Biological Age Acceleration as a Marker of Chronic Kidney Disease: A Population-Based Study

Biological Age Acceleration as a Marker of Chronic Kidney Disease: A Population-Based Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Biological Age Acceleration as a Marker of Chronic Kidney Disease: A Population-Based Study Quan Zhou, Mingcheng Chen, Xiaojun Huang, Wentao Luo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8972599/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Chronic kidney disease (CKD) remains a major global health concern. Biological age (BA), which reflects the functional state of multiple physiological systems, may capture aging-related vulnerability more effectively than chronological age. This study evaluated whether acceleration in BA—estimated by the Klemera–Doubal method (KDM)—is linked to CKD in a nationally representative Chinese cohort. The original number of participants was 17,708. Data were drawn from 6,830 participants in the baseline wave of the China Health and Retirement Longitudinal Study (CHARLS). KDM-based BA acceleration (KDM-BAacc) was calculated as the residual from regressing biological age on chronological age and categorized into quartiles. CKD was identified from self-reported physician diagnoses. Logistic regression models with stepwise adjustment for sociodemographic, lifestyle, and clinical factors were applied to examine the association. Each interquartile range increase in KDM-BAacc was linked with markedly higher odds of CKD (OR = 2.13, 95% CI: 1.68–2.70, p < 0.001). Participants in the top quartile of acceleration exhibited a risk of CKD that was over three times greater compared to those in the bottom quartile (OR = 3.10, 95% CI: 1.99–4.85). Restricted cubic spline models supported a steady, approximately linear increase in CKD risk across the entire spectrum of BA acceleration ( p for non-linearity = 0.758). Subgroup analyses showed consistency across demographic strata, with significant interactions observed for smoking history, education level, and sleep duration. Sensitivity analyses yielded consistent results. KDM-derived biological age acceleration is a strong, independent, and linearly increasing risk factor for CKD, suggesting that BA may hold substantial value for CKD risk prediction and early identification. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Nephrology Health sciences/Risk factors Biological Age Aging Chronic Kidney Disease KDM CHARLS Risk Factor Dose-Response Figures Figure 1 Figure 2 Figure 3 Introduction Chronic kidney disease (CKD) has become an increasingly pressing global health issue, leading to substantial illness burden, premature deaths, and rising healthcare costs 1 . From 1990 to 2017, both the prevalence and mortality of CKD showed marked upward trends—cases increased by nearly one-third and deaths by more than 40%, amounting to approximately 35.8 million disability-adjusted life years (DALYs) 2 . Although chronological age is recognized as a major non-modifiable determinant of CKD—with markedly higher rates observed in older adults 3 —individuals of the same age often exhibit markedly different health statuses. This variability highlights the limitation of measuring aging solely as time elapsed since birth, as it does not fully reflect true physiological decline. To address this, the concept of biological age (BA) has been proposed. BA integrates multiple physiological indicators to better characterize an individual's functional status and degree of aging 4 , thereby providing a more nuanced assessment of health than chronological age alone. Among various approaches, the Klemera–Doubal method (KDM) is a widely used and validated algorithm for estimating BA 5 . It combines a panel of clinical biomarkers into a composite score representing overall physiological integrity. The discrepancy between BA and chronological age, commonly termed BA acceleration, captures whether a person is aging more rapidly or more slowly than expected. A positive acceleration value indicates that an individual's physiological profile resembles that of someone older than their actual age, suggesting an accelerated aging process. Previous research has shown that individuals with higher biological age tend to experience a greater burden of aging-related diseases, including elevated risks of mortality, cardiovascular disorders, and cognitive impairment 6 , 7 . However, the specific nature of the association between BA acceleration and CKD risk remains unclear, particularly in terms of the dose-response relationship in large, community-based populations. Clarifying this relationship is important for understanding how systemic physiological dysregulation contributes to renal impairment. To address this research gap, we examined the association between accelerated biological aging and CKD in a population-based sample of middle-aged and elderly Chinese individuals from the China Health and Retirement Longitudinal Study (CHARLS). We employed a comprehensive analytical strategy, incorporating categorical, continuous, and flexible modeling approaches, to rigorously characterize this association. Results Baseline characteristics Table 1 outlines the baseline characteristics of the 6,830 participants, stratified by quartiles of KDM-BAacc. The mean age of the study population was 58.4 years, and 54.1% were female. As BA acceleration increased across quartiles, participants exhibited progressively less favourable health profiles. Individuals in the highest quartile (Q4) were more likely to be male, current smokers, and frequent alcohol drinkers, with all comparisons showing significant differences. Q4 participants also had a substantially higher prevalence of cardiometabolic conditions, including diabetes (13.4% in Q4 vs. 4.9% in Q1), dyslipidemia, and hypertension. In terms of biomarker profiles, those in Q4 demonstrated elevated levels of urea, creatinine, and cystatin C, along with higher glycated hemoglobin, total cholesterol, and LDL-C. They also had lower platelet counts and higher hs-CRP concentrations. Together, these patterns indicate broad physiological dysregulation across multiple systems among individuals with the greatest BA acceleration. Association between KDM-BAacc and CKD Logistic regression analyses validated a significant association between KDM-BAacc and CKD. In the initial univariate analysis (Model 1), every interquartile range increment in KDM-BAacc demonstrated a significant 43% elevation in the likelihood of developing CKD (OR = 1.43, 95% CI: 1.26–1.63, p < 0.001). This robust correlation persisted with minimal variation following the incorporation of sociodemographic covariates in Model 2 (OR = 1.43, 95% CI: 1.25–1.63, p < 0.001). After further adjustment for lifestyle and clinical factors in Model 3, the association strengthened markedly, with each IQR increase in KDM-BAacc linked to a 113% increase in the odds of CKD (OR = 2.13, 95% CI: 1.68–2.70, p < 0.001). Stratification by KDM-BAacc quartiles demonstrated a significant dose-dependent association with CKD risk. In the fully adjusted multivariate model (Model 3), when compared to individuals in the lowest quartile (Q1), the adjusted odds ratios for CKD incidence progressively increased across higher quartiles: 1.47 (95% CI: 1.04–2.09) for Q2, 1.38 (0.94–2.04) for Q3, and 3.10 (1.99–4.85) for Q4. Although the OR for Q3 was slightly lower than that for Q2, the overall trend across quartiles remained highly significant ( p < 0.001), showing a consistent increase in risk (Table 2 ). This pattern was further supported by the restricted cubic spline (RCS) analysis. Dose-response relationship analysis RCS analysis showed a significant overall association between KDM-BAacc and CKD risk (Wald χ² = 28.81, 2 df, p < 0.0001). The test for non-linearity was non-significant ( p for non-linearity = 0.7576), indicating that a linear relationship adequately described the association. As illustrated in Fig. 1 , the OR for CKD increased steadily across the full range of KDM-BAacc values, reinforcing the dose-response pattern observed in the quartile analyses. No threshold or saturation effect was evident. In Fig. 1 , the solid line illustrates the OR for CKD, while the shaded region indicates the associated 95% confidence interval. The OR is standardized to a value of 1 at the median of the KDM-BAacc measure. The model was adjusted for age, sex, education, residential location, marital status, alcohol consumption, smoking status, and sleep duration. Subgroup and interaction analyses Subgroup analyses showed a consistently positive association between KDM-BAacc (per IQR increase) and CKD across nearly all examined strata, including age (< 65 vs. ≥65 years), sex, residential location, and smoking. In all subgroups, the odds ratios exceeded 1.9 and reached statistical significance ( p < 0.05) (Fig. 3 ). The association appeared stronger among married individuals and participants reporting more than 6 hours of sleep per night. Interaction analyses further revealed significant effect modification by smoking history (particularly among former smokers), education level (primary school and high school groups), and sleep duration, as indicated by significant interaction terms ( p for interaction < 0.05) (Fig. 3 ). Sensitivity analyses Sensitivity analyses demonstrated a robust positive association between KDM-BAacc and CKD across the majority of examined subgroups. In the fully adjusted model (Model 3), an interquartile range (IQR) increment in KDM-BAacc corresponded to a 92% elevated risk of CKD (OR = 1.92, 95% CI: 1.60–2.30). A statistically significant dose-response pattern was evident across KDM-BAacc quartiles (P for trend < 0.001). This linear association was further substantiated by restricted cubic spline (RCS) modeling ( P for nonlinearity = 0.758). Significant effect modification was observed, with interactions identified for smoking status, educational attainment, and sleep duration ( P for interaction < 0.05 for each), suggesting that the relationship between KDM-BAacc and CKD risk is modified by these factors ( Supplementary materials). Discussion This epidemiological investigation revealed a statistically significant correlation between KDM-BAacc and the development of CKD. Baseline comparisons showed progressively poorer health profiles across increasing KDM-BAacc quartiles, including higher prevalences of diabetes, hypertension, and dyslipidemia, accompanied by adverse biomarker patterns. Logistic regression analyses demonstrated that each interquartile range increase in KDM-BAacc was associated with a substantially elevated odds of CKD, an association that became stronger after full adjustment for sociodemographic, lifestyle, and clinical factors (OR = 2.13). Both the quartile-based analyses and RCS models supported a linear dose–response relationship, with no indication of threshold or saturation effects. Subgroup analyses further confirmed the consistency of this association across a wide range of demographic and behavioral characteristics, while interaction analyses revealed significant effect modification by smoking history, education level, and sleep duration. Sensitivity analyses confirmed consistent results, further supporting the main results. Collectively, these findings establish KDM-BAacc as an independent and robust risk factor for CKD and highlight its potential value in risk stratification and early prevention strategies. KDM-BAacc has increasingly been used to quantify the pace of biological aging and has been examined in relation to several age-related diseases, including cardiovascular, neurodegenerative, and renal disorders 8 , 9 . Previous research on aging markers and CKD has primarily focused on epigenetic aging indicators such as Horvath's clock 10 , 11 . In contrast, evidence on KDM-BAacc, which is based on clinical biomarkers rather than DNA methylation profiles, remains limited. Some prospective cohort studies have reported associations between epigenetic age acceleration and renal function decline or CKD incidence, although effect sizes were generally modest (ORs around 1.1–1.3 per IQR increase) and often estimated without extensive adjustment for confounders 12 , 13 . A major strength of the present study is its demonstration of a comparatively strong association between KDM-BAacc and CKD risk in a large community-based sample, with a fully adjusted OR of 2.13 and a clearly delineated linear dose–response relationship. These findings are consistent with the few comparable studies available 14 , though the magnitude of association observed here is notably larger, potentially reflecting more comprehensive covariate adjustment or population-specific influences. Furthermore, prior studies seldom evaluated effect modification by lifestyle or psychosocial factors, whereas our analysis identified significant interactions with smoking history, education level, and sleep duration. These results underscore the possibility that the impact of accelerated biological aging on renal health may differ across population subgroups, suggesting avenues for more tailored risk assessment. Overall, our findings strengthen the evidence supporting the utility of KDM-BAacc in predicting CKD risk and indicate that it may offer advantages over certain traditional aging metrics. The mechanisms linking KDM-BAacc to CKD are likely multifactorial and may involve several interconnected physiological pathways. Firstly, accelerated aging is characterized by key biological processes such as cellular senescence, telomere shortening, and elevated oxidative stress, all of which can directly damage glomerular and tubular structures, promote renal fibrosis, and contribute to progressive loss of kidney function 15 , 16 . Secondly, the strong correlations observed in this study between KDM-BAacc and cardiometabolic risk factors, including diabetes, hypertension, and dyslipidemia, suggest that metabolic dysregulation and chronic inflammation may act as important mediators. Elevated hs-CRP and glycated hemoglobin levels among individuals with high BA acceleration reflect systemic inflammation and impaired glucose metabolism, both well-recognized contributors to renal microenvironment injury 17 , 18 . Additionally, accelerated biological aging may influence CKD risk through epigenetic mechanisms, such as aberrant DNA methylation patterns that modify the expression of genes involved in renal homeostasis 19 . Another relevant mechanism concerns the interplay between physiological dysregulation and the natural age-related decline in renal function. Individuals with accelerated biological aging often demonstrate multisystem impairment, as reflected in the elevated urea and creatinine levels and hematological abnormalities observed in this study. Such cumulative organ dysfunction likely heightens susceptibility to CKD progression 20 . Finally, lifestyle factors identified as effect modifiers, particularly smoking and insufficient sleep, may further amplify the detrimental impact of BA acceleration on renal health by intensifying oxidative stress, inflammation, and immune dysregulation [21] . Together, these pathways form a plausible mechanistic framework supporting the strong and linear association observed between KDM-BAacc and CKD risk. There are several notable strengths in this study. This study utilizes a comprehensive, nationally representative dataset, which strengthens the external validity of its conclusions. The use of detailed multivariable adjustment strengthens the robustness of the observed associations, while the application of RCS analysis provides compelling evidence for a linear dose–response relationship that extends beyond simple linear assumptions. Furthermore, subgroup and interaction analyses offer valuable insights into potential effect modification, contributing to a more nuanced understanding of how BA acceleration may influence CKD risk among different population groups. Nevertheless, several limitations should be acknowledged. Firstly, the cross-sectional design precludes causal inference. Prospective cohort studies or interventional trials are needed to confirm whether KDM-BAacc predicts CKD incidence or progression. Secondly, although extensive covariates were included, the possibility of residual confounding, such as genetic susceptibility, unmeasured environmental exposures, or medication use, cannot be excluded. For instance, the biological mechanisms underlying interactions with sleep duration and marital status remain unclear and warrant further investigation. Additionally, the study population's specific characteristics may limit generalizability to other demographic or ethnic groups. Future research should incorporate multi-omics data, including genomic and epigenomic markers, to deepen mechanistic insights. Developing dynamic measures of BA acceleration that capture changes over time would also enhance understanding of aging trajectories. From a clinical perspective, integrating KDM-BAacc or similar biomarkers into CKD risk assessment tools could facilitate earlier identification of high-risk individuals and support targeted preventive strategies. Methods Study population The dataset was obtained from the initial 2011 survey of the China Health and Retirement Longitudinal Study (CHARLS), which represents a nationally representative cohort of Chinese residents aged 45 years or older. This cohort was constructed using a rigorous, multi-stage probability sampling methodology to ensure population representativeness 22 . The study collects detailed information on participants' demographic background, socioeconomic circumstances, health status, and biochemical indicators. For the current analysis, we included individuals who provided blood samples and had complete biomarker data necessary for computing KDM-based biological age as well as for determining CKD status. The original number of participants was 17,708. After applying these criteria, 6,830 participants remained eligible for inclusion. The CHARLS project received ethical approval from the Peking University Institutional Review Board (IRB00001052–11015), and written informed consent was obtained from all participants. Calculation of KDM-BA acceleration BA was estimated utilizing the Klemera–Doubal method (KDM), an established algorithm that integrates multiple biomarkers representing the functional status of various physiological systems. Following prior research, the KDM-BA algorithm was trained via data from the China Health and Nutrition Survey (CHNS), which included adults aged 20–79 years. For the CHARLS population, we applied an adapted and previously validated version of the KDM - BA model for Chinese adults 23 . The revised predictive model integrated eight key clinical indicators: serum cholesterol levels, triglyceride concentrations, glycated hemoglobin (HbA1c), blood urea, serum creatinine, high - sensitivity C - reactive protein (hsCRP), platelet count, and systolic arterial pressure measurements 23 . These biomarkers capture renal function (e.g., creatinine), metabolic status (e.g., HbA1c and lipid profiles), inflammation (hsCRP), and hematopoietic function (platelet count), all of which were available in the CHARLS dataset. KDM - based BA acceleration (KDM-BAacc) was calculated as the residual from a linear regression of KDM-estimated BA on chronological age. A positive residual indicates accelerated aging, meaning that an individual's BA exceeds their chronological age; conversely, a negative residual reflects slower than expected aging. For statistical analyses, KDM-BAacc was examined both as a continuous variable—standardized by its interquartile range to improve interpretability—and as a categorical variable divided into quartiles, with Q1 indicating the slowest aging and Q4 the fastest. Assessment of CKD CKD was identified based on participants' self - reported physician diagnosis. Participants who had been previously diagnosed with chronic kidney disease prior to the study's initiation in 2011 were excluded in the cohort. For the remaining participants, CKD status was validated during the 2015 follow - up, where respondents directly confirmed the accuracy of their previously reported diagnosis. Covariates Drawing on prior literature and guided by directed acyclic graphs, we selected a comprehensive set of covariates to address potential confounding. Sociodemographic factors included chronological age (treated as a continuous variable), sex, educational attainment (categorized as college or above, high school, or primary school and below), place of residence (urban or rural), and marital status (married versus unmarried, separated, or widowed). Lifestyle characteristics considered in the models were smoking behaviour (current, former, or never smoker), alcohol consumption (never, occasional use—less than once per month—or more frequent intake), and average sleep duration per night (hours, continuous). Clinical indicators incorporated into the adjustment set were systolic and diastolic blood pressure (both continuous, mmHg). These covariates were included to minimize residual confounding as much as possible. Statistical analysis Baseline characteristics of the study population were summarized overall and across quartiles of KDM-BAacc. Depending on their distribution, continuous variables were presented as mean ± standard deviation or median (interquartile range), with between - group comparisons performed using parametric ANOVA or non-parametric Kruskal–Wallis tests as appropriate. For categorical variables, we reported absolute numbers and proportions, with statistical significance assessed through Pearson's chi - square tests. The association between KDM-BAacc and CKD was evaluated using multivariable logistic regression. Three progressively adjusted regression models were developed with sequential covariate adjustments: The baseline Model 1 contained no covariates; Model 2 incorporated adjustments for key sociodemographic characteristics including age, gender, educational attainment, geographic residence, and marital status; while Model 3 extended these adjustments to encompass behavioral and clinical covariates such as tobacco use, alcohol intake, nightly sleep duration, and both systolic and diastolic blood pressure measurements. Results were presented as odds ratios (ORs) with 95% confidence intervals (CIs). A p value for linear trend across KDM-BAacc quartiles was obtained by modeling the quartile variable as a continuous term. To assess the dose - response relationship between continuous KDM-BAacc and CKD risk without assuming linearity, restricted cubic spline (RCS) models with three knots positioned at the 10th, 50th, and 90th percentiles of the KDM-BAacc distribution were fitted, adjusting for all covariates included in Model 3. Linearity was evaluated using a likelihood ratio test comparing the spline model with a corresponding linear model. Subgroup analyses were performed by applying Model 3 within strata of key demographic and clinical characteristics. Multiplicative interactions were assessed by introducing cross- product terms into the regression models and evaluating significance using Wald tests. For sensitivity analysis,to handle potential bias from missing data and verify the robustness of our findings, we employed multiple imputation using the `mice` package (m = 5 imputations, maxit = 10, method = "pmm"). The regression analysis and subgroup analysis were repeated across the imputed datasets, and the results were pooled. All statistical analyses were conducted using R software (version 4.1.0; R Foundation for Statistical Computing, Vienna, Austria). Statistical significance was defined as a two - sided p - value of less than 0.05. Declarations Data availability The data that support the findings of this study are available from the China Health and Retirement Longitudinal Study (CHARLS) but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the corresponding author upon reasonable request and with permission of CHARLS. Funding This work did not receive any financial support. Author contributions QZ, MCC: Conceptualization, Methodology, Formal analysis, Writing – original draft. QZ,XJH: Data curation, Validation, Writing – review & editing. XJH, ETL: Supervision, Project administration, Funding acquisition. All authors reviewed and approved the final manuscript. Competing interests The authors declare no competing interests. Additional information Correspondence and requests for materials should be addressed to [Wentao Luo and Xiaojun Huang]. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References Jha, V. et al. Chronic kidney disease: global dimension and perspectives. Lancet. 382, 260–272 (2013). GBD Chronic Kidney Disease Collaboration. Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 395 , 709–733 (2020). Stevens, L. A. et al. Prevalence of CKD and comorbid illness in elderly patients in the United States: results from the Kidney Early Evaluation Program (KEEP). Am. J. Kidney Dis. 55 , S23–S33 (2010). Jylhävä, J., Pedersen, N. 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Baseline Characteristics of Study Participants Stratified by KDM Biological Age Acceleration Quartiles Characteristic Overall (N=6830) Q1 (Slowest) (N=1708) Q2 (N=1707) Q3 (N=1707) Q4 (Fastest) (N=1708) p-value Demographics Age, years (Mean (SD)) 58.4 (8.6) 58.9 (8.7) 57.8 (8.4) 58.3 (8.6) 58.7 (8.4) 0.001 Male, n (%) 3135 (45.9) 469 (27.5) 646 (37.8) 924 (54.1) 1096 (64.2) <0.001 Education, n (%) <0.001 Primary school or below 4789 (70.1) 1299 (76.1) 1194 (69.9) 1151 (67.4) 1145 (67.0) High school 1840 (26.9) 378 (22.1) 465 (27.2) 495 (29.0) 502 (29.4) College or above 201 (2.9) 31 (1.8) 48 (2.8) 61 (3.6) 61 (3.6) Location, n (%) <0.001 City/Town 1087 (15.9) 200 (11.7) 258 (15.1) 312 (18.3) 317 (18.6) Village 5735 (84.1) 1507 (88.3) 1446 (84.9) 1394 (81.7) 1388 (81.4) Marital Status, n (%) 0.677 Married 6088 (89.2) 1515 (88.8) 1533 (90.0) 1518 (88.9) 1522 (89.2) Never/Separated/Widowed 737 (10.8) 192 (11.2) 171 (10.0) 189 (11.1) 185 (10.8) Lifestyle Factors Smoking Status, n (%) <0.001 Current Smoker 2008 (29.9) 356 (21.0) 440 (26.3) 567 (34.0) 645 (38.7) Ex-smoker 545 (8.1) 87 (5.1) 112 (6.7) 160 (9.6) 186 (11.2) Non-smoker 4153 (61.9) 1255 (73.9) 1122 (67.0) 940 (56.4) 836 (50.1) Drinking Status, n (%) Once/month 1332 (20.7) 221 (13.5) 279 (17.2) 397 (25.0) 435 (27.6) ≤Once/month 534 (8.3) 114 (7.0) 144 (8.9) 128 (8.1) 148 (9.4) Never 4555 (70.9) 1297 (79.5) 1202 (74.0) 1063 (66.9) 993 (63.0) Sleep Time, hours (Mean (SD)) 6.4 (1.9) 6.3 (1.9) 6.4 (1.9) 6.4 (1.8) 6.5 (1.8) 0.008 Clinical Measures SBP, mmHg (Mean (SD)) 129.1 (21.1) 112.0 (11.5) 122.3 (12.7) 132.0 (14.3) 150.7 (20.9) <0.001 DBP, mmHg (Mean (SD)) 75.3 (12.2) 66.8 (9.1) 72.3 (9.4) 77.4 (9.9) 85.0 (11.6) <0.001 Comorbidities, n (%) Diabetes 579 (8.6) 82 (4.9) 97 (5.8) 176 (10.6) 224 (13.4) <0.001 Dyslipidemia (Mean (SD)) 0.2 (0.4) 0.2 (0.4) 0.2 (0.4) 0.3 (0.4) 0.3 (0.5) <0.001 Hypertension (Mean (SD)) 0.4 (0.5) 0.1 (0.3) 0.2 (0.4) 0.5 (0.5) 0.8 (0.4) <0.001 Cardiovascular Disease 894 (13.2) 203 (12.0) 212 (12.6) 216 (12.7) 263 (15.5) 0.013 Biomarkers (Mean (SD)) Total Cholesterol, mg/dL 193.2 (38.3) 183.5 (35.5) 191.6 (37.7) 195.2 (37.3) 202.5 (40.2) <0.001 LDL-C, mg/dL 116.2 (34.7) 107.3 (31.5) 114.7 (32.4) 118.1 (33.7) 124.5 (38.4) <0.001 HDL-C, mg/dL 50.9 (15.3) 50.5 (14.5) 51.5 (15.1) 51.4 (16.0) 50.2 (15.5) 0.032 Triglycerides, mg/dL 134.7 (110.2) 137.7 (142.3) 131.0 (104.3) 132.0 (95.6) 138.3 (90.4) 0.119 Urea, mmol/L 4.4 (1.2) 3.6 (0.9) 4.1 (0.9) 4.6 (1.0) 5.3 (1.3) <0.001 Creatinine, mg/dL 0.8 (0.2) 0.7 (0.1) 0.7 (0.1) 0.8 (0.1) 0.9 (0.2) <0.001 Cystatin C, mg/L 1.0 (0.2) 0.9 (0.2) 1.0 (0.2) 1.0 (0.2) 1.0 (0.2) <0.001 Glycated Hemoglobin, % 5.3 (0.8) 5.1 (0.5) 5.2 (0.7) 5.3 (0.8) 5.5 (1.1) <0.001 hs-CRP, mg/L 0.1 (0.2) 0.0 (0.1) 0.0 (0.1) 0.1 (0.1) 0.1 (0.3) <0.001 Platelet Count, 10⁹/L 212.5 (76.4) 234.8 (91.8) 216.6 (70.1) 204.1 (68.5) 194.4 (66.4) <0.001 SD: Standard Deviation; SBP: Systolic Blood Pressure; DBP: Diastolic Blood Pressure; LDL-C: Low-Density Lipoprotein Cholesterol; HDL-C: High-Density Lipoprotein Cholesterol; hs-CRP: high-sensitivity C-Reactive Protein. Q1-Q4: Quartiles of KDM Biological Age Acceleration. p-values are derived from ANOVA for continuous variables and Chi-square test for categorical variables. Table 2. Multivariable Logistic Regression Analysis of the Association Between KDM Biological Age Acceleration and Chronic Kidney Disease Risk Variable Model 1 (Crude) OR (95% CI) Model 2 (Sociodemographic)† OR (95% CI) Model 3 (Fully Adjusted)‡ OR (95% CI) KDM_BAacc (per IQR) 1.43 (1.26, 1.63) * 1.43 (1.25, 1.63) * 2.13 (1.68, 2.70) * KDM_BAacc Quartiles Q1 (Reference) 1.00 1.00 1.00 Q2 1.22 (0.88, 1.70) 1.31 (0.94, 1.82) 1.47 (1.04, 2.09) * Q3 1.11 (0.79, 1.55) 1.12 (0.79, 1.57) 1.38 (0.94, 2.04) Q4 2.06 (1.54, 2.78) * 2.08 (1.54, 2.83) * 3.10 (1.99, 4.85) * P for trend <0.001 <0.001 <0.001 †Model 2 adjusts for: Age, Sex, Education, Location, Marital Status. ‡Model 3 adjusts for all variables in Model 2 plus: Smoking, Drinking, Sleep_time, SBP, DBP. *** p < 0.05, ** p < 0.01, *** p < 0.001* OR: Odds Ratio; CI: Confidence Interval; IQR: Interquartile Range. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 18 May, 2026 Reviews received at journal 18 May, 2026 Reviewers agreed at journal 18 May, 2026 Reviews received at journal 14 May, 2026 Reviewers agreed at journal 13 May, 2026 Reviewers agreed at journal 12 May, 2026 Reviewers agreed at journal 12 May, 2026 Reviewers agreed at journal 07 May, 2026 Reviewers invited by journal 06 May, 2026 Editor invited by journal 05 Mar, 2026 Editor assigned by journal 26 Feb, 2026 Submission checks completed at journal 26 Feb, 2026 First submitted to journal 25 Feb, 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. 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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-8972599","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":640359692,"identity":"ea9bbdbf-26b6-4294-b443-66e8c7319963","order_by":0,"name":"Quan Zhou","email":"","orcid":"","institution":"Hubei University of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Quan","middleName":"","lastName":"Zhou","suffix":""},{"id":640359694,"identity":"7d5a44a2-8797-456e-9aaa-ffbde924db66","order_by":1,"name":"Mingcheng Chen","email":"","orcid":"","institution":"Hubei University of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Mingcheng","middleName":"","lastName":"Chen","suffix":""},{"id":640359695,"identity":"c4cd8958-fac7-4ded-8c2e-a3942b0c2ecc","order_by":2,"name":"Xiaojun Huang","email":"","orcid":"","institution":"Hubei University of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaojun","middleName":"","lastName":"Huang","suffix":""},{"id":640359697,"identity":"71317c55-da39-4774-98c0-c3958f164f55","order_by":3,"name":"Wentao Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIie3RsQrCMBCA4SuF6HDYTVoK9hUiWRz6MC0FJ0FHVxGc6t7iS/QRUgJOdRd0KBR0zdhJbFYREjeHfHN+jrsAWNYfmgA4bbJ9zbzRnnPZGyQEXJfKhrMgP6d1mZslJCgPPK2uKybGxCTx1zxEch+mNFIAQuRNuS7JgCE+hl2OldgsYF6eEk0SZpCh7w5TLpUoEBJ6M0gEUlft0gokZomzKxKhEjBMoo6B5Et1ZDoc2dfv4mH97NNXrL6yk7KPIy/UJJ/8355blmVZ370B0GtIP356ZfAAAAAASUVORK5CYII=","orcid":"","institution":"Hubei University of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Wentao","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2026-02-26 02:55:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8972599/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8972599/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109330698,"identity":"8b28a60c-d185-4393-b733-016c8081fc79","added_by":"auto","created_at":"2026-05-15 16:07:49","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":656053,"visible":true,"origin":"","legend":"\u003cp\u003eDose-response relationship between KDM-BAacc and CKD risk.\u003c/p\u003e\n\u003cp\u003eThe solid line represents the odds ratio (OR) for CKD, and the shaded area indicates the 95% confidence interval. The OR is set to 1 at the median of KDM-BAacc. The model is adjusted for age, sex, education, residence, marital status, alcohol consumption, smoking, and sleep duration.\u003c/p\u003e","description":"","filename":"Fig101.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8972599/v1/9b9de2d5279be84d664f6221.jpg"},{"id":109330699,"identity":"7c6d6fde-4189-433c-91a6-bc35b1b376ac","added_by":"auto","created_at":"2026-05-15 16:07:49","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1812918,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of the association between KDM-BAacc and CKD risk.\u003cbr\u003e\nForest plot displays odds ratios (OR) with 95% confidence intervals for CKD across demographic, behavioral, and clinical subgroups. The association was positive and statistically significant in most subgroups, including all age, sex, and residential categories. Non-significant associations were observed for non-married status and college education or above. All estimates are derived from multivariable logistic regression models adjusted for covariates in Model 3 (age, sex, education, residence, marital status, alcohol consumption, smoking, and sleep duration).\u003c/p\u003e","description":"","filename":"Fig201.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8972599/v1/b8c094f638de42332a8009c5.jpg"},{"id":109330700,"identity":"d22cf372-5cd4-4c11-9f82-08f0000591ce","added_by":"auto","created_at":"2026-05-15 16:07:49","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1170087,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction analysis between KDM-BAacc and selected covariates on CKD risk.\u003cbr\u003e\nThe forest plot illustrates the coefficient estimates (beta) with 95% confidence intervals for multiplicative interaction terms introduced into the fully adjusted logistic regression model. Statistically significant interactions (\u003cem\u003ep\u003c/em\u003e for interaction \u0026lt; 0.05) were identified for smoking history (especially ex-smoker status), education level (primary and high school groups), and sleep duration. No significant interaction was found for marital status, age, sex, blood pressure, residence, or alcohol consumption. The analysis suggests that the magnitude of the association between KDM-BAacc and CKD risk may be modified by these specific lifestyle and socioeconomic factors.\u003c/p\u003e","description":"","filename":"Fig301.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8972599/v1/1b7ee486e192907e5b0d6c05.jpg"},{"id":109330727,"identity":"b620fa7a-0c0d-4d6f-815b-3ef8faca9301","added_by":"auto","created_at":"2026-05-15 16:08:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3966934,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8972599/v1/c15f9e02-18c5-4efd-8a25-05bebb26f9c3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Biological Age Acceleration as a Marker of Chronic Kidney Disease: A Population-Based Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic kidney disease (CKD) has become an increasingly pressing global health issue, leading to substantial illness burden, premature deaths, and rising healthcare costs\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. From 1990 to 2017, both the prevalence and mortality of CKD showed marked upward trends\u0026mdash;cases increased by nearly one-third and deaths by more than 40%, amounting to approximately 35.8\u0026nbsp;million disability-adjusted life years (DALYs)\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Although chronological age is recognized as a major non-modifiable determinant of CKD\u0026mdash;with markedly higher rates observed in older adults\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u0026mdash;individuals of the same age often exhibit markedly different health statuses. This variability highlights the limitation of measuring aging solely as time elapsed since birth, as it does not fully reflect true physiological decline. To address this, the concept of biological age (BA) has been proposed. BA integrates multiple physiological indicators to better characterize an individual's functional status and degree of aging\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, thereby providing a more nuanced assessment of health than chronological age alone.\u003c/p\u003e \u003cp\u003eAmong various approaches, the Klemera\u0026ndash;Doubal method (KDM) is a widely used and validated algorithm for estimating BA\u003csup\u003e5\u003c/sup\u003e. It combines a panel of clinical biomarkers into a composite score representing overall physiological integrity. The discrepancy between BA and chronological age, commonly termed BA acceleration, captures whether a person is aging more rapidly or more slowly than expected. A positive acceleration value indicates that an individual's physiological profile resembles that of someone older than their actual age, suggesting an accelerated aging process.\u003c/p\u003e \u003cp\u003ePrevious research has shown that individuals with higher biological age tend to experience a greater burden of aging-related diseases, including elevated risks of mortality, cardiovascular disorders, and cognitive impairment\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. However, the specific nature of the association between BA acceleration and CKD risk remains unclear, particularly in terms of the dose-response relationship in large, community-based populations. Clarifying this relationship is important for understanding how systemic physiological dysregulation contributes to renal impairment. To address this research gap, we examined the association between accelerated biological aging and CKD in a population-based sample of middle-aged and elderly Chinese individuals from the China Health and Retirement Longitudinal Study (CHARLS). We employed a comprehensive analytical strategy, incorporating categorical, continuous, and flexible modeling approaches, to rigorously characterize this association.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eBaseline characteristics\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;1 outlines the baseline characteristics of the 6,830 participants, stratified by quartiles of KDM-BAacc. The mean age of the study population was 58.4 years, and 54.1% were female. As BA acceleration increased across quartiles, participants exhibited progressively less favourable health profiles. Individuals in the highest quartile (Q4) were more likely to be male, current smokers, and frequent alcohol drinkers, with all comparisons showing significant differences. Q4 participants also had a substantially higher prevalence of cardiometabolic conditions, including diabetes (13.4% in Q4 vs. 4.9% in Q1), dyslipidemia, and hypertension. In terms of biomarker profiles, those in Q4 demonstrated elevated levels of urea, creatinine, and cystatin C, along with higher glycated hemoglobin, total cholesterol, and LDL-C. They also had lower platelet counts and higher hs-CRP concentrations. Together, these patterns indicate broad physiological dysregulation across multiple systems among individuals with the greatest BA acceleration.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eAssociation between KDM-BAacc and CKD\u003c/h3\u003e\n\u003cp\u003eLogistic regression analyses validated a significant association between KDM-BAacc and CKD. In the initial univariate analysis (Model 1), every interquartile range increment in KDM-BAacc demonstrated a significant 43% elevation in the likelihood of developing CKD (OR\u0026thinsp;=\u0026thinsp;1.43, 95% CI: 1.26\u0026ndash;1.63,\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This robust correlation persisted with minimal variation following the incorporation of sociodemographic covariates in Model 2 (OR\u0026thinsp;=\u0026thinsp;1.43, 95% CI: 1.25\u0026ndash;1.63,\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After further adjustment for lifestyle and clinical factors in Model 3, the association strengthened markedly, with each IQR increase in KDM-BAacc linked to a 113% increase in the odds of CKD (OR\u0026thinsp;=\u0026thinsp;2.13, 95% CI: 1.68\u0026ndash;2.70,\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Stratification by KDM-BAacc quartiles demonstrated a significant dose-dependent association with CKD risk. In the fully adjusted multivariate model (Model 3), when compared to individuals in the lowest quartile (Q1), the adjusted odds ratios for CKD incidence progressively increased across higher quartiles: 1.47 (95% CI: 1.04\u0026ndash;2.09) for Q2, 1.38 (0.94\u0026ndash;2.04) for Q3, and 3.10 (1.99\u0026ndash;4.85) for Q4. Although the OR for Q3 was slightly lower than that for Q2, the overall trend across quartiles remained highly significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), showing a consistent increase in risk (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This pattern was further supported by the restricted cubic spline (RCS) analysis.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eDose-response relationship analysis\u003c/h3\u003e\n\u003cp\u003eRCS analysis showed a significant overall association between KDM-BAacc and CKD risk (Wald \u0026chi;\u0026sup2; = 28.81, 2 df,\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The test for non-linearity was non-significant (\u003cem\u003ep\u003c/em\u003efor non-linearity\u0026thinsp;=\u0026thinsp;0.7576), indicating that a linear relationship adequately described the association. As illustrated in Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the OR for CKD increased steadily across the full range of KDM-BAacc values, reinforcing the dose-response pattern observed in the quartile analyses. No threshold or saturation effect was evident. In Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the solid line illustrates the OR for CKD, while the shaded region indicates the associated 95% confidence interval. The OR is standardized to a value of 1 at the median of the KDM-BAacc measure. The model was adjusted for age, sex, education, residential location, marital status, alcohol consumption, smoking status, and sleep duration.\u003c/p\u003e\n\u003ch3\u003eSubgroup and interaction analyses\u003c/h3\u003e\n\u003cp\u003eSubgroup analyses showed a consistently positive association between KDM-BAacc (per IQR increase) and CKD across nearly all examined strata, including age (\u0026lt;\u0026thinsp;65 vs. \u0026ge;65 years), sex, residential location, and smoking. In all subgroups, the odds ratios exceeded 1.9 and reached statistical significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The association appeared stronger among married individuals and participants reporting more than 6 hours of sleep per night. Interaction analyses further revealed significant effect modification by smoking history (particularly among former smokers), education level (primary school and high school groups), and sleep duration, as indicated by significant interaction terms (\u003cem\u003ep\u003c/em\u003e for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eSensitivity analyses\u003c/h3\u003e\n\u003cp\u003eSensitivity analyses demonstrated a robust positive association between KDM-BAacc and CKD across the majority of examined subgroups. In the fully adjusted model (Model 3), an interquartile range (IQR) increment in KDM-BAacc corresponded to a 92% elevated risk of CKD (OR\u0026thinsp;=\u0026thinsp;1.92, 95% CI: 1.60\u0026ndash;2.30). A statistically significant dose-response pattern was evident across KDM-BAacc quartiles (P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This linear association was further substantiated by restricted cubic spline (RCS) modeling (\u003cem\u003eP\u003c/em\u003e for nonlinearity\u0026thinsp;=\u0026thinsp;0.758). Significant effect modification was observed, with interactions identified for smoking status, educational attainment, and sleep duration (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for each), suggesting that the relationship between KDM-BAacc and CKD risk is modified by these factors ( Supplementary materials).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis epidemiological investigation revealed a statistically significant correlation between KDM-BAacc and the development of CKD. Baseline comparisons showed progressively poorer health profiles across increasing KDM-BAacc quartiles, including higher prevalences of diabetes, hypertension, and dyslipidemia, accompanied by adverse biomarker patterns. Logistic regression analyses demonstrated that each interquartile range increase in KDM-BAacc was associated with a substantially elevated odds of CKD, an association that became stronger after full adjustment for sociodemographic, lifestyle, and clinical factors (OR\u0026thinsp;=\u0026thinsp;2.13). Both the quartile-based analyses and RCS models supported a linear dose\u0026ndash;response relationship, with no indication of threshold or saturation effects. Subgroup analyses further confirmed the consistency of this association across a wide range of demographic and behavioral characteristics, while interaction analyses revealed significant effect modification by smoking history, education level, and sleep duration. Sensitivity analyses confirmed consistent results, further supporting the main results. Collectively, these findings establish KDM-BAacc as an independent and robust risk factor for CKD and highlight its potential value in risk stratification and early prevention strategies.\u003c/p\u003e \u003cp\u003eKDM-BAacc has increasingly been used to quantify the pace of biological aging and has been examined in relation to several age-related diseases, including cardiovascular, neurodegenerative, and renal disorders\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Previous research on aging markers and CKD has primarily focused on epigenetic aging indicators such as Horvath's clock\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. In contrast, evidence on KDM-BAacc, which is based on clinical biomarkers rather than DNA methylation profiles, remains limited. Some prospective cohort studies have reported associations between epigenetic age acceleration and renal function decline or CKD incidence, although effect sizes were generally modest (ORs around 1.1\u0026ndash;1.3 per IQR increase) and often estimated without extensive adjustment for confounders\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. A major strength of the present study is its demonstration of a comparatively strong association between KDM-BAacc and CKD risk in a large community-based sample, with a fully adjusted OR of 2.13 and a clearly delineated linear dose\u0026ndash;response relationship. These findings are consistent with the few comparable studies available\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, though the magnitude of association observed here is notably larger, potentially reflecting more comprehensive covariate adjustment or population-specific influences. Furthermore, prior studies seldom evaluated effect modification by lifestyle or psychosocial factors, whereas our analysis identified significant interactions with smoking history, education level, and sleep duration. These results underscore the possibility that the impact of accelerated biological aging on renal health may differ across population subgroups, suggesting avenues for more tailored risk assessment. Overall, our findings strengthen the evidence supporting the utility of KDM-BAacc in predicting CKD risk and indicate that it may offer advantages over certain traditional aging metrics.\u003c/p\u003e \u003cp\u003eThe mechanisms linking KDM-BAacc to CKD are likely multifactorial and may involve several interconnected physiological pathways. Firstly, accelerated aging is characterized by key biological processes such as cellular senescence, telomere shortening, and elevated oxidative stress, all of which can directly damage glomerular and tubular structures, promote renal fibrosis, and contribute to progressive loss of kidney function\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Secondly, the strong correlations observed in this study between KDM-BAacc and cardiometabolic risk factors, including diabetes, hypertension, and dyslipidemia, suggest that metabolic dysregulation and chronic inflammation may act as important mediators. Elevated hs-CRP and glycated hemoglobin levels among individuals with high BA acceleration reflect systemic inflammation and impaired glucose metabolism, both well-recognized contributors to renal microenvironment injury\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Additionally, accelerated biological aging may influence CKD risk through epigenetic mechanisms, such as aberrant DNA methylation patterns that modify the expression of genes involved in renal homeostasis\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Another relevant mechanism concerns the interplay between physiological dysregulation and the natural age-related decline in renal function. Individuals with accelerated biological aging often demonstrate multisystem impairment, as reflected in the elevated urea and creatinine levels and hematological abnormalities observed in this study. Such cumulative organ dysfunction likely heightens susceptibility to CKD progression\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Finally, lifestyle factors identified as effect modifiers, particularly smoking and insufficient sleep, may further amplify the detrimental impact of BA acceleration on renal health by intensifying oxidative stress, inflammation, and immune dysregulation\u003csup\u003e[21]\u003c/sup\u003e. Together, these pathways form a plausible mechanistic framework supporting the strong and linear association observed between KDM-BAacc and CKD risk.\u003c/p\u003e \u003cp\u003eThere are several notable strengths in this study. This study utilizes a comprehensive, nationally representative dataset, which strengthens the external validity of its conclusions. The use of detailed multivariable adjustment strengthens the robustness of the observed associations, while the application of RCS analysis provides compelling evidence for a linear dose\u0026ndash;response relationship that extends beyond simple linear assumptions. Furthermore, subgroup and interaction analyses offer valuable insights into potential effect modification, contributing to a more nuanced understanding of how BA acceleration may influence CKD risk among different population groups. Nevertheless, several limitations should be acknowledged. Firstly, the cross-sectional design precludes causal inference. Prospective cohort studies or interventional trials are needed to confirm whether KDM-BAacc predicts CKD incidence or progression. Secondly, although extensive covariates were included, the possibility of residual confounding, such as genetic susceptibility, unmeasured environmental exposures, or medication use, cannot be excluded. For instance, the biological mechanisms underlying interactions with sleep duration and marital status remain unclear and warrant further investigation. Additionally, the study population's specific characteristics may limit generalizability to other demographic or ethnic groups. Future research should incorporate multi-omics data, including genomic and epigenomic markers, to deepen mechanistic insights. Developing dynamic measures of BA acceleration that capture changes over time would also enhance understanding of aging trajectories. From a clinical perspective, integrating KDM-BAacc or similar biomarkers into CKD risk assessment tools could facilitate earlier identification of high-risk individuals and support targeted preventive strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThe dataset was obtained from the initial 2011 survey of the China Health and Retirement Longitudinal Study (CHARLS), which represents a nationally representative cohort of Chinese residents aged 45 years or older. This cohort was constructed using a rigorous, multi-stage probability sampling methodology to ensure population representativeness\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The study collects detailed information on participants' demographic background, socioeconomic circumstances, health status, and biochemical indicators. For the current analysis, we included individuals who provided blood samples and had complete biomarker data necessary for computing KDM-based biological age as well as for determining CKD status. The original number of participants was 17,708. After applying these criteria, 6,830 participants remained eligible for inclusion. The CHARLS project received ethical approval from the Peking University Institutional Review Board (IRB00001052\u0026ndash;11015), and written informed consent was obtained from all participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCalculation of KDM-BA acceleration\u003c/h2\u003e \u003cp\u003eBA was estimated utilizing the Klemera\u0026ndash;Doubal method (KDM), an established algorithm that integrates multiple biomarkers representing the functional status of various physiological systems. Following prior research, the KDM-BA algorithm was trained via data from the China Health and Nutrition Survey (CHNS), which included adults aged 20\u0026ndash;79 years. For the CHARLS population, we applied an adapted and previously validated version of the KDM - BA model for Chinese adults\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The revised predictive model integrated eight key clinical indicators: serum cholesterol levels, triglyceride concentrations, glycated hemoglobin (HbA1c), blood urea, serum creatinine, high - sensitivity C - reactive protein (hsCRP), platelet count, and systolic arterial pressure measurements\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. These biomarkers capture renal function (e.g., creatinine), metabolic status (e.g., HbA1c and lipid profiles), inflammation (hsCRP), and hematopoietic function (platelet count), all of which were available in the CHARLS dataset. KDM - based BA acceleration (KDM-BAacc) was calculated as the residual from a linear regression of KDM-estimated BA on chronological age. A positive residual indicates accelerated aging, meaning that an individual's BA exceeds their chronological age; conversely, a negative residual reflects slower than expected aging. For statistical analyses, KDM-BAacc was examined both as a continuous variable\u0026mdash;standardized by its interquartile range to improve interpretability\u0026mdash;and as a categorical variable divided into quartiles, with Q1 indicating the slowest aging and Q4 the fastest.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of CKD\u003c/h2\u003e \u003cp\u003eCKD was identified based on participants' self - reported physician diagnosis. Participants who had been previously diagnosed with chronic kidney disease prior to the study's initiation in 2011 were excluded in the cohort. For the remaining participants, CKD status was validated during the 2015 follow - up, where respondents directly confirmed the accuracy of their previously reported diagnosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eDrawing on prior literature and guided by directed acyclic graphs, we selected a comprehensive set of covariates to address potential confounding. Sociodemographic factors included chronological age (treated as a continuous variable), sex, educational attainment (categorized as college or above, high school, or primary school and below), place of residence (urban or rural), and marital status (married versus unmarried, separated, or widowed). Lifestyle characteristics considered in the models were smoking behaviour (current, former, or never smoker), alcohol consumption (never, occasional use\u0026mdash;less than once per month\u0026mdash;or more frequent intake), and average sleep duration per night (hours, continuous). Clinical indicators incorporated into the adjustment set were systolic and diastolic blood pressure (both continuous, mmHg). These covariates were included to minimize residual confounding as much as possible.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eBaseline characteristics of the study population were summarized overall and across quartiles of KDM-BAacc. Depending on their distribution, continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range), with between - group comparisons performed using parametric ANOVA or non-parametric Kruskal\u0026ndash;Wallis tests as appropriate. For categorical variables, we reported absolute numbers and proportions, with statistical significance assessed through Pearson's chi - square tests. The association between KDM-BAacc and CKD was evaluated using multivariable logistic regression. Three progressively adjusted regression models were developed with sequential covariate adjustments: The baseline Model 1 contained no covariates; Model 2 incorporated adjustments for key sociodemographic characteristics including age, gender, educational attainment, geographic residence, and marital status; while Model 3 extended these adjustments to encompass behavioral and clinical covariates such as tobacco use, alcohol intake, nightly sleep duration, and both systolic and diastolic blood pressure measurements. Results were presented as odds ratios (ORs) with 95% confidence intervals (CIs). A p value for linear trend across KDM-BAacc quartiles was obtained by modeling the quartile variable as a continuous term. To assess the dose - response relationship between continuous KDM-BAacc and CKD risk without assuming linearity, restricted cubic spline (RCS) models with three knots positioned at the 10th, 50th, and 90th percentiles of the KDM-BAacc distribution were fitted, adjusting for all covariates included in Model 3. Linearity was evaluated using a likelihood ratio test comparing the spline model with a corresponding linear model. Subgroup analyses were performed by applying Model 3 within strata of key demographic and clinical characteristics. Multiplicative interactions were assessed by introducing cross- product terms into the regression models and evaluating significance using Wald tests. For sensitivity analysis,to handle potential bias from missing data and verify the robustness of our findings, we employed multiple imputation using the `mice` package (m\u0026thinsp;=\u0026thinsp;5 imputations, maxit\u0026thinsp;=\u0026thinsp;10, method = \"pmm\"). The regression analysis and subgroup analysis were repeated across the imputed datasets, and the results were pooled. All statistical analyses were conducted using R software (version 4.1.0; R Foundation for Statistical Computing, Vienna, Austria). Statistical significance was defined as a two - sided p - value of less than 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the China Health and Retirement Longitudinal Study (CHARLS) but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the corresponding author upon reasonable request and with permission of CHARLS.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work did not receive any financial support. \u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003eQZ, MCC: Conceptualization, Methodology, Formal analysis, Writing \u0026ndash; original draft.\u003c/p\u003e\n\u003cp\u003eQZ,XJH: Data curation, Validation, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eXJH, ETL: Supervision, Project administration, Funding acquisition.\u003c/p\u003e\n\u003cp\u003eAll authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eAdditional information\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eCorrespondence\u0026nbsp;\u003c/strong\u003eand requests for materials should be addressed to [Wentao Luo and Xiaojun Huang].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher\u0026rsquo;s note\u0026nbsp;\u003c/strong\u003eSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJha, V. et al. 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P., Strauss, J. \u0026amp; Yang, G. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). \u003cem\u003eInt. J. Epidemiol.\u003c/em\u003e \u003cb\u003e43\u003c/b\u003e, 61\u0026ndash;68 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL\u0026oacute;pez-Ot\u0026iacute;n, C., Blasco, M. A., Partridge, L., Serrano, M. \u0026amp; Kroemer, G. \u003cem\u003eHallm. aging Cell\u003c/em\u003e \u003cb\u003e153\u003c/b\u003e, 1194\u0026ndash;1217 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, B. H. et al. DNA methylation-based measures of biological age: meta-analysis predicting time to death.\u003cem\u003eAging.\u003c/em\u003e8, 1844\u0026ndash;1865 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, X., Liu, H. \u0026amp; Li, Y. Epigenetic age acceleration and chronic kidney disease: a review. \u003cem\u003eCurr. 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J., McKay, G. J., Maxwell, A. P. \u0026amp; McKnight, A. J. DNA hypermethylation and DNA hypomethylation is present at different loci in chronic kidney disease.\u003cem\u003eEpigenetics\u003c/em\u003e.\u003cb\u003e9\u003c/b\u003e, 366\u0026ndash;377 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'Sullivan, E. D., Hughes, J. \u0026amp; Ferenbach, D. A. Renal aging: causes and consequences. \u003cem\u003eJ. Am. Soc. Nephrol.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e, 407\u0026ndash;420 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRicardo, A. C. \u0026amp; Madero, M. Lifestyle and kidney disease. In\u003cem\u003eChronic Kidney Disease, Dialysis, and Transplantation.\u003c/em\u003e153\u0026ndash;165 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, Y., Hu, Y., Smith, J. P., Strauss, J. \u0026amp; Yang, G. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). \u003cem\u003eInt. J. Epidemiol.\u003c/em\u003e \u003cb\u003e43\u003c/b\u003e, 61\u0026ndash;68 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, Z. Development and Validation of 2 Composite Aging Measures Using Routine Clinical Biomarkers in the Chinese Population: Analyses From 2 Prospective Cohort Studies. \u003cem\u003eJ. Gerontol. Biol. Sci. Med. Sci.\u003c/em\u003e \u003cb\u003e76\u003c/b\u003e, 1627\u0026ndash;1632 (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Baseline Characteristics of Study Participants Stratified by KDM Biological Age Acceleration Quartiles\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"944\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall (N=6830)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1 (Slowest) (N=1708)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2 (N=1707)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3 (N=1707)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ4 (Fastest) (N=1708)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eDemographics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eAge, years (Mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e58.4 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e58.9 (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e57.8 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e58.3 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e58.7 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eMale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e3135 (45.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e469 (27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e646 (37.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e924 (54.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e1096 (64.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eEducation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003ePrimary school or below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e4789 (70.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e1299 (76.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1194 (69.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1151 (67.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e1145 (67.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e1840 (26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e378 (22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e465 (27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e495 (29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e502 (29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eCollege or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e201 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e31 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e48 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e61 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e61 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eLocation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eCity/Town\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e1087 (15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e200 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e258 (15.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e312 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e317 (18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eVillage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e5735 (84.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e1507 (88.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1446 (84.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1394 (81.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e1388 (81.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eMarital Status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e6088 (89.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e1515 (88.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1533 (90.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1518 (88.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e1522 (89.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eNever/Separated/Widowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e737 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e192 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e171 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e189 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e185 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eLifestyle Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eSmoking Status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eCurrent Smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e2008 (29.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e356 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e440 (26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e567 (34.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e645 (38.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eEx-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e545 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e87 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e112 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e160 (9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e186 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eNon-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e4153 (61.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e1255 (73.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1122 (67.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e940 (56.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e836 (50.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eDrinking Status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026gt;Once/month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e1332 (20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e221 (13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e279 (17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e397 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e435 (27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026le;Once/month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e534 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e114 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e144 (8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e128 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e148 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e4555 (70.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e1297 (79.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1202 (74.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1063 (66.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e993 (63.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eSleep Time, hours (Mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e6.4 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e6.3 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e6.4 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e6.4 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e6.5 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eClinical Measures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eSBP, mmHg (Mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e129.1 (21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e112.0 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e122.3 (12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e132.0 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e150.7 (20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eDBP, mmHg (Mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e75.3 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e66.8 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e72.3 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e77.4 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e85.0 (11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eComorbidities, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e579 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e82 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e97 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e176 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e224 (13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eDyslipidemia (Mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e0.2 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e0.2 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.2 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.3 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.3 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eHypertension (Mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e0.4 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e0.1 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.2 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.5 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.8 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eCardiovascular Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e894 (13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e203 (12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e212 (12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e216 (12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e263 (15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eBiomarkers (Mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eTotal Cholesterol, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e193.2 (38.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e183.5 (35.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e191.6 (37.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e195.2 (37.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e202.5 (40.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eLDL-C, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e116.2 (34.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e107.3 (31.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e114.7 (32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e118.1 (33.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e124.5 (38.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eHDL-C, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e50.9 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e50.5 (14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e51.5 (15.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e51.4 (16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e50.2 (15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eTriglycerides, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e134.7 (110.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e137.7 (142.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e131.0 (104.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e132.0 (95.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e138.3 (90.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eUrea, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e4.4 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e3.6 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4.1 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e4.6 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e5.3 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eCreatinine, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e0.8 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e0.7 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.7 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.8 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.9 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eCystatin C, mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e1.0 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e0.9 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.0 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.0 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e1.0 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eGlycated Hemoglobin, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e5.3 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e5.1 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e5.2 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e5.3 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e5.5 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003ehs-CRP, mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e0.1 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e0.0 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.0 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.1 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.1 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003ePlatelet Count, 10⁹/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e212.5 (76.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e234.8 (91.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e216.6 (70.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e204.1 (68.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e194.4 (66.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eSD: Standard Deviation; SBP: Systolic Blood Pressure; DBP: Diastolic Blood Pressure; LDL-C: Low-Density Lipoprotein Cholesterol; HDL-C: High-Density Lipoprotein Cholesterol; hs-CRP: high-sensitivity C-Reactive Protein.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eQ1-Q4: Quartiles of KDM Biological Age Acceleration.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ep-values are derived from ANOVA for continuous variables and Chi-square test for categorical variables.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Multivariable Logistic Regression Analysis of the Association Between KDM Biological Age Acceleration and Chronic Kidney Disease Risk\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"944\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1 (Crude) OR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 300px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2 (Sociodemographic)\u0026dagger; OR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 272px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3 (Fully Adjusted)\u0026Dagger; OR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKDM_BAacc (per IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.43 (1.26, 1.63)\u003c/strong\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 300px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.43 (1.25, 1.63)\u003c/strong\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 272px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.13 (1.68, 2.70)\u003c/strong\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKDM_BAacc Quartiles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 300px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 272px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1 (Reference)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 300px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 272px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e1.22 (0.88, 1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 300px;\"\u003e\n \u003cp\u003e1.31 (0.94, 1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 272px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.47 (1.04, 2.09)\u003c/strong\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e1.11 (0.79, 1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 300px;\"\u003e\n \u003cp\u003e1.12 (0.79, 1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 272px;\"\u003e\n \u003cp\u003e1.38 (0.94, 2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.06 (1.54, 2.78)\u003c/strong\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 300px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.08 (1.54, 2.83)\u003c/strong\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 272px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.10 (1.99, 4.85)\u003c/strong\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 300px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 272px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e\u0026dagger;Model 2 adjusts for: Age, Sex, Education, Location, Marital Status.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026Dagger;Model 3 adjusts for all variables in Model 2 plus: Smoking, Drinking, Sleep_time, SBP, DBP.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e*** p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001*\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOR: Odds Ratio; CI: Confidence Interval; IQR: Interquartile Range.\u003c/em\u003e\u003c/p\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Biological Age, Aging, Chronic Kidney Disease, KDM, CHARLS, Risk Factor, Dose-Response","lastPublishedDoi":"10.21203/rs.3.rs-8972599/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8972599/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChronic kidney disease (CKD) remains a major global health concern. Biological age (BA), which reflects the functional state of multiple physiological systems, may capture aging-related vulnerability more effectively than chronological age. This study evaluated whether acceleration in BA\u0026mdash;estimated by the Klemera\u0026ndash;Doubal method (KDM)\u0026mdash;is linked to CKD in a nationally representative Chinese cohort. The original number of participants was 17,708. Data were drawn from 6,830 participants in the baseline wave of the China Health and Retirement Longitudinal Study (CHARLS). KDM-based BA acceleration (KDM-BAacc) was calculated as the residual from regressing biological age on chronological age and categorized into quartiles. CKD was identified from self-reported physician diagnoses. Logistic regression models with stepwise adjustment for sociodemographic, lifestyle, and clinical factors were applied to examine the association. Each interquartile range increase in KDM-BAacc was linked with markedly higher odds of CKD (OR\u0026thinsp;=\u0026thinsp;2.13, 95% CI: 1.68\u0026ndash;2.70,\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Participants in the top quartile of acceleration exhibited a risk of CKD that was over three times greater compared to those in the bottom quartile (OR\u0026thinsp;=\u0026thinsp;3.10, 95% CI: 1.99\u0026ndash;4.85). Restricted cubic spline models supported a steady, approximately linear increase in CKD risk across the entire spectrum of BA acceleration (\u003cem\u003ep\u003c/em\u003efor non-linearity\u0026thinsp;=\u0026thinsp;0.758). Subgroup analyses showed consistency across demographic strata, with significant interactions observed for smoking history, education level, and sleep duration. Sensitivity analyses yielded consistent results. KDM-derived biological age acceleration is a strong, independent, and linearly increasing risk factor for CKD, suggesting that BA may hold substantial value for CKD risk prediction and early identification.\u003c/p\u003e","manuscriptTitle":"Biological Age Acceleration as a Marker of Chronic Kidney Disease: A Population-Based Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-15 16:07:45","doi":"10.21203/rs.3.rs-8972599/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-18T16:29:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-18T10:21:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256215030974066637047492858610189770937","date":"2026-05-18T10:00:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-14T14:01:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"193302273390112964646266955741926040883","date":"2026-05-13T07:50:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"133589401493474890374705739195341237319","date":"2026-05-12T09:40:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159857314342098925052800144702157670182","date":"2026-05-12T08:56:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"308640359824404371690706202951804248834","date":"2026-05-07T16:32:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-06T09:26:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-05T10:51:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-26T13:40:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-26T13:39:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-26T02:38:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cc1915d7-2919-4fd1-a113-6e11b1bf319e","owner":[],"postedDate":"May 15th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-18T16:29:52+00:00","index":103,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-18T10:21:34+00:00","index":102,"fulltext":""},{"type":"reviewerAgreed","content":"256215030974066637047492858610189770937","date":"2026-05-18T10:00:43+00:00","index":101,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-14T14:01:54+00:00","index":100,"fulltext":""},{"type":"reviewerAgreed","content":"193302273390112964646266955741926040883","date":"2026-05-13T07:50:13+00:00","index":98,"fulltext":""},{"type":"reviewerAgreed","content":"133589401493474890374705739195341237319","date":"2026-05-12T09:40:32+00:00","index":97,"fulltext":""},{"type":"reviewerAgreed","content":"159857314342098925052800144702157670182","date":"2026-05-12T08:56:45+00:00","index":96,"fulltext":""},{"type":"reviewerAgreed","content":"308640359824404371690706202951804248834","date":"2026-05-07T16:32:33+00:00","index":88,"fulltext":""},{"type":"reviewersInvited","content":"12","date":"2026-05-06T09:26:16+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":68146615,"name":"Health sciences/Diseases"},{"id":68146616,"name":"Health sciences/Health care"},{"id":68146617,"name":"Health sciences/Medical research"},{"id":68146618,"name":"Health sciences/Nephrology"},{"id":68146619,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-15T16:07:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-15 16:07:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8972599","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8972599","identity":"rs-8972599","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}