Abstract
Chronic kidney disease (CKD) imposes a substantial global health burden. Emerging evidence implicates respiratory sarcopenia as a potential mortality accelerator. This dual-cohort study aimed to clarify the correlation between respiratory sarcopenia and all-cause mortality in CKD populations and evaluate prognostic impacts of longitudinal transitions. We analyzed 1,300 CKD participants without preexisting chronic respiratory conditions from the China Health and Retirement Longitudinal Study (CHARLS) and 1,346 from the U.S. Health and Retirement Study (HRS). Respiratory sarcopenia was defined using surrogate measures, including peak expiratory flow rate for respiratory muscle strength and appendicular skeletal muscle mass index for muscle mass. Multivariable Cox proportional hazards models were employed to assess the associations between respiratory sarcopenia and all-cause mortality. Over median follow-ups of 88 and 96 months, respiratory sarcopenia was associated with a significantly elevated risk of mortality (CHARLS: Hazard Ratio [HR] = 1.61, 95% Confidence Interval [CI] 1.17–2.22; HRS: HR = 1.48, 95% CI 1.07–2.04). Longitudinal progression from non-sarcopenic to respiratory sarcopenic status was associated with an increased risk of mortality (CHARLS: HR = 1.94, 95% CI 1.01–3.76; HRS: HR = 1.82, 95% CI 1.02–3.28), while reversion was associated with a reduced risk of death in the HRS cohort (HR = 0.15, 95% CI 0.03–0.94). Respiratory sarcopenia was independently associated with an increased risk of all-cause mortality in CKD populations using multivariable-adjusted analyses, and reversion from respiratory sarcopenia was associated with a lower risk of mortality. The multi-cohort design strengthens the clinical relevance of these findings, despite limitations from the indirect diagnosis and potential confounding.
Keywords: Respiratory sarcopenia, sarcopenia, chronic kidney disease, all-cause mortality, cohort study
1. Introduction
Chronic kidney disease (CKD) represents a major global health burden characterized by progressive renal dysfunction and elevated mortality rates [1]. Although traditional risk factors are well-documented, emerging evidence suggests that sarcopenia, an age-related loss of muscle mass and function, may significantly contribute to adverse outcomes in CKD populations [2]. Both pre-sarcopenia and confirmed sarcopenia have been associated with accelerated renal function decline and CKD progression [3]. The coexistence of CKD and sarcopenia correlates with significantly increased all-cause and cardiovascular mortality [4,5].
Recent diagnostic criteria updates have defined respiratory sarcopenia as a distinct clinical subtype marked by the co-occurrence of reduced respiratory muscle strength and low respiratory muscle mass, thereby differentiating it from general sarcopenia and isolated pulmonary dysfunction [6]. Peak expiratory flow rate (PEFR), a practical index for pulmonary impairment screening and a key criterion for respiratory sarcopenia diagnosis, has demonstrated associations with renal function deterioration and a heightened risk of end-stage kidney disease [7,8]. Notably, respiratory sarcopenia independently predicts all-cause mortality in community-dwelling older adults [9], yet its prognostic impact on CKD mortality remains not yet characterized.
While previous studies have established the prognostic value of general sarcopenia in CKD, the role of respiratory sarcopenia, a phenotype that may reflect both systemic muscle wasting and specific respiratory compromise, has not been evaluated in this high-risk population. Given that patients with CKD exhibit accelerated muscle wasting due to uremic metabolic dysregulation, chronic inflammation, and protein-energy wasting, which may disproportionately affect respiratory muscles [10], we hypothesize that respiratory sarcopenia represents a distinct and potent risk factor for mortality in CKD. Leveraging data from the China Health and Retirement Longitudinal Study (CHARLS) and the University of Michigan Health and Retirement Study (HRS), this investigation aimed to determine the prevalence of respiratory sarcopenia in community-dwelling older adults with CKD and to evaluate its prognostic significance for all-cause mortality. The use of two ethnically and geographically distinct cohorts enhances the generalizability of our findings, which may help inform future risk stratification in CKD population.
2. Methods
2.1. Ethics and study population
The datasets analyzed in this study were publicly available from the CHARLS and HRS cohorts. CHARLS is a national population-based survey targeting community-dwelling Chinese adults aged ≥45 years, with the baseline survey (2010–2011) conducted across 150 county-level and 450 village-level units in China [11]. Ethical approval for CHARLS was granted by Peking University Biomedical Ethics Committee (IRB00001052-11014; IRB00001052-11015). HRS is an ongoing longitudinal panel study encompassing approximately 20,000 U.S. participants, supported jointly by the National Institute on Aging and Social Security Administration [12]. The 2010 HRS wave was designated as baseline to ensure chronological consistency with CHARLS. The University of Michigan Institutional Review Board approved the study procedures. All the participants provided written informed consent for their participation in the original study. Additional ethical approvals and consents are not required, because this study relied on publicly available de-identified data.
CKD was defined as either a clinically confirmed diagnosis or an estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2 [13]. Given that cystatin C demonstrates reduced muscle mass dependency, eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) cystatin C equation [14,15]. Eligibility required the availability of complete data on respiratory sarcopenia status, CKD, and mortality. Exclusion criteria encompassed active malignancies and preexisting pulmonary pathologies, including chronic obstructive pulmonary disease (COPD) and asthma. Furthermore, participants on dialysis were effectively excluded, as the community-based design. From 17,705 CHARLS and 22,034 HRS participants, 1,300 and 1,346 CKD-eligible individuals met baseline inclusion criteria respectively. We analyzed 925 CHARLS and 978 HRS participants from 2014 to 2015 follow-up waves, for longitudinal respiratory sarcopenia assessment. The complete selection process is illustrated in Figure 1.
Figure 1.
Flowchart for participants screening process. CHARLS: China Health and Retirement Longitudinal Study; HRS: the University of Michigan Health and Retirement Study; eGFR: estimated glomerular filtration rate; CKD: chronic kidney disease; ASM: appendicular skeletal muscle mass; PEFR: peak expiratory flow rate. Longitudinal analysis subset (2014–2015) was derived from the baseline CKD cohort, with attrition due to mortality and loss to follow-up.
2.2. Patient and public involvement
Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
2.3. Assessment of respiratory sarcopenia
Respiratory sarcopenia was defined as the co-occurrence of low respiratory muscle strength and reduced respiratory muscle mass, according to consensus guidelines from four professional societies [16]. PEFR served as the primary indicator of respiratory muscle strength, which has been validated as a reliable metric in community-dwelling older populations [6,17]. Sex-specific diagnostic thresholds of PEFR were defined as one standard deviation below cohort means, based on demonstrated predictive accuracy for respiratory sarcopenia identification [7]. To minimize confounding by airway obstruction, participants with preexisting COPD or asthma were excluded (see the Ethics and study population section). Given practical limitations in direct respiratory muscle mass measurement, appendicular skeletal muscle mass (ASM) was utilized as a surrogate biomarker for identifying respiratory sarcopenia [16,18]. Consistent with prior methodology, low muscle mass thresholds corresponded to sex-specific 20th percentiles of height-adjusted ASM (ASM/ht2) [19,20]. Cohort-specific diagnostic criteria were: PEFR <3.38 L/s and ASM/ht2 <6.98 kg/m2 for CHARLS males; PEFR <2.55 L/s and ASM/ht2 <5.25 kg/m2 for CHARLS females; PEFR <5.46 L/s and ASM/ht2 <7.58 kg/m2 for HRS males; PEFR <3.76 L/s and ASM/ht2 <5.80 kg/m2 for HRS females. While sex-specific PEFR cutoffs based on cohort means may vary across populations, this internal standardization ensures relative comparability within each cohort’s context. Longitudinal assessments at baseline (2010–2011) and follow-up (2014–2015) were evaluated for the impact of respiratory sarcopenia progression on clinical outcomes.
2.4. Calculation of key variables
The eGFR was calculated for all participants using the CKD-EPI cystatin C equation [14]:
eGFR = 133 × min(Cystatin C/0.8,1)−0.499 × max(Cystatin C/0.8,1)−1.328 × 0.9962age × 0.932 [if female] where Cystatin C is serum Cystatin C levels.
For CHARLS participants, ASM was calculated using the equation recommended by the Asian Working Group for Sarcopenia (AWGS) 2019, which showed strong concordance with dual-energy X-ray absorptiometry (DXA) in Chinese populations [21,22]:
ASM = 0.193 × weight in kg+ 0.107 × height in meter − 4.157 × gender (1 for male; 2 for female) − 0.037 × age − 2.631
ASM of HRS participants was derived from a population-specific prediction equation for U.S. adults with minimal DXA measurement bias [23]:
ASM = 0.485 × 0.998age × 0.814[1 for female, 0 for male] × 1.006height in cm × weight in kg0.680
These equations demonstrated high concordance with gold-standard methods (r2 = 0.90 in CHARLS [22], r2 = 0.89 in HRS [23]), minimizing misclassification bias.
2.5. Outcome ascertainment and follow-up
The primary outcome was all-cause mortality. The survival status of CHARLS participants was determined through follow-up interviews (2014, 2015, 2018 waves), with survival duration computed via validated algorithms [24]. HRS participants’ survival data (2010–2018) were extracted from the longitudinally updated cross-wave tracker file, which documented death events with month-level precision through exit interviews, reports from a spouse/partner, and active tracking by the study team. Status change of respiratory sarcopenia was defined as transition between baseline (2010–2011) and follow-up (2014–2015), with mortality events tracked subsequently until 2018.
2.6. Potential covariates
Potential covariates encompassed: 1) demographic factors, including age, sex, race/ethnicity, educational attainment, marital status, and family income; 2) lifestyle factors, including smoking status, alcohol consumption, and physical activity patterns; 3) comorbidities, including hypertension, diabetes mellitus, cardiovascular diseases, and stroke; and 4) clinical biomarkers, including body mass index (BMI), eGFR, C-reactive protein (CRP), and dyslipidemia. Baseline demographic and clinical characteristics were documented through standardized questionnaires, biochemical assays, and physical examinations. Educational attainment was categorized into two categories: below high school and high school or above. Smoking status was categorized as never smokers (<100 lifetime cigarettes) versus ever smokers (current or former tobacco users). Alcohol consumption was stratified as never drinkers (<12 lifetime alcoholic beverages) versus ever drinkers. Physical activity levels were classified into three tiers: vigorous (vigorous activity more than once a week), moderate (moderate activity more than once a week), and inactive (the rest). Hypertension was ascertained through self-reported physician diagnosis, antihypertensive medication use, or systolic blood pressure ≥140 mmHg, or diastolic blood pressure ≥90 mmHg during the physical examination. Diabetes mellitus was defined by self-reported clinical diagnosis, use of glucose-lowering medications, glycated hemoglobin (HbA1c) ≥ 6.5%, or fasting plasma glucose ≥ 7.0 mmol/L (126 mg/dL). Cardiovascular disease encompassed self-reported diagnoses of coronary artery disease, angina pectoris, myocardial infarction, or congestive heart failure.
2.7. Statistical analysis
Descriptive statistics were expressed as counts (percentages) for categorical variables and medians with interquartile ranges (IQR) for continuous variables. Between-group comparisons across respiratory sarcopenia status (respiratory sarcopenia vs. no respiratory sarcopenia) employed appropriate statistical tests: Student’s t-test for normally distributed continuous variables, Wilcoxon rank-sum test for skewed distributions, and χ2 test for categorical variables.
Cox proportional hazards models were employed to estimate the association between respiratory sarcopenia status and the risk of all-cause mortality in participants with CKD. Hazard ratios (HR) and 95% confidence intervals (CI) were derived from three models. Model 1 was unadjusted. Model 2 was adjusted for age (continuous), sex (male or female), eGFR (continuous), and race/ethnicity (only for HRS, White/Caucasian, Black/African, Asian, or Other). Model 3 was additionally adjusted for education level (below high school, or high school or above), marital status (yes or no), family income (≥ median or < median levels), BMI (<25, 25–29.9, or ≥30 kg/m2), smoking status (never or ever), alcohol consumption (never or ever), physical activity (vigorous, moderate, or inactive), hypertension (yes or no), diabetes (yes or no), cardiovascular disease (yes or no), stroke (yes or no), CRP (mg/L, continuous), total cholesterol (dichotomized at 240 mg/dL), and high-density lipoprotein (HDL-C, dichotomized at 50 mg/dL). Proportional-hazards assumptions were validated via Grambsch-Therneau tests on Schoenfeld residuals (global p value >.05). Kaplan–Meier survival curves were plotted to visualize the association between respiratory sarcopenia status and all-cause mortality. Differences between survival curves were assessed using the log-rank test.
The proportion of missing data was assessed for all variables included in Model 3. In the HRS cohort, the missingness rates were 0.01% for CRP, 2.45% for total cholesterol, and 2.60% for HDL. In the CHARLS cohort, data were incomplete for comorbidities: hypertension (0.05%), diabetes (1.00%), cardiovascular diseases (0.08%), and stroke (0.07%). Smoking status data had missingness rates of 0.01% in CHARLS and 36.77% in HRS, while family income data was missing for 0.06% of the CHARLS cohort and 49.40% of the HRS cohort. All other key variables, including age, sex, eGFR, PEFR, ASM/Ht2, BMI, marital status, and alcohol consumption, had complete data. Missing data were imputed via multiple imputation with chained Equations (50 iterations; R package mice) to handle incomplete covariates.
Subgroup analyses stratified by age (<65 or ≥65 years), sex (male or female), hypertension (yes or no), diabetes (yes or no), smoking behavior (yes or no), alcohol consumption (yes or no), and CRP (≥ median or < median levels) were performed to evaluate effect heterogeneity. This community-dwelling elderly cohort was predominantly composed of individuals with CKD Stages 2–3, with very few participants in Stage 1, 4, and 5. Analyses in these small subgroups would be statistically underpowered and yield unreliable estimates. To robustly assess effect modification by renal function, we instead performed a subgroup analysis dichotomized at eGFR 45 mL/min/1.73 m2, a clinically relevant cutoff that distinguishes moderate-to-severe impairment from milder disease. Benjamini-Hochberg false discovery rate (FDR) correction was conducted for the p-values of interactive effects. Subgroup-specific p-values are presented unadjusted.
Several sensitivity analyses were applied to validate the robustness of the results. Participants who died within one year of follow-up were excluded to address potential reverse causality. Participants with extreme values of PEFR or ASM/Ht2 (<5th percentile or >95th percentile), aged >85 years or <60 years were respectively excluded for sensitivity analysis. Additionally, the cutoff values of ASM/Ht2 recommended by AWGS 2019 and European Working Group on Sarcopenia in Older People 2 (EWGSOP2) were implemented for respiratory sarcopenia assessment [21,25]. As a sensitivity analysis to assess the robustness of our findings to the operational definition of the exposure, we additionally evaluated the association between PEFR as a continuous variable and all-cause mortality using Model 3. We also conducted E-value analysis to assess the potential influence of unmeasured confounding on our primary results [26,27].
All statistical analyses and figures were performed using R software (version 4.4.2) via R Studio (version 2024.12.0 + 467). A p value of less than .05 was considered statistically significant.
3. Results
3.1. Characteristics of included participants
The prevalence of respiratory sarcopenia was 10.69% in CHARLS-CKD participants versus 6.76% in HRS-CKD counterparts. Compared with individuals without respiratory sarcopenia, those with respiratory sarcopenia were older (median [IQR]: CHARLS 75 [70–80] vs. 67 [58–73] years; HRS 83 [76–88] vs. 72 [64–79] years), exhibited lower PEFR (CHARLS 2.33 [1.67–2.67] vs. 4.50 [3.33–6.00] L/s; HRS 3.17 [2.33–3.67] vs. 5.67 [4.50–7.17] L/s), reduced muscle mass (ASM/ht2: CHARLS 6.10 [4.83–6.65] vs. 6.88 [5.89–7.49] kg/m2; HRS 5.59 [5.30–6.79] vs. 7.33 [6.44–8.27] kg/m2), lower BMI (CHARLS 19.03 [17.50–20.11] vs. 23.04 [20.86–25.85] kg/m2; HRS 23.01 [21.05–25.11] vs. 30.13 [26.65–34.39] kg/m2), and compromised renal function (eGFR: CHARLS 50.10 [43.10–56.40] vs. 54.50 [47.10–59.50] mL/min/1.73 m2; HRS 39.30 [30.80–51.80] vs. 46.80 [37.20–53.90] mL/min/1.73 m2), while demonstrating elevated HDL-C levels (CHARLS 56.06 [46.78–65.72] vs. 49.87 [40.98–59.92] mg/dL; HRS 56.74 [48.12–66.44] vs. 51.36 [42.74–62.13] mg/dL). Sarcopenic participants had lower educational attainment (high school completion: CHARLS 7.91% vs. 21.19%; HRS 65.93% vs. 75.22%), higher rates of inactive physical inactivity (CHARLS 83.45% vs. 74.94%; HRS 49.45% vs. 35.38%), increased smoking prevalence (CHARLS 47.48% vs. 43.46%; HRS 100% vs. 95.06%), yet paradoxically lower cardiometabolic risk profiles (hypertension: CHARLS 56.83% vs. 60.12%; HRS 76.92% vs. 85.34%; diabetes: CHARLS 10.07% vs. 18.26%; HRS 25.27% vs. 36.02%). Cohort-specific disparities were observed in CRP, total cholesterol levels, and cardiovascular disease prevalence, whereas gender distribution, stroke incidence, and alcohol consumption patterns showed no significant intergroup differences. Comprehensive baseline characteristics are presented in Table 1. Baseline characteristics according to their CKD stage distribution are shown in Supplementary Table 1 and Supplementary Table 2.
Table 1.
Baseline characteristics of the included participants.
| The CHARLS cohort |
The HRS cohort |
|||||
|---|---|---|---|---|---|---|
| No respiratory sarcopenia | Respiratory sarcopenia | p Value | No respiratory sarcopenia | Respiratory sarcopenia | p Value | |
| Deaths/total, no. (%) | 224/1,161 (19.29%) | 57/139 (41.01%) | <.001 | 344/1,255 (27.41%) | 65/91 (71.43%) | <.001 |
| Age, years | 67 (58, 73) | 75 (70, 80) | <.001 | 72 (64, 79) | 83 (76, 88) | <.001 |
| Sex, no. male (%) | 603 (51.94%) | 78 (56.12%) | .351 | 461.00 (36.73%) | 34.00 (37.36%) | .904 |
| PEFR, L/s | 4.50 (3.33, 6.00) | 2.33 (1.67, 2.67) | <.001 | 5.67 (4.50, 7.17) | 3.17 (2.33, 3.67) | <.001 |
| ASM/Ht2, kg/m2 | 6.88 (5.89, 7.49) | 6.10 (4.83, 6.65) | <.001 | 7.33 (6.44, 8.27) | 5.59 (5.30, 6.79) | <.001 |
| BMI, kg/m2 | 23.04 (20.86, 25.85) | 19.03 (17.50, 20.11) | <.001 | 30.13 (26.65, 34.39) | 23.01 (21.05, 25.11) | <.001 |
| eGFR, mL/min/1.73m2 | 54.50 (47.10, 59.50) | 50.10 (43.10, 56.40) | <.001 | 46.80 (37.20, 53.90) | 39.30 (30.80, 51.80) | <.001 |
| CRP, mg/L | 1.30 (0.71, 2.93) | 1.31 (0.60, 3.64) | .840 | 2.30 (1.05, 5.19) | 1.54 (0.64, 3.81) | .009 |
| Cholesterol, mg/dL | 188.6 (165.8, 216.1) | 180.5 (159.6, 210.3) | .032 | 188.6 (157.5, 221.1) | 188.6 (152.3, 217.2) | .436 |
| HDL, mg/dL | 49.87 (40.98, 59.92) | 56.06 (46.78, 65.72) | <.001 | 51.36 (42.74, 62.13) | 56.74 (48.12, 66.44) | .002 |
| Hypertension, no. (%) | 698 (60.12%) | 79 (56.83%) | .455 | 1,071 (85.34%) | 70 (76.92%) | .031 |
| Diabetes, no. (%) | 212 (18.26%) | 14 (10.07%) | .016 | 452 (36.02%) | 23 (25.27%) | .038 |
| Cardiovascular disease, no. (%) | 193 (16.62%) | 9 (6.47%) | .002 | 413 (32.91%) | 33 (36.26%) | .511 |
| Stroke, no. (%) | 36 (3.10%) | 3 (2.16%) | .792 | 110 (8.76%) | 8 (8.79%) | .993 |
| Education level | <.001 | .049 | ||||
| Below high school, no. (%) | 915 (78.81%) | 128 (92.09%) | 311 (24.78%) | 31 (34.07%) | ||
| High school or above, no. (%) | 246 (21.19%) | 11 (7.91%) | 944 (75.22%) | 60 (65.93%) | ||
| Physical activity | .084 | .008 | ||||
| Inactive, no. (%) | 870 (74.94%) | 116 (83.45%) | 444 (35.38%) | 45 (49.45%) | ||
| Moderate, no. (%) | 131 (11.28%) | 11 (7.91%) | 573 (45.66%) | 27 (29.67%) | ||
| Vigorous, no. (%) | 160 (13.78%) | 12 (8.64%) | 238 (18.96%) | 19 (20.88%) | ||
| Smoking history, no. (%) | 515 (44.36%) | 66 (47.48%) | .484 | 1,193 (95.06%) | 91 (100.00%) | .019 |
| Alcohol consumption history, no. (%) | 357 (30.75%) | 45 (32.37%) | .695 | 198 (15.78%) | 21 (23.08%) | .068 |
| Marital status, no. married (%) | 929 (80.02%) | 94 (67.63%) | <.001 | 699 (55.70%) | 30 (32.97%) | <.001 |
| Income, no. low (%) | 564 (48.58%) | 90 (64.75%) | <.001 | 758 (60.40%) | 52 (57.14%) | .540 |
Note: CHARLS: China Health and Retirement Longitudinal Study; HRS: The University of Michigan Health and Retirement Study; eGFR: estimated glomerular filtration rate; ASM: appendicular skeletal muscle mass; PEFR: peak expiratory flow rate; BMI: body mass index; CRP: C reactive protein; HDL: high-density lipoprotein.
3.2. Association between respiratory sarcopenia and all-cause mortality
During median follow-up durations of 88 months (8,356 person-years) and 96 months (9,516 person-years), 281 deaths (21.61%) occurred in the CHARLS cohort and 409 deaths (30.38%) in the HRS cohort. Mortality rates differed markedly between respiratory sarcopenia and non-sarcopenia groups: 41.01% vs. 19.09% (CHARLS) and 71.43% vs. 27.41% (HRS) (Table 1). The higher absolute mortality in HRS respiratory sarcopenia group might be associated with their older baseline age and lower eGFR, potentially explaining the differential.
Multivariable Cox proportional-hazards analysis revealed respiratory sarcopenia might be an independent mortality predictor in CKD across adjustment models: CHARLS cohort (Model 1: HR = 2.53, 95% CI 1.89–3.39; Model 2: HR = 1.49, 95% CI 1.10–2.03; Model 3: HR = 1.61, 95% CI 1.17–2.22) and HRS cohort (Model 1: HR = 3.65, 95% CI 2.80–4.76; Model 2: HR = 1.56, 95% CI 1.15–2.13; Model 3: HR = 1.48, 95% CI 1.07–2.04) (Table 2). Kaplan–Meier curves are shown in Figure 2, and the log-rank tests confirmed significant survival differences between groups (p < .001), providing visual support for the potential associations.
Table 2.
Association between respiratory sarcopenia and all-cause mortality in chronic kidney disease.
| No. deaths/total | Model 1 |
Model 2 |
Model 3 |
||||
|---|---|---|---|---|---|---|---|
| HR (95% CI) | p Value | HR (95% CI) | p Value | HR (95% CI) | p Value | ||
| The CHARLS cohort | 281/1,300 | ||||||
| No respiratory sarcopenia | 224/1,161 | Ref. | Ref. | Ref. | |||
| Respiratory sarcopenia | 57/139 | 2.53 (1.89, 3.39) | <.001 | 1.49 (1.10, 2.03) | .011 | 1.61 (1.17, 2.22) | .003 |
| The HRS cohort | 409/1,346 | ||||||
| No respiratory sarcopenia | 344/1,255 | Ref. | Ref. | Ref. | |||
| Respiratory sarcopenia | 65/91 | 3.65 (2.80, 4.76) | <.001 | 1.56 (1.15, 2.13) | .005 | 1.48 (1.07, 2.04) | .018 |
Notes: Model 1 was not adjusted for any covariates. Model 2 was adjusted for age (continuous), sex (male or female), eGFR (continuous), and race (only for HRS, White/Caucasian, Black/African, Asian, or Other). Model 3 was further adjusted for education level (below high school, or high school or above), marital status (yes or no), income (≥ median or < median levels), BMI (<25, 25–29.9, or ≥30 kg/m2), smoking status (never or ever), alcohol consumption (never or ever), physical activity (vigorous, moderate, or inactive), hypertension (yes or no), diabetes (yes or no), cardiovascular diseases (yes or no), stroke (yes or no), CRP (mg/dL, continuous), cholesterol (≥240 or <240 mg/dL), and high-density lipoprotein (≥50 or <50 mg/dL). CHARLS: China Health and Retirement Longitudinal Study; HRS: the University of Michigan Health and Retirement Study; HR: hazard ratio; CI: confidence interval.
Figure 2.
Kaplan–Meier curves for all-cause mortality by respiratory sarcopenia status. (a) Kaplan–Meier curves in the CHARLS cohorts; (b) Kaplan–Meier curves in the HRS cohorts.
3.3. Longitudinal change of respiratory sarcopenia status and all-cause mortality
Among 925 CHARLS and 978 HRS participants with repeated assessments for longitudinal analysis, progression from no respiratory sarcopenia to respiratory sarcopenia was associated with elevated mortality risk compared with those maintaining non-sarcopenic status: CHARLS cohort (Model 1: HR = 2.94, 95% CI 1.63–5.32; Model 2: HR = 2.13, 95% CI 1.15–3.93; Model 3: HR = 1.94, 95% CI 1.01–3.76) and HRS cohort (Model 1: HR = 4.27, 95% CI 2.57–7.11; Model 2: HR = 1.78, 95% CI 1.03–3.07; Model 3: HR = 1.82, 95% CI 1.02–3.28) (Figure 3). Notably, reversion from respiratory sarcopenia to non-sarcopenic status in HRS participants was associated with reduced mortality risk (Model 1: HR = 0.21, 95% CI 0.06–0.72; Model 2: HR = 0.21, 95% CI 0.06–0.74; Model 3: HR = 0.15, 95% CI 0.03–0.94), though this protective association was non-significant in CHARLS (Model 3: HR = 0.88, 95% CI 0.14–5.56). However, this analysis was based on a limited number of events and should be interpreted with caution as hypothesis-generating.
Figure 3.
Association between longitudinal respiratory sarcopenia status change and all-cause mortality among participants with CKD. Model 1 was not adjusted for any covariates. Model 2 was adjusted for age (continuous), sex (male or female), eGFR (continuous), and race (only for HRS, White/Caucasian, Black/African, Asian, or Other). Model 3 was further adjusted for education level (below high school, or high school or above), marital status (yes or no), income (≥ median or < median levels), BMI (<25, 25–29.9, or ≥ 30 kg/m2), smoking status (never or ever), alcohol consumption (never or ever), physical activity (vigorous, moderate, or inactive), hypertension (yes or no), diabetes (yes or no), cardiovascular diseases (yes or no), stroke (yes or no), CRP (continuous), cholesterol (≥ 240 or < 240 mg/dL), and high-density lipoprotein (≥ 50 or < 50 mg/dL). CHARLS: China Health and Retirement Longitudinal Study; HRS: the University of Michigan Health and Retirement Study; HR: hazard ratio; CI: confidence interval; RS: respiratory sarcopenia.
3.4. Subgroup analysis
Stratified analyses across pre-specified subgroups (age, sex, eGFR, hypertension, diabetes, smoking status, alcohol consumption, CRP levels) demonstrated consistent associations between respiratory sarcopenia and elevated all-cause mortality in CKD populations with specific risk profiles: participants aged ≥65 years, preserved renal function (eGFR ≥45 mL/min/1.73 m2), current/former smokers, and lifetime abstainers (Figure 4). However, the interaction effects between subgroups were not significant after FDR correction. Longitudinal subgroup analyses are shown in Supplementary Table 3.
Figure 4.
Subgroup analysis of the association between respiratory sarcopenia and all-cause mortality among participants with CKD. Regression model was adjusted for age (continuous), sex (male or female), eGFR (continuous), and race (only for HRS, White/Caucasian, Black/African, Asian, or Other), education level (below high school, or high school or above), marital status (yes or no), income (≥ median or < median levels), BMI (<25, 25–29.9, or ≥30 kg/m2), smoking status (never or ever), alcohol consumption (never or ever), physical activity (vigorous, moderate, or inactive), hypertension (yes or no), diabetes (yes or no), cardiovascular diseases (yes or no), stroke (yes or no), CRP (mg/dL, continuous), cholesterol (≥240 or <240 mg/dL), and high-density lipoprotein (≥ 50 or < 50 mg/dL). CHARLS, China Health and Retirement Longitudinal Study; HRS, the University of Michigan Health and Retirement Study; HR, hazard ratio; CI, confidence interval; RS, respiratory sarcopenia.
3.5. Sensitivity analysis
Exclusion of participants with early mortality (≤1 year follow-up) maintained robust associations between respiratory sarcopenia and mortality. Sensitivity assessments addressing potential confounders, including age restriction thresholds (≤85 or ≥60 years), extreme PEFR/ASM/ht2 values exclusion (<5th or >95th percentiles), and alternative sarcopenia criteria per AWGS2019/EWGSOP2 guidelines, confirmed result stability (Figure 5).
Figure 5.
Sensitivity analysis of the association between respiratory sarcopenia and all-cause mortality among participants with CKD. Model 3 was adjusted for age (continuous), sex (male or female), eGFR (continuous), and race (only for HRS, White/Caucasian, Black/African, Asian, or Other), education level (below high school, or high school or above), marital status (yes or no), income (≥ median or < median levels), BMI (<25, 25–29.9, or ≥30 kg/m2), smoking status (never or ever), alcohol consumption (never or ever), physical activity (vigorous, moderate, or inactive), hypertension (yes or no), diabetes (yes or no), cardiovascular diseases (yes or no), stroke (yes or no), CRP (mg/dL, continuous), cholesterol (≥240 or <240 mg/dL), and high-density lipoprotein (≥50 or <50 mg/dL). CHARLS: China Health and Retirement Longitudinal Study; HRS: the University of Michigan Health and Retirement Study; HR: hazard ratio; CI: confidence interval; ASM: appendicular skeletal muscle mass; PEFR: peak expiratory flow rate; AWGS: Asian Working Group for Sarcopenia; EWGSOP: European Working Group on Sarcopenia in Older People.
In a complementary analysis treating PEFR as a continuous variable, each 1 L/s decrease in PEFR was associated with a significantly increased risk of all-cause mortality in both the CHARLS (HR = 1.20, 95% CI 1.12–1.30; p < .001) and the HRS (HR = 1.14, 95% CI 1.06–1.20; p < .001) cohorts (Figure 5). The results of longitudinal sensitivity analyses are detailed in Supplementary Table 4.
To assess the potential influence of unmeasured confounding, we performed an E-value analysis. The E-values for the observed primary associations were considerable (CHARLS: 2.60 for the point estimate; HRS: 2.17 for the point estimate). This suggests that an unmeasured confounder would need to be associated with both respiratory sarcopenia and all-cause mortality by hazard ratios of at least 2.60 and 2.17, respectively, above and beyond the measured covariates, to fully explain the observed results. Although the considerable E-values suggest that the observed associations are unlikely to be entirely due to weak or moderate unmeasured confounding, the inherent limitations of an observational design preclude definitive conclusions. The potential for residual confounding by strong, unknown factors persists. Therefore, our findings, while robust, should be interpreted as indicative of a strong association rather than definitive proof of causation, underscoring the necessity for future research to validate these results.
4. Discussion
This study found that respiratory sarcopenia was associated with an increased risk of all-cause mortality among community-dwelling older adults with CKD. Furthermore, longitudinal progression to respiratory sarcopenia was associated with elevated mortality risk. An exploratory analysis also suggested a potential trend where reversion from respiratory sarcopenia was associated with reduced mortality risk in the HRS cohort, although this requires confirmation in larger studies due to a limited number of events.
Our findings align with evidence from the Otassha study, which identified respiratory sarcopenia as a mortality predictor in older adults aged ≥65 years [28]. However, the specific relationship between respiratory sarcopenia and mortality risk in patients with CKD remains previously unreported. The heightened susceptibility of CKD patients to multimorbidity may provide a pathophysiological basis for the strong association between respiratory sarcopenia and mortality observed in our study [29]. In contrast to the high prevalence of general sarcopenia in CKD (24.5%) [30], our study revealed a lower prevalence of respiratory sarcopenia (6.76% to 10.69%) when employing combined PEFR and ASM/ht2 criteria, also lower than rates of obstructive (15.6%) and restrictive (9.8%) pulmonary dysfunction reported in U.S. adults with CKD [31].
Although our study does not provide direct mechanistic data, the observed association between respiratory sarcopenia and mortality is biologically plausible and can be contextualized within existing literature. Multiple pathophysiological mechanisms may interconnect CKD, sarcopenia, and respiratory dysfunction, including chronic inflammation, hypoxia, oxidative stress, protein-energy wasting (PEW), and aging-related degeneration [32]. Chronic inflammation and oxidative stress could be central to this triad: CKD induces a proinflammatory state characterized by elevated cytokines that activate NF-κB signaling, promoting muscle protein degradation while suppressing synthesis [33]. Concurrently, CKD-related oxidative stress and uremic toxin accumulation drive reactive oxygen species overproduction, inducing mitochondrial dysfunction and accelerating muscle atrophy [34]. Experimental models using muscle cells isolated from patients with CKD demonstrated enhanced proteolytic activity [35], while hypoxia-driven impairments in satellite cell autophagy and capillary maturation exacerbated muscle wasting [36]. From a respiratory perspective, reduced PEFR reflects both expiratory muscle weakness and intrinsic pulmonary pathology, including airway remodeling, bronchospasm, and mucosal edema, serving as a composite biomarker of respiratory compromise and tissue hypoxia [37]. PEW, a hallmark CKD complication involving progressive loss of muscle and energy reserves, may preferentially affect respiratory musculature [38]. Emerging evidence highlights synergistic mortality risks when malnutrition coexists with pulmonary dysfunction in CKD [39], which aligns with the association between respiratory sarcopenia and mortality observed in our study. Collectively, this evidence supports a hypothesis in which respiratory sarcopenia in CKD may arise from multifactorial insults (e.g., hypoxia, inflammation, oxidative stress, PEW, senescence) and could contribute to poor prognosis through disruptions in the lung-kidney-muscle axis. We speculate that the heightened mortality risk may be attributable to compounded cardiorespiratory vulnerability: reduced respiratory reserve could impair oxygen delivery to renal and skeletal muscle tissues already affected by hypoxia, while CKD-associated inflammation might amplify muscle catabolism. This proposed triad represents a potential self-perpetuating cycle that warrants further investigation. We postulate that this hypothesized triad may underlie the associations observed in our study. However, this integrative model remains speculative and serves primarily as a framework to guide future hypothesis-driven research in CKD.
The observed disparity in absolute mortality rates between the HRS and CHARLS cohorts may reflect differences in baseline characteristics between the two populations. As shown in Table 1, participants with respiratory sarcopenia in HRS were older and had more advanced renal impairment compared to those in CHARLS, both of which are established risk factors for mortality in CKD populations [2]. Additional factors such as variations in comorbidity profiles and environmental contexts between the U.S. and China may also contribute to these differences. Notably, despite these variations in baseline risk and absolute mortality, the relative association between respiratory sarcopenia and mortality remained consistent and significant in both cohorts. This consistency across distinct populations suggests that the relationship between respiratory sarcopenia and survival may be robust and generalizable. The higher absolute risk observed in the HRS cohort suggests that assessing respiratory sarcopenia might be particularly relevant in older, sicker CKD populations. However, the utility of our operational definition, which relies on surrogate measures, for clinical decision-making requires further validation.
Several limitations merit consideration. First and most importantly, our operational definition of respiratory sarcopenia relied on pragmatic surrogate measures (PEFR and ASM/ht2) rather than direct gold-standard assessments. Specifically, ASM was estimated using population-specific regression equations, not directly measured by imaging techniques. Similarly, PEFR served as an indirect proxy for respiratory muscle strength and could be influenced by non-muscular factors such as airway obstruction. This pragmatic approach, while necessary and well-validated for large-scale epidemiological studies [20,21], inevitably introduces a degree of non-differential misclassification bias. Consequently, our reported associations may represent conservative estimates of the true relationship between respiratory sarcopenia and mortality. The PEFR and ASM/ht2 thresholds used are cohort-standardized epidemiologic cut-points intended for research and risk stratification, not for clinical diagnosis. Their absolute values should not be directly applied to other populations without validation. Future prospective studies are urgently needed to establish and validate simple, universally applicable clinical cut-points for PEFR that can be used to diagnose respiratory sarcopenia. Another key limitation is the lack of albuminuria data for CKD diagnosis and classification. Consequently, our CKD definition relied on eGFR and self-reported diagnoses, which may have biased our samples toward more advanced disease stages. Third, while we adjusted for numerous covariates, residual confounding from unmeasured factors cannot be excluded. Additionally, despite the use of multiple imputation, the high proportion of missing data for specific variables remains a potential source of bias. Fourth, although we validated the proportional hazards assumption, our analysis did not account for competing risks. Consequently, the absolute risk estimates should be interpreted with caution, although the reported hazard ratios remain valid measures of association. Therefore, future prospective studies should incorporate gold-standard respiratory muscle imaging and broader CKD stage representation to validate these findings. Additionally, the consistent association observed across both cohorts, despite differences in mortality ascertainment methodologies, strengthens the robustness of our findings against outcome misclassification. However, it is important to reaffirm that, as an observational study, our findings demonstrate a potential association but cannot establish a causal relationship between respiratory sarcopenia and mortality among CKD population.
This dual-cohort design leveraging CHARLS and HRS provides unique insights into respiratory sarcopenia across distinct healthcare environments and genetic backgrounds. Despite inherent heterogeneity, the consistent mortality association observed in both cohorts strengthens the generalizability of our findings to diverse aging populations. To our knowledge, this investigation represents the first multinational evaluation of respiratory sarcopenia’s prognostic significance in CKD, employing rigorous methodology including multivariable-adjusted Cox models, subgroup stratification, and sensitivity analyses across two ethnogeographically distinct cohorts.
5. Conclusions
Respiratory sarcopenia is associated with elevated all-cause mortality risk in CKD populations. Future intervention trials are needed to determine whether improving respiratory function can reduce mortality risk in this population, which would help to establish a causal link.
Supplementary Material
Acknowledgment
We thank the participants and staff of CHARLS and HRS involved in this study, as all the data were derived from these databases.
Funding Statement
This work was supported by the National Natural Science Foundation of China under Grant 82274391; Project of Shanghai Yueyang Hospital under Grant QY71.42.04; Traditional Chinese Medicine Inheritance and Innovation Studio from Shanghai Municipal Health Commission under Grant 2025CXGZS-17 and National Key Research and Development Program of China under Grant 2019YFC1709401.
Ethical approval
The CHARLS protocol was approved by the Biomedical Ethics Review Committee of Peking University (approval number: IRB00001052-11015 and IRB00001052-11014). The protocol of HRS was approved by the Institutional Review Board of the University of Michigan. Additional ethical approvals and consents are not required, because this study relied on publicly available de-identified data.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. The original publicly available questionnaire data and biomarker data are available from the CHARLS [11] and HRS study [12].
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. The original publicly available questionnaire data and biomarker data are available from the CHARLS [11] and HRS study [12].