Association between frailty and physical-psychological multimorbidity in middle-aged and elderly Chinese adults: Evidence from the China Health and Retirement Longitudinal Study (CHARLS)

doi:10.21203/rs.3.rs-5203605/v1

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Association between frailty and physical-psychological multimorbidity in middle-aged and elderly Chinese adults: Evidence from the China Health and Retirement Longitudinal Study (CHARLS)

https://doi.org/10.21203/rs.3.rs-5203605/v1

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Background

As the population ages, chronic diseases, frailty, and physical-psychological multimorbidity (PP-MM) increase. However, the association between frailty and PP-MM remains unclear. This study aimed to investigate this relationship in middle-aged and elderly Chinese individuals.

Methods

This study used four waves of data from the Chinese Longitudinal Study of Health and Aging. Frailty was assessed using the frailty index (FI), and physical illness was defined as having one or more self-reported chronic diseases. Psychological status was examined using the 10-item version of the Centre for Epidemiological Studies Depression Scale. The participants were inquired about any previous diagnoses of affective, emotional, nervous, or psychiatric disorders. PP-MM was defined as the concurrent presence of two kinds of diseases (physical illness and psychological disorders). The relationship between FI and PP-MM was evaluated using COX risk regression models and restricted cubic spline (RCS) curves.

Results

This study included 10,707 subjects, and after adjusting for potential confounders, the HR was 3.01 (95% confidence interval (CI) = 2.05–4.23) for pre-frail and 6.11 (95% CI = 3.79–9.84) for frail. COX regression analysis indicated a potential association between FI and PP-MM progression. RCS analysis revealed that the risk of PP-MM prevalence increased faster with an FI between 0.10 and 0.25.

Conclusion

Our study suggests that FI is positively associated with the prevalence of PP-MM and that the pre-frail phase may be a better opportunity to implement interventions for PP-MM prevention, with early monitoring of FI to identify patients at high risk for PP-MM and to provide direction and rationale for preventing PP-MM.

Frailty

Physical conditions

Psychological multimorbidity

Middle-aged and people

As the population ages and lives longer, chronic diseases have become a significant global public health challenge(Bauer et al., 2014). Many people also experience multimorbidity, characterised by multiple health conditions(Wu et al., 2023). Patients with chronic diseases may experience negative emotions, including depression. Due to the long-term nature of chronic diseases and their impact on quality of life, many patients with chronic diseases suffer from depression(2013; Miao et al., 2023), and the prevalence of depression in patients with chronic diseases has been estimated to be 9.3–25%(Ingle et al., 2017). Chronic diseases are associated with a significantly higher risk of depression in middle-aged and elderly Chinese(Bi et al., 2021).

Multimorbidity has been associated with frailty(Vetrano et al., 2019). Frailty—an emerging global health concern—is characterised by a decline in the functioning of multiple physiological systems and increased vulnerability to stressors (Clegg et al., 2013). It is commonly related to various adverse outcomes, including falls, disability, hospitalisation, and even death(Carrasco-Ribelles et al., 2022). The National Institute for Health and Care Excellence and the British Geriatrics Society have emphasised the importance of identifying frail and multimorbid patients at higher risk of adverse outcomes and more likely to benefit from treatment [10]. These patients are at greater risk of adverse outcomes and are more likely to benefit from treatment optimisation.

The prevalence of multimorbidity and frailty increases with age. Research on frailty and multimorbidity has primarily focused on the elderly population(Tazzeo et al., 2021; Wong et al., 2010; Zazzara et al., 2019). However, as there are more middle-aged individuals than older individuals, there are more adults under 65 years of age with multiple long-term conditions than those aged 65 years or older(Arakelyan et al., 2023). To date, no study has assessed the correlation between frailty and the occurrence of physical-psychological multimorbidity (PP-MM) in middle-aged and older adults. This longitudinal study aimed to explore the concurrent trajectories of frailty and physical-mental multimorbidity in a national cohort of middle-aged and older adults in China and to quantify the dose-response relationship between frailty and PP-MM.

2.1 Population and Data Sources

We used data from the China Health and Retirement Longitudinal Study (CHARLS). CHARLS was established in 2011 as a national longitudinal cohort survey on ageing. It was designed based on the US Health and Retirement Study (Zhao et al., 2014) and used a multistage cluster sampling method to select participants and conduct a series of data collections. The Ethical Application for Collecting Human Subject Data in CHARLS was approved by the Biomedical Ethics Review Board of Peking University (IRB00001052-11015), and all participants provided written informed consent. Detailed information regarding the CHARLS data is available on their website at http://charls.pku.edu.cn/en.

We conducted four rounds of the CHARLS core survey [2011 (baseline), 2013, 2015, and 2018], encompassing 17,708 participants. Of these, 7001 participants were excluded for the following reasons: having PP-MM at baseline (n = 634), missing baseline PP-MM data (n = 68), missing demographic data (n = 124), health-related factors (n = 3469), missing sleep data (n = 1553), and missing lifestyle data follow-up data (n = 1153). Ultimately, 10707 participants were included in this study. Details of the selection process are displayed in Fig. 1.

2.2 Assessment of Frailty

The frailty index (FI) was constructed using the deficit accumulation model developed by Searle et al.(Searle et al., 2008). To construct the FI, we selected 40 items, including activities of daily living (11 items), functional limitations (9 items), diagnosis (14 items), disabilities (5 items), and self-reported health status. We defined the following three frailty state categories: non-frail (FI ≤ 0.10), pre-frail (FI > 0.10 to < 0.25), and frail (FI ≥ 0.25) (Cao et al., 2022).

2.3 Assessment of PP-MM

This research resulted in the occurrence of PP-MM, characterised by the presence of physical illness and psychological disorders(Zhou et al., 2024).

As in previous studies(Guo et al., 2023), a physical illness was identified if a physician informed the participant that they had at least one of the following twelve chronic diseases: hypertension, diabetes, dyslipidemia, heart disease, stroke, chronic lung disease, asthma, liver disease, cancer, digestive disease, kidney disease, and arthritis.

As in previous studies, a psychological condition was determined by the following two methods: assessment based on study-specific symptoms and self-reported diagnosis. Using the 10-item Centre for Epidemiological Studies Depression Scale (CES-D; 0–30 on the CHALS), participants were defined as having depression when they exhibited a CES-D score of ≥ 10(Ni et al., 2023). Alternatively, a physician informed participants that they were experiencing affective, emotional, nervous, or psychiatric issues.

2.4 Accompanying factors

The covariates included information on socio-demographics, lifestyle behaviours, and health-related factors.

The sociodemographic variables encompassed age, sex, marital status (married and other), Hukou (Agricultual Hukou and other), and educational level (Primary school below, Primary school, and Middle school or above).

Lifestyle behaviours and health-related factors included body mass index (BMI), smoking (yes or no), alcohol consumption (yes or no), sleep, and hypertension (yes or no).

2.5 Statistical Methods

Baseline characteristics are presented by the following three frailty status categories: non-frail, pre-frailty, and frail. Data are expressed as mean ± standard deviation (SD) for continuous variables and as a percentage for categorical variables. Categorical and continuous variables were tested using the chi-squared and t-tests, respectively. We recorded the number of individuals who were followed up from the baseline date to the date of diagnosis or December 31, 2018, whichever occurred first. We employed COX proportional risk models to estimate the association between frailty indices (included as continuous and categorical variables in the models, respectively) and PP-MM. We built model 1 (the crude model) for the initial analyses, with model 2 incorporating confounders, including age, gender, residence, marriage, education level, drinking, smoking, sleep, and BMI. We used the restricted cubic spline (RCS) to further assess the potential nonlinear association between FI and PP-MM progression. For subgroup analyses, the samples were stratified by various factors, including age, gender, residence, hukou, education, marriage, alcohol consumption, sleep, smoking, and BMI. A P < 0.05 was considered statistically significant in each analysis. R 4.4.1 was used for the analysis.

3.1 Descriptive Statistics

The characteristics of the 10,707 subjects in this study according to FI subgroups are summarised in Table 1. The mean age (± SD) of the subjects was 56.4 (± 8.64) years, of which 5760 (53.80%) were female. Pre-frailty was observed in 4502 (42.04%) participants, whereas deficient status was identified in 854 (7.97%) participants. Analyses indicated that older age and shorter sleep duration were associated with pre-frail development. Moreover, being female, residing in a rural household, having a lower educational level, being unmarried or living alone, not smoking or consuming alcohol, and having a BMI ≥ 30 were associated with frailty development.

Table 1

**The descriptive statistics of participants.**
Characteristic	Overall(n = 10707)	Healthy (n = 5351)	Pro-frailty(n = 4502)	Frailty (n = 854)	P value
Age_group (Mean (± SD))	56.4 (8.64)	59.3 (9.24)	63.1 (9.56)	58.2 (9.18)	< 0.001
gender (%)
Men	4947 (46.2)	2796 ( 52.3)	1847 ( 41.0)	304 ( 35.6)	< 0.001
Women	5760 (53.8)	2555 ( 47.7)	2655 ( 59.0)	550 ( 64.4)
Hukou (%)
Agricultual Hukou	8771 (81.9)	4230 ( 79.1)	3783 ( 84.0)	758 ( 88.8)	< 0.001
Other	1936 (18.1)	1121 ( 20.9)	719 ( 16.0)	96 ( 11.2)
Marriage (%)
Married and living with spouse	9166 (85.6)	4765 ( 89.0)	3727 ( 82.8)	674 ( 78.9)	< 0.001
Other	1541 (14.4)	586 ( 11.0)	775 ( 17.2)	180 ( 21.1)
Education (%)
Primary school below	4863 (45.4)	1923 ( 35.9)	2378 ( 52.8)	562 ( 65.8)	< 0.001
Primary school	2390 (22.3)	1223 ( 22.9)	1013 ( 22.5)	154 ( 18.0)
Middle school or above	3454 (32.3)	2205 ( 41.2)	1111 ( 24.7)	138 ( 16.2)
Smoking (%)
Smoker	4081 (38.1)	2183 ( 40.8)	1606 ( 35.7)	292 ( 34.2)	< 0.001
Non-smoker	6626 (61.9)	3168 ( 59.2)	2896 ( 64.3)	562 ( 65.8)
Drinking (%)
Drinker	3522 (32.9)	2015 ( 37.7)	1334 ( 29.6)	173 ( 20.3)	< 0.001
Non-drinker	7185 (67.1)	3336 ( 62.3)	3168 ( 70.4)	681 ( 79.7)
Sleep_group (Mean (SD))	6.77 (1.60)	6.17 (1.94)	5.54 (2.30)	6.42 (1.85)	< 0.001
BMI_group (%)
Under 25	7294 (68.1)	3703 ( 69.2)	3032 ( 67.3)	559 ( 65.5)	0.007
25–30.0	2851 (26.6)	1398 ( 26.1)	1220 ( 27.1)	233 ( 27.3)
30.0 and over	562 ( 5.2)	250 ( 4.7)	250 ( 5.6)	62 ( 7.3)
hypertension (%)
Yes	3635 (33.9)	1866 ( 34.9)	1529 ( 34.0)	240 ( 28.1)	0.001
No	7072 (66.1)	3485 ( 65.1)	2973 ( 66.0)	614 ( 71.9)

3.2 Association between FI and PP-MM

In univariate analyses, the risk of physical-psychological multimorbidity (PP-MM) increased by 1.57 (95% confidence interval (CI) = 1.40–1.76) for each 0.1 increase in FI (Table 2). After adjusting for confounders including age, sex, race, education, household income, marital status, social security, smoking, alcohol consumption, and BMI, the HR (Hazard Ratio,HR) was 1.53 (95% CI = 1.34–1.74). After categorising FI into non-frail (FI ≤ 0.10), pre-frail (0.10 < FI < 0.25), and frail (FI ≥ 0.25), the results of the univariate analyses revealed that, compared to non-frail, the HR was 3.33 (95% CI = 2.28–4.84) for pre-frail and 6.50 (95% CI = 4.15–10.16) for frail. After adjusting for the confounders of age, sex, race, education, household income, marital status, social security, smoking, alcohol consumption, and BMI, the HR was 3.01 (95% CI = 2.05–4.23) for pre-frail and 6.11 (95% CI = 3.79–9.84) for frail.

Table 2

**Hazard ratios and 95% CI of frailty status for PP-MM.**
	Unadjusted model		Adjusted model
	HR (95% CI)	P value	HR (95% CI)	P value
Continuous	1.57(1.40, 1.76)	< 0.001	1.53(1.34, 1.74)	< 0.001
Categories
Healthy	Ref		Ref
Pro-frailty	3.33(2.28, 4.84)	< 0.001	3.01(2.05, 4.23)	< 0.001
Frailty	6.50(4.15,10.16)	< 0.001	6.11(3.79, 9.84)	< 0.001
Notes: Model adjusted for age, gender, hukou, marriage, education level, drinking, smoking, sleep, BMI and Hypertension.

Moreover, the RCS model demonstrated a nonlinear relationship between frailty (FI) and CMM incidence in all subjects (overall, P < 0.001; nonlinear, P = 0.001; Fig. 2). The incidence of PP-MM increased as FI increased. The 95% CI for the HR of CMM were all above 1 at an FI of approximately 0.1, and the rate of increase in the incidence of PP-MM was faster when the FI was between 0.10 and 0.25 and decreased after an FI of 0.25. These findings indicate that the association between increased FI and the risk of developing PP-MM in subjects was more significant in the pre-frailty period.

3.3 Subgroup analyses and interactions

Subgroup and interaction analyses were performed to further evaluate the relationship between FI and PP-MM incidence. These analyses were based on various characteristics, including age, gender, household, education, smoking, alcohol consumption, and sleep. The effects of FI and PP-MM were consistent across subgroups, testing the robustness of the results (Fig. 3).

This prospective nationwide study utilised a 4-node RCS curve and a COX regression model for the first time to evaluate the relationship between FI and PP-MM. This study identified 4,502 cases of pre-frailty and 854 cases of frailty. The COX regression model indicated a correlation between FI and PP-MM prevalence in middle-aged and older Chinese individuals. Moreover, pre-frail and frail participants exhibited a higher risk of PP-MM. The RCS findings revealed that the prevalence risk of PP-MM increased as FI increased, and the rate of increase was more rapid in the pre-frail period.

Positive associations between frailty and the risk of a single chronic condition have been studied, including heart failure, coronary artery disease(Ijaz et al., 2022), chronic obstructive pulmonary disease, and asthma(Qu et al., 2024). Several studies have also explored frailty and multimorbidity, demonstrating that frailty is strongly associated with chronic disease multimorbidity(Hanlon et al., 2018; Luo et al., 2023). This is consistent with our findings. A study discovered that depression partially mediated the association between prior frailty and subsequent multimorbidity(Feng et al., 2023), possibly because most frail patients have chronic inflammatory conditions, including elevated levels of interleukin-6, which is a significant risk factor for various chronic illnesses(Ferrucci and Fabbri, 2018). Frail older adults with multiple chronic diseases experience an accumulation of inflammatory conditions that may prolong the pro-inflammatory state, increase cortisol levels, and reduce muscle mass, physiological reserve, and immunocompetence, creating a vicious cycle(Chang et al., 2010). Frail older adults may experience disability or functional dependence, pain due to chronic conditions, activity limitations, and poor endurance, leading to depression and psychological problems(Woods et al., 2005). Therefore, we suggest incorporating frailty assessment into the routine monitoring and evaluation of patients with chronic conditions, leading to the identification of those at greater risk of PP-MM and facilitating targeted clinical care and health management.

A comprehensive longitudinal study on frailty transitions in community-dwelling adults revealed that over 50% of participants transitioned between frail states. Moreover, although most of these transitions were to more frail states, some made transitions to less frail states, suggesting that frailty is reversible(Gill et al., 2006) and can be reversed through interventions(Shi et al., 2021). This study determined that the HR and 95% CI for PP-MM were greater than 1 at FI of 0.10 and that the risk of developing PP-MM increased more rapidly as FI increased between 0.10 and 0.25 and slowed down after FI of 0.25. This revealed a significant relationship between the increase in FI and the risk of PP-MM prevalence in the pre-frail period. Moreover, the pre-frail period may present a better opportunity to implement interventions to prevent PP-MM, providing new evidence for PP-MM prevention.

In this study, we found that the HR and 95% CI for PP-MM were greater than 1 at FI of 0.10 and that the prevalence risk of PP-MM rose faster with increasing FI between 0.10 and 0.25 and slowed down after FI of 0.25. This suggests that the relationship between rising FI and PP-MM prevalence risk is meaningful in the pre-frail period and the pre-frail period may be a better opportunity to implement interventions for PP-MM prevention. This provides new evidence for preventing PP-MM.

This is the first time that the association between FI and PP-MM has been explored longitudinally using a large sample from a nationally representative sample. This study used the COX regression and RCS models to estimate the association between FI and PP-MM in middle-aged and older adults. A series of subgroup analyses and interactions were conducted to confirm the robustness of our results.

This study has several limitations. First, the respondents self-reported physical, psychological, and cognitive impairments, which may have introduced recall bias and neglected undiagnosed disorders. Second, despite adjusting for many potential confounders, the possibility of residual confounding factors could not be eliminated. Finally, although longitudinal studies have yielded stronger correlations between FI and PP-MM than cross-sectional studies, we remain unable to establish a causal relationship or elucidate the underlying biological mechanisms. Further experimental studies are required to confirm this association.

In summary, the prevalence of PP-MM increased with increasing FI. Pre-frail may represent a better opportunity to implement interventions to prevent PP-MM. Early identification of pre-frailty and frailty can assist in identifying individuals at a greater risk of PP-MM and facilitate the provision of targeted clinical treatment and health management.

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Ethical Approval and consent to participate

The data comes from a public database and requires no ethical review.

Clinical trial number

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Data availability statement

The CHARLS dataset is publicly available online, accessible at http://charls.pku.edu.cn/en.

Conflicts of Interest

The authors declare no conflict of interest in this study.

Funding

This research received no external funding.

Acknowledgement

There is no acknowledgement.

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The authors of this article consent.

Authors’ contributions

ZhiYing Fei: Data curation, Formal analysis, and Writing - original draft; Qian Yin:Writing- Reviewing and Editing; YingYing Tu：Writing- Reviewing and Editing；ChunQiao Wu: Conceptualization, Writing - original draft, Writing - review & editing, and Validation.

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No competing interests reported.

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Association between frailty and physical-psychological multimorbidity in middle-aged and elderly Chinese adults: Evidence from the China Health and Retirement Longitudinal Study (CHARLS)

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Abstract

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1. Introduction

2. Materials and Methods

2.1 Population and Data Sources

2.2 Assessment of Frailty

2.3 Assessment of PP-MM

2.4 Accompanying factors

2.5 Statistical Methods

3. Results

3.1 Descriptive Statistics

3.2 Association between FI and PP-MM

3.3 Subgroup analyses and interactions

4. Discussion

5. Conclusions

Declarations

References

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