Association between frailty and chronic lung diseases in middle aged and older population

Abstract

Background

Previous studies have linked frailty in older adults to poor health outcomes, particularly chronic lung disease (CLD). However, this study innovatively concentrated on changes over time rather than solely on the initial frailty status, aiming to investigate the relationship between initial frailty status, its progression, and the incidence of CLD.

Methods

Using data from the China Health and Retirement Longitudinal Study (CHARLS), frailty was assessed with the Rockwood frailty index. Among 6,943 participants, 4,624 (66.6%) were robust, 2,140 (30.8%) prefrail, and 179 (2.6%) frail. Changes in frailty status were evaluated over a two-year follow-up, with CLD identified through self-reported physician diagnoses. Cox proportional hazards models calculated hazard ratios (HR) and 95% confidence intervals (CI), adjusting for confounders.

Results

Robust individuals who progressed to pre-frail or frail status had a higher risk of CLD (HR = 1.57, 95% CI: 1.27–1.93). Conversely, frail participants who improved to robust or pre-frail status showed a lower risk (HR = 0.78, 95% CI: 0.37–1.61). Additionally, pre-frail participants who regained robust status had reduced CLD risk (HR = 0.59, 95% CI: 0.44–0.80).

Conclusions

Frailty progression increases CLD risk, while recovery decreases it, underscoring the importance of frailty screening in clinical practice for preventing and managing CLD in the ageing population.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-026-27240-4.

Background

Population aging has emerged as a global phenomenon, with China currently hosting the largest elderly population worldwide. According to the 2020 national census data, there are over 260 million individuals aged 60 and above in China, presenting significant challenges to the healthcare system [1]. Among various issues stemming from population aging, frailty is particularly concerning [2, 3]. Frailty is an age-related clinical condition marked by a decline in the physiological capacity of various organ systems. It is characterized by multiple functional impairments and reduced physiological reserves, leading to increased vulnerability and a decreased ability to cope with stressors in individuals [4–6]. This condition heightens dependence, fragility, and mortality risk. Compared to their non-frail counterparts, frail older adults exhibit greater susceptibility to adverse health outcomes such as falls, disability, delirium, emergency hospital admissions, and even death [7]. Thus, interventions to prevent or slow the progression of frailty among older adults, before it leads to significant functional decline, are key concerns for health­care policy and provision [8, 9]. While some leading health authorities and organizations have recommended that frailty should be identified in all healthcare encounters with older adults, particularly those with multimorbidity [10, 11].

Concurrently, this increased longevity is accompanied by a rising prevalence of chronic non-communicable diseases [12]. As the primary contributors to the global health burden, chronic diseases represent a significant obstacle to China’s efforts in achieving the “Healthy China 2030” initiative [13, 14]. Chronic lung diseases (CLD) encompass a range of conditions, including chronic obstructive pulmonary disease (COPD), chronic bronchitis, interstitial lung disease (ILD), pulmonary heart disease, emphysema, and other persistent pulmonary disorders; however, lung tumors are excluded from this classification [15, 16]. Patients suffering from CLD frequently experience severe respiratory symptoms and endure a diminished quality of life [17]. Notably, both the Global Status Report on Noncommunicable Diseases published in 2012 and the Global Burden of Diseases Report released in 2019 identified CLD as the third leading cause of mortality worldwide [18]. Prior research has established a bidirectional relationship between frailty and respiratory illnesses, but evidence linking chronic lung diseases—such as chronic bronchitis, pulmonary heart disease, emphysema, and other persistent pulmonary conditions—with frailty in older adults remains limited [19]. A recent meta-analysis revealed that frailty is highly prevalent in CLD and is strongly associated with worse disease progression, higher all-cause mortality (HR: 2.64, 95% CI 1.74–4.02), and increased risk of hospitalizations (OR: 1.65, 95% CI: 1.05–2.61) [20]. Furthermore, previous studies have predominantly concentrated on baseline frailty status without accounting for changes in frailty over the course of follow-up assessments [21]. In contrast to a single evaluation of frailty at baseline, examining fluctuations in frailty status may provide a more comprehensive understanding of biological associations, including the relationships between frailty progression and CLD. Importantly, emerging evidence suggests that interventions can potentially reverse frailty outcomes [22, 23]. Evaluating the risk of incident CLD in individuals who recover from frailty is essential for substantiating the need for targeted interventions addressing frailty within respiratory illness management. Consequently, it is imperative to investigate the associations between changes in frailty status and risks for incident CLD.

In the present study, we utilized the prospective cohorts from the China Health and Retirement Longitudinal Study (CHARLS). Our objective was to investigate the associations between changes in frailty status and the risk of incident CLD. We hypothesized that an increase in frailty status would correspond with a heightened risk of incident CLD, whereas a recovery in frailty status would be associated with a reduced risk of incident CLD.

Method

Study design and participants

The China Health and Retirement Longitudinal Study (CHARLS) is a nationally representative longitudinal survey targeting individuals in China aged 45 years or older. All participants underwent face-to-face interviews conducted by trained staff using a standardized questionnaire to gather data on sociodemographic information, lifestyle factors, and health-related information. Following the initial assessment, CHARLS participants were revisited and interviewed every two years through face-to-face Computer Assisted Personal Interviews (CAPI) for ongoing data collection. In this study, wave 1 (2011) of CHARLS was designated as the baseline, while wave 2 (2013) served as the second survey period. We utilized data from both the baseline and second survey to assess the dynamic nature of frailty status over time. Additionally, subsequent waves—culminating in wave 4 in 2018—enabled continuous tracking of outcomes, facilitating longitudinal monitoring of changes and developments in frailty status. The CHARLS received approval from the Ethics Review Committees of Peking University, and informed consent was obtained from each participant involved in this cohort study.

The selection process of the study population is illustrated in Fig. 1. The data for our study were obtained from the CHARLS. Among the 17,235 participants aged ≥ 45 years from CHARLS, we first excluded 5,290 individuals due to lung disease (n = 1,795) or missing data necessary for calculating the frailty index (FI) at baseline (2011) (n = 3,485), as well as those with missing data on lung disease status (n = 10). Furthermore, at wave 2 (2013), an additional 3,435 participants were excluded: this included those with lung disease (n = 448) and those lacking complete FI calculation data (n = 2,987). Ultimately, we also removed an additional 1,567 participants who had incomplete covariate data. As a result, a total of 6,943 participants remained for inclusion in the final analyses.

Fig. 1.

Flowchart of study participants. CHARLS: China Health and Retirement Longitudinal Stud

Assessment of frailty

A common approach for measuring frailty is the calculation of a FI, based on the proportion of measured health deficits that individuals accrue with age [24]. In this study, we constructed the FI following standard procedures described previously after screening baseline self-reported or measured data. Based on the data of CHARLS, we selected 30 items for FI construction, involving disease, symptoms, disability, physical function, depression, and cognition (see Supplementary Table S1). Each deficit was dichotomized or mapped into the 0–1 interval according to corresponding cut-off values, with 0 (absence of a deficit) and 1 indicating the maximal expression of the deficit (the unhealthiest state). A higher value of cognitive score indicated a worse, frailer status. For each participant, FI was calculated by the sum of the present health deficits divided by 30. Thus, the FI was a continuous variable ranging from 0 to 1, and a higher FI indicated a higher level of frailty. As suggested by previous studies, frailty status was further categorized into three levels: robust (FI ≤ 0.10), pre-frail (0.10 < FI < 0.25), and frail (FI ≥ 0.25) [25, 26]. Changes in frailty status were evaluated by frailty status at baseline and the second survey was conducted two years after the baseline (see Supplementary Figure S1). Total FI and change in FI were also used to assess the dynamic nature of frailty status. Total FI was calculated by the FI at baseline plus the FI at the second survey, while change in FI was calculated by the FI at the second survey minus the FI at baseline.

Covariates

The covariates considered in this study included age, sex, education level, marital status, residence, body mass index (BMI), smoking status, and alcohol consumption. Education level was specifically categorized into three distinct groups: illiterate individuals, those with a primary school education or below, and those with a middle school education or higher. Marital status was classified into two categories: married or partnered individuals and those with other marital statuses (including separated, divorced, unmarried, or widowed). Smoking status was divided into three categories: never smokers, former smokers who have smoked at some point in their lives but do not currently smoke, and current smokers. Similarly, drinking habits were categorized as never drinkers, ever drinkers who have consumed alcohol at any time but do not currently drink it regularly, and current drinkers.

Outcome ascertainment and follow-up

The primary outcome of this study was the incidence of CLD. CLD encompasses a range of conditions, including chronic bronchitis, pulmonary heart disease, emphysema, and other chronic pulmonary disorders; however, lung tumors were excluded from this definition. In this study, diagnoses of CLD were both self-reported by participants and confirmed by physicians. Data regarding CLD were collected by trained investigators who posed the question: “Have you been diagnosed by a doctor with CLD (excluding tumors or cancer), such as chronic bronchitis, pulmonary heart disease, or emphysema?” A participant was classified as having physician-diagnosed CLD if they answered ‘yes’ and the onset time was recorded at baseline and during follow-up assessments. Investigators verified the accuracy of these responses through double-checking procedures. In analyzing baseline frailty status, follow-up commenced from wave 1 of CHARLS. For analyses concerning changes in frailty status, follow-up began during wave 2 of CHARLS. The follow-up period concluded upon the first observation of CLD or at a predetermined censoring date if it occurred earlier. The final survey date attended by each participant served as the censoring date for data collection purposes. The most recent survey available prior to this analysis was CHARLS wave 4 conducted in 2018 [27–29].

Statistical analyses

For descriptive statistics, continuous variables were presented as mean [standard deviation (SD)] or median [interquartile range (IQR)], while categorical variables were reported as number (percentage).

Cox proportional hazards regression was employed to calculate the hazard ratio (HR) along with its 95% confidence interval (95% CI), to explore the relationship between the change in frailty status and the risk of developing chronic liver disease (CLD). Two Cox regression models were fitted with robust participants as the reference. Model 1 was unadjusted, whereas Model 2 was multivariable-adjusted, accounting for factors such as age, sex, education, marital status, residence, body mass index, smoking, and alcohol consumption. The similar methodologies were also used to examine the associations between changes in FI and total FI with the incidence of CLD. The changes in FI and total FI were categorized into quartiles, with the lower quartiles serving as the reference group. Furthermore, we conducted a trend test by treating quartiles as a continuous variable [30, 31]. The proportional hazards assumptions for these Cox regression models were verified using Schoenfeld residuals [32, 33]. And all cox model satisfied the proportional hazards assumption (all P-values > 0.05).

Several sensitivity analyses were conducted. First, we repeated the analyses after addressing the missing values for these covariates through multiple imputations using chained equations. Second, to minimize the reverse causality, the main analyses were repeated after excluding participants who occurred CLD in the first year of follow-up. Third, stratified analyses were performed by sex and age (middle-aged: < 65 years; older: ≥ 65 years). The statistical significance of interaction was evaluated by the likelihood ratio test [34, 35]. Finally, we performed a sensitivity analysis in which the FI was modeled as a continuous variable instead of quartiles.

All statistical analyses were carried out by Stata SE, version 15.0 (Stata Corp LP., College Station, Texas, USA). All P-values were two-sided, and P < 0.05 was considered statistically significant.

Results

Baseline characteristics of the study population

According to the inclusion and exclusion criteria, a total of 6,943 participants from CHARLS (female: 52.63%; age: 57.83 ± 8.54 years) were included in the analyses of baseline frailty status. The baseline characteristics of study participants categorized by frailty status are presented in Table 1. Among all participants, 4,624 (66.6%) individuals were classified as robust, 2,140 (30.8%) as prefrail, and 179 (2.6%) as frail. In comparison to robust participants, those identified as frail were older, more likely to be female, less likely to be married, non-smoking, non-drinking, and exhibited lower levels of education and physical activity, living in urban areas. Additionally, prefrail participants exhibited the highest BMI. Association of changes in frailty status with incident chronic lung disease.

Table 1.

Baseline characteristics of participants for frailty status (n = 6943)

Characteristics	Total	Robust	Pre-frail	Frail	P
	N = 6943	N = 4624	N = 2140	N = 179
Age (years)	57.83 ± 8.54	57.05 ± 8.47	59.10 ± 8.45	62.75 ± 7.82	< 0.001
Female	3654 (52.63)	2210 (47.79)	1326 (61.96)	118 (65.92)	< 0.001
Education level					< 0.001
Illiterate	1630 (23.48)	971 (21.00)	583 (27.24)	76 (42.46)
Primary school and below	2877 (41.44)	1820 (39.36)	987 (46.12)	70 (39.11)
Middle school and above	2436 (35.09)	1833 (39.64)	570 (26.64)	33 (18.44)
Marital status					< 0.001
Married	748 (10.77)	439 (9.49)	272 (12.71)	37 (20.67)
Othera	6195 (89.23)	4185 (90.51)	1868 (87.29)	142 (79.33)
Residence					< 0.001
Urban	4559 (65.66)	2960 (64.01)	1458 (68.13)	141 (78.77)
Rural	2384 (34.34)	1664 (35.99)	682 (31.87)	38 (21.23)
BMIb	23.66 ± 3.82	23.53 ± 3.72	23.97 ± 4.06	23.50 ± 3.35	< 0.001
Smoking status					< 0.001
Never	4241 (61.08)	2713 (58.67)	1411 (65.93)	117 (65.36)
Ever smoker	553 (7.96)	348 (7.53)	179 (8.36)	26 (14.53)
Current smoker	2149 (30.95)	1563 (33.80)	550 (25.70)	36 (20.11)
Alcohol consumption					< 0.001
Never drinker	4010 (57.76)	2572 (55.62)	1319 (61.64)	119 (66.48)
Former drinker	512 (7.37)	286 (6.19)	203 (9.49)	23 (12.85)
Current drinker	2421 (34.87)	1766 (38.19)	618 (28.88)	37 (20.67)

Data are presented as mean ± standard deviations, number (proportion %)

^a Including divorced, widowed, and never married

^b BMI Body mass index

Table 2 presents the number and percentage of changes in frailty status. Among participants who were robust at baseline, 931 individuals (20.2%) progressed to pre-frail or frail status. Conversely, among those classified as frail at baseline, 93 individuals (51.9%) recovered to either pre-frail or robust status. Additionally, for participants identified as pre-frail at baseline, 544 individuals (25.4%) returned to robust levels, while 168 individuals (7.9%) advanced to a frail state.

Table 2.

Number and percentage of the changes in frailty status

The time interval between baseline and the second survey was two years in the CHARLS

^arobust: FI ≤ 0.10)

^bpre-frail: 0.10 < FI < 0.25)

^cfrail: FI ≥ 0.25.

Table 3 illustrates the association between changes in frailty status and the risk of incident chronic lung disease (CLD). Robust participants who transitioned to frail/pre-frail status exhibited a significantly increased risk of developing incident CLD compared to stable robust participants (HR = 1.57, 95% CI: 1.27–1.93).

Table 3.

Hazards ratios (HRs) and 95% CIs (confidence intervals) for the association of changes in frailty status with risks of incident chronic lung disease

Changes in frailty status	n	Cases (%)	Model 1a		Model 2b
			HR (95% CI)	P-value	HR (95% CI)	P-value
Stable robust	3693	324 (8.77%)	1 (reference)		1 (reference)
Robust to pre-frail/frail	931	124 (13.32%)	1.56 (1.27, 1.92)	< 0.001	1.57 (1.27, 1.93)	< 0.001
Stable pre-frail	1428	229 (16.04%)	1 (reference)		1 (reference)
Pre-frail to robust	544	56 (10.29%)	0.63 (0.47, 0.84)	0.002	0.59 (0.44, 0.80)	0.001
Pre-frail to frail	168	32 (19.05%)	1.20 (0.83, 1.74)	0.325	1.26 (0.87, 1.84)	0.216
Stable frail	86	16 (18.60%)	1 (reference)		1 (reference)
Frail to robust/pre-frail	93	15 (16.13%)	0.86 (0.43, 1.75)	0.681	0.78 (0.37, 1.61)	0.496

^a Model 1 was unadjusted

^b Model 2 was adjusted for age, sex, education, marital status, residence, body mass index, smoking, and alcohol consumption

In contrast, a significant reduction in the risk of incident CLD was observed among frail participants who regained robustness or shifted to pre-frail status when compared with stable frail counterparts (HR = 0.78, 95% CI: 0.37–1.61).

For baseline pre-frail participants, those who reverted to robust status demonstrated a significantly lower risk of incident CLD relative to those maintaining stable pre-frailty (HR = 0.59, 95% CI: 0.44–0.80). Although pre-frail participants progressing to a frail state also showed an elevated risk for incident CLD, this finding did not attain statistical significance (HR = 1.26, 95% CI: 0.87–1.84).

Associations of total FI and change in FI with incident chronic lung disease

Total FI ranged from 0 to 1.09 and was categorized into quartiles as follows: Q1 (Total FI ≤ 0.06), Q2 (0.06 < Total FI ≤ 0.13), Q3 (0.13 < Total FI ≤ 0.23), Q4 (Total FI > 0.23). Table 4 illustrates the association between total FI and the risks of incident CLD. After adjusting for confounding variables, participants in the upper quartile of total FI exhibited a significantly higher risk of developing incident CLD compared to those in the lower quartile (HR = 2.44, 95% CI: 1.96–3.05). Additionally, participants within the third quartile of total FI also demonstrated significantly increased risks for CLD (HR = 1.59, 95% CI: 1.26-2.00). Trend analyses revealed a positive correlation between increasing levels of total FI and rising risks of incident CLD.

Table 4.

Hazards ratios (HRs) and 95% CIs (confidence intervals) for the association of total frailty index with risks of incident chronic lung disease

Total frailty index (quartile)	n	Cases (%)	Model 1a		Model 2b
			HR (95% CI)	P-value	HR (95% CI)	P-value
Q1	1544	116 (7.51%)	1 (reference)		1 (reference)
Q2	1716	157 (9.15%)	1.22 (0.96, 1.55)	0.109	1.22 (0.96, 1.55)	0.103
Q3	1818	210 (11.55%)	1.56 (1.24, 1.95)	< 0.001	1.59 (1.26, 2.00)	< 0.001
Q4	1865	313 (16.78%)	2.31 (1.87, 2.86)	< 0.001	2.44 (1.96, 3.05)	< 0.001
P for trend	6943	796 (11.46%)	< 0.001		< 0.001

^a Model 1 was unadjusted

^b Model 2 was adjusted for age, sex, education, marital status, residence, body mass index, smoking, and alcohol consumption

Changes in FI (ΔFI) ranged from − 0.27 to 0.60 and were divided into quartiles: Q1 (ΔFI ≤ -0.02), Q2 (-0.02 < ΔFI ≤ 0), Q3 (0 < ΔFI ≤ 0.04), Q4 (ΔFI > 0.04). Table 5 presents the relationship between changes in FI (ΔFI) and risks associated with incident CLD. Participants falling within the first quartile of ΔFI showed a significantly elevated risk for incident CLD when compared to those in the fourth quartile (HR = 1.39, 95% CI: 1.15–1.67). However, no significant increase in CLD risk was observed among participants categorized within the middle quartiles of ΔFI when compared to other quartiles.

Table 5.

Hazards ratios (HRs) and 95% CIs (confidence intervals) for the association of changes in frailty index with risks of incident chronic lung disease

Changes in frailty index (quartile)	n	Cases (%)	Model 1a		Model 2b
			HR (95% CI)	P-value	HR (95% CI)	P-value
Q1	1769	204 (11.53%)	1 (reference)		1 (reference)
Q2	1875	174 (9.28%)	0.80 (0.66, 0.98)	0.033	0.83 (0.68, 1.02)	0.070
Q3	1615	162 (10.03%)	0.87 (0.71, 1.07)	0.174	0.90 (0.73, 1.10)	0.302
Q4	1684	256 (15.20%)	1.35 (1.12, 1.62)	0.002	1.39 (1.15, 1.67)	0.001
P for trend	6943	796 (11.46%)	0.001		< 0.001

^a Model 1 was unadjusted

^b Model 2 was adjusted for age, sex, education, marital status, residence, body mass index, smoking, and alcohol consumption

Sensitivity analysis

The results were consistent with the main analysis, when we imputed the missing covariates (supplementary Tables 2–4), or excluded participants who occurred CLD in the first year of follow-up (supplementary Tables 5–7). In the stratified analyses, the results were similar in males and females (supplementary Tables 8–10) or in middle-aged and older participants (supplementary Tables 11–13). Meanwhile, the interaction of FI with sex or age were not significant. When the FI was used as continuous, each 0.1 point increase in total FI or ΔFI were associated with a 18% (HR = 1.18; 95% CI 1.05–1.31) or 24% (HR = 1.24; 95% CI 1.18–1.29) higher risk of incident CLD, respectively (supplementary Table 14).

Discussion

By analyzing the prospective cohort study CHARLS, we identified associations between changes in frailty status and the risk of CLD occurrence. Participants classified as pre-frail or frail exhibited a heightened risk for incident CLD compared to their fit counterparts. Furthermore, robust participants who transitioned to a pre-frail or frail state also demonstrated an increased risk of CLD relative to stable robust individuals. Conversely, pre-frail participants who reverted to a frail state, as well as frail participants returning to either a frail or pre-frail condition, showed a diminished risk of incident CLD when compared to stable participants. Additionally, higher total FI scores and increases in FI were linked with elevated risks of incident CLD.

As the global older population continues to expand rapidly, frailty is becoming an increasingly prevalent condition. It has been demonstrated that there was a close association between frailty status and respiratory health in previous studies [36, 37]. Observational studies in ILD have shown that frailty is associated with mortality and adverse health outcomes such as quality of life [38]. A systematic review on frailty impact in chronic respiratory disease concluded that frailty negatively affects the quality of life and increases the risk of mortality in patients with idiopathic pulmonary fibrosis/ILD [39]. Besides, frailty is considered an independent risk factor for hospital admissions and mortality in ILD, including pre- and post-lung transplantation [40]. In our study, we observed consistent results indicating elevated risks of incident CLD in pre-frail and frail participants compared to robust participants. Basic research suggests that the biological mechanisms linking frailty with CLD may involve shared physiological pathways, such as systemic inflammation, endocrine/metabolic dysfunction, and physical function (lower limb muscle strength, exercise capacity, physical activity) [41–44]. Notably, our results remained significant even after adjusting for traditional respiratory risk factors. These findings further support the notion of frailty as an independent risk factor for CLD.

Our study also investigated the associations between changes in frailty status and incident CLD—an aspect not previously examined. In a study of 42 775 community-dwelling older participants, 42.8% experienced a change in frailty status (13.7% improvement and 29.1% deterioration) during a mean follow-up of 3.9 years [45]. The dynamically changing nature of the frailty states in CHARLS can also be confirmed in our study. Importantly, we found that robust participants who progressed to pre-frail or frail status exhibited increased risks of incident CLD compared to those who remained stable in their robust condition. This finding underscores the detrimental impact of frailty progression on incident CLD.

Although there is no statistical significance between the change from pre-frail state to frail state and incident CLD, which may be due to a small number of participants, the results showed a trend of increasing risk of incident CLD. Moreover, given that the transition from pre-frail state to frail state has been demonstrated to increase mortality and the incidence of cardiovascular disease, it is reasonable to suggest the change from pre-frail state to frail state may have an adverse impact on the incidence of CLD [38, 46]. Conversely, decreased risks of incident CLD were observed among frail participants who recovered to either robust or pre-frail statuses and among pre-frail individuals returning to robust status when compared with their stable counterparts. This suggests substantial benefits associated with reversing frailty concerning incident CLD outcomes.

Beyond the categorized assessments of frailty status, we further examined its dynamic nature through the total FI and the changes therein. Higher total FI scores and increases in FI were correlated with elevated risks for incident CLD, thereby corroborating our earlier findings.

The results of our study hold significant potential to inform clinical practice and public health policy. However, frailty was mentioned very briefly in the Global Initiative for Chronic Obstructive Lung Disease international guidelines or in the Global Initiative for Asthma international guidelines [47, 48]. The COPD-X Plan, a guidelines document in Australia and New Zealand, was one of the few sources that dedicated significant attention to frailty-related issues [49]. It is essential that frailty assessment be integrated into routine CLD management, particularly within the older population. We recommend identifying individuals exhibiting frailty or pre-frailty as primary targets for preventing adverse CLD outcomes or related events. Furthermore, it is imperative that robust individuals assess risk factors associated with frailty, enabling early identification of at-risk individuals and facilitating timely implementation of appropriate prevention strategies aimed at delaying the progression of frailty.

Moreover, it is important to note that frailty represents a reversible state, this is evidenced by observations indicating reduced CLD risks among individuals who successfully reversed their frailty status. Consequently, substantial efforts must be dedicated to implementing effective interventions for reversing frail conditions. In contrast to established frailty, pre-frailty may present a more promising golden period for intervention regarding CLD prevention due to its higher likelihood of transitioning towards robust status.

As far as we are aware, there has been no previous research investigating the associations between changes in frailty status and risks of incident CLD. Besides, we utilized the prospective cohorts from the nationally representative sample of CHARLS, which has high response rates and data quality. Furthermore, the diverse sensitivity analyses also ensured the robustness of our results and enhanced the reliability of our findings.

However, some limitations should also be highlighted. Firstly, recall bias cannot be fully excluded since our research data were based on self-reported. The absence of standardized objective assessments (e.g., spirometry, CT imaging) complicates distinguishing true CLD cases from undiagnosed or asymptomatic individuals, potentially biasing prevalence estimates. Secondary, although we adjusted for multiple confounders, residual confounding such as dietary patterns, physical activity levels, and comorbidities (e.g., diabetes) may independently influence CLD risk, which could potentially bias our observed frailty-CLD association. Third, some categories contain fewer individuals, for instance, those who progressed from robust to frail or from frail to robust, which have to merge with proximate categorization, precluding further detailed, individual analysis. Future research should employ longitudinal cohort designs with extended follow-up periods to capture dynamic frailty transitions and integrate objective biomarkers (e.g., inflammatory cytokines) and comprehensive lifestyle assessments to elucidate the biological pathways linking frailty and CLD.

Conclusion

Different changes in frailty status are associated with varying risks of incident CLD. Specifically, a deterioration in frailty status correlates with an increased risk of CLD, while improvement in frailty status is linked to a reduced risk. Future research should focus on developing targeted preventive strategies aimed at slowing the progression of frailty and implementing interventions designed to reverse frailty within the context of CLD control and management practices.

Authors' contributions

Study concept and design: D.Z.; Drafting of the manuscript: X.H.; Analysis and interpretation of data: J.L., S.C., Y.S.; Acquisition of data: Z.C., X.J., J.L.; Critical revision of the manuscript for important intellectual content: X.Z., K.C., X.G. All authors read and approved the final manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (82273621, 82103819), the Ningbo Natural Science Foundation (2023J162), 2023–2025 the First Batch of Provincial Clinical Key Specialty Construction Projects in Jiangxi (Gan Wei Yi Zi [2023] No.82) and Gannan Soviet Area talent support program (Gan Shi Cai Zi [2021] No.1).

Data availability

Details of the CHARLS data are available from http://charls.pku.edu.cn/pages/data/111/zh-cn.html.

Declarations

Ethics approval and consent to participate

This study utilized publicly available de-identified data from CHARLS, and did not require additional ethics approval.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.