Association between periodontitis and NAFLD-related diseases: Results from the NHANES and Mendelian randomization study

Association between periodontitis and NAFLD-related diseases: Results from the NHANES and Mendelian randomization study | 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 Association between periodontitis and NAFLD-related diseases: Results from the NHANES and Mendelian randomization study Yanqiu Huang, Wenhui Wang, Xiaoyu Wang, Jie Yuan, Jinfan Xu, Yang Yang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3966322/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background & Aim There are contradictory causal links between disorders associated to non-alcoholic fatty liver and periodontitis. The purpose of this research is to use Mendelian randomization (MR) to establish a causal association between periodontitis and non-alcoholic fatty liver disease (NAFLD), including the latter's development to liver fibrosis. Materials and Methods The study included 4,425 people from the National Health and Nutrition Examination Survey (NHANES) conducted in the United States between 2009 and 2014. The study employed two multivariable logistic regression models to evaluate the correlation between advanced fibrosis (AF) and periodontitis, as well as NAFLD. Model 1 did not involve any covariate adjustments; model 2 controlled for age, gender, and race; model 3 was additionally adjusted for Body Mass Index (BMI), education level, household income poverty ratio, smoking status, physical activity, and history of diabetes. Periodontitis (n:17,353 cases/28,210 controls) was used as the exposure, and NAFLD (n:2,275 cases/375,002 controls), fibrosis (n:146 cases/373,307 controls), cirrhosis (n:1,142 cases/373,307 controls) and fibrosis/cirrhosis (n:1,841 cases/366, 450 cases control) as outcomes and causality validation was performed. Sensitivity studies, such as heterogeneity tests, multiple validity tests, and exclusion analyses, were also carried out to guarantee the trustworthiness of the findings. Results In the observational study, there was no significant correlation between periodontitis and NAFLD (OR: 0.82, 95% CI: 0.64–1.95) or AF (OR: 1.06, 95% CI: 0.72–1.56). The MR analysis found no significant association between genetically predicted periodontitis and liver conditions in the IVW method (NAFLD: OR: 1.12, 95% CI: 0.98 − 1.27; fibrosis: OR: 0.84, 95% CI: 0.50 − 1.42; cirrhosis: OR:0.99, 95% CI: 0.82 − 1.19; fibrosis/cirrhosis: OR: 0.92, 95% CI: 0.83 − 1.26). There is consistency in sensitivity results. Conclusions According to cross-sectional research, there is no discernible link between NAFLD or liver fibrosis and periodontal disease, and the MR analysis does not support a causal relationship between them. Periodontitis Non-alcoholic fatty liver disease Liver fibrosis/cirrhosis National Health and Nutrition Examination Survey Mendelian randomization λ Figures Figure 1 Figure 2 Figure 3 Figure 4 Background An ecological dysregulation of the dental biofilm is the cause of periodontal disease, a chronic infectious illness. ( 1 ), with an overall prevalence of 45%-48% ( 2 ) among US adults. At 11.2% of the world's population, it is the sixth most common human disease in its most severe form ( 3 ). Unhealthy lifestyles such as smoking, low-quality diets (high-fat, high-sugar diets) and mental stress (depression, etc.) can significantly increase the risk of periodontitis. Prior research has demonstrated how periodontal disease may exacerbate the onset of systemic disorders, including cardio-vascular disease ( 4 ), hypertension ( 5 ), metabolic disorder-related disease disorders ( 6 ), Non-alcoholic fatty liver disease (NAFLD) ( 6 )), and others. Without a history of heavy alcohol consumption, NAFLD is characterized by abnormal fat buildup in the liver. ( 8 ). The estimated global prevalence of NAFLD was 24% from 1989 to 2015 ( 4 ). Due to the global obesity epidemic, smoking, high-fat and high-sugar diets, and mental stress, Currently, NAFLD is among the most prevalent liver conditions in adults and children worldwide ( 5 ). Increasing evidence now implies that NAFLD is a multisystem disease that affects not only the liver but also other organs and regulatory pathways ( 6 ). For instance, NAFLD increases the risk of developing type 2 diabetes (T2DM) ( 7 ), cardiovascular disease (CVD) ( 8 ), and chronic kidney disease (CKD) ( 9 ). NAFLD can be one of the factors leading to liver fibrosis and, ultimately, cirrhosis ( 10 ). Steatosis in NALFD continues to progress to abnormal proliferation of fibrous tissue forming hepatic fibrosis ( 11 ). The severity of liver fibrosis can range from mild to severe and usually progresses gradually. Without proper management, especially in cases of severe NAFLD or NASH, liver fibrosis can progressively worsen, ultimately leading to cirrhosis ( 12 ). Cirrhosis is an advanced liver disease marked by the replacement of healthy liver tissue with fibrous tissue, resulting in severe impairment of liver structure and function ( 13 ). Treatment for cirrhosis often requires a liver transplant, as the disease has reached an irreversible stage ( 14 ). Recently, epidemiological studies have suggested that periodontitis increases the prevalence of NAFLD and fibrosis ( 15 )( 16 )( 17 ). The pathogenesis may involve systemic inflammation and oxidative damage ( 18 )( 19 ). Porphyromonas gingivalis has also gained popularity in the field of etiology ( 20 )( 21 ). Another plausible mechanism that links dental health to liver function is the idea of the "oral-intestinal-liver axis" ( 22 )( 23 ) ( 24 )( 25 )( 26 ). However, Mendelian randomization (MR) came to the conclusion that there is no evidence to support the claim that periodontitis has no causal effect on fibrosis and NAFLD. The potential lack of further validation due to conflicting results from population studies and genetic data, as well as the lack of other MR studies may explain the value of our study. The relationship between periodontal disease and NAFLD has been discussed from in vitro, in vivo, and epidemiological perspectives. This study looked into the possible cause-and-effect link between liver disorders and periodontitis using data from both MR and the NHANES database. Materials and Methods The study was divided into two phases, as illustrated in Fig. 1. In the first stage, we conducted multivariable logistic regression analyses using data from the NHANES database to examine the connection between liver disorders and periodontitis. In the second stage, we conducted four MR analyses using data from the Gene-Lifestyle Interactions in Dental Endpoints (GLIDE) consortium and FinnGen database to look at the mutual relationship that exists between liver disorders and periodontitis. NHANES Data sources and study population NHANES is a collection of a sequence of cross-sectional surveys aimed at the non-institutionalized population in the US. It selects a nationally representative sample using a multi-stage probability sampling technique and assesses the nutritional and health condition of the participants. The survey includes interviews conducted at participants' households, physical evaluations, and laboratory examinations. NHANES is administered by the National Center for Health Statistics, a division of the Centers for Disease Control and Prevention (CDC). The National Center for Health Statistics Ethics Review Board has given the study ethical approval, and all participants have given written informed permission (Fig. 2). Figure 2 depicts the process of participant selection for our NHANES study. Initially, there were 30,468 individuals enrolled. Nevertheless, 11,756 participants had to be excluded due to the unavailability of periodontal data. As a result, the study ultimately included a total of 4,425 participants. Assessment of periodontitis and liver diseases The oral health statistics from NHANES (2009 to 2014) show that qualified examiners evaluated the clinical attachment loss (CAL) and periodontal probing depth (PD) at six specific locations for every tooth (wisdom teeth excluded), for a total of 28 teeth. The 2018 global classification criteria were used to determine the diagnosis of periodontitis: when interdental CAL is evident in ≥ 2 non-adjacent teeth or when buccal or oral CAL ≥ 3 mm is observed in ≥ 2 teeth with a pocket depth exceeding 3 mm, it is classified as periodontitis ( 27 ). NAFLD was diagnosed by Fatty Liver Index (FLI) ≥ 60 or ultrasonographic FLI (USFLI) ≥ 30. FLI is a numerical number used in medicine to assess a person's risk of having hepatic steatosis or fatty liver disease. Instead of using FLI as a diagnostic sign in our investigation, we opted to employ USFLI. Here is how FLI and USFLI were determined ( 28 ): ("Mexican American" and "non-Hispanic Black" have a value of 0 if the participant is not of that ethnicity, and 1 if they are.) Participants who were at risk of atrial fibrillation (AF) were identified by elevated scores for the non-alcoholic fatty liver disease fibrosis (NFS), fibrosis-4 index (FIB-4), or the aspartate aminotransferase (AST)/platelet ratio index (APRI). The NFS is a scoring system that's used to determine how likely or severe it is that NAFLD may cause fibrosis (the production of scar tissue) in the liver. Without requiring a liver biopsy, medical personnel can assess the degree of fibrosis using the easily accessible and non-invasive FIB-4 instrument. To assess the level of liver fibrosis (scarring) in individuals suffering from liver disease, especially those with chronic hepatitis C, doctors employ the non-invasive APRI scoring system. Here is how these indicators were computed: (AST: The upper limit of normal (ULN) for the 1999–2000 cycle was 40 U/L; thereafter, the ULN was 33 U/L) ( 29 ) Those with an APRI exceeding 1, FIB-4 greater than 2.67, or NFS surpassing 0.676 were classified as individuals at high risk of AF, as per the criteria outlined in reference. Assessment of covariates The thorough evaluation of covariates plays a pivotal role in research, as it serves to control potential confounding factors, thereby ensuring a more precise and dependable assessment of the primary associations. In our study, we have carefully chosen a set of covariates to enhance our comprehension of the link between periodontal disease and liver conditions. We included several key covariates such as age, gender, ethnicity, BMI, drinking, recent tobacco use, history of diabetes, education, marriage, family monthly poverty level index, energy intake and leisure time physical activity (LTPA), all of which were gathered through standardized questionnaires. Additionally, the weight and height of each participant were measured during physical examinations, with BMI calculated as weight (kg)/ height (m2). Statistical analyses We combined data from the years 2009 to 2014 and created 6-year sampling weights in accordance with NHANES' sampling methodology, which were incorporated into all of our analyses. To assess variances in continuous and categorical variables across various analysis groups, we used the t-test for continuous data and the Rao Scott chi-square test for categorical variables. The impact of liver disorders on the risk of periodontitis was investigated using a multivariable logistic regression analysis. We computed odds ratios and the associated 95% confidence intervals. In the multivariable analysis, we made the following adjustments: In Model 1, no covariate adjustments were performed; in Model 2, age, gender, and race adjustments were applied; in Model 3, BMI, education level, household income poverty ratio, smoking status, physical activity, and history of diabetes were adjusted. All analyses were performed using SAS 9.4. The significance threshold was set at 0.05 and two-sided levels of significance were computed. MR Analysis Basic concept of MR analysis When compared to traditional observational approaches, MR analysis is less susceptible to errors resulting from reverse causation and confounding since genetic differences are assigned randomly during gamete development and are not connected to environmental variables. As a result, we used MR analysis in this work to find single nucleotide polymorphisms (SNPs) connected to liver disorders and periodontitis. Following this, we integrated these identified SNPs to ascertain the connection between periodontitis and liver diseases. Study design description The investigation of the relationship between liver disorders and periodontitis used a two-sample MR analysis. We conducted the MR analyses utilizing summary statistics from open-access databases to examine the association between periodontitis as the exposure and liver conditions including NAFLD, fibrosis, cirrhosis and fibrosis/cirrhosis as the outcomes. The study does not require ethical approval because it is based on publically available summary statistics. Data sources and selection of instrumental variables (IVs) The summary statistics derived from two European datasets served as the foundation for the two-sample MR analysis: data for periodontitis (17,353 cases; 28,210 controls) from a genome-wide association study (GWAS) of the European studies of GLIDE consortium ( 30 ) ; and liver conditions including NAFLD (2,275 cases; 375,002 controls), fibrosis (146 cases; 373,307 controls), cirrhosis (1,142 cases; 373,307 controls) and fibrosis/cirrhosis (1,841 cases; 375002 controls) from FinnGen ( 31 ) which was based on over 370,000 Finnland residents with trustable diagnoses in wide genres of diseases. SNPs were chosen for IV selection at a genome-wide significance criterion (p 0.001. Palindromic SNPs were excluded from exposure and outcome data when harmonizing them. Finally, we vertificated that all IVs did not impact the outcomes through other pathways by applying Phenoscanner for possible related traits. In order to assure the trustworthiness of results and reduce the interference of confounders related to SNPs with P-value < 1 ×10 − 5, the PhenoScanner database was utilized ( 32 ). Statistical analysis The random-effects inverse variance weighting (IVW) approach is the main statistical methodology used in this study to investigate the possibility of bidirectional causation between liver illness and periodontitis. To further enhance our results, we also used the weighted mode, weighted median, and MR Egger techniques. The IVW method operates on the premise that all fundamental assumptions of MR are satisfied. Nevertheless, given that the inclusion of pleiotropic IVs can introduce bias into IVW estimates, we conducted sensitivity analyses to account for any pleiotropic effects. R (version 4.3.2) was utilized to conduct the MR analysis. Three packages, "TwoSampleMR"( 33 ), "MRPRESSO"( 34 ), and "forestploter," were specifically used for the data processing and visualization. The study was reported using the STROBE-MR (STrengthening the Reporting of OBservational studies in Epidemiology using Mendelian randomization) criteria ( 35 ). Pleiotropy and Sensitivity Analysis MR-Egger regression was used to determine if horizontal pleiotropy would exist. The average pleiotropic impact of the IVs is reflected in the intercept term of the MR-Egger regression. In addition, we looked for the existence of pleiotropy using the MR Pleiotropy REsidual Sum and Outlier (MR-PRESSO) test. MR-PRESSO serves multiple purposes, including the detection of horizontal pleiotropy, the correction of horizontal pleiotropy by identifying and removing outliers, and the evaluation of significant differences in causal effects both before and after the elimination of outliers. We used MR-Egger regression and the IVW technique to measure heterogeneity, and we calculated the degree of heterogeneity using the Cochran's Q statistic. We also performed a leave-one-out analysis to assess the consistency and robustness of our results. Results Baseline Characteristics of Periodontitis, NAFLD, Fibrosis and Cirrhosis in NHANES The baseline features of NAFLD and periodontitis in the research subjects are listed in Table 1 . 4425 participants were enrolled in our study of which 2636 were diagnosed with periodontitis. With a mean age of 54.5 years, periodontitis patient group’s age was comparatively high. Gender distribution showed that 56.2% of the population was male, somewhat more than the 43.8% of females. Compared to the healthy control group, those with periodontitis were more likely to be male, older, smokers, diabetics, had lower levels of education, and have a lower family income. Table 1 Baseline characteristics of study population in NHANES 2009–2014 and prevalence of periodontitis and NAFLD by characteristics. Characteristics Total (N = 4425) Periodontitis NAFLD No (N = 1789) Yes (N = 2636) P -value No (N = 3625) Yes (N = 800) P -value Age, years, Mean (SD) 53.1(0.2) 51.1(0.4) 54.5(0.3) < 0.01 51.9(0.2) 58.9(0.5) < 0.01 Gender, n(%) < 0.01 < 0.01 Male 2226(50.3) 745(41.6) 1481(56.2) 1761(48.6) 465(58.1) Female 2199(49.7) 1044(58.4) 1155(43.8) 1864(51.4) 335(41.9) USFLI, Mean(SD) 27.6(0.3) 25.4(0.5) 29.1(0.4) < 0.01 22.0(0.3) 53.0(0.6) < 0.01 NFS, Mean(SD) -1.5(0.02) -1.6(0.03) -1.4(0.03) < 0.01 -1.7(0.02) -0.7(0.05) < 0.01 FIB-4, Mean(SD) 1.3(0.01) 1.3(0.02) 1.3(0.02) < 0.01 1.3(0.01) 1.4(0.03) < 0.01 APRI, Mean(SD) 0.4(0.01) 0.4(0.01) 0.4(0.01) 0.90 0.4(0.01) 0.4(0.01) 0.37 Body mass index, kg/m 2 , Mean(SD) 29.0(0.09) 28.9(0.1) 29.1(0.1) 0.27 28.1(0.1) 33.0(0.2) < 0.01 Ethnicity, n(%) < 0.01 < 0.01 Mexican American 614(13.9) 179(10.0) 435(16.5) 455(12.6) 159(19.9) Other Hispanic 424(9.6) 150(8.9) 274(10.4) 338(9.3) 86(10.8) Non-Hispanic White 2106(47.6) 1026(57.4) 1080(41.0) 1721(47.5) 385(48.1) Non-Hispanic Black 814(18.4) 267(14.9) 547(20.8) 718(19.8) 96(12.0) Other Race - Including Multi-Racial 467(10.6) 167(9.3) 300(11.4) 393(10.8) 74(9.3) Education levels, n(%) < 0.01 < 0.01 Less than high school 1081(24.4) 357(20.0) 724(27.5) 789(21.8) 292(36.5) High school or above 3344(75.6) 1432(80.0) 1912(72.5) 2836(78.2) 508(63.5) Marriage status, n(%) 0.56 0.12 Have a partner or be married 2899(65.5) 1181(66.0) 1718(65.2) 2356(65.0) 543(67.9) Single or widowed 1526(34.5) 608(34.0) 918(34.8) 1269(35.0) 257(32.1) Poverty status, n(%) < 0.01 3.50 2293(51.8) 1014(56.7) 1279(48.5) 1951(53.8) 342(42.8) Energy intake levels, n(%) 0.54 < 0.01 Inadequate 1871(42.3) 740(41.4) 1131(42.9) 1464(40.4) 407(50.9) Adequate 1855(41.9) 767(42.9) 1088(41.3) 1573(43.4) 282(35.3) Excessive 699(15.8) 282(16.8) 417(15.8) 588(16.2) 111(13.9) Smoking status, n(%) < 0.01 < 0.01 Never 2422(54.7) 1050(58.7) 1372(52.0) 1980(54.6) 442(55.3) Ever 1175(26.6) 459(25.7) 716(27.2) 916(25.3) 259(32.4) Current 828(18.7) 280(15.7) 548(20.8) 729(20.1) 99(12.4) Leisure time physical activity, minutes, n(%) 0.04 < 0.01 0 2295(51.9) 887(49.6) 1408(53.4) 1779(49.1) 516(64.5) 0-150 787(17.8) 328(18.3) 459(17.4) 663(18.3) 124(15.5) ≥ 150 1343(30.4) 574(32.1) 769(29.2) 1183(32.6) 160(20.0) Diabetes, n(%) < 0.01 < 0.01 Yes 826(18.7) 281(15.7) 545(20.7) 509(14.0) 317(39.6) No 3599(18.7) 1508(84.3) 2091(79.3) 3116(86.0) 483(60.4) NAFLD, non-alcoholic fatty liver disease; SF, significant fibrosis; AF, advanced fibrosis; SE, standard error of mean. 800 NAFLD patients had a mean age of 58.9 years, with a somewhat higher percentage of males (58.1%) than females (41.9%). Individuals with NAFLD were more likely to be male, older, obese, current smokers, and diabetics as compared to healthy controls. Their LTPA, household income, and education levels were also lower. Baseline Characteristics of Periodontitis, NAFLD, Fibrosis and Cirrhosis in NHANES Multivariate logistic regression analysis was employed to investigate the correlation between liver illnesses and periodontal disease. Periodontitis was only statistically significant in Model 1 in AF (OR: 1.48, 95% CI: 1.08–2.07), and it was not substantially linked with NAFLD or liver fibrosis in the overall group (P > 0.05). The relationship between AF and periodontitis was significant in Model 1 (OR: 1.79, 95% CI: 1.32–2.43) when stratified by sex, but not in the other models (P > 0.05) (Fig. 3). Causal Relationship of Periodontitis on Liver Conditions in MR In the MR analysis where periodontitis acted as exposure, 6 SNPs were selected as IVs (detailed information described in Table S2 ). The results indicated an overall non-significant association between genetically predicted periodontitis and an increased risk of NAFLD in the IVW method (OR: 1.12, 95% CI: 0.98–1.27). The weighted mean (OR 1.06, 95% CI 0.91–1.23) and weighted median (OR: 1.07, 95% CI: 0.91–1.25) approaches further supported this conclusion. Notably, IVW validation revealed no indication of considerable SNP heterogeneity, and no pleiotropy was seen. Similarly, in the context of cirrhosis, genetically predicted periodontitis demonstrated an insignificant elevated risk in the IVW analysis (OR: 0.99, 95% CI 0.82–1.19), which was consistent with the findings from the weighted median (OR: 1.02, 95% CI: 0.81–1.28) and the weighted mode (OR: 1.02, 95% CI: 0.83–1.26) methods. Like in the previous analyses, there was no significant SNP heterogeneity based on IVW validation, and no pleiotropic effects were detected (Fig. 4). In summary, our MR analyses provide insights into the relationships between periodontitis and various liver diseases, highlighting generally non-significant associations and the absence of significant SNP heterogeneity or pleiotropy in our results. The regional associational plot indicating the lead SNP and nearby genes associated with periodontitis was depicted as Figure S4 . After conducting a heterogeneity analysis, it was determined that there was no significant potential heterogeneity in the way that periodontitis affected the liver conditions (fibrosis: MR Egger P: 0.88, IVW P: 0.95; cirrhosis: MR Egge` r P: 0.58, IVW P: 0.71; fibrosis/cirrhosis: MR Egger P: 0.63, IVW P: 0.67). According to Table S3 , S4, the Egger intercept (Egger intercept P: NAFLD:0.85; fibrosis: 0.95; cirrhosis: 0.79; fibrosis/cirrhosis: 0.48) did not demonstrate any horizontal pleiotropy between the impact of periodontitis on liver diseases. And Figure S3 displayed the findings of the MR sensitivity study as scatter plots, leave-one-out analyses, and funnel plots. Discussion This is the first study that we are aware of that combines genetic Mendelian randomization in European countries with cross-sectional US samples to investigate bidirectional connections between periodontitis and NAFLD-associated illnesses. The analysis indicates that there is no significant correlation between periodontal disease and NAFLD or liver fibrosis, and the MR analysis does not support a causal relationship between them. The sensitivity analysis further confirms our conclusions. While previous research has hinted at a certain link between periodontitis and NAFLD, the exact cause-and-effect relationship remains unclear ( 36 ). According to research by Saito et al., individuals with periodontitis had considerably higher levels of aspartate aminotransferase ( 41 ) than those without the condition, indicating a clear correlation between periodontitis and liver damage. According to a cross-sectional study, in a representative sample of Koreans, the existence of periodontal pockets may be substantially correlated with NAFLD markers ( 37 ). Nevertheless, F Xu et al.'s meta-analysis revealed that the available data does not support a causal relationship between periodontitis and NAFLD ( 38 ). In light of the meta-analysis's conclusions, we applied the latest MR periodontitis genetic data to similarly obtain consistent conclusions. The recent MR study suggested that NAFLD moderately increases the chances of periodontitis by Li Tan et al. However, we believe that both the exposure and outcome samples were derived from the FinnGen database, which goes against the premise of a two-sample MR study and makes the conclusions unreliable ( 39 )( 40 ). Most of the pathologic associations between periodontitis and human NAFLD have been provided by observational epidemiologic studies, and causality has not been established ( 41 ). Smoking and poor diet, among others are all common etiologic factors for periodontitis and NAFLD ( 42 )( 43 )( 44 ), and when participants had these risk factors, periodontitis and NAFLD often co-occurred and therefore showed statistically significant associations, which no longer existed in our model after adjusting for these important confounders. Currently, some studies have suggested that oral diffusion of some pathogenic factors such as bacteria and inflammatory factors in patients with periodontitis affects the liver through the oral-intestinal-hepatic axis or blood-borne pathways ( 45 )( 46 )( 23 ). As well as periodontitis is closely associated with insulin resistance or metabolic syndrome also may promote the development of NAFLD ( 41 ). Nevertheless, the impact of treating periodontitis on liver illness has not been the subject of previous research, and the findings of several meta-analyses are inconsistent ( 41 ). It is undeniable that periodontitis affects the development of systemic diseases, and MR studies have only inferred causality, ignoring the biological mechanisms between them. Therefore, future studies on the mechanisms of periodontitis diagnosis and treatment on NAFLD-related diseases need to be further explored. This study possesses several notable strengths: ( 1 ) This represents the first-ever combination of bidirectional MR analysis with NHANES data to investigate the possible causative relationship between chronic liver illnesses and periodontitis. Meanwhile our NHANES data results were consistent with MR results. ( 2 ) The data we utilized originated from NHANES 2009–2014, representing a substantial dataset. Furthermore, we leveraged data from the Finnish database for MR analysis, which offers comprehensive insights into periodontitis, NAFLD, liver fibrosis, and cirrhosis. ( 3 ) Our study stands out by introducing three distinct indicators for assessing liver fibrosis, enhancing the reliability and persuasiveness of our findings in comparison to prior research. Nonetheless, our study is not without its limitations: ( 1 ) We refrained from further subclassifying periodontitis, even though previous research has linked moderate to severe periodontitis with chronic liver diseases. ( 2 ) Our approach employed three indicators to gauge liver fibrosis, which may exhibit limitations regarding specificity and sensitivity. ( 3 ) NHANES data represent the circumstances of Americans, while the MR data were obtained from the European population. This disparity between observational and genetic data could introduce bias into the results, necessitating further enhancements and refinements. Conclusions To sum up, observational study of NHANES data suggests that there is no significant correlation between periodontal disease and the risk of NAFLD and liver diseases. Furthermore, there is no discernible causal relationship between the incidence of NAFLD or liver fibrosis/cirrhosis and periodontal disease, according to the MR study. Consequently, further standardized cohort studies in a more diverse population are warranted to gain a deeper insight into the interrelationship between oral health and liver function, as well as the mechanisms underlying it. Abbreviations NAFLD: non-alcoholic fatty liver disease; NHANES: National Health and Nutrition Examination Survey; AF: advanced fibrosis; MR: Mendelian randomization; BMI: Body Mass Index; MetS: metabolic syndrome; NASH: non-alcoholic steatohepatitis; T2DM: type 2 diabetes; CVD: cardiovascular disease; CKD: chronic kidney disease; GLIDE: Gene-Lifestyle Interactions in Dental Endpoints; PD: probing depth; CAL: clinical attachment loss; FLI: Fatty Liver Index; USFLI: ultrasonographic FLI; NFS: non-alcoholic fatty liver disease fibrosis; FIB-4: fibrosis-4 index; AST: aspartate aminotransferase; APRI: platelet ratio index; ULN: upper limit of normal; LTPA: leisure time physical activity; SNPs: single nucleotide polymorphisms; GWAS : genome-wide association study; IVW: inverse variance weighting; STROBE-MR: Strengthening the reporting of observational studies in epidemiology using mendelian randomization; MR-PRESSO: MR Pleiotropy residual sum and outlier Declarations i ) Ethics approval and consent to participate NHANES is conducted by the Centers for Disease Control and Prevention (CDC) and the National Center for Health Statistics (NCHS). And the NHANES study protocol was reviewed and approved by the NCHS Research Ethics Review Committee. All participants in NHANES provided written informed consent. ii ) Consent for publication Not applicable. iii ) Availability of Data and Material (ADM) Data used for this study are available on the following websites: NHANES: (https://www.cdc.gov/nchs/nhanes/index.htm); FinnGen: (https://r9.finngen.fi). GWAS summary statistics for dental caries and periodontitis: (https://data.bris.ac.uk/data/dataset/2j2rqgzedxlq02oqbb4vmycnc2); iv ) Competing interests The authors declare no competing interests. v ) Funding H.W. is funded by the National Natural Science Foundation of China (81972820 and 82030099), the National Key Research and Development Program of China (2018YFC2000700 and 2022YFD2101500), the Science and Technology Commission of Shanghai Municipality (22DZ2303000), Natural Science Foundation of Shanghai Municipality (23ZR1435900), Innovative research team of high-level local universities in Shanghai and Shanghai Jiao Tong University Key Program of Medical Engineering (YG2021ZD01); W.S. is funded by Three Year Action Plan for Promoting Clinical Skills and Clinical Innovation in Municipal Hospitals (SHDC2022CRS025). vi ) Author’s contributions Conceptualization, Y.H., W.W., X.W., J.Y., Y.Y. and J.X.; Methodology and formal analysis, Y.H., W.W., and X.W.; Investigation, J.Y., Y.Y. and J.X; Writing—original draft preparation, Y.H., W.W. and X.W.; writing—review and editing, J.Y., Y.Y., J.X., W.S., X.L. and H.W.; supervision, W.S., X.L. and H.W.; funding acquisition, H.W., X.L. and W.S.; Y.H., X.W. and W.W. contributed equally to this work. All authors have read and agreed to the published version of the manuscript. vii ) Acknowledgments The authors thank the GLIDE and FinnGen consortium for contributing data and all participants involved in this study. Supplementary Material The following supporting information can be downloaded at: www.mdpi.com/xxx/s1, Table S1: Data source for Mendelian randomization (MR); Table S2: Detailed information of the SNPs of periodontitis applied in MR analyses.; Table S3: The results of heterogeneity analyses for MR; Table S4: The results horizontal pleiotropy analyses for MR; Figure S1: The RCS plot of the results in different populations and different models using NHANES data; Figure S2: The effect of periodontitis on FIB-4, NFS and APRI according to data from NHANES; Figure S3: The sensitivity results of the MR analyses; Figure S4: The regional association plot of periodontitis Informed Consent Statement Not applicable. Disclaimer/Publisher’s Note The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. References Y. Z et al., ‘Global burden of NAFLD and NASH: trends, predictions, risk factors and prevention’, Nat. Rev. Gastroenterol. Hepatol., vol. 15, no. 1, Jan. 2018, doi: 10.1038/nrgastro.2017.109. E. Pi, D. Ba, W. L, T.-E. Go, and G. Rj, ‘Prevalence of periodontitis in adults in the United States: 2009 and 2010’, J. Dent. Res., vol. 91, no. 10, Oct. 2012, doi: 10.1177/0022034512457373. S. M et al., ‘Periodontitis and Cardiovascular Diseases. Consensus Report’, Glob. Heart, vol. 15, no. 1, Mar. 2020, doi: 10.5334/gh.400. Z. M. Younossi, A. B. Koenig, D. Abdelatif, Y. Fazel, L. Henry, and M. 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Amv et al., ‘Inflammatory profile of apical periodontitis exacerbated by cigarette smoke inhalation: Histological and immunohistochemical analysis in rats’, Int. Endod. J., vol. 56, no. 4, Apr. 2023, doi: 10.1111/iej.13883. W. Dm, M. G, N. A, W. L, L. Gj, and W. Jv, ‘Association between diet and periodontitis: a cross-sectional study of 10,000 NHANES participants’, Am. J. Clin. Nutr., vol. 112, no. 6, Oct. 2020, doi: 10.1093/ajcn/nqaa266. Y. S et al., ‘Lifestyle and metabolic factors for nonalcoholic fatty liver disease: Mendelian randomization study’, Eur. J. Epidemiol., vol. 37, no. 7, Jul. 2022, doi: 10.1007/s10654-022-00868-3. K. Y et al., ‘Visceral obesity and hypoadiponectinemia are significant determinants of hepatic dysfunction: An epidemiologic study of 3827 Japanese subjects’, J. Clin. Gastroenterol., vol. 43, no. 10, Dec. 2009, doi: 10.1097/MCG.0b013e3181962de8. K. L, B. N, C. S, and K. Df, ‘Adipokines and inflammatory mediators after initial periodontal treatment in patients with type 2 diabetes and chronic periodontitis’, J. Periodontol., vol. 81, no. 1, Jan. 2010, doi: 10.1902/jop.2009.090267. Additional Declarations No competing interests reported. Supplementary Files FigureS1.tif FigureS2.tif FigureS3.tif FigureS4.tif TableS1.docx TableS2.docx TableS3.docx TableS4.docx Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-3966322","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":275171960,"identity":"5b9ab701-1236-4028-84a9-b9293702e3c5","order_by":0,"name":"Yanqiu Huang","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yanqiu","middleName":"","lastName":"Huang","suffix":""},{"id":275171961,"identity":"94e3bb6b-b9f5-4f36-87a2-86f2897014a1","order_by":1,"name":"Wenhui Wang","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenhui","middleName":"","lastName":"Wang","suffix":""},{"id":275171962,"identity":"e81f743d-d71b-4740-9272-58ccfb5d8b70","order_by":2,"name":"Xiaoyu Wang","email":"","orcid":"","institution":"Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Wang","suffix":""},{"id":275171963,"identity":"52c4dcb3-7599-49a5-a102-b6d3c4499c23","order_by":3,"name":"Jie Yuan","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Yuan","suffix":""},{"id":275171964,"identity":"7fdaf0c1-8b97-476d-b443-75c9ec32f6be","order_by":4,"name":"Jinfan Xu","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jinfan","middleName":"","lastName":"Xu","suffix":""},{"id":275171965,"identity":"d5082b6a-de9d-461e-8566-1eb7ad80e2d5","order_by":5,"name":"Yang Yang","email":"","orcid":"","institution":"Johns Hopkins University Bloomberg School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Yang","suffix":""},{"id":275171966,"identity":"3191857b-1ebd-4fca-9d4a-70a25d6ea5d8","order_by":6,"name":"Wentao Shi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYDACCcYGGJONgaFCQk6eSC0GIA1ALWcsjA0bCOhgkACTUC2MbRWJDAcI6JCf3dwm8XHHH3l++eZjDz7Ok0hgbGB++OgGHi0Gdw62Sc48Y2A4s40t3XDmNok8dgY2Y+McfFokEttu87YZMG44xmMmzbtNopixgYdNGp8W+RlALX/bDOzBWv7OkUhsOEBAC8MNoBbGNoNEsBbGBiK0GNxIbP/Z22acPLMtLd2w55iEsWEzAb/Iz0h/bPCzTc62n/nwsQc/aurk5NmbHz7G6zBMwEya8lEwCkbBKBgFWAAAYWFKsXq92C8AAAAASUVORK5CYII=","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Wentao","middleName":"","lastName":"Shi","suffix":""},{"id":275171967,"identity":"4c875e6f-7e75-4c92-a294-2a1bd4f07a20","order_by":7,"name":"Xiaoguang Li","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaoguang","middleName":"","lastName":"Li","suffix":""},{"id":275171968,"identity":"4eed2453-911b-463c-9c0e-a26396250d52","order_by":8,"name":"Hui Wang","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-02-18 08:14:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3966322/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3966322/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51825773,"identity":"881fba9a-42de-4d9a-aac2-24892aef7304","added_by":"auto","created_at":"2024-02-29 17:02:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":108652,"visible":true,"origin":"","legend":"\u003cp\u003eOverall study design. NAFLD, non-alcoholic fatty liver disease.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3966322/v1/5c38425ded127848dea63e2e.png"},{"id":51825774,"identity":"1855d1e3-3814-45b4-8fc9-685ebb1e3ea8","added_by":"auto","created_at":"2024-02-29 17:02:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":99096,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant inclusion algorithm of NHANES. NHANES, National Health and Nutrition Examination\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3966322/v1/f9ffff0fbbb2095267009e74.png"},{"id":51825772,"identity":"e0b99528-883d-40fc-9a5f-6a850bd4f9de","added_by":"auto","created_at":"2024-02-29 17:02:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":164752,"visible":true,"origin":"","legend":"\u003cp\u003eThe effect of periodontitis on liver diseases in NHANES.NAFLD, non-alcoholic fatty liver disease; AF, advanced liver fibrosis; OR, odds ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3966322/v1/773450c43d6a92b592bdcd2c.png"},{"id":51825779,"identity":"0155c164-d295-4e79-a125-bc9505d05842","added_by":"auto","created_at":"2024-02-29 17:02:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":139239,"visible":true,"origin":"","legend":"\u003cp\u003eThe effect of periodontitis on liver diseases in MR analysis. 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(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), with an overall prevalence of 45%-48% (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) among US adults. At 11.2% of the world's population, it is the sixth most common human disease in its most severe form (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Unhealthy lifestyles such as smoking, low-quality diets (high-fat, high-sugar diets) and mental stress (depression, etc.) can significantly increase the risk of periodontitis. Prior research has demonstrated how periodontal disease may exacerbate the onset of systemic disorders, including cardio-vascular disease (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), hypertension (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), metabolic disorder-related disease disorders (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), Non-alcoholic fatty liver disease (NAFLD) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)), and others.\u003c/p\u003e \u003cp\u003eWithout a history of heavy alcohol consumption, NAFLD is characterized by abnormal fat buildup in the liver. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The estimated global prevalence of NAFLD was 24% from 1989 to 2015 (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Due to the global obesity epidemic, smoking, high-fat and high-sugar diets, and mental stress, Currently, NAFLD is among the most prevalent liver conditions in adults and children worldwide (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Increasing evidence now implies that NAFLD is a multisystem disease that affects not only the liver but also other organs and regulatory pathways (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). For instance, NAFLD increases the risk of developing type 2 diabetes (T2DM) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), cardiovascular disease (CVD) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), and chronic kidney disease (CKD) (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). NAFLD can be one of the factors leading to liver fibrosis and, ultimately, cirrhosis (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Steatosis in NALFD continues to progress to abnormal proliferation of fibrous tissue forming hepatic fibrosis (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The severity of liver fibrosis can range from mild to severe and usually progresses gradually. Without proper management, especially in cases of severe NAFLD or NASH, liver fibrosis can progressively worsen, ultimately leading to cirrhosis (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Cirrhosis is an advanced liver disease marked by the replacement of healthy liver tissue with fibrous tissue, resulting in severe impairment of liver structure and function (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Treatment for cirrhosis often requires a liver transplant, as the disease has reached an irreversible stage (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecently, epidemiological studies have suggested that periodontitis increases the prevalence of NAFLD and fibrosis (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The pathogenesis may involve systemic inflammation and oxidative damage (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Porphyromonas gingivalis has also gained popularity in the field of etiology (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Another plausible mechanism that links dental health to liver function is the idea of the \"oral-intestinal-liver axis\" (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). However, Mendelian randomization (MR) came to the conclusion that there is no evidence to support the claim that periodontitis has no causal effect on fibrosis and NAFLD. The potential lack of further validation due to conflicting results from population studies and genetic data, as well as the lack of other MR studies may explain the value of our study.\u003c/p\u003e \u003cp\u003eThe relationship between periodontal disease and NAFLD has been discussed from in vitro, in vivo, and epidemiological perspectives. This study looked into the possible cause-and-effect link between liver disorders and periodontitis using data from both MR and the NHANES database.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThe study was divided into two phases, as illustrated in Fig.\u0026nbsp;1. In the first stage, we conducted multivariable logistic regression analyses using data from the NHANES database to examine the connection between liver disorders and periodontitis. In the second stage, we conducted four MR analyses using data from the Gene-Lifestyle Interactions in Dental Endpoints (GLIDE) consortium and FinnGen database to look at the mutual relationship that exists between liver disorders and periodontitis.\u003c/p\u003e\n\u003ch3\u003eNHANES\u003c/h3\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eData sources and study population\u003c/h2\u003e\n \u003cp\u003eNHANES is a collection of a sequence of cross-sectional surveys aimed at the non-institutionalized population in the US. It selects a nationally representative sample using a multi-stage probability sampling technique and assesses the nutritional and health condition of the participants. The survey includes interviews conducted at participants\u0026apos; households, physical evaluations, and laboratory examinations. NHANES is administered by the National Center for Health Statistics, a division of the Centers for Disease Control and Prevention (CDC). The National Center for Health Statistics Ethics Review Board has given the study ethical approval, and all participants have given written informed permission (Fig.\u0026nbsp;2).\u003c/p\u003e\n \u003cp\u003eFigure 2 depicts the process of participant selection for our NHANES study. Initially, there were 30,468 individuals enrolled. Nevertheless, 11,756 participants had to be excluded due to the unavailability of periodontal data. As a result, the study ultimately included a total of 4,425 participants.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eAssessment of periodontitis and liver diseases\u003c/h2\u003e\n \u003cp\u003eThe oral health statistics from NHANES (2009 to 2014) show that qualified examiners evaluated the clinical attachment loss (CAL) and periodontal probing depth (PD) at six specific locations for every tooth (wisdom teeth excluded), for a total of 28 teeth. The 2018 global classification criteria were used to determine the diagnosis of periodontitis: when interdental CAL is evident in \u0026ge;\u0026thinsp;2 non-adjacent teeth or when buccal or oral CAL\u0026thinsp;\u0026ge;\u0026thinsp;3 mm is observed in \u0026ge;\u0026thinsp;2 teeth with a pocket depth exceeding 3 mm, it is classified as periodontitis (\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eNAFLD was diagnosed by Fatty Liver Index (FLI)\u0026thinsp;\u0026ge;\u0026thinsp;60 or ultrasonographic FLI (USFLI)\u0026thinsp;\u0026ge;\u0026thinsp;30. FLI is a numerical number used in medicine to assess a person\u0026apos;s risk of having hepatic steatosis or fatty liver disease. Instead of using FLI as a diagnostic sign in our investigation, we opted to employ USFLI. Here is how FLI and USFLI were determined (\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e):\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1709191151.png\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e(\u0026quot;Mexican American\u0026quot; and \u0026quot;non-Hispanic Black\u0026quot; have a value of 0 if the participant is not of that ethnicity, and 1 if they are.)\u003c/p\u003e\n \u003cp\u003eParticipants who were at risk of atrial fibrillation (AF) were identified by elevated scores for the non-alcoholic fatty liver disease fibrosis (NFS), fibrosis-4 index (FIB-4), or the aspartate aminotransferase (AST)/platelet ratio index (APRI). The NFS is a scoring system that\u0026apos;s used to determine how likely or severe it is that NAFLD may cause fibrosis (the production of scar tissue) in the liver. Without requiring a liver biopsy, medical personnel can assess the degree of fibrosis using the easily accessible and non-invasive FIB-4 instrument. To assess the level of liver fibrosis (scarring) in individuals suffering from liver disease, especially those with chronic hepatitis C, doctors employ the non-invasive APRI scoring system. Here is how these indicators were computed:\u003c/p\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1709191199.png\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cp\u003e(AST: The upper limit of normal (ULN) for the 1999\u0026ndash;2000 cycle was 40 U/L; thereafter, the ULN was 33 U/L) (\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eThose with an APRI exceeding 1, FIB-4 greater than 2.67, or NFS surpassing 0.676 were classified as individuals at high risk of AF, as per the criteria outlined in reference.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eAssessment of covariates\u003c/h2\u003e\n \u003cp\u003eThe thorough evaluation of covariates plays a pivotal role in research, as it serves to control potential confounding factors, thereby ensuring a more precise and dependable assessment of the primary associations. In our study, we have carefully chosen a set of covariates to enhance our comprehension of the link between periodontal disease and liver conditions. We included several key covariates such as age, gender, ethnicity, BMI, drinking, recent tobacco use, history of diabetes, education, marriage, family monthly poverty level index, energy intake and leisure time physical activity (LTPA), all of which were gathered through standardized questionnaires. Additionally, the weight and height of each participant were measured during physical examinations, with BMI calculated as weight (kg)/ height (m2).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analyses\u003c/h2\u003e\n \u003cp\u003eWe combined data from the years 2009 to 2014 and created 6-year sampling weights in accordance with NHANES\u0026apos; sampling methodology, which were incorporated into all of our analyses. To assess variances in continuous and categorical variables across various analysis groups, we used the t-test for continuous data and the Rao Scott chi-square test for categorical variables. The impact of liver disorders on the risk of periodontitis was investigated using a multivariable logistic regression analysis. We computed odds ratios and the associated 95% confidence intervals. In the multivariable analysis, we made the following adjustments: In Model 1, no covariate adjustments were performed; in Model 2, age, gender, and race adjustments were applied; in Model 3, BMI, education level, household income poverty ratio, smoking status, physical activity, and history of diabetes were adjusted. All analyses were performed using SAS 9.4. The significance threshold was set at 0.05 and two-sided levels of significance were computed.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eMR Analysis\u003c/h2\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003eBasic concept of MR analysis\u003c/h2\u003e\n \u003cp\u003eWhen compared to traditional observational approaches, MR analysis is less susceptible to errors resulting from reverse causation and confounding since genetic differences are assigned randomly during gamete development and are not connected to environmental variables. As a result, we used MR analysis in this work to find single nucleotide polymorphisms (SNPs) connected to liver disorders and periodontitis. Following this, we integrated these identified SNPs to ascertain the connection between periodontitis and liver diseases.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003eStudy design description\u003c/h2\u003e\n \u003cp\u003eThe investigation of the relationship between liver disorders and periodontitis used a two-sample MR analysis. We conducted the MR analyses utilizing summary statistics from open-access databases to examine the association between periodontitis as the exposure and liver conditions including NAFLD, fibrosis, cirrhosis and fibrosis/cirrhosis as the outcomes. The study does not require ethical approval because it is based on publically available summary statistics.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eData sources and selection of instrumental variables (IVs)\u003c/h2\u003e\n \u003cp\u003eThe summary statistics derived from two European datasets served as the foundation for the two-sample MR analysis: data for periodontitis (17,353 cases; 28,210 controls) from a genome-wide association study (GWAS) of the European studies of GLIDE consortium (\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e) ; and liver conditions including NAFLD (2,275 cases; 375,002 controls), fibrosis (146 cases; 373,307 controls), cirrhosis (1,142 cases; 373,307 controls) and fibrosis/cirrhosis (1,841 cases; 375002 controls) from FinnGen (\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e) which was based on over 370,000 Finnland residents with trustable diagnoses in wide genres of diseases.\u003c/p\u003e\n \u003cp\u003eSNPs were chosen for IV selection at a genome-wide significance criterion (p\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u0026thinsp;\u0026minus;\u0026thinsp;6). SNPs that were deemed eligible for clumping were selected using a 10,000 kb window size and linkage disequilibrium as determined by r2\u0026thinsp;\u0026gt;\u0026thinsp;0.001. Palindromic SNPs were excluded from exposure and outcome data when harmonizing them. Finally, we vertificated that all IVs did not impact the outcomes through other pathways by applying Phenoscanner for possible related traits. In order to assure the trustworthiness of results and reduce the interference of confounders related to SNPs with P-value\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;5, the PhenoScanner database was utilized (\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eThe random-effects inverse variance weighting (IVW) approach is the main statistical methodology used in this study to investigate the possibility of bidirectional causation between liver illness and periodontitis. To further enhance our results, we also used the weighted mode, weighted median, and MR Egger techniques. The IVW method operates on the premise that all fundamental assumptions of MR are satisfied. Nevertheless, given that the inclusion of pleiotropic IVs can introduce bias into IVW estimates, we conducted sensitivity analyses to account for any pleiotropic effects. R (version 4.3.2) was utilized to conduct the MR analysis. Three packages, \u0026quot;TwoSampleMR\u0026quot;(\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e), \u0026quot;MRPRESSO\u0026quot;(\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e), and \u0026quot;forestploter,\u0026quot; were specifically used for the data processing and visualization. The study was reported using the STROBE-MR (STrengthening the Reporting of OBservational studies in Epidemiology using Mendelian randomization) criteria (\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003ePleiotropy and Sensitivity Analysis\u003c/h2\u003e\n \u003cp\u003eMR-Egger regression was used to determine if horizontal pleiotropy would exist. The average pleiotropic impact of the IVs is reflected in the intercept term of the MR-Egger regression. In addition, we looked for the existence of pleiotropy using the MR Pleiotropy REsidual Sum and Outlier (MR-PRESSO) test. MR-PRESSO serves multiple purposes, including the detection of horizontal pleiotropy, the correction of horizontal pleiotropy by identifying and removing outliers, and the evaluation of significant differences in causal effects both before and after the elimination of outliers. We used MR-Egger regression and the IVW technique to measure heterogeneity, and we calculated the degree of heterogeneity using the Cochran\u0026apos;s Q statistic. We also performed a leave-one-out analysis to assess the consistency and robustness of our results.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics of Periodontitis, NAFLD, Fibrosis and Cirrhosis in NHANES\u003c/h2\u003e \u003cp\u003eThe baseline features of NAFLD and periodontitis in the research subjects are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. 4425 participants were enrolled in our study of which 2636 were diagnosed with periodontitis. With a mean age of 54.5 years, periodontitis patient group\u0026rsquo;s age was comparatively high. Gender distribution showed that 56.2% of the population was male, somewhat more than the 43.8% of females. Compared to the healthy control group, those with periodontitis were more likely to be male, older, smokers, diabetics, had lower levels of education, and have a lower family income.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of study population in NHANES 2009\u0026ndash;2014 and prevalence of periodontitis and NAFLD by characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;4425)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003ePeriodontitis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eNAFLD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;1789)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;2636)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003cb\u003e-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;3625)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;800)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003cb\u003e-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years, Mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e53.1(0.2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e51.1(0.4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e54.5(0.3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e51.9(0.2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e58.9(0.5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2226(50.3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e745(41.6)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1481(56.2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1761(48.6)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e465(58.1)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2199(49.7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1044(58.4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1155(43.8)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1864(51.4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e335(41.9)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSFLI, Mean(SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e27.6(0.3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e25.4(0.5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e29.1(0.4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e22.0(0.3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e53.0(0.6)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFS, Mean(SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-1.5(0.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-1.6(0.03)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-1.4(0.03)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-1.7(0.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-0.7(0.05)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIB-4, Mean(SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.3(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.3(0.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.3(0.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.3(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.4(0.03)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPRI, Mean(SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.4(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.4(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.4(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.4(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.4(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index, kg/m\u003csup\u003e2\u003c/sup\u003e, Mean(SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e29.0(0.09)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e28.9(0.1)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e29.1(0.1)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e28.1(0.1)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e33.0(0.2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e614(13.9)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e179(10.0)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e435(16.5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e455(12.6)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e159(19.9)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e424(9.6)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e150(8.9)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e274(10.4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e338(9.3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e86(10.8)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2106(47.6)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1026(57.4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1080(41.0)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1721(47.5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e385(48.1)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e814(18.4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e267(14.9)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e547(20.8)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e718(19.8)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e96(12.0)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race - Including Multi-Racial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e467(10.6)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e167(9.3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e300(11.4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e393(10.8)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e74(9.3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation levels, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1081(24.4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e357(20.0)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e724(27.5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e789(21.8)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e292(36.5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3344(75.6)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1432(80.0)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1912(72.5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2836(78.2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e508(63.5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarriage status, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave a partner or be married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2899(65.5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1181(66.0)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1718(65.2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2356(65.0)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e543(67.9)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle or widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1526(34.5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e608(34.0)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e918(34.8)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1269(35.0)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e257(32.1)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoverty status, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1506(34.0)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e532(29.7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e974(36.9)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1185(32.7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e321(40.1)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.30\u0026ndash;3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e626(14.1)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e243(13.6)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e383(14.5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e489(13.5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e137(17.1)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2293(51.8)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1014(56.7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1279(48.5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1951(53.8)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e342(42.8)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy intake levels, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.54\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInadequate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1871(42.3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e740(41.4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1131(42.9)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1464(40.4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e407(50.9)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdequate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1855(41.9)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e767(42.9)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1088(41.3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1573(43.4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e282(35.3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcessive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e699(15.8)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e282(16.8)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e417(15.8)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e588(16.2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e111(13.9)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2422(54.7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1050(58.7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1372(52.0)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1980(54.6)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e442(55.3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1175(26.6)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e459(25.7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e716(27.2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e916(25.3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e259(32.4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e828(18.7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e280(15.7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e548(20.8)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e729(20.1)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e99(12.4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeisure time physical activity, minutes, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2295(51.9)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e887(49.6)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1408(53.4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1779(49.1)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e516(64.5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0-150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e787(17.8)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e328(18.3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e459(17.4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e663(18.3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e124(15.5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1343(30.4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e574(32.1)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e769(29.2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1183(32.6)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e160(20.0)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e826(18.7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e281(15.7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e545(20.7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e509(14.0)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e317(39.6)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3599(18.7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1508(84.3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2091(79.3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3116(86.0)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e483(60.4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNAFLD, non-alcoholic fatty liver disease; SF, significant fibrosis; AF, advanced fibrosis; SE, standard error of mean.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e800 NAFLD patients had a mean age of 58.9 years, with a somewhat higher percentage of males (58.1%) than females (41.9%). Individuals with NAFLD were more likely to be male, older, obese, current smokers, and diabetics as compared to healthy controls. Their LTPA, household income, and education levels were also lower.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics of Periodontitis, NAFLD, Fibrosis and Cirrhosis in NHANES\u003c/h2\u003e \u003cp\u003eMultivariate logistic regression analysis was employed to investigate the correlation between liver illnesses and periodontal disease. Periodontitis was only statistically significant in Model 1 in AF (OR: 1.48, 95% CI: 1.08\u0026ndash;2.07), and it was not substantially linked with NAFLD or liver fibrosis in the overall group (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThe relationship between AF and periodontitis was significant in Model 1 (OR: 1.79, 95% CI: 1.32\u0026ndash;2.43) when stratified by sex, but not in the other models (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCausal Relationship of Periodontitis on Liver Conditions in MR\u003c/h2\u003e \u003cp\u003eIn the MR analysis where periodontitis acted as exposure, 6 SNPs were selected as IVs (detailed information described in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The results indicated an overall non-significant association between genetically predicted periodontitis and an increased risk of NAFLD in the IVW method (OR: 1.12, 95% CI: 0.98\u0026ndash;1.27). The weighted mean (OR 1.06, 95% CI 0.91\u0026ndash;1.23) and weighted median (OR: 1.07, 95% CI: 0.91\u0026ndash;1.25) approaches further supported this conclusion. Notably, IVW validation revealed no indication of considerable SNP heterogeneity, and no pleiotropy was seen. Similarly, in the context of cirrhosis, genetically predicted periodontitis demonstrated an insignificant elevated risk in the IVW analysis (OR: 0.99, 95% CI 0.82\u0026ndash;1.19), which was consistent with the findings from the weighted median (OR: 1.02, 95% CI: 0.81\u0026ndash;1.28) and the weighted mode (OR: 1.02, 95% CI: 0.83\u0026ndash;1.26) methods. Like in the previous analyses, there was no significant SNP heterogeneity based on IVW validation, and no pleiotropic effects were detected (Fig.\u0026nbsp;4).\u003c/p\u003e \u003cp\u003eIn summary, our MR analyses provide insights into the relationships between periodontitis and various liver diseases, highlighting generally non-significant associations and the absence of significant SNP heterogeneity or pleiotropy in our results. The regional associational plot indicating the lead SNP and nearby genes associated with periodontitis was depicted as Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAfter conducting a heterogeneity analysis, it was determined that there was no significant potential heterogeneity in the way that periodontitis affected the liver conditions (fibrosis: MR Egger P: 0.88, IVW P: 0.95; cirrhosis: MR Egge` r P: 0.58, IVW P: 0.71; fibrosis/cirrhosis: MR Egger P: 0.63, IVW P: 0.67). According to Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e, S4, the Egger intercept (Egger intercept P: NAFLD:0.85; fibrosis: 0.95; cirrhosis: 0.79; fibrosis/cirrhosis: 0.48) did not demonstrate any horizontal pleiotropy between the impact of periodontitis on liver diseases. And Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e displayed the findings of the MR sensitivity study as scatter plots, leave-one-out analyses, and funnel plots.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first study that we are aware of that combines genetic Mendelian randomization in European countries with cross-sectional US samples to investigate bidirectional connections between periodontitis and NAFLD-associated illnesses. The analysis indicates that there is no significant correlation between periodontal disease and NAFLD or liver fibrosis, and the MR analysis does not support a causal relationship between them. The sensitivity analysis further confirms our conclusions.\u003c/p\u003e \u003cp\u003eWhile previous research has hinted at a certain link between periodontitis and NAFLD, the exact cause-and-effect relationship remains unclear (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). According to research by Saito et al., individuals with periodontitis had considerably higher levels of aspartate aminotransferase (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) than those without the condition, indicating a clear correlation between periodontitis and liver damage. According to a cross-sectional study, in a representative sample of Koreans, the existence of periodontal pockets may be substantially correlated with NAFLD markers (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Nevertheless, F Xu et al.'s meta-analysis revealed that the available data does not support a causal relationship between periodontitis and NAFLD (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). In light of the meta-analysis's conclusions, we applied the latest MR periodontitis genetic data to similarly obtain consistent conclusions. The recent MR study suggested that NAFLD moderately increases the chances of periodontitis by Li Tan et al. However, we believe that both the exposure and outcome samples were derived from the FinnGen database, which goes against the premise of a two-sample MR study and makes the conclusions unreliable (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e)(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMost of the pathologic associations between periodontitis and human NAFLD have been provided by observational epidemiologic studies, and causality has not been established (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Smoking and poor diet, among others are all common etiologic factors for periodontitis and NAFLD (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e)(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e), and when participants had these risk factors, periodontitis and NAFLD often co-occurred and therefore showed statistically significant associations, which no longer existed in our model after adjusting for these important confounders. Currently, some studies have suggested that oral diffusion of some pathogenic factors such as bacteria and inflammatory factors in patients with periodontitis affects the liver through the oral-intestinal-hepatic axis or blood-borne pathways (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e)(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e)(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). As well as periodontitis is closely associated with insulin resistance or metabolic syndrome also may promote the development of NAFLD (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Nevertheless, the impact of treating periodontitis on liver illness has not been the subject of previous research, and the findings of several meta-analyses are inconsistent (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). It is undeniable that periodontitis affects the development of systemic diseases, and MR studies have only inferred causality, ignoring the biological mechanisms between them. Therefore, future studies on the mechanisms of periodontitis diagnosis and treatment on NAFLD-related diseases need to be further explored.\u003c/p\u003e \u003cp\u003eThis study possesses several notable strengths: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) This represents the first-ever combination of bidirectional MR analysis with NHANES data to investigate the possible causative relationship between chronic liver illnesses and periodontitis. Meanwhile our NHANES data results were consistent with MR results. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) The data we utilized originated from NHANES 2009\u0026ndash;2014, representing a substantial dataset. Furthermore, we leveraged data from the Finnish database for MR analysis, which offers comprehensive insights into periodontitis, NAFLD, liver fibrosis, and cirrhosis. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Our study stands out by introducing three distinct indicators for assessing liver fibrosis, enhancing the reliability and persuasiveness of our findings in comparison to prior research. Nonetheless, our study is not without its limitations: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) We refrained from further subclassifying periodontitis, even though previous research has linked moderate to severe periodontitis with chronic liver diseases. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Our approach employed three indicators to gauge liver fibrosis, which may exhibit limitations regarding specificity and sensitivity. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) NHANES data represent the circumstances of Americans, while the MR data were obtained from the European population. This disparity between observational and genetic data could introduce bias into the results, necessitating further enhancements and refinements.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eTo sum up, observational study of NHANES data suggests that there is no significant correlation between periodontal disease and the risk of NAFLD and liver diseases. Furthermore, there is no discernible causal relationship between the incidence of NAFLD or liver fibrosis/cirrhosis and periodontal disease, according to the MR study. Consequently, further standardized cohort studies in a more diverse population are warranted to gain a deeper insight into the interrelationship between oral health and liver function, as well as the mechanisms underlying it.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNAFLD: non-alcoholic fatty liver disease; NHANES: National Health and Nutrition Examination Survey; AF: advanced fibrosis; MR: Mendelian randomization; BMI: Body Mass Index; MetS: metabolic syndrome; NASH: non-alcoholic steatohepatitis; T2DM: type 2 diabetes; CVD: cardiovascular disease; CKD: chronic kidney disease; GLIDE: Gene-Lifestyle Interactions in Dental Endpoints; PD: probing depth; CAL: clinical attachment loss; FLI: Fatty Liver Index; USFLI: ultrasonographic FLI; NFS: non-alcoholic fatty liver disease fibrosis; FIB-4: fibrosis-4 index; AST: aspartate aminotransferase; APRI: platelet ratio index; ULN: upper limit of normal; LTPA: leisure time physical activity; SNPs: single nucleotide polymorphisms; GWAS : genome-wide association study; IVW: inverse variance weighting; STROBE-MR: Strengthening the reporting of observational studies in epidemiology using mendelian randomization; MR-PRESSO: MR Pleiotropy residual sum and outlier\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003ei ) Ethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNHANES is conducted by the Centers for Disease Control and Prevention (CDC) and the National Center for Health Statistics (NCHS). And the NHANES study protocol was reviewed and approved by the NCHS Research Ethics Review Committee. All participants in NHANES provided written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eii ) Consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eiii ) Availability of Data and Material (ADM)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData used for this study are available on the following websites: NHANES: (https://www.cdc.gov/nchs/nhanes/index.htm); FinnGen: (https://r9.finngen.fi). GWAS summary statistics for dental caries and periodontitis: (https://data.bris.ac.uk/data/dataset/2j2rqgzedxlq02oqbb4vmycnc2);\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eiv ) Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ev ) Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.W. is funded by the National Natural Science Foundation of China (81972820 and 82030099), the National Key Research and Development Program of China (2018YFC2000700 and 2022YFD2101500), the Science and Technology Commission of Shanghai Municipality (22DZ2303000), Natural Science Foundation of Shanghai Municipality (23ZR1435900), Innovative research team of high-level local universities in Shanghai and Shanghai Jiao Tong University Key Program of Medical Engineering (YG2021ZD01); W.S. is funded by Three Year Action Plan for Promoting Clinical Skills and Clinical Innovation in Municipal Hospitals (SHDC2022CRS025).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003evi ) Author\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, Y.H., W.W., X.W., J.Y., Y.Y. and J.X.; Methodology and formal analysis, Y.H., W.W., and X.W.; Investigation, J.Y., Y.Y. and J.X; Writing\u0026mdash;original draft preparation, Y.H., W.W. and X.W.; writing\u0026mdash;review and editing, J.Y., Y.Y., J.X., W.S., X.L. and H.W.; supervision, W.S., X.L. and H.W.; funding acquisition, H.W., X.L. and W.S.; Y.H., X.W. and W.W. contributed equally to this work. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003evii ) Acknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the GLIDE and FinnGen consortium for contributing data and all participants involved in this study.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eSupplementary Material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following supporting information can be downloaded at: www.mdpi.com/xxx/s1, Table S1: Data source for Mendelian randomization (MR); Table S2: Detailed information of the SNPs of periodontitis applied in MR analyses.; Table S3: The results of heterogeneity analyses for MR; Table S4: The results horizontal pleiotropy analyses for MR; Figure S1: The RCS plot of the results in different populations and different models using NHANES data; Figure S2: The effect of periodontitis on FIB-4, NFS and APRI according to data from NHANES; Figure S3: The sensitivity results of the MR analyses; Figure S4: The regional association plot of periodontitis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclaimer/Publisher\u0026rsquo;s Note\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eY. Z et al., \u0026lsquo;Global burden of NAFLD and NASH: trends, predictions, risk factors and prevention\u0026rsquo;, Nat. Rev. Gastroenterol. Hepatol., vol. 15, no. 1, Jan. 2018, doi: 10.1038/nrgastro.2017.109.\u003c/li\u003e\n\u003cli\u003eE. Pi, D. Ba, W. L, T.-E. Go, and G. Rj, \u0026lsquo;Prevalence of periodontitis in adults in the United States: 2009 and 2010\u0026rsquo;, J. Dent. Res., vol. 91, no. 10, Oct. 2012, doi: 10.1177/0022034512457373.\u003c/li\u003e\n\u003cli\u003eS. M et al., \u0026lsquo;Periodontitis and Cardiovascular Diseases. Consensus Report\u0026rsquo;, Glob. Heart, vol. 15, no. 1, Mar. 2020, doi: 10.5334/gh.400.\u003c/li\u003e\n\u003cli\u003eZ. M. Younossi, A. B. Koenig, D. Abdelatif, Y. Fazel, L. 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Periodontol., vol. 81, no. 1, Jan. 2010, doi: 10.1902/jop.2009.090267.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Periodontitis, Non-alcoholic fatty liver disease, Liver fibrosis/cirrhosis, National Health and Nutrition Examination Survey, Mendelian randomization λ","lastPublishedDoi":"10.21203/rs.3.rs-3966322/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3966322/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground \u0026amp; Aim\u003c/h2\u003e \u003cp\u003eThere are contradictory causal links between disorders associated to non-alcoholic fatty liver and periodontitis. The purpose of this research is to use Mendelian randomization (MR) to establish a causal association between periodontitis and non-alcoholic fatty liver disease (NAFLD), including the latter's development to liver fibrosis.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eThe study included 4,425 people from the National Health and Nutrition Examination Survey (NHANES) conducted in the United States between 2009 and 2014. The study employed two multivariable logistic regression models to evaluate the correlation between advanced fibrosis (AF) and periodontitis, as well as NAFLD. Model 1 did not involve any covariate adjustments; model 2 controlled for age, gender, and race; model 3 was additionally adjusted for Body Mass Index (BMI), education level, household income poverty ratio, smoking status, physical activity, and history of diabetes. Periodontitis (n:17,353 cases/28,210 controls) was used as the exposure, and NAFLD (n:2,275 cases/375,002 controls), fibrosis (n:146 cases/373,307 controls), cirrhosis (n:1,142 cases/373,307 controls) and fibrosis/cirrhosis (n:1,841 cases/366, 450 cases control) as outcomes and causality validation was performed. Sensitivity studies, such as heterogeneity tests, multiple validity tests, and exclusion analyses, were also carried out to guarantee the trustworthiness of the findings.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn the observational study, there was no significant correlation between periodontitis and NAFLD (OR: 0.82, 95% CI: 0.64\u0026ndash;1.95) or AF (OR: 1.06, 95% CI: 0.72\u0026ndash;1.56). The MR analysis found no significant association between genetically predicted periodontitis and liver conditions in the IVW method (NAFLD: OR: 1.12, 95% CI: 0.98\u0026thinsp;\u0026minus;\u0026thinsp;1.27; fibrosis: OR: 0.84, 95% CI: 0.50\u0026thinsp;\u0026minus;\u0026thinsp;1.42; cirrhosis: OR:0.99, 95% CI: 0.82\u0026thinsp;\u0026minus;\u0026thinsp;1.19; fibrosis/cirrhosis: OR: 0.92, 95% CI: 0.83\u0026thinsp;\u0026minus;\u0026thinsp;1.26). There is consistency in sensitivity results.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAccording to cross-sectional research, there is no discernible link between NAFLD or liver fibrosis and periodontal disease, and the MR analysis does not support a causal relationship between them.\u003c/p\u003e","manuscriptTitle":"Association between periodontitis and NAFLD-related diseases: Results from the NHANES and Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-29 17:01:57","doi":"10.21203/rs.3.rs-3966322/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"58d97ef4-ba9c-4f6b-9e20-d57c15f16c94","owner":[],"postedDate":"February 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-20T14:59:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-29 17:01:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3966322","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3966322","identity":"rs-3966322","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}