Skeletal Muscle Mass and Pulmonary Function in Institutionalized Older Men: Correlations Versus Independent Effects in a Cross-Sectional Study

Abstract

Introduction: Aging is associated with progressive changes in the muscular and respiratory systems, leading to functional limitations. Sarcopenia, the loss of muscle mass and strength, may negatively impact respiratory mechanics. While relationships between muscle mass and lung function are documented in community-dwelling older adults, data regarding institutionalized populations who experience standard of living homogenization and high clinical heterogeneity remain scarce. This study aimed to determine whether skeletal muscle mass (SMM) and body composition components are independently associated with spirometric parameters in institutionalized older men. Materials and Methods: A cross-sectional study was conducted on 58 men aged 60–85 living in Polish nursing homes, divided into younger (≤75 years, n = 42) and older (>75 years, n = 16) subgroups. Body composition was assessed via bioelectrical impedance analysis (BIA), and pulmonary function was measured using standard spirometry (VC, FVC, FEV₁, PEF, FEV₁/VC). Statistical analysis included Spearman/Pearson correlations and multiple linear regression models. Results: In the global sample, SMM showed only weak-to-moderate positive correlations with volumetric parameters (VC, FVC, FEV₁). However, trunk skeletal muscle mass (SMMT) did not maintain statistical significance in multiple regression models after controlling for specific outcomes-dependent sets of predictors (including age, body height, or body weight). Body height was the only independent predictor of VC (β = 0.49; p < 0.001). Although the regression model for the FEV₁/VC ratio did not reach global significance (p = 0.096), age presented a significant negative partial effect within the model (β = −0.33; p = 0.014). However, subgroup analysis revealed strong, positive muscle–respiratory correlations (rho from 0.46 to 0.71) exclusively in the oldest subgroup (>75 years). Conclusions: SMM is not an independent predictor of respiratory function across the entire sample of institutionalized older men. Although age-stratified subgroup analyses tentatively suggested positive muscle–respiratory correlations in participants aged >75 years, these subgroup differences cannot be statistically confirmed as a formal age interaction due to sample size limitations. Therefore, these findings must be treated as strictly exploratory and require confirmation in larger, adequately powered samples. This highlights the potential clinical importance of monitoring lower muscle mass parameters as indicators concurrently associated with lower respiratory performance in the oldest nursing home residents, although future prospective longitudinal studies are needed to evaluate any dynamic patterns.

1. Introduction

Aging is accompanied by progressive alterations in both the muscular and respiratory systems, leading to a gradual decline in functional capacity and an increased susceptibility to chronic diseases. One of the key physiological processes associated with aging is sarcopenia, defined as the loss of skeletal muscle mass and strength. It should be noted however, that while contemporary consensus definitions require both low muscle strength and low mass for a formal diagnosis of sarcopenia, the present study focuses specifically on its quantitative component, namely skeletal muscle mass (SMM). This phenomenon affects not only functional performance but also the functioning of the respiratory system, as skeletal muscles contribute to breathing mechanics, stabilization of the thoracic cage, and the generation of expiratory force [1].

In the general population, lower skeletal muscle mass has been shown to be associated with reduced values of fundamental spirometric parameters, including vital capacity (VC), forced vital capacity (FVC), and forced expiratory volume in one second (FEV₁). These relationships are typically attributed to both weakened respiratory musculature and an overall decline in physical fitness. However, most available studies have been conducted among community-dwelling older adults, who lead diverse lifestyles and exhibit greater clinical heterogeneity [2].

Far less is known about the relationships between skeletal muscle mass and pulmonary function in older adults residing in long-term care institutions. This population is particularly vulnerable to limited physical activity, chronic diseases, malnutrition, and environmental factors that may influence both body composition and ventilatory parameters. Institutionalization also leads to a homogenization of daily routines and lifestyle, which may attenuate or obscure associations commonly observed in community-dwelling older adults [3]. Despite this, scientific evidence concerning this group remains scarce, and existing analyses rarely account for the simultaneous effects of age, height, body mass, or hydration-related parameters on respiratory function.

In light of these research gaps, conducting more extensive investigations and in-depth analyses in this area is fully justified. The aim of the present study was to determine whether skeletal muscle mass and selected components of body composition are associated with spirometric parameters in older men residing in long-term care facilities in Poland. The analysis encompassed both simple correlations and multiple linear regression models, allowing for an assessment of the independent contribution of skeletal muscle mass after controlling for key demographic and anthropometric variables. This study provides new evidence regarding an institutionalized population in which muscle–respiratory relationships may differ from those observed in community-dwelling older adults, with implications for health risk assessment, rehabilitation planning, and respiratory monitoring in long-term care settings.

2. Materials and Methods

2.1. Study Participants

A cross-sectional study was conducted among 58 men aged 60–85 years residing in long-term care facilities located in the south-western region of Poland. Participants were stratified into two age groups: a younger group (≤75 years) and an older group (>75 years). The assessments were carried out between 2023 and 2025 across 15 institutions.

2.1.1. Inclusion Criteria

• Age 60–85 years
• Permanent residence in a Polish long-term care facility
• Independent mobility and ability to perform basic daily activities
• No medical contraindications to functional fitness assessment
• No difficulties with verbal communication
• No physical or cognitive impairments
• Ability to perform spirometry in accordance with ATS/ERS standards and stable health status on the day of testing

2.1.2. Exclusion Criteria

Exclusion criteria included, among others, cancer, acute injuries, recent myocardial infarction, acute respiratory infections or other infections, exacerbations of chronic diseases, respiratory failure requiring oxygen therapy, other medical contraindications, and lack of informed consent.

All participants provided written informed consent. The study was approved by the Research Ethics Committee of the University of Opole (KB/202/FI/2019) and conducted in accordance with the Declaration of Helsinki.

Due to the specific characteristics of long-term care environments, the recruited group exhibited a high degree of lifestyle homogeneity. However, it is important to note that the strict inclusion criteria required for a reliable spirometric assessment resulted in a highly selected, relatively healthy subgroup of residents. Therefore, this sample may not fully represent the broader, more biologically heterogeneous and frail population of older individuals residing in long-term care facilities.

2.2. Somatic Measurements

Body height was measured using a SECA 764 stadiometer (SECA GmbH & Co. KG, Hamburg, Germany). Body mass was assessed with the ACCUNIQ BC310 body composition analyzer (Selvas Healthcare, Daejeon, Republic of Korea), and these measurements were used to calculate the body mass index (BMI).

Body Composition Assessment

Body composition was evaluated using bioelectrical impedance analysis (BIA) with the ACCUNIQ BC310 analyzer. This device employs multi-frequency BIA and an 8-point electrode system (hand-foot), enabling segmental assessment of body composition. The following parameters were measured:

• Total skeletal muscle mass (SMM)
• Trunk skeletal muscle mass (SMMT)
• Segmental skeletal muscle mass:

Right upper limb (SMM-RUL) and left upper limb (SMM-LUL)

Right lower limb (SMM-RLL) and left lower limb (SMM-LLL)

• Intracellular water (ICW)
• Extracellular water to total body water ratio (ECW/TBW)

Measurements were performed in a standing position, in accordance with the manufacturer’s instructions, at least 3 h after the last meal. Participants refrained from vigorous physical activity for 12 h prior to testing and from alcohol consumption for 24 h. All assessments were conducted at a consistent time of day, following bladder emptying. The use of BIA for evaluating skeletal muscle mass is recommended for assessing muscle quality within body composition research in older adults, in line with EWGSOP2 guidelines [4].

2.3. Respiratory Function Assessment

Pulmonary function was assessed using the BTL-08 Spiro Pro spirometer (BTL Industries Ltd., Hertfordshire, United Kingdom), a CE-certified diagnostic device compliant with ATS/ERS standards for spirometry performance and quality control [5,6]. The following parameters were measured:

• Vital Capacity (VC)
• Forced Vital Capacity (FVC)
• Forced Expiratory Volume in 1 s (FEV₁)
• FEV₁/FVC ratio
• Peak Expiratory Flow (PEF)

Participants were tested in a seated position with a nose clip. Each participant performed at least three acceptable and repeatable maneuvers, and the best values were used for analysis. All measurements were conducted by trained personnel.

In addition to absolute values, percent-predicted values for VC, FVC, FEV₁, FEV₁/VC ratio, and PEF were calculated using the Global Lung Function Initiative (GLI) reference equations, which incorporate age, sex, and height [5,6].

Quality Control of Spirometry

To ensure high-quality spirometric data, a multi-stage quality control procedure was applied. Measurements considered physiologically impossible or indicative of technical error were excluded. Criteria included:

• Values > 150% of predicted for VC, FVC, or FEV₁, classified as non-physiological according to GLI reference ranges [5].
• FEV₁/VC > 100%, which is physiologically impossible and indicates measurement artifact [6,7,8].
• Extreme PEF values, particularly >150–180% predicted, which are highly susceptible to technical errors such as suboptimal start of exhalation, coughing, or incomplete inhalation [6].

All spirometry results were evaluated according to ATS/ERS criteria for acceptability and repeatability, including assessment of start of test quality, absence of cough, exhalation duration, and stability of the end of test plateau [6]. Measurements not meeting these criteria were excluded.

2.4. Statistical Analysis

The conformity of variable distributions with the theoretical normal distribution was assessed using the Shapiro–Wilk test. Measures of central tendency for normally distributed variables were compared between age groups using the independent-samples Student’s t-test. Differences in central tendency for skewed variables were analyzed using the Mann–Whitney U test.

Associations between skeletal muscle mass and ventilatory parameters were evaluated using the Pearson linear correlation coefficient (r) for normally distributed variables and the Spearman rank correlation coefficient (ρ), applied as the primary measure of association for skewed variables or when assumptions for Pearson correlation were not met.

To determine the independent effects of skeletal muscle mass and body composition components on pulmonary function, multiple linear regression analysis was performed. Four regression models were specified, with the following dependent variables: VC, FEV₁, PEF, and FEV₁/VC. Predictors included age, height, body mass, trunk skeletal muscle mass (SMMT), intracellular water (ICW), and the extracellular-to-total body water ratio (ECW/TBW), selected based on their potential physiological relevance.

For each model, the following statistics were reported: β coefficients, p-values, R², adjusted R², standard errors, and overall model significance (F-test). Statistical significance was set at p < 0.05.

Multiple regression models were constructed for the entire sample (N = 58) to evaluate the independent contribution of trunk skeletal muscle mass (SMMT) and fluid status to pulmonary function. The specific configurations of predictors varied across the models depending on the outcome variable to optimize physiological relevance and prevent multicollinearity, while controlling for age and height (Model 1 and 4) or body mass (Model 2 and 3). To avoid model overfitting and instability of parameter estimates, reporting was limited to standardized β coefficients, unstandardized b weights, standard errors (SE), and global model fit (F-test). To ensure the mathematical validity of the multipredictor regression models, diagnostic collinearity statistics (Variance Inflation Factor, VIF) and 95% confidence intervals (95% CL) for the regression coefficients were computed. Residual diagnostics, including independence of residuals (Durbin-Watson test) and influential data points (Cook’s distance), were verified to confirm model assumptions. Primary data management was conducted via Statistica 13.3 (license JPZ007B482801ARACD9) (TIBCO Software Inc., Palo Alto, CA, USA), and regression assumption testing was supplemented using jamovi software (Version 2.4.41.0) (The jamovi project, Sydney, Australia) [9,10,11]. Given the extensive number of continuous variables evaluated across multiple physiological systems, these age-stratified correlation analyses were executed without formal adjustments for multiple comparisons and are thus explicitly treated as strictly exploratory, post-hoc analyses intended for hypothesis generation rather than formal confirmation.

Additionally, a priori established age-stratified subgroup analyses (≤75 and >75 years) were conducted as exploratory (post-hoc) evaluations. The 75-year threshold was adopted in accordance with gerontological and World Health Organization standards, which recognize 75 years as the biological transition into advanced old age (late elderly), where functional and body composition declines often accelerate non-linearly [12].

3. Results

3.1. Characteristics of the Study Group

The study included 58 men aged 60–85 years (mean 70.5 ± 7.9 years). Detailed descriptive statistics for body composition parameters and pulmonary function measures are presented in Table 1.

Table 1. The general statistical characteristic (N = 58).

Variable	M ± SD	Me.	Min-Max	Skewness	Kurtosis	Shapiro–Wilk p-Value
Age (years)	70.50 ± 7.93	69.50	60–85	0.44	–0.87	0.001
SMM (kg)	29.82 ± 5.33	29.30	13.9–47.4	0.55	2.44	0.021
SMMT (kg)	24.48 ± 4.19	24.28	11.86–36.74	0.48	2.11	0.016
SMM-RUL (kg)	3.60 ± 0.62	3.58	1.55–5.45	0.11	1.95	0.071
SMM-LUL (kg)	3.63 ± 0.66	3.56	1.64–5.49	0.37	1.69	0.028
SMM-RLL (kg)	8.99 ± 1.87	8.73	4.03–15.65	1.04	3.10	0.001
SMM-LLL (kg)	9.04 ± 1.95	8.77	4.21–15.77	1.22	3.44	<0.001
ICW (l)	22.77 ± 3.97	22.50	11.8–35.6	0.59	1.93	0.062
ECW/TBW	0.414 ± 0.007	0.414	0.399–0.428	–0.29	–0.14	0.332
Body height (cm)	169.86 ± 6.78	169.00	152–182	–0.28	–0.33	0.504
Body weight (kg)	75.27 ± 15.32	74.90	51.4–119	0.77	0.63	0.011
BMI (kg/m2)	26.16 ± 5.14	25.10	18.65–42.16	0.89	0.59	0.006
VC (l)	2.98 ± 0.88	2.93	1.19–5.73	0.70	0.76	0.107
FVC (l)	2.58 ± 0.69	2.58	0.84–3.98	–0.09	–0.51	0.814
FEV1 (l)	1.97 ± 0.67	1.93	0.80–3.34	0.22	–0.66	0.228
PEF (l/s)	4.28 ± 1.88	4.23	0.75–8.15	0.19	–0.66	0.352
FEV1/VC ratio	76.06 ± 15.28	79.59	35.21–100	–0.58	–0.36	0.043

SMM—Skeletal Muscle Mass, SMMT—Trunk Skeletal Muscle Mass, SMM-RUL—Skeletal Muscle Mass right upper limb, SMM-LUL—Skeletal Muscle Mass left upper limb, SMM-RLL—Skeletal Muscle Mass right lower limb, SMM-LLL—Skeletal Muscle Mass left lower limb, ICW—Intracellular Water, ECW/TBW—Extracellular Water/Total Body Water, BMI—Body Mass Index, VC—Vital Capacity; FVC—Forced Vital Capacity; FEV₁—Forced Expiratory Volume in 1 s; PEF—Peak Expiratory Flow, M—mean, SD—standard deviation, Me—median, Min—minimum, Max—maximum.

3.2. Differences Between Age Groups

Participants were divided into a younger group (≤75 years; n = 42) and an older group (>75 years; n = 16). Combined results of significance testing for differences in arithmetic means and medians of the analyzed variables between younger and older men are presented in Table 2.

Table 2. Comparison of parameters between the younger group (n = 42) and the older group (n = 16).

Variable	Younger Group (n = 42) M ± SD	Older Group (n = 16) M ± SD	Test	p-Value
SMM (kg)	30.45 ± 5.87	28.18 ± 3.15	t	0.150
SMMT (kg)	24.73 ± 4.27	23.85 ± 4.04	U	0.244
SMM-RUL (kg)	3.67 ± 0.68	3.43 ± 0.44	t	0.20
SMM-LUL (kg)	3.71 ± 0.72	3.44 ± 0.45	t	0.17
SMM-RLL (kg)	9.21 ± 2.08	8.44 ± 1.01	U	0.230
SMM-LLL (kg)	9.26 ± 2.19	8.45 ± 0.95	U	0.210
ICW (l)	23.30 ± 4.30	21.37 ± 2.60	t	0.100
ECW/TBW	0.41 ± 0.01	0.42 ± 0.01	t	0.374
Body height (cm)	170.74 ± 6.77	167.56 ± 6.47	t	0.112
Body weight (kg)	77.35 ± 15.10	69.81 ± 15.00	U	0.076
VC (l)	3.05 ± 0.95	2.82 ± 0.63	t	0.388
FVC (l)	2.57 ± 0.71	2.60 ± 0.67	t	0.886
FEV1 (l)	2.04 ± 0.65	1.77 ± 0.69	t	0.169
PEF (l/s)	4.49 ± 1.84	3.73 ± 1.95	t	0.172
FEV1/VC ratio	78.96 ± 11.76	68.47 ± 20.59	t	0.018

SMM—Skeletal Muscle Mass, SMMT—Trunk Skeletal Muscle Mass, SMM-RUL—Skeletal Muscle Mass right upper limb, SMM-LUL—Skeletal Muscle Mass left upper limb, SMM-RLL—Skeletal Muscle Mass right lower limb, SMM-LLL—Skeletal Muscle Mass left lower limb, ICW—Intracellular Water, ECW/TBW—Extracellular Water/Total Body Water, VC—Vital Capacity; FVC—Forced Vital Capacity; FEV₁—Forced Expiratory Volume in 1 s; PEF—Peak Expiratory Flow, M—mean, SD—standard deviation, t—Student’s t-test, U—Mann–Whitney U test Exact p-values are reported; the level of statistical significance was set at p < 0.05.

Statistically significant differences between the age subgroups were observed only for the Tiffeneau index (FEV₁/VC) (p = 0.018). Younger men demonstrated significantly higher values of this parameter, whereas all remaining body composition and pulmonary function variables showed no statistically significant differences between age groups (p > 0.05).

3.3. Correlations Between Body Composition and Pulmonary Function

In the Spearman rank-correlation analysis performed for the entire sample (N = 58), statistically significant correlations of weak to moderate strength were found exclusively for absolute volume-based spirometric parameters: vital capacity (VC), forced vital capacity (FVC), and forced expiratory volume in one second (FEV₁) (correlation coefficients ranging from 0.25 to 0.31; p < 0.05). These associations involved:

• total skeletal muscle mass (SMM),
• trunk skeletal muscle mass (SMMT),
• skeletal muscle mass of the lower limbs (SMM-RLL and SMM-LLL).

No significant correlations were observed for flow-based parameters (PEF), the FEV₁/VC ratio, intracellular water (ICW), or the ECW/TBW ratio.

3.4. Age-Stratified Correlation Analysis

To assess the influence of age on muscle–respiratory relationships, separate correlation analyses were conducted for the two age groups: younger men (≤75 years) and older men (>75 years).

Younger Men (≤75 years)

In this subgroup, Pearson’s r was used for most variables, while Spearman’s ρ was applied to lower-limb muscle mass (SMM-RLL and SMM-LLL) due to skewed distributions. No statistically significant correlations were found between total or segmental skeletal muscle mass (SMM), hydration parameters (ICW, ECW/TBW), and either absolute or percent-predicted spirometric values (all p > 0.05). No statistically significant muscle–respiratory associations were detected in men aged ≤75 years. While these findings suggest that linear correlations do not reach the threshold of statistical significance in this specific younger subgroup, they should not be interpreted as definitive proof of a complete absence of relationships, as weaker or non-linear associations might remain undetected due to sample size limitations.

In contrast, several significant correlations were observed in the oldest subgroup (n = 16); however, these findings should be interpreted cautiously due to the limited sample size and the inherently wide confidence intervals surrounding the correlation coefficients.

Clear positive associations were found between muscle mass, hydration parameters, and absolute lung volume indices (VC, FVC). These included:

• Total skeletal muscle mass (SMM):

VC: r = 0.634, p = 0.008

FVC: r = 0.609, p = 0.012

• Trunk skeletal muscle mass (SMMT):

VC: ρ = 0.643, p = 0.007

• Upper-limb muscle mass (SMM-RUL, SMM-LUL)—significant positive correlations with VC and FVC
• Hydration parameters (ICW, ECW/TBW) -significant positive correlations with VC and FVC
• Right lower-limb muscle mass (SMM-RLL):

VC: r = 0.571, p = 0.021

FVC: r = 0.507, p = 0.045

Additionally, significant negative correlations were observed with percent-predicted values of the FEV₁/VC ratio and PEF. For example:

• SMMT:

FEV₁/VC %pred: ρ = –0.503, p = 0.047

PEF %pred: ρ = –0.570, p = 0.021

Other correlations, including those for FEV₁ and left lower-limb muscle mass (SMM-LLL), did not reach statistical significance (p > 0.05).

Multiple Linear Regression—Independent Predictors of Pulmonary Function.

To evaluate the independent effects of body composition parameters on spirometric indices while controlling for demographic and anthropometric factors, four multiple linear regression models were constructed for the entire study sample (N = 58). The results of this analysis are presented in Table 3.

Table 3. Multiple linear regression models (VC, FEV₁, PEF, FEV₁/VC).

Predictors	Model 1: VC			Model 2: FEV1			Model 3: PEF			Model 4: FEV1/VC Ratio
	β	b	p	β	b	p	β	b	p	β	b	p
Intercept	-	−6.293	0.041	-	6.910	0.241	-	7.874	0.012	-	120.971	0.036
Age (years)	−0.124	−0.014	0.328	−0.172	−0.014	0.230	−0.228	−0.054	0.096	−0.331	−0.636	0.014
Body height (cm)	0.489	0.063	0.001	-	-	-	-	-	-	−0.002	−0.005	0.987
Body weight (kg)	-	-	-	0.154	0.007	0.339	0.122	0.015	0.423	-	-	-
SMMT (kg)	−0.063	−0.013	0.810	0.091	0.015	0.554	−0.083	−0.037	0.581	0.010	0.035	0.947
ICW (l)	−0.032	−0.007	0.904	-	-	-	-	-	-	-	-	-
ECW/TBW	-	-	-	−0.115	−11.556	0.450	-	-	-	-	-	-
R2	0.237			0.091			0.071			0.110
Adjusted R2	0.180			0.022			0.020			0.061
F	4.121			1.323			1.378			2.225
p-value	0.006			0.273			0.260			0.096
SEE	0.793			0.660			1.863			14.806

N = 58. beta—standardized regression coefficient; b—unstandardized regression coefficient; p—p-value (statistical significance); R²—coefficient of determination; Adjusted R²—adjusted coefficient of determination; F—F-test statistic; SEE—standard error of estimate; VC—Vital Capacity; FEV₁—Forced Expiratory Volume in 1 s; PEF—Peak Expiratory Flow; SMMT—Trunk Skeletal Muscle Mass; ICW—Intracellular Water; ECW/TBW—Extracellular Water to Total Body Water ratio. Body height was excluded from Model 2 due to multicollinearity with body weight. (r = 0.54), (p < 0.001), which induced parameter instability when entered simultaneously. Full baseline collinearity diagnostics (VIF ranges) confirming the mathematical validity of all finalized configurations are reported in the diagnostic section below. Model diagnostics and bivariate correlation precision metrics: —Model 1 (VC) unstandardized b 95% CI: Height (0.029 to 0.099), Age (−0.043 to 0.015), SMMT (−0.119 to 0.096), ICW (−0.118 to 0.104); VIF: 1.13–4.81 (SMMT VIF = 4.64, ICW VIF = 4.81). —Model 2 (FEV₁) 95% CI: Weight (−0.007 to 0.021), Age (−0.038 to 0.009), SMMT (−0.034 to 0.063), ECW/TBW (−42.062 to 18.949); VIF: 1.14–1.41. —Model 3 (PEF) 95% CI: Weight (−0.023 to 0.054), Age (−0.118 to 0.010), SMMT (−0.171 to 0.097); VIF: 1.12–1.38. —Model 4 (FEV₁/VC) 95% CI: Age (−1.140 to −0.133), Height (−0.669 to 0.658), SMMT (−1.016 to 1.085); VIF: 1.03–1.25. All models were free from severe multicollinearity (VIF < 5.0). —Table 2 (>75 years subgroup, n = 16) correlation 95% CI metrics total SMM with VC (95% CI: 0.198 to 0.860), total SMM with FVC (95% CI: 0.158 to 0.849), trunk SMMT with VC (95% CI: 0.211 to 0.864), and SMM-RLL with VC (95% CI: 0.101 to 0.831). The wide confidence intervals reflect the limited sample size and reinforce the exploratory nature of these advanced-age estimations.

Model 1 was statistically significant (F = 4.12; p = 0.006) and explained approximately 18% of the variance in vital capacity (adjusted R² = 0.180). Height was the only significant independent predictor of VC (β = 0.49; p < 0.001). The following linear regression equation was obtained: $\mathrm{VC}=-6.293-0.014\times \mathrm{Age}+0.063\times \mathrm{Height}-0.013\times \mathrm{SMMT}-0.007\times \mathrm{ICW}.$Model 4, in which the dependent variable was the FEV₁/VC ratio, did not reach global statistical significance (F = 2.225; p = 0.096). Therefore, the individual predictors within this model must be interpreted with extreme caution: while a negative partial coefficient for age was formally calculated (β = –0.33; p = 0.014), it cannot be considered robust or definitive due to the lack of global model significance.

4. Discussion

This study examined the relationships between selected body composition parameters, including skeletal muscle mass, and pulmonary ventilatory function in older men residing in long-term care facilities. The findings revealed a complex and potentially descriptive pattern of muscle–respiratory interactions across different age strata. While separate subgroup correlation analyses showed significant results only in the oldest men (>75 years), this does not statistically confirm that the muscle–respiratory relationships are definitively modulated by age, as a formal interaction test could not be reliably performed due to sample size constraints. Thus, the trend toward stronger associations in late old age remains exploratory rather than definitive, differing from relationships typically described in the general population.

When evaluating associations in the entire sample, only weak correlations and no independent effects were observed. In the full group analysis, only weak positive correlations were found between total skeletal muscle mass (SMM) and VC, FVC, and FEV₁. Importantly, when trunk skeletal muscle mass (SMMT) was evaluated within the multiple regression models under differentiated control sets, these independent effects disappeared after adjusting for age, height, or body mass. This result is consistent with population-based studies showing that pulmonary function in older adults is largely determined by anatomical and biological factors-such as height, thoracic compliance, and age-related changes in lung structure-rather than by variability in muscle mass. Previous research indicates that height is one of the strongest predictors of VC and FVC in older adults, which is confirmed by our regression model, where height was the only significant predictor of VC [13,14].

In the younger subgroup (≤75 years), no significant correlations were observed between muscle mass and ventilatory parameters. Similar findings have been reported by other authors, who demonstrated that in individuals aged 60–75 years, pulmonary function is more closely related to anthropometric characteristics than to muscle mass [13,14]. National literature also emphasizes that involutional processes in older adults substantially modify tissue composition, and somatic indices (including BMI) may co-occur more strongly with functional capacity than isolated skeletal muscle mass [15,16,17]. The asymmetry observed in lower-limb associations in our study requires cautious interpretation, as structural involutional changes in the thoracic musculoskeletal system and respiratory muscles do not always progress linearly relative to changes in total skeletal muscle mass, as widely described in functional morphology research [17].

Population-based studies by other authors have highlighted that muscle–respiratory relationships are most evident in individuals with markedly reduced muscle mass or chronic disease, whereas in those with moderate muscle function, correlations tend to be weak or non-significant [14,18,19,20,21]. Our findings align with this trend: in men aged 60–75 years, pulmonary function remained relatively stable, and variability in muscle mass was insufficient to influence spirometric parameters.

In contrast, exploratory analyses revealed several significant correlations in the oldest subgroup (>75 years), although these findings should be interpreted cautiously because of the limited sample size. The most striking results concern this subgroup. Although muscle–respiratory correlations were calculated for the entire sample, comparative analysis of spirometric parameters and body composition across age groups suggests that these relationships differ between younger and older seniors. In men ≤ 75 years, VC, FVC, and FEV₁ values were relatively homogeneous, and muscle mass variability was small, limiting the statistical potential for strong correlations. In men > 75 years, greater variability in both muscle and respiratory parameters facilitated the emergence of muscle–respiratory associations. This phenomenon is consistent with literature indicating that in late old age, declines in muscle mass and pulmonary function occur more parallelly than in earlier decades of life [19,21,22]. Specifically, prior evidence confirms that skeletal muscle reduction in institutionalized cohorts deeply impacts respiratory muscle strength [23], and longitudinal changes in physical capacities are strictly associated with systemic health outcomes in aging populations [24]. Furthermore, recent population-based data underscore that unfavorable alterations in body composition compartments are directly linked to lung function decline and higher risks of airflow obstruction [25]. As a result, muscle–respiratory relationships may appear stronger in the oldest seniors, although this observation should be interpreted as exploratory because of the limited subgroup size [26].

A particularly interesting and initially counterintuitive finding in the >75 years subgroup was the significant negative association between trunk skeletal muscle mass (SMMT) and percent-predicted FEV₁/VC (Tiffeneau index). In classical respiratory pathophysiology, a reduced Tiffeneau index is typically associated with airway obstruction. One plausible, albeit strictly hypothetical, explanation for this trend is a potential mathematical artifact resulting from asymmetrical shifts in lung parameters. Since greater trunk muscle mass correlated strongly and positively with VC and FVC while displaying no significant association with FEV₁, an increase in the denominator (FVC) could mathematically depress the overall ratio. However, because our cross-sectional data cannot verify intra-thoracic pressure dynamics or true airway physical properties, this mathematical rationale must be treated cautiously as only one of several possible explanations rather than a proven mechanism, and it does not rule out other latent age-related changes in airway mechanics.

• In this subgroup, strong and very strong positive correlations were observed between ventilatory parameters (especially VC and FVC) and total skeletal muscle mass, trunk muscle mass, and upper-limb muscle mass. Segmental analysis revealed a differentiated pattern in the lower limbs: significant positive associations with VC and FVC were found only for the right lower limb, whereas correlations for the left lower limb were non-significant. Flow-based parameters (FEV₁) showed no significant associations with muscle mass in either lower limb. While these exploratory findings provide a conceptual background in line with existing literature on respiratory sarcopenia [19,21], the apparent asymmetry between the significant correlation for the right lower limb and the non-significant correlation for the left lower limb must be interpreted with extreme caution. This isolated finding lacks a clear biological or functional foundation in our sample. Given the extensive multiple testing performed across multiple somatic segments, this divergent pattern does not imply true physiological asymmetry or movement lateralization: rather, it is highly likely to reflect random sampling variability or an artifact of multiple comparisons within a small subgroup. Other authors have noted that while loss of muscle mass in very advanced age generally progresses in parallel with declines in pulmonary function, regional discrepancies should not be over-interpreted without formal statistical corrections for multiple testing [21,23,24,25,27].

One possible explanation for this observation involves two hypothetical mechanisms: Cohort effect: Individuals who reach >75 years in institutional care represent a specific cohort with distinct health profiles. Longitudinal studies show that in late old age, declines in muscle mass and pulmonary function often occur in parallel, strengthening muscle–respiratory associations [23,24,25].

2.

Survivor bias: Only individuals with resilient physiological profiles or specific synchronous adaptations in fitness and respiratory mechanics survive into late old age within institutional settings. This interpretation aligns with recent data from Polish residential care homes [28], emphasizing that physical fitness levels profiles are heavily intertwined with the risk factors of frailty syndrome in and biological institutionalized populations. However, within the cross-sectional framework of our study, these pathways remain strictly speculative. Due to the nature of data collected at a single time point from two age strata, it is methodologically impossible to definitively differentiate between true biological aging, cohort differences, selective survival, sampling variability, or potential residual confounding.

Our findings in older men complement our recent preliminary study conducted among institutionalized women in Poland, which similarly revealed that higher skeletal muscle mass coexisted with significantly better spirometric parameters [29]. Despite strong correlations observed in the oldest subgroup (>75 years), global multiple regression models for the entire sample (N = 58) did not identify independent effects of muscle parameters on spirometry. This suggests that body composition–respiratory relationships may become more evident in advanced old age (as shown in subgroup analyses), whereas in the full age spectrum they are masked by stronger anthropometric influences such as height. Additionally, although formal diagnostics ruled out critical multicollinearity (all VIF < 5.0), the shared variance among chronological age, body size, and specific lean tissue compartments within a constrained sample size (N = 58) inherently limits the statistical power required to isolate weak, independent muscle–respiratory effects in a global multi-predictor framework. This heavily aligns with recent evidence from a larger cohort of older Polish men, which demonstrated that institutionalization, low physical activity, and poor subjective health status significantly degrade functional and physical capacity compared to community-dwelling peers [27]. Similar methodological phenomena have been described in studies on muscle–respiratory relationships, where full-sample regression models do not always confirm relationships specific to the oldest age subgroups [21,27,30].

In summary, our study suggests that the statistical associations between muscle mass and lung function parameters vary across age strata, appearing more pronounced in the oldest seniors. While these cross-sectional findings align with the physiological context of age-related muscle differences described in the literature, they demonstrate a distinct pattern in institutionalized populations that represents a meaningful baseline for future longitudinal research.

4.1. Clinical Implications

The findings have important implications for clinical practice in long-term care facilities:

• skeletal muscle mass should not be considered an independent indicator of pulmonary function in older men,
• in the oldest seniors, muscle–respiratory relationships appear to be more pronounced, suggesting that monitoring muscle parameters could serve as a valuable concurrent indicator of overall physical and respiratory decline in late old age.

4.2. Strengths and Limitations

The strengths of this study include the analysis of a specific population of institutionalized older adults, which is rarely described in the literature, as well as the combined assessment of segmental body composition and full spirometry using both correlation and multiple regression approaches.

4.3. Limitations:

• The size of the oldest subgroup (>75 years; n = 16) was relatively small, which may have limited the statistical power of correlation analyses conducted within this specific age cohort. Although the smaller sample reflects the natural demographic structure and the challenges of recruiting individuals of very advanced age in long-term care facilities, the strong bivariate correlation coefficients observed in this subgroup suggest the presence of clear physiological trends. Nevertheless, the limited sample size prevented the development of separate, mathematically stable multiple regression models for this subgroup, necessitating the restriction of multivariable analyses to the full study sample (N = 58). Therefore, the subgroup findings should be considered exploratory and hypothesis-generating rather than confirmatory. Consequently, while the global sample (N − 58) provided adequate statistical power to detect moderate-to-strong bivariate correlation effects, the post-hoc statistical power for the stratified oldest subgroup was inherently limited, reinforcing the strictly exploratory nature of these advanced age observations. This small sample size inherently generates wider 95% confidence intervals around the calculated bivariate correlation coefficients for the oldest stratum, which reduces estimation precision and underscores that these strong correlations must be interpreted cautiously as preliminary, hypothesis-generating findings.
• The strict selection criteria significantly limit the external validity of the study. By requiring independent mobility, stable health status, and the absence of cognitive or physical impairments necessary to perform valid spirometry, our sample consists of a relatively healthy subgroup of nursing home residents. Consequently, these findings cannot be automatically generalized to the wider population of institutionalized older adults, many of whom exhibit severe frailty, advanced dementia, or multi-morbidity that precludes compliance with strict respiratory testing protocols. Furthermore, the lack of global statistical significance in the regression model for the FEV₁/VC ratio underscores the exploratory nature of our multivariable findings and prevents us from drawing firm conclusions regarding age-specific rates of decline in relational spirometric parameters.
• The study included only men residing in long-term care facilities, which limits the generalizability of the findings to women or community-dwelling older adults.
• Important potential confounding factors, including smoking status (and smoking history), physical activity levels, specific chronic respiratory and cardiovascular diseases, medication regiments, and objective nutritional status (e.g., dietary intake or laboratory biomarkers), were not comprehensively assessed. Since these variables are heavily intertwined and can independently modulate both skeletal muscle mass architecture and pulmonary function parameters, their absence introduces a risk of residual confounding. Consequently, the coefficients in our multiple linear regression models should be interpreted with caution, as these unmeasured factors might have attenuated or masked independent muscle–respiratory relationships across the full sample.
• The cross-sectional design represents a major methodological limitation when comparing the two age strata. Because data were collected at a single time point, it is impossible to distinguish chronological or biological aging processes from cohort effects, selective survival biases, inherent sampling variability, or residual confounding variables. Crucially, without longitudinal data, this design completely precludes any causal inference and does not allow us to confirm whether reduced muscle mass contributes to subsequent pulmonary decline or vice versa. Consequently, any observed differences or correlations must be interpreted with extreme caution as purely synchronous associations rather than directional trajectories. Despite these limitations, the study provides valuable data on a population that is rarely examined, as reflected in the literature, and highlights distinct patterns of muscle–respiratory relationships in late old age.

5. Conclusions

In this population of institutionalized older men, total skeletal muscle mass (SMM) demonstrated only weak positive correlations with selected ventilatory parameters (VC, FVC, FEV₁). Furthermore, trunk skeletal muscle mass (SMMT) did not emerge as an independent predictor of pulmonary function in the multiple regression models after adjusting for outcome-specific covariates (such as age, height, or weight). This indicates that localized muscle quantity within the trunk is not independently associated with respiratory outcomes in this group. Although a negative age-related trend for the FEV₁/VC ratio was noted in the preliminary analysis, this model did not achieve global statistical significance, meaning that further research on larger cohorts is required to establish whether relational indices exhibit unique sensitivity to the aging process in this population.

An important, yet strictly preliminary, finding of the study was the observation of positive muscle–respiratory correlations in the oldest participants (>75 years). Because a formal test of interaction was not performed and the oldest subgroup was relatively small, these differences between age categories did not establish a verified age-dependent causal or statistical mechanism. These findings should be considered purely exploratory and hypothesis-generating, requiring validation in large cohorts. The results emphasize the need for individualized respiratory fitness screenings in nursing homes and indicate that the coexistence of lower muscle quantity and lower respiratory performance values may present a more relevant clinical pattern in late old age than in previous decades of life.