Abstract
Introduction
Sarcopenia is recognized as a consequence of hormones, immune system changes, and chronic inflammatory diseases that occur with aging. For the diagnosis of sarcopenia, the European Working Group on Sarcopenia in Older People (EWGSOP2) criteria are used. Furthermore, the fat-free mass index (FFMI), a marker of sarcopenia, is used to predict sarcopenic patients. In patients with chronic obstructive pulmonary disease (COPD), systemic inflammation, advanced age, sedentary lifestyle, and poor nutrition may lead to sarcopenia. This study aimed to investigate the contribution of the SARC-F questionnaire, a simple questionnaire to rapidly diagnose sarcopenia, in the prediction of sarcopenic patients secondary to COPD.
Method
Our study included patients aged 50 years and older who were diagnosed with COPD and who signed an informed consent form. Demographic data, symptoms, anthropometric measurements, pulmonary function tests, a 6-minute walk test, and blood parameters were evaluated. The SARC-F questionnaire was administered to the participants. In our study, the correlation of sarcopenic patients according to FFMI with the SARC-F questionnaire was analyzed. The significance value was accepted as p < 0.05 in the statistical analysis of the study’s data.
Results
The data from 130 participants were analyzed in the study. Of the patients, 99 (76.2%) were male, 31 (23.8%) were female, and the mean age was 68.0 ± 9.6 years. According to the SARC-F results, the number of patients with < 4 points was 103 and the number of patients with ≥ 4 points was 27. According to the FFMI, the number of patients without sarcopenia was 96 and the number of patients with sarcopenia was 34. A statistically significant correlation was found between the FFMI and the sarcopenia indicators assessed by the SARC-F (p < 0.001).
Conclusion
Patients’ adaptation to a sedentary lifestyle, COPD exacerbations causing systemic inflammation, and advanced age increase the likelihood of sarcopenia. The advanced age of patients diagnosed with COPD normalizes the loss of muscle strength, which delays the early diagnosis and treatment of sarcopenia. This study emphasizes the importance and practical usefulness of the SARC-F questionnaire in achieving the goals of early diagnosis and treatment of sarcopenia in all COPD patients.
Keywords: COPD, Sarcopenia, BIA, SARC-F, FFM index
Introduction
Sarcopenia includes the loss of functional capacity in addition to the loss of muscle strength and muscle mass [1]. Sarcopenia is recognized as a consequence of hormonal and immune system changes that occur with aging. Recent studies have revealed that chronic inflammatory diseases may also lead to sarcopenia. Increased levels of inflammatory cytokines such as Tumor Necrosis Factor-α (TNF-α) and Interleukin-6 (IL-6) have been shown to increase muscle loss in chronic inflammatory diseases [2]. The most widely accepted diagnostic criteria for sarcopenia have been established by the European Working Group on Sarcopenia in Older People (EWGSOP2). According to the EWGSOP2, sarcopenia is defined as a diffuse and progressive skeletal muscle disorder associated with an increased risk of adverse outcomes such as physical disability, falls, bone fractures, and death. According to the EWGSOP2, the diagnostic criteria for sarcopenia include reduced muscle strength, reduced muscle volume or quality, and poor physical capacity. The presence of the first criterion indicates possible sarcopenia; the presence of the first and second criteria together indicates a sarcopenia diagnosis; and the presence of all three criteria together indicates severe sarcopenia [3]. Appendicular skeletal muscle mass (ASM) that is normalized for body size is used as an indicator of muscle mass. Hence, the assessment of ASM is fundamental to identify low muscle mass in sarcopenia. In diagnosing sarcopenia, dual-energy X-ray absorptiometry (DXA) is currently the most effective method for assessing ASM. Nevertheless, the high cost, immobility, and exposure to X-ray radiation of the DXA system are potential limitations to its use for muscle mass assessment in community settings. It has been shown that appendicular skeletal muscle mass index (ASMI) may be a useful simple surrogate marker for screening for low muscle mass in sarcopenia, as fat-free body mass index (FFMI) shows a strong positive correlation with ASM measured using Bioelectrical Impedance Analysis (BIA) and DXA regardless of age and obesity [4]. BIA, which is used to diagnose sarcopenia, is a simple and non-invasive method that can estimate components of body composition such as fat, fat-free mass, and muscle mass by measuring electrical properties in the body. However, changes in hydration level and nutrient intake may cause errors in the measurement, as it does not directly measure muscle mass [5]. Due to the lack of easy accessibility of BIA, delays in diagnosing sarcopenia or a lack of diagnosis may occur. Therefore, researchers have searched for more accessible methods to diagnose sarcopenia. The SARC-F, a simple questionnaire to rapidly diagnose sarcopenia, is one of the methods recommended by international sarcopenia guidelines and is used for screening purposes to rapidly diagnose sarcopenia. The criteria assessed in this questionnaire include gait, muscle strength, difficulty climbing stairs, the ability to get up from a chair, and whether there has been a fall in the last 12 months [6].
Chronic obstructive pulmonary disease (COPD) refers to a heterogeneous condition characterized by chronic respiratory symptoms (dyspnea, cough, and sputum) resulting from airway or alveolar abnormalities and is characterized by persistent, and often progressive, airway obstruction [7]. COPD is an inflammatory condition. It is associated with multiple systemic manifestations in addition to airflow obstruction, which greatly affect the prognosis and cost of treatment. An abnormal nutritional status and changes in body composition are among the most common comorbidities in people with COPD and have a significant, negative impact on prognosis. Individuals with COPD often have comorbid diseases that affect systemic health. Moreover, health conditions such as smoking, advanced age, and a sedentary lifestyle are closely associated with COPD [8]. Catabolic processes increase in both COPD and aging and lead to decreased muscle mass. The loss of muscle mass is a diagnostic criterion for both malnutrition and sarcopenia [3, 9]. In several studies, decreased muscle mass has been associated with a lower body mass index, more frequent exacerbations, more severe dyspnea, worse lung function, and lower results in exercise tests [10]. Therefore, those diagnosed with COPD should be closely monitored for sarcopenia. Early detection of sarcopenia in COPD may favorably affect the disease and prognosis.
This study aimed to investigate the contribution of the SARC-F questionnaire, which is used as a sarcopenia screening test in patients with COPD, in the diagnosis of sarcopenia and to detect patients with sarcopenia more rapidly with this questionnaire.
Methods
Study population and data sources
The study was designed as an observational prospective study and the study was started after receiving approval from the local ethics committee (Session No:2024/09 − 02). Patients aged 50 years and over who applied to the Chest Diseases Polyclinic of XXX University, Faculty of Medicine, who were diagnosed with COPD according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) guideline, and who signed the informed consent form were included in the study. Patients who were in the acute exacerbation period of COPD, were under steroid treatment, had any muscle disease, were under 50 years of age, had disabilities in their extremities, had end-stage renal failure and liver failure, had lung transplantation, were diagnosed with asthma, had severe heart failure, and who refused to participate in the study were excluded from the study.
Data collection
Demographic data, anthropometric measurements, pulmonary function tests, 6-minute walk test (6MWT) results, white blood cell count, neutrophils, hemoglobin, Blood urea nitrogen (BUN), creatinine, Lactate Dehydrogenase (LDH), prealbumin, and C-reactive protein (CRP) values were recorded. The Modified Medical Research Council (mMRC) dyspnea scale was used to determine the stage of dyspnea. The BODE index was calculated according to Body Mass Index (BMI), airflow obstruction (FEV1), dyspnea (mMRC dyspnea score) and exercise capacity (6MWT).The BODE Index was calculated [11]. The SARC-F questionnaire was applied to each patient. The COPD treatment regimen of each patient was recorded. The treatment regimens included long-acting beta-2 agonist (LABA), long-acting anticholinergic (LAMA), inhaled corticosteroid (ICS), or their combinations according to the stage of the patient.
For the anthropometric measurements, BMI, FFM, fat mass (FM), skeletal muscle mass (SMM), skeletal muscle index (SMM/body surface area) (SMI), and lean body mass index (FFM/body surface area) (FFMI) were measured using the BIA method. These measurements were obtained using the TANITA DC 360 ST device in the endocrinology outpatient clinic. The participants were asked to remove metal objects and electronic devices at the time of measurement. They were also advised to go barefoot and wear light clothing during the device measurement. It was ensured that the patients had not engaged in strenuous physical activity before the muscle mass measurement and had not consumed alcohol before the procedure. To measure gait speed, the participants were asked to walk in a straight line over a four-meter interval at a speed similar to their daily walking speed. Walking speeds were recorded in meters/second to measure physical capacity. A Handgrip digital handgrip dynamometer (Kyto EH 101, Guangdong, China) was used to determine the muscle strength of the participants in kilograms. During the measurement, the participants were asked to stand upright and squeeze the dynamometer with all their strength. The averages of three measurements were calculated and recorded. Furthermore, skeletal muscle strength, SMM, and physical capacity values were recorded, and the probability of sarcopenia was then calculated according to the EWGSOP2 [3].
The participants were asked a five-question SARC-F questionnaire about gait, muscle strength, their difficulty climbing stairs, their ability to get up from a chair, and whether they had fallen in the last year [6] (Table 1). A SARC-F score of ≥ 4 was selected as the cut-off point. The participants were classified according to the presence or absence of sarcopenia based on their BIA results. The relationship between sarcopenia and the SARC-F was then analyzed.
Table 1.
SARC-F Screen for Sarcopenia: Component Question Scoring
| SARC-F items | Question | Points |
|---|---|---|
| Strength | How much difficulty do you have in lifting and carrying 10 pounds? |
None = 0 Some = 1 A lot or unable = 2 |
| Assistance in walking | How much difficulty do you have walking across a room? |
None = 0 Some= 1 A lot/use aids/unable= 2 |
| Climb stairs | How much difficulty do you have climbing a flight of 10 stairs? |
None = 0 Some= 1 A lot or unable = 2 |
| Falls in the past year | How many times have you fallen in the past year? |
None = 0 Less than 3 falls = 1 4 or more falls= 2 |
Statistical analysis
Statistical analyses were performed using the Statistical Package for Social Sciences (SPSS) version 22.0 (IBM, Chicago, IL) computer software. Data distribution was assessed visually (histograms and probability plots) and analytically (the Kolmogorov-Smirnov test). The categorical data were expressed as numbers (n) and percentages, the non-parametric data were expressed as median and quartiles (25-75%), and the parametric data were expressed as mean ± standard deviation. For the correlation analysis of the continuous data, Spearman’s correlation test was used for the nonparametric data and Pearson’s correlation test was used for the parametric data. An alpha value of less than 0.05 was considered statistically significant.
Results
The data from 130 participants were analyzed in the study. The mean age was 68.0 ± 9.6 years. Of the participants, 76.2% were male and 23.8% were female. The mean height of the participants was 167.0 ± 8.9 cm and the mean weight was 75.7 ± 14.2 kg. Regarding comorbidities, 22.3% of the participants had diabetes mellitus (DM), 51.5% had hypertension(HT) and 35.4% had coronary artery disease(CAD) (Table 2).
Table 2.
Demographic and laboratory findings
| Parameters | Patients (N=130) | |
|---|---|---|
| Mean | Standard Deviation (SD) | |
| Age, year | 68,0 | 9,6 |
| N (Number) | Percentage (%) | |
| Sex, female/male | 31/99 | 23,8/76,2 |
| Comorbiditiesa | ||
| Diabetes Mellitus (DM) | 29 | 22,3 |
| Hypertension (HT) | 67 | 51,5 |
| Coronary artery disease (CAD) | 46 | 35,4 |
| None/Other | 16 | 12,3 |
| Mean | Standard Deviation (SD) | |
| WBC, μL | 8,58 | 2,84 |
| Hemoglobin, g/dL | 14,2 | 1,81 |
| Lactatdehydrogenase(LDH),U/L | 186,00 | 83,00 |
| C-reactive protein (CRP), mg/L | 5,00 | 33,44 |
WBC White blood cell, LABA Long-acting β2-agonist, LAMA Long-acting muscarinic antagonist, ICS Inhaled corticosteroid
a“n” varied because more than one option was selected
The mean BMI of the participants was 27.43, FM was 24.71, FFMI was 18.42, the duration of the four-meter walk test was 0.68 s, and the grip strength was 23.73. Using combined staging, there were 63 patients in group A, 40 in group B, and 27 in group E. Spirometric classification showed that the number of patients in stages 1, 2, 3 and 4 were 17, 69, 42 and 2, respectively. According to the SARC-F results, the number of patients with < 4 points was 103 and the number of patients with ≥ 4 points was 27 (Table 3).
Table 3.
Anthropometric measurement and functional assessment findings
| Parameters | Patients ( N =130) | |
|---|---|---|
| Mean | Standard Deviation (SD) | |
| BMI, kg/m2 | 27,43 | 5,63 |
| FM, kg | 24,71 | 10,85 |
| FFM, kg | 51,20 | 10,88 |
| FM/FFM | 0,49 | 0,24 |
| SMI, kg/m2 | 10,49 | 1,20 |
| FMI, kg/m2 | 9,04 | 4,49 |
| FFMI, kg/m2 | 18,42 | 2,37 |
| Muscle mass | 48,99 | 8,84 |
| Skeletal muscle | 29,12 | 5,34 |
| 4 m walk test (Sn) | 0,68 | 0,19 |
| 6minwalktest,(m) | 273, 38 | 108,28 |
| Gripping forceHGS, (kg) | 23,73 | 8,19 |
| FEV1, % | 57,68 | 19,67 |
| FVC, % | 69,62 | 17,59 |
| FEV1/FVC | 70,70 | 16,35 |
| Number (n) | Percentage (%) | |
| Staging | ||
| Grup A | 63 | 48,5 |
| Grup B | 40 | 30,8 |
| Grup E | 27 | 20,8 |
| GOLDClassification | ||
| Evre 1 | 17 | 13,1 |
| Evre 2 | 69 | 53,1 |
| Evre 3 | 42 | 32,3 |
| Evre 4 | 2 | 1,5 |
| SARC-F score | ||
| <4 puan | 103 | 79,2 |
| ≥4 puan | 27 | 20,8 |
| FFMI | ||
| No sarcopenia | 96 | 73,9 |
| Sarcopenia | 34 | 26,2 |
| EWGSOP-2 staging | ||
| Possible sarcopenia | 96 | 73,9 |
| Sarcopenia | 12 | 9,2 |
| Severe sarcopenia | 22 | 16,9 |
BMI Body mass index, FM Fat mass, FFM Fat free muscle, SMI Skeletal Muscle Mass İndex, FMI Fat mass index, FFMI Fat free mass index, FEV1 Forced Expiratory Volume, FVC Forced vital capacity, GOLD Global Initiative for Chronic Obstructive Lung Disease, SARC-F A Simple Questionnaire to Rapidly Diagnose Sarcopenia, EWGSOP The European Working Group on Sarcopenia in Older People
According to the FFMI, there were 96 patients without sarcopenia and 34 patients with sarcopenia. Additionally, according to the EWGSOP-2 staging, there were 96 patients with probable sarcopenia, 12 with sarcopenia, and 22 with severe sarcopenia. A statistically significant correlation was found between the FFMI and the sarcopenia indicators assessed by the SARC-F questionnaire (p < 0.001) (Table 4)
Table 4.
The relationship between FFMI and SARC-F
| SARC-F Nosarcopenia N =103 | SARC-F Sarcopenia N =27 | P value | |||
|---|---|---|---|---|---|
| FFMI | n | % | n | % | <0,001 |
| No sarcopenia | 87 | 66,9 | 9 | 6,9 | |
| Sarcopenia | 16 | 12,3 | 18 | 13,8 | |
FFMI Fat free muscle index, SARC-F A Simple Questionnaire to Rapidly Diagnose Sarcopenia
For treatment, 20% of the participants only used LABA, while 19.2% only used LAMA, 20% used LABA and LAMA, and 40.8% used LABA, LAMA, and ICS together. Accordingly, the use of LAMA, LABA, and ICS together was found to be associated with sarcopenia according to FFMI and severe sarcopenia according to the EWGSOP-2, with p < 0.001 for the FFMI and p = 0.001 for the EWGSOP-2 (Table 5).
Table 5.
Association between treatment and FFMI and EWGSOP-2
| LABA N =26 | LAMA N =25 | LABA+LAMA N =26 | LABA+LAMA+ICS a N =53 | P value | |||||
|---|---|---|---|---|---|---|---|---|---|
| FFMI | n | % | n | % | n | % | n | % | <0,001 |
| Nosarcopenia | 23 | 17,7 | 23 | 17,7 | 21 | 16,2 | 29 | 22,3 | |
| Sarcopenia | 3 | 2,3 | 2 | 1,5 | 5 | 3,8 | 24 | 18,5 | |
| EWGSOP2 | n | % | n | % | n | % | n | % | 0,001 |
| Possible sarcopenia | 23 | 17,7 | 23 | 17,7 | 21 | 16,2 | 29 | 22,3 | |
| Sarcopenia | 3 | 2,3 | 1 | 0,8 | 2 | 1,5 | 6 | 4,6 | |
| Severesarcopenia | 0 | 0,0 | 1 | 0,8 | 3 | 2,3 | 18 | 13,8 | |
LABA Long-acting β2-agonist, LAMA Long-acting muscarinic antagonist, ICS Inhaled corticosteroid, FFMI Fat free mass index, EWGSOP The European Working Group on Sarcopenia in Older People
aParameter with significant difference between groups
In the comparison of the hemogram and biochemistry parameters according to the SARC-F sarcopenia classification, WBC (p = 0.024), NE (p = 0.002), lactate dehydrogenase (p = 0.004), and C-reactive protein (p = 0.008) were statistically significantly lower, and hemoglobin (p = 0.011) was statistically significantly higher in patients classified as low-risk versus high-risk according to SARC-F classification. There was no statistically significant difference in the BUN, creatinine, and prealbumin values (p > 0.05) (Table 6).
Table 6.
Comparison of laboratory parameters according to SARC-F sarcopenia classification
| SARC-F < 4N>=103 Median (%25-%75) | SARC-F ≥ 4 N =27 Median (%25-%75) | P value | |
|---|---|---|---|
| WBC, μl | 8,45 (7,00-9,85) | 9,19 (8,26-12,00) | 0,024 |
| NE, μl | 5,46 (4,20-6,42) | 6,52 (5,10-9,00) | 0,002 |
| Hemoglobin, g/dL | 14,3 (13,5-15,3) | 13,4 (11,7-14,8) | 0,011 |
| Blood urea nitrogen, mg/dL | 15, 0 (12,0-19,0) | 16,0 (12,0-21,0) | 0,575 |
| Creatinine, mg/dL | 0,93 (0,78-1,05) | 0,84 (0,69-1,14) | 0,220 |
| Lactate dehydrogenase, U/L | 181,0 (160,0-207,0) | 205,0 (182,0-276,0) | 0,004 |
| Prealbumin, mg/dL | 0,22 (0,19-0,24) | 0,20 (0,14-0,26) | 0,117 |
| C-reactive protein, mg/L | 4,0 (3,0-11,0) | 10,0 (4,0-27,0) | 0,008 |
SARC-F A Simple Questionnaire to Rapidly Diagnose Sarcopenia, WBC White blood cell, NE Neutrophil
Regarding the COPD functional assessment results, FEV (p = 0.002), FVC (p = 0.001) and the 6MWT (p < 0.001) were found to be statistically significantly higher in the group with a SARC-F score < 4 compared to the group with a SARC-F score ≥ 4. Additionally, according to the mMRC classification, the number of people with mMRC 1 and the number of people with mMRC 4 were higher and lower in the group with a SARC-F score < 4 compared to the group with a SARC-F score ≥ 4 (p < 0.001). However, no significant difference was observed between the groups for FEV1/FVC and the GOLD SARC-F sarcopenia classification (p > 0.05) (Table 7)
Table 7.
Comparison of COPD functional assessment findings according to SARC-F sarcopenia classification
| SARC-F < 4 N =103 | SARC-F ≥ 4 N =27 | P value | |
|---|---|---|---|
| FEV1a | 60,0 (44,0-73,0) | 45,0 (35,0-57,0) | 0,002 |
| FVCb | 72,11±17,21 | 60,11±15,93 | 0,001 |
| FEV1/FVCb | 71,26±16,08 | 68,56±17,48 | 0,446 |
| 6MWTa | 320,0 (240,0-380,0) | 160,0 (80,0-250,0) | <0,001 |
| MMRCc | |||
| Grade 0 | 9 (6,9) | 1 (0,8) | <0,001 |
| Grade 1* | 48 (36,9) | 0 (0,0) | |
| Grade 2 | 24 (18,5) | 2 (1,5) | |
| Grade 3 | 20 (15,4) | 8 (6,6) | |
| Grade 4* | 2 (1,5) | 16 (12,3) | |
| GOLDc | |||
| Stage 1 | 16 (12,3) | 1 (0,8) | 0,092 |
| Stage 2 | 57 (43,9) | 12 (9,2) | |
| Stage 3 | 29 (22,3) | 13 (10,0) | |
| Stage 4 | 1 (0,8) | 1 (0,8) | |
SARC-F A Simple Questionnaire to Rapidly Diagnose Sarcopenia, FEV1 Forced Expiratory Volume, FVC Forced vital capacity, mMRC Modifiye Medikal Research Council, GOLD Global Initiative for Chronic Obstructive Lung Disease, 6 MWT 6min walk test
aMedian (%25-%75)
bmean ± standard deviation
cnumber (%)
*Categorical parameter for which there is a significant difference between groups
There was a negative and low to moderate statistically significant correlation between SARC-F stage and FEV1 (rho=−0.269, p = 0.002), FVC (r=−0.278, p = 0.001), and 6MWT (rho=−0.421, p < 0.001). In addition, a positive and high-low statistically significant association was observed with mMRC (rho = 0.583, p < 0.001) and GOLD (r = 0.219, p = 0.012). There was no significant correlation between the FEV1/FVC value and SARC-F staging (p > 0.05) (Fig. 1).
Fig. 1.
Correlation of continuous variables significantly associated with SARC-F staging of COPD functional assessment findings. SARC-F: A Simple Questionnaire to Rapidly Diagnose Sarcopenia, FEV1: Forced Expiratory Volume, FVC Forced vital capacity, 6 MWT:6 min walk test
Discussion
Sarcopenia is recognized as a consequence of hormonal and immune system changes that occur with aging. Sarcopenia is another known extrapulmonary symptom of COPD and is associated with various factors such as physical inactivity, malnutrition, and chronic disease [12]. Since it is difficult to use the BIA device that is used in the diagnosis of sarcopenia in outpatient clinic conditions due to reasons such as accessibility, being a time-consuming muscle mass measurement, and cost, we included the contribution of the SARC-F questionnaire to the diagnosis in our study in terms of accessibility, cost, and speed in the identification of patients with sarcopenia.
The SARC-F questionnaire has been used previously for elderly patients with hip fractures and elderly patients with cardiovascular disease and it was concluded that it is a useful screening tool for sarcopenia [13, 14]. McIntosh EI et al. classified sarcopenia with the FFMI and used it together with grip strength and gait speed measurements, where significant results were obtained for predicting sarcopenia in patients [15]. In our study, which evaluated the correlation between FFMI and the SARC-F questionnaire, a statistically significant relationship was found as a result of the data. We could not find any publication that directly overlaps with our study. Prediction of sarcopenia in patients using the FFMI is costly and time-consuming. In terms of rapid screening of patients and cost, Woo J et al. stated that the questionnaire could be conducted at the expense of missing people who are sarcopenic but not sarcopenic according to the results of the SARC-F questionnaire [16].
Oral corticosteroid (OCS) use is an essential medication for acute exacerbations in patients with COPD. The association of OCS and components of sarcopenia in elderly patients has not been largely investigated. Benz, Elizabeth et al. found that OCS use was associated with a decrease in hand grip strength in people with COPD in a cumulative frequency- and duration-dependent manner [17]. Hand grip strength is included in the EWGSOP2 criteria and plays an important role in the diagnosis of sarcopenia. An analysis of the results of our study revealed that patients using inhaled corticosteroids were sarcopenic, which was similar to the findings of the study by Benz Elizabeth et al. Apart from directly associating ICS use with sarcopenia, systemic inflammation in the group E patients should not be ignored. The fact that most of the sarcopenic patients were in group E may be secondary to the high exposure of patients to systemic inflammation. Considering these studies, the association of corticosteroids and sarcopenia is an open and important topic for research. The significant correlation observed between FFMI and the SARC-F questionnaire suggests that while SARC-F does not directly assess muscle mass, it may still reflect underlying reductions in muscle mass through its focus on physical performance. Reduced FFMI likely contributes to functional impairments that are captured by the SARC-F tool, making the questionnaire a practical and cost-effective method for identifying patients at risk for sarcopenia in settings where direct body composition measurement tools are not readily available.
Sarcopenia is the loss of muscle strength, mass, and function that may progress with chronic comorbidities. In our study, we analyzed the correlation between comorbidities and sarcopenia classification according to the FFMI and found that comorbidities such as DM, HT, and CAD did not have a statistically significant relationship with sarcopenia. In the literature, several studies have found a correlation between sarcopenia and comorbidities. In the study conducted by Purnamasari D et al., a strong association was found between Type 2 DM and sarcopenia and insulin resistance was thought to be the main mediator of impaired physical function and mobility that may lead to sarcopenia [18]. Similarly, P. Duarte M. et al. showed that patients receiving hemodialysis via the SARC-F questionnaire were associated with sarcopenia [19]. Sarcopenia in COPD is caused by similar causes such as chronic inflammation and decreased physical activity. It is important to evaluate patients with COPD and comorbid conditions using the SARC-F questionnaire when a BIA device is not available so that sarcopenic patients are not overlooked.
Our study examined the correlation of the COPD functional assessment findings with SARC-F staging. A negative and low-moderate statistically significant correlation was found between the SARC-F stage and FEV1, FVC, and the 6MWT. Furthermore, a positive and high-low statistically significant association was observed with the mMRC and GOLD functional stage. In the group with a SARC-F score < 4, FEV1, FVC and 6MWT were statistically significantly higher than in the group with a SARC-F score ≥ 4. In addition, according to the mMRC classification, the number of people with mMRC 1 was higher and the number of people with mMRC 4 was lower in the group with a SARC-F score < 4 compared to the group with a SARC-F score ≥ 4. However, no significant difference was found between the groups for the SARC-F FEV1/FVC value and the GOLD sarcopenia classification. In their study, Benz E et al. showed that sarcopenia is common in the elderly population and is associated with chronic airway diseases [20]. It was concluded that there is a need for early diagnosis of sarcopenia in elderly people with chronic airway diseases by applying the recommendations of the EWGSOP2. Considering this study, we conclude that the SARC-F questionnaire used in our study provides faster results than the BIA method for patients with chronic airway disease. Likewise, Ohkubo H et al. used the SARC-F questionnaire for patients with idiopathic pulmonary fibrosis and showed that the SARC-F score was associated with lung function tests, the CAT score, and the daily step count. In the same study, the SARC-F score was shown to be significantly associated with the distance a patient walked during the 6-minute walk test and their daily steps [21]. Sarcopenia is associated with a worse prognosis and a higher prevalence in patients with more severe lung disease (37.6% in GOLD stage III-IV versus 19.1% in GOLD stage I-II) [22]. Leivseth et al. reported that in a 15-year follow-up, people with GOLD stage III and IV disease severity had a more than sixfold increased mortality risk in women and a more than twofold increased mortality risk in men [23]. Costa et al. reported an increased prevalence of sarcopenia in GOLD stage III and IV patients and indicated that these groups were associated with a lower 4-year survival rate [24]. Based on the results of our study and the results of studies in the literature, it is important to use the SARC-F questionnaire in COPD patient assessment. An increased SARC-F score in terms of COPD dyspnea scoring and the clinical prediction of patients indicates that they may have advanced stage according to the mMRC and that the losses in spirometric measurements and the 6-minute walk test may be significantly increased.
The limitations of the study are thought to be the reasons for the non-significant association between GOLD stage, comorbidities, FEV1/FVC, and sarcopenia, prospective study design, time limitation, and insufficient number of COPD patients attending our clinic. Since participation was voluntary and the participants had to visit the study center, the number of participants was not sufficient. Furthermore, the low number of comorbidities, such as diabetes mellitus associated with COPD, suggests that comorbidities were not statistically significant.
Conclusion
COPD is an important health problem in terms of the number of patients and mortality. COPD is recognized as a systemic inflammatory disease rather than a lung-specific disease. Therefore, the source of mortality is not only COPD but also the comorbidities associated with COPD. We found that the score on the SARC-F questionnaire increased with increasing COPD stage and that the questionnaire correlated with several parameters. In our study, we observed the presence of sarcopenia in COPD patients, the relationship between sarcopenia and patients receiving inhaled corticosteroids, the relationship between the FFMI, a marker of sarcopenia, and the SARC-F questionnaire, the correlation of spirometric measurements with the SARC-F questionnaire, and significant relationships between the sarcopenia diagnostic criteria and the SARC-F questionnaire results despite the relatively small number of participants. Most of our results are similar to the results found in the literature. The recent pandemic and the increasing demand for technology have caused patients to move inadequately. Another situation is that COPD exacerbations cause systemic inflammation and increase the likelihood of sarcopenia. Additionally, the fact that patients are usually older and diagnosed with COPD normalizes the loss of muscle strength, which delays early detection and the treatment of sarcopenia. Sarcopenia secondary to COPD and an increase in COPD stage secondary to sarcopenia create a vicious circle, leading to a rapid increase in mortality, reduced quality of life, and increased hospitalizations. Effective, multifaceted, and successful treatment of COPD patients is extremely important in terms of patient survival and reduced morbidity, as well as for workforce participation and reduced health care costs. At this point, we believe that the early diagnosis and treatment of sarcopenia in all COPD patients and the SARC-F questionnaire are vital to achieve these goals.
Acknowledgements
Not applicable.
Abbreviations
- COPD
Chronic obstructive pulmonary disease
- SARC-F
A simple questionnaire to rapidly diagnose sarcopenia
- GOLD
Global iniative for chronic Obstructive Lung Disease
- EWGSOP
EuropeanWorkingGroup on Sarcopenia in Older People
- Mmrc
Modified Medical Research Council Dispne Skalası
- CAT
COPD Assessment Test
- FFM
Fat Free Mass
- FFMI
Fat free Mass Index
- TNF-α
Tumor Necrosis Factor-α
- IL-6
Interleukin 6
- CRP
C-reactive protein
- BODE
Body Mass Index (BMI), airflow obstruction (FEV1), breathing dyspnea (mMRC dyspneascore)
- FEV1
1st Second Forced Expiratory Volume
- FVC
Forced Vital Capacity
- BIA
Bioelectric Impedance Analysis
- DEXA
Dual Energy X-Ray Absorptiometry
- HGS
Dynamic hand grip test
- ASM
Appendicular skeletal muscle mass
- BMI
Body Mass Index
- SABA
Short-acting beta 2 agonist
- SAMA
Short-acting muscarinic antagonist
- LABA
Long-acting beta-2 agonist
- LAMA
Long-acting muscarinic antagonist
- ICS
Inhaled corticosteroids
- PFT
Pulmonary function tests
- CAD
Coronary artery disease
- DM
Diabetes Mellitus
- HT
Hypertension
- LDH
Lactate Dehydrogenaz
- WBC
White blood cell
- NE
Neutrophil
- FM
Fat mass
- 6 MWT
6 minute walk test
- SMI
Skeletal Muscle Mass Index
Authors’ contributions
CB and BA designed this investigation and wrote the manuscript. BA and CB contributed to study design. HS and MS collected the data. NA and CB conducted statistical analysis and revision of the manuscript. HK designed this investigation and reviewed the manuscript. All authors read and approved the final manuscript. All authors reviewed the manuscript.
Funding
No funding was obtained for this study.
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
Ethical permission was obtained from the Sutcu Imam University Medical Research Ethics Committee for this study with number 2024/09 − 02, and Helsinki Declaration rules were followed to conduct this study. Since our study was retrospective, informed consent could not be obtained from the patients. This information was given to the ethics committee and ethics committee approval was obtained.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.