Sarcopenia and It's Influencing Factors Among Adults With Asthma, Chronic Obstructive Pulmonary Disease, and Tuberculosis in Penang, Malaysia

Sarcopenia and It's Influencing Factors Among Adults With Asthma, Chronic Obstructive Pulmonary Disease, and Tuberculosis in Penang, Malaysia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sarcopenia and It's Influencing Factors Among Adults With Asthma, Chronic Obstructive Pulmonary Disease, and Tuberculosis in Penang, Malaysia Radhiyatul Akma Mohamad Zani, Hazwani Ahmad Yusof, Nurul ‘Ain Azizan, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5460176/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Apr, 2025 Read the published version in BMC Public Health → Version 1 posted 10 You are reading this latest preprint version Abstract Background Chronic respiratory diseases like asthma, chronic obstructive pulmonary disease (COPD), and tuberculosis (TB) are increasing globally, leading to systemic symptoms like skeletal muscle dysfunction. Ageing and physical inactivity exacerbate sarcopenia, reducing functional capacity, disability, and quality of life. However, limited research exists on the prevalence of sarcopenia among chronic respiratory diseases in low-middle-income countries like Malaysia. Hence, this study aims to investigate the prevalence of sarcopenia and its associated risk factors among adults with asthma, COPD, and TB in Penang, Malaysia. Methods A cross-sectional study was conducted from June 2023 to March 2024. This study included 469 patients (mean age: 52.62 ± 16.61 years) diagnosed with asthma (n = 180), COPD (n = 186), or TB (n = 103) receiving treatment in chest clinics of two governmental hospitals in Penang. The SARC-F and SARC-CalF questionnaires were used to assess the participants' risk of sarcopenia. Sarcopenia was identified using the 2019 criteria of the Asian Working Group for Sarcopenia (AWGS). The risk factors for sarcopenia in asthma, COPD, and TB patients were investigated using multivariable logistic regression. Results The prevalence of sarcopenia was 18.9% (95% CI 13.5–25.4) in patients with asthma, 33.9% (95% CI 27.1–41.2) in those with TB, and 35.9% (95% CI 26.7–46.0) in those with COPD, according to AWGS 2019 criteria. The SARC-CalF screening tool showed that 27.3% of participants had a positive risk of having sarcopenia. The independent risk factors associated with sarcopenia in asthma patients were age, physical activity and body mass index (BMI). For TB patients, significant risk factors included Chinese and other ethnicities, foreigners, lower daily protein intake, and BMI. In COPD patients, independent risk factors included age, moderate physical activity, BMI and history of heart failure. Conclusion This study highlighted a significant burden of sarcopenia among patients with asthma, COPD and TB. Non-clinical interventions such as lifestyle modification and nutritional support to the patients are crucial to maintain muscle strength and delay the onset of sarcopenia, particularly in people with chronic respiratory diseases. Sarcopenia Chronic Respiratory Disease Asthma COPD TB Risk Factors Prevalence Figures Figure 1 Figure 2 Background Chronic respiratory diseases (CRDs) were the third leading cause of death globally in 2019, accounting for 4.0 million deaths. Among these, COPD was the leading cause of mortality, while asthma had the highest prevalence [1]. Asthma affects approximately 262 million people worldwide [2]. In Malaysia, the prevalence of asthma ranges from 8.9–13% in children (6–14 years) and 6.3% in adults, while the prevalence of COPD in suburban areas is estimated at 3.4–5.1% [3]. Meanwhile, TB remains a major public health concern, with cases rising by 5.47% within a year, from 25,391 new TB cases in 2022 to 26,781 cases in 2023 [4]. Beyond respiratory impairment, CRDs contribute to muscle loss and reduced physical activity, increasing the risk of sarcopenia [5]. With an ageing population, sarcopenia has become a significant health concern due to its association with physical disability, hospitalisation, and increased mortality rates [6, 7]. It also places a substantial burden on healthcare systems, particularly due to the need for long-term care [7]. In Malaysia, CRDs are among the top three causes of hospitalisation and mortality [8]. However, challenges remain in the healthcare system, including limited awareness among healthcare providers, time constraints, and a lack of confidence in diagnosing and managing these conditions [9]. Additionally, pulmonary rehabilitation services are scarce, and palliative care for severe COPD remains limited [9]. These gaps may further hinder the recognition and management of sarcopenia in CRD patients. Sarcopenia affects up to 17.6% of individuals with asthma, with 5.5% experiencing severe sarcopenia [10]. However, in Malaysia, data on sarcopenia prevalence among CRD patients—particularly those with asthma, COPD, and TB—remain scarce [11]. Reported prevalence in these populations varies widely, from 9.5–57.1%, depending on the study population and diagnostic criteria used. Research on sarcopenia in CRD patients across Asian countries, particularly Malaysia, remains limited. The Asian Working Group for Sarcopenia (AWGS) updated its diagnostic criteria in 2019, establishing specific cut-off values for the Asian population [12]. Understanding sarcopenia prevalence among CRD patients in Malaysia is essential for developing targeted prevention and management strategies. Therefore, this study aims to assess the prevalence of sarcopenia among adults with asthma, COPD, and TB in Penang, Malaysia, and identify associated risk factors. Methods This comparative cross-sectional study was conducted from June 2023 to March 2024. Participants were recruited from two government hospitals: one located on Penang Island and the other on the mainland. The high prevalence of CRD patients, including TB [13], and Penang’s representation of Malaysia’s multicultural population made it a particularly suitable research setting. The sample size for this study was carefully determined to ensure adequate statistical power for detecting meaningful associations. Sample size calculations were performed separately using a single proportion formula for each disease group based on the previous literatures. The calculations were conducted using the Sample Size Calculator (Version 2.0) by Ariffin et al. [14], considering a significance level (α) of 0.05 and a power (1-β) of 80%. The minimum total sample size required was estimated at 282 participants. The sample size was increased by 40%, making it 395 samples to account for potential high dropouts. Among the strategies used in this study to minimize non-response and ensure data completeness was approaching the patients early during their waiting time to avoid them getting anxious for their queue number to be called, hence less rejection. A polite and proper explanation about the need for the study was also shown to positively impact a good response rate. The researcher will also check for any questions that they missed answering to ensure data completeness. 500 patients were approached. However, 469 participants were successfully recruited, further enhancing the reliability of the study findings. The final total sample achieved for this study was 469 participants diagnosed with asthma (n = 180), COPD (n = 103), or TB (n = 186) were included. Recruitment was conducted using a convenience sampling technique. Potential eligible patients were derived from the weekly patient follow-up lists obtained from the chest clinics, categorised by diagnosis, with assistance from in-charge nurses. All patients meeting the inclusion criteria were invited to participate. Post-TB patients were defined as individuals who had completed the full course of TB treatment and had been officially declared cured or treatment-completed by a physician. Data collection involved researcher-guided interviews, each lasting approximately 20–30 minutes. Researcher served as data collectors and underwent multiple training sessions led by university-affiliated science officers. The training included theoretical instruction on study protocols, hands-on practice with SF-BIA and handgrip dynamometry, and standardised procedures for conducting the gait speed test using a stopwatch and a pre-measured walking distance. Additionally, role-playing exercises were conducted to standardise interview techniques. To ensure proficiency, data collectors practised assessments on public volunteers before conducting measurements on study participants. All measurements were carried out in the morning between 8:00 AM and 12:00 PM to minimise variations due to circadian influences and participant fatigue. Study population The inclusion and exclusion criteria for each group are shown in Table 1 . As for the general exclusion criteria, Asthma, TB or COPD patients who were fasting on the day of data collection or with conditions that limit mobility (e.g., advanced osteoarthritis, stroke with significant disability, or spinal cord injuries) were excluded from this study since sarcopenia in these cases is primarily due to immobility rather than disease-related mechanisms. Table 1 Inclusion and exclusion criteria for participant selection Inclusion and Exclusion Criteria for Participant Selection Disease Group Inclusion Criteria Exclusion Criteria Asthma Adults aged ≥ 18 years, diagnosed with asthma based on Global Initiative for Asthma (GINA) 2023 criteria [ Global Initiative for Asthma, 2023 ]. The diagnosis was confirmed by a respiratory physician using clinical history, spirometry showing reversible airflow limitation (> 12% & >200 mL improvement in FEV1 post-bronchodilator), and patient-reported symptoms. Severe cardiovascular or neurological diseases affecting study participation COPD Adults aged ≥ 18 years, diagnosed with COPD based on Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2023 criteria [ Agustí et al., 2023 ]. The diagnosis was confirmed via spirometry (FEV1/FVC < 0.70 post-bronchodilator). Patients experiencing an exacerbation within the preceding four weeks TB Adults aged ≥ 18 years, diagnosed with active TB (on treatment for at least two weeks) or post-TB (completed treatment and attending follow-ups). Diagnosis was confirmed through clinical, radiological, and microbiological evidence. Extensive lung damage affecting functional assessments, Patients with asthma-COPD overlap (ACO) were classified based on their most recent diagnosis and prescribed treatment, as determined by respiratory physicians. If their latest diagnosis and on-going treatment indicated COPD, they were classified within the COPD group for analysis. Data collection Researchers personally recruited patients to participate in the chest clinics at both hospitals, obtaining informed consent prior to enrolment. Following written informed consent, participants’ socio-demographic data, medical history data, lifestyle factors (smoking, alcohol drinking, and drug addiction history), and anthropometric measurements were gathered. The researchers evaluated the participants for sarcopenia risk using questionnaires like the Sarcopenia Rapid Assessment Tool—Functional (SARC-F) and the Sarcopenia Rapid Assessment Tool—Calf (SARC-CalF). Bioelectrical impedance analysis (BIA), handgrip dynamometers, and gait speed testing were implemented to evaluate the participants further to identify sarcopenia. The participants’ physical activity and dietary status were assessed using the International Physical Activity Questionnaires (IPAQ). This study has been ethically approved by the Medical Research Ethics Committee (MREC) of the Ministry of Health (NMRR ID-23-00257-WV6(IIR)) and the Human Research Ethics Committee, Universiti Sains Malaysia (USM/JEPeM/22110707). Assessment of risk of sarcopenia with SARC-F and SARC-CalF The SARC-F questionnaire used in this study includes five questions assessing strength, walking assistance, chair rise, stair climbing, and falls to screen for sarcopenia risk. The SARC-CalF expands on the SARC-F by incorporating calf circumference, with a score of 11 or more indicating possible sarcopenia [15]. Calf circumference measurement enhances sensitivity compared to the SARC-F alone, reducing the likelihood of underestimation [16–20]. However, according to AWGS 2019, the SARC-F and SARC-CalF are screening tools and do not confirm sarcopenia. AWGS 2019 serves as a full diagnostic framework, incorporating muscle mass, strength, and physical performance for a more accurate diagnosis. This distinction is important when interpreting findings, as screening tools help identify individuals at risk but require further assessment using AWGS 2019 criteria for definitive diagnosis. Anthropometric measurements The anthropometric measurements such as height, weight, BMI, and calf circumference were measured in this study. A BIA device was used to determine body weight to the nearest 0.1 kg, while a stadiometer was used to determine height to the nearest 0.01 cm. Weight (in kilograms) was divided by height (in square meters) to calculate BMI. Participants stood while the most visible portion of the dominant side's calf was measured with a tape measure in 0.1 cm increments without tightening it to determine calf circumference. The AWGS 2019 criteria identified suboptimal calf circumference as < 33 cm for females and < 34 cm for males. Diagnostic criteria of sarcopenia The AWGS 2019 criteria define sarcopenia as a condition characterised by a combination of low muscle mass, reduced muscle strength, and poor physical performance. Possible sarcopenia is defined as the presence of either low muscle strength or poor physical performance, without low muscle mass. Severe sarcopenia is characterised by the presence of low muscle mass, reduced muscle strength, and poor physical performance. Assessment of muscle mass The method of BIA is widely recognised for the evaluation of muscle mass. A muscle mass measurement was conducted in this investigation using a BIA device (OMRON HBF-206IT). Prior to standing on the metal footpads of the BIA device, participants are required to remove any jewellery, metals, or shoes and wear lightweight clothing. BIA is the primary method for determining appendicular skeletal muscle mass (ASMM), which is subsequently used to calculate appendicular skeletal muscle index (ASMI) using the following formula: ASMM/height2 [21]. The AWGS 2019 criteria for diagnosing sarcopenia are consistent with this calculation [15]. Based on the AWGS 2019 criteria, the thresholds for low muscle mass are ASMI < 7.0 kg/m2 for men and ASMI < 5.7 kg/m2 for women [22]. Assessment of muscle strength Muscle strength was assessed using handgrip strength measured by a digital Smedley spring hand dynamometer (Takei T.K.K. 5401; Takei Scientific Instruments Co. Ltd., Tokyo, Japan) as recommended by AWGS 2019. Participants were instructed to squeeze the dynamometer with maximum effort while standing with fully extended arms at their sides, and 3 trials were performed. Based on AWGS 2019 criteria, handgrip strengths < 28 kg in males and < 18 kg in females were classified as low muscle strength. Assessment of physical performance The participant's physical performance was assessed using a gait speed test. In this study, the 6-meter walk test was used to determine gait speed test. Participants were instructed to walk the distance at their normal pace over 6-meter course. The time it took to walk the distance was measured to the nearest 0.01 second. Gait speed was determined by dividing the distance walked by the total time required to complete the test in meters per second (m/s) [23]. According to the AWGS 2019 guidelines, low gait speed is less than 1.0 m/s [22]. International of Physical Activity Questionnaire - Long Form (IPAQ - LF) The IPAQ is a widely used self-reported tool designed to measure physical activity levels over the past seven days [24]. The English and Malay versions, which are publicly available on the IPAQ website ( https://sites.google.com/view/ipaq/home ), were obtained and adapted with permission from the original authors [25]. For this study, the IPAQ-LF, which consists of 27 items, was used to assess various domains of physical activity, including job-related, transport-related, domestic, and leisure activities. It also includes questions on sedentary behaviour, such as sitting time [24]. To score the IPAQ, responses are converted into Metabolic Equivalent of Task (MET) values. The IPAQ-LF captures the frequency and duration of activities performed at different intensities—walking, moderate-intensity, and vigorous-intensity activities. The total physical activity score is calculated based on the weekly time spent in each activity. Participants are then classified as sedentary, irregularly active, active, or very active [24]. Activities are classified by their MET values, which contribute to a total score that indicates health-enhancing physical activity (HEPA) [26]. The IPAQ-LF has been widely validated in cross-sectional studies assessing physical activity levels in adults, particularly in chronic respiratory diseases like asthma and COPD [27–30]. As the IPAQ is a self-reported measure, potential biases, such as recall bias and social desirability bias, must be considered. To minimise these biases, participants were given ample time to complete the questionnaire without feeling rushed. They were also encouraged to seek clarification if needed. This approach ensures that the information provided by participants is as accurate and reliable as possible. Additionally, the researcher was available to assist participants in understanding the questions and addressing any uncertainties they might have regarding the questionnaire Food Frequency Questionnaire (FFQ) Daily food intake was assessed using an FFQ developed by the Institute for Public Health Malaysia [31]. This questionnaire is recognised as a reliable method for estimating habitual dietary intake in the Malaysian population [32]. In this study, the FFQ recorded the frequency and quantity of foods consumed by participants across fourteen categories: cereals and cereal products, fast food, meat and meat products, fish and seafood, eggs, legumes, milk and milk products, vegetables, fruits, beverages, alcoholic drinks, confectionery, spreads, and flavourings. Participants were asked to recall their food intake over the past week, selecting the frequency of consumption from predefined options and specifying portion sizes. The collected data were analysed using FFQ Nutritionist Pro to estimate nutrient intake, including energy (calories), protein, calcium, potassium, phosphorus, magnesium, iron, zinc, selenium, cobalamin, pyridoxine, vitamin C, vitamin D, vitamin E, and vitamin K. The FFQ is one of the most commonly used methods for assessing dietary intake in large-scale epidemiological studies [33]. Furthermore, FFQs have been widely utilised in clinical nutrition studies, including a recent study on TB patients in China that assessed protein and overall nutrient intake using both FFQ and 24-hour recall methods [34]. Similarly, Li et al. [35] employed the FFQ to examine dietary patterns in TB patients, emphasising the importance of a balanced diet, including high-quality protein, in improving disease outcomes. Statistical analysis Continuous variables were expressed as means and standard deviations. Categorical variables are presented as numbers and percentages. Following the initial analysis, a multivariable logistic regression model was used to determine the independent associations between each potential variable (risk factors) and the sarcopenia condition. The model included variables with univariate significance levels of 0.25 or less. The adjusted odds ratios and 95% confidence intervals were reported for the final regression model. IBM SPSS Statistics, version 27 (SPSS Inc., Chicago, IL, USA), was used to conduct the analyses. A p-value of less than 0.05 was considered statistically significant. Results Participants’ characteristics Table 2 presents the socio-demographic characteristics of the study population (n = 469), stratified by disease groups (asthma, TB, and COPD). Among the disease groups, males were more prevalent in TB (48.9%) and COPD (42.0%), while females were dominant in asthma (66.8%). The mean age of participants was 52.6 ± 16.6 years, with COPD patients being the oldest (65.1 ± 9.9 years) and TB patients the youngest (46.2 ± 16.5 years). In terms of ethnicity, Malays constituted the majority (49.9%), followed by Chinese (25.4%), Indians (19.2%), and other ethnicities (5.5%). Chinese ethnicity was more common among TB patients (48.7%), while Malays had the highest proportion in asthma (40.6%). Regarding marital status, 57.1% were married, with TB patients having the highest proportion of never-married individuals (52.1%). Divorced and separated individuals were more common among asthma and TB patients. For education level, most had secondary education (45.6%), while university graduates were more frequent among asthma patients (58.7%) but rare in COPD (2.7%). In terms of employment, 45.4% were employed, with the highest employment rate among TB patients (43.7%). COPD patients had the highest proportion of retired individuals (26.4%). Regarding financial income, 40.1% relied on family or friends for support, and only 0.6% earned more than MYR 5,000. TB patients had the highest proportion of self-supporting individuals (50.0%), while COPD patients were mostly in the lower-income group (< MYR 2,000; 28.0%). Table 2. Socio-demographic characteristics of all patients (n=469) Factors Category Overall (n=180) (n=186) (n=103) n (%) mean±SD Asthma n (%) TB n (%) COPD n (%) Gender Male 231 (49.3) 21 (9.1) 113 (48.9) 97 (42.0) Female 238 (50.7) 159 (66.8) 73 (30.7) 6 (2.5) Age, years Mean age 52.6±16.6 52.2 ± 15.8 46.2 ± 16.5 65.1 ± 9.9 Ethnicity Malay 234 (49.9) 95 (40.6) 86 (36.8) 53 (22.6) Chinese 119 (25.4) 27 (22.7) 58 (48.7) 34 (28.6) Indian 90 (19.2) 50 (55.6) 26 (28.9) 14 (15.6) Other 10 (2.1) 5 (50.0) 4 (40.0) 1 (10.0) Foreigners 16 (3.4) 3 (18.8) 12 (75.0) 1 (6.3) Marital Status Never married 121 (25.8) 33 (27.3) 63 (52.1) 25 (20.7) Married 268 (57.1) 114 (42.5) 94 (35.1) 60 (22.4) Separated 2 (0.4) 0 (0.0) 2 (100.0) 0 (0.0) Divorcee 76 (16.2) 33 (43.4) 26 (34.2) 17 (22.4) Cohabiting 2 (0.4) 0 (0.0) 1 (50.0) 1 (50.0) Education Level No formal schooling 30 (6.4) 9 (30.0) 13 (43.3) 8 (26.7) Primary 86 (18.3) 21 (24.4) 30 (34.9) 35 (40.7) Secondary 214 (45.6) 83 (38.8) 84 (39.3) 47 (22.0) College 64 (13.6) 23 (35.9) 30 (46.9) 11 (17.2) University 75 (16.0) 44 (58.7) 29 (38.7) 2 (2.7) Employment Status Not employed 184 (39.2) 67 (36.4) 68 (37.0) 49 (26.6) Employed 213 (45.4) 85 (39.9) 93 (43.7) 35 (16.4) Retired 72 (15.4) 28 (38.9) 25 (34.7) 19 (26.4) Financial Income Financial support (family/friends) 188 (40.1) 68 (36.2) 71 (37.8) 49 (26.1) Self-support (saving) 44 (9.4) 10 (22.7) 22 (50.0) 12 (27.3) < MYR 2 000 114 (24.3) 41 (36.0) 41 (36.0) 32 (28.0) MYR 5 000 3 (0.6) 2 (66.7) 1 (33.3) 0 (0.0) Abbreviation: NS = Values are presented as frequency, n = total number of participants; % = percentage; mean ± standard deviation (SD). Prevalence of sarcopenia Table 3 categorises participants based on sarcopenia status using the Asian Working Group for Sarcopenia (AWGS) 2019 criteria, which include low muscle mass, reduced handgrip strength, and slow gait speed to determine sarcopenia severity. The prevalence of sarcopenia, as classified by the AWGS 2019 criteria, revealed that 34.3% of participants had low muscle mass, with the highest prevalence among COPD patients (39.8%), followed by TB (43.5%) and asthma (21.7%). Additionally, 67.8% of participants had reduced handgrip strength, with COPD patients showing the highest prevalence (74.8%). Impaired physical performance, as measured by gait speed, was observed in 59.1% of participants, with COPD patients again showing the highest prevalence (65.0%). Overall, the prevalence of sarcopenia was highest among COPD patients (35.9%), followed by TB (33.9%) and asthma (18.9%), with severe sarcopenia most common in COPD patients (28.2%). The progressive muscle loss associated with chronic respiratory diseases is especially pronounced in COPD and TB patients. Table 3 Diagnosis of sarcopenia in patients with asthma, COPD, and TB based on AWGS 2019 criteria Criteria Asthma, Total (n = 180) TB, Total (n = 186) COPD, Total (n = 103) Overall, n (%) NS, n (%) PS, n (%) S, n (%) SS, n (%) NS, n (%) PS, n (%) S, n (%) SS, n (%) NS, n (%) PS, n (%) S, n (%) SS, n (%) Muscle Mass: (ASMI, kg/m 2 ) NS: 308 (65.7) S: 161 (34.3) 141 (78.3) 39 (21.7) 105 (56.5) 81 (43.5) 62 (60.2) 41 (39.8) Muscle Strength: (Handgrip strength, kg) NS: 151 (32.2) S: 318 (67.8) 65 (36.1) 115 (63.9) 60 (32.3) 126 (67.7) 26 (25.2) 77 (74.8) Physical Performance: Gait speed, m/s NS: 192 (40.9) S: 277 (59.1) 65 (36.1) 115 (63.9) 91 (48.9) 95 (51.1) 36 (35.0) 67 (65.0) Final Assessment Sarcopenia (muscle mass, handgrip strength, gait speed) NS: 138 (29.4) PS: 197 (42.0) S: 38 (8.1) SS: 96 (20.5) 62 (34.4) 84 (46.7) 6 (3.3) 28 (15.6) 53 (28.5) 70 (37.6) 24 (12.9) 39 (21.0) 23 (22.3) 43 (41.7) 8 (7.8) 29 (28.2) Abbreviation: AWGS = Asian Working Group for Sarcopenia; NS = Non-Sarcopenia; PS = Possible Sarcopenia; S = Sarcopenia; SS = Severe Sarcopenia; ASMI = Appendicular Skeletal Muscle Mass Index. Values are presented as frequency, n = is total number of participants; % = percentage; mean ± standard deviation (SD). Table 4 present the prevalence of sarcopenia among patients with asthma, TB, and COPD. Sarcopenia was most prevalent among COPD patients (35.9%, 95% CI 26.7-46.0), followed by TB patients (33.9%, 95% CI 27.1-41.2), while asthma patients had the lowest prevalence (18.9%, 95% CI 13.5-25.4). These findings suggest that sarcopenia is more common in TB and COPD populations. Table 4 Prevalence of sarcopenia among all three types of chronic respiratory disorders (asthma, TB, and COPD) Types of Disease Non-sarcopenia, n (%) Sarcopenia, n (%) Asthma 146 (81.1) 34 (18.9) TB 123 (66.1) 63 (33.9) COPD 66 (64.1) 37 (35.9) The prevalence of sarcopenia was stratified by gender and age groups based on the recommended standardised age groups for life stage analysis, as proposed by Diaz et al. (2021). Age groups for the analysis therefore consisted of: Older adolescents (18 – 19 years), Young adults (20 – 24 years), Adults (25 – 59 years), and Older adults (60 – 99 years) (Table 5). Table 5 Prevalence of sarcopenia, stratified by age groups and gender composition Age group (years) Male Sarcopenia (%) Female Sarcopenia (%) 18 - 19 60.0 40.0 20 - 24 75.0 25.0 25 - 59 40.4 59.6 60 - 99 63.0 37.0 Factors associated with sarcopenia among asthma, TB and COPD patients a) Bivariable analysis of factors associated with sarcopenia Table 6 presents the prevalence of sarcopenia across different socio-demographic characteristics. Males had a higher prevalence of sarcopenia (31.6%) compared to females (20.7%), suggesting a potential gender-related risk. Ethnic differences were also notable, with Chinese COPD patients exhibiting the highest sarcopenia prevalence (43.2%), indicating a possible link between ethnicity and sarcopenia risk. Education level appeared to be an influencing factor, as individuals with no formal schooling had a higher prevalence of sarcopenia, particularly among TB (12.8%) and COPD (16.2%) patients. Employment status was another significant factor, with unemployed participants showing the highest prevalence of sarcopenia (37.0%), reinforcing the potential impact of socioeconomic status. To further explore these associations, univariable logistic regression analysis was conducted to identify factors with p-values < 0.25 for inclusion in the multiple logistic regression analysis. Among asthma patients, variables meeting this threshold were age, Chinese ethnicity, other races, no formal schooling, primary education level, and financial support. For TB patients, Indian ethnicity, marital status (married, divorced/separated), no formal schooling, primary education level, and unemployment status were included in the multivariable analysis. Similarly, among COPD patients, no formal schooling, primary education level, unemployment, retirement, and financial support were identified as potential predictors of Table 7 examines lifestyle and dietary factors associated with sarcopenia risk, particularly physical activity levels, dietary intake, and overall nutritional status. Lifestyle and nutritional factors showed a strong association with sarcopenia, with physical activity levels and dietary intake being particularly influential. Low physical activity was linked to a higher prevalence of sarcopenia (41.2%), with COPD patients being the most affected (81.1%). Nutritional intake analysis revealed that sarcopenic individuals had significantly lower protein consumption, with TB patients having the lowest daily intake (22.29 ± 8.43 g/day), potentially contributing to accelerated muscle loss. Interestingly, energy intake was relatively higher among COPD patients with sarcopenia (518.73 ± 261.33 kcal/day), yet this group still exhibited a high prevalence of sarcopenia, suggesting possible metabolic inefficiencies or inadequate nutrient absorption. To further investigate these associations, univariable logistic regression analysis was conducted to identify factors with p-values ˂ 0.25 for inclusion in the multiple logistic regression analysis. Among asthma patients, the selected variables were passive smoking, low and moderate physical activity, energy intake (kcal/day), protein intake (g/day), and key micronutrients, including potassium (g/day), phosphorus (mg/day), magnesium (mg/day), iron (mg/day), zinc (mg/day), selenium (µg/day), cobalamin (µg/day), pyridoxine (mg/day), vitamin C (mg/day), and vitamin E (mg/day). Among TB patients, the included factors were passive smoking, active or former chaser status, occasional alcohol consumption, low physical activity, energy intake (kcal/day), protein intake (g/day), and micronutrients such as potassium, phosphorus, magnesium, iron, zinc, selenium, cobalamin, pyridoxine, vitamin E, and vitamin K. Among COPD patients, significant variables included low physical activity, energy intake, protein intake, and micronutrients such as potassium, phosphorus, magnesium, zinc, selenium, pyridoxine, and vitamin E Table 8 evaluates clinical risk factors for sarcopenia, including hospitalisation history, body mass index (BMI), and common comorbidities such as hypertension and diabetes. Clinical factors played a crucial role in the development of sarcopenia, with hospitalisation history emerging as a significant predictor. Among hospitalised individuals, the prevalence of sarcopenia was highest in asthma patients (97.1%), followed closely by COPD patients (94.6%). Hypertension was the most common comorbidity, with COPD patients experiencing the highest burden (40.5%). Other less prevalent comorbidities are detailed in Supplementary I. Additionally, a history of falls was strongly associated with sarcopenia, particularly among COPD patients (21.6%), indicating an increased risk of frailty and fractures. To further investigate these associations, univariable logistic regression analysis was conducted to identify factors with p-values < 0.25 for inclusion in the multiple logistic regression analysis. Among asthma patients, the selected variables were hospitalisation history, BMI (kg/m²), and a history of rheumatoid arthritis. For TB patients, hospitalisation history and BMI were included. Among COPD patients, significant variables included a history of falls, BMI, type 2 diabetes mellitus (T2DM), and heart failure. b. Multivariable analysis of independent factors associated with sarcopenia Multivariable logistic regression analysis identified several independent risk factors for sarcopenia across disease groups (Table 9). In asthma patients, older age (OR = 1.05, 95% CI = 1.01 to 1.09, p < 0.05) and lower BMI (OR = 0.61, 95% CI = 0.51 to 0.73, p < 0.05) increased the risk, while high physical activity was protective (OR = 0.13, 95% CI = 0.03 to 0.58, p < 0.05). Among TB patients, Chinese ethnicity (OR = 0.34, 95% CI = 0.12 to 0.99, p < 0.05) and other ethnicities (OR = 0.17, 95% CI = 0.04 to 0.83, p < 0.05) were linked to higher sarcopenia prevalence, while lower protein intake (OR = 0.88, 95% CI = 0.83 to 0.92, p < 0.05) and lower BMI (OR = 0.63, 95% CI = 0.53 to 0.75, p < 0.05) were significant risk factors. For COPD patients, older age (OR = 1.03, 95% CI: 1.03 to 1.18, p < 0.05), lower BMI (OR = 0.77, 95% CI = 0.68 to 0.88, p < 0.05), and history of heart failure (OR = 15.30, 95% CI = 1.48 to 158.06, p < 0.05) were major contributors to sarcopenia, whereas moderate physical activity (OR = 0.14, 95% CI = 0.03 to 0.70, p < 0.05) was a protective factor. The findings are summarised in Figure 2. Discussion To the best of our knowledge, this is the first study to investigate sarcopenia in asthma, COPD, and TB in Penang, Malaysia. The study found that sarcopenia was prevalent among COPD patients at a rate of 35.9%. This finding is consistent with international research using comparable diagnostic tools. For instance, studies indicate that the prevalence of sarcopenia among COPD patients is 25.0% in Brazil [36], 44.3% in Turkey [37], and 22.0% in China [38]. Despite using BIA, handgrip strength, and gait speed in accordance with the AWGS 2019 definition, a study [38] reported a lower prevalence of 22.0%. Other studies have reported prevalence rates of 31.1%, 38.36%, and 33.6%, respectively [39-41]. Variations in diagnostic tools, definitions of sarcopenia, and sample sizes can account for these variations. Furthermore, the current study found that 33.9% of TB patients had sarcopenia, a finding that aligns with Choi et al.'s retrospective study, which discovered that sarcopenia affected 19.6% to 45.0% of Korean TB survivors [42]. This alignment in prevalence rates shows that sarcopenia is widespread in this population, despite differences in diagnostic methods like DXA, BIA, and WHO/AWGS 2019 cut-off values. Dual-Energy X-ray Absorptiometry (DXA) is widely regarded as the gold standard for assessing muscle mass due to its precision in measuring appendicular lean mass (ALM) [22]. However, DXA's high cost and limited portability hinder its widespread use, particularly in resource-constrained settings [43]. Bioelectrical Impedance Analysis (BIA) offers a more accessible alternative, being cost-effective and portable [44]. Nonetheless, BIA's accuracy can be compromised by factors such as hydration status and device-specific variations, leading to discrepancies in sarcopenia diagnosis [45]. The Asian Working Group for Sarcopenia (AWGS) 2019 consensus provides specific cut-off values for ALM, recommending <7.0 kg/m² for men and <5.4 kg/m² for women when using DXA, and <7.0 kg/m² for men and <5.7 kg/m² for women when using BIA, aiming to accommodate ethnic and methodological differences [22]. However, discrepancies between devices have been observed; for instance, a study comparing two segmental multi-frequency BIA devices found significant differences in ALM values, suggesting that device-specific adjustments may be necessary to ensure diagnostic accuracy [46]. Furthermore, factors such as hydration status, electrode placement, and patient positioning can influence BIA measurements, necessitating standardized protocols to enhance reliability [45]. While BIA provides a practical alternative to DXA, clinicians should be cautious of its limitations and consider device-specific and population-specific adjustments when diagnosing sarcopenia. In this study, ethnicity significantly influenced sarcopenia in TB patients. The study identified an association between sarcopenia and ethnicity in TB patients, including Chinese, other races, and foreigners. No other studies have reported similar findings. However, a study in Singapore found that Chinese people had a higher risk of sarcopenia than Malays and Indians, suggesting that ethnic lifestyle differences may affect likelihood [43]. Additionally, this study found that 18.9% of asthmatics had sarcopenia. This is similar to Hu's study, which reported that 12.3%–21.3% of asthma patients in China have sarcopenia [44]. Both studies used the AWGS 2019 definition of sarcopenia, albeit using different assessment tools. This study used BIA for muscle mass, handgrip strength for muscle strength, and gait speed for physical performance, while Hu et al.’s research used the 5-time chair stand test. Despite these differences in assessment methods, the prevalence rates of sarcopenia in Southeast Asian asthma patients are similar. Like Won et al.'s study [45],, this study found that age and lower BMI were risk factors for sarcopenia in asthma patients. Additionally, Yu et al. found that sarcopenia was associated with age and BMI in 4,000 community-dwelling Chinese adults 65 and older [46]. Kim et al. [47] also identified age and low BMI as risk factors for muscle strength decline, with regular exercise providing protective effects against grip strength decline in women aged 75 and older. Physical inactivity is associated with a higher risk of sarcopenia in asthma patients. Sánchez-Sánchez et al. [48] conducted a comprehensive systematic review and meta-analysis, demonstrating that higher levels of physical activity are significantly associated with a reduced risk of sarcopenia. Evidence suggests that highly active individuals have a lower incidence of sarcopenia than less active ones [49]. Despite the well-documented benefits of physical activity in preventing and managing sarcopenia, research on its effects in asthma patients remains limited. Next, this study identified that lower daily protein intake significantly impacts sarcopenia in TB patients. This supports the findings of Shin et al. [50], who found that TB survivors who ate enough protein and calories had a lower risk of sarcopenia. Furthermore, this study discovered a significant influence of BMI on sarcopenia in TB patients. No other study has explored the relationship between sarcopenia and BMI among TB patients. Curtis et al. found a significant association between low BMI and an elevated risk of sarcopenia in older adults, suggesting that low BMI is a consistent risk factor across various populations [51]. Ethnicity was found to significantly impact sarcopenia in TB patients. This study revealed associations between sarcopenia and ethnicity among TB patients, including Chinese, other races, and foreigners. Lifestyle and cultural factors, which vary among ethnic groups, may also affect sarcopenia risk. For example, a study in Singapore found that Chinese individuals had a higher risk of sarcopenia compared to Malays and Indians, indicating that ethnic differences in lifestyle practices may influence sarcopenia risk [52]. Different ethnic groups have different living environments, lifestyles, and eating habits [53]. Furthermore, this study also found that age is a significant variable influencing sarcopenia in COPD patients. This result is consistent with the findings of Zhou et al. [54], who identified age as a risk factor for sarcopenia in this population. Schols et al. reported that muscle loss occurs at an annual rate of 1% to 2% in COPD patients aged 50 and older [55]. In addition, this study found a significant link between moderate physical activity and a lower risk of sarcopenia among COPD patients. These findings suggest that COPD patients may have the potential to develop sarcopenia by limiting physical activity. Additionally, Kim et al. found that a decline in physical activity is a key factor in developing sarcopenia among COPD patients [56]. BMI was significantly associated with sarcopenia in COPD patients. Zhou et al. [54] and Morisawa and Katzmarzyk [57] reported a strong link between low BMI and sarcopenia, likely due to malnutrition and hypoproteinemia in this population [58]. Additionally, this study identified a significant association between a history of heart failure and sarcopenia among COPD patients. Consistently, Leem et al. [59] found that sarcopenia was significantly linked to an increased risk of atherosclerotic cardiovascular disease (ASCVD) in men with COPD, independent of central obesity and fat mass. Given the shared cardiovascular burden in COPD, these findings further emphasise the interplay between sarcopenia, cardiovascular disease, and COPD severity. Heart failure (HF) is another condition frequently associated with sarcopenia, with prevalence rates ranging from 10.1% to 68%, depending on disease severity [60]. Sarcopenia and HF share common mechanisms, including systemic inflammation, malnutrition, neurohormonal dysregulation, and oxidative stress, leading to a vicious cycle that worsens both conditions [61-62]. Additionally, hormonal deficiencies, such as low levels of insulin-like growth factor-1 (IGF-1), testosterone, and vitamin D, may accelerate muscle wasting in HF patients [63]. Sarcopenia in HF has been linked to reduced functional capacity, increased hospitalisation rates, and higher mortality risk [64]. Moreover, cardiovascular diseases, including hypertension, are closely linked to sarcopenia. Hypertension contributes to muscle loss through decreased capillary density around muscle fibres, impairing nutrient and oxygen delivery [65]. Studies have reported a higher prevalence of sarcopenia in individuals with CVD compared to the general population [66], with sarcopenic obesity—characterised by excessive fat accumulation alongside muscle loss—further elevating the risk of cardiovascular mortality [67-68]. The main strength of this study is that it is the first to investigate the prevalence of sarcopenia and its related risk factors in Malaysia, specifically within the Penang population. It systematically examines patients with chronic respiratory diseases—namely asthma, COPD, and TB—in two regions of Penang, using the AWGS 2019 criteria. However, certain limitations must be acknowledged. The cross-sectional design limits the capacity to determine causal relationships among variables. The identified risk factors are correlated with sarcopenia but may not be direct causes. Future longitudinal studies are strongly recommended to provide more substantial evidence for causality. Additionally, future research should explore a wider range of clinical factors that may be associated with sarcopenia. Integrating qualitative assessment to understand differences in health or cultural practices among different ethnicities in Malaysia may give a holistic understanding of its impact to the risk of sarcopenia. Secondly, the use of convenience sampling may limit the generalisability of the findings, as participants were selected based on availability rather than random selection. This, along with the limited sample size, reduces the robustness of the results. Increasing the sample size in future studies would enhance the reliability of the findings. Moreover, the use of food frequency questionnaires for dietary intake assessment and self-reported physical activity questionnaires may introduce inaccuracies due to potential over- or under-reporting or recall bias. To mitigate this, the researcher used a short recall period (e.g., past 7 days instead of past month) and provide prompts or examples to improve accuracy when needed. The reliance on HBF-206IT-based bioelectrical impedance analysis (BIA) for sarcopenia diagnosis also has limitations, as factors such as body composition and hydration status can affect accuracy. Financial constraints further restricted the use of Dual-energy X-ray Absorptiometry (DXA), a more precise diagnostic tool. Finally, the participants were recruited from government hospital chest clinics, meaning they may have had more severe disease, potentially leading to an overestimation of sarcopenia prevalence. As a result, the findings should be interpreted with caution and cannot be fully generalised to all individuals with asthma, COPD, or TB in Malaysia. Conclusions In conclusion, this study found that sarcopenia prevalence was highest in COPD patients, followed by TB patients, while asthma patients had a lower prevalence, highlighting the varying impact of respiratory diseases on muscle mass loss. Older age and low physical activity increase the risk associated with sarcopenia among asthma patients, while a higher BMI is associated with a lower likelihood of having sarcopenia in asthma patients. For TB patients, the risk of developing sarcopenia is elevated by Chinese ethnicity, other races and foreigners, lower protein intake, and a lower BMI. In COPD patients, advancing age and a history of heart failure are significant risk factors, while moderate physical activity appears to offer protection against sarcopenia. Based on the findings, prevention, detection, and management of sarcopenia in chronic respiratory disease patients should focus on addressing key risk factors, with tailored clinical management strategies for each group. For asthma patients, clinicians should recommend regular physical activity, ideally incorporating resistance training, and encourage maintaining a healthy BMI through balanced nutrition. For TB patients, strategies should include improving protein intake and monitoring BMI, along with addressing ethnic and socioeconomic factors that may contribute to increased sarcopenia risk. In COPD patients, management should focus on promoting moderate physical activity, particularly resistance exercises, and carefully managing comorbidities like heart failure. Nutritional support, including protein supplementation, should be prioritized to improve muscle mass and strength. Clinicians should also conduct regular screenings for sarcopenia to ensure early detection and prompt physiotherapy or rehabilitation intervention, improving quality of life and overall health outcomes for these patients. Abbreviations ASMI Appendicular skeletal muscle mass index ASMM Appendicular Skeletal Muscle Mass AWGS Asian Working Group for Sarcopenia BIA Bioelectrical Impedance Analysis BMI Body Mass Index CI Confidence Interval COPD Chronic Obstructive Pulmonary Disease FFQ Food Frequency Questionnaire HF Heart Failure IPAQ-LF International Physical Activity Questionnaire Long Form SARC-CalF Sarcopenia Risk Assessment using Calf circumference and Functional performance SARC-F Sarcopenia Rapid Assessment Tool - Functional TB Tuberculosis Declarations Acknowledgements We extend our sincere gratitude to Advanced Medical and Dental Institute, Universiti Sains Malaysia, for instrumental support in this study’s fruition. Special recognition goes to the dedicated health staff from the two public hospitals. Above all, we are indebted to the study participants. Author contributions NMS, HAY, NAA and RAMZ design the study. RAMZ, SI and IAHA involved in data collection. RAMZ, NAA and NMS analyzed and interpreted the patient data regarding the prevalence of sarcopenia and risk factors associated with sarcopenia and chronic respiratory disease patients. NMS, HAY and NAA also provided guidance and supervision to RAMZ. RAMZ and NMS are major contributors to writing the manuscript. All authors read and approved the final manuscript. Funding No funding. Data availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate Ethical approval for this study was obtained from the Human Research Ethics Committee, Universiti Sains Malaysia (USM/JEPeM/22110707) and the Medical Research Ethics Committee MREC, Ministry of Health Malaysia (NMRR ID-23-00257-WV6(IIR)). Before enrolment, trained research assistants obtained written informed consent from all participants. Consent for publication Not applicable. 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Multiple hormonal and metabolic deficiency syndrome in chronic heart failure: rationale, design, and demographic characteristics of the T.O.S.CA. Registry. Intern Emerg Med. 2018;13(5):661-71. doi:10.1007/s11739-018-1844-8. Zhang Y, Zhang J, Ni W, Yuan X, Liu D, Li P, Xu J, Zhao Z. Sarcopenia in heart failure: a systematic review and meta-analysis. ESC Heart Fail. 2021;8(2):1007-17. doi:10.1002/ehf2.13255. Gueugneau M, Coudy-Gandilhon C, Meunier B, Combaret L, Taillandier D, Polge C, Attaix D, Roche F, Féasson L, Barthélémy JC, Béchet D. Lower skeletal muscle capillarisation in hypertensive elderly men. Exp Gerontol. 2016;76:80-8. doi:10.1016/j.exger.2016.01.013. Lutski M, Weinstein G, Tanne D, Goldbourt U. Overweight, obesity, and late-life sarcopenia among men with cardiovascular disease, Israel. Prev Chronic Dis. 2020;17:E164. doi:10.5888/pcd17.200167. Polyzos SA, Margioris AN. Sarcopenic obesity. Hormones (Athens). 2018;17(3):321-31. doi:10.1007/s42000-018-0049-x Herzog W. Reflections on obesity, exercise, and musculoskeletal health. J Sport Health Sci. 2020;9(2):108-9. doi:10.1016/j.jshs.2019.11.004. Tables Table 6 To 9 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SupplementaryI.docx Table6to9.docx Cite Share Download PDF Status: Published Journal Publication published 28 Apr, 2025 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Accepted 16 Apr, 2025 Reviews received at journal 14 Apr, 2025 Reviewers agreed at journal 14 Apr, 2025 Reviews received at journal 12 Apr, 2025 Reviews received at journal 09 Apr, 2025 Reviewers agreed at journal 09 Apr, 2025 Reviewers agreed at journal 08 Apr, 2025 Reviewers invited by journal 08 Apr, 2025 Submission checks completed at journal 28 Mar, 2025 First submitted to journal 27 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Hazwani","middleName":"Ahmad","lastName":"Yusof","suffix":""},{"id":440163655,"identity":"7e56782a-360d-4b44-afd6-d45c4fd5bb7b","order_by":2,"name":"Nurul ‘Ain Azizan","email":"","orcid":"","institution":"University of Nottingham Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Nurul","middleName":"‘Ain","lastName":"Azizan","suffix":""},{"id":440163656,"identity":"48f289b5-d89b-4578-b59a-c87c7b87cb99","order_by":3,"name":"Irfhan Ali Hyder Ali","email":"","orcid":"","institution":"Ministry of Health Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Irfhan","middleName":"Ali Hyder","lastName":"Ali","suffix":""},{"id":440163657,"identity":"abe1730f-deb3-4487-a4d9-d3452276f2c5","order_by":4,"name":"Suhaila Ismail","email":"","orcid":"","institution":"Ministry of Health Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Suhaila","middleName":"","lastName":"Ismail","suffix":""},{"id":440163658,"identity":"d4d4f0a9-e584-4a12-bb33-2d71fde90281","order_by":5,"name":"Noorsuzana Mohd Shariff","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYBCDBAYGxoYPFRUkammcceYMaVoYGGecbSNCqfyM9IefC2rq8vhnH25sODjvsL1uA3eaBD4tBjcSkqVnHDtcLHEuEahl2+HEbQd4t+HXIpFwQJqH7UBiwxnG9scftx1OMCOkRX5GYvNvnn91ifPPMAJtmXPYnqAWhhvJbNK8bcyJG8BaGg4zEnbYmWds1rx9hxM3grQcOJaeuO0w72YLvA5rT398m+dbXeK8M+wPGw7UWNubHe/deAOvwzABMwMLfr9g1fSBZC2jYBSMglEwnAEAlN5VYQe0N74AAAAASUVORK5CYII=","orcid":"","institution":"Universiti Sains Malaysia","correspondingAuthor":true,"prefix":"","firstName":"Noorsuzana","middleName":"Mohd","lastName":"Shariff","suffix":""}],"badges":[],"createdAt":"2024-11-15 11:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5460176/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5460176/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-22819-9","type":"published","date":"2025-04-28T15:57:35+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80275844,"identity":"67dae639-af9f-40e0-81e1-0ac092e1380c","added_by":"auto","created_at":"2025-04-10 04:55:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":168292,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant recruitment and data collection flowchart\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5460176/v1/070c6d8d88a02f2731ad07ad.png"},{"id":80276230,"identity":"18ef9709-b263-4b54-b9fa-9385d1ca5e2f","added_by":"auto","created_at":"2025-04-10 05:03:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":117799,"visible":true,"origin":"","legend":"\u003cp\u003eIndependent factors associated with sarcopenia in asthma, TB, and COPD patients\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5460176/v1/fe511379ee4993015174fa4a.png"},{"id":81987760,"identity":"9850810c-8001-4f7e-a186-9ef9817f8048","added_by":"auto","created_at":"2025-05-05 16:05:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1464074,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5460176/v1/fac8afbe-9a5c-4576-9ce5-b3b230cb9926.pdf"},{"id":80277387,"identity":"f3587e45-231a-4d29-b215-970e6b24370c","added_by":"auto","created_at":"2025-04-10 05:11:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20355,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryI.docx","url":"https://assets-eu.researchsquare.com/files/rs-5460176/v1/fbf687dc729719d0264270a3.docx"},{"id":80275841,"identity":"926f3f5b-6b00-47c8-826a-b3d39ba8d251","added_by":"auto","created_at":"2025-04-10 04:55:53","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":71831,"visible":true,"origin":"","legend":"","description":"","filename":"Table6to9.docx","url":"https://assets-eu.researchsquare.com/files/rs-5460176/v1/9c92e38c6b77400bf3a9de87.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eSarcopenia and It's Influencing Factors Among Adults With Asthma, Chronic Obstructive Pulmonary Disease, and Tuberculosis in Penang, Malaysia\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eChronic respiratory diseases (CRDs) were the third leading cause of death globally in 2019, accounting for 4.0\u0026nbsp;million deaths. Among these, COPD was the leading cause of mortality, while asthma had the highest prevalence [1]. Asthma affects approximately 262\u0026nbsp;million people worldwide [2]. In Malaysia, the prevalence of asthma ranges from 8.9\u0026ndash;13% in children (6\u0026ndash;14 years) and 6.3% in adults, while the prevalence of COPD in suburban areas is estimated at 3.4\u0026ndash;5.1% [3]. Meanwhile, TB remains a major public health concern, with cases rising by 5.47% within a year, from 25,391 new TB cases in 2022 to 26,781 cases in 2023 [4].\u003c/p\u003e \u003cp\u003eBeyond respiratory impairment, CRDs contribute to muscle loss and reduced physical activity, increasing the risk of sarcopenia [5]. With an ageing population, sarcopenia has become a significant health concern due to its association with physical disability, hospitalisation, and increased mortality rates [6, 7]. It also places a substantial burden on healthcare systems, particularly due to the need for long-term care [7].\u003c/p\u003e \u003cp\u003eIn Malaysia, CRDs are among the top three causes of hospitalisation and mortality [8]. However, challenges remain in the healthcare system, including limited awareness among healthcare providers, time constraints, and a lack of confidence in diagnosing and managing these conditions [9]. Additionally, pulmonary rehabilitation services are scarce, and palliative care for severe COPD remains limited [9]. These gaps may further hinder the recognition and management of sarcopenia in CRD patients.\u003c/p\u003e \u003cp\u003eSarcopenia affects up to 17.6% of individuals with asthma, with 5.5% experiencing severe sarcopenia [10]. However, in Malaysia, data on sarcopenia prevalence among CRD patients\u0026mdash;particularly those with asthma, COPD, and TB\u0026mdash;remain scarce [11]. Reported prevalence in these populations varies widely, from 9.5\u0026ndash;57.1%, depending on the study population and diagnostic criteria used. Research on sarcopenia in CRD patients across Asian countries, particularly Malaysia, remains limited.\u003c/p\u003e \u003cp\u003eThe Asian Working Group for Sarcopenia (AWGS) updated its diagnostic criteria in 2019, establishing specific cut-off values for the Asian population [12]. Understanding sarcopenia prevalence among CRD patients in Malaysia is essential for developing targeted prevention and management strategies. Therefore, this study aims to assess the prevalence of sarcopenia among adults with asthma, COPD, and TB in Penang, Malaysia, and identify associated risk factors.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis comparative cross-sectional study was conducted from June 2023 to March 2024. Participants were recruited from two government hospitals: one located on Penang Island and the other on the mainland. The high prevalence of CRD patients, including TB [13], and Penang\u0026rsquo;s representation of Malaysia\u0026rsquo;s multicultural population made it a particularly suitable research setting. The sample size for this study was carefully determined to ensure adequate statistical power for detecting meaningful associations. Sample size calculations were performed separately using a single proportion formula for each disease group based on the previous literatures. The calculations were conducted using the Sample Size Calculator (Version 2.0) by Ariffin et al. [14], considering a significance level (\u0026alpha;) of 0.05 and a power (1-\u0026beta;) of 80%. The minimum total sample size required was estimated at 282 participants. The sample size was increased by 40%, making it 395 samples to account for potential high dropouts. Among the strategies used in this study to minimize non-response and ensure data completeness was approaching the patients early during their waiting time to avoid them getting anxious for their queue number to be called, hence less rejection. A polite and proper explanation about the need for the study was also shown to positively impact a good response rate. The researcher will also check for any questions that they missed answering to ensure data completeness. 500 patients were approached. However, 469 participants were successfully recruited, further enhancing the reliability of the study findings. The final total sample achieved for this study was 469 participants diagnosed with asthma (n\u0026thinsp;=\u0026thinsp;180), COPD (n\u0026thinsp;=\u0026thinsp;103), or TB (n\u0026thinsp;=\u0026thinsp;186) were included. Recruitment was conducted using a convenience sampling technique. Potential eligible patients were derived from the weekly patient follow-up lists obtained from the chest clinics, categorised by diagnosis, with assistance from in-charge nurses. All patients meeting the inclusion criteria were invited to participate.\u003c/p\u003e\n\u003cp\u003ePost-TB patients were defined as individuals who had completed the full course of TB treatment and had been officially declared cured or treatment-completed by a physician. Data collection involved researcher-guided interviews, each lasting approximately 20\u0026ndash;30 minutes. Researcher served as data collectors and underwent multiple training sessions led by university-affiliated science officers. The training included theoretical instruction on study protocols, hands-on practice with SF-BIA and handgrip dynamometry, and standardised procedures for conducting the gait speed test using a stopwatch and a pre-measured walking distance. Additionally, role-playing exercises were conducted to standardise interview techniques. To ensure proficiency, data collectors practised assessments on public volunteers before conducting measurements on study participants. All measurements were carried out in the morning between 8:00 AM and 12:00 PM to minimise variations due to circadian influences and participant fatigue.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy population\u003c/h2\u003e\n \u003cp\u003eThe inclusion and exclusion criteria for each group are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. As for the general exclusion criteria, Asthma, TB or COPD patients who were fasting on the day of data collection or with conditions that limit mobility (e.g., advanced osteoarthritis, stroke with significant disability, or spinal cord injuries) were excluded from this study since sarcopenia in these cases is primarily due to immobility rather than disease-related mechanisms. \u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eInclusion and exclusion criteria for participant selection\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eInclusion and Exclusion Criteria for Participant Selection\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInclusion Criteria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eExclusion Criteria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsthma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdults aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years, diagnosed with asthma based on Global Initiative for Asthma (GINA) 2023 criteria [\u003cstrong\u003eGlobal Initiative for Asthma, 2023\u003c/strong\u003e]. The diagnosis was confirmed by a respiratory physician using clinical history, spirometry showing reversible airflow limitation (\u0026gt;\u0026thinsp;12% \u0026amp; \u0026gt;200 mL improvement in FEV1 post-bronchodilator), and patient-reported symptoms.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSevere cardiovascular or neurological diseases affecting study participation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdults aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years, diagnosed with COPD based on Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2023 criteria [\u003cstrong\u003eAgust\u0026iacute; et al., 2023\u003c/strong\u003e]. The diagnosis was confirmed via spirometry (FEV1/FVC\u0026thinsp;\u0026lt;\u0026thinsp;0.70 post-bronchodilator).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatients experiencing an exacerbation within the preceding four weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdults aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years, diagnosed with active TB (on treatment for at least two weeks) or post-TB (completed treatment and attending follow-ups). Diagnosis was confirmed through clinical, radiological, and microbiological evidence.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExtensive lung damage affecting functional assessments,\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003ePatients with asthma-COPD overlap (ACO) were classified based on their most recent diagnosis and prescribed treatment, as determined by respiratory physicians. If their latest diagnosis and on-going treatment indicated COPD, they were classified within the COPD group for analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eResearchers personally recruited patients to participate in the chest clinics at both hospitals, obtaining informed consent prior to enrolment. Following written informed consent, participants\u0026rsquo; socio-demographic data, medical history data, lifestyle factors (smoking, alcohol drinking, and drug addiction history), and anthropometric measurements were gathered. The researchers evaluated the participants for sarcopenia risk using questionnaires like the Sarcopenia Rapid Assessment Tool\u0026mdash;Functional (SARC-F) and the Sarcopenia Rapid Assessment Tool\u0026mdash;Calf (SARC-CalF). Bioelectrical impedance analysis (BIA), handgrip dynamometers, and gait speed testing were implemented to evaluate the participants further to identify sarcopenia. The participants\u0026rsquo; physical activity and dietary status were assessed using the International Physical Activity Questionnaires (IPAQ). This study has been ethically approved by the Medical Research Ethics Committee (MREC) of the Ministry of Health (NMRR ID-23-00257-WV6(IIR)) and the Human Research Ethics Committee, Universiti Sains Malaysia (USM/JEPeM/22110707).\u003c/p\u003e\n\u003ch3\u003eAssessment of risk of sarcopenia with SARC-F and SARC-CalF\u003c/h3\u003e\n\u003cp\u003eThe SARC-F questionnaire used in this study includes five questions assessing strength, walking assistance, chair rise, stair climbing, and falls to screen for sarcopenia risk. The SARC-CalF expands on the SARC-F by incorporating calf circumference, with a score of 11 or more indicating possible sarcopenia [15]. Calf circumference measurement enhances sensitivity compared to the SARC-F alone, reducing the likelihood of underestimation [16\u0026ndash;20]. However, according to AWGS 2019, the SARC-F and SARC-CalF are screening tools and do not confirm sarcopenia. AWGS 2019 serves as a full diagnostic framework, incorporating muscle mass, strength, and physical performance for a more accurate diagnosis. This distinction is important when interpreting findings, as screening tools help identify individuals at risk but require further assessment using AWGS 2019 criteria for definitive diagnosis.\u003c/p\u003e\n\u003ch3\u003eAnthropometric measurements\u003c/h3\u003e\n\u003cp\u003eThe anthropometric measurements such as height, weight, BMI, and calf circumference were measured in this study. A BIA device was used to determine body weight to the nearest 0.1 kg, while a stadiometer was used to determine height to the nearest 0.01 cm. Weight (in kilograms) was divided by height (in square meters) to calculate BMI. Participants stood while the most visible portion of the dominant side\u0026apos;s calf was measured with a tape measure in 0.1 cm increments without tightening it to determine calf circumference. The AWGS 2019 criteria identified suboptimal calf circumference as \u0026lt;\u0026thinsp;33 cm for females and \u0026lt;\u0026thinsp;34 cm for males.\u003c/p\u003e\n\u003ch3\u003eDiagnostic criteria of sarcopenia\u003c/h3\u003e\n\u003cp\u003eThe AWGS 2019 criteria define sarcopenia as a condition characterised by a combination of low muscle mass, reduced muscle strength, and poor physical performance. Possible sarcopenia is defined as the presence of either low muscle strength or poor physical performance, without low muscle mass. Severe sarcopenia is characterised by the presence of low muscle mass, reduced muscle strength, and poor physical performance.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eAssessment of muscle mass\u003c/h2\u003e\n \u003cp\u003eThe method of BIA is widely recognised for the evaluation of muscle mass. A muscle mass measurement was conducted in this investigation using a BIA device (OMRON HBF-206IT). Prior to standing on the metal footpads of the BIA device, participants are required to remove any jewellery, metals, or shoes and wear lightweight clothing. BIA is the primary method for determining appendicular skeletal muscle mass (ASMM), which is subsequently used to calculate appendicular skeletal muscle index (ASMI) using the following formula: ASMM/height2 [21]. The AWGS 2019 criteria for diagnosing sarcopenia are consistent with this calculation [15]. Based on the AWGS 2019 criteria, the thresholds for low muscle mass are ASMI\u0026thinsp;\u0026lt;\u0026thinsp;7.0 kg/m2 for men and ASMI\u0026thinsp;\u0026lt;\u0026thinsp;5.7 kg/m2 for women [22].\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eAssessment of muscle strength\u003c/h3\u003e\n\u003cp\u003eMuscle strength was assessed using handgrip strength measured by a digital Smedley spring hand dynamometer (Takei T.K.K. 5401; Takei Scientific Instruments Co. Ltd., Tokyo, Japan) as recommended by AWGS 2019. Participants were instructed to squeeze the dynamometer with maximum effort while standing with fully extended arms at their sides, and 3 trials were performed. Based on AWGS 2019 criteria, handgrip strengths\u0026thinsp;\u0026lt;\u0026thinsp;28 kg in males and \u0026lt;\u0026thinsp;18 kg in females were classified as low muscle strength.\u003c/p\u003e\n\u003ch3\u003eAssessment of physical performance\u003c/h3\u003e\n\u003cp\u003eThe participant\u0026apos;s physical performance was assessed using a gait speed test. In this study, the 6-meter walk test was used to determine gait speed test. Participants were instructed to walk the distance at their normal pace over 6-meter course. The time it took to walk the distance was measured to the nearest 0.01 second. Gait speed was determined by dividing the distance walked by the total time required to complete the test in meters per second (m/s) [23]. According to the AWGS 2019 guidelines, low gait speed is less than 1.0 m/s [22].\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eInternational of Physical Activity Questionnaire - Long Form (IPAQ - LF)\u003c/h2\u003e\n \u003cp\u003eThe IPAQ is a widely used self-reported tool designed to measure physical activity levels over the past seven days [24]. The English and Malay versions, which are publicly available on the IPAQ website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sites.google.com/view/ipaq/home\u003c/span\u003e\u003c/span\u003e), were obtained and adapted with permission from the original authors [25].\u003c/p\u003e\n \u003cp\u003eFor this study, the IPAQ-LF, which consists of 27 items, was used to assess various domains of physical activity, including job-related, transport-related, domestic, and leisure activities. It also includes questions on sedentary behaviour, such as sitting time [24].\u003c/p\u003e\n \u003cp\u003eTo score the IPAQ, responses are converted into Metabolic Equivalent of Task (MET) values. The IPAQ-LF captures the frequency and duration of activities performed at different intensities\u0026mdash;walking, moderate-intensity, and vigorous-intensity activities. The total physical activity score is calculated based on the weekly time spent in each activity. Participants are then classified as sedentary, irregularly active, active, or very active [24]. Activities are classified by their MET values, which contribute to a total score that indicates health-enhancing physical activity (HEPA) [26].\u003c/p\u003e\n \u003cp\u003eThe IPAQ-LF has been widely validated in cross-sectional studies assessing physical activity levels in adults, particularly in chronic respiratory diseases like asthma and COPD [27\u0026ndash;30]. As the IPAQ is a self-reported measure, potential biases, such as recall bias and social desirability bias, must be considered. To minimise these biases, participants were given ample time to complete the questionnaire without feeling rushed. They were also encouraged to seek clarification if needed. This approach ensures that the information provided by participants is as accurate and reliable as possible. Additionally, the researcher was available to assist participants in understanding the questions and addressing any uncertainties they might have regarding the questionnaire\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eFood Frequency Questionnaire (FFQ)\u003c/h2\u003e\n \u003cp\u003eDaily food intake was assessed using an FFQ developed by the Institute for Public Health Malaysia [31]. This questionnaire is recognised as a reliable method for estimating habitual dietary intake in the Malaysian population [32].\u003c/p\u003e\n \u003cp\u003eIn this study, the FFQ recorded the frequency and quantity of foods consumed by participants across fourteen categories: cereals and cereal products, fast food, meat and meat products, fish and seafood, eggs, legumes, milk and milk products, vegetables, fruits, beverages, alcoholic drinks, confectionery, spreads, and flavourings. Participants were asked to recall their food intake over the past week, selecting the frequency of consumption from predefined options and specifying portion sizes. The collected data were analysed using FFQ Nutritionist Pro to estimate nutrient intake, including energy (calories), protein, calcium, potassium, phosphorus, magnesium, iron, zinc, selenium, cobalamin, pyridoxine, vitamin C, vitamin D, vitamin E, and vitamin K.\u003c/p\u003e\n \u003cp\u003eThe FFQ is one of the most commonly used methods for assessing dietary intake in large-scale epidemiological studies [33]. Furthermore, FFQs have been widely utilised in clinical nutrition studies, including a recent study on TB patients in China that assessed protein and overall nutrient intake using both FFQ and 24-hour recall methods [34]. Similarly, Li et al. [35] employed the FFQ to examine dietary patterns in TB patients, emphasising the importance of a balanced diet, including high-quality protein, in improving disease outcomes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eContinuous variables were expressed as means and standard deviations. Categorical variables are presented as numbers and percentages. Following the initial analysis, a multivariable logistic regression model was used to determine the independent associations between each potential variable (risk factors) and the sarcopenia condition. The model included variables with univariate significance levels of 0.25 or less. The adjusted odds ratios and 95% confidence intervals were reported for the final regression model. IBM SPSS Statistics, version 27 (SPSS Inc., Chicago, IL, USA), was used to conduct the analyses. A p-value of less than 0.05 was considered statistically significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u0026rsquo; characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;2 presents the socio-demographic characteristics of the study population (n\u0026thinsp;=\u0026thinsp;469), stratified by disease groups (asthma, TB, and COPD). Among the disease groups, males were more prevalent in TB (48.9%) and COPD (42.0%), while females were dominant in asthma (66.8%).\u003c/p\u003e \u003cp\u003eThe mean age of participants was 52.6\u0026thinsp;\u0026plusmn;\u0026thinsp;16.6 years, with COPD patients being the oldest (65.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9 years) and TB patients the youngest (46.2\u0026thinsp;\u0026plusmn;\u0026thinsp;16.5 years). In terms of ethnicity, Malays constituted the majority (49.9%), followed by Chinese (25.4%), Indians (19.2%), and other ethnicities (5.5%). Chinese ethnicity was more common among TB patients (48.7%), while Malays had the highest proportion in asthma (40.6%).\u003c/p\u003e \u003cp\u003eRegarding marital status, 57.1% were married, with TB patients having the highest proportion of never-married individuals (52.1%). Divorced and separated individuals were more common among asthma and TB patients. For education level, most had secondary education (45.6%), while university graduates were more frequent among asthma patients (58.7%) but rare in COPD (2.7%).\u003c/p\u003e \u003cp\u003eIn terms of employment, 45.4% were employed, with the highest employment rate among TB patients (43.7%). COPD patients had the highest proportion of retired individuals (26.4%). Regarding financial income, 40.1% relied on family or friends for support, and only 0.6% earned more than MYR 5,000. TB patients had the highest proportion of self-supporting individuals (50.0%), while COPD patients were mostly in the lower-income group (\u0026lt;\u0026thinsp;MYR 2,000; 28.0%).\u003c/p\u003e\u003cp\u003eTable 2. Socio-demographic characteristics of all patients (n=469)\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(n=180)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(n=186)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(n=103)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003cp\u003emean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eAsthma\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eTB\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eCOPD\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e231 (49.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e21 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e113 (48.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e97 (42.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e238 (50.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e159 (66.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e73 (30.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e6 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eMean age\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e52.6\u0026plusmn;16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e52.2 \u0026plusmn; 15.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e46.2 \u0026plusmn; 16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e65.1 \u0026plusmn; 9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eMalay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e234 (49.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e95 (40.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e86 (36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e53 (22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eChinese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e119 (25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e27 (22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e58 (48.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e34 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eIndian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e90 (19.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e50 (55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e26 (28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e14 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eOther\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e10 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e5 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e4 (40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eForeigners\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e16 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e3 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e12 (75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e121 (25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e33 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e63 (52.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e25 (20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e268 (57.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e114 (42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e94 (35.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e60 (22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eSeparated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2 (100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eDivorcee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e76 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e33 (43.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e26 (34.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e17 (22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eCohabiting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eNo formal schooling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e30 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e9 (30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e13 (43.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e8 (26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e86 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e21 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e30 (34.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e35 (40.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e214 (45.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e83 (38.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e84 (39.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e47 (22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eCollege\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e64 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e23 (35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e30 (46.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e11 (17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eUniversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e75 (16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e44 (58.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e29 (38.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eNot employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e184 (39.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e67 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e68 (37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e49 (26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e213 (45.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e85 (39.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e93 (43.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e35 (16.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eRetired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e72 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e28 (38.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e25 (34.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e19 (26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFinancial Income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eFinancial support (family/friends)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e188 (40.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e68 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e71 (37.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e49 (26.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eSelf-support (saving)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e44 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e10 (22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e22 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e12 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026lt; MYR 2 000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e114 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e41 (36.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e41 (36.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e32 (28.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026lt; MYR 3 000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e84 (17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e40 (47.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e36 (42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e8 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eMYR 3 000 -\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMYR 5 000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e36 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e19 (52.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e15 (41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026gt; MYR 5 000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e3 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviation: NS = Values are presented as frequency, n = total number of participants; % = percentage; mean \u0026plusmn; standard deviation (SD). \u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePrevalence of sarcopenia\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 categorises participants based on sarcopenia status using the Asian Working Group for Sarcopenia (AWGS) 2019 criteria, which include low muscle mass, reduced handgrip strength, and slow gait speed to determine sarcopenia severity.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; The prevalence of sarcopenia, as classified by the AWGS 2019 criteria, revealed that 34.3% of participants had low muscle mass, with the highest prevalence among COPD patients (39.8%), followed by TB (43.5%) and asthma (21.7%). Additionally, 67.8% of participants had reduced handgrip strength, with COPD patients showing the highest prevalence (74.8%). Impaired physical performance, as measured by gait speed, was observed in 59.1% of participants, with COPD patients again showing the highest prevalence (65.0%). Overall, the prevalence of sarcopenia was highest among COPD patients (35.9%), followed by TB (33.9%) and asthma (18.9%), with severe sarcopenia most common in COPD patients (28.2%). The progressive muscle loss associated with chronic respiratory diseases is especially pronounced in COPD and TB patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 Diagnosis of sarcopenia in patients with asthma, COPD, and TB based on AWGS 2019 criteria\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"606\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCriteria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Asthma, Total (n = 180)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eTB, Total (n = 186)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eCOPD, Total (n = 103)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNS,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePS,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSS,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNS,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePS,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSS,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNS,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePS,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSS,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eMuscle Mass:\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(ASMI, kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003eNS: 308 (65.7)\u003c/p\u003e\n \u003cp\u003eS: 161 (34.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e141\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(78.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e39\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003cp\u003e(56.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003cp\u003e(43.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003cp\u003e(60.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003cp\u003e(39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eMuscle Strength:\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(Handgrip strength, kg)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003eNS: 151 (32.2)\u003c/p\u003e\n \u003cp\u003eS: 318 (67.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003cp\u003e(36.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003cp\u003e(63.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003cp\u003e(32.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003cp\u003e(67.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003cp\u003e(25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003cp\u003e(74.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003ePhysical Performance:\u003c/p\u003e\n \u003cp\u003eGait speed, m/s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003eNS: 192 (40.9)\u003c/p\u003e\n \u003cp\u003eS: 277 (59.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003cp\u003e(36.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003cp\u003e(63.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003cp\u003e(48.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003cp\u003e(51.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003cp\u003e(35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003cp\u003e(65.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eFinal Assessment Sarcopenia (muscle mass, handgrip strength, gait speed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003eNS: 138 (29.4)\u003c/p\u003e\n \u003cp\u003ePS: 197 (42.0)\u003c/p\u003e\n \u003cp\u003eS: 38 (8.1)\u003c/p\u003e\n \u003cp\u003eSS: 96 (20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003cp\u003e(34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003cp\u003e(46.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e(3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003cp\u003e(15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003cp\u003e(28.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003cp\u003e(37.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003cp\u003e(12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003cp\u003e(21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003cp\u003e(22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e43\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e(7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003cp\u003e(28.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviation: AWGS = Asian Working Group for Sarcopenia; NS = Non-Sarcopenia; PS = Possible Sarcopenia; S = Sarcopenia; SS = Severe Sarcopenia; ASMI = Appendicular Skeletal Muscle Mass Index. Values are presented as frequency, n = is total number of participants; % = percentage; mean \u0026plusmn; standard deviation (SD).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Table 4 present the prevalence of sarcopenia among patients with asthma, TB, and COPD. Sarcopenia was most prevalent among COPD patients (35.9%, 95% CI 26.7-46.0), followed by TB patients (33.9%, 95% CI 27.1-41.2), while asthma patients had the lowest prevalence (18.9%, 95% CI 13.5-25.4). These findings suggest that sarcopenia is more common in TB and COPD populations. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4 Prevalence of sarcopenia among all three types of chronic respiratory disorders (asthma, TB, and COPD)\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"566\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTypes of Disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-sarcopenia, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSarcopenia, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAsthma\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e146 (81.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e34 (18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e123 (66.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e63 (33.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOPD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e66 (64.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e37 (35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;The prevalence of sarcopenia was stratified by gender and age groups based on the recommended standardised age groups for life stage analysis, as proposed by Diaz et al. (2021). Age groups for the analysis therefore consisted of: Older adolescents (18 \u0026ndash; 19 years), Young adults (20 \u0026ndash; 24 years), Adults (25 \u0026ndash; 59 years), and Older adults (60 \u0026ndash; 99 years) (Table 5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 5 Prevalence of sarcopenia, stratified by age groups and gender composition\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale Sarcopenia (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale Sarcopenia (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e18 - 19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e60.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e40.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20 - 24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e75.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e25 - 59\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e40.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e59.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e60 - 99\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e63.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e37.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFactors associated with sarcopenia among asthma, TB and COPD patients\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ea) Bivariable analysis of factors associated with sarcopenia\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 6 presents the prevalence of sarcopenia across different socio-demographic characteristics. Males had a higher prevalence of sarcopenia (31.6%) compared to females (20.7%), suggesting a potential gender-related risk. Ethnic differences were also notable, with Chinese COPD patients exhibiting the highest sarcopenia prevalence (43.2%), indicating a possible link between ethnicity and sarcopenia risk. Education level appeared to be an influencing factor, as individuals with no formal schooling had a higher prevalence of sarcopenia, particularly among TB (12.8%) and COPD (16.2%) patients. Employment status was another significant factor, with unemployed participants showing the highest prevalence of sarcopenia (37.0%), reinforcing the potential impact of socioeconomic status.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; To further explore these associations, univariable logistic regression analysis was conducted to identify factors with p-values \u0026lt; 0.25 for inclusion in the multiple logistic regression analysis. Among asthma patients, variables meeting this threshold were age, Chinese ethnicity, other races, no formal schooling, primary education level, and financial support. For TB patients, Indian ethnicity, marital status (married, divorced/separated), no formal schooling, primary education level, and unemployment status were included in the multivariable analysis. Similarly, among COPD patients, no formal schooling, primary education level, unemployment, retirement, and financial support were identified as potential predictors of\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Table 7 examines lifestyle and dietary factors associated with sarcopenia risk, particularly physical activity levels, dietary intake, and overall nutritional status. Lifestyle and nutritional factors showed a strong association with sarcopenia, with physical activity levels and dietary intake being particularly influential. Low physical activity was linked to a higher prevalence of sarcopenia (41.2%), with COPD patients being the most affected (81.1%). Nutritional intake analysis revealed that sarcopenic individuals had significantly lower protein consumption, with TB patients having the lowest daily intake (22.29 \u0026plusmn; 8.43 g/day), potentially contributing to accelerated muscle loss. Interestingly, energy intake was relatively higher among COPD patients with sarcopenia (518.73 \u0026plusmn; 261.33 kcal/day), yet this group still exhibited a high prevalence of sarcopenia, suggesting possible metabolic inefficiencies or inadequate nutrient absorption.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; To further investigate these associations, univariable logistic regression analysis was conducted to identify factors with p-values ˂ 0.25 for inclusion in the multiple logistic regression analysis. Among asthma patients, the selected variables were passive smoking, low and moderate physical activity, energy intake (kcal/day), protein intake (g/day), and key micronutrients, including potassium (g/day), phosphorus (mg/day), magnesium (mg/day), iron (mg/day), zinc (mg/day), selenium (\u0026micro;g/day), cobalamin (\u0026micro;g/day), pyridoxine (mg/day), vitamin C (mg/day), and vitamin E (mg/day). Among TB patients, the included factors were passive smoking, active or former chaser status, occasional alcohol consumption, low physical activity, energy intake (kcal/day), protein intake (g/day), and micronutrients such as potassium, phosphorus, magnesium, iron, zinc, selenium, cobalamin, pyridoxine, vitamin E, and vitamin K. Among COPD patients, significant variables included low physical activity, energy intake, protein intake, and micronutrients such as potassium, phosphorus, magnesium, zinc, selenium, pyridoxine, and vitamin E\u003c/p\u003e\n\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Table 8 evaluates clinical risk factors for sarcopenia, including hospitalisation history, body mass index (BMI), and common comorbidities such as hypertension and diabetes. Clinical factors played a crucial role in the development of sarcopenia, with hospitalisation history emerging as a significant predictor. Among hospitalised individuals, the prevalence of sarcopenia was highest in asthma patients (97.1%), followed closely by COPD patients (94.6%). Hypertension was the most common comorbidity, with COPD patients experiencing the highest burden (40.5%). Other less prevalent comorbidities are detailed in Supplementary I. Additionally, a history of falls was strongly associated with sarcopenia, particularly among COPD patients (21.6%), indicating an increased risk of frailty and fractures.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; To further investigate these associations, univariable logistic regression analysis was conducted to identify factors with p-values \u0026lt; 0.25 for inclusion in the multiple logistic regression analysis. Among asthma patients, the selected variables were hospitalisation history, BMI (kg/m\u0026sup2;), and a history of rheumatoid arthritis. For TB patients, hospitalisation history and BMI were included. Among COPD patients, significant variables included a history of falls, BMI, type 2 diabetes mellitus (T2DM), and heart failure. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eb. Multivariable analysis of independent factors associated with sarcopenia\u003c/p\u003e\n\u003cp\u003eMultivariable logistic regression analysis identified several independent risk factors for sarcopenia across disease groups (Table 9). In asthma patients, older age (OR = 1.05, 95% CI = 1.01 to 1.09, p \u0026lt; 0.05) and lower BMI (OR = 0.61, 95% CI = 0.51 to 0.73, p \u0026lt; 0.05) increased the risk, while high physical activity was protective (OR = 0.13, 95% CI = 0.03 to 0.58, p \u0026lt; 0.05). Among TB patients, Chinese ethnicity (OR = 0.34, 95% CI = 0.12 to 0.99, p \u0026lt; 0.05) and other ethnicities (OR = 0.17, 95% CI = 0.04 to 0.83, p \u0026lt; 0.05) were linked to higher sarcopenia prevalence, while lower protein intake (OR = 0.88, 95% CI = 0.83 to 0.92, p \u0026lt; 0.05) and lower BMI (OR = 0.63, 95% CI = 0.53 to 0.75, p \u0026lt; 0.05) were significant risk factors. For COPD patients, older age (OR = 1.03, 95% CI: 1.03 to 1.18, p \u0026lt; 0.05), lower BMI (OR = 0.77, 95% CI = 0.68 to 0.88, p \u0026lt; 0.05), and history of heart failure (OR = 15.30, 95% CI = 1.48 to 158.06, p \u0026lt; 0.05) were major contributors to sarcopenia, whereas moderate physical activity (OR = 0.14, 95% CI = 0.03 to 0.70, p \u0026lt; 0.05) was a protective factor. The findings are summarised in Figure 2.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this is the first study to investigate sarcopenia in asthma, COPD, and TB in Penang, Malaysia. The study found that sarcopenia was prevalent among COPD patients at a rate of 35.9%. This finding is consistent with international research using comparable diagnostic tools. For instance, studies indicate that the prevalence of sarcopenia among COPD patients is 25.0% in Brazil [36], 44.3% in Turkey [37], and 22.0% in China [38]. Despite using BIA, handgrip strength, and gait speed in accordance with the AWGS 2019 definition, a study [38] reported a lower prevalence of 22.0%. Other studies have reported prevalence rates of 31.1%, 38.36%, and 33.6%, respectively [39-41]. Variations in diagnostic tools, definitions of sarcopenia, and sample sizes can account for these variations.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Furthermore, the current study found that 33.9% of TB patients had sarcopenia, a finding that aligns with Choi et al.\u0026apos;s retrospective study, which discovered that sarcopenia affected 19.6% to 45.0% of Korean TB survivors [42]. This alignment in prevalence rates shows that sarcopenia is widespread in this population, despite differences in diagnostic methods like DXA, BIA, and WHO/AWGS 2019 cut-off values. Dual-Energy X-ray Absorptiometry (DXA) is widely regarded as the gold standard for assessing muscle mass due to its precision in measuring appendicular lean mass (ALM) [22]. However, DXA\u0026apos;s high cost and limited portability hinder its widespread use, particularly in resource-constrained settings [43]. Bioelectrical Impedance Analysis (BIA) offers a more accessible alternative, being cost-effective and portable [44]. Nonetheless, BIA\u0026apos;s accuracy can be compromised by factors such as hydration status and device-specific variations, leading to discrepancies in sarcopenia diagnosis [45].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Asian Working Group for Sarcopenia (AWGS) 2019 consensus provides specific cut-off values for ALM, recommending \u0026lt;7.0 kg/m\u0026sup2; for men and \u0026lt;5.4 kg/m\u0026sup2; for women when using DXA, and \u0026lt;7.0 kg/m\u0026sup2; for men and \u0026lt;5.7 kg/m\u0026sup2; for women when using BIA, aiming to accommodate ethnic and methodological differences [22]. However, discrepancies between devices have been observed; for instance, a study comparing two segmental multi-frequency BIA devices found significant differences in ALM values, suggesting that device-specific adjustments may be necessary to ensure diagnostic accuracy [46]. Furthermore, factors such as hydration status, electrode placement, and patient positioning can influence BIA measurements, necessitating standardized protocols to enhance reliability [45]. While BIA provides a practical alternative to DXA, clinicians should be cautious of its limitations and consider device-specific and population-specific adjustments when diagnosing sarcopenia.\u003c/p\u003e\n\u003cp\u003eIn this study, ethnicity significantly influenced sarcopenia in TB patients. The study identified an association between sarcopenia and ethnicity in TB patients, including Chinese, other races, and foreigners. No other studies have reported similar findings. However, a study in Singapore found that Chinese people had a higher risk of sarcopenia than Malays and Indians, suggesting that ethnic lifestyle differences may affect likelihood [43].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Additionally, this study found that 18.9% of asthmatics had sarcopenia. This is similar to Hu\u0026apos;s study, which reported that 12.3%\u0026ndash;21.3% of asthma patients in China have sarcopenia [44]. Both studies used the AWGS 2019 definition of sarcopenia, albeit using different assessment tools. This study used BIA for muscle mass, handgrip strength for muscle strength, and gait speed for physical performance, while Hu et al.\u0026rsquo;s research used the 5-time chair stand test. Despite these differences in assessment methods, the prevalence rates of sarcopenia in Southeast Asian asthma patients are similar. Like Won et al.\u0026apos;s study [45],, this study found that age and lower BMI were risk factors for sarcopenia in asthma patients. Additionally, Yu et al. found that sarcopenia was associated with age and BMI in 4,000 community-dwelling Chinese adults 65 and older [46]. Kim et al. [47] also identified age and low BMI as risk factors for muscle strength decline, with regular exercise providing protective effects against grip strength decline in women aged 75 and older.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Physical inactivity is associated with a higher risk of sarcopenia in asthma patients. S\u0026aacute;nchez-S\u0026aacute;nchez et al. [48] conducted a comprehensive systematic review and meta-analysis, demonstrating that higher levels of physical activity are significantly associated with a reduced risk of sarcopenia. Evidence suggests that highly active individuals have a lower incidence of sarcopenia than less active ones [49]. Despite the well-documented benefits of physical activity in preventing and managing sarcopenia, research on its effects in asthma patients remains limited.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Next, this study identified that lower daily protein intake significantly impacts sarcopenia in TB patients. This supports the findings of Shin et al. [50], who found that TB survivors who ate enough protein and calories had a lower risk of sarcopenia. Furthermore, this study discovered a significant influence of BMI on sarcopenia in TB patients. No other study has explored the relationship between sarcopenia and BMI among TB patients. Curtis et al. found a significant association between low BMI and an elevated risk of sarcopenia in older adults, suggesting that low BMI is a consistent risk factor across various populations [51]. Ethnicity was found to significantly impact sarcopenia in TB patients. This study revealed associations between sarcopenia and ethnicity among TB patients, including Chinese, other races, and foreigners. Lifestyle and cultural factors, which vary among ethnic groups, may also affect sarcopenia risk. For example, a study in Singapore found that Chinese individuals had a higher risk of sarcopenia compared to Malays and Indians, indicating that ethnic differences in lifestyle practices may influence sarcopenia risk [52]. Different ethnic groups have different living environments, lifestyles, and eating habits [53].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Furthermore, this study also found that age is a significant variable influencing sarcopenia in COPD patients. This result is consistent with the findings of Zhou et al. [54], who identified age as a risk factor for sarcopenia in this population. Schols et al. reported that muscle loss occurs at an annual rate of 1% to 2% in COPD patients aged 50 and older [55].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; In addition, this study found a significant link between moderate physical activity and a lower risk of sarcopenia among COPD patients. These findings suggest that COPD patients may have the potential to develop sarcopenia by limiting physical activity. Additionally, Kim et al. found that a decline in physical activity is a key factor in developing sarcopenia among COPD patients [56].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;BMI was significantly associated with sarcopenia in COPD patients. Zhou et al. [54] and Morisawa and Katzmarzyk [57] reported a strong link between low BMI and sarcopenia, likely due to malnutrition and hypoproteinemia in this population [58].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; Additionally, this study identified a significant association between a history of heart failure and sarcopenia among COPD patients. Consistently, Leem et al. [59] found that sarcopenia was significantly linked to an increased risk of atherosclerotic cardiovascular disease (ASCVD) in men with COPD, independent of central obesity and fat mass. Given the shared cardiovascular burden in COPD, these findings further emphasise the interplay between sarcopenia, cardiovascular disease, and COPD severity.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Heart failure (HF) is another condition frequently associated with sarcopenia, with prevalence rates ranging from 10.1% to 68%, depending on disease severity [60]. Sarcopenia\u0026nbsp;and HF share common mechanisms, including systemic inflammation, malnutrition, neurohormonal dysregulation, and oxidative stress, leading to a vicious cycle that worsens both conditions [61-62]. Additionally, hormonal deficiencies, such as low levels of insulin-like growth factor-1 (IGF-1), testosterone, and vitamin D, may accelerate muscle wasting in HF patients [63]. Sarcopenia in HF has been linked to reduced functional capacity, increased hospitalisation rates, and higher mortality risk [64].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Moreover, cardiovascular diseases, including hypertension, are closely linked to sarcopenia. Hypertension contributes to muscle loss through decreased capillary density around muscle fibres, impairing nutrient and oxygen delivery [65]. Studies have reported a higher prevalence of sarcopenia in individuals with CVD compared to the general population [66], with sarcopenic obesity\u0026mdash;characterised by excessive fat accumulation alongside muscle loss\u0026mdash;further elevating the risk of cardiovascular mortality [67-68].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; The main strength of this study is that it is the first to investigate the prevalence of sarcopenia and its related risk factors in Malaysia, specifically within the Penang population. It systematically examines patients with chronic respiratory diseases\u0026mdash;namely asthma, COPD, and TB\u0026mdash;in two regions of Penang, using the AWGS 2019 criteria.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; However, certain limitations must be acknowledged. The cross-sectional design limits the capacity to determine causal relationships among variables. The identified risk factors are correlated with sarcopenia but may not be direct causes. Future longitudinal studies are strongly recommended to provide more substantial evidence for causality. Additionally, future research should explore a wider range of clinical factors that may be associated with sarcopenia. Integrating qualitative assessment to understand differences in health or cultural practices among different ethnicities in Malaysia may give a holistic understanding of its impact to the risk of sarcopenia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Secondly, the use of convenience sampling may limit the generalisability of the findings, as participants were selected based on availability rather than random selection. This, along with the limited sample size, reduces the robustness of the results. Increasing the sample size in future studies would enhance the reliability of the findings.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Moreover, the use of food frequency questionnaires for dietary intake assessment and self-reported physical activity questionnaires may introduce inaccuracies due to potential over- or under-reporting or recall bias. To mitigate this, the researcher used a short recall period (e.g., past 7 days instead of past month) and provide prompts or examples to improve accuracy when needed. The reliance on HBF-206IT-based bioelectrical impedance analysis (BIA) for sarcopenia diagnosis also has limitations, as factors such as body composition and hydration status can affect accuracy. Financial constraints further restricted the use of Dual-energy X-ray Absorptiometry (DXA), a more precise diagnostic tool.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Finally, the participants were recruited from government hospital chest clinics, meaning they may have had more severe disease, potentially leading to an overestimation of sarcopenia prevalence. As a result, the findings should be interpreted with caution and cannot be fully generalised to all individuals with asthma, COPD, or TB in Malaysia.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this study found that sarcopenia prevalence was highest in COPD patients, followed by TB patients, while asthma patients had a lower prevalence, highlighting the varying impact of respiratory diseases on muscle mass loss. Older age and low physical activity increase the risk associated with sarcopenia among asthma patients, while a higher BMI is associated with a lower likelihood of having sarcopenia in asthma patients. For TB patients, the risk of developing sarcopenia is elevated by Chinese ethnicity, other races and foreigners, lower protein intake, and a lower BMI. In COPD patients, advancing age and a history of heart failure are significant risk factors, while moderate physical activity appears to offer protection against sarcopenia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Based on the findings, prevention, detection, and management of sarcopenia in chronic respiratory disease patients should focus on addressing key risk factors, with tailored clinical management strategies for each group. For asthma patients, clinicians should recommend regular physical activity, ideally incorporating resistance training, and encourage maintaining a healthy BMI through balanced nutrition. For TB patients, strategies should include improving protein intake and monitoring BMI, along with addressing ethnic and socioeconomic factors that may contribute to increased sarcopenia risk. In COPD patients, management should focus on promoting moderate physical activity, particularly resistance exercises, and carefully managing comorbidities like heart failure. Nutritional support, including protein supplementation, should be prioritized to improve muscle mass and strength. Clinicians should also conduct regular screenings for sarcopenia to ensure early detection and prompt physiotherapy or rehabilitation intervention, improving quality of life and overall health outcomes for these patients.\u003c/p\u003e\n"},{"header":"Abbreviations","content":"\u003cp\u003eASMI Appendicular skeletal muscle mass index\u003c/p\u003e\n\u003cp\u003eASMM Appendicular Skeletal Muscle Mass\u003c/p\u003e\n\u003cp\u003eAWGS Asian Working Group for Sarcopenia\u003c/p\u003e\n\u003cp\u003eBIA Bioelectrical Impedance Analysis\u003c/p\u003e\n\u003cp\u003eBMI Body Mass Index\u003c/p\u003e\n\u003cp\u003eCI Confidence Interval\u003c/p\u003e\n\u003cp\u003eCOPD Chronic Obstructive Pulmonary Disease\u003c/p\u003e\n\u003cp\u003eFFQ Food Frequency Questionnaire\u003c/p\u003e\n\u003cp\u003eHF Heart Failure\u003c/p\u003e\n\u003cp\u003eIPAQ-LF International Physical Activity Questionnaire Long Form\u003c/p\u003e\n\u003cp\u003eSARC-CalF Sarcopenia Risk Assessment using Calf circumference and Functional performance\u003c/p\u003e\n\u003cp\u003eSARC-F Sarcopenia Rapid Assessment Tool - Functional\u003c/p\u003e\n\u003cp\u003eTB Tuberculosis\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our sincere gratitude to Advanced Medical and Dental Institute, Universiti Sains Malaysia, for instrumental support in this study\u0026rsquo;s fruition. Special recognition goes to the dedicated health staff from the two public hospitals. Above all, we are indebted to the study participants.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNMS, HAY, NAA and RAMZ design the study. RAMZ, SI and IAHA involved in data collection. RAMZ, NAA and NMS analyzed and interpreted the patient data regarding the prevalence of sarcopenia and risk factors associated with sarcopenia and chronic respiratory disease patients. NMS, HAY and NAA also provided guidance and supervision to RAMZ. RAMZ and NMS are major contributors to writing the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was obtained from the Human Research Ethics Committee, Universiti Sains Malaysia (USM/JEPeM/22110707) and the Medical Research Ethics Committee MREC, Ministry of Health Malaysia (NMRR ID-23-00257-WV6(IIR)). Before enrolment, trained research assistants obtained written informed consent from all participants.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\n\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMomtazmanesh S, Moghaddam SS, Ghamari SH, Rad EM, Rezaei N, Shobeiri P, et al. Global burden of chronic respiratory diseases and risk factors, 1990\u0026ndash;2019: An update from the global burden of disease study 2019. eClin Med. 2023;59:101936.\u003c/li\u003e\n\u003cli\u003eVos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990\u0026ndash;2019: A systematic analysis for the Global Burden of Disease Study 2019. 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ESC Heart Fail. 2021;8(2):1007-17. doi:10.1002/ehf2.13255.\u003c/li\u003e\n\u003cli\u003eGueugneau M, Coudy-Gandilhon C, Meunier B, Combaret L, Taillandier D, Polge C, Attaix D, Roche F, F\u0026eacute;asson L, Barth\u0026eacute;l\u0026eacute;my JC, B\u0026eacute;chet D. Lower skeletal muscle capillarisation in hypertensive elderly men. Exp Gerontol. 2016;76:80-8. doi:10.1016/j.exger.2016.01.013.\u003c/li\u003e\n\u003cli\u003eLutski M, Weinstein G, Tanne D, Goldbourt U. Overweight, obesity, and late-life sarcopenia among men with cardiovascular disease, Israel. Prev Chronic Dis. 2020;17:E164. doi:10.5888/pcd17.200167.\u003c/li\u003e\n\u003cli\u003ePolyzos SA, Margioris AN. Sarcopenic obesity. Hormones (Athens). 2018;17(3):321-31. doi:10.1007/s42000-018-0049-x\u003c/li\u003e\n\u003cli\u003eHerzog W. Reflections on obesity, exercise, and musculoskeletal health. J Sport Health Sci. 2020;9(2):108-9. doi:10.1016/j.jshs.2019.11.004.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 6 To 9 are available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sarcopenia, Chronic Respiratory Disease, Asthma, COPD, TB, Risk Factors, Prevalence","lastPublishedDoi":"10.21203/rs.3.rs-5460176/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5460176/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eChronic respiratory diseases like asthma, chronic obstructive pulmonary disease (COPD), and tuberculosis (TB) are increasing globally, leading to systemic symptoms like skeletal muscle dysfunction. Ageing and physical inactivity exacerbate sarcopenia, reducing functional capacity, disability, and quality of life. However, limited research exists on the prevalence of sarcopenia among chronic respiratory diseases in low-middle-income countries like Malaysia. Hence, this study aims to investigate the prevalence of sarcopenia and its associated risk factors among adults with asthma, COPD, and TB in Penang, Malaysia.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted from June 2023 to March 2024. This study included 469 patients (mean age: 52.62\u0026thinsp;\u0026plusmn;\u0026thinsp;16.61 years) diagnosed with asthma (n\u0026thinsp;=\u0026thinsp;180), COPD (n\u0026thinsp;=\u0026thinsp;186), or TB (n\u0026thinsp;=\u0026thinsp;103) receiving treatment in chest clinics of two governmental hospitals in Penang. The SARC-F and SARC-CalF questionnaires were used to assess the participants' risk of sarcopenia. Sarcopenia was identified using the 2019 criteria of the Asian Working Group for Sarcopenia (AWGS). The risk factors for sarcopenia in asthma, COPD, and TB patients were investigated using multivariable logistic regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe prevalence of sarcopenia was 18.9% (95% CI 13.5\u0026ndash;25.4) in patients with asthma, 33.9% (95% CI 27.1\u0026ndash;41.2) in those with TB, and 35.9% (95% CI 26.7\u0026ndash;46.0) in those with COPD, according to AWGS 2019 criteria. The SARC-CalF screening tool showed that 27.3% of participants had a positive risk of having sarcopenia. The independent risk factors associated with sarcopenia in asthma patients were age, physical activity and body mass index (BMI). For TB patients, significant risk factors included Chinese and other ethnicities, foreigners, lower daily protein intake, and BMI. In COPD patients, independent risk factors included age, moderate physical activity, BMI and history of heart failure.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study highlighted a significant burden of sarcopenia among patients with asthma, COPD and TB. Non-clinical interventions such as lifestyle modification and nutritional support to the patients are crucial to maintain muscle strength and delay the onset of sarcopenia, particularly in people with chronic respiratory diseases.\u003c/p\u003e","manuscriptTitle":"Sarcopenia and It's Influencing Factors Among Adults With Asthma, Chronic Obstructive Pulmonary Disease, and Tuberculosis in Penang, Malaysia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-10 04:55:48","doi":"10.21203/rs.3.rs-5460176/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-04-16T05:31:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-14T17:19:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"75421372292178977315986637895336911695","date":"2025-04-14T17:06:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-13T02:49:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-10T03:32:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210265999026790077138004094754682798909","date":"2025-04-10T03:11:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280945047598462946162363898975045033581","date":"2025-04-08T14:30:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-08T08:45:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-28T08:56:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-03-27T15:12:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"619a08c9-1d04-4fcc-bca7-8fb1eade4bc8","owner":[],"postedDate":"April 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-05T16:01:14+00:00","versionOfRecord":{"articleIdentity":"rs-5460176","link":"https://doi.org/10.1186/s12889-025-22819-9","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-04-28 15:57:35","publishedOnDateReadable":"April 28th, 2025"},"versionCreatedAt":"2025-04-10 04:55:48","video":"","vorDoi":"10.1186/s12889-025-22819-9","vorDoiUrl":"https://doi.org/10.1186/s12889-025-22819-9","workflowStages":[]},"version":"v1","identity":"rs-5460176","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5460176","identity":"rs-5460176","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}