JinHua Qian1,2
XiaoYun Fan- 1Department of Geriatric Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- 2Department of Geriatric Medicine, Huangshan city People’s Hospital, Huangshan, China
Introduction: The phase angle (PhA), derived from bioelectrical impedance analysis (BIA), serves as an indicator of cellular health and body composition. While associated with muscle strength and exercise capacity in various conditions, its clinical relevance in chronic obstructive pulmonary disease (COPD) requires further characterization. This study aimed to evaluate the relationship between PhA, muscle strength, and physical function among individuals with COPD.
Methods: Between June 2024 and August 2025, 112 male patients with COPD and 20 healthy male controls were enrolled in this cross-sectional study. Assessments included pulmonary function, body composition via BIA, handgrip strength, knee extension strength, walking speed, and other clinical indicators. Relationships were analyzed using multivariable linear and least absolute shrinkage and selection operator (LASSO) regression models.
Results: PhA values were significantly lower in COPD patients than in healthy controls. Stratification of COPD patients by PhA revealed that a lower PhA was associated with progressively worse muscle strength, exercise capacity, and other clinical markers. Multivariable linear regression analyses demonstrated that a lower PhA was independently associated with slower walking speed (β = 0.061, p < 0.001) and reduced knee extension strength (β = 1.15, p = 0.002). Furthermore, PhA was selected as a key predictor in a prognostic model for severe physical impairment derived from the LASSO regression analysis.
Conclusion: In this cross-sectional study, a lower PhA is independently associated with muscle weakness and impaired physical performance in men with COPD. These findings suggest that PhA may serve as a useful biomarker for assessing nutritional and functional status in this population. However, the cross-sectional design precludes causal inference, and the diagnostic utility of PhA for COPD itself is not established.
Introduction
Chronic obstructive pulmonary disease (COPD) is a prevalent respiratory condition primarily caused by long-term tobacco use, environmental pollutants, and recurrent respiratory infections (1, 2). In China, over 27% of elderly individuals are affected by COPD (3), which ranks as the fourth leading cause of death globally (4). Beyond impaired lung function, patients frequently experience systemic manifestations, including anemia, depression, sarcopenia, malnutrition, and cardiovascular comorbidities (5, 6). Malnutrition further impairs respiratory muscle function (7, 8), potentially leading to respiratory failure and increased mortality (9, 10).
Bioelectrical impedance analysis (BIA) is a recognized technique for assessing body composition and diagnosing sarcopenia (7, 8). The phase angle (PhA), calculated directly from resistance (R) and reactance (Xc) without estimation equations (11), serves as a marker of cellular membrane integrity and viability (12). Higher PhA values suggest better membrane integrity and functional capacity (13). PhA levels are typically lower in women than in men, and they decrease as they age (14, 15). PhA also reflects muscle mass, strength, fat-free mass (FFM), and hydration status, underscoring its utility in sarcopenia diagnosis (16). Clinically, PhA holds prognostic value for older adults and patients with COPD, cancer, or those undergoing hemodialysis. In COPD populations, PhA inversely correlates with poor prognostic markers but shows a positive association between fat-free mass and physical performance (17). Notably, PhA proves to be a more robust metric than fat-free mass alone, as it correlates with exercise tolerance and the body mass index, airflow obstruction, dyspnea, and exercise capacity index (BODE) index independent of fat-free mass (18). Thus, PhA is a valuable BIA-derived indicator of nutritional status and cellular health (19). Despite existing studies linking BIA-derived PhA to nutritional status, data specifically focusing on elderly COPD patients remain limited (20).
Given the established relevance of PhA in various diseases, including cancer, hepatic fibrosis, heart failure, and renal failure (11, 13), this study aimed to investigate whether PhA can serve as an indicator of systemic involvement in COPD. Demonstrating such an association would extend the clinical significance of PhA beyond its role in skeletal muscle assessment, positioning it as a comprehensive marker of systemic impairment in COPD.
Materials and methods
Participants
This single-center, observational, cross-sectional study was conducted at our hospital and was approved by the institutional review board and ethics committee (Approval number: 2024-A-009, approval date: June 10, 2024). All participants provided written informed consent, and the study was conducted in accordance with the Declaration of Helsinki.
From June 2024 and August 2025, we recruited 112 male patients with clinically stable COPD undergoing routine follow-up, along with 20 healthy male volunteers. The exclusion criteria were as follows: (1) COPD exacerbation within the past month, (2) contraindications to BIA such as cardiac pacemaker implantation, (3) incomplete data, or (4) unwillingness to participate. Participants were informed of their right to withdraw at any time, and only routine clinical data were collected without additional invasive procedures.
Sample size consideration
The sample size of 112 COPD patients and 20 controls was determined by the available cohort during the study period. A post-hoc precision analysis was performed for the primary correlation between phase angle (PhA) and the 6-min walk distance (6MWD), which yielded a correlation coefficient of r = 0.69. For this effect size, with an alpha error of 0.05 and a power of 90%, the required sample size would be approximately 25 participants. Our sample of 112 patients, therefore, provides ample statistical power to detect the observed association and allows for precise estimation, as evidenced by the narrow confidence intervals around our correlation and regression coefficients.
Blood test
The following parameters were retrieved from electronic medical records: serum albumin, total lymphocyte count, total cholesterol, hemoglobin, and C-reactive protein.
Body composition
Body composition was assessed via whole-body bioelectrical impedance analysis (BIA) using a Bodystat Quadscan 4000 analyzer (Bodystat Ltd., Isle of Man, United Kingdom) (21). The measured parameters included fat-free mass (FFM), from which the fat-free mass index (FFMI, FFM/height2 in kg/m2) and skeletal muscle mass index (SMI) were derived. PhA and the extracellular water-to-total body water ratio (ECW/TBW) were also obtained (22).
Bioelectrical impedance
Prior to measurement, participants fasted for at least 1.5 h, emptied their bladders, and refrained from moderate-to-vigorous physical activity for 12 h. Following a 15-min rest period in a supine position on a non-conductive surface, the BIA was performed. The measurement used a tetrapolar electrode configuration on the dominant side of the body. An alternating current of 800 μA at a frequency of 50 kHz was applied. Surface electrodes (Bodystat Ltd., Isle of Man, United Kingdom) were placed at standard anatomical sites: on the dorsum of the hand proximal to the metacarpal-phalangeal joint, on the wrist at the midline between the distal prominences of the radius and ulna, on the foot between the medial and lateral malleoli at the ankle, and on the dorsum of the foot proximal to the metatarsal-phalangeal joint.
Resistance (R) and reactance (Xc) were measured directly by the device. The intraday measurement variability was below 2% for R and 3.5% for Xc. The PhA was computed at 50 kHz using the following formula: PhA (°) = arctangent (Xc/R) × (180/π) with dedicated software (Bodystat Ltd., Isle of Man, United Kingdom). PhA values were classified as low or normal based on age-, sex-, and body mass index (BMI)-stratified reference percentiles from a large healthy cohort (n = 214,732) (23). The standardized phase angle was calculated as follows: (observed PhA − mean reference PhA)/SD of reference PhA. The PhA was analyzed both as a raw variable (units: degrees) and as a z-standardized score. The z-standardization was performed to facilitate interpretation relative to a healthy reference population and was used in the studies. We applied the reference values from Barbosa-Silva et al.’s study, which provide mean and standard deviation values for PhA stratified by sex and age groups in a large healthy sample (n = 1,967). For each participant, the z-score was calculated as follows: (observed PhA − mean reference PhA for their sex and age group)/standard deviation of the reference PhA for their sex and age group.
Fat-free mass (FFM) was estimated using sex-specific regression equations (24, 25): For women, FFM was calculated as follows: FFM = 7.610 + 0.474 × (height2/R) + 0.184 × weight, and for men, FFM was calculated as follows: FFM = 0.383 + 0.465 × (height2/R) + 0.213 × weight.
Patient background
Data collected from medical records included: age, chronic obstructive lung disease (GOLD) stage (I: mild, forced expiratory volume in 1 second (FEV₁) ≥ 80% predicted; II: moderate, 50% ≤ FEV₁ < 80%; III: severe, 30% ≤ FEV₁ < 50%; and IV: very severe, FEV₁ < 30% predicted), modified Medical Research Council (mMRC) dyspnea score, exacerbation history in the previous year, use of home oxygen therapy, comorbidities, and medications (26).
Pulmonary function
Pulmonary function was assessed using the CHESTAC-8800 system (Chest Co., Ltd., Tokyo, Japan) following standard guidelines (27). Forced vital capacity (FVC), FEV₁, and FEV₁/FVC ratio were measured via forced spirometry (28). Inspiratory capacity (IC) was determined using the slow vital capacity technique. Diffusing capacity for carbon monoxide (DLCO) was measured using the single-breath method (29).
Predicted values for FVC and FEV₁ were derived from the 1988 Chinese national spirometric reference equations, which were established from a large sample (n = 7,115) across six major administrative regions in China and were recommended by the Chinese Expert Consensus on Adult Pulmonary Function Diagnosis (2022) for their applicability to the Chinese population (27, 30, 31). For DLCO, predicted values were calculated using the 2011 revised reference equations specifically derived for Chinese adults (28, 32–34). The use of these population-specific equations minimizes potential bias in the derived percentage of predicted values.
Physical function assessment
The 6-min walk test (6MWT) was administered according to the American Thoracic Society guidelines (35), and the 6-min walk distance (6MWD) was recorded. Usual walking speed was assessed as a separate, direct measure of gait speed. Participants were instructed to walk at their usual, comfortable pace over a straight, flat distance of 4 m. The time taken to cover the central 3 m (to exclude acceleration and deceleration phases) was measured using a stopwatch. Walking speed was then calculated in meters per second (m/s). This short walk test is a widely used and reliable measure of usual gait speed in older and clinical populations. Walking speed was calculated based on the 6MWD. Handgrip strength was measured using a Smedley-type dynamometer (Grip-D; Takei Scientific Instruments Co., Ltd., Niigata, Japan), with the highest reading used (36). Knee extension strength was assessed using a μTas F-1 system (Anima Co., Ltd., Tokyo, Japan), and the maximum value was recorded.
Prognostic nomogram analysis
Least absolute shrinkage and selection operator (LASSO) regression analysis was used for variable selection and dimensionality reduction. Non-zero coefficients from the LASSO model were included in a multivariable logistic regression analysis to develop a predictive nomogram. Model calibration was evaluated using calibration curves, and discriminative ability was assessed using Harrell’s C-index. A decision curve analysis was used to evaluate clinical utility, and diagnostic performance was determined using the receiver operating characteristic curve analysis (AUC) in the training and validation cohorts (37–39).
Statistical analysis
Non-normally distributed data are presented as median [interquartile range (IQR)] or proportions with 95% confidence intervals (CIs). Spearman’s correlation coefficient was used to evaluate associations between PhA, FFM, FFMI, and other variables. Group comparisons (low vs. normal PhA or FFMI) were performed using the Mann–Whitney U-test. Multivariable regression analyses were conducted to identify determinants of square-root-transformed indicators, including incremental shuttle walk (ISW) distance, 4-m gait speed (4MGS), 5-repetition sit-to-stand test (5STS), and Age, Dyspnea, and Airflow Obstruction Index Score (ADO) index scores. Independent variables included PhA, FFMI, age, sex, BMI, percent predicted FEV₁, mMRC dyspnea score, quadriceps maximal voluntary contraction (QMVC), and the Charlson comorbidity index. For the ADO model, age, percent predicted FEV₁, and mMRC score were excluded, as they are components of this composite index. The variables were checked for collinearity (r < 0.5) and included or excluded through a stepwise selection process (entry p < 0.05, removal p ≥ 0.10).
Results
A total of 112 male patients diagnosed with COPD and 20 healthy male volunteers were enrolled in this study. The demographic and clinical characteristics of the participants are summarized in Table 1. The mean age of the COPD group was 75.12 ± 1.27 years, which was comparable to that of the control group (74.23 ± 3.99 years). The average body mass index (BMI) was also similar between the two groups (COPD: 23.11 ± 2.61 vs. control: 23.58 ± 3.26). However, significant differences were observed in body composition and functional parameters. The fat-free mass index (FFMI) was notably lower in the COPD group (16.24 ± 2.64) than in the control group (18.25 ± 3.25). Furthermore, the COPD group exhibited significant reductions in the skeletal muscle mass index (SMI), PhA, and walking speed. The prevalence of sarcopenia and Global Leadership Initiative on Malnutrition (GLIM)-defined malnutrition was also substantially higher among COPD patients.
Table 1. Characteristics of patients.
When patients were stratified based on PhA values (Table 2), the subgroup with lower PhA demonstrated a significantly higher extracellular water-to-total body water ratio (ECW/TBW) than the higher PhA subgroup. This lower PhA subgroup also exhibited markedly reduced values for multiple parameters, including BMI, FFMI, SMI, lung function parameters (FEV₁, FEV₁/FVC, DLCO, and IC), muscle strength (handgrip strength and knee extension strength), functional capacity (walking speed and 6MWD), and biochemical markers (total cholesterol and hemoglobin).
Table 2. Patient data comparison between the low- and high-PhA groups.
Correlation analyses confirmed these associations (Table 3). The PhA showed statistically significant positive correlations with FFMI (r = 0.44, p = 0.01), SMI (r = 0.36, p = 0.02), lung function (FEV₁%: r = 0.25, p = 0.02; FVC: r = 0.34, p = 0.01; FEV₁/FVC: r = 0.62, p = 0.02), and physical function (handgrip strength: r = 0.25, p = 0.03; walking speed: r = 0.22, p = 0.02; 6MWD: r = 0.69, p = 0.03).
Table 3. Correlation between PhA and indicators of sarcopenia and malnutrition in patients with COPD.
Independent association between the PhA and physical function
Independent associations between PhA and key physical function parameters were assessed using multivariable linear regression models, after adjusting for age, sex, BMI, and comorbidities. As shown in Table 4, a one-degree increase in the PhA was significantly associated with a 0.061 m/s increase in walking speed (β = 0.061, 95% CI: 0.035–0.085, p < 0.001). Similarly, for knee extension strength, a one-degree increase in the PhA was associated with a 1.15 kg increase in strength (β = 1.15, 95% CI: 1.05–1.62, p = 0.002).
Table 4. Multivariate logistic regression analysis with sarcopenia and malnutrition as the dependent variable in patients with COPD.
Development of a prognostic model for severe physical impairment
To develop a clinically applicable tool for identifying patients at high risk, we constructed a prognostic model for “severe physical impairment,” defined as the coexistence of low handgrip strength and low 6-min walk distance. A LASSO regression analysis, applied to an initial set of 20 variables, selected five key predictors: age, number of comorbidities, PhA, handgrip strength, and forced vital capacity (FVC). A nomogram was created based on these predictors to estimate individual patient risk (Figure 1).
Figure 1. Prognostic nomogram for predicting the probability of severe physical impairment in COPD patients. (A) Calibration curves of the non-adherence nomogram prediction in the cohort. (B) Optimal parameter (lambda) selection in the LASSO model uses fivefold cross-validation using minimal criteria. (C) Decision curve analysis for the non-adherence nomogram. (D) LASSO coefficient profiles of the 22 characteristics. (E) Decision curve analysis for the non-adherence nomogram. (F) A vertical line indicates the optimum lambda value from fivefold cross-validation, yielding five features with non-zero coefficients. BMI, body mass index; FFMI, fat-free mass index; SMI, skeletal muscle mass index; PhA, phase angle; ECW/TBW, extracellular water-to-total body water ratio; FEV₁%, percent predicted forced expiratory volume in 1 second; 6MWD, 6-min walk distance. Data are presented as mean ± standard deviation.
The model was subjected to rigorous internal validation using the 0.632 + bootstrap method (1,000 resamples) to correct for over-optimism. The validated model demonstrated good discriminative ability, with a bootstrap-corrected C-index of 0.81 (95% CI: 0.75–0.87). The calibration curve showed acceptable agreement between predicted probabilities and observed outcomes (Figure 1A). The decision curve analysis (Figure 1B) confirmed the clinical utility of the nomogram, indicating that its use for risk stratification provides a positive net benefit across a wide range of decision thresholds.
Discussion
Globally, respiratory diseases often receive comparatively less attention and funding relative to their significant contribution to morbidity and mortality (9). Chronic obstructive pulmonary disease (COPD), in particular, is a major public health issue and a persistent challenge for healthcare systems in the 21st century (40). In our cohort, we observed a significantly lower PhA in COPD patients than in healthy controls. Subsequent stratification of COPD patients based on PhA, followed by multivariate and LASSO regression analyses, established PhA as an independent predictor of handgrip strength and knee extension strength in this population.
The PhA, a raw parameter derived from the bioelectrical impedance analysis (BIA), is increasingly used to assess malnutrition across various diseases, including respiratory conditions such as COPD (41, 42). COPD patients with lower PhA are typically older and more likely to experience hypoxia and hypercapnia. Those with more severe disease exhibit reduced body cell mass, significant skeletal muscle wasting, and impaired gas exchange (8, 43). The relationship between PhA and all-cause mortality in COPD patients has been demonstrated using the Cox regression analysis, the Kaplan–Meier analysis, and log-rank test (44, 45). Furthermore, PhA serves as a marker for the role of malnutrition in idiopathic pulmonary fibrosis (IPF), independent of body weight. It correlates with reduced muscle mass in these patients, which impairs their physical strength and exercise capacity and adversely affects prognosis (46). Our research also confirmed a substantial reduction in PhA among individuals with COPD, reinforcing its value as a crucial biomarker.
Within health assessment tools, walking pace (WP) and hand grip strength (HGS) are fundamental metrics that correlate widely with various health outcomes. Numerous studies demonstrated that poorer muscle function is associated with higher mortality and morbidity (47, 48). For example, a 24-year follow-up study of 1,142,599 adolescent male individuals (aged 16–19 years) found that reduced muscular strength was associated with increased all-cause and cardiovascular mortality, but not with cancer mortality (49). Recent data from the Prospective Urban–Rural Epidemiology (PURE) study, which included 139,691 adults aged 35–70 years with a 4-year follow-up, demonstrated an inverse relationship between grip strength and all-cause, non-cardiovascular, and cardiovascular mortality. However, no significant association was observed with respiratory diseases, including COPD (50). A bidirectional causal relationship has been suggested between usual walking pace and COPD risk, whereas a decrease in right-hand grip strength exhibits a unidirectional relationship with an increased incidence of COPD (51). HGS is also linked to peak inspiratory flow rate (PIFR) in clinically stable COPD patients, particularly in those with significant symptoms but infrequent exacerbations. Threshold values of HGS associated with poor PIFR have been identified (52). Moreover, HGS in male COPD patients exhibited a positive correlation with the EQ-5D utility score index, an indicator of quality of life (53).
Handgrip strength is closely associated with lower limb muscle function and serves as a meaningful indicator of overall limb strength across different age groups (54). Evidence suggests that grip strength may reflect nutritional status, with findings from the Hertfordshire Cohort Study indicating that healthier dietary patterns—characterized by prudent food choices, adequate dietary protein, antioxidants, vitamin D, and fatty fish consumption—are positively correlated with better grip strength (55). Furthermore, higher levels of physical activity and reduced sedentary behavior have been associated with greater grip strength (56, 57), underscoring the importance of lifestyle factors in maintaining muscular function. Additional determinants include ethnicity, age, sex, height, and socioeconomic status, with studies suggesting a heritability of approximately 52% for grip strength (58, 59).
Inspiratory muscle training may offer benefits for COPD patients with inspiratory muscle weakness (60). Although maximal inspiratory pressure is frequently reduced in COPD, only a minority of patients meet the criteria for profound weakness. While inspiratory muscle strength correlates with leg muscle strength, it does not directly associate with walking distance or symptom burden (61). Pulmonary obstruction in COPD significantly contributes to losses in muscle strength and mobility, and age-related declines in physical function may be influenced not only by reduced muscle strength and power but also by deteriorating pulmonary function (62). Quadriceps weakness is a recognized consequence of physical inactivity, particularly following hospitalizations for acute exacerbation of COPD (AECOPD). In such patients, progressive resistance training using ankle weights has been shown to be a feasible rehabilitation strategy even in severe cases (63). Impaired voluntary activation of the quadriceps is also common in advanced COPD and can be ameliorated with targeted exercise interventions (64).
Currently, it is estimated that 30–60% of COPD patients are affected by malnutrition, which can accelerate pulmonary cachexia and sarcopenia through progressive weight loss (65). The prevalence of cachexia increases with disease severity (66). Muscle atrophy and diaphragmatic dysfunction may further reduce exercise tolerance, creating a vicious cycle that worsens disease outcomes. In addition to the body mass index (BMI), more nuanced nutritional assessments are necessary, as BMI alone may underestimate malnutrition. Bioelectrical impedance analysis (BIA) is a non-invasive and practical method for body composition evaluation. It operates on the principle of electrical resistance through body tissues and provides estimates based on a three-compartment model (7, 67). The BIA can be applied across COPD disease stages and often reveals deficits in fat-free mass (FFM) and the PhA, which correlate with FEV₁, FFM, and prediction of mortality. Exacerbation frequency has also been linked to lower BMI and FFM (68), highlighting the importance of incorporating body composition and PhA assessment in COPD management.
Several studies report positive correlations between PhA and markers such as hemoglobin and albumin in hospitalized patients with cardiovascular disease, supporting the utility of PhA in identifying sarcopenia, malnutrition, and cachexia (69). Patients with lower PhA are typically older, at higher nutritional risk, and exhibit more pronounced malnutrition. They also exhibit unfavorable changes in anthropometric measures (e.g., weight, BMI, and arm and waist circumference), body composition parameters [e.g., skeletal muscle mass and appendicular skeletal mass index (ASMI)], and biochemical parameters (e.g., hemoglobin, albumin, and lipids) compared to those with normal PhA. Significant positive correlations have been observed between PhA and these nutritional metrics, indicating that lower PhA reflects poorer nutritional status (44, 45, 70).
An important consideration is that PhA is influenced by factors such as hydration, inflammation, and disease severity (67). While our models adjusted for key demographics and comorbidities, we could not include all potential confounders (71). However, this sensitivity is precisely what makes PhA a valuable composite biomarker (72). Evidence suggests that PhA integratively reflects the overall health of cell membranes and body cell mass, serving as a summary measure of the cumulative impact of various pathological processes (73). This includes the recent 2025 study (74) linking low PhA to computed tomography (CT)-based functional and pathological changes in smokers, the 2024 systematic review consolidating evidence on PhA’s association with disease severity, function, and prognosis in COPD, as well as prognostic studies (8, 70, 75–77). All statements regarding mortality and prognosis, especially in the Discussion, have been carefully reviewed and are currently specifically grounded in the findings from the COPD patient cohorts (43, 45, 78). Therefore, its persistent association with physical function, despite the potential influence of other variables, underscores its role as a robust, pragmatic indicator of global cellular health and functional status in COPD (79).
Limitations
This study has several limitations. First, as a small, single-center investigation, the generalizability of the findings is limited, and future prospective randomized controlled trials are needed to confirm these results. Second, certain measures, such as walking speed and grip strength, are subject to human performance variability; and future studies would benefit from incorporating more objective biomechanical or device-based assessments. Finally, the relatively short observation period limits the evaluation of long-term relationships, highlighting the need for extended follow-up in subsequent research.
Conclusion
PhA was identified as an independent predictor of both handgrip strength and knee extension strength in patients with COPD. These findings underscore the clinical relevance of PhA as a practical biomarker for identifying impaired muscle function and nutritional status in COPD, supporting its potential incorporation into routine assessment and management of individuals with COPD.
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Ethics statement
This study is a single center, observational, cross-sectional study. This study was approved by the ethics committee of our hospital and the institutional review board (IRB) (approval number: 2036-58-55, approval date: March 10, 2021). All patients were informed and signed an informed consent form.
Author contributions
JQ: Data curation, Methodology, Supervision, Writing – original draft, Writing – review & editing. MX: Formal analysis, Project administration, Software, Validation, Writing – original draft. ZZ: Funding acquisition, Resources, Visualization, Writing – review & editing. YW: Conceptualization, Investigation, Software, Writing – review & editing. GW: Formal analysis, Project administration, Validation, Writing – review & editing. XF: Conceptualization, Investigation, Software, Writing – original draft, Writing – review & editing.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. This research was funded by Scientific Research Project of Colleges and Universities in Anhui Province (No. 2024AH51904).
Acknowledgments
The authors would like to thank all participants and our hospital.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Keywords: PHA, COPD, grip strength, knee extension strength, BIA
Citation: Qian J, Xu M, Zhang Z, Wu Y, Wang G and Fan X (2026) Lower bioelectrical impedance phase angle is associated with COPD and is a marker for increased risks in elderly COPD patients. Front. Med. 12:1703621. doi: 10.3389/fmed.2025.1703621
Edited by:
Roberto Giovanni Carbone, University of Genoa, ItalyReviewed by:
Rodrigo Alvaro Brandão Lopes-Martins, Hospital do câncer de Muriaé, BrazilFrancesco De Blasio, Clinic Center, Italy
Copyright © 2026 Qian, Xu, Zhang, Wu, Wang and Fan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: XiaoYun Fan, eGlhb3l1bmZhbkBhaG11LmVkdS5jbg==