Skip to main content
Download PDF

Association between muscle quality index and pulmonary function in post-COVID-19 subjects

BMC Pulmonary Medicine volume 23, Article number: 442 (2023) Cite this article

Abstract

Background

The SARS-CoV2 pandemic impacted many critically ill patients, causing sequelae, affecting lung function, and involving the musculoskeletal system. We evaluated the association between lung function and muscle quality index in severely ill post-COVID-19 patients.

Methods

A cross-sectional study was conducted on a post-COVID-19 cohort at a third-level center. The study included patients who had experienced severe-to-critical COVID-19. Anthropometric measurements, such as body mass index (BMI) and handgrip strength, were obtained to calculate the muscle quality index (MQI). Additionally, spirometry, measurements of expiratory and inspiratory pressure, and an assessment of DLCO in the lungs were performed. The MQI was categorized into two groups: low-MQI (below the 50th percentile) and high-MQI (above the 50th percentile), based on sex. Group differences were analyzed, and a multivariate linear regression analysis was performed to assess the association between respiratory function and MQI.

Results

Among the 748 patients analyzed, 61.96% required mechanical ventilation, and the median hospital stay was 17 days. In patients with a low MQI, it was observed that both mechanical respiratory function and DLCO were lower. The multivariate analysis revealed significantly lower findings in mechanical respiratory function among patients with a low MQI.

Conclusion

The Low-MQI is an independent predictor associated with pulmonary function parameters in subjects with Post-COVID-19 syndrome.

Peer Review reports

Background

Post-COVID-19 syndrome is characterized by the development of signs and symptoms during or after an infection caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), with symptoms persisting for more than 12 weeks and not attributable to alternative diagnoses [1]. A longitudinal cohort study revealed that 68% of subjects experienced symptoms at 6 months, and 49% experienced symptoms at the 1-year mark following the initial acute viral infection caused by SARS-CoV-2 [2].

Additionally, the severity of COVID-19 infections is associated with the presence or persistence of signs and symptoms in post-COVID-19 individuals [3]. The most common manifestations include pulmonary, musculoskeletal, hematologic, cardiovascular, endocrine, renal, and gastrointestinal symptoms [4]. Muscle weakness or fatigue, dyspnea, and sleep disturbance are among the most frequently reported symptoms [2].

Regarding musculoskeletal manifestations, in the acute phase of COVID-19 many subjects exhibit body composition changes, including a loss of muscle mass and strength. The muscular impairment can be attributed to various factors such as anorexia, malnutrition, and especially the severity of the illness. Hospitalized subjects have more significant pro-inflammatory states, oxidative stress, increased protein catabolism, and prolonged hospital stays, negatively impacting muscular mass. In addition, in subjects who require intensive care unit, invasive mechanical ventilation (IMV) and using neuromuscular blockers and corticosteroids negatively affect the peripheral muscles, the intercostal muscles, and the diaphragm, the primary muscle in charge of breathing [5,6,7,8,9,10].

Concerning the post-COVID phase, the pro-inflammatory state and endothelial dysfunction persist, as well as an increase in adipose tissue [11, 12]. Low muscle mass/strength and adipose tissue excess possess strongly interconnected physiopathologic mechanisms that exacerbate one another, resulting in a vicious cycle. This cycle leads to a reduction in protein synthesis, increased protein degradation, fat infiltration into skeletal muscle, promotion of lipotoxicity, exacerbation of inflammation [13], oxidative stress, and mitochondrial dysfunction [13,14,15,16]. Low muscle mass/strength loss and the accumulation of intramuscular fat contribute to muscle contractility impairment [17, 18].

The muscle strength can be easily assessed through handgrip strength (HGS). HGS has been demonstrated to be associated with whole-body muscle strength and skeletal muscle mass index (SMI), as well as pulmonary function, morbidity, and mortality in diverse populations [19,20,21,22,23,24].

The muscle quality index (MQI), obtained by dividing HGS by body mass index (BMI) (i.e., MQI = HGS/BMI), has emerged as a health and physical function indicator [25,26,27,28]. Existing evidence suggests that low MQI is linked to metabolic markers [28], metabolic syndrome [25], the prediction of cardiovascular disease risk factors [27], and physical function [28]. However, the association between the strength/BMI index and pulmonary function remains undefined. We aim to assess the association between MQI and pulmonary function in post-COVID-19 subjects.

Methods

A cross-sectional study was conducted at the Instituto Nacional de Enfermedades Respiratorias “Ismael Cosío Villegas” in Mexico City.

The study focused on moderate to severe COVID-19 subjects with a confirmed diagnosis of COVID-19 by PCR testing. Moderate to severe COVID-19 was considered in those patients who, during the acute phase of the disease, required hospitalization with blood oxygen saturation ≤ 93%, PaO2/FiO2 ratio < 300 (arterial partial pressure of oxygen/fraction of inspired oxygen).

In the study, those patients during the acute phase were moderate to severe and were subsequently discharged were included. Data were collected from outpatient evaluations 3 months post-acute COVID-19 infection during routine clinical examinations of post-COVID-19 subjects between June 1, 2020, and May 30, 2023 (Fig. 1). Subjects who could not be contacted, declined to participate, or died before the follow-up visit were excluded.

Fig. 1
figure 1

Study flow diagram

Full size image

Outcome measures

Anthropometric, clinical, and demographic variables were evaluated during the post-COVID-19 clinical management delivered to patients at our institute.

Anthropometry

Weight and height were measured according to the manual reference for anthropometric standardization [29]. All subjects wore light clothing and were barefoot.

Body mass index (BMI)

BMI was estimated by weight in kilograms divided by height in meters squared.

Handgrip strength (HGS)

Handgrip strength was measured using a mechanical Smedley Hand Dynamometer (Stoelting, Wood Dale, UK) according to the technique described in Rodriguez et al., which consists of subjects standing with their arms stretched parallel to the trunk, then picking up the dynamometer and applying the maximum force with the dominant hand. The measurement was repeated three times, one minute apart, to avoid fatigue. The maximum value was recorded in kg [30].

Muscle quality index (MQI)

MQI was calculated by dividing HGS by BMI and subsequently categorised as follows: Low-MQI ≤ 50th percentile and High-MQI > 50th percentile, considering gender (50th percentile MQI: 0.54 for women and 0.99 for men).

Pulmonary function

Forced spirometry was performed using a portable spirometer (EasyOne Pro Lab, Ndd Medical Technologies Inc., Zürich, Switzerland) and carried out by an experienced respiratory medicine technician in accordance with American Thoracic Society/European Respiratory Society standards [31] The analyzed spirometric variables included forced expiratory volume in the first second (FEV1), forced vital capacity (FVC) before and after administering a bronchodilator, peak expiratory flow rate (PEFR), and maximum expiratory flow between 25 and 75% of the FVC (MEF 25–75). Following a 15-minute rest, participants performed a maximal forced inspiration and a forceful expiration using a nose clip. Spirometry reference values were derived from Mexican-American individuals [32].

Respiratory muscle strength

Maximal inspiratory pressures (MIP) and maximal expiratory pressures (MEP) were measured in accordance with ATS/ERS 2002 guidelines using MicroRPM equipment (CareFusion, Micromedical, UK) [33].

Carbon monoxide diffusing capacity (DLCO)

A skilled respiratory technician conducted tests for DLCO using EasyOne pro® equipment from Ndd Medical Technologies Inc., Zürich, Switzerland. The assessment accounted for altitude and hemoglobin, employing predicted values for the Latino population [34].

Statistical analysis

Analyses were conducted using the commercially available software STATA version 14 (Stata Corp., College Station, TX, USA). Categorical variables were expressed as frequencies and percentages. The Shapiro-Wilk test assessed the normality of continuous variables; normal variables were expressed as mean and standard deviation, while non-normal variables were reported as median and percentiles 25–75. Comparisons between study groups (Low-MQI vs. High-MQI) were analyzed using the chi-square test for categorical variables and the t-test or Mann-Whitney U test for continuous variables. To evaluate the association between low-MQI and pulmonary function, linear regression models were performed using each variable of pulmonary function as a dependent variable and Low-MQI as an independent variable. The multivariate linear regression models were adjusted by bivariate analysis for variables with p < 0.10, such as age, diabetes, hypertension, ischaemic cardiopathy, and IMV, and multicollinearity was checked with the variance inflation factor. A p-value of 0.05 was considered statistically significant. A p-value of 0.05 was considered statistically significant.

Results

Seven hundred and forty-eight patients were assessed, with a mean age of 54.61 ± 0.44 years; 63.90% were male and BMI were 30.39 ± 6.21. The low-MQI group comprised older individuals with a higher prevalence of hypertension, obesity and fatigue compared to the high-MQI group. Respect to hospitalary parameters during COVID-19 acute phase, low-MQI group had higher IMV, duration of IMV, and a longer hospital stay compared to the high-MQI group (Table 1).

Table 1 Demographic and clinical characteristics in Post-COVID-19 patients
Full size table

In terms of pulmonary function, the Low-MQI group exhibited lower FEV1 in liters and percentage, FVC in liters and percentage, MEF 25–75, PEFR, FEV1/FVC ratio, DLCO, MIP, MEP than high-MQI group (Table 2).

Table 2 Pulmonary function according to muscle quality index
Full size table

Additionally, Table 3 showed that Low-MQI was lower FEV1 in liters and percentage, FVC in liters and percentage, MEF 25–75, PEF, MIP, and MEP in both bivariate and multivariate models adjusted for age, diabetes, hypertension, ischaemic cardiopathy, and IMV. However, no significant difference was found in DLCO in either the crude or adjusted models.

Table 3 Association between low muscle quality index and pulmonary function
Full size table

Discussion

The primary finding of our research demonstrated a negative association between low-MQI and mechanical pulmonary function, as well as respiratory muscle strength in subjects with post-COVID syndrome.

In relation to pulmonary function, this is influenced by various factors such as age, sex, gestational weeks, muscular strength, the immune system, and exposure to toxic agents such as tobacco, wood smoke, asbestos, and respiratory infections. In a post-COVID-19 infection, a meta-analysis conducted by Lee and Cols demonstrated that impaired diffusion capacity was the most prevalent abnormality on pulmonary function tests at 35%. Restrictive patterns were identified in 8%, while persistent ground-glass opacities and pulmonary fibrosis had a prevalence of 34% [35]. FEV1 reduction is a significant predictor of mortality in the general population [36, 37], and serves as a marker for cardiovascular mortality [38]. It was observed that low-MQI independently predicts lower FEV1, as subjects with low-MQI had 4.87% less FEV1 (β: -4.87, CI 95%; − 7.58 to − 2.17, p < 0.001) and 3.53% less FVC (β: -3.53, CI 95%; − 6.45 to − 0.62, p < 0.018) than those with high-MQI, adjusted for confounding variables. Various researchers have demonstrated a negative association between low muscle mass, impaired in muscular performance and low muscle strength, or sarcopenia, with FEV1 and FVC [39,40,41]. van Gassel et al. observed that 3 months after hospital discharge in post-COVID-19 subjects requiring IVM, those subjects with decreased physical function had lower FEV1 and DLCO [42].

In the general population, both the BMI and central obesity have exhibited an inverse relationship with FEV1% and FVC% [43, 44].

Research indicates an inverse association between BMI and both FEV1 and FVC [6, 8, 45]. This is attributed to the detrimental impact of excess adiposity, particularly centralized fat, on pulmonary function. Studies by Kwack and Cols demonstrated significant associations between subcutaneous thoracic fat, intra-thoracic fat, subcutaneous abdominal fat, and lower FEV1 and FVC [46]. The presence of excessive adipose tissue, particularly around the chest and abdomen, has been linked to worse lung function, likely due to difficulties in respiratory mechanics. The latter results from restrictions on lung expansion and increased resistance to diaphragmatic contraction during respiration, consequently causing reduced lung volume [45, 46].

Nonetheless, Koo et al. demonstrated that COPD patients with sarcopenic obesity exhibited poorer lung function compared to individuals without sarcopenia or obesity [47]. Moreover, those with sarcopenic obesity presented elevated levels of C-reactive protein, IL-6, and reduced exercise tolerance [48]. Additionally, sarcopenic obesity is linked to an increased risk of restrictive lung disease among the elderly [49].

As previously stated, a series of interconnected mechanisms between low muscle mass and strength and excess adipose tissue lead to alterations in the catabolism and anabolism of proteins and glycogen, as well as energy utilization [13, 50]. Additionally, mitochondrial dysfunction, diminishing muscle fiber number, decreased capillary density, increased oxidative stress, and inflammation occur along with adipose tissue infiltration. Such events promote myofibril atrophy and loss of muscle function both in peripheral muscles and those responsible for respiration, such as the diaphragm and intercostal muscles [13,14,15,16, 51].

Low muscle mass/strength and an excess of adipose tissue independently impair lung function [24, 40, 44]. Moreover, they possess synergistic mechanisms that result in a vicious cycle [47, 48].

The MQI is an emerging indicator of health and physical function [28] that represents a valuable tool for clinical practice, as it is a low-cost and easy tool to assess skeletal muscle quality, taking into account the important relationship between muscle strength and adipose tissue, which and predicting lung function, respiratory muscle strength in post-COVID-19 subjects. The assessment of MQI allows the identification of subjects at risk for opportune therapeutic management.

In terms of respiratory muscle functionality, in post-COVID-19 subjects who were hospitalized for severe COVID-19 infection, after resolution of the active infection, there was a lower thickening ratio between diaphragm thickness at end-inspiration/end-expiration compared with non-COVID subjects [52]. Inspiratory muscle functions can be assessed by MIP, while expiratory muscle strength is evaluated through MEP or PEFR.

Our study demonstrated a negative association between low-MQI and respiratory muscle strength, as evaluated by PEF, MIP, and MEP. Participants with low-MQI exhibited a 0.74 L lower PEFR than those with High-MQI (β: -0.79, CI 95%; − 1.15 to − 0.43, p < 0.001). PEFR is associated with respiratory muscle strength, low skeletal muscle mass, and sarcopenia [21, 53, 54] Kera and Cols. employed PEFR to define respiratory sarcopenia [54]. The PEFR was found to be associated with the 5-year mortality rate in an older population [55]. Additionally, we observed that individuals with low-MQI exhibited 8.14 cmH2O lower MEP (β: - 8.14, CI 95%; − 13.57 to − 2.71, p = 0.003) and 5.84 cmH2O less MIP (β: - 5.84, CI 95%; − 9.83 to − 1.84, p = 0.004) than those with High-MQI. MEP evaluates the strength of abdominal and intercostal muscles, while MIP measures the diaphragm’s strength—the most crucial muscle for respiration [56] Various studies have demonstrated a positive association between HGS and respiratory muscle strength; Shin and Cols showed that in adults over 60 years of age, for each kilogram of HGS, MIP increased by 1.96 cmH2O and MEP by 1.10 cmH2O [56]. Moreover, it has been noted that protein synthesis deterioration and mitochondrial degradation are more prominent in sarcopenic obesity than in sarcopenia or obesity alone [51].

Strengths and limitations

This study possesses inherent limitations due to its cross-sectional design, such as not being able to determine causality between variables. In addition, we do not know the subjects’ lung function and muscle quality before COVID-19 infection. Another significant limitation is that as this is the first study to evaluate the association between MQI and lung function in the post-COVID syndrome population, it is impossible to contrast our results in other post-COVID syndrome populations. Nonetheless, the study’s strengths include a large sample size to provide sufficient statistical power for conducting multiple linear regression analyses and adjusting for confounding variables.

Conclusions

The low-MQI serves as an independent predictor linked to pulmonary function parameters among individuals experiencing post-COVID-19 syndrome. The MQI could function as an indicator that determines the requirement for muscle training within pulmonary rehabilitation programs.

Availability of data and materials

The datasets generated and/or analysed 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.

Abbreviations

BMI:

Body mass index

FEV1:

Forced expiratory volume in 1 second

FVC:

Forced vital capacity

DLCO:

Carbon monoxide diffusing capacity

HSG:

Handgrip strength

MEP:

Maximal expiratory pressure

MEF 25–75:

Maximum expiratory flow between 25 and 75%

MIP:

Maximal inspiratory pressures

MQI:

Muscle quality index

SARS-CoV-2:

Severe acute respiratory syndrome coronavirus-2

SMI:

Skeletal muscle mass index

References

  1. Shah W, Hillman T, Playford ED, Hishmeh L. Managing the long term effects of covid-19: summary of NICE, SIGN, and RCGP rapid guideline. Bmj. 2021;372:n136.

    Article  PubMed  Google Scholar 

  2. Huang L, Yao Q, Gu X, Wang Q, Ren L, Wang Y, et al. 1-year outcomes in hospital survivors with COVID-19: a longitudinal cohort study. Lancet. 2021;398(10302):747–58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Fernández-de-Las-Peñas C, Palacios-Ceña D, Gómez-Mayordomo V, Florencio LL, Cuadrado ML, Plaza-Manzano G, et al. Prevalence of post-COVID-19 symptoms in hospitalized and non-hospitalized COVID-19 survivors: a systematic review and meta-analysis. Eur J Intern Med. 2021;92:55–70.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Gupta A, Madhavan MV, Sehgal K, Nair N, Mahajan S, Sehrawat TS, et al. Extrapulmonary manifestations of COVID-19. Nat Med. 2020;26(7):1017–32.

    Article  CAS  PubMed  Google Scholar 

  5. Reid MB, Li YP. Tumor necrosis factor-alpha and muscle wasting: a cellular perspective. Respir Res. 2001;2(5):269–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Yang T, Li Z, Jiang L, Xi X. Corticosteroid use and intensive care unit-acquired weakness: a systematic review and meta-analysis. Critical care (London, England). 2018;22(1):187.

    Article  PubMed  Google Scholar 

  7. Forcina L, Miano C, Scicchitano BM, Rizzuto E, Berardinelli MG, De Benedetti F, et al. Increased circulating levels of Interleukin-6 affect the redox balance in skeletal muscle. Oxidative Med Cell Longev. 2019;2019:3018584.

    Article  Google Scholar 

  8. Disser NP, De Micheli AJ, Schonk MM, Konnaris MA, Piacentini AN, Edon DL, et al. Musculoskeletal consequences of COVID-19. J Bone Joint Surg Am. 2020;102(14):1197–204.

    Article  PubMed  Google Scholar 

  9. 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.

    Article  PubMed  Google Scholar 

  10. González-Islas D, Sánchez-Moreno C, Orea-Tejeda A, Hernández-López S, Salgado-Fernández F, Keirns-Davis C, et al. Body composition and risk factors associated with sarcopenia in post-COVID patients after moderate or severe COVID-19 infections. BMC Pulm Med. 2022;22(1):223.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Lemos MM, Cavalini GR, Pugliese Henrique CR, Perli VAS, de Moraes MG, Marchiori LLM, et al. Body composition and cardiorespiratory fitness in overweight or obese people post COVID-19: a comparative study. Front Physiol. 2022;13:949351.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Jacobs LMC, Wintjens M, Nagy M, Willems L, Ten Cate H, Spronk HMH, et al. Biomarkers of sustained systemic inflammation and microvascular dysfunction associated with post-COVID-19 condition symptoms at 24 months after SARS-CoV-2-infection. Front Immunol. 2023;14:1182182.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Kalinkovich A, Livshits G. Sarcopenic obesity or obese sarcopenia: a cross talk between age-associated adipose tissue and skeletal muscle inflammation as a main mechanism of the pathogenesis. Ageing Res Rev. 2017;35:200–21.

    Article  CAS  PubMed  Google Scholar 

  14. Aon MA, Bhatt N, Cortassa SC. Mitochondrial and cellular mechanisms for managing lipid excess. Front Physiol. 2014;5:282.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Di Meo S, Iossa S, Venditti P. Skeletal muscle insulin resistance: role of mitochondria and other ROS sources. J Endocrinol. 2017;233(1):R15–r42.

    Article  PubMed  Google Scholar 

  16. Wu H, Ballantyne CM. Skeletal muscle inflammation and insulin resistance in obesity. J Clin Invest. 2017;127(1):43–54.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Baker JF, Mostoufi-Moab S, Long J, Zemel B, Ibrahim S, Taratuta E, et al. Intramuscular fat accumulation and associations with body composition, strength, and physical functioning in patients with rheumatoid arthritis. Arthritis care & research. 2018;70(12):1727–34.

    Article  CAS  Google Scholar 

  18. Wu H, Liu M, Chi VTQ, Wang J, Zhang Q, Liu L, et al. Handgrip strength is inversely associated with metabolic syndrome and its separate components in middle aged and older adults: a large-scale population-based study. Metab Clin Exp. 2019;93:61–7.

    Article  CAS  PubMed  Google Scholar 

  19. Rantanen T, Harris T, Leveille SG, Visser M, Foley D, Masaki K, et al. Muscle strength and body mass index as long-term predictors of mortality in initially healthy men. J Gerontol A Biol Sci Med Sci. 2000;55(3):M168–73.

    Article  CAS  PubMed  Google Scholar 

  20. Izawa KP, Watanabe S, Osada N, Kasahara Y, Yokoyama H, Hiraki K, et al. Handgrip strength as a predictor of prognosis in Japanese patients with congestive heart failure. Eur J Cardiovasc Prev Rehabil. 2009;16(1):21–7.

    Article  PubMed  Google Scholar 

  21. Ro HJ, Kim DK, Lee SY, Seo KM, Kang SH, Suh HC. Relationship between respiratory muscle strength and conventional Sarcopenic indices in young adults: a preliminary study. Ann Rehabil Med. 2015;39(6):880–7.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Sayer AA, Kirkwood TB. Grip strength and mortality: a biomarker of ageing? Lancet. 2015;386(9990):226–7.

    Article  PubMed  Google Scholar 

  23. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Martínez-Luna N, Orea-Tejeda A, González-Islas D, Flores-Cisneros L, Keirns-Davis C, Sánchez-Santillán R, et al. Association between body composition, sarcopenia and pulmonary function in chronic obstructive pulmonary disease. BMC Pulm Med. 2022;22(1):106.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Moreira MA, Vafaei A, da Câmara SMA, Nascimento RAD, de Morais M, Almeida MDG, et al. Metabolic syndrome (MetS) and associated factors in middle-aged women: a cross-sectional study in Northeast Brazil. Women Health. 2020;60(6):601–17.

    Article  PubMed  Google Scholar 

  26. Choi S, Nah S, Jang H, Moon J, Han S. Association between relative handgrip strength and chronic lower Back pain: a Nationwide cross-sectional analysis of the Korea National Health and nutrition examination survey. Int J Environ Res Public Health. 2021;18(20)

  27. Gao Y, Huang H, Ni C, Feng Y, Yu J, Huang Y, et al. Comparison of five expressions of handgrip strength for predicting cardiovascular disease risk factors in Chinese middle-aged community residents. Front Public Health. 2022;10:903036.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Caamaño-Navarrete F, Jerez-Mayorga D, Alvarez C, Del-Cuerpo I, Cresp-Barría M, Delgado-Floody P. Muscle quality index in morbidly obesity patients related to metabolic syndrome markers and cardiorespiratory fitness. Nutrients. 2023;15(11)

  29. Tg L. Anthropometric standardization reference manual. Human kinetics books. 1988:55–68.

  30. 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. Clinical nutrition ESPEN. 2017;19:54–8.

    Article  Google Scholar 

  31. Graham BL, Steenbruggen I, Miller MR, Barjaktarevic IZ, Cooper BG, Hall GL, et al. Standardization of Spirometry 2019 update. An official American Thoracic Society and European Respiratory Society technical statement. Am J Respir Crit Care Med. 2019;200(8):e70–88.

    Article  PubMed  PubMed Central  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.

    Article  CAS  PubMed  Google Scholar 

  33. ATS/ERS Statement on respiratory muscle testing. Am J Respir Crit Care Med. 2002;166(4):518–624.

    Article  Google Scholar 

  34. Vázquez-García JC, Pérez-Padilla R, Casas A, Schönffeldt-Guerrero P, Pereira J, Vargas-Domínguez C, et al. Reference values for the diffusing capacity determined by the single-breath technique at different altitudes: the Latin American single-breath diffusing capacity reference project. Respir Care. 2016;61(9):1217–23.

    Article  PubMed  Google Scholar 

  35. Lee JH, Yim JJ, Park J. Pulmonary function and chest computed tomography abnormalities 6-12 months after recovery from COVID-19: a systematic review and meta-analysis. Respir Res. 2022;23(1):233.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. 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.

    Article  PubMed  Google Scholar 

  37. 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. J Clin Med. 2020;9(3)

  38. Sin DD, Man SP. Why are patients with chronic obstructive pulmonary disease at increased risk of cardiovascular diseases? The potential role of systemic inflammation in chronic obstructive pulmonary disease. Circulation. 2003;107(11):1514–9.

    Article  PubMed  Google Scholar 

  39. Moon JH, Kong MH, Kim HJ. Implication of sarcopenia and Sarcopenic obesity on lung function in healthy elderly: using Korean National Health and nutrition examination survey. J Korean Med Sci. 2015;30(11):1682–8.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Chen L, Liu X, Wang Q, Jia L, Song K, Nie S, et al. Better pulmonary function is associated with greater handgrip strength in a healthy Chinese Han population. BMC Pulm Med. 2020;20(1):114.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Benz E, Wijnant SRA, Trajanoska K, Arinze JT, de Roos EW, de Ridder M, et al. Sarcopenia, systemic immune-inflammation index and all-cause mortality in middle-aged and older people with COPD and asthma: a population-based study. ERJ open research. 2022;8(1)

  42. van Gassel RJJ, Bels J, Remij L, van Bussel BCT, Posthuma R, Gietema HA, et al. Functional outcomes and their association with physical performance in mechanically ventilated coronavirus disease 2019 survivors at 3 months following hospital discharge: a cohort study. Crit Care Med. 2021;49(10):1726–38.

    Article  PubMed  PubMed Central  Google Scholar 

  43. 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.

    Article  PubMed  Google Scholar 

  44. 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.

    Article  PubMed  Google Scholar 

  45. Ohara DG, Moreira YP, Silva CFR, Matos AP, Gama TO, Jamami M, et al. Impaired pulmonary function is associated with dynapenia, but not with abdominal obesity and dynapenic abdominal obesity in older adults. European geriatric medicine. 2022;13(2):475–82.

    Article  PubMed  Google Scholar 

  46. Kwack WG, Kang YS, 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. Journal of thoracic disease. 2019;11(12):5300–9.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Koo HK, Park JH, Park HK, Jung H, Lee SS. Conflicting role of sarcopenia and obesity in male patients with chronic obstructive pulmonary disease: Korean National Health and nutrition examination survey. PLoS One. 2014;9(10):e110448.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Joppa P, Tkacova R, Franssen FM, Hanson C, Rennard SI, Silverman EK, et al. Sarcopenic obesity, functional outcomes, and systemic inflammation in patients with chronic obstructive pulmonary disease. J Am Med Dir Assoc. 2016;17(8):712–8.

    Article  PubMed  Google Scholar 

  49. Lee SE, Park JH, Kim KA, Kang YS, Choi HS. Association between Sarcopenic obesity and pulmonary function in Korean elderly: results from the Korean National Health and nutrition examination survey. Calcif Tissue Int. 2020;106(2):124–30.

    Article  CAS  PubMed  Google Scholar 

  50. Hunter GR, Singh H, Carter SJ, Bryan DR, Fisher G. Sarcopenia and its implications for metabolic health. J Obes. 2019;2019:8031705.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Kim KW, Baek MO, Yoon MS, Son KH. Deterioration of mitochondrial function in the human intercostal muscles differs among individuals with sarcopenia, obesity, and sarcopenic obesity. Clin Nutr. 2021;40(5):2697–706.

    Article  CAS  PubMed  Google Scholar 

  52. Farr E, Wolfe AR, Deshmukh S, Rydberg L, Soriano R, Walter JM, et al. Diaphragm dysfunction in severe COVID-19 as determined by neuromuscular ultrasound. Ann Clin Transl Neurol. 2021;8(8):1745–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Kera T, Kawai H, Hirano H, Kojima M, Fujiwara Y, Ihara K, et al. Relationships among peak expiratory flow rate, body composition, physical function, and sarcopenia in community-dwelling older adults. Aging Clin Exp Res. 2018;30(4):331–40.

    Article  PubMed  Google Scholar 

  54. Kera T, Kawai H, Hirano H, Kojima M, Watanabe Y, Motokawa K, et al. Definition of respiratory sarcopenia with peak expiratory flow rate. J Am Med Dir Assoc. 2019;20(8):1021–5.

    Article  PubMed  Google Scholar 

  55. Cook NR, Evans DA, Scherr PA, Speizer FE, Taylor JO, Hennekens CH. Peak expiratory flow rate and 5-year mortality in an elderly population. Am J Epidemiol. 1991;133(8):784–94.

    Article  CAS  PubMed  Google Scholar 

  56. Shin HI, Kim DK, Seo KM, Kang SH, Lee SY, Son S. Relation between respiratory muscle strength and skeletal muscle mass and hand grip strength in the healthy elderly. Ann Rehabil Med. 2017;41(4):686–92.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

To LGA.

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Heart Failure and Respiratory Distress Clinic at the Instituto Nacional de Enfermedades Respiratorias “Ismael Cosío Villegas”, Calzada de Tlalpan 4502 Col Sec XVI CP 14080 Del Tlalpan, Mexico City, Mexico

    Dulce González-Islas, Arturo Orea-Tejeda, Nadia Hernández-López & Rocío Sánchez-Santillán

  2. Department of Research in Tobacco Smoking and COPD at the Instituto Nacional de Enfermedades Respiratorias “Ismael Cosío Villegas”, Mexico City, Mexico

    Robinson Robles-Hernández

  3. Department of Epidemiological Information Analysis at Dirección General de Epidemiología, Secretaría de Salud, Gobierno de México, Mexico City, 01480, Mexico

    Laura Flores-Cisneros

  4. Pulmonary Rehabilitation Department at the Instituto Nacional de Enfermedades Respiratorias “Ismael Cosío Villegas”, Mexico City, Mexico

    Susana Galicia-Amor & Juan Carlos García-Hernández

  5. Licenciatura en Nutriología Facultad de Estudios Superiores Zaragoza Universidad Nacional Autónoma de México, C.P. 09230, Mexico City, Mexico

    Mariana I. Valdés-Moreno

  6. Direction for Medical Care in Pneumology at the Instituto Nacional de Enfermedades Respiratorias “Ismael Cosío Villegas”|, Mexico City, Mexico

    Armando Castorena-Maldonado

Authors
  1. Dulce González-Islas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Robinson Robles-Hernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Laura Flores-Cisneros
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Arturo Orea-Tejeda
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Susana Galicia-Amor
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nadia Hernández-López
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mariana I. Valdés-Moreno
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Rocío Sánchez-Santillán
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Juan Carlos García-Hernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Armando Castorena-Maldonado
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: G-I D., F-C L., and O-T A.; Data curation: H-L N., S-S R., and G-H JC; Formal analysis; G-I D., and F-C L.; Investigation; O-T A., R-H R., and V-M MI.; Resources: G-A S., and S-S R.; Methodology: G-I D., F-C L., O-T A., and H-L N.; Project administration: R-H R., V-M MI, and C-M A.; Supervision; R-H R., G-A S., G-HJC., and C-M A.; Roles/Writing: original draft; G-I D., F-C L., O-T A., R-H R., G-Ar S., H-L N., S-S R., G-H JC., G-H JC., V-M MI, and C-M A.; Writing: review and editing; G-I D., F-C L.,O-T A., R-H R., G-A S., H-L N., S-S R., V-M MI, G-H JC., and C-M A.

Corresponding author

Correspondence to Arturo Orea-Tejeda.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the Institutional Ethics and Research Committee for Biomedical Research in Humans at the Instituto Nacional de Enfermedades Respiratorias “Ismael Cosío Villegas” (approval number C57–21). All participants provided informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

González-Islas, D., Robles-Hernández, R., Flores-Cisneros, L. et al. Association between muscle quality index and pulmonary function in post-COVID-19 subjects. BMC Pulm Med 23, 442 (2023). https://doi.org/10.1186/s12890-023-02745-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12890-023-02745-5

Keywords

BMC Pulmonary Medicine

ISSN: 1471-2466

Contact us