Association between insulin and sarcopenia in elderly Chinese individuals: a cross-sectional and longitudinal study

Association between insulin and sarcopenia in elderly Chinese individuals: a cross-sectional and longitudinal study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between insulin and sarcopenia in elderly Chinese individuals: a cross-sectional and longitudinal study Guofang Sun, Jianjun Liang, Dechao Chen, Kongjun Zhao, Wangmi Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4581143/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: The link between insulin and sarcopenia among older adults in China is not yet fully understood. This study seeks to investigate this association using data from a nationally representative large-scale survey. Methods: The study utilized data from two waves of the China Health and Nutrition Survey (CHNS) conducted in 2009 and 2015. Subjects meeting the inclusion criteria were classified according to the Asia Working Group for Sarcopenia2019 criteria. The study employed ordinary least squares (OLS) regression models to analyze the cross-sectional association between muscle mass and insulin. Additionally, logistic regression models were utilized to examine the longitudinal association between sarcopenia and insulin. Results: In 2009, a cross-sectional association study enrolled a total of 2329 participants aged over 60 years, with 53.1% women and a median age of 68.00 years. The prevalence of sarcopenia in the study population was 30.83%, with a higher prevalence in females (60.03%). In the adjusted OLS regression model based on blood biomarker, insulin was positively associated with muscle mass (β=0.075, 95% confidence interval (CI): 0.034 - 0.117, P <0.01). In 2009, individuals without sarcopenia were divided into two groups based on the median value of insulin in the total population. When 944 individuals were followed up in 2015 to assess the incidence of sarcopenia, a significant difference was found between the two groups (12.44% vs 7.45%, P=0.01). The adjusted logistic regression models indicated that higher insulin levels were associated with a reduced incidence of sarcopenia (Hazard ratio =0.958, 95% CI: 0.925 - 0.989, P=0.01). Conclusions: Adequate insulin could potentially serve as a protective factor in preserving healthy muscle mass among Chinese adults aged 60 and above. CHNS sarcopenia muscle mass insulin Figures Figure 1 Figure 2 Figure 3 Introduction As aging process accelerates in China, the proportion of elderly population is steadily rising, leading to a significant increase in societal burdens [ 1 ]. With the growing elderly population, the demand for social pension security, healthcare services, and other related resources will also see a substantial surge. This will impose heavy pressure on the country's social security system, healthcare system, and family economies [ 2 , 3 ]. Therefore, addressing the challenges brought about by aging to society, such as sarcopenia, requires concerted efforts from the government, various sectors of society, and families. Sarcopenia, characterized by progressive loss of skeletal muscle mass, strength, and function, represents a prevalent geriatric syndrome associated with adverse outcomes [ 4 ]. Its epidemiology demonstrates a rising prevalence worldwide, particularly in aging populations. The multifactorial etiology involves age-related hormonal changes, chronic inflammation, sedentary lifestyle, and inadequate nutrition. Clinically, sarcopenia manifests as decreased muscle mass and strength, impaired physical performance, and increased risk of falls and fractures [ 5 ]. Prevention and management strategies encompass a multidisciplinary approach, including nutritional optimization with adequate protein intake and vitamin D supplementation, resistance exercise training, and addressing underlying comorbidities [ 6 , 7 ]. Pharmacological interventions such as anabolic agents and myostatin inhibitors are emerging as potential therapeutic options [ 8 ]. Insulin, primarily secreted by pancreatic beta cells, plays a pivotal role in glucose homeostasis by facilitating glucose uptake into muscle and adipose tissue. Impaired insulin signaling pathways contribute to insulin resistance, leading to decreased glucose uptake and utilization in skeletal muscle [ 9 ]. Therefore, insulin resistance has been unequivocally established as a pivotal contributor to the pathogenesis of sarcopenia, eliciting muscle wasting primarily through the subsequent mechanisms: (1) heightened protein catabolism coupled with diminished protein synthesis within the skeletal musculature; (2) upregulated expression of the FoxO family, orchestrating skeletal muscle attenuation either through direct means or by fostering protein degradation; and (3) induction of autophagy within skeletal muscle cells [ 10 ]. Given the shared underlying determinants between muscle mass and insulin resistance, recent investigations have posited a potential nexus between sarcopenia and insulin. Therefore, the present study utilized data sourced from the China Health and Nutrition Survey (CHNS), a nationally representative dataset. By conducting a cross-sectional analysis in 2009, we scrutinized the relationship between sarcopenia and insulin in Chinese elderly individuals aged 60 years and above. Furthermore, we undertook longitudinal analyses based on data from 2015 to explore the enduring association of insulin with sarcopenia. This endeavor aimed to furnish robust scientific evidence pertaining to the etiology, early intervention, and preventive measures against sarcopenia. Methods Data source The study cohort was drawn from the CHNS, a nationwide longitudinal study administered by the Chinese Center for Disease Control and Prevention in collaboration with the University of North Carolina. Employing a multi-stage, random cluster sampling methodology, the CHNS sought representation across socioeconomic strata, encompassing low, middle, and high-income brackets. Within each province, a weighted sampling framework guided the selection of four counties and two cities. Subsequently, villages within counties and urban as well as suburban neighborhoods within cities were chosen through random sampling procedures. Within these locales, households were randomly identified, and all household members were included in the survey. Comprehensive descriptions of the cohort and sampling methodology have been previously published [ 11 ]. The assessment of blood biomarkers was conducted in 2009, and the final follow-up time was in 2015. Therefore, these two waves of data were respectively utilized for cross-sectional studies and longitudinal cohort analyses. Figure 1 presents the schematic diagram of research process. Assessment of sarcopenia Sarcopenia evaluation followed the Asia Working Group for Sarcopenia (AWGS) 2019 guidelines, incorporating assessments of muscle strength, appendicular skeletal muscle mass (ASM), and physical performance [ 12 ]. Among these, ASM plays a pivotal role in fundamental functions such as mobility. The calculation formula is as follows: ASM = 0.193×weight (kg) + 0.107×height (cm) − 4.157×gender − 0.037×age (years) − 2.631. Gender is set to 1 if male, and 2 if female [ 13 ]. This equation exhibits a robust R 2 value of 0.90, indicating its commendable predictive capability for ASM in Chinese adults. Cross-validation further demonstrated a strong correlation coefficient of 0.941 with dual X-ray absorptiometry, underscoring its reliability. Moreover, this equation has been consistently applied in various research endeavors involving similar study populations to ours [ 14 , 15 ]. The ASM index (ASMI), calculated by dividing ASM by the square of height in meters, serves as a key metric for categorizing low muscle mass. In accordance with the 2021 Chinese consensus on sarcopenia, low muscle mass was delineated by ASMI values below 7.0 kg/m 2 for males and 5.4 kg/m 2 for females. Covariates This study employed adjustments for demographic factors, medical history, and blood biomarkers. Covariates were carefully chosen in alignment with existing research and clinical directives [ 16 , 17 ]. Demographic variables encompassed age, sex, and ASM. Medical history, including a record of diabetes mellitus (DM) [ 18 ] and hypertension [ 19 ], was included due to their potential influence on dietary habits owing to therapeutic regimens. In the survey conducted in 2009, blood samples constituted vital specimens. All individuals aged seven years and older (inclusive of seven years) were mandated to provide a 12ml blood sample, drawn from an empty stomach, distributed across three tubes each containing 4ml. The intricate procedure for measurement can be referenced in the CHNS operational manual. The blood sample indicators included in this study encompassed the following items: insulin, glucose, triglyceride (TG), total cholesterol (TC), apolipoprotein a1 (ApoA1), apolipoprotein b (Apo-B), lipoprotein a (LPA), C-reactive protein (CRP), creatinine (CR), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). Statistical analysis Descriptive statistics were utilized to compare patients from different groups. Continuous variables were summarized using mean and standard deviation or medians and interquartile ranges, while categorical variables were presented as frequencies. Normality of continuous variables were assessed using the Shapiro-Wilk test. Parametric data were analyzed using the two-tailed T-test, while nonparametric data were compared using the Wilcoxon rank-sum test. Categorical data were analyzed using the χ2 test. In 2009, we utilized an Ordinary Least Squares (OLS) regression model to explore the cross-sectional relationship between ASMI and insulin. The findings were articulated through regression coefficients (β) accompanied by their corresponding 95% confidence interval (CI). Moreover, longitudinal data spanning from 2009 to 2015 allowed us to delve into the association between insulin and the onset of sarcopenia using a logistic regression model, expressed in Hazard ratio (HR) and 95% CI. We delineated three distinct models, each integrating various covariates to discern their influence. Initially, Model 1 solely incorporated insulin as the independent variable. Subsequently, Model 2 expanded to include other blood sample indicators, including glucose, TG, TC, Apo-A1, Apo-B, LPA, CRP, and CR. The factors ultimately included in the Model 2 were determined by the backward method. Building upon Model 2, Model 3 further incorporated personal basic information, including age, gender, hypertension, and DM. A significance level of P < 0.05 was used. All statistical analyses were performed using R version 4.4.0 (The R Foundation, Vienna, Austria). Results Table 1 provides the baseline characteristics of the study population in 2009. The median age of the 2329 participants was 68.00 years (63.00–73.00 years), with females comprising 53.07% of the cohort. According to AWGS criteria, 718 participants (30.83%) were diagnosed with sarcopenia. The sarcopenia group was more likely to comprise females and older individuals (P < 0.01). Regarding blood test indicators, the sarcopenia group exhibited lower levels of glucose, insulin, TC, TG, LDL-C, ApoB, and CRP (P < 0.01). On the other hand, the sarcopenia group had higher levels of HDL-C, ApoA1, and LPA (P < 0.01). There was no significant difference in CR levels between the two groups (P = 0.11). Table 1 Baseline characteristics of study population in 2009 Total (N = 2329) Non-sarcopenia (N = 1611) Sarcopenia (N = 718) P value Age (year) 68.00 (63.00,73.00) 66.00 (63.00,72.00) 71.00 (65.00,77.00) < 0.01 Gender < 0.01 Female 1236 (53.07%) 805 (49.97%) 431 (60.03%) Male 1093 (46.93%) 806 (50.03%) 287 (39.97%) Hypertension < 0.01 No 1681 (72.18%) 1090 (67.66%) 591 (82.31%) Yes 648 (27.82%) 521 (32.34%) 127 (17.69%) DM < 0.01 No 2183 (93.73%) 1472 (91.37%) 711 (99.03%) Yes 146 (6.27%) 139 (8.63%) 7 (0.97%) ASMI (Kg/m 2 ) 6.61 (5.66,7.44) 7.11 (6.12,7.72) 5.27 (4.90,6.62) < 0.01 Glucose (mmol/L) 5.29 (4.84,5.93) 5.39 (4.92,6.11) 5.13 (4.70,5.61) < 0.01 Insulin (uIU/mL) 10.49 (7.19,15.90) 11.90 (8.16,17.51) 8.36 (6.05,12.18) < 0.01 TG (mg/dL) 116.03 (79.72,173.60) 131.09 (89.46,195.31) 91.23 (67.32,130.20) < 0.01 TC (mg/dL) 192.96 (169.76,219.64) 196.06 (171.69,222.35) 187.16 (165.89,212.68) < 0.01 HDL-C (mg/dL) 54.52 (45.63,64.58) 51.82 (44.08,61.87) 59.94 (51.04,70.38) < 0.01 LDL-C (mg/dL) 120.26 (98.61,145.01) 122.97 (100.93,148.30) 114.66 (93.58,138.44) < 0.01 Apo-A1 (mg/dL) 112.00 (97.00,131.00) 110.00 (95.00,128.00) 116.00 (102.00,138.00) < 0.01 Apo-B (mg/dL) 95.00 (78.00,113.00) 98.00 (81.00,116.00) 87.00 (73.00,103.00) < 0.01 LPA (mg/L) 0.92 (0.50,2.00) 0.87 (0.50,1.79) 1.06 (0.53,2.48) < 0.01 CRP (mg/L) 2.00 (1.00,4.00) 2.00 (1.00,4.00) 1.00 (1.00,3.00) < 0.01 CR (mg/dL) 1.01 (0.89,1.15) 1.01 (0.89,1.15) 1.00 (0.88,1.14) 0.11 ApoA1: apolipoprotein a1; Apo-B: apolipoprotein b; ASMI: appendicular skeletal muscle mass index; CR: creatinine; CRP: C-reactive protein; DM: diabetes mellitus; HDL-C: high-density lipoprotein cholestero; LDL-C: low-density lipoprotein cholesterol; LPA: lipoprotein a; TC: total cholesterol; TG: triglyceride. The median level of ASMI of the two groups were 7.11 and 5.27, respectively. These scores demonstrated a significant decline from the non-sarcopenia group to the sarcopenia group (P < 0.01). Figure 2 illustrates the cross-sectional relationship between insulin and ASMI in the 2009 wave. In the crude model, insulin levels were positively correlated with ASMI (P < 0.01; Fig. 2 A). In the adjusted model by glucose, HDL-C, LPA, CR, and insulin, similar patterns were also observed with statistical significance (P < 0.01, Fig. 2 B). Among the 944 longitudinal analytic samples, 92 participants (9.75%) developed new-onset sarcopenia in 2015. The incidence rate of sarcopenia was 7.45% in the high insulin group, whereas it was 12.44% in the low insulin group, indicating a significant difference (P = 0.01) (Table 2 ). Figure 3 presents the longitudinal association between sarcopenia and insulin using logistic regression models. In the unadjusted model, compared to the high insulin group, individuals with low insulin level had a higher risk of developing sarcopenia (HR = 0.955, 95% CI: 0.923–0.985, P < 0.01). This pattern persisted in the fully adjusted model, accounting for age, gender, hypertension, DM, HDL-C, and insulin, with the result remaining statistically significant (HR = 0.958, 95% CI: 0.925–0.989, P = 0.01). Table 2 Baseline characteristics of the follow-up cohort from 2015 Total (N = 944) High insulin level (N = 510) Low insulin level (N = 434) P value Age (year) 71.00 (68.00,75.00) 71.00 (68.00,76.00) 71.00 (68.00,75.00) 0.73 Gender < 0.01 Female 485 (51.38%) 288 (56.47%) 197 (45.39%) Male 459 (48.62%) 222 (43.53%) 237 (54.61%) Hypertension 0.29 No 593 (62.82%) 312 (61.18%) 281 (64.75%) Yes 351 (37.18%) 198 (38.82%) 153 (35.25%) DM < 0.01 No 853 (90.36%) 443 (86.86%) 410 (94.47%) Yes 91 (9.64%) 67 (13.14%) 24 (5.53%) Sarcopenia : 0.01 No 852 (90.25%) 472 (92.55%) 380 (87.56%) Yes 92 (9.75%) 38 (7.45%) 54 (12.44%) Glucose (mmol/L) 5.35 (4.88,5.98) 5.59 (5.07,6.53) 5.15 (4.71,5.54) < 0.01 Insulin (uIU/mL) 11.37 (7.71,17.15) 16.58 (13.35,23.04) 7.34 (5.51,9.01) < 0.01 HDL-C (mg/dL) 52.20 (44.76,61.87) 49.11 (42.92,59.16) 55.10 (46.79,63.81) < 0.01 LPA (mg/L) 0.85 (0.50,1.76) 0.76 (0.50,1.55) 0.94 (0.55,1.92) 0.01 CR (mg/dL) 1.00 (0.88,1.13) 1.00 (0.90,1.12) 0.98 (0.87,1.13) 0.37 CR: creatinine; DM: diabetes mellitus; HDL-C: high-density lipoprotein cholestero; LPA: lipoprotein a. Discussion This study investigated the relationship between sarcopenia and insulin among individuals aged over 60 within Chinese communities, utilizing nationally representative data. Our cross-sectional analysis revealed a positive correlation between ASMI and insulin. Moreover, our longitudinal analysis demonstrated that older adults with lower insulin level were at an elevated risk of developing new-onset sarcopenia. In this study, the prevalence of sarcopenia falls within the intermediate range compared to previous research [ 20 , 21 ]. There are several reasons that could explain this discrepancy [ 22 ]. First, estimates of sarcopenia prevalence are influenced by the diagnostic criteria employed. Second, prevalence estimates may differ based on the assessment techniques utilized. Third, prevalence estimates can vary across different populations and regions. On the other hand, the incidence of new-onset sarcopenia observed in this study is similar to that reported in a previous study [ 23 ]. As an anabolic hormone, insulin promotes protein synthesis by facilitating the uptake of amino acids into muscle tissues [ 24 ]. Our study results indicate that insulin levels are positively correlated with ASMI and serve as a protective factor against sarcopenia. A previous study has reached similar conclusions, but our larger sample size strengthens the evidence in this area [ 25 ]. The relationship between insulin levels and the decline in muscle mass and function in older adults, particularly those with DM, is complex. Traditionally, insulin resistance has been considered central to the onset of DM, leading to opposing hypotheses [ 26 ]: one suggests that insulin resistance contributes to the development of sarcopenia, while the other posits that sarcopenia is a risk factor for insulin resistance and DM. However, mounting evidence indicates that disordered insulin secretion, rather than insulin resistance, plays a crucial role in the progression of DM [ 27 , 28 ]. In aging and diabetes, diminished insulin signaling impairs muscle protein synthesis and enhances muscle protein degradation, resulting in muscle mass loss and eventual sarcopenia. Therefore, insulin therapy slows the progression of sarcopenia in individuals with DM [ 29 ]. However, in a cohort study from Mexico involving community-dwelling older adults without other chronic health conditions, hyperinsulinemia, an early indicator of insulin resistance, was linked to a reduction in ASM [ 30 ]. Given the considerable heterogeneity of sarcopenia across diverse populations, further investigation is warranted to determine whether this could elucidate the conflicting results observed in different population. In addition to insulin, ASMI exhibited positive correlations with glucose and CR, while demonstrating negative correlations with HDL-C and LPR among the blood indicators. Similar findings have also been reported in a previous study [ 31 ]. Therefore, ASMI is also associated with liver function, and renal function other than β cell function. Sarcopenia correlates with fibrotic burden in individuals diagnosed with chronic hepatitis B. Moreover, ASMI experiences a notable decrease during antiviral therapy for chronic hepatitis B [ 32 ]. Progressive renal dysfunction is linked to diminished muscle strength and physical performance. Among older men residing in the community, even mild-to-moderate renal impairment at the outset is correlated with deteriorations in grip strength, gait speed, and overall muscle function over time [ 33 ]. Our longitudinal analysis, utilizing nationally representative data, indicated that the protective factors of sarcopenia include hypertension besides insulin, while the risk factors include age and HDL-C. Within this study, hypertension was found to reduce the risk of sarcopenia, a finding consistent with prior research [ 34 ]. Generally, nutritional and exercise therapies are advocated for hypertension management [ 35 ], both of which have been shown to mitigate sarcopenia [ 36 ]. Nevertheless, further investigation is warranted to elucidate the relationship between hypertension and sarcopenia prevention. In middle-aged and older Chinese adults, each incremental unit rise in HDL-C levels corresponds to a 42% increase in the likelihood of developing sarcopenia at 4 years follow up, emphasizing the importance of effectively managing high HDL-C levels in sarcopenia prevention [ 37 ]. A study from China indicates that the prevalence of sarcopenia among males aged 60–69 years, 70–79 years, and over 80 years is 1.5%, 9.6%, and 33.1%, respectively. Therefore, prior to reaching 80 years of age, preserving muscle mass warrants primary consideration, whereas after surpassing this age threshold, emphasis should shift towards enhancing muscle strength and function to mitigate disability risk [ 38 ]. It is important to acknowledge the limitations of this study. Firstly, while we adjusted for a comprehensive set of potential confounders based on existing knowledge, certain additional confounding factors, such as physical activity and dietary intake, were not accounted for in our analysis. Secondly, the observational nature of our study made it susceptible to recall bias inherent in questionnaire surveys. Thirdly, while our longitudinal study revealed a stronger correlation between sarcopenia and insulin compared to the cross-sectional analysis, the underlying biological mechanisms remain unclear. Therefore, further experimental studies are warranted to elucidate and confirm this association. Conclusions In summary, this study highlights a potential association between insulin and the onset of sarcopenia in Chinese individuals aged 60 and above, offering novel insights into a potential causal relationship. This endeavor will facilitate the exploration of efficacious approaches for treating sarcopenia using insulin prior to the establishment of evidence-based clinical guidelines. Abbreviations ApoA1: apolipoprotein a1; Apo-B: apolipoprotein b; ASM: appendicular skeletal muscle mass; ASMI: appendicular skeletal muscle mass index; AWGS: Asia Working Group for Sarcopenia; CHNS: China Health and Nutrition Survey; CI: confidence interval; CR: creatinine; CRP: C-reactive protein; DM: diabetes mellitus; HDL-C: high-density lipoprotein cholestero; HR: Hazard ratio; LDL-C: low-density lipoprotein cholesterol; LPA: lipoprotein a; OLS: ordinary least squares; TC: total cholesterol; TG: triglyceride. Declarations Ethics approval and consent to participate The data used in this paper are publicly available, ethically approved, and the subjects have given their informed consent. Consent for publication All authors read and approved the final manuscript. Availability of data and materials The datasets for this study can be found in the China Health and Nutrition Survey (https://www.cpc.unc.edu/projects/china). Codes are available on request from the authors. Competing interests The authors report no conflicts of interest in this work. Funding This study was supported by the National Natural Science Foundation of China (No. 82002837). Authors’ contributions Wangmi Liu conceived and designed the experiments. Guofang Sun and Jianjun Liang performed the experiments and analyzed the data. Guofang Sun wrote the manuscript. Dechao Chen and Kongjun Zhao proofread the manuscript and data. All authors read and approved the manuscript. Informed consent Not applicable. Clinical Trial Number Not applicable. References Chen X, Giles J, Yao Y, Yip W, Meng Q, Berkman L, Chen H, Chen X, Feng J, Feng Z et al : The path to healthy ageing in China: a Peking University-Lancet Commission . Lancet 2022, 400 (10367):1967-2006. Fang EF, Scheibye-Knudsen M, Jahn HJ, Li J, Ling L, Guo H, Zhu X, Preedy V, Lu H, Bohr VA et al : A research agenda for aging in China in the 21st century . Ageing Res Rev 2015, 24 (Pt B):197-205. 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Wang M, Yang Z, Zhai H: Association of high-density lipoprotein cholesterol with sarcopenia in Chinese community-dwelling middle-aged and older adults: Evidence from 4-year longitudinal study from the CHARLS . Gerontology 2024. Cao M, Lian J, Lin X, Liu J, Chen C, Xu S, Ma S, Wang F, Zhang N, Qi X et al : Prevalence of sarcopenia under different diagnostic criteria and the changes in muscle mass, muscle strength, and physical function with age in Chinese old adults . BMC Geriatr 2022, 22 (1):889. 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. 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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-4581143","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":321189208,"identity":"abde5d60-588f-4e9a-bd26-94da8da5423f","order_by":0,"name":"Guofang Sun","email":"","orcid":"","institution":"Shengzhou Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Guofang","middleName":"","lastName":"Sun","suffix":""},{"id":321189210,"identity":"465f6ee4-f7c2-4955-9a2d-1511e96fd945","order_by":1,"name":"Jianjun Liang","email":"","orcid":"","institution":"Daishan First People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jianjun","middleName":"","lastName":"Liang","suffix":""},{"id":321189211,"identity":"a659be45-9e42-4665-b516-eac5772aa88c","order_by":2,"name":"Dechao Chen","email":"","orcid":"","institution":"Daishan First People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dechao","middleName":"","lastName":"Chen","suffix":""},{"id":321189212,"identity":"0c69a1e9-ce84-423b-b846-50bf83b18483","order_by":3,"name":"Kongjun Zhao","email":"","orcid":"","institution":"Daishan First People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kongjun","middleName":"","lastName":"Zhao","suffix":""},{"id":321189213,"identity":"4620a981-afc0-47ab-bdbd-bc783edc2857","order_by":4,"name":"Wangmi Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIie3RsQrCMBCA4StCpkjWuNRXiLgICr5Ki2Anwcn5QkEXHyDg4DP4BikHTqWuDg5Ozrp1EYxOTjVugvm3g/sISQBCoR9MCL2qk1rGANaNzIN0DKG8rUd9f6Jshh3Dpim+Rh8CtsA+55RtdanguiAQG2wWUa5xwgc001iqyFQE8mSbSQsKJHfKLIdStdpLAiWTZsIg1TlnlLEnufsQDmkeuesn/EkiHyJlsQT3yD0D+3mxrjIujx/I+LC6gPvKbtfQ7lwvhrEwH8jbefb1mdx33yXwi+VQKBT6qx6bxkcG6IohhAAAAABJRU5ErkJggg==","orcid":"","institution":"Zhejiang University","correspondingAuthor":true,"prefix":"","firstName":"Wangmi","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-06-14 09:51:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4581143/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4581143/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60610717,"identity":"1d98a8a1-7c7d-4f97-8efe-5860fbf1f1cc","added_by":"auto","created_at":"2024-07-18 18:28:50","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3181825,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram illustrating the process of sample selection.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4581143/v1/5702ebae6912ead77762f2b0.jpg"},{"id":60610716,"identity":"defbb2b1-a016-4717-8de8-78232a9755c1","added_by":"auto","created_at":"2024-07-18 18:28:50","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2395082,"visible":true,"origin":"","legend":"\u003cp\u003eOLS regression model on ASMI and insulin. A: The crude model based on insulin only. B: The adjusted model using backward method included glucose, HDL-C, LPA, CR, and insulin.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4581143/v1/2dd67114e1b437290701c176.jpg"},{"id":60610718,"identity":"00fba860-1865-4bb8-bb82-08ad8010b939","added_by":"auto","created_at":"2024-07-18 18:28:51","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4139887,"visible":true,"origin":"","legend":"\u003cp\u003eLogistic regression model on sarcopenia and insulin. A: The crude model based on insulin only. B: The adjusted model based on blood biomarkers included glucose, HDL-C, LPA, CR, and insulin. C: The fully adjusted model included age, gender, hypertension, DM, HDL-C, and insulin.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4581143/v1/e88da49f933b99d35b0dc946.jpg"},{"id":62612459,"identity":"ab53bc72-92e4-4a18-8982-505828866f11","added_by":"auto","created_at":"2024-08-16 12:24:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11363808,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4581143/v1/3daaaa58-1fec-4caa-b65d-4e9938917e78.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between insulin and sarcopenia in elderly Chinese individuals: a cross-sectional and longitudinal study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs aging process accelerates in China, the proportion of elderly population is steadily rising, leading to a significant increase in societal burdens [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. With the growing elderly population, the demand for social pension security, healthcare services, and other related resources will also see a substantial surge. This will impose heavy pressure on the country's social security system, healthcare system, and family economies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, addressing the challenges brought about by aging to society, such as sarcopenia, requires concerted efforts from the government, various sectors of society, and families.\u003c/p\u003e \u003cp\u003eSarcopenia, characterized by progressive loss of skeletal muscle mass, strength, and function, represents a prevalent geriatric syndrome associated with adverse outcomes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Its epidemiology demonstrates a rising prevalence worldwide, particularly in aging populations. The multifactorial etiology involves age-related hormonal changes, chronic inflammation, sedentary lifestyle, and inadequate nutrition. Clinically, sarcopenia manifests as decreased muscle mass and strength, impaired physical performance, and increased risk of falls and fractures [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Prevention and management strategies encompass a multidisciplinary approach, including nutritional optimization with adequate protein intake and vitamin D supplementation, resistance exercise training, and addressing underlying comorbidities [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Pharmacological interventions such as anabolic agents and myostatin inhibitors are emerging as potential therapeutic options [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInsulin, primarily secreted by pancreatic beta cells, plays a pivotal role in glucose homeostasis by facilitating glucose uptake into muscle and adipose tissue. Impaired insulin signaling pathways contribute to insulin resistance, leading to decreased glucose uptake and utilization in skeletal muscle [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, insulin resistance has been unequivocally established as a pivotal contributor to the pathogenesis of sarcopenia, eliciting muscle wasting primarily through the subsequent mechanisms: (1) heightened protein catabolism coupled with diminished protein synthesis within the skeletal musculature; (2) upregulated expression of the FoxO family, orchestrating skeletal muscle attenuation either through direct means or by fostering protein degradation; and (3) induction of autophagy within skeletal muscle cells [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven the shared underlying determinants between muscle mass and insulin resistance, recent investigations have posited a potential nexus between sarcopenia and insulin. Therefore, the present study utilized data sourced from the China Health and Nutrition Survey (CHNS), a nationally representative dataset. By conducting a cross-sectional analysis in 2009, we scrutinized the relationship between sarcopenia and insulin in Chinese elderly individuals aged 60 years and above. Furthermore, we undertook longitudinal analyses based on data from 2015 to explore the enduring association of insulin with sarcopenia. This endeavor aimed to furnish robust scientific evidence pertaining to the etiology, early intervention, and preventive measures against sarcopenia.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eThe study cohort was drawn from the CHNS, a nationwide longitudinal study administered by the Chinese Center for Disease Control and Prevention in collaboration with the University of North Carolina. Employing a multi-stage, random cluster sampling methodology, the CHNS sought representation across socioeconomic strata, encompassing low, middle, and high-income brackets. Within each province, a weighted sampling framework guided the selection of four counties and two cities. Subsequently, villages within counties and urban as well as suburban neighborhoods within cities were chosen through random sampling procedures. Within these locales, households were randomly identified, and all household members were included in the survey. Comprehensive descriptions of the cohort and sampling methodology have been previously published [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The assessment of blood biomarkers was conducted in 2009, and the final follow-up time was in 2015. Therefore, these two waves of data were respectively utilized for cross-sectional studies and longitudinal cohort analyses. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the schematic diagram of research process.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of sarcopenia\u003c/h2\u003e \u003cp\u003eSarcopenia evaluation followed the Asia Working Group for Sarcopenia (AWGS) 2019 guidelines, incorporating assessments of muscle strength, appendicular skeletal muscle mass (ASM), and physical performance [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Among these, ASM plays a pivotal role in fundamental functions such as mobility. The calculation formula is as follows: ASM\u0026thinsp;=\u0026thinsp;0.193\u0026times;weight (kg)\u0026thinsp;+\u0026thinsp;0.107\u0026times;height (cm)\u0026thinsp;\u0026minus;\u0026thinsp;4.157\u0026times;gender\u0026thinsp;\u0026minus;\u0026thinsp;0.037\u0026times;age (years)\u0026thinsp;\u0026minus;\u0026thinsp;2.631. Gender is set to 1 if male, and 2 if female [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This equation exhibits a robust R\u003csup\u003e2\u003c/sup\u003e value of 0.90, indicating its commendable predictive capability for ASM in Chinese adults. Cross-validation further demonstrated a strong correlation coefficient of 0.941 with dual X-ray absorptiometry, underscoring its reliability. Moreover, this equation has been consistently applied in various research endeavors involving similar study populations to ours [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The ASM index (ASMI), calculated by dividing ASM by the square of height in meters, serves as a key metric for categorizing low muscle mass. In accordance with the 2021 Chinese consensus on sarcopenia, low muscle mass was delineated by ASMI values below 7.0 kg/m\u003csup\u003e2\u003c/sup\u003e for males and 5.4 kg/m\u003csup\u003e2\u003c/sup\u003e for females.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eThis study employed adjustments for demographic factors, medical history, and blood biomarkers. Covariates were carefully chosen in alignment with existing research and clinical directives [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Demographic variables encompassed age, sex, and ASM. Medical history, including a record of diabetes mellitus (DM) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and hypertension [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], was included due to their potential influence on dietary habits owing to therapeutic regimens.\u003c/p\u003e \u003cp\u003eIn the survey conducted in 2009, blood samples constituted vital specimens. All individuals aged seven years and older (inclusive of seven years) were mandated to provide a 12ml blood sample, drawn from an empty stomach, distributed across three tubes each containing 4ml. The intricate procedure for measurement can be referenced in the CHNS operational manual. The blood sample indicators included in this study encompassed the following items: insulin, glucose, triglyceride (TG), total cholesterol (TC), apolipoprotein a1 (ApoA1), apolipoprotein b (Apo-B), lipoprotein a (LPA), C-reactive protein (CRP), creatinine (CR), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were utilized to compare patients from different groups. Continuous variables were summarized using mean and standard deviation or medians and interquartile ranges, while categorical variables were presented as frequencies. Normality of continuous variables were assessed using the Shapiro-Wilk test. Parametric data were analyzed using the two-tailed T-test, while nonparametric data were compared using the Wilcoxon rank-sum test. Categorical data were analyzed using the χ2 test.\u003c/p\u003e \u003cp\u003eIn 2009, we utilized an Ordinary Least Squares (OLS) regression model to explore the cross-sectional relationship between ASMI and insulin. The findings were articulated through regression coefficients (β) accompanied by their corresponding 95% confidence interval (CI). Moreover, longitudinal data spanning from 2009 to 2015 allowed us to delve into the association between insulin and the onset of sarcopenia using a logistic regression model, expressed in Hazard ratio (HR) and 95% CI. We delineated three distinct models, each integrating various covariates to discern their influence. Initially, Model 1 solely incorporated insulin as the independent variable. Subsequently, Model 2 expanded to include other blood sample indicators, including glucose, TG, TC, Apo-A1, Apo-B, LPA, CRP, and CR. The factors ultimately included in the Model 2 were determined by the backward method. Building upon Model 2, Model 3 further incorporated personal basic information, including age, gender, hypertension, and DM. A significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used. All statistical analyses were performed using R version 4.4.0 (The R Foundation, Vienna, Austria).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides the baseline characteristics of the study population in 2009. The median age of the 2329 participants was 68.00 years (63.00\u0026ndash;73.00 years), with females comprising 53.07% of the cohort. According to AWGS criteria, 718 participants (30.83%) were diagnosed with sarcopenia. The sarcopenia group was more likely to comprise females and older individuals (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Regarding blood test indicators, the sarcopenia group exhibited lower levels of glucose, insulin, TC, TG, LDL-C, ApoB, and CRP (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). On the other hand, the sarcopenia group had higher levels of HDL-C, ApoA1, and LPA (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). There was no significant difference in CR levels between the two groups (P\u0026thinsp;=\u0026thinsp;0.11).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of study population in 2009\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (N\u0026thinsp;=\u0026thinsp;2329)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-sarcopenia (N\u0026thinsp;=\u0026thinsp;1611)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSarcopenia (N\u0026thinsp;=\u0026thinsp;718)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (year)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.00 (63.00,73.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.00 (63.00,72.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.00 (65.00,77.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1236 (53.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e805 (49.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e431 (60.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1093 (46.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e806 (50.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e287 (39.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1681 (72.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1090 (67.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e591 (82.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e648 (27.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e521 (32.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127 (17.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2183 (93.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1472 (91.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e711 (99.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146 (6.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139 (8.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (0.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eASMI (Kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.61 (5.66,7.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.11 (6.12,7.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.27 (4.90,6.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlucose (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.29 (4.84,5.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.39 (4.92,6.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.13 (4.70,5.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInsulin (uIU/mL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.49 (7.19,15.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.90 (8.16,17.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.36 (6.05,12.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTG (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116.03 (79.72,173.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131.09 (89.46,195.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.23 (67.32,130.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTC (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e192.96 (169.76,219.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e196.06 (171.69,222.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e187.16 (165.89,212.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL-C (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.52 (45.63,64.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.82 (44.08,61.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.94 (51.04,70.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL-C (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120.26 (98.61,145.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122.97 (100.93,148.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114.66 (93.58,138.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eApo-A1 (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112.00 (97.00,131.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110.00 (95.00,128.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116.00 (102.00,138.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eApo-B (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.00 (78.00,113.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.00 (81.00,116.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.00 (73.00,103.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLPA (mg/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.50,2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87 (0.50,1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 (0.53,2.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRP (mg/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.00 (1.00,4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00 (1.00,4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (1.00,3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCR (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.89,1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01 (0.89,1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.88,1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eApoA1: apolipoprotein a1; Apo-B: apolipoprotein b; ASMI: appendicular skeletal muscle mass index; CR: creatinine; CRP: C-reactive protein; DM: diabetes mellitus; HDL-C: high-density lipoprotein cholestero; LDL-C: low-density lipoprotein cholesterol; LPA: lipoprotein a; TC: total cholesterol; TG: triglyceride.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe median level of ASMI of the two groups were 7.11 and 5.27, respectively. These scores demonstrated a significant decline from the non-sarcopenia group to the sarcopenia group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the cross-sectional relationship between insulin and ASMI in the 2009 wave. In the crude model, insulin levels were positively correlated with ASMI (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In the adjusted model by glucose, HDL-C, LPA, CR, and insulin, similar patterns were also observed with statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e Among the 944 longitudinal analytic samples, 92 participants (9.75%) developed new-onset sarcopenia in 2015. The incidence rate of sarcopenia was 7.45% in the high insulin group, whereas it was 12.44% in the low insulin group, indicating a significant difference (P\u0026thinsp;=\u0026thinsp;0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the longitudinal association between sarcopenia and insulin using logistic regression models. In the unadjusted model, compared to the high insulin group, individuals with low insulin level had a higher risk of developing sarcopenia (HR\u0026thinsp;=\u0026thinsp;0.955, 95% CI: 0.923\u0026ndash;0.985, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This pattern persisted in the fully adjusted model, accounting for age, gender, hypertension, DM, HDL-C, and insulin, with the result remaining statistically significant (HR\u0026thinsp;=\u0026thinsp;0.958, 95% CI: 0.925\u0026ndash;0.989, P\u0026thinsp;=\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the follow-up cohort from 2015\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (N\u0026thinsp;=\u0026thinsp;944)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh insulin level (N\u0026thinsp;=\u0026thinsp;510)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow insulin level (N\u0026thinsp;=\u0026thinsp;434)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (year)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.00 (68.00,75.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.00 (68.00,76.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.00 (68.00,75.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e485 (51.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e288 (56.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e197 (45.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e459 (48.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e222 (43.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e237 (54.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e593 (62.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e312 (61.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e281 (64.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e351 (37.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e198 (38.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e153 (35.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e853 (90.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e443 (86.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e410 (94.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91 (9.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (13.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (5.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSarcopenia\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e852 (90.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e472 (92.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e380 (87.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92 (9.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (7.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (12.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlucose (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.35 (4.88,5.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.59 (5.07,6.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.15 (4.71,5.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInsulin (uIU/mL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.37 (7.71,17.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.58 (13.35,23.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.34 (5.51,9.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL-C (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.20 (44.76,61.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.11 (42.92,59.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.10 (46.79,63.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLPA (mg/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85 (0.50,1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76 (0.50,1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94 (0.55,1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCR (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.88,1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (0.90,1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.87,1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eCR: creatinine; DM: diabetes mellitus; HDL-C: high-density lipoprotein cholestero; LPA: lipoprotein a.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the relationship between sarcopenia and insulin among individuals aged over 60 within Chinese communities, utilizing nationally representative data. Our cross-sectional analysis revealed a positive correlation between ASMI and insulin. Moreover, our longitudinal analysis demonstrated that older adults with lower insulin level were at an elevated risk of developing new-onset sarcopenia.\u003c/p\u003e \u003cp\u003eIn this study, the prevalence of sarcopenia falls within the intermediate range compared to previous research [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. There are several reasons that could explain this discrepancy [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. First, estimates of sarcopenia prevalence are influenced by the diagnostic criteria employed. Second, prevalence estimates may differ based on the assessment techniques utilized. Third, prevalence estimates can vary across different populations and regions. On the other hand, the incidence of new-onset sarcopenia observed in this study is similar to that reported in a previous study [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs an anabolic hormone, insulin promotes protein synthesis by facilitating the uptake of amino acids into muscle tissues [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Our study results indicate that insulin levels are positively correlated with ASMI and serve as a protective factor against sarcopenia. A previous study has reached similar conclusions, but our larger sample size strengthens the evidence in this area [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The relationship between insulin levels and the decline in muscle mass and function in older adults, particularly those with DM, is complex. Traditionally, insulin resistance has been considered central to the onset of DM, leading to opposing hypotheses [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]: one suggests that insulin resistance contributes to the development of sarcopenia, while the other posits that sarcopenia is a risk factor for insulin resistance and DM. However, mounting evidence indicates that disordered insulin secretion, rather than insulin resistance, plays a crucial role in the progression of DM [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In aging and diabetes, diminished insulin signaling impairs muscle protein synthesis and enhances muscle protein degradation, resulting in muscle mass loss and eventual sarcopenia. Therefore, insulin therapy slows the progression of sarcopenia in individuals with DM [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, in a cohort study from Mexico involving community-dwelling older adults without other chronic health conditions, hyperinsulinemia, an early indicator of insulin resistance, was linked to a reduction in ASM [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Given the considerable heterogeneity of sarcopenia across diverse populations, further investigation is warranted to determine whether this could elucidate the conflicting results observed in different population.\u003c/p\u003e \u003cp\u003eIn addition to insulin, ASMI exhibited positive correlations with glucose and CR, while demonstrating negative correlations with HDL-C and LPR among the blood indicators. Similar findings have also been reported in a previous study [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Therefore, ASMI is also associated with liver function, and renal function other than β cell function. Sarcopenia correlates with fibrotic burden in individuals diagnosed with chronic hepatitis B. Moreover, ASMI experiences a notable decrease during antiviral therapy for chronic hepatitis B [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Progressive renal dysfunction is linked to diminished muscle strength and physical performance. Among older men residing in the community, even mild-to-moderate renal impairment at the outset is correlated with deteriorations in grip strength, gait speed, and overall muscle function over time [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur longitudinal analysis, utilizing nationally representative data, indicated that the protective factors of sarcopenia include hypertension besides insulin, while the risk factors include age and HDL-C. Within this study, hypertension was found to reduce the risk of sarcopenia, a finding consistent with prior research [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Generally, nutritional and exercise therapies are advocated for hypertension management [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], both of which have been shown to mitigate sarcopenia [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Nevertheless, further investigation is warranted to elucidate the relationship between hypertension and sarcopenia prevention. In middle-aged and older Chinese adults, each incremental unit rise in HDL-C levels corresponds to a 42% increase in the likelihood of developing sarcopenia at 4 years follow up, emphasizing the importance of effectively managing high HDL-C levels in sarcopenia prevention [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. A study from China indicates that the prevalence of sarcopenia among males aged 60\u0026ndash;69 years, 70\u0026ndash;79 years, and over 80 years is 1.5%, 9.6%, and 33.1%, respectively. Therefore, prior to reaching 80 years of age, preserving muscle mass warrants primary consideration, whereas after surpassing this age threshold, emphasis should shift towards enhancing muscle strength and function to mitigate disability risk [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is important to acknowledge the limitations of this study. Firstly, while we adjusted for a comprehensive set of potential confounders based on existing knowledge, certain additional confounding factors, such as physical activity and dietary intake, were not accounted for in our analysis. Secondly, the observational nature of our study made it susceptible to recall bias inherent in questionnaire surveys. Thirdly, while our longitudinal study revealed a stronger correlation between sarcopenia and insulin compared to the cross-sectional analysis, the underlying biological mechanisms remain unclear. Therefore, further experimental studies are warranted to elucidate and confirm this association.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, this study highlights a potential association between insulin and the onset of sarcopenia in Chinese individuals aged 60 and above, offering novel insights into a potential causal relationship. This endeavor will facilitate the exploration of efficacious approaches for treating sarcopenia using insulin prior to the establishment of evidence-based clinical guidelines.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eApoA1: apolipoprotein a1; Apo-B: apolipoprotein b; ASM: appendicular skeletal muscle mass; ASMI: appendicular skeletal muscle mass index; AWGS: Asia Working Group for Sarcopenia; CHNS: China Health and Nutrition Survey; CI: confidence interval; CR: creatinine; CRP: C-reactive protein; DM: diabetes mellitus; HDL-C: high-density lipoprotein cholestero; HR: Hazard ratio; LDL-C: low-density lipoprotein cholesterol; LPA: lipoprotein a; OLS: ordinary least squares; TC: total cholesterol; TG: triglyceride.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e The data used in this paper are publicly available, ethically approved, and the subjects have given their informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e The datasets for this study can be found in the China Health and Nutrition Survey (https://www.cpc.unc.edu/projects/china). Codes are available on request from the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors report no conflicts of interest in this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis study was supported by the National Natural Science Foundation of China (No. 82002837).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e Wangmi Liu conceived and designed the experiments. Guofang Sun and Jianjun Liang performed the experiments and analyzed the data. Guofang Sun wrote the manuscript. Dechao Chen and Kongjun Zhao proofread the manuscript and data. All authors read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChen X, Giles J, Yao Y, Yip W, Meng Q, Berkman L, Chen H, Chen X, Feng J, Feng Z\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eThe path to healthy ageing in China: a Peking University-Lancet Commission\u003c/strong\u003e. \u003cem\u003eLancet \u003c/em\u003e2022, \u003cstrong\u003e400\u003c/strong\u003e(10367):1967-2006.\u003c/li\u003e\n\u003cli\u003eFang EF, Scheibye-Knudsen M, Jahn HJ, Li J, Ling L, Guo H, Zhu X, Preedy V, Lu H, Bohr VA\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eA research agenda for aging in China in the 21st century\u003c/strong\u003e. \u003cem\u003eAgeing Res Rev \u003c/em\u003e2015, 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adults\u003c/strong\u003e. \u003cem\u003eBMC Geriatr \u003c/em\u003e2022, \u003cstrong\u003e22\u003c/strong\u003e(1):889.\u003c/li\u003e\n\u003c/ol\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":"CHNS, sarcopenia, muscle mass, insulin","lastPublishedDoi":"10.21203/rs.3.rs-4581143/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4581143/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003eThe link between insulin and sarcopenia among older adults in China is not yet fully understood. This study seeks to investigate this association using data from a nationally representative large-scale survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe study utilized data from two waves of the China Health and Nutrition Survey (CHNS) conducted in 2009 and 2015. Subjects meeting the inclusion criteria were classified according to the Asia Working Group for Sarcopenia2019 criteria. The study employed ordinary least squares (OLS) regression models to analyze the cross-sectional association between muscle mass and insulin. Additionally, logistic regression models were utilized to examine the longitudinal association between sarcopenia and insulin.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eIn 2009, a cross-sectional association study enrolled a total of 2329 participants aged over 60 years, with 53.1% women and a median age of 68.00 years. The prevalence of sarcopenia in the study population was 30.83%, with a higher prevalence in females (60.03%). In the adjusted OLS regression model based on blood biomarker, insulin was positively associated with muscle mass (β=0.075, 95% confidence interval (CI): 0.034 - 0.117, P \u0026lt;0.01). In 2009, individuals without sarcopenia were divided into two groups based on the median value of insulin in the total population. When 944 individuals were followed up in 2015 to assess the incidence of sarcopenia, a significant difference was found between the two groups (12.44% vs 7.45%, P=0.01). The adjusted logistic regression models indicated that higher insulin levels were associated with a reduced incidence of sarcopenia (Hazard ratio =0.958, 95% CI: 0.925 - 0.989, P=0.01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eAdequate insulin could potentially serve as a protective factor in preserving healthy muscle mass among Chinese adults aged 60 and above.\u003c/p\u003e","manuscriptTitle":"Association between insulin and sarcopenia in elderly Chinese individuals: a cross-sectional and longitudinal study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-18 18:28:46","doi":"10.21203/rs.3.rs-4581143/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":"f9b43111-e035-4499-8c9a-b96fc6f62126","owner":[],"postedDate":"July 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-16T12:16:31+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-18 18:28:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4581143","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4581143","identity":"rs-4581143","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}