Exploring influencing factors and predictive analysis of sarcopenia in the Chinese population using the body composition analyzer

Abstract

Objective:

Employing body composition analysis, this study aims to examine the influencing factors and conduct predictive analysis regarding sarcopenia incidence in the middle-aged and elderly population in China.

Methods:

This study recruited inpatients from the General Medicine Department of Tongji Medical College Affiliated Union Hospital, Huazhong University of Science and Technology, as the subjects for a single-center retrospective study. Diagnosis was conducted according to the 2019 criteria from the Asian Working Group for Sarcopenia. Binary logistic regression analysis was utilized to identify factors influencing sarcopenia, and predictive modeling for sarcopenia occurrence was performed based on the area under the ROC curve (AUC).

Results:

This study comprised 1258 hospitalized patients, of whom 340 were diagnosed with sarcopenia and 918 were not, resulting in a prevalence of 27%. The baseline characteristics showed statistically significant differences between the two groups. Binary logistic regression analysis revealed that low protein, low total body water, low minerals, low basal metabolic rate, and age were risk factors for sarcopenia (OR > 1, P < 0.05). Conversely, being male, having a higher BMI, greater fat-free mass index, and a higher InBody score were identified as protective factors against sarcopenia (OR < 1, P < 0.05). The AUC values for predicting sarcopenia occurrence based on low protein, low total body water, low minerals, low basal metabolic rate, and age were 0.871, 0.846, 0.757, 0.645, and 0.649, respectively, indicating their significance as predictive indicators. Combining these five indicators into a new predictive model for sarcopenia yielded an area under the curve (AUC) value of 0.932, demonstrating excellent sensitivity and specificity concurrently.

Conclusion:

The results of body composition analysis indicate that sarcopenia occurrence in the middle-aged and elderly population in China is associated with factors such as low protein, low total body water, low minerals, low basal metabolic rate, age, gender, BMI, fat-free mass index, and InBody score. The combination of specific body composition indicators facilitates the effective prediction of sarcopenia. Clinical practitioners should proactively identify the risk factors influencing sarcopenia, accurately predict.

Keywords

Sarcopenia inBody body composition analysis

Introduction

According to the 2020 national census data from the National Bureau of Statistics, the population aged 60 and above in China is nearly 270 million, accounting for 18.7% of the total population, while those aged 65 and above have surpassed 190 million, constituting 13.5% of the total population.¹ With China gradually transitioning into an aging society, degenerative conditions such as sarcopenia emerge as significant health concerns for the elderly population. Sarcopenia (SP) is a chronic syndrome characterized by a decline in overall muscle mass, reduced muscle strength, and physiological function decline.² It can result in impaired limb mobility, an elevated risk of falls, and fractures.³ Recent research indicates that the prevalence of sarcopenia among elderly individuals residing in Chinese communities can reach approximately 12%. However, the overall prevalence of sarcopenia among elderly populations in both community and hospital settings is even higher, ranging from 20% to 30%. Among women in nursing homes, the prevalence of sarcopenia can be as high as 33.7%.⁴The InBody body composition analyzer employs bioelectrical impedance analysis (BIA) to detect and analyze body composition and content, including proteins, water, minerals, etc. Research has identified a close association between human body composition and the occurrence and development of malnutrition, as well as various chronic diseases,^5–7 including sarcopenia. Compared to dual-energy X-ray absorptiometry (DXA), BIA offers advantages such as simple and convenient operation, lower cost, non-radioactivity, and ease of repeat measurements.⁸ Thus, it can be applied in hospitals and even at the primary healthcare level.^9–11 In this study, we employed the InBody body composition analyzer in conjunction with the diagnostic and treatment consensus proposed by the 2019 Asian Working Group for Sarcopenia.¹² We analyzed the body composition results of 1258 hospitalized patients, identified individuals meeting the diagnostic criteria for sarcopenia, and examined the influencing factors contributing to its occurrence. This research aims to provide a theoretical basis for reducing or preventing the onset of sarcopenia. Additionally, it suggests that healthcare professionals should early identify the risk factors of sarcopenia as well as effectively screen and manage high-risk populations.

Methods

Patients

The study cohort comprised 1258 patients admitted for inpatient treatment in the General Medicine Department of Tongji Medical College Affiliated Union Hospital, Huazhong University of Science and Technology, from December 2021 to October 2023. Our study is a retrospective research.

Inclusion criteria

Admission for inpatient care in the General Medicine Department between December 2021 and October 2023.

Underwent a body composition examination using the InBody analyzer while hospitalized.

Provided comprehensive informed consent to participate in this study and volunteered.

Exclusion criteria

Did not undergo body composition assessment using the InBody analyzer while hospitalized.

Were unable to cooperate in completing the required assessments.

Diagnosis

This study utilized the 2019 Asian Working Group for Sarcopenia diagnostic consensus,¹² which defines sarcopenia as a condition characterized by a reduction in muscle mass accompanied by a decrease in muscle strength, with or without a decline in muscle function. The assessment of skeletal muscle mass was conducted using bioelectrical impedance analysis (InBody body composition analyzer), and the Appendicular Skeletal Muscle Index (ASMI) was calculated. ASMI is calculated as the appendicular skeletal muscle mass (in kilograms) divided by the square of the height (in meters): ASMI = Appendicular Skeletal Muscle Mass (kg) / Height (m)². Sarcopenia is diagnosed when the ASMI is less than 7.0 kg/m² for males and less than 5.7 kg/m² for females.

Data collection

This study was conducted in the General Medicine Department of Tongji Medical College Affiliated Union Hospital, Huazhong University of Science and Technology. General patient information was extracted from the hospital's Haitai inpatient system, while body composition analysis data were obtained from the InBody body composition analyzer (InBody270, provided by BISON Medical Trading (Shanghai) Co., Ltd). For eligible study participants meeting the inclusion criteria, informed and trained research personnel from our department explained the research objectives and precautions. Patient confidentiality was maintained, and participants, under the guidance of trained research personnel, completed the body composition analysis examination. Prior to testing, participants were advised to avoid vigorous exercise, dietary changes, or excessive fluid intake to ensure optimal conditions for measurement. Participants were instructed to remove footwear and socks, stand in the designated position on the instrument with slightly separated legs, ensuring full contact of both feet with the InBody electrode plate. Participants maintained an upright position, ensuring good contact with the instrument, with arms slightly apart and naturally hanging. The testing environment remained quiet and stable throughout the measurement process until completion. To minimize operational errors, research personnel underwent professional training, and all measurements were conducted by the same trained individual. This approach aimed to ensure consistency and reliability in the data collection process.

Statistic analysis

Statistical analysis was conducted using SPSS 25.0 and Excel 2021 software for data analysis, and GraphPad Prism 9.5 software was utilized for graphical representation. Metric data with a normal distribution were expressed as mean ± standard deviation (x ± s), and inter-group comparisons were performed using independent sample t-tests. Data with skewed distribution were presented as the median with interquartile range [M (P25, P75)], and group comparisons were performed using the Mann-Whitney U test. Qualitative data were expressed as frequencies and percentages (%), and inter-group comparisons were conducted using the chi-square test. Logistic stepwise regression analysis was applied to analyze all detection indicators of the InBody analyzer, aiming to identify potentially meaningful indicators. Subsequently, binary logistic regression analysis was employed to identify significant indicators influencing the development of sarcopenia. Two-sided tests were employed, with a significance level set at α = 0.05. Finally, ROC curves were generated to assess the diagnostic capability of each individual indicator and the combined multi-indicator approach in predicting sarcopenia. The AUC values were calculated and evaluated to assess the diagnostic performance.

Results

Baseline data

This study included a total of 1258 participants, with ages ranging from 31 to 93 years and an average age of (58.48 ± 10.86) years. Among them, there were 691 males and 567 females, with 340 cases diagnosed with sarcopenia and 918 without, resulting in a prevalence rate of 27%. As indicated in Table 1, significant statistical differences (P < 0.05) were observed between the sarcopenia and non-sarcopenia groups in general characteristics such as age, gender, height, weight, and BMI. Based on Table 2, the incidence of sarcopenia gradually increases with age. Between the ages of 30 and 80, the incidence of sarcopenia increases by approximately 10% per decade. After the age of 80, the incidence of sarcopenia can reach up to 70.2%. According to the results from the InBody analyzer (Table 3), significant statistical differences (P < 0.05) were found between the two groups in total body water, protein, minerals, body fat mass, fat mass index, fat-free mass, fat-free mass index, and InBody score. Additionally, the InBody analyzer could display the normal ranges for various indicators based on the subjects’ age, gender, and other basic information. Evaluation of these indicators within their respective ranges, such as low total body water, low protein, high body fat mass, and high waist-to-hip ratio, was conducted in a binary format (Yes/No). The statistical analysis using the chi-square test revealed significant differences (P < 0.05) between the two groups. Furthermore, all count data from the measurement indicators were presented in a categorical bar chart, as illustrated in Figure 1.

Figure 1.

Count data of InBody composition analysis.

Table 1.

Baseline characteristics of the study population.

Group	Sarcopenia	Non-sarcopenia	t/χ2	P
Age	63.08 ± 12.32	56.77 ± 9.73	t = 8.51	<0.0001
Gender
Male	154 (22.29%)	537 (77.71%)	χ2 = 17.469	<0.0001
Female	186 (32.8%)	381 (67.2%)	χ2 = 17.469	<0.0001
Height (cm)	161.62 ± 6.99	166.98 ± 7.25	t = −11.753	<0.0001
Weight (Kg)	54.88 ± 8.02	70.20 ± 11.42	t = 26.624	<0.0001
BMI	21.00 ± 2.71	25.09 ± 3.10	t = −22.88	<0.0001

Table 2.

The morbidity of sarcopenia varies across different age group.

Age group	Number	Sarcopenia	Morbidity
31–40	14	1	7.1%
41–50	265	40	17.8%
51–60	551	129	23.4%
61–70	248	79	31.9%
71–80	123	51	41.5%
81–99	57	40	70.2%

Table 3.

Inbody body composition analysis results.

Group	Sarcopenia	Non-sarcopenia	t/χ2	P
Total Body Water (Kg)	28.85 ± 4.09	36.29 ± 6.10	t = −24.81	<0.0001
Protein (Kg)	7.63 ± 1.09	9.67 ± 1.67	t = −25.282	<0.0001
Minerals (Kg)	2.70 ± 0.35	3.35 ± 0.55	t = −24.631	<0.0001
Body Fat Mass (Kg)	15.69 ± 5.59	20.89 ± 6.35	t = −14.098	<0.0001
Fat Mass Index	6.06 ± 2.29	7.52 ± 2.31	t = −9.96	<0.0001
Fat Free Mass (Kg)	39.18 ± 5.51	49.31 ± 8.29	t = −25.01	<0.0001
Fat Free Mass Index	14.94 ± 1.26	17.57 ± 1.87	t = −28.628	<0.0001
Inbody Score	67.54 ± 5.43	69.60 ± 5.83	t = −5.66	<0.0001
Low Total Body Water
Yes	257 (81.33%)	59 (18.67%)	χ2 = 630.942	<0.0001
No	83 (8.81%)	859 (91.19%)	χ2 = 630.942	<0.0001
Low Protein
Yes	276 (81.18%)	64 (18.82%)	χ2 = 630.942	<0.0001
No	64 (6.97%)	854 (93.03%)	χ2 = 630.942	<0.0001
Low Minerals
Yes	210(68.63%)	96(31.37%)	χ2 = 354.814	<0.0001
No	130((13.66%)	822(86.34%)	χ2 = 354.814	<0.0001
High Body Fat Mass
Yes	160 (18.37%)	711 (81.63%)	χ2 = 107.596	<0.0001
No	180 (46.51%)	207 (53.49%)	χ2 = 107.596	<0.0001
Low Fat Free Mass
Yes	260 (80.50%)	63 (19.50%)	χ2 = 629.948	<0.0001
No	80 (8.56%)	855 (91.44%)	χ2 = 629.948	<0.0001
High Percent Body Fat
Yes	243 (23.55%)	789 (76.45%)	χ2 = 35.284	<0.0001
No	97 (42.92%)	129 (57.08%)	χ2 = 35.284	<0.0001
High Waist-Hip Ratio
Yes	163 (20.58%)	629 (79.42%)	χ2 = 45.047	<0.0001
No	177 (37.98%)	289 (62.02%)	χ2 = 45.047	<0.0001
High Obesity Degree
Yes	48 (7.67%)	578 (92.33%)	χ2 = 236.786	<0.0001
No	292 (46.20%)	340 (53.80%)	χ2 = 236.786	<0.0001
High Visceral Fat Level
Yes	70 (15.28%)	388 (84.72%)	χ2 = 50.358	<0.0001
No	270 (33.75%)	530 (66.25%)	χ2 = 50.358	<0.0001
Low Basal Metabolic Rate
Yes	258 (37.45%)	431 (62.55%)	χ2 = 83.838	<0.0001
No	82 (14.41%)	487 (85.59%)	χ2 = 83.838	<0.0001

Analysis of factors influencing sarcopenia

Utilizing the presence or absence of sarcopenia as the dependent variable (1 = sarcopenia, 0 = non-sarcopenia), Logistic Stepwise Regression analysis was initially conducted, incorporating all variables from Tables 1 and 3 as independent variables. Drawing upon relevant literature and clinical experience, the analysis identified significant factors potentially influencing the occurrence of sarcopenia. The selected factors included low protein, low total body water, low minerals, gender, high body fat mass, low basal metabolic rate, high visceral fat level, high waist-to-hip ratio, high percent body fat, high obesity degree, age, BMI, fat-free mass index, and InBody score. Subsequently, the aforementioned variables were entered as independent variables into Binary Logistic Regression analysis, and the results are presented in Table 4. The findings revealed that low protein, low total body water, low minerals, low basal metabolic rate, and age were identified as risk factors for sarcopenia (OR > 1, P < 0.05). Conversely, being male, having a higher BMI, greater fat-free mass index, and a higher InBody score were identified as protective factors against sarcopenia (OR < 1, P < 0.05). The odds ratios (OR) were visually represented as shown in Figure 2.

Figure 2.

Or visual processing.

Table 4.

Results of binary multiple-factor logistic regression analysis.

Variable	B	Stand error	Wald	OR(95%CI)	P
Low Protein	2.505	0.339	54.702	12.240(6.032–23.770)	<0.0001
Low Total Body Water	1.268	0.364	12.143	3.553(1.741–7.249)	<0.0001
Low Minerals	0.669	0.276	5.887	1.952(1.137–3.350)	0.015
Low Basal Metabolic Rate	0.948	0.229	17.122	2.581(1.647–4.045)	<0.0001
Gender	−0.698	0.248	7.959	0.497(0.306–0.808)	0.005
Age	0.050	0.009	32.815	1.052(1.034–1.070)	<0.0001
BMI	−0.618	0.061	104.407	0.539(0.478–0.607)	<0.0001
Fat Free Mass Index	−0.670	0.089	57.212	0.512(0.430–0.609)	<0.0001
Inbody Score	−0.222	0.024	84.902	0.801(0.764–0.839)	<0.0001

Predictive analysis of sarcopenia based on inbody body composition analysis

Based on the conclusions drawn from Section 2.2, it is evident that low protein, low total body water, low minerals, low basal metabolic rate, and age are risk factors associated with the occurrence of sarcopenia. Conversely, being male, having a higher BMI, greater fat-free mass index, and a higher InBody score are identified as protective factors against sarcopenia. Incorporating these variables into the diagnostic model, a ROC curve was plotted (Figure 3). Table 5 presents the predictive value of different indicators for sarcopenia. Among the single-factor indicators, low protein exhibited an AUC of 0.871 (95% CI = 0.845–0.897, P = 0.000), with sensitivity of 82.2% and specificity of 93%; low total body water demonstrated an AUC of 0.846 (95% CI = 0.817–0.874, P = 0.000), with sensitivity of 75.6% and specificity of 93.6%; low minerals showed an AUC of 0.757 (95% CI = 0.723–0.790, P = 0.000), with sensitivity of 61.8% and specificity of 89.5%; low basal metabolic rate presented an AUC of 0.645 (95% CI = 0.611–0.678, P = 0.000), with sensitivity of 75.9% and specificity of 53.1%; and age exhibited an AUC of 0.649 (95% CI = 0.614–0.684, P = 0.000), with sensitivity of 46.2% and specificity of 76.1%. Finally, combining these five indicators, a new model incorporating low protein, low total body water, low minerals, low basal metabolic rate, and age was established (Figure 4). The AUC for the new model was 0.932 (95% CI = 0.916–0.947, P = 0.000), demonstrating higher predictive performance. Sensitivity (83.2%) and specificity (92.6%) also showed improvement (Table 6).

Figure 3.

ROC curves for different indicators in predicting sarcopenia.

Figure 4.

ROC curve for the combined model (low protein, low total body water, low minerals, low basal metabolic rate, age) in predicting sarcopenia.

Table 5.

Predictive value of different indicators for sarcopenia.

Predictor variable	Sensibility	Specificity	AUC	P	95%CI
Predictor variable	Sensibility	Specificity	AUC	P	Min	Max
Low Protein	81.2%	93%	0.871	<0.0001	0.845	0.897
Low Total Body Water	75.6%	93.6%	0.846	<0.0001	0.817	0.874
Low Minerals	61.8%	89.5%	0.757	<0.0001	0.723	0.790
Gender	45.3%	41.%	0.434	<0.0001	0.398	0.470
Low Basal Metabolic Rate	75.9%	53,1%	0.645	<0.0001	0.611	0.678
Age	46.2%	76.1%	0.649	<0.0001	0.614	0.684
BMI	23.2%	19%	0.146	<0.0001	0.123	0.170
Fat Free Mass Index	9.1%	32.9%	0.121	<0.0001	0.103	0.140
Inbody Score	14.4%	69.2%	0.399	<0.0001	0.366	0.433

Table 6.

Predictive value of the combined model (low protein, low total body water, low minerals, low basal metabolic rate, age) for sarcopenia.

Predictor variable	Sensibility	Specificity	AUC	P	95%CI
Predictor variable	Sensibility	Specificity	AUC	P	Min	Max
Low Protein + Low Total Body Water + Low Minerals + Low Basal Metabolic Rate + Age	83.2%	92.6%	0.932	<0.0001	0.916	0.947

Discussion

Sarcopenia is an age-related geriatric syndrome characterized by progressive loss of muscle mass, strength, and/or decline in muscle physiological function. According to statistics, there are currently 50 million sarcopenia patients worldwide, with an estimated increase to 500 million by the year 2050.¹³ Human muscle mass peaks at the age of 25, and a decline of 3%–5% in muscle mass occurs every decade from the age of 40, followed by a 1%–2% reduction per year after the age of 50.^14,15 The prevalence of sarcopenia among individuals aged 80 and above is reported to be as high as 67.1%.¹⁶ Additionally, sarcopenia often coexists with osteoporosis (OP), collectively referred to as "sarcopenia-osteoporosis (OS)," thereby significantly increasing the risk of falls, fractures, and hospitalization in the elderly. The InBody body composition analyzer, utilizing the principle of bioelectrical impedance analysis (BIA), is widely employed as a non-invasive, convenient, cost-effective, and easily repeatable method for assessing and analyzing human body composition, including proteins, water, minerals, and fat. In recent years, it has gained popularity as a diagnostic tool that is ionizing radiation-free, thus making it more readily accepted by patients compared to dual-energy X-ray absorptiometry (DXA) examinations. In this study, we employed the InBody analyzer to assess the body composition characteristics of sarcopenia patients in China, analyzed influencing factors, and ultimately conducted predictive analysis for sarcopenia based on identified influencing factors.

Low protein, low total body water, low minerals, and low basal metabolic rate are risk factors for the occurrence of sarcopenia

The findings of this study suggest that inadequate protein intake significantly increases the risk of sarcopenia, in line with prior research.¹⁷ Protein is a major component of muscle tissue and plays a vital role in muscle synthesis and repair. Inadequate protein intake can result in suboptimal muscle protein synthesis, consequently precipitating sarcopenia. The recommended dietary nutrient reference intake for Chinese residents in 2018 is 65 g/d protein intake for adult men and 55 g/d for women.¹⁸ According to the European Society for Clinical Nutrition and Metabolism guidelines, healthy elderly individuals are advised to consume 1.0–1.2 g of protein per kilogram of body weight per day, while those engaging in resistance exercise should aim for 1.2 g/kg/day. For elderly patients with chronic diseases, the recommendation is 1.2–1.5 g/kg/day, and for individuals facing severe illness, intake exceeding 1.5 g/kg/day is suggested.¹⁹ Disparities in body composition between domestic and international populations may necessitate additional investigation into daily protein requirements. This result underscores the significance of incorporating protein intake into dietary planning, particularly for high-risk groups susceptible to sarcopenia. Augmenting protein intake could serve as a potential preventive measure. Furthermore, future research endeavors should prioritize elucidating the precise role of protein in the mechanisms governing muscle synthesis. This entails investigating variables such as protein type, timing of consumption, and quantity, aiming to foster a more holistic comprehension of the intricate pathways through which protein influences the onset of sarcopenia.

Muscle tissue contains a substantial proportion of water, and age-related cellular fluid loss can contribute to reduced body water content, potentially exacerbating sarcopenia. Minerals are primarily located in the skeleton, and insufficient mineral levels can predispose individuals to bone metabolic disorders such as osteoporosis. Nevertheless, our research demonstrates that decreased mineral content also influences the development of sarcopenia. Intriguingly, sarcopenia frequently coexists with osteoporosis, referred to as OS,²⁰ thereby underscoring the intimate connection between these two conditions.²¹ Notably, vitamin D and calcium emerge as pivotal factors. Results from a multicenter, double-blind, randomized controlled trial indicate that vitamin D supplementation upregulates gene expression and muscle protein synthesis, thereby enhancing muscle mass and lower limb function in elderly individuals with diminished skeletal muscle mass, ultimately improving strength and balance.²² This highlights the significant role that minerals play in preserving skeletal and muscle health.

The basal metabolic rate (BMR) indicates the body's energy expenditure during a state of rest. Our findings suggest that a diminished BMR may influence the onset of sarcopenia, likely due to decreased energy utilization efficiency, which could impair the maintenance and repair processes of muscle tissue. Moreover, metabolic rate closely correlates with energy balance, which is critical for preserving the health of muscle tissue. Diminished BMR may signify decreased energy requirements during rest, potentially resulting in reduced protein utilization by muscles. This physiological condition may induce muscle tissue to enter a negative energy balance, consequently precipitating sarcopenia. Furthermore, the body's metabolic rate is subject to regulation by the endocrine system, including thyroid hormones and insulin,²³ among other factors. These hormones can directly or indirectly influence the synthesis and breakdown processes of muscle tissue.

Male gender, BMI, and fat-free mass index are protective factors against sarcopenia

The study findings suggest that females exhibit a higher susceptibility to sarcopenia compared to males, contradicting certain previous research findings.²⁴ Males typically possess greater muscle mass and density, with androgen secretion potentially contributing to enhanced muscle protein synthesis, thus retarding the decline in muscle mass.²⁵ Conversely, in females, the substantial reduction in estrogen levels following menopause, a hormone crucial for musculoskeletal health maintenance, underscores the necessity for heightened awareness of musculoskeletal diseases among the elderly female demographic. Our observation revealed a decreased risk of sarcopenia among individuals with higher BMI. This finding underscores the significance of BMI in preserving muscle mass, as individuals with higher BMI may possess greater muscle mass, offering a protective advantage against muscle decline. Moderate fat storage may positively contribute by furnishing supplementary energy reserves to facilitate muscle synthesis and repair. Nevertheless, the protective impact of BMI may extend beyond muscle mass and should encompass the balance between fat and muscle, as observed in individuals with sarcopenic obesity.²⁶ Hence, evaluating muscle mass and strength remains essential for diagnosing sarcopenia, even in individuals with a normal BMI. In contrast to BMI, the fat-free mass index (FFMI) excludes fat from body weight, primarily assessing the quality of non-fat mass, notably muscle. Thus, FFMI is regarded as a superior indicator of muscle mass. Elevated FFMI levels may indicate a reduced risk of sarcopenia development. Nonetheless, this should be evaluated alongside overall muscle strength and functionality for a comprehensive assessment.

Regarding the predictive analysis of sarcopenia

This study integrated factors significantly linked to sarcopenia occurrence into a diagnostic model, and ROC curves were generated. Low levels of protein, total body water, minerals, basal metabolic rate, and age exhibited favorable AUC values, sensitivity, and specificity. Subsequently, we amalgamated these five indicators to formulate a novel predictive model, yielding an AUC of 0.932, accompanied by commendable sensitivity (83.2%) and specificity (92.6%). Prior research has suggested that in the absence of DXA, BIA serves as a viable method for evaluating muscle mass and diagnosing sarcopenia.^27,28 BIA quantifies muscle mass and body composition via whole-body impedance, presenting a convenient, cost-effective, and radiation-free option for recurrent assessments, applicable across diverse settings including community and care facilities. Despite the excellent predictive capability of our new model, it depends on BIA/DXA measurements to derive these indicators. This underscores the importance that even if muscle mass assessed by BIA/DXA fails to meet the diagnostic criteria for sarcopenia in the elderly population, healthcare professionals should remain vigilant regarding the potential development of sarcopenia when the aforementioned indicators are low or borderline. In such instances, educating patients about the significance of supplementing protein, vitamin D, calcium, and participating in suitable physical exercises becomes imperative for averting sarcopenia and enhancing quality of life.

Limitations and constraints

While the sample size in this study is adequate, a comprehensive assessment and calculation of the sample size were not performed. Moreover, this study overlooks the investigation of Socioeconomic Status (SES) indicators, including educational level, economic status, and family environment of the subjects, which could influence the health status of the subjects and may consequently limit the comprehensiveness of this study. Finally, this study does not include an assessment of muscle function, such as the Timed Up and Go (TUG) test and the time taken for five sit-to-stand repetitions. The absence of muscle function assessments may limit the accuracy of sarcopenia diagnosis and the evaluation of its severity, thereby constituting limitations of this study.

Conclusion

Sarcopenia exhibits a relatively high prevalence among elderly individuals in China, emerging as a primary degenerative disease that poses a substantial threat to the health and quality of life of the aging population. The study employed the InBody analyzer, focusing on body composition analysis, to delineate the key influencing factors of sarcopenia. This study furnishes a theoretical foundation for the clinical prevention or treatment of sarcopenia, contributing to enhanced management strategies for individuals with sarcopenia. Moreover, the practicality and safety of the InBody make it a viable candidate for promotion in community settings and even larger hospital facilities. Subsequent research endeavors could contemplate conducting a comparative study between Bioelectrical Impedance Analysis (BIA) and the gold standard for body composition analysis, Dual-Energy X-ray Absorptiometry (DXA), to thoroughly validate the accuracy of BIA. Owing to the merits of BIA, including its non-radiative nature and capacity for repeated measurements, consideration could be given to substituting DXA with BIA as an innovative detection method.

Footnotes

Acknowledgements

This work was supported by the General Medicine Department of Tongji Medical College Affiliated Union Hospital, Huazhong University of Science and Technology, Wuhan, China.

Author contribution

Shaotian Li proposed the main research objectives, took responsibility for conceptualizing, designing, and implementing the research, and authored the paper. Jingfeng Zou, Liping Wang, Guqiao Nie were responsible for data collection, organization, statistical analysis, as well as the creation of charts and graphs. Wen Peng was responsible for quality control and review of the article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical statement

This study was approved by the Medical Ethics Committee of Union Hospital Affiliated with Tongji Medical College, Huazhong University of Science and Technology. Ethics number: 0906–01.

Note: Considering the rigor of ethical standards in this study, ethical approval was sought from the Medical Ethics Committee of Union Hospital Affiliated with Tongji Medical College, Huazhong University of Science and Technology, and obtained ethical approval number.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Informed consent

Our study is a retrospective research, and therefore, the subjects do not need to directly participate or provide any additional information. No additional intervention or interaction is required. Therefore, we obtained verbal informed consent from the research subjects, which was approved.The data for this research topic are sourced from the hospital's inpatient medical record system. We will strictly maintain the confidentiality of subjects’ personal privacy and data information, ensuring that all data is treated with utmost confidentiality.

Note: In this study, verbal informed consent of the participants was obtained with approval from the Medical Ethics Committee of Union Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology. We recorded the informed consent process using a voice recorder or smartphone. Therefore, we obtained verbal informed consent from the research subjects, which was approved.

ORCID iD

Shaotian Li

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