Diabetes Mellitus Complicated by Renal Failure: Burden in a 3.30-Million Resident Cohort in Pudong New Area, Shanghai, China

Diabetes Mellitus Complicated by Renal Failure: Burden in a 3.30-Million Resident Cohort in Pudong New Area, Shanghai, China | 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 Mellitus Complicated by Renal Failure: Burden in a 3.30-Million Resident Cohort in Pudong New Area, Shanghai, China Yichen Chen, Sheng Chen, Ru Liu, Lianghong Sun, Caoyi Xue, Xiaobing Qu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9427155/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Diabetes mellitus complicated by renal failure (DM-RF) is an emerging driver of premature death in China. Objective To quantify 19-year mortality trends attributable to DM-RF in Pudong New Area of Shanghai and to project the burden to 2035. Methods Using population-based vital-registration data covering 3.30 million residents (2005–2023), we calculated crude mortality, world-standardized age-standardized mortality rates (ASMRW), years-of-life-lost (YLL) rates and average YLL (AYLL). Decomposition analysis identified key drivers; Bayesian age–period–cohort models forecast rates for 2024–2035. Results Over the study period, 3,699 DM-RF deaths were documented. ASMRW more than tripled, rising from 1.29 to 4.15 per 100,000 (average annual percentage change 6.89%). Males consistently experienced higher mortality than females, with ASMRW of 5.44 vs 2.94 per 100,000 in 2023. The YLL rate increased by 8.64% annually, whereas AYLL declined modestly (− 0.62%; all P < 0.001). Epidemiological factors explained 311% of the 376 excess deaths, with males accounting for 521%. Bayesian projections estimated that ASMRW will reach 6.06 (95% CI: 3.77–8.35) per 100,000 by 2035. Conclusion DM-RF mortality is escalating rapidly in Pudong New Area of Shanghai, propelled by deteriorating glycaemic control and population aging. Sex-specific, age-targeted and community-integrated prevention strategies are urgently needed if Shanghai is to meet Sustainable Development Goal 3.4. Diabetes mellitus renal failure comorbidity mortality years of life lost China Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Diabetes mellitus complicated by renal failure (DM-RF) is now among the fastest-growing contributors to global disability-adjusted life years (DALYs) [ 1 , 2 ]. Global Burden of Disease (GBD) data show that the age-standardized DALY rate attributable to diabetic kidney disease surged 74% from 1990 to 2021, with East Asia experiencing the steepest rise [ 3 ]. China witnessed a 2.3-fold increase in DM-RF–attributable deaths, while Japan and Korea, despite favorable all-cause mortality trends, recorded increases of 40% and 65%, respectively [ 4 – 6 ]. These patterns mark a clear epidemiological shift in which chronic hyperglycemia and progressive nephron loss now act synergistically to shorten life expectancy [ 7 ]. The drivers of DM-RF mortality vary greatly by setting. In high-income countries, late nephrology referral and sub-optimal glycaemic control dominate, whereas in middle-income regions, inadequate access to renin–angiotensin blockade and dialysis capacity are the principal constraints [ 8 ]. Within-country comparisons reveal two- to four-fold differences in case fatality across Chinese prefectures and Japanese prefectures alike, implicating health-system performance rather than genetic background as the principal determinant [ 9 ]. Yet GBD estimates still rely heavily on vital-registration tabulations, in which up to one-third of DM-RF deaths are misclassified, especially in Asia where multiple comorbidities are common [ 10 ]. Discrepancies between modeled and empirical estimates reached 42% in China and 28% in Japan, hampering the calibration of prevention priorities [ 10 , 11 ]. Real-world, individual-level mortality datasets are therefore the key to validating GBD estimates and to exposing age- and sex-specific patterns essential for targeted public health action against DM-RF [ 12 , 13 ]. Although DM-RF remains associated with excess mortality, therapeutic gains have been considerable. Widespread adoption of sodium glucose cotransporter 2 (SGLT-2) inhibitors and non-steroidal mineralocorticoid receptor antagonists has shifted chronic kidney disease (CKD) management earlier in the disease course, cutting progression to renal failure [ 14 ]. Japan’s Diabetes Outcome Intervention Trial-2 and nationwide Korean cohorts indicate that systematic implementation of these therapies reduces RF events by 25%–35% [ 15 , 16 ]. Real-world mortality surveillance is now required to quantify how much of this benefit is actually achieved at population level and to inform policy refinement and resource allocation. Crude mortality rate (CMR), age-standardised mortality rate (ASMRW), years of life lost (YLL) and average YLL (AYLL) translate individual deaths into measurable societal loss, allowing health authorities to rank DM-RF against competing threats [ 17 ]. By estimating these metrics with high-resolution data from Pudong New Area, Shanghai, we provide an empirical platform for precision prevention: identifying high-risk demographic strata, calibrating local glycaemic and blood-pressure control targets, and forecasting future dialysis demand [ 18 , 19 ]. The dataset can subsequently support nested pharmaco-epidemiological studies (e.g., impact of SGLT-2 inhibitor diffusion), economic evaluations of community screening, and quasi-experimental assessments of interventions such as integrated diabetes–kidney clinics [ 18 ]. Ultimately, this work can evolve into a sentinel surveillance model for China and comparable East-Asian megacities, aligning real-world evidence with the global agenda to curb the rising tide of DM-RF [ 20 ]. METHODS Data source and case definition We extracted 3,699 deaths attributed to DM-RF from 426,750 all-cause deaths recorded in Pudong New Area, Shanghai, China, between 1 January 2005 and 31 December 2023 (mid-2023 population: 3.30 million) (Fig. 1 ). The regional mortality registry, maintained by the Pudong Center for Disease Control and Prevention, provided an individual-level dataset that includes age, sex, date of death and all underlying and contributing causes. Record linkage, field verification and annual active surveillance are performed to ensure ≥ 98% completeness and internal consistency [ 17 ]. DM-RF deaths were identified using the International Classification of Diseases, 10th Revision (ICD-10) codes E10–E14 (diabetes mellitus) as the underlying cause and N17–N19 (renal failure) as an associated condition. Certifying physicians, reviewing available medical records and laboratory data, confirmed that RF was a direct complication of diabetes. Only deaths meeting this causal sequence were retained [ 19 ]. The study protocol was approved by the Institutional Review Board of the Pudong Health Commission, Shanghai (IRB#2016-04-0586). All data were de-identified before analysis; no additional contact with patients or families occurred. Data Analyses CMR and ASMRW, with Segi's world standard population as the standard, were calculated and expressed per 100,000 person-years. Sex-specific CMRs were compared using Poisson regression, whereas ASMRWs were contrasted with the Mantel–Haenszel test. To rank the leading causes of premature death, YLL and AYLL were computed according to the Global Burden of Disease (GBD) methodology [ 21 ]. YLL was estimated using the World Health Organization (WHO) formula: where C represents the age-weighting fit with a constant (0.1658), K is the age weighting modulation factor, e is Napier's constant (or Euler’s number), r is the discount rate (3%), a is age at death, β is the age-weighting parameter (0.04), and L is the standard life expectancy at age a from the GBD reference life table. Age strata were 0–4, 5–14, 15–29, 30–44, 45–59, 60–69, 70–79, and ≥ 80 years. Because DM-RF deaths were uncommon before age 45, temporal trends in CMR, ASMRW, AYLL, and YLL rate were examined for < 45, 45–59, 60–69, 70–79, and ≥ 80 years. Temporal patterns were summarized with the average annual percentage change (AAPC) [ 17 ]; the natural logarithm of the age-standardised rate was regressed on calendar year, and AAPC and its 95% confidence interval (CI) were obtained as described previously [ 17 ]. Drivers of change in DM-RF mortality between 2005 and 2023 were quantified with Das Gupta’s decomposition [ 22 ], partitioning contributions from population size, age structure, and DM-RF risk. Bayesian age–period–cohort (BAPC) modelling with integrated nested Laplace approximations was used to project rates and burden for 2024–2035; this approach yields better coverage than alternative forecasting models [ 23 ]. All analyses were conducted in R 4.3.2 with the BAPC package in R (version 4.3.2), following validated protocols [ 24 ]. Two-sided P < 0.05 indicated statistical significance. RESULTS Overall DM-RF Burden During the 19-year study period, 3,699 deaths were attributed to DM-RF, accounting for 0.87% of all-cause mortality, 16.36% of diabetes-related deaths, and 13.56% of deaths with any RF code. Among diabetes-specific deaths, the RF-attributable proportion climbed from 11.29% to 13.84%, peaking at 19.20% in 2020. Conversely, among all RF deaths, the diabetic subset grew from 11.88% to 16.28% ( Table S1 ). Annual DM-RF deaths rose from 72 (0.39% of all-cause mortality) in 2005 to 448 (1.35%) in 2023, accumulating 36,874 YLL. The corresponding CMR was 6.75 per 100,000, the ASMRW was 67.26 per 100,000, and the AYLL per death was 9.97 years ( Table S2 ). In 2023, male CMR was 15.85 per 100,000 and female CMR 11.30; corresponding ASMRWs were 5.44 and 2.94 per 100,000, YLL rates 152.24 and 101.07 per 100,000, and AYLLs 9.60 and 8.94 years, respectively. Over the entire period, men contributed 1,922 deaths (51.96%) and exhibited consistently higher metrics: CMR 7.03, ASMRW 2.93, and YLL rate 72.18 per 100,000 (all P < 0.05 vs women). Women recorded 1,777 deaths (48.04%) with CMR 6.47, ASMRW 2.05, and YLL rate 62.37 per 100,000. Median age at death was younger in males (73.92 years) than in females (78.87 years), resulting in a male AYLL of 10.27 years versus 9.65 years in females (Table 1 ) . Table 1 Burden of DM-RF deaths by sex and age groups in Pudong New Area of Shanghai, China, 2005–2023 Overall No. of deaths Proportion (%) Age at death (Mean ± SD) Age at death (Median) CMR (1/100,000) ASMRW (1/100,000) YLL (years) YLL rate (1/100,000) AYLL (year/person) 3699 100.00 74.37 ± 12.10 76.23 6.75 2.48 36873.98 67.26 9.97 By sex Male 1922 51.96 72.45 ± 12.29 73.92 7.03 2.93 19733.92 72.18 10.27 Female 1777 48.04 76.43 ± 11.54 78.57 6.47 2.05 17140.06 62.37 9.65 By age (years) < 45 65 1.76 39.05 ± 4.85 40.34 0.25 / 1553.06 5.83 23.89 45–59 375 10.14 54.30 ± 4.10 55.22 2.75 / 7089.34 52.07 18.90 60–69 731 19.76 65.40 ± 2.79 65.56 9.25 / 10317.33 130.34 14.11 70–79 1144 30.93 75.28 ± 2.84 75.49 26.57 / 10730.57 249.42 9.38 ≥ 80 1384 37.42 85.44 ± 6.45 85.14 58.87 / 7183.68 305.32 5.19 Note: DM-RF, diabetes mellitus combined with renal failure; CMR, crude mortality rate; ASMRW, age-standardized mortality rate by Segi’s world standard population; YLL, years of life lost; AYLL, average years of life lost CMRs increased sharply with age during the study period, particularly rising from 4.41 per 100,000 in individuals aged 45–59 years to 13.98 in those aged 60–69, 39.90 in the 70–79 group, and reaching 115.51 among those aged 80 and above in 2023 ( Table S3 ). As shown in Table 1 , subjects under 45 years represented merely 65 deaths (1.76%) while registering the largest AYLL (23.9 years) and the most attenuated CMR (0.25 per 100 000). The 45–59, 60–69, 70–79, and ≥ 80 age groups contributed 375, 731, 1,144, and 1,384 deaths, respectively. Across these groups, the CMR rose from 2.75 per 100,000 in the youngest cohort to 58.87 per 100,000 among those aged 80 or above. Meanwhile, the AYLLs declined from 18.90 to 5.19 years per person, while YLL rates climbed steadily from 5.83 to 305.32 per 100,000. Males consistently outpaced females across all metrics. Table S4 presents the annual age- and sex-specific burden from 2005 to 2023. Similar patterns by sex and along age were observed each year. Temporal Trends During the study period, all major DM-RF mortality metrics rose steeply. The CMR climbed from 2.83 to 13.56 per 100,000 (AAPC: 9.14%; 95% CI: 6.48–11.87%), the ASMRW from 1.29 to 4.15 per 100,000 (AAPC: 6.89%; 95%CI: 6.48–11.87), and the YLL rate from 28.82 to 126.48 per 100,000 (AAPC: 8.64%; 95% CI: 5.99–11.36), whereas the AYLL declined modestly from 10.17 to 9.33 years (AAPC: − 0.62%; 95% CI: -0.97–-0.26) (all P < 0.001). Increases were consistently faster in men: AAPCs for CMR, ASMRW and YLL rate were 11.48%, 7.85% and 9.11%, respectively, versus 6.67%, 4.23% and 5.97% in women (all P < 0.001) ( Table S5 ). Age-specific AAPCs in CMR displayed a stepwise deceleration: 6.62% (45–59 years), 6.15% (60–69), 2.46% (70–79) and 5.96% (≥ 80). YLL-rate AAPCs followed an identical gradient: 6.35%, 6.22%, 2.43% and 5.43%, respectively. Concurrently, the share of total DM-RF deaths attributable to the 60–69 and ≥ 80 age groups expanded annually (AAPC: 2.17% and 2.07%, respectively; all P < 0.001). Drivers of DM-RF burden Decomposition analysis revealed pronounced sex-specific heterogeneity in the factors driving the DM-RF epidemic from 2005 to 2023 (Fig. 2 ). Epidemiological change, i.e. the rising risk exposure among populations, was the dominant driver of growth across every metric. Specifically, DM-RF deaths rose by 376 during the 19-year period. Population ageing accounted for 92 additional deaths (128% of the observed increment), population growth for 59 (82%), while epidemiological factors alone generated 224 extra cases (311%). Among men, 232 extra deaths were attributable to aging (56 deaths; 200%), population growth (30; 108%)], and predominantly epidemiological escalation (146, 521%). Among women, the corresponding increments were 144 deaths, with ageing contributing 38 (86%), population growth 29 (65%), and epidemiological change 77 (176%) ( Table S6) . Projections for DM-RF burden The Bayesian model indicates that the CMR of DM-RF will continue its steep ascent, reaching 33.01 per 100,000 (95% CI: 13.86–52.16) by 2035 (an AAPC of 7.60%; 95% CI: 6.57–8.64%; P < 0.001). The ASMRW is forecast to climb to 6.06 per 100,000 (95% CI: 3.77–8.35; AAPC: 3.05%, 95% CI: 2.37–3.73%; P < 0.001), while the YLL rate is expected to reach 191.59 per 100,000 (95% CI 121.00–262.17) (AAPC: 3.36%, 95% CI: 2.60–4.13%; P < 0.001) (Fig. 3 ). DISCUSSION Diabetic nephropathy is already the second-leading cause of end-stage renal disease (ESRD) in China and the dominant aetiology after age 45 [ 25 ]. Without intensified control, DM-RF will account for one-quarter of all diabetes deaths by 2035 [ 26 ]. Our 19-year, 3.3-million-resident Pudong cohort, among Asia’s largest, quantifies this burden and supports WHO’s NCD agenda and SDG 3.4: a one-third reduction in premature NCD mortality by 2030 [ 27 ]. We document a steep upward trend in DM-RF mortality. The ASMRW rose every year from 2005 to 2023 while the AYLL declined modestly, indicating a growing burden of the disease. By comparison, US rates peaked at 4.8 per 100 000 in 2010 and fell to 3.9 by 2021 [ 28 ]. Sweden’s EHR-integrated National Diabetes Register delivered real-time feedback to primary-care physicians and cut DM-RF deaths 35% during 2005–2018 [ 29 ]. The UK Quality and Outcomes Framework, incentivizing annual albuminuria/eGFR screening, shortened nephrology referral times and reduced incident ESRD 25% [ 30 ]. Japan’s universal Tokutei-Kenshin programme (urine dipstick, serum creatinine, HbA1c < 7% target, widespread SGLT-2 use) has held DM-RF mortality at 2.1 per 100 000 for ten years [ 31 ], while Korea’s insurer-funded “diabetes schools” achieved a 15% reduction in composite renal endpoints [ 32 ]. Shanghai now lags high-income Western regions in DM-RF controls by roughly a decade. The observed increase in Pudong is multifactorial. Diabetes mortality rose between 2005 and 2020, propelled by ageing, obesogenic environments and non-demographic drivers [ 19 ]. Glycaemic control remains poor: a 2022 local survey found that only 38% of adults with diabetes achieved HbA1c < 7% and 24% were unaware of their renal status [ 33 ]. Use of renoprotective agents such as SGLT-2 inhibitors and RAAS blockers remains below global averages [ 34 ]. Health literacy and medication adherence gaps persist, especially among migrant populations. Decomposition analyses show that, after adjusting for population ageing and growth, the dominant drivers of DM-RF mortality are epidemiological: rising diabetes incidence, longer disease duration and increasing prevalence of renal impairment. Men in this cohort exhibited consistently higher DM-RF mortality across all metrics, paralleling global patterns illustrated by USRDS male-to-female mortality ratios of 1.4–1.6 for diabetic ESRD [ 27 ]. The excess risk is substantiated by biological determinants (greater visceral adiposity, testosterone-mediated endothelial dysfunction) and compounded by sociocultural barriers: lower health-seeking behaviour and inferior treatment adherence in this subgroup. Decomposition shows epidemiological factors influence men more, propelled by higher smoking, central obesity and delayed clinic visits, while women’s smaller share reflects better primary-care engagement and antihypertensive adherence [ 5 ]. These disparities are nevertheless modifiable. Following Mexico’s introduction of universal health coverage and targeted education, the PURE cohort documented complete attenuation of the sex gap [ 35 ]. Narrowing the sex differential would alleviate individual suffering and avert catastrophic household expenditure, as the premature loss of male breadwinners routinely triggers acute financial shock. Age-specific analyses revealed a steep gradient: CMR rose from 4.4 per 100,000 at 45–59 years to 116 per 100,000 at ≥ 80 years, reflecting cumulative hyperglycaemic exposure, arterial stiffening and age-related decline in renal reserve. Intriguingly, AAPCs in both CMR and YLL rate plateaued in the 70–79 age stratum (2.46% and 2.43%, respectively) before re-accelerating after age 80, plausibly because of survivor bias and more aggressive risk-factor modification among septuagenarians under China’s Healthy Ageing Action Plan [ 20 ]. For the population aged ≥ 50 years, community-based comorbidity management offers substantial potential. Evidence from Hong Kong’s Risk Assessment and Management Program indicates that nurse-led risk stratification, combined with quarterly eGFR monitoring and lifestyle coaching, reduced composite renal endpoints by 29% in adults aged 50–75 years [ 36 ]. These findings argue for gender-responsive and age-specific interventions. Evidence-based interventions include: (1) workplace screening in male-dominated industries; (2) gamified digital tools that enhance male engagement [ 37 ]; and (3) family-centred counselling that mobilizes spousal support to raise adherence at lower cost than gender-neutral approaches. For Pudong New Area, we propose a tiered, community-embedded strategy. First, annual albuminuria screening should be implemented across all community health centres using point-of-care devices, with automated EHR alerts triggered when eGFR falls below 60 ml/min/1.73 m². Second, integrated care pathways should provide real-time virtual consultations with endocrinologists and nephrologists from primary-care clinics, supported by district-level, cloud-based clinical decision aids. Third, pharmacist-led medication management should be adopted [ 38 ];. In addition, culturally tailored dietary counselling should emphasize traditional, low-sodium, plant-forward recipes. Finally, a community peer-mentor programme, modeled on Japan’s “diabetes supporters” initiative [ 39 ], should be introduced to enhance self-management and reduce social isolation. Strengths of this study include the exceptionally large sample, high-quality surveillance data, and exhaustive analytic strategy. Two limitations merit consideration. First, the lack of individual-level clinical parameters (e.g., longitudinal HbA1c or eGFR trajectories) precludes causal inference; linkage to Pudong’s HER repository is underway to address this. Second, under-reporting of diabetes on death certificates may underestimate true burden, an issue we mitigated by cross-validating hospital discharge diagnoses and prescription-dispensing databases. In conclusion, 19 years of population-based mortality data covering 3.3 million residents reveal a steep and sustained rise in DM-RF mortality and premature mortality in Pudong New Area of Shanghai. These findings underscore the urgent need for integrated, sex-responsive and age-specific strategies to curb this burgeoning burden and accelerate progress toward global NCD targets. Declarations DATA SHARING STATEMENT The data that support the findings of this study are available from the corresponding author upon request. ACKNOWLEDGEMENTS The authors thank all staff of the Shanghai Pudong Vital Statistics for their great work in data collection and the assurance of high data quality. The authors also thank Medjaden Inc. for scientific editing of this manuscript. ETHICAL STATEMENT Our study did not involve participant intervention. The Ethics Committee of the Health Department of Shanghai Pudong, China approved this study (IRB#2016-04-0586) and waived the need for informed consent. We ensured strict confidentiality of the data. FUNDING This work was supported by the grants from General Program of Shanghai Pudong New Area Health Commission (No. PW2023A-27 to YC), the Project of the 2025 Open Fund of the Shanghai Key Laboratory of Meteorology and Health (No.QXJK202509 to XL), the Shanghai Municipal Program for Medical Leading Talents (2019LJ15) and the National Key Research and Development Program of China (2022YFC3600901). The funder had no role in study design, data collection, data analysis, data interpretation, or writing of the report. All authors had full access to all data in the study and had responsibility for the decision to submit for publication. DECLARATION OF INTERESTS The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. AUTHOR CONTRIBUTIONS YC and SC conducted the statistical analyses and wrote the manuscript together. RL gave the advice for the design of this study and participated in the coordination during the study period. The three authors are responsible for the accuracy of the analysis and contributed equally to this study. CX provided significant intellectual advice. LS, XQ and HC contributed to the mortality data collection and took responsibility for the integrity of this data. LH,and SJ conducted the literature review and gave suggestions for analysis. XL conceived of the study, and participated in the development of this work. WX directed the implementation and reviewed the manuscript. All authors read and approved the final manuscript. CONSENT FOR PUBLICATION The study was approved for publication by all authors. References Collaboration GBDCKD. Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395(10225):709–33. Wang Z, You Q, Wang Y, Wang J, Shao L. 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Saudi J Kidney Dis Transpl. 2023;34(Suppl 1):S177–218. Kobayashi K, Toyoda M, Hatori N, Furuki T, Sakai H, Sato K, Miyakawa M, Tamura K, Kanamori A. Sodium-glucose cotransporter 2 inhibitor-induced reduction in the mean arterial pressure improved renal composite outcomes in type 2 diabetes mellitus patients with chronic kidney disease: A propensity score-matched model analysis in Japan. J Diabetes Investig. 2021;12(8):1408–16. Kim HJ, Song SH. Steps to understanding diabetes kidney disease: a focus on metabolomics. Korean J Intern Med. 2024;39(6):898–905. Hou X, Wang L, Zhu D, Guo L, Weng J, Zhang M, Zhou Z, Zou D, Ji Q, Guo X, et al. Prevalence of diabetic retinopathy and vision-threatening diabetic retinopathy in adults with diabetes in China. Nat Commun. 2023;14(1):4296. Wu B, Liu K, Mao H. Clinical Characteristics, Treatment Patterns, and Renal-Related Events in Patients with Diagnosed CKD: Results from Regional Health Information System in China. J Am Soc Nephrol. 2023;34(11S):1172–1172. Kautzky-Willer A, Harreiter J, Pacini G. Sex and Gender Differences in Risk, Pathophysiology and Complications of Type 2 Diabetes Mellitus. Endocr Rev. 2016;37(3):278–316. Greenwood DA, Litchman ML, Isaacs D, Blanchette JE, Dickinson JK, Hughes A, Colicchio VD, Ye J, Yehl K, Todd A, et al. A New Taxonomy for Technology-Enabled Diabetes Self-Management Interventions: Results of an Umbrella Review. J Diabetes Sci Technol. 2022;16(4):812–24. Collaborators GBDAB. Global, regional, and national prevalence of adult overweight and obesity, 1990–2021, with forecasts to 2050: a forecasting study for the Global Burden of Disease Study 2021. Lancet. 2025;405(10481):813–38. Ramos T, Verma A, Speirits I, Zhang L, McInally J, McShane C, Kennon B, Forsyth P, Lowrie R, Johnson CF. Evaluating a pharmacist-led cardio-renal-metabolic service to reduce healthcare inequities in a socioeconomically deprived population: a prospective intervention study. Int J Clin Pharm. 2025;47(5):1395–405. Araki A. Individualized treatment of diabetes mellitus in older adults. Geriatr Gerontol Int. 2024;24(12):1257–68. Additional Declarations No competing interests reported. Supplementary Files TableSlegends.docx Supplementarymaterials.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 28 Apr, 2026 Editor invited by journal 16 Apr, 2026 Editor assigned by journal 16 Apr, 2026 Submission checks completed at journal 16 Apr, 2026 First submitted to journal 15 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9427155","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633696901,"identity":"fb907c09-9113-414d-90fd-cf4c2ac2b3db","order_by":0,"name":"Yichen Chen","email":"","orcid":"","institution":"Fudan University School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Yichen","middleName":"","lastName":"Chen","suffix":""},{"id":633696902,"identity":"02b4f318-cdd1-4de2-94d8-25d3bc8b58ef","order_by":1,"name":"Sheng Chen","email":"","orcid":"","institution":"Department of Health Management 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Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYJACZijN+KACypYgVguzwRlStbBJEKXF4PjZw68LKu7YNUjkmFUcbLOWN2dgPnibh8EuD6eWM3lp1jPOPEtu4DljduNgW7rhzga2ZGsehuRiXFrMDuSYGfO2HU5mYO8xu/2x7TDjhgM8ZtI8DAcSG3BpOf8GqoWZx6zgYNth+w0H+L/h13Ijx/gxUIsdyBYGoJZEoC1seLXY33hjxsxz5nACG8+xYokD59KTNxxmM7acY5CMU4tkf47xZ56Kw/b8EskbPxwos7bdcLz54Y03FXY4tTCAogNIJLbB+eCoMcCtHqTkA8iBeJWMglEwCkbByAYAIMpXm2Gsw/oAAAAASUVORK5CYII=","orcid":"","institution":"Fudan University School of Public Health","correspondingAuthor":true,"prefix":"","firstName":"Wanghong","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2026-04-15 12:40:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9427155/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9427155/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108646808,"identity":"89944f80-dc7e-4bde-9b7f-da44c721a0b0","added_by":"auto","created_at":"2026-05-06 23:43:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":283923,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of DM-RF death ascertainment within all-cause mortality, Pudong New Area of Shanghai, China, 2005–2023\u003c/p\u003e","description":"","filename":"Fig1FlowChart.png","url":"https://assets-eu.researchsquare.com/files/rs-9427155/v1/c351420ab268701da01d33ea.png"},{"id":108646809,"identity":"224f074b-754b-4fe3-8180-427cfdfca234","added_by":"auto","created_at":"2026-05-06 23:43:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":85265,"visible":true,"origin":"","legend":"\u003cp\u003eDecomposition of changes in the number of DM-FR deaths by potential drivers in Pudong New area of Shanghai, China, 2005-2023\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-9427155/v1/8ca8b92fe7308d928f755e27.png"},{"id":108805247,"identity":"229bdc9a-1ca2-47c7-b0d0-5cdf72d65b97","added_by":"auto","created_at":"2026-05-08 15:25:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":650254,"visible":true,"origin":"","legend":"\u003cp\u003eCMR, ASMRW, and YLL rate for DM-RF deaths in Pudong New Area of Shanghai, 2005–2023, with projections to 2024–2035\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: DM-RF, diabetes mellitus combined with renal failure;CMR, crude mortality rate (per 100,000); ASMRW, age-standardized mortality rate by Segi’s world standard population (per 100,000); YLL, years of life lost; AAPC, average annual percentage change; CI, confidence interval.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig3BAPC.png","url":"https://assets-eu.researchsquare.com/files/rs-9427155/v1/32a0545eee2cb15531e2b23e.png"},{"id":108810131,"identity":"870f243b-0201-4fd5-b2d4-732f345fab31","added_by":"auto","created_at":"2026-05-08 15:57:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1094189,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9427155/v1/c9bf0f76-58e3-4f3b-99c0-7948fcd9c1e0.pdf"},{"id":108646807,"identity":"327062da-1624-4ebe-9b07-49bd4512711d","added_by":"auto","created_at":"2026-05-06 23:43:48","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14325,"visible":true,"origin":"","legend":"","description":"","filename":"TableSlegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-9427155/v1/44d31a487d68d4436bee77c1.docx"},{"id":108806449,"identity":"9b80e7c6-b9b7-4ab4-bbec-24bfecbdbcb3","added_by":"auto","created_at":"2026-05-08 15:28:36","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":49383,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-9427155/v1/503de4ebc89819331f029b76.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diabetes Mellitus Complicated by Renal Failure: Burden in a 3.30-Million Resident Cohort in Pudong New Area, Shanghai, China","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eDiabetes mellitus complicated by renal failure (DM-RF) is now among the fastest-growing contributors to global disability-adjusted life years (DALYs) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Global Burden of Disease (GBD) data show that the age-standardized DALY rate attributable to diabetic kidney disease surged 74% from 1990 to 2021, with East Asia experiencing the steepest rise [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. China witnessed a 2.3-fold increase in DM-RF\u0026ndash;attributable deaths, while Japan and Korea, despite favorable all-cause mortality trends, recorded increases of 40% and 65%, respectively [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These patterns mark a clear epidemiological shift in which chronic hyperglycemia and progressive nephron loss now act synergistically to shorten life expectancy [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe drivers of DM-RF mortality vary greatly by setting. In high-income countries, late nephrology referral and sub-optimal glycaemic control dominate, whereas in middle-income regions, inadequate access to renin\u0026ndash;angiotensin blockade and dialysis capacity are the principal constraints [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Within-country comparisons reveal two- to four-fold differences in case fatality across Chinese prefectures and Japanese prefectures alike, implicating health-system performance rather than genetic background as the principal determinant [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Yet GBD estimates still rely heavily on vital-registration tabulations, in which up to one-third of DM-RF deaths are misclassified, especially in Asia where multiple comorbidities are common [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Discrepancies between modeled and empirical estimates reached 42% in China and 28% in Japan, hampering the calibration of prevention priorities [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Real-world, individual-level mortality datasets are therefore the key to validating GBD estimates and to exposing age- and sex-specific patterns essential for targeted public health action against DM-RF [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough DM-RF remains associated with excess mortality, therapeutic gains have been considerable. Widespread adoption of sodium glucose cotransporter 2 (SGLT-2) inhibitors and non-steroidal mineralocorticoid receptor antagonists has shifted chronic kidney disease (CKD) management earlier in the disease course, cutting progression to renal failure [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Japan\u0026rsquo;s Diabetes Outcome Intervention Trial-2 and nationwide Korean cohorts indicate that systematic implementation of these therapies reduces RF events by 25%\u0026ndash;35% [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Real-world mortality surveillance is now required to quantify how much of this benefit is actually achieved at population level and to inform policy refinement and resource allocation.\u003c/p\u003e \u003cp\u003eCrude mortality rate (CMR), age-standardised mortality rate (ASMRW), years of life lost (YLL) and average YLL (AYLL) translate individual deaths into measurable societal loss, allowing health authorities to rank DM-RF against competing threats [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. By estimating these metrics with high-resolution data from Pudong New Area, Shanghai, we provide an empirical platform for precision prevention: identifying high-risk demographic strata, calibrating local glycaemic and blood-pressure control targets, and forecasting future dialysis demand [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The dataset can subsequently support nested pharmaco-epidemiological studies (e.g., impact of SGLT-2 inhibitor diffusion), economic evaluations of community screening, and quasi-experimental assessments of interventions such as integrated diabetes\u0026ndash;kidney clinics [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Ultimately, this work can evolve into a sentinel surveillance model for China and comparable East-Asian megacities, aligning real-world evidence with the global agenda to curb the rising tide of DM-RF [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source and case definition\u003c/h2\u003e \u003cp\u003eWe extracted 3,699 deaths attributed to DM-RF from 426,750 all-cause deaths recorded in Pudong New Area, Shanghai, China, between 1 January 2005 and 31 December 2023 (mid-2023 population: 3.30\u0026nbsp;million) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The regional mortality registry, maintained by the Pudong Center for Disease Control and Prevention, provided an individual-level dataset that includes age, sex, date of death and all underlying and contributing causes. Record linkage, field verification and annual active surveillance are performed to ensure\u0026thinsp;\u0026ge;\u0026thinsp;98% completeness and internal consistency [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDM-RF deaths were identified using the International Classification of Diseases, 10th Revision (ICD-10) codes E10\u0026ndash;E14 (diabetes mellitus) as the underlying cause and N17\u0026ndash;N19 (renal failure) as an associated condition. Certifying physicians, reviewing available medical records and laboratory data, confirmed that RF was a direct complication of diabetes. Only deaths meeting this causal sequence were retained [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study protocol was approved by the Institutional Review Board of the Pudong Health Commission, Shanghai (IRB#2016-04-0586). All data were de-identified before analysis; no additional contact with patients or families occurred.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Analyses\u003c/h3\u003e\n\u003cp\u003eCMR and ASMRW, with Segi's world standard population as the standard, were calculated and expressed per 100,000 person-years. Sex-specific CMRs were compared using Poisson regression, whereas ASMRWs were contrasted with the Mantel\u0026ndash;Haenszel test. To rank the leading causes of premature death, YLL and AYLL were computed according to the Global Burden of Disease (GBD) methodology [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. YLL was estimated using the World Health Organization (WHO) formula:\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eC\u003c/em\u003e represents the age-weighting fit with a constant (0.1658), K is the age weighting modulation factor, e is Napier's constant (or Euler\u0026rsquo;s number), r is the discount rate (3%), \u003cem\u003ea\u003c/em\u003e is age at death, \u003cem\u003eβ\u003c/em\u003e is the age-weighting parameter (0.04), and \u003cem\u003eL\u003c/em\u003e is the standard life expectancy at age a from the GBD reference life table.\u003c/p\u003e \u003cp\u003eAge strata were 0\u0026ndash;4, 5\u0026ndash;14, 15\u0026ndash;29, 30\u0026ndash;44, 45\u0026ndash;59, 60\u0026ndash;69, 70\u0026ndash;79, and \u0026ge;\u0026thinsp;80 years. Because DM-RF deaths were uncommon before age 45, temporal trends in CMR, ASMRW, AYLL, and YLL rate were examined for \u0026lt;\u0026thinsp;45, 45\u0026ndash;59, 60\u0026ndash;69, 70\u0026ndash;79, and \u0026ge;\u0026thinsp;80 years. Temporal patterns were summarized with the average annual percentage change (AAPC) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]; the natural logarithm of the age-standardised rate was regressed on calendar year, and AAPC and its 95% confidence interval (CI) were obtained as described previously [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDrivers of change in DM-RF mortality between 2005 and 2023 were quantified with Das Gupta\u0026rsquo;s decomposition [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], partitioning contributions from population size, age structure, and DM-RF risk. Bayesian age\u0026ndash;period\u0026ndash;cohort (BAPC) modelling with integrated nested Laplace approximations was used to project rates and burden for 2024\u0026ndash;2035; this approach yields better coverage than alternative forecasting models [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAll analyses were conducted in R 4.3.2 with the BAPC package in R (version 4.3.2), following validated protocols [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated statistical significance.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOverall DM-RF Burden\u003c/h2\u003e \u003cp\u003eDuring the 19-year study period, 3,699 deaths were attributed to DM-RF, accounting for 0.87% of all-cause mortality, 16.36% of diabetes-related deaths, and 13.56% of deaths with any RF code. Among diabetes-specific deaths, the RF-attributable proportion climbed from 11.29% to 13.84%, peaking at 19.20% in 2020. Conversely, among all RF deaths, the diabetic subset grew from 11.88% to 16.28% (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Annual DM-RF deaths rose from 72 (0.39% of all-cause mortality) in 2005 to 448 (1.35%) in 2023, accumulating 36,874 YLL. The corresponding CMR was 6.75 per 100,000, the ASMRW was 67.26 per 100,000, and the AYLL per death was 9.97 years (\u003cb\u003eTable S2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eIn 2023, male CMR was 15.85 per 100,000 and female CMR 11.30; corresponding ASMRWs were 5.44 and 2.94 per 100,000, YLL rates 152.24 and 101.07 per 100,000, and AYLLs 9.60 and 8.94 years, respectively. Over the entire period, men contributed 1,922 deaths (51.96%) and exhibited consistently higher metrics: CMR 7.03, ASMRW 2.93, and YLL rate 72.18 per 100,000 (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 vs women). Women recorded 1,777 deaths (48.04%) with CMR 6.47, ASMRW 2.05, and YLL rate 62.37 per 100,000. Median age at death was younger in males (73.92 years) than in females (78.87 years), resulting in a male AYLL of 10.27 years versus 9.65 years in females (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\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\u003eBurden of DM-RF deaths by sex and age groups in Pudong New Area of Shanghai, China, 2005\u0026ndash;2023\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=\"\u0026plusmn;\" 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=\"left\" 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=\"left\" 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\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of deaths\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProportion (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge at death (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAge at death (Median)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCMR (1/100,000)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eASMRW (1/100,000)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYLL (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYLL rate (1/100,000)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAYLL (year/person)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3699\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.37\u0026thinsp;\u0026plusmn;\u0026thinsp;12.10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.23\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.75\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.48\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36873.98\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e67.26\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.97\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBy sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e72.45\u0026thinsp;\u0026plusmn;\u0026thinsp;12.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19733.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e72.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e76.43\u0026thinsp;\u0026plusmn;\u0026thinsp;11.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e17140.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e62.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBy age (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e39.05\u0026thinsp;\u0026plusmn;\u0026thinsp;4.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1553.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e54.30\u0026thinsp;\u0026plusmn;\u0026thinsp;4.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7089.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e52.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e65.40\u0026thinsp;\u0026plusmn;\u0026thinsp;2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10317.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e130.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e75.28\u0026thinsp;\u0026plusmn;\u0026thinsp;2.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e75.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10730.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e249.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e85.44\u0026thinsp;\u0026plusmn;\u0026thinsp;6.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e58.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7183.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e305.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cem\u003eNote: DM-RF, diabetes mellitus combined with renal failure; CMR, crude mortality rate; ASMRW, age-standardized mortality rate by Segi\u0026rsquo;s world standard population; YLL, years of life lost; AYLL, average years of life lost\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCMRs increased sharply with age during the study period, particularly rising from 4.41 per 100,000 in individuals aged 45\u0026ndash;59 years to 13.98 in those aged 60\u0026ndash;69, 39.90 in the 70\u0026ndash;79 group, and reaching 115.51 among those aged 80 and above in 2023 (\u003cb\u003eTable S3\u003c/b\u003e). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, subjects under 45 years represented merely 65 deaths (1.76%) while registering the largest AYLL (23.9 years) and the most attenuated CMR (0.25 per 100 000). The 45\u0026ndash;59, 60\u0026ndash;69, 70\u0026ndash;79, and \u0026ge;\u0026thinsp;80 age groups contributed 375, 731, 1,144, and 1,384 deaths, respectively. Across these groups, the CMR rose from 2.75 per 100,000 in the youngest cohort to 58.87 per 100,000 among those aged 80 or above. Meanwhile, the AYLLs declined from 18.90 to 5.19 years per person, while YLL rates climbed steadily from 5.83 to 305.32 per 100,000. Males consistently outpaced females across all metrics. \u003cb\u003eTable S4\u003c/b\u003e presents the annual age- and sex-specific burden from 2005 to 2023. Similar patterns by sex and along age were observed each year.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTemporal Trends\u003c/h3\u003e\n\u003cp\u003eDuring the study period, all major DM-RF mortality metrics rose steeply. The CMR climbed from 2.83 to 13.56 per 100,000 (AAPC: 9.14%; 95% CI: 6.48\u0026ndash;11.87%), the ASMRW from 1.29 to 4.15 per 100,000 (AAPC: 6.89%; 95%CI: 6.48\u0026ndash;11.87), and the YLL rate from 28.82 to 126.48 per 100,000 (AAPC: 8.64%; 95% CI: 5.99\u0026ndash;11.36), whereas the AYLL declined modestly from 10.17 to 9.33 years (AAPC: \u0026minus;\u0026thinsp;0.62%; 95% CI: -0.97\u0026ndash;-0.26) (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Increases were consistently faster in men: AAPCs for CMR, ASMRW and YLL rate were 11.48%, 7.85% and 9.11%, respectively, versus 6.67%, 4.23% and 5.97% in women (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cb\u003eTable S5\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eAge-specific AAPCs in CMR displayed a stepwise deceleration: 6.62% (45\u0026ndash;59 years), 6.15% (60\u0026ndash;69), 2.46% (70\u0026ndash;79) and 5.96% (\u0026ge;\u0026thinsp;80). YLL-rate AAPCs followed an identical gradient: 6.35%, 6.22%, 2.43% and 5.43%, respectively. Concurrently, the share of total DM-RF deaths attributable to the 60\u0026ndash;69 and \u0026ge;\u0026thinsp;80 age groups expanded annually (AAPC: 2.17% and 2.07%, respectively; all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDrivers of DM-RF burden\u003c/h2\u003e \u003cp\u003eDecomposition analysis revealed pronounced sex-specific heterogeneity in the factors driving the DM-RF epidemic from 2005 to 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Epidemiological change, i.e. the rising risk exposure among populations, was the dominant driver of growth across every metric.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSpecifically, DM-RF deaths rose by 376 during the 19-year period. Population ageing accounted for 92 additional deaths (128% of the observed increment), population growth for 59 (82%), while epidemiological factors alone generated 224 extra cases (311%). Among men, 232 extra deaths were attributable to aging (56 deaths; 200%), population growth (30; 108%)], and predominantly epidemiological escalation (146, 521%). Among women, the corresponding increments were 144 deaths, with ageing contributing 38 (86%), population growth 29 (65%), and epidemiological change 77 (176%) (\u003cb\u003eTable S6)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProjections for DM-RF burden\u003c/h3\u003e\n\u003cp\u003eThe Bayesian model indicates that the CMR of DM-RF will continue its steep ascent, reaching 33.01 per 100,000 (95% CI: 13.86\u0026ndash;52.16) by 2035 (an AAPC of 7.60%; 95% CI: 6.57\u0026ndash;8.64%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The ASMRW is forecast to climb to 6.06 per 100,000 (95% CI: 3.77\u0026ndash;8.35; AAPC: 3.05%, 95% CI: 2.37\u0026ndash;3.73%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the YLL rate is expected to reach 191.59 per 100,000 (95% CI 121.00\u0026ndash;262.17) (AAPC: 3.36%, 95% CI: 2.60\u0026ndash;4.13%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eDiabetic nephropathy is already the second-leading cause of end-stage renal disease (ESRD) in China and the dominant aetiology after age 45 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Without intensified control, DM-RF will account for one-quarter of all diabetes deaths by 2035 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Our 19-year, 3.3-million-resident Pudong cohort, among Asia\u0026rsquo;s largest, quantifies this burden and supports WHO\u0026rsquo;s NCD agenda and SDG 3.4: a one-third reduction in premature NCD mortality by 2030 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe document a steep upward trend in DM-RF mortality. The ASMRW rose every year from 2005 to 2023 while the AYLL declined modestly, indicating a growing burden of the disease. By comparison, US rates peaked at 4.8 per 100 000 in 2010 and fell to 3.9 by 2021 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Sweden\u0026rsquo;s EHR-integrated National Diabetes Register delivered real-time feedback to primary-care physicians and cut DM-RF deaths 35% during 2005\u0026ndash;2018 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The UK Quality and Outcomes Framework, incentivizing annual albuminuria/eGFR screening, shortened nephrology referral times and reduced incident ESRD 25% [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Japan\u0026rsquo;s universal Tokutei-Kenshin programme (urine dipstick, serum creatinine, HbA1c\u0026thinsp;\u0026lt;\u0026thinsp;7% target, widespread SGLT-2 use) has held DM-RF mortality at 2.1 per 100 000 for ten years [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], while Korea\u0026rsquo;s insurer-funded \u0026ldquo;diabetes schools\u0026rdquo; achieved a 15% reduction in composite renal endpoints [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Shanghai now lags high-income Western regions in DM-RF controls by roughly a decade.\u003c/p\u003e \u003cp\u003eThe observed increase in Pudong is multifactorial. Diabetes mortality rose between 2005 and 2020, propelled by ageing, obesogenic environments and non-demographic drivers [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Glycaemic control remains poor: a 2022 local survey found that only 38% of adults with diabetes achieved HbA1c\u0026thinsp;\u0026lt;\u0026thinsp;7% and 24% were unaware of their renal status [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Use of renoprotective agents such as SGLT-2 inhibitors and RAAS blockers remains below global averages [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Health literacy and medication adherence gaps persist, especially among migrant populations. Decomposition analyses show that, after adjusting for population ageing and growth, the dominant drivers of DM-RF mortality are epidemiological: rising diabetes incidence, longer disease duration and increasing prevalence of renal impairment.\u003c/p\u003e \u003cp\u003eMen in this cohort exhibited consistently higher DM-RF mortality across all metrics, paralleling global patterns illustrated by USRDS male-to-female mortality ratios of 1.4\u0026ndash;1.6 for diabetic ESRD [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The excess risk is substantiated by biological determinants (greater visceral adiposity, testosterone-mediated endothelial dysfunction) and compounded by sociocultural barriers: lower health-seeking behaviour and inferior treatment adherence in this subgroup. Decomposition shows epidemiological factors influence men more, propelled by higher smoking, central obesity and delayed clinic visits, while women\u0026rsquo;s smaller share reflects better primary-care engagement and antihypertensive adherence [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These disparities are nevertheless modifiable. Following Mexico\u0026rsquo;s introduction of universal health coverage and targeted education, the PURE cohort documented complete attenuation of the sex gap [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Narrowing the sex differential would alleviate individual suffering and avert catastrophic household expenditure, as the premature loss of male breadwinners routinely triggers acute financial shock.\u003c/p\u003e \u003cp\u003eAge-specific analyses revealed a steep gradient: CMR rose from 4.4 per 100,000 at 45\u0026ndash;59 years to 116 per 100,000 at \u0026ge;\u0026thinsp;80 years, reflecting cumulative hyperglycaemic exposure, arterial stiffening and age-related decline in renal reserve. Intriguingly, AAPCs in both CMR and YLL rate plateaued in the 70\u0026ndash;79 age stratum (2.46% and 2.43%, respectively) before re-accelerating after age 80, plausibly because of survivor bias and more aggressive risk-factor modification among septuagenarians under China\u0026rsquo;s Healthy Ageing Action Plan [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. For the population aged\u0026thinsp;\u0026ge;\u0026thinsp;50 years, community-based comorbidity management offers substantial potential. Evidence from Hong Kong\u0026rsquo;s Risk Assessment and Management Program indicates that nurse-led risk stratification, combined with quarterly eGFR monitoring and lifestyle coaching, reduced composite renal endpoints by 29% in adults aged 50\u0026ndash;75 years [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese findings argue for gender-responsive and age-specific interventions. Evidence-based interventions include: (1) workplace screening in male-dominated industries; (2) gamified digital tools that enhance male engagement [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]; and (3) family-centred counselling that mobilizes spousal support to raise adherence at lower cost than gender-neutral approaches. For Pudong New Area, we propose a tiered, community-embedded strategy. First, annual albuminuria screening should be implemented across all community health centres using point-of-care devices, with automated EHR alerts triggered when eGFR falls below 60 ml/min/1.73 m\u0026sup2;. Second, integrated care pathways should provide real-time virtual consultations with endocrinologists and nephrologists from primary-care clinics, supported by district-level, cloud-based clinical decision aids. Third, pharmacist-led medication management should be adopted [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e];. In addition, culturally tailored dietary counselling should emphasize traditional, low-sodium, plant-forward recipes. Finally, a community peer-mentor programme, modeled on Japan\u0026rsquo;s \u0026ldquo;diabetes supporters\u0026rdquo; initiative [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], should be introduced to enhance self-management and reduce social isolation.\u003c/p\u003e \u003cp\u003eStrengths of this study include the exceptionally large sample, high-quality surveillance data, and exhaustive analytic strategy. Two limitations merit consideration. First, the lack of individual-level clinical parameters (e.g., longitudinal HbA1c or eGFR trajectories) precludes causal inference; linkage to Pudong\u0026rsquo;s HER repository is underway to address this. Second, under-reporting of diabetes on death certificates may underestimate true burden, an issue we mitigated by cross-validating hospital discharge diagnoses and prescription-dispensing databases.\u003c/p\u003e \u003cp\u003eIn conclusion, 19 years of population-based mortality data covering 3.3\u0026nbsp;million residents reveal a steep and sustained rise in DM-RF mortality and premature mortality in Pudong New Area of Shanghai. These findings underscore the urgent need for integrated, sex-responsive and age-specific strategies to curb this burgeoning burden and accelerate progress toward global NCD targets.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDATA SHARING STATEMENT\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all staff of the Shanghai Pudong Vital Statistics for their great work in data collection and the assurance of high data quality. The authors also thank Medjaden Inc. for scientific editing of\u0026nbsp;this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICAL STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study did not involve participant intervention. The Ethics Committee\u0026nbsp;of the Health Department of Shanghai Pudong, China\u0026nbsp;approved this study (IRB#2016-04-0586) and waived the need for informed consent. We ensured\u0026nbsp;strict confidentiality of the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the grants from General Program of Shanghai Pudong New Area Health Commission (No. PW2023A-27 to YC), the Project of the 2025 Open Fund of the Shanghai Key Laboratory of Meteorology and Health (No.QXJK202509 to XL), the Shanghai Municipal Program for Medical Leading Talents (2019LJ15) and the National Key Research and Development Program of China (2022YFC3600901). The funder had no role in study design, data collection, data analysis, data interpretation, or writing of the report. All authors had full access to all data in the study and had responsibility for the decision to submit for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDECLARATION OF INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYC and SC conducted the statistical analyses and wrote the manuscript together. RL gave the advice for the design of this study and participated in the coordination during the study period. The three authors are responsible for the accuracy of the analysis and contributed equally to this study. CX provided significant intellectual advice.\u0026nbsp;LS, XQ and HC contributed to the mortality data collection and took responsibility for the integrity of this data. LH,and SJ conducted the literature review and gave suggestions for analysis. XL conceived of the study, and participated in the development of this work. WX directed the implementation and reviewed the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONSENT FOR PUBLICATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved for publication by all authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCollaboration GBDCKD. Global, regional, and national burden of chronic kidney disease, 1990\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017. 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Saudi J Kidney Dis Transpl. 2023;34(Suppl 1):S177\u0026ndash;218.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKobayashi K, Toyoda M, Hatori N, Furuki T, Sakai H, Sato K, Miyakawa M, Tamura K, Kanamori A. Sodium-glucose cotransporter 2 inhibitor-induced reduction in the mean arterial pressure improved renal composite outcomes in type 2 diabetes mellitus patients with chronic kidney disease: A propensity score-matched model analysis in Japan. J Diabetes Investig. 2021;12(8):1408\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim HJ, Song SH. Steps to understanding diabetes kidney disease: a focus on metabolomics. Korean J Intern Med. 2024;39(6):898\u0026ndash;905.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHou X, Wang L, Zhu D, Guo L, Weng J, Zhang M, Zhou Z, Zou D, Ji Q, Guo X, et al. Prevalence of diabetic retinopathy and vision-threatening diabetic retinopathy in adults with diabetes in China. Nat Commun. 2023;14(1):4296.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu B, Liu K, Mao H. Clinical Characteristics, Treatment Patterns, and Renal-Related Events in Patients with Diagnosed CKD: Results from Regional Health Information System in China. J Am Soc Nephrol. 2023;34(11S):1172\u0026ndash;1172.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKautzky-Willer A, Harreiter J, Pacini G. Sex and Gender Differences in Risk, Pathophysiology and Complications of Type 2 Diabetes Mellitus. Endocr Rev. 2016;37(3):278\u0026ndash;316.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreenwood DA, Litchman ML, Isaacs D, Blanchette JE, Dickinson JK, Hughes A, Colicchio VD, Ye J, Yehl K, Todd A, et al. A New Taxonomy for Technology-Enabled Diabetes Self-Management Interventions: Results of an Umbrella Review. J Diabetes Sci Technol. 2022;16(4):812\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollaborators GBDAB. Global, regional, and national prevalence of adult overweight and obesity, 1990\u0026ndash;2021, with forecasts to 2050: a forecasting study for the Global Burden of Disease Study 2021. Lancet. 2025;405(10481):813\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamos T, Verma A, Speirits I, Zhang L, McInally J, McShane C, Kennon B, Forsyth P, Lowrie R, Johnson CF. Evaluating a pharmacist-led cardio-renal-metabolic service to reduce healthcare inequities in a socioeconomically deprived population: a prospective intervention study. Int J Clin Pharm. 2025;47(5):1395\u0026ndash;405.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAraki A. Individualized treatment of diabetes mellitus in older adults. Geriatr Gerontol Int. 2024;24(12):1257\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Diabetes mellitus, renal failure, comorbidity, mortality, years of life lost, China","lastPublishedDoi":"10.21203/rs.3.rs-9427155/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9427155/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDiabetes mellitus complicated by renal failure (DM-RF) is an emerging driver of premature death in China.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo quantify 19-year mortality trends attributable to DM-RF in Pudong New Area of Shanghai and to project the burden to 2035.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing population-based vital-registration data covering 3.30\u0026nbsp;million residents (2005\u0026ndash;2023), we calculated crude mortality, world-standardized age-standardized mortality rates (ASMRW), years-of-life-lost (YLL) rates and average YLL (AYLL). Decomposition analysis identified key drivers; Bayesian age\u0026ndash;period\u0026ndash;cohort models forecast rates for 2024\u0026ndash;2035.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOver the study period, 3,699 DM-RF deaths were documented. ASMRW more than tripled, rising from 1.29 to 4.15 per 100,000 (average annual percentage change 6.89%). Males consistently experienced higher mortality than females, with ASMRW of 5.44 vs 2.94 per 100,000 in 2023. The YLL rate increased by 8.64% annually, whereas AYLL declined modestly (\u0026minus;\u0026thinsp;0.62%; all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Epidemiological factors explained 311% of the 376 excess deaths, with males accounting for 521%. Bayesian projections estimated that ASMRW will reach 6.06 (95% CI: 3.77\u0026ndash;8.35) per 100,000 by 2035.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eDM-RF mortality is escalating rapidly in Pudong New Area of Shanghai, propelled by deteriorating glycaemic control and population aging. Sex-specific, age-targeted and community-integrated prevention strategies are urgently needed if Shanghai is to meet Sustainable Development Goal 3.4.\u003c/p\u003e","manuscriptTitle":"Diabetes Mellitus Complicated by Renal Failure: Burden in a 3.30-Million Resident Cohort in Pudong New Area, Shanghai, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-06 23:43:44","doi":"10.21203/rs.3.rs-9427155/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-28T12:43:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-16T09:52:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-16T04:14:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-16T04:14:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-04-15T12:32:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d518f010-eb39-435d-ad52-798f9a68980f","owner":[],"postedDate":"May 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T23:43:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-06 23:43:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9427155","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9427155","identity":"rs-9427155","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}