analyses were performed to investigate causal relationships between sleep-related phenotypes and periodontitis risk using large-scale GWAS summary statistics. Multi-omics bioinformatics approaches integrated periodontitis transcriptomic data with insomnia-associated and lactylation-related gene sets to elucidate shared molecular mechanisms.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eGlobal analysis revealed a 44% increase in absolute periodontitis burden (12.8 to 18.4\u0026nbsp;million DALYs) despite modest improvements in age-standardized rates (-0.34% annually). Pronounced regional disparities persisted, with Sub-Saharan Africa and South Asia bearing disproportionate burden. MR analysis identified insomnia as a primary causal risk factor for periodontitis (OR\u0026thinsp;=\u0026thinsp;1.245, 95% CI: 1.016\u0026ndash;1.526, P\u0026thinsp;=\u0026thinsp;0.034 in multivariable analysis), with effect sizes comparable to established risk factors. Educational attainment demonstrated robust protective effects (15.7% risk reduction per additional year). Transcriptomic analysis identified 25 cross-talk genes predominantly enriched in inflammatory pathways, including IL1B, CXCL8, and AGE-RAGE signaling. Novel lactylation-mediated epigenetic regulation was revealed through correlations between transcriptional regulators XBP1/MEF2C and lactylation enzymes HDAC1/SIRT1. Five core biomarkers showed excellent diagnostic performance (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7), with CD93 demonstrating superior discriminatory capacity (AUC\u0026thinsp;=\u0026thinsp;0.879).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis study establishes sleep disorders as major modifiable risk factors for periodontitis, with robust causal evidence and molecular validation. The identification of lactylation-mediated epigenetic mechanisms provides novel therapeutic targets. Integration of sleep assessment into periodontal care represents a paradigm shift toward precision oral health interventions addressing upstream determinants of disease.\u003c/p\u003e","manuscriptTitle":"Global Burden of Periodontitis and Causal Links with Sleep Disorders: A Mendelian Randomization and Multi-Omics Analysis with Focus on Lactylation-Mediated Mechanisms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 18:41:42","doi":"10.21203/rs.3.rs-7547880/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"75559631106429961530876754961490862900","date":"2025-11-10T12:29:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"94616847030353065866916796683801118977","date":"2025-11-06T21:52:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-30T07:52:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-28T11:07:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-07T12:40:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-07T09:12:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Oral Health","date":"2025-10-07T08:19:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b7e65377-8cd3-46fa-b837-dfc5c478d84f","owner":[],"postedDate":"November 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-11T18:41:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-11 18:41:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7547880","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7547880","identity":"rs-7547880","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}