Skip to main content
Download PDF

Intrinsic capacity trajectories and cardiovascular disease incidence among Chinese older adults: a population-based prospective cohort study

BMC Geriatrics volume 25, Article number: 269 (2025) Cite this article

Abstract

Background

Intrinsic capacity (IC), a composite of physical and mental capacities, is a marker of healthy aging. However, the association between changes in IC and trajectories in older adults and the onset of cardiovascular diseases (CVD) remains unclear.

Objective

To identify IC trajectories over time and assess the associations between IC trajectories and the incidence of CVD among older adults.

Methods

The prospective cohort study used data from the China Health and Retirement Longitudinal Study (CHARLS) between 2011 and 2020. To determine IC trajectory during 2011–2015, we included adults aged 60 years or older without CVD at baseline, who completed the follow-up visits in 2013 and 2015. We assessed IC through five domains (locomotion, sensory, vitality, cognition, and psychology) using the WHO framework. We determined the onset of CVD by self-reported diagnoses of heart disease or stroke. We performed a group-based trajectory model to identify IC trajectory, and cox proportional hazard models to estimate the adjusted hazard ratio (HR) for CVD incident across different IC trajectory groups, adjusting for sociodemographic factors, lifestyle health behaviors, and cardiovascular metabolic factors.

Results

The study included 3 336 adults without CVD at baseline, with the mean age of 66.64 (SD:5.53) years, and 49.4% were male. The baseline mean IC score was 5.40 (SD:1.70). We identified three IC trajectory: (1) moderate IC with subsequent increase (61.3%), (2) low IC with slow decline (27.61%), and (3) high IC with subsequent decline (11.09%). During the average follow-up of 6.78 years, we identified 1,351 cases of incident CVD. After adjusting for covariates, adults who in the low IC with slow decline group were 1.68 (95% CI: 1.38–2.04) times more likely to develop CVD, compared to adults in the high IC with subsequent decline group.

Conclusion

IC trajectory among Chinese older adults is heterogeneous. Low IC with slow declining is associated with an increased risk of CVD incidence.

Peer Review reports

Background

The global population’s aging is currently one of the most significant medical and sociodemographic issues [1]. In 2023, nearly 297 million people in China were aged 60 years or older [2], this number is expected to increase to 402 million by 2040, comprising around 28% of the population [3]. A progressive loss of physiological reserve accompanies the aging process, which is associated with an increasing incidence of major noncommunicable diseases, multimorbidity, and socioeconomic burdens [4].

Intrinsic capacity (IC), a composite of physical and mental capacities, is a marker of healthy aging [5]. In 2015, the World Health Organization (WHO) conceptualized a framework for healthy aging, defining it as a holistic concept based on functional capacity rather than merely the absence of disease [6]. According to this model, functional ability is influenced by an individual’s IC, the environment they live in, and their interaction. In 2017, the WHO proposed the integrated care for older people (ICOPE) approach, which aims to enhance IC to promote healthy aging [7]. In 2022, Susana López-Ortiz et al. developed a new composite quantified IC assessment tool based on the ICOPE framework and existing evidence [8]. It weights five domains (cognition, vitality, sensory, psychological, and locomotion) equally, using a 0–2 range to stratify the status of each domain. The IC score ranges from 0 (worst IC) to 10 (highest IC), simplifying the evaluation process and enabling easier comparisons between studies. To date, this quantified assessment tool has not been used to evaluate IC among older adults. Furthermore, the WHO has emphasized that IC should be viewed as a dynamic concept that changes over time [9]. Measuring IC and identifying different trajectories in older adults is crucial for informing valuable insights into their health status and identifying those at risk of adverse health outcomes.

Declining IC serves as a predictor of negative health outcomes in older adults, such as disability [10, 11], dementia [12], falls [13], and mortality [10, 14]. The prevalence of cardiovascular disease (CVD) rises with age [15], indicating that a decline in IC may increase vulnerability to CVD events. A study of 384,380 participants from the UK with a 10.6-year follow-up period, found that IC deficits were associated with CVD morbidity (hazard ratios were 1.56 for men and 1.78 for women) and higher risk of mortality (hazard ratio were 2.10 for men and 2.29 for woman) [16]. Given the dynamic nature of IC, recent studies have analyzed the patterns of IC changes and their influencing factors [17,18,19]. Nevertheless, there is limited evidence on the impact of IC trajectories on CVD outcomes. Thus, this study intends to delineate the IC trajectories over time among Chinese older adults and assess their predictive value for CVD outcomes using a population-based prospective cohort. Our hypothesis posits that the IC trajectory exhibits heterogeneity among Chinese older adults and IC declines were associated with increased CVD incidence. By elucidating the predictive value of IC trajectories, this study aims to inform public health initiatives and interventions designed to improve the cardiovascular health and well-being of China’s aging population.

Materials and methods

Study design and participants

We used accessible deidentified data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative survey conducted by the National Development Research Institute at Peking University [20]. The baseline survey employed a multi-stage probability-proportionate sampling method, including over 17 708 individuals aged 45 and older, residing in approximately 10 000 households distributed across 150 counties in 28 provinces [21]. The CHARLS survey is conducted every 2 to 3 years, with respondents being personally interviewed face-to-face in their residences. The Biomedical Ethics Review Committee of Peking University approved the CHARLS study (IRB00001052-11015), and CHARLS’ study participants provided consent for access to their data for secondary research. The study followed the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [22].

In this study, we included a subset of participants aged 60 years or older at baseline, without a history of CVD, who completed the follow-up visit in 2013 (wave 2) and 2015 (wave 3). We excluded participants with missing data for IC assessment variables, resulting in a final sample of 3 336 participants, as shown in Fig. 1. The analysis was conducted in 2024. Clinical trial number: not applicable.

Fig. 1
figure 1

Flowchart of participants screening

Full size image

Measures

Intrinsic capacity (IC)

We defined and assessed IC across five domains according to the proposed approach by Susana López-Ortiz et al. [8]. Locomotion was evaluated using the Short Physical Performance Battery (SPPB), which included chair-stand, walking speed, and standing balance tests [23]. Sensory was assessed through self-reported hearing and vision disorders [24]. Vitality was measured using the respiratory function (peak flow test) and dominant hand strength measurement [10, 25, 26]. Psychological capacity was evaluated using the Center for Epidemiological Studies Depression Scale-10 (CES-D10) [27,28,29]. Cognitive function was measured using the Mini-Mental State Examination (MMSE) [30]. Each IC domain was stratified into three levels with scores ranging from 0 to 2: 0 = severely impaired, 1 = partially impaired, and 2 = slightly impaired or fully preserved. The IC score ranged from 0 (worst IC) to 10 (highest IC) [8]. More details on the definition, measurement, and stratification criteria of each IC domain were described in additional file table S1. The data used to calculate the IC index were collected in three waves: 2011, 2013, and 2015 wave.

CVD incidence

The first occurrence of CVD is the primary outcome of this study. The CHARLS employed self-reported diagnoses of heart disease or stroke recorded to assess the incidence of CVD, with assessments conducted every two years during the follow-up period (2013, 2015, 2018, and 2020 wave). Participants were queried about any incident of diagnosed heart disease (including heart attack, coronary heart disease, angina, congestive, heart failure, or other heart problems) or stroke. All participants were followed up until incident CVD diagnosis, death or September 2020, whichever came first.

Covariates measurement

Covariates included demographic, body mass index (BMI), lifestyle factors, and cardiovascular metabolic factors that might be associated with IC and CVD [17, 18, 31, 32]. Demographic variables consisted of age (year), gender, residence (urban or rural), education level (International Standard Classification of Education, ISCED-97) [33], marital status, and current work status. Lifestyle fators contained smoking, drinking status, and social activities participation. Cardiovascular metabolic factors comprised hypertension, diabetes, and dyslipidemia. Additional file table s2 showed the details on the measurement of each variable in CHARLS questionnaire.

Statistical analysis

We conducted all statistical analyses using R 4.4.0 software. We identified the IC trajectory during 2011 to 2015 in Chinese older adults by Group-Based Trajectory Model (GBTM) [34, 35]. The Akaike Information Criterion (AIC), adjusted Akaike Information Criterion (aAIC) [36], and Bayesian Information Criterion (BIC) [37] were adopted to assess the model fit. Lower AIC and BIC values, higher entropy indicate a better fit and classification accuracy [35]. Additionally, the average posterior probability (AvePP) was calculated, with values above 0.7 considered acceptable [38]. Trajectories were fitted with quadratic functions and the Expectation-Maximization (EM) algorithm [39]. We also performed the prediction two steps ahead with the function (n.ahead) to identify the trends of IC trajectories based on the previous three time-point measurements [40].

For the baseline description, we presented continuous variables with normally distributed as the mean ± standard deviation (SD), while categorical variables using counts and percentages (%). We conducted the chi-square test for categorical variables among IC trajectory groups. We measured incidence density as the number of events divided by total person-years of follow-up, presenting results as events per 1000 person-years. The Kaplan–Meier method was applied to compute cumulative incidence of CVD for each trajectory group, with comparisons made using the log-rank test. Cox proportional hazard models were constructed to analyze hazard ratios (HRs) and 95% confidence intervals (CIs) for CVD in other trajectory groups, compared to the high IC with subsequent decline group, with the proportional hazards assumption satisfied. We developed four models to adjust for covariates across three categories. In addition, subgroup analyses were stratified by gender, age, hypertension, diabetes, and dyslipidemia. We also performed sensitivity analysis to verify the robustness of the results. First, we adjusted for IC in 2011 and IC in 2015 to assess whether a separate IC could explain this association during the follow-up. Second, we excluded outcome events from the first follow-up time point (2013) to mitigate the potential for reverse causality.

Results

Participants and IC score

Table 1 presents the demographic characteristics, cardiovascular metabolic factors, and three-wave IC scores of the participants. Among 3 336 older adults, males and females each made up approximately 50%. The mean age at baseline was (66.64 ± 5.53) years. The average IC score across the three waves showed a decline, but the difference was not statistically significant (F = 1.879, P = 0.153). The highest IC score was observed in the baseline wave (5.40 ± 1.70), while the lowest was in the 2015 wave (5.32 ± 1.73).

Table 1 Participants characteristics by IC score from wave 1 to wave 3
Full size table

Trajectories of IC

Table 2 reports the fit indices of four quadratic function models of trajectories of IC. The 3-group model had the lowest BIC and aAIC when compared with other models; additionally, the AvePP values were all exceeding 0.80, the proportion of each group was more than 5%, making it the best fit model. The trajectory of IC was identified across three waves (additional file Fig S1), and a two-step ahead of prediction was also performed (Fig. 2).

Table 2 AvePP of group assignment and statistics of model fit
Full size table
Fig. 2
figure 2

Trajectories of intrinsic capacity in Chinese older adults with next two steps prediction (Note: Trajectory 1, Low IC with Slow Decline; Trajectory 2, High IC with Subsequent Decline; Trajectory 3, Moderate IC with Subsequent Increase)

Full size image

Trajectory 1 was characterized by a lower IC score that experienced a slow but steady decline, earning the label “Low IC with Slow Decline.” This group included 27.61% of participants, with the IC score decreasing from 3.77 ± 1.24 to 3.59 ± 1.19 over the five-year period. Trajectory 2 exhibited a higher IC score at two initial time points, followed by a gradual decline. This decreasing trend continued when predicting the next two steps ahead, leading to the designation “High IC with Subsequent Decline.” This group represented the smallest proportion of participants (11.09%) with an estimated mean IC score of 7.48 ± 0.86 at baseline, 7.72 ± 0.75 in 2013, and 7.64 ± 0.78 in 2015. Trajectory 3, labeled “Moderate IC with Subsequent Increase”, displayed a moderate IC score that gradually increased, converging towards score of trajectory 2 as they continued to decline in future predictions. This group comprised most participants (61.30%), showing a steady but slow increase in the IC score from 5.67 ± 1.30 to 5.69 ± 1.29 (additional file table s3). In addition, the characteristics of participants among three trajectory groups were shown in additional file table s4.

The relationship between CVD risk and IC trajectories

After an average follow-up of 6.78 years (range: 2–9 y), 1,351 cases of incident of CVD were identified (additional file table s5). The incidence density was 59.7/1 000 person-year. The Kaplan-Meier curve showed that participants in Low IC with slow decline group had a higher risk of CVD than those in other two groups (log-rank test, p < 0.0001, Fig. 3a). Similar differences were observed between gender subgroups (log-rank test, p < 0.01, Fig. 3b, c).

Fig. 3
figure 3

Kaplan–Meier curves of CVD incidence according to IC trajectory. (a. total dataset; b. male; c. female)

Full size image

The Low IC with Slow Decline group had the highest incidence density (72.6 per1 000 person-year), when compared with other trajectory groups. After adjusting for potential confounders, the HR and 95% CI for CVD incidence were 1.15 (0.97, 1.38) in the moderate IC with subsequent increase group, 1.68 (1.38, 2.04) in the low IC with slow decline group, respectively, when compared with the high IC with subsequent decline group (Table 3).

Table 3 The hazard ratio of CVD among older adults according to IC trajectories
Full size table

Subgroup and sensitivity analysis

We did not find significant interaction between IC trajectories and gender, age, hypertension, diabetes, and dyslipidemia on CVD incidence, as shown in Table 4. In the sensitivity analysis, there was no substantial change in the relationship between IC trajectory and CVD risk after additional adjustment for IC score in 2011 or 2015 (additional file: table s6, s7). In addition, the results of excluding CVD events that occurred in the first follow-up wave (2013) was consistent with the main results, except for a reduction in incidence density (additional file: table s8).

Table 4 Subgroup analysis of hazard ratio of CVD among older adults according to IC trajectories
Full size table

Discussion

The current study employed a GBTM approach to identify three distinct IC trajectory patterns over 5 years among 3 336 Chinese older adults. Older adults with a low IC with slow decline trajectory, as compared with those in high IC or moderate IC group, had a higher risk of CVD during an average follow-up of 7 years. Similar results were also found in the subgroup analysis and sensitivity analysis.

Intrinsic Capacity (IC) has been proposed as a comprehensive measure of overall health status, useful for monitoring health decline in older adults [10]. Our study found that the average IC score ranged from 5.32 to 5.40, indicating a decline in capacity (9–10 points = high and stable capacity, 5–8 points = declining capacity, 0–4 points = significant loss of capacity). We observed a progressive decline in IC with advancing age, consistent with findings from the Yale project on adults aged 70+ [10], and CCGAS project on those aged 60+ [41]. However, this trend varied when stratified by age group. The decline was consistent among those aged 70–79 and 80+, whereas a fluctuating pattern was observed in the 60–69 age group. Notably, one study found that 29.4% of older adults even showed improvements in their IC score over time [17]. These findings highlight the critical importance of early monitoring and proactive interventions targeting IC changes in older adults, which may help mitigate and potentially reverse these declining trends.

We identified three distinct IC trajectory patterns among Chinese older adults, and the majority were in a “Moderate IC with Subsequent Increase” group (61.3%), followed by “Low IC with Slow Decline” group (27.61%) and the “High IC with Subsequent Decline” group (11.61%). Similarly, Zhao YN et al. [19] analyzed the same database with 3893 older adults, both with or without baseline CVD, and identified three trajectory groups: high trajectory (15.7%), stable (52.7%), and declining (31.6%). Additionally, Yu R et al. [31] examined IC trajectories among community-dwelling older adults aged 60 + in Hong Kong, also revealing three groups: high IC level with the least declining (36.3%), low IC level (46.9%), and lowest IC level with most declining (16.8%). Despite identification of similar trajectory groups and distributions, our study reveals significant differences. By employing a quadratic model to fit and predict trajectory changes for the subsequent two steps, we achieved a more comprehensive depiction of trajectory characteristics. For example, the “High IC with Subsequent Decline” group showed minor fluctuations in IC score during the first three measurement points. However, projections based on this trend indicate a decline in IC score over the next two points, approaching a medium level rather than maintaining high score. This trend elucidates the non-significant differences in CVD outcomes between the “High IC with Subsequent Decline” group and “Moderate IC with Subsequent Increase” group in 2020. These predictions provide critical forward-looking insights, enabling healthcare professionals to proactively implement measures to maintain IC and promote primary prevention. However, our analysis excluded subjects diagnosed with CVD at baseline, and these studies utilized different IC scoring criteria, which may further explain the observed differences in trajectory distributions. Since Susana López-Ortiz et al. have proposed a comprehensive and detailed IC assessment tool, we also recommend its use in further related research to ensure comparability.

The observation that poorer IC correlates directly with adverse health outcomes is consistent with findings from previous studies [10, 42, 43]. In addition, the accumulation of impairments of IC was linked with increasing higher risk of morbidity and mortality of CVD [16]. Our study enriches existing findings by examining the dynamic changes in IC. We found that the highest CVD incidence density in the “Low IC with Slow Decline” group (72.6 per 1 000 person-year), compared with the other trajectory groups. After adjusting for potential confounding factors [44,45,46,47], we confirmed that older adults with a low IC with decline trajectory had a 67.5% higher risk of CVD, relative to those in a high IC with subsequent decline group. This indicates that identifying low levels of IC coupled with a continuous decline represents a significant risk factor for CVD outcomes. However, participants in moderate IC with subsequent increase group have no significant increased risk of CVD. This finding offers an innovative perspective, contrasting with previous research which suggested that high IC levels provide a protective health benefit compared to lower IC levels [10, 42, 43]. While a decline in IC is indeed harmful to health, it is crucial to consider not only a single measurement of IC but also its dynamic changes over time. A gradual and continuous decline in IC can negate the benefits of initially high levels, and healthcare professionals’ focus on those with low IC often overlooks those high IC experiencing declining, thereby exacerbating their health risks. Therefore, integrating IC assessments into annual health checkups at the early stage of life course, and delivering timely information are essential for identifying declining IC early and implementing tailored primary prevention strategies effectively, ultimately enhancing the health and well-being of older adults.

Strengths and limitations of this study

The present study has important strengths, including the population-based sample, which ensures the representativeness and generalizability of the results. Furthermore, this is the first prospective study to investigate the relationship of longitudinal pattern of IC and CVD events among Chinese older adults. The trajectories were utilized to illustrate changes in IC over time, while adjusting for potential confounders that could affect the risk of CVD and performing sensitivity analysis, which ensures the reliability of the research findings. Importantly, our findings underscore that monitoring IC trends over time is as crucial as assessing IC levels themselves.

Nevertheless, we also acknowledge several limitations. Firstly, our analysis utilized the most recent database version available since 2020. We will maintain the currency of our findings and incorporate updated data in subsequent analyses upon its release. Second, due to the necessity of fitting trajectory requirements, we only included participants with complete IC measurements from waves 1–3 without employing multiple imputation techniques. Notably, the locomotion domain (assessed by chair-stand, walking speed, and standing balance tests) and the vitality domain (evaluated by respiratory function and hand strength tests) in our IC composite were only recorded for seniors aged 60 years and older in CHARLS. This decision led to a relatively smaller sample size of elderly participants in our study but enabled more robust conclusions. Third, our IC assessments utilized the tools as those reported by Susana López-Ortiz et al. [8], although differences were noted in the psychological and vitality dimensions. While Susana López-Ortiz et al. recommended the Cornell Scale for Depression in Dementia (CSDD) for psychological evaluation and the mini nutritional assessment (MNA) for vitality, we used the Center for Epidemiological Studies Depression Scale-10 (CES-D10) from the CHARLS database to assess psychological status. Furthermore, based on previous research recommendations [10, 48], we substituted respiratory function (peak flow test) and dominant hand strength measurement for MNA in evaluating vitality. Thus, our IC measurement criteria differed somewhat from Susana López-Ortiz et al..‘s suggestions. When comparing results with future studies using this assessment tool, consistency in measurement tools should be carefully considered. Lastly, the CVD outcomes were self-reported rather than comprehensive medical record reviews, may marginally impact result reliability. Given the diverse range of cardiovascular conditions covered, cautious interpretation of outcomes is advised.

Conclusion

IC trajectory among Chinese older adults is heterogeneous, and Low IC with slow decline predicts an increased risk of CVD incidence. These observed associations were consistent and robust after adjustment for potential confounders, which underscore that monitoring IC trends over time is as crucial as assessing IC levels themselves. This finding implies that regular monitoring of IC may assist in identifying individuals at a higher risk of CVD among older adults.

Data availability

The datasets generated and analysed during the current study are available in the National Development Research Institute at Peking University repository, https://charls.charlsdata.com/pages/data/111/zh-cn.html.

References

  1. World Population Ageing. 2023: Challenges and opportunities of population ageing in the least developed countries [https://desapublications.un.org/publications/world-population-ageing-2023-challenges-and-opportunities-population-ageing-least]

  2. Over one-fifth of Chinese. population older than 60, says official report [https://english.www.gov.cn/news/202410/12/content_WS6709cb9ac6d0868f4e8ebbda.html#:~:text=BEIJING%2 C%20Oct.,an%20official%20report%20released%20Friday.].

  3. The L. Population ageing in China: crisis or opportunity? Lancet (London England). 2022;400(10366):1821.

    Article  Google Scholar 

  4. Low LL, Kwan YH, Ko MSM, Yeam CT, Lee VSY, Tan WB, Thumboo J. Epidemiologic characteristics of multimorbidity and sociodemographic factors associated with multimorbidity in a rapidly aging Asian country. JAMA Netw Open. 2019;2(11):e1915245.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Zhou Y, Ma L. Intrinsic capacity in older adults: recent advances. Aging Dis. 2022;13(2):353–9.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Organization WH. World report on ageing and health. Luxembourg: World Health Organization; 2015.

    Google Scholar 

  7. Organization WH. Integrated care for older people: guidelines on Community-level to manage declines in intrinsic capacity. Switzerland: World Health Organization; 2017.

    Google Scholar 

  8. Lopez-Ortiz S, Lista S, Penin-Grandes S, Pinto-Fraga J, Valenzuela PL, Nistico R, Emanuele E, Lucia A, Santos-Lozano A. Defining and assessing intrinsic capacity in older people: a systematic review and a proposed scoring system. Ageing Res Rev. 2022;79:101640.

    Article  PubMed  Google Scholar 

  9. Integrated Care for Older People (ICOPE). Guidance for person-centred assessment and pathways in primary care [https://www.who.int/publications/i/item/WHO-FWC-ALC-19.1]

  10. Stolz E, Mayerl H, Freidl W, Roller-Wirnsberger R, Gill TM. Intrinsic capacity predicts negative health outcomes in older adults. J Gerontol Biol Sci Med Sci. 2022;77(1):101–5.

    Article  Google Scholar 

  11. Zhang N, Zhang H, Sun M-Z, Zhu Y-S, Shi G-P, Wang Z-D, Wang J-C, Wang X-F. Intrinsic capacity and 5-year late-life functional ability trajectories of Chinese older population using ICOPE tool: the Rugao longevity and ageing study. Aging Clin Exp Res. 2023;35(10):2061–8.

    Article  PubMed  Google Scholar 

  12. Sun M, He Q, Sun N, Han Q, Wang Y, Zhao H, Li G, Ma Z, Feng Z, Li T, et al. Intrinsic capacity, polygenic risk score, APOE genotype, and risk of dementia: a prospective cohort study based on the UK biobank. Neurology. 2024;102(12):e209452.

    Article  CAS  PubMed  Google Scholar 

  13. Cacciatore S, Marzetti E, Calvani R, Picca A, Salini S, Russo A, Tosato M, Landi F. Intrinsic capacity and recent falls in adults 80 years and older living in the community: results from the IlSIRENTE study. Aging Clin Exp Res. 2024;36(1):169.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Yu R, Lai ETC, Leung G, Ho SC, Woo J. Intrinsic capacity and 10-year mortality: findings from a cohort of older people. Exp Gerontol. 2022;167:111926.

    Article  PubMed  Google Scholar 

  15. Zhang J, Tong H, Jiang L, Zhang Y, Hu J. Trends and disparities in China’s cardiovascular disease burden from 1990 to 2019. Nutr Metab Cardiovasc Dis. 2023;33(12):2344–54.

    Article  PubMed  Google Scholar 

  16. Ramírez-Vélez R, Iriarte-Fernández M, Santafé G, Malanda A, Beard JR, Garcia-Hermoso A, Izquierdo M. Association of intrinsic capacity with incidence and mortality of cardiovascular disease: prospective study in UK biobank. J Cachexia Sarcopenia Muscle. 2023;14(5):2054–63.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Zhou Y, Wang G, Li J, Liu P, Pan Y, Li Y, Ma L. Trajectory of intrinsic capacity among community-dwelling older adults in China: the China health and retirement longitudinal study. Arch Gerontol Geriatr. 2024;124:105452.

    Article  PubMed  Google Scholar 

  18. Salinas-Rodríguez A, Fernández-Niño JA, Rivera-Almaraz A, Manrique-Espinoza B. Intrinsic capacity trajectories and socioeconomic inequalities in health: the contributions of wealth, education, gender, and ethnicity. Int J Equity Health. 2024;23(1):48.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Zhao Y, Chen Y, Xiao LD, Liu Q, Nan J, Li X, Feng H. Intrinsic capacity trajectories, predictors and associations with care dependence in community-dwelling older adults: a social determinant of health perspective. Geriatric Nurs (New York NY). 2024;56:46–54.

    Article  Google Scholar 

  20. Zhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China health and retirement longitudinal study (CHARLS). Int J Epidemiol. 2014;43(1):61–8.

    Article  PubMed  Google Scholar 

  21. Zhao Yaohui S, Gonghuan JY, John G, Peifeng H (Perry), Yisong H, Xiaoyan L, Man L, editors. Park Albert, Smith James P.: China Health and Retirement Longitudinal Study: 2011–2012 National Baseline User’s Guide. In.; 2013.

  22. von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke JP, Initiative S. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370(9596):1453–7.

    Article  Google Scholar 

  23. de Fátima Ribeiro Silva C, Ohara DG, Matos AP, Pinto A, Pegorari MS. Short physical performance battery as a measure of physical performance and mortality predictor in older adults: a comprehensive literature review. Int J Environ Res Public Health. 2021;18(20).

  24. Gutiérrez-Robledo LM, García-Chanes RE, Pérez-Zepeda MU. Allostatic load as a biological substrate to intrinsic capacity: a secondary analysis of CRELES. J Nutr Health Aging. 2019;23(9):788–95.

    Article  PubMed  Google Scholar 

  25. China Health and Retirement Longitudinal Study (CHARLS). 2013 Biomarker Questionnaire (Used for physical measurement) [https://gero.usc.edu/cbph/network/studies-with-biomarkers/china-health-and-retirement-longitudinal-study-charls/]

  26. Magave JA, Bezerra SJS, Matos AP, Pinto ACPN, Pegorari MS, Ohara DG. Peak expiratory flow as an index of frailty syndrome in older adults: a cross-sectional study. J Nutr Health Aging. 2020;24(9):993–8.

    Article  CAS  PubMed  Google Scholar 

  27. Andresen EM, Malmgren JA, Carter WB, Patrick DL. Screening for depression in well older adults: evaluation of a short form of the CES-D (Center for epidemiologic studies depression scale). Am J Prev Med. 1994;10(2):77–84.

    Article  CAS  PubMed  Google Scholar 

  28. Chen H, Mui AC. Factorial validity of the center for epidemiologic studies depression scale short form in older population in China. Int Psychogeriatr. 2014;26(1):49–57.

    Article  CAS  PubMed  Google Scholar 

  29. Fu H, Si L, Guo R. What is the optimal cut-off point of the 10-item center for epidemiologic studies depression scale for screening depression among Chinese individuals aged 45 and over?? An exploration using latent profile analysis. Front Psychiatry. 2022;13:820777.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Folstein MF, Folstein SE, McHugh PR. Mini-mental state. A practical method for grading the cognitive State.of patients for the clinician. J Psychiatr Res. 1975;12(3):189–98.

    Article  CAS  PubMed  Google Scholar 

  31. Yu R, Lai D, Leung G, Woo J. Trajectories of intrinsic capacity: determinants and associations with disability. J Nutr Health Aging. 2023;27(3):174–81.

    Article  CAS  PubMed  Google Scholar 

  32. Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, Barengo NC, Beaton AZ, Benjamin EJ, Benziger CP, et al. Global burden of cardiovascular diseases and risk factors, 1990–2019: update from the GBD 2019 study. J Am Coll Cardiol. 2020;76(25):2982–3021.

    Article  PubMed  PubMed Central  Google Scholar 

  33. United Nations Educational SaCOU. International Standard Classification of Education ISCED 1997 In.; 1997.

  34. Nagin DS, Jones BL, Elmer J. Recent advances in Group-Based trajectory modeling for clinical research. Annu Rev Clin Psychol. 2024;20(1):285–305.

  35. Nagin DS, Odgers CL. Group-based trajectory modeling in clinical research. Annu Rev Clin Psychol. 2010;6:109–38.

    Article  PubMed  Google Scholar 

  36. Akaike H. A new look at the statistical model identification. IEEE Trans Autom Control. 1974;19(6):716–23.

    Article  Google Scholar 

  37. Schwarz G. Estimating the dimension of a model. Annals Stat. 1978;6(2):461–4.

    Article  Google Scholar 

  38. Walsh CA, Mucherino S, Orlando V, Bennett KE, Menditto E, Cahir C. Mapping the use of group-based trajectory modelling in medication adherence research: a scoping review protocol. HRB Open Res. 2020;3:25.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Magrini A. Assessment of agricultural sustainability in European union countries: a group-based multivariate trajectory approach. AStA Adv Stat Anal. 2022;106(4):673–703.

    Article  Google Scholar 

  40. Nagin DS, Jones BL, Passos VL, Tremblay RE. Group-based multi-trajectory modeling. Stat Methods Med Res. 2018;27(7):2015–23.

    Article  PubMed  Google Scholar 

  41. Ma L, Chhetri JK, Zhang L, Sun F, Li Y, 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(1):e043062.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Cheong CY, Yap P, Nyunt MSZ, Qi G, Gwee X, Wee SL, Yap KB, Ng TP. Functional health index of intrinsic capacity: multi-domain operationalisation and validation in the Singapore longitudinal ageing study (SLAS2). Age Ageing. 2022;51(3).

  43. Locquet M, Sanchez-Rodriguez D, Bruyere O, Geerinck A, Lengele L, Reginster JY, Beaudart C. Intrinsic capacity defined using four domains and mortality risk: a 5-year follow-up of the sarcophage cohort. J Nutr Health Aging. 2022;26(1):23–9.

    Article  CAS  PubMed  Google Scholar 

  44. Zhiting G, Jiaying T, Haiying H, Yuping Z, Qunfei Y, Jingfen J. Cardiovascular disease risk prediction models in the Chinese population- a systematic review and meta-analysis. BMC Public Health. 2022;22(1):1608.

    Article  PubMed  PubMed Central  Google Scholar 

  45. China Tjtffgotaamocri. Guideline on the assessment and management of cardiovascular risk in China. Zhonghua Yu Fang Yi Xue Za Zhi. 2019;53(1):13–35.

    Google Scholar 

  46. Hu D, Han Y, Ning G, Ma C. Guidelines for primary prevention of cardiovascular diseases in China. Chin J Cardiovasc Disease. 2020;48(12):1000–38.

    Google Scholar 

  47. Arnett DK, Khera A, Blumenthal RS. 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: part 1, lifestyle and behavioral factors. JAMA Cardiol. 2019;4(10):1043–4.

    Article  PubMed  Google Scholar 

  48. Charles A, Buckinx F, Locquet M, Reginster JY, Petermans J, Gruslin B, Bruyere O. Prediction of adverse outcomes in nursing home residents according to intrinsic capacity proposed by the world health organization. J Gerontol Biol Sci Med Sci. 2020;75(8):1594–9.

    Article  Google Scholar 

Download references

Acknowledgements

Thank you to Peking University for providing me with the opportunity to access the dataset and for the guidance on data processing. I also want to thank The Center for Global Initiatives (CGI) for their support, funding, and guidance during my visiting program.

Funding

Scientific Research Fund of National Health Commission of the People’s Republic of China - Major Project of the Major Science and Technology Program for Medicine and Health in Zhejiang Province (WKJ-ZJ-2406). The funder played no role in the design, execution, analysis, interpretation of data, writing of the study, or decision to submit it for publication.

Author information

Authors and Affiliations

  1. The Second Affiliated Hospital of Zhejiang University School of Medicine (SAHZU), No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009, Zhejiang Province, China

    Zhiting Guo, Yingying Jia & Jingfen Jin

  2. Faculty of Nursing, Zhejiang University school of medicine, No.866 Yuhangtang Road, Xihu District, Hangzhou, Zhejiang Province, China

    Zhiting Guo & Yingying Jia

  3. Johns Hopkins University School of Nursing, 525N. Wolfe Street, 21205, Baltimore, MD, USA

    Yuling Chen, Binu Koirala, Jiaying Li & Cheryl R. Dennison Himmelfarb

  4. Key Laboratory of The Diagnosis and Treatment of Severe Trauma and Burn of Zhejiang Province, No.88 Jiefang Road, Shangcheng District, 310009, Hangzhou, Zhejiang Province, China

    Jingfen Jin

Authors
  1. Zhiting Guo
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Yuling Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Binu Koirala
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Jiaying Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Yingying Jia
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Cheryl R. Dennison Himmelfarb
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Jingfen Jin
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

GZ: designed the work, acquired and analyzed data, drafted the manuscript; CY, LJ, JY: reviewed the data analysis process and revised the manuscript; KB and HC: provided supervision, reviewed the writing and editing; JJ: provided funding support, supervision, and reviewed the writing and editing. All authors reviewed the manuscript.

Corresponding author

Correspondence to Jingfen Jin.

Ethics declarations

Ethics approval and consent to participate

The Biomedical Ethics Review Committee of Peking University approved the CHARLS study (IRB00001052-11015), and CHARLS’ study participants provided consent for access to their data for secondary research. This study was conducted in accordance with the principles of the Declaration of Helsinki.

Consent for publication

None.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, Z., Chen, Y., Koirala, B. et al. Intrinsic capacity trajectories and cardiovascular disease incidence among Chinese older adults: a population-based prospective cohort study. BMC Geriatr 25, 269 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12877-025-05910-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12877-025-05910-z

Keywords

BMC Geriatrics

ISSN: 1471-2318

Contact us