Analysis of prevalence and associated biomarkers of sarcopenia living in rural community older adults in Wuhan, China

Analysis of prevalence and associated biomarkers of sarcopenia living in rural community older adults in Wuhan, China | 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 Article Analysis of prevalence and associated biomarkers of sarcopenia living in rural community older adults in Wuhan, China Yun Zhou, Kemeng Zhang, Yanling Zhang, Ping He, Zhaohui Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4814100/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Sarcopenia is a syndrome of loss of muscle mass and decreased skeletal muscle function with impaired ability in the activities of daily life and cause some adverse consequences in the elderly. In China, where the aging trend is obvious, the incidence of sarcopenia is increasing. Exploring potential biomarkers for sarcopenia may lead to early screening and intervention for sarcopenia.This study investigated the prevalence and potential biomarkers of sarcopenia in older adult living in rural community in Wuhan,China. Methods: This cross-sectional study involved 236 older participants (age ≥65 years) who received a health examination that included body composition and 23 circulating biomarkers.Sarcopenia was defined by the Asian Working Group for Sarcopenia revised in 2019 (AWGS2019). We divided the participants into a non-sarcopeniagroup and a sarcopenia group. The correlation between biomarkers and sarcopenia was analyzed by independent sample t -test, and then the significant variables of the t -test ( p < 0.05) were included in the multivariate logistic regression model to determine the independent factors associated with sarcopenia. Results: Among the 236 participants, 92 were men and 144 were females, with a mean age of 70.6 ± 4.4years. The prevalence of sarcopenia in rural community was 25.4%(men 20.7%, women 28.5%). Analyses were conducted using multivariate logistic regression，growth differentiation factor 11(GDF11), was an independent risk factor for sarcopenia [Exp (B) 1.031, 95% CI: 1.010-1.052, p =0.003]. However, body mass index, albumin（ALB）, fibroblast growth factor 19(FGF19), and tumour necrosis factor alpha(TNF-α ) were independent protective factors for sarcopenia [BMI: Exp (B) 0.007, 95% CI: 0.000-0.244, p =0.006；ALB: Exp (B) 0.490, 95% CI: 0.281-0.853， p =0.012; FGF19: Exp(B) 0.804, 95% CI: 0.683-0.946, p =0.009; TNF-α: Exp (B) 0.379, 95% CI: 0.194-0.742, p =0.005]. Conclusions: About a quarter of elderly people in rural Chinese communities are at risk of sarcopenia. Lower BMI, lower serum ALB, FGF19, TNF-α, and higher circulating GDF11 are associated with sarcopenia. Health sciences/Diseases Health sciences/Medical research Health sciences/Risk factors Sarcopenia Disease incidence Biomarkers，Elderly Rural community Background Sarcopenia is a geriatric syndrome characterized by age-related decline in skeletal muscle mass and muscle strength or physical function [ 1 ] . According to the national statistics of China in 2021, the elderly population aged 65 and above has increased to 190 million, accounting for about 13.5% of the total population [ 2 ] . China is facing severe challenges related to population aging. Chronic diseases such as age-related sarcopenia have a huge impact on family medical burden and social public health expenditure. At present, there are limited epidemiological information regarding sarcopenia, especially those on the elderly living in the rural community of China [ 3 , 4 ] . The etiology and pathogenesis of sarcopenia are complex and varied, and there may be multiple interactions, such as neuromuscular degeneration, chronic inflammation, oxidative stress response and changes in the secretion of anabolic hormones [ 5 , 6 ] . Currently, standardized diagnostic criteria for sarcopenia have not been determined internationally, and different populations, measuring devices and means have a great impact on the diagnosis of sarcopenia.However, exploring biomarkers related to sarcopenia can quickly and economically support the diagnosis of sarcopenia. Therefore, people's interest in exploring biomarkers related to sarcopenia is gradually increasing. Although some biomarkers, such as albumin, inflammatory factor IL-6, growth differentiation factor 11, and recently the emerging fibroblast growth factor 19 (FGF19), have been considered as biomarkers related to sarcopenia, the types and mechanisms of these biomarkers have not been clearly defined and accepted internationally [ 16 , 17 ] .Some studies have shown that chronic inflammation associated with aging may cause imbalance of protein synthesis and metabolism, leading to muscle loss [ 18 – 20 ] ,but there are still other studies with contradictory results [ 21 ] Therefore, we hope to help identify biomarkers associated with sarcopenia through our study. In this study, a total of 23 biomarkers that may be related to the pathogenesis of sarcopenia were analyzed. The main objective of this study was to estimate the prevalence of sarcopenia among the elderly in rural China and to search for potentially reliable biomarkers of sarcopenia that could contribute to the prevention, diagnosis and treatment of sarcopenia. Methods Participants and study design This study is a single-center rural community-based cross-sectional study.The subjects of our study were residents of Huangling Community and Fenghuang Yuan Community in Wuhan, China. From September 26, 2020 to October 30, 2020, the elderly ≥ 65 years old participated in the routine health examination in Junshan Health Center of Huangling Community, Wuhan. Participants were excluded from the study if they were: (1) unable to communicate with the investigator or refused to sign the informed consent; (2) the participants were unable to complete a series of tests such as height, weight, body composition, grip strength and 6-meter walking speed test; (3) having a physical disability. The total number of people who underwent health examinations in 2020 was 571. Based on the exclusion criteria established, the final 236 participants（92 men and 144 women) met the inclusion criteria.For the sample size calculation of this cross-sectional study, the estimated prevalence of sarcopenia was 13.5%, α=0.05, precision 10%, the interval to be used will be a two-sided confidence interval, The sample size was calculated by PASS 11 software, N=198. Considering the 10% loss rate, at least 220 patients should be included in this study. According to our set into the exclusion criteria, this study finally included in the number of cases to 236. The protocol of this study was approved by the ethics committee of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology (UHCT-IEC-SOP-016-03-01) and followed the tenets of the Helsinki Declaration. Body composition We use the 8- contact electrode bioelectrical impedance analysis (BIA) device (BCA-2A, TFHT, Beijing, China) to measure the body composition. A multi-frequency electrical impedance body composition analysers, the BCA-2A uses a quadrupole 8-point haptic electrode system with five different measurement frequencies (5, 50, 100, 250 and 500 kHz). Skeletal muscle content (ASM) of extremities is the sum of skeletal muscle content of both upper and lower limbs [7] . Skeletal muscle mass normalized for height (RASM, ASM/Ht 2 ) is defined as the ratio of ASM (kg) to height square (m 2 ) [8] . The ASM/Ht 2 < 7.0kg/m 2 in men or < 5.7kg/m 2 in women is diagnosed as skeletal muscle loss. Handgrip strength Hand grip strength was measured using the standard Adjustable Digital Grip Strength Tester (CSTF-WL, TFHT, Beijing, China). Participants were asked to perform two maximum grip strength tests with their favorable hand for data analysis with the best results [9,10] . The criteria for low grip strength is less than 28kg for men or less than 18kg for women. Gait speed A digital walking speed tester (CSTF-6MBS, TFHT, Beijing, China) was used to measure walking speed (m/s). Participants were asked to perform the test at their usual walking speed on a 6-meter track. If participants preferred using a walker, they were allowed to use a walker during the test. Slow walking speed is defined as a walking speed < 1.0m/s [11] . Anthropometry Height and weight were measured using a digital height and weight tester (CSTF-ST, TFHT, Beijing, China). Participants were asked to remove their shoes during measurement. Body mass index (BMI) was calculated by dividing their weight by the square of their height (kg/m 2 ). Definition of sarcopenia We identified patients with sarcopenia by using the AWGS2019 sarcopenia consensus. The diagnosis of sarcopenia requires ASM/Ht 2 < 7.0 kg/m 2 in male and < 5.7 kg/m 2 in female; grip strength < 28kg in male and < 18kg in female and/or gait speed < 1.0m/s [11] . Laboratory testing Fasting blood samples were collected by venipuncture and centrifuged within 1 hour after sampling. 15 parameters including ALT, AST, DBIL, TBIL, ALB, ALP, γ-GT, CREA, UA, GLU, TG, TC, HDL, LDL and GSP were analyzed. 8 biomarkers including ADP, FGF19, GDF11, GDF15, IGF-1, TNF-α, IL-6 and DHEA were determined by human enzyme-related immunosorbent assay kit (ELISA) (ELK Biotechnology CO., LTD). Statistical analysis SPSS 25.0 software was used to analyze the data. Continuous variables and categorical variables were expressed as mean ± SD and percentage, respectively. Independent sample t-test was used for comparing continuous variables,Use tˊ-test when the variance of variables is Unequal or does not follow a normal distribution.,and Chi-square test was used for figuring out the difference among categorical variables. The independent association factors of sarcopenia were analyzed by multiple logistics regression analysis, and the statistical test was two-tail test. p<0.05 was considered to be statistically significant. Result Clinical characteristics The participant characteristics are shown in Table 1. During the data collection period from September 2019 to June 2020, a total of 236 elderly people (aged ≥65 years) from rural communities in Wuhan participated in the sarcopenia screening program, including 92 males and 144 females. The average baseline physical fitness measurements of participants were as follows: Age: 70.6 ± 4.4 years; BMI: 23.8 ± 3.4kg/m 2 ; walking speed: 0.86 ± 0.19m/s; grip strength: 23.0 ± 8.3kg; upper limb fat content: 2.54 ± 0.98kg; lower limb fat content: 6.09 ± 1.85kg; upper limb muscle content: 4.22 ± 1.08kg; The lower extremity muscle content was 12.81 ± 3.37kg, body fat percentage was 28.8 ± 6.3%, and ASM/Ht 2 was 6.82 ± 1.39kg/m 2 . A total of 60 patients were diagnosed with sarcopenia according to the 2019AWGS diagnostic criteria, including 19 males and 41 females. The prevalence of sarcopenia was 25.4%. The prevalence was 20.7% in elderly men and 28.5% in elderly women. Variables Associated With Sarcopenia In Rural Community Older Adults The mean BMI, grip Strength and body fat percentage of participants in sarcopenia group were significantly lower than those in non-sarcopenia group, (BMI, non-sarcopenia group: 25.1± 2.7, sarcopenia group: 20.1 ± 1.8, p < 0.001), (handgrip strength, non-sarcopenia group: 24.0 ± 8.8, sarcopenia group: 20.0 ± 6.0, p < 0.001) (body fat percentage, non-sarcopenia group:30.1 ± 5.9, sarcopenia group: 24.7 ± 5.8, p < 0.001), and the fat content and muscle content of limbs in sarcopenia group were significantly lower than those in non-sarcopenia group. There were no significant differences in age, sex and walking speed between sarcopenia group and non-sarcopenia groups ( p > 0.05). The circulating biomarker tests of the participants are shown in Table 2. t -test or t ˊ-test analysis revealed differences in circulation markers between older subjects with and without sarcopenia. Compared with circulating factors in non-sarcopenia elderly subjects, FGF19 and GDF11 were significantly higher. The circulating factors in sarcopenia group were significantly decreased: ALT, AST, ALB, CREA, UA, GLU, TG, LDL, GDF15, TNF-α and IL6. There were no significant differences in other circulating factors (DBIL, TBIL, ALP, γ-GT, TC, HDL, GSP, ADP, IGF-1 and DHEA) between non-sarcopenia and sarcopenia groups ( p ≥ 0.05). Independent Factors Associated With Sarcopenia In Rural Community Older Adults In order to further elaborate the related factors of sarcopenia, We included the 14 significantly variables BMI,FGF19 ,GDF11 ,ALT, AST, ALB, CREA, UA, GLU, TG, LDL, GDF15, TNF-α and IL6 from the above univariate analysis into the multivariate analysis,an analysis of multiple determinants of the sarcopenia was conducted using a stepwise conditional logistic regression with p < 0.05 (Table3). The results showed that GDF11 was an independent risk factor for sarcopenia. BMI, ALB, FGF19 and TNF-α were independent protective factors (Table3). In addition, LDL was not an independent factor in the development of sarcopenia (p > 0.05). Discussion In this study, our main finding was that the prevalence of sarcopenia in rural elderly in China was 25.4%, in which 20.7% for men and 28.5% for women. The subjects with sarcopenia had a lower BMI than the non-sarcopenic older subjects. There were no significant differences in age, sex, or walking speed between non-sarcopenic and sarcopenic subjects. Compared with non-sarcopenic subjects, sarcopenic subjects had higher levels of GDF11 and lower levels of ALB, FGF19, and TNF-α. In addition, inflammatory cytokine IL-6 and β-superfamily cytokine GDF15 are not independent factors of sarcopenia. In recent years, epidemiological survey results of sarcopenia in Chinese population show that the prevalence rate of sarcopenia in elderly people aged 60 and above is 3.1%-62.9% [ 12 ] . A study in Taiwan showed that the prevalence of sarcopenia was 18.6% in 302 aged 65 and older men and 23.0% in women, which is similar to our results [ 13 ] . A New Mexico senior health survey found that the prevalence of sarcopenia in older men and women was 28.5% and 33.9%, respectively [ 14 ] . However, other studies have reported low prevalence of sarcopenia, and differences in prevalence results may be due to differences in the definition of sarcopenia, diagnostic cut-off values, or ethnic background of the population. BMI, waist circumference, and/or waist-to-hip ratio are commonly used to define obesity, and BMI in older adults needs to be interpreted with caution because of reduced physiological height and lack of correlation between BMI and body fat percentage, distribution, or body composition in older adults.That is why we discussed here is just sarcopenia and non-sarcopenia groups of relatively high low BMI, and overweight and obese relations with less muscle disease needs further research.Our finding that a lower BMI is a risk factor for sarcopenia is consistent with the findings of multiple previous studies suggesting that the higher the BMI in older adults, the lower the risk of sarcopenia [ 15 , 16 ] .While obesity is considered a risk factor for many adverse outcomes, in older adults, being slightly overweight may be beneficial. Studies have also shown that older men are resistant to the dangers of overweight and obesity; Mild overweight, obesity, and even central obesity were beneficial for survival [ 17 ] .Similarly, a study of hospitalized older adults found that fat mass was associated with a lower risk of death or complications [ 18 ] .In addition, studies have shown that BMI is positively correlated with muscle mass and fat mass [ 19 – 21 ] .It has been pointed out that fat is an energy store for the elderly and helps individuals survive in disease or poor physical condition. The amount of fat can affect lean body mass for several ages, and people with high fat amount may have a higher protein intake, which is a protective mechanism against muscle loss [ 22 , 23 ] .Given this, we believe that high BMI may play a protective role against loss of muscle mass and strength in older adults. The results of this study further support the negative association between BMI and sarcopenia, so maintaining a healthy BMI in older adults may help maintain muscle mass and strength. Studies have shown that the serum albumin of the elderly with sarcopenia is lower than that of the non-sarcopenia elderly participants [ 24 , 25 ] . A meta-study of sarcopenia involving 4,904 community-living and institutional older adults (68-87.6 years) from 9 countries and another meta-analysis of 4,071 participants from 5 countries showed that regardless of age and setting, Albumin is negatively associated with sarcopenia, and given that there is no conclusive evidence that serum albumin can be definitively used as a biomarker for sarcopenia, which is easier and less costly to implement than other approaches and ensures timely assessment and early intervention in elderly patients with sarcopenia, Therefore, serum albumin decline can be considered as a reference indicator for biomarkers of sarcopenia. However, albumin concentrations are also affected by processes such as inflammation and infection, so there is no consensus on optimal limits and reference ranges for serum values to assess nutritional status in older adults [ 26 – 28 ] . Albumin is the most abundant plasma protein in the body, and its basic role is to regulate the passage of water and solutes through capillaries by maintaining colloidal pressure in the vascular system. Albumin has long been considered integral to the assessment of nutritional status, it is considered to be a very important plasma protein in the assessment of nutritional status, reducing it can alter wound healing, cause immune problems, and reduce lean body mass [ 29 ] . Improving the nutritional status of the elderly can increase the serum albumin content, which may play a certain role in the treatment of sarcopenia in the elderly. In elderly patients with sarcopenia, the level of GDF11 was higher in the sarcopenia group than in the non-sarcopenia group, while GDF15 was not statistically different between the two groups. GDF11 and GDF15 are members of the transforming growth factor β superfamily of cytokines, and GDF11 is closely related to myostatin (MSTN). GDF15 is also known as MIC-1 [ 30 ] . However, some scholars prefer to believe that GDF15 belongs to the glial cell derived neurotrophic factor (GDNF) -like growth factor or cytokine family, because GDF15 receptor GFRAL is a co-receptor of Ret tyrosine kinase [ 31 , 32 ] . Multiple studies have suggested that GDF11 inhibits skeletal muscle regeneration [ 33 – 35 ] . In a recent study, Juli E. Jones found that elevated GDF11 could induce the expression of oxia in mice with decreased muscle mass, food intake and body weight, and that GDF11 could also up-regulate plasma GDF15. Blocking the GDF15 receptor alleviates anorexia but does not reduce muscle loss, whereas blocking the GDF11 receptor ActR II prevents muscle loss and reduces anorexia [ 33 ] . Therefore, elevated GDF11 can be considered as an independent predictor of sarcopenia. We also found that FGF19 was lower in the sarcopenia group than in the non-sarcopenia group. FGF19 is a member of the FGF family, a family of proteins involved in differentiation, development and metabolism [ 36 ] . FGF19 is similar to endocrine hormone and is secreted by ileum intestinal epithelial cells. The pathogenesis of sarcopenia is related to skeletal muscle metabolism, and FGF19 has been found to play a role in muscle metabolism [ 37 ] . Therefore, FGF19 may be an emerging factor for the treatment of sarcopenia, and can contribute to the diagnosis and prevention of sarcopenia. The negative association between FGF19 and sarcopenia was also confirmed by a recent cross-sectional study in Turkey of 88 elderly outpatient participants aged 65 years or older [ 38 ] . A number of literatures have shown that sarcopenia is related to inflammatory cytokines, but the existing studies are controversial on whether inflammatory factors play a positive or negative role in sarcopenia. Studies have shown that chronic inflammation leads to a loss of muscle mass by affecting protein synthesis and catabolism. However, most of the people in these studies were obese or had other chronic conditions, so the role of inflammatory factors in sarcopenia remains controversial [ 39 ] . In a recent study, 299 Japanese residents (127 males and 172 females) participated in urban health check-ups. It was found that IL-6 is not an independent factor in sarcopenia [ 40 ] . This conclusion is similar to ours. A recent community sarcopenia study in Taiwan also found no significant correlation between serum IL-6 levels and ASMI, grip strength, or gait speed [ 41 ] . IL-6 is an effective regulator of human fat metabolism, which can increase lipolysis and fat oxidation. IL-6 acts in an anti-inflammatory way during muscle contraction. Because IL-6 has pro-inflammatory and anti-inflammatory effects, and the IL-6 secreted into the blood cannot be determined whether it is derived from muscle tissue [ 42 ] . The role of TNF-α played in sarcopenia remains controversial. Results in this study demonstrated that a low TNF-α level in sarcopenic old adults. In vitro studies have shown that TNF-α has a direct inhibitory effect on insulin signaling and can cause insulin resistance in skeletal muscle, thereby increasing free fatty acids and causing chronic inflammation. A clinical study showed the level of TNF-α in the elderly sarcopenia group was significantly lower than that in the non-sarcopenia group. In multiple logistic regression analysis, low level of TNF-α was identified as an independent risk factor for sarcopenia [ 40 , 43 ] . Further research is needed to better understand the role of TNF-α in sarcopenia. There are some limitations to our study. First, because the participants came from rural communities, most of whom may have different occupations and living standards than older people in urban environments, the study has population limitations. Secondly, there are some unmeasured or unknown confounding factors that may affect the results of the study. Sarcopenia is an emerging and complex syndrome, and its influencing factors and pathogenesis are complex and diverse. Therefore, it is impossible to make a definitive diagnosis of sarcopenia through a single serum marker. Another, the lack of investigation of the clinical characteristics of the participants with noncommunicable diseases is also a limitation of this study, and the noncommunicable diseases of the participants may also affect the conclusions of the study. In addition, this is a cross-sectional, single-center study that cannot definitively establish a causal relationship between serum biomarkers and sarcopenia. Further longitudinal and multi-center collaborative studies are needed to confirm our results. Conclusion This study shows that the prevalence of sarcopenia in rural elderly in China is 25.4%, 20.7% for men and 28.5% for women. BMI, serum ALB, FGF19, and TNF-α levels were lower in the elderly population with sarcopenia, while GDF11 was increased in the serum of patients with sarcopenia. Although this study cannot draw a cause-and-effect relationship, our results may help to identify reliable biomarkers related to sarcopenia that could benefit the early detection, diagnosis, treatment, and prevention of sarcopenia. Declarations Ethics approval and consent to participate All participants signed a written informed consent prior to enrollment.It was approved by the Ethics committee of Union Hospital, Tongji Medical College, and Huazhong University of Science and Technology. It followed the tenets of the Helsinki Declaration in conducting this study. Consent for publication Not applicable. Availability of data and materials The datasets used and / or analyzed in the current study are available from the corresponding authors,without improper reservations. Competing interests The authors declare that they have no competing interest. Funding This research was supported by the National Key Research and Development Programs of China (No. 2018YFC2002100, 2018YFC2002102, and 2020YFC2006000). Authors' contributions ZHW and PH has contributed to the design of this study.YZ performed statistical analysis and wrote the first draft. YZ, PH, KMZ and YLZ participated in patient examination and data collection. YZ, ZHW and PH performed laboratory analysis. All authors approved the final version of the manuscript. Acknowledgements Not applicable. Ethics Statement All participants signed a written informed consent prior to enrollment.It was approved by the Ethics committee of Union Hospital, Tongji Medical College, and Huazhong University of Science and Technology. It followed the tenets of the Helsinki Declaration in conducting this study. Authors’ contributions ZHW and PH has contributed to the design of this study.YZ performed statistical analysis and wrote the first draft. YZ, PH, KMZ and YLZ participated in patient examination and data collection. YZ, ZHW and PH performed laboratory analysis. All authors approved the final version of the manuscript. Funding This research was supported by the National Key Research and Development Programs (No. 2018YFC2002100, 2018YFC2002102, and 2020YFC2006000). Availability of data and materials The datasets used and / or analyzed in the current study are available from the corresponding authors,without improper reservations. Conflict of Interest The authors declare that they have no competing interest. References Morley J E, Abbatecola A M, Argiles J M, et al. Sarcopenia with limited mobility: an international consensus. J Am Med Dir Assoc, 2011,12(6):403-409. National Bureau of Statistics of China. China Statistical Yearbook. China Statistics Press; 2021 Gallagher D, DeLegge M. Body composition (sarcopenia) in obese patients: implications for care in the intensive care unit. JPEN J Parenter Enteral Nutr, 2011,35(5 Suppl):21S-28S. Janssen I, Shepard D S, Katzmarzyk P T, et al. The healthcare costs of sarcopenia in the United States. J Am Geriatr Soc, 2004,52(1):80-85. Cruz-Jentoft A J, Sayer A A. Sarcopenia. Lancet, 2019,393(10191):2636-2646. Curcio F, Ferro G, Basile C, et al. Biomarkers in sarcopenia: A multifactorial approach. Exp Gerontol, 2016,85:1-8. Heymsfield S B, Smith R, Aulet M, et al. Appendicular skeletal muscle mass: measurement by dual-photon absorptiometry. Am J Clin Nutr, 1990,52(2):214-218. Janssen I, Baumgartner R N, Ross R, et al. Skeletal muscle cutpoints associated with elevated physical disability risk in older men and women. Am J Epidemiol, 2004,159(4):413-421. Han P, Kang L, Guo Q, et al. Prevalence and Factors Associated With Sarcopenia in Suburb-dwelling Older Chinese Using the Asian Working Group for Sarcopenia Definition. J Gerontol A Biol Sci Med Sci, 2016,71(4):529-535. Zhang W, Shen S, Wang W, et al. Poor lower extremity function was associated with pre-diabetes and diabetes in older chinese people. PLoS One, 2014,9(12):e115883. Chen L K, Woo J, Assantachai P, et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J Am Med Dir Assoc, 2020,21(3):300-307. Chen Z, Li W Y, Ho M, et al. The Prevalence of Sarcopenia in Chinese Older Adults: Meta-Analysis and Meta-Regression. Nutrients, 2021,13(5). Chien M Y, Huang T Y, Wu Y T. Prevalence of sarcopenia estimated using a bioelectrical impedance analysis prediction equation in community-dwelling elderly people in Taiwan. J Am Geriatr Soc, 2008,56(9):1710-1715. Baumgartner R N, Koehler K M, Gallagher D, et al. Epidemiology of sarcopenia among the elderly in New Mexico. Am J Epidemiol, 1998,147(8):755-763. Wu L C, Kao H H, Chen H J, et al. Preliminary screening for sarcopenia and related risk factors among the elderly. Medicine (Baltimore), 2021,100(19):e25946. Linge J, Heymsfield S B, Dahlqvist L O. On the Definition of Sarcopenia in the Presence of Aging and Obesity-Initial Results from UK Biobank. J Gerontol A Biol Sci Med Sci, 2020,75(7):1309-1316. Auyeung T W, Lee J S, Leung J, et al. Survival in older men may benefit from being slightly overweight and centrally obese--a 5-year follow-up study in 4,000 older adults using DXA. J Gerontol A Biol Sci Med Sci, 2010,65(1):99-104. Bouillanne O, Dupont-Belmont C, Hay P, et al. Fat mass protects hospitalized elderly persons against morbidity and mortality. Am J Clin Nutr, 2009,90(3):505-510. Woo J, Leung J, Kwok T. BMI, body composition, and physical functioning in older adults. Obesity (Silver Spring), 2007,15(7):1886-1894. Beavers K M, Beavers D P, Houston D K, et al. Associations between body composition and gait-speed decline: results from the Health, Aging, and Body Composition study. Am J Clin Nutr, 2013,97(3):552-560. Yu R, Wong M, Leung J, et al. Incidence, reversibility, risk factors and the protective effect of high body mass index against sarcopenia in community-dwelling older Chinese adults. Geriatr Gerontol Int, 2014,14 Suppl 1:15-28. Kuk J L, Ardern C I. Influence of age on the association between various measures of obesity and all-cause mortality. J Am Geriatr Soc, 2009,57(11):2077-2084. Cheng Q, Zhu X, Zhang X, et al. A cross-sectional study of loss of muscle mass corresponding to sarcopenia in healthy Chinese men and women: reference values, prevalence, and association with bone mass. J Bone Miner Metab, 2014,32(1):78-88. Picca A, Coelho-Junior H J, Calvani R, et al. Biomarkers shared by frailty and sarcopenia in older adults: A systematic review and meta-analysis. Ageing Res Rev, 2022,73:101530. Silva-Fhon J R, Rojas-Huayta V M, Aparco-Balboa J P, et al. Sarcopenia and blood albumin: A systematic review with meta-analysis. Biomedica, 2021,41(3):590-603. Cabrerizo S, Cuadras D, Gomez-Busto F, et al. Serum albumin and health in older people: Review and meta analysis. Maturitas, 2015,81(1):17-27. Keller U. Nutritional Laboratory Markers in Malnutrition. J Clin Med, 2019,8(6). Zhang Z, Pereira S L, Luo M, et al. Evaluation of Blood Biomarkers Associated with Risk of Malnutrition in Older Adults: A Systematic Review and Meta-Analysis. Nutrients, 2017,9(8). Smith S H. Using albumin and prealbumin to assess nutritional status. Nursing, 2017,47(4):65-66. Nakashima M, Toyono T, Akamine A, et al. Expression of growth/differentiation factor 11, a new member of the BMP/TGFbeta superfamily during mouse embryogenesis. Mech Dev, 1999,80(2):185-189. Emmerson P J, Wang F, Du Y, et al. The metabolic effects of GDF15 are mediated by the orphan receptor GFRAL. Nat Med, 2017,23(10):1215-1219. Hsu J Y, Crawley S, Chen M, et al. Non-homeostatic body weight regulation through a brainstem-restricted receptor for GDF15. Nature, 2017,550(7675):255-259. Jones J E, Cadena S M, Gong C, et al. Supraphysiologic Administration of GDF11 Induces Cachexia in Part by Upregulating GDF15. Cell Rep, 2018,22(6):1522-1530. Egerman M A, Cadena S M, Gilbert J A, et al. GDF11 Increases with Age and Inhibits Skeletal Muscle Regeneration. Cell Metab, 2015,22(1):164-174. Ma Y, Liu Y, Han F, et al. Growth differentiation factor 11: a "rejuvenation factor" involved in regulation of age-related diseases?[J]. Aging (Albany NY), 2021,13(8):12258-12272. Ornitz D M, Itoh N. The Fibroblast Growth Factor signaling pathway. Wiley Interdiscip Rev Dev Biol, 2015,4(3):215-266. Tomlinson E, Fu L, John L, et al. Transgenic mice expressing human fibroblast growth factor-19 display increased metabolic rate and decreased adiposity. Endocrinology, 2002,143(5):1741-1747. Bag S R, Suzan V, Arman P, et al. Association of FGF-19 and FGF-21 levels with primary sarcopenia. Geriatr Gerontol Int, 2021,21(10):959-962. Pan L, Xie W, Fu X, et al. Inflammation and sarcopenia: A focus on circulating inflammatory cytokines. Exp Gerontol, 2021,154:111544. Ito S, Nakashima H, Ando K, et al. Association between Low Muscle Mass and Inflammatory Cytokines. Biomed Res Int, 2021,2021:5572742. Liu H C, Han D S, Hsu C C, et al. Circulating MicroRNA-486 and MicroRNA-146a serve as potential biomarkers of sarcopenia in the older adults. BMC Geriatr, 2021,21(1):86. Pedersen B K, Febbraio M A. Muscle as an endocrine organ: focus on muscle-derived interleukin-6. Physiol Rev, 2008,88(4):1379-1406. Brandt C, Pedersen B K. The role of exercise-induced myokines in muscle homeostasis and the defense against chronic diseases. J Biomed Biotechnol, 2010,2010:520258. Tables Table 1 Demographics characteristics and the index of body examination of 236 living in rural community older adults in Wuhan, China Variable Total （ n =236） Non-sarcopenia （ n =176） Sarcopenia （ n =60） t /χ 2 P -value Age (years) 70.6±4.4 70.5±4.3 70.7±4.9 -0.345 0.731 Male / Female 92/144 73/103 19/41 1.811 0.178 BMI (kg/m 2 ) 23.8±3.4 25.1±2.7 20.1±1.8 16.021 ＜ 0.001 * GS (m/s) 0.86±0.19 0.87±0.20 0.82±0.17 1.694 0.092 HS (kg) 23.0±8.3 24.0±8.8 20.0±6.0 3.850 ＜ 0.001 * ASM/Ht 2 (kg/m 2 ) 6.81±1.38 7.28±1.21 5.41±0.81 13.503 ＜ 0.001 * Upper limb fat ( kg) 2.54±0.98 2.79±0.80 1.84±1.12 7.152 ＜ 0.001 * Lower limb fat (kg) 6.09±1.85 6.72±1.60 4.24±1.19 11.032 ＜ 0.001 * Upper limb muscles (kg) 4.22±1.08 4.59±0.95 3.15±0.63 13.229 ＜ 0.001 * Lower limb muscles (kg) 12.81±3.37 13.77±3.17 10.01±2.17 10.205 ＜ 0.001 * PBF (%) 28.8±6.3 30.1±5.9 24.7±5.8 6.148 ＜ 0.001 * BMI: body mass index, HS: handgrip strength, GS: gait speed, ASM/Ht 2 : appendicular skeletal muscle mass, appendicular soft lean mass/(height) 2 , PBF: percentage body fat. Table 2 Biomarker characteristics of 236 participants living in rural community older adults in Wuhan,China Variable Non-sarcopenia Sarcopenia t P -value ALT (U/L) 18.84±11.72 12.57±6.69 5.074 ＜ 0.001* AST (U/L) 25.08±9.56 22.42±6.94 1.984 0.048* DBIL (μmol/L) 9.82±2.72 9.75±2.67 0.185 0.853 TBIL (μmol/L) 11.80±3.50 11.83±3.41 -0.720 0.943 ALB (g/L) 36.60±3.68 35.45±4.00 2.047 0.042* ALP (U/L) 80.82±26.66 79.43±28.67 0.342 0.732 γ-GT (U/L) 33.15±35.95 25.66±35.12 1.400 0.163 CREA(μmol/L) 64.81±25.57 57.94±14.38 1.976 0.049* UA (μmol/L) 278.49±79.24 239.54±77.71 3.304 0.001* GLU (mmol/L) 4.58±1.02 4.29±0.92 1.990 0.048* TG (mmol) 0.99±0.52 0.76±0.40 3.111 0.002* TC (mmol) 4.14±0.79 4.06±0.77 0.719 0.473 HDL (mmol) 1.04±0.29 1.10±0.35 -1.325 0.186 LDL (mmol) 2.65±0.71 2.44±0.72 2.005 0.046* GSP (mmol/L) 1.81±0.30 1.85±0.30 -0.884 0.377 ADP (μg/mL) 9.46±2.70 9.34±1.76 0.402 0.688 FGF19 (pg/mL) 111.08±25.09 124.49±18.75 -4.367 ＜ 0.001* GDF11 (ng/mL) 191.97±142.53 417.33±214.55 -7.586 ＜ 0.001* GDF15 (ng/mL) 0.75±0.48 0.54±0.35 3.153 0.002* IGF-1 (ng/mL) 383.80±96.33 376.55±62.43 0.668 0.505 TNFa (pg/mL) 25.00±13.06 16.18±7.22 6.508 ＜ 0.001* IL6 (pg/mL) 5.59±3.20 3.19±2.64 5.221 ＜ 0.001* DHEA (ng/mL) 574.74±193.38 550.28±246.49 0.699 0.487 ALT: alanine aminotransferase, AST: aspartate aminotransferase, DBIL: direct bilirubin, TBiL: total bilirubin, ALB: albumin, ALP: alkaline - phosphatase, γ-GT: gamma - glutamyl transferase, CREA: creatinine, UA: uric acid, GLU: glucose, TG: triglycerides, TC: total cholesterol, HDL: high-density lipoprotein, LDL: low-density lipoprotein, GSP: glycosylated serum protein, ADP: adiponectin, FGF19: fibroblast growth factor 19, GDF11: Growth differentiation factor 11, GDF15: growth differentiation factor 15, IGF - 1: insulin-like growth factor 1, TNF-α: tumour necrosis factor alpha, (IL-6): interleukin 6, DHEA: dehydroepiandrosterone. Table 3 Models of logistic regression were used to identify factors associated with sarcopenia living in rural community older adults in Wuhan, China B SE Wald df p Exp(B) 95%CI BMI -5.015 1.839 7.437 1 0.006 0.007 0.000-0.244 ALB -0.714 0.283 6.350 1 0.012 0.490 0.281-0.853 LDL 1.709 0.889 3.699 1 0.054 5.522 0.968-31.508 FGF19 -0.218 0.083 6.875 1 0.009 0.804 0.683-0.946 GDF11 0.030 0.010 8.773 1 0.003 1.031 1.010-1.052 TNF-α -0.970 0.343 8.024 1 0.005 0.379 0.194-0.742 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4814100","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":344901766,"identity":"3e5b11f3-164a-423a-a106-48aa747b46da","order_by":0,"name":"Yun Zhou","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"Zhou","suffix":""},{"id":344901767,"identity":"6203eeca-f6cd-4f52-b5bd-5b2e9931cbcd","order_by":1,"name":"Kemeng Zhang","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Kemeng","middleName":"","lastName":"Zhang","suffix":""},{"id":344901768,"identity":"49e76db6-8603-486c-b218-d6deb2eda26d","order_by":2,"name":"Yanling Zhang","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yanling","middleName":"","lastName":"Zhang","suffix":""},{"id":344901769,"identity":"88f5699f-583e-4b28-8df7-bafe6cc02755","order_by":3,"name":"Ping He","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"He","suffix":""},{"id":344901771,"identity":"ce8e95cc-0821-4fd4-b027-0f10c1539337","order_by":4,"name":"Zhaohui Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYJACZgYDGzl+ZubDD0jQUpBmLNnOlmZAgpYPhxM3nOdRkCBKucGNHOPPBQbMiZsP8zAYMNTYRBOjxcB4hgGb8bbDvAceMBxLy20gpMXsdu6GZB4DHtlth/kSDBgbDhOn5TCPgQTj5mYQSaSWjUDFBoobmInVYn///Weg4gRjicPAQE4gxi+SPceSP/P8+S/H33/48IMPNTaEtaCCBNKUj4JRMApGwSjABQBSpD451ZbSkgAAAABJRU5ErkJggg==","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Zhaohui","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-07-27 17:31:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4814100/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4814100/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82681629,"identity":"b0a3c006-5234-4765-bc54-83caf401f9c1","added_by":"auto","created_at":"2025-05-14 06:02:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":855874,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4814100/v1/0f2a09d8-861b-41c5-a91d-3a49e9e34a05.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of prevalence and associated biomarkers of sarcopenia living in rural community older adults in Wuhan, China","fulltext":[{"header":"Background","content":"\u003cp\u003eSarcopenia is a geriatric syndrome characterized by age-related decline in skeletal muscle mass and muscle strength or physical function\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. According to the national statistics of China in 2021, the elderly population aged 65 and above has increased to 190\u0026nbsp;million, accounting for about 13.5% of the total population\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. China is facing severe challenges related to population aging. Chronic diseases such as age-related sarcopenia have a huge impact on family medical burden and social public health expenditure. At present, there are limited epidemiological information regarding sarcopenia, especially those on the elderly living in the rural community of China\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe etiology and pathogenesis of sarcopenia are complex and varied, and there may be multiple interactions, such as neuromuscular degeneration, chronic inflammation, oxidative stress response and changes in the secretion of anabolic hormones\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Currently, standardized diagnostic criteria for sarcopenia have not been determined internationally, and different populations, measuring devices and means have a great impact on the diagnosis of sarcopenia.However, exploring biomarkers related to sarcopenia can quickly and economically support the diagnosis of sarcopenia. Therefore, people's interest in exploring biomarkers related to sarcopenia is gradually increasing. Although some biomarkers, such as albumin, inflammatory factor IL-6, growth differentiation factor 11, and recently the emerging fibroblast growth factor 19 (FGF19), have been considered as biomarkers related to sarcopenia, the types and mechanisms of these biomarkers have not been clearly defined and accepted internationally\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.Some studies have shown that chronic inflammation associated with aging may cause imbalance of protein synthesis and metabolism, leading to muscle loss\u003csup\u003e[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e,but there are still other studies with contradictory results\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003eTherefore, we hope to help identify biomarkers associated with sarcopenia through our study. In this study, a total of 23 biomarkers that may be related to the pathogenesis of sarcopenia were analyzed.\u003c/p\u003e \u003cp\u003eThe main objective of this study was to estimate the prevalence of sarcopenia among the elderly in rural China and to search for potentially reliable biomarkers of sarcopenia that could contribute to the prevention, diagnosis and treatment of sarcopenia.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eParticipants and study design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a single-center rural\u0026nbsp;community-based cross-sectional study.The subjects of our study were residents of Huangling Community and Fenghuang Yuan Community\u0026nbsp;in\u0026nbsp;Wuhan, China. From September 26, 2020 to October 30, 2020, the elderly \u0026ge; 65 years old participated in the\u0026nbsp;routine\u0026nbsp;health examination in Junshan Health Center of Huangling Community, Wuhan. Participants were excluded from the study if they were: (1) unable to communicate with the investigator or refused to sign the informed consent; (2) the participants were unable to complete a series of tests such as height, weight, body composition, grip strength and 6-meter walking speed test; (3) having a physical disability. The total number of people who underwent health examinations in 2020 was\u0026nbsp;571. Based on the exclusion criteria established, the final 236 participants（92 men and 144 women) met the inclusion criteria.For the sample size calculation of this cross-sectional study, the estimated prevalence of sarcopenia was 13.5%,\u0026nbsp;\u0026alpha;=0.05, precision 10%, the interval to be used will be a two-sided confidence interval, The sample size was calculated by PASS 11 software, N=198. Considering the 10% loss rate, at least 220 patients should be included in this study. According to our set into the exclusion criteria, this study finally included in the number of cases to 236.\u0026nbsp;The protocol of this study was approved by the\u0026nbsp;ethics committee of Union\u0026nbsp;Hospital, Tongji Medical College, Huazhong University of Science and Technology\u0026nbsp;(UHCT-IEC-SOP-016-03-01)\u0026nbsp;and\u0026nbsp;followed\u0026nbsp;the tenets of the\u0026nbsp;Helsinki\u0026nbsp;Declaration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBody composition\u003c/strong\u003e\u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe use the 8- contact electrode bioelectrical impedance analysis (BIA) device (BCA-2A, TFHT, Beijing, China) to measure the body composition. A multi-frequency electrical impedance body composition analysers, the BCA-2A uses a quadrupole 8-point haptic electrode system with five different measurement frequencies (5, 50, 100, 250 and 500 kHz). Skeletal muscle content (ASM) of extremities is the sum of skeletal muscle content of both upper and lower limbs\u003csup\u003e[7]\u003c/sup\u003e. Skeletal muscle mass normalized for height (RASM, ASM/Ht\u003csup\u003e2\u003c/sup\u003e) is defined as the ratio of ASM (kg) to height square (m\u003csup\u003e2\u003c/sup\u003e)\u003csup\u003e[8]\u003c/sup\u003e. The ASM/Ht\u003csup\u003e2\u003c/sup\u003e \u0026lt; 7.0kg/m\u003csup\u003e2\u003c/sup\u003e in men or \u0026lt; 5.7kg/m\u003csup\u003e2\u003c/sup\u003e in women is diagnosed as skeletal muscle loss.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHandgrip strength\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHand grip strength was measured using the standard Adjustable Digital Grip Strength Tester (CSTF-WL, TFHT, Beijing, China). Participants were asked to perform two maximum grip strength tests with their favorable hand for data analysis with the best results\u003csup\u003e[9,10]\u003c/sup\u003e.\u0026nbsp;The criteria for low grip strength is less than 28kg for men or less than 18kg for women.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGait speed\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA digital walking speed tester (CSTF-6MBS, TFHT, Beijing, China) was used to measure walking speed (m/s). Participants were asked to perform the test at their usual walking speed on a 6-meter track. If participants preferred using a walker, they were allowed to use a walker during the test. Slow walking speed is defined as a walking speed \u0026lt; 1.0m/s\u003csup\u003e[11]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnthropometry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeight and weight were measured using a digital height and weight tester (CSTF-ST, TFHT, Beijing, China). Participants were asked to remove their shoes during measurement. Body mass index (BMI) was calculated by dividing their weight by the square of their height (kg/m\u003csup\u003e2\u003c/sup\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDefinition of sarcopenia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe identified patients with sarcopenia by using the AWGS2019 sarcopenia consensus. The diagnosis of sarcopenia requires ASM/Ht\u003csup\u003e2\u003c/sup\u003e \u0026lt; 7.0 kg/m\u003csup\u003e2\u003c/sup\u003e in male and \u0026lt; 5.7 kg/m\u003csup\u003e2\u0026nbsp;\u003c/sup\u003ein female; grip strength \u0026lt; 28kg in male and \u0026lt; 18kg in female and/or\u0026nbsp;gait\u0026nbsp;speed\u0026nbsp;\u0026lt; 1.0m/s\u003csup\u003e[11]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLaboratory\u0026nbsp;testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFasting blood samples were collected by venipuncture and centrifuged within 1 hour after sampling. 15 parameters including ALT, AST, DBIL, TBIL, ALB, ALP, \u0026gamma;-GT, CREA, UA, GLU, TG, TC, HDL, LDL and GSP were analyzed. 8 biomarkers including ADP, FGF19, GDF11, GDF15, IGF-1, TNF-\u0026alpha;, IL-6 and DHEA were determined by human enzyme-related immunosorbent assay kit (ELISA) (ELK Biotechnology CO., LTD).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSPSS 25.0 software was used to analyze the data. Continuous variables and categorical variables were expressed as mean \u0026plusmn; SD and percentage, respectively. Independent sample t-test was used for comparing continuous variables,Use\u0026nbsp;tˊ-test\u0026nbsp;when the variance of variables is\u0026nbsp;\u003ca href=\"file:///D%3A/Download/baidu-translate-client/resources/app.asar/app.html#/#\"\u003eUnequal\u003c/a\u003e or does not follow a normal distribution.,and Chi-square test was used for figuring out the difference among categorical variables. The independent association factors of sarcopenia were analyzed by multiple logistics regression analysis, and the statistical test was two-tail test. p\u0026lt;0.05 was considered to be statistically significant.\u0026nbsp;\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003e\u003cstrong\u003eClinical\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003echaracteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe participant characteristics are shown in Table 1. During the data collection period from September 2019 to June 2020, a total of 236 elderly people (aged ≥65 years) from rural communities in Wuhan participated in the sarcopenia screening program, including 92\u0026nbsp;males and 144\u0026nbsp;females. The average baseline physical fitness measurements of participants were as follows: Age: 70.6 ± 4.4 years; BMI: 23.8 ± 3.4kg/m\u003csup\u003e2\u003c/sup\u003e; walking speed: 0.86 ± 0.19m/s; grip strength: 23.0 ± 8.3kg; upper limb fat content: 2.54 ± 0.98kg; lower limb fat content: 6.09 ± 1.85kg; upper limb muscle content: 4.22 ± 1.08kg; The lower extremity muscle content was 12.81 ± 3.37kg, body fat percentage was 28.8 ± 6.3%, and ASM/Ht\u003csup\u003e2\u003c/sup\u003e was 6.82 ± 1.39kg/m\u003csup\u003e2\u003c/sup\u003e. A total of 60 patients were diagnosed with sarcopenia according to the 2019AWGS diagnostic criteria, including 19 males and 41 females. The prevalence of sarcopenia was 25.4%. The prevalence was 20.7% in elderly men and 28.5% in elderly women.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariables Associated With Sarcopenia In Rural Community\u0026nbsp;Older Adults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean BMI, grip Strength and body fat percentage of participants in \u0026nbsp;sarcopenia\u0026nbsp;group\u0026nbsp;were significantly lower than those in\u0026nbsp;non-sarcopenia\u0026nbsp;group, (BMI,\u0026nbsp;non-sarcopenia group: 25.1± 2.7,\u0026nbsp;sarcopenia group: 20.1 ± 1.8, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), (handgrip strength,\u0026nbsp;non-sarcopenia group: 24.0 ± 8.8,\u0026nbsp;sarcopenia group: 20.0 ± 6.0, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) (body fat percentage,\u0026nbsp;non-sarcopenia group:30.1 ± 5.9,\u0026nbsp;sarcopenia group: 24.7 ± 5.8, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and the fat content and muscle content of limbs in\u0026nbsp;sarcopenia group\u0026nbsp;were significantly lower than those in\u0026nbsp;non-sarcopenia group. There were no significant differences in age, sex and walking speed between\u0026nbsp;sarcopenia group\u0026nbsp;and\u0026nbsp;non-sarcopenia groups\u0026nbsp;(\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe circulating biomarker tests of the participants are shown in Table 2. \u0026nbsp;\u003cem\u003et\u003c/em\u003e-test\u0026nbsp;or\u0026nbsp;\u003cem\u003et\u003c/em\u003eˊ-test\u0026nbsp;analysis revealed differences in circulation markers between older subjects with and without sarcopenia. Compared with circulating factors in non-sarcopenia\u0026nbsp;elderly subjects, FGF19 and GDF11 were significantly higher. The circulating factors in sarcopenia\u0026nbsp;group were significantly decreased: ALT, AST, ALB, CREA, UA, GLU, TG, LDL, GDF15, TNF-α and IL6.\u0026nbsp;There were no significant differences in other circulating factors (DBIL, TBIL, ALP, γ-GT, TC, HDL, GSP, ADP, IGF-1 and DHEA) between non-sarcopenia and sarcopenia groups (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e≥ 0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndependent Factors Associated With Sarcopenia In Rural Community\u003c/strong\u003e \u003cstrong\u003eOlder Adults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to further\u0026nbsp;elaborate the related factors of sarcopenia, We included the 14\u0026nbsp;significantly variables\u0026nbsp;BMI,FGF19\u0026nbsp;,GDF11\u0026nbsp;,ALT, AST, ALB, CREA, UA, GLU, TG, LDL, GDF15, TNF-α and IL6\u0026nbsp;from the above univariate analysis into the multivariate analysis,an\u0026nbsp;analysis\u0026nbsp;of\u0026nbsp;multiple\u0026nbsp;determinants\u0026nbsp;of\u0026nbsp;the sarcopenia was\u0026nbsp;conducted\u0026nbsp;using\u0026nbsp;a stepwise conditional logistic regression with\u0026nbsp;p\u0026nbsp;\u0026lt; 0.05\u0026nbsp;(Table3). The results showed that GDF11 was an independent risk factor for sarcopenia. BMI, ALB, FGF19 and TNF-α were independent protective factors (Table3). In addition, LDL was not an independent factor in the development of sarcopenia (p \u0026gt; 0.05).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, our main finding was that the prevalence of sarcopenia in rural elderly in China was 25.4%, in which 20.7% for men and 28.5% for women. The subjects with sarcopenia had a lower BMI than the non-sarcopenic older subjects. There were no significant differences in age, sex, or walking speed between non-sarcopenic and sarcopenic subjects. Compared with non-sarcopenic subjects, sarcopenic subjects had higher levels of GDF11 and lower levels of ALB, FGF19, and TNF-α. In addition, inflammatory cytokine IL-6 and β-superfamily cytokine GDF15 are not independent factors of sarcopenia.\u003c/p\u003e \u003cp\u003eIn recent years, epidemiological survey results of sarcopenia in Chinese population show that the prevalence rate of sarcopenia in elderly people aged 60 and above is 3.1%-62.9%\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. A study in Taiwan showed that the prevalence of sarcopenia was 18.6% in 302 aged 65 and older men and 23.0% in women, which is similar to our results\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. A New Mexico senior health survey found that the prevalence of sarcopenia in older men and women was 28.5% and 33.9%, respectively\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. However, other studies have reported low prevalence of sarcopenia, and differences in prevalence results may be due to differences in the definition of sarcopenia, diagnostic cut-off values, or ethnic background of the population.\u003c/p\u003e \u003cp\u003eBMI, waist circumference, and/or waist-to-hip ratio are commonly used to define obesity, and BMI in older adults needs to be interpreted with caution because of reduced physiological height and lack of correlation between BMI and body fat percentage, distribution, or body composition in older adults.That is why we discussed here is just sarcopenia and non-sarcopenia groups of relatively high low BMI, and overweight and obese relations with less muscle disease needs further research.Our finding that a lower BMI is a risk factor for sarcopenia is consistent with the findings of multiple previous studies suggesting that the higher the BMI in older adults, the lower the risk of sarcopenia\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.While obesity is considered a risk factor for many adverse outcomes, in older adults, being slightly overweight may be beneficial. Studies have also shown that older men are resistant to the dangers of overweight and obesity; Mild overweight, obesity, and even central obesity were beneficial for survival\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.Similarly, a study of hospitalized older adults found that fat mass was associated with a lower risk of death or complications\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.In addition, studies have shown that BMI is positively correlated with muscle mass and fat mass\u003csup\u003e[\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.It has been pointed out that fat is an energy store for the elderly and helps individuals survive in disease or poor physical condition. The amount of fat can affect lean body mass for several ages, and people with high fat amount may have a higher protein intake, which is a protective mechanism against muscle loss\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.Given this, we believe that high BMI may play a protective role against loss of muscle mass and strength in older adults. The results of this study further support the negative association between BMI and sarcopenia, so maintaining a healthy BMI in older adults may help maintain muscle mass and strength.\u003c/p\u003e \u003cp\u003eStudies have shown that the serum albumin of the elderly with sarcopenia is lower than that of the non-sarcopenia elderly participants\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. A meta-study of sarcopenia involving 4,904 community-living and institutional older adults (68-87.6 years) from 9 countries and another meta-analysis of 4,071 participants from 5 countries showed that regardless of age and setting, Albumin is negatively associated with sarcopenia, and given that there is no conclusive evidence that serum albumin can be definitively used as a biomarker for sarcopenia, which is easier and less costly to implement than other approaches and ensures timely assessment and early intervention in elderly patients with sarcopenia, Therefore, serum albumin decline can be considered as a reference indicator for biomarkers of sarcopenia. However, albumin concentrations are also affected by processes such as inflammation and infection, so there is no consensus on optimal limits and reference ranges for serum values to assess nutritional status in older adults\u003csup\u003e[\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Albumin is the most abundant plasma protein in the body, and its basic role is to regulate the passage of water and solutes through capillaries by maintaining colloidal pressure in the vascular system. Albumin has long been considered integral to the assessment of nutritional status, it is considered to be a very important plasma protein in the assessment of nutritional status, reducing it can alter wound healing, cause immune problems, and reduce lean body mass\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Improving the nutritional status of the elderly can increase the serum albumin content, which may play a certain role in the treatment of sarcopenia in the elderly.\u003c/p\u003e \u003cp\u003eIn elderly patients with sarcopenia, the level of GDF11 was higher in the sarcopenia group than in the non-sarcopenia group, while GDF15 was not statistically different between the two groups. GDF11 and GDF15 are members of the transforming growth factor β superfamily of cytokines, and GDF11 is closely related to myostatin (MSTN). GDF15 is also known as MIC-1\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. However, some scholars prefer to believe that GDF15 belongs to the glial cell derived neurotrophic factor (GDNF) -like growth factor or cytokine family, because GDF15 receptor GFRAL is a co-receptor of Ret tyrosine kinase\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Multiple studies have suggested that GDF11 inhibits skeletal muscle regeneration\u003csup\u003e[\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. In a recent study, Juli E. Jones found that elevated GDF11 could induce the expression of oxia in mice with decreased muscle mass, food intake and body weight, and that GDF11 could also up-regulate plasma GDF15. Blocking the GDF15 receptor alleviates anorexia but does not reduce muscle loss, whereas blocking the GDF11 receptor ActR II prevents muscle loss and reduces anorexia\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Therefore, elevated GDF11 can be considered as an independent predictor of sarcopenia.\u003c/p\u003e \u003cp\u003eWe also found that FGF19 was lower in the sarcopenia group than in the non-sarcopenia group. FGF19 is a member of the FGF family, a family of proteins involved in differentiation, development and metabolism\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. FGF19 is similar to endocrine hormone and is secreted by ileum intestinal epithelial cells. The pathogenesis of sarcopenia is related to skeletal muscle metabolism, and FGF19 has been found to play a role in muscle metabolism\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Therefore, FGF19 may be an emerging factor for the treatment of sarcopenia, and can contribute to the diagnosis and prevention of sarcopenia. The negative association between FGF19 and sarcopenia was also confirmed by a recent cross-sectional study in Turkey of 88 elderly outpatient participants aged 65 years or older\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA number of literatures have shown that sarcopenia is related to inflammatory cytokines, but the existing studies are controversial on whether inflammatory factors play a positive or negative role in sarcopenia. Studies have shown that chronic inflammation leads to a loss of muscle mass by affecting protein synthesis and catabolism. However, most of the people in these studies were obese or had other chronic conditions, so the role of inflammatory factors in sarcopenia remains controversial\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. In a recent study, 299 Japanese residents (127 males and 172 females) participated in urban health check-ups. It was found that IL-6 is not an independent factor in sarcopenia\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. This conclusion is similar to ours. A recent community sarcopenia study in Taiwan also found no significant correlation between serum IL-6 levels and ASMI, grip strength, or gait speed\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. IL-6 is an effective regulator of human fat metabolism, which can increase lipolysis and fat oxidation. IL-6 acts in an anti-inflammatory way during muscle contraction. Because IL-6 has pro-inflammatory and anti-inflammatory effects, and the IL-6 secreted into the blood cannot be determined whether it is derived from muscle tissue\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe role of TNF-α played in sarcopenia remains controversial. Results in this study demonstrated that a low TNF-α level in sarcopenic old adults. \u003cem\u003eIn vitro\u003c/em\u003e studies have shown that TNF-α has a direct inhibitory effect on insulin signaling and can cause insulin resistance in skeletal muscle, thereby increasing free fatty acids and causing chronic inflammation. A clinical study showed the level of TNF-α in the elderly sarcopenia group was significantly lower than that in the non-sarcopenia group. In multiple logistic regression analysis, low level of TNF-α was identified as an independent risk factor for sarcopenia\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. Further research is needed to better understand the role of TNF-α in sarcopenia.\u003c/p\u003e \u003cp\u003eThere are some limitations to our study. First, because the participants came from rural communities, most of whom may have different occupations and living standards than older people in urban environments, the study has population limitations. Secondly, there are some unmeasured or unknown confounding factors that may affect the results of the study. Sarcopenia is an emerging and complex syndrome, and its influencing factors and pathogenesis are complex and diverse. Therefore, it is impossible to make a definitive diagnosis of sarcopenia through a single serum marker. Another, the lack of investigation of the clinical characteristics of the participants with noncommunicable diseases is also a limitation of this study, and the noncommunicable diseases of the participants may also affect the conclusions of the study. In addition, this is a cross-sectional, single-center study that cannot definitively establish a causal relationship between serum biomarkers and sarcopenia. Further longitudinal and multi-center collaborative studies are needed to confirm our results.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study shows that the prevalence of sarcopenia in rural elderly in China is 25.4%, 20.7% for men and 28.5% for women. BMI, serum ALB, FGF19, and TNF-α levels were lower in the elderly population with sarcopenia, while GDF11 was increased in the serum of patients with sarcopenia. Although this study cannot draw a cause-and-effect relationship, our results may help to identify reliable biomarkers related to sarcopenia that could benefit the early detection, diagnosis, treatment, and prevention of sarcopenia.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll participants signed a written informed consent prior to enrollment.It\u0026nbsp;was\u0026nbsp;approved\u0026nbsp;by\u0026nbsp;the\u0026nbsp;Ethics committee of Union\u0026nbsp;Hospital, Tongji Medical College, and Huazhong University of Science and Technology. It\u0026nbsp;followed\u0026nbsp;the tenets of the\u0026nbsp;Helsinki\u0026nbsp;Declaration\u0026nbsp;in\u0026nbsp;conducting\u0026nbsp;this\u0026nbsp;study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and / or analyzed in the current study are available from the corresponding authors,without improper reservations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Key Research and Development Programs of China (No. 2018YFC2002100, 2018YFC2002102, and 2020YFC2006000).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZHW and PH has contributed to the design of this study.YZ performed statistical analysis and wrote the first draft. YZ, PH, KMZ and YLZ participated in patient examination and data collection. YZ, ZHW and PH performed laboratory analysis. All authors approved the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants signed a written informed consent prior to enrollment.It\u0026nbsp;was\u0026nbsp;approved\u0026nbsp;by\u0026nbsp;the\u0026nbsp;Ethics committee of Union\u0026nbsp;Hospital, Tongji Medical College, and Huazhong University of Science and Technology.\u0026nbsp;It\u0026nbsp;followed\u0026nbsp;the tenets of the\u0026nbsp;Helsinki\u0026nbsp;Declaration\u0026nbsp;in\u0026nbsp;conducting\u0026nbsp;this\u0026nbsp;study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZHW and PH has contributed to the design of this study.YZ performed statistical analysis and wrote the first draft. YZ, PH, KMZ and YLZ participated in patient examination and data collection. YZ, ZHW and PH performed laboratory analysis. All authors approved the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Key Research and Development Programs\u0026nbsp;(No. 2018YFC2002100, 2018YFC2002102, and 2020YFC2006000).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and / or analyzed in the current study are available from the corresponding authors,without improper reservations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interest.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMorley J E, Abbatecola A M, Argiles J M, et al. Sarcopenia with limited mobility: an international consensus. J Am Med Dir Assoc, 2011,12(6):403-409.\u003c/li\u003e\n\u003cli\u003eNational Bureau of Statistics of China. China Statistical Yearbook. China Statistics Press; 2021 \u003c/li\u003e\n\u003cli\u003eGallagher D, DeLegge M. Body composition (sarcopenia) in obese patients: implications for care in the intensive care unit. JPEN J Parenter Enteral Nutr, 2011,35(5 Suppl):21S-28S.\u003c/li\u003e\n\u003cli\u003eJanssen I, Shepard D S, Katzmarzyk P T, et al. The healthcare costs of sarcopenia in the United States. J Am Geriatr Soc, 2004,52(1):80-85.\u003c/li\u003e\n\u003cli\u003eCruz-Jentoft A J, Sayer A A. Sarcopenia. Lancet, 2019,393(10191):2636-2646.\u003c/li\u003e\n\u003cli\u003eCurcio F, Ferro G, Basile C, et al. Biomarkers in sarcopenia: A multifactorial approach. Exp Gerontol, 2016,85:1-8.\u003c/li\u003e\n\u003cli\u003eHeymsfield S B, Smith R, Aulet M, et al. Appendicular skeletal muscle mass: measurement by dual-photon absorptiometry. Am J Clin Nutr, 1990,52(2):214-218.\u003c/li\u003e\n\u003cli\u003eJanssen I, Baumgartner R N, Ross R, et al. Skeletal muscle cutpoints associated with elevated physical disability risk in older men and women. Am J Epidemiol, 2004,159(4):413-421.\u003c/li\u003e\n\u003cli\u003eHan P, Kang L, Guo Q, et al. Prevalence and Factors Associated With Sarcopenia in Suburb-dwelling Older Chinese Using the Asian Working Group for Sarcopenia Definition. J Gerontol A Biol Sci Med Sci, 2016,71(4):529-535.\u003c/li\u003e\n\u003cli\u003eZhang W, Shen S, Wang W, et al. Poor lower extremity function was associated with pre-diabetes and diabetes in older chinese people. PLoS One, 2014,9(12):e115883.\u003c/li\u003e\n\u003cli\u003eChen L K, Woo J, Assantachai P, et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J Am Med Dir Assoc, 2020,21(3):300-307.\u003c/li\u003e\n\u003cli\u003eChen Z, Li W Y, Ho M, et al. The Prevalence of Sarcopenia in Chinese Older Adults: Meta-Analysis and Meta-Regression. Nutrients, 2021,13(5).\u003c/li\u003e\n\u003cli\u003eChien M Y, Huang T Y, Wu Y T. Prevalence of sarcopenia estimated using a bioelectrical impedance analysis prediction equation in community-dwelling elderly people in Taiwan. J Am Geriatr Soc, 2008,56(9):1710-1715.\u003c/li\u003e\n\u003cli\u003eBaumgartner R N, Koehler K M, Gallagher D, et al. Epidemiology of sarcopenia among the elderly in New Mexico. Am J Epidemiol, 1998,147(8):755-763.\u003c/li\u003e\n\u003cli\u003eWu L C, Kao H H, Chen H J, et al. Preliminary screening for sarcopenia and related risk factors among the elderly. Medicine (Baltimore), 2021,100(19):e25946.\u003c/li\u003e\n\u003cli\u003eLinge J, Heymsfield S B, Dahlqvist L O. On the Definition of Sarcopenia in the Presence of Aging and Obesity-Initial Results from UK Biobank. J Gerontol A Biol Sci Med Sci, 2020,75(7):1309-1316.\u003c/li\u003e\n\u003cli\u003eAuyeung T W, Lee J S, Leung J, et al. Survival in older men may benefit from being slightly overweight and centrally obese--a 5-year follow-up study in 4,000 older adults using DXA. J Gerontol A Biol Sci Med Sci, 2010,65(1):99-104.\u003c/li\u003e\n\u003cli\u003eBouillanne O, Dupont-Belmont C, Hay P, et al. Fat mass protects hospitalized elderly persons against morbidity and mortality. Am J Clin Nutr, 2009,90(3):505-510.\u003c/li\u003e\n\u003cli\u003eWoo J, Leung J, Kwok T. BMI, body composition, and physical functioning in older adults. Obesity (Silver Spring), 2007,15(7):1886-1894.\u003c/li\u003e\n\u003cli\u003eBeavers K M, Beavers D P, Houston D K, et al. Associations between body composition and gait-speed decline: results from the Health, Aging, and Body Composition study. Am J Clin Nutr, 2013,97(3):552-560.\u003c/li\u003e\n\u003cli\u003eYu R, Wong M, Leung J, et al. Incidence, reversibility, risk factors and the protective effect of high body mass index against sarcopenia in community-dwelling older Chinese adults. Geriatr Gerontol Int, 2014,14 Suppl 1:15-28.\u003c/li\u003e\n\u003cli\u003eKuk J L, Ardern C I. Influence of age on the association between various measures of obesity and all-cause mortality. J Am Geriatr Soc, 2009,57(11):2077-2084.\u003c/li\u003e\n\u003cli\u003eCheng Q, Zhu X, Zhang X, et al. A cross-sectional study of loss of muscle mass corresponding to sarcopenia in healthy Chinese men and women: reference values, prevalence, and association with bone mass. J Bone Miner Metab, 2014,32(1):78-88.\u003c/li\u003e\n\u003cli\u003ePicca A, Coelho-Junior H J, Calvani R, et al. Biomarkers shared by frailty and sarcopenia in older adults: A systematic review and meta-analysis. Ageing Res Rev, 2022,73:101530.\u003c/li\u003e\n\u003cli\u003eSilva-Fhon J R, Rojas-Huayta V M, Aparco-Balboa J P, et al. Sarcopenia and blood albumin: A systematic review with meta-analysis. Biomedica, 2021,41(3):590-603.\u003c/li\u003e\n\u003cli\u003eCabrerizo S, Cuadras D, Gomez-Busto F, et al. Serum albumin and health in older people: Review and meta analysis. Maturitas, 2015,81(1):17-27.\u003c/li\u003e\n\u003cli\u003eKeller U. Nutritional Laboratory Markers in Malnutrition. J Clin Med, 2019,8(6).\u003c/li\u003e\n\u003cli\u003eZhang Z, Pereira S L, Luo M, et al. Evaluation of Blood Biomarkers Associated with Risk of Malnutrition in Older Adults: A Systematic Review and Meta-Analysis. Nutrients, 2017,9(8).\u003c/li\u003e\n\u003cli\u003eSmith S H. Using albumin and prealbumin to assess nutritional status. Nursing, 2017,47(4):65-66.\u003c/li\u003e\n\u003cli\u003eNakashima M, Toyono T, Akamine A, et al. Expression of growth/differentiation factor 11, a new member of the BMP/TGFbeta superfamily during mouse embryogenesis. Mech Dev, 1999,80(2):185-189.\u003c/li\u003e\n\u003cli\u003eEmmerson P J, Wang F, Du Y, et al. The metabolic effects of GDF15 are mediated by the orphan receptor GFRAL. Nat Med, 2017,23(10):1215-1219.\u003c/li\u003e\n\u003cli\u003eHsu J Y, Crawley S, Chen M, et al. Non-homeostatic body weight regulation through a brainstem-restricted receptor for GDF15. Nature, 2017,550(7675):255-259.\u003c/li\u003e\n\u003cli\u003eJones J E, Cadena S M, Gong C, et al. Supraphysiologic Administration of GDF11 Induces Cachexia in Part by Upregulating GDF15. Cell Rep, 2018,22(6):1522-1530.\u003c/li\u003e\n\u003cli\u003eEgerman M A, Cadena S M, Gilbert J A, et al. GDF11 Increases with Age and Inhibits Skeletal Muscle Regeneration. Cell Metab, 2015,22(1):164-174.\u003c/li\u003e\n\u003cli\u003eMa Y, Liu Y, Han F, et al. Growth differentiation factor 11: a \u0026quot;rejuvenation factor\u0026quot; involved in regulation of age-related diseases?[J]. Aging (Albany NY), 2021,13(8):12258-12272.\u003c/li\u003e\n\u003cli\u003eOrnitz D M, Itoh N. The Fibroblast Growth Factor signaling pathway. Wiley Interdiscip Rev Dev Biol, 2015,4(3):215-266.\u003c/li\u003e\n\u003cli\u003eTomlinson E, Fu L, John L, et al. Transgenic mice expressing human fibroblast growth factor-19 display increased metabolic rate and decreased adiposity. Endocrinology, 2002,143(5):1741-1747.\u003c/li\u003e\n\u003cli\u003eBag S R, Suzan V, Arman P, et al. Association of FGF-19 and FGF-21 levels with primary sarcopenia. Geriatr Gerontol Int, 2021,21(10):959-962.\u003c/li\u003e\n\u003cli\u003ePan L, Xie W, Fu X, et al. Inflammation and sarcopenia: A focus on circulating inflammatory cytokines. Exp Gerontol, 2021,154:111544.\u003c/li\u003e\n\u003cli\u003eIto S, Nakashima H, Ando K, et al. Association between Low Muscle Mass and Inflammatory Cytokines. Biomed Res Int, 2021,2021:5572742.\u003c/li\u003e\n\u003cli\u003eLiu H C, Han D S, Hsu C C, et al. Circulating MicroRNA-486 and MicroRNA-146a serve as potential biomarkers of sarcopenia in the older adults. BMC Geriatr, 2021,21(1):86.\u003c/li\u003e\n\u003cli\u003ePedersen B K, Febbraio M A. Muscle as an endocrine organ: focus on muscle-derived interleukin-6. Physiol Rev, 2008,88(4):1379-1406.\u003c/li\u003e\n\u003cli\u003eBrandt C, Pedersen B K. The role of exercise-induced myokines in muscle homeostasis and the defense against chronic diseases. J Biomed Biotechnol, 2010,2010:520258.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Demographics\u0026nbsp;characteristics\u0026nbsp;and the index of body examination of 236 living in rural community older adults in Wuhan, China\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.347826086956523%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e（\u003cem\u003en\u003c/em\u003e=236）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.17391304347826%\" valign=\"top\"\u003e\n \u003cp\u003eNon-sarcopenia\u003c/p\u003e\n \u003cp\u003e（\u003cem\u003en\u003c/em\u003e=176）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.17391304347826%\" valign=\"top\"\u003e\n \u003cp\u003eSarcopenia\u003c/p\u003e\n \u003cp\u003e（\u003cem\u003en\u003c/em\u003e=60）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.521739130434783%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e/\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.91304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.347826086956523%\" valign=\"top\"\u003e\n \u003cp\u003e70.6\u0026plusmn;4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.17391304347826%\" valign=\"top\"\u003e\n \u003cp\u003e70.5\u0026plusmn;4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.17391304347826%\" valign=\"top\"\u003e\n \u003cp\u003e70.7\u0026plusmn;4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.521739130434783%\" valign=\"top\"\u003e\n \u003cp\u003e-0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.91304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003eMale / Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.347826086956523%\" valign=\"top\"\u003e\n \u003cp\u003e92/144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.17391304347826%\" valign=\"top\"\u003e\n \u003cp\u003e73/103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.17391304347826%\" valign=\"top\"\u003e\n \u003cp\u003e19/41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.521739130434783%\" valign=\"top\"\u003e\n \u003cp\u003e1.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.91304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.347826086956523%\" valign=\"top\"\u003e\n \u003cp\u003e23.8\u0026plusmn;3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.17391304347826%\" valign=\"top\"\u003e\n \u003cp\u003e25.1\u0026plusmn;2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.17391304347826%\" valign=\"top\"\u003e\n \u003cp\u003e20.1\u0026plusmn;1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.521739130434783%\" valign=\"top\"\u003e\n \u003cp\u003e16.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.91304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003eGS (m/s)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.347826086956523%\" valign=\"top\"\u003e\n \u003cp\u003e0.86\u0026plusmn;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.17391304347826%\" valign=\"top\"\u003e\n \u003cp\u003e0.87\u0026plusmn;0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.17391304347826%\" valign=\"top\"\u003e\n \u003cp\u003e0.82\u0026plusmn;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.521739130434783%\" valign=\"top\"\u003e\n \u003cp\u003e1.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.91304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003eHS (kg)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.347826086956523%\" valign=\"top\"\u003e\n \u003cp\u003e23.0\u0026plusmn;8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.17391304347826%\" valign=\"top\"\u003e\n \u003cp\u003e24.0\u0026plusmn;8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.17391304347826%\" valign=\"top\"\u003e\n \u003cp\u003e20.0\u0026plusmn;6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.521739130434783%\" valign=\"top\"\u003e\n \u003cp\u003e3.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.91304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003cstrong\u003e\u003cs\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/s\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003eASM/Ht\u003csup\u003e2\u003c/sup\u003e (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.347826086956523%\" valign=\"top\"\u003e\n \u003cp\u003e6.81\u0026plusmn;1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.17391304347826%\" valign=\"top\"\u003e\n \u003cp\u003e7.28\u0026plusmn;1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.17391304347826%\" valign=\"top\"\u003e\n \u003cp\u003e5.41\u0026plusmn;0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.521739130434783%\" valign=\"top\"\u003e\n \u003cp\u003e13.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.91304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003eUpper limb fat (\u003c/p\u003ekg)\u003cbr\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.347826086956523%\" valign=\"top\"\u003e\n \u003cp\u003e2.54\u0026plusmn;0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.17391304347826%\" valign=\"top\"\u003e\n \u003cp\u003e2.79\u0026plusmn;0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.17391304347826%\" valign=\"top\"\u003e\n \u003cp\u003e1.84\u0026plusmn;1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.521739130434783%\" valign=\"top\"\u003e\n \u003cp\u003e7.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.91304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003eLower limb fat (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.347826086956523%\" valign=\"top\"\u003e\n \u003cp\u003e6.09\u0026plusmn;1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.17391304347826%\" valign=\"top\"\u003e\n \u003cp\u003e6.72\u0026plusmn;1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.17391304347826%\" valign=\"top\"\u003e\n \u003cp\u003e4.24\u0026plusmn;1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.521739130434783%\" valign=\"top\"\u003e\n \u003cp\u003e11.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.91304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003eUpper limb muscles (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.347826086956523%\" valign=\"top\"\u003e\n \u003cp\u003e4.22\u0026plusmn;1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.17391304347826%\" valign=\"top\"\u003e\n \u003cp\u003e4.59\u0026plusmn;0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.17391304347826%\" valign=\"top\"\u003e\n \u003cp\u003e3.15\u0026plusmn;0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.521739130434783%\" valign=\"top\"\u003e\n \u003cp\u003e13.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.91304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003eLower limb muscles (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.347826086956523%\" valign=\"top\"\u003e\n \u003cp\u003e12.81\u0026plusmn;3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.17391304347826%\" valign=\"top\"\u003e\n \u003cp\u003e13.77\u0026plusmn;3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.17391304347826%\" valign=\"top\"\u003e\n \u003cp\u003e10.01\u0026plusmn;2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.521739130434783%\" valign=\"top\"\u003e\n \u003cp\u003e10.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.91304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003ePBF\u0026nbsp;(%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.347826086956523%\" valign=\"top\"\u003e\n \u003cp\u003e28.8\u0026plusmn;6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.17391304347826%\" valign=\"top\"\u003e\n \u003cp\u003e30.1\u0026plusmn;5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.17391304347826%\" valign=\"top\"\u003e\n \u003cp\u003e24.7\u0026plusmn;5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.521739130434783%\" valign=\"top\"\u003e\n \u003cp\u003e6.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.91304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003eBMI: body mass index, HS: handgrip strength, GS: gait speed, ASM/Ht\u003csup\u003e2\u003c/sup\u003e: \u0026nbsp; \u0026nbsp; appendicular skeletal muscle mass, appendicular soft lean mass/(height)\u003csup\u003e2\u003c/sup\u003e, PBF: \u0026nbsp;percentage body fat.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Biomarker characteristics of 236 participants living in rural community older adults in Wuhan,China\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"540\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003eNon-sarcopenia\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003eSarcopenia\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e18.84\u0026plusmn;11.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003e12.57\u0026plusmn;6.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e5.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003eAST (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e25.08\u0026plusmn;9.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003e22.42\u0026plusmn;6.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e1.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.048*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003eDBIL (\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e9.82\u0026plusmn;2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003e9.75\u0026plusmn;2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e0.853\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003eTBIL (\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e11.80\u0026plusmn;3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003e11.83\u0026plusmn;3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e-0.720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e0.943\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003eALB (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e36.60\u0026plusmn;3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003e35.45\u0026plusmn;4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e2.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.042*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003eALP (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e80.82\u0026plusmn;26.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003e79.43\u0026plusmn;28.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gamma;-GT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e33.15\u0026plusmn;35.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003e25.66\u0026plusmn;35.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e1.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003eCREA(\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e64.81\u0026plusmn;25.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003e57.94\u0026plusmn;14.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e1.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.049*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003eUA (\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e278.49\u0026plusmn;79.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003e239.54\u0026plusmn;77.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e3.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003eGLU (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e4.58\u0026plusmn;1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003e4.29\u0026plusmn;0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e1.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.048*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003eTG (mmol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e0.99\u0026plusmn;0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003e0.76\u0026plusmn;0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e3.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003eTC (mmol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e4.14\u0026plusmn;0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003e4.06\u0026plusmn;0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e0.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003eHDL (mmol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e1.04\u0026plusmn;0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003e1.10\u0026plusmn;0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e-1.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003eLDL (mmol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e2.65\u0026plusmn;0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003e2.44\u0026plusmn;0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e2.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.046*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003eGSP (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e1.81\u0026plusmn;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003e1.85\u0026plusmn;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e-0.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003eADP (\u0026mu;g/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e9.46\u0026plusmn;2.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003e9.34\u0026plusmn;1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e0.688\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003eFGF19 (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e111.08\u0026plusmn;25.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003e124.49\u0026plusmn;18.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e-4.367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003eGDF11 (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e191.97\u0026plusmn;142.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003e417.33\u0026plusmn;214.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e-7.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003eGDF15 (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e0.75\u0026plusmn;0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003e0.54\u0026plusmn;0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e3.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"bottom\"\u003e\n \u003cp\u003eIGF-1 \u0026nbsp;(ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e383.80\u0026plusmn;96.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003e376.55\u0026plusmn;62.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e0.505\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003eTNFa (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e25.00\u0026plusmn;13.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003e16.18\u0026plusmn;7.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e6.508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003eIL6 (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e5.59\u0026plusmn;3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003e3.19\u0026plusmn;2.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e5.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003eDHEA (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e574.74\u0026plusmn;193.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.962962962962962%\" valign=\"top\"\u003e\n \u003cp\u003e550.28\u0026plusmn;246.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e0.699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.925925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eALT: alanine aminotransferase, AST: aspartate aminotransferase, DBIL: direct bilirubin, TBiL: total bilirubin, ALB: albumin, ALP: alkaline - phosphatase, \u0026gamma;-GT: gamma - glutamyl transferase, CREA: creatinine, UA: uric acid, GLU: glucose, TG: triglycerides, TC: total cholesterol, HDL: high-density lipoprotein, LDL: low-density lipoprotein, GSP: glycosylated serum protein, ADP: adiponectin, FGF19: fibroblast growth factor 19, GDF11: Growth differentiation factor 11, GDF15: growth differentiation factor 15, IGF - 1: insulin-like growth factor 1, TNF-\u0026alpha;: tumour necrosis factor alpha, (IL-6): interleukin 6, DHEA: dehydroepiandrosterone.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eModels\u0026nbsp;of\u0026nbsp;logistic regression were\u0026nbsp;used\u0026nbsp;to\u0026nbsp;identify\u0026nbsp;factors associated\u0026nbsp;with\u003cem\u003e\u0026nbsp;\u003c/em\u003esarcopenia living in rural community older adults in Wuhan, China\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"574\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.67247386759582%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.195121951219512%\" valign=\"top\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.67247386759582%\" valign=\"top\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.846689895470384%\" valign=\"top\"\u003e\n \u003cp\u003eWald\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.059233449477352%\" valign=\"top\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.32404181184669%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.67247386759582%\" valign=\"top\"\u003e\n \u003cp\u003eExp(B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.557491289198605%\" valign=\"top\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.67247386759582%\" valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.195121951219512%\" valign=\"top\"\u003e\n \u003cp\u003e-5.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.67247386759582%\" valign=\"top\"\u003e\n \u003cp\u003e1.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.846689895470384%\" valign=\"top\"\u003e\n \u003cp\u003e7.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.059233449477352%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.32404181184669%\" valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.67247386759582%\" valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.557491289198605%\" valign=\"top\"\u003e\n \u003cp\u003e0.000-0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.67247386759582%\" valign=\"top\"\u003e\n \u003cp\u003eALB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.195121951219512%\" valign=\"top\"\u003e\n \u003cp\u003e-0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.67247386759582%\" valign=\"top\"\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.846689895470384%\" valign=\"top\"\u003e\n \u003cp\u003e6.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.059233449477352%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.32404181184669%\" valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.67247386759582%\" valign=\"top\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.557491289198605%\" valign=\"top\"\u003e\n \u003cp\u003e0.281-0.853\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.67247386759582%\" valign=\"top\"\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.195121951219512%\" valign=\"top\"\u003e\n \u003cp\u003e1.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.67247386759582%\" valign=\"top\"\u003e\n \u003cp\u003e0.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.846689895470384%\" valign=\"top\"\u003e\n \u003cp\u003e3.699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.059233449477352%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.32404181184669%\" valign=\"top\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.67247386759582%\" valign=\"top\"\u003e\n \u003cp\u003e5.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.557491289198605%\" valign=\"top\"\u003e\n \u003cp\u003e0.968-31.508\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.67247386759582%\" valign=\"top\"\u003e\n \u003cp\u003eFGF19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.195121951219512%\" valign=\"top\"\u003e\n \u003cp\u003e-0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.67247386759582%\" valign=\"top\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.846689895470384%\" valign=\"top\"\u003e\n \u003cp\u003e6.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.059233449477352%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.32404181184669%\" valign=\"top\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.67247386759582%\" valign=\"top\"\u003e\n \u003cp\u003e0.804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.557491289198605%\" valign=\"top\"\u003e\n \u003cp\u003e0.683-0.946\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.67247386759582%\" valign=\"top\"\u003e\n \u003cp\u003eGDF11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.195121951219512%\" valign=\"top\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.67247386759582%\" valign=\"top\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.846689895470384%\" valign=\"top\"\u003e\n \u003cp\u003e8.773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.059233449477352%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.32404181184669%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.67247386759582%\" valign=\"top\"\u003e\n \u003cp\u003e1.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.557491289198605%\" valign=\"top\"\u003e\n \u003cp\u003e1.010-1.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.67247386759582%\" valign=\"top\"\u003e\n \u003cp\u003eTNF-\u0026alpha;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.195121951219512%\" valign=\"top\"\u003e\n \u003cp\u003e-0.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.67247386759582%\" valign=\"top\"\u003e\n \u003cp\u003e0.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.846689895470384%\" valign=\"top\"\u003e\n \u003cp\u003e8.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.059233449477352%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.32404181184669%\" valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.67247386759582%\" valign=\"top\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.557491289198605%\" valign=\"top\"\u003e\n \u003cp\u003e0.194-0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sarcopenia, Disease incidence, Biomarkers，Elderly, Rural community ","lastPublishedDoi":"10.21203/rs.3.rs-4814100/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4814100/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eSarcopenia is a syndrome of loss of muscle mass and decreased skeletal muscle function with impaired ability in the activities of daily life and cause some adverse consequences in the elderly. In China, where the aging trend is obvious, the incidence of sarcopenia is increasing. Exploring potential biomarkers for sarcopenia may lead to early screening and intervention for sarcopenia.This study investigated the prevalence and potential biomarkers of sarcopenia in older adult living in rural community in Wuhan,China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis cross-sectional study involved 236 older participants (age ≥65 years) who received a health examination that included body composition and 23 circulating biomarkers.Sarcopenia was defined by the Asian Working Group for Sarcopenia revised in 2019 (AWGS2019). We divided the participants into a non-sarcopeniagroup and a sarcopenia group. The correlation between biomarkers and sarcopenia was analyzed by independent sample \u003cem\u003et\u003c/em\u003e-test, and then the significant variables of the \u003cem\u003et\u003c/em\u003e-test (\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05) were included in the multivariate logistic regression model to determine the independent factors associated with sarcopenia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Among the 236 participants, 92 were men and 144 were females, with a mean age of 70.6 ± 4.4years. The prevalence of sarcopenia in rural community was 25.4%(men 20.7%, women 28.5%). \u0026nbsp;Analyses were conducted using multivariate logistic regression，growth differentiation factor 11(GDF11), was an independent risk factor for sarcopenia [Exp (B) 1.031, 95% CI: 1.010-1.052, \u003cem\u003ep\u003c/em\u003e=0.003]. However, body mass index, albumin（ALB）, fibroblast growth factor 19(FGF19), and tumour necrosis factor alpha(TNF-α ) were independent protective factors for sarcopenia [BMI: Exp (B) 0.007, 95% CI: 0.000-0.244, \u003cem\u003ep\u003c/em\u003e=0.006；ALB: Exp (B) 0.490, 95% CI: 0.281-0.853，\u003cem\u003ep\u003c/em\u003e=0.012; FGF19: Exp(B) 0.804, 95% CI: 0.683-0.946, \u003cem\u003ep\u003c/em\u003e=0.009; TNF-α: Exp (B) 0.379, 95% CI: 0.194-0.742, \u003cem\u003ep\u003c/em\u003e=0.005].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eAbout a quarter of elderly people in rural Chinese communities are at risk of sarcopenia. Lower BMI, lower serum ALB, FGF19, TNF-α, and higher circulating GDF11 are associated with sarcopenia.\u003c/p\u003e","manuscriptTitle":"Analysis of prevalence and associated biomarkers of sarcopenia living in rural community older adults in Wuhan, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-02 10:09:26","doi":"10.21203/rs.3.rs-4814100/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"91e6886b-b702-402f-ba41-c9b76a41b395","owner":[],"postedDate":"September 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":36551524,"name":"Health sciences/Diseases"},{"id":36551525,"name":"Health sciences/Medical research"},{"id":36551526,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-05-14T05:53:54+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-02 10:09:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4814100","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4814100","identity":"rs-4814100","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}