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Association between body composition, sarcopenia and pulmonary function in chronic obstructive pulmonary disease



Chronic Obstructive Pulmonary Disease (COPD) is characterized by progressive and irreversible airflow limitation. Different factors that modify pulmonary function include age, sex, muscular strength, and a history of exposure to toxic agents. However, the impact of body composition compartments and sarcopenia on pulmonary function is not well-established. This study aimed to evaluate how body composition compartments and sarcopenia affect pulmonary function in COPD patients.


In a cross-sectional study, patients with a confirmed diagnosis of COPD, > 40 years old, and forced expiratory volume in the first second /forced vital capacity ratio (FEV1/FVC) < 0.70 post-bronchodilator were included. Patients with cancer, HIV, and asthma were excluded. Body composition was measured with bioelectrical impedance. Sarcopenia was defined according to EWGSOP2, and pulmonary function was assessed by spirometry.


185 patients were studied. The mean age was 72.20 ± 8.39 years; 55.14% were men. A linear regression adjusted model showed associations between body mass index, fat-free mass, skeletal muscle mass index, appendicular skeletal muscle mass index, and phase angle (PhA), and sarcopenia with FEV1 (%). As regards FVC (%), PhA and exercise tolerance had positive associations.


Body composition, especially PhA, SMMI, ASMMI, and sarcopenia, has a significant impact on pulmonary function. Early detection of disturbances of these indexes enables the early application of such therapeutic strategies in COPD patients.

Peer Review reports


Chronic Obstructive Pulmonary Disease (COPD) is a treatable and avoidable illness characterized by persistent respiratory symptoms and progressive and irreversible airflow limitation [1]. According to the World Health Organization, COPD is considered a public health problem because globally it is the third leading cause of death [2].

Pulmonary function is modified by different factors, including age, sex, weeks of gestation, muscular strength, the immune system, and a history of exposure to toxic agents such as tobacco, wood smoke, and asbestos [3,4,5].

The pulmonary function can be estimated by spirometry. Spirometry reveals pulmonary dynamic volumes: forced expiratory volume in the first second (FEV1), forced vital capacity (FVC), and the ratio of forced expiratory volume in the first second to forced vital capacity (FEV1/FVC). Several studies have demonstrated that FEV1 reduction is a significant predictor of mortality in the general population [6, 7] and a marker of cardiovascular mortality [8]. Therefore, it is necessary to know which factors affect it.

Patients with COPD have alterations in the body composition compartments of fat-free mass (FFM), appendicular skeletal muscle mass index (ASMMI), skeletal muscle mass index (SMMI), fat mass (FM), phase angle (PhA), and muscular function. Loss of muscle mass and muscular function have multifactorial origins. The factors involved include oxidative stress, hypoxia, disuse, malnutrition, a higher catabolic state, and glucocorticoid use [9]. The prevalence of muscle wasting ranges from 15 to 40% in patients with COPD [10, 11]. Weight and muscle mass loss are associated with diminished muscle strength, walking speed, exercise tolerance, pulmonary alterations, and worse prognosis in COPD patients [10, 12,13,14].

Previous studies in other populations have shown that reduced skeletal muscle mass is associated with lower pulmonary function [15, 16]. Moreover, the strength of the respiratory muscles impacts pulmonary function [17]. In COPD patients, upper limb muscle strength has been positively associated with the FEV1/FVC ratio and respiratory muscle strength [5]. However, the role of the fat-free mass index (FFMI) is unclear. Maddocks et al. found no difference in predicted FEV1% between subjects with low and normal FFMI. On the other hand, Machado et al. showed that subjects with low FFMI had lower predicted FEV1% [18]. No evidence has been reported about the impact of SMMI or ASMMI on pulmonary function.

Sarcopenia is a progressive and generalized skeletal muscle disorder characterized by low muscle strength and low muscle mass that can lead to falls, fractures, physical disability, and mortality [10, 19]. The loss of muscle mass and sarcopenia have multifactorial origins, including mitochondrial abnormalities, diminished protein synthesis, diminished intake of essential amino acids for protein synthesis, hypoxemia which interferes with protein synthesis, and increased proteolysis due to a pro-inflammatory state. In addition, glucocorticoid use, which promotes proteolysis and acidosis, is common in COPD patients. Combined with these factors, physical inactivity promotes muscle atrophy [20]. A meta-analysis performed by Benz et al. estimates that the prevalence of sarcopenia in COPD patients is 21.6% (95% CI; 14.6 to 30.9%) [21]. In COPD, sarcopenia patients have lower FEV1 than patients without sarcopenia [22].

Phase angle (PhA) is an important body composition variable of bioelectrical impedance analysis (BIA). A low PhA suggests lower cellularity, membrane integrity, cellular function, malnutrition, status, impaired quality of life, and worse prognosis [14, 22, 23]. In COPD patients, this manifests as reduced muscle strength [14, 22] Maddocks et al. showed that COPD patients with a PhA below of fifth percentile of age, sex and BMI-stratified reference values had low predicted FEV1% and lower physical activity than COPD patients with normal PhA [13].

However, the association between body composition compartments and sarcopenia with pulmonary function in COPD patients is not well-established. Thus, the objective of this study was to evaluate how body composition compartments and sarcopenia may affect pulmonary function in COPD patients.

Materials and methods

A cross-sectional study was performed. The data were obtained during outpatient evaluations carried out during routine consultations of patients of Mexican origin with COPD between August 1, 2019, and March 31, 2020.

Patients with a confirmed diagnosis of COPD according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) recommendations [24] were included. The subjects were > 40 years old, and spirometry with a post-bronchodilator FEV1/FVC ratio < 0.70. Patients with diagnoses of cancer, HIV, and asthma were excluded.

Outcome measures

Body composition, anthropometry, pulmonary function, clinical and demographic variables were evaluated as part of the clinical management provided to the patients who came to the Institute.


Weight and height were measured according to the manual reference of anthropometric standardization [25]. All subjects wore light clothing and were barefoot. Body mass index (BMI) was calculated by dividing the total body weight (kilograms) by the height in meters squared.

Body composition

Body composition and phase angle (PhA) were measured with whole-body bioelectrical impedance using a four-pole mono-frequency equipment RJL Quantum IV analyzer (RJL Systems®, Clinton Township, MI, USA). Phase angle was calculated using the equation: arctan (Reactance/Resistance) x (180º /π) using the PhA Software (RJL Systems®). The standard technique [26] was used. The measurements were all performed by the same operator, in the morning, in a comfortable area, free of drafts, and with portable electric heaters. The subjects were fasting and should not have exercised eight hours before or consumed alcohol 12 h before the study. During the entire study, the person was supine with arms separated from the trunk at about 30º and legs separated at about 45º.

The area was cleaned with alcohol, and electrodes were placed on the hand and ipsilateral foot. Resistance and reactance were registered, and PhA. Fat-free mass (FFM) and fat mass were estimated by RJL Systems’ software BC 4.2.2. Appendicular skeletal muscle mass index (ASMMI) was assessed according to Sergi’s formula (27): ASMMI (Kg/m2) = [-3.964 + (0.227*(Height2 (cm)/Resistance) + (0.095*Weight) + (1.384*Sex) + (0.064*Reactance) /Height (m2)].

Skeletal muscle mass index (SMMI) was assessed according Janssen’s formula (28): SMM (kg) = [(Height2 cm/Resistance × 401) + (gender × 3.825) + (age x − 0.071) + 5.102], and SMMI (kg/m2) = SMMI /Height (m2).

Handgrip strength

Handgrip strength was measured with a mechanical Smedley Hand Dynamometer (Stoelting, Wood Dale, UK) according to the technique described in Rodriguez et al. [29].


Sarcopenia was defined according to EWGSOP2 [19] in men as ASMMI < 7 kg/m2 and handgrip strength < 27 kg and in women as ASMMI < 6 kg/m2 and handgrip strength < 16 kg.

Exercise tolerance

Exercise tolerance was assessed by a 6-min walk, performed according to American Thoracic Society standards [30].

Pulmonary function

Spirometry testing was conducted by an experienced pulmonary technician using a portable spirometer (EasyOnePC, Ndd Medical Technologies Inc., Zürich, Switzerland) according to the criteria of the American Thoracic Society/European Respiratory Society standards [31]. The spirometry variables analyzed were FEV1, and FVC after using a bronchodilator. After 15 min resting, a maximum forced inhalation and a powerful forced expiration were performed by the participant wearing a nose clip. The reference values used for spirometry were obtained in Mexican–American individuals [32].

Statistical analysis

Analyses were performed using the commercially available package STATA version 14 (Stata Corp., College Station, TX, U.S.A.). The Shapiro–Wilk test was used to test the normality of continuous variables. Normal continuous variables were expressed as mean and standard deviation, while non-normal variables were expressed as median and percentiles 25–75. A comparison among study groups was analyzed with a chi-square test for categorical variables and unpaired Student's t-test or Wilcoxon tests for continuous variables.

Linear regression analysis was performed to examine the association between the dependent variable: pulmonary function (predicted FEV1% and predicted FVC %) and independent variables: body composition compartments, handgrip strength, exercise tolerance, and sarcopenia. The models were adjusted for sex, height, and age. A p-value < 0.05 was considered statistically significant.


One hundred eighty-five patients with COPD were evaluated, of whom 78 (42%) had sarcopenia. The mean population age was 72.20 ± 8.39 years; 55.14% were men, 50.27% had systemic hypertension, 29.19% were obese, and 51.80% were in heart failure.

COPD patients with sarcopenia were older, with a lower prevalence of obesity, low FEV1 (L), FVC (L), BMI, FM, FFM, handgrip strength, middle-upper arm circumference, SMMI, ASMMI, abdominal obesity, PhA, and exercise tolerance compared to those COPD patients without sarcopenia (Table 1).

Table 1 Clinical and body composition characteristic in sarcopenia subjects

Table 2 shows the linear regression results adjusted for age, sex, and height, in which the variables associated with FEV1 and FVC are presented. BMI (β:0.670, 95% CI; 0.181–1.159), FFM (β:0.648, 95% CI;0.217–1.079), middle-upper arm circumference, SMMI, ASMMI, PhA, and exercise tolerance were positively associated with FEV1. Sarcopenia, however, had a negative association with FEV1. As regards the FVC, PhA and exercise tolerance showed positive associations (β:0.064, 95% CI; 0.036–0.093 for exercise tolerance).

Table 2 Association between body composition, sarcopenia and pulmonary function


The main finding in our study was that FEV1 had a positive correlation with SMMI, ASMMI, BMI, FFM, PhA, middle-upper arm circumference, and exercise tolerance, while sarcopenia had a negative impact. As far as FVC was concerned, PhA and exercise tolerance showed positive associations.

Muscle mass is the largest tissue in the human body [33]. It can also be an independent prognostic factor for daily disability, mobility, and mortality in COPD patients [10, 12, 34]. Our study showed positive associations between FEV1, and ASMMI (β: 4.896, 95% CI; 1.982 to 7.810, p = 0.001) and SMMI (β: 2.876, 95% CI; 0.534 to 5.218, p = 0.016). That is, for every 1 kg/m2 of ASMMI that increased, there was an increase of 4.89% in FEV1, and for every 1 k/m2 of SMMI, FEV1 increased 2.87%. Previous studies have also reported that subjects with low muscle mass have worse pulmonary function. A study performed by Jeon et al. in elderly Koreans found that subjects with low FEV1 were at higher risk for decreased muscle mass [15]. Also, a study performed by Park et al. in subjects without pulmonary disease showed that lower muscle mass was associated with a higher risk of FEV1 < 80% (OR: 2.97, CI 95% 2.74 to 3.17) and FVC < 80% (OR: 2.64, CI 95%; 2.43 to 2.83) [16].

On the other hand, in the present study, a higher BMI was also positively associated with FEV1. Currently, a controversy exists about the impact of obesity evaluated by BMI on pulmonary function and prognostic patients, which has been called the paradox of obesity [35, 36]. There is evidence that a higher BMI improves pulmonary function [36, 37]. A meta-analysis performed on COPD patients from clinical trials observed that overweight-obesity and morbid obesity subjects evaluated by BMI had minor decreases in FEV1 per year compared to normal weight and underweight subjects [37]. However, evidence shows the deleterious effects of obesity on pulmonary function [38,39,40]. While a study by Ochs-Balcom et al. found a negative association between BMI > 25 kg/m2 with both FEV1 and FVC, a study by Peralta et al. reported that increased weight in overweight and obese subjects was associated with a reduction only in FVC. However, no association was observed with the FEV1/FVC ratio [39]. Similar results have been reported in other studies [41, 42].

Nevertheless, it is essential to bear in mind that BMI is the quotient of body weight in kg and height in m2, but BMI cannot distinguish the two most significant components of body composition: FM and FFM. Our study observed that FFM positively impacted FEV1, while no such association was observed with fat mass when evaluating these two components. These results suggest that the FFM, specifically SMMI or ASMMI, plays an essential role in pulmonary function. Won et al. likewise observed that SMMI was associated with FEV1 and FVC [43]. Apart from FFM, SMMI and ASMMI correlate with prognosis [10,11,12].

In this study PhA proved to be a predictor for FEV1 (β: 5.745, CI 95%; 2.596 to 8.895, p < 0.001) and FVC (β: 3.871, CI 95%; 1.077 to 6.665, p = 0.007) adjusted for age, sex and height. That is, for every 1 degree that PhA increases, there is an increment of 5.74% for FEV1 and 3.87% for FVC.

A lower PhA has been associated with reduced FEV1, muscle strength, exercise tolerance, diminished quality of life, prolonged hospitalization, and exacerbations [13], as well as being an independent predictor of death (HR: 0.53, CI 95%; 0.36–0.77 p < 0.001) in COPD subjects [14].

It is noteworthy that muscle strength is a significant predictor of mortality [12]. Although an association between diminished handgrip strength and pulmonary function was not observed in the present study, it was found in other studies. Liu et al. reported a positive correlation between the strength of upper limb muscles and inspiratory and expiratory respiratory muscles and the FEV1/FVC ratio [5]. Likewise, subjects hospitalized because of exacerbations of COPD showed a positive association between handgrip strength and effective peak inspiratory flow rate determined by the inspiratory muscle force [44].

As regards sarcopenia, the prevalence in COPD patients is 22% [21], representing an increase according to the GOLD [20, 22]. The present study demonstrated that sarcopenia was an independent predictor for FEV1 since subjects had 6.992% lower FEV1 than those without sarcopenia (β: − 6.992, 95% CI; − 13.722 to − 0.261, p < 0.042) adjusted for age, sex, and height. Different studies have shown that sarcopenia, muscle depletion, and low muscle strength are associated with deteriorating lung function, higher inflammatory biomarkers, poor quality of life, and worse prognosis [10, 19, 20, 45].

When adipose tissue distribution is examined in the context of obesity, abdominal obesity plays an important role in pulmonary function. A study in subjects over 50 years of age showed that those with abdominal obesity had decreased pulmonary function. Moreover, pulmonary function was even lower in those with generalized obesity [42]. In a meta-analysis Wehrmeister et al. found similar results. They concluded that an inverse association exists between pulmonary function and abdominal circumference [41], which is a good indicator of abdominal obesity. This could be explained by the fact that obesity causes dysfunction in small airways and limitation of expiratory flow as well as mechanical respiratory changes, restricted movement of the thoracic cage, decreased strength of the respiratory muscles, gas exchange, and reduced tolerance to exercise due to excess adipose tissue around the rib cage and abdomen [46]. The present study did not establish an association between abdominal obesity and pulmonary function, but this may be explained by the population's higher prevalence of sarcopenia.

In addition, obesity, especially central obesity, is associated with an increase in inflammatory factors such as interleukin 6 and tumor necrosis α [47], which negatively impact pulmonary function and increase morbidity and mortality [48]. That is why it is essential to distinguish among body composition compartments–FM, FFM, ASMMI, SMMI, and PhA which have different impacts on pulmonary function and prognosis, both in healthy subjects and in COPD patients.

The clinical picture is further complicated by comorbidities such as hypertension, diabetes, and heart failure, which are frequent in COPD [49]. These pathologies are independent risk factors for sarcopenia [49, 50]. The presence of one or more comorbidities leads to more significant loss of skeletal muscle mass, physical performance, and worse prognosis [51]. This low skeletal muscle mass had a negative impact on pulmonary function [15, 16].

Limitations and strengths

This study has the inherent limitations of a cross-sectional study. Another limitation is the small sample size. However, among the strengths, this is the first study in COPD patients that evaluates the association of different compartments of body composition on pulmonary function using a multivariate prediction model adjusted for confounding variables.


The components of body composition, especially PhA, SMMI, ASMMI, and sarcopenia, have significant impacts on pulmonary function. Early detection of disturbances in these indexes and muscle wasting enables the early application of such therapeutic strategies as physical and pulmonary rehabilitation, and nutritional treatment may improve pulmonary function in COPD patients.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due to the fact that individual privacy could be compromised but are available from the corresponding author on reasonable request.



Appendicular skeletal muscle mass index


Bioelectrical impedance analysis


Body Mass Index


Chronic Obstructive Pulmonary Disease


Fat mass

FEV1 :

Forced expiratory volume in first second


Forced expiratory volume in first second/Forced vital capacity


Fat-free mass


Forced vital capacity


Global Initiative for Chronic Obstructive Lung Disease


Phase angle


Skeletal muscle mass index


  1. Mirza S, Clay RD, Koslow MA, Scanlon PD. COPD Guidelines: a review of the 2018 GOLD Report. Mayo Clin Proc. 2018;93(10):1488–502.

    PubMed  Google Scholar 

  2. Organization WH. Top 10 Causes of Death in 2016 2018 2019. Available from:

  3. Sharma G, Goodwin J. Effect of aging on respiratory system physiology and immunology. Clin Interv Aging. 2006;1(3):253–60.

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Suzuki M, Makita H, Ito YM, Nagai K, Konno S, Nishimura M, et al. Clinical features and determinants of COPD exacerbation in the Hokkaido COPD cohort study. Eur Respir J. 2014;43(5):1289–97.

    PubMed  Google Scholar 

  5. Liu X, Li P, Wang Z, Lu Y, Li N, Xiao L, et al. Evaluation of isokinetic muscle strength of upper limb and the relationship with pulmonary function and respiratory muscle strength in stable COPD patients. Int J Chron Obstruct Pulmon Dis. 2019;14:2027–36.

    PubMed  PubMed Central  Google Scholar 

  6. Schünemann HJ, Dorn J, Grant BJ, Winkelstein W Jr, Trevisan M. Pulmonary function is a long-term predictor of mortality in the general population: 29-year follow-up of the Buffalo Health Study. Chest. 2000;118(3):656–64.

    PubMed  Google Scholar 

  7. Ji Z, de Miguel-Diez J, Castro-Riera CR, Bellon-Cano JM, Gallo-Gonzalez V, Giron-Matute WI, et al. Differences in the outcome of patients with COPD according to Body Mass Index. Journal of clinical medicine. 2020;9(3).

  8. Sin DD, Anthonisen NR, Soriano JB, Agusti AG. Mortality in COPD: role of comorbidities. Eur Respir J. 2006;28(6):1245–57.

    CAS  PubMed  Google Scholar 

  9. Langen RC, Gosker HR, Remels AH, Schols AM. Triggers and mechanisms of skeletal muscle wasting in chronic obstructive pulmonary disease. Int J Biochem Cell Biol. 2013;45(10):2245–56.

    CAS  PubMed  Google Scholar 

  10. Schols AM, Broekhuizen R, Weling-Scheepers CA, Wouters EF. Body composition and mortality in chronic obstructive pulmonary disease. Am J Clin Nutr. 2005;82(1):53–9.

    CAS  PubMed  Google Scholar 

  11. Vestbo J, Prescott E, Almdal T, Dahl M, Nordestgaard BG, Andersen T, et al. Body mass, fat-free body mass, and prognosis in patients with chronic obstructive pulmonary disease from a random population sample: findings from the Copenhagen City Heart Study. Am J Respir Crit Care Med. 2006;173(1):79–83.

    PubMed  Google Scholar 

  12. Swallow EB, Reyes D, Hopkinson NS, Man WD, Porcher R, Cetti EJ, et al. Quadriceps strength predicts mortality in patients with moderate to severe chronic obstructive pulmonary disease. Thorax. 2007;62(2):115–20.

    PubMed  Google Scholar 

  13. Maddocks M, Kon SS, Jones SE, Canavan JL, Nolan CM, Higginson IJ, et al. Bioelectrical impedance phase angle relates to function, disease severity and prognosis in stable chronic obstructive pulmonary disease. Clin Nutr. 2015;34(6):1245–50.

    PubMed  Google Scholar 

  14. de Blasio F, Scalfi L, Di Gregorio A, Alicante P, Bianco A, Tantucci C, et al. Raw bioelectrical impedance analysis variables are independent predictors of early all-cause mortality in patients with COPD. Chest. 2019;155(6):1148–57.

    PubMed  Google Scholar 

  15. Jeon YK, Shin MJ, Kim MH, Mok JH, Kim SS, Kim BH, et al. Low pulmonary function is related with a high risk of sarcopenia in community-dwelling older adults: the Korea National Health and Nutrition Examination Survey (KNHANES) 2008–2011. Osteoporosis Int. 2015;26(10):2423–9.

    CAS  Google Scholar 

  16. Park CH, Yi Y, Do JG, Lee YT, Yoon KJ. Relationship between skeletal muscle mass and lung function in Korean adults without clinically apparent lung disease. Medicine. 2018;97(37):e12281.

  17. Bahat G, Tufan A, Ozkaya H, Tufan F, Akpinar TS, Akin S, et al. Relation between hand grip strength, respiratory muscle strength and spirometric measures in male nursing home residents. Aging Male. 2014;17(3):136–40.

    PubMed  Google Scholar 

  18. Machado FVC, Spruit MA, Groenen MTJ, Houben-Wilke S, van Melick PP, Hernandes NA, et al. Frequency and functional translation of low muscle mass in overweight and obese patients with COPD. Respir Res. 2021;22(1):93.

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyere O, Cederholm T, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019;48(4):601.

    PubMed  PubMed Central  Google Scholar 

  20. Jones SE, Maddocks M, Kon SS, Canavan JL, Nolan CM, Clark AL, et al. Sarcopenia in COPD: prevalence, clinical correlates and response to pulmonary rehabilitation. Thorax. 2015;70(3):213–8.

    PubMed  Google Scholar 

  21. Benz E, Trajanoska K, Lahousse L, Schoufour JD, Terzikhan N, De Roos E, et al. Sarcopenia in COPD: a systematic review and meta-analysis. Eur Respir Rev. 2019;28:154.

    Google Scholar 

  22. de Blasio F, Di Gregorio A, de Blasio F, Bianco A, Bellofiore B, Scalfi L. Malnutrition and sarcopenia assessment in patients with chronic obstructive pulmonary disease according to international diagnostic criteria, and evaluation of raw BIA variables. Respir Med. 2018;134:1–5.

    PubMed  Google Scholar 

  23. Norman K, Wirth R, Neubauer M, Eckardt R, Stobäus N. The bioimpedance phase angle predicts low muscle strength, impaired quality of life, and increased mortality in old patients with cancer. Journal of the American Medical Directors Association. 2015;16(2):173. e17–e22 %@ 1525–8610.

  24. Vogelmeier CF, Criner GJ, Martinez FJ, Anzueto A, Barnes PJ, Bourbeau J, et al. Global strategy for the diagnosis, management and prevention of chronic obstructive lung disease 2017 report: GOLD executive summary. Respirology. 2017;22(3):575–601.

    PubMed  Google Scholar 

  25. Lohman TG, Roche AF, Martorell R. Anthropometric standardization reference manual: Human kinetics books Champaign; 1988.

  26. Lukaski HC, Johnson PE, Bolonchuk WW, Lykken GI. Assessment of fat-free mass using bioelectrical impedance measurements of the human body. Am J Clin Nutr. 1985;41(4):810–7.

    CAS  PubMed  Google Scholar 

  27. Sergi G, De Rui M, Veronese N, Bolzetta F, Berton L, Carraro S, et al. Assessing appendicular skeletal muscle mass with bioelectrical impedance analysis in free-living Caucasian older adults. Clin Nutr. 2015;34(4):667–73.

    PubMed  Google Scholar 

  28. Janssen I, Heymsfield SB, Baumgartner RN, Ross R. Estimation of skeletal muscle mass by bioelectrical impedance analysis. J Appl Physiol. 2000;89(2):465–71.

    CAS  PubMed  Google Scholar 

  29. Rodríguez-García WD, García-Castañeda L, Orea-Tejeda A, Mendoza-Núñez V, González-Islas DG, Santillán-Díaz C, et al. Handgrip strength: Reference values and its relationship with bioimpedance and anthropometric variables. Clin Nutr ESPEN. 2017;19:54–8.

    Google Scholar 

  30. ATS statement: guidelines for the six-minute walk test. American journal of respiratory and critical care medicine. 2002;166(1):111–7.

  31. Miller MR, Hankinson J, Brusasco V, Burgos F, Casaburi R, Coates A, et al. Standardisation of spirometry. Eur Respir J. 2005;26(2):319–38.

    CAS  PubMed  Google Scholar 

  32. Hankinson JL, Odencrantz JR, Fedan KB. Spirometric reference values from a sample of the general U.S. population. Am J Respir Crit Care Med. 1999;159(1):179–87.

  33. Snyder W, Cook M, Nasset E, Karhausen L, Howells GP, Tipton I. Report of the task group on reference man: Pergamon Oxford; 1975.

  34. Janssen I, Baumgartner RN, Ross R, Rosenberg IH, Roubenoff R. Skeletal muscle cutpoints associated with elevated physical disability risk in older men and women. Am J Epidemiol. 2004;159(4):413–21.

    PubMed  Google Scholar 

  35. Yamauchi Y, Hasegawa W, Yasunaga H, Sunohara M, Jo T, Takami K, et al. Paradoxical association between body mass index and in-hospital mortality in elderly patients with chronic obstructive pulmonary disease in Japan. Int J Chron Obstruct Pulmon Dis. 2014;9:1337–46.

    PubMed  PubMed Central  Google Scholar 

  36. Wu Z, Yang D, Ge Z, Yan M, Wu N, Liu Y. Body mass index of patients with chronic obstructive pulmonary disease is associated with pulmonary function and exacerbations: a retrospective real world research. J Thorac Dis. 2018;10(8):5086–99.

    PubMed  PubMed Central  Google Scholar 

  37. Sun Y, Milne S, Jaw JE, Yang CX, Xu F, Li X, et al. BMI is associated with FEV decline in chronic obstructive pulmonary disease: a meta-analysis of clinical trials. Respir Res. 2019;20(1):236.

    PubMed  PubMed Central  Google Scholar 

  38. Lazarus R, Sparrow D, Weiss ST. Effects of obesity and fat distribution on ventilatory function: the normative aging study. Chest. 1997;111(4):891–8.

    CAS  PubMed  Google Scholar 

  39. Ochs-Balcom HM, Grant BJ, Muti P, Sempos CT, Freudenheim JL, Trevisan M, et al. Pulmonary function and abdominal adiposity in the general population. Chest. 2006;129(4):853–62.

    PubMed  Google Scholar 

  40. Kwack WG, Kang Y-S, Jeong YJ, Oh JY, Cha YK, Kim JS, et al. Association between thoracic fat measured using computed tomography and lung function in a population without respiratory diseases. J Thorac Dis. 2019;11(12):5300.

    PubMed  PubMed Central  Google Scholar 

  41. Wehrmeister FC, Menezes AM, Muniz LC, Martinez-Mesa J, Domingues MR, Horta BL. Waist circumference and pulmonary function: a systematic review and meta-analysis. Syst Rev. 2012;1:55.

    PubMed  PubMed Central  Google Scholar 

  42. Pan J, Xu L, Lam TH, Jiang CQ, Zhang WS, Jin YL, et al. Association of adiposity with pulmonary function in older Chinese: Guangzhou Biobank Cohort Study. Respir Med. 2017;132:102–8.

    PubMed  Google Scholar 

  43. Won YH, Cho YS, Kim DH, Joo SY, Seo CH. relation between low pulmonary function and skeletal muscle index in burn patients with major burn injury and smoke inhalation: a retrospective study. J Burn Care Res. 2020;41(3):695–9.

    PubMed  Google Scholar 

  44. Samarghandi A, Ioachimescu OC, Qayyum R. Association between peak inspiratory flow rate and hand grip muscle strength in hospitalized patients with acute exacerbation of chronic obstructive pulmonary disease. PLoS ONE. 2020;15(1):e0227737.

  45. Byun MK, Cho EN, Chang J, Ahn CM, Kim HJ. Sarcopenia correlates with systemic inflammation in COPD. Int J Chron Obstruct Pulmon Dis. 2017;12:669–75.

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Salome CM, King GG, Berend N. Physiology of obesity and effects on lung function. J Appl Physiol. 2010;108(1):206–11.

    PubMed  Google Scholar 

  47. Kern PA, Ranganathan S, Li C, Wood L, Ranganathan G. Adipose tissue tumor necrosis factor and interleukin-6 expression in human obesity and insulin resistance. Am J Physiol Endocrinol Metab. 2001;280(5):E745–51.

    CAS  PubMed  Google Scholar 

  48. Lavie CJ, Milani RV. Obesity and cardiovascular disease: The hippocrates paradox? J Am Coll Cardiol. 2003;42(4):677–9.

    PubMed  Google Scholar 

  49. Pacifico J, Geerlings MAJ, Reijnierse EM, Phassouliotis C, Lim WK, Maier AB. Prevalence of sarcopenia as a comorbid disease: a systematic review and meta-analysis. Exp Gerontol. 2020;131:110801.

    PubMed  Google Scholar 

  50. Dodds RM, Granic A, Robinson SM, Sayer AA. Sarcopenia, long-term conditions, and multimorbidity: findings from UK Biobank participants. J Cachexia Sarcopenia Muscle. 2020;11(1):62–8.

    PubMed  Google Scholar 

  51. Gong G, Wan W, Zhang X, Liu Y, Liu X, Yin J. Correlation between the Charlson comorbidity index and skeletal muscle mass/physical performance in hospitalized older people potentially suffering from sarcopenia. BMC Geriatr. 2019;19(1):367.

    PubMed  PubMed Central  Google Scholar 

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The authors declare that there is no funding in the present study.

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Conceptualization: ML.N., GI.D, OT.A. Data curation: ML.N. GI.D., FC.L., SS.R., MV.V. Formal analysis: GI.D., FC.L. Investigation: KD.C., SS.R., PG.I., MV.V., MR.O., MR.O. Methodology: GI.D., FC.L., OT. A., KD.C., PG.I., GA.Y. Project administration: GI.D, OT. A., ML.N., KD.C., GA.Y. Writing—original draft: All authors. Writing—review and editing: All authors read and approved the final manuscript.

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Correspondence to Dulce González-Islas.

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This study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Ethics and Research Committee of Biomedical Research in Humans (approval number E-02-18), and all participants signed informed consent.

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Martínez-Luna, N., Orea-Tejeda, A., González-Islas, D. et al. Association between body composition, sarcopenia and pulmonary function in chronic obstructive pulmonary disease. BMC Pulm Med 22, 106 (2022).

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  • Chronic obstructive pulmonary disease
  • Pulmonary function
  • Sarcopenia
  • Body composition
  • Skeletal muscle mass