Association between intrinsic capacity, changes in intrinsic capacity, and cardiometabolic multimorbidity: results from three prospective cohort studies

Background: Intrinsic capacity (IC)is closely associated with cardiometabolic health in middle-aged and older adults. The purpose of this study was to determine the associations of baseline IC, cumulative IC scores, and their dynamic changes with the risk of incident cardiometabolic multimorbidity (CMM).

Methods: Using data from three prospective cohorts, the China Health and Retirement Longitudinal Study (CHARLS), the English Longitudinal Study of Ageing (ELSA), and the Health and Retirement Study (HRS), participants who met the eligibility criteria were included in this study. Kaplan-Meier curves and Cox models analyzed risk trends and associations.

Results: A total of 11,916 participants were included based on the inclusion and exclusion criteria. At baseline, the risk of CMM in the injured group was significantly higher than that in the non-injured group(Pooled: HR = 1.40, 95% CI 1.30-1.52, P < 0.001); Cumulative IC scores showed a graded association with CMM risk: individuals with moderately reduced scores (≈1 SD below intact) had higher risk (HR = 1.22, 95% CI 1.09–1.36), and those with substantially reduced scores (>1 SD below intact) had even higher risk (HR = 1.61, 95% CI 1.47–1.78). For dynamic changes, CMM risk was significantly higher in persistent impairment and decline groups; even the improvement group had higher risk than the no-decline group (Decline: HR = 1.25, 95% CI 1.10-1.42, P < 0.001; Improvement: HR = 1.28, 95% CI 1.13-1.45, P < 0.001; Persistent impairment: HR = 1.63, 95% CI 1.48-1.80, P < 0.001).

Conclusions: IC and its changes relate to CMM risk. Even with IC improvement, risk remains higher than in those with intact IC. Precise strategies to delay IC decline and individualized interventions are needed for CMM control.

Introduction

Cardiometabolic diseases (CMD) are a group of diseases related to cardiac function and metabolic processes, with common types including hypertension, diabetes mellitus, coronary heart disease, and stroke. When an individual has two or more of these diseases concurrently, it is defined as cardiometabolic multimorbidity (CMM) (1). Evidence suggests that CMM is associated with a variety of adverse health outcomes; compared with individuals without diseases, the life expectancy of patients with CMM can be shortened by 15 years (2, 3). In recent years, with the annual increase in the prevalence and incidence of CMM, CMM triggered by the decline in individuals’ physical capacity and mental health has gradually attracted significant attention from experts (4–6).

In 2015, the World Health Organization (WHO) officially proposed a new public health action framework (7). This framework innovatively introduced the concept of intrinsic capacity (IC), which is defined as a core indicator for comprehensively assessing an individual’s physical and mental capacities, and mainly includes locomotion, vitality, cognition, psychological, and sensory as its five dimensions (8). Data from a large-scale study based on the UK Biobank demonstrates that participants with low IC levels at baseline were significantly associated with subsequent disease-specific mortality (hypertension, stroke, and coronary heart disease), suggesting that IC may be a key predictive indicator for the prognosis of these diseases (5). From a behavioral perspective, the decline in IC may lead to changes in lifestyle, such as reduced physical activity or increased social isolation, both of which can further increase the risk of CMM (9).

Currently, evidence regarding the association between IC and adverse health outcomes has also been accumulating. However, several research gaps remain. First, most study outcomes focus on the association between IC and a single CMD, with no targeted exploration of CMM as a distinct form of multimorbidity. This fails to reflect the true characteristics of multimorbidity in this population, limiting clinical applicability (4). Second, study designs are mainly based on single IC measurement at baseline or cross-sectional analyses, failing to capture the dynamic trajectories of IC. This limitation prevents clarification of the association between changes in IC and the risk of CMM, thus hindering the development of early warning protocols (10). Third, study populations are mostly limited to single national cohorts, lacking cross-country validation, which leads to restricted external validity and further limits its standardized application in the prevention and control of CMM among the global population aged ≥50 years.

Based on the discussed context, this study aims to investigate the association between IC, its changes, and CMM. The analysis was performed using three prospective databases: the China Health and Retirement Longitudinal Study (CHARLS), the English Longitudinal Study of Ageing (ELSA), and the Health and Retirement Study (HRS). This study enhances the generalizability of results through cross-national cohorts; meanwhile, it can explore the heterogeneity of the associations between IC, its changes, and CMM among populations in different regions. The study will overcome the limitation of sole baseline measurement in a single cohort and combine it with the Cox proportional hazards model to analyze the impact of different IC levels and trajectories on incident CMM. Theoretically, this study can fill the research gap in the association between dynamic IC trajectories and CMM in populations aged ≥50 years, while verifying the necessity of multi-cohort analysis in geriatric cardiometabolic health research. These methods can inform the development of future intervention strategies for CMM.

Methods

Study design and participants

CHARLS, ELSA, and HRS are nationally representative prospective cohort studies conducted in China, the United Kingdom, and the United States, respectively (11–13). In this study, Wave 1 (2011) of CHARLS, Wave 2 (2004-05) of ELSA, and Wave 10 (2010) of HRS were defined as the baselines. Wave 2 (2013) of CHARLS, Wave 4 (2008-09) of ELSA, and Wave 11 (2012) of HRS were regarded as the second surveys. This study used data from the baselines and the second surveys to assess the status and dynamic changes of IC. Subsequent follow-up surveys were used to track the outcome until the final follow-up surveys which were Wave 5 (2020) of CHARLS, Wave 9 (2018-19) of ELSA, and Wave 15 (2020) of HRS. CHARLS, ELSA, and HRS have obtained approvals from the Ethics Review Boards of Peking University, the London Multi-Center Study, and the University of Michigan, respectively. All participants provided informed consent forms. The authors did not participate in direct data collection, and the ownership of the data remains with the original data custodians.

Figure 1 presents the selection process of the study population. A total of 44,032 participants aged over 50 years from CHARLS, ELSA, and HRS were included in the screening. First, this study excluded 15,205 participants with missing IC data at baseline. Second, 6,747 participants who had already been diagnosed with CMM at baseline were excluded. To explore the dynamic changes of IC, 9,425 participants with missing IC data in the second survey were further excluded. Finally, 739 participants who had CMM in the second survey were excluded. Ultimately, 11,916 participants were included in the final analysis.

Figure 1

Figure 1. Selection process of the study population.

IC assessment

IC was assessed following the World Health Organization’s Integrated Care for Older People (ICOPE) screening tool, which evaluates an individual’s physical and mental reserve across five domains—cognition, psychological, sensory(vision and hearing), vitality, and locomotion. This operational approach has been widely applied and validated in population-based aging studies (14–17). To ensure comparability between CHARLS, ELSA, and HRS, each domain was modeled using tunable variables from each cohort. Each field is encoded as 0 (damaged) or 1 (complete). The IC score ranges from 0 to 6 points. A higher score indicates a better IC level—with 6 points indicating fully intact IC and a score of ≤5 points indicating impaired IC (18–20). Although IC consists of five domains, the sensory domain includes two separate indicators (vision and hearing), resulting in six items in total and therefore a total score of 0 to 6.

To further characterize IC status across time, two IC-related indicators were derived using data from the baseline and the second follow-up assessments: cumulative IC scores and changes in IC. Cumulative IC scores represent the overall IC level across the two waves and were calculated as the sum of the IC scores at baseline and follow-up, using the formula:

$$Cumulative IC scores=I{C}_{baseline}+I{C}_{follow-up}$$

Based on its empirical distribution, cumulative IC scores were categorized into three levels (T1–T3), where T1 represents intact cumulative IC, T2 represents cumulative IC scores approximately one standard deviation below the intact level, and T3 indicates cumulative IC scores more than one standard deviation below the intact level (distribution shown in Supplementary Figure S1).

Changes in IC were evaluated both continuously and categorically. The continuous change score was defined as:

$$changes in IC=I{C}_{follow-up}-I{C}_{baseline}$$

For categorical analyses, participants were classified into four mutually exclusive groups reflecting their IC trajectories: no decline (intact IC at both assessments), decline (intact at baseline but impaired at follow-up), improvement (impaired at baseline but intact at follow-up), and persistent impairment (impaired at both assessments).

Details of the domain construction and variable harmonization are provided in Supplementary Table S1.

Covariates assessment

In the analytical framework of this study, the following covariates were included: age, gender, educational level, marital status, smoking status, drinking status, systolic blood pressure (SBP), diastolic blood pressure (DBP), and handgrip strength. In order to ensure data consistency across the three databases (CHARLS, ELSA, HRS), this study standardized definitions for covariates. Educational level was uniformly categorized into three groups: below high school, high school, and above high school. Marital status was divided into two categories: the married group, which included married individuals and those who were married but had absent spouses, and the other group, which encompassed separated, divorced, widowed, and never-married individuals. Smoking status was classified into two categories: past or current smokers and never smokers. Drinking status was similarly divided into two groups: past or current drinkers and never drinkers.

Outcome definition and follow-up

The primary outcome of this study was incident CMM. Across CHARLS, ELSA, and HRS, CMM was defined based on participants’ self-reported doctor-diagnosed conditions, including hypertension, diabetes or elevated blood glucose, heart disease (e.g., myocardial infarction, coronary heart disease, angina pectoris, congestive heart failure, and other heart diseases), and stroke. Coexistence of 2 or more of these cardiometabolic diseases was considered as CMM.

During the follow-up period, the follow-up endpoints were the first occurrence of CMM, death, and the observation cutoff date, whichever came first. Death data in CHARLS and HRS were obtained from the most recent survey, while death data in ELSA were only available up to Wave 6 (2012-13). The observation cutoff date was the date of the last survey that participants attended; ideally, this corresponded to Wave 5 (2020) of CHARLS, Wave 9 (2018-19) of ELSA, and Wave 15 (2020) of HRS.

Statistical analysis

In descriptive statistics, continuous variables were expressed as mean [standard deviation (SD)], and categorical variables were presented as counts (percentages).

Pooled associations across CHARLS, ELSA, and HRS were estimated using a one-stage stratified Cox proportional hazards model, with cohort specified as a stratification variable to allow cohort-specific baseline hazards.

To analyze the association between baseline IC and the risk of incident CMM, Cox proportional hazards regression models were used to calculate the hazard ratio (HR) and its 95% confidence interval (95% CI). With the intact IC group as the reference group, three regression models were fitted: Model 1: Unadjusted model; Model 2: Adjusted for demographic characteristics including age, gender, educational level, and marital status; Model 3: Further adjusted for smoking status, drinking status, systolic blood pressure (SBP), diastolic blood pressure (DBP), and handgrip strength on the basis of Model 2. Missing values of covariates were handled using multiple imputation. In addition to baseline IC, the associations of cumulative IC scores and changes in IC with incident CMM were also examined. Cumulative IC scores were categorized into three levels (T1–T3) based on their empirical distribution, and changes in IC were classified into four trajectory groups (no decline, decline, improvement, and persistent impairment), as described previously. All the above analyses were adjusted for covariates, and the proportional hazards assumption of each Cox regression model was verified using the Schoenfeld residual test (21).

To verify the robustness of the results, multiple sensitivity analyses were conducted in this study: (I) To reduce potential bias introduced by multiple imputation, after excluding samples with missing covariates, the associations between baseline IC score, cumulative IC scores, changes in IC, and the risk of incident CMM were re-analyzed; (II) To reduce potential residual confounding bias, a supplementary analysis was performed using the propensity score matching (PSM) method; (III) To treat death as a competing event for CMM, we used a competing risks model to re-verify the association effects. (IV) To address potential heterogeneity within the improvement group, we optimized the behavior of IC changes in sensitivity analysis. Based on the transition pattern from baseline to follow-up, IC changes were divided into five mutually exclusive categories: No decline (The IC is intact both at baseline and the second survey), Decline (The IC is intact at baseline but impaired at the second survey), Minor improvement (Baseline IC shows mild impairment (IC = 5), and becomes intact at the second survey), Major improvement (Baseline IC shows substantial impairment (IC< 5), and becomes intact at the second survey), Persistent impairment (The IC is impaired both at baseline and the second survey). (V) To address potential confounding caused by baseline heterogeneity in pre-existing single cardiometabolic conditions (e.g., hypertension, diabetes, heart disease, or stroke), we conducted an additional sensitivity analysis stratified by single-disease subgroups.

Results

Characteristics of the study population

According to the inclusion and exclusion criteria, a total of 11,916 participants (females: 54.6%, mean age: 66.7 years) were included from CHARLS, ELSA, and HRS for the baseline IC characteristic analysis, with specific breakdowns as follows: 4,361 participants from CHARLS (females: 43.5%, mean age: 60.8 years), 2,738 participants from ELSA (females: 60.8%, mean age: 63.1 years), and 4,817 participants from HRS (females: 61.1%, mean age: 74.0 years). Detailed baseline characteristics of these participants are presented in Table 1. Across CHARLS, ELSA, and HRS, a higher proportion of participants had an educational level of high school or below. Specifically, this proportion reached 98.8% in CHARLS, which was much higher than the 81.3% in ELSA and 76.3% in HRS. Regarding marital status, CHARLS had the highest proportion of married participants (88.9%), while HRS had the lowest (59.6%). Compared with non-drinkers, the proportion of drinkers was extremely high in ELSA (93.4%), whereas the lowest proportion of drinkers was observed in CHARLS (46.6%). For smoking status, 58.3% of participants in ELSA were smokers, which was higher than the proportions in CHARLS (46.3%) and HRS (54.7%). In terms of health indicators, compared with the other cohorts, HRS had the highest mean DBP (78.5 mmHg) and the lowest mean grip strength (30.0 kg). Details of baseline characteristics of IC status for CHARLS, ELSA, and HRS participants are presented in Supplementary Tables S2–S4.

Table 1

Table 1. Baseline characteristics of participants in CHARLS, ELSA, and HRS.

Baseline IC and its association with CMM

To assess the association between baseline IC and CMM, this study utilized Kaplan-Meier survival curves stratified by baseline IC score. Results showed that participants with impaired baseline IC had a higher risk of developing CMM than those with intact baseline IC (Supplementary Figure S2). Furthermore, this study further adjusted Cox proportional hazards models to explore the association between baseline IC and CMM in depth (Table 2). Results indicated that, with the intact baseline IC group as the reference, participants with impaired baseline IC had a significantly increased risk of CMM in all individual cohorts and the pooled population (CHARLS: HR = 1.36, 95% CI: 1.17-1.52, P < 0.001; ELSA: HR = 1.51, 95% CI: 1.25-1.82, P < 0.001; HRS = 1.35, 95% CI: 1.20-1.53, P < 0.001; Pooled: HR = 1.40, 95% CI: 1.30-1.52, P < 0.001). This association remained stable even after adjusting for confounding factors.

Table 2

Table 2. Association between baseline IC and CMM.

Cumulative IC scores and its association with CMM

Table 3 presents the association between cumulative IC scores and CMM. In the CHARLS cohort, compared with the T1 group, the CMM risk was significantly higher in both the T2 group (HR = 1.33, 95% CI: 1.12–1.57, P = 0.001) and the T3 group (HR = 1.60, 95% CI: 1.37–1.88, P < 0.001). In the ELSA cohort, the CMM risk was also significantly elevated in the T2 group (HR = 1.36, 95% CI: 1.07–1.72, P = 0.009) and the T3 group (HR = 2.15, 95% CI: 1.73–2.66, P < 0.001), with the T3 group showing the largest risk increase among all cohorts. In the HRS cohort, only the T3 group exhibited a significantly higher CMM risk than the T1 group (HR = 1.43, 95% CI: 1.24–1.65, P < 0.001). Analysis of the pooled population revealed that the CMM risk was significantly increased in both the T2 group (HR = 1.22, 95% CI: 1.09–1.36, P < 0.001) and the T3 group (HR = 1.61, 95% CI: 1.47–1.78, P < 0.001). Additionally, the Kaplan-Meier survival curve based on cumulative IC scores classification in this study showed an overall trend that the lower the cumulative IC scores were, the higher the CMM risk was. (Supplementary Figure S3).

Table 3

Table 3. Association between cumulative IC scores and CMM.

Association between changes in IC and CMM

Table 4 presents the association between changes in IC and CMM. Results showed that, with the group without IC decline as the reference, the group with persistent impairment had a significantly increased risk of CMM across all cohorts (CHARLS: HR = 1.65, 95% CI: 1.40–1.95, P < 0.001; ELSA: HR = 2.26, 95% CI: 1.79–2.79, P < 0.001; HRS: HR = 1.41, 95% CI: 1.22–1.63, P < 0.001; Pooled: HR = 1.63, 95% CI: 1.48–1.80, P < 0.001). The group with IC decline also exhibited a significantly elevated risk of CMM(CHARLS: HR = 1.41, 95% CI: 1.17–1.71, P < 0.001; ELSA: HR = 1.44, 95% CI: 1.08–1.92, P = 0.012; HRS: HR = 1.04, 95% CI:0.85–1.28, P = 0.689; Pooled: HR = 1.25, 95% CI: 1.10–1.42, P < 0.001). Notably, the group with IC improvement also had a significantly higher risk of CMM (CHARLS: HR = 1.28, 95% CI: 1.05–1.56, P = 0.015; ELSA: HR = 1.37, 95% CI: 1.04–1.79, P = 0.023; HRS: HR = 1.24, 95% CI: 1.02–1.51, P = 0.032; Pooled: HR = 1.28, 95% CI: 1.13–1.45, P < 0.001). Additionally, Kaplan-Meier survival curves stratified by changes in IC, used in this study, are provided in Supplementary Figure S4.

Table 4

Table 4. Association between changes in IC and CMM.

Sensitivity analyses

(I) To reduce potential bias introduced by multiple imputation, after excluding samples with missing covariates, the associations between baseline IC score, cumulative IC scores, changes in IC, and the risk of incident CMM were re-analyzed. The results were consistent with those of the primary analysis (Supplementary Tables S5–S7). (II) To mitigate potential residual confounding bias, a supplementary analysis was conducted using the propensity score matching (PSM) method (Supplementary Tables S8-S10). (III) With death treated as a competing event for CMM, a competing risks model was used to re-verify the association effects (Supplementary Tables S11–S13). (IV) To further refine the improvement group, we re-analyzed the association between changes in IC and risk of CMM (Supplementary Table S14). (V) To minimize potential confounding introduced by baseline heterogeneity in pre-existing single cardiometabolic conditions, we performed a subgroup sensitivity analysis restricted to participants with only one condition at baseline. The pooled results were generally consistent with the main findings (Supplementary Tables S15–S17).

Discussion

Using three prospective cohorts, we explored associations of baseline IC, cumulative IC scores, and their dynamic changes with incident CMM risk via Kaplan-Meier curves and Cox models. Results showed baseline IC-impaired participants had higher incident CMM risk than intact ones, with this association stable after adjusting for confounders. Cumulative IC scores correlated negatively with CMM risk, most notably in the largest decline group. IC decline and persistent impairment significantly increased CMM risk; even improved IC carried higher risk than the no-decline reference group. These trends were consistent across individual cohorts and the pooled population.

Previous studies have gradually revealed the association between IC and CMD in middle-aged and older populations. A national cohort study found that for each 1-point increase in baseline IC, the risk of cardiovascular disease (CVD) decreased by 8% (22). Another study further confirmed that impaired baseline IC is an independent predictor of incident CVD (5). These findings are highly consistent with the core results of the present study—across the three cohorts CHARLS, ELSA, HRS and the pooled population, we observed that impaired baseline IC significantly increased the risk of CMM. This cross-ethnic and cross-regional consistency further validates the universality of IC as a risk factor for CMM. From the perspective of basic research, IC and CMD may share common physiological pathways, such as chronic inflammation (23, 24). This provides further evidence to support the core conclusion of this study.

IC dynamic characteristics are not isolated but reflect physiological function evolution in middle-aged and older adults. Two prospective studies showed IC changes dynamically during follow-up, with three trajectories in the Chinese population (6, 25). confirming its non-fixed nature and highlighting the significance of exploring its dynamic associations with CMM risk. Our results reveal differential impacts of IC changes on CMM risk: First, those with persistent IC impairment face the highest risk, as prolonged core physiological dysfunction (e.g., metabolic regulation) maintains low reserve, failing to counter risks and accelerating cardiometabolic disease accumulation (26, 27). This suggests that persistent IC impairment is a high-risk signal for CMM and that this population should be prioritized for clinical intervention. Second, IC decline significantly elevates risk, possibly reflecting worsened lifestyles or underlying diseases; timely intervention to halt decline may reduce comorbidity. Most notably, though IC improvement is positive, their risk remains higher than the no-decline reference group, indicating improvement does not eliminate risk, necessitating long-term monitoring to prevent rebound.

Beyond these findings, it is important to recognize that the two indicators used to characterize IC dynamics—changes in IC and cumulative IC scores—capture distinct aspects of functional evolution, which may help explain their different associations with CMM risk. Changes in IC reflect short-term transitions between two assessments and emphasize whether an individual’s functional reserve improves, declines, or remains stable. In contrast, cumulative IC scores represent the overall functional reserve accumulated across the period and reflect long-term physiological robustness. Consequently, an individual who experiences a small decline from a high IC level may still maintain strong cumulative reserve, whereas someone with persistently low IC may exhibit chronically insufficient reserve despite no recent decline. These conceptual differences suggest that cumulative IC scores may be more relevant to chronic cardiometabolic dysregulation, while IC changes primarily capture transient fluctuations that influence risk when sufficiently marked or sustained.

In addition, the negative correlation between cumulative IC scores and comorbidity risk further supports the importance of long-term IC reserves in shaping cardiometabolic vulnerability. Individuals with lower cumulative IC scores—whether persistent impairment or significant decline—consistently exhibit higher CMM risk. This pattern highlights the accumulation of physiological deficits over time and is a key mechanism linking IC deterioration to cardiometabolic deterioration. These findings break the perception that IC only requires a single assessment and clarify the importance of dynamically monitoring IC changes for CMM risk prediction.

This study holds several strengths. First, it is the first study to investigate the association between IC, changes in IC, and CMM. Additionally, this study includes three prospective cohorts with diverse ethnicities; this cross-population design not only avoids the geographical limitation of a single cohort but also significantly enhances the generalizability of the conclusions through the consistency of results across different populations. Furthermore, the study results were consistent across the three cohorts, indicating the generality of the conclusions. Finally, diverse sensitivity analyses were conducted, which also ensured the robustness of the study results.

This study has several limitations. First, consistent with many previous studies (28–30), outcome variables were defined based on participants’ self-reported doctor-diagnosed information, which may introduce bias. Second, differences in follow-up intervals across cohorts may affect the comparability of results: the interval between CHARLS Wave 1 (2011) and Wave 2 (2013) was 2 years, between ELSA Wave 2 (2004–05) and Wave 4 (2008–09) was 4 years, and between HRS Wave 10 (2010) and Wave 11 (2012) was 2 years. The reason for the different interval between baseline and the second follow-up in ELSA was the lack of available BMI data in ELSA Wave 3, which may have caused minor discrepancies. Finally, in ELSA, death data were only available up to Wave 6, while outcome data were tracked until Wave 9. The lack of death data between Wave 7 and Wave 9 may have led to incorrect estimation of follow-up duration.

Conclusion

Both IC and changes in IC are associated with the risk of CMM. Even with IC improvement, the associated risk remains higher than that in individuals with intact IC. In the future, it is necessary to develop targeted strategies to delay the decline in IC and implement individualized interventions to improve IC, thereby contributing to the prevention and control of CMM.

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found here: https://g2aging.org/home.

Ethics statement

The studies involving humans were approved by CHARLS (approved by the Biomedical Ethics Review Committee of Peking University, IRB00001052-11015), HRS (approved by the University of Michigan Institutional Review Board), and ELSA (approved by the South Central–Berkshire Research Ethics Committee on 22nd March 2021 (21/SC/0030). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author contributions

ZW: Conceptualization, Project administration, Data curation, Validation, Investigation, Methodology, Supervision, Writing – review & editing, Resources, Writing – original draft, Software, Visualization, Formal Analysis. LW: Supervision, Software, Writing – review & editing, Conceptualization, Methodology, Formal Analysis, Visualization, Project administration, Investigation, Resources, Validation, Data curation. LT: Investigation, Conceptualization, Data curation, Methodology, Writing – review & editing. XL: Visualization, Writing – review & editing. YL: Visualization, Writing – review & editing. XY: Visualization, Writing – review & editing. FL: Writing – review & editing, Supervision, Funding acquisition, Resources. XZ: Funding acquisition, Writing – review & editing, Supervision, Resources.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This study was supported by National Natural Science Foundation of China (NO.72074168; NO.72304210).

Acknowledgments

We thank the Gateway to Global Aging Data for providing harmonized data information. We also appreciate the work done by staff and investigators from the China Health and Retirement Longitudinal Study; the Health and Retirement Study; and the English Longitudinal Study of Ageing, and we appreciate the participation of respondents in these studies.

Conflict of interest

The authors declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2025.1738997/full#supplementary-material

References

1. Glynn LG. Multimorbidity: another key issue for cardiovascular medicine. Lancet. (2009) 374:1421–2. doi: 10.1016/S0140-6736(09)61863-8

PubMed Abstract | Crossref Full Text | Google Scholar

2. Lyall DM, Celis-Morales CA, Anderson J, Gill JM, Mackay DF, McIntosh AM, et al. Associations between single and multiple cardiometabolic diseases and cognitive abilities in 474–129 UK Biobank participants. Eur Heart J. (2017) 38:577–83. doi: 10.1093/eurheartj/ehw528

PubMed Abstract | Crossref Full Text | Google Scholar

3. Di Angelantonio E, Kaptoge S, Wormser D, Willeit P, Butterworth AS, Bansal N, et al. Association of cardiometabolic multimorbidity with mortality. JAMA. (2015) 314:52–60. doi: 10.1001/jama.2015.7008

PubMed Abstract | Crossref Full Text | Google Scholar

4. Zeng X, Guan H, Wang Y, and Shen S. Declined intrinsic capacity is associated with one-year functional disability in older hypertensive inpatients. BMC Geriatr. (2025) 25:649. doi: 10.1186/s12877-025-06289-7

PubMed Abstract | Crossref Full Text | Google Scholar

5. Ramírez-Vélez R, Iriarte-Fernández M, Santafé G, Malanda A, Beard JR, Garcia-Hermoso A, et al. Association of intrinsic capacity with incidence and mortality of cardiovascular disease: Prospective study in UK Biobank. J Cachexia Sarcopenia Muscle. (2023) 14:2054–63. doi: 10.1002/jcsm.13283

PubMed Abstract | Crossref Full Text | Google Scholar

6. Guo Z, Chen Y, Koirala B, Li J, Jia Y, Dennison Himmelfarb CR, et al. Intrinsic capacity trajectories and cardiovascular disease incidence among Chinese older adults: a population-based prospective cohort study. BMC Geriatr. (2025) 25:269. doi: 10.1186/s12877-025-05910-z

PubMed Abstract | Crossref Full Text | Google Scholar

7. WHO Guidelines Approved by the Guidelines Review Committee. Integrated Care for Older People: Guidelines on Community-Level Interventions to Manage Declines in Intrinsic Capacity Vol. 2017. . Geneva: World Health Organization Copyright © World Health Organization (2017).

Google Scholar

8. Cesari M, Araujo de Carvalho I, Amuthavalli Thiyagarajan J, Cooper C, Martin FC, Reginster JY, et al. Evidence for the domains supporting the construct of intrinsic capacity. J Gerontol A Biol Sci Med Sci. (2018) 73:1653–60. doi: 10.1093/gerona/gly011

PubMed Abstract | Crossref Full Text | Google Scholar

9. Zhang Z, Xu H, Zhang R, Yan Y, Ling X, Meng Y, et al. Frailty and depressive symptoms in relation to cardiovascular disease risk in middle-aged and older adults. Nat Commun. (2025) 16:6008. doi: 10.1038/s41467-025-61089-2

PubMed Abstract | Crossref Full Text | Google Scholar

10. Ma L, Chhetri JK, Zhang L, Sun F, Li Y, and Tang Z. Cross-sectional study examining the status of intrinsic capacity decline in community-dwelling older adults in China: prevalence, associated factors and implications for clinical care. BMJ Open. (2021) 11:e043062. doi: 10.1136/bmjopen-2020-043062

PubMed Abstract | Crossref Full Text | Google Scholar

11. Zhao Y, Hu Y, Smith JP, Strauss J, and Yang G. Cohort profile: the China health and retirement longitudinal study (CHARLS). Int J Epidemiol. (2014) 43:61–8. doi: 10.1093/ije/dys203

PubMed Abstract | Crossref Full Text | Google Scholar

12. Steptoe A, Breeze E, Banks J, and Nazroo J. Cohort profile: the English longitudinal study of ageing. Int J Epidemiol. (2013) 42:1640–8. doi: 10.1093/ije/dys168

PubMed Abstract | Crossref Full Text | Google Scholar

13. Sonnega A, Faul JD, Ofstedal MB, Langa KM, Phillips JW, and Weir DR. Cohort profile: the health and retirement study (HRS). Int J Epidemiol. (2014) 43:576–85. doi: 10.1093/ije/dyu067

PubMed Abstract | Crossref Full Text | Google Scholar

14. Sum G, Lau LK, Jabbar KA, Lun P, George PP, Munro YL, et al. The world health organization (WHO) integrated care for older people (ICOPE) framework: A narrative review on its adoption worldwide and lessons learnt. Int J Environ Res Public Health. (2022) 20:154. doi: 10.3390/ijerph20010154

PubMed Abstract | Crossref Full Text | Google Scholar

15. Leung AYM, Su JJ, Lee ESH, Fung JTS, and Molassiotis A. Intrinsic capacity of older people in the community using WHO Integrated Care for Older People (ICOPE) framework: a cross-sectional study. BMC Geriatr. (2022) 22:304. doi: 10.1186/s12877-022-02980-1

PubMed Abstract | Crossref Full Text | Google Scholar

16. de Oliveira VP, Ferriolli E, Lourenço RA, González-Bautista E, de Souto Barreto P, and de Mello RGB. The sensitivity and specificity of the WHO’s ICOPE screening tool, and the prevalence of loss of intrinsic capacity in older adults: A scoping review. Maturitas. (2023) 177:107818. doi: 10.1016/j.maturitas.2023.107818

PubMed Abstract | Crossref Full Text | Google Scholar

17. Yan Wang N, Liu X, Kong X, Sumi Y, Chhetri JK, Hu L, et al. Implementation and impact of the World Health Organization integrated care for older people (ICOPE) program in China: a randomised controlled trial. Age Ageing. (2024) 53:afad249. doi: 10.1093/ageing/afad249

PubMed Abstract | Crossref Full Text | Google Scholar

18. Chang YH, Hung CC, Chiang YY, Chen CY, Liao LC, Ma MH, et al. Effects of osteoporosis treatment and multicomponent integrated care on intrinsic capacity and happiness among rural community-dwelling older adults: the Healthy Longevity and Ageing in Place (HOPE) randomised controlled trial. Age Ageing. (2025) 54:afaf017. doi: 10.1093/ageing/afaf017

PubMed Abstract | Crossref Full Text | Google Scholar

19. Beard JR, Hanewald K, Si Y, Amuthavalli Thiyagarajan J, and Moreno-Agostino D. Cohort trends in intrinsic capacity in England and China. Nat Aging. (2025) 5:87–98. doi: 10.1038/s43587-024-00741-w

PubMed Abstract | Crossref Full Text | Google Scholar

20. Huang Z, Lai ETC, Hashimoto H, Marmot M, and Woo J. Life-course socioeconomic inequalities, social mobility and healthy aging in older adults: A multi-cohort study. Arch Gerontol Geriatrics. (2025) 133:105829. doi: 10.1016/j.archger.2025.105829

PubMed Abstract | Crossref Full Text | Google Scholar

21. Westers F, Leder S, and Tealdi L. Horizontal federated learning and assessment of Cox models. Front Digit Health. (2025) 7:1603630. doi: 10.3389/fdgth.2025.1603630

PubMed Abstract | Crossref Full Text | Google Scholar

22. Song F, Hu MJ, Xia J, and Yin C. Intrinsic capacity and its change predict cardiovascular disease risk in early cardiovascular-kidney-metabolic syndrome: a nationwide cohort study. Open Heart. (2025) 12:2025-003395. doi: 10.1136/openhrt-2025-003395

PubMed Abstract | Crossref Full Text | Google Scholar

23. Li X and Ma L. From biological aging to functional decline: Insights into chronic inflammation and intrinsic capacity. Ageing Res Rev. (2024) 93:102175. doi: 10.1016/j.arr.2023.102175

PubMed Abstract | Crossref Full Text | Google Scholar

24. He D, Wang Z, Li J, Yu K, He Y, He X, et al. Changes in frailty and incident cardiovascular disease in three prospective cohorts. Eur Heart J. (2024) 45:1058–68. doi: 10.1093/eurheartj/ehad885

PubMed Abstract | Crossref Full Text | Google Scholar

25. Cui Y, Lei S, Xia Y, and Yang F. The trajectory of intrinsic capacity and its related factors among elderly Chinese patients with cardiovascular disease: a prospective cohort study. Front Endocrinol (Lausanne). (2025) 16:1539982. doi: 10.3389/fendo.2025.1539982

PubMed Abstract | Crossref Full Text | Google Scholar

26. Chhetri JK, Xue QL, Ma L, Chan P, and Varadhan R. Intrinsic capacity as a determinant of physical resilience in older adults. J Nutr Health Aging. (2021) 25:1006–11. doi: 10.1007/s12603-021-1629-z

PubMed Abstract | Crossref Full Text | Google Scholar

27. Zhu L, Zong X, Shi X, and Ouyang X. Association between intrinsic capacity and sarcopenia in hospitalized older patients. J nutrition Health aging. (2023) 27:542–9. doi: 10.1007/s12603-023-1946-5

PubMed Abstract | Crossref Full Text | Google Scholar

28. Gao K, Cao LF, Ma WZ, Gao YJ, Luo MS, Zhu J, et al. Association between sarcopenia and cardiovascular disease among middle-aged and older adults: Findings from the China health and retirement longitudinal study. EClinicalMedicine. (2022) 44:101264. doi: 10.1016/j.eclinm.2021.101264

PubMed Abstract | Crossref Full Text | Google Scholar

29. Yang Y, Cao L, Xia Y, and Li J. The effect of living environmental factors on cardiovascular diseases in Chinese adults: results from a cross-sectional and longitudinal study. Eur J Prev Cardiol. (2022) 30:1063–73. doi: 10.1093/eurjpc/zwac304

PubMed Abstract | Crossref Full Text | Google Scholar

30. Bu F, Zaninotto P, and Fancourt D. Longitudinal associations between loneliness, social isolation and cardiovascular events. Heart. (2020) 106:1394–9. doi: 10.1136/heartjnl-2020-316614

PubMed Abstract | Crossref Full Text | Google Scholar