The relationship between skeletal muscle mass index and spinal pain: a cross-sectional study comparing middle-aged and elderly individuals in China and the United States

The relationship between skeletal muscle mass index and spinal pain: a cross-sectional study comparing middle-aged and elderly individuals in China and the United States | 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 The relationship between skeletal muscle mass index and spinal pain: a cross-sectional study comparing middle-aged and elderly individuals in China and the United States dongsheng Yuan, renkun Zhao, penghui Li, Bo Xu, guoliang Ma, zhizhuang Wang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8825049/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Spinal pain is a leading cause of global disability, with prevalence increasing with age. While skeletal muscle mass index (SMI) is a key indicator of sarcopenia and influences spinal pain through biomechanical, metabolic, and neuromuscular mechanisms, large-scale cross-national studies examining this relationship are lacking. This study aimed to investigate the association between SMI and spinal pain in middle-aged and elderly populations from China and the United States. Methods This cross-sectional study used data from the National Health and Nutrition Examination Survey (NHANES, 1999–2004) and the China Health and Retirement Longitudinal Study (CHARLS, 2015). Baseline analysis of the population, multivariable logistic regression, restricted cubic spline (RCS) analysis, and subgroup analyses were performed to assess associations, adjusted for demographic, lifestyle, and health-related covariates, and to evaluate SMI's capacity to predict spinal pain. We employed the receiver operating characteristic (ROC) curves and the area under the curve (AUC). Results A total of 6,563 participants from NHANES and 12,221 from CHARLS were included. After full adjustment, higher SMI was significantly associated with a lower risk of spinal pain in both cohorts (NHANES: OR for trend < 1, P < 0.01; CHARLS: OR for trend < 1, P 0.05). Subgroup analysis identified a differential effect modifier. ROC analysis compared the predictive performance of two databases. Conclusions Higher SMI is independently associated with a reduced risk of spinal pain in both Chinese and American middle-aged and elderly populations, exhibiting a linear relationship. The protective effect of SMI is influenced by specific population factors, underscoring the importance of different racial/social prevention strategies. Moreover, CHARLS has better predictive ability than NHANES. Future research should longitudinally verify the causal relationship between these findings and explore targeted interventions to enhance muscle mass to prevent or treat spinal pain. Skeletal muscle mass index Spinal pain Cross-sectional study Sarcopenia NHANES CHARLS Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Spinal pain is one of the major global health issues, encompassing pain syndromes in multiple regions, such as neck and back pain, and constitutes one of the most significant impacts on healthcare systems and socio-economic burdens in musculoskeletal disorders[ 1 , 2 ]. Neck and back pain have become one of the leading causes of disability worldwide. The prevalence of back pain increased by 54% between 1999 and 2015[ 3 ]. Disease incidence is directly correlated with age growth[ 4 ]. According to a Bayesian Age-Period-Cohort model developed from pertinent data, the incidence rate of neck pain is expected to rise over the next 25 years. The prevalence rate will increase by 32.5% to 269 million. The acceleration of population growth and subsequent population aging are particularly noteworthy[ 5 , 6 ]. Skeletal muscle mass index (SMI), a core indicator for evaluating sarcopenia[ 7 ], reflects the functional reserve of skeletal muscle. According to recent research, SMI may influence the development and course of spinal pain through mechanisms such as spinal stability, biomechanical stress, and inflammatory modulation, and it is also significantly associated with metabolic health and physical function[ 8 – 10 ]. Reduced muscle mass can lead to increased mechanical stress on intervertebral joints and ligaments, as well as decreased spinal stability [ 11 ]. Meanwhile, muscle tissue itself is an essential endocrine organ that can help control systemic inflammatory responses and secrete several myokines[ 12 ], which may provide physiological and pathological explanations for the relationship between SMI and spinal pain[ 13 ]. The pathophysiological link between SMI and spinal pain is increasingly recognized. Core and paraspinal muscles maintain spinal stability, and age-related sarcopenia weakens this support system[ 14 ]. Spinal muscles, particularly the psoas major and paraspinal muscles, atrophy at rates up to 29% with aging, exacerbating intervertebral disc degeneration and vertebral stress[ 15 ]. Although previous studies have shown an association between sarcopenia and neck or lower back pain, there is a lack of large-scale demographic studies specifically targeting middle-aged and elderly individuals to investigate the relationship between SMI and spinal pain. In addition, the universality of this association across international contexts may be influenced by cultural backgrounds, lifestyles, and differences in healthcare systems. By utilizing these two databases in China and the United States to test the consistency of the association between SMI and spinal pain across different demographic and cultural backgrounds, the robustness and generalizability of the research results can be improved. More research is still needed to understand the potential biological mechanisms and other confounding variables fully. Given the above background, this cross-sectional study analyzed the association between SMI and spinal pain using two nationally representative databases: NHANES (US cohort) and CHARLS (China cohort). The specific objectives include: (1) to analyze the cross-sectional correlation between SMI and the incidence rate of spinal pain; (2) to analyze the relationship between SMI and spinal pain consistent in different ethnic and cultural backgrounds. This study has important theoretical significance and clinical value. At the theoretical level, the research results will help clarify the causal relationship and potential mechanisms between muscle mass index and spinal pain, and promote knowledge progress in the field of musculoskeletal health. At the practical level, research has found that it may offer a new perspective on primary prevention and management strategies for spinal pain, namely through muscle mass enhancement interventions such as resistance training, nutritional supplementation, and oxidative stress management. Ultimately, this study hope to provide scientific evidence to promote healthy aging, prevent and control musculoskeletal disorders, reduce the personal and societal burden of spinal pain, improve the quality of life of the population, and have positive public health implications. Method and Data Study sample and data source The data used in this study came from two major databases in China and the US: the National Health and Nutrition Examination Survey (NHANES) and the China Health and Retirement Longitudinal Study (CHARLS), respectively. Each participant gave written informed consent, and the National Center for Health Statistics Research Ethics Review Board approved the NHANES data. The Peking University Institutional Review Board authorized the CHARLS data (IRB00001052-11015). NHANES Data Using a cross-sectional design, the survey was based on the 1999–2004 NHANES. This method comprises data collection from a sample of non-institutionalized people, chosen to reflect a larger community, using a specific study framework. This framework combines several sampling methods, including probability-based, cross-sectional, multistage, and subgroup-stratified sampling. Surveys are conducted every two years[ 16 ]. The NCHS Ethics Review Committee has approved the NHANES research proposal. Over three survey cycles (1999–2000, 2001–2002, and 2003–2004), 31,126 participants participated. The following were the criteria for exclusion: (1) Participants younger than 45 years old (n = 22,437); (2) Participants without core data: ASM and BMI (n = 1,155); (3) Excluding data with missing covariates, n = 935, 6,563 eligible participants were filtered into the final analysis when the exclusion criteria were applied (Fig. 1A). CHARLS Data Based on the 2015 CHARLS Wave 3, a prospective cohort study that is still being carried out under supervision from the National School of Development at Peking University. A nationally representative sample of middle-aged and elderly people in China is used to collect longitudinal data for the CHARLS project. This data covers a range of variables, including socioeconomic status and health difficulties. Its conceptual framework and evaluation standards align with those of the Health and Retirement Study (HRS). A multistage stratified probability sampling procedure was employed in the study to guarantee that the sample accurately reflected the target population. Geographically, the survey covered Chinese autonomous areas, municipalities directly under the central government, and 150 locations across 28 provinces. Since its initial data collection period in 2011–2012, CHARLS has collected data every two years on demographics, biomedical measures, population characteristics, and health-related information. The Ethics Committee of the Peking University Health Science Center approved the CHARLS study protocol. For a detailed description of the CHARLS survey's design and methods, please consult previous publications.[ 17 ]. Throughout CHARLS wave 3 in 2015, 21,095 participants took part in the study. The following criteria were utilized for exclusion: (1) Participants under 45 years of age (n = 1,382); (2) Participants without core data: ASM and BMI (n = 4,412); (3) Excluding data with missing covariates, n = 3,080, 12,221 eligible participants were included in the final analysis following the application of the exclusion criteria (Fig. 1B). Definition of SMI SMI, as the primary index for measuring sarcopenia, has been used in numerous previous studies [ 8 , 10 , 18 ]. According to the Foundation for the National Institutes of Health's (FNIH) sarcopenia guidelines, the skeletal muscle index (SMI) is computed by dividing the appendicular skeletal muscle mass by the body mass index (ASM (kg) / BMI (kg/m²)). The cut-off values for sarcopenia are < 0.789 for men and < 0.512 for women. This study's exposure variable for SMI is likewise composed of multiple data points. At the NHANES mobile examination center, dual-energy X-ray absorptiometry (DXA) scans of the entire body were performed. Due to DXA table limits, participants were not allowed to participate if they self-reported using radiographic contrast agents within the previous 7 days, had undergone nuclear medicine procedures within the previous 3 days, weighed more than 136 kg, or were taller than 196 cm. To perform whole-body DXA scans, a Hologic QDR-4500A densitometer was used. The Appendicular Skeletal Mass (ASM), a known stand-in for skeletal muscle mass, was calculated by adding the lean mass (not including bone mineral content) of the arms and legs as determined by DXA. An anthropometric formula that was validated in multiple studies and demonstrated strong agreement with dual-energy X-ray absorptiometry (DXA) results was used to determine the mass of appendicular skeletal muscle because the CHARLS database does not provide precise measurements of this mass[ 7 , 9 , 19 ]: Appendicular Skeletal Muscle = 0.193 * weight (kg) + 0.107 * height (cm) − 4.157 *sex − 0.037 * age (years) − 2.631, where sex was coded as 1 for men and 2 for women. Definition of spinal pain Spinal pain was the outcome variable of this study, and its definition was compounded from questionnaire data. For NHANES, information is collected using the Miscellaneous Pain Questionnaire (MPQ)[ 10 , 20 ]. Participants were asked if they had neck or low back pain and, if so, which parts of their bodies were impacted. The reaction to neck, back, or spine pain is classified as spinal pain if the patient describes it as such; if the patient reports it as non-spinal, it is classified as non-spinal pain. Similar results about spinal pain were obtained via the CHARLS questionnaire. The question "What part of your body do you feel pain in?" is posed to participants. Any one or more of the following areas—neck, waist, or back—are considered to be experiencing spinal pain, and vice versa[ 21 , 22 ]. Covariates Given that this study focuses on two databases, the covariates are selected using coding logic and consistent variable names to ensure the stability of the entire model[ 23 ]. Included are factors such as age, gender, height, weight, marital status, education level, BMI (Body Mass Index), HDL (High-Density Lipoprotein), SBP (Systolic Blood Pressure), DBP (Diastolic Blood Pressure), WC (Waist Circumference), drinking (alcohol usage), smoking, diabetes, hypertension, and heart disease. More detailed information on the covariates mentioned above was provided in Supplementary Tables 1A and 1B. Statistical analysis Since CHARLS and NHANES have multi-stage, intricate sample designs, we describe their sampling structures using the R survey package. Categorical variables are reported as frequencies (percentages) when discussing participants' baseline characteristics, whereas continuous variables are reported as means ± standard deviations. For inter-group comparisons, the survey employed a chi-square test or t-test for categorical variables and a weighted Wilcoxon rank-sum test for continuous variables. Assess the association between SMI and spinal pain using a weighted multivariate logistic regression model; findings are presented as odds ratios (OR) and 95% CI. Model 1 (unadjusted), Model 2 (adjusted for age, gender, education, marital status, SBP, DBP, BMI, WC, HDL, height, and weight), and Model 3 (fully adjusted for all factors) were the three successive models built. Linear trends between spinal pain and SMI quartiles were investigated by modeling the median of each quartile as a continuous variable, and potential relationships were assessed using limited cubic spline regression after controlling for all other variables. The categories sorted by age, gender, education level, marital status, smoking status, alcohol consumption, hypertension, diabetes, arthritis, and cardiovascular disease were subjected to cross-testing and subgroup analysis, in addition to assessing SMI's capacity to predict spinal pain. We employed the receiver operating characteristic (ROC) curves and the area under the curve (AUC). To analyze the data,we used EmpowerStats (version 4.2) and R (version 4.5.1). The significance level for the analysis was set at P < 0.05. Results Baseline characteristics of study participants The study included 6,563 NHANES participants, of whom 3,054 reported spinal pain and 3,509 did not. The study also included 12,221 participants with CHARLS, of whom 2,826 reported spinal pain and 9,395 did not. The baseline characteristics of the two groups in the two databases are shown in Table 1 A, B. Compared with the non spinal pain group, individuals with spinal pain were more likely to exhibit the following characteristics age, sex, education, smoking, drinking, height, weight, WC, BMI, HDL, diabetes, heart disease, hypertension, arthritis (all P < 0.05). Multivariate logistic regression analysis of SMI and Spinal Pain In this work, we further investigated the association between spinal pain and SMI using multiple logistic regressions using data from NHANES and CHARLS. To demonstrate the trend relationship as SMI groups expanded, we first used quartiles of group trends. Q1 is considered the reference group for Q2, 3, and 4 in all models. Even after adjusting for confounding variables, the Q2 group's risk of spinal pain was significantly and marginally lower than that of the Q1 group in Model 1. Compared to the Q1 group, the risk was decreased in the Q3 and Q4 groups. The group trend (OR < 1; P for trend significant) showed that as the SMI group grew, the risk of spinal pain decreased significantly. A similar pattern was found in the CHARLS data (Table 2 B). Although it was not significant in the Q2 group, the risk of spinal pain was much lower in the Q3 and Q4 groups compared to the Q1 and Q2 groups. The variables included in Model 2 were age, gender, education level, marital status, height, weight, SBP and DBP, BMI, WC, and HDL. Although the risk was lower in the Q2, Q3, and Q4 groups than in the Q1 group, the study showed that the total decline was not as significant as in Model 1. In a similar vein, Table 2 B shows that while the risk of spinal pain declined over time in Q3 and Q4, the Q2 group did not show any discernible change. After adjusting for the basic factors, the trend's OR decreased (its degree diminished), yet P < 0.01 remained significant. In Model 3, we further alter lifestyle choices and chronic illnesses (drinking, smoking, heart disease, hypertension, diabetes, and arthritis) based on Model 2. The Q2 group was no longer significant in Table 2 A, but Q3 and Q4 were significant, and the risk decreased progressively. This implies that, even if the risk reduction was not as significant as in Models 1 and 2, the confounding variables in Model 3 were unaffected. While the Q2 group remains negligible, Table 2 B shows that the Q3 group is marginally significant. Confounding variables, such as lifestyle and chronic illnesses, affected the association in the Q3 group, but a residual association persisted and was more pronounced in the Q4 group. Even after adjusting for covariates, the overall trend of higher SMI and lower risk of spinal pain remained visible in the group trend. Restrictive cubic spline analysis of SMI and spinal pain Figures 2A and 2B show the results of a restricted cubic spline (RCS) analysis after all variables have been controlled for, including age, gender, education level, marital status, BMI, WC, HDL, height, weight, SBP, DBP, smoking, drinking, diabetes, heart disease, hypertension, and arthritis. Both showed a strong linear correlation between overall spinal pain risk and SMI in the NHANES and CHARLS cohorts. The P -values of the nonlinear values are 0.238 and 0.542, respectively. The non-linear correlation between the two is not statistically significant, and the correlation form is typically linear or almost linear. The two figures show a negative correlation between a higher SMI and a lower incidence of spinal pain, despite fluctuations in the effect size. With a relatively slight change, the OR in the NHANES cohort reduced from about 1.2 to 0.8 as the SMI rose from 0.4 to 1.2. As SMI increased from 0.4 to 1.2, the CHARLS cohort's OR changed more significantly, from about 2.0 to about 0.6. As a result, the risk increased more sharply at low SMI and fell more sharply at high SMI. Subgroup analysis of SMI and Spinal Pain In order to further explore the relationship between SMI and spinal pain, we conducted a subgroup analysis. After adjusting the continuous variables of SBP, DBP, weight, height, WC, and HDL, we analyzed the following categorical variables: age, gender, marital status, education, smoking, drinking, diabetes, arthritis, hypertension, and heart disease. Figure 3A and 3B show the forest maps for NHANES and the CHARLS subgroup analysis, respectively. The P -value of the interaction indicates whether the stratification factor affects the association between SMI and spinal pain. If P < 0.05, it suggests that the stratification factor is effect-modifying (the effect of SMI varies across subgroups). In Fig. 3A, NHANES data, only the interaction between Age ( P = 0.005) and heart disease ( P = 0.011) was statistically significant, indicating that age and the presence of heart disease alter the association between SMI and spinal pain. In Fig. 3B, the CHARLS data, the interaction of Smoking ( P = 0.027), Diabetes ( P = 0.045), and Hypertension ( P = 0.004) is statistically significant, suggesting that "smoking status", "whether there is diabetes", and "whether there is hypertension" will change the relationship between SMI and spinal pain. In the age groups, Fig. 3A shows substantial associations with OR = 0.626 (95% CI 0.625–0.627) for ages 45–56, OR = 0.295 (95% CI 0.294–0.296) for ages 57–68, and OR = 0.968 (95% CI 0.963–0.974) for ages 69–85+, with an interaction P -value of 0.005, indicating that age is an effect-modifying factor. In Fig. 3B, all age groups had statistically significant OR, but the interaction P was 0.117, indicating no significant interaction. Both databases observed that the risk of spinal pain gradually increases with age. In the Smoking group, Fig. 3A shows that only non-smokers OR = 0.432 (95%CI 0.226–0.823) differ significantly, while the interaction P = 0.957 indicates no difference. Figure 3B: Non-smoking OR = 0.114 (95%CI 0.058–0.222), smoking OR = 0.04 (95%CI 0.01–0.161), interaction P = 0.027, indicating that smoking is an effect-modifying factor. In the Diabetes group, Fig. 3A: diabetes OR = 0.142 (95%CI 0.03–0.675) interaction P = 0.447. Figure 3B: No diabetes OR = 0.109 (95%CI 0.055–0.213), diabetes OR = 0.026 (95%CI 0.003–0.264), interactive P = 0.045, suggesting that diabetes is an effect-modifying factor. In the hypertension group, Fig. 3A: No hypertension OR = 0.41 (95%CI 0.192–0.875) interaction P = 0.185, no difference. Figure 3B: No hypertension OR = 0.081 (95%CI 0.037–0.174), with hypertension OR = 0.181 (95%CI 0.055–0.601), interaction P = 0.004, indicating that hypertension is an effect-modifying factor. In the heart disease grouping, Fig. 3A: No heart disease OR = 0.48 (95%CI 0.26–0.887), interaction P = 0.011, indicating that heart disease is an effect-modifying factor. Figure 3B: OR for no heart disease = 0.122 (95%CI 0.061–0.251), OR for heart disease = 0.087 (95%CI 0.019–0.403), Interaction P = 0.187, no difference. ROC prediction of spinal pain using SMI To evaluate the predictive effectiveness of SMI for spinal pain in NHANES and CHARLS, ROC analysis was performed. The AUC of CHARLS is 0.602 (95% CI = 0.590–0.614), corresponding to a specificity of 54.9% and a sensitivity of 67.7%. At the ideal threshold of NHANES, the sensitivity was 41.8%, the specificity was 65.3%, and the AUC was 0.540 (95% CI = 0.526–0.553) (Fig. 4A, B). After integrating WC markers, the predictive performance was improved, but not significantly. Based on the above findings, SMI has higher prediction accuracy than NHANES in CHARLS. Discussion This study investigates the connection between spinal pain and SMI using a variety of methods, primarily based on the NHANES (1999–2004) and CHARLS (2015 wave 3) databases. The two populations differ significantly in terms of cultural background, lifestyle, health care, disease prevention, perception, and the incidence rate of population epidemiology; yet, this study has produced three main findings:(1) After adjusting for confounding factors, SMI and spinal pain were significantly negatively correlated in both cohorts, meanwhile the older the age, the higher the risk of spinal pain and there exists a linear correlation relationship. (2) Restrictive cubic spline (RCS) analysis confirmed a linear association between SMI and spinal pain (NHANES cohort non-linear P = 0.31, CHARLS cohort non-linear P = 0.28), which supplemented previous epidemiological evidence that only reported a "negative correlation" but did not specify the form of the association[ 10 , 24 ] ; (3) subgroup analysis showed that the effect modifying factors were population specific: age and heart disease were significant modifying factors associated with SMI-spinal pain in the NHANES cohort, while smoking, diabetes and hypertension played this role in the CHARLS cohort, suggesting that there were cross-cultural differences in the protective effects of SMI;(4) ROC prediction curve analysis revealed the interaction between "indicators population outcomes" and evaluated the predictive effect of SMI on spinal pain. Its predictive performance in CHARLS was better than NHANES, indicating that the predictive value of indicators has population specificity, which may be due to differences in measurement methods, population disease spectrum, and other factors. Subsequent research can further explore the underlying reasons for population heterogeneity or combine more dimensional indicators (such as molecular markers) to improve prediction accuracy. Both mechanistic research and clinical data corroborate the negative linear association between spinal pain and SMI, which is driven by coordinated regulation of multiple physiological systems. As the "dynamic stability system" of the spine, the mass of the paraspinal (erector spinae, psoas major) and core (transversus abdominis, multifidus) muscle groups directly affects how effectively spinal mechanical loads are distributed[ 25 , 26 ]. The multifidus muscle, a "segment-specific stabilizer" of the lumbar spine, has a larger cross-sectional area among individuals with higher SMI. This allows for displacement between vertebrae (shear and rotational forces), lowers the risk of disc annulus fibrosus tears, and lessens the risk of injury to the small-joint capsules. Multifidus muscle atrophy was shown to be 32.6% to 48.9% more common in CLBP patients than in healthy controls, according to a case-control study that included 217 CLBP patients and 189 healthy controls. The visual analog scale (VAS) and the extent of atrophy had a positive correlation (r = 0.42, P < 0.001)[ 27 ]. The elastic potential energy reserve of skeletal muscles increases with the increase in SMI[ 28 ]. The "buffer chain" created by muscle contraction and relaxation can reduce the instantaneous impact force on the spine during dynamic activities such as bending and rotation. This prevents pain signals from being triggered by the sudden high pressure in the intervertebral disc nucleus pulposus[ 29 ]. By secreting actin, skeletal muscle, an endocrine organ, can control systemic inflammation and affect when spinal pain first appears[ 30 ]. Include cytokines that reduce inflammation, like interleukin-6 (IL-6) and interleukin-10 (IL-10). Individuals with high SMI also release irisin, which can decrease the production of pro-inflammatory factors, including interleukin-1β (IL-1β) and tumor necrosis factor alpha (TNF-α), and prevent the activation of the nuclear factor kappa B (NF-κB) inflammatory pathway[ 31 ]. The oxidative capacity of skeletal muscle positively regulates whole-body vibration of muscle factors, thereby verifying the key role of skeletal muscle health in preventing and improving chronic diseases[ 32 ]. Irisin is one of the mediators that postpones intervertebral disc degeneration (IDD), the primary cause of spinal pain in middle-aged and older adults. It can also enhance the proliferation of nucleus pulposus cells, glycosaminoglycan (GAG) content, metabolic activity, and type II collagen synthesis[ 33 ]. The "signal sources" for spinal motion control are the proprioceptors found in skeletal muscles, such as muscle spindles and Golgi tendon organs. By controlling the signaling effectiveness of this system, SMI may affect spinal pain risk[ 34 ]. Muscle spindle density can decrease with reduced muscle mass, compromising the accuracy of spinal position and motion perception and leading to a loss of proprioceptive signals. For older people with lower back pain (LBP), this is one of the primary causes of deterioration in postural balance control. It is hypothesized that age-related muscle loss is inversely correlated with proprioceptive sensitivity of the lower limbs, increasing the likelihood of lower back discomfort[ 35 ]. Exercise control compensation disorders: A lack of muscle mass can lead to compensatory exercise patterns, which include an over-reliance on the chest and abdominal muscles to compensate for the function of the lower back muscles. This can result in spinal line deviation, leading to greater lumbar lordosis. Chronic spinal pain can develop from long-term force-line deviation, which can cause inflammation of the lumbar myofascial and supraspinatus ligaments [ 36 ]. Reduced SMI can accelerate the loss of vertebral bone density, increase the risk of vertebral compression fractures, and transfer axial strain from the spine to the vertebral endplate[ 37 ]. Oxidative stress is an early potential biomarker of muscle atrophy[ 38 ]. Reactive oxygen species (ROS) production is elevated, and skeletal muscle mitochondrial activity is aberrant in those with lower SMI. Excessive ROS can further reduce muscle mass by oxidizing muscle proteins and activating the caspase-3 apoptotic pathway[ 39 ]. In addition, ROS can enter the intervertebral disc via the blood supply, inhibiting nucleus pulposus cell proliferation and promoting their death. This results in decreased nucleus pulposus cell proliferation and increased intervertebral disc water content and flexibility, which eventually leads to intervertebral disc herniation and root spinal pain[ 40 ]. This may constitute a biological mechanism shared by SMI and spinal pain. The negative correlation between SMI and spinal pain in the two queues is consistent. However, there are differences in the effect-modifying factors, which reflect differences in population characteristics, lifestyle, and disease spectrum: US queue: The "aging metabolism" superposition effect of age and heart disease: According to the US Census Bureau and the China Health Commission, the proportion of elderly people in US population is higher than that in China, and the obesity rate (BMI ≥ 30) is also significantly higher than that in China. According to related studies, a low skeletal muscle index raises the possibility of predicting all-cause mortality in coronary heart disease and is linked to low peak oxygen uptake. The preventive impact of SMI on SP may be diminished by the overlap between age-related muscle loss and circulatory problems associated with heart disease[ 41 , 42 ]."Metabolic vascular" synergistic effect of smoking, diabetes, and hypertension: nicotine produced by smoking inhibits the myogenic ability and myotube formation of C2C12 cells[ 43 ], diabetes microvascular disease reduces muscle blood supply[ 44 ], hypertension vascular endothelial damage impairs actin secretion[ 45 , 46 ], We speculate that these three factors can synergistically affect the association between SMI and spinal pain. The advantages of research are cross-racial extrapolation (Use two nationally representative large cohorts to ensure strong external validity of the results) and a comprehensive analysis across multiple dimensions — biomechanics, metabolism, neurology, and endocrinology — was conducted to elucidate the underlying factors driving these correlations and to provide a theoretical basis for clinical interventions. The limitations of research include: cross-sectional design: It is not possible to establish a causal relationship, and reverse causality cannot be ruled out (such as chronic spinal pain leading to reduced physical activity, which subsequently causes muscle loss)[ 47 ]; Self-reported spinal pain is subject to recall bias and cannot distinguish pain subtypes (radicular, myofascial) or severity (acute, chronic) through self-report by subjects; Residual confounding factors: unmeasured factors in the database (such as dietary protein intake, spinal imaging results, and exclusion of other diseases) may affect the research results. Future research should focus on using longitudinal data to confirm causal relationships. Specifically, they should examine the predictive utility of baseline SMI for diagnosing spinal pain, conduct multicenter, multivariate, randomized controlled trials (RCTs), and develop relevant intervention plans to enhance the preventive and therapeutic effects of skeletal muscle mass index on spinal pain. Conclusion In conclusion, a higher SMI is an independent protective factor for spinal pain in middle-aged and older individuals in China and the US, and the two countries show a linear negative correlation. SMI has predictive value for spinal pain, with a more pronounced effect in CHARLS. Several mechanisms, including hormone balance, neuromuscular control, inflammation regulation, and biomechanical support, drive this relationship. The negative correlation linear relationship between SMI and spinal pain has cross racial consistency, and the characteristics of specific populations regulate the protective effect of SMI, emphasizing the necessity of tailored prevention strategies. Abbreviations SMI Skeletal muscle mass index NHANES National Health and Nutrition Examination Survey CHARLS China Health and Retirement Longitudinal Study BMI Body Mass Index HDL High-Density Lipoprotein SBP Systolic Blood Pressure DBP Diastolic Blood Pressure WC Waist Circumference Declarations Ethics approval and consent to participate Ethics approval and consent to participate all data used in this study are all from the public domain. Clinical trial number Not applicable. Consent for publication Not applicable. Availability of data and materials This study analyzed publicly available datasets; relevant data can be obtained from the CHARLS and NHANES at https://charls.pku.edu.cn/. and https://www.cdc.gov/nchs/nhanes/about/. Competing interests The authors declare no competing interests. Acknowledgements The authors thank the Chinese Center for Social Sciences Survey of Peking University for providing the CHARLS data and the NHANES data provided by the National Health and Nutrition Examination Survey in the United States. We want to express our gratitude to the participants and researchers who contributed to the database used in this investigation. References Qiu K, Wang C, Mo X, Yang G, Huang L, Wu Y, Pan Z (2025) The global macroeconomic burden of musculoskeletal disorders. Int J Surg. https://doi.org/10.1097/js9.0000000000003072 Wei J, Chen L, Huang S, Li Y, Zheng J, Cheng Z, Xie Z (2022) Time Trends in the Incidence of Spinal Pain in China, 1990 to 2019 and Its Prediction to 2030: The Global Burden of Disease Study 2019. Pain Ther 11(4):1245–1266. https://doi.org/10.1007/s40122-022-00422-9 Hartvigsen J, Hancock MJ, Kongsted A, Louw Q, Ferreira ML, Genevay S, Hoy D, Karppinen J, Pransky G, Sieper J, Smeets RJ, Underwood M (2018) What low back pain is and why we need to pay attention. Lancet 391(10137):2356–2367. https://doi.org/10.1016/s0140-6736(18)30480-x Wu A, March L, Zheng X, Huang J, Wang X, Zhao J, Blyth FM, Smith E, Buchbinder R, Hoy D (2020) Ann Transl Med 8(6):299. https://doi.org/10.21037/atm.2020.02.175 . Global low back pain prevalence and years lived with disability from 1990 to 2017: estimates from the Global Burden of Disease Study 2017 Xu F, Zhang X, Yang M, Zhao Q, Wang Q, Lian J, Zhang R, Chu T, Kou Z, Zhao M (2025) Magnitude, temporal trend and inequality in burden of neck pain: an analysis of the Global Burden of Disease Study 2019. BMC Musculoskelet Disord 26(1):202. https://doi.org/10.1186/s12891-025-08342-3 Global regional, national burden of neck pain (2024) 1990–2020, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021. Lancet Rheumatol 6(3):e142–e155. https://doi.org/10.1016/s2665-9913(23)00321-1 Chen LK, Woo J, Assantachai P, Auyeung TW, Chou MY, Iijima K, Jang HC, Kang L, Kim M, Kim S, Kojima T, Kuzuya M, Lee JSW, Lee SY, Lee WJ, Lee Y, Liang CK, Lim JY, Lim WS, Peng LN, Sugimoto K, Tanaka T, Won CW, Yamada M, Zhang T, Akishita M, Arai H, Asian Working Group for Sarcopenia (2020) 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J Am Med Dir Assoc 21(3):300–307e2. https://doi.org/10.1016/j.jamda.2019.12.012 Jiang J, Chen G, Li Y, Zhao Q, Chen Z (2025) Serum calcium levels and the risk of sarcopenia in young adults: insights from NHANES 2011–2018. Front Nutr 12:1526879. https://doi.org/10.3389/fnut.2025.1526879 Hu Z, Tian Y, Song X, Zeng F, Yang A (2023) Associations between indoor air pollution for cooking and heating with muscle and sarcopenia in Chinese older population. J Cachexia Sarcopenia Muscle 14(5):2029–2043. https://doi.org/10.1002/jcsm.13281 Chen R, Yang C, Tang X, Han S, Kuang M, Li X (2025) The relationship between muscle mass and low back pain: a cross-sectional study. Eur Spine J 34(7):2597–2604. https://doi.org/10.1007/s00586-025-09026-1 Yuan S, Larsson SC (2023) Epidemiology of sarcopenia: Prevalence, risk factors, and consequences. Metabolism 144:155533. https://doi.org/10.1016/j.metabol.2023.155533 Li L, Zuo A, Yin R, Liu Q, Liu C, Li N, Xu D, Zhang S, Li J, Lei S, Ruan S, Li T, Guo Y (2025) CTRP9 as a myokine mitigates sarcopenia via the LAMP-2A/NLRP3 pathway. Cell Death Dis 16(1):710. https://doi.org/10.1038/s41419-025-08025-w Lin T, Dai M, Xu P, Sun L, Shu X, Xia X, Zhao Y, Song Q, Guo D, Deng C, Yue J (2022) Prevalence of Sarcopenia in Pain Patients and Correlation Between the Two Conditions: A Systematic Review and Meta-Analysis. J Am Med Dir Assoc 23(5):902e1–902e20. https://doi.org/10.1016/j.jamda.2022.02.005 Guven AE, Schönnagel L, Camino-Willhuber G, Chiapparelli E, Amoroso K, Zhu J, Tani S, Caffard T, Arzani A, Zadeh AT, Shue J, Tan ET, Sama AA, Girardi FP, Cammisa FP, Hughes AP (2024) Relationship between facet joint osteoarthritis and lumbar paraspinal muscle atrophy: a cross-sectional study. J Neurosurg Spine 41(3):360–368. https://doi.org/10.3171/2024.4.Spine231018 Sakai Y, Watanabe T, Matsui H, Osada N, Adachi Y, Takeichi Y, Katsumi A, Watanabe K (2025) Factors Related to the Prognosis of Chronic Low Back Pain in Older Adults. Phys Ther. https://doi.org/10.1093/ptj/pzaf123 Patel CJ, Pho N, McDuffie M, Easton-Marks J, Kothari C, Kohane IS, Avillach P (2016) A database of human exposomes and phenomes from the US National Health and Nutrition Examination Survey. Sci Data 3:160096. https://doi.org/10.1038/sdata.2016.96 Zhao Y, Hu Y, Smith JP, Strauss J, Yang G (2014) Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol 43(1):61–68. https://doi.org/10.1093/ije/dys203 Liu J, Zhang F (2025) Associations between sarcopenia (defined by low muscle mass), inflammatory markers, and all-cause mortality in older adults: mediation analyses in a large U.S. NHANES community sample, 1999–2006. Front Med (Lausanne) 12:1515839. https://doi.org/10.3389/fmed.2025.1515839 Luo YX, Zhou XH, Heng T, Yang LL, Zhu YH, Hu P, Yao XQ (2024) Bidirectional transitions of sarcopenia states in older adults: The longitudinal evidence from CHARLS. J Cachexia Sarcopenia Muscle 15(5):1915–1929. https://doi.org/10.1002/jcsm.13541 Zhang W, Li Y, Shao P, Du Y, Zhao K, Zhan J, Tan LA (2025) Association of weight-adjusted waist index and body mass index with chronic low back pain in American adults: a retrospective cohort study and predictive model development based on machine learning algorithms (NHANES 2009–2010). Front Public Health 13:1617732. https://doi.org/10.3389/fpubh.2025.1617732 Zhou S, Liu Y, Zhang Y, Luo N, Chen Q, Ge M, Shen B (2024) Association between persistent musculoskeletal pain and incident sarcopenia in China: the mediating effect of depressive symptoms. Front Public Health 12:1416796. https://doi.org/10.3389/fpubh.2024.1416796 Ren S, Jiang X, Wang S, Wong AYL, Bi X, Wang X (2024) The prevalence and factors associated with neck and low back pain in patients with stroke: insights from the CHARLS. BMC Public Health 24(1):2362. https://doi.org/10.1186/s12889-024-19847-2 Fan Z, Feng C, Li X, Zhang Z, Gong H, Di J (2025) Association between uric acid to high-density lipoprotein cholesterol ratio and chronic kidney disease in middle-aged and older adults: results from two nationally representative population-based study. Ren Fail 47(1):2575920. https://doi.org/10.1080/0886022x.2025.2575920 Chen J, Wang X, Xu Z (2023) Sarcopenia and Chronic Pain in the Elderly: A Systematic Review and Meta-Analysis. J Pain Res 16:3569–3581. https://doi.org/10.2147/jpr.S435866 Indahl A, Velund L, Reikeraas O (1995) Good prognosis for low back pain when left untampered. A randomized clinical trial. Spine (Phila Pa 1976) 20(4):473–477. https://doi.org/10.1097/00007632-199502001-00011 Panjabi MM (1992) The stabilizing system of the spine. Part I. Function, dysfunction, adaptation, and enhancement. J Spinal Disord 5(4):383–389 discussion 397. https://doi.org/10.1097/00002517-199212000-00001 Hides JA, Stokes MJ, Saide M, Jull GA, Cooper DH (1994) Evidence of lumbar multifidus muscle wasting ipsilateral to symptoms in patients with acute/subacute low back pain. Spine (Phila Pa 1976) 19(2):165–172. https://doi.org/10.1097/00007632-199401001-00009 Prokopidis K, Cacciatore S, Piaggi P, Vetrano DL, Schlögl M (2025) Muscle strengthening activities: cross-sectional associations with skeletal muscle outcomes in adults aged 50–64 and 65 years and above. Eur Geriatr Med. https://doi.org/10.1007/s41999-025-01327-4 Kang SH, Mirka GA (2025) Load transfer between active and passive lumbar tissues and its implications in time-dependent EMG-assisted biomechanical modeling. J Biomech 183:112600. https://doi.org/10.1016/j.jbiomech.2025.112600 Pedersen BK, Febbraio MA (2012) Muscles, exercise and obesity: skeletal muscle as a secretory organ. Nat Rev Endocrinol 8(8):457–465. https://doi.org/10.1038/nrendo.2012.49 Boström P, Wu J, Jedrychowski MP, Korde A, Ye L, Lo JC, Rasbach KA, Boström EA, Choi JH, Long JZ, Kajimura S, Zingaretti MC, Vind BF, Tu H, Cinti S, Højlund K, Gygi SP, Spiegelman BM (2012) A PGC1-α-dependent myokine that drives brown-fat-like development of white fat and thermogenesis. Nature 481(7382):463–468. https://doi.org/10.1038/nature10777 Broniec MN, Norland K, Looney J, Crandall R, Thomas J, Wang X, Harris RA (2025) Increased Skeletal Muscle Oxidative Capacity Augments the Myokine Response to Whole Body Vibration. Med Sci Sports Exerc. https://doi.org/10.1249/mss.0000000000003854 Vadalà G, Di Giacomo G, Ambrosio L, Cicione C, Tilotta V, Russo F, Papalia R, Denaro V (2023) Effect of Irisin on Human Nucleus Pulposus Cells: New Insights into the Biological Cross-talk Between Muscle and Intervertebral Disk. Spine (Phila Pa 1976) 48(7):468–475. https://doi.org/10.1097/brs.0000000000004488 Proske U, Gandevia SC (2012) The proprioceptive senses: their roles in signaling body shape, body position and movement, and muscle force. Physiol Rev 92(4):1651–1697. https://doi.org/10.1152/physrev.00048.2011 Sakai Y, Watanabe T, Wakao N, Matsui H, Osada N, Sugiura T, Morita Y, Kawai K, Ito T (2022) Yamazaki, Proprioception and Geriatric Low Back Pain. Spine Surg Relat Res 6(5):422–432. https://doi.org/10.22603/ssrr.2021-0269 van Dieën JH, Cholewicki J, Radebold A (2003) Trunk muscle recruitment patterns in patients with low back pain enhance the stability of the lumbar spine. Spine (Phila Pa 1976) 28(8) 834 – 41 Nguyen BTT, Nguyen TT, Kuo YJ, Chen YP (2025) Impact of sarcopenia on outcomes following vertebral augmentation for osteoporotic vertebral compression fracture: a systematic review and meta-analysis. Asian Spine J 19(3):476–489. https://doi.org/10.31616/asj.2024.0467 Coto Montes A, Boga JA, Bermejo Millo C, Rubio González A, Potes Ochoa Y, Vega Naredo I, Martínez Reig M, Romero Rizos L, Sánchez Jurado PM, Solano JJ, Abizanda P, Caballero B (2017) Potential early biomarkers of sarcopenia among independent older adults. Maturitas 104:117–122. https://doi.org/10.1016/j.maturitas.2017.08.009 Zhang X, Wang X, Huang L, Zhou C, Qian B, Luo Z, Chen Y (2025) Effects of statins on sarcopenia with focus on mechanistic insights and future perspectives. Front Pharmacol 16:1669591. https://doi.org/10.3389/fphar.2025.1669591 Zhou H, Wu C, Jin Y, Wu O, Chen L, Guo Z, Wang X, Chen Q, Kwan KYH, Li YM, Xia D, Chen T, Wu A (2024) Role of oxidative stress in mitochondrial dysfunction and their implications in intervertebral disc degeneration: Mechanisms and therapeutic strategies. J Orthop Translat 49:181–206. https://doi.org/10.1016/j.jot.2024.08.016 Nichols S, O'Doherty AF, Taylor C, Clark AL, Carroll S, Ingle L (2019) Low skeletal muscle mass is associated with low aerobic capacity and increased mortality risk in patients with coronary heart disease - a CARE CR study. Clin Physiol Funct Imaging 39(1):93–102. https://doi.org/10.1111/cpf.12539 Fonseca G, Garfias Macedo T, Ebner N, Dos Santos MR, de Souza FR, Mady C, Takayama L, Pereira RMR, Doehner W, Anker SD, Negrão CE, Alves M, von Haehling S (2020) Muscle mass, muscle strength, and functional capacity in patients with heart failure of Chagas disease and other aetiologies. ESC Heart Fail 7(5):3086–3094. https://doi.org/10.1002/ehf2.12936 Zheng G, Li C, Chen X, Deng Z, Xie T, Huo Z, Wei X, Huang Y, Zeng X, Luo Y, Bai J (2024) HDAC9 inhibition reduces skeletal muscle atrophy and enhances regeneration in mice with cigarette smoke-induced COPD. Biochim Biophys Acta Mol Basis Dis 1870(3):167023. https://doi.org/10.1016/j.bbadis.2024.167023 Ruggiero AD, Davis A, Sherrill C, Westwood B, Hawkins GA, Palmer ND, Chou JW, Reeves T, Cox LA, Kavanagh K (2021) Skeletal muscle extracellular matrix remodeling with worsening glycemic control in nonhuman primates. Am J Physiol Regul Integr Comp Physiol 320(3):R226–r235. https://doi.org/10.1152/ajpregu.00240.2020 Radwan E, Mali V, Haddox S, El-Noweihi A, Mandour M, Ren J, Belmadani S, Matrougui K (2019) Treg cells depletion is a mechanism that drives microvascular dysfunction in mice with established hypertension. Biochim Biophys Acta Mol Basis Dis 1865(2):403–412. https://doi.org/10.1016/j.bbadis.2018.10.031 Bennai F, Morsing P, Paliege A, Ketteler M, Mayer B, Tapp R, Bachmann S (1999) Normalizing the expression of nitric oxide synthase by low-dose AT1 receptor antagonism parallels improved vascular morphology in hypertensive rats. J Am Soc Nephrol 10(11):S104–S115 Richardson PE (2015) David Sackett and the birth of Evidence Based Medicine: How to Practice and Teach EBM. BMJ 350:h3089. https://doi.org/10.1136/bmj.h3089 Tables Table 1 A Baseline characteristics of NHANES individuals Variable Spinal pain P -value No,n = 3509 Yes,n = 3054 Age (years) 60.02 ± 11.27 58.88 ± 10.85 < 0.0001 Gender n(%) < 0.001 Male 1757 (50.06%) 1363 (44.64%) Female 1752(49.94%) 1691(55.36%) Education n(%) < 0.001 Primary school 671(19.11%) 737 (24.13%) Middle school 886(25.26%) 864(28.30%) college 1952(55.63%) 1453 (47.56%) Marital status n(%) 0.126 Married or partnered 2401(68.42%) 2035 (66.65%) Never married 1108 (31.58%) 1019(33.35%) Smoking n(%) < 0.001 yes 599 (17.08%) 672(22.00%) no 2910 (82.92%) 2382 (78.00%) Drinking n(%) 0.005 yes 2235 (63.68%) 1843 (60.35%) no 1274 (36.32%) 1211 (39.65%) Diabetes n(%) < 0.001 yes 388(11.07%) 434 (14.22%) no 3121 (88.93%) 2620(85.78%) Heart disease n(%) < 0.001 yes 385(10.97%) 453 (14.82%) no 3124 (89.03%) 2601(85.18%) Hypertension n(%) < 0.001 yes 1331 (37.94%) 1329(43.52%) no 2178 (62.06%) 1725(56.48%) Arthritis n(%) < 0.001 yes 924(26.33%) 1452 (47.56%) no 2585(73.67%) 1602 (52.44%) SBP (mmHg) 130.31 ± 19.72 130.09 ± 20.21 0.654 DBP (mmHg) 73.44 ± 11.80 73.31 ± 12.30 0.669 Height (cm) 168.43 ± 10.03 167.65 ± 10.00 < 0.001 Weight (kg) 80.14 ± 18.59 81.37 ± 19.50 < 0.001 BMI (kg/m 2 ) 28.15 ± 5.68 28.86 ± 6.14 < 0.001 WC (cm) 98.58 ± 14.31 100.10 ± 15.12 < 0.001 HDL (mg/dL) 53.22 ± 15.90 53.54 ± 16.98 0.4367 SMI 0.77 ± 0.19 0.74 ± 0.19 < 0.001 Table 1 B Baseline characteristics of CHARLS individuals Variable Spinal pain P -value No,n = 9395 Yes,n = 2826 Age (years) 60.51 ± 9.78 60.90 ± 9.36 0.008 Gender n(%) < 0.001 Male 4802 (51.05%) 951 (33.62%) Female 4603 (48.95%) 1875(66.38%) Education n(%) < 0.001 Primary school 1562 (16.61%) 580 (20.50%) Middle school 7793 (82.86%) 2244(79.43%) college 50 (0.53%) 2 (0.07%) Marital status n(%) 0.424 Married or partnered 9342 (99.33%) 2804 (99.19%) Never married 63 (0.67%) 22 (0.81%) Smoking n(%) < 0.001 yes 710 (7.55%) 155 (5.59%) no 8695 (92.45%) 2671 (94.41%) Drinking n(%) < 0.001 yes 3511 (37.33%) 795 (28.21%) no 5894 (62.67%) 2031 (71.79%) Diabetes n(%) < 0.001 yes 724 (7.71%) 304 (10.78%) no 8681 (92.29%) 2522 (89.22%) Heart disease n(%) < 0.001 yes 1183 (12.58%) 654 (23.19%) no 8222 (87.42%) 2172 (76.81%) Hypertension n(%) < 0.001 yes 2564 (27.27%) 963 (34.11%) no 6841 (72.73%) 1863 (65.89%) Arthritis n(%) < 0.001 yes 2888 (30.70%) 1613 (57.09%) no 6517 (69.30%) 1213 (42.91%) SBP (mmHg) 128.64 ± 19.77 127.88 ± 20.26 0.018 DBP (mmHg) 75.83 ± 11.62 75.37 ± 11.81 0.035 Height (cm) 158.86 ± 8.49 155.98 ± 8.20 < 0.001 Weight (kg) 60.63 ± 11.83 58.34 ± 11.43 < 0.001 BMI (kg/m 2 ) 23.95 ± 3.90 23.92 ± 4.03 0.380 WC (cm) 85.67 ± 12.74 84.71 ± 14.00 0.007 HDL (mg/dL) 51.06 ± 11.52 51.71 ± 11.61 0.005 SMI 0.74 ± 0.17 0.68 ± 0.16 < 0.001 Table 2 A NHANES: Logistic regression analysis for associations between the SMI and spinal pain Q1 was used as a control for the other SMI groups and was referred to as "Ref" in all models (the reference group). Model1 :adjust for none. Model2 : adjust for Age,Gender,Education,Marital status, SBP, DBP, BMI, WC, HDL, Height, Weight. Model3 : adjust for age, gender, education, marital-status, SBP, DBP, BMI, WC, HDL, Height, Weight, smoking, drinking, Diabetes, Heart-disease, Hypertension, Arthritis. Exposure Model1 Model2 Model3 OR(95%) P OR(95%) P OR(95%) P SMI GROUP Q1(0.29–0.58) Ref Ref Ref Q2(0.58–0.72) 0.951(0.807,1.006) < 0.001 0.970(0.969,0.971) < 0.001 0.978(0.946,1.012) 0.219 Q3(0.72–0.88) 0.782(0.781,0783) < 0.001 0.847(0.846,0.848) < 0.001 0.939(0.908,0.972) < 0.001 Q4(0.88–1.46) 0.749(0.748,0.750) < 0.001 0.834(0.833,0.836) < 0.001 0.922(0.891,0.954) < 0.001 Group trend 0.493(0.492,0.494) < 0.001 0.683(0.681,0.685) < 0.001 0.635(0.633,0.637) < 0.001 Table 2 B CHARLS: Logistic regression analysis for associations between the SMI and Spinal pain Exposure Model1 Model2 Model3 OR(95%) P OR(95%) P OR(95%) P SMI GROUP Q1(0.14–0.58) Ref Ref Ref Q2(0.58–0.69) 0.901(0.807,1.006) 0.063 0.941(0.837,0.106) 0.312 0.958(0.845,1.082) 0.496 Q3(0.69–0.88) 0.511(0.453,0.576) < 0.001 0.736(0.579,0.935) 0.012 0.793(0.619,1.015) 0.065 Q4(0.88–1.29) 0.430(0.380,0.487) < 0.001 0.652(0.489,0.869) 0.003 0.723(0.537,0.973) 0.032 Group trend 0.107(0.081,0.140) < 0.001 0.364(0.183,0.726) 0.004 0.466(0.228,0.953) 0.036 Q1, Model1,2,3 adjust variables the same as Table 2A Additional Declarations No competing interests reported. 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figure legend.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8825049/v1/2a390a1e2315a061dcc73179.png"},{"id":106068606,"identity":"41d03813-50cc-45a5-96a3-555cef71ad8e","added_by":"auto","created_at":"2026-04-03 06:11:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1452003,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8825049/v1/90f8691a6b864e82f2e1c24a.png"},{"id":106068608,"identity":"fc743c49-d106-47c9-a86e-b0c38da25cfe","added_by":"auto","created_at":"2026-04-03 06:11:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":189773,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8825049/v1/3bb6b3d2cf2dccb38743997d.png"},{"id":106068610,"identity":"a349832f-2570-4c5b-a9d9-4219cf0b9ee1","added_by":"auto","created_at":"2026-04-03 06:11:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":142128,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8825049/v1/1d676bfdac217bf39063babf.png"},{"id":106094517,"identity":"221231d5-b4c2-4588-9adb-b8aa2ee61700","added_by":"auto","created_at":"2026-04-03 11:42:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2790281,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8825049/v1/cd78d375-7cf3-4b9e-91f5-e10cd075f12c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The relationship between skeletal muscle mass index and spinal pain: a cross-sectional study comparing middle-aged and elderly individuals in China and the United States","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSpinal pain is one of the major global health issues, encompassing pain syndromes in multiple regions, such as neck and back pain, and constitutes one of the most significant impacts on healthcare systems and socio-economic burdens in musculoskeletal disorders[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Neck and back pain have become one of the leading causes of disability worldwide. The prevalence of back pain increased by 54% between 1999 and 2015[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Disease incidence is directly correlated with age growth[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. According to a Bayesian Age-Period-Cohort model developed from pertinent data, the incidence rate of neck pain is expected to rise over the next 25 years. The prevalence rate will increase by 32.5% to 269\u0026nbsp;million. The acceleration of population growth and subsequent population aging are particularly noteworthy[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSkeletal muscle mass index (SMI), a core indicator for evaluating sarcopenia[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], reflects the functional reserve of skeletal muscle. According to recent research, SMI may influence the development and course of spinal pain through mechanisms such as spinal stability, biomechanical stress, and inflammatory modulation, and it is also significantly associated with metabolic health and physical function[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Reduced muscle mass can lead to increased mechanical stress on intervertebral joints and ligaments, as well as decreased spinal stability [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Meanwhile, muscle tissue itself is an essential endocrine organ that can help control systemic inflammatory responses and secrete several myokines[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], which may provide physiological and pathological explanations for the relationship between SMI and spinal pain[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe pathophysiological link between SMI and spinal pain is increasingly recognized. Core and paraspinal muscles maintain spinal stability, and age-related sarcopenia weakens this support system[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Spinal muscles, particularly the psoas major and paraspinal muscles, atrophy at rates up to 29% with aging, exacerbating intervertebral disc degeneration and vertebral stress[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough previous studies have shown an association between sarcopenia and neck or lower back pain, there is a lack of large-scale demographic studies specifically targeting middle-aged and elderly individuals to investigate the relationship between SMI and spinal pain. In addition, the universality of this association across international contexts may be influenced by cultural backgrounds, lifestyles, and differences in healthcare systems. By utilizing these two databases in China and the United States to test the consistency of the association between SMI and spinal pain across different demographic and cultural backgrounds, the robustness and generalizability of the research results can be improved. More research is still needed to understand the potential biological mechanisms and other confounding variables fully.\u003c/p\u003e \u003cp\u003eGiven the above background, this cross-sectional study analyzed the association between SMI and spinal pain using two nationally representative databases: NHANES (US cohort) and CHARLS (China cohort). The specific objectives include: (1) to analyze the cross-sectional correlation between SMI and the incidence rate of spinal pain; (2) to analyze the relationship between SMI and spinal pain consistent in different ethnic and cultural backgrounds. This study has important theoretical significance and clinical value. At the theoretical level, the research results will help clarify the causal relationship and potential mechanisms between muscle mass index and spinal pain, and promote knowledge progress in the field of musculoskeletal health. At the practical level, research has found that it may offer a new perspective on primary prevention and management strategies for spinal pain, namely through muscle mass enhancement interventions such as resistance training, nutritional supplementation, and oxidative stress management. Ultimately, this study hope to provide scientific evidence to promote healthy aging, prevent and control musculoskeletal disorders, reduce the personal and societal burden of spinal pain, improve the quality of life of the population, and have positive public health implications.\u003c/p\u003e"},{"header":"Method and Data","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy sample and data source\u003c/h2\u003e \u003cp\u003eThe data used in this study came from two major databases in China and the US: the National Health and Nutrition Examination Survey (NHANES) and the China Health and Retirement Longitudinal Study (CHARLS), respectively. Each participant gave written informed consent, and the National Center for Health Statistics Research Ethics Review Board approved the NHANES data. The Peking University Institutional Review Board authorized the CHARLS data (IRB00001052-11015).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNHANES Data\u003c/h3\u003e\n\u003cp\u003eUsing a cross-sectional design, the survey was based on the 1999\u0026ndash;2004 NHANES. This method comprises data collection from a sample of non-institutionalized people, chosen to reflect a larger community, using a specific study framework. This framework combines several sampling methods, including probability-based, cross-sectional, multistage, and subgroup-stratified sampling. Surveys are conducted every two years[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e The NCHS Ethics Review Committee has approved the NHANES research proposal. Over three survey cycles (1999\u0026ndash;2000, 2001\u0026ndash;2002, and 2003\u0026ndash;2004), 31,126 participants participated. The following were the criteria for exclusion: (1) Participants younger than 45 years old (n\u0026thinsp;=\u0026thinsp;22,437); (2) Participants without core data: ASM and BMI (n\u0026thinsp;=\u0026thinsp;1,155); (3) Excluding data with missing covariates, n\u0026thinsp;=\u0026thinsp;935, 6,563 eligible participants were filtered into the final analysis when the exclusion criteria were applied (Fig.\u0026nbsp;1A).\u003c/p\u003e\n\u003ch3\u003eCHARLS Data\u003c/h3\u003e\n\u003cp\u003eBased on the 2015 CHARLS Wave 3, a prospective cohort study that is still being carried out under supervision from the National School of Development at Peking University. A nationally representative sample of middle-aged and elderly people in China is used to collect longitudinal data for the CHARLS project. This data covers a range of variables, including socioeconomic status and health difficulties. Its conceptual framework and evaluation standards align with those of the Health and Retirement Study (HRS). A multistage stratified probability sampling procedure was employed in the study to guarantee that the sample accurately reflected the target population. Geographically, the survey covered Chinese autonomous areas, municipalities directly under the central government, and 150 locations across 28 provinces. Since its initial data collection period in 2011\u0026ndash;2012, CHARLS has collected data every two years on demographics, biomedical measures, population characteristics, and health-related information. The Ethics Committee of the Peking University Health Science Center approved the CHARLS study protocol. For a detailed description of the CHARLS survey's design and methods, please consult previous publications.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThroughout CHARLS wave 3 in 2015, 21,095 participants took part in the study. The following criteria were utilized for exclusion: (1) Participants under 45 years of age (n\u0026thinsp;=\u0026thinsp;1,382); (2) Participants without core data: ASM and BMI (n\u0026thinsp;=\u0026thinsp;4,412); (3) Excluding data with missing covariates, n\u0026thinsp;=\u0026thinsp;3,080, 12,221 eligible participants were included in the final analysis following the application of the exclusion criteria (Fig.\u0026nbsp;1B).\u003c/p\u003e\n\u003ch3\u003eDefinition of SMI\u003c/h3\u003e\n\u003cp\u003eSMI, as the primary index for measuring sarcopenia, has been used in numerous previous studies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. According to the Foundation for the National Institutes of Health's (FNIH) sarcopenia guidelines, the skeletal muscle index (SMI) is computed by dividing the appendicular skeletal muscle mass by the body mass index (ASM (kg) / BMI (kg/m\u0026sup2;)). The cut-off values for sarcopenia are \u0026lt;\u0026thinsp;0.789 for men and \u0026lt;\u0026thinsp;0.512 for women.\u003c/p\u003e \u003cp\u003eThis study's exposure variable for SMI is likewise composed of multiple data points. At the NHANES mobile examination center, dual-energy X-ray absorptiometry (DXA) scans of the entire body were performed. Due to DXA table limits, participants were not allowed to participate if they self-reported using radiographic contrast agents within the previous 7 days, had undergone nuclear medicine procedures within the previous 3 days, weighed more than 136 kg, or were taller than 196 cm. To perform whole-body DXA scans, a Hologic QDR-4500A densitometer was used. The Appendicular Skeletal Mass (ASM), a known stand-in for skeletal muscle mass, was calculated by adding the lean mass (not including bone mineral content) of the arms and legs as determined by DXA. An anthropometric formula that was validated in multiple studies and demonstrated strong agreement with dual-energy X-ray absorptiometry (DXA) results was used to determine the mass of appendicular skeletal muscle because the CHARLS database does not provide precise measurements of this mass[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]: Appendicular Skeletal Muscle\u0026thinsp;=\u0026thinsp;0.193 * weight (kg)\u0026thinsp;+\u0026thinsp;0.107 * height (cm)\u0026thinsp;\u0026minus;\u0026thinsp;4.157 *sex\u0026thinsp;\u0026minus;\u0026thinsp;0.037 * age (years)\u0026thinsp;\u0026minus;\u0026thinsp;2.631, where sex was coded as 1 for men and 2 for women.\u003c/p\u003e\n\u003ch3\u003eDefinition of spinal pain\u003c/h3\u003e\n\u003cp\u003eSpinal pain was the outcome variable of this study, and its definition was compounded from questionnaire data. For NHANES, information is collected using the Miscellaneous Pain Questionnaire (MPQ)[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Participants were asked if they had neck or low back pain and, if so, which parts of their bodies were impacted. The reaction to neck, back, or spine pain is classified as spinal pain if the patient describes it as such; if the patient reports it as non-spinal, it is classified as non-spinal pain. Similar results about spinal pain were obtained via the CHARLS questionnaire. The question \"What part of your body do you feel pain in?\" is posed to participants. Any one or more of the following areas\u0026mdash;neck, waist, or back\u0026mdash;are considered to be experiencing spinal pain, and vice versa[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eGiven that this study focuses on two databases, the covariates are selected using coding logic and consistent variable names to ensure the stability of the entire model[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Included are factors such as age, gender, height, weight, marital status, education level, BMI (Body Mass Index), HDL (High-Density Lipoprotein), SBP (Systolic Blood Pressure), DBP (Diastolic Blood Pressure), WC (Waist Circumference), drinking (alcohol usage), smoking, diabetes, hypertension, and heart disease. More detailed information on the covariates mentioned above was provided in Supplementary Tables\u0026nbsp;1A and 1B.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eSince CHARLS and NHANES have multi-stage, intricate sample designs, we describe their sampling structures using the R survey package. Categorical variables are reported as frequencies (percentages) when discussing participants' baseline characteristics, whereas continuous variables are reported as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations. For inter-group comparisons, the survey employed a chi-square test or t-test for categorical variables and a weighted Wilcoxon rank-sum test for continuous variables. Assess the association between SMI and spinal pain using a weighted multivariate logistic regression model; findings are presented as odds ratios (OR) and 95% CI.\u003c/p\u003e \u003cp\u003eModel 1 (unadjusted), Model 2 (adjusted for age, gender, education, marital status, SBP, DBP, BMI, WC, HDL, height, and weight), and Model 3 (fully adjusted for all factors) were the three successive models built. Linear trends between spinal pain and SMI quartiles were investigated by modeling the median of each quartile as a continuous variable, and potential relationships were assessed using limited cubic spline regression after controlling for all other variables. The categories sorted by age, gender, education level, marital status, smoking status, alcohol consumption, hypertension, diabetes, arthritis, and cardiovascular disease were subjected to cross-testing and subgroup analysis, in addition to assessing SMI's capacity to predict spinal pain. We employed the receiver operating characteristic (ROC) curves and the area under the curve (AUC). To analyze the data,we used EmpowerStats (version 4.2) and R (version 4.5.1). The significance level for the analysis was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of study participants\u003c/h2\u003e \u003cp\u003eThe study included 6,563 NHANES participants, of whom 3,054 reported spinal pain and 3,509 did not. The study also included 12,221 participants with CHARLS, of whom 2,826 reported spinal pain and 9,395 did not.\u003c/p\u003e \u003cp\u003eThe baseline characteristics of the two groups in the two databases are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B. Compared with the non spinal pain group, individuals with spinal pain were more likely to exhibit the following characteristics age, sex, education, smoking, drinking, height, weight, WC, BMI, HDL, diabetes, heart disease, hypertension, arthritis (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMultivariate logistic regression analysis of SMI and Spinal Pain\u003c/h2\u003e \u003cp\u003eIn this work, we further investigated the association between spinal pain and SMI using multiple logistic regressions using data from NHANES and CHARLS. To demonstrate the trend relationship as SMI groups expanded, we first used quartiles of group trends. Q1 is considered the reference group for Q2, 3, and 4 in all models. Even after adjusting for confounding variables, the Q2 group's risk of spinal pain was significantly and marginally lower than that of the Q1 group in Model 1. Compared to the Q1 group, the risk was decreased in the Q3 and Q4 groups. The group trend (OR\u0026thinsp;\u0026lt;\u0026thinsp;1; \u003cem\u003eP\u003c/em\u003e for trend significant) showed that as the SMI group grew, the risk of spinal pain decreased significantly. A similar pattern was found in the CHARLS data (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Although it was not significant in the Q2 group, the risk of spinal pain was much lower in the Q3 and Q4 groups compared to the Q1 and Q2 groups. The variables included in Model 2 were age, gender, education level, marital status, height, weight, SBP and DBP, BMI, WC, and HDL. Although the risk was lower in the Q2, Q3, and Q4 groups than in the Q1 group, the study showed that the total decline was not as significant as in Model 1. In a similar vein, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e2\u003c/span\u003eB shows that while the risk of spinal pain declined over time in Q3 and Q4, the Q2 group did not show any discernible change. After adjusting for the basic factors, the trend's OR decreased (its degree diminished), yet \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 remained significant. In Model 3, we further alter lifestyle choices and chronic illnesses (drinking, smoking, heart disease, hypertension, diabetes, and arthritis) based on Model 2. The Q2 group was no longer significant in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, but Q3 and Q4 were significant, and the risk decreased progressively. This implies that, even if the risk reduction was not as significant as in Models 1 and 2, the confounding variables in Model 3 were unaffected. While the Q2 group remains negligible, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e2\u003c/span\u003eB shows that the Q3 group is marginally significant. Confounding variables, such as lifestyle and chronic illnesses, affected the association in the Q3 group, but a residual association persisted and was more pronounced in the Q4 group. Even after adjusting for covariates, the overall trend of higher SMI and lower risk of spinal pain remained visible in the group trend.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRestrictive cubic spline analysis of SMI and spinal pain\u003c/h2\u003e \u003cp\u003eFigures 2A and 2B show the results of a restricted cubic spline (RCS) analysis after all variables have been controlled for, including age, gender, education level, marital status, BMI, WC, HDL, height, weight, SBP, DBP, smoking, drinking, diabetes, heart disease, hypertension, and arthritis. Both showed a strong linear correlation between overall spinal pain risk and SMI in the NHANES and CHARLS cohorts. The \u003cem\u003eP\u003c/em\u003e-values of the nonlinear values are 0.238 and 0.542, respectively. The non-linear correlation between the two is not statistically significant, and the correlation form is typically linear or almost linear. The two figures show a negative correlation between a higher SMI and a lower incidence of spinal pain, despite fluctuations in the effect size. With a relatively slight change, the OR in the NHANES cohort reduced from about 1.2 to 0.8 as the SMI rose from 0.4 to 1.2. As SMI increased from 0.4 to 1.2, the CHARLS cohort's OR changed more significantly, from about 2.0 to about 0.6. As a result, the risk increased more sharply at low SMI and fell more sharply at high SMI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analysis of SMI and Spinal Pain\u003c/h2\u003e \u003cp\u003eIn order to further explore the relationship between SMI and spinal pain, we conducted a subgroup analysis. After adjusting the continuous variables of SBP, DBP, weight, height, WC, and HDL, we analyzed the following categorical variables: age, gender, marital status, education, smoking, drinking, diabetes, arthritis, hypertension, and heart disease. Figure\u0026nbsp;3A and 3B show the forest maps for NHANES and the CHARLS subgroup analysis, respectively. The \u003cem\u003eP\u003c/em\u003e-value of the interaction indicates whether the stratification factor affects the association between SMI and spinal pain. If \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, it suggests that the stratification factor is effect-modifying (the effect of SMI varies across subgroups).\u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;3A, NHANES data, only the interaction between Age (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) and heart disease (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011) was statistically significant, indicating that age and the presence of heart disease alter the association between SMI and spinal pain. In Fig.\u0026nbsp;3B, the CHARLS data, the interaction of Smoking (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027), Diabetes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045), and Hypertension (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) is statistically significant, suggesting that \"smoking status\", \"whether there is diabetes\", and \"whether there is hypertension\" will change the relationship between SMI and spinal pain.\u003c/p\u003e \u003cp\u003eIn the age groups, Fig.\u0026nbsp;3A shows substantial associations with OR\u0026thinsp;=\u0026thinsp;0.626 (95% CI 0.625\u0026ndash;0.627) for ages 45\u0026ndash;56, OR\u0026thinsp;=\u0026thinsp;0.295 (95% CI 0.294\u0026ndash;0.296) for ages 57\u0026ndash;68, and OR\u0026thinsp;=\u0026thinsp;0.968 (95% CI 0.963\u0026ndash;0.974) for ages 69\u0026ndash;85+, with an interaction \u003cem\u003eP\u003c/em\u003e-value of 0.005, indicating that age is an effect-modifying factor. In Fig.\u0026nbsp;3B, all age groups had statistically significant OR, but the interaction \u003cem\u003eP\u003c/em\u003e was 0.117, indicating no significant interaction. Both databases observed that the risk of spinal pain gradually increases with age. In the Smoking group, Fig.\u0026nbsp;3A shows that only non-smokers OR\u0026thinsp;=\u0026thinsp;0.432 (95%CI 0.226\u0026ndash;0.823) differ significantly, while the interaction \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.957 indicates no difference. Figure\u0026nbsp;3B: Non-smoking OR\u0026thinsp;=\u0026thinsp;0.114 (95%CI 0.058\u0026ndash;0.222), smoking OR\u0026thinsp;=\u0026thinsp;0.04 (95%CI 0.01\u0026ndash;0.161), interaction \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027, indicating that smoking is an effect-modifying factor. In the Diabetes group, Fig.\u0026nbsp;3A: diabetes OR\u0026thinsp;=\u0026thinsp;0.142 (95%CI 0.03\u0026ndash;0.675) interaction \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.447. Figure\u0026nbsp;3B: No diabetes OR\u0026thinsp;=\u0026thinsp;0.109 (95%CI 0.055\u0026ndash;0.213), diabetes OR\u0026thinsp;=\u0026thinsp;0.026 (95%CI 0.003\u0026ndash;0.264), interactive \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045, suggesting that diabetes is an effect-modifying factor. In the hypertension group, Fig.\u0026nbsp;3A: No hypertension OR\u0026thinsp;=\u0026thinsp;0.41 (95%CI 0.192\u0026ndash;0.875) interaction \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.185, no difference. Figure\u0026nbsp;3B: No hypertension OR\u0026thinsp;=\u0026thinsp;0.081 (95%CI 0.037\u0026ndash;0.174), with hypertension OR\u0026thinsp;=\u0026thinsp;0.181 (95%CI 0.055\u0026ndash;0.601), interaction \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004, indicating that hypertension is an effect-modifying factor. In the heart disease grouping, Fig.\u0026nbsp;3A: No heart disease OR\u0026thinsp;=\u0026thinsp;0.48 (95%CI 0.26\u0026ndash;0.887), interaction \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011, indicating that heart disease is an effect-modifying factor. Figure\u0026nbsp;3B: OR for no heart disease\u0026thinsp;=\u0026thinsp;0.122 (95%CI 0.061\u0026ndash;0.251), OR for heart disease\u0026thinsp;=\u0026thinsp;0.087 (95%CI 0.019\u0026ndash;0.403), Interaction \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.187, no difference.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eROC prediction of spinal pain using SMI\u003c/h2\u003e \u003cp\u003eTo evaluate the predictive effectiveness of SMI for spinal pain in NHANES and CHARLS, ROC analysis was performed. The AUC of CHARLS is 0.602 (95% CI\u0026thinsp;=\u0026thinsp;0.590\u0026ndash;0.614), corresponding to a specificity of 54.9% and a sensitivity of 67.7%. At the ideal threshold of NHANES, the sensitivity was 41.8%, the specificity was 65.3%, and the AUC was 0.540 (95% CI\u0026thinsp;=\u0026thinsp;0.526\u0026ndash;0.553) (Fig.\u0026nbsp;4A, B). After integrating WC markers, the predictive performance was improved, but not significantly. Based on the above findings, SMI has higher prediction accuracy than NHANES in CHARLS.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigates the connection between spinal pain and SMI using a variety of methods, primarily based on the NHANES (1999\u0026ndash;2004) and CHARLS (2015 wave 3) databases. The two populations differ significantly in terms of cultural background, lifestyle, health care, disease prevention, perception, and the incidence rate of population epidemiology; yet, this study has produced three main findings:(1) After adjusting for confounding factors, SMI and spinal pain were significantly negatively correlated in both cohorts, meanwhile the older the age, the higher the risk of spinal pain and there exists a linear correlation relationship. (2) Restrictive cubic spline (RCS) analysis confirmed a linear association between SMI and spinal pain (NHANES cohort non-linear \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.31, CHARLS cohort non-linear \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.28), which supplemented previous epidemiological evidence that only reported a \"negative correlation\" but did not specify the form of the association[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] ; (3) subgroup analysis showed that the effect modifying factors were population specific: age and heart disease were significant modifying factors associated with SMI-spinal pain in the NHANES cohort, while smoking, diabetes and hypertension played this role in the CHARLS cohort, suggesting that there were cross-cultural differences in the protective effects of SMI;(4) ROC prediction curve analysis revealed the interaction between \"indicators population outcomes\" and evaluated the predictive effect of SMI on spinal pain. Its predictive performance in CHARLS was better than NHANES, indicating that the predictive value of indicators has population specificity, which may be due to differences in measurement methods, population disease spectrum, and other factors. Subsequent research can further explore the underlying reasons for population heterogeneity or combine more dimensional indicators (such as molecular markers) to improve prediction accuracy.\u003c/p\u003e \u003cp\u003eBoth mechanistic research and clinical data corroborate the negative linear association between spinal pain and SMI, which is driven by coordinated regulation of multiple physiological systems. As the \"dynamic stability system\" of the spine, the mass of the paraspinal (erector spinae, psoas major) and core (transversus abdominis, multifidus) muscle groups directly affects how effectively spinal mechanical loads are distributed[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The multifidus muscle, a \"segment-specific stabilizer\" of the lumbar spine, has a larger cross-sectional area among individuals with higher SMI. This allows for displacement between vertebrae (shear and rotational forces), lowers the risk of disc annulus fibrosus tears, and lessens the risk of injury to the small-joint capsules. Multifidus muscle atrophy was shown to be 32.6% to 48.9% more common in CLBP patients than in healthy controls, according to a case-control study that included 217 CLBP patients and 189 healthy controls. The visual analog scale (VAS) and the extent of atrophy had a positive correlation (r\u0026thinsp;=\u0026thinsp;0.42, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001)[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The elastic potential energy reserve of skeletal muscles increases with the increase in SMI[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The \"buffer chain\" created by muscle contraction and relaxation can reduce the instantaneous impact force on the spine during dynamic activities such as bending and rotation. This prevents pain signals from being triggered by the sudden high pressure in the intervertebral disc nucleus pulposus[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBy secreting actin, skeletal muscle, an endocrine organ, can control systemic inflammation and affect when spinal pain first appears[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Include cytokines that reduce inflammation, like interleukin-6 (IL-6) and interleukin-10 (IL-10). Individuals with high SMI also release irisin, which can decrease the production of pro-inflammatory factors, including interleukin-1β (IL-1β) and tumor necrosis factor alpha (TNF-α), and prevent the activation of the nuclear factor kappa B (NF-κB) inflammatory pathway[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The oxidative capacity of skeletal muscle positively regulates whole-body vibration of muscle factors, thereby verifying the key role of skeletal muscle health in preventing and improving chronic diseases[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Irisin is one of the mediators that postpones intervertebral disc degeneration (IDD), the primary cause of spinal pain in middle-aged and older adults. It can also enhance the proliferation of nucleus pulposus cells, glycosaminoglycan (GAG) content, metabolic activity, and type II collagen synthesis[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe \"signal sources\" for spinal motion control are the proprioceptors found in skeletal muscles, such as muscle spindles and Golgi tendon organs. By controlling the signaling effectiveness of this system, SMI may affect spinal pain risk[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Muscle spindle density can decrease with reduced muscle mass, compromising the accuracy of spinal position and motion perception and leading to a loss of proprioceptive signals. For older people with lower back pain (LBP), this is one of the primary causes of deterioration in postural balance control. It is hypothesized that age-related muscle loss is inversely correlated with proprioceptive sensitivity of the lower limbs, increasing the likelihood of lower back discomfort[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Exercise control compensation disorders: A lack of muscle mass can lead to compensatory exercise patterns, which include an over-reliance on the chest and abdominal muscles to compensate for the function of the lower back muscles. This can result in spinal line deviation, leading to greater lumbar lordosis. Chronic spinal pain can develop from long-term force-line deviation, which can cause inflammation of the lumbar myofascial and supraspinatus ligaments [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Reduced SMI can accelerate the loss of vertebral bone density, increase the risk of vertebral compression fractures, and transfer axial strain from the spine to the vertebral endplate[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Oxidative stress is an early potential biomarker of muscle atrophy[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Reactive oxygen species (ROS) production is elevated, and skeletal muscle mitochondrial activity is aberrant in those with lower SMI. Excessive ROS can further reduce muscle mass by oxidizing muscle proteins and activating the caspase-3 apoptotic pathway[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In addition, ROS can enter the intervertebral disc via the blood supply, inhibiting nucleus pulposus cell proliferation and promoting their death. This results in decreased nucleus pulposus cell proliferation and increased intervertebral disc water content and flexibility, which eventually leads to intervertebral disc herniation and root spinal pain[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This may constitute a biological mechanism shared by SMI and spinal pain.\u003c/p\u003e \u003cp\u003eThe negative correlation between SMI and spinal pain in the two queues is consistent. However, there are differences in the effect-modifying factors, which reflect differences in population characteristics, lifestyle, and disease spectrum: US queue: The \"aging metabolism\" superposition effect of age and heart disease: According to the US Census Bureau and the China Health Commission, the proportion of elderly people in US population is higher than that in China, and the obesity rate (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30) is also significantly higher than that in China. According to related studies, a low skeletal muscle index raises the possibility of predicting all-cause mortality in coronary heart disease and is linked to low peak oxygen uptake. The preventive impact of SMI on SP may be diminished by the overlap between age-related muscle loss and circulatory problems associated with heart disease[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\"Metabolic vascular\" synergistic effect of smoking, diabetes, and hypertension: nicotine produced by smoking inhibits the myogenic ability and myotube formation of C2C12 cells[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], diabetes microvascular disease reduces muscle blood supply[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], hypertension vascular endothelial damage impairs actin secretion[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], We speculate that these three factors can synergistically affect the association between SMI and spinal pain.\u003c/p\u003e \u003cp\u003eThe advantages of research are cross-racial extrapolation (Use two nationally representative large cohorts to ensure strong external validity of the results) and a comprehensive analysis across multiple dimensions \u0026mdash; biomechanics, metabolism, neurology, and endocrinology \u0026mdash; was conducted to elucidate the underlying factors driving these correlations and to provide a theoretical basis for clinical interventions.\u003c/p\u003e \u003cp\u003eThe limitations of research include: cross-sectional design: It is not possible to establish a causal relationship, and reverse causality cannot be ruled out (such as chronic spinal pain leading to reduced physical activity, which subsequently causes muscle loss)[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]; Self-reported spinal pain is subject to recall bias and cannot distinguish pain subtypes (radicular, myofascial) or severity (acute, chronic) through self-report by subjects; Residual confounding factors: unmeasured factors in the database (such as dietary protein intake, spinal imaging results, and exclusion of other diseases) may affect the research results.\u003c/p\u003e \u003cp\u003eFuture research should focus on using longitudinal data to confirm causal relationships. Specifically, they should examine the predictive utility of baseline SMI for diagnosing spinal pain, conduct multicenter, multivariate, randomized controlled trials (RCTs), and develop relevant intervention plans to enhance the preventive and therapeutic effects of skeletal muscle mass index on spinal pain.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, a higher SMI is an independent protective factor for spinal pain in middle-aged and older individuals in China and the US, and the two countries show a linear negative correlation. SMI has predictive value for spinal pain, with a more pronounced effect in CHARLS. Several mechanisms, including hormone balance, neuromuscular control, inflammation regulation, and biomechanical support, drive this relationship. The negative correlation linear relationship between SMI and spinal pain has cross racial consistency, and the characteristics of specific populations regulate the protective effect of SMI, emphasizing the necessity of tailored prevention strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eSMI Skeletal muscle mass index\u003c/p\u003e \u003cp\u003eNHANES National Health and Nutrition Examination Survey\u003c/p\u003e \u003cp\u003eCHARLS China Health and Retirement Longitudinal Study\u003c/p\u003e \u003cp\u003eBMI Body Mass Index\u003c/p\u003e \u003cp\u003eHDL High-Density Lipoprotein\u003c/p\u003e \u003cp\u003eSBP Systolic Blood Pressure\u003c/p\u003e \u003cp\u003eDBP Diastolic Blood Pressure\u003c/p\u003e \u003cp\u003eWC Waist Circumference\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate all data used in this study are all from the public domain.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study analyzed publicly available datasets; relevant data can be obtained from the CHARLS and NHANES at https://charls.pku.edu.cn/. and https://www.cdc.gov/nchs/nhanes/about/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the Chinese Center for Social Sciences Survey of Peking University for providing the CHARLS data and the NHANES data provided by the National Health and Nutrition Examination Survey in the United States. We want to express our gratitude to the participants and researchers who contributed to the database used in this investigation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eQiu K, Wang C, Mo X, Yang G, Huang L, Wu Y, Pan Z (2025) The global macroeconomic burden of musculoskeletal disorders. Int J Surg. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/js9.0000000000003072\u003c/span\u003e\u003cspan address=\"10.1097/js9.0000000000003072\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei J, Chen L, Huang S, Li Y, Zheng J, Cheng Z, Xie Z (2022) Time Trends in the Incidence of Spinal Pain in China, 1990 to 2019 and Its Prediction to 2030: The Global Burden of Disease Study 2019. Pain Ther 11(4):1245\u0026ndash;1266. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40122-022-00422-9\u003c/span\u003e\u003cspan address=\"10.1007/s40122-022-00422-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHartvigsen J, Hancock MJ, Kongsted A, Louw Q, Ferreira ML, Genevay S, Hoy D, Karppinen J, Pransky G, Sieper J, Smeets RJ, Underwood M (2018) What low back pain is and why we need to pay attention. Lancet 391(10137):2356\u0026ndash;2367. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s0140-6736(18)30480-x\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(18)30480-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu A, March L, Zheng X, Huang J, Wang X, Zhao J, Blyth FM, Smith E, Buchbinder R, Hoy D (2020) Ann Transl Med 8(6):299. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21037/atm.2020.02.175\u003c/span\u003e\u003cspan address=\"10.21037/atm.2020.02.175\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Global low back pain prevalence and years lived with disability from 1990 to 2017: estimates from the Global Burden of Disease Study 2017\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu F, Zhang X, Yang M, Zhao Q, Wang Q, Lian J, Zhang R, Chu T, Kou Z, Zhao M (2025) Magnitude, temporal trend and inequality in burden of neck pain: an analysis of the Global Burden of Disease Study 2019. BMC Musculoskelet Disord 26(1):202. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12891-025-08342-3\u003c/span\u003e\u003cspan address=\"10.1186/s12891-025-08342-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal regional, national burden of neck pain (2024) 1990\u0026ndash;2020, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021. Lancet Rheumatol 6(3):e142\u0026ndash;e155. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s2665-9913(23)00321-1\u003c/span\u003e\u003cspan address=\"10.1016/s2665-9913(23)00321-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen LK, Woo J, Assantachai P, Auyeung TW, Chou MY, Iijima K, Jang HC, Kang L, Kim M, Kim S, Kojima T, Kuzuya M, Lee JSW, Lee SY, Lee WJ, Lee Y, Liang CK, Lim JY, Lim WS, Peng LN, Sugimoto K, Tanaka T, Won CW, Yamada M, Zhang T, Akishita M, Arai H, Asian Working Group for Sarcopenia (2020) 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J Am Med Dir Assoc 21(3):300\u0026ndash;307e2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jamda.2019.12.012\u003c/span\u003e\u003cspan address=\"10.1016/j.jamda.2019.12.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang J, Chen G, Li Y, Zhao Q, Chen Z (2025) Serum calcium levels and the risk of sarcopenia in young adults: insights from NHANES 2011\u0026ndash;2018. Front Nutr 12:1526879. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnut.2025.1526879\u003c/span\u003e\u003cspan address=\"10.3389/fnut.2025.1526879\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu Z, Tian Y, Song X, Zeng F, Yang A (2023) Associations between indoor air pollution for cooking and heating with muscle and sarcopenia in Chinese older population. J Cachexia Sarcopenia Muscle 14(5):2029\u0026ndash;2043. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jcsm.13281\u003c/span\u003e\u003cspan address=\"10.1002/jcsm.13281\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen R, Yang C, Tang X, Han S, Kuang M, Li X (2025) The relationship between muscle mass and low back pain: a cross-sectional study. Eur Spine J 34(7):2597\u0026ndash;2604. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00586-025-09026-1\u003c/span\u003e\u003cspan address=\"10.1007/s00586-025-09026-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan S, Larsson SC (2023) Epidemiology of sarcopenia: Prevalence, risk factors, and consequences. Metabolism 144:155533. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.metabol.2023.155533\u003c/span\u003e\u003cspan address=\"10.1016/j.metabol.2023.155533\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi L, Zuo A, Yin R, Liu Q, Liu C, Li N, Xu D, Zhang S, Li J, Lei S, Ruan S, Li T, Guo Y (2025) CTRP9 as a myokine mitigates sarcopenia via the LAMP-2A/NLRP3 pathway. Cell Death Dis 16(1):710. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41419-025-08025-w\u003c/span\u003e\u003cspan address=\"10.1038/s41419-025-08025-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin T, Dai M, Xu P, Sun L, Shu X, Xia X, Zhao Y, Song Q, Guo D, Deng C, Yue J (2022) Prevalence of Sarcopenia in Pain Patients and Correlation Between the Two Conditions: A Systematic Review and Meta-Analysis. J Am Med Dir Assoc 23(5):902e1\u0026ndash;902e20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jamda.2022.02.005\u003c/span\u003e\u003cspan address=\"10.1016/j.jamda.2022.02.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuven AE, Sch\u0026ouml;nnagel L, Camino-Willhuber G, Chiapparelli E, Amoroso K, Zhu J, Tani S, Caffard T, Arzani A, Zadeh AT, Shue J, Tan ET, Sama AA, Girardi FP, Cammisa FP, Hughes AP (2024) Relationship between facet joint osteoarthritis and lumbar paraspinal muscle atrophy: a cross-sectional study. J Neurosurg Spine 41(3):360\u0026ndash;368. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3171/2024.4.Spine231018\u003c/span\u003e\u003cspan address=\"10.3171/2024.4.Spine231018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSakai Y, Watanabe T, Matsui H, Osada N, Adachi Y, Takeichi Y, Katsumi A, Watanabe K (2025) Factors Related to the Prognosis of Chronic Low Back Pain in Older Adults. Phys Ther. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ptj/pzaf123\u003c/span\u003e\u003cspan address=\"10.1093/ptj/pzaf123\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel CJ, Pho N, McDuffie M, Easton-Marks J, Kothari C, Kohane IS, Avillach P (2016) A database of human exposomes and phenomes from the US National Health and Nutrition Examination Survey. Sci Data 3:160096. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/sdata.2016.96\u003c/span\u003e\u003cspan address=\"10.1038/sdata.2016.96\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Y, Hu Y, Smith JP, Strauss J, Yang G (2014) Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol 43(1):61\u0026ndash;68. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ije/dys203\u003c/span\u003e\u003cspan address=\"10.1093/ije/dys203\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu J, Zhang F (2025) Associations between sarcopenia (defined by low muscle mass), inflammatory markers, and all-cause mortality in older adults: mediation analyses in a large U.S. NHANES community sample, 1999\u0026ndash;2006. Front Med (Lausanne) 12:1515839. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmed.2025.1515839\u003c/span\u003e\u003cspan address=\"10.3389/fmed.2025.1515839\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo YX, Zhou XH, Heng T, Yang LL, Zhu YH, Hu P, Yao XQ (2024) Bidirectional transitions of sarcopenia states in older adults: The longitudinal evidence from CHARLS. J Cachexia Sarcopenia Muscle 15(5):1915\u0026ndash;1929. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jcsm.13541\u003c/span\u003e\u003cspan address=\"10.1002/jcsm.13541\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang W, Li Y, Shao P, Du Y, Zhao K, Zhan J, Tan LA (2025) Association of weight-adjusted waist index and body mass index with chronic low back pain in American adults: a retrospective cohort study and predictive model development based on machine learning algorithms (NHANES 2009\u0026ndash;2010). Front Public Health 13:1617732. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpubh.2025.1617732\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2025.1617732\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou S, Liu Y, Zhang Y, Luo N, Chen Q, Ge M, Shen B (2024) Association between persistent musculoskeletal pain and incident sarcopenia in China: the mediating effect of depressive symptoms. Front Public Health 12:1416796. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpubh.2024.1416796\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2024.1416796\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRen S, Jiang X, Wang S, Wong AYL, Bi X, Wang X (2024) The prevalence and factors associated with neck and low back pain in patients with stroke: insights from the CHARLS. BMC Public Health 24(1):2362. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-024-19847-2\u003c/span\u003e\u003cspan address=\"10.1186/s12889-024-19847-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan Z, Feng C, Li X, Zhang Z, Gong H, Di J (2025) Association between uric acid to high-density lipoprotein cholesterol ratio and chronic kidney disease in middle-aged and older adults: results from two nationally representative population-based study. Ren Fail 47(1):2575920. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/0886022x.2025.2575920\u003c/span\u003e\u003cspan address=\"10.1080/0886022x.2025.2575920\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen J, Wang X, Xu Z (2023) Sarcopenia and Chronic Pain in the Elderly: A Systematic Review and Meta-Analysis. J Pain Res 16:3569\u0026ndash;3581. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2147/jpr.S435866\u003c/span\u003e\u003cspan address=\"10.2147/jpr.S435866\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIndahl A, Velund L, Reikeraas O (1995) Good prognosis for low back pain when left untampered. A randomized clinical trial. Spine (Phila Pa 1976) 20(4):473\u0026ndash;477. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/00007632-199502001-00011\u003c/span\u003e\u003cspan address=\"10.1097/00007632-199502001-00011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanjabi MM (1992) The stabilizing system of the spine. Part I. Function, dysfunction, adaptation, and enhancement. J Spinal Disord 5(4):383\u0026ndash;389 discussion 397. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/00002517-199212000-00001\u003c/span\u003e\u003cspan address=\"10.1097/00002517-199212000-00001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHides JA, Stokes MJ, Saide M, Jull GA, Cooper DH (1994) Evidence of lumbar multifidus muscle wasting ipsilateral to symptoms in patients with acute/subacute low back pain. Spine (Phila Pa 1976) 19(2):165\u0026ndash;172. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/00007632-199401001-00009\u003c/span\u003e\u003cspan address=\"10.1097/00007632-199401001-00009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eProkopidis K, Cacciatore S, Piaggi P, Vetrano DL, Schl\u0026ouml;gl M (2025) Muscle strengthening activities: cross-sectional associations with skeletal muscle outcomes in adults aged 50\u0026ndash;64 and 65 years and above. Eur Geriatr Med. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s41999-025-01327-4\u003c/span\u003e\u003cspan address=\"10.1007/s41999-025-01327-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang SH, Mirka GA (2025) Load transfer between active and passive lumbar tissues and its implications in time-dependent EMG-assisted biomechanical modeling. J Biomech 183:112600. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jbiomech.2025.112600\u003c/span\u003e\u003cspan address=\"10.1016/j.jbiomech.2025.112600\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePedersen BK, Febbraio MA (2012) Muscles, exercise and obesity: skeletal muscle as a secretory organ. Nat Rev Endocrinol 8(8):457\u0026ndash;465. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nrendo.2012.49\u003c/span\u003e\u003cspan address=\"10.1038/nrendo.2012.49\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBostr\u0026ouml;m P, Wu J, Jedrychowski MP, Korde A, Ye L, Lo JC, Rasbach KA, Bostr\u0026ouml;m EA, Choi JH, Long JZ, Kajimura S, Zingaretti MC, Vind BF, Tu H, Cinti S, H\u0026oslash;jlund K, Gygi SP, Spiegelman BM (2012) A PGC1-α-dependent myokine that drives brown-fat-like development of white fat and thermogenesis. Nature 481(7382):463\u0026ndash;468. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nature10777\u003c/span\u003e\u003cspan address=\"10.1038/nature10777\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBroniec MN, Norland K, Looney J, Crandall R, Thomas J, Wang X, Harris RA (2025) Increased Skeletal Muscle Oxidative Capacity Augments the Myokine Response to Whole Body Vibration. Med Sci Sports Exerc. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1249/mss.0000000000003854\u003c/span\u003e\u003cspan address=\"10.1249/mss.0000000000003854\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVadal\u0026agrave; G, Di Giacomo G, Ambrosio L, Cicione C, Tilotta V, Russo F, Papalia R, Denaro V (2023) Effect of Irisin on Human Nucleus Pulposus Cells: New Insights into the Biological Cross-talk Between Muscle and Intervertebral Disk. Spine (Phila Pa 1976) 48(7):468\u0026ndash;475. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/brs.0000000000004488\u003c/span\u003e\u003cspan address=\"10.1097/brs.0000000000004488\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eProske U, Gandevia SC (2012) The proprioceptive senses: their roles in signaling body shape, body position and movement, and muscle force. Physiol Rev 92(4):1651\u0026ndash;1697. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1152/physrev.00048.2011\u003c/span\u003e\u003cspan address=\"10.1152/physrev.00048.2011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSakai Y, Watanabe T, Wakao N, Matsui H, Osada N, Sugiura T, Morita Y, Kawai K, Ito T (2022) Yamazaki, Proprioception and Geriatric Low Back Pain. Spine Surg Relat Res 6(5):422\u0026ndash;432. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.22603/ssrr.2021-0269\u003c/span\u003e\u003cspan address=\"10.22603/ssrr.2021-0269\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Die\u0026euml;n JH, Cholewicki J, Radebold A (2003) Trunk muscle recruitment patterns in patients with low back pain enhance the stability of the lumbar spine. Spine (Phila Pa 1976) 28(8) 834\u0026thinsp;\u0026ndash;\u0026thinsp;41\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen BTT, Nguyen TT, Kuo YJ, Chen YP (2025) Impact of sarcopenia on outcomes following vertebral augmentation for osteoporotic vertebral compression fracture: a systematic review and meta-analysis. Asian Spine J 19(3):476\u0026ndash;489. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.31616/asj.2024.0467\u003c/span\u003e\u003cspan address=\"10.31616/asj.2024.0467\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoto Montes A, Boga JA, Bermejo Millo C, Rubio Gonz\u0026aacute;lez A, Potes Ochoa Y, Vega Naredo I, Mart\u0026iacute;nez Reig M, Romero Rizos L, S\u0026aacute;nchez Jurado PM, Solano JJ, Abizanda P, Caballero B (2017) Potential early biomarkers of sarcopenia among independent older adults. Maturitas 104:117\u0026ndash;122. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.maturitas.2017.08.009\u003c/span\u003e\u003cspan address=\"10.1016/j.maturitas.2017.08.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Wang X, Huang L, Zhou C, Qian B, Luo Z, Chen Y (2025) Effects of statins on sarcopenia with focus on mechanistic insights and future perspectives. Front Pharmacol 16:1669591. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fphar.2025.1669591\u003c/span\u003e\u003cspan address=\"10.3389/fphar.2025.1669591\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou H, Wu C, Jin Y, Wu O, Chen L, Guo Z, Wang X, Chen Q, Kwan KYH, Li YM, Xia D, Chen T, Wu A (2024) Role of oxidative stress in mitochondrial dysfunction and their implications in intervertebral disc degeneration: Mechanisms and therapeutic strategies. J Orthop Translat 49:181\u0026ndash;206. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jot.2024.08.016\u003c/span\u003e\u003cspan address=\"10.1016/j.jot.2024.08.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNichols S, O'Doherty AF, Taylor C, Clark AL, Carroll S, Ingle L (2019) Low skeletal muscle mass is associated with low aerobic capacity and increased mortality risk in patients with coronary heart disease - a CARE CR study. Clin Physiol Funct Imaging 39(1):93\u0026ndash;102. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/cpf.12539\u003c/span\u003e\u003cspan address=\"10.1111/cpf.12539\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFonseca G, Garfias Macedo T, Ebner N, Dos Santos MR, de Souza FR, Mady C, Takayama L, Pereira RMR, Doehner W, Anker SD, Negr\u0026atilde;o CE, Alves M, von Haehling S (2020) Muscle mass, muscle strength, and functional capacity in patients with heart failure of Chagas disease and other aetiologies. ESC Heart Fail 7(5):3086\u0026ndash;3094. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ehf2.12936\u003c/span\u003e\u003cspan address=\"10.1002/ehf2.12936\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng G, Li C, Chen X, Deng Z, Xie T, Huo Z, Wei X, Huang Y, Zeng X, Luo Y, Bai J (2024) HDAC9 inhibition reduces skeletal muscle atrophy and enhances regeneration in mice with cigarette smoke-induced COPD. Biochim Biophys Acta Mol Basis Dis 1870(3):167023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bbadis.2024.167023\u003c/span\u003e\u003cspan address=\"10.1016/j.bbadis.2024.167023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuggiero AD, Davis A, Sherrill C, Westwood B, Hawkins GA, Palmer ND, Chou JW, Reeves T, Cox LA, Kavanagh K (2021) Skeletal muscle extracellular matrix remodeling with worsening glycemic control in nonhuman primates. Am J Physiol Regul Integr Comp Physiol 320(3):R226\u0026ndash;r235. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1152/ajpregu.00240.2020\u003c/span\u003e\u003cspan address=\"10.1152/ajpregu.00240.2020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRadwan E, Mali V, Haddox S, El-Noweihi A, Mandour M, Ren J, Belmadani S, Matrougui K (2019) Treg cells depletion is a mechanism that drives microvascular dysfunction in mice with established hypertension. Biochim Biophys Acta Mol Basis Dis 1865(2):403\u0026ndash;412. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bbadis.2018.10.031\u003c/span\u003e\u003cspan address=\"10.1016/j.bbadis.2018.10.031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBennai F, Morsing P, Paliege A, Ketteler M, Mayer B, Tapp R, Bachmann S (1999) Normalizing the expression of nitric oxide synthase by low-dose AT1 receptor antagonism parallels improved vascular morphology in hypertensive rats. J Am Soc Nephrol 10(11):S104\u0026ndash;S115\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichardson PE (2015) David Sackett and the birth of Evidence Based Medicine: How to Practice and Teach EBM. BMJ 350:h3089. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/bmj.h3089\u003c/span\u003e\u003cspan address=\"10.1136/bmj.h3089\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":" \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 \u003cdiv class=\"SimplePara\"\u003eA Baseline characteristics of NHANES individuals\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cdiv class=\"SimplePara\"\u003eVariable\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eSpinal pain\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e-value\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo,n\u0026thinsp;=\u0026thinsp;3509\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eYes,n\u0026thinsp;=\u0026thinsp;3054\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eAge (years)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e60.02\u0026thinsp;\u0026plusmn;\u0026thinsp;11.27\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e58.88\u0026thinsp;\u0026plusmn;\u0026thinsp;10.85\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eGender n(%)\u003c/span\u003e\u003c/div\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMale\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1757 (50.06%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1363 (44.64%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eFemale\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1752(49.94%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1691(55.36%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eEducation n(%)\u003c/span\u003e\u003c/div\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePrimary school\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e671(19.11%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e737 (24.13%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMiddle school\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e886(25.26%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e864(28.30%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ecollege\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1952(55.63%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1453 (47.56%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eMarital status n(%)\u003c/span\u003e\u003c/div\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.126\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMarried or partnered\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2401(68.42%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2035 (66.65%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNever married\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1108 (31.58%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1019(33.35%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eSmoking n(%)\u003c/span\u003e\u003c/div\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eyes\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e599 (17.08%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e672(22.00%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eno\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2910 (82.92%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2382 (78.00%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eDrinking n(%)\u003c/span\u003e\u003c/div\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.005\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eyes\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2235 (63.68%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1843 (60.35%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eno\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1274 (36.32%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1211 (39.65%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eDiabetes n(%)\u003c/span\u003e\u003c/div\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eyes\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e388(11.07%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e434 (14.22%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eno\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3121 (88.93%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2620(85.78%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eHeart disease n(%)\u003c/span\u003e\u003c/div\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eyes\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e385(10.97%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e453 (14.82%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eno\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3124 (89.03%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2601(85.18%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eHypertension n(%)\u003c/span\u003e\u003c/div\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eyes\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1331 (37.94%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1329(43.52%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eno\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2178 (62.06%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1725(56.48%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eArthritis n(%)\u003c/span\u003e\u003c/div\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eyes\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e924(26.33%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1452 (47.56%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eno\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2585(73.67%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1602 (52.44%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eSBP (mmHg)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e130.31\u0026thinsp;\u0026plusmn;\u0026thinsp;19.72\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e130.09\u0026thinsp;\u0026plusmn;\u0026thinsp;20.21\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.654\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eDBP (mmHg)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e73.44\u0026thinsp;\u0026plusmn;\u0026thinsp;11.80\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e73.31\u0026thinsp;\u0026plusmn;\u0026thinsp;12.30\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.669\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eHeight (cm)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e168.43\u0026thinsp;\u0026plusmn;\u0026thinsp;10.03\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e167.65\u0026thinsp;\u0026plusmn;\u0026thinsp;10.00\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eWeight (kg)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e80.14\u0026thinsp;\u0026plusmn;\u0026thinsp;18.59\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e81.37\u0026thinsp;\u0026plusmn;\u0026thinsp;19.50\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eBMI (kg/m\u003c/span\u003e\u003csup\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e28.15\u0026thinsp;\u0026plusmn;\u0026thinsp;5.68\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e28.86\u0026thinsp;\u0026plusmn;\u0026thinsp;6.14\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eWC (cm)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e98.58\u0026thinsp;\u0026plusmn;\u0026thinsp;14.31\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e100.10\u0026thinsp;\u0026plusmn;\u0026thinsp;15.12\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eHDL (mg/dL)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e53.22\u0026thinsp;\u0026plusmn;\u0026thinsp;15.90\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e53.54\u0026thinsp;\u0026plusmn;\u0026thinsp;16.98\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.4367\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eSMI\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eB Baseline characteristics of CHARLS individuals\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cdiv class=\"SimplePara\"\u003eVariable\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eSpinal pain\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e-value\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo,n\u0026thinsp;=\u0026thinsp;9395\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eYes,n\u0026thinsp;=\u0026thinsp;2826\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eAge (years)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e60.51\u0026thinsp;\u0026plusmn;\u0026thinsp;9.78\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e60.90\u0026thinsp;\u0026plusmn;\u0026thinsp;9.36\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.008\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eGender n(%)\u003c/span\u003e\u003c/div\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMale\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e4802 (51.05%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e951 (33.62%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eFemale\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e4603 (48.95%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1875(66.38%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eEducation n(%)\u003c/span\u003e\u003c/div\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePrimary school\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1562 (16.61%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e580 (20.50%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMiddle school\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e7793 (82.86%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2244(79.43%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ecollege\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e50 (0.53%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2 (0.07%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eMarital status n(%)\u003c/span\u003e\u003c/div\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.424\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMarried or partnered\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e9342 (99.33%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2804 (99.19%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNever married\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e63 (0.67%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e22 (0.81%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eSmoking n(%)\u003c/span\u003e\u003c/div\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eyes\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e710 (7.55%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e155 (5.59%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eno\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e8695 (92.45%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2671 (94.41%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eDrinking n(%)\u003c/span\u003e\u003c/div\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eyes\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3511 (37.33%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e795 (28.21%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eno\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e5894 (62.67%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2031 (71.79%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eDiabetes n(%)\u003c/span\u003e\u003c/div\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eyes\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e724 (7.71%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e304 (10.78%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eno\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e8681 (92.29%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2522 (89.22%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eHeart disease n(%)\u003c/span\u003e\u003c/div\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eyes\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1183 (12.58%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e654 (23.19%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eno\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e8222 (87.42%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2172 (76.81%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eHypertension n(%)\u003c/span\u003e\u003c/div\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eyes\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2564 (27.27%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e963 (34.11%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eno\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e6841 (72.73%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1863 (65.89%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eArthritis n(%)\u003c/span\u003e\u003c/div\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eyes\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2888 (30.70%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1613 (57.09%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eno\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e6517 (69.30%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1213 (42.91%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eSBP (mmHg)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e128.64\u0026thinsp;\u0026plusmn;\u0026thinsp;19.77\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e127.88\u0026thinsp;\u0026plusmn;\u0026thinsp;20.26\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.018\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eDBP (mmHg)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e75.83\u0026thinsp;\u0026plusmn;\u0026thinsp;11.62\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e75.37\u0026thinsp;\u0026plusmn;\u0026thinsp;11.81\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.035\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eHeight (cm)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e158.86\u0026thinsp;\u0026plusmn;\u0026thinsp;8.49\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e155.98\u0026thinsp;\u0026plusmn;\u0026thinsp;8.20\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eWeight (kg)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e60.63\u0026thinsp;\u0026plusmn;\u0026thinsp;11.83\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e58.34\u0026thinsp;\u0026plusmn;\u0026thinsp;11.43\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eBMI (kg/m\u003c/span\u003e\u003csup\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e23.95\u0026thinsp;\u0026plusmn;\u0026thinsp;3.90\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e23.92\u0026thinsp;\u0026plusmn;\u0026thinsp;4.03\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.380\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eWC (cm)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e85.67\u0026thinsp;\u0026plusmn;\u0026thinsp;12.74\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e84.71\u0026thinsp;\u0026plusmn;\u0026thinsp;14.00\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.007\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eHDL (mg/dL)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e51.06\u0026thinsp;\u0026plusmn;\u0026thinsp;11.52\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e51.71\u0026thinsp;\u0026plusmn;\u0026thinsp;11.61\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.005\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eSMI\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eA NHANES: Logistic regression analysis for associations between the SMI and spinal pain Q1\u003c/span\u003e was used as a control for the other SMI groups and was referred to as \"Ref\" in all models (the reference group). \u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eModel1\u003c/span\u003e:adjust for none. \u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eModel2\u003c/span\u003e: adjust for Age,Gender,Education,Marital status, SBP, DBP, BMI, WC, HDL, Height, Weight. \u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eModel3\u003c/span\u003e: adjust for age, gender, education, marital-status, SBP, DBP, BMI, WC, HDL, Height, Weight, smoking, drinking, Diabetes, Heart-disease, Hypertension, Arthritis.\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eExposure\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eModel1\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eModel2\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eModel3\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eOR(95%)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eOR(95%)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eOR(95%)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSMI GROUP\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eQ1(0.29\u0026ndash;0.58)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eRef\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eRef\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eRef\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eQ2(0.58\u0026ndash;0.72)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.951(0.807,1.006)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.970(0.969,0.971)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.978(0.946,1.012)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.219\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eQ3(0.72\u0026ndash;0.88)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.782(0.781,0783)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.847(0.846,0.848)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.939(0.908,0.972)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eQ4(0.88\u0026ndash;1.46)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.749(0.748,0.750)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.834(0.833,0.836)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.922(0.891,0.954)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eGroup\u003c/span\u003e \u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003etrend\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.493(0.492,0.494)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.683(0.681,0.685)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.635(0.633,0.637)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eB CHARLS: Logistic regression analysis for associations between the SMI and Spinal pain\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eExposure\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eModel1\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eModel2\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eModel3\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eOR(95%)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eOR(95%)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eOR(95%)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSMI GROUP\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eQ1(0.14\u0026ndash;0.58)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eRef\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eRef\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eRef\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eQ2(0.58\u0026ndash;0.69)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.901(0.807,1.006)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.063\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.941(0.837,0.106)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.312\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.958(0.845,1.082)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.496\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eQ3(0.69\u0026ndash;0.88)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.511(0.453,0.576)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.736(0.579,0.935)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.012\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.793(0.619,1.015)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.065\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eQ4(0.88\u0026ndash;1.29)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.430(0.380,0.487)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.652(0.489,0.869)\u003c/div\u003e \u003c/td\u003e \u003ctd 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\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.364(0.183,0.726)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.004\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.466(0.228,0.953)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.036\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eQ1, Model1,2,3 adjust variables the same as Table\u0026nbsp;2A\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"european-spine-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esjo","sideBox":"Learn more about [European Spine Journal](http://link.springer.com/journal/586)","snPcode":"586","submissionUrl":"https://submission.springernature.com/new-submission/586/3","title":"European Spine Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Skeletal muscle mass index, Spinal pain, Cross-sectional study, Sarcopenia, NHANES, CHARLS","lastPublishedDoi":"10.21203/rs.3.rs-8825049/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8825049/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSpinal pain is a leading cause of global disability, with prevalence increasing with age. While skeletal muscle mass index (SMI) is a key indicator of sarcopenia and influences spinal pain through biomechanical, metabolic, and neuromuscular mechanisms, large-scale cross-national studies examining this relationship are lacking. This study aimed to investigate the association between SMI and spinal pain in middle-aged and elderly populations from China and the United States.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional study used data from the National Health and Nutrition Examination Survey (NHANES, 1999\u0026ndash;2004) and the China Health and Retirement Longitudinal Study (CHARLS, 2015). Baseline analysis of the population, multivariable logistic regression, restricted cubic spline (RCS) analysis, and subgroup analyses were performed to assess associations, adjusted for demographic, lifestyle, and health-related covariates, and to evaluate SMI's capacity to predict spinal pain. We employed the receiver operating characteristic (ROC) curves and the area under the curve (AUC).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 6,563 participants from NHANES and 12,221 from CHARLS were included. After full adjustment, higher SMI was significantly associated with a lower risk of spinal pain in both cohorts (NHANES: OR for trend\u0026thinsp;\u0026lt;\u0026thinsp;1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; CHARLS: OR for trend\u0026thinsp;\u0026lt;\u0026thinsp;1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). RCS analysis revealed a linear negative association between SMI and spinal pain (non-linear \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Subgroup analysis identified a differential effect modifier. ROC analysis compared the predictive performance of two databases.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eHigher SMI is independently associated with a reduced risk of spinal pain in both Chinese and American middle-aged and elderly populations, exhibiting a linear relationship. The protective effect of SMI is influenced by specific population factors, underscoring the importance of different racial/social prevention strategies. Moreover, CHARLS has better predictive ability than NHANES. Future research should longitudinally verify the causal relationship between these findings and explore targeted interventions to enhance muscle mass to prevent or treat spinal pain.\u003c/p\u003e","manuscriptTitle":"The relationship between skeletal muscle mass index and spinal pain: a cross-sectional study comparing middle-aged and elderly individuals in China and the United States","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-03 06:10:38","doi":"10.21203/rs.3.rs-8825049/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-14T15:22:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-08T15:22:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"187314828461587025217195496319853546301","date":"2026-03-31T13:45:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191278697791513609855395677756007047079","date":"2026-03-31T12:17:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311809477659585672780279108041429226062","date":"2026-03-31T11:34:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-29T11:15:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-10T20:54:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-10T07:12:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Spine Journal","date":"2026-02-09T02:29:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-spine-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esjo","sideBox":"Learn more about [European Spine Journal](http://link.springer.com/journal/586)","snPcode":"586","submissionUrl":"https://submission.springernature.com/new-submission/586/3","title":"European Spine Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"72e16260-0cc3-4ae0-8267-8aa7cc8a3bc9","owner":[],"postedDate":"April 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-03T06:10:38+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-03 06:10:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8825049","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8825049","identity":"rs-8825049","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}