Association between appendicular lean mass-to-body weight ratio and metabolic syndrome in community-dwelling older adults in Taiwan: A cross-sectional study

Abstract

Objective

Metabolic syndrome increases the risk of cardiovascular diseases and diabetes. This study examined the associations of body mass index and the appendicular lean mass-to-body weight ratio with metabolic syndrome in Taiwanese older adults.

Methods

A cross-sectional study of 3739 participants from community surveys (2017–2019) was conducted. Anthropometric measurements, blood pressure, handgrip strength, and biochemical markers were assessed. Appendicular lean mass-to-body weight ratio was calculated using a validated equation. Lifestyle factors and comorbidities were evaluated using questionnaires.

Results

The prevalence of metabolic syndrome was 47.1% in males and 50.0% in females. Participants with metabolic syndrome had higher body mass index and waist circumference but lower appendicular lean mass-to-body weight ratio. Higher appendicular lean mass-to-body weight ratio was inversely associated with metabolic syndrome (odds ratio = 0.877 in males; odds ratio = 0.885 in females).

Conclusions

Higher body mass index was positively associated with metabolic syndrome, whereas higher appendicular lean mass-to-body weight ratio was inversely associated with metabolic syndrome. Maintaining greater muscle mass relative to body weight may help reduce the risk of metabolic syndrome in older adults.

Keywords

Metabolic syndrome sarcopenia body composition appendicular lean mass older adults

Introduction

Metabolic syndrome (MS), defined as a cluster of cardiometabolic risk factors in a pre-disease state, is associated with various cardiovascular diseases and increased mortality in the older population.¹ The risk factors for MS include elevated fasting blood glucose (FBG), high blood pressure (BP), low high-density lipoprotein (HDL), high triglycerides (TG), and increased waist circumference (WC), although the specific thresholds for each factor may vary by ethnicity and sex.² The pathophysiology of MS is complex and remains incompletely understood. Abdominal adiposity, chronic inflammation, neurohormonal activation, and insulin resistance are considered the most plausible underlying mechanisms.^3,4

Sarcopenia, defined as the age-related loss of muscle mass, is associated with frailty and mortality in older individuals.^5,6 The coexistence of sarcopenia and obesity, known as sarcopenic obesity, has recently gained attention owing to its relationship with multiple cardiometabolic risk factors.^7–9 The simultaneous decrease in muscle mass and increase in adiposity play a key role in sarcopenic obesity and also contribute to MS.¹⁰ Given the shared pathophysiological mechanisms among sarcopenia, obesity, and MS, prior studies have examined the association between body composition and MS;¹¹ however, their findings remain inconsistent. In a Taiwanese study, low muscle mass was positively associated with MS in females but not in males.¹² A cross-racial study reported that low lean body mass index was associated with MS in Australians but not in Koreans.¹³ Even within the same population, varying definitions of body composition have resulted in different conclusions.¹⁴ Furthermore, there is a lack of evidence directly comparing the relative impact of the lean body mass ratio in a large-scale, community-based cohort in Taiwan. The present study aimed to address this gap by evaluating the independent association between the lean body mass ratio and MS.

Although dual-energy X-ray absorptiometry (DXA) is considered the gold standard for measuring lean body mass, its clinical application remains challenging in large-scale community-based screenings owing to high costs and limited equipment accessibility. To address this limitation, we used a validated equation specifically developed for the Taiwanese population to estimate appendicular lean mass (ALM).¹⁵ Furthermore, we developed the ALM-to-body weight ratio (ALM/W) as a practical indicator. Our aim was to identify a more accessible and easily obtainable factor that is significantly associated with MS, thereby providing a convenient tool for early screening and risk assessment in community settings.

Our working hypothesis was that a higher ALM/W ratio is inversely and independently associated with the risk of MS, potentially offering a more nuanced marker of cardiometabolic health than body mass index (BMI) alone. Therefore, this study aimed to examine the associations of BMI and ALM/W with MS in Taiwanese older adults and to explore the clinical utility of this lean mass-to-weight ratio.

Materials and methods

Study design

This cross-sectional study used data from a community-based health survey conducted in Chiayi County, Taiwan, between 2017 and 2019. Community-dwelling individuals aged >65 years and who had resided in Chiayi County for >1 year were recruited. Participants aged 65–85 years without acute illness or infectious disease within 3 weeks prior to enrollment were included. The reporting of this study conforms to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.¹⁶

Ethics statement

This study was conducted in accordance with the Declaration of Helsinki of 1975, as revised in 2024, and the protocol was approved by the Institutional Review Board of Tri-Service General Hospital, Taipei, Taiwan (Approval Number: TSGHIRB-1-108-05-073). Written informed consent was obtained from all participants prior to enrollment. Furthermore, all participant data were fully deidentified prior to analysis to ensure that individuals could not be identified.

Demographic and lifestyle data

A standardized questionnaire was used to collect demographic information and lifestyle factors, including age, sex, residency, education level, occupation, dietary habits, cigarette smoking, alcohol consumption, and daily activities. Trained research technicians assisted participants in completing the questionnaires.

Anthropometric measurements

Anthropometric parameters, including height, weight, and WC, were measured by trained personnel using regularly calibrated clinical-grade instruments in accordance with standardized health screening protocols. Participants were barefoot and wore light clothing. Height was measured to the nearest 0.5 cm using a digital stadiometer, and weight was recorded to the nearest 0.1 kg using a beam balance scale. WC was measured to the nearest 0.1 cm at the midpoint between the lower margin of the last palpable rib and the top of the iliac crest, in accordance with World Health Organization (WHO) guidelines. BMI was calculated as weight in kilograms divided by height in meters squared (kg/m²).

BP measurement

BP was measured using regularly calibrated clinical-grade automated sphygmomanometers in accordance with national health screening protocols across community centers. Participants were seated and rested for at least 5 min prior to measurement. The right arm was positioned at heart level and fitted with an appropriately sized cuff. BP was measured twice, and the average of the two readings was used for analysis.

Grip strength (GS) measurement

Participants were seated and rested for 2–3 min before prior to testing. GS of the dominant hand was measured twice using a digital dynamometer (TKK 5101 Grip-D, Takey; Tokyo, Japan),¹⁷ with the elbow fully extended. The average of the two measurements was used for analysis.

Estimation of ALM

Estimated ALM was calculated using the following equation, which incorporates body weight, sex, height, and GS:

\begin{array}{l} ALM = - 9.833 + 0.397 \times weight (kg) \\ + 4.433 \times sex + 0.121 \times height (cm) \\ + 0.061 \times hand GS (kg) \end{array}

This equation, developed from a Taiwanese cohort of community-dwelling older adults,¹⁵ provides a more accurate prediction of ALM than bioelectrical impedance analysis (BIA), with a reported adjusted R² of 0.914 and a standard error of 2.062 (p < 0.001). The original validation study reported 95% limits of agreement ranging from −3.98 to 3.92 kg. Although the original reference used the term appendicular muscle mass (AMM), the actual target measure estimated by this equation is ALM, defined as the sum of non-bone, fat-free lean soft tissue in the arms and legs, as measured by a three-compartment DXA model. We adopted this standardized terminology in accordance with recent guidelines.¹⁸ ALM/W was calculated to reflect the proportion of lean mass.

Blood specimen collection and analysis

Participants fasted overnight for 10–12 h prior to venipuncture. Blood samples were processed within 1 h and stored at −80°C until analysis. FBG was determined using the glucose oxidase method (Beckman Instruments; Fullerton, CA, USA).¹⁹ TGs were measured enzymatically²⁰ using a Hitachi 7150 auto-analyzer. Total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were measured using enzymatic colorimetric methods.^21,22

Definition of MS and chronic diseases

MS was defined using the modified National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) criteria for Asians.^2,23 A diagnosis was made if three or more of the following were present: (a) FBG ≥100 mg/dL or current use of antidiabetic medication; (b) systolic BP (SBP) ≥130 mmHg, diastolic BP (DBP) ≥85 mmHg, or use of antihypertensive medications; (c) WC ≥90 cm in men or ≥80 cm in women; (d) TG ≥150 mg/dL; or (e) HDL-C <40 mg/dL in men or <50 mg/dL in women.

Chronic diseases, including diabetes, hypertension (HTN), and dyslipidemia were identified based on self-reported history (assisted by trained technicians) and corroborated by laboratory data. Diabetes was defined as FBG ≥126 mg/dL, use of antidiabetic medications, or a prior diagnosis. HTN was defined as SBP ≥140 mmHg, DBP ≥90 mmHg, or use of antihypertensive medications. Dyslipidemia was defined as TC ≥240 mg/dL, TG ≥200 mg/dL, LDL-C ≥160 mg/dL, or use of lipid-lowering agents.

Statistical methods

Statistical analyses were conducted using IBM Statistical Package for Social Sciences (SPSS) Statistics version 22.0. Continuous variables were reported as means ± SDs, and categorical variables were reported as frequencies and percentages. Independent t-tests and chi-square tests were used for group comparisons. Analysis of covariance (ANCOVA) was used to compare subgroups after adjustment for covariates. Multivariate linear and logistic regression models were used to assess associations between variables. A two-tailed p-value <0.05 was considered statistically significant.

Declaration of generative artificial intelligence (AI) and AI-assisted technologies in the writing process

During the preparation of this manuscript, the authors used Gemini (Google, Mountain View; CA, USA) to improve language clarity and grammar. All content was reviewed and edited by the authors, who take full responsibility for the integrity and accuracy of the manuscript.

Results

Table 1 presents the characteristics of anthropometry, adverse behaviors, BP, lipid profile, chronic diseases, and ALM/W among community-based older male population. The prevalence of MS was 47.1% in males. Compared with participants without MS, those with MS had higher BMI (26.5 ± 3.2 vs. 23.5 ± 3.0 kg/m², p < 0.001), higher WC (93.0 ± 8.0 vs. 84.2 ±7.9 cm, p < 0.001), higher ALM (44.2 ± 4.4 vs. 40.7 ± 4.1 Kg, p < 0.001), and lower ALM/W (0.632 ± 0.027 vs. 0.663 ± 0.032, p < 0.001). Additionally, the prevalence of diabetes mellitus (DM) (33.4% vs 9.7%, p < 0.001) and HTN (57.7% vs. 30.3%, p < 0.001) was higher in participants with MS.

Table 1.

Characteristics of anthropometry, lipids profile, chronic disease, adverse behaviors, blood pressure, and ALM/W among community-based older male population (n = 1600).

	MS (+)	MS (−)
Variables	(n = 754)	(n = 846)	p-value
	Mean ± SD	Mean ± SD
Age (years)^a	73.1 ± 6.1	73.3 ± 6.0	0.501
BMI (kg/m²)^a	26.5 ± 3.2	23.5 ± 3.0	<0.001***
Body waist (cm)^a	93.0 ± 8.0	84.2 ± 7.9	<0.001***
GS (kg)	33.1 ± 7.4	32.6 ± 6.9	0.125
ALM (kg)	44.2 ± 4.4	40.7 ± 4.1	<0.001***
ALM/W	0.632 ± 0.027	0.663 ± 0.032	<0.001***
SBP (mmHg)	142.2 ± 16.9	135.6 ± 18.7	<0.001***
DBP (mmHg)	82.9 ± 11.2	80.6 ± 10.9	<0.001***
AC (mg/dL)	114.4 ± 35.4	96.0 ± 22.2	<0.001***
TG (mg/dL)	149.3 ± 92.5	92.5 ± 37.2	<0.001***
HDL-C (mg/dL)	45.1 ± 11.1	57.7 ± 12.9	<0.001***
CVD (n, %)^b	143 ± 19.0	109 ± 12.9	0.001***
CVA (n, %)^b	32 ± 4.2	30 ± 3.5	0.470
Hypertension (n, %)^b	435 ± 57.7	256 ± 30.3	<0.001***
DM (n, %)^b	252 ± 33.4	82 ± 9.7	<0.001***
Dyslipidemia (n, %)^b	173 ± 22.9	46 ± 5.4	<0.001***
CKD (n, %)^b	35 ± 4.6	30 ± 3.5	0.268
Smoking (n, %)^b	94 ± 12.5	119 ± 14.1	0.347
Alcohol consumption (n, %)^b	137 ± 18.2	140 ± 16.5	0.392

t-test was applied to compare MS (+) and MS (−) groups for age, obesity-related characteristics, and lipid profile; TG was log-transformed for analysis; ***p < 0.001, **p < 0.01, *p < 0.05.

Chi-square test was applied to compare MS (+) and MS (−) groups for chronic diseases and adverse behaviors; ***p < 0.001, **p < 0.01, *p < 0.05.

AC: glucose ante cibum; BMI: body mass index; CKD: chronic kidney disease; CVA: cerebrovascular disease; CVD: cardiovascular disease; DBP: diastolic blood pressure; DM: diabetes mellitus; HDL-C: high-density lipoprotein cholesterol; SBP: systolic blood pressure; TG: triglyceride; MS: metabolic syndrome; ALM: appendicular lean mass; ALM/W: appendicular lean mass-to-body weight ratio; GS: grip strength.

The characteristics of anthropometry, lipid profile, chronic diseases, adverse behaviors, BP, and ALM/W among older female population are presented in Table 2. The prevalence of MS was 49.98% in females. Compared with participants without MS, those with MS had higher BMI (26.8 ± 3.7 vs. 23.9 ± 3.5 kg/m², p < 0.001), higher WC (87.9 ± 8.0 vs. 78.9 ± 8.7 cm, p < 0.001), higher ALM (34.1 ± 3.9 vs. 31.3 ± 3.9 Kg, p < 0.001), and lower ALM/W (0.5595 ± 0.0217 vs. 0.5789 ± 0.0254, p < 0.001). Additionally, the prevalence of DM (36.6% vs. 8.3%, p < 0.001) and HTN (60.6% vs. 33.5%, p < 0.001) was also higher in participants with MS.

Table 2.

Characteristics of anthropometry, lipids profile, chronic disease, adverse behaviors, blood pressure, and ALM/W among community-based older female population (n = 2139).

	MS (+)	MS (−)
Variables	(n = 1069)	(n = 1070)	p-value
	Mean ± SD	Mean ± SD
Age (years)^a	73.1 ± 6.0	72.4 ± 5.9	0.008**
BMI (kg/m²)^a	26.8 ± 3.7	23.9 ± 3.5	<0.001***
Body waist (cm)^a	87.9 ± 8.0	79.9 ± 8.7	<0.001***
GS (kg)	21.8 ± 5.1	21.5 ± 4.6	0.073
ALM (kg)	34.1 ± 3.9	31.3 ± 3.9	<0.001***
ALM/W	0.560 ± 0.022	0.579 ± 0.025	<0.001***
SBP (mmHg)	145.5 ± 16.7	136.7 ± 18.1	<0.001***
DBP (mmHg)	80.2 ± 10.4	77.7 ± 10.0	<0.001***
AC (mg/dL)	120.8 ± 44.4	95.0 ± 18.0	<0.001***
TG (mg/dL)	157.8 ± 85.0	102.0 ± 46.0	<0.001***
HDL-C (mg/dL)	54.8 ± 13.3	63.5 ± 14.2	<0.001***
CVD (n, %)^b	153 ± 17.7	169 ± 13.3	0.005**
CVA (n, %)^b	21 ± 2.4	15 ± 1.2	0.028*
Hypertension (n, %)^b	525 ± 60.6	426 ± 33.5	<0.001***
DM (n, %)^b	317 ± 36.6	106 ± 8.3	<0.001***
Dyslipidemia (n, %)^b	227 ± 26.2	110 ± 8.6	<0.001***
CKD (n, %)^b	29 ± 3.3	23 ± 1.8	0.023*
Smoking (n, %)^b	7 ± 0.8	12 ± 0.9	0.745
Alcohol consumption (n, %)^b	23 ± 2.7	24 ± 1.9	0.233

t-test was applied to compare MS (+) and MS (−) groups for age, obesity-related characteristics, and lipid profile; TG was log-transformed for analysis; ***p < 0.001, **p < 0.01, *p < 0.05.

Chi-square test was applied to compare MS (+) and MS (−) groups for chronic diseases and adverse behaviors; ***p < 0.001, **p < 0.01, *p < 0.05.

Multiple regression coefficients for BMI and MS, stratified by sex, are presented in Table 3. BMI was positively associated with MS in both sexes (odds ratio (OR) = 1.087, 95% confidence interval (CI): 1.018–1.161 for males; OR = 1.071, 95% CI: 1.026–1.117 for females). BMI was also directly associated with the number of MS factors (β = 0.052, p < 0.001 in males; β = 0.025, p = 0.001 in females). Particularly, every 10-unit increase in BMI was associated with an increase of 0.52 in the MS factor score in males and 0.25 in females.

Table 3.

Sex-specific multiple regression coefficients of BMI in relation to metabolic syndrome status (n = 3739).

Dependent	Model I^a			Model II^b
Variables	β	SE β	p-value	β	SE β	p-value
Male (n = 1600)
MS	OR = 1.397	95% CI: 1.340–1.457		OR = 1.087	95% CI: 1.018–1.161
MS (score)	0.200	0.008	<0.001***	0.052	0.013	<0.001***
Female (n = 2139)
MS	OR = 1.255	95% CI: 1.221–1.291		OR = 1.071	95% CI: 1.026–1.117
MS (score)	0.135	0.006	<0.001***	0.025	0.008	0.001**

Model I: Linear regression model adjusted for age, cigarette smoking, and alcohol consumption.

Model II: Further adjusted for age, cigarette smoking, alcohol consumption, waist circumference, hypertension, and DM.

***p < 0.001, **p < 0.01.

MS: metabolic syndrome; SE: standard error; β: regression coefficient; BMI: body mass index; OR: odds ratio; CI: confidence interval; DM: diabetes mellitus.

Table 4 presents the multiple regression coefficients for ALM/W and MS, stratified by sex. In the final model, ALM/W was negatively associated with MS in both sexes (OR = 0.877, 95% CI: 0.818–0.941 for males; OR = 0.885, 95% CI: 0.831–0.943 for females). There was also an inverse dose–response relationship between ALM/W and the number of MS factors (β = −7.279, p = 0.001 in males; β = −5.367, p < 0.001 in females). Specifically, every 10% increase in ALM/W was associated with a decrease of 0.73 in the MS factor score in males and 0.54 in females.

Table 4.

Sex-specific multiple regression coefficients of ALM/W in relation to metabolic syndrome status (n = 3739).

Dependent	Model I^a			Model II^b
Variables	β	SE β	p-value	β	SE β	p-value
Male (n = 1600)
MS	OR = 0.698	95% CI: 0.668–0.728		OR = 0.877	95% CI: 0.818–0.941
MS (score)	−20.906	0.818	<0.001***	−7.279	1.336	0.001**
Female (n = 2139)
MS	OR = 0.702	95% CI: 0.672–0.733		OR = 0.885	95% CI: 0.831–0.943
MS (factor score)	−20.603	0.536	<0.001***	−5.367	1.095	<0.001***

Model I: Linear regression model adjusted for age, cigarette smoking, and alcohol consumption.

Model II: Further adjusted for age, cigarette smoking, alcohol consumption, waist circumference, hypertension, and DM.

***p < 0.001, **p < 0.01.

MS: metabolic syndrome; SE: standard error; β: regression coefficient; ALM/W: appendicular lean mass-to-body weight ratio; OR: odds ratio; CI: confidence interval; DM: diabetes mellitus.

Discussion

This study found that the prevalence of MS among community-dwelling older individuals in Taiwan was 47.1% in males and 50.0% in females. Compared with participants without MS, those with MS had higher BMI and lower ALM/W. Furthermore, BMI was positively associated with MS, whereas ALM/W was inversely associated with MS. An inverse dose–response relationship between ALM/W and the number of MS components was also observed.

Our results demonstrated that although participants with MS had higher BMI, they also had higher absolute ALM. Because BMI encompasses both fat and lean mass, it is not an ideal indicator of body composition. The observed increase in BMI may be attributable to fat, muscle, or both. Therefore, we used ALM/W as a more specific indicator of lean mass proportion. Prior studies have shown that sarcopenic obesity, defined by the ratio of appendicular skeletal muscle mass to total body weight, is more strongly associated with MS than lean mass alone.^11,24 Our findings confirmed that ALM/W is inversely associated with MS and its individual components.

A prior Taiwanese study found that women with low muscle mass ratios had a higher risk of MS.¹² Another study reported that sarcopenic obesity was associated with an increased risk of MS.⁹ However, these associations often depend strongly on the specific index used to define muscle mass. In contrast, a study by Scott et al.¹³ found that sarcopenia, defined by lean mass relative to height squared, was inversely associated with MS, whereas a positive association was observed when lean mass relative to BMI was used as the index in the same population. A Korean study using data from the Korean National Health and Nutrition Examination Survey reported similar findings, with differing associations depending on the lean mass index used.¹⁴

To understand these discrepancies, it is important to consider the underlying pathophysiology. Sarcopenic obesity contributes to MS through multiple mechanisms. Visceral fat accumulation induces oxidative stress, chronic inflammation, and insulin resistance.^25,26 Muscle tissue is a major site for insulin-mediated glucose uptake,²⁷ and muscle loss contributes to insulin resistance. The coexistence of increased fat mass and decreased muscle mass is believed play a central role in the development of MS due to shared pathophysiology involving insulin resistance.²⁸ Because ALM/W can increase through higher muscle mass or lower fat mass (FM), it may provide a more integrative measure of cardiometabolic risk than lean mass alone.

Consistent with this mechanistic rationale, our study extends prior research by demonstrating a robust, dose-dependent inverse association between ALM/W and MS risk factors. Although a Korean study by Kim et al.²⁹ reported that the risk of MS decreased with each quartile increase in lean body mass ratio, our study further demonstrated that ALM/W is inversely associated with each individual MS component. Notably, this dose–response relationship remained significant even after adjustment for age, lifestyle factors, WC, DM, and HTN, underscoring its independent clinical utility.

Our study had several limitations. First, owing to the cross-sectional design, causal relationships could not be established. Second, a notable limitation is the lack of direct measurements of total body FM. Because ALM was adjusted by body weight, the ALM/W ratio inherently reflects the proportion of both muscle and fat. Therefore, ALM/W should be interpreted as a composite indicator of body composition balance rather than a measure of muscle mass independent of FM. However, our multivariable models adjusted for WC (a robust proxy for central adiposity), and ALM/W remained a significant predictor of MS, suggesting its independent clinical utility beyond central obesity. Finally, the inherent estimation error of the ALM prediction equation represents a significant limitation. The original validation study reported wide limits of agreement (−3.98 to 3.92 kg). Although this estimation method is more feasible than direct DXA for large-scale community-based screenings and is valid for identifying broad epidemiological associations, an error margin of approximately 4 kg is substantial at the individual level. For older adults with low baseline muscle mass, this lack of precision may lead to misclassification of their true body composition status. Therefore, the ALM/W ratio in our study should be interpreted as an indicator of population-level trends rather than a precise diagnostic tool for individual patients. Definitive clinical diagnoses still require direct imaging modalities.

Conclusion

This study found that the ALM/W ratio was negatively associated with MS among community-dwelling older individuals in Taiwan. ALM/W demonstrated an inverse dose–response relationship with each MS risk factor, even after adjusting for age, lifestyle patterns, WC, HTN, and diabetes. These findings suggest that the ALM/W ratio may serve as a practical and accessible indicator for screening metabolic disorders in large-scale, community-based public health settings.

Footnotes

Acknowledgments

The authors thank Winnie Chu for English editing and corrections. We also thank the Chiayi Health Bureau, Yu-Chen Lin, and Chun-Yin Liu for their technical assistance and data collection. During the preparation of this manuscript, the authors used Gemini (Google, Mountain View, CA, USA) to improve language clarity and grammar. All content was reviewed and edited by the authors, who take full responsibility for the integrity and accuracy of the manuscript.

Author contributions

Chun-Yung Chang: Investigation and writing—original draft.

Nain-Feng Chu: Conceptualization, methodology, supervision, and writing—review and editing.

Der-Min Wu: Data curation and methodology.

Wen-Chen Liang: Writing—review and editing.

Shu-Chuan Yeh: Resources and supervision.

Cheng-Hsin Chuang: Formal analysis, project administration, resources, and supervision.

Data availability statement

The data that support the findings of this study are not publicly available due to privacy reasons but are available from the corresponding author upon request.

Declaration of conflicting interests

The authors declare no conflict of interest.

Ethics statement

This study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of Tri-Service General Hospital, Taipei, Taiwan (Approval Number: TSGHIRB-1-108-05-073). All participants provided written informed consent prior to enrollment.

Funding

This research received no external funding.

ORCID iDs

Chun-Yung Chang

Shu-Chuan Yeh

Cheng-Hsin Chuang

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