Abstract
Background
Evidence suggests an association between sarcopenia and impaired respiratory function; however, longitudinal population-based data on the relationship between sarcopenia, its individual components, and peak expiratory flow (PEF) decline remain limited. This study aimed to investigate the cross-sectional and longitudinal associations of sarcopenia and its components with PEF measures among middle-aged and older Chinese adults.
Methods
Data were obtained from the 2011 and 2015 waves of the China Health and Retirement Longitudinal Study (CHARLS). Sarcopenia was defined according to the Asian Working Group for Sarcopenia 2019 criteria. Cross-sectional associations between sarcopenia and PEF measures were examined using multivariable logistic and linear regression models. Longitudinal associations between baseline sarcopenia and subsequent PEF decline were evaluated using multivariable logistic regression models.
Results
A total of 9452 participants were included in the cross-sectional analysis. Compared with participants without sarcopenia, those with sarcopenia and severe sarcopenia had higher odds of severe airflow limitation (SAL) (OR = 1.41, 95% CI: 1.20–1.65 and OR = 1.56, 95% CI: 1.26–1.94, respectively). Sarcopenia and its individual components were also associated with lower PEF and PEF% predicted. In the longitudinal analysis of 4804 participants, low muscle strength was the strongest and most consistent predictor of subsequent PEF decline. Participants with low muscle strength had a higher risk of new-onset SAL (OR = 1.54, 95% CI: 1.20–1.98) and a 56%–64% greater risk of PEF% predicted decline across different thresholds. Severe sarcopenia and low muscle mass were also associated with greater risks of moderate-to-severe PEF% predicted decline.
Conclusion
Sarcopenia was associated with poorer cross-sectional PEF measures, whereas low muscle strength showed the strongest and most consistent longitudinal associations with subsequent PEF decline. These findings highlight the potential importance of muscle strength preservation for maintaining respiratory health in middle-aged and older adults.
Graphical Abstract
This study aimed to investigate the association between sarcopenia and its components with PEF decline using data from the China Health and Retirement Longitudinal Study (CHARLS), employing both cross-sectional and longitudinal analyses. The findings demonstrated that sarcopenia was associated with poorer cross-sectional PEF measures, whereas low muscle strength emerged as the strongest and most consistent predictor of subsequent PEF decline among middle-aged and older adults in China.
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1186/s12890-026-04451-4.
Keywords: Sarcopenia, Muscle strength, Peak expiratory flow, Severe airflow limitation
Introduction
As the global population ages, pulmonary dysfunction has become an increasingly important public health concern [1]. Pulmonary function gradually declines with age and is influenced by multiple factors, including low physical activity, malnutrition, chronic diseases, immune dysfunction, and tobacco exposure [2, 3]. Severe airflow limitation (SAL) represents an important manifestation of pulmonary dysfunction and is associated with substantial morbidity among older adults [4]. Peak expiratory flow (PEF), an easily accessible measure of pulmonary function, has been widely used to assess respiratory health and airflow limitation. Previous studies have shown that reduced PEF is associated with adverse health outcomes, including frailty, falls, hospitalization, disability, and mortality in older adults [5–7].
Muscle health, particularly sarcopenia, has become a critical area of research in aging studies due to its profound implications for physical function, quality of life, and comorbidities. According to the latest definition by the Asian Working Group for Sarcopenia (AWGS), sarcopenia is characterized by a decline in skeletal muscle mass accompanied by reductions in muscle strength or physical performance, with a prevalence as high as 16.5% among community-dwelling adults aged 60 years and older in Asia [8]. Accumulating evidence suggests a bidirectional relationship between sarcopenia and pulmonary function [9, 10]. On one hand, sarcopenia may impair respiratory muscle function, immune regulation, and systemic inflammation, contributing to deteriorated lung function [9]. On the other hand, chronic respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD) are associated with accelerated muscle loss, potentially creating a vicious cycle of functional decline [10]. In addition, chronic inflammation, oxidative stress, mitochondrial dysfunction, and other aging-related biological processes have been implicated in both muscle deterioration and respiratory impairment [11–15]. These shared mechanisms may partly explain why reduced peripheral muscle strength has been associated with poorer respiratory outcomes in older adults.
Although accumulating evidence supports an association between sarcopenia and impaired lung function, several important questions remain unresolved. Most existing studies have focused on patients with chronic respiratory diseases, such as COPD or asthma, whereas longitudinal evidence from community-dwelling populations remains relatively limited. In addition, the heterogeneous nature of sarcopenia, including differences in severity and the distinct contributions of muscle mass, muscle strength, and physical performance, has not been fully characterized with respect to pulmonary function outcomes. We hypothesized that sarcopenia and its individual components would be associated with poorer PEF measures and that low muscle strength would show the strongest longitudinal association with subsequent PEF decline. Therefore, this study aimed to investigate the cross-sectional associations of sarcopenia and its components with pulmonary dysfunction and the longitudinal associations with subsequent PEF decline among community-dwelling middle-aged and older adults.
Methods
Study design and population
This study combined cross-sectional and longitudinal analyses using data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative longitudinal survey of Chinese adults aged 45 years or older and their spouses, designed to investigate health and socioeconomic adaptations in response to China’s rapidly aging population [16]. All data used in this study, including demographic information, health status, sarcopenia-related measurements, and PEF assessment, were obtained from standardized questionnaires and physical examinations implemented by the CHARLS study team.
We used wave 1 (2011) as baseline and wave 3 (2015) as follow-up. First, baseline data were used to examine the cross-sectional association between sarcopenia/sarcopenia components and PEF indices. Then, baseline sarcopenia status and sarcopenia components were used to examine their longitudinal associations with subsequent PEF decline and new-onset SAL in 2015.
We initially included 9452 participants aged ≥ 50 years at baseline. Participants with missing data regarding PEF assessments and sarcopenia evaluations at baseline were excluded. Furthermore, participants identified as having SAL at baseline, participants lost to follow-up in 2015, and participants who failed to complete PEF assessment in 2015 were further excluded from the longitudinal analysis. Consequently, the final analytical sample consisted of 4804 participants. The detailed selection process of the study population is illustrated in Fig. 1. Missing covariate data were handled using multiple imputation by chained equations (MICE). The distributions of imputed variables before and after multiple imputation were summarized in Supplementary Table S1 and were largely comparable, suggesting that the imputation procedure did not materially alter the distributions of these variables.
Fig. 1.
Study flow
Sarcopenia assessment
Sarcopenia assessment was operationalized according to the 2019 AWGS diagnostic algorithm, comprising three core diagnostic domains: (1) muscle strength, (2) muscle mass and (3) physical performance [17]. Sarcopenia was defined as low muscle mass accompanied by either low muscle strength or poor physical performance. The diagnosis of severe sarcopenia required concomitant impairment across all three domains. Based on these criteria, participants were stratified into three categories: non-sarcopenia, sarcopenia, and severe sarcopenia.
Muscle strength quantification was performed using a digital handgrip dynamometer (Yuejian™ WL-1000, China) through standardized protocol. Participants maintained neutral wrist position with elbow flexed at 90° while executing maximal isometric contractions. Four measurements were performed on the dominant and non-dominant hands over two trials. The average of the available maximum strength data was used. Low muscle strength status was defined using sex-specific thresholds (< 28 kg males; < 18 kg females) according to the AWGS 2019 consensus [17].
Muscle mass was assessed by height-adjusted appendicular skeletal muscle mass (ASM/height2). ASM was calculated using an anthropometric formula validated for the Chinese population, which is highly consistent with dual-energy X-ray absorptiometry (DXA) [18]:
 |
Weight was measured by an Omron™ HN-286 weight scale, and height was measured by a SecaTM213 stadiometer (Seca Trading, Hangzhou, China). Sex was coded as 1 for males and 0 for females. Because direct DXA measurements were unavailable in CHARLS and no universally accepted anthropometry-specific AWGS cutoffs have been established for this equation, low muscle mass was defined using the sex-specific lowest 20th percentile of ASM/height² in the study population (< 6.79 kg/m² for males and < 4.89 kg/m² for females), consistent with previous epidemiological studies using CHARLS data [19, 20].
Physical performance assessment included gait speed and 5-time chair stand test. For gait speed, each participant was asked to complete a 4-m walk along a marked 2.5-m linear course at a normal pace. Two valid trials were recorded, and the fastest walking speed was used for analysis. For the 5‐time chair stand test, participants were asked to stand up from a chair five times in a row with their arms crossed over their chest, and the total completion time was recorded. Diagnostic thresholds followed the AWGS 2019 operational definitions: low physical performance was designated as (a) gait speed < 1.0 m/s or (b) 5‐time chair stand test ≥ 12 s.
PEF assessment
PEF (L/min) data were obtained from the CHARLS database, which was performed using a calibrated digital peak flow meter (Shanghai Everpure Medical Plastic Co. Ltd, Shanghai, China) with a single-use mouthpiece. Participants were instructed to inhale as deeply as possible, open their mouth, forcefully close their lips to the outside of the mouthpiece, and then blow air into the mouthpiece as hard and as quickly as possible. The PEF value was derived from the highest value of three repeatable measurements with a rest interval of 30 s. Predicted PEF was calculated using the Zhong Nanshan equations for Chinese adults [21]:
 |
 |
The percentage of actual PEF in predicted PEF (PEF% pred) is the ratio between PEF and predicted PEF values. SAL was defined as PEF% pred < 60% [21]. PEF indices required for longitudinal analysis included PEF decline, PEF% pred decline, and new-onset SAL.
Covariates
Covariates included age, sex (male/female), body mass index (BMI), residence (urban/rural), marital status (married/ divorced/ widowed/ others), education, smoking status, drinking status, physical activity level, chronic lung diseases, and asthma. The covariates were selected based on previous literature and clinical relevance [4, 6, 9]. Education was categorized as illiterate, unfinished or completed primary school, middle school, and high school or above, corresponding approximately to 0 years, 1–6 years, 7–9 years, and ≥ 10 years of formal education, respectively. Smoking/drinking status was categorized as never, former, or current smoking/drinking. Current smoking was defined as currently smoking cigarettes at the time of the survey; former smoking was defined as having smoked previously but not currently; and never smoking was defined as no history of smoking. Current drinking was defined as alcohol consumption during the past year; former drinking was defined as previous alcohol consumption but no alcohol consumption during the past year; and never drinking was defined as no history of alcohol consumption.
For physical activity level, we calculated total physical activity volume (PAV) by metabolic equivalent (MET), PAV = 8.0 × weekly vigorous physical activity duration + 4.0 × weekly moderate physical activity duration + 3.3 × weekly low intensity physical activity duration [22, 23]. According to previous studies, physical activity levels were categorized into active (≥ 1500 MET-min/week), less active (600–1499 MET-min/week), and sedentary (< 600 MET-min/week) based on the total PAV score [24, 25]. The interviews on chronic lung disease and asthma used self-report questionnaires: “Have you been diagnosed with chronic lung diseases, such as chronic bronchitis, emphysema (excluding tumors, or cancer) by a doctor?” and “Have you been diagnosed with asthma by a doctor?”.
Statistics
Continuous variables are presented as mean with standard deviation, and categorical variables are presented as number with percentage. Missing covariate data were imputed using MICE for a total of 20 imputations. Multiple imputation was applied only to missing covariate data; participants with missing primary exposure or outcome variables were excluded from the corresponding analyses and were not imputed. The imputation model included all variables used in the main analytical models, including age, sex, BMI, residence, marital status, education, smoking status, drinking status, chronic lung diseases, asthma, physical activity level, baseline sarcopenia status, baseline lung function indices, and follow-up lung function outcomes. Continuous variables were imputed using predictive mean matching, binary variables using logistic regression, and unordered categorical variables using multinomial logistic regression. Estimates across the imputed datasets were combined using Rubin’s rules.
Logistic regression and linear regression models were used to examine the cross-sectional associations between sarcopenia and PEF indices after adjustment for the aforementioned confounders. Logistic regression models were also used to examine the longitudinal associations between baseline sarcopenia and four-year PEF decline. For longitudinal analyses, both unadjusted and fully adjusted models were constructed. The fully adjusted models included the aforementioned confounders and baseline PEF. As a sensitivity analysis, the main analyses were repeated after excluding participants with self-reported chronic lung diseases or asthma at baseline. All analyses were performed using Stata version 17.0 (StataCorp LLC, College Station, TX, USA).
Results
Baseline characteristics of study population
A total of 9452 participants were included in the cross-sectional analysis. The baseline characteristics of the study population are presented in Table 1. Participants were categorized into three mutually exclusive groups: non-sarcopenia (n = 7590, 80.30%), sarcopenia (n = 1335, 14.12%), and severe sarcopenia (n = 527, 5.58%).
Table 1.
Baseline characteristics of study participants (n = 9452)
| Characteristics | Overall (n = 9452) |
Non-sarcopenia (n = 7590) | Sarcopenia (n = 1335) | Severe sarcopenia (n = 527) |
|---|---|---|---|---|
| Age, mean ± SD | 62.04 ± 8.13 | 60.41 ± 7.23 | 67.29 ± 7.88 | 72.20 ± 8.07 |
| Sex, n (%) | ||||
| Male | 4682(49.53) | 3776(49.75) | 639(47.87) | 267(50.66) |
| Female | 4770(50.47) | 3814(50.25) | 696(52.13) | 260(49.34) |
| BMI, mean ± SD | 23.52 ± 3.91 | 24.61 ± 3.50 | 19.05 ± 1.73 | 19.18 ± 1.87 |
| Residence, n (%) | ||||
| Rural | 5975(63.21) | 4585(60.41) | 1011(75.73) | 379(71.92) |
| Urban | 3477(36.79) | 3005(39.59) | 324(24.27) | 148(28.08) |
| Marital status, n (%) | ||||
| Married | 8033(84.99) | 6667(87.84) | 1019(76.33) | 347(65.84) |
| Divorced | 118(1.25) | 85 (1.12) | 23(1.72) | 10(1.90) |
| Widowed | 1223(12.94) | 787(10.37) | 279(20.90) | 157(29.79) |
| Others | 78(0.83) | 51(0.67) | 14(1.05) | 13(2.47) |
| Education, n (%) | ||||
| Illiterate | 4854(51.37) | 3611(47.58) | 846(63.37) | 397(75.62) |
| Primary school | 2144(22.69) | 1740(22.93) | 309(23.15) | 95(18.10) |
| Middle school | 1549(16.39) | 1397(18.41) | 128(9.59) | 24(4.57) |
| High school and above | 902(9.55) | 841(11.08) | 52(3.90) | 9(1.71) |
| Smoking, n (%) | ||||
| Never smoke | 5511(58.60) | 4487(59.34) | 736(55.67) | 288(55.38) |
| Former smoke | 929(9.88) | 762(10.08) | 111(8.40) | 56(10.77) |
| Current smoke | 2964(31.52) | 2313(30.59) | 475(35.93) | 176(33.85) |
| Drinking, n (%) | ||||
| Never drink | 5437(57.60) | 4339(57.21) | 772(58.09) | 326(61.98) |
| Former drink | 903(9.57) | 717(9.45) | 125(9.41) | 61(11.60) |
| Current drink | 3099(32.83) | 2528(33.33) | 432(32.51) | 139(26.43) |
| Physical activity, n (%) | ||||
| Active | 3068(78.63) | 2538(79.91) | 421(77.82) | 109(58.92) |
| Less active | 134(3.43) | 111(3.49) | 16(2.96) | 7(3.78) |
| Sedentary | 700(17.94) | 527(16.59) | 104(19.22) | 69(37.30) |
| Chronic lung diseases, n (%) | ||||
| Yes | 1054 (11.21) | 740(9.79) | 221(16.69) | 93(17.78) |
| No | 8351 (88.79) | 6818(90.21) | 1103(83.31) | 430(82.22) |
| Asthma, n (%) | ||||
| Yes | 381(4.05) | 281(3.72) | 69(5.20) | 31(5.92) |
| No | 9022(95.95) | 7270(96.28) | 1259(94.80) | 493(94.08) |
| Severe airflow limitation, n (%) | ||||
| Yes | 2850(30.15) | 2061(27.15) | 545(40.82) | 244(46.30) |
| No | 6602(69.85) | 5529(72.85) | 790(59.18) | 283(53.70) |
| PEF, mean ± SD | 286.82 ± 121.31 | 301.61 ± 120.86 | 236.96 ± 106.31 | 200.06 ± 89.49 |
| PEF% pred, mean ± SD | 0.76 ± 0.28 | 0.78 ± 0.27 | 0.70 ± 0.30 | 0.65 ± 0.26 |
Abbreviations: BMI body mass index, PEF peak expiratory flow, PEF% pred percentage of actual PEF in predicted PEF
The mean age increased from 60.41 years in the non-sarcopenia group to 72.20 years in the severe sarcopenia group, whereas mean BMI decreased from 24.61 to 19.18 kg/m². Participants with sarcopenia were also more likely to reside in rural areas and had lower educational attainment. The proportion of married individuals decreased across sarcopenia categories, from 87.84% in the non-sarcopenia group to 65.84% in the severe sarcopenia group. Current smoking was more common among participants with sarcopenia and severe sarcopenia, whereas current drinking was more frequent among those without sarcopenia. Physical activity data were available for 3902 participants. Among those with available data, participants with sarcopenia or severe sarcopenia exhibited lower levels of physical activity and were more likely to be classified as sedentary. In addition, the prevalence of chronic lung diseases, asthma, and SAL increased across sarcopenia categories. PEF indicators showed a similar pattern, with lower PEF and PEF% pred values observed among participants with sarcopenia and severe sarcopenia compared with those without sarcopenia.
Cross-sectional associations between sarcopenia, sarcopenia components, and PEF measures
Table 2 presents the cross-sectional associations of sarcopenia and its components with PEF parameters. After adjustment for the aforementioned confounders, both sarcopenia and severe sarcopenia were associated with a higher likelihood of SAL and lower PEF measures, with larger effect estimates observed for severe sarcopenia. Among the individual sarcopenia components, low muscle strength demonstrated the strongest associations with a higher likelihood of SAL (OR = 1.75, 95% CI: 1.55–1.99), lower PEF (β = -36.32, 95% CI: -42.29 to -30.35), and lower PEF% pred (β = -0.10, 95% CI: -0.11 to -0.08), whereas the associations for low muscle mass and low physical performance were comparatively smaller.
Table 2.
Associations between sarcopenia and lung function
| Severe airflow limitation | PEF | PEF% pred | ||||
|---|---|---|---|---|---|---|
| OR (95%CI) | P value | β (95%CI) | P value | β (95%CI) | P value | |
| Sarcopenia | ||||||
| Non-sarcopenia | 1(Reference) | NA | 0 (Reference) | NA | 0 (Reference) | NA |
| Sarcopenia | 1.41 (1.20–1.65) | < 0.001 | -19.47 (-26.65- -12.29) | < 0.001 | -0.05 (-0.07- -0.03) | < 0.001 |
| Severe sarcopenia | 1.56 (1.26–1.94) | < 0.001 | -34.02 (-44.27- -23.78) | < 0.001 | -0.10 (-0.13- -0.07) | < 0.001 |
| Sarcopenia item | ||||||
| Low muscle mass | 1.45 (1.25–1.67) | < 0.001 | -23.13 (-29.84- -16.43) | < 0.001 | -0.06 (-0.08- -0.05) | < 0.001 |
| Low muscle function | 1.65 (1.46–1.86) | < 0.001 | -27.63 (-32.84- -22.42) | < 0.001 | -0.07 (-0.09- -0.06) | < 0.001 |
| Low muscle strength | 1.75 (1.55–1.99) | < 0.001 | -36.32 (-42.29- -30.35) | < 0.001 | -0.10 (-0.11- -0.08) | < 0.001 |
Adjusted for age, sex, body mass index, residence, marital status, education, smoking status, drinking status, chronic lung diseases, asthma and physical activity
Abbreviations: NA not applicable, OR odds ratio, CI confidence interval, PEF peak expiratory flow, PEF% pred percentage of actual PEF in predicted PEF
Longitudinal associations between sarcopenia, sarcopenia components, and PEF decline
Figure 2 illustrates the longitudinal associations between sarcopenia, sarcopenia components, and PEF% pred decline. Sarcopenia was associated with a 39% higher risk of PEF% pred decline greater than 30%. In contrast, severe sarcopenia was associated with progressively greater risks of moderate-to-severe declines in PEF% pred, with ORs of 2.01 (95% CI: 1.38–2.93) for a > 20% decline and 2.40 (95% CI: 1.55–3.70) for a > 30% decline. Among the individual sarcopenia components, low muscle strength showed the most consistent associations across all thresholds of PEF% pred decline, with ORs ranging from 1.56 to 1.64. Low muscle mass was associated with moderate-to-severe declines in PEF% pred.
Fig. 2.
Longitudinal associations between sarcopenia and PEF% pred decline. Notes: Adjusted for age, sex, body mass index, residence, marital status, education, smoking status, drinking status, chronic lung diseases, asthma, physical activity level and baseline PEF. Abbreviations: OR= odds ratio; CI = confidence interval; PEF% pred, percentage of actual PEF in predicted PEF
Similar patterns were observed for 4-year PEF decline (Supplementary Table S2). Low muscle strength demonstrated the largest and most consistent associations across all thresholds of PEF decline, whereas severe sarcopenia was associated with progressively greater risks of moderate-to-severe declines in PEF. Low muscle mass showed larger effect estimates for more substantial declines (> 30%). For new-onset SAL (Supplementary Table S3), only low muscle strength was associated with a 54% higher risk (OR = 1.54, 95% CI: 1.20–1.98).
Sensitivity analysis
Considering chronic lung diseases and asthma may strongly influence PEF, we performed a sensitivity analysis after excluding participants with self-reported chronic lung diseases or asthma. The results were generally consistent with the main findings and did not materially alter our conclusions, supporting the robustness of the observed associations (Supplementary Table S4-7).
Discussion
In this nationally representative cohort of middle-aged and older Chinese adults, sarcopenia and its individual components were associated with poorer baseline PEF measures. Notably, low muscle strength was the only sarcopenia component consistently associated with both new-onset SAL and all examined thresholds of decline in PEF and PEF% pred during follow-up. Furthermore, participants with severe sarcopenia and low muscle mass showed greater risks of moderate-to-severe declines in PEF% pred. Collectively, these findings highlight the potential importance of muscle strength as a marker of future respiratory health and support the close relationship between muscle health and pulmonary function in aging populations.
In our study, 19.70% of participants were diagnosed with sarcopenia or severe sarcopenia, which was similar to other studies [26]. Previous research indicated that chronic lung diseases often coexist with sarcopenia in the older adults [11]. Approximately one-quarter of COPD patients suffer from sarcopenia [27], and older adults with impaired PEF are more likely to develop sarcopenia. In our study, the prevalence of chronic lung diseases, asthma and SAL increased with the severity of sarcopenia. Furthermore, PEF and PEF% pred were lower in the sarcopenia/severe sarcopenia group. Research has shown that other indicators related to lung function, such as forced expiratory volume in one second (FEV1), forced vital capacity (FVC), FEV1/FVC ratio, maximal expiratory/inspiratory pressure, and phase angle, are also associated with sarcopenia [28]. Further longitudinal research is needed to explore the bidirectional relationship between sarcopenia and lung function and to identify intervention strategies to mitigate lung function decline and preserve muscle health.
Consistent with previous studies, we observed associations between sarcopenia and impaired PEF measures. Similar findings have been reported in cohorts from Brazil, Japan, the United States, and China, where sarcopenia was associated with poorer respiratory function and greater respiratory symptoms [4, 9, 28, 29]. Several mechanisms may explain these associations. First, sarcopenia may impair respiratory mechanics through deterioration of respiratory and trunk muscles, thereby reducing expiratory performance [30–32]. Second, chronic low-grade inflammation, characterized by elevated cytokines such as TNF-α and IL-6, contributes to both muscle loss and pulmonary dysfunction [12, 13]. Third, mitochondrial dysfunction and oxidative stress may simultaneously affect skeletal muscle and lung tissues, promoting a cycle of declining muscle and respiratory health [14, 15]. In addition, alterations in muscle-derived signaling molecules may further contribute to muscle–lung interactions [33, 34].
While sarcopenia has previously been linked to chronic respiratory disease and respiratory mortality [35, 36], longitudinal evidence regarding PEF decline remains limited. In our study, the predictive value appeared to increase with sarcopenia severity, and low muscle strength showed the strongest and most consistent associations with subsequent PEF decline. This finding is supported by previous studies demonstrating that reduced handgrip strength is associated with adverse respiratory outcomes and increased respiratory mortality [37, 38]. Because muscle strength declines earlier than muscle mass during the progression of sarcopenia [39], handgrip strength may serve as a simple, low-cost and practical screening tool for identifying individuals at risk of future PEF decline in primary care settings. Shared mechanisms, including inflammation, neuromuscular dysfunction, and mitochondrial impairment, may partly explain this relationship [12–15, 40]. These findings suggest that maintaining muscle strength through lifestyle or exercise interventions may represent a potential strategy for preserving respiratory health in older adults [41, 42].
This study has several strengths. First, it was based on a large, nationally representative cohort of middle-aged and older Chinese adults, enhancing the generalizability of the findings. Second, we simultaneously evaluated the associations of sarcopenia status, sarcopenia severity, and individual sarcopenia components with PEF decline, providing a more comprehensive understanding of the muscle–respiratory relationship. Finally, missing covariate data were handled using multiple imputation, which may have reduced potential bias and improved statistical efficiency compared with complete-case analyses. Several limitations should be acknowledged. First, muscle mass was estimated using a validated anthropometric equation rather than direct DXA measurements. Although this equation has been validated in the Chinese population and shows good agreement with DXA [18], DXA remains the reference standard for muscle mass assessment. In addition, low muscle mass was defined using the sex-specific lowest 20th percentile of the study population, consistent with previous CHARLS-based studies, which may limit direct comparability with studies applying AWGS-recommended DXA-based cutoffs. Second, although the CHARLS provides extensive data, the lack of detailed respiratory parameters other than PEF limited our ability to comprehensively evaluate pulmonary function. Third, muscle strength was assessed using handgrip strength, a surrogate marker of overall muscle strength, rather than direct measures of respiratory muscle function. Therefore, we could not directly evaluate the contribution of respiratory muscle dysfunction to PEF decline. Fourth, despite adjustment for multiple covariates and the use of multiple imputation, residual and unmeasured confounding cannot be completely excluded. Fifth, selection bias may have occurred because participants with missing exposure, outcome, or follow-up data were excluded from the analyses, potentially limiting the representativeness of the final analytical sample. Finally, the cross-sectional analyses cannot establish temporal relationships, and the longitudinal analyses relied on a single follow-up assessment of PEF, limiting our ability to characterize longitudinal trajectories and temporal patterns of decline.
Sarcopenia, particularly low muscle strength, was associated with a higher risk of PEF decline among middle-aged and older Chinese adults. Our findings extend the knowledge regarding the relationship between muscle health and respiratory function. In the future, more longitudinal studies are needed to elucidate the temporal relationship and potential causal pathways between sarcopenia and pulmonary function decline, as well as the potential benefits of exercise training in altering these trajectories.
Supplementary Information
Acknowledgements
We acknowledge the CHARLS team for their efforts in collecting and making the data publicly available to the research community. Our gratitude also extends to all participants and staff involved in this study.
Abbreviations
- ASM
Appendicular skeletal muscle mass
- AWGS
The Asian Working Group for Sarcopenia
- BMI
Body mass index
- CHARLS
China Health and Retirement Longitudinal Study
- CI
Confidence interval
- COPD
Chronic obstructive pulmonary disease
- DXA
Dual-energy X-ray absorptiometry
- FEV1
Forced expiratory volume in one second
- FVC
Forced vital capacity
- MET
Metabolic equivalent
- MICE
Multiple imputation by chained equations
- OR
Odds ratio
- PAV
Physical activity volume
- PEF
Peak expiratory flow
- PEF% pred
Percentage of actual PEF in predicted PEF
- SAL
Severe airflow limitation
Authors’ contributions
Conceptualization: Fengjuan Hu, Gongchang Zhang, Meiling Ge, Birong Dong.Data curation, formal analysis and visualization: Fengjuan Hu, Hu Liu.Methodology: Fengjuan Hu, Gongchang Zhang, Tingting Huang, Wanyu Zhao, Xiaolei Liu, Ning Huang, Meiling Ge.Supervision: Tingting Huang, Wanyu Zhao, Xiaolei Liu, Ning Huang, Birong Dong, Meiling Ge.Project administration: Xiaolei Liu, Ning Huang, Meiling Ge, Birong Dong.Writing – original draft: Fengjuan Hu, Gongchang Zhang.Writing – review & editing: Hu Liu, Tingting Huang, Wanyu Zhao, Xiaolei Liu, Ning Huang, Birong Dong, Meiling Ge.
Funding
This work was supported by the National Natural Science Foundation of China (No.82401845, PI: MLG; No.92248304, PI: BRD; No.32400972, PI: NH), National Key Research and Development Program of China (2023YFC3605900, PI: MLG), National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University (Z2024JC006, PI: NH; Z2024JC008, PI: XLL), The Funds of Technology Department of Sichuan Provincial (No.2024NSFSC1601, PI: MLG), Project of Sichuan Province Health Commission (24LCYJPT01, PI: XLL), Project of Chengdu Science and Technology (2024-YF05-02255-SN, PI: XLL). The financial sponsors had no role in the design, implementation, analysis, or reporting of the results. The authors declare that they have no competing interests.
Data availability
The data for CHARLS are available at https://charls.pku.edu.cn/en/.
Declarations
Ethics approval and consent to participate
The CHARLS study protocol received approval from the Institutional Review Board of Peking University (approval number: IRB00001052-11015; IRB00001052-11014), and written informed consent was obtained from all participants. This research is a secondary analysis of the de-identified CHARLS public data. For detailed information regarding ethical review and participant consent in CHARLS, please refer to the official study documentation at https://charls.pku.edu.cn/en. All methods were performed in accordance with the relevant guidelines and regulations. This study was conducted in compliance with the Declaration of Helsinki.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Fengjuan Hu and Gongchang Zhang contributed equally to this work.
Contributor Information
Birong Dong, Email: birongdong123@outlook.com.
Meiling Ge, Email: gml006587@wchscu.edu.cn.
References
- 1.GBD Chronic Respiratory Disease Collaborators. Prevalence and attributable health burden of chronic respiratory diseases, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Respir Med. 2020;8(6):585–96. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Bush A. Impact of early life exposures on respiratory disease. Paediatr Respir Rev. 2021;40:24–32. [DOI] [PubMed] [Google Scholar]
- 3.Bui DS, Idrose NS, Dharmage SC. Lifetime lung function trajectories: insights into risk factors, consequences and implications. Thorax. 2024;79(8):700–1. [DOI] [PubMed] [Google Scholar]
- 4.Ohara DG, Pegorari MS, Oliveira Dos Santos NL, de Fátima Ribeiro Silva C, Oliveira MSR, Matos AP, Jamami M. Cross-sectional study on the association between pulmonary function and sarcopenia in Brazilian community-dwelling elderly from the Amazon Region. J Nutr Health Aging. 2020;24(2):181–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Roberts MH, Mapel DW. Limited lung function: impact of reduced peak expiratory flow on health status, health-care utilization, and expected survival in older adults. Am J Epidemiol. 2012;176(2):127–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Trevisan C, Rizzuto D, Maggi S, Sergi G, Welmer AK, Vetrano DL. Cross-sectional and longitudinal associations between peak expiratory flow and frailty in older adults. J Clin Med. 2019;8(11):1901. [DOI] [PMC free article] [PubMed]
- 7.Trevisan C, Rizzuto D, Ek S, Maggi S, Sergi G, Fratiglioni L, Welmer AK. Peak expiratory flow and the risk of injurious falls in older adults: the role of physical and cognitive deficits. J Am Med Dir Assoc. 2020;21(9):1288–1294.e4. [DOI] [PubMed] [Google Scholar]
- 8.Weng SE, Huang YW, Tseng YC, Peng HR, Lai HY, Akishita M, Arai H, Hsiao FY, Chen LK. The evolving landscape of sarcopenia in Asia: a systematic review and meta-analysis following the 2019 Asian Working Group for Sarcopenia (AWGS) diagnostic criteria. Arch Gerontol Geriatr. 2025;128:105596. [DOI] [PubMed] [Google Scholar]
- 9.Hu Z, Tian Y, Song X, Zeng F, Yang A. Associations between sarcopenia with asthmatic prevalence, lung function and comorbidity. BMC Geriatr. 2022;22(1):703. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Su Y, Zhang Y, Zhang D, Xu J. Exploring the relationship between sarcopenia and 11 respiratory diseases: a comprehensive mendelian randomization analysis. Aging Clin Exp Res. 2024;36(1):205. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Lima TRL, Almeida VP, Ferreira AS, Guimarães FS, Lopes AJ. Handgrip strength and pulmonary disease in the elderly: what is the link? Aging Dis. 2019;10(5):1109–29. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Cheng Y, Lin S, Cao Z, Yu R, Fan Y, Chen J. The role of chronic low-grade inflammation in the development of sarcopenia: advances in molecular mechanisms. Int Immunopharmacol. 2025;147:114056. [DOI] [PubMed] [Google Scholar]
- 13.Iliakis CS, Crotta S, Wack A. The interplay between innate immunity and nonimmune cells in lung damage, inflammation, and repair. Annu Rev Immunol. 2025;43(1):395–422. [DOI] [PubMed] [Google Scholar]
- 14.Marzetti E, Calvani R, Coelho-Júnior HJ, Landi F, Picca A. Mitochondrial quantity and quality in age-related sarcopenia. Int J Mol Sci. 2024;25(4):2052. [DOI] [PMC free article] [PubMed]
- 15.Chan SMH, Selemidis S, Vlahos R. The double-edged sword of ROS in muscle wasting and COPD: insights from aging-related sarcopenia. Antioxid (Basel). 2024;13(7):882. [DOI] [PMC free article] [PubMed]
- 16.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. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Chen LK, Woo J, Assantachai P, Auyeung TW, Chou MY, Iijima K, Jang HC, Kang L, Kim M, Kim S, et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J Am Med Dir Assoc. 2020;21(3):300–307.e2. [DOI] [PubMed] [Google Scholar]
- 18.Wen X, Wang M, Jiang CM, Zhang YM. Anthropometric equation for estimation of appendicular skeletal muscle mass in Chinese adults. Asia Pac J Clin Nutr. 2011;20(4):551–6. [PubMed] [Google Scholar]
- 19.Hu Y, Peng W, Ren R, Wang Y, Wang G. Sarcopenia and mild cognitive impairment among elderly adults: the first longitudinal evidence from CHARLS. J Cachexia Sarcopenia Muscle. 2022;13(6):2944–52. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Luo YX, Zhou XH, Heng T, Yang LL, Zhu YH, Hu P, Yao XQ. Bidirectional transitions of sarcopenia states in older adults: the longitudinal evidence from CHARLS. J Cachexia Sarcopenia Muscle. 2024;15(5):1915–29. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Wang K, Jia S, Zhao W, Ge M, Dong B. The creatinine-to-cystatin C ratio (a surrogate marker of muscle mass) as a predictor of lung function decline in older adults: a nationwide longitudinal study in China. Respir Med. 2023;211:107197. [DOI] [PubMed] [Google Scholar]
- 22.Tian Y, Shi Z. Effects of physical activity on daily physical function in Chinese middle-aged and older adults: a longitudinal study from CHARLS. J Clin Med. 2022;11(21):6514. [DOI] [PMC free article] [PubMed]
- 23.Li X, Zhang W, Zhang W, Tao K, Ni W, Wang K, Li Z, Liu Q, Lin J. Level of physical activity among middle-aged and older Chinese people: evidence from the China health and retirement longitudinal study. BMC Public Health. 2020;20(1):1682. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Bahadoran Z, Mirmiran P, Shabani M, Azizi F. Higher daily physical activity levels may facilitate pre-diabetes regression to normoglycemia: a longitudinal study among an Iranian population. Prev Med Rep. 2023;34:102233. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Chwal BC, Dos Reis RCP, Schmidt MI, Duncan BB, Barreto SM, Griep RH. Levels and correlates of risk factor control in diabetes mellitus -ELSA-Brasil. Diabetol Metab Syndr. 2023;15(1):4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Petermann-Rocha F, Balntzi V, Gray SR, Lara J, Ho FK, Pell JP, Celis-Morales C. Global prevalence of sarcopenia and severe sarcopenia: a systematic review and meta-analysis. J Cachexia Sarcopenia Muscle. 2022;13(1):86–99. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Sepúlveda-Loyola W, Osadnik C, Phu S, Morita AA, Duque G, Probst VS. Diagnosis, prevalence, and clinical impact of sarcopenia in COPD: a systematic review and meta-analysis. J Cachexia Sarcopenia Muscle. 2020;11(5):1164–76. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Sawaya Y, Ishizaka M, Kubo A, Shiba T, Hirose T, Onoda K, Maruyama H, Urano T. Association between skeletal muscle mass index and lung function/respiratory muscle strength in older adults requiring long-term care or support. J Phys Ther Sci. 2020;32(11):754–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Weng L, Xu Z, Chen Y, Chen C. Associations between the muscle quality index and adult lung functions from NHANES 2011–2012. Front Public Health. 2023;11:1146456. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Kim KW, Cho HJ, Khaliq SA, Son KH, Yoon MS. Comparative analyses of mTOR/Akt and muscle atrophy-related signaling in aged respiratory and gastrocnemius muscles. Int J Mol Sci. 2020;21(8):2862. [DOI] [PMC free article] [PubMed]
- 31.Feng Z, Zhao H, Kang W, Liu Q, Wu J, Bragazzi NL, Ma X, Wang W, Rong P. Association of paraspinal muscle measurements on chest computed tomography with clinical outcomes in patients with severe coronavirus disease 2019. J Gerontol Biol Sci Med Sci. 2021;76(3):e78–84. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Zhang L, Okazaki T, Ebihara S, Izumi SI. High-intensity intermittent inspiratory and abdominal muscle combined training in respiratory, swallowing and systemic muscles of healthy adults. J Oral Rehabil. 2024;51(9):1805–12. [DOI] [PubMed] [Google Scholar]
- 33.Han Z, Ma J, Han Y, Yuan G, Jiao R, Meng A. Irisin attenuates acute lung injury by suppressing the pyroptosis of alveolar macrophages. Int J Mol Med. 2023;51(4):32. [DOI] [PMC free article] [PubMed]
- 34.Quan BH, Liu XY, Li ZY, Han SZ, Cui QC, Chang SY, Lv ST, Yin XJ, Liang S. MSTN gene knockout suppresses the activation of lung fibroblasts through the inhibition of the Smad/AKT signaling pathway, thereby ameliorating pulmonary fibrosis. Cell Signal. 2025;129:111673. [DOI] [PubMed] [Google Scholar]
- 35.Wang H, Qiu H, Gu X, Zhang Y, Wang S. The association between sarcopenia and incident chronic lung disease in the general population: a longitudinal study based on CHARLS data. Exp Gerontol. 2023;180:112257. [DOI] [PubMed] [Google Scholar]
- 36.Ibarra-Fernández AA, Robles-Hernández R, Orea-Tejeda A, González-Islas D, Jiménez-Valentín A, Sánchez-Santillán R, Arcos-Pacheco LP, Gutiérrez-Luna E, Zurita-Sandoval A, Peña-Espinosa T, et al. Dynapenia and sarcopenia as risk factors for mortality in interstitial lung disease. Respirology. 2025;30(5):424–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Martinez CH, Diaz AA, Meldrum CA, McDonald MN, Murray S, Kinney GL, Hokanson JE, Curtis JL, Bowler RP, Han MK, et al. Handgrip strength in chronic obstructive pulmonary disease. associations with acute exacerbations and body composition. Ann Am Thorac Soc. 2017;14(11):1638–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Mey R, Calatayud J, Casaña J, Torres-Castro R, Cuenca-Martínez F, Suso-Martí L, Andersen LL, López-Bueno R. Handgrip strength and respiratory disease mortality: longitudinal analyses from SHARE. Pulmonology. 2024;30(5):445–51. [DOI] [PubMed] [Google Scholar]
- 39.Cruz-Jentoft AJ, Sayer AA. Sarcopenia. Lancet. 2019;393(10191):2636–46. [DOI] [PubMed] [Google Scholar]
- 40.Martínez-Arnau FM, Buigues C, Fonfría-Vivas R, Cauli O. Respiratory muscle strengths and their association with lean mass and handgrip strengths in older institutionalized individuals. J Clin Med. 2020;9(9):2727. [DOI] [PMC free article] [PubMed]
- 41.Nystoriak MA, Bhatnagar A. Cardiovascular effects and benefits of exercise. Front Cardiovasc Med. 2018;5:135. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Coskun Benlidayi I. Exercise therapy for improving cardiovascular health in rheumatoid arthritis. Rheumatol Int. 2024;44(1):9–23. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data for CHARLS are available at https://charls.pku.edu.cn/en/.