Cognitive Frailty and Cardiometabolic Risk in Middle-Aged and Older Adults: Evidence from the UK and China

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Aims: This study aimed to examine the associations between cognitive frailty and cardiometabolic risk in two nationally representative cohorts from China and the United Kingdom. Methods: We analyzed data from 7,628 participants in the China Health and Retirement Longitudinal Study (CHARLS) and 4,703 from the English Longitudinal Study of Ageing (ELSA), all aged ≥50 years. Frailty was assessed using the frailty index (FI) in the main analysis. Cox proportional hazards models were applied to estimate hazard ratios (HRs) for incident cardiometabolic diseases (CMDs), cardiovascular diseases (CVDs), and diabetes. Subgroup and interaction analyses were performed to examine effect modification. Restricted cubic spline (RCS) models were used to assess the shape of the association between FI and CMD risk among individuals with cognitive impairment. Sensitivity analyses employed competing risk models and the physical frailty phenotype (PFP) as an alternative frailty measure. Results: Cognitive frailty was associated with higher risks of CMDs (HR 1.58, 95% CI 1.39–1.79), CVDs (HR 1.64, 95% CI 1.42–1.89), and diabetes (HR 1.39, 95% CI 1.11–1.75). Cognitive impairment alone showed no significant association with these outcomes. Significant dose–response associations between the FI and CMDs and CVDs were observed among individuals with cognitive impairment. Results were consistent across cohorts and robust in sensitivity analyses. Conclusions: Cognitive frailty is a consistent predictor of cardiometabolic risk across distinct populations, supporting integrated screening and prevention strategies targeting both cognitive and physical deficits in aging populations. Frailty Cognitive impairment Cognitive frailty Cardiometabolic disease Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION As the global population ages at an unprecedented rate, age-related health conditions have become increasingly prevalent. Among these, frailty and cognitive impairment are two of the most prevalent geriatric syndromes [ 1 – 3 ]. Globally, an estimated 12% of individuals aged 50 years and older are currently classified as frail [ 1 ], while the prevalence of mild cognitive impairment in this age group is approximately 20% [ 3 ]. Frailty is a biological syndrome characterized by reduced physiological reserve and diminished resistance to stressors, resulting from cumulative declines across multiple physiological systems [ 4 , 5 ]. Cognitive impairment refers to a decline in intellectual functions, including thinking, memory, reasoning, and planning [ 2 ]. Given the potential bidirectional relationship and shared pathophysiological mechanisms—such as chronic inflammation, oxidative stress, and mitochondrial dysfunction—these two conditions have been conceptualized collectively as cognitive frailty, defined as the concurrent presence of both conditions in older adults without dementia [ 6 , 7 ]. Both cognitive impairment and frailty are key markers of the aging process and are associated with a range of adverse outcomes [ 8 – 10 ], including disability, hospitalization, mortality, and cardiometabolic diseases (CMDs) [ 11 , 12 ]. Previous studies suggest that the synergistic effect of these two conditions is associated with a heightened risk of adverse health outcomes. For instance, a longitudinal study across 17 countries found that individuals with cognitive frailty (subdistribution hazard ratio [SHR] 2.34, 95% CI 2.01–2.72) had a greater risk of mortality than those with either physical frailty (SHR 1.83, 95% CI 1.72–1.95) or cognitive impairment (SHR 1.36, 95% CI 1.25–1.48) alone [ 13 ]. However, whether cognitive frailty confers greater risk of CMDs—a leading cause of death worldwide—than either condition in isolation remains unclear [ 12 ]. Furthermore, the underlying mechanisms linking cognitive frailty with the development and progression of CMDs have yet to be fully elucidated. Our study utilized data from two nationally representative cohorts of middle-aged and older adults in the UK (English Longitudinal Study of Ageing [ELSA]) and China (China Health and Retirement Longitudinal Study [CHARLS]) to examine the associations of cognitive frailty with the risk of incident CMDs [ 14 , 15 ]. Given the differing sociocultural and healthcare contexts in these two countries, this cross-national approach may help to identify context-specific patterns and inform globally relevant strategies for addressing cardiometabolic vulnerability associated with frailty and cognitive impairment in diverse aging populations. METHODS Study population Detailed study designs of the CHARLS and ELSA can be found in Supplementary Information . For the current study, participants from these two cohorts were excluded if they met any of the following criteria: (1) were below 50 years of age at baseline; (2) had prevalent CMDs, dementia, or Parkinson’s disease at baseline; (3) lacked follow-up data on cardiometabolic outcomes of interest; or (4) had insufficient data to assess frailty status and cognitive function. Participant selection procedures are presented in Fig. S1 . Assessment of frailty Frailty was evaluated using the frailty index (FI) and the physical frailty phenotype (PFP) approaches [ 16 , 17 ]. To harmonize frailty assessments across CHARLS and ELSA datasets, 26 items were selected for FI construction, comprising self-reported health status, chronic diseases (excluding CMDs), physical function, and psychological conditions ( Table S1 ). For all variables included in the FI, a score of 0 indicated the absence of a deficit, and a score of 1 indicated the presence of a deficit. The FI score was calculated as the ratio of the number of deficits present to the total number of deficits considered, with a higher score indicating greater frailty. Based on previous studies [ 18 , 19 ], FI scores were categorized into three groups: robust (FI score ≤ 0.10), prefrail (FI score > 0.10 and < 0.25), and frail (FI score ≥ 0.25). Participants with more than 20% missing data across FI items (i.e., more than 5 items) were excluded ( Fig. S1 ) [ 20 ]. The details of the PFP approach can be found in Supplementary Information . Assessment of cognitive impairment For CHARLS participants, cognitive impairment was assessed using a combination of three tests: specifically, the Telephone Interview for Cognitive Status (TICS-10), a word recall test, and a figure drawing test. The composite score of these three tests ranges from 0 to 21, with higher scores indicating better cognitive function [ 21 ]. For ELSA participants, cognitive impairment was assessed based on three tests: the word recall test (immediate and delayed recall, with a total score of 20 [10 points each]), the date naming test (maximum score of 4), and the verbal fluency test. In the verbal fluency test, participants were asked to name as many animals as possible within 60 seconds, and the number of animals named was recorded as the test score [ 22 ]. Participants scoring more than one standard deviation (SD) below age-appropriate norms were classified as cognitively impaired. Those scoring within one standard deviation of the norm or above were considered cognitively normal [ 23 ]. The procedures for these cognitive tests in the two cohorts are described in detail elsewhere [ 21 , 22 ]. Assessment of cognitive frailty Participants were categorized according to their frailty and cognitive status into the following groups: normal (non-frail and normal cognition), frailty only, cognitive impairment only, and cognitive frailty (co-occurrence of cognitive impairment and frailty). Ascertainment of outcomes and endpoints The primary outcome was incident major cardiometabolic diseases (CMDs), defined as the occurrence of either major cardiovascular diseases (CVDs; including heart diseases and stroke) or diabetes. Incident major CVDs and diabetes were also examined separately as secondary outcomes. In each wave of CHARLS and ELSA, participants were asked whether a doctor had informed them of a diagnosis of diabetes, heart diseases (including angina, heart attack, congestive heart failure, and other heart problems), or stroke. Participants reporting a diagnosis of heart disease or stroke were classified as having incident CVDs, and those reporting a diagnosis of diabetes were classified as having incident diabetes. Follow-up continued until the first occurrence of a major CMD, death, or the end of the follow-up period, whichever came first. Covariates The covariates included age (in years), sex (female or male), study region (China or the UK), marital status, education level, smoking status, alcohol consumption, and physician-diagnosed hypertension [ 13 ]. To ensure consistency between CHARLS and ELSA, marital status was dichotomized as either married/partnered or other (including separated, divorced, unmarried, or widowed). Education level was classified into two categories: less than high school and high school or above. Smoking status was grouped as current smokers, former smokers, or never smokers. Alcohol consumption was categorized as never drinkers and ever drinkers. Physician-diagnosed hypertension was included as a covariate but was not considered part of the major CMDs definition. Statistical analysis Descriptive characteristics were summarized across the four frailty and cognitive groups. Cox proportional hazards regression models were used to investigate the associations of cognitive frailty with the risk of three cardiometabolic outcomes. The proportional hazards assumption was assessed using Schoenfeld residuals, with no violations detected. In the main analysis, frailty was assessed using the FI, and the normal group served as the reference category. For the primary outcome (major CMDs) and two secondary outcomes (major CVDs and diabetes), hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using two models: Model 1 adjusted for age and sex; Model 2 further adjusted for study region (China or the UK), marital status, education level, smoking status, alcohol consumption, and hypertension. To assess potential synergistic effects, the risks in participants with cognitive frailty were compared with those in participants with frailty alone or cognitive impairment alone. Subgroup analyses were conducted by study region (China vs. UK) using separate Cox models based on Model 2 (excluding the stratification variable). Region-specific adjusted survival curves were generated to illustrate time-to-event differences across cognitive–frailty groups. Further stratified analyses were conducted within each cohort by age group (50–65 vs. >65 years) and by sex, with stratification variables excluded from the respective models. Multiplicative interaction terms between cognitive–frailty status and age group or sex were included to evaluate effect modification. To further examine the role of physical frailty in cardiometabolic risk among individuals with cognitive impairment, we conducted restricted cubic spline (RCS) regression models treating the FI as a continuous exposure, using Cox proportional hazards models (Model 2) with FI = 0.25 set as the reference value. To evaluate the robustness of our findings, two sets of sensitivity analyses were performed. First, all primary and region-stratified analyses were re-estimated using Fine – Gray subdistribution hazard models to account for death as a competing risk [ 24 ]. Second, the associations were re-evaluated using the PFP as an alternative frailty measure [ 17 ], applying the same modelling strategy and covariate adjustments as in the main analyses. All statistical analyses were performed using Stata version 18.0 (StataCorp LLC, College Station, TX, USA) and R version 4.4.1 (R Foundation for Statistical Computing, Vienna, Austria). A P -value of less than 0.05 was considered statistically significant. RESULTS Participant selection and characteristics A total of 7,628 participants from CHARLS and 4,703 from ELSA were included in the final analytical sample ( Fig. S1 ). Baseline characteristics of participants stratified by cognitive and frailty status are presented in Table 1 . In both CHARLS and ELSA, statistically significant differences were observed across the four groups for all sociodemographic and health-related variables ( P < 0.0001). Individuals with cognitive frailty tended to be older, more likely to be female, less educated, and less likely to be married or partnered. Table 1 Baseline characteristics of participants by cognitive impairment and physical status. CHARLS (n = 7,628) ELSA (n = 4,703) Normal (n = 4,948) Frailty only (n = 1,194) Cognitive impairment only (n = 928) Cognitive frailty (n = 558) P value Normal (n = 3,181) Frailty only (n = 455) Cognitive impairment only (n = 831) Cognitive frailty (n = 236) P value Age (years), mean (SD) 60.6 (7.5) 63.3 (8.1) 61.4 (8.2) 65.0 (9.3) < 0.0001 62.8 (7.8) 65.1 (9.1) 69.2 (9.7) 71.3 (10.5) < 0.0001 Sex, n (%) < 0.0001 < 0.0001 Female 1,972 (39.9) 663 (55.5) 663 (71.4) 444 (79.6) 1,748 (55.0) 333 (73.2) 457 (55.0) 167 (70.8) Male 2,976 (60.2) 531 (44.5) 265 (28.6) 114 (20.4) 1,433 (45.1) 122 (26.8) 374 (45.0) 69 (29.2) Education, n (%) < 0.0001 < 0.0001 High school not completed 4,304 (87.0) 1,137 (95.2) 916 (98.7) 557 (99.8) 941 (32.1) 199 (49.8) 489 (64.2) 162 (76.8) High school or above 644 (13.0) 57 (4.8) 12 (1.3) 1 (0.2) 1,992 (67.9) 201 (50.3) 273 (35.8) 49 (23.2) Marital status, n (%) < 0.0001 < 0.0001 Married or partnered 4,219 (85.3) 942 (78.9) 743 (80.1) 396 (71.0) 2,361 (74.2) 260 (57.1) 504 (60.7) 113 (47.9) Others 729 (14.7) 252 (21.1) 185 (19.9) 162 (29.0) 829 (25.8) 195 (42.9) 326 (39.3) 123 (52.1) Alcohol consumption, n (%) < 0.0001 < 0.0001 Never drinkers 2,656 (53.7) 738 (61.8) 664 (71.6) 399 (71.6) 166 (5.5) 49 (12.0) 88 (12.1) 46 (24.3) Ever drinkers 2,289 (46.3) 456 (38.2) 264 (28.5) 158 (28.4) 2843 (94.5) 359 (88.0) 641 (87.9) 143 (75.7) Smoking status, n (%) < 0.0001 < 0.0001 Never smokers 2,553 (51.6) 710 (59.5) 673 (72.5) 434 (77.8) 1,301 (40.9) 150 (33.0) 339 (40.8) 74 (31.4) Previous smokers 481 (9.73) 120 (10.1) 37 (4.0) 23 (4.1) 1,468 (46.2) 204 (44.8) 377 (45.4) 113 (47.9) Current smokers 1,912 (38.7) 364 (30.5) 218 (23.49) 101 (18.1) 411 (12.9) 101 (22.2) 114 (13.7) 49 (20.76) Hypertension, n (%) < 0.0001 < 0.0001 No 3957 (80.2) 770 (64.9) 784 (84.8) 391 (70.8) 2240 (70.4) 223 (49.0) 551 (66.3) 98 (41.5) Yes 975 (19.8) 417 (35.1) 141 (15.2) 161 (29.2) 941 (29.6) 232 (51.0) 280 (33.7) 138 (58.5) BMI (kg/m 2 ), mean (SD) 23.2 (3.7) 23.0 (3.9) 22.5 (3.8) 22.6 (4.1) < 0.0001 27.5 (4.6) 29.8 (5.7) 27.3 (4.5) 29.1 (5.4) < 0.0001 Abbreviations: CHARLS, China Health and Retirement Longitudinal Study; ELSA, English Longitudinal Study of Ageing; SD, standard deviation. Numbers may not sum to total due to missing data. Continuous variables were reported as means with SD, while categorical variables were presented as frequencies and percentages. Group differences were examined using analysis of variance (ANOVA), the Wilcoxon rank-sum test, or the Chi-square test, as appropriate. Association of cognitive frailty with CMD outcomes Table 2 presents the associations of cognitive and frailty status with the risk of three cardiometabolic outcomes in the combined cohorts. Compared with normal participants, those with cognitive frailty had significantly higher risks across all outcomes after full adjustment for covariates. Specifically, cognitive frailty was associated with increased risks of CMDs (HR 1.58; 95% CI 1.39–1.79), CVDs (HR 1.64; 95% CI 1.42–1.89), and diabetes (HR 1.39; 95% CI 1.11–1.75). Frailty alone was also significantly associated with increased risks of CMDs (HR 1.53; 95% CI 1.39–1.67), CVDs (HR 1.48; 95% CI 1.34–1.65), and diabetes (HR 1.55; 95% CI 1.32–1.82). In contrast, cognitive impairment alone was not significantly associated with any of the outcomes. Direct comparisons further showed that individuals with cognitive frailty had a significantly higher risk of CMDs and CVDs than those with cognitive impairment only (CMDs: HR 1.48, 95% CI 1.28–1.72; CVDs: HR 1.62, 95% CI 1.36–1.92), whereas the excess risk for diabetes was not statistically significant (HR 1.21, 95% CI 0.93–1.58). No significant differences were observed between cognitive frailty and frailty alone across any of the three outcomes. The distribution of these three cardiometabolic outcomes by cognitive–frailty group in the combined cohorts is presented in Fig. S2 . Table 2 Associations of cognitive and frailty status with incident cardiometabolic outcomes in the combined CHARLS and ELSA cohorts. Group Cases, n (%) CMDs a CVDs Diabetes Model 1 b HR (95%CI) Model 2 c HR (95%CI) Model 1 HR (95%CI) Model 2 HR (95%CI) Model 1 HR (95%CI) Model 2 HR (95%CI) Individual and combined effect Normal 8,129 (65.9%) Ref. Ref. Ref. Ref. Ref. Ref. Frailty only 1,649 (13.4%) 1.87 (171, 2.04) 1.53 (1.39, 1.67) 1.81 (1.64, 2.00) 1.48 (1.34, 1.65) 1.88 (1.62, 2.19) 1.55 (1.32, 1.82) Cognitive impairment only 1,759 (14.3%) 1.03 (0.94, 1.14) 1.06 (0.96, 1.18) 0.97 (0.86, 1.08) 1.01 (0.89, 1.14) 1.15 (0.97, 1.36) 1.15 (0.96, 1.38) Cognitive frailty 794 (6.4%) 1.91 (1.70, 2.16) 1.58 (1.39, 1.79) 1.94 (1.69, 2.22) 1.64 (1.42, 1.89) 1.74 (1.40, 2.16) 1.39 (1.11, 1.75) Combined effect vs. individual effect Cognitive frailty vs. frailty only NA 1.02 (0.90, 1.17) 1.03 (0.90, 1.19) 1.07 (0.92, 1.25) 1.10 (0.94, 1.29) 0.92 (0.73, 1.17) 0.90 (0.70, 1.15) Cognitive frailty vs. cognitive impairment only NA 1.85 (1.61, 2.14) 1.48 (1.28, 1.72) 2.01 (1.70, 2.37) 1.62 (1.36, 1.92) 1.51 (1.18, 1.95) 1.21 (0.93, 1.58) CHARLS, China Health and Retirement Longitudinal Study; ELSA, English Longitudinal Study of Ageing; CMDs, cardiometabolic diseases; CVDs, cardiovascular diseases; HR, hazard ratio; NA, not applicable. a CMDs were defined as the presence of either CVDs or diabetes. b Model 1 adjusted for age and gender. c Model 2 adjusted for age, sex, study region, marital status, education level, smoking status, alcohol consumption, and hypertension Cohort-specific analyses Figure 1 presents adjusted survival curves and corresponding HRs for CMDs, CVDs, and diabetes by cognitive–frailty groups in CHARLS (panels A–C) and ELSA (panels D–F). In both cohorts, compared with the normal group, cognitive frailty was significantly associated with higher risks of CMDs and CVDs. In CHARLS, cognitive frailty was also linked to increased diabetes risk (HR 1.33, 95% CI 1.03–1.72), whereas the association was not significant in ELSA (HR 1.31, 95% CI 0.77–2.24). Frailty alone was consistently associated with higher risks of all three outcomes across both cohorts, while cognitive impairment alone was not significantly associated with any outcome. Compared to cognitive impairment alone, cognitive frailty conferred significantly greater risks of CMDs and CVDs in both cohorts, but not for diabetes. Subgroup analyses by sex and age Subgroup analyses by sex and age group are presented in Fig. 2 (CHARLS) and Fig. 3 (ELSA). In CHARLS, no significant interactions were found between sex or age group and cognitive–frailty status for any of the outcomes (all P for interaction > 0.05). In contrast, significant effect modification by sex and age was observed in ELSA. Specifically, the association between cognitive frailty and CMDs or CVDs was significantly stronger in males than in females ( P for interaction = 0.015 for CMDs, 0.020 for CVDs). Similarly, the association between cognitive frailty and CMDs varied significantly by age group ( P for interaction = 0.048), with higher hazard ratios observed in participants aged > 65 years. No significant interactions were identified for diabetes in either cohort. RCS analysis among cognitively impaired participants To further clarify the role of frailty in cardiometabolic risk among individuals with cognitive impairment, we used RCS models to examine the shape and strength of the association between the FI and cardiometabolic outcomes (Fig. 4 ). A positive association was observed for both CMDs and CVDs, with statistically significant overall trends (overall P < 0.001 for both). The association with diabetes followed a similar pattern but did not reach statistical significance (overall P = 0.186). Evidence of non-linearity was detected for CVDs (non-linearity P = 0.023), while no such evidence was observed for CMDs or diabetes (non-linearity P > 0.05). Sensitivity analyses Results from Fine–Gray models were consistent with the main analyses, with cognitive frailty remaining significantly associated with increased risks of CMDs, CVDs, and diabetes ( Tables S2–S4 ). When frailty was defined using the PFP, cognitive frailty remained significantly associated with increased risks of CMDs and CVDs, but not diabetes, after full adjustment for covariates ( Table S5 ). Notably, in contrast to the main analysis using the FI, cognitive impairment alone was modestly but significantly associated with higher risks of CMDs (HR 1.10, 95% CI 1.01–1.20). No significant association was observed between cognitive impairment alone and diabetes. The overall pattern of results remained broadly consistent with the main results. Cognitive frailty was associated with higher risks of CMDs and CVDs than cognitive impairment alone, although the differences did not reach statistical significance. DISCUSSION In this large, population-based study involving cohorts from China and the UK, we found that individuals with cognitive frailty had significantly higher risks of CMDs, CVDs, and diabetes compared with those with normal cognitive function and frailty status. These associations were consistently observed across both cohorts and remained robust after adjustment for demographic, behavioral, and clinical covariates. Notably, frailty alone was also associated with elevated risks of all three outcomes, while cognitive impairment alone was not significantly associated with any. Subgroup analyses further revealed effect modification by sex and age in ELSA, but not in CHARLS. Furthermore, results remained comparable in sensitivity analyses using competing risk models and alternative frailty definitions. One notable finding was that cognitive impairment, in the absence of physical frailty, may not substantially elevate cardiometabolic risk. Individuals who retain physical robustness may be better able to engage in self-care and maintain healthier behaviors, thereby mitigating the adverse impact of cognitive decline. In addition, some cases of cognitive impairment may have been transient or subclinical, attenuating long-term associations [ 25 ]. Interestingly, the addition of cognitive impairment to frailty did not significantly increase risk beyond that conferred by frailty alone. This may suggest that frailty alone captures much of the systemic vulnerability relevant to cardiometabolic risk, thereby limiting the incremental contribution of cognitive deficits. Subgroup analyses revealed effect modification by sex and age in ELSA but not in CHARLS. In ELSA, the association between cognitive frailty and CMDs or CVDs was stronger among men and individuals aged > 65 years. This is consistent with previous research reporting stronger associations between cognitive frailty and mortality among older adults (≥ 70 years) and males [ 13 ]. It is plausible that older individuals with cognitive frailty are more susceptible to adverse cardiometabolic outcomes due to accumulated physiological burden, multisystem dysregulation, and reduced resilience with age, whereas younger individuals may still retain greater functional reserves that mitigate such risks. While the reasons underlying the sex-specific differences remain unclear, these findings underscore the need for further research into how biological, behavioral, and social factors may differentially influence cardiometabolic risk across population subgroups. The absence of interaction effects in CHARLS may reflect more uniform risk distributions or population-specific differences in the expression of frailty. Furthermore, RCS analysis demonstrated significant dose–response associations between the FI and the risks of CMDs and CVDs, with similar but non-significant trends observed for diabetes. These findings reinforce the role of frailty as a fundamental determinant of cardiometabolic vulnerability, even among individuals with cognitive impairment. The mechanisms underlying the observed associations between cognitive frailty and cardiometabolic risk are likely to be multifactorial. Cognitive frailty reflects the co-occurrence of neurocognitive decline and physical vulnerability, both of which have been linked to pathophysiological mechanisms such as chronic systemic inflammation, mitochondrial dysfunction, and oxidative stress [ 7 , 26 ]. These mechanisms may accelerate vascular aging, impair glucose metabolism, and promote atherosclerosis, thereby increasing susceptibility to both CVDs and diabetes [ 27 – 30 ]. Furthermore, individuals with cognitive frailty may be less likely to adhere to medical treatment, maintain physical activity, or follow dietary recommendations [ 2 ], thereby further exacerbating cardiometabolic risk. This study has several strengths. First, we utilized data from two nationally representative cohorts, enhancing generalizability and cross-cultural relevance. Second, the prospective design, large sample size, and long follow-up enabled robust risk estimation. Third, comprehensive adjustment for covariates and multiple sensitivity analyses—including competing risk models and alternative frailty definitions—supported the robustness of our findings. However, several limitations warrant consideration. First, cognitive impairment and frailty were assessed at a single time point, limiting insight into their temporal progression. Second, residual confounding from unmeasured factors, such as dietary patterns, may exist. Third, differences in variable operationalization across cohorts may have introduced heterogeneity. Fourth, CMD outcomes were self-reported, raising the possibility of misclassification due to variations in diagnosis rates or healthcare access across socioeconomic strata. Fifth, in ELSA, mortality data were unavailable beyond wave 6, which may have affected follow-up estimates and the accuracy of competing risk adjustment [ 19 ]. Finally, the observational nature of the study precludes causal inference. CONCLUSIONS Cognitive frailty was independently associated with an elevated risk of CMDs, particularly CVDs, in both Chinese and UK cohorts. These associations were stronger than those observed for cognitive impairment alone and remained robust across alternative frailty definitions and analytical approaches. The inclusion of culturally diverse populations enhances the generalizability of our findings and underscores the need for integrated, contextually appropriate strategies to identify and address both cognitive and frailty in aging populations. Declarations Acknowledgement The authors gratefully acknowledge all investigators and participants involved in the CHARLS and ELSA cohorts for their valuable contributions. Data availability The CHARLS data are available upon request (https://charls.pku.edu.cn/), while the ELSA data are available after registration (https://beta.ukdataservice.ac.uk/datacatalogue/series/series?id=200011). Authorship contribution Conceptualization: HY, JJL, ZG. Methodology: JJL, CL, ZG. Data curation: HY, JJL. Formal analysis: HY, JJL, ZG. Original draft: HY, JJL, ZG. Critical review and revision: CL, SE, GJ, JL, LS, CJTH, ZG. Final approval of the version to be published: all authors. Funding CJTH is supported by the Forrest Research Foundation Scholarship and the ECU Higher Degree by Research Scholarship. ZG is supported by the Curtin Higher Degree by Research Scholarship and the Dementia Centre of Excellence (DCE) and Curtin enAble Institute Seed Funding. Ethical approval and consent to participate CHARLS and ELSA were approved by the Ethical Review Committee of Peking University and the London Multi-Centre Research Ethics Committee, respectively. Informed consent was obtained from all participants in both cohorts. Competing interests The authors declare no competing interests. References O’Caoimh R, Sezgin D, O’Donovan MR, Molloy DW, Clegg A, Rockwood K, et al (2021) Prevalence of frailty in 62 countries across the world: a systematic review and meta-analysis of population-level studies. 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The American Journal of Geriatric Psychiatry 33(2):178-91. https://doi.org/10.1016/j.jagp.2024.08.009 Zhao Y, Hu Y, Smith JP, Strauss J, Yang G (2014) Cohort profile: the China health and retirement longitudinal study (CHARLS). Int J Epidemiol 43(1):61-8. https://doi.org/10.1093/ije/dys203 Steptoe A, Breeze E, Banks J, Nazroo J (2013) Cohort profile: the English longitudinal study of ageing. Int J Epidemiol 42(6):1640-8. https://doi.org/10.1093/ije/dys168 Searle SD, Mitnitski A, Gahbauer EA, Gill TM, Rockwood K (2008) A standard procedure for creating a frailty index. BMC Geriatr 8:1-10. https://doi.org/10.1186/1471-2318-8-24 Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al (2001) Frailty in older adults: evidence for a phenotype. 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J Am Geriatr Soc 61(9):1537-51. https://doi.org/10.1111/jgs.12420 Li J, Cacchione PZ, Hodgson N, Riegel B, Keenan BT, Scharf MT, et al (2017) Afternoon napping and cognition in Chinese older adults: findings from the China health and retirement longitudinal study baseline assessment. J Am Geriatr Soc 65(2):373-80. https://doi.org/10.1111/jgs.14368 Ragusa FS, Veronese N, Vernuccio L, Dominguez LJ, Smith L, Bolzetta F, et al (2024) Mild cognitive impairment predicts the onset of Sarcopenia: a longitudinal analysis from the English Longitudinal Study on Ageing. Aging Clin Exp Res 36(1):129. https://doi.org/10.1007/s40520-024-02781-z Jak AJ, Bondi MW, Delano-Wood L, Wierenga C, Corey-Bloom J, Salmon DP, et al (2009) Quantification of five neuropsychological approaches to defining mild cognitive impairment. The American Journal of Geriatric Psychiatry 17(5):368-75. https://doi.org/10.1097/JGP.0b013e31819431d5 Fine JP, Gray RJ (1999) A proportional hazards model for the subdistribution of a competing risk. Journal of the American statistical association 94(446):496-509. https://doi.org/10.1080/01621459.1999.10474144 Sachdev PS, Lipnicki DM, Crawford J, Reppermund S, Kochan NA, Trollor JN, et al (2013) Factors predicting reversion from mild cognitive impairment to normal cognitive functioning: a population-based study. PLoS One 8(3):e59649. https://doi.org/10.1371/journal.pone.0059649 Soysal P, Stubbs B, Lucato P, Luchini C, Solmi M, Peluso R, et al (2016) Inflammation and frailty in the elderly: a systematic review and meta-analysis. Ageing research reviews 31:1-8. https://doi.org/10.1016/j.arr.2016.08.006 Maloberti A, Vallerio P, Triglione N, Occhi L, Panzeri F, Bassi I, et al (2019) Vascular aging and disease of the large vessels: role of inflammation. High Blood Press Cardiovasc Prev 26:175-82. https://doi.org/10.1007/s40292-019-00318-4 Stumvoll M, Goldstein BJ, Van Haeften TW (2005) Type 2 diabetes: principles of pathogenesis and therapy. The Lancet 365(9467):1333-46. https://doi.org/10.1016/S0140-6736(05)61032-X Lowell BB, Shulman GI (2005) Mitochondrial dysfunction and type 2 diabetes. Science 307(5708):384-7. https://doi.org/10.1126/science.1104343 James K, Jamil Y, Kumar M, Kwak MJ, Nanna MG, Qazi S, et al (2024) Frailty and cardiovascular health. Journal of the American Heart Association 13(15):e031736. https://doi.org/10.1161/JAHA.123.031736 Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial4.25.docx Cite Share Download PDF Status: Published Journal Publication published 04 Sep, 2025 Read the published version in Aging Clinical and Experimental Research → Version 1 posted Editorial decision: Revision requested 25 Jun, 2025 Reviews received at journal 15 Jun, 2025 Reviews received at journal 11 Jun, 2025 Reviewers agreed at journal 03 Jun, 2025 Reviews received at journal 22 May, 2025 Reviewers agreed at journal 21 May, 2025 Reviewers agreed at journal 12 May, 2025 Reviewers invited by journal 12 May, 2025 Editor assigned by journal 07 May, 2025 Submission checks completed at journal 06 May, 2025 First submitted to journal 04 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6590850","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":456778638,"identity":"1353924d-6a38-4d9f-8975-2d1ee4d25948","order_by":0,"name":"Haiyang Yan","email":"","orcid":"","institution":"Yancheng Municipal Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Haiyang","middleName":"","lastName":"Yan","suffix":""},{"id":456778640,"identity":"64af70af-bafc-4eac-adc1-47a9f458ce12","order_by":1,"name":"Jingjing Lang","email":"","orcid":"","institution":"Children’s Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Jingjing","middleName":"","lastName":"Lang","suffix":""},{"id":456778645,"identity":"9d7b93ae-b192-4515-b6b3-6c9d86ebfb28","order_by":2,"name":"Chengfeng Li","email":"","orcid":"","institution":"Edith Cowan University","correspondingAuthor":false,"prefix":"","firstName":"Chengfeng","middleName":"","lastName":"Li","suffix":""},{"id":456778647,"identity":"4ea44cae-3035-4d90-a168-1412172948a6","order_by":3,"name":"Samaneh Eftekhariranjbar","email":"","orcid":"","institution":"University G. D'Annunzio of Chieti-Pescara","correspondingAuthor":false,"prefix":"","firstName":"Samaneh","middleName":"","lastName":"Eftekhariranjbar","suffix":""},{"id":456778648,"identity":"c1621cea-d415-4406-be45-427d436522b3","order_by":4,"name":"Guoyan Jiang","email":"","orcid":"","institution":"Yancheng Municipal Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Guoyan","middleName":"","lastName":"Jiang","suffix":""},{"id":456778649,"identity":"b7d08627-6033-4ea1-82a7-90b3cd50c3cc","order_by":5,"name":"Jing Lei","email":"","orcid":"","institution":"Curtin University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Lei","suffix":""},{"id":456778652,"identity":"529b6daf-8aab-497d-a39b-a686ff4206cd","order_by":6,"name":"Lixin Sun","email":"","orcid":"","institution":"Jiangxi Science and Technology Normal University","correspondingAuthor":false,"prefix":"","firstName":"Lixin","middleName":"","lastName":"Sun","suffix":""},{"id":456778664,"identity":"60c19f1d-4bf4-4af9-ab43-d7fbf611ab56","order_by":7,"name":"Carlos J. Toro-Huamanchumo","email":"","orcid":"","institution":"Edith Cowan University","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"J.","lastName":"Toro-Huamanchumo","suffix":""},{"id":456778665,"identity":"56f2d7a1-a827-450c-93b0-09b1a2825f14","order_by":8,"name":"Zhongyang Guan","email":"data:image/png;base64,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","orcid":"","institution":"Curtin University","correspondingAuthor":true,"prefix":"","firstName":"Zhongyang","middleName":"","lastName":"Guan","suffix":""}],"badges":[],"createdAt":"2025-05-05 03:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6590850/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6590850/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s40520-025-03179-1","type":"published","date":"2025-09-04T15:56:56+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82886828,"identity":"756702c6-6b5a-4264-83fb-9cefeb257bbd","added_by":"auto","created_at":"2025-05-16 11:54:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":368986,"visible":true,"origin":"","legend":"\u003cp\u003eAdjusted survival curves for cardiometabolic outcomes by cognitive and frailty status in the CHARLS and ELSA cohorts.\u003c/p\u003e\n\u003cp\u003eAdjusted survival curves for incident cardiometabolic diseases (CMDs), cardiovascular diseases (CVDs), and diabetes by cognitive and frailty status. Panels A–C present results from the China Health and Retirement Longitudinal Study (CHARLS); panels D–F present results from the English Longitudinal Study of Ageing (ELSA). Models were adjusted for age, sex, marital status, education level, smoking status, alcohol consumption, and hypertension. HR, hazard ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6590850/v1/243a679f59b7e764abd6c252.png"},{"id":82886826,"identity":"adca7f38-f72c-4747-a6a2-7c63e76efc50","added_by":"auto","created_at":"2025-05-16 11:54:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":245477,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses of the associations between cognitive and frailty status and cardiometabolic outcomes by sex and age in CHARLS.\u003c/p\u003e\n\u003cp\u003eForest plots showing hazard ratios (HRs) and 95% confidence intervals (CIs) for incident cardiometabolic diseases (CMDs), cardiovascular diseases (CVDs), and diabetes across cognitive and frailty status categories, stratified by sex (Panel A) and age group (Panel B) among the China Health and Retirement Longitudinal Study (CHARLS) participants. Interaction \u003cem\u003eP\u003c/em\u003e values indicate effect modification by subgroup. Models adjusted for age (in sex-stratified models), sex (in age-stratified models), marital status, education level, smoking status, alcohol consumption, and hypertension.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6590850/v1/52cc8e000445e8894ffc8f5a.png"},{"id":82886832,"identity":"286bde87-9e00-44a2-8d8c-ff6cde4180f7","added_by":"auto","created_at":"2025-05-16 11:54:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":246245,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses of the associations between cognitive and frailty status and cardiometabolic outcomes by sex and age in ELSA.\u003c/p\u003e\n\u003cp\u003eForest plots showing hazard ratios (HRs) and 95% confidence intervals (CIs) for incident cardiometabolic diseases (CMDs), cardiovascular diseases (CVDs), and diabetes across cognitive and frailty status categories, stratified by sex (Panel A) and age group (Panel B) among English Longitudinal Study of Ageing (ELSA) participants. Interaction \u003cem\u003eP\u003c/em\u003e values indicate effect modification by subgroup. Models adjusted for age (in sex-stratified models), sex (in age-stratified models), marital status, education level, smoking status, alcohol consumption, and hypertension.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6590850/v1/1a31c0cb51fbb519cb0165c4.png"},{"id":82886824,"identity":"13fe180c-f0f9-4145-a63e-24c3f2cf49e1","added_by":"auto","created_at":"2025-05-16 11:54:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":116438,"visible":true,"origin":"","legend":"\u003cp\u003eDose–response associations between FI and cardiometabolic outcomes among cognitively impaired individuals in the combined CHARLS and ELSA cohorts.\u003c/p\u003e\n\u003cp\u003eRestricted cubic spline models showing the association between frailty index (FI) and risk of cardiometabolic diseases (CMDs, panel A), cardiovascular diseases (CVDs, panel B), and diabetes (panel C) among cognitively impaired participants from the combined cohort. Models were adjusted for age, sex, study region, marital status, education level, smoking status, alcohol consumption, and hypertension. The reference value was set at FI = 0.25. Shaded areas represent 95% confidence intervals. Bars indicate the distribution of FI. \u003cem\u003eP\u003c/em\u003e values indicate overall association and test for non-linearity.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6590850/v1/3ec928bcb02d3adfa56e86db.png"},{"id":90827866,"identity":"01052ecf-b457-45df-8728-406409c5d90f","added_by":"auto","created_at":"2025-09-08 16:01:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2040107,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6590850/v1/a167cf4d-b0e1-4f4b-89c0-db67579b232b.pdf"},{"id":82886835,"identity":"df9f94b0-0eac-4582-8d52-4d7c37d5a080","added_by":"auto","created_at":"2025-05-16 11:54:36","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":430028,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial4.25.docx","url":"https://assets-eu.researchsquare.com/files/rs-6590850/v1/68cee51792ecec8f20dfe059.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cognitive Frailty and Cardiometabolic Risk in Middle-Aged and Older Adults: Evidence from the UK and China","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAs the global population ages at an unprecedented rate, age-related health conditions have become increasingly prevalent. Among these, frailty and cognitive impairment are two of the most prevalent geriatric syndromes [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Globally, an estimated 12% of individuals aged 50 years and older are currently classified as frail [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], while the prevalence of mild cognitive impairment in this age group is approximately 20% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Frailty is a biological syndrome characterized by reduced physiological reserve and diminished resistance to stressors, resulting from cumulative declines across multiple physiological systems [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Cognitive impairment refers to a decline in intellectual functions, including thinking, memory, reasoning, and planning [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Given the potential bidirectional relationship and shared pathophysiological mechanisms\u0026mdash;such as chronic inflammation, oxidative stress, and mitochondrial dysfunction\u0026mdash;these two conditions have been conceptualized collectively as cognitive frailty, defined as the concurrent presence of both conditions in older adults without dementia [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBoth cognitive impairment and frailty are key markers of the aging process and are associated with a range of adverse outcomes [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], including disability, hospitalization, mortality, and cardiometabolic diseases (CMDs) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Previous studies suggest that the synergistic effect of these two conditions is associated with a heightened risk of adverse health outcomes. For instance, a longitudinal study across 17 countries found that individuals with cognitive frailty (subdistribution hazard ratio [SHR] 2.34, 95% CI 2.01\u0026ndash;2.72) had a greater risk of mortality than those with either physical frailty (SHR 1.83, 95% CI 1.72\u0026ndash;1.95) or cognitive impairment (SHR 1.36, 95% CI 1.25\u0026ndash;1.48) alone [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, whether cognitive frailty confers greater risk of CMDs\u0026mdash;a leading cause of death worldwide\u0026mdash;than either condition in isolation remains unclear [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Furthermore, the underlying mechanisms linking cognitive frailty with the development and progression of CMDs have yet to be fully elucidated.\u003c/p\u003e \u003cp\u003eOur study utilized data from two nationally representative cohorts of middle-aged and older adults in the UK (English Longitudinal Study of Ageing [ELSA]) and China (China Health and Retirement Longitudinal Study [CHARLS]) to examine the associations of cognitive frailty with the risk of incident CMDs [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Given the differing sociocultural and healthcare contexts in these two countries, this cross-national approach may help to identify context-specific patterns and inform globally relevant strategies for addressing cardiometabolic vulnerability associated with frailty and cognitive impairment in diverse aging populations.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eDetailed study designs of the CHARLS and ELSA can be found in \u003cb\u003eSupplementary Information\u003c/b\u003e. For the current study, participants from these two cohorts were excluded if they met any of the following criteria: (1) were below 50 years of age at baseline; (2) had prevalent CMDs, dementia, or Parkinson\u0026rsquo;s disease at baseline; (3) lacked follow-up data on cardiometabolic outcomes of interest; or (4) had insufficient data to assess frailty status and cognitive function. Participant selection procedures are presented in \u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssessment of frailty\u003c/h3\u003e\n\u003cp\u003eFrailty was evaluated using the frailty index (FI) and the physical frailty phenotype (PFP) approaches [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. To harmonize frailty assessments across CHARLS and ELSA datasets, 26 items were selected for FI construction, comprising self-reported health status, chronic diseases (excluding CMDs), physical function, and psychological conditions (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). For all variables included in the FI, a score of 0 indicated the absence of a deficit, and a score of 1 indicated the presence of a deficit. The FI score was calculated as the ratio of the number of deficits present to the total number of deficits considered, with a higher score indicating greater frailty. Based on previous studies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], FI scores were categorized into three groups: robust (FI score\u0026thinsp;\u0026le;\u0026thinsp;0.10), prefrail (FI score\u0026thinsp;\u0026gt;\u0026thinsp;0.10 and \u0026lt;\u0026thinsp;0.25), and frail (FI score\u0026thinsp;\u0026ge;\u0026thinsp;0.25). Participants with more than 20% missing data across FI items (i.e., more than 5 items) were excluded (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The details of the PFP approach can be found in \u003cb\u003eSupplementary Information\u003c/b\u003e.\u003c/p\u003e\n\u003ch3\u003eAssessment of cognitive impairment\u003c/h3\u003e\n\u003cp\u003eFor CHARLS participants, cognitive impairment was assessed using a combination of three tests: specifically, the Telephone Interview for Cognitive Status (TICS-10), a word recall test, and a figure drawing test. The composite score of these three tests ranges from 0 to 21, with higher scores indicating better cognitive function [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. For ELSA participants, cognitive impairment was assessed based on three tests: the word recall test (immediate and delayed recall, with a total score of 20 [10 points each]), the date naming test (maximum score of 4), and the verbal fluency test. In the verbal fluency test, participants were asked to name as many animals as possible within 60 seconds, and the number of animals named was recorded as the test score [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Participants scoring more than one standard deviation (SD) below age-appropriate norms were classified as cognitively impaired. Those scoring within one standard deviation of the norm or above were considered cognitively normal [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The procedures for these cognitive tests in the two cohorts are described in detail elsewhere [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eAssessment of cognitive frailty\u003c/h3\u003e\n\u003cp\u003eParticipants were categorized according to their frailty and cognitive status into the following groups: normal (non-frail and normal cognition), frailty only, cognitive impairment only, and cognitive frailty (co-occurrence of cognitive impairment and frailty).\u003c/p\u003e\n\u003ch3\u003eAscertainment of outcomes and endpoints\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was incident major cardiometabolic diseases (CMDs), defined as the occurrence of either major cardiovascular diseases (CVDs; including heart diseases and stroke) or diabetes. Incident major CVDs and diabetes were also examined separately as secondary outcomes. In each wave of CHARLS and ELSA, participants were asked whether a doctor had informed them of a diagnosis of diabetes, heart diseases (including angina, heart attack, congestive heart failure, and other heart problems), or stroke. Participants reporting a diagnosis of heart disease or stroke were classified as having incident CVDs, and those reporting a diagnosis of diabetes were classified as having incident diabetes. Follow-up continued until the first occurrence of a major CMD, death, or the end of the follow-up period, whichever came first.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eThe covariates included age (in years), sex (female or male), study region (China or the UK), marital status, education level, smoking status, alcohol consumption, and physician-diagnosed hypertension [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. To ensure consistency between CHARLS and ELSA, marital status was dichotomized as either married/partnered or other (including separated, divorced, unmarried, or widowed). Education level was classified into two categories: less than high school and high school or above. Smoking status was grouped as current smokers, former smokers, or never smokers. Alcohol consumption was categorized as never drinkers and ever drinkers. Physician-diagnosed hypertension was included as a covariate but was not considered part of the major CMDs definition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive characteristics were summarized across the four frailty and cognitive groups. Cox proportional hazards regression models were used to investigate the associations of cognitive frailty with the risk of three cardiometabolic outcomes. The proportional hazards assumption was assessed using Schoenfeld residuals, with no violations detected. In the main analysis, frailty was assessed using the FI, and the normal group served as the reference category. For the primary outcome (major CMDs) and two secondary outcomes (major CVDs and diabetes), hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using two models: Model 1 adjusted for age and sex; Model 2 further adjusted for study region (China or the UK), marital status, education level, smoking status, alcohol consumption, and hypertension. To assess potential synergistic effects, the risks in participants with cognitive frailty were compared with those in participants with frailty alone or cognitive impairment alone. Subgroup analyses were conducted by study region (China vs. UK) using separate Cox models based on Model 2 (excluding the stratification variable). Region-specific adjusted survival curves were generated to illustrate time-to-event differences across cognitive\u0026ndash;frailty groups. Further stratified analyses were conducted within each cohort by age group (50\u0026ndash;65 vs. \u0026gt;65 years) and by sex, with stratification variables excluded from the respective models. Multiplicative interaction terms between cognitive\u0026ndash;frailty status and age group or sex were included to evaluate effect modification.\u003c/p\u003e \u003cp\u003eTo further examine the role of physical frailty in cardiometabolic risk among individuals with cognitive impairment, we conducted restricted cubic spline (RCS) regression models treating the FI as a continuous exposure, using Cox proportional hazards models (Model 2) with FI\u0026thinsp;=\u0026thinsp;0.25 set as the reference value. To evaluate the robustness of our findings, two sets of sensitivity analyses were performed. First, all primary and region-stratified analyses were re-estimated using Fine\u003cem\u003e\u0026ndash;\u003c/em\u003eGray subdistribution hazard models to account for death as a competing risk [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Second, the associations were re-evaluated using the PFP as an alternative frailty measure [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], applying the same modelling strategy and covariate adjustments as in the main analyses.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using Stata version 18.0 (StataCorp LLC, College Station, TX, USA) and R version 4.4.1 (R Foundation for Statistical Computing, Vienna, Austria). A \u003cem\u003eP\u003c/em\u003e-value of less than 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eParticipant selection and characteristics\u003c/h2\u003e \u003cp\u003eA total of 7,628 participants from CHARLS and 4,703 from ELSA were included in the final analytical sample (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Baseline characteristics of participants stratified by cognitive and frailty status are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In both CHARLS and ELSA, statistically significant differences were observed across the four groups for all sociodemographic and health-related variables (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Individuals with cognitive frailty tended to be older, more likely to be female, less educated, and less likely to be married or partnered.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of participants by cognitive impairment and physical status.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eCHARLS (n\u0026thinsp;=\u0026thinsp;7,628)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c12\" namest=\"c8\"\u003e \u003cp\u003eELSA (n\u0026thinsp;=\u0026thinsp;4,703)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;4,948)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrailty only\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,194)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCognitive impairment only\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;928)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCognitive frailty\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;558)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3,181)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFrailty only\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;455)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCognitive impairment only\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;831)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCognitive frailty\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;236)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60.6 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.3 (8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.4 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.0 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e62.8 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e65.1 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e69.2 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e71.3 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, 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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,972 (39.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e663 (55.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e663 (71.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e444 (79.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1,748 (55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e333 (73.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e457 (55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e167 (70.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,976 (60.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e531 (44.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e265 (28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e114 (20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1,433 (45.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e122 (26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e374 (45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e69 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation, 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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school not completed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,304 (87.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,137 (95.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e916 (98.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e557 (99.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e941 (32.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e199 (49.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e489 (64.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e162 (76.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e644 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1,992 (67.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e201 (50.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e273 (35.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e49 (23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried or partnered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,219 (85.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e942 (78.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e743 (80.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e396 (71.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2,361 (74.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e260 (57.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e504 (60.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e113 (47.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e729 (14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e252 (21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e185 (19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e162 (29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e829 (25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e195 (42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e326 (39.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e123 (52.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption, 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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever drinkers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,656 (53.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e738 (61.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e664 (71.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e399 (71.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e166 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e49 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e88 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e46 (24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver drinkers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,289 (46.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e456 (38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e264 (28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e158 (28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2843 (94.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e359 (88.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e641 (87.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e143 (75.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever smokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,553 (51.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e710 (59.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e673 (72.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e434 (77.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1,301 (40.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e150 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e339 (40.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e74 (31.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious smokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e481 (9.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1,468 (46.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e204 (44.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e377 (45.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e113 (47.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,912 (38.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e364 (30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e218 (23.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e101 (18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e411 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e101 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e114 (13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e49 (20.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, 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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3957 (80.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e770 (64.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e784 (84.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e391 (70.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2240 (70.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e223 (49.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e551 (66.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e98 (41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e975 (19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e417 (35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e141 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e161 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e941 (29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e232 (51.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e280 (33.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e138 (58.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.2 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.0 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.5 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.6 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e27.5 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e29.8 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e27.3 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e29.1 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eAbbreviations: CHARLS, China Health and Retirement Longitudinal Study; ELSA, English Longitudinal Study of Ageing; SD, standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eNumbers may not sum to total due to missing data. Continuous variables were reported as means with SD, while categorical variables were presented as frequencies and percentages. Group differences were examined using analysis of variance (ANOVA), the Wilcoxon rank-sum test, or the Chi-square test, as appropriate.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of cognitive frailty with CMD outcomes\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the associations of cognitive and frailty status with the risk of three cardiometabolic outcomes in the combined cohorts. Compared with normal participants, those with cognitive frailty had significantly higher risks across all outcomes after full adjustment for covariates. Specifically, cognitive frailty was associated with increased risks of CMDs (HR 1.58; 95% CI 1.39\u0026ndash;1.79), CVDs (HR 1.64; 95% CI 1.42\u0026ndash;1.89), and diabetes (HR 1.39; 95% CI 1.11\u0026ndash;1.75). Frailty alone was also significantly associated with increased risks of CMDs (HR 1.53; 95% CI 1.39\u0026ndash;1.67), CVDs (HR 1.48; 95% CI 1.34\u0026ndash;1.65), and diabetes (HR 1.55; 95% CI 1.32\u0026ndash;1.82). In contrast, cognitive impairment alone was not significantly associated with any of the outcomes. Direct comparisons further showed that individuals with cognitive frailty had a significantly higher risk of CMDs and CVDs than those with cognitive impairment only (CMDs: HR 1.48, 95% CI 1.28\u0026ndash;1.72; CVDs: HR 1.62, 95% CI 1.36\u0026ndash;1.92), whereas the excess risk for diabetes was not statistically significant (HR 1.21, 95% CI 0.93\u0026ndash;1.58). No significant differences were observed between cognitive frailty and frailty alone across any of the three outcomes. The distribution of these three cardiometabolic outcomes by cognitive\u0026ndash;frailty group in the combined cohorts is presented in \u003cb\u003eFig. S2\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations of cognitive and frailty status with incident cardiometabolic outcomes in the combined CHARLS and ELSA cohorts.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCases, n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eCMDs\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eCVDs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndividual and combined effect\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,129 (65.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrailty only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,649 (13.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.87 (171, 2.04)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.53 (1.39, 1.67)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.81 (1.64, 2.00)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.48 (1.34, 1.65)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.88 (1.62, 2.19)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.55 (1.32, 1.82)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive impairment only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,759 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03 (0.94, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 (0.96, 1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97 (0.86, 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01 (0.89, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.15 (0.97, 1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.15 (0.96, 1.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive frailty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e794 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.91 (1.70, 2.16)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.58 (1.39, 1.79)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.94 (1.69, 2.22)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.64 (1.42, 1.89)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.74 (1.40, 2.16)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.39 (1.11, 1.75)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCombined effect vs. individual effect\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive frailty vs. frailty only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02 (0.90, 1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03 (0.90, 1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07 (0.92, 1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.10 (0.94, 1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.92 (0.73, 1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.90 (0.70, 1.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive frailty vs. cognitive impairment only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.85 (1.61, 2.14)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.48 (1.28, 1.72)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.01 (1.70, 2.37)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.62 (1.36, 1.92)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.51 (1.18, 1.95)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.21 (0.93, 1.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eCHARLS, China Health and Retirement Longitudinal Study; ELSA, English Longitudinal Study of Ageing; CMDs, cardiometabolic diseases; CVDs, cardiovascular diseases; HR, hazard ratio; NA, not applicable.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ea\u003c/sup\u003e CMDs were defined as the presence of either CVDs or diabetes.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003eb\u003c/sup\u003e Model 1 adjusted for age and gender.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ec\u003c/sup\u003e Model 2 adjusted for age, sex, study region, marital status, education level, smoking status, alcohol consumption, and hypertension\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCohort-specific analyses\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents adjusted survival curves and corresponding HRs for CMDs, CVDs, and diabetes by cognitive\u0026ndash;frailty groups in CHARLS (panels A\u0026ndash;C) and ELSA (panels D\u0026ndash;F). In both cohorts, compared with the normal group, cognitive frailty was significantly associated with higher risks of CMDs and CVDs. In CHARLS, cognitive frailty was also linked to increased diabetes risk (HR 1.33, 95% CI 1.03\u0026ndash;1.72), whereas the association was not significant in ELSA (HR 1.31, 95% CI 0.77\u0026ndash;2.24). Frailty alone was consistently associated with higher risks of all three outcomes across both cohorts, while cognitive impairment alone was not significantly associated with any outcome. Compared to cognitive impairment alone, cognitive frailty conferred significantly greater risks of CMDs and CVDs in both cohorts, but not for diabetes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analyses by sex and age\u003c/h2\u003e \u003cp\u003eSubgroup analyses by sex and age group are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (CHARLS) and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (ELSA). In CHARLS, no significant interactions were found between sex or age group and cognitive\u0026ndash;frailty status for any of the outcomes (all \u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In contrast, significant effect modification by sex and age was observed in ELSA. Specifically, the association between cognitive frailty and CMDs or CVDs was significantly stronger in males than in females (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.015 for CMDs, 0.020 for CVDs). Similarly, the association between cognitive frailty and CMDs varied significantly by age group (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.048), with higher hazard ratios observed in participants aged\u0026thinsp;\u0026gt;\u0026thinsp;65 years. No significant interactions were identified for diabetes in either cohort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRCS analysis among cognitively impaired participants\u003c/h2\u003e \u003cp\u003eTo further clarify the role of frailty in cardiometabolic risk among individuals with cognitive impairment, we used RCS models to examine the shape and strength of the association between the FI and cardiometabolic outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A positive association was observed for both CMDs and CVDs, with statistically significant overall trends (overall \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both). The association with diabetes followed a similar pattern but did not reach statistical significance (overall \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.186). Evidence of non-linearity was detected for CVDs (non-linearity \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023), while no such evidence was observed for CMDs or diabetes (non-linearity \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analyses\u003c/h2\u003e \u003cp\u003eResults from Fine\u0026ndash;Gray models were consistent with the main analyses, with cognitive frailty remaining significantly associated with increased risks of CMDs, CVDs, and diabetes (\u003cb\u003eTables S2\u0026ndash;S4\u003c/b\u003e). When frailty was defined using the PFP, cognitive frailty remained significantly associated with increased risks of CMDs and CVDs, but not diabetes, after full adjustment for covariates (\u003cb\u003eTable S5\u003c/b\u003e). Notably, in contrast to the main analysis using the FI, cognitive impairment alone was modestly but significantly associated with higher risks of CMDs (HR 1.10, 95% CI 1.01\u0026ndash;1.20). No significant association was observed between cognitive impairment alone and diabetes. The overall pattern of results remained broadly consistent with the main results. Cognitive frailty was associated with higher risks of CMDs and CVDs than cognitive impairment alone, although the differences did not reach statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this large, population-based study involving cohorts from China and the UK, we found that individuals with cognitive frailty had significantly higher risks of CMDs, CVDs, and diabetes compared with those with normal cognitive function and frailty status. These associations were consistently observed across both cohorts and remained robust after adjustment for demographic, behavioral, and clinical covariates. Notably, frailty alone was also associated with elevated risks of all three outcomes, while cognitive impairment alone was not significantly associated with any. Subgroup analyses further revealed effect modification by sex and age in ELSA, but not in CHARLS. Furthermore, results remained comparable in sensitivity analyses using competing risk models and alternative frailty definitions.\u003c/p\u003e \u003cp\u003eOne notable finding was that cognitive impairment, in the absence of physical frailty, may not substantially elevate cardiometabolic risk. Individuals who retain physical robustness may be better able to engage in self-care and maintain healthier behaviors, thereby mitigating the adverse impact of cognitive decline. In addition, some cases of cognitive impairment may have been transient or subclinical, attenuating long-term associations [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Interestingly, the addition of cognitive impairment to frailty did not significantly increase risk beyond that conferred by frailty alone. This may suggest that frailty alone captures much of the systemic vulnerability relevant to cardiometabolic risk, thereby limiting the incremental contribution of cognitive deficits. Subgroup analyses revealed effect modification by sex and age in ELSA but not in CHARLS. In ELSA, the association between cognitive frailty and CMDs or CVDs was stronger among men and individuals aged\u0026thinsp;\u0026gt;\u0026thinsp;65 years. This is consistent with previous research reporting stronger associations between cognitive frailty and mortality among older adults (\u0026ge;\u0026thinsp;70 years) and males [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. It is plausible that older individuals with cognitive frailty are more susceptible to adverse cardiometabolic outcomes due to accumulated physiological burden, multisystem dysregulation, and reduced resilience with age, whereas younger individuals may still retain greater functional reserves that mitigate such risks. While the reasons underlying the sex-specific differences remain unclear, these findings underscore the need for further research into how biological, behavioral, and social factors may differentially influence cardiometabolic risk across population subgroups. The absence of interaction effects in CHARLS may reflect more uniform risk distributions or population-specific differences in the expression of frailty. Furthermore, RCS analysis demonstrated significant dose\u0026ndash;response associations between the FI and the risks of CMDs and CVDs, with similar but non-significant trends observed for diabetes. These findings reinforce the role of frailty as a fundamental determinant of cardiometabolic vulnerability, even among individuals with cognitive impairment.\u003c/p\u003e \u003cp\u003eThe mechanisms underlying the observed associations between cognitive frailty and cardiometabolic risk are likely to be multifactorial. Cognitive frailty reflects the co-occurrence of neurocognitive decline and physical vulnerability, both of which have been linked to pathophysiological mechanisms such as chronic systemic inflammation, mitochondrial dysfunction, and oxidative stress [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These mechanisms may accelerate vascular aging, impair glucose metabolism, and promote atherosclerosis, thereby increasing susceptibility to both CVDs and diabetes [\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Furthermore, individuals with cognitive frailty may be less likely to adhere to medical treatment, maintain physical activity, or follow dietary recommendations [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], thereby further exacerbating cardiometabolic risk.\u003c/p\u003e \u003cp\u003eThis study has several strengths. First, we utilized data from two nationally representative cohorts, enhancing generalizability and cross-cultural relevance. Second, the prospective design, large sample size, and long follow-up enabled robust risk estimation. Third, comprehensive adjustment for covariates and multiple sensitivity analyses\u0026mdash;including competing risk models and alternative frailty definitions\u0026mdash;supported the robustness of our findings. However, several limitations warrant consideration. First, cognitive impairment and frailty were assessed at a single time point, limiting insight into their temporal progression. Second, residual confounding from unmeasured factors, such as dietary patterns, may exist. Third, differences in variable operationalization across cohorts may have introduced heterogeneity. Fourth, CMD outcomes were self-reported, raising the possibility of misclassification due to variations in diagnosis rates or healthcare access across socioeconomic strata. Fifth, in ELSA, mortality data were unavailable beyond wave 6, which may have affected follow-up estimates and the accuracy of competing risk adjustment [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Finally, the observational nature of the study precludes causal inference.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eCognitive frailty was independently associated with an elevated risk of CMDs, particularly CVDs, in both Chinese and UK cohorts. These associations were stronger than those observed for cognitive impairment alone and remained robust across alternative frailty definitions and analytical approaches. The inclusion of culturally diverse populations enhances the generalizability of our findings and underscores the need for integrated, contextually appropriate strategies to identify and address both cognitive and frailty in aging populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge all investigators and participants involved in the CHARLS and ELSA cohorts for their valuable contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CHARLS data are available upon request (https://charls.pku.edu.cn/), while the ELSA data are available after registration (https://beta.ukdataservice.ac.uk/datacatalogue/series/series?id=200011).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: HY, JJL, ZG. Methodology: JJL, CL, ZG. Data curation: HY, JJL. Formal analysis: HY, JJL, ZG. Original draft: HY, JJL, ZG. Critical review and revision: CL, SE, GJ, JL, LS, CJTH, ZG. Final approval of the version to be published: all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCJTH is supported by the Forrest Research Foundation Scholarship and the ECU Higher Degree by Research Scholarship. ZG is supported by the Curtin Higher Degree by Research Scholarship and the Dementia Centre of Excellence (DCE) and Curtin enAble Institute Seed Funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCHARLS and ELSA were approved by the Ethical Review Committee of Peking University and the London Multi-Centre Research Ethics Committee, respectively. Informed consent was obtained from all participants in both cohorts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eO\u0026rsquo;Caoimh R, Sezgin D, O\u0026rsquo;Donovan MR, Molloy DW, Clegg A, Rockwood K, et al (2021) Prevalence of frailty in 62 countries across the world: a systematic review and meta-analysis of population-level studies. Age Ageing 50(1):96-104. https://doi.org/10.1093/ageing/afaa219\u003c/li\u003e\n \u003cli\u003eRobertson DA, Savva GM, Kenny RA (2013) Frailty and cognitive impairment\u0026mdash;A review of the evidence and causal mechanisms. 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PLoS One 8(3):e59649. https://doi.org/10.1371/journal.pone.0059649\u003c/li\u003e\n \u003cli\u003eSoysal P, Stubbs B, Lucato P, Luchini C, Solmi M, Peluso R, et al (2016) Inflammation and frailty in the elderly: a systematic review and meta-analysis. Ageing research reviews 31:1-8. https://doi.org/10.1016/j.arr.2016.08.006\u003c/li\u003e\n \u003cli\u003eMaloberti A, Vallerio P, Triglione N, Occhi L, Panzeri F, Bassi I, et al (2019) Vascular aging and disease of the large vessels: role of inflammation. High Blood Press Cardiovasc Prev 26:175-82. https://doi.org/10.1007/s40292-019-00318-4\u003c/li\u003e\n \u003cli\u003eStumvoll M, Goldstein BJ, Van Haeften TW (2005) Type 2 diabetes: principles of pathogenesis and therapy. The Lancet 365(9467):1333-46. https://doi.org/10.1016/S0140-6736(05)61032-X\u003c/li\u003e\n \u003cli\u003eLowell BB, Shulman GI (2005) Mitochondrial dysfunction and type 2 diabetes. Science 307(5708):384-7. https://doi.org/10.1126/science.1104343\u003c/li\u003e\n \u003cli\u003eJames K, Jamil Y, Kumar M, Kwak MJ, Nanna MG, Qazi S, et al (2024) Frailty and cardiovascular health. Journal of the American Heart Association 13(15):e031736. https://doi.org/10.1161/JAHA.123.031736\u003cem\u003e\u003c/em\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"aging-clinical-and-experimental-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"acer","sideBox":"Learn more about [Aging Clinical and Experimental Research](http://link.springer.com/journal/40520)","snPcode":"40520","submissionUrl":"https://submission.nature.com/new-submission/40520/3","title":"Aging Clinical and Experimental Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Frailty, Cognitive impairment, Cognitive frailty, Cardiometabolic disease","lastPublishedDoi":"10.21203/rs.3.rs-6590850/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6590850/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Cognitive frailty, a novel construct integrating cognitive and physical deficits, is increasingly recognized in aging research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAims:\u003c/strong\u003e This study aimed to examine the associations between cognitive frailty and cardiometabolic risk in two nationally representative cohorts from China and the United Kingdom.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We analyzed data from 7,628 participants in the China Health and Retirement Longitudinal Study (CHARLS) and 4,703 from the English Longitudinal Study of Ageing (ELSA), all aged ≥50 years. Frailty was assessed using the frailty index (FI) in the main analysis. Cox proportional hazards models were applied to estimate hazard ratios (HRs) for incident cardiometabolic diseases (CMDs), cardiovascular diseases (CVDs), and diabetes. Subgroup and interaction analyses were performed to examine effect modification. Restricted cubic spline (RCS) models were used to assess the shape of the association between FI and CMD risk among individuals with cognitive impairment. Sensitivity analyses employed competing risk models and the physical frailty phenotype (PFP) as an alternative frailty measure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Cognitive frailty was associated with higher risks of CMDs (HR 1.58, 95% CI 1.39–1.79), CVDs (HR 1.64, 95% CI 1.42–1.89), and diabetes (HR 1.39, 95% CI 1.11–1.75). Cognitive impairment alone showed no significant association with these outcomes. Significant dose–response associations between the FI and CMDs and CVDs were observed among individuals with cognitive impairment. Results were consistent across cohorts and robust in sensitivity analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eCognitive frailty is a consistent predictor of cardiometabolic risk across distinct populations, supporting integrated screening and prevention strategies targeting both cognitive and physical deficits in aging populations.\u003c/p\u003e","manuscriptTitle":"Cognitive Frailty and Cardiometabolic Risk in Middle-Aged and Older Adults: Evidence from the UK and China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 11:54:31","doi":"10.21203/rs.3.rs-6590850/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-25T11:16:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-15T13:37:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-11T17:22:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"299550694166166071478678615379170492116","date":"2025-06-03T07:32:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-23T03:58:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8182128243088882372355916501318517691","date":"2025-05-21T09:22:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285306896968882496688899697955320944236","date":"2025-05-13T02:00:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-13T00:55:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-07T19:25:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-06T06:32:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Aging Clinical and Experimental Research","date":"2025-05-05T03:04:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"aging-clinical-and-experimental-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"acer","sideBox":"Learn more about [Aging Clinical and Experimental Research](http://link.springer.com/journal/40520)","snPcode":"40520","submissionUrl":"https://submission.nature.com/new-submission/40520/3","title":"Aging Clinical and Experimental Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a01e0270-dc1a-4dd1-ae7f-9fd647da6dcc","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-08T15:58:30+00:00","versionOfRecord":{"articleIdentity":"rs-6590850","link":"https://doi.org/10.1007/s40520-025-03179-1","journal":{"identity":"aging-clinical-and-experimental-research","isVorOnly":false,"title":"Aging Clinical and Experimental Research"},"publishedOn":"2025-09-04 15:56:56","publishedOnDateReadable":"September 4th, 2025"},"versionCreatedAt":"2025-05-16 11:54:31","video":"","vorDoi":"10.1007/s40520-025-03179-1","vorDoiUrl":"https://doi.org/10.1007/s40520-025-03179-1","workflowStages":[]},"version":"v1","identity":"rs-6590850","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6590850","identity":"rs-6590850","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}