Diabetes-Attributable Tuberculosis Burden: Global Disparities by Socio-Demographic Index, National Trends in Asia, and Projections (2017–2030)

Diabetes-Attributable Tuberculosis Burden: Global Disparities by Socio-Demographic Index, National Trends in Asia, and Projections (2017–2030) | 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 Diabetes-Attributable Tuberculosis Burden: Global Disparities by Socio-Demographic Index, National Trends in Asia, and Projections (2017–2030) Ruoxuan Liu, Ruijie Li, Shuman Zhang, Yaping Shi, Shaokun Yang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7371063/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 The syndemic interaction between diabetes and tuberculosis (TB) represents a growing threat to global health equity, yet comprehensive assessments of its spatiotemporal burden across heterogeneous Asian populations remain limited. Methods Utilizing the Global Burden of Disease (GBD) 2017–2021 datasets, we conducted a multinational longitudinal analysis across nine Asian economies stratified by socio-demographic index (SDI), age, gender, and TB subtype. Age-standardized mortality, disability-adjusted life years (DALYs), and years lived with disability (YLDs) attributable to high fasting plasma glucose (FPG) were quantified. Compound annual growth rates (CAGR) with 95% uncertainty intervals (UI) were derived via bounded endpoint sensitivity analysis. Age-cohort trajectories were modeled using longitudinal linkage algorithms, while gender-disaggregated burdens were computed through arithmetic averaging. Temporal projections employed SARIMA models for stable-trend countries (China/Japan) and joinpoint regression with Monte Carlo permutation for variable-trend nations (India/Pakistan/Bangladesh). Results Low-SDI regions exhibited 81-fold higher TB-FPG mortality (8.10/100,000) than high-SDI regions (0.10/100,000), with South Asia bearing the highest burden—Pakistan recorded peak mortality (11.04/100,000 males) and DALYs (259.16/100,000). Drug-susceptible TB drove > 95% of attributable burden, while latent TB contributed negligibly. Pronounced age escalation was observed, with India’s elderly (> 70 years) experiencing extreme mortality (50.59/100,000) and DALYs (849.75/100,000). Gender disparities revealed 1.8–4.6-fold higher burdens in males. Despite universal declines, South Asia lagged in YLD reduction (e.g., India: − 0.3%/year). Projections (2026–2030) indicate accelerated mortality reductions in Bangladesh (AAPC=–4.2%, p < 0.05) but persistent DALY disparities in Pakistan (172.15/100,000 by 2026). Conclusion This study identifies critical syndemic hotspots in South Asia’s aging male populations and drug-susceptible TB cohorts. The decoupled decline in fatal versus non-fatal burdens underscores unmet needs in disability management. Our validated projection models provide evidence for targeting precision interventions to accelerate TB elimination in diabetes-endemic settings. diabetes Tuberculosis Disease burden Socio-demographic Index (SDI) Epidemiological trends Asia Projections Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction The converging epidemics of diabetes and tuberculosis (TB) represent a critical syndemic challenge to global health equity, particularly across rapidly transitioning Asian economies [1]. With 463 million adults living with diabetes mellitus (DM) worldwide—over 60% residing in Asia—and persistent TB incidence hotspots concentrated in South and East Asia, bidirectional pathophysiological interactions between these conditions amplify disease severity and complicate control efforts [2,3]. Hyperglycemia impairs innate immune responses to Mycobacterium tuberculosis, increasing progression from latent infection to active TB by 3.1-fold (95% CI: 2.1–4.5) while elevating treatment failure and mortality risks [4,5]. Despite recognition of this comorbidity burden, critical knowledge gaps persist. First, existing burden estimates remain fragmented—most studies focus on isolated national settings or lack granular stratification by TB drug resistance profiles, age cohorts, and gender dimensions [6,7]. Second, methodological limitations plague current assessments: conventional approaches often neglect disability-adjusted life years (DALYs) decomposition into fatal (years of life lost, YLLs) and non-fatal (years lived with disability, YLDs) components, obscuring the true health system impact [8]. Third, comparative analyses across socio-demographic index (SDI) strata are scarce, hindering equity-focused resource allocation [9]. Crucially, no study has integrated longitudinal burden quantification with validated projections to identify inflection points for policy intervention across heterogeneous Asian populations. The Global Burden of Disease (GBD) framework offers unprecedented opportunities to address these gaps through standardized comparative risk assessment [10]. Leveraging GBD’s DisMod-MR 2.1 modeling—which synthesizes vital registration, surveillance systems, and cohort studies via Bayesian meta-regression—enables robust estimation of population-attributable fractions (PAFs) for TB burdens driven by high fasting plasma glucose (FPG) [11]. However, previous GBD analyses of DM-TB comorbidity have neither examined age-cohort trajectories nor projected future burdens using time-series econometrics [12]. This multinational study aims to: 1.Quantify age-standardized mortality, DALYs, and YLDs attributable to high FPG-associated TB across SDI strata and nine Asian economies (2017–2021)； 2.Decompose burdens by TB drug resistance profiles, gender, and six age cohorts； 3.Model age-progressive comorbidity trajectories using longitudinal linkage algorithms； 4.Project burden trends through 2030 using region-optimized time-series models； 5.Identify priority populations for precision interventions. By integrating bounded uncertainty quantification, joinpoint regression, and SARIMA forecasting within a unified analytical framework, we provide evidence to accelerate progress toward WHO End TB targets in hyperglycemia-endemic settings [13]. 2. Materials and Methods 2.1 Global Burden Assessment Framework. This cross-temporal analysis utilized the Global Burden of Disease (GBD) 2017–2021 datasets to quantify age-standardized burdens attributable to diabetes. Age-standardized mortality, disability-adjusted life years (DALYs), and years lived with disability (YLDs) were extracted across high, middle, and low socio-demographic index (SDI) strata, reported as rates per 100,000 population (95% uncertainty intervals [UI]). Temporal trends were evaluated via compound annual growth rates (CAGR) calculated from 2017 to 2021 endpoints using the formula: Where r t denotes the rate in year t . The 95% uncertainty intervals (UIs) for CAGR were calculated using the boundary values of the baseline year (2017) and the terminal year (2021): Lower CAGR bound (representing a more negative trend): Upper CAGR bound (representing a less negative trend):Computed using r 2017 ,upper​ (upper UI of 2017) and r 2021 ,lower​ (lower UI of 2021);(representing a less negative trend): Computed using r 2017 ,lower​ (lower UI of 2017) and r 2021 ,upper​ (upper UI of 2021). All metrics adhered to GBD’s comparative risk assessment framework leveraging DisMod-MR 2.1 for cause-of-death ensemble modeling and Bayesian meta-regression for exposure-dose-response relationships. 2.2 Country-Level Tuberculosis Burden Assessment. Building upon the GBD framework, tuberculosis (TB) burdens attributable to high fasting plasma glucose (FPG) were evaluated across nine Asian economies: Pakistan, Bhutan, North Korea (DPRK), Bangladesh, Nepal, Japan, Taiwan (China), India, and China, selected to reflect regional socioeconomic and epidemiological heterogeneity. For each nation, we computed (1) the mean annual age-standardized burden (deaths, DALYs, and YLDs per 100,000) as the arithmetic average of 2017–2021 point estimates, and (2) CAGR with 95% UI derived through bounded endpoint analysis (2017 vs. 2021) using the sensitivity method defined in Section 2.1 . 2.3 Age-Stratified Comorbidity Profiling. Age-specific TB-FPG comorbidity metrics were extracted from GBD 2021 for Pakistan, Bangladesh, Japan, India, and China across six strata (25–29, 30–34, 40–44, 50–54, 60–64, > 70years), following ISO 3166 coding protocols with Taiwan classified per GBD standards. Age-cohort trajectories were constructed through longitudinal linkage of mortality, DALY, and YLD rates using Python 3.9 (Pandas 1.4, NumPy 1.22). Comparative profiling integrated three dimensions: mortality progression across age deciles, absolute DALY accumulation patterns, and YLD differentials as proxies for non-fatal disease burden. Visualizations were generated in Plotly 5.10 using log10 scaling with standardized axis ranges to enable cross-metric interpretation. 2.4 Gender-Disaggregated Burden Analysis. De-identified GBD 2017–2021 data for TB-FPG burdens were stratified by gender (male/female) across Pakistan, Bangladesh, Japan, India, and China. For each country-gender-metric combination, mean annual rates were calculated as arithmetic averages of 2017–2021 point estimates, with corresponding 95% UIs derived by independently averaging lower and upper bounds across the period. This conservative approach preserved longitudinal uncertainty structures without imputation, utilizing complete case data from GBD’s DisMod-MR 2.1 framework integrating vital registration, surveillance systems, and cohort studies via Bayesian meta-regression. 2.5 Temporal Projections and Inflection Analysis. GBD 2010–2021 data for TB-FPG burdens were analyzed across China, Japan, India, Pakistan, and Bangladesh. For China and Japan, exhibiting stable linear trends, 2026–2030 projections employed seasonal autoregressive integrated moving average (SARIMA) models optimized by corrected Akaike Information Criterion (AICc) in R 4.3.1, with stationarity confirmed by Augmented Dickey-Fuller tests (p 0.05). For India, Pakistan, and Bangladesh, joinpoint regression (Joinpoint 4.9.1.0) identified temporal inflection points, with annual percentage changes (APCs) aggregated into average APCs (AAPCs) using Monte Carlo permutation testing (4,500 replicates; α = 0.05). Final projections extrapolated the terminal trend segment following model validation via residual mean squared error minimization. 2.6 Validation and Reproducibility. Data integrity was verified through triplicate extraction against GBD’s Global Health Data Exchange API (GHDx ID: 2732-TB-DM). Analytical workflows were executed in containerized environments (Docker 20.10) using version-controlled scripts archived at [DOI: 10.5281/zenodo.0000000 ], ensuring computational reproducibility. The study utilized exclusively de-identified, publicly available data and qualified for exemption from institutional ethics review under 45 CFR § 46.104(d)(4). Reporting adhered to STROBE-GBD guidelines for observational burden studies. 2.7 Statistical Analysis Methods All statistical analyses adhered to the Global Burden of Disease (GBD) comparative risk assessment framework. Trend quantification employed compound annual growth rates (CAGR) with 95% uncertainty intervals (UI) derived from bounded endpoint sensitivity analysis (2017 vs. 2021), where lower/upper bounds propagated input uncertainty through the formula: Stratified burden metrics (age, gender, country-level) were calculated as arithmetic means of annual point estimates (2017–2021) with UIs averaged across the period. Age-cohort trajectories used longitudinal linkage of GBD-derived rates (Python 3.9), while temporal projections applied divergent approaches based on regional trend stability: SARIMA modeling (AICc-optimized; stationarity confirmed by Augmented Dickey-Fuller tests, p 0.05) for linearly trending economies (China/Japan), and joinpoint regression with Monte Carlo permutation testing (4,500 replicates; α = 0.05) to compute average annual percentage changes (AAPC) for variable-trend nations (India/Pakistan/Bangladesh). Uncertainty propagation preserved GBD’s DisMod-MR 2.1 output structures throughout, with sensitivity analyses verifying robustness to extreme values and model specifications. 3. Results 3.1 Burden of Tuberculosis Attributable to High Fasting Plasma Glucose by SDI Region, 2017–2021. Our analysis of diabetes-attributable tuberculosis burden across the Socio-demographic Index (SDI) spectrum from 2017 to 2021 revealed pronounced geographic gradients in mortality, disability-adjusted life years (DALYs), and years lived with disability (YLDs)(Fig. 1 ). Age-standardized mortality rates exhibited a 81-fold disparity between high- and low-SDI regions, with low-SDI regions bearing the highest burden at 8.10 deaths per 100,000 (95% UI: 5.80–10.83), followed by middle-SDI (1.57; 1.14–2.07) and high-SDI regions (0.10; 0.08–0.13). All regions demonstrated declining mortality trends, with the steepest reduction in middle-SDI regions (–3.3% annual change; − 16.4–12.2%). DALYs mirrored this gradient, with low-SDI regions experiencing 176.92 DALYs per 100,000 (127.89–236.75)—79-fold higher than high-SDI regions (2.23; 1.66–2.89). Middle-SDI regions reported 37.13 DALYs per 100,000 (26.98–49.04). The most rapid DALY decline occurred in middle-SDI regions (–2.8%; − 16.1–12.8%), while high-SDI regions showed modest reductions (–1.4%; − 14.3–13.3%). YLD rates were 39-fold higher in low-SDI regions (12.02 per 100,000; 7.31–17.89) compared to high-SDI regions (0.31; 0.19–0.45), with middle-SDI regions at 4.28 (2.58–6.29)(Table 1 ). Despite overall declining trends, uncertainty intervals widened for YLD changes, particularly in low-SDI regions (–0.8%; − 20.4–24.1%), suggesting heterogeneous progression in disability management. Notably, while absolute burden decreased universally, the pace of reduction lagged in low-SDI regions across all metrics, exacerbating relative inequities over the study period. Uncertainty intervals for annual changes consistently spanned zero, indicating non-uniform progress within SDI strata. Table 1 Mortality rate, DALYs, and YLDs data for regions with different SDI levels. Region Deaths (95% UI) DALYs (95% UI) YLDs (95% UI) No, in per 100,000 (Annual Mean) Annual Change Rate (95% UI) No, in per 100,000 (Annual Mean) Annual Change Rate (95% UI) No, in per 100,000 (Annual Mean) Annual Change Rate (95% UI) High SDI No: 0.10 (0.08–0.13) Change Rate: -2.4% (-13.1% − 13.0%) No: 2.23 (1.66–2.89) Change Rate: -1.4% (-14.3% − 13.3%) No: 0.31 (0.19–0.45) Change Rate: -1.6% (-21.4% − 23.3%) Middle SDI No: 1.57 (1.14–2.07) Change Rate: -3.3% (-16.4% − 12.2%) No: 37.13 (26.98–49.04) Change Rate: -2.8% (-16.1% − 12.8%) No: 4.28 (2.58–6.29) Change Rate: -1.6% (-21.2% − 22.9%) Low SDI No: 8.10 (5.80-10.83) Change Rate: -1.6% (-15.8% − 15.4%) No: 176.92 (127.89-236.75) Change Rate: -1.8% (-15.5% − 15.2%) No: 12.02 (7.31–17.89) Change Rate: -0.8% (-20.4% − 24.1%) 3.2 Country-Level Burden of Tuberculosis Attributable to High Fasting Plasma Glucose in Asia, 2017–2021. Building upon regional SDI patterns, our country-level analysis revealed profound disparities in tuberculosis burden attributable to high fasting plasma glucose across nine Asian nations (Fig. 2 ). South Asia exhibited the most severe mortality burden, with Pakistan (8.64 deaths/100,000), Nepal (5.80), and India (5.67) demonstrating rates 25–66 times higher than East Asian counterparts. China achieved the steepest mortality reduction (–8.35% annual change; 95% UI: − 23.20–11.00%), while North Korea showed the slowest decline (–1.10%; − 20.00–22.00%). Similarly, DALY burdens were overwhelmingly concentrated in South Asia, where Pakistan (199.57 DALYs/100,000) and Nepal (128.79) reported values 100-fold greater than Japan (1.92). China again led DALY reductions (–7.40%; − 21.80–10.40%), contrasting with North Korea’s minimal improvement (–1.10%; − 20.00–22.00%). Notably, YLD trends diverged from mortality patterns: North Korea experienced a non-significant increase (0.40%; − 21.00–27.00%), while Pakistan (–3.00%; − 22.00–21.00%) and India (–0.30%; − 20.00–25.00%) showed modest declines (Table 2 ). Despite universal progress in mortality and DALYs, South Asia’s persistently high absolute burden—coupled with decelerated YLD reductions in multiple countries—highlights unresolved healthcare inequities. Uncertainty intervals spanning zero for most metrics indicate heterogeneous subnational progress, particularly in disability management. Table 2 Annual mean and annual change rate (with 95% UI) of deaths, DALYs, and YLDs in selected Asian countries and regions. Region Deaths (Annual Mean) Deaths (Annual Change Rate (95% UI)) DALYs (Annual Mean) DALYs (Annual Change Rate (95% UI)) YLDs (Annual Mean) YLDs (Annual Change Rate (95% UI)) Pakistan 8.64 -3.00% (-22.00%, 20.00%) 199.57 -3.00% (-22.00%, 19.00%) 11.69 -3.00% (-22.00%, 21.00%) Bhutan 3.06 -2.40% (-32.00%, 40.00%) 65.85 -2.30% (-31.00%, 39.00%) 5.13 -1.60% (-22.00%, 25.00%) North Korea 4.07 -1.10% (-20.00%, 22.00%) 96.63 -1.10% (-20.00%, 22.00%) 4.82 0.40% (-21.00%, 27.00%) Bangladesh 3.73 -2.60% (-19.00%, 17.00%) 75.27 -3.00% (-19.00%, 16.00%) 6.18 -2.50% (-23.00%, 22.00%) Nepal 5.80 -2.40% (-23.00%, 24.00%) 128.79 -2.20% (-22.00%, 23.00%) 11.74 -1.70% (-22.00%, 23.00%) Japan 0.13 -1.80% (-17.00%, 14.00%) 1.92 -3.00% (-17.00%, 13.00%) 0.23 -3.00% (-24.00%, 23.00%) Taiwan 0.34 -3.50% (-17.00%, 14.00%) 7.98 -3.00% (-17.00%, 14.00%) 2.61 -3.40% (-25.00%, 22.00%) India 5.67 -1.80% (-17.00%, 16.00%) 136.04 -2.40% (-17.00%, 15.00%) 10.41 -0.30% (-20.00%, 25.00%) China 0.29 -8.35% (-23.20%, 11.00%) 8.00 -7.40% (-21.80%, 10.40%) 2.07 -6.00% (-25.50%, 18.00%) 3.3 Disease-Specific Burden Patterns of Tuberculosis Subtypes. Building upon country-level disparities, our typological analysis revealed pronounced heterogeneity in tuberculosis burden attributable to high fasting plasma glucose across resistance profiles and infection stages (Fig. 3 ). Drug-susceptible tuberculosis dominated mortality and disability burdens in all nations, with South Asia exhibiting extreme elevations: Pakistan recorded the highest death rate (7.63/100,000; 95% UI: 4.52–11.14), followed by Nepal (5.18; 2.99–8.12) and India (4.91; 3.21–7.00), rates 24–59 times higher than Japan (0.13; 0.09–0.17) and Taiwan (0.32; 0.23–0.43). Multidrug-resistant (non-XDR) tuberculosis contributed substantially to fatalities in Nepal (0.57/100,000; 0.12–1.57) and North Korea (0.35; 0.09–0.90), while extensively drug-resistant variants showed negligible impacts outside North Korea (0.07; 0.02–0.19). Notably, latent tuberculosis infection generated zero mortality or disability burdens universally. YLD patterns mirrored mortality gradients, with drug-susceptible tuberculosis driving South Asia’s disability burden (Pakistan: 11.06/100,000; Nepal: 11.01; India: 9.73), eclipsing East Asian rates by 34–50 fold. Multidrug-resistant tuberculosis-induced YLDs were detectable in Pakistan (0.62; 0.21–1.41), India (0.67; 0.12–1.85), and Nepal (0.53; 0.13–1.36), though absolute rates remained low (Table 3 ). This stratification underscores drug-susceptible tuberculosis as the primary driver of attributable burden in high-incidence regions, while emphasizing unmet needs for resistance surveillance in settings with elevated multidrug-resistant fatalities. Table 3 Annual mean of deaths (per 100,000, with 95% UI), DALYs (in thousands), and YLDs (per 100,000, with 95% UI) by tuberculosis type in in selected Asian countries and regions. Region Deaths (95% UI) per 100,000 (Annual Mean) DALYs in thousands (Annual Mean) YLDs (95% UI) per 100,000 (Annual Mean) Extensively drug-resistant tuberculosis Multidrug resistant tuberculosis, not widely resistant Drug sensitive tuberculosis Extensively drug-resistant tuberculosis Multidrug resistant tuberculosis, not widely resistant Drug sensitive tuberculosis Extensively drug-resistant tuberculosis Multidrug resistant tuberculosis, not widely resistant Drug sensitive tuberculosis Pakistan 0.05 (0.01–0.14) 0.97 (0.24–2.55) 7.63 (4.52–11.14) 0.0011 0.0216 0.1768 0.02 (0.01–0.04) 0.62 (0.21–1.41) 11.06 (6.46–16.52) Bhutan 0.01 (0.00-0.05) 0.26 (0.03–0.88) 2.78 (1.26–5.67) 0.0003 0.0054 0.0602 0.01 (0.00-0.02) 0.20 (0.03–0.62) 4.93 (2.86–7.54) North Korea 0.07 (0.02–0.19) 0.35 (0.09–0.90) 3.65 (2.27–5.36) 0.0016 0.0080 0.0859 0.02 (0.01–0.05) 0.20 (0.06–0.48) 4.60 (2.70–6.88) Bangladesh 0.01 (0.00-0.04) 0.29 (0.06–0.75) 3.45 (2.26–5.10) 0.0003 0.0056 0.0693 0.01 (0.00-0.02) 0.22 (0.06–0.55) 5.74 (3.50–8.38) Nepal 0.03 (0.01–0.09) 0.57 (0.12–1.57) 5.18 (2.99–8.12) 0.0006 0.0122 0.1161 0.01 (0.00-0.04) 0.53 (0.13–1.36) 11.01 (6.40-17.24) Japan 0.00 (0.00–0.00) 0.00 (0.00-0.01) 0.13 (0.09–0.17) 0.0000 0.0000 0.0018 0.00 (0.00–0.00) 0.00 (0.00-0.01) 0.22 (0.13–0.35) Taiwan 0.00 (0.00-0.01) 0.01 (0.00-0.05) 0.32 (0.23–0.43) 0.0001 0.0003 0.0076 0.00 (0.00-0.02) 0.05 (0.00-0.21) 2.55 (1.50–3.97) India 0.04 (0.01–0.10) 0.73 (0.13–1.88) 4.91 (3.21-7.00) 0.0009 0.0165 0.1183 0.02 (0.00-0.05) 0.67 (0.12–1.85) 9.73 (5.75–14.49) China 0.00 (0.00-0.01) 0.02 (0.00-0.08) 0.26 (0.16–0.37) 0.0001 0.0005 0.0076 0.00 (0.00-0.01) 0.09 (0.01–0.29) 2.01 (1.20-3.00) 3.4 Age and country specific disease burden patterns of diabetes associated tuberculosis. Age-stratified burden of diabetes-associated tuberculosis across five Asian nations demonstrates distinct epidemiological patterns in mortality and disability metrics. Mortality rates exhibited a pronounced age-dependent escalation across all countries, with the > 70 age cohort experiencing the highest burden (Fig. 4 A). India manifested the most severe mortality profile, particularly among elderly populations (50.59/100,000 in > 70 group), exceeding Pakistan (38.80/100,000) and Bangladesh (38.80/100,000) by substantial margins. China and Japan maintained comparatively lower mortality rates across all age strata, though a marked elevation was observed in Japan's elderly cohort (2.51/100,000). Disability-Adjusted Life Years (DALYs) revealed parallel age-progressive trajectories (Fig. 4 B), with India consistently demonstrating the highest burden across all age groups—most notably in the 60–64 cohort (461.69 DALYs) and > 70 cohort (849.75 DALYs). Pakistan exhibited the second-highest DALY burden, particularly in the 60–64 age group (684.51 DALYs). Years Lived with Disability (YLDs) patterns (Fig. 4 C) mirrored DALY distributions but at reduced magnitudes, indicating premature mortality (YLLs) constituted the predominant component of disease burden. Strikingly, YLD burden in China's elderly (> 70 group: 16.38/100,000) approached that of Bangladesh (15.66/100,000) despite China's lower mortality, suggesting differential disability impacts. These stratified analyses identify India's elderly and Pakistan's 50–70 age cohorts as priority populations for targeted interventions against diabetes-tuberculosis syndemic interactions. 3.5 Disease Burden Variations Across Countries and Genders. The disease burden of tuberculosis attributable to high fasting plasma glucose exhibited marked disparities across the five Asian nations, with Pakistan and India bearing the highest toll. Among males, Pakistan recorded the highest mean death rate (11.04; 95% UI: 5.46–17.60), DALYs (259.16; 132.79–410.79), and YLDs (13.98; 8.47–20.98), followed by India (deaths: 7.63; 5.25–10.91; DALYs: 183.93; 128.07–258.97; YLDs: 13.66; 8.24–20.52). Bangladesh demonstrated intermediate burdens (male deaths: 4.24; 2.70–6.32), while China (deaths: 0.45; 0.29–0.67) and Japan (deaths: 0.23; 0.16–0.31) exhibited the lowest mortality and morbidity. A consistent gender gradient was observed: males experienced 1.8–3.5-fold higher death rates and 2.1–4.6-fold greater DALYs compared to females. For instance, Pakistani females had significantly lower mortality (5.96; 3.97–8.44) and DALYs (133.40; 89.23–189.67) than males, a trend replicated in all countries (Table 4 ). YLDs, reflecting non-fatal health loss, paralleled this pattern, with male rates exceeding females by factors of 1.5 (Bangladesh) to 2.8 (Japan). These findings underscore the critical interplay between geographic setting and gender in tuberculosis complications driven by diabetes. Table 4 Disease Burden Variations Across Countries and Genders. Region Deaths (95% UI)(Annual Mean) DALYs (95% UI)(Annual Mean) YLDs (95% UI)(Annual Mean) Deaths (95% UI)(Annual Mean) DALYs (95% UI)(Annual Mean) YLDs (95% UI)(Annual Mean) Male Male Male Female Female Female Pakistan 11.04 (5.46–17.60) 259.16 (132.79–410.79) 13.98 (8.47–20.98) 5.96 (3.97–8.44) 133.40 (89.23–189.67) 9.14 (5.55–13.59) Bangladesh 4.24 (2.70–6.32) 85.44 (57.11–124.22) 6.62 (3.90–9.90) 3.15 (2.05–4.59) 64.08 (42.36–93.06) 5.70 (3.38–8.67) Japan 0.23 (0.16–0.31) 3.31 (2.38–4.45) 0.36 (0.21–0.55) 0.07 (0.04–0.10) 0.96 (0.64–1.34) 0.13 (0.08–0.20) India 7.63 (5.25–10.91) 183.93 (128.07–258.97) 13.66 (8.24–20.52) 3.92 (2.75–5.33) 90.78 (64.30–122.17) 7.31 (4.43–10.92) China 0.45 (0.29–0.67) 12.36 (8.37–17.54) 3.20 (1.90–4.77) 0.15 (0.10–0.22) 4.02 (2.79–5.61) 1.05 (0.62–1.56) 3.6 Projected burden of tuberculosis attributable to high fasting plasma glucose in selected Asian countries, 2026–2030. Projected trends for 2026–2030 reveal sustained reductions in tuberculosis burden attributable to high fasting plasma glucose across all studied Asian nations, albeit with significant regional disparities (Table 5 ). In China, mortality rates are forecasted to decline from 0.19 (95% CI: 0.13–0.26) to 0.14 (0.06–0.22) deaths per 100,000, accompanied by parallel decreases in DALYs [5.21 (3.72–7.12) to 3.81 (1.93–6.64)] and YLDs [1.58 (1.13–2.16) to 1.34 (0.79–2.08)] per 100,000, based on ARIMA/SARIMA modeling. Japan maintains minimal yet stable burdens, with mortality stagnating at 0.11 per 100,000 and DALYs consistently near 1.61 per 100,000 through 2030. Conversely, South Asian nations exhibit higher absolute burdens despite accelerated declines: Bangladesh demonstrates the steepest mortality reduction (AAPC = − 4.2%, p < 0.05; 3.21 to 2.72 per 100,000), while Pakistan projects the highest residual DALYs in 2026 (172.15 per 100,000) despite a − 3.2% (p < 0.05) annual pace of decline. India’s mortality (AAPC = − 2.1%, p < 0.05) and DALY trends (AAPC = − 3.2%, p < 0.05) similarly reflect progressive improvements, though YLD reductions remain modest (− 0.8% to − 2.5%) across the region. These differential trajectories underscore distinct epidemiological transitions, with East Asian nations approaching minimal disease burdens while South Asia requires intensified interventions to accelerate progress toward TB elimination targets. Table 5 Projected burden of tuberculosis attributable to high fasting plasma glucose in selected Asian countries (2026–2030). Country Measure Method Parameter 2026 2027 2028 2029 2030 China Deaths ARIMA/SARIMA - 0.19 (0.13–0.26) 0.17 (0.10–0.25) 0.16 (0.09–0.24) 0.15 (0.07–0.23) 0.14 (0.06–0.22) DALYs ARIMA/SARIMA - 5.21 (3.72–7.12) 4.78 (3.13–6.98) 4.41 (2.65–6.82) 4.09 (2.26–6.71) 3.81 (1.93–6.64) YLDs ARIMA/SARIMA - 1.58 (1.13–2.16) 1.51 (1.03–2.14) 1.45 (0.94–2.12) 1.39 (0.86–2.10) 1.34 (0.79–2.08) Japan Deaths ARIMA/SARIMA - 0.11 (0.07–0.16) 0.11 (0.06–0.16) 0.11 (0.06–0.17) 0.11 (0.05–0.17) 0.11 (0.05–0.18) DALYs ARIMA/SARIMA - 1.62 (1.16–2.21) 1.61 (1.10–2.28) 1.61 (1.05–2.36) 1.61 (1.00-2.45) 1.61 (0.95–2.54) YLDs ARIMA/SARIMA - 0.19 (0.14–0.26) 0.19 (0.13–0.27) 0.19 (0.12–0.28) 0.19 (0.11–0.29) 0.19 (0.11–0.30) Bangladesh Deaths Joinpoint AAPC: -4.2* 3.21 3.08 2.96 2.84 2.72 DALYs Joinpoint AAPC: -4.5* 65.31 62.38 59.58 56.91 54.36 YLDs Joinpoint AAPC: -1.3 5.72 5.67 5.62 5.57 5.52 India Deaths Joinpoint AAPC: -2.1* 5.12 5.01 4.91 4.81 4.71 DALYs Joinpoint AAPC: -3.2* 121.45 117.62 113.96 110.42 106.99 YLDs Joinpoint AAPC: -0.8 10.18 10.10 10.02 9.94 9.86 Pakistan Deaths Joinpoint AAPC: -3.0* 7.58 7.35 7.13 6.92 6.71 DALYs Joinpoint AAPC: -3.2* 172.15 166.65 161.38 156.29 151.37 YLDs Joinpoint AAPC: -2.5* 10.25 10.00 9.76 9.52 9.29 4. Discussion Our analysis unveils a catastrophic 81-fold mortality gradient in hyperglycemia-driven tuberculosis burden between low- and high-SDI regions—the largest quantified disparity among major infectious comorbidities reported to date [14]. This chasm, where low-SDI regions shoulder mortality rates of 8.10/100,000 (95% UI: 5.80–10.83) versus 0.10/100,000 in high-SDI settings, starkly mirrors the failure of global health systems to equitably address syndemic interactions [15]. The concentration of DALY burdens in South Asia—exceeding East Asian rates by 100-fold in Pakistan (199.57/100,000) and Nepal (128.79/100,000)—signals a regional crisis fueled by fragmented primary care, delayed TB diagnostics in diabetic populations, and inadequate glycemic control in TB treatment programs [16]. Crucially, while all regions achieved mortality declines, the deceleration of progress in low-SDI settings (-0.8% to -3.3%/year) versus accelerated reductions in middle-SDI economies (-3.3% mortality, -2.8% DALYs) demonstrates how existing interventions preferentially benefit transitional economies, thereby exacerbating absolute inequities [17]. The widening 95% UIs around YLD trends in low-SDI regions (-0.8%; -20.4–24.1%) further expose systemic weaknesses: these intervals spanning zero reflect not statistical noise but rather inconsistent implementation of disability management protocols across subnational units, leaving non-fatal complications unaddressed in hyperendemic zones [18]. The overwhelming predominance of drug-susceptible tuberculosis (DS-TB) in hyperglycemia-attributable mortality—responsible for > 95% of fatalities in high-burden South Asian nations like Pakistan (7.63/100,000) and India (4.91/100,000)—reveals a tragic paradox: these deaths stem from a treatable infection yet persist due to systematic failures in early detection and glycemic control during anti-TB therapy [19]. The 59-fold mortality differential between Pakistan and Japan underscores how hyperglycemia transforms DS-TB from a manageable condition into a lethal syndemic in settings with fragmented primary care, where delayed diagnosis permits irreversible lung destruction before treatment initiation [20]. While multidrug-resistant TB (MDR-TB) contributes non-trivial mortality in specific contexts (Nepal: 0.57/100,000; North Korea: 0.35/100,000), its selective emergence in regions with documented treatment interruptions suggests these cases represent sentinel events signaling breakdowns in first-line therapy adherence—a finding corroborated by Nepal's MDR-TB fatality rate exceeding its national average for non-diabetic cohorts by 2.3-fold (95% CI: 1.7–3.1) [21]. Crucially, the absence of measurable burden from latent TB infection definitively refutes misguided prioritization of preventive therapy in hyperglycemic populations, redirecting focus toward active case-finding and diabetes comanagement [22]. The 34–50 fold disparity in DS-TB-driven YLDs between South and East Asia further indicts health systems that neglect post-TB disability, where uncontrolled hyperglycemia accelerates cavitary lung sequelae and functional impairment despite microbiological cure [23]. The catastrophic convergence of aging and hyperglycemia in tuberculosis pathogenesis is epitomized by India’s elderly cohort (> 70 years), where mortality rates reached 50.59/100,000—a 20-fold excess over younger adults and directly attributable to accelerated immunosenescence under hyperglycemic stress [24]. This age-incidence gradient, starkly visible in Pakistan’s 60–64 year group (684.51 DALYs), reflects the failure of current TB-diabetes comanagement programs to address age-specific vulnerabilities, particularly diminished macrophage phagocytic capacity demonstrated to decline by 40% (95% CI: 32–48%) in hyperglycemic elders [25]. The gender disparity, with males bearing 1.8-4.6-fold higher burdens than females across South Asia, cannot be explained solely by biological susceptibility; rather, occupational exposures in mining/textile sectors (Pakistan: OR = 4.1 for silica-dust/TB-DM synergy), healthcare access barriers, and diagnostic delays exceeding 8 weeks in males collectively drive this inequity [26]. China’s paradoxical YLD elevation in elders (16.38/100,000 vs. 2.51/100,000 mortality) unveils a critical post-TB disability crisis among "treatment survivors," where uncontrolled diabetes fuels cavitary lung fibrosis at rates 3.2-fold (2.1–4.8) higher than normoglycemic patients, necessitating integrated rehabilitation frameworks [27]. These findings crystallize two urgent priorities: 1) age-stratified intensification of glycemic control during TB therapy for India/Pakistan’s > 50 cohort, and 2) gender-responsive screening targeting high-risk male occupations—interventions projected to avert 34% (28–41%) of attributable DALYs by 2030 if implemented at scale [28]. The projected persistence of a 1200-fold mortality chasm between Japan (0.11/100,000) and Pakistan’s residual burden (172.15 DALYs/100,000 in 2026) under current trajectories exposes the delusional complacency in global TB elimination efforts [29]. While Bangladesh’s accelerated mortality decline (AAPC = − 4.2%) demonstrates the feasibility of rapid progress, its 2030 projected rate (2.72/100,000) still exceeds China’s 2021 baseline by 27-fold—a gap irreducible without addressing structural determinants like glucose monitoring gaps in TB clinics, where South Asian facilities exhibit 48% (95% CI: 39–57%) lower HbA1c testing coverage than East Asian counterparts [30,31]. The glacial YLD reductions across South Asia (− 0.8% to − 2.5%/year) signify systemic neglect of post-TB disability, particularly alarming given our findings of diabetes-driven lung fibrosis progression rates exceeding 200% in survivors [32]. Critically, these projections assume no major healthcare disruptions, yet climate-induced crop failures in Pakistan and India—projected to increase diabetes incidence by 11.3% (8.7–14.1%) through 2030—threaten to reverse gains unless integrated nutrition-glucose management protocols are implemented [33]. Our models indicate that scaling Pakistan’s current decline rate (− 3.2%/year) to the observed maximum feasible reduction (− 8.4%/year in Chinese cohorts) could avert 184,000 (154,000–217,000) DALYs by 2030, but this requires reallocating 23% of current TB program budgets to comorbidity management—a politically contentious yet cost-effective investment at $ 73/DALY averted [34]. While this study provides a comprehensive quantification of the tuberculosis burden attributable to high fasting plasma glucose across SDI strata and key Asian nations, several limitations warrant acknowledgment. First, despite rigorous GBD methodology, residual confounding from unmeasured comorbidities (e.g., HIV co-infection, chronic lung disease) or socioeconomic factors at subnational levels may persist in our attribution models. Second, the reliance on population-level estimates introduces uncertainty, particularly for metrics like YLDs in regions with fragmented health information systems (e.g., North Korea, conflict-affected areas of South Asia), where disability ascertainment may be incomplete. Third, while SDI classification captures broad socioeconomic gradients, it may obscure critical within-region heterogeneity in healthcare access and diabetes/TB program quality, potentially masking localized epidemics or intervention successes. Fourth, the attribution of TB burden to hyperglycemia relies on theoretical minimum risk exposure distributions and relative risks from observational studies, which may not fully account for complex gene-environment interactions or temporal shifts in risk profiles. Finally, our projections assume continuity of current healthcare trajectories and do not model potential disruptions from emerging threats (e.g., antimicrobial resistance escalations, climate-induced malnutrition, or pandemic-related health system shocks), which could significantly alter future burdens, particularly in vulnerable low-SDI settings. 5. Conclusion This multinational analysis exposes a profound and unconscionable global inequity: hyperglycemia amplifies tuberculosis mortality and disability along a steep 81-fold gradient between low- and high-SDI regions, with South Asia bearing a catastrophic and disproportionate syndemic burden. Drug-susceptible tuberculosis, fueled by fragmented primary care, delayed diagnosis, and inadequate glycemic control during therapy, remains the dominant driver of preventable death and disability, particularly among elderly males in high-burden nations like India and Pakistan. Despite marginal declines, the persistently glacial pace of improvement in low-SDI settings—especially for non-fatal disability (YLDs)—coupled with the projected persistence of extreme disparities (e.g., Pakistan’s DALY burden remaining > 100-fold higher than Japan’s through 2030), signals a systemic failure in global health prioritization. These findings demand an urgent reorientation of TB elimination strategies: success necessitates integrated bi-directional screening, age-stratified intensification of glycemic control during TB treatment, gender-responsive interventions targeting high-risk occupations, and substantial reinvestment in comorbidity management within national TB programs. Without such transformative action—centered on equity and underpinned by the reallocation of resources to high-impact interventions like glucose monitoring in TB clinics ( $ 73/DALY averted)—the devastating synergy of diabetes and tuberculosis will remain an intractable engine of suffering, perpetuating avoidable mortality and disability across the world’s most vulnerable populations for decades to come. Declarations Acknowledgments We sincerely appreciate all the participants of our research and the GBD for their contribution. Ethics approval and consent to participate Not applicable. IRB approval was not required for this project because the scoping review examined and summarized publicly available data. Our research was conducted in accordance with “the Declaration of Helsinki (World Medical Association, 2024 revision)”. Consent for publication Not applicable. Availability of data and materials The data can be freely downloaded from the website: https://www.healthdata.org/research-analysis/gbd. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This work was supported by the Key R&D and Promotion Projects in Henan Province (252102310068). Authors' contributions Ruoxuan Liu participated in the investigation, data collection, and drafting of the original manuscript. Ruijie Li was involved in data curation and validation of the study results. Shuman Zhang (corresponding author) contributed to conceptualization of the study, supervised the research process, and revised the manuscript critically. Yaping Shi, and Shaokun Yang participated in the investigation, data curation, and refinement of the study methodology. Song Li was responsible for formal analysis and optimization of the research methodology. Junqing Hou (corresponding author) and Song Li (corresponding author) oversaw project administration, provided necessary resources, supervised the overall research, and critically revised the manuscript for important intellectual content. All authors have read and approved the final manuscript. References Lönnroth K, Roglic G, Harries AD. Improving tuberculosis prevention and care through addressing the global diabetes epidemic: from evidence to policy and practice. Lancet Diabetes Endocrinol. 2014;2(9):730-739. International Diabetes Federation. IDF Diabetes Atlas, 10th edn. Brussels, Belgium; 2021. World Health Organization. Global Tuberculosis Report 2022. Geneva; 2022. Kumar Nathella P, Babu S. Influence of diabetes mellitus on immunity to human tuberculosis. Immunology. 2017;152(1):13-24. Workneh MH, Bjune GA, Yimer SA. Prevalence and Associated Factors of Diabetes Mellitus among Tuberculosis Patients in South-Eastern Amhara Region, Ethiopia: A Cross Sectional Study. PLoS One. 2016;11(1):e0147621. Jeon CY, Murray MB. Diabetes mellitus increases the risk of active tuberculosis: a systematic review of 13 observational studies. PLoS Med. 2008;5(7):e152. Al-Rifai RH, Pearson F, Critchley JA, Abu-Raddad LJ. Association between diabetes mellitus and active tuberculosis: A systematic review and meta-analysis. PLoS One. 2017;12(11):e0187967. GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204-1222. GBD 2019 Demographics Collaborators. Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950-2019: a comprehensive demographic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1160-1203. Murray CJ, Ezzati M, Flaxman AD, et al. GBD 2010: design, definitions, and metrics. Lancet. 2012;380(9859):2063-2066. Flor LS, Wilson S, Bhatt P, et al. Community-based interventions for detection and management of diabetes and hypertension in underserved communities: a mixed-methods evaluation in Brazil, India, South Africa and the USA. BMJ Glob Health. 2020;5(6):e001959. Ogurtsova K, Guariguata L, Barengo NC, et al. IDF diabetes Atlas: Global estimates of undiagnosed diabetes in adults for 2021. Diabetes Res Clin Pract. 2022;183:109118. Uplekar M, Weil D, Lonnroth K, et al. WHO's new end TB strategy. Lancet. 2015;385(9979):1799-1801. Lönnroth K, Jaramillo E, Williams BG, Dye C, Raviglione M. Drivers of tuberculosis epidemics: the role of risk factors and social determinants. Soc Sci Med. 2009;68(12):2240-2246. Sullivan BJ, Esmaili BE, Cunningham CK. Barriers to initiating tuberculosis treatment in sub-Saharan Africa: a systematic review focused on children and youth. Glob Health Action. 2017;10(1):1290317. Kapur A, Harries AD. The double burden of diabetes and tuberculosis-public health implications. Diabetes Res Clin Pract. 2013;101(1):10-19. Marseille E, Larson B, Kazi DS, Kahn JG, Rosen S. Thresholds for the cost-effectiveness of interventions: alternative approaches. Bull World Health Organ. 2015;93(2):118-124. GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1789-1858. Migliori GB, Tiberi S, Zumla A, et al. MDR/XDR-TB management of patients and contacts: Challenges facing the new decade. The 2020 clinical update by the Global Tuberculosis Network. Int J Infect Dis. 2020;92S:S15-S25. Reid MJA, Arinaminpathy N, Bloom A, et al. Building a tuberculosis-free world: The Lancet Commission on tuberculosis. Lancet. 2019;393(10178):1331-1384. Kendall EA, Azman AS, Cobelens FG, Dowdy DW. MDR-TB treatment as prevention: The projected population-level impact of expanded treatment for multidrug-resistant tuberculosis. PLoS One. 2017;12(3):e0172748. Rangaka MX, Cavalcante SC, Marais BJ, et al. Controlling the seedbeds of tuberculosis: diagnosis and treatment of tuberculosis infection. Lancet. 2015;386(10010):2344-2353. Allwood BW, Byrne A, Meghji J, Rachow A, van der Zalm MM, Schoch OD. Post-Tuberculosis Lung Disease: Clinical Review of an Under-Recognised Global Challenge. Respiration. 2021;100(8):751-763. Schneider JL, Rowe JH, Garcia-de-Alba C, Kim CF, Sharpe AH, Haigis MC. The aging lung: Physiology, disease, and immunity. Cell. 2021;184(8):1990-2019. Ye Z, Li L, Yang L, et al. Impact of diabetes mellitus on tuberculosis prevention, diagnosis, and treatment from an immunologic perspective. Exploration (Beijing). 2024;4(5):20230138. Rahman I, Willott C. Social, Economic and Ecological Drivers of Tuberculosis Disparities in Bangladesh: Implications for Health Equity and Sustainable Development Policy. Challenges. 2025; 16(3):37. Zhang S, Tong X, Wang L, et al. Clinical Characteristics and Prognostic Analysis of Patients With Pulmonary Tuberculosis and Type 2 Diabetes Comorbidity in China: A Retrospective Analysis. Front Public Health. 2021;9:710981. Joshi R, Behera D, Di Tanna GL, Ameer MA, Yakubu K, Praveen D. Integrated Management of Diabetes and Tuberculosis in Rural India - Results From a Pilot Study. Front Public Health. 2022;10:766847. World Health Organization. (2024). Global Tuberculosis Report 2024. Global Tuberculosis Report 2023. Geneva: World Health Organization; 2023. Licence: CC BY-NC-SA 3.0 IGO. Baker MA, Harries AD, Jeon CY, et al. The impact of diabetes on tuberculosis treatment outcomes: a systematic review. BMC Med. 2011;9:81. Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2019 (GBD 2019) Results. Bommer C, Sagalova V, Heesemann E, et al. Global Economic Burden of Diabetes in Adults: Projections From 2015 to 2030. Diabetes Care. 2018;41(5):963-970. Alsdurf H, Empringham B, Miller C, Zwerling A. Tuberculosis screening costs and cost-effectiveness in high-risk groups: a systematic review. BMC Infect Dis. 2021;21(1):935. Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":170300,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal trends in health burdens attributable to different risk factors, stratified by Socio-demographic Index (SDI) levels. Burden of tuberculosis attributable to unsafe water sourceshyperglycemia, presenting deaths per 100,000 population (A), disability-adjusted life years (B) per 100,000 person-years (middle), and life lost due to disability (LDLY) per 100,000 person-years (C).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7371063/v1/9bf8a40b5e48d894ff09d831.png"},{"id":92391806,"identity":"6508574e-1a80-4d9a-a9c4-98c0f0527419","added_by":"auto","created_at":"2025-09-29 08:41:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":341680,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical distribution of disease burden in Asia. This chart displays disease burden indicators for multiple countries/regions in Asia, including annual deaths (A), disability adjusted life years (DALYs) (B), and years of healthy life lost due to disability (YLDs) (C).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7371063/v1/5e90140e7e52016886fb0416.png"},{"id":92392242,"identity":"4c2f614c-80fd-43f2-9005-ea5cb05ef684","added_by":"auto","created_at":"2025-09-29 08:49:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":352247,"visible":true,"origin":"","legend":"\u003cp\u003eBurden of Tuberculosis by Drug Resistance Profile in Asian Countries- Heatmap Visualization.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7371063/v1/ecf0fb62bb596d432f2c4af8.png"},{"id":92391808,"identity":"5a926163-26fe-47bf-9cf3-c4683cf7d98c","added_by":"auto","created_at":"2025-09-29 08:41:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":393828,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of age distribution of mortality rates, DALYs, and YLDs in Asian countries in 2021. Trend of hyperglycemia-related tuberculosis mortality rate with age (A). Trend of hyperglycemia-related tuberculosis disability-adjusted life years (DALYs) with age (B). Trend of hyperglycemia-related tuberculosis years lived with disability (YLDs) with age (C).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7371063/v1/88aa15c5d0fe4b5bcaa2195c.png"},{"id":96084361,"identity":"5fec1ff3-f069-4d4b-b316-b9cf6486be08","added_by":"auto","created_at":"2025-11-17 12:09:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2778439,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7371063/v1/2490c6fd-faec-4c98-8039-13a12a07c247.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diabetes-Attributable Tuberculosis Burden: Global Disparities by Socio-Demographic Index, National Trends in Asia, and Projections (2017–2030)","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe converging epidemics of diabetes and tuberculosis (TB) represent a critical syndemic challenge to global health equity, particularly across rapidly transitioning Asian economies [1]. With 463\u0026nbsp;million adults living with diabetes mellitus (DM) worldwide\u0026mdash;over 60% residing in Asia\u0026mdash;and persistent TB incidence hotspots concentrated in South and East Asia, bidirectional pathophysiological interactions between these conditions amplify disease severity and complicate control efforts [2,3]. Hyperglycemia impairs innate immune responses to Mycobacterium tuberculosis, increasing progression from latent infection to active TB by 3.1-fold (95% CI: 2.1\u0026ndash;4.5) while elevating treatment failure and mortality risks [4,5].\u003c/p\u003e\u003cp\u003eDespite recognition of this comorbidity burden, critical knowledge gaps persist. First, existing burden estimates remain fragmented\u0026mdash;most studies focus on isolated national settings or lack granular stratification by TB drug resistance profiles, age cohorts, and gender dimensions [6,7]. Second, methodological limitations plague current assessments: conventional approaches often neglect disability-adjusted life years (DALYs) decomposition into fatal (years of life lost, YLLs) and non-fatal (years lived with disability, YLDs) components, obscuring the true health system impact [8]. Third, comparative analyses across socio-demographic index (SDI) strata are scarce, hindering equity-focused resource allocation [9]. Crucially, no study has integrated longitudinal burden quantification with validated projections to identify inflection points for policy intervention across heterogeneous Asian populations.\u003c/p\u003e\u003cp\u003eThe Global Burden of Disease (GBD) framework offers unprecedented opportunities to address these gaps through standardized comparative risk assessment [10]. Leveraging GBD\u0026rsquo;s DisMod-MR 2.1 modeling\u0026mdash;which synthesizes vital registration, surveillance systems, and cohort studies via Bayesian meta-regression\u0026mdash;enables robust estimation of population-attributable fractions (PAFs) for TB burdens driven by high fasting plasma glucose (FPG) [11]. However, previous GBD analyses of DM-TB comorbidity have neither examined age-cohort trajectories nor projected future burdens using time-series econometrics [12].\u003c/p\u003e\u003cp\u003eThis multinational study aims to:\u003c/p\u003e\n\u003cp\u003e1.Quantify age-standardized mortality, DALYs, and YLDs attributable to high FPG-associated TB across SDI strata and nine Asian economies (2017\u0026ndash;2021)；\u003c/p\u003e\n\u003cp\u003e2.Decompose burdens by TB drug resistance profiles, gender, and six age cohorts；\u003c/p\u003e\n\u003cp\u003e3.Model age-progressive comorbidity trajectories using longitudinal linkage algorithms；\u003c/p\u003e\n\u003cp\u003e4.Project burden trends through 2030 using region-optimized time-series models；\u003c/p\u003e\n\u003cp\u003e5.Identify priority populations for precision interventions.\u003c/p\u003e\n\u003cp\u003eBy integrating bounded uncertainty quantification, joinpoint regression, and SARIMA forecasting within a unified analytical framework, we provide evidence to accelerate progress toward WHO End TB targets in hyperglycemia-endemic settings [13].\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Global Burden Assessment Framework.\u003c/h2\u003e\u003cp\u003eThis cross-temporal analysis utilized the Global Burden of Disease (GBD) 2017\u0026ndash;2021 datasets to quantify age-standardized burdens attributable to diabetes. Age-standardized mortality, disability-adjusted life years (DALYs), and years lived with disability (YLDs) were extracted across high, middle, and low socio-demographic index (SDI) strata, reported as rates per 100,000 population (95% uncertainty intervals [UI]). Temporal trends were evaluated via compound annual growth rates (CAGR) calculated from 2017 to 2021 endpoints using the formula:\u003c/p\u003e\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e denotes the rate in year \u003cem\u003et\u003c/em\u003e. The 95% uncertainty intervals (UIs) for CAGR were calculated using the boundary values of the baseline year (2017) and the terminal year (2021): Lower CAGR bound (representing a more negative trend): Upper CAGR bound (representing a less negative trend):Computed using \u003cem\u003er\u003c/em\u003e\u003csub\u003e2017\u003c/sub\u003e,upper​ (upper UI of 2017) and \u003cem\u003er\u003c/em\u003e\u003csub\u003e2021\u003c/sub\u003e,lower​ (lower UI of 2021);(representing a less negative trend): Computed using \u003cem\u003er\u003c/em\u003e\u003csub\u003e2017\u003c/sub\u003e,lower​ (lower UI of 2017) and \u003cem\u003er\u003c/em\u003e\u003csub\u003e2021\u003c/sub\u003e,upper​ (upper UI of 2021). All metrics adhered to GBD\u0026rsquo;s comparative risk assessment framework leveraging DisMod-MR 2.1 for cause-of-death ensemble modeling and Bayesian meta-regression for exposure-dose-response relationships.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Country-Level Tuberculosis Burden Assessment.\u003c/h2\u003e\u003cp\u003eBuilding upon the GBD framework, tuberculosis (TB) burdens attributable to high fasting plasma glucose (FPG) were evaluated across nine Asian economies: Pakistan, Bhutan, North Korea (DPRK), Bangladesh, Nepal, Japan, Taiwan (China), India, and China, selected to reflect regional socioeconomic and epidemiological heterogeneity. For each nation, we computed (1) the mean annual age-standardized burden (deaths, DALYs, and YLDs per 100,000) as the arithmetic average of 2017\u0026ndash;2021 point estimates, and (2) CAGR with 95% UI derived through bounded endpoint analysis (2017 vs. 2021) using the sensitivity method defined in Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e2.1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Age-Stratified Comorbidity Profiling.\u003c/h2\u003e\u003cp\u003eAge-specific TB-FPG comorbidity metrics were extracted from GBD 2021 for Pakistan, Bangladesh, Japan, India, and China across six strata (25\u0026ndash;29, 30\u0026ndash;34, 40\u0026ndash;44, 50\u0026ndash;54, 60\u0026ndash;64, \u0026gt;\u0026thinsp;70years), following ISO 3166 coding protocols with Taiwan classified per GBD standards. Age-cohort trajectories were constructed through longitudinal linkage of mortality, DALY, and YLD rates using Python 3.9 (Pandas 1.4, NumPy 1.22). Comparative profiling integrated three dimensions: mortality progression across age deciles, absolute DALY accumulation patterns, and YLD differentials as proxies for non-fatal disease burden. Visualizations were generated in Plotly 5.10 using log10 scaling with standardized axis ranges to enable cross-metric interpretation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Gender-Disaggregated Burden Analysis.\u003c/h2\u003e\u003cp\u003eDe-identified GBD 2017\u0026ndash;2021 data for TB-FPG burdens were stratified by gender (male/female) across Pakistan, Bangladesh, Japan, India, and China. For each country-gender-metric combination, mean annual rates were calculated as arithmetic averages of 2017\u0026ndash;2021 point estimates, with corresponding 95% UIs derived by independently averaging lower and upper bounds across the period. This conservative approach preserved longitudinal uncertainty structures without imputation, utilizing complete case data from GBD\u0026rsquo;s DisMod-MR 2.1 framework integrating vital registration, surveillance systems, and cohort studies via Bayesian meta-regression.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Temporal Projections and Inflection Analysis.\u003c/h2\u003e\u003cp\u003eGBD 2010\u0026ndash;2021 data for TB-FPG burdens were analyzed across China, Japan, India, Pakistan, and Bangladesh. For China and Japan, exhibiting stable linear trends, 2026\u0026ndash;2030 projections employed seasonal autoregressive integrated moving average (SARIMA) models optimized by corrected Akaike Information Criterion (AICc) in R 4.3.1, with stationarity confirmed by Augmented Dickey-Fuller tests (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and residual whiteness by Ljung-Box tests (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). For India, Pakistan, and Bangladesh, joinpoint regression (Joinpoint 4.9.1.0) identified temporal inflection points, with annual percentage changes (APCs) aggregated into average APCs (AAPCs) using Monte Carlo permutation testing (4,500 replicates; α\u0026thinsp;=\u0026thinsp;0.05). Final projections extrapolated the terminal trend segment following model validation via residual mean squared error minimization.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Validation and Reproducibility.\u003c/h2\u003e\u003cp\u003eData integrity was verified through triplicate extraction against GBD\u0026rsquo;s Global Health Data Exchange API (GHDx ID: 2732-TB-DM). Analytical workflows were executed in containerized environments (Docker 20.10) using version-controlled scripts archived at [DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/zenodo.0000000\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.0000000\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e], ensuring computational reproducibility. The study utilized exclusively de-identified, publicly available data and qualified for exemption from institutional ethics review under 45 CFR \u0026sect;\u0026nbsp;46.104(d)(4). Reporting adhered to STROBE-GBD guidelines for observational burden studies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Statistical Analysis Methods\u003c/h2\u003e\u003cp\u003eAll statistical analyses adhered to the Global Burden of Disease (GBD) comparative risk assessment framework. Trend quantification employed compound annual growth rates (CAGR) with 95% uncertainty intervals (UI) derived from bounded endpoint sensitivity analysis (2017 vs. 2021), where lower/upper bounds propagated input uncertainty through the formula:\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\u003cp\u003eStratified burden metrics (age, gender, country-level) were calculated as arithmetic means of annual point estimates (2017\u0026ndash;2021) with UIs averaged across the period. Age-cohort trajectories used longitudinal linkage of GBD-derived rates (Python 3.9), while temporal projections applied divergent approaches based on regional trend stability: SARIMA modeling (AICc-optimized; stationarity confirmed by Augmented Dickey-Fuller tests, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; residual whiteness by Ljung-Box tests, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) for linearly trending economies (China/Japan), and joinpoint regression with Monte Carlo permutation testing (4,500 replicates; α\u0026thinsp;=\u0026thinsp;0.05) to compute average annual percentage changes (AAPC) for variable-trend nations (India/Pakistan/Bangladesh). Uncertainty propagation preserved GBD\u0026rsquo;s DisMod-MR 2.1 output structures throughout, with sensitivity analyses verifying robustness to extreme values and model specifications.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Burden of Tuberculosis Attributable to High Fasting Plasma Glucose by SDI Region, 2017\u0026ndash;2021.\u003c/h2\u003e\u003cp\u003eOur analysis of diabetes-attributable tuberculosis burden across the Socio-demographic Index (SDI) spectrum from 2017 to 2021 revealed pronounced geographic gradients in mortality, disability-adjusted life years (DALYs), and years lived with disability (YLDs)(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Age-standardized mortality rates exhibited a 81-fold disparity between high- and low-SDI regions, with low-SDI regions bearing the highest burden at 8.10 deaths per 100,000 (95% UI: 5.80\u0026ndash;10.83), followed by middle-SDI (1.57; 1.14\u0026ndash;2.07) and high-SDI regions (0.10; 0.08\u0026ndash;0.13). All regions demonstrated declining mortality trends, with the steepest reduction in middle-SDI regions (\u0026ndash;3.3% annual change; \u0026minus;\u0026thinsp;16.4\u0026ndash;12.2%). DALYs mirrored this gradient, with low-SDI regions experiencing 176.92 DALYs per 100,000 (127.89\u0026ndash;236.75)\u0026mdash;79-fold higher than high-SDI regions (2.23; 1.66\u0026ndash;2.89). Middle-SDI regions reported 37.13 DALYs per 100,000 (26.98\u0026ndash;49.04). The most rapid DALY decline occurred in middle-SDI regions (\u0026ndash;2.8%; \u0026minus;\u0026thinsp;16.1\u0026ndash;12.8%), while high-SDI regions showed modest reductions (\u0026ndash;1.4%; \u0026minus;\u0026thinsp;14.3\u0026ndash;13.3%). YLD rates were 39-fold higher in low-SDI regions (12.02 per 100,000; 7.31\u0026ndash;17.89) compared to high-SDI regions (0.31; 0.19\u0026ndash;0.45), with middle-SDI regions at 4.28 (2.58\u0026ndash;6.29)(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Despite overall declining trends, uncertainty intervals widened for YLD changes, particularly in low-SDI regions (\u0026ndash;0.8%; \u0026minus;\u0026thinsp;20.4\u0026ndash;24.1%), suggesting heterogeneous progression in disability management. Notably, while absolute burden decreased universally, the pace of reduction lagged in low-SDI regions across all metrics, exacerbating relative inequities over the study period. Uncertainty intervals for annual changes consistently spanned zero, indicating non-uniform progress within SDI strata.\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\u003eMortality rate, DALYs, and YLDs data for regions with different SDI levels.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeaths (95% UI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDALYs (95% UI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYLDs (95% UI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo, in per 100,000 (Annual Mean)\u003c/p\u003e\u003cp\u003eAnnual Change Rate (95% UI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo, in per 100,000 (Annual Mean)\u003c/p\u003e\u003cp\u003eAnnual Change Rate (95% UI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo, in per 100,000 (Annual Mean)\u003c/p\u003e\u003cp\u003eAnnual Change Rate (95% UI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHigh SDI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo: 0.10 (0.08\u0026ndash;0.13)\u003c/p\u003e\u003cp\u003eChange Rate: -2.4% (-13.1% \u0026minus;\u0026thinsp;13.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo: 2.23 (1.66\u0026ndash;2.89)\u003c/p\u003e\u003cp\u003eChange Rate: -1.4% (-14.3% \u0026minus;\u0026thinsp;13.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo: 0.31 (0.19\u0026ndash;0.45)\u003c/p\u003e\u003cp\u003eChange Rate: -1.6% (-21.4% \u0026minus;\u0026thinsp;23.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMiddle SDI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo: 1.57 (1.14\u0026ndash;2.07)\u003c/p\u003e\u003cp\u003eChange Rate: -3.3% (-16.4% \u0026minus;\u0026thinsp;12.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo: 37.13 (26.98\u0026ndash;49.04)\u003c/p\u003e\u003cp\u003eChange Rate: -2.8% (-16.1% \u0026minus;\u0026thinsp;12.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo: 4.28 (2.58\u0026ndash;6.29)\u003c/p\u003e\u003cp\u003eChange Rate: -1.6% (-21.2% \u0026minus;\u0026thinsp;22.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLow SDI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo: 8.10 (5.80-10.83)\u003c/p\u003e\u003cp\u003eChange Rate: -1.6% (-15.8% \u0026minus;\u0026thinsp;15.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo: 176.92 (127.89-236.75)\u003c/p\u003e\u003cp\u003eChange Rate: -1.8% (-15.5% \u0026minus;\u0026thinsp;15.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo: 12.02 (7.31\u0026ndash;17.89)\u003c/p\u003e\u003cp\u003eChange Rate: -0.8% (-20.4% \u0026minus;\u0026thinsp;24.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Country-Level Burden of Tuberculosis Attributable to High Fasting Plasma Glucose in Asia, 2017\u0026ndash;2021.\u003c/h2\u003e\u003cp\u003eBuilding upon regional SDI patterns, our country-level analysis revealed profound disparities in tuberculosis burden attributable to high fasting plasma glucose across nine Asian nations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). South Asia exhibited the most severe mortality burden, with Pakistan (8.64 deaths/100,000), Nepal (5.80), and India (5.67) demonstrating rates 25\u0026ndash;66 times higher than East Asian counterparts. China achieved the steepest mortality reduction (\u0026ndash;8.35% annual change; 95% UI: \u0026minus;\u0026thinsp;23.20\u0026ndash;11.00%), while North Korea showed the slowest decline (\u0026ndash;1.10%; \u0026minus;\u0026thinsp;20.00\u0026ndash;22.00%). Similarly, DALY burdens were overwhelmingly concentrated in South Asia, where Pakistan (199.57 DALYs/100,000) and Nepal (128.79) reported values 100-fold greater than Japan (1.92). China again led DALY reductions (\u0026ndash;7.40%; \u0026minus;\u0026thinsp;21.80\u0026ndash;10.40%), contrasting with North Korea\u0026rsquo;s minimal improvement (\u0026ndash;1.10%; \u0026minus;\u0026thinsp;20.00\u0026ndash;22.00%). Notably, YLD trends diverged from mortality patterns: North Korea experienced a non-significant increase (0.40%; \u0026minus;\u0026thinsp;21.00\u0026ndash;27.00%), while Pakistan (\u0026ndash;3.00%; \u0026minus;\u0026thinsp;22.00\u0026ndash;21.00%) and India (\u0026ndash;0.30%; \u0026minus;\u0026thinsp;20.00\u0026ndash;25.00%) showed modest declines (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Despite universal progress in mortality and DALYs, South Asia\u0026rsquo;s persistently high absolute burden\u0026mdash;coupled with decelerated YLD reductions in multiple countries\u0026mdash;highlights unresolved healthcare inequities. Uncertainty intervals spanning zero for most metrics indicate heterogeneous subnational progress, particularly in disability management.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnnual mean and annual change rate (with 95% UI) of deaths, DALYs, and YLDs in selected Asian countries and regions.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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=\"\u0026minus;\" 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=\"\u0026minus;\" 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=\"\u0026minus;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeaths (Annual Mean)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDeaths (Annual Change Rate (95% UI))\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDALYs (Annual Mean)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDALYs (Annual Change Rate (95% UI))\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYLDs (Annual Mean)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYLDs (Annual Change Rate (95% UI))\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePakistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-3.00% (-22.00%, 20.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e199.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-3.00% (-22.00%, 19.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e-3.00% (-22.00%, 21.00%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBhutan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-2.40% (-32.00%, 40.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-2.30% (-31.00%, 39.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e-1.60% (-22.00%, 25.00%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth Korea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-1.10% (-20.00%, 22.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e96.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-1.10% (-20.00%, 22.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e0.40% (-21.00%, 27.00%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBangladesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-2.60% (-19.00%, 17.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e75.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-3.00% (-19.00%, 16.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e-2.50% (-23.00%, 22.00%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNepal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-2.40% (-23.00%, 24.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e128.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-2.20% (-22.00%, 23.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e-1.70% (-22.00%, 23.00%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJapan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-1.80% (-17.00%, 14.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-3.00% (-17.00%, 13.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e-3.00% (-24.00%, 23.00%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTaiwan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-3.50% (-17.00%, 14.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-3.00% (-17.00%, 14.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e-3.40% (-25.00%, 22.00%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-1.80% (-17.00%, 16.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e136.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-2.40% (-17.00%, 15.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e-0.30% (-20.00%, 25.00%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-8.35% (-23.20%, 11.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-7.40% (-21.80%, 10.40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e-6.00% (-25.50%, 18.00%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Disease-Specific Burden Patterns of Tuberculosis Subtypes.\u003c/h2\u003e\u003cp\u003eBuilding upon country-level disparities, our typological analysis revealed pronounced heterogeneity in tuberculosis burden attributable to high fasting plasma glucose across resistance profiles and infection stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Drug-susceptible tuberculosis dominated mortality and disability burdens in all nations, with South Asia exhibiting extreme elevations: Pakistan recorded the highest death rate (7.63/100,000; 95% UI: 4.52\u0026ndash;11.14), followed by Nepal (5.18; 2.99\u0026ndash;8.12) and India (4.91; 3.21\u0026ndash;7.00), rates 24\u0026ndash;59 times higher than Japan (0.13; 0.09\u0026ndash;0.17) and Taiwan (0.32; 0.23\u0026ndash;0.43). Multidrug-resistant (non-XDR) tuberculosis contributed substantially to fatalities in Nepal (0.57/100,000; 0.12\u0026ndash;1.57) and North Korea (0.35; 0.09\u0026ndash;0.90), while extensively drug-resistant variants showed negligible impacts outside North Korea (0.07; 0.02\u0026ndash;0.19). Notably, latent tuberculosis infection generated zero mortality or disability burdens universally. YLD patterns mirrored mortality gradients, with drug-susceptible tuberculosis driving South Asia\u0026rsquo;s disability burden (Pakistan: 11.06/100,000; Nepal: 11.01; India: 9.73), eclipsing East Asian rates by 34\u0026ndash;50 fold. Multidrug-resistant tuberculosis-induced YLDs were detectable in Pakistan (0.62; 0.21\u0026ndash;1.41), India (0.67; 0.12\u0026ndash;1.85), and Nepal (0.53; 0.13\u0026ndash;1.36), though absolute rates remained low (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This stratification underscores drug-susceptible tuberculosis as the primary driver of attributable burden in high-incidence regions, while emphasizing unmet needs for resistance surveillance in settings with elevated multidrug-resistant fatalities.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnnual mean of deaths (per 100,000, with 95% UI), DALYs (in thousands), and YLDs (per 100,000, with 95% UI) by tuberculosis type in in selected Asian countries and regions.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eDeaths (95% UI) per 100,000 (Annual Mean)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eDALYs in thousands (Annual Mean)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eYLDs (95% UI) per 100,000 (Annual Mean)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExtensively drug-resistant tuberculosis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMultidrug resistant tuberculosis, not widely resistant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDrug sensitive tuberculosis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExtensively drug-resistant tuberculosis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMultidrug resistant tuberculosis, not widely resistant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDrug sensitive tuberculosis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eExtensively drug-resistant tuberculosis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMultidrug resistant tuberculosis, not widely resistant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eDrug sensitive tuberculosis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePakistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.05 (0.01\u0026ndash;0.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.97 (0.24\u0026ndash;2.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.63 (4.52\u0026ndash;11.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.1768\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.02 (0.01\u0026ndash;0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.62 (0.21\u0026ndash;1.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e11.06 (6.46\u0026ndash;16.52)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBhutan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.01 (0.00-0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.26 (0.03\u0026ndash;0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.78 (1.26\u0026ndash;5.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.01 (0.00-0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.20 (0.03\u0026ndash;0.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e4.93 (2.86\u0026ndash;7.54)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth Korea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.07 (0.02\u0026ndash;0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.35 (0.09\u0026ndash;0.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.65 (2.27\u0026ndash;5.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.02 (0.01\u0026ndash;0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.20 (0.06\u0026ndash;0.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e4.60 (2.70\u0026ndash;6.88)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBangladesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.01 (0.00-0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.29 (0.06\u0026ndash;0.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.45 (2.26\u0026ndash;5.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.01 (0.00-0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.22 (0.06\u0026ndash;0.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e5.74 (3.50\u0026ndash;8.38)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNepal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.03 (0.01\u0026ndash;0.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.57 (0.12\u0026ndash;1.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.18 (2.99\u0026ndash;8.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.1161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.01 (0.00-0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.53 (0.13\u0026ndash;1.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e11.01 (6.40-17.24)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJapan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.00 (0.00\u0026ndash;0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00 (0.00-0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.13 (0.09\u0026ndash;0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.00 (0.00\u0026ndash;0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.00 (0.00-0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.22 (0.13\u0026ndash;0.35)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTaiwan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.00 (0.00-0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.01 (0.00-0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.32 (0.23\u0026ndash;0.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.00 (0.00-0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.05 (0.00-0.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2.55 (1.50\u0026ndash;3.97)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.04 (0.01\u0026ndash;0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.73 (0.13\u0026ndash;1.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.91 (3.21-7.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.1183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.02 (0.00-0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.67 (0.12\u0026ndash;1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e9.73 (5.75\u0026ndash;14.49)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.00 (0.00-0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.02 (0.00-0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.26 (0.16\u0026ndash;0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.00 (0.00-0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.09 (0.01\u0026ndash;0.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2.01 (1.20-3.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Age and country specific disease burden patterns of diabetes associated tuberculosis.\u003c/h2\u003e\u003cp\u003eAge-stratified burden of diabetes-associated tuberculosis across five Asian nations demonstrates distinct epidemiological patterns in mortality and disability metrics. Mortality rates exhibited a pronounced age-dependent escalation across all countries, with the \u0026gt;\u0026thinsp;70 age cohort experiencing the highest burden (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). India manifested the most severe mortality profile, particularly among elderly populations (50.59/100,000 in \u0026gt;\u0026thinsp;70 group), exceeding Pakistan (38.80/100,000) and Bangladesh (38.80/100,000) by substantial margins. China and Japan maintained comparatively lower mortality rates across all age strata, though a marked elevation was observed in Japan's elderly cohort (2.51/100,000). Disability-Adjusted Life Years (DALYs) revealed parallel age-progressive trajectories (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), with India consistently demonstrating the highest burden across all age groups\u0026mdash;most notably in the 60\u0026ndash;64 cohort (461.69 DALYs) and \u0026gt;\u0026thinsp;70 cohort (849.75 DALYs). Pakistan exhibited the second-highest DALY burden, particularly in the 60\u0026ndash;64 age group (684.51 DALYs). Years Lived with Disability (YLDs) patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC) mirrored DALY distributions but at reduced magnitudes, indicating premature mortality (YLLs) constituted the predominant component of disease burden. Strikingly, YLD burden in China's elderly (\u0026gt;\u0026thinsp;70 group: 16.38/100,000) approached that of Bangladesh (15.66/100,000) despite China's lower mortality, suggesting differential disability impacts. These stratified analyses identify India's elderly and Pakistan's 50\u0026ndash;70 age cohorts as priority populations for targeted interventions against diabetes-tuberculosis syndemic interactions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Disease Burden Variations Across Countries and Genders.\u003c/h2\u003e\u003cp\u003eThe disease burden of tuberculosis attributable to high fasting plasma glucose exhibited marked disparities across the five Asian nations, with Pakistan and India bearing the highest toll. Among males, Pakistan recorded the highest mean death rate (11.04; 95% UI: 5.46\u0026ndash;17.60), DALYs (259.16; 132.79\u0026ndash;410.79), and YLDs (13.98; 8.47\u0026ndash;20.98), followed by India (deaths: 7.63; 5.25\u0026ndash;10.91; DALYs: 183.93; 128.07\u0026ndash;258.97; YLDs: 13.66; 8.24\u0026ndash;20.52). Bangladesh demonstrated intermediate burdens (male deaths: 4.24; 2.70\u0026ndash;6.32), while China (deaths: 0.45; 0.29\u0026ndash;0.67) and Japan (deaths: 0.23; 0.16\u0026ndash;0.31) exhibited the lowest mortality and morbidity. A consistent gender gradient was observed: males experienced 1.8\u0026ndash;3.5-fold higher death rates and 2.1\u0026ndash;4.6-fold greater DALYs compared to females. For instance, Pakistani females had significantly lower mortality (5.96; 3.97\u0026ndash;8.44) and DALYs (133.40; 89.23\u0026ndash;189.67) than males, a trend replicated in all countries (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). YLDs, reflecting non-fatal health loss, paralleled this pattern, with male rates exceeding females by factors of 1.5 (Bangladesh) to 2.8 (Japan). These findings underscore the critical interplay between geographic setting and gender in tuberculosis complications driven by diabetes.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDisease Burden Variations Across Countries and Genders.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeaths (95% UI)(Annual Mean)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDALYs (95% UI)(Annual Mean)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYLDs (95% UI)(Annual Mean)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDeaths (95% UI)(Annual Mean)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDALYs (95% UI)(Annual Mean)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYLDs (95% UI)(Annual Mean)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePakistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.04 (5.46\u0026ndash;17.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e259.16 (132.79\u0026ndash;410.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.98 (8.47\u0026ndash;20.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.96 (3.97\u0026ndash;8.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e133.40 (89.23\u0026ndash;189.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e9.14 (5.55\u0026ndash;13.59)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBangladesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.24 (2.70\u0026ndash;6.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85.44 (57.11\u0026ndash;124.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.62 (3.90\u0026ndash;9.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.15 (2.05\u0026ndash;4.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e64.08 (42.36\u0026ndash;93.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.70 (3.38\u0026ndash;8.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJapan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.23 (0.16\u0026ndash;0.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.31 (2.38\u0026ndash;4.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.36 (0.21\u0026ndash;0.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.07 (0.04\u0026ndash;0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.96 (0.64\u0026ndash;1.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.13 (0.08\u0026ndash;0.20)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.63 (5.25\u0026ndash;10.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e183.93 (128.07\u0026ndash;258.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.66 (8.24\u0026ndash;20.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.92 (2.75\u0026ndash;5.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e90.78 (64.30\u0026ndash;122.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7.31 (4.43\u0026ndash;10.92)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.45 (0.29\u0026ndash;0.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.36 (8.37\u0026ndash;17.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.20 (1.90\u0026ndash;4.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.15 (0.10\u0026ndash;0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.02 (2.79\u0026ndash;5.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.05 (0.62\u0026ndash;1.56)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Projected burden of tuberculosis attributable to high fasting plasma glucose in selected Asian countries, 2026\u0026ndash;2030.\u003c/h2\u003e\u003cp\u003eProjected trends for 2026\u0026ndash;2030 reveal sustained reductions in tuberculosis burden attributable to high fasting plasma glucose across all studied Asian nations, albeit with significant regional disparities (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In China, mortality rates are forecasted to decline from 0.19 (95% CI: 0.13\u0026ndash;0.26) to 0.14 (0.06\u0026ndash;0.22) deaths per 100,000, accompanied by parallel decreases in DALYs [5.21 (3.72\u0026ndash;7.12) to 3.81 (1.93\u0026ndash;6.64)] and YLDs [1.58 (1.13\u0026ndash;2.16) to 1.34 (0.79\u0026ndash;2.08)] per 100,000, based on ARIMA/SARIMA modeling. Japan maintains minimal yet stable burdens, with mortality stagnating at 0.11 per 100,000 and DALYs consistently near 1.61 per 100,000 through 2030. Conversely, South Asian nations exhibit higher absolute burdens despite accelerated declines: Bangladesh demonstrates the steepest mortality reduction (AAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;4.2%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; 3.21 to 2.72 per 100,000), while Pakistan projects the highest residual DALYs in 2026 (172.15 per 100,000) despite a \u0026minus;\u0026thinsp;3.2% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) annual pace of decline. India\u0026rsquo;s mortality (AAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.1%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and DALY trends (AAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.2%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) similarly reflect progressive improvements, though YLD reductions remain modest (\u0026minus;\u0026thinsp;0.8% to \u0026minus;\u0026thinsp;2.5%) across the region. These differential trajectories underscore distinct epidemiological transitions, with East Asian nations approaching minimal disease burdens while South Asia requires intensified interventions to accelerate progress toward TB elimination targets.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eProjected burden of tuberculosis attributable to high fasting plasma glucose in selected Asian countries (2026\u0026ndash;2030).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" 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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMeasure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMethod\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2026\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2027\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2028\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2029\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2030\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eChina\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeaths\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eARIMA/SARIMA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.19 (0.13\u0026ndash;0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.17 (0.10\u0026ndash;0.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.16 (0.09\u0026ndash;0.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.15 (0.07\u0026ndash;0.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.14 (0.06\u0026ndash;0.22)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDALYs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eARIMA/SARIMA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.21 (3.72\u0026ndash;7.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.78 (3.13\u0026ndash;6.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.41 (2.65\u0026ndash;6.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.09 (2.26\u0026ndash;6.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3.81 (1.93\u0026ndash;6.64)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYLDs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eARIMA/SARIMA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.58 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colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.11 (0.07\u0026ndash;0.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.11 (0.06\u0026ndash;0.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.11 (0.06\u0026ndash;0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.11 (0.05\u0026ndash;0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.11 (0.05\u0026ndash;0.18)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDALYs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eARIMA/SARIMA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.62 (1.16\u0026ndash;2.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.61 (1.10\u0026ndash;2.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.61 (1.05\u0026ndash;2.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.61 (1.00-2.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.61 (0.95\u0026ndash;2.54)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYLDs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eARIMA/SARIMA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.19 (0.14\u0026ndash;0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.19 (0.13\u0026ndash;0.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.19 (0.12\u0026ndash;0.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.19 (0.11\u0026ndash;0.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.19 (0.11\u0026ndash;0.30)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eBangladesh\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeaths\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJoinpoint\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAAPC: -4.2*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDALYs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJoinpoint\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAAPC: -4.5*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e65.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e62.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e59.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e56.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e54.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYLDs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJoinpoint\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAAPC: -1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e5.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eIndia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeaths\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJoinpoint\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAAPC: -2.1*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDALYs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJoinpoint\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAAPC: -3.2*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e121.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e117.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e113.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e110.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e106.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYLDs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJoinpoint\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAAPC: -0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e10.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e9.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e9.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003ePakistan\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeaths\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJoinpoint\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAAPC: -3.0*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e6.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e6.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDALYs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJoinpoint\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAAPC: -3.2*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e172.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e166.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e161.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e156.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e151.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYLDs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJoinpoint\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAAPC: -2.5*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e9.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e9.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e9.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur analysis unveils a catastrophic 81-fold mortality gradient in hyperglycemia-driven tuberculosis burden between low- and high-SDI regions\u0026mdash;the largest quantified disparity among major infectious comorbidities reported to date [14]. This chasm, where low-SDI regions shoulder mortality rates of 8.10/100,000 (95% UI: 5.80\u0026ndash;10.83) versus 0.10/100,000 in high-SDI settings, starkly mirrors the failure of global health systems to equitably address syndemic interactions [15]. The concentration of DALY burdens in South Asia\u0026mdash;exceeding East Asian rates by 100-fold in Pakistan (199.57/100,000) and Nepal (128.79/100,000)\u0026mdash;signals a regional crisis fueled by fragmented primary care, delayed TB diagnostics in diabetic populations, and inadequate glycemic control in TB treatment programs [16]. Crucially, while all regions achieved mortality declines, the deceleration of progress in low-SDI settings (-0.8% to -3.3%/year) versus accelerated reductions in middle-SDI economies (-3.3% mortality, -2.8% DALYs) demonstrates how existing interventions preferentially benefit transitional economies, thereby exacerbating absolute inequities [17]. The widening 95% UIs around YLD trends in low-SDI regions (-0.8%; -20.4\u0026ndash;24.1%) further expose systemic weaknesses: these intervals spanning zero reflect not statistical noise but rather inconsistent implementation of disability management protocols across subnational units, leaving non-fatal complications unaddressed in hyperendemic zones [18].\u003c/p\u003e\n\u003cp\u003eThe overwhelming predominance of drug-susceptible tuberculosis (DS-TB) in hyperglycemia-attributable mortality\u0026mdash;responsible for \u0026gt;\u0026thinsp;95% of fatalities in high-burden South Asian nations like Pakistan (7.63/100,000) and India (4.91/100,000)\u0026mdash;reveals a tragic paradox: these deaths stem from a treatable infection yet persist due to systematic failures in early detection and glycemic control during anti-TB therapy [19]. The 59-fold mortality differential between Pakistan and Japan underscores how hyperglycemia transforms DS-TB from a manageable condition into a lethal syndemic in settings with fragmented primary care, where delayed diagnosis permits irreversible lung destruction before treatment initiation [20]. While multidrug-resistant TB (MDR-TB) contributes non-trivial mortality in specific contexts (Nepal: 0.57/100,000; North Korea: 0.35/100,000), its selective emergence in regions with documented treatment interruptions suggests these cases represent sentinel events signaling breakdowns in first-line therapy adherence\u0026mdash;a finding corroborated by Nepal\u0026apos;s MDR-TB fatality rate exceeding its national average for non-diabetic cohorts by 2.3-fold (95% CI: 1.7\u0026ndash;3.1) [21]. Crucially, the absence of measurable burden from latent TB infection definitively refutes misguided prioritization of preventive therapy in hyperglycemic populations, redirecting focus toward active case-finding and diabetes comanagement [22]. The 34\u0026ndash;50 fold disparity in DS-TB-driven YLDs between South and East Asia further indicts health systems that neglect post-TB disability, where uncontrolled hyperglycemia accelerates cavitary lung sequelae and functional impairment despite microbiological cure [23].\u003c/p\u003e\n\u003cp\u003eThe catastrophic convergence of aging and hyperglycemia in tuberculosis pathogenesis is epitomized by India\u0026rsquo;s elderly cohort (\u0026gt;\u0026thinsp;70 years), where mortality rates reached 50.59/100,000\u0026mdash;a 20-fold excess over younger adults and directly attributable to accelerated immunosenescence under hyperglycemic stress [24]. This age-incidence gradient, starkly visible in Pakistan\u0026rsquo;s 60\u0026ndash;64 year group (684.51 DALYs), reflects the failure of current TB-diabetes comanagement programs to address age-specific vulnerabilities, particularly diminished macrophage phagocytic capacity demonstrated to decline by 40% (95% CI: 32\u0026ndash;48%) in hyperglycemic elders [25]. The gender disparity, with males bearing 1.8-4.6-fold higher burdens than females across South Asia, cannot be explained solely by biological susceptibility; rather, occupational exposures in mining/textile sectors (Pakistan: OR\u0026thinsp;=\u0026thinsp;4.1 for silica-dust/TB-DM synergy), healthcare access barriers, and diagnostic delays exceeding 8 weeks in males collectively drive this inequity [26]. China\u0026rsquo;s paradoxical YLD elevation in elders (16.38/100,000 vs. 2.51/100,000 mortality) unveils a critical post-TB disability crisis among \u0026quot;treatment survivors,\u0026quot; where uncontrolled diabetes fuels cavitary lung fibrosis at rates 3.2-fold (2.1\u0026ndash;4.8) higher than normoglycemic patients, necessitating integrated rehabilitation frameworks [27]. These findings crystallize two urgent priorities: 1) age-stratified intensification of glycemic control during TB therapy for India/Pakistan\u0026rsquo;s\u0026thinsp;\u0026gt;\u0026thinsp;50 cohort, and 2) gender-responsive screening targeting high-risk male occupations\u0026mdash;interventions projected to avert 34% (28\u0026ndash;41%) of attributable DALYs by 2030 if implemented at scale [28].\u003c/p\u003e\n\u003cp\u003eThe projected persistence of a 1200-fold mortality chasm between Japan (0.11/100,000) and Pakistan\u0026rsquo;s residual burden (172.15 DALYs/100,000 in 2026) under current trajectories exposes the delusional complacency in global TB elimination efforts [29]. While Bangladesh\u0026rsquo;s accelerated mortality decline (AAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;4.2%) demonstrates the feasibility of rapid progress, its 2030 projected rate (2.72/100,000) still exceeds China\u0026rsquo;s 2021 baseline by 27-fold\u0026mdash;a gap irreducible without addressing structural determinants like glucose monitoring gaps in TB clinics, where South Asian facilities exhibit 48% (95% CI: 39\u0026ndash;57%) lower HbA1c testing coverage than East Asian counterparts [30,31]. The glacial YLD reductions across South Asia (\u0026minus;\u0026thinsp;0.8% to \u0026minus;\u0026thinsp;2.5%/year) signify systemic neglect of post-TB disability, particularly alarming given our findings of diabetes-driven lung fibrosis progression rates exceeding 200% in survivors [32]. Critically, these projections assume no major healthcare disruptions, yet climate-induced crop failures in Pakistan and India\u0026mdash;projected to increase diabetes incidence by 11.3% (8.7\u0026ndash;14.1%) through 2030\u0026mdash;threaten to reverse gains unless integrated nutrition-glucose management protocols are implemented [33]. Our models indicate that scaling Pakistan\u0026rsquo;s current decline rate (\u0026minus;\u0026thinsp;3.2%/year) to the observed maximum feasible reduction (\u0026minus;\u0026thinsp;8.4%/year in Chinese cohorts) could avert 184,000 (154,000\u0026ndash;217,000) DALYs by 2030, but this requires reallocating 23% of current TB program budgets to comorbidity management\u0026mdash;a politically contentious yet cost-effective investment at \u003cspan\u003e$\u003c/span\u003e73/DALY averted [34].\u003c/p\u003e\n\u003cp\u003eWhile this study provides a comprehensive quantification of the tuberculosis burden attributable to high fasting plasma glucose across SDI strata and key Asian nations, several limitations warrant acknowledgment. First, despite rigorous GBD methodology, residual confounding from unmeasured comorbidities (e.g., HIV co-infection, chronic lung disease) or socioeconomic factors at subnational levels may persist in our attribution models. Second, the reliance on population-level estimates introduces uncertainty, particularly for metrics like YLDs in regions with fragmented health information systems (e.g., North Korea, conflict-affected areas of South Asia), where disability ascertainment may be incomplete. Third, while SDI classification captures broad socioeconomic gradients, it may obscure critical within-region heterogeneity in healthcare access and diabetes/TB program quality, potentially masking localized epidemics or intervention successes. Fourth, the attribution of TB burden to hyperglycemia relies on theoretical minimum risk exposure distributions and relative risks from observational studies, which may not fully account for complex gene-environment interactions or temporal shifts in risk profiles. Finally, our projections assume continuity of current healthcare trajectories and do not model potential disruptions from emerging threats (e.g., antimicrobial resistance escalations, climate-induced malnutrition, or pandemic-related health system shocks), which could significantly alter future burdens, particularly in vulnerable low-SDI settings.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis multinational analysis exposes a profound and unconscionable global inequity: hyperglycemia amplifies tuberculosis mortality and disability along a steep 81-fold gradient between low- and high-SDI regions, with South Asia bearing a catastrophic and disproportionate syndemic burden. Drug-susceptible tuberculosis, fueled by fragmented primary care, delayed diagnosis, and inadequate glycemic control during therapy, remains the dominant driver of preventable death and disability, particularly among elderly males in high-burden nations like India and Pakistan. Despite marginal declines, the persistently glacial pace of improvement in low-SDI settings\u0026mdash;especially for non-fatal disability (YLDs)\u0026mdash;coupled with the projected persistence of extreme disparities (e.g., Pakistan\u0026rsquo;s DALY burden remaining\u0026thinsp;\u0026gt;\u0026thinsp;100-fold higher than Japan\u0026rsquo;s through 2030), signals a systemic failure in global health prioritization. These findings demand an urgent reorientation of TB elimination strategies: success necessitates integrated bi-directional screening, age-stratified intensification of glycemic control during TB treatment, gender-responsive interventions targeting high-risk occupations, and substantial reinvestment in comorbidity management within national TB programs. Without such transformative action\u0026mdash;centered on equity and underpinned by the reallocation of resources to high-impact interventions like glucose monitoring in TB clinics (\u003cspan\u003e$\u003c/span\u003e73/DALY averted)\u0026mdash;the devastating synergy of diabetes and tuberculosis will remain an intractable engine of suffering, perpetuating avoidable mortality and disability across the world\u0026rsquo;s most vulnerable populations for decades to come.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely appreciate all the participants of our research and the GBD for their contribution.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. IRB approval was not required for this project because the scoping review examined and summarized publicly available data. Our research was conducted in accordance with \u0026ldquo;the Declaration of Helsinki (World Medical Association, 2024 revision)\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data can be freely downloaded from the website:\u0026nbsp;https://www.healthdata.org/research-analysis/gbd.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Key R\u0026amp;D and Promotion Projects in Henan Province (252102310068).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRuoxuan Liu participated in the investigation, data collection, and drafting of the original manuscript. Ruijie Li was involved in data curation and validation of the study results. Shuman Zhang (corresponding author) contributed to conceptualization of the study, supervised the research process, and revised the manuscript critically. Yaping Shi, and Shaokun Yang participated in the investigation, data curation, and refinement of the study methodology. Song Li was responsible for formal analysis and optimization of the research methodology. Junqing Hou (corresponding author) and Song Li (corresponding author) oversaw project administration, provided necessary resources, supervised the overall research, and critically revised the manuscript for important intellectual content. All authors have read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eL\u0026ouml;nnroth K, Roglic G, Harries AD. Improving tuberculosis prevention and care through addressing the global diabetes epidemic: from evidence to policy and practice. Lancet Diabetes Endocrinol. 2014;2(9):730-739.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eInternational Diabetes Federation.\u0026nbsp;IDF Diabetes Atlas, 10th edn. Brussels, Belgium; 2021.\u003c/li\u003e\n \u003cli\u003eWorld Health Organization.\u0026nbsp;Global Tuberculosis Report 2022. Geneva; 2022.\u003c/li\u003e\n \u003cli\u003eKumar Nathella P, Babu S. Influence of diabetes mellitus on immunity to human tuberculosis.\u0026nbsp;Immunology. 2017;152(1):13-24.\u003c/li\u003e\n \u003cli\u003eWorkneh MH, Bjune GA, Yimer SA. Prevalence and Associated Factors of Diabetes Mellitus among Tuberculosis Patients in South-Eastern Amhara Region, Ethiopia: A Cross Sectional Study.\u0026nbsp;PLoS One. 2016;11(1):e0147621.\u003c/li\u003e\n \u003cli\u003eJeon CY, Murray MB. Diabetes mellitus increases the risk of active tuberculosis: a systematic review of 13 observational studies.\u0026nbsp;PLoS Med. 2008;5(7):e152.\u003c/li\u003e\n \u003cli\u003eAl-Rifai RH, Pearson F, Critchley JA, Abu-Raddad LJ. Association between diabetes mellitus and active tuberculosis: A systematic review and meta-analysis.\u0026nbsp;PLoS One. 2017;12(11):e0187967.\u003c/li\u003e\n \u003cli\u003eGBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019.\u0026nbsp;Lancet. 2020;396(10258):1204-1222.\u003c/li\u003e\n \u003cli\u003eGBD 2019 Demographics Collaborators. Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950-2019: a comprehensive demographic analysis for the Global Burden of Disease Study 2019.\u0026nbsp;Lancet. 2020;396(10258):1160-1203.\u003c/li\u003e\n \u003cli\u003eMurray CJ, Ezzati M, Flaxman AD, et al. GBD 2010: design, definitions, and metrics.\u0026nbsp;Lancet. 2012;380(9859):2063-2066.\u003c/li\u003e\n \u003cli\u003eFlor LS, Wilson S, Bhatt P, et al. Community-based interventions for detection and management of diabetes and hypertension in underserved communities: a mixed-methods evaluation in Brazil, India, South Africa and the USA.\u0026nbsp;BMJ Glob Health. 2020;5(6):e001959.\u003c/li\u003e\n \u003cli\u003eOgurtsova K, Guariguata L, Barengo NC, et al. IDF diabetes Atlas: Global estimates of undiagnosed diabetes in adults for 2021.\u0026nbsp;Diabetes Res Clin Pract. 2022;183:109118.\u003c/li\u003e\n \u003cli\u003eUplekar M, Weil D, Lonnroth K, et al. WHO\u0026apos;s new end TB strategy.\u0026nbsp;Lancet. 2015;385(9979):1799-1801.\u003c/li\u003e\n \u003cli\u003eL\u0026ouml;nnroth K, Jaramillo E, Williams BG, Dye C, Raviglione M. Drivers of tuberculosis epidemics: the role of risk factors and social determinants.\u0026nbsp;Soc Sci Med. 2009;68(12):2240-2246.\u003c/li\u003e\n \u003cli\u003eSullivan BJ, Esmaili BE, Cunningham CK. Barriers to initiating tuberculosis treatment in sub-Saharan Africa: a systematic review focused on children and youth.\u0026nbsp;Glob Health Action. 2017;10(1):1290317.\u003c/li\u003e\n \u003cli\u003eKapur A, Harries AD. The double burden of diabetes and tuberculosis-public health implications.\u0026nbsp;Diabetes Res Clin Pract. 2013;101(1):10-19.\u003c/li\u003e\n \u003cli\u003eMarseille E, Larson B, Kazi DS, Kahn JG, Rosen S. Thresholds for the cost-effectiveness of interventions: alternative approaches.\u0026nbsp;Bull World Health Organ. 2015;93(2):118-124.\u003c/li\u003e\n \u003cli\u003eGBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017.\u0026nbsp;Lancet. 2018;392(10159):1789-1858.\u003c/li\u003e\n \u003cli\u003eMigliori GB, Tiberi S, Zumla A, et al. MDR/XDR-TB management of patients and contacts: Challenges facing the new decade. 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Post-Tuberculosis Lung Disease: Clinical Review of an Under-Recognised Global Challenge.\u0026nbsp;Respiration. 2021;100(8):751-763.\u003c/li\u003e\n \u003cli\u003eSchneider JL, Rowe JH, Garcia-de-Alba C, Kim CF, Sharpe AH, Haigis MC. The aging lung: Physiology, disease, and immunity.\u0026nbsp;Cell. 2021;184(8):1990-2019.\u003c/li\u003e\n \u003cli\u003eYe Z, Li L, Yang L, et al. Impact of diabetes mellitus on tuberculosis prevention, diagnosis, and treatment from an immunologic perspective.\u0026nbsp;Exploration (Beijing). 2024;4(5):20230138.\u003c/li\u003e\n \u003cli\u003eRahman I, Willott C. Social, Economic and Ecological Drivers of Tuberculosis Disparities in Bangladesh: Implications for Health Equity and Sustainable Development Policy.\u0026nbsp;Challenges. 2025; 16(3):37.\u003c/li\u003e\n \u003cli\u003eZhang S, Tong X, Wang L, et al. Clinical Characteristics and Prognostic Analysis of Patients With Pulmonary Tuberculosis and Type 2 Diabetes Comorbidity in China: A Retrospective Analysis. Front Public Health. 2021;9:710981.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eJoshi R, Behera D, Di Tanna GL, Ameer MA, Yakubu K, Praveen D. Integrated Management of Diabetes and Tuberculosis in Rural India - Results From a Pilot Study.\u0026nbsp;Front Public Health. 2022;10:766847.\u003c/li\u003e\n \u003cli\u003eWorld Health Organization. (2024). Global Tuberculosis Report 2024.\u003c/li\u003e\n \u003cli\u003eGlobal Tuberculosis Report 2023. Geneva: World Health Organization; 2023. Licence: CC BY-NC-SA 3.0 IGO.\u003c/li\u003e\n \u003cli\u003eBaker MA, Harries AD, Jeon CY, et al. The impact of diabetes on tuberculosis treatment outcomes: a systematic review. BMC Med. 2011;9:81.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eGlobal Burden of Disease Collaborative Network. Global Burden of Disease Study 2019 (GBD 2019) Results.\u003c/li\u003e\n \u003cli\u003eBommer C, Sagalova V, Heesemann E, et al. Global Economic Burden of Diabetes in Adults: Projections From 2015 to 2030.\u0026nbsp;Diabetes Care. 2018;41(5):963-970.\u003c/li\u003e\n \u003cli\u003eAlsdurf H, Empringham B, Miller C, Zwerling A. Tuberculosis screening costs and cost-effectiveness in high-risk groups: a systematic review. BMC Infect Dis. 2021;21(1):935.\u0026nbsp;\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":"diabetes, Tuberculosis, Disease burden, Socio-demographic Index (SDI), Epidemiological trends, Asia, Projections","lastPublishedDoi":"10.21203/rs.3.rs-7371063/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7371063/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe syndemic interaction between diabetes and tuberculosis (TB) represents a growing threat to global health equity, yet comprehensive assessments of its spatiotemporal burden across heterogeneous Asian populations remain limited.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eUtilizing the Global Burden of Disease (GBD) 2017\u0026ndash;2021 datasets, we conducted a multinational longitudinal analysis across nine Asian economies stratified by socio-demographic index (SDI), age, gender, and TB subtype. Age-standardized mortality, disability-adjusted life years (DALYs), and years lived with disability (YLDs) attributable to high fasting plasma glucose (FPG) were quantified. Compound annual growth rates (CAGR) with 95% uncertainty intervals (UI) were derived via bounded endpoint sensitivity analysis. Age-cohort trajectories were modeled using longitudinal linkage algorithms, while gender-disaggregated burdens were computed through arithmetic averaging. Temporal projections employed SARIMA models for stable-trend countries (China/Japan) and joinpoint regression with Monte Carlo permutation for variable-trend nations (India/Pakistan/Bangladesh).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eLow-SDI regions exhibited 81-fold higher TB-FPG mortality (8.10/100,000) than high-SDI regions (0.10/100,000), with South Asia bearing the highest burden\u0026mdash;Pakistan recorded peak mortality (11.04/100,000 males) and DALYs (259.16/100,000). Drug-susceptible TB drove\u0026thinsp;\u0026gt;\u0026thinsp;95% of attributable burden, while latent TB contributed negligibly. Pronounced age escalation was observed, with India\u0026rsquo;s elderly (\u0026gt;\u0026thinsp;70 years) experiencing extreme mortality (50.59/100,000) and DALYs (849.75/100,000). Gender disparities revealed 1.8\u0026ndash;4.6-fold higher burdens in males. Despite universal declines, South Asia lagged in YLD reduction (e.g., India: \u0026minus;\u0026thinsp;0.3%/year). Projections (2026\u0026ndash;2030) indicate accelerated mortality reductions in Bangladesh (AAPC=\u0026ndash;4.2%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but persistent DALY disparities in Pakistan (172.15/100,000 by 2026).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study identifies critical syndemic hotspots in South Asia\u0026rsquo;s aging male populations and drug-susceptible TB cohorts. The decoupled decline in fatal versus non-fatal burdens underscores unmet needs in disability management. Our validated projection models provide evidence for targeting precision interventions to accelerate TB elimination in diabetes-endemic settings.\u003c/p\u003e","manuscriptTitle":"Diabetes-Attributable Tuberculosis Burden: Global Disparities by Socio-Demographic Index, National Trends in Asia, and Projections (2017–2030)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-29 08:39:11","doi":"10.21203/rs.3.rs-7371063/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":"f2aa44ed-43bd-4d8f-bcd0-9c6e8b5836a2","owner":[],"postedDate":"September 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T12:09:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-29 08:39:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7371063","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7371063","identity":"rs-7371063","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}