Sex-Specific Mediation of Biological Age Acceleration in the Associations between Cardiovascular– Kidney–Metabolic and All-Cause Mortality

Sex-Specific Mediation of Biological Age Acceleration in the Associations between Cardiovascular– Kidney–Metabolic and All-Cause Mortality | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sex-Specific Mediation of Biological Age Acceleration in the Associations between Cardiovascular– Kidney–Metabolic and All-Cause Mortality Jiawen Ke, Zuolin Lu, He Xu, Yue Zhang, Yachen Wang, Xiaoxia Wei, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7951175/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Cardiovascular-kidney-metabolic (CKM) syndrome represents a major contributor to premature mortality and is strongly linked to the aging process. However, it remains unclear whether the association between CKM and mortality risk is mediated through biological aging acceleration (BAacc), particularly with respect to potential sex-specific differences. Methods We included 12,722 participants from the China Health and Retiremalet Longitudinal Study (CHARLS) from 2011 to 2020. BAacc was estimated using the Klemera-Doubal method. Mediation analysis was used to assess the mediating role of BAacc in associations between CKM and all-cause mortality. Models were adjusted for key cardiovascular risk factors. Results During the follow-up, 439 participants (3.45%) died. After multivariable adjustment, CKM was significantly associated with an increased risk of all-cause mortality in both men (hazard ratio [HR], 1.33; 95% confidence interval [CI], 1.30–1.36) and women (1.28; 1.25–1.31). Notably, the mediating effect of biological aging acceleration (BAacc) on the association between CKM and mortality was more pronounced among women (natural indirect effect [NIE]: 1.05; 1.02–1.08) than among men (1.02; 1.00-1.05; P-for-interaction = 0.01). Regarding individual CKM components, the associations of hypertension (1.24; 1.05–1.47 for women vs. 1.06; 0.93–1.21 for men; P-for-interaction = 0.04), diabetes (1.15; 1.05–1.26 for women vs. 1.06; 0.98–1.14 for men), and heart disease (1.03; 1.00-1.06 for women vs. 1.02; 1.00-1.04 for men) with mortality were significantly mediated by BAacc among women but not among men. BAacc did not significantly mediate the associations of stroke or chronic kidney disease with mortality in either sex. Conclusions We found that aging acceleration significantly mediates the association between CKM and all-cause mortality, and this mediating pathway appears more pronounced among women. These findings emphasize the importance of incorporating aging-targeted strategies into CKM management to reduce residual mortality risk. cardiovascular–kidney–metabolic syndrome biological age all-cause mortality mediation analysis Figures Figure 1 Figure 2 Figure 3 Research Insights What is currently known about this topic? (max. 3 highlights) CKM syndrome is a major cause of premature death and is closely tied to aging. It is unclear whether biological aging acceleration mediates the link between CKM and mortality, with attention to sex-specific differences. What is the key research question? (formatted as a question) How does biological aging acceleration mediate the association between CKM syndrome and all-cause mortality across sexes? What is new? (max. 3 highlights) This is the first cohort study to investigate the sex-specific mediating role of biological age in the relationship between CKM syndrome and mortality. Furthermore, it highlights that the mediation effect can be largely attributed to cardiometabolic disease, rather than cerebrovascular or kidney disorders. How might this study influence clinical practice? (max. 1 highlight) Findings could inform personalized interventions to delay or reverse aging in CKM syndrome, particularly among women, to reduce mortality risk. Background Cardiovascular diseases (CVD) and metabolic dysfunction represent a significant global health burden, contributing substantially to mortality and disability worldwide ( 1 , 2 ). With the acceleration of global population aging, the prevalence of CVD and metabolic dysfunction are expected to rise markedly by 2050 ( 3 , 4 ). Cardiovascular-kidney-metabolic syndrome (CKM), newly defined by American Heart Association (AHA) ( 5 ), is characterized by the coexistence of metabolic dysfunction, chronic kidney disease (CKD), and cardiovascular impairment. Adverse CKM status has been associated with multi-organ damage and premature mortality ( 6 ). Given the complex interplay among cardiovascular, renal, and metabolic systems, it is crucial to clarify how CKM contributes to overall mortality risk. Biological age acceleration (BAacc), reflects the extent to which an individual’s biological age (BA) exceeds what would be expected for their chronological age, indicating physiological deterioration and accelerated aging ( 7 – 9 ). Accelerated DNA methylation age was associated with increased all-cause mortality among individuals with cardiometabolic disease ( 10 ), indicating a potential mediation role of BAacc in the relationship between CKM and mortality. Lifestyle and pharmacological interventions may slow or reverse biological aging and reduce mortality ( 11 , 12 ). Elucidating the role of BAacc in the association between CKM and mortality is of importance for guiding clinical management and informing public health strategies aimed at lowering the risk of death among CKM patients. The epidemiology and pathophysiological mechanisms of CKM and its major components display pronounced sex-related differences. ( 13 , 14 ). Likewise, the biological aging process exhibits distinct trajectories between men and women. ( 15 , 16 ). Therefore, taking advantage of a large population-based cohort in China, this study aimed to investigate the sex-specific mediating role of BAacc in the association between CKM-and its individual components—and all-cause mortality. Methods Study population The primary analysis utilized data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative longitudinal survey conducted among adult residents in China ( 17 ). Written informed consent for participation in both the baseline and follow-up surveys was obtained from all participants or their legal guardians. The study protocol was approved by the Biomedical Ethics Committee of Peking University (IRB00001052-11015). This study was reported followed the STROBE guidelines ( 18 ). Detailed descriptions of the study design and cohort have been published previously ( 19 ). Data from three survey waves were utilized to establish a temporal sequence linking CKM status and baseline characteristics with biological age (assessed at wave 1 [2011] or wave 3 [2015]) and subsequent all-cause mortality (wave 5 [2020]). Because blood samples were collected only during waves 1 and 3, a dynamic baseline approach was applied to estimate biological aging acceleration (BAacc), thereby maximizing the inclusion of eligible participants. Among 19,108 participants initially enrolled, individuals with missing key variables (follow-up data, BA, CKM diagnosis, age, or sex) were excluded (N = 5,296). Additionally, values of each biomarker used to calculate BA exceeded three standard deviations from the mean were excluded (N = 1,090). Therefore, 12,722 participants were included in the final analysis ( Fig. 1 ) . Assessment of cardiovascular-kidney-metabolic syndrome Based on the guideline ( 5 ), CKM was classified into 5 stages. Stage 0 was defined as having a normal body mass index (BMI) < 23 kg/m 2 and a normal waist circumstance (WC) < 80 cm for women or < 90 cm for men. Stage 1 included individuals with elevated BMI ≥ 23 kg/m 2 , elevated WC (≥ 80 cm for women or ≥ 90 cm for men), or prediabetes diagnosed through fasting blood glucose (100–126 mg/dL) or glycated hemoglobin (5.7%-6.5%). Stage 2 comprised individuals with metabolic risk factors (elevated fasting serum triglycerides (≥ 135mg/dL), hypertension, diabetes, or metabolic syndrome ( 20 ), moderate-to-high-risk CKD (estimated glomerular filtration rate [eGFR] 45–60 mL/min/1.73m 2 ), or both. Metabolic syndrome is defined as the presence of 3 or more conditions: elevated WC, low high-density lipoprotein cholesterol levels < 40 mg/dL for male or < 50 mg/dL for female, elevated triglyceride levels ≥ 150 mg/dL, systolic blood pressure ≥ 130 mmHg or diastolic blood pressure ≥ 80 mmHg, or prediabetes). Stage 3 was defined by the presence of very high-risk CKD (eGFR 15–45 mL/min/1.73 m 2 ), self-reported CKD, or a predicted 10-year CVD risk ≥ 20% using the American Heart Association's Predicting Risk of CVD Events (PREVENT) equations (21). Stage 4 was identified based on self-reported heart disease or stroke among individuals with excess/dysfunctional adiposity, other metabolic risk factors, or CKD. Self-reported disease history was determined from response to the following questions in each wave: “Have you been diagnosed with hypertension, diabetes, CKD, heart disease (including heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems), or stroke?” or “Are you currently receiving any of the following treatments (Chinese traditional medicine/Western medicine/Other treatments/None of the above) for hypertension, diabetes, CKD, heart disease, stroke or their complications?” ( 22 ). In addition, hypertension was defined as a systolic blood pressure (SBP) ≥ 140 mmHg or a diastolic blood pressure (DBP) ≥ 90 mmHg, and diabetes was defined as a glycated hemoglobin (HbA1c) level ≥ 6.5%. Development of biological age acceleration We used the Klemera-Doubal method to calculate biological age and indicate the degree of ageing since it could captures distinct dimensions of aging ( 23 ). BA calculation was performed by “BioAge” R package ( 24 ). Biological age acceleration was calculated as the residual from a linear regression of BA on CA to adjust for the effect of CA. Positive BAacc values indicate accelerated physiological aging, whereas negative values reflect a comparatively younger clinical profile ( 25 ). Because four biomarkers-albumin, red blood cell count (RBC), ferritin, and transferrin-were unavailable in the CHARLS dataset, BA was estimated using the remaining eight biomarkers. Despite this limitation, these biomarkers have been shown to possess predictive validity for aging and aging-related outcomes in Chinese populations ( 25 ). The final set of biomarkers included total cholesterol (TC), triglycerides (TG), glycated hemoglobin (HbA1c), urea, creatinine, high-sensitivity C-reactive protein (hsCRP), platelet count (PLT), and SBP, representing various domains of physical functions. Biomarker values were log-transformed to approximate normal distribution, and further BA and BAacc were calculated. The BA calculation formula is expressed as follows: $$\:Biological\:age=\:\frac{{\sum\:}_{j=1}^{\:m}\left({x}_{j}-{q}_{j}\right)\left(\frac{{k}_{j}}{{s}_{j}^{2}}\right)+\frac{CA}{{s}_{BA}^{2}}}{{\sum\:}_{j=1}^{m}{\left(\frac{{k}_{j}}{{s}_{j}}\right)}^{2}+\frac{1}{{s}_{BA}^{2}}}$$ Where: $$\:{BA}_{E}=\frac{{\sum\:}_{j=1}^{m}({x}_{j}-{q}_{j})\left(\frac{{k}_{j}}{{s}_{j}^{2}}\right)}{{\sum\:}_{j=1}^{m}{\left(\frac{{k}_{j}}{{s}_{j}}\right)}^{2}}$$ $$\:{r}_{char}=\frac{{\sum\:}_{j=1}^{m}\frac{{r}_{j}^{2}}{\sqrt{1-{r}_{j}^{2}}}}{{\sum\:}_{j=1}^{m}\frac{{r}_{j}}{\sqrt{1-{r}_{j}^{2}}}}$$ and: $$\:{S}_{BA}^{2}=\frac{{\sum\:}_{j=1}^{n}{(\left({BA}_{Ei}-{CA}_{I}\right)-\frac{{\sum\:}_{i=1}^{n}({BA}_{Ei}-{CA}_{i})}{n})}^{2}}{n}-\left(\frac{1-{r}_{char}^{2}}{{r}_{char}^{2}}\right)\times\:\left(\frac{{({CA}_{max}-{CA}_{min})}^{2}}{12m}\right)$$ The values j and i represent the number of biomarkers and samples respectively. The values k, q, and s are the regression slope, intercept, and the root means squared error of a biomarker regressed on chronological age, respectively. The value r j 2 represents the variance explained by regression of chronological age on biomarkers. Assessment of covariates Socioeconomic characteristics and lifestyle factors were obtained through standardized, face-to-face interviews conducted at baseline and during follow-up assessments ( 26 , 27 ). Educational attainment was categorized as elementary school or below, middle school, and high school or above. Marital status was classified as married, never married, or other (including separated, divorced, or widowed). Residential area was defined as rural or urban. Smoking and drinking behaviors were assessed based on current status and dichotomized as yes or no. Body mass index (BMI) was calculated as weight (kg) divided by height squared (m²). Height and weight were measured without shoes and in light clothing using calibrated devices. Waist circumference was measured at the midpoint between the lower rib margin and the iliac crest using a non-elastic tape, with participants standing and breathing normally. Blood pressure was measured on the left arm using a calibrated Omron HEM-7200 monitor, three consecutive readings were taken and the mean value was used for analysis. Blood samples were collected and analyzed following standardized laboratory procedures to ensure data comparability across participants. TC and TG were quantified using enzymatic colorimetric assays. HbA1c was determined by the boronate affinity high-performance liquid chromatography (HPLC) method. Urea was measured using an enzymatic ultraviolet (UV) method with urease, and creatinine concentrations were assessed by the rate-blanked and compensated Jaffe method. High-sensitivity C-reactive protein was analyzed using an immunoturbidimetric assay, and PLT was determined by an automated hematology analyzer based on electrical impedance or optical detection. All assays were conducted at the Youanmen Center for Clinical Laboratory, Capital Medical University, which is accredited by the Beijing Health Bureau and participates in regular external quality assessments organized by the Chinese Ministry of Health. Statistical analysis Baseline characteristics between men and women were compared. Continuous variables were presented as mean (standard deviation [SD]) or median (interquartile range [IQR]), as appropriate, whereas categorical variables were expressed as frequencies and percentages. Between-group differences were assessed using the Wilcoxon rank-sum test for continuous variables and the χ² test for categorical variables. Mediation analyses were conducted separately for men and women. To examine the pathway from CKM to biological age acceleration, linear regression models were applied. Regression coefficients (β) and corresponding 95% confidence intervals (CIs) per stage change in CKM status were reported. Cox proportional hazards models were employed to evaluate the associations of CKM and BAacc with all-cause mortality, estimating hazard ratios (HRs) with 95% CIs. Mediation analyses were subsequently conducted to assess whether BAacc mediated the effect of CKM on all-cause mortality, and to quantify the proportion of the total effect explained by the mediation. The total causal effect (TCE) of CKM on mortality was decomposed into the natural direct effect (NDE), representing effects not operating through biological age acceleration, and the natural indirect effect (NIE), representing effects mediated through biological age acceleration. TCE, NDE, and NIE were each reported as HRs with corresponding 95% CIs. The proportion mediated (PM) represents the share of the TCE attributable to the NIE. To evaluate potential effect modification by sex in the mediation pathway, sex and its interactions with the exposure and mediator were included in the mediation analyses of the overall population. The statistical significance of these interaction terms (P-interaction) was determined using analysis of variance (ANOVA) to compare models with and without the interaction terms, indicating whether the mediation effect differs by sex. Furthermore, the medication analysis was performed among each individual components of CKM, including hypertension, diabetes, heart disease, CKD, and stroke. Missing values were imputed using the median for continuous variables and the mode for categorical variables (all < 15%). Statistical analyses were conducted using R software (version 4.4.0), and a two-tailed P < 0.05 was considered statistically significant. Result Population characteristics Table 1 summarized the characteristics of participants stratified by sex. The biological age developed in this study showed good accuracy in r (0.989), MAE (1.141) and RMSE (1.459). Among the 12,722 participants (55% were female), the median age was 57 [49, 64] years old and the biological age was 57.17 [49.76, 64.18] years old. A total of 11,169 participants (87.79%) were in stage 1–4 CKM. The mean follow-up duration was 7.32 (1.97) years. Overall, 439 participants (3.45%) had died by the 2020 follow-up. Men had higher biological age and positive BA, indicating signs of accelerated aging. The prevalence of CKM stages 1–4 was higher in women than in men. Table 1 Baseline characteristics Overall (N = 12,722) Men (N = 5,725) Women (N = 6,997) P Age (median [IQR]), years 57 [49, 64] 58 [51, 65] 56 [49, 63] < 0.001 Education, n (%) Elementary school and below 6,069 (47.7) 2,291 (40.0) 3,778 (54.0) < 0.001 Middle school 1,905 (15.0) 1,135 (19.8) 770 (11.0) High school and above 4,748 (37.3) 2,299 (40.2) 2,449 (35.0) Marital status, n (%) < 0.001 Married 11,365 (89.3) 5,233 (91.4) 6,132 (87.6) Never married 82 (0.6) 82 (1.4) 0 (0.0) Other (separated, divorced, widowed) 1,275 (10.0) 410 (7.2) 865 (12.4) Residential area, n (%) 0.101 Rural 9,036 (71.0) 4,024 (70.3) 5,012 (71.6) Urban 9,400 (73.9) 1,701 (29.7) 1,985 (28.4) Current smokers, yes, n (%) 3,322 (26.1) 2,979 (52.0) 343 (4.9) < 0.001 Alcohol intake, yes, n (%) 4,379 (34.4) 3,363 (58.7) 1,016 (14.5) < 0.001 BMI, mean (SD), kg/m 2 23.7 (3.9) 23.3 (3.8) 24.1 (4.0) < 0.001 Systolic BP (median [IQR]), mmHg 125 [113, 139] 126 [115, 139] 124 [112, 139] < 0.001 Diastolic BP (median [IQR]), mmHg 74 [67, 83] 76 [68, 84] 73.67 [66, 82] < 0.001 Biochemical testing HbA1c, mean (SD), % 5.4 (0.6) 5.4 (0.6) 5.5 (0.6) 0.564 Platelets, mean (SD), ×10 9 /L 207.1 (66.1) 198.5 (63.7) 214.2 (67.3) < 0.001 Total cholesterol, mean (SD), mmol/L 4.9 (1.0) 4.7 (1.0) 5.0 (1.0) < 0.001 Triglycerides (median [IQR]), mmol/L 1.2 [0.9, 1.8] 1.1 [0.8, 1.7] 1.3 [0.9, 1.8] < 0.001 Creatinine (median [IQR]), µmol/L 66.6 [57.4, 77.4] 76.0 [67.9, 85.2] 59.9 [53.6, 66.9] < 0.001 high sensitivity C-reactive protein (median [IQR]), mg/dL 1.07 [0.58, 2.10] 1.10 [0.60, 2.15] 1.00 [0.54, 2.00] < 0.001 Urea nitrogen (median [IQR]), mmol/L 5.3 [4.4, 6.3] 5.6 [4.6, 6.6] 5.1 [4.2, 6.0] < 0.001 BA, (median [IQR]), years 57.17 [49.76, 64.18] 58.49 [51.57, 65.18] 55.91 [48.30, 63.05] < 0.001 BAacc, (median [IQR]), years -0.01 [-1.01, 1.02] 0.46 [-0.47, 1.43] -0.42 [-1.37, 0.56] < 0.001 10-CVD risk score (median [IQR]) 0.05 [0.03, 0.10] 0.07 [0.04, 0.12] 0.04 [0.02, 0.08] < 0.001 MetS, n (%) 4,950 (38.9) 1,645 (28.7) 3,305 (47.2) < 0.001 Cardiovascular-kidney-metabolic syndrome, n (%) < 0.001 stage 0 1,553 (12.2) 899 (15.7) 654 (9.3) stage 1 3,214 (25.3) 1,521 (26.6) 1,693 (24.2) stage 2 5,559 (43.7) 2,219 (38.8) 3,340 (47.7) stage 3 902 (7.1) 518 (9.0) 384 (5.5) stage 4 1,494 (11.7) 568 (9.9) 926 (13.2) Bold text in the table represented statistically significant results.IQR: interquartile range. Mediation analysis CKM As illustrated in Fig. 2 , higher CKM stages were strongly associated with greater BAacc in both men and women. In fully adjusted linear regression models, each incremental CKM stage was associated with an average increase of 0.33 years in BAacc among men (95% CI: 0.30–0.36) and 0.28 years among women (95% CI: 0.25–0.31). When BAacc was considered as the exposure, it showed a significant positive association with all-cause mortality in women but not in men. Among women, each additional year of BAacc was linked to an 18% higher risk of all-cause mortality (HR: 1.18, 95% CI: 1.06–1.31). In contrast, the corresponding association in men was weaker and not statistically significant (HR: 1.07, 95% CI: 0.98–1.16). In the mediation models, the NDE was significant in both sexes (men: 1.23, 95% CI: 1.11–1.37; women: 1.33, 95% CI: 1.16–1.52), indicating a substantial direct contribution of CKM to mortality independent of BAacc. However, after adjustment, the direct effects were notably attenuated and became statistically non-significant (men: 1.07, 95% CI: 0.96–1.19; women: 1.09, 95% CI: 0.95–1.26). The indirect effect mediated through BAacc remained significant in women (NIE: 1.05, 95% CI: 1.02–1.08). Quantitatively, BAacc explained approximately 39.3% of the total association between CKM and all-cause mortality in women. For men, the mediated effect was small and not statistically significant (NIE: 1.02, 95% CI: 1.00-1.05). A significant sex-specific difference was observed in the mediation effect (P for interaction = 0.01). Hypertension Among men (n = 5,478), hypertension was associated with an increased risk of BAacc (β = 1.37, 95% CI: 1.31–1.44), but BAacc was not significantly associated with all-cause mortality (HR: 1.04, 95% CI: 0.95–1.15). The TCE of hypertension on mortality was 1.29 (95% CI: 1.01–1.65), with a nonsignificant NIE through BAacc (HR: 1.06, 95% CI: 0.93–1.21). Among women (n = 6,724), hypertension was strongly associated with both BAacc (β = 1.43, 95% CI: 1.36–1.50) and all-cause mortality (HR: 1.16, 95% CI: 1.03–1.31). The TCE of hypertension on mortality was 1.49 (95% CI: 1.07–2.09), with a significant indirect pathway via BAacc (NIE: 1.24, 95% CI: 1.05–1.47), explaining 58.7% of the total effect. The sex interaction was significant (P for interaction = 0.04) ( Fig. 3 . A) . Diabetes Among men (n = 5,511), diabetes was significantly associated with higher BA acceleration (β = 0.87, 95% CI: 0.74–1.01), but BA acceleration was not significantly related to all-cause mortality (HR: 1.07, 95% CI: 0.98–1.16). The TCE of diabetes on mortality was 1.92 (95% CI: 1.33–2.77), with a nonsignificant NIE through BAacc (HR: 1.06, 95% CI: 0.98–1.14). Among women (n = 6,699), diabetes was significantly associated with higher BAacc (β = 0.84, 95% CI: 0.72–0.96) and with increased risk of all-cause mortality (HR: 1.18, 95% CI: 1.06–1.31). The TCE of diabetes on mortality was 1.60 (95% CI: 1.01–2.54), with a significant indirect effect mediated through BAacc (NIE: 1.15, 95% CI: 1.05–1.26), explaining 34.0% of the total effect. The sex interaction was not statistically significant (P for interaction = 0.10) ( Fig. 3 . B) . Heart disease Among men (n = 5,462), heart disease was significantly associated with higher BAacc (β = 0.26, 95% CI: 0.14–0.39), but BAacc was not significantly associated with all-cause mortality (HR: 1.07, 95% CI: 0.98–1.16). The TCE of heart disease on mortality was 1.37 (95% CI: 0.98–1.91), with a nonsignificant NIE through BAacc (HR: 1.02, 95% CI: 1.00-1.04). Among women (n = 6,639), heart disease was also significantly associated with higher BAacc (β = 0.19, 95% CI: 0.09–0.29) and with an increased risk of all-cause mortality (HR: 1.17, 95% CI: 1.05–1.30). The TCE of heart disease on mortality was 1.66 (95% CI: 1.15–2.40), with a modest but significant indirect effect through BAacc (NIE: 1.03, 95% CI: 1.00-1.06), accounting for 7.3% of the total effect. No significant sex difference was observed in the mediation pathway (P for interaction = 0.54) ( Fig. 3 . C) . Stroke Among men (n = 5,483), stroke was significantly associated with higher BAacc (β = 0.31, 95% CI: 0.07–0.55), but BA acceleration was not significantly associated with all-cause mortality (HR: 1.08, 95% CI: 0.99–1.17). The TCE of stroke on mortality was 2.29 (95% CI: 1.37–3.81), with a nonsignificant NIE through BAacc (HR: 1.02, 95% CI: 0.99–1.06). Among women (n = 6,765), stroke was not significantly associated with BAacc (β = 0.00, 95% CI: − 0.25–0.26), but was associated with an increased risk of all-cause mortality (HR: 1.18, 95% CI: 1.07–1.32). The TCE of stroke on mortality was 1.74 (95% CI: 0.85–3.56), with a nonsignificant indirect effect via BAacc (NIE: 1.00, 95% CI: 0.96–1.04). The test for interaction by sex was not statistically significant (P for interaction = 0.35) ( Fig. 3 . D) . CKD Among men (n = 5,466), CKD was significantly associated with higher BAacc (β = 0.15, 95% CI: 0.01–0.30), but BAacc was not significantly associated with all-cause mortality (HR: 1.09, 95% CI: 1.00-1.18). The TCE of CKD on mortality was 1.16 (95% CI: 0.76–1.85), with a nonsignificant NIE via BAacc (HR: 1.01, 95% CI: 1.00-1.30). Among women (n = 6,725), CKD was not significantly associated with BAacc (β = 0.02, 95% CI: − 0.13–0.17), but was associated with an increased risk of all-cause mortality (HR: 1.19, 95% CI: 1.07–1.32). The TCE of CKD on mortality was 1.35 (95% CI: 0.71–2.57), with a nonsignificant indirect effect via BAacc (NIE: 1.00, 95% CI: 0.98–1.03). The test for interaction by sex was not statistically significant (P for interaction = 0.64) ( Fig. 3 . E) . Discussion In this large, population-based cohort study, we observed significant mediating effect of BAacc on association between CKM syndrome and all-cause mortality, more pronounced among women compared to men. This finding suggests that accelerated biological aging may play a stronger contributory role in mortality risk among women with CKM. For individual components, BAacc significantly mediated the associations of hypertension, diabetes, and heart disease with mortality in women, but not in men. In contrast, BAacc did not mediate the associations of stroke or CKD with mortality in either sex, indicating that the influence of biological aging on mortality may differ across specific cardiometabolic conditions. Our findings support the mediating role of BAacc in disease-to-mortality pathways, consistent with prior studies showing that BAacc partially mediates the effects of metabolic dysfunction, depression, and lifestyle factors on mortality ( 29 – 31 ). Importantly, emerging evidence suggests that this mediation may differ by sex. For example, BAacc differentially mediates the association between obesity-related traits and incident cardiovascular disease in men and women ( 27 , 32 ). To our knowledge, this study is the first to provide direct evidence of a sex-specific mediation role of BAacc in the pathway from CKM to all-cause mortality, highlighting the need to consider both biological aging and sex differences in risk assessment and intervention strategies. CKM represents a systematic, persistent, and progressive state of metabolic stress. Its molecular underpinnings involve a network of interconnected processes, including insulin resistance, activation of the renin–angiotensin–aldosterone system, oxidative stress, lipotoxicity, endoplasmic reticulum stress, mitochondrial dysfunction with impaired energy production, inflammation, and apoptosis ( 33 – 35 ). The aforementioned mechanisms are widely recognized as key drivers of biological aging( 36 ), suggesting that CKM may increase mortality risk by accelerating biological aging and promoting aging-related deterioration across multiple organ systems. We found that CKM more evidently influenced mortality risk through accelerated biological aging in women, compared to men. The women included in our study were generally in the peri- or post-menopausal age range, suggesting that this sex difference may stem from hormonal changes associated with menopause ( 37 , 38 ). These hormonal alterations can exacerbate metabolic dysfunction, insulin resistance, systemic inflammation, and oxidative stress ( 39 – 42 ). Such alterations could amplify the biological impact of CKM, thereby accelerating aging-related deterioration and increasing mortality risk among women. The mediation effect of BAacc on the association between CKM and mortality was mainly attributable to cardiometabolic diseases such as hypertension, diabetes, and heart disease, rather than to cerebrovascular disease or CKD. A possible explanation is that the increased mortality risk associated with cardiometabolic dysfunction is largely mediated by accelerated biological aging ( 43 , 44 ). Large-scale analyses using UK Biobank data demonstrated that each standard deviation increase in PhenoAge acceleration was associated with higher risks of progression from cardiometabolic disease to death ( 45 , 46 ). Individuals who delayed or reversed accelerated aging exhibited lower risks of cardiovascular disease and mortality ( 47 ). In contrast, cerebrovascular disease or CKD appears to exert a more direct effect on mortality risk, less influenced by biological aging, likely because their lethality stems from acute vascular injury or organ-specific damage ( 48 , 49 ). Clinical implications Findings suggest that interventions capable of slowing or reversing aging processes ( 50 – 52 ) could reduce mortality among patients with CKM and provide substantial survival benefits, particularly in women. Prioritizing aging-focused strategies in individuals with cardiometabolic disease may be more cost-effective, as these conditions are more strongly influenced by biological aging. Furthermore, incorporating biological age assessment into clinical practice could enable early identification of high-risk individuals and more precise prevention, ultimately improving both survival and quality of life. Strengths and limitations The key strength of this study is that it is the first to investigate the mediation pathway linking CKM, biological age acceleration, and its impact on all-cause mortality. The nationally representative, large-scale cohort CHARLS enhances the reliability of the findings by capturing temporal relationships. However, several limitations should be acknowledged. First, the definitions of CKM components in CHARLS partially relied on self-reported physician diagnoses, which may introduce recall bias. Nevertheless, self-reported health data are commonly employed in large-scale epidemiological research, and in this study, objective physical examinations and biomarker measurements were incorporated to enhance diagnostic accuracy. Second, owing to data availability constraints, biological age was estimated using a limited panel of routinely measured clinical biomarkers, which may not fully capture the multidimensional complexity of biological aging. However, prior validation studies have demonstrated the reliability and predictive value of the Klemera–Doubal method within the CHARLS cohort ( 25 , 53 ). Third, although extensive covariate adjustment was performed, the possibility of residual confounding cannot be entirely excluded. Finally, as the present study was conducted among Chinese adults, the generalizability of these findings to other ethnic or national populations warrants further investigation in diverse cohorts. Conclusions In conclusion, leveraging nationally representative cohort data, this study is the first to reveal a mediation pathway linking CKM to biological age acceleration and subsequently to all-cause mortality. Cardiometabolic burden accounts for a substantial portion of this effect, with the mediation being more pronounced in women, likely reflecting sex-specific hormonal and metabolic mechanisms. These findings underscore the potential of targeting biological aging to mitigate mortality risk in individuals with CKM, particularly those with cardiometabolic conditions, and support the development of personalized strategies to enhance longevity and overall health outcomes. Abbreviations AHA American Heart Association CKM Cardiovascular-renal-metabolic CVD Cardiovascular disease CKD Chroic kidney disease CA Chronological age BA Biological age BAacc Biological age acceleration HbA1c Glycated hemoglobin TC Total cholesterol TG Triglycerides hsCRP High-sensitivity C-reactive protein PLT Platelet count SBP Systolic blood pressure DBP Diastolic blood pressure BMI Body Mass Index WC Waist circumference HRs Hazard ratios CI Confidence interval TCE Total causal effect NDE Natural direct effect NIE Natural indirect effect PM Proportion mediated CHARLS China Health and Retirement Longitudinal Study Declarations Ethics approval and consent to participate The CHARLS was approved by the Biomedical Ethics Committee of Peking University, and all participants were required to sign informed consent. Consent for publication Not applicable. Conflict of interests The authors declare that they have no competing interests Funding This study was supported by the Special Research Fund for Central Universities, Peking Union Medical College (Grant No.: 3332024094); the CAMS Innovation Fund for Medical Sciences (CIFMS) (Grant No.: 2023-I2M-2-001); the Noncommunicable Chronic Diseases-National Science and Technology Major Project (Grant No.: 2023ZD0506001); the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (Grant No.: 2022-ZHCH330-01); the non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (Grant No.: 2021-RC330-004) and the Disciplines Construction Project: Population Medicine (Grant No.: WH10022022010). None of the funders had any role in the design and conduct of the study; the collection, management, analysis and interpretation of the data; and in the preparation, review or approval of the manuscript. Author Contribution J.K. and Dr. Z.L. had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.Concept and design: J.K., Z.L..Acquisition, analysis, or interpretation of data: All.Drafting of the manuscript: J.K., Z.L., Y.K..Statistical analysis: J.K., Z.L., Y.K..Obtained funding: R.S., Z.L., Y.K..Administrative, technical, or material support: H.X., Y.Z., X.W., X.C..Supervision: R.S..Review and revision: All.The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. Acknowledgements This study was conducted based on the China Health and Retirement Longitudinal Study (CHARLS). The authors would like to thank the CHARLS research team, field staff, and all the CHARLS participants for their time and efforts. 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1","display":"","copyAsset":false,"role":"figure","size":55059,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the study population\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7951175/v1/bfd0200d0fe1d5800f7badf8.png"},{"id":94633631,"identity":"e8fad41e-e02f-495f-baa4-b9386d9c5128","added_by":"auto","created_at":"2025-10-29 06:39:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":171300,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe mediating role of BAacc in the relationship between CKM and all-cause mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eModel 1 was unadjusted.\u003c/p\u003e\n\u003cp\u003eModel 2 was adjusted for age, education, marital status, residential area, smoking status, and alcohol intake.\u003c/p\u003e\n\u003cp\u003eBold text in the figure represented statistically significant results. * P\u0026lt;0.05; ** P\u0026lt;0.01; *** P\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7951175/v1/89b61c59c8974f21abad36c2.png"},{"id":94633566,"identity":"e1dac5df-7824-45c4-b43b-92511f42906e","added_by":"auto","created_at":"2025-10-29 06:38:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":643793,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe mediating role of BAacc in the relationship between components of CKM and all-cause mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eModel 1 was unadjusted.\u003c/p\u003e\n\u003cp\u003eModel 2 was adjusted for age, education, marital status, residential area, smoking status, and alcohol intake.\u003c/p\u003e\n\u003cp\u003eBold text in the figure represented statistically significant results. * P\u0026lt;0.05; ** P\u0026lt;0.01; *** P\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7951175/v1/c4d32b2713e2975c80b7eacf.png"},{"id":94672635,"identity":"10bc97ce-1919-4872-ab78-9216a649d518","added_by":"auto","created_at":"2025-10-29 13:40:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1786029,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7951175/v1/7312988b-231d-44a1-9fa7-27003e960acb.pdf"},{"id":94633575,"identity":"52663c6c-943d-4ba6-95f5-0701dbe99002","added_by":"auto","created_at":"2025-10-29 06:38:58","extension":"jpeg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":995792,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical abstract image\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7951175/v1/79d49af204f2fde886739b6c.jpeg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sex-Specific Mediation of Biological Age Acceleration in the Associations between Cardiovascular– Kidney–Metabolic and All-Cause Mortality","fulltext":[{"header":"Research Insights","content":" \u003cp\u003e\u003cstrong\u003eWhat is currently known about this topic? (max. 3 highlights)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eCKM syndrome is a major cause of premature death and is closely tied to aging. It is unclear whether biological aging acceleration mediates the link between CKM and mortality, with attention to sex-specific differences.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eWhat is the key research question? (formatted as a question)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eHow does biological aging acceleration mediate the association between CKM syndrome and all-cause mortality across sexes?\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eWhat is new? (max. 3 highlights)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThis is the first cohort study to investigate the sex-specific mediating role of biological age in the relationship between CKM syndrome and mortality. Furthermore, it highlights that the mediation effect can be largely attributed to cardiometabolic disease, rather than cerebrovascular or kidney disorders.\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eHow might this study influence clinical practice? (max. 1 highlight)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFindings could inform personalized interventions to delay or reverse aging in CKM syndrome, particularly among women, to reduce mortality risk.\u0026nbsp;\u003c/p\u003e"},{"header":"Background","content":"\u003cp\u003eCardiovascular diseases (CVD) and metabolic dysfunction represent a significant global health burden, contributing substantially to mortality and disability worldwide (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). With the acceleration of global population aging, the prevalence of CVD and metabolic dysfunction are expected to rise markedly by 2050 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Cardiovascular-kidney-metabolic syndrome (CKM), newly defined by American Heart Association (AHA) (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), is characterized by the coexistence of metabolic dysfunction, chronic kidney disease (CKD), and cardiovascular impairment. Adverse CKM status has been associated with multi-organ damage and premature mortality (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Given the complex interplay among cardiovascular, renal, and metabolic systems, it is crucial to clarify how CKM contributes to overall mortality risk.\u003c/p\u003e\u003cp\u003eBiological age acceleration (BAacc), reflects the extent to which an individual\u0026rsquo;s biological age (BA) exceeds what would be expected for their chronological age, indicating physiological deterioration and accelerated aging (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Accelerated DNA methylation age was associated with increased all-cause mortality among individuals with cardiometabolic disease (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), indicating a potential mediation role of BAacc in the relationship between CKM and mortality. Lifestyle and pharmacological interventions may slow or reverse biological aging and reduce mortality (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Elucidating the role of BAacc in the association between CKM and mortality is of importance for guiding clinical management and informing public health strategies aimed at lowering the risk of death among CKM patients.\u003c/p\u003e\u003cp\u003eThe epidemiology and pathophysiological mechanisms of CKM and its major components display pronounced sex-related differences. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Likewise, the biological aging process exhibits distinct trajectories between men and women. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Therefore, taking advantage of a large population-based cohort in China, this study aimed to investigate the sex-specific mediating role of BAacc in the association between CKM-and its individual components\u0026mdash;and all-cause mortality.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy population\u003c/h2\u003e\u003cp\u003eThe primary analysis utilized data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative longitudinal survey conducted among adult residents in China (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Written informed consent for participation in both the baseline and follow-up surveys was obtained from all participants or their legal guardians. The study protocol was approved by the Biomedical Ethics Committee of Peking University (IRB00001052-11015). This study was reported followed the STROBE guidelines (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Detailed descriptions of the study design and cohort have been published previously (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Data from three survey waves were utilized to establish a temporal sequence linking CKM status and baseline characteristics with biological age (assessed at wave 1 [2011] or wave 3 [2015]) and subsequent all-cause mortality (wave 5 [2020]). Because blood samples were collected only during waves 1 and 3, a dynamic baseline approach was applied to estimate biological aging acceleration (BAacc), thereby maximizing the inclusion of eligible participants.\u003c/p\u003e\u003cp\u003eAmong 19,108 participants initially enrolled, individuals with missing key variables (follow-up data, BA, CKM diagnosis, age, or sex) were excluded (N = 5,296). Additionally, values of each biomarker used to calculate BA exceeded three standard deviations from the mean were excluded (N = 1,090). Therefore, 12,722 participants were included in the final analysis \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAssessment of cardiovascular-kidney-metabolic syndrome\u003c/h3\u003e\n\u003cp\u003eBased on the guideline (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), CKM was classified into 5 stages. Stage 0 was defined as having a normal body mass index (BMI) \u0026lt; 23 kg/m\u003csup\u003e2\u003c/sup\u003e and a normal waist circumstance (WC) \u0026lt; 80 cm for women or \u0026lt; 90 cm for men. Stage 1 included individuals with elevated BMI ≥ 23 kg/m\u003csup\u003e2\u003c/sup\u003e, elevated WC (≥ 80 cm for women or ≥ 90 cm for men), or prediabetes diagnosed through fasting blood glucose (100–126 mg/dL) or glycated hemoglobin (5.7%-6.5%). Stage 2 comprised individuals with metabolic risk factors (elevated fasting serum triglycerides (≥ 135mg/dL), hypertension, diabetes, or metabolic syndrome (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), moderate-to-high-risk CKD (estimated glomerular filtration rate [eGFR] 45–60 mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e), or both. Metabolic syndrome is defined as the presence of 3 or more conditions: elevated WC, low high-density lipoprotein cholesterol levels \u0026lt; 40 mg/dL for male or \u0026lt; 50 mg/dL for female, elevated triglyceride levels ≥ 150 mg/dL, systolic blood pressure ≥ 130 mmHg or diastolic blood pressure ≥ 80 mmHg, or prediabetes). Stage 3 was defined by the presence of very high-risk CKD (eGFR 15–45 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e), self-reported CKD, or a predicted 10-year CVD risk ≥ 20% using the American Heart Association's Predicting Risk of CVD Events (PREVENT) equations (21). Stage 4 was identified based on self-reported heart disease or stroke among individuals with excess/dysfunctional adiposity, other metabolic risk factors, or CKD. Self-reported disease history was determined from response to the following questions in each wave: “Have you been diagnosed with hypertension, diabetes, CKD, heart disease (including heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems), or stroke?” or “Are you currently receiving any of the following treatments (Chinese traditional medicine/Western medicine/Other treatments/None of the above) for hypertension, diabetes, CKD, heart disease, stroke or their complications?” (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). In addition, hypertension was defined as a systolic blood pressure (SBP) ≥ 140 mmHg or a diastolic blood pressure (DBP) ≥ 90 mmHg, and diabetes was defined as a glycated hemoglobin (HbA1c) level ≥ 6.5%.\u003c/p\u003e\n\u003ch3\u003eDevelopment of biological age acceleration\u003c/h3\u003e\n\u003cp\u003eWe used the Klemera-Doubal method to calculate biological age and indicate the degree of ageing since it could captures distinct dimensions of aging (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). BA calculation was performed by “BioAge” R package (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Biological age acceleration was calculated as the residual from a linear regression of BA on CA to adjust for the effect of CA. Positive BAacc values indicate accelerated physiological aging, whereas negative values reflect a comparatively younger clinical profile (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBecause four biomarkers-albumin, red blood cell count (RBC), ferritin, and transferrin-were unavailable in the CHARLS dataset, BA was estimated using the remaining eight biomarkers. Despite this limitation, these biomarkers have been shown to possess predictive validity for aging and aging-related outcomes in Chinese populations (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The final set of biomarkers included total cholesterol (TC), triglycerides (TG), glycated hemoglobin (HbA1c), urea, creatinine, high-sensitivity C-reactive protein (hsCRP), platelet count (PLT), and SBP, representing various domains of physical functions. Biomarker values were log-transformed to approximate normal distribution, and further BA and BAacc were calculated.\u003c/p\u003e\u003cp\u003eThe BA calculation formula is expressed as follows:\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Biological\\:age=\\:\\frac{{\\sum\\:}_{j=1}^{\\:m}\\left({x}_{j}-{q}_{j}\\right)\\left(\\frac{{k}_{j}}{{s}_{j}^{2}}\\right)+\\frac{CA}{{s}_{BA}^{2}}}{{\\sum\\:}_{j=1}^{m}{\\left(\\frac{{k}_{j}}{{s}_{j}}\\right)}^{2}+\\frac{1}{{s}_{BA}^{2}}}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{BA}_{E}=\\frac{{\\sum\\:}_{j=1}^{m}({x}_{j}-{q}_{j})\\left(\\frac{{k}_{j}}{{s}_{j}^{2}}\\right)}{{\\sum\\:}_{j=1}^{m}{\\left(\\frac{{k}_{j}}{{s}_{j}}\\right)}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{r}_{char}=\\frac{{\\sum\\:}_{j=1}^{m}\\frac{{r}_{j}^{2}}{\\sqrt{1-{r}_{j}^{2}}}}{{\\sum\\:}_{j=1}^{m}\\frac{{r}_{j}}{\\sqrt{1-{r}_{j}^{2}}}}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eand:\u003c/p\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:{S}_{BA}^{2}=\\frac{{\\sum\\:}_{j=1}^{n}{(\\left({BA}_{Ei}-{CA}_{I}\\right)-\\frac{{\\sum\\:}_{i=1}^{n}({BA}_{Ei}-{CA}_{i})}{n})}^{2}}{n}-\\left(\\frac{1-{r}_{char}^{2}}{{r}_{char}^{2}}\\right)\\times\\:\\left(\\frac{{({CA}_{max}-{CA}_{min})}^{2}}{12m}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe values j and i represent the number of biomarkers and samples respectively. The values k, q, and s are the regression slope, intercept, and the root means squared error of a biomarker regressed on chronological age, respectively. The value r\u003csub\u003ej\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e represents the variance explained by regression of chronological age on biomarkers.\u003c/p\u003e\n\u003ch3\u003eAssessment of covariates\u003c/h3\u003e\n\u003cp\u003eSocioeconomic characteristics and lifestyle factors were obtained through standardized, face-to-face interviews conducted at baseline and during follow-up assessments (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Educational attainment was categorized as elementary school or below, middle school, and high school or above. Marital status was classified as married, never married, or other (including separated, divorced, or widowed). Residential area was defined as rural or urban. Smoking and drinking behaviors were assessed based on current status and dichotomized as yes or no.\u003c/p\u003e\u003cp\u003eBody mass index (BMI) was calculated as weight (kg) divided by height squared (m²). Height and weight were measured without shoes and in light clothing using calibrated devices. Waist circumference was measured at the midpoint between the lower rib margin and the iliac crest using a non-elastic tape, with participants standing and breathing normally. Blood pressure was measured on the left arm using a calibrated Omron HEM-7200 monitor, three consecutive readings were taken and the mean value was used for analysis.\u003c/p\u003e\u003cp\u003eBlood samples were collected and analyzed following standardized laboratory procedures to ensure data comparability across participants. TC and TG were quantified using enzymatic colorimetric assays. HbA1c was determined by the boronate affinity high-performance liquid chromatography (HPLC) method. Urea was measured using an enzymatic ultraviolet (UV) method with urease, and creatinine concentrations were assessed by the rate-blanked and compensated Jaffe method. High-sensitivity C-reactive protein was analyzed using an immunoturbidimetric assay, and PLT was determined by an automated hematology analyzer based on electrical impedance or optical detection. All assays were conducted at the Youanmen Center for Clinical Laboratory, Capital Medical University, which is accredited by the Beijing Health Bureau and participates in regular external quality assessments organized by the Chinese Ministry of Health.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eBaseline characteristics between men and women were compared. Continuous variables were presented as mean (standard deviation [SD]) or median (interquartile range [IQR]), as appropriate, whereas categorical variables were expressed as frequencies and percentages. Between-group differences were assessed using the Wilcoxon rank-sum test for continuous variables and the χ² test for categorical variables.\u003c/p\u003e\u003cp\u003eMediation analyses were conducted separately for men and women. To examine the pathway from CKM to biological age acceleration, linear regression models were applied. Regression coefficients (β) and corresponding 95% confidence intervals (CIs) per stage change in CKM status were reported. Cox proportional hazards models were employed to evaluate the associations of CKM and BAacc with all-cause mortality, estimating hazard ratios (HRs) with 95% CIs. Mediation analyses were subsequently conducted to assess whether BAacc mediated the effect of CKM on all-cause mortality, and to quantify the proportion of the total effect explained by the mediation. The total causal effect (TCE) of CKM on mortality was decomposed into the natural direct effect (NDE), representing effects not operating through biological age acceleration, and the natural indirect effect (NIE), representing effects mediated through biological age acceleration. TCE, NDE, and NIE were each reported as HRs with corresponding 95% CIs. The proportion mediated (PM) represents the share of the TCE attributable to the NIE. To evaluate potential effect modification by sex in the mediation pathway, sex and its interactions with the exposure and mediator were included in the mediation analyses of the overall population. The statistical significance of these interaction terms (P-interaction) was determined using analysis of variance (ANOVA) to compare models with and without the interaction terms, indicating whether the mediation effect differs by sex. Furthermore, the medication analysis was performed among each individual components of CKM, including hypertension, diabetes, heart disease, CKD, and stroke.\u003c/p\u003e\u003cp\u003eMissing values were imputed using the median for continuous variables and the mode for categorical variables (all \u0026lt; 15%). Statistical analyses were conducted using R software (version 4.4.0), and a two-tailed P \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Result","content":"\u003ch2\u003ePopulation characteristics\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarized the characteristics of participants stratified by sex. The biological age developed in this study showed good accuracy in \u003cem\u003er\u003c/em\u003e (0.989), \u003cem\u003eMAE\u003c/em\u003e (1.141) and \u003cem\u003eRMSE\u003c/em\u003e (1.459). Among the 12,722 participants (55% were female), the median age was 57 [49, 64] years old and the biological age was 57.17 [49.76, 64.18] years old. A total of 11,169 participants (87.79%) were in stage 1–4 CKM. The mean follow-up duration was 7.32 (1.97) years. Overall, 439 participants (3.45%) had died by the 2020 follow-up. Men had higher biological age and positive BA, indicating signs of accelerated aging. The prevalence of CKM stages 1–4 was higher in women than in men.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u0026nbsp;(N = 12,722)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMen\u0026nbsp;(N = 5,725)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWomen (N = 6,997)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u0026nbsp;(median\u0026nbsp;[IQR]),\u0026nbsp;years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57\u0026nbsp;[49,\u0026nbsp;64]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58\u0026nbsp;[51,\u0026nbsp;65]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56\u0026nbsp;[49,\u0026nbsp;63]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation,\u0026nbsp;n\u0026nbsp;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElementary\u0026nbsp;school\u0026nbsp;and\u0026nbsp;below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,069\u0026nbsp;(47.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,291\u0026nbsp;(40.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3,778\u0026nbsp;(54.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u0026nbsp;school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,905\u0026nbsp;(15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,135\u0026nbsp;(19.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e770\u0026nbsp;(11.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u0026nbsp;school\u0026nbsp;and\u0026nbsp;above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,748\u0026nbsp;(37.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,299\u0026nbsp;(40.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,449\u0026nbsp;(35.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital\u0026nbsp;status,\u0026nbsp;n\u0026nbsp;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11,365\u0026nbsp;(89.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5,233\u0026nbsp;(91.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6,132\u0026nbsp;(87.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u0026nbsp;married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82\u0026nbsp;(0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82\u0026nbsp;(1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u0026nbsp;(0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u0026nbsp;(separated,\u0026nbsp;divorced,\u0026nbsp;widowed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,275\u0026nbsp;(10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e410\u0026nbsp;(7.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e865\u0026nbsp;(12.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidential\u0026nbsp;area,\u0026nbsp;n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,036\u0026nbsp;(71.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4,024\u0026nbsp;(70.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5,012\u0026nbsp;(71.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,400\u0026nbsp;(73.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,701\u0026nbsp;(29.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,985\u0026nbsp;(28.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smokers,\u0026nbsp;yes, n\u0026nbsp;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,322\u0026nbsp;(26.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,979\u0026nbsp;(52.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e343\u0026nbsp;(4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol intake,\u0026nbsp;yes, n\u0026nbsp;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,379\u0026nbsp;(34.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,363\u0026nbsp;(58.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,016\u0026nbsp;(14.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI,\u0026nbsp;mean\u0026nbsp;(SD),\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.7\u0026nbsp;(3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.3\u0026nbsp;(3.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.1\u0026nbsp;(4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic\u0026nbsp;BP\u0026nbsp;(median\u0026nbsp;[IQR]),\u0026nbsp;mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e125\u0026nbsp;[113,\u0026nbsp;139]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e126\u0026nbsp;[115,\u0026nbsp;139]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e124\u0026nbsp;[112,\u0026nbsp;139]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiastolic\u0026nbsp;BP\u0026nbsp;(median\u0026nbsp;[IQR]),\u0026nbsp;mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74\u0026nbsp;[67,\u0026nbsp;83]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76\u0026nbsp;[68,\u0026nbsp;84]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73.67\u0026nbsp;[66,\u0026nbsp;82]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBiochemical\u0026nbsp;testing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1c, mean\u0026nbsp;(SD),\u0026nbsp;%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.4\u0026nbsp;(0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.4\u0026nbsp;(0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.5\u0026nbsp;(0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.564\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelets,\u0026nbsp;mean\u0026nbsp;(SD),\u0026nbsp;×10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e207.1\u0026nbsp;(66.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e198.5\u0026nbsp;(63.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e214.2\u0026nbsp;(67.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u0026nbsp;cholesterol,\u0026nbsp;mean\u0026nbsp;(SD),\u0026nbsp;mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.9\u0026nbsp;(1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.7\u0026nbsp;(1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.0\u0026nbsp;(1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglycerides\u0026nbsp;(median\u0026nbsp;[IQR]),\u0026nbsp;mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2\u0026nbsp;[0.9,\u0026nbsp;1.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.1\u0026nbsp;[0.8,\u0026nbsp;1.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.3\u0026nbsp;[0.9,\u0026nbsp;1.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine\u0026nbsp;(median\u0026nbsp;[IQR]),\u0026nbsp;µmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66.6\u0026nbsp;[57.4,\u0026nbsp;77.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76.0\u0026nbsp;[67.9,\u0026nbsp;85.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59.9\u0026nbsp;[53.6,\u0026nbsp;66.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehigh sensitivity C-reactive\u0026nbsp;protein\u0026nbsp;(median\u0026nbsp;[IQR]),\u0026nbsp;mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.07\u0026nbsp;[0.58,\u0026nbsp;2.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.10\u0026nbsp;[0.60,\u0026nbsp;2.15]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u0026nbsp;[0.54,\u0026nbsp;2.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrea\u0026nbsp;nitrogen\u0026nbsp;(median\u0026nbsp;[IQR]),\u0026nbsp;mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.3\u0026nbsp;[4.4,\u0026nbsp;6.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.6\u0026nbsp;[4.6,\u0026nbsp;6.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.1\u0026nbsp;[4.2,\u0026nbsp;6.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBA,\u0026nbsp;(median\u0026nbsp;[IQR]),\u0026nbsp;years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57.17\u0026nbsp;[49.76,\u0026nbsp;64.18]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.49\u0026nbsp;[51.57,\u0026nbsp;65.18]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55.91\u0026nbsp;[48.30,\u0026nbsp;63.05]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBAacc,\u0026nbsp;(median\u0026nbsp;[IQR]),\u0026nbsp;years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.01\u0026nbsp;[-1.01,\u0026nbsp;1.02]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.46\u0026nbsp;[-0.47,\u0026nbsp;1.43]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.42\u0026nbsp;[-1.37,\u0026nbsp;0.56]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10-CVD\u0026nbsp;risk\u0026nbsp;score\u0026nbsp;(median\u0026nbsp;[IQR])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.05\u0026nbsp;[0.03,\u0026nbsp;0.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.07\u0026nbsp;[0.04,\u0026nbsp;0.12]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04\u0026nbsp;[0.02,\u0026nbsp;0.08]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetS, n\u0026nbsp;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,950\u0026nbsp;(38.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,645\u0026nbsp;(28.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3,305\u0026nbsp;(47.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiovascular-kidney-metabolic\u0026nbsp;syndrome,\u0026nbsp;n\u0026nbsp;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003estage\u0026nbsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,553\u0026nbsp;(12.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e899\u0026nbsp;(15.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e654\u0026nbsp;(9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003estage\u0026nbsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,214\u0026nbsp;(25.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,521\u0026nbsp;(26.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,693\u0026nbsp;(24.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003estage\u0026nbsp;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,559\u0026nbsp;(43.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,219\u0026nbsp;(38.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3,340\u0026nbsp;(47.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003estage\u0026nbsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e902\u0026nbsp;(7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e518\u0026nbsp;(9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e384\u0026nbsp;(5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003estage\u0026nbsp;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,494\u0026nbsp;(11.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e568\u0026nbsp;(9.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e926\u0026nbsp;(13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eBold text in the table represented statistically significant results.IQR: interquartile range.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch3\u003eMediation analysis\u003c/h3\u003e\u003ch2\u003eCKM\u003c/h2\u003e\u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e, higher CKM stages were strongly associated with greater BAacc in both men and women. In fully adjusted linear regression models, each incremental CKM stage was associated with an average increase of 0.33 years in BAacc among men (95% CI: 0.30–0.36) and 0.28 years among women (95% CI: 0.25–0.31).\u003c/p\u003e\u003cp\u003eWhen BAacc was considered as the exposure, it showed a significant positive association with all-cause mortality in women but not in men. Among women, each additional year of BAacc was linked to an 18% higher risk of all-cause mortality (HR: 1.18, 95% CI: 1.06–1.31). In contrast, the corresponding association in men was weaker and not statistically significant (HR: 1.07, 95% CI: 0.98–1.16).\u003c/p\u003e\u003cp\u003eIn the mediation models, the NDE was significant in both sexes (men: 1.23, 95% CI: 1.11–1.37; women: 1.33, 95% CI: 1.16–1.52), indicating a substantial direct contribution of CKM to mortality independent of BAacc. However, after adjustment, the direct effects were notably attenuated and became statistically non-significant (men: 1.07, 95% CI: 0.96–1.19; women: 1.09, 95% CI: 0.95–1.26). The indirect effect mediated through BAacc remained significant in women (NIE: 1.05, 95% CI: 1.02–1.08). Quantitatively, BAacc explained approximately 39.3% of the total association between CKM and all-cause mortality in women. For men, the mediated effect was small and not statistically significant (NIE: 1.02, 95% CI: 1.00-1.05). A significant sex-specific difference was observed in the mediation effect (P for interaction = 0.01).\u003c/p\u003e\u003ch2\u003eHypertension\u003c/h2\u003e\u003cp\u003eAmong men (n = 5,478), hypertension was associated with an increased risk of BAacc (β = 1.37, 95% CI: 1.31–1.44), but BAacc was not significantly associated with all-cause mortality (HR: 1.04, 95% CI: 0.95–1.15). The TCE of hypertension on mortality was 1.29 (95% CI: 1.01–1.65), with a nonsignificant NIE through BAacc (HR: 1.06, 95% CI: 0.93–1.21). Among women (n = 6,724), hypertension was strongly associated with both BAacc (β = 1.43, 95% CI: 1.36–1.50) and all-cause mortality (HR: 1.16, 95% CI: 1.03–1.31). The TCE of hypertension on mortality was 1.49 (95% CI: 1.07–2.09), with a significant indirect pathway via BAacc (NIE: 1.24, 95% CI: 1.05–1.47), explaining 58.7% of the total effect. The sex interaction was significant (P for interaction = 0.04) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003cb\u003eA)\u003c/b\u003e.\u003c/p\u003e\u003ch2\u003eDiabetes\u003c/h2\u003e\u003cp\u003eAmong men (n = 5,511), diabetes was significantly associated with higher BA acceleration (β = 0.87, 95% CI: 0.74–1.01), but BA acceleration was not significantly related to all-cause mortality (HR: 1.07, 95% CI: 0.98–1.16). The TCE of diabetes on mortality was 1.92 (95% CI: 1.33–2.77), with a nonsignificant NIE through BAacc (HR: 1.06, 95% CI: 0.98–1.14). Among women (n = 6,699), diabetes was significantly associated with higher BAacc (β = 0.84, 95% CI: 0.72–0.96) and with increased risk of all-cause mortality (HR: 1.18, 95% CI: 1.06–1.31). The TCE of diabetes on mortality was 1.60 (95% CI: 1.01–2.54), with a significant indirect effect mediated through BAacc (NIE: 1.15, 95% CI: 1.05–1.26), explaining 34.0% of the total effect. The sex interaction was not statistically significant (P for interaction = 0.10) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003cb\u003eB)\u003c/b\u003e.\u003c/p\u003e\u003ch2\u003eHeart disease\u003c/h2\u003e\u003cp\u003eAmong men (n = 5,462), heart disease was significantly associated with higher BAacc (β = 0.26, 95% CI: 0.14–0.39), but BAacc was not significantly associated with all-cause mortality (HR: 1.07, 95% CI: 0.98–1.16). The TCE of heart disease on mortality was 1.37 (95% CI: 0.98–1.91), with a nonsignificant NIE through BAacc (HR: 1.02, 95% CI: 1.00-1.04). Among women (n = 6,639), heart disease was also significantly associated with higher BAacc (β = 0.19, 95% CI: 0.09–0.29) and with an increased risk of all-cause mortality (HR: 1.17, 95% CI: 1.05–1.30). The TCE of heart disease on mortality was 1.66 (95% CI: 1.15–2.40), with a modest but significant indirect effect through BAacc (NIE: 1.03, 95% CI: 1.00-1.06), accounting for 7.3% of the total effect. No significant sex difference was observed in the mediation pathway (P for interaction = 0.54) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003cb\u003eC)\u003c/b\u003e.\u003c/p\u003e\u003ch2\u003eStroke\u003c/h2\u003e\u003cp\u003eAmong men (n = 5,483), stroke was significantly associated with higher BAacc (β = 0.31, 95% CI: 0.07–0.55), but BA acceleration was not significantly associated with all-cause mortality (HR: 1.08, 95% CI: 0.99–1.17). The TCE of stroke on mortality was 2.29 (95% CI: 1.37–3.81), with a nonsignificant NIE through BAacc (HR: 1.02, 95% CI: 0.99–1.06). Among women (n = 6,765), stroke was not significantly associated with BAacc (β = 0.00, 95% CI: − 0.25–0.26), but was associated with an increased risk of all-cause mortality (HR: 1.18, 95% CI: 1.07–1.32). The TCE of stroke on mortality was 1.74 (95% CI: 0.85–3.56), with a nonsignificant indirect effect via BAacc (NIE: 1.00, 95% CI: 0.96–1.04). The test for interaction by sex was not statistically significant (P for interaction = 0.35) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003cb\u003eD)\u003c/b\u003e.\u003c/p\u003e\u003ch2\u003eCKD\u003c/h2\u003e\u003cp\u003eAmong men (n = 5,466), CKD was significantly associated with higher BAacc (β = 0.15, 95% CI: 0.01–0.30), but BAacc was not significantly associated with all-cause mortality (HR: 1.09, 95% CI: 1.00-1.18). The TCE of CKD on mortality was 1.16 (95% CI: 0.76–1.85), with a nonsignificant NIE via BAacc (HR: 1.01, 95% CI: 1.00-1.30). Among women (n = 6,725), CKD was not significantly associated with BAacc (β = 0.02, 95% CI: − 0.13–0.17), but was associated with an increased risk of all-cause mortality (HR: 1.19, 95% CI: 1.07–1.32). The TCE of CKD on mortality was 1.35 (95% CI: 0.71–2.57), with a nonsignificant indirect effect via BAacc (NIE: 1.00, 95% CI: 0.98–1.03). The test for interaction by sex was not statistically significant (P for interaction = 0.64) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003cb\u003eE)\u003c/b\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large, population-based cohort study, we observed significant mediating effect of BAacc on association between CKM syndrome and all-cause mortality, more pronounced among women compared to men. This finding suggests that accelerated biological aging may play a stronger contributory role in mortality risk among women with CKM. For individual components, BAacc significantly mediated the associations of hypertension, diabetes, and heart disease with mortality in women, but not in men. In contrast, BAacc did not mediate the associations of stroke or CKD with mortality in either sex, indicating that the influence of biological aging on mortality may differ across specific cardiometabolic conditions.\u003c/p\u003e\u003cp\u003eOur findings support the mediating role of BAacc in disease-to-mortality pathways, consistent with prior studies showing that BAacc partially mediates the effects of metabolic dysfunction, depression, and lifestyle factors on mortality (\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Importantly, emerging evidence suggests that this mediation may differ by sex. For example, BAacc differentially mediates the association between obesity-related traits and incident cardiovascular disease in men and women (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). To our knowledge, this study is the first to provide direct evidence of a sex-specific mediation role of BAacc in the pathway from CKM to all-cause mortality, highlighting the need to consider both biological aging and sex differences in risk assessment and intervention strategies.\u003c/p\u003e\u003cp\u003eCKM represents a systematic, persistent, and progressive state of metabolic stress. Its molecular underpinnings involve a network of interconnected processes, including insulin resistance, activation of the renin\u0026ndash;angiotensin\u0026ndash;aldosterone system, oxidative stress, lipotoxicity, endoplasmic reticulum stress, mitochondrial dysfunction with impaired energy production, inflammation, and apoptosis (\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). The aforementioned mechanisms are widely recognized as key drivers of biological aging(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), suggesting that CKM may increase mortality risk by accelerating biological aging and promoting aging-related deterioration across multiple organ systems.\u003c/p\u003e\u003cp\u003eWe found that CKM more evidently influenced mortality risk through accelerated biological aging in women, compared to men. The women included in our study were generally in the peri- or post-menopausal age range, suggesting that this sex difference may stem from hormonal changes associated with menopause (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). These hormonal alterations can exacerbate metabolic dysfunction, insulin resistance, systemic inflammation, and oxidative stress (\u003cspan additionalcitationids=\"CR40 CR41\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Such alterations could amplify the biological impact of CKM, thereby accelerating aging-related deterioration and increasing mortality risk among women.\u003c/p\u003e\u003cp\u003eThe mediation effect of BAacc on the association between CKM and mortality was mainly attributable to cardiometabolic diseases such as hypertension, diabetes, and heart disease, rather than to cerebrovascular disease or CKD. A possible explanation is that the increased mortality risk associated with cardiometabolic dysfunction is largely mediated by accelerated biological aging (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Large-scale analyses using UK Biobank data demonstrated that each standard deviation increase in PhenoAge acceleration was associated with higher risks of progression from cardiometabolic disease to death (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Individuals who delayed or reversed accelerated aging exhibited lower risks of cardiovascular disease and mortality (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). In contrast, cerebrovascular disease or CKD appears to exert a more direct effect on mortality risk, less influenced by biological aging, likely because their lethality stems from acute vascular injury or organ-specific damage (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eClinical implications\u003c/h2\u003e\u003cp\u003eFindings suggest that interventions capable of slowing or reversing aging processes (\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e) could reduce mortality among patients with CKM and provide substantial survival benefits, particularly in women. Prioritizing aging-focused strategies in individuals with cardiometabolic disease may be more cost-effective, as these conditions are more strongly influenced by biological aging. Furthermore, incorporating biological age assessment into clinical practice could enable early identification of high-risk individuals and more precise prevention, ultimately improving both survival and quality of life.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and limitations\u003c/h2\u003e\u003cp\u003eThe key strength of this study is that it is the first to investigate the mediation pathway linking CKM, biological age acceleration, and its impact on all-cause mortality. The nationally representative, large-scale cohort CHARLS enhances the reliability of the findings by capturing temporal relationships. However, several limitations should be acknowledged. First, the definitions of CKM components in CHARLS partially relied on self-reported physician diagnoses, which may introduce recall bias. Nevertheless, self-reported health data are commonly employed in large-scale epidemiological research, and in this study, objective physical examinations and biomarker measurements were incorporated to enhance diagnostic accuracy. Second, owing to data availability constraints, biological age was estimated using a limited panel of routinely measured clinical biomarkers, which may not fully capture the multidimensional complexity of biological aging. However, prior validation studies have demonstrated the reliability and predictive value of the Klemera\u0026ndash;Doubal method within the CHARLS cohort (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Third, although extensive covariate adjustment was performed, the possibility of residual confounding cannot be entirely excluded. Finally, as the present study was conducted among Chinese adults, the generalizability of these findings to other ethnic or national populations warrants further investigation in diverse cohorts.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, leveraging nationally representative cohort data, this study is the first to reveal a mediation pathway linking CKM to biological age acceleration and subsequently to all-cause mortality. Cardiometabolic burden accounts for a substantial portion of this effect, with the mediation being more pronounced in women, likely reflecting sex-specific hormonal and metabolic mechanisms. These findings underscore the potential of targeting biological aging to mitigate mortality risk in individuals with CKM, particularly those with cardiometabolic conditions, and support the development of personalized strategies to enhance longevity and overall health outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eAHA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAmerican Heart Association\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCKM\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCardiovascular-renal-metabolic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCVD\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCardiovascular disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCKD\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eChroic kidney disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eChronological age\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eBA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBiological age\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eBAacc\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBiological age acceleration\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eHbA1c\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGlycated hemoglobin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eTC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTotal cholesterol\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eTG\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTriglycerides\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ehsCRP\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHigh-sensitivity C-reactive protein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ePLT\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePlatelet count\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eSBP\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSystolic blood pressure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eDBP\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDiastolic blood pressure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody Mass Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eWC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWaist circumference\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eHRs\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHazard ratios\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfidence interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eTCE\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTotal causal effect\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eNDE\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNatural direct effect\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eNIE\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNatural indirect effect\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ePM\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eProportion mediated\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCHARLS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eChina Health and Retirement Longitudinal Study\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CHARLS was approved by the Biomedical Ethics Committee of Peking University, and all participants were required to sign informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eConflict of interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study was supported by the Special Research Fund for Central Universities, Peking Union Medical College (Grant No.: 3332024094); the CAMS Innovation Fund for Medical Sciences (CIFMS) (Grant No.: 2023-I2M-2-001); the Noncommunicable Chronic Diseases-National Science and Technology Major Project (Grant No.: 2023ZD0506001); the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (Grant No.: 2022-ZHCH330-01); the non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (Grant No.: 2021-RC330-004) and the Disciplines Construction Project: Population Medicine (Grant No.: WH10022022010). None of the funders had any role in the design and conduct of the study; the collection, management, analysis and interpretation of the data; and in the preparation, review or approval of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eJ.K. and Dr. Z.L. had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.Concept and design: J.K., Z.L..Acquisition, analysis, or interpretation of data: All.Drafting of the manuscript: J.K., Z.L., Y.K..Statistical analysis: J.K., Z.L., Y.K..Obtained funding: R.S., Z.L., Y.K..Administrative, technical, or material support: H.X., Y.Z., X.W., X.C..Supervision: R.S..Review and revision: All.The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThis study was conducted based on the China Health and Retirement Longitudinal Study (CHARLS). The authors would like to thank the CHARLS research team, field staff, and all the CHARLS participants for their time and efforts.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data that support the findings of this study are available for download at [http://CHARLS.pku.edu.cn/en](http:/CHARLS.pku.edu.cn/en) .\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMensah George A, Fuster V, Murray Christopher JL, Roth Gregory A, null, n M, George A et al. Global Burden of Cardiovascular Diseases and Risks, 1990\u0026ndash;2022. JACC. 2023;82(25):2350\u0026thinsp;\u0026ndash;\u0026thinsp;473.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChew NWS, Ng CH, Tan DJH, Kong G, Lin C, Chin YH, et al. Cell Metab. 2023;35(3):414\u0026ndash;e283. 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Sci Rep. 2025;15(1):17616.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"cardiovascular–kidney–metabolic syndrome, biological age, all-cause mortality, mediation analysis","lastPublishedDoi":"10.21203/rs.3.rs-7951175/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7951175/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCardiovascular-kidney-metabolic (CKM) syndrome represents a major contributor to premature mortality and is strongly linked to the aging process. However, it remains unclear whether the association between CKM and mortality risk is mediated through biological aging acceleration (BAacc), particularly with respect to potential sex-specific differences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe included 12,722 participants from the China Health and Retiremalet Longitudinal Study (CHARLS) from 2011 to 2020. BAacc was estimated using the Klemera-Doubal method. Mediation analysis was used to assess the mediating role of BAacc in associations between CKM and all-cause mortality. Models were adjusted for key cardiovascular risk factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the follow-up, 439 participants (3.45%) died. After multivariable adjustment, CKM was significantly associated with an increased risk of all-cause mortality in both men (hazard ratio [HR], 1.33; 95% confidence interval [CI], 1.30–1.36) and women (1.28; 1.25–1.31). Notably, the mediating effect of biological aging acceleration (BAacc) on the association between CKM and mortality was more pronounced among women (natural indirect effect [NIE]: 1.05; 1.02–1.08) than among men (1.02; 1.00-1.05; P-for-interaction = 0.01). Regarding individual CKM components, the associations of hypertension (1.24; 1.05–1.47 for women vs. 1.06; 0.93–1.21 for men; P-for-interaction = 0.04), diabetes (1.15; 1.05–1.26 for women vs. 1.06; 0.98–1.14 for men), and heart disease (1.03; 1.00-1.06 for women vs. 1.02; 1.00-1.04 for men) with mortality were significantly mediated by BAacc among women but not among men. BAacc did not significantly mediate the associations of stroke or chronic kidney disease with mortality in either sex.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe found that aging acceleration significantly mediates the association between CKM and all-cause mortality, and this mediating pathway appears more pronounced among women. These findings emphasize the importance of incorporating aging-targeted strategies into CKM management to reduce residual mortality risk.\u003c/p\u003e","manuscriptTitle":"Sex-Specific Mediation of Biological Age Acceleration in the Associations between Cardiovascular– Kidney–Metabolic and All-Cause Mortality","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-29 06:38:23","doi":"10.21203/rs.3.rs-7951175/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d9e84ae5-2b51-401c-9d76-29a253fd3654","owner":[],"postedDate":"October 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-29T06:38:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-29 06:38:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7951175","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7951175","identity":"rs-7951175","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}