Cholesterol-lowering medications and sarcopenia: Large cross- sectional Study ：NHANES 2011-2014

Cholesterol-lowering medications and sarcopenia: Large cross- sectional Study ：NHANES 2011-2014 | 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 Cholesterol-lowering medications and sarcopenia: Large cross- sectional Study ：NHANES 2011-2014 Wei Gong, Tingting Liu, Jie Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3968474/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective Cholesterol-lowering medications, especially statins, are widely prescribed for primary and secondary prevention of cardiovascular diseases. The association between those medications and sarcopenia has garnered attention and remains a topic of contention. Our aim is to investigate whether cholesterol-lowering medications are a risk factor for sarcopenia. Methods We utilized data from the National Health and Nutrition Examination Survey (NHANES) database, extracting data from the 2011–2014 survey cycles. By constructed univariate and multivariate regression models, we elucidated the relationship between the X variable and the Y variable. By conducted predictive models by the ROC curve to assess the average predictive value based on AUC. Results The ratio of usage of cholesterol-lowering medication showed a significant difference between the sarcopenia group and non-sarcopenia group (77 (25.0%) vs. 396 (12.0%), p < 0.001), but when further analyzing the subgroups of obese and non-obese individuals, this difference disappeared. In the multivariable logistic regression analysis BMI demonstrated a significant and independent impact on sarcopenia (OR = 1.13, 95%CI 1.01–1.27, p = 0.036). The ROC curve analysis of the model incorporating age, grip strength, and BMI as predictors yielded an area under the curve (AUC) of 0.7433. Conclusion There is no direct correlation between cholesterol-lowering medications and sarcopenia. Instead, obesity emerges as an independent risk factor for sarcopenia. Additionally, the combination of BMI, age, and grip strength demonstrates good predictive value for identifying the risk of sarcopenia in clinical patients. sarcopenia Cholesterol-lowering medications obesity risk factor grip strength Figures Figure 1 Figure 2 Figure 3 Introduction Cholesterol-lowering medications (CLM), especially statins, are widely prescribed for primary and secondary prevention of cardiovascular diseases[ 1 ]. They work by inhibiting the activity of HMG-CoA reductase, blocking the conversion of HMG-CoA to mevalonic acid and reducing hepatic cholesterol synthesis[ 2 ]. It reported that approximately 25% of individuals aged 65 and above undergoing statin therapy[ 3 , 4 ]. Sarcopenia is a progressive and systemic skeletal muscle disease that has been associated with an increased risk of adverse outcomes, including falls, fractures, physical disability, and mortality[ 5 ]. It is becoming a significant global health concern. As early as 2010, the European Working Group on Sarcopenia in Older People (EWGSOP) reported a prevalence rate of 5–13% in individuals aged 60–70, and a range of 11–50% in individuals aged over 80[ 6 ]. In 2019, the Asian Working Group for Sarcopenia (AWGS) reported a prevalence range of 5.5–25.7%, with a higher prevalence in males (5.1%-21.0%) compared to females (4.1%-16.3%) [ 7 ]. With the widespread use of CLM in clinical practice, there have been reports on their adverse effects and both short-term and long-term impacts on the human body, leading to ongoing debates and inconsistent findings. Specifically, the association between CLM and sarcopenia has garnered attention and remains a topic of contention. While the majority of studies suggest that these medications do not have a detrimental effect on skeletal muscle function, as symptoms associated with medication use typically resolve upon discontinuation[ 8 , 9 ], there is evidence indicating that CLM may have discernible effects on muscle function, potentially exacerbating the risk of developing sarcopenia to varying degrees[ 10 , 11 ]. However, a minority of studies present opposing viewpoints, suggesting that these medications not only do not negatively impact muscle function but may even offer protection to skeletal muscle to some extent[ 12 , 13 ]. The purpose of this study is to investigate whether CLM are a risk factor for sarcopenia. We utilized data from the National Health and Nutrition Examination Survey (NHANES) database, specifically extracting data from the 2011–2014 survey cycles. Demographic, dietary, examination, laboratory, and questionnaire data were collected to construct the necessary variables for analysis, following clinical practice guidelines. CLM were defined as the key independent variable (X) in this study, while the diagnosis of sarcopenia, strictly adhering to the definition requirements from the Foundation for the National Institutes of Health (FNIH), was designated as the dependent variable (Y). By constructing univariate and multivariate regression models, we aimed to elucidate the relationship between the X variable (CLM) and the Y variable (sarcopenia), while also analyzing the impact of various covariates on their correlation. Furthermore, significant variables were incorporated into predictive models to enhance the clinical screening capability for sarcopenia. Methods Data source and population The data for this study was derived from the National Health and Nutrition Examination Survey (NHANES), an ongoing health assessment conducted by the Centers for Disease Control and Prevention (CDC) in the United States. NHANES employs a complex, multi-stage, probability sampling strategy to gather comprehensive health information representative of the general U.S. population. NHANES data collection includes household interviews, mobile examination center visits, blood sample collection, and follow-up interviews. The survey has been conducted annually since 1999, providing over 20 years of data. Each survey cycle involves approximately 10,000 participants who are selected through rigorous random sampling methods. For our study, we focused on the 2011 and 2014 NHANES cycles, as these were the only cycles that included the grip test for assessing muscle strength, which is a key component in defining sarcopenia. The grip test was carried out specifically for the 8–59 age group. Additionally, information regarding the use of cholesterol-lowering medication was obtained through questionnaire surveys, limited to adults aged 20 and above. Following the flowchart depicted in Fig. 1 , a total of 19,931 individuals were initially included in the NHANES cycles under consideration. To narrow down our study population to those relevant to sarcopenia assessment, we applied further inclusion criteria. Firstly, we focused on adults aged 20 to 60 years, as the grip test for muscle strength assessment and cholesterol-lowering medication survey were only conducted in this age range. Consequently, after the initial screening process, 7,697 participants remained for our analysis. In the following, we excluded observations with missing values on key variables, including the use of cholesterol-lowering medicines and the variables directly related to assessing skeletal muscle strength and mass: grip strength and appendicular lean mass. For grip strength, individuals with missing values in both upper extremities were excluded. If only one side of the upper extremity was tested for grip strength, the strength of that side was used as an indicator of the individual's muscle strength and they were not excluded. For appendicular lean mass, individuals with missing values for lean mass in all four limbs (including both upper and lower extremities) were excluded, as some patients had missing values for lean mass in one limb due to measurement technique issues. The handling of missing values for grip strength and appendicular lean mass will be addressed in the next section of the paper. Next, we removed the variable used for body classification, BMI, due to missing values. Individuals with missing values for basic demographic data such as gender, age, race, and education level were also removed. After these two stages of screening, the study population consisted of 4,075 individuals. Subsequently, we excluded individuals with missing values for key covariates such as combined diabetes and chronic kidney disease. Finally, individuals with stroke and tumors were excluded from the study population as stroke may affect muscle strength testing and tumors are a wasting disease that can lead to loss of skeletal muscle protein. After the aforementioned screening process, a final total of 3,820 individuals were included in this study. Definition of Cholesterol-lowering treatment The information regarding the status of cholesterol-lowering treatment was derived from the Blood Pressure/Cholesterol section of the questionnaire. In this section, participants were asked the following questions: "To lower your blood cholesterol, have you ever been told by a doctor or other health professional to take prescribed medicine?" If the response to this question was "yes," participants were then asked, "Are you now following this advice to take prescribed medicine?" Based on the responses to these two questions, individuals were classified into different categories. If the response to both questions was "yes," the individual was defined as being "Under cholesterol-lowering treatment." If the response to the first question was "yes" but the response to the second question was "no," the individual was defined as "without treatment," indicating that there was an indication for treatment but they were not currently receiving it. If the response to both questions was "no," it indicated that the individual did not require medication for cholesterol-lowering treatment. Dual-energy X-ray absorptiometry measurement and sarcopenia definition Dual-energy X-ray absorptiometry (DXA) is a widely accepted method for measuring body composition, owing to its efficiency, ease of use, and low radiation exposure. In the survey cycles from 1999 to 2006, DXA examinations were conducted as part of the mobile examination center, but only the calculation of lean mass in the four limbs was performed in the survey cycles from 2011 to 2014. This examination primarily targeted individuals aged 8–59 years, while excluding the following criteria: 1) Pregnancy: Participants who had a positive urine pregnancy test and/or self-reported being pregnant at the time of the DXA examination were excluded. 2) History of radiographic contrast material use: Individuals who reported using radiographic contrast material (such as barium) within the past 7 days were excluded. 3) Weight and height limitations: Individuals who self-reported weighing over 450 pounds or having a height over 6'5" were excluded due to limitations of the DXA table. The DXA examination provides various components of body composition, and in this study, the focus is on extracting the Lean excl Body Mineral Content of the four limbs. The definition of sarcopenia primarily relies on the analysis of the skeletal muscle mass index (SMI), also known as the sarcopenia index. This index is calculated by adjusting appendicular lean mass (ALM) according to the body weight index recommended by the Foundation for the National Institutes of Health (FNIH) Sarcopenia Project[ 14 – 16 ]. Males with an SMI < 0.789 or females with an SMI < 0.512 are considered to have low muscle mass, defining pre-sarcopenia[ 14 , 17 , 18 ]. Furthermore, the use of sarcopenia instead of pre-sarcopenia in this study is reliable because sarcopenia is widely accepted and recognized as the formal term for low muscle mass according to international guidelines, providing a more accurate representation of the pathological state being studied. ALM represents the sum of lean mass in the four limbs as measured by DXA. During the processing of variables, observations with missing data for lean mass in all four limbs were excluded. However, if an individual had missing lean mass data in one limb due to specific reasons, we imputed the average lean mass of individuals of the same age and gender group to compensate for this missing value. Finally, SMI was calculated by dividing the sum of lean mass in the four limbs (converted to kilograms) by the body mass index (BMI). Definition of obesity In defining obesity, we continued to use the Body Mass Index (BMI) to categorize individuals in this study. BMI is calculated using body measures, dividing weight (in kilograms) by height (in meters) squared. Individuals with a BMI ≤ 18.5 are classified as underweight, those with a BMI > 18.5 and < 25 are classified as normal weight, individuals with a BMI ≥ 25 and < 30 are classified as overweight, and those with a BMI ≥ 30 are classified as obese. Definitions of covariates Demographic data, including age, sex, race, and education level, as well as information on alcohol consumption, smoking status, and the presence of diabetes and chronic kidney disease, history of stroke, and cancer, were obtained through self-reported questionnaires. Total protein intake and total energy intake were assessed using two consecutive 24-hour dietary recalls, and the average of these two recalls was used as the final value. In the case of a missing second dietary recall, the values from the first recall were used as the final values for protein and energy intake. Relevant laboratory measurements were obtained from blood specimens collected from participants, which were processed, stored, and shipped to the University of Minnesota in Minneapolis, MN for analysis. The grip strength results were derived from the muscle strength component of the examination data. Participants aged 6 years and above were included in this assessment. Individuals who were unable to hold the dynamometer with both hands, such as those missing both arms, both hands, thumbs on both hands, or paralyzed in both hands, were excluded from this component. However, participants who were able to grip the dynamometer with one hand still performed the grip strength test. Participants who had undergone surgery on either hand or wrist in the last three months were not tested on that particular hand. The grip strength assessment required participants to stand with maximum effort while gripping the dynamometer. Participants were instructed to exhale during the exertion to avoid intra-thoracic pressure accumulation. The test was performed with both hands, and each hand was tested three times. The tests were carried out alternately between hands, with a rest period of at least 60 seconds between measurements. Originally, our design aimed to use grip strength in conjunction with SMI to define sarcopenia. However, in the population we included, the proportion of individuals with low grip strength levels was very low, accounting for only 1.75% of the population. Therefore, we had to abandon this approach. Statistical analysis All data processing and statistical analysis were performed using R (version 4.3.2). Continuous variables were expressed as mean (standard deviation), while categorical variables were presented as counts (percentages). For categorical variables in complex survey data, statistical analysis to compare differences between groups was conducted using the chi-squared test with Rao & Scott's second-order correction. For continuous variables in complex survey data, the non-parametric Wilcoxon rank-sum test was used to analyze differences between two groups. In the correlation analysis between independent and dependent variables, the dependent variable in this study was a binary variable. Therefore, logistic regression analysis was performed to construct both univariate and multivariate regression models. The predictive modeling analysis utilized the ROC curve to assess the average predictive value based on the area under the curve (AUC). A significance level of p < 0.05 was considered to indicate statistical differences, and p < 0.01 was considered to indicate significant statistical differences. Results The Usage of Cholesterol-lowering Medication Shows Significant Differences between Non-sarcopenic and Sarcopenic Groups In this study, we investigated the usage of cholesterol-lowering medication and observed significant differences between the non-sarcopenic and sarcopenic groups. We included a total of 3,820 participants from two cycles of NHANES data, ensuring comprehensive information on key variables such as appendicular lean mass, BMI, and cholesterol-lowering medication. Additionally, important covariates such as demographic information, diabetes, CKD, smoking status, alcohol consumption, and relevant laboratory tests were also well-documented. Participants were divided into non-sarcopenic and sarcopenic groups based on their sarcopenia index. Table 1 presents the baseline characteristics of both groups and compares the differences in various variables. Notably, we found that the prevalence of sarcopenia in the 20-60-year-old population surveyed was approximately 8.0%. The average age in the sarcopenic group was higher than that in the non-sarcopenic group, with a statistically significant difference (49.0 (11.1) vs. 43.0 (10.9), p<0.001). Age stratification further revealed that the highest prevalence of sarcopenia was concentrated in the 50-60 years age group among the participants included in our study. Moreover, the distribution of sarcopenia exhibited variations among different ethnicities, with Mexican Americans and Other Hispanics showing higher levels of disease prevalence compared to other ethnic groups. Interestingly, an unexpected observation surfaced when examining the association between sarcopenia and educational attainment. It appears that the incidence of sarcopenia is positively correlated with the level of education received by individuals. There were significant differences in the indicators related to physical examination between the two groups. The sarcopenic group had significantly higher levels of Waist Circumference and BMI compared to the non-sarcopenic group (113.30 (19.14) vs. 96.70 (15.58), p<0.001), (35.50 (8.67) vs. 27.70 (6.36), p<0.001). Moreover, in the sarcopenic group, a significant proportion of individuals reached the obesity category based on BMI levels. The average level of Triglycerides was significantly higher in the sarcopenic group compared to the non-sarcopenic group, but due to the substantial amount of missing data, its interpretability may be limited. On the other hand, the average levels of HDL-Cholesterol, Calcium, and Grip strength were significantly lower in the sarcopenic group compared to the non-sarcopenic group. There was a difference in the distribution of diabetes between the two groups, with a higher proportion of diabetes in the sarcopenic group. Of particular interest was the usage of cholesterol-lowering medication, which showed a significant difference between the two groups (77 (25.0%) vs. 396 (12.0%), p<0.001). Additionally, based on the predefined grouping criteria, the sarcopenic group had a significantly higher proportion of individuals who met the criteria for cholesterol-lowering medication usage compared to the non-sarcopenic group. Other variables, such as gender, showed no difference in distribution between the two groups, with a relatively balanced ratio of sarcopenia in males and females. Alcohol consumption, cigarette consumption, total protein intake, total energy intake, kidney disease, and laboratory measurements including total cholesterol, LDL-cholesterol, iron, and phosphorus did not show statistically significant differences between the two groups. In the single-factor logistic regression analysis, Cholesterol-lowering medication demonstrated a significant association with sarcopenia ， but not in the multivariable logistic regression analysis . To investigate the relationship between Cholesterol-lowering medication treatment and sarcopenia, logistic regression analysis was performed, with Model 1 representing the single-factor logistic regression analysis considering only Cholesterol-lowering medication. Model 2 included adjustments for waist circumference, BMI, cholesterol levels, triglyceride levels, HDL-cholesterol levels, and age. Model 3 encompassed adjustments for all variables. (Figure 2) In the single-factor regression analysis (Model 1), Cholesterol-lowering medication was identified as a risk factor for sarcopenia, with an odds ratio (OR) of 2.42 (95% confidence interval [CI]: 1.78-3.29, p<0.001). However, after adjusting for age, obesity-related variables, and serum lipid levels in Model 2 and Model 3, the association between Cholesterol-lowering medication and sarcopenia disappeared. This suggests that the use of cholesterol-lowering medication is not a true risk factor for sarcopenia. The observed difference in Model 1 may be attributed to potential confounding factors related to medication usage. Cholesterol-lowering medication is not a direct predictor of sarcopenia To further explore the specific factors that contribute to the differences observed in Cholesterol-lowering medication usage and to identify the true correlates of sarcopenia, we divided the population into two groups: those who received Cholesterol-lowering medication (under treatment) and those who did not (non-treatment). We compared various variables between these two groups, and Table 2 presents the variables that showed significant differences. In the under treatment-group, there was a slightly higher proportion of males, accounting for 56% of the population (p=0.041). Additionally, the average age was significantly higher in this group (53.0 (7.2), p<0.001), indicating that Cholesterol-lowering medication users were predominantly in the 50-60 age range. The average waist circumference and BMI were significantly higher in the under treatment-group compared to the non-treatment group. Moreover, the obesity rate was higher in the under treatment-group, accounting for 55% of the study sample. In terms of laboratory tests, the total cholesterol level and low-density lipoprotein cholesterol level were significantly lower in the under treatment-group compared to the non-medication group. This suggests that the majority of individuals, which under treated were effectively able to achieve cholesterol reduction through medication. However, the under treatment-group showed significantly higher levels of triglycerides compared to the non-treatment group. Additionally, there were differences observed in the average levels of calcium and Sarcopenia index between the two groups. Furthermore, we noticed an interesting phenomenon: the under treatment-group had a significantly higher proportion of individuals with comorbid diabetes compared to the non-treatment group. Our analysis suggests that this could be attributed to the fact that individuals with diabetes are more likely to experience disturbances in lipid metabolism, leading to elevated cholesterol levels and the need for Cholesterol-lowering medication. In other words, the use of Cholesterol-lowering medication is a consequence of diabetes, rather than its cause. Strong Association between BMI and Sarcopenia in Multivariable Regression Analysis Through the previous analysis, we have become more convinced that Cholesterol-lowering medication is not directly related to sarcopenia. The results of the univariable regression analysis in Table 2 may be influenced by one or more covariates. Therefore, we included these variables in a new fitted model and conducted a multivariable logistic regression analysis to examine their relationships with sarcopenia. As show in Table 3 the results indicate that only BMI, total cholesterol, and low-density lipoprotein cholesterol are significantly associated with sarcopenia. Among these factors, BMI exhibits the most prominent relationship. This finding suggests that higher BMI values correlate with an increased risk of sarcopenia. Influence of Obesity on the Association between Cholesterol-lowering Medication and Sarcopenia Analysis Combining the Findings from Table 3, it is likely that the association between the use of Cholesterol-lowering medication and sarcopenia is influenced by the medication being taken under different body conditions, rather than being an independent risk factor. Individuals who require Cholesterol-lowering treatment are typically those with abnormally high levels of serum total cholesterol or low-density lipoprotein cholesterol. Moreover, a substantial proportion of these patients are obese. To investigate this hypothesis, we divided the study population based on a body mass index (BMI) threshold of 30, defining individuals with a BMI greater than or equal to 30 as obese and those with a BMI less than 30 as non-obese. We further explored the impact of obesity on medication usage differences related to sarcopenia. We then divided each subgroup into sarcopenia and non-sarcopenia groups based on the sarcopenia index. Table 4 presents the variable differences observed in these two groups. From the results presented, we observe that the prevalence of sarcopenia is mainly concentrated in the obese group. Although there were no significant differences in mean age between the two groups, the sarcopenia group in the obese population was primarily composed of individuals aged 50-60, which aligns with the overall population distribution. Similar patterns were observed for waist circumference and BMI as well. Within the non-obese group, there were no significant differences in the use of Cholesterol-lowering medication between the sarcopenia and non-sarcopenia groups. However, in the obese group, the differences persisted. Based on these findings, we speculate that obesity may truly be the influencing factor for sarcopenia. Obesity —the key culprit To validate our hypothesis, we performed a multivariable logistic regression analysis by incorporating all the meaningful variables mentioned earlier. The final analysis revealed that only BMI demonstrated a significant and independent impact on sarcopenia, displaying clear statistical significance. This finding supports the notion that obesity is the true independent risk factor for sarcopenia. The results are presented in Table 5. The results indicate that for every unit increase in BMI, the odds of developing sarcopenia increase by a factor of 1.13. This suggests that higher BMI values are associated with a significantly increased risk of sarcopenia. Given the strong association between obesity and sarcopenia, we aimed to develop a predictive model to assist in clinical screening for sarcopenia. As the ROC plot shown in Figure 3, we evaluated the predictive performance of four different models. Model 1: Predicting Sarcopenia using Obesity Alone. In this model, we solely used obesity as a predictor for sarcopenia. The area under the receiver operating characteristic curve (AUC) was calculated to be 0.6927, indicating some predictive value but relatively low accuracy. Model 2: Incorporating Obesity, Age, and Grip Strength. To enhance the predictive accuracy, we included obesity, age, and grip strength as predictors for sarcopenia. The AUC increased to 0.7433, signifying a significant improvement in prediction accuracy compared to Model 1. Model 3: Adjusting for Cholesterol-lowering Medication. In Model 3, we adjusted for the use of cholesterol-lowering medication in addition to obesity, age, and grip strength. The AUC showed a minimal change, slightly increasing to 0.7448. These findings suggest that the use of cholesterol-lowering medication does not contribute significantly to the prediction of sarcopenia. Model 4: Comprehensive Prediction with All Variables. In Model 4, we incorporated all variables, including obesity, age, grip strength, and cholesterol-lowering medication, to predict sarcopenia. The AUC remained the same as in Model 3, indicating that the inclusion of additional variables did not improve the predictive capability beyond obesity, age, and grip strength. Overall, these results demonstrate that while obesity, age, and grip strength are important predictors for sarcopenia, the inclusion of other variables did not contribute significantly to the predictive ability of the model. Therefore, a combined assessment of obesity, age, and grip strength provides the most reliable and accurate prediction of sarcopenia in the context of this study. Discussion Since the relationship between cholesterol and cardiovascular diseases has been thoroughly elucidated, significant effects have been observed in clinical practice through early reduction of serum cholesterol levels, particularly low-density lipoprotein cholesterol, for primary and secondary prevention of cardiovascular diseases[ 1 ]. CLM, especially statins, are widely prescribed for this purpose. Statins work by inhibiting the activity of HMG-CoA reductase, blocking the conversion of HMG-CoA to mevalonic acid and reducing hepatic cholesterol synthesis[ 2 ]. In our study, the usage rate of CLM among adults aged 20–60 in the United States was found to be 22.75%. This proportion is expected to be higher among individuals aged 60 and above. Literature reports suggest that economically developed countries have approximately 25% of individuals aged 65 and above undergoing statin therapy for primary or secondary prevention of cardiovascular diseases [ 3 , 4 ]. The discussion has highlighted the safety and tolerability of commonly used CLM in clinical practice. However, it is important to acknowledge that over the past 40 years of their use, various clinical adverse reactions have been reported. Specifically, statins, a class of CLM, have been associated with Statin-Associated Muscle Symptoms (SAMS), which is the most common and significant adverse event, with up to 72% of adverse events related to muscle-related issues. These events manifest as muscle pain, myopathy, myositis with elevated creatine kinase (CK), or in its most severe form, rhabdomyolysis, along with reports of additional joint and abdominal pain [ 1 , 19 ]. The pathogenic mechanisms underlying muscle-related adverse effects involve membrane excitability, mitochondrial function, coenzyme Q10 depletion, calcium homeostasis, apoptosis induction, and genetic determinants[ 2 , 20 ]. Sarcopenia is a progressive and systemic skeletal muscle disease that has been associated with an increased risk of adverse outcomes, including falls, fractures, physical disability, and mortality[ 5 ]. It is becoming a significant global health concern. As early as 2010, the European Working Group on Sarcopenia in Older People (EWGSOP) reported a prevalence rate of 5–13% in individuals aged 60–70, and a range of 11–50% in individuals aged over 80[ 6 ]. However, these numbers have been increasing over the years. In 2019, while the updated guidelines by EWGSOP did not include epidemiological descriptions of sarcopenia, the report by the Asian Working Group for Sarcopenia (AWGS) that same year showed a prevalence range of 5.5–25.7%, with a higher prevalence in males (5.1%-21.0%) compared to females (4.1%-16.3%) [ 7 ]. It is important to note that while the report from AWGS primarily focused on Asian populations, the prevalence range reported does not differentiate by age. This indicates that in the past decade, the population affected by sarcopenia has at least doubled. In the population included in this study, the weighted analysis revealed a prevalence rate of approximately 8.0% for sarcopenia, which aligns with the current literature. Additionally, there was a clear age gradient in the distribution of the population, with individuals aged 50–60 representing nearly half of the total. In recent years, there has been a debate regarding the association between cholesterol-lowering medication and sarcopenia. Our study aimed to examine this relationship and found some interesting results. We observed differences in the use of cholesterol-lowering medication between the sarcopenia and non-sarcopenia groups. However, after conducting regression analysis to account for multiple factors, the correlation between medication use and sarcopenia was eliminated. This suggests that the relationship between cholesterol-lowering medication and sarcopenia is not direct and may be influenced by other variables. Our findings are consistent with a cohort study conducted on a larger population of 639 participants. This study, which followed participants for an average period of 4.4 years, found no difference in grip strength decline between those who took statin cholesterol-lowering medication and those who did not. This indicates that long-term use of cholesterol-lowering medication does not seem to affect muscle strength in relation to sarcopenia[ 8 ].Furthermore, a cross-sectional study conducted in China among 441 elderly hospitalized patients also reported no statistically significant difference in cholesterol-lowering medication use between the sarcopenia and non-sarcopenia groups[ 9 ]. Similarly, an observational cohort study on 756 community-dwelling male veterans aged 65 and above found no difference in muscle strength testing between long-term users of statin medication and non-users[ 21 ]. However, despite the findings that suggest no direct correlation between cholesterol-lowering medication and sarcopenia, there are clinical studies that present a different perspective based on the theoretical basis linking these medications to muscle function. A prospective cohort study involving 774 community-dwelling participants (48% females, mean age 62 ± 7) observed 147 individuals taking statin cholesterol-lowering medication at the beginning, which increased to 179 over an average follow-up period of 2.6 years. During the follow-up, falls risk and assessment of lower limb muscle strength were evaluated. The results indicated that the observed subjects taking cholesterol-lowering medication had a significantly higher risk of falls and a greater decline in lower limb muscle function compared to those who did not take medication[ 10 ].Furthermore, a systematic review conducted by Lluis and colleagues, exploring the relationship between various oral medications and sarcopenia, supports the conclusion that cholesterol-lowering medication is detrimental to muscle function and contributes to the development of sarcopenia. They also summarized the most important pathways and factors contributing to the loss of muscle mass, which include immobility, nutritional deficiencies, inflammation, endocrine factors, and genetic influence. Ultimately, these factors may lead to the occurrence of sarcopenia[ 11 ]. Based on several recent research studies, there have been contrasting findings. To begin with, a meta-analysis conducted in 2020[ 12 ], examined 67,001 patients with chronic kidney disease over a time span from 1997 to 2011. The study primarily aimed to evaluate the risk of new-onset muscle loss in these patients who were using statin drugs. Preliminary analysis using the Cox proportional hazards model demonstrated that statin drugs used in chronic kidney disease patients effectively prevented the occurrence of muscle loss. Additionally, higher doses and lipophilic formulations of statin drugs appeared to provide more significant protective effects, and these results were consistent during long-term follow-up. Furthermore, a retrospective study conducted by Iisa Lindström and colleagues in 2021[ 22 ], focused on patients with abdominal aortic aneurysm who underwent endovascular repair. The results indicated that long-term use of statin drugs in these patients, with an average age of 77.7 years, reduced the long-term mortality rate without increasing the risk of sarcopenia. Moreover, a recent meta-analysis examined the impact of diet and medications on sarcopenia. Findings from the study suggested that long-term use of certain medications, including statins, metformin, GLP-1 agonists, losartan, growth hormone, and dipeptidyl peptidase-4 inhibitors, not only improved metabolic parameters associated with sarcopenia but also protected muscle while preventing cardiovascular disease[ 13 ]. The debate regarding the association between cholesterol-lowering medication and sarcopenia is still ongoing, and more comprehensive clinical and basic research is needed to elucidate the relationship between the two. Our study utilized the NHANES database to analyze the correlation between them from the perspective of the general population, which is an advantage not possessed by the aforementioned studies that focused on specific populations. The extrapolation value of their conclusions is debatable. In our analysis, we found that obesity is an independent risk factor for sarcopenia. Furthermore, in the construction of our predictive model, we found that age, BMI, and grip strength have a high predictive value for sarcopenia. Specifically, older age, higher BMI, and lower grip strength were associated with an increased risk of developing sarcopenia. The 2018 Second Edition of the EWGSOP guidelines introduced the concept of sarcopenic obesity, bringing attention to the impact of obesity on muscle loss[ 5 ]. After years of clinical practice and research, the correlation between obesity and sarcopenia has been well established[ 23 – 26 ], which is consistent with the results of our study. In conclusion, our study has several limitations. Firstly, the age range of the subjects included was limited to adults under 60 years old, which may have influenced the observed muscle strength and muscle quality compared to individuals over 60 years old. Additionally, we investigated the proportion of cholesterol-lowering medication use among individuals over 60 years old during the 2011–2014 period, and astonishingly, it was as high as 42.28%. Including this population in the analysis may lead to different results. Secondly, while we strictly followed the definition requirements from the Foundation for the National Institutes of Health (FNIH) and utilized skeletal muscle index (SMI) to assess muscle wasting, existing guidelines also suggest that the diagnosis of sarcopenia should ideally consider muscle strength, muscle quality, and physical performance. This is an area that needs continuous improvement in our future research. Lastly, it is regrettable that the specific duration of medication use was not provided in the database, which could also impact the results of the analysis. Despite these limitations, our study provides important insights into the association between cholesterol-lowering medication and sarcopenia. It highlights the independent risk factor of obesity for sarcopenia and identifies age, BMI, and grip strength as potential predictors for sarcopenia. However, further research is warranted to validate our findings in larger and more diverse populations, and to address the aforementioned limitations. Understanding the relationship between cholesterol-lowering medication and sarcopenia can contribute to the development of targeted interventions and strategies for preventing and managing sarcopenia in clinical practice. Declarations Funding This research was funded by the Jilin Province Medical and Health Talents Project, grant number JLSWSRCZX2023-25 Author Contribution Jie Li: Conceived and designed the study, analyzed the data, and contributed to the interpretation of results. Wrote the manuscript and critically revised it for intellectual content. Acted as the corresponding author and oversaw the entire research process.Wei Gong: Contributed to the study design, data analysis, and interpretation. Conducted the statistical analyses and assisted in writing the manuscript. Reviewed and provided critical feedback for intellectual content.Tingting Liu: Contributed to the study design, data collection, and analysis. Assisted in the interpretation of results and manuscript writing. Reviewed and provided critical revisions for intellectual content.All authors read and approved the final version of the manuscript for submission to the journal. Acknowledgments We would like to thank any individuals, institutions, or resources that have provided support in other ways towards the completion of this study. Availability of Data and Materials The data utilized in this research were derived from the National Health and Nutrition Examination Survey (NHANES) database. Access to the NHANES database can be obtained through the official website of the Centers for Disease Control and Prevention (CDC). Researchers interested in accessing the data are encouraged to visit the CDC website ( https://www.cdc.gov/nchs/nhanes/index.htm ) for detailed information on data availability and the required procedures for data access. References Ward, N.C., G.F. Watts and R.H. Eckel, Statin Toxicity. Circ Res, 2019 . 124(2): p. 328-350. Bouitbir, J., G.M. Sanvee, M.V. Panajatovic, F. Singh and S. Krähenbühl, Mechanisms of statin-associated skeletal muscle-associated symptoms. Pharmacol Res, 2020 . 154: p. 104201. Gu, Q., R. Paulose-Ram, V.L. Burt and B.K. Kit, Prescription cholesterol-lowering medication use in adults aged 40 and over: United States, 2003-2012. NCHS Data Brief, 2014 (177): p. 1-8. Wallach-Kildemoes, H., H. Stovring, E. Holme Hansen, K. Howse and H. Pétursson, Statin prescribing according to gender, age and indication: what about the benefit-risk balance? J Eval Clin Pract, 2016 . 22(2): p. 235-46. Cruz-Jentoft, A.J., G. Bahat, J. Bauer, Y. Boirie, O. Bruyère, et al., Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing, 2019 . 48(1): p. 16-31. Cruz-Jentoft, A.J., J.P. Baeyens, J.M. Bauer, Y. Boirie, T. Cederholm, et al., Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People. Age Ageing, 2010 . 39(4): p. 412-23. Chen, L.K., J. Woo, P. Assantachai, T.W. Auyeung, M.Y. Chou, et al., Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J Am Med Dir Assoc, 2020 . 21(3): p. 300-307.e2. Witham, M.D., H.E. Syddall, E. Dennison, C. Cooper, M.E. McMurdo, et al., ACE inhibitors, statins and thiazides: no association with change in grip strength among community dwelling older men and women from the Hertfordshire Cohort Study. Age Ageing, 2014 . 43(5): p. 661-6. Lu, B., L. Shen, H. Zhu, L. Xi, W. Wang, et al., Association between serum homocysteine and sarcopenia among hospitalized older Chinese adults: a cross-sectional study. BMC Geriatr, 2022 . 22(1): p. 896. Scott, D., L. Blizzard, J. Fell and G. Jones, Statin therapy, muscle function and falls risk in community-dwelling older adults. Qjm, 2009 . 102(9): p. 625-33. Campins, L., M. Camps, A. Riera, E. Pleguezuelos, J.C. Yebenes, et al., Oral Drugs Related with Muscle Wasting and Sarcopenia. A Review. Pharmacology, 2017 . 99(1-2): p. 1-8. Lin, M.H., S.Y. Chiu, P.H. Chang, Y.L. Lai, P.C. Chen, et al., Hyperlipidemia and Statins Use for the Risk of New Diagnosed Sarcopenia in Patients with Chronic Kidney: A Population-Based Study. Int J Environ Res Public Health, 2020 . 17(5). Mellen, R.H., O.S. Girotto, E.B. Marques, L.F. Laurindo, P.C. Grippa, et al., Insights into Pathogenesis, Nutritional and Drug Approach in Sarcopenia: A Systematic Review. Biomedicines, 2023 . 11(1). Kim, K.M., H.C. Jang and S. Lim, Differences among skeletal muscle mass indices derived from height-, weight-, and body mass index-adjusted models in assessing sarcopenia. Korean J Intern Med, 2016 . 31(4): p. 643-50. Cawthon, P.M., K.W. Peters, M.D. Shardell, R.R. McLean, T.T. Dam, et al., Cutpoints for low appendicular lean mass that identify older adults with clinically significant weakness. J Gerontol A Biol Sci Med Sci, 2014 . 69(5): p. 567-75. Kitsuda, Y., T. Wada, S. Tanishima, M. Osaki, H. Nagashima, et al., Impact of Sarcopenia on Spinal Spondylosis: A Literature Review. J Clin Med, 2023 . 12(16). Golabi, P., L. Gerber, J.M. Paik, R. Deshpande, L. de Avila, et al., Contribution of sarcopenia and physical inactivity to mortality in people with non-alcoholic fatty liver disease. JHEP Rep, 2020 . 2(6): p. 100171. Li, R., S. Lin, J. Tu, Y. Chen, B. Cheng, et al., Establishment and evaluation of a novel practical tool for the diagnosis of pre-sarcopenia in young people with diabetes mellitus. J Transl Med, 2023 . 21(1): p. 393. Tomaszewski, M., K.M. Stępień, J. Tomaszewska and S.J. Czuczwar, Statin-induced myopathies. Pharmacol Rep, 2011 . 63(4): p. 859-66. Muñoz-Blanco, A., R. Gómez-Huelgas and J.F. Gómez-Cerezo, Statin-associated muscle symptoms: Myth or reality? Rev Clin Esp (Barc), 2022 . 222(10): p. 602-611. Agostini, J.V., M.E. Tinetti, L. Han, G. McAvay, J.M. Foody, et al., Effects of statin use on muscle strength, cognition, and depressive symptoms in older adults. J Am Geriatr Soc, 2007 . 55(3): p. 420-5. Lindström, I., S. Protto, N. Khan, S. Väärämäki, N. Oksala, et al., Statin use, development of sarcopenia, and long-term survival after endovascular aortic repair. J Vasc Surg, 2021 . 74(5): p. 1651-1658.e1. Choi, K.M., Sarcopenia and sarcopenic obesity. Korean J Intern Med, 2016 . 31(6): p. 1054-1060. Bilski, J., P. Pierzchalski, M. Szczepanik, J. Bonior and J.A. Zoladz, Multifactorial Mechanism of Sarcopenia and Sarcopenic Obesity. Role of Physical Exercise, Microbiota and Myokines. Cells, 2022 . 11(1). Zamboni, M., S. Rubele and A.P. Rossi, Sarcopenia and obesity. Curr Opin Clin Nutr Metab Care, 2019 . 22(1): p. 13-19. Donini, L.M., L. Busetto, S.C. Bischoff, T. Cederholm, M.D. Ballesteros-Pomar, et al., Definition and Diagnostic Criteria for Sarcopenic Obesity: ESPEN and EASO Consensus Statement. Obes Facts, 2022 . 15(3): p. 321-335. Tables Table 1. Demographic and Clinical Characteristics of Participants according Sarcopenia Index Group Sarcopenia index group Characteristic N 1 Overall N = 90422607 (100%) 2 Non-sarcopenia N = 90422608 (92%) 2 Sarcopenia N = 90422608 (8.0%) 2 p Value 3 Sex 3,820 0.14 Female 1,928 (49%) 1,779 (50%) 149 (44%) Age (years) 3,820 43.0 (11.0) 43.0 (10.9) 49.0 (11.1) <0.001 Age group 3,820 <0.001 20-30 years 639 (16%) 604 (17%) 35 (12%) 30-40 years 936 (23%) 874 (23%) 62 (16%) 40-50 years 1,137 (30%) 1,062 (30%) 75 (23%) 50-60 years 1,108 (31%) 955 (30%) 153 (48%) Race 3,820 <0.001 Mexican American 410 (7.7%) 340 (7.0%) 70 (15%) Other Hispanic 310 (5.5%) 264 (5.2%) 46 (9.7%) Non-Hispanic White 1,517 (67%) 1,391 (67%) 126 (63%) Non-Hispanic Black 900 (12%) 863 (12%) 37 (5.9%) Non-Hispanic Asian 546 (5.6%) 508 (5.6%) 38 (4.9%) Other/multiracial 137 (2.8%) 129 (2.9%) 8 (1.6%) Education level 3,820 <0.001 9-11th grade 412 (8.2%) 372 (8.2%) 40 (8.8%) College graduate or above 1,261 (37%) 1,197 (38%) 64 (22%) High school graduate 766 (20%) 679 (20%) 87 (26%) Less than 9th grade 141 (2.6%) 110 (2.0%) 31 (9.1%) Some college or AA degree 1,240 (32%) 1,137 (32%) 103 (35%) Waist Circumference(cm) 3,793 97.60 (16.53) 96.70 (15.58) 113.30 (19.14) <0.001 BMI(kg/m^2) 3,820 28.20 (6.94) 27.70 (6.36) 35.50 (8.67) <0.001 BMI level 3,820 <0.001 Normal weight 1,040 (26%) 1,019 (28%) 21 (3.5%) Obesity 1,521 (39%) 1,277 (35%) 244 (80%) Overweight 1,207 (34%) 1,149 (35%) 58 (15%) Underweight 52 (1.0%) 50 (1.0%) 2 (0.9%) Alcohol consumrtion group 3,611 0.6 1-5 drinks/month 1,906 (52%) 1,729 (53%) 177 (51%) 10+ drinks/month 543 (19%) 506 (19%) 37 (18%) 5-10 drinks/month 331 (11%) 311 (11%) 20 (9.6%) Non-drinker 831 (17%) 752 (17%) 79 (21%) Cigarette consumption group 3,820 0.3 Current somker 663 (16%) 622 (17%) 41 (13%) Former smoker 854 (25%) 771 (24%) 83 (29%) Never smoker 2,303 (59%) 2,102 (59%) 201 (58%) Total protein intake(gm) 3,661 81.22 (36.90) 81.45 (37.07) 78.85 (34.84) 0.4 Total energy intake(kcal) 3,661 2,073.50 (851.64) 2,084.62 (863.88) 1,929.97 (680.66) 0.058 Total Cholesterol(mg/dL) 3,681 193.00 (42.13) 193.00 (42.43) 188.00 (38.46) 0.3 Triglyceride(mg/dL) 1,767 101.00 (139.03) 98.70 (140.59) 131.07 (118.14) <0.001 LDL-cholesterol(mg/dL) 1,725 115.00 (35.54) 114.00 (35.60) 116.00 (35.07) 0.6 HDL-Cholesterol (mg/dL) 3,681 50.00 (15.69) 50.00 (15.83) 46.00 (13.34) <0.001 Calcium(mg/dL) 3,649 9.40 (0.35) 9.40 (0.36) 9.30 (0.31) 0.013 Iron(ug/dL) 3,655 82.00 (35.05) 83.00 (35.17) 73.13 (33.55) 0.14 Phosphorus (mg/dL) 3,664 3.80 (0.56) 3.80 (0.56) 3.70 (0.58) 0.3 Kidney disease 3,820 0.056 CKD 82 (1.8%) 67 (1.7%) 15 (3.3%) Non-CKD 3,738 (98%) 3,428 (98%) 310 (97%) Diabetes 3,820 <0.001 Diabetes 377 (7.8%) 314 (6.9%) 63 (18%) Non-diabetes 3,443 (92%) 3,181 (93%) 262 (82%) CLM 3,820 <0.001 No medication 3,103 (81%) 2,883 (82%) 220 (68%) Under treatment 473 (13%) 396 (12%) 77 (25%) Without treatment 244 (5.9%) 216 (5.8%) 28 (7.4%) Sarcopenia index 3,820 0.80 (0.20) 0.83 (0.19) 0.69 (0.14) <0.001 Grip strength 3,820 36.54 (11.05) 37.15 (11.01) 32.22 (10.61) <0.001 BMI, Body mass index; CLM, Cholesterol-lowering medications; CKD, Chronic kidney disease 1 N not Missing (unweighted) 2 N(weighted)(%);n (unweighted)(%); Median (SD) 3 chi-squared test with Rao & Scott's second-order correction; Wilcoxon rank-sum test for complex survey samples Table 2. Comparison of Variables between Cholesterol-lowering Medication Users and Non-users Characteristic N 1 Overall N = 90422607 (100%) 2 Non-treatment N = 90422608 (87%) 2 Under treatment N = 90422608 (13%) 2 p Value 3 Sex 3,820 0.041 Male 1,892 (51%) 1,634 (50%) 258 (56%) Age (years) 3,820 43.0 (11.0) 41.0 (10.8) 53.0 (7.2) <0.001 Age group 3,820 <0.001 20-30 years 639 (16%) 633 (19%) 6 (1.1%) 30-40 years 936 (23%) 898 (25%) 38 (7.2%) 40-50 years 1,137 (30%) 1,021 (31%) 116 (23%) 50-60 years 1,108 (31%) 795 (25%) 313 (69%) Waist Circumference(cm) 3,793 97.60 (16.53) 96.50 (16.31) 105.70 (15.53) <0.001 BMI (kg/m^2) 3,820 28.20 (6.94) 27.80 (6.88) 30.60 (6.87) <0.001 BMI level 3,820 <0.001 Underweight 52 (1.0%) 51 (1.2%) 1 (<0.1%) Normal weight 1,040 (26%) 977 (28%) 63 (13%) Overweight 1,207 (34%) 1,060 (34%) 147 (33%) Obesity 1,521 (39%) 1,259 (37%) 262 (55%) Total Cholesterol(mg/dL) 3,681 193.00 (42.13) 194.00 (41.63) 184.60 (44.74) <0.001 Triglyceride(mg/dL) 1,767 101.00 (139.03) 98.68 (107.86) 122.00 (253.70) <0.001 LDL-cholesterol(mg/dL) 1,725 115.00 (35.54) 116.00 (35.35) 106.00 (36.11) 0.020 HDL-Cholesterol (mg/dL) 3,681 50.00 (15.69) 50.00 (16.01) 48.00 (12.72) <0.001 Calcium(mg/dL) 3,649 9.40 (0.35) 9.40 (0.36) 9.40 (0.34) 0.017 Diabetes 3,820 <0.001 Diabetes 377 (7.8%) 190 (4.1%) 187 (32%) Non-diabetes 3,443 (92%) 3,157 (96%) 286 (68%) Sarcopenia index 3,820 0.80 (0.20) 0.80 (0.20) 0.79 (0.19) 0.018 BMI, Body mass index; CKD, Chronic kidney disease 1 N not Missing (unweighted) 2 N(weighted)(%);n (unweighted)(%); Median (SD) 3 chi-squared test with Rao & Scott's second-order correction; Wilcoxon rank-sum test for complex survey samples Table 3. Multivariable Logistic Regression Analysis of Variables Associated with Sarcopenia Characteristic OR 1 95% CI 1 p value Age (years) 1.00 0.94, 1.06 >0.9 Age group 20-30 years — — 30-40 years 0.73 0.27, 2.01 0.5 40-50 years 1.07 0.30, 3.80 >0.9 50-60 years 2.09 0.33, 13.2 0.4 Waist Circumference(cm) 0.99 0.95, 1.03 0.6 BMI (kg/m^2) 1.13 1.05, 1.22 0.002 BMI level Underweight — — Normal weight 0.62 0.05, 8.05 0.7 Overweight 1.16 0.08, 16.5 >0.9 Obesity 2.06 0.13, 34.0 0.6 Total Cholesterol(mg/dl) 1.02 1.00, 1.04 0.028 Triglyceride(mg/dl) 1.00 1.00, 1.00 0.7 LDL-cholesterol(mg/dl) 0.98 0.96, 1.00 0.026 Calcium(mg/dL) 1.20 0.67, 2.14 0.5 Diabetic group Diabetes — — Non-diabetes 1.37 0.56, 3.37 0.5 1 OR = Odds Ratio, CI = Confidence Interval; BMI, Body mass index Table 4. Differences in Variables between Sarcopenia and Non-sarcopenia Groups stratified by Obesity Status Obese group Non-obese group Characteristic N 1 Non-sarcopenia N = 1277 (83%) 2 Sarcopenia N = 244 (17%) 2 p Value 3 N 1 Non-sarcopenia N = 2218 (97%) 2 Sarcopenia , N = 81 (2.6%) 2 p Value 3 Age group 1,521 <0.001 2,299 0.5 20-30 years 176 (13%) 26 (11%) 428 (19%) 9 (18%) 30-40 years 329 (24%) 43 (15%) 545 (23%) 19 (23%) 40-50 years 396 (32%) 62 (24%) 666 (29%) 13 (19%) 50-60 years 376 (31%) 113 (51%) 579 (29%) 40 (40%) Waist Circumference(cm) 1,505 111.40 (12.41) 120.63 (16.41) <0.001 2,288 90.40 (9.49) 93.98 (10.36) 0.031 BMI (kg/m^2) 1,521 33.90 (5.30) 37.36 (7.57) <0.001 2,299 25.40 (2.96) 27.39 (3.21) <0.001 Triglyceride(mg/dL) 705 114.00 (185.85) 137.17 (121.51) 0.012 1,062 91.00 (104.11) 98.43 (101.55) 0.080 CLM 1,521 0.016 2,299 0.063 No medication 976 (76%) 159 (66%) 1,907 (86%) 61 (74%) Under treatment 198 (17%) 64 (27%) 198 (9.5%) 13 (18%) Without treatment 103 (7.8%) 21 (7.3%) 113 (4.7%) 7 (7.8%) Sarcopenia index 1,521 0.74 (0.17) 0.67 (0.14) <0.001 2,299 0.86 (0.19) 0.72 (0.14) <0.001 Grip strength 1,521 37.75 (11.66) 33.17 (10.85) <0.001 2,299 36.50 (10.60) 31.12 (8.82) <0.001 BMI, Body mass index; CLM, Cholesterol-lowering medications 1 N not Missing (unweighted) 2 N(unweighted)(%);n (unweighted)(%); Median (SD) 3 chi-squared test with Rao & Scott's second-order correction; Wilcoxon rank-sum test for complex survey samples Table 5. Multivariable Logistic Regression Analysis of Variables Associated with Sarcopenia Characteristic OR 1 95% CI 1 p value Age (years) 0.97 0.92, 1.03 0.3 Age group 20-30 years — — 30-40 years 1.05 0.31, 3.62 >0.9 40-50 years 2.06 0.49, 8.64 0.3 50-60 years 4.13 0.59, 29.1 0.13 Waist Circumference(cm) 1.02 0.96, 1.08 0.5 BMI (kg/m^2) 1.13 1.01, 1.27 0.036 Total Cholesterol(mg/dL) 0.55 0.25, 1.22 0.12 Triglyceride(mg/dL) 1.13 0.96, 1.32 0.11 LDL-cholesterol(mg/dL) 1.80 0.82, 3.97 0.12 HDL-Cholesterol (mg/dL) 1.84 0.84, 4.02 0.11 Calcium(mg/dL) 1.37 0.75, 2.52 0.3 Kidney disease CKD — — Non-CKD 0.28 0.04, 2.06 0.2 Diabetes Diabetes — — Non-diabetes 2.26 0.71, 7.19 0.14 CLM No medication — — Under treatment 1.58 0.63, 4.00 0.3 Without treatment 0.80 0.23, 2.76 0.7 Grip strength 0.97 0.93, 1.00 0.078 1 OR = Odds Ratio, CI = Confidence Interval; BMI, Body mass index; CLM, Cholesterol-lowering medications; CKD, Chronic kidney disease Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3968474","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276222542,"identity":"02298d46-2d65-4715-804f-b5e6f750dea2","order_by":0,"name":"Wei Gong","email":"","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Gong","suffix":""},{"id":276222543,"identity":"42a117f7-eedc-4210-a5d4-072e86df8013","order_by":1,"name":"Tingting Liu","email":"","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Liu","suffix":""},{"id":276222544,"identity":"2858900d-6cec-4504-a814-eafc17026d8d","order_by":2,"name":"Jie Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBACA2aGhAMMBhJyjO0gLhvRWiosjBmbidYCJs9UJDYwE62FneHhgY9tEunNzTwGDB/KDjPwz24g7LCDM9skchuBWhhnnDvMIHHnAGEth3mhWph52w4DgyKBCC1/gQ5jBGn5S7QWhjMSCWAtjMRqOdhTIWHY2MxWcLDnXDqPxA0CWuz7zyR/+GFQJ2/Y3rzxwY8yazn+GQS0MDDwQFQYNjAwHABxCakHAvYDYEqeCKWjYBSMglEwQgEAEac++krC8ogAAAAASUVORK5CYII=","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":true,"prefix":"","firstName":"Jie","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-02-19 01:07:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3968474/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3968474/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52105772,"identity":"57e986ba-5e46-449a-ba98-578f1ce0ab94","added_by":"auto","created_at":"2024-03-06 19:31:18","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":282538,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the selection process of the final study population from the NHANES database. Each box represents a specific decision point or criteria used for participant inclusion or exclusion.\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3968474/v1/57d8e3b3c39e81ebe37055ed.jpg"},{"id":52104143,"identity":"940cfc49-4b55-42d7-b958-ff89d5e35a9d","added_by":"auto","created_at":"2024-03-06 19:23:18","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":85154,"visible":true,"origin":"","legend":"\u003cp\u003eLogistic Regression Analysis Models for Cholesterol-lowering Medication and Sarcopenia. CLM=Cholesterol-lowering medication; OR = Odds Ratio; CI = Confidence Interval.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3968474/v1/d046ea7c5b68874c9bbf7673.jpg"},{"id":52104145,"identity":"4f5a2222-5522-497c-b204-195382433317","added_by":"auto","created_at":"2024-03-06 19:23:18","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":156894,"visible":true,"origin":"","legend":"\u003cp\u003eROC Plots Comparing the Predictive Performance of Different Models for Sarcopenia Prediction\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3968474/v1/4c831efbbbddbe619060ddd0.jpg"},{"id":55712647,"identity":"bb8a8410-5f6d-4d80-8f1a-e44acaae6c99","added_by":"auto","created_at":"2024-05-02 06:53:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1743655,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3968474/v1/18fc951d-76c2-4ef9-888d-90f1b8062786.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cholesterol-lowering medications and sarcopenia: Large cross- sectional Study ：NHANES 2011-2014","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCholesterol-lowering medications (CLM), especially statins, are widely prescribed for primary and secondary prevention of cardiovascular diseases[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. They work by inhibiting the activity of HMG-CoA reductase, blocking the conversion of HMG-CoA to mevalonic acid and reducing hepatic cholesterol synthesis[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It reported that approximately 25% of individuals aged 65 and above undergoing statin therapy[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSarcopenia is a progressive and systemic skeletal muscle disease that has been associated with an increased risk of adverse outcomes, including falls, fractures, physical disability, and mortality[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It is becoming a significant global health concern. As early as 2010, the European Working Group on Sarcopenia in Older People (EWGSOP) reported a prevalence rate of 5\u0026ndash;13% in individuals aged 60\u0026ndash;70, and a range of 11\u0026ndash;50% in individuals aged over 80[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In 2019, the Asian Working Group for Sarcopenia (AWGS) reported a prevalence range of 5.5\u0026ndash;25.7%, with a higher prevalence in males (5.1%-21.0%) compared to females (4.1%-16.3%) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWith the widespread use of CLM in clinical practice, there have been reports on their adverse effects and both short-term and long-term impacts on the human body, leading to ongoing debates and inconsistent findings. Specifically, the association between CLM and sarcopenia has garnered attention and remains a topic of contention. While the majority of studies suggest that these medications do not have a detrimental effect on skeletal muscle function, as symptoms associated with medication use typically resolve upon discontinuation[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], there is evidence indicating that CLM may have discernible effects on muscle function, potentially exacerbating the risk of developing sarcopenia to varying degrees[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, a minority of studies present opposing viewpoints, suggesting that these medications not only do not negatively impact muscle function but may even offer protection to skeletal muscle to some extent[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe purpose of this study is to investigate whether CLM are a risk factor for sarcopenia. We utilized data from the National Health and Nutrition Examination Survey (NHANES) database, specifically extracting data from the 2011\u0026ndash;2014 survey cycles. Demographic, dietary, examination, laboratory, and questionnaire data were collected to construct the necessary variables for analysis, following clinical practice guidelines.\u003c/p\u003e \u003cp\u003eCLM were defined as the key independent variable (X) in this study, while the diagnosis of sarcopenia, strictly adhering to the definition requirements from the Foundation for the National Institutes of Health (FNIH), was designated as the dependent variable (Y). By constructing univariate and multivariate regression models, we aimed to elucidate the relationship between the X variable (CLM) and the Y variable (sarcopenia), while also analyzing the impact of various covariates on their correlation. Furthermore, significant variables were incorporated into predictive models to enhance the clinical screening capability for sarcopenia.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source and population\u003c/h2\u003e \u003cp\u003eThe data for this study was derived from the National Health and Nutrition Examination Survey (NHANES), an ongoing health assessment conducted by the Centers for Disease Control and Prevention (CDC) in the United States. NHANES employs a complex, multi-stage, probability sampling strategy to gather comprehensive health information representative of the general U.S. population.\u003c/p\u003e \u003cp\u003eNHANES data collection includes household interviews, mobile examination center visits, blood sample collection, and follow-up interviews. The survey has been conducted annually since 1999, providing over 20 years of data. Each survey cycle involves approximately 10,000 participants who are selected through rigorous random sampling methods.\u003c/p\u003e \u003cp\u003eFor our study, we focused on the 2011 and 2014 NHANES cycles, as these were the only cycles that included the grip test for assessing muscle strength, which is a key component in defining sarcopenia. The grip test was carried out specifically for the 8\u0026ndash;59 age group.\u003c/p\u003e \u003cp\u003eAdditionally, information regarding the use of cholesterol-lowering medication was obtained through questionnaire surveys, limited to adults aged 20 and above.\u003c/p\u003e \u003cp\u003eFollowing the flowchart depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, a total of 19,931 individuals were initially included in the NHANES cycles under consideration. To narrow down our study population to those relevant to sarcopenia assessment, we applied further inclusion criteria. Firstly, we focused on adults aged 20 to 60 years, as the grip test for muscle strength assessment and cholesterol-lowering medication survey were only conducted in this age range. Consequently, after the initial screening process, 7,697 participants remained for our analysis.\u003c/p\u003e \u003cp\u003eIn the following, we excluded observations with missing values on key variables, including the use of cholesterol-lowering medicines and the variables directly related to assessing skeletal muscle strength and mass: grip strength and appendicular lean mass. For grip strength, individuals with missing values in both upper extremities were excluded. If only one side of the upper extremity was tested for grip strength, the strength of that side was used as an indicator of the individual's muscle strength and they were not excluded. For appendicular lean mass, individuals with missing values for lean mass in all four limbs (including both upper and lower extremities) were excluded, as some patients had missing values for lean mass in one limb due to measurement technique issues. The handling of missing values for grip strength and appendicular lean mass will be addressed in the next section of the paper.\u003c/p\u003e \u003cp\u003eNext, we removed the variable used for body classification, BMI, due to missing values. Individuals with missing values for basic demographic data such as gender, age, race, and education level were also removed. After these two stages of screening, the study population consisted of 4,075 individuals. Subsequently, we excluded individuals with missing values for key covariates such as combined diabetes and chronic kidney disease. Finally, individuals with stroke and tumors were excluded from the study population as stroke may affect muscle strength testing and tumors are a wasting disease that can lead to loss of skeletal muscle protein.\u003c/p\u003e \u003cp\u003eAfter the aforementioned screening process, a final total of 3,820 individuals were included in this study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDefinition of Cholesterol-lowering treatment\u003c/h2\u003e \u003cp\u003eThe information regarding the status of cholesterol-lowering treatment was derived from the Blood Pressure/Cholesterol section of the questionnaire. In this section, participants were asked the following questions: \"To lower your blood cholesterol, have you ever been told by a doctor or other health professional to take prescribed medicine?\" If the response to this question was \"yes,\" participants were then asked, \"Are you now following this advice to take prescribed medicine?\"\u003c/p\u003e \u003cp\u003eBased on the responses to these two questions, individuals were classified into different categories. If the response to both questions was \"yes,\" the individual was defined as being \"Under cholesterol-lowering treatment.\" If the response to the first question was \"yes\" but the response to the second question was \"no,\" the individual was defined as \"without treatment,\" indicating that there was an indication for treatment but they were not currently receiving it. If the response to both questions was \"no,\" it indicated that the individual did not require medication for cholesterol-lowering treatment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDual-energy X-ray absorptiometry measurement and sarcopenia definition\u003c/h2\u003e \u003cp\u003eDual-energy X-ray absorptiometry (DXA) is a widely accepted method for measuring body composition, owing to its efficiency, ease of use, and low radiation exposure. In the survey cycles from 1999 to 2006, DXA examinations were conducted as part of the mobile examination center, but only the calculation of lean mass in the four limbs was performed in the survey cycles from 2011 to 2014. This examination primarily targeted individuals aged 8\u0026ndash;59 years, while excluding the following criteria:\u003c/p\u003e \u003cp\u003e1) Pregnancy: Participants who had a positive urine pregnancy test and/or self-reported being pregnant at the time of the DXA examination were excluded.\u003c/p\u003e \u003cp\u003e2) History of radiographic contrast material use: Individuals who reported using radiographic contrast material (such as barium) within the past 7 days were excluded.\u003c/p\u003e \u003cp\u003e3) Weight and height limitations: Individuals who self-reported weighing over 450 pounds or having a height over 6'5\" were excluded due to limitations of the DXA table.\u003c/p\u003e \u003cp\u003eThe DXA examination provides various components of body composition, and in this study, the focus is on extracting the Lean excl Body Mineral Content of the four limbs. The definition of sarcopenia primarily relies on the analysis of the skeletal muscle mass index (SMI), also known as the sarcopenia index. This index is calculated by adjusting appendicular lean mass (ALM) according to the body weight index recommended by the Foundation for the National Institutes of Health (FNIH) Sarcopenia Project[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Males with an SMI\u0026thinsp;\u0026lt;\u0026thinsp;0.789 or females with an SMI\u0026thinsp;\u0026lt;\u0026thinsp;0.512 are considered to have low muscle mass, defining pre-sarcopenia[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e Furthermore, the use of sarcopenia instead of pre-sarcopenia in this study is reliable because sarcopenia is widely accepted and recognized as the formal term for low muscle mass according to international guidelines, providing a more accurate representation of the pathological state being studied.\u003c/p\u003e \u003cp\u003eALM represents the sum of lean mass in the four limbs as measured by DXA. During the processing of variables, observations with missing data for lean mass in all four limbs were excluded. However, if an individual had missing lean mass data in one limb due to specific reasons, we imputed the average lean mass of individuals of the same age and gender group to compensate for this missing value. Finally, SMI was calculated by dividing the sum of lean mass in the four limbs (converted to kilograms) by the body mass index (BMI).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDefinition of obesity\u003c/h2\u003e \u003cp\u003eIn defining obesity, we continued to use the Body Mass Index (BMI) to categorize individuals in this study. BMI is calculated using body measures, dividing weight (in kilograms) by height (in meters) squared. Individuals with a BMI\u0026thinsp;\u0026le;\u0026thinsp;18.5 are classified as underweight, those with a BMI\u0026thinsp;\u0026gt;\u0026thinsp;18.5 and \u0026lt;\u0026thinsp;25 are classified as normal weight, individuals with a BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 and \u0026lt;\u0026thinsp;30 are classified as overweight, and those with a BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 are classified as obese.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDefinitions of covariates\u003c/h2\u003e \u003cp\u003eDemographic data, including age, sex, race, and education level, as well as information on alcohol consumption, smoking status, and the presence of diabetes and chronic kidney disease, history of stroke, and cancer, were obtained through self-reported questionnaires. Total protein intake and total energy intake were assessed using two consecutive 24-hour dietary recalls, and the average of these two recalls was used as the final value. In the case of a missing second dietary recall, the values from the first recall were used as the final values for protein and energy intake. Relevant laboratory measurements were obtained from blood specimens collected from participants, which were processed, stored, and shipped to the University of Minnesota in Minneapolis, MN for analysis.\u003c/p\u003e \u003cp\u003eThe grip strength results were derived from the muscle strength component of the examination data. Participants aged 6 years and above were included in this assessment. Individuals who were unable to hold the dynamometer with both hands, such as those missing both arms, both hands, thumbs on both hands, or paralyzed in both hands, were excluded from this component. However, participants who were able to grip the dynamometer with one hand still performed the grip strength test. Participants who had undergone surgery on either hand or wrist in the last three months were not tested on that particular hand.\u003c/p\u003e \u003cp\u003eThe grip strength assessment required participants to stand with maximum effort while gripping the dynamometer. Participants were instructed to exhale during the exertion to avoid intra-thoracic pressure accumulation. The test was performed with both hands, and each hand was tested three times. The tests were carried out alternately between hands, with a rest period of at least 60 seconds between measurements.\u003c/p\u003e \u003cp\u003eOriginally, our design aimed to use grip strength in conjunction with SMI to define sarcopenia. However, in the population we included, the proportion of individuals with low grip strength levels was very low, accounting for only 1.75% of the population. Therefore, we had to abandon this approach.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll data processing and statistical analysis were performed using R (version 4.3.2). Continuous variables were expressed as mean (standard deviation), while categorical variables were presented as counts (percentages). For categorical variables in complex survey data, statistical analysis to compare differences between groups was conducted using the chi-squared test with Rao \u0026amp; Scott's second-order correction. For continuous variables in complex survey data, the non-parametric Wilcoxon rank-sum test was used to analyze differences between two groups. In the correlation analysis between independent and dependent variables, the dependent variable in this study was a binary variable. Therefore, logistic regression analysis was performed to construct both univariate and multivariate regression models. The predictive modeling analysis utilized the ROC curve to assess the average predictive value based on the area under the curve (AUC). A significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to indicate statistical differences, and p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 was considered to indicate significant statistical differences.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eThe Usage of Cholesterol-lowering Medication Shows Significant Differences between Non-sarcopenic and Sarcopenic Groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we investigated the usage of cholesterol-lowering medication and observed significant differences between the non-sarcopenic and sarcopenic groups. We included a total of 3,820 participants from two cycles of NHANES data, ensuring comprehensive information on key variables such as appendicular lean mass, BMI, and cholesterol-lowering medication. Additionally, important covariates such as demographic information, diabetes, CKD, smoking status, alcohol consumption, and relevant laboratory tests were also well-documented.\u003c/p\u003e\n\u003cp\u003eParticipants were divided into non-sarcopenic and sarcopenic groups based on their sarcopenia index. Table 1 presents the baseline characteristics of both groups and compares the differences in various variables. Notably, we found that the prevalence of sarcopenia in the 20-60-year-old population surveyed was approximately 8.0%. The average age in the sarcopenic group was higher than that in the non-sarcopenic group, with a statistically significant difference (49.0 (11.1) vs. 43.0 (10.9), p\u0026lt;0.001). Age stratification further revealed that the highest prevalence of sarcopenia was concentrated in the 50-60 years age group among the participants included in our study. Moreover, the distribution of sarcopenia exhibited variations among different ethnicities, with Mexican Americans and Other Hispanics showing higher levels of disease prevalence compared to other ethnic groups. Interestingly, an unexpected observation surfaced when examining the association between sarcopenia and educational attainment. It appears that the incidence of sarcopenia is positively correlated with the level of education received by individuals.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere were significant differences in the indicators related to physical examination between the two groups. The sarcopenic group had significantly higher levels of Waist Circumference and BMI compared to the non-sarcopenic group (113.30 (19.14) vs. 96.70 (15.58), p\u0026lt;0.001), (35.50 (8.67) vs. 27.70 (6.36), p\u0026lt;0.001). Moreover, in the sarcopenic group, a significant proportion of individuals reached the obesity category based on BMI levels. The average level of Triglycerides was significantly higher in the sarcopenic group compared to the non-sarcopenic group, but due to the substantial amount of missing data, its interpretability may be limited. On the other hand, the average levels of HDL-Cholesterol, Calcium, and Grip strength were significantly lower in the sarcopenic group compared to the non-sarcopenic group. There was a difference in the distribution of diabetes between the two groups, with a higher proportion of diabetes in the sarcopenic group. Of particular interest was the usage of cholesterol-lowering medication, which showed a significant difference between the two groups (77 (25.0%) vs. 396 (12.0%), p\u0026lt;0.001). Additionally, based on the predefined grouping criteria, the sarcopenic group had a significantly higher proportion of individuals who met the criteria for cholesterol-lowering medication usage compared to the non-sarcopenic group. Other variables, such as gender, showed no difference in distribution between the two groups, with a relatively balanced ratio of sarcopenia in males and females. Alcohol consumption, cigarette consumption, total protein intake, total energy intake, kidney disease, and laboratory measurements including total cholesterol, LDL-cholesterol, iron, and phosphorus did not show statistically significant differences between the two groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIn the single-factor logistic regression analysis, Cholesterol-lowering medication demonstrated a significant association with sarcopenia\u003c/strong\u003e\u003cstrong\u003e，\u003c/strong\u003e\u003cstrong\u003ebut not in the multivariable logistic regression analysis\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the relationship between Cholesterol-lowering medication treatment and sarcopenia, logistic regression analysis was performed, with Model 1 representing the single-factor logistic regression analysis considering only Cholesterol-lowering medication. Model 2 included adjustments for waist circumference, BMI, cholesterol levels, triglyceride levels, HDL-cholesterol levels, and age. Model 3 encompassed adjustments for all variables. (Figure 2)\u003c/p\u003e\n\u003cp\u003eIn the single-factor regression analysis (Model 1), Cholesterol-lowering medication was identified as a risk factor for sarcopenia, with an odds ratio (OR) of 2.42 (95% confidence interval [CI]: 1.78-3.29, p\u0026lt;0.001). However, after adjusting for age, obesity-related variables, and serum lipid levels in Model 2 and Model 3, the association between Cholesterol-lowering medication and sarcopenia disappeared. This suggests that the use of cholesterol-lowering medication is not a true risk factor for sarcopenia. The observed difference in Model 1 may be attributed to potential confounding factors related to medication usage.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCholesterol-lowering medication\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eis\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;not a direct predictor of sarcopenia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further explore the specific factors that contribute to the differences observed in Cholesterol-lowering medication usage and to identify the true correlates of sarcopenia, we divided the population into two groups: those who received Cholesterol-lowering medication (under treatment) and those who did not (non-treatment). We compared various variables between these two groups, and Table 2 presents the variables that showed significant differences.\u003c/p\u003e\n\u003cp\u003eIn the under treatment-group, there was a slightly higher proportion of males, accounting for 56% of the population (p=0.041). Additionally, the average age was significantly higher in this group (53.0 (7.2), p\u0026lt;0.001), indicating that Cholesterol-lowering medication users were predominantly in the 50-60 age range. The average waist circumference and BMI were significantly higher in the under treatment-group compared to the non-treatment group. Moreover, the obesity rate was higher in the under treatment-group, accounting for 55% of the study sample.\u003c/p\u003e\n\u003cp\u003eIn terms of laboratory tests, the total cholesterol level and low-density lipoprotein cholesterol level were significantly lower in the under treatment-group compared to the non-medication group. This suggests that the majority of individuals, which under treated were effectively able to achieve cholesterol reduction through medication.\u003c/p\u003e\n\u003cp\u003eHowever, the under treatment-group showed significantly higher levels of triglycerides compared to the non-treatment group. Additionally, there were differences observed in the average levels of calcium and Sarcopenia index between the two groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, we noticed an interesting phenomenon: the under treatment-group had a significantly higher proportion of individuals with comorbid diabetes compared to the non-treatment group. Our analysis suggests that this could be attributed to the fact that individuals with diabetes are more likely to experience disturbances in lipid metabolism, leading to elevated cholesterol levels and the need for Cholesterol-lowering medication. In other words, the use of Cholesterol-lowering medication is a consequence of diabetes, rather than its cause.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrong Association between BMI and Sarcopenia in Multivariable Regression Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThrough the previous analysis, we have become more convinced that Cholesterol-lowering medication is not directly related to sarcopenia. The results of the univariable regression analysis in Table 2 may be influenced by one or more covariates. Therefore, we included these variables in a new fitted model and conducted a multivariable logistic regression analysis to examine their relationships with sarcopenia. As show in Table 3 the results indicate that only BMI, total cholesterol, and low-density lipoprotein cholesterol are significantly associated with sarcopenia. Among these factors, BMI exhibits the most prominent relationship. This finding suggests that higher BMI values correlate with an increased risk of sarcopenia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInfluence of Obesity on the Association between Cholesterol-lowering Medication and Sarcopenia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis Combining the Findings from Table 3, it is likely that the association between the use of Cholesterol-lowering medication and sarcopenia is influenced by the medication being taken under different body conditions, rather than being an independent risk factor. Individuals who require Cholesterol-lowering treatment are typically those with abnormally high levels of serum total cholesterol or low-density lipoprotein cholesterol. Moreover, a substantial proportion of these patients are obese.\u003c/p\u003e\n\u003cp\u003eTo investigate this hypothesis, we divided the study population based on a body mass index (BMI) threshold of 30, defining individuals with a BMI greater than or equal to 30 as obese and those with a BMI less than 30 as non-obese. We further explored the impact of obesity on medication usage differences related to sarcopenia. We then divided each subgroup into sarcopenia and non-sarcopenia groups based on the sarcopenia index. Table 4 presents the variable differences observed in these two groups.\u003c/p\u003e\n\u003cp\u003eFrom the results presented, we observe that the prevalence of sarcopenia is mainly concentrated in the obese group. Although there were no significant differences in mean age between the two groups, the sarcopenia group in the obese population was primarily composed of individuals aged 50-60, which aligns with the overall population distribution. Similar patterns were observed for waist circumference and BMI as well.\u003c/p\u003e\n\u003cp\u003eWithin the non-obese group, there were no significant differences in the use of Cholesterol-lowering medication between the sarcopenia and non-sarcopenia groups. However, in the obese group, the differences persisted. Based on these findings, we speculate that obesity may truly be the influencing factor for sarcopenia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObesity —the key culprit\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo validate our hypothesis, we performed a multivariable logistic regression analysis by incorporating all the meaningful variables mentioned earlier. The final analysis revealed that only BMI demonstrated a significant and independent impact on sarcopenia, displaying clear statistical significance. This finding supports the notion that obesity is the true independent risk factor for sarcopenia. The results are presented in Table 5. The results indicate that for every unit increase in BMI, the odds of developing sarcopenia increase by a factor of 1.13. This suggests that higher BMI values are associated with a significantly increased risk of sarcopenia.\u003c/p\u003e\n\u003cp\u003eGiven the strong association between obesity and sarcopenia, we aimed to develop a predictive model to assist in clinical screening for sarcopenia. As the ROC plot shown in Figure 3, we evaluated the predictive performance of four different models.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel 1: Predicting Sarcopenia using Obesity Alone. In this model, we solely used obesity as a predictor for sarcopenia. The area under the receiver operating characteristic curve (AUC) was calculated to be 0.6927, indicating some predictive value but relatively low accuracy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel 2: Incorporating Obesity, Age, and Grip Strength. To enhance the predictive accuracy, we included obesity, age, and grip strength as predictors for sarcopenia. The AUC increased to 0.7433, signifying a significant improvement in prediction accuracy compared to Model 1.\u003c/p\u003e\n\u003cp\u003eModel 3: Adjusting for Cholesterol-lowering Medication. In Model 3, we adjusted for the use of cholesterol-lowering medication in addition to obesity, age, and grip strength. The AUC showed a minimal change, slightly increasing to 0.7448. These findings suggest that the use of cholesterol-lowering medication does not contribute significantly to the prediction of sarcopenia.\u003c/p\u003e\n\u003cp\u003eModel 4: Comprehensive Prediction with All Variables. In Model 4, we incorporated all variables, including obesity, age, grip strength, and cholesterol-lowering medication, to predict sarcopenia. The AUC remained the same as in Model 3, indicating that the inclusion of additional variables did not improve the predictive capability beyond obesity, age, and grip strength.\u003c/p\u003e\n\u003cp\u003eOverall, these results demonstrate that while obesity, age, and grip strength are important predictors for sarcopenia, the inclusion of other variables did not contribute significantly to the predictive ability of the model. Therefore, a combined assessment of obesity, age, and grip strength provides the most reliable and accurate prediction of sarcopenia in the context of this study.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSince the relationship between cholesterol and cardiovascular diseases has been thoroughly elucidated, significant effects have been observed in clinical practice through early reduction of serum cholesterol levels, particularly low-density lipoprotein cholesterol, for primary and secondary prevention of cardiovascular diseases[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. CLM, especially statins, are widely prescribed for this purpose. Statins work by inhibiting the activity of HMG-CoA reductase, blocking the conversion of HMG-CoA to mevalonic acid and reducing hepatic cholesterol synthesis[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study, the usage rate of CLM among adults aged 20\u0026ndash;60 in the United States was found to be 22.75%. This proportion is expected to be higher among individuals aged 60 and above. Literature reports suggest that economically developed countries have approximately 25% of individuals aged 65 and above undergoing statin therapy for primary or secondary prevention of cardiovascular diseases [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe discussion has highlighted the safety and tolerability of commonly used CLM in clinical practice. However, it is important to acknowledge that over the past 40 years of their use, various clinical adverse reactions have been reported. Specifically, statins, a class of CLM, have been associated with Statin-Associated Muscle Symptoms (SAMS), which is the most common and significant adverse event, with up to 72% of adverse events related to muscle-related issues. These events manifest as muscle pain, myopathy, myositis with elevated creatine kinase (CK), or in its most severe form, rhabdomyolysis, along with reports of additional joint and abdominal pain [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The pathogenic mechanisms underlying muscle-related adverse effects involve membrane excitability, mitochondrial function, coenzyme Q10 depletion, calcium homeostasis, apoptosis induction, and genetic determinants[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSarcopenia is a progressive and systemic skeletal muscle disease that has been associated with an increased risk of adverse outcomes, including falls, fractures, physical disability, and mortality[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It is becoming a significant global health concern. As early as 2010, the European Working Group on Sarcopenia in Older People (EWGSOP) reported a prevalence rate of 5\u0026ndash;13% in individuals aged 60\u0026ndash;70, and a range of 11\u0026ndash;50% in individuals aged over 80[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, these numbers have been increasing over the years. In 2019, while the updated guidelines by EWGSOP did not include epidemiological descriptions of sarcopenia, the report by the Asian Working Group for Sarcopenia (AWGS) that same year showed a prevalence range of 5.5\u0026ndash;25.7%, with a higher prevalence in males (5.1%-21.0%) compared to females (4.1%-16.3%) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is important to note that while the report from AWGS primarily focused on Asian populations, the prevalence range reported does not differentiate by age. This indicates that in the past decade, the population affected by sarcopenia has at least doubled.\u003c/p\u003e \u003cp\u003eIn the population included in this study, the weighted analysis revealed a prevalence rate of approximately 8.0% for sarcopenia, which aligns with the current literature. Additionally, there was a clear age gradient in the distribution of the population, with individuals aged 50\u0026ndash;60 representing nearly half of the total.\u003c/p\u003e \u003cp\u003eIn recent years, there has been a debate regarding the association between cholesterol-lowering medication and sarcopenia. Our study aimed to examine this relationship and found some interesting results. We observed differences in the use of cholesterol-lowering medication between the sarcopenia and non-sarcopenia groups. However, after conducting regression analysis to account for multiple factors, the correlation between medication use and sarcopenia was eliminated. This suggests that the relationship between cholesterol-lowering medication and sarcopenia is not direct and may be influenced by other variables. Our findings are consistent with a cohort study conducted on a larger population of 639 participants. This study, which followed participants for an average period of 4.4 years, found no difference in grip strength decline between those who took statin cholesterol-lowering medication and those who did not. This indicates that long-term use of cholesterol-lowering medication does not seem to affect muscle strength in relation to sarcopenia[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].Furthermore, a cross-sectional study conducted in China among 441 elderly hospitalized patients also reported no statistically significant difference in cholesterol-lowering medication use between the sarcopenia and non-sarcopenia groups[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Similarly, an observational cohort study on 756 community-dwelling male veterans aged 65 and above found no difference in muscle strength testing between long-term users of statin medication and non-users[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, despite the findings that suggest no direct correlation between cholesterol-lowering medication and sarcopenia, there are clinical studies that present a different perspective based on the theoretical basis linking these medications to muscle function. A prospective cohort study involving 774 community-dwelling participants (48% females, mean age 62\u0026thinsp;\u0026plusmn;\u0026thinsp;7) observed 147 individuals taking statin cholesterol-lowering medication at the beginning, which increased to 179 over an average follow-up period of 2.6 years. During the follow-up, falls risk and assessment of lower limb muscle strength were evaluated. The results indicated that the observed subjects taking cholesterol-lowering medication had a significantly higher risk of falls and a greater decline in lower limb muscle function compared to those who did not take medication[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].Furthermore, a systematic review conducted by Lluis and colleagues, exploring the relationship between various oral medications and sarcopenia, supports the conclusion that cholesterol-lowering medication is detrimental to muscle function and contributes to the development of sarcopenia. They also summarized the most important pathways and factors contributing to the loss of muscle mass, which include immobility, nutritional deficiencies, inflammation, endocrine factors, and genetic influence. Ultimately, these factors may lead to the occurrence of sarcopenia[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on several recent research studies, there have been contrasting findings. To begin with, a meta-analysis conducted in 2020[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], examined 67,001 patients with chronic kidney disease over a time span from 1997 to 2011. The study primarily aimed to evaluate the risk of new-onset muscle loss in these patients who were using statin drugs. Preliminary analysis using the Cox proportional hazards model demonstrated that statin drugs used in chronic kidney disease patients effectively prevented the occurrence of muscle loss. Additionally, higher doses and lipophilic formulations of statin drugs appeared to provide more significant protective effects, and these results were consistent during long-term follow-up. Furthermore, a retrospective study conducted by Iisa Lindstr\u0026ouml;m and colleagues in 2021[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], focused on patients with abdominal aortic aneurysm who underwent endovascular repair. The results indicated that long-term use of statin drugs in these patients, with an average age of 77.7 years, reduced the long-term mortality rate without increasing the risk of sarcopenia. Moreover, a recent meta-analysis examined the impact of diet and medications on sarcopenia. Findings from the study suggested that long-term use of certain medications, including statins, metformin, GLP-1 agonists, losartan, growth hormone, and dipeptidyl peptidase-4 inhibitors, not only improved metabolic parameters associated with sarcopenia but also protected muscle while preventing cardiovascular disease[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe debate regarding the association between cholesterol-lowering medication and sarcopenia is still ongoing, and more comprehensive clinical and basic research is needed to elucidate the relationship between the two. Our study utilized the NHANES database to analyze the correlation between them from the perspective of the general population, which is an advantage not possessed by the aforementioned studies that focused on specific populations. The extrapolation value of their conclusions is debatable.\u003c/p\u003e \u003cp\u003eIn our analysis, we found that obesity is an independent risk factor for sarcopenia. Furthermore, in the construction of our predictive model, we found that age, BMI, and grip strength have a high predictive value for sarcopenia. Specifically, older age, higher BMI, and lower grip strength were associated with an increased risk of developing sarcopenia. The 2018 Second Edition of the EWGSOP guidelines introduced the concept of sarcopenic obesity, bringing attention to the impact of obesity on muscle loss[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. After years of clinical practice and research, the correlation between obesity and sarcopenia has been well established[\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], which is consistent with the results of our study.\u003c/p\u003e \u003cp\u003eIn conclusion, our study has several limitations. Firstly, the age range of the subjects included was limited to adults under 60 years old, which may have influenced the observed muscle strength and muscle quality compared to individuals over 60 years old. Additionally, we investigated the proportion of cholesterol-lowering medication use among individuals over 60 years old during the 2011\u0026ndash;2014 period, and astonishingly, it was as high as 42.28%. Including this population in the analysis may lead to different results. Secondly, while we strictly followed the definition requirements from the Foundation for the National Institutes of Health (FNIH) and utilized skeletal muscle index (SMI) to assess muscle wasting, existing guidelines also suggest that the diagnosis of sarcopenia should ideally consider muscle strength, muscle quality, and physical performance. This is an area that needs continuous improvement in our future research. Lastly, it is regrettable that the specific duration of medication use was not provided in the database, which could also impact the results of the analysis.\u003c/p\u003e \u003cp\u003eDespite these limitations, our study provides important insights into the association between cholesterol-lowering medication and sarcopenia. It highlights the independent risk factor of obesity for sarcopenia and identifies age, BMI, and grip strength as potential predictors for sarcopenia. However, further research is warranted to validate our findings in larger and more diverse populations, and to address the aforementioned limitations. Understanding the relationship between cholesterol-lowering medication and sarcopenia can contribute to the development of targeted interventions and strategies for preventing and managing sarcopenia in clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was funded by the Jilin Province Medical and Health Talents Project, grant number JLSWSRCZX2023-25\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJie Li: Conceived and designed the study, analyzed the data, and contributed to the interpretation of results. Wrote the manuscript and critically revised it for intellectual content. Acted as the corresponding author and oversaw the entire research process.Wei Gong: Contributed to the study design, data analysis, and interpretation. Conducted the statistical analyses and assisted in writing the manuscript. Reviewed and provided critical feedback for intellectual content.Tingting Liu: Contributed to the study design, data collection, and analysis. Assisted in the interpretation of results and manuscript writing. Reviewed and provided critical revisions for intellectual content.All authors read and approved the final version of the manuscript for submission to the journal.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eWe would like to thank any individuals, institutions, or resources that have provided support in other ways towards the completion of this study.\u003c/p\u003e\u003ch2\u003eAvailability of Data and Materials\u003c/h2\u003e \u003cp\u003eThe data utilized in this research were derived from the National Health and Nutrition Examination Survey (NHANES) database. Access to the NHANES database can be obtained through the official website of the Centers for Disease Control and Prevention (CDC). Researchers interested in accessing the data are encouraged to visit the CDC website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/index.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/index.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for detailed information on data availability and the required procedures for data access.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWard, N.C., G.F. Watts and R.H. Eckel, \u003cem\u003eStatin Toxicity.\u003c/em\u003e Circ Res, \u003cstrong\u003e2019\u003c/strong\u003e. 124(2): p. 328-350.\u003c/li\u003e\n\u003cli\u003eBouitbir, J., G.M. Sanvee, M.V. Panajatovic, F. Singh and S. 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McMurdo, et al., \u003cem\u003eACE inhibitors, statins and thiazides: no association with change in grip strength among community dwelling older men and women from the Hertfordshire Cohort Study.\u003c/em\u003e Age Ageing, \u003cstrong\u003e2014\u003c/strong\u003e. 43(5): p. 661-6.\u003c/li\u003e\n\u003cli\u003eLu, B., L. Shen, H. Zhu, L. Xi, W. Wang, et al., \u003cem\u003eAssociation between serum homocysteine and sarcopenia among hospitalized older Chinese adults: a cross-sectional study.\u003c/em\u003e BMC Geriatr, \u003cstrong\u003e2022\u003c/strong\u003e. 22(1): p. 896.\u003c/li\u003e\n\u003cli\u003eScott, D., L. Blizzard, J. Fell and G. Jones, \u003cem\u003eStatin therapy, muscle function and falls risk in community-dwelling older adults.\u003c/em\u003e Qjm, \u003cstrong\u003e2009\u003c/strong\u003e. 102(9): p. 625-33.\u003c/li\u003e\n\u003cli\u003eCampins, L., M. Camps, A. Riera, E. Pleguezuelos, J.C. Yebenes, et al., \u003cem\u003eOral Drugs Related with Muscle Wasting and Sarcopenia. A Review.\u003c/em\u003e Pharmacology, \u003cstrong\u003e2017\u003c/strong\u003e. 99(1-2): p. 1-8.\u003c/li\u003e\n\u003cli\u003eLin, M.H., S.Y. Chiu, P.H. Chang, Y.L. Lai, P.C. Chen, et al., \u003cem\u003eHyperlipidemia and Statins Use for the Risk of New Diagnosed Sarcopenia in Patients with Chronic Kidney: A Population-Based Study.\u003c/em\u003e Int J Environ Res Public Health, \u003cstrong\u003e2020\u003c/strong\u003e. 17(5).\u003c/li\u003e\n\u003cli\u003eMellen, R.H., O.S. Girotto, E.B. Marques, L.F. Laurindo, P.C. Grippa, et al., \u003cem\u003eInsights into Pathogenesis, Nutritional and Drug Approach in Sarcopenia: A Systematic Review.\u003c/em\u003e Biomedicines, \u003cstrong\u003e2023\u003c/strong\u003e. 11(1).\u003c/li\u003e\n\u003cli\u003eKim, K.M., H.C. Jang and S. Lim, \u003cem\u003eDifferences among skeletal muscle mass indices derived from height-, weight-, and body mass index-adjusted models in assessing sarcopenia.\u003c/em\u003e Korean J Intern Med, \u003cstrong\u003e2016\u003c/strong\u003e. 31(4): p. 643-50.\u003c/li\u003e\n\u003cli\u003eCawthon, P.M., K.W. Peters, M.D. Shardell, R.R. McLean, T.T. Dam, et al., \u003cem\u003eCutpoints for low appendicular lean mass that identify older adults with clinically significant weakness.\u003c/em\u003e J Gerontol A Biol Sci Med Sci, \u003cstrong\u003e2014\u003c/strong\u003e. 69(5): p. 567-75.\u003c/li\u003e\n\u003cli\u003eKitsuda, Y., T. Wada, S. Tanishima, M. Osaki, H. Nagashima, et al., \u003cem\u003eImpact of Sarcopenia on Spinal Spondylosis: A Literature Review.\u003c/em\u003e J Clin Med, \u003cstrong\u003e2023\u003c/strong\u003e. 12(16).\u003c/li\u003e\n\u003cli\u003eGolabi, P., L. Gerber, J.M. Paik, R. Deshpande, L. de Avila, et al., \u003cem\u003eContribution of sarcopenia and physical inactivity to mortality in people with non-alcoholic fatty liver disease.\u003c/em\u003e JHEP Rep, \u003cstrong\u003e2020\u003c/strong\u003e. 2(6): p. 100171.\u003c/li\u003e\n\u003cli\u003eLi, R., S. Lin, J. Tu, Y. Chen, B. Cheng, et al., \u003cem\u003eEstablishment and evaluation of a novel practical tool for the diagnosis of pre-sarcopenia in young people with diabetes mellitus.\u003c/em\u003e J Transl Med, \u003cstrong\u003e2023\u003c/strong\u003e. 21(1): p. 393.\u003c/li\u003e\n\u003cli\u003eTomaszewski, M., K.M. Stępień, J. Tomaszewska and S.J. Czuczwar, \u003cem\u003eStatin-induced myopathies.\u003c/em\u003e Pharmacol Rep, \u003cstrong\u003e2011\u003c/strong\u003e. 63(4): p. 859-66.\u003c/li\u003e\n\u003cli\u003eMu\u0026ntilde;oz-Blanco, A., R. G\u0026oacute;mez-Huelgas and J.F. G\u0026oacute;mez-Cerezo, \u003cem\u003eStatin-associated muscle symptoms: Myth or reality?\u003c/em\u003e Rev Clin Esp (Barc), \u003cstrong\u003e2022\u003c/strong\u003e. 222(10): p. 602-611.\u003c/li\u003e\n\u003cli\u003eAgostini, J.V., M.E. Tinetti, L. Han, G. McAvay, J.M. Foody, et al., \u003cem\u003eEffects of statin use on muscle strength, cognition, and depressive symptoms in older adults.\u003c/em\u003e J Am Geriatr Soc, \u003cstrong\u003e2007\u003c/strong\u003e. 55(3): p. 420-5.\u003c/li\u003e\n\u003cli\u003eLindstr\u0026ouml;m, I., S. Protto, N. Khan, S. V\u0026auml;\u0026auml;r\u0026auml;m\u0026auml;ki, N. Oksala, et al., \u003cem\u003eStatin use, development of sarcopenia, and long-term survival after endovascular aortic repair.\u003c/em\u003e J Vasc Surg, \u003cstrong\u003e2021\u003c/strong\u003e. 74(5): p. 1651-1658.e1.\u003c/li\u003e\n\u003cli\u003eChoi, K.M., \u003cem\u003eSarcopenia and sarcopenic obesity.\u003c/em\u003e Korean J Intern Med, \u003cstrong\u003e2016\u003c/strong\u003e. 31(6): p. 1054-1060.\u003c/li\u003e\n\u003cli\u003eBilski, J., P. Pierzchalski, M. Szczepanik, J. Bonior and J.A. Zoladz, \u003cem\u003eMultifactorial Mechanism of Sarcopenia and Sarcopenic Obesity. Role of Physical Exercise, Microbiota and Myokines.\u003c/em\u003e Cells, \u003cstrong\u003e2022\u003c/strong\u003e. 11(1).\u003c/li\u003e\n\u003cli\u003eZamboni, M., S. Rubele and A.P. Rossi, \u003cem\u003eSarcopenia and obesity.\u003c/em\u003e Curr Opin Clin Nutr Metab Care, \u003cstrong\u003e2019\u003c/strong\u003e. 22(1): p. 13-19.\u003c/li\u003e\n\u003cli\u003eDonini, L.M., L. Busetto, S.C. Bischoff, T. Cederholm, M.D. Ballesteros-Pomar, et al., \u003cem\u003eDefinition and Diagnostic Criteria for Sarcopenic Obesity: ESPEN and EASO Consensus Statement.\u003c/em\u003e Obes Facts, \u003cstrong\u003e2022\u003c/strong\u003e. 15(3): p. 321-335.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Demographic and Clinical Characteristics of Participants according\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSarcopenia Index Group\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"130%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.484848484848484%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSarcopenia index group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eN = 90422607 (100%)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-sarcopenia\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eN = 90422608 (92%)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSarcopenia\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eN = 90422608 (8.0%)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep Value\u003c/strong\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e3,820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e1,928 (49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e1,779 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e149 (44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e3,820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e43.0 (11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e43.0 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e49.0 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e3,820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e20-30 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e639 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e604 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e35 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e30-40 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e936 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e874 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e62 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e40-50 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e1,137 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e1,062 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e75 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e50-60 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e1,108 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e955 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e153 (48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e3,820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e410 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e340 (7.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e70 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e310 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e264 (5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e46 (9.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e1,517 (67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e1,391 (67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e126 (63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e900 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e863 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e37 (5.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eNon-Hispanic Asian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e546 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e508 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e38 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eOther/multiracial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e137 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e129 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e8 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e3,820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e9-11th grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e412 (8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e372 (8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e40 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eCollege graduate or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e1,261 (37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e1,197 (38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e64 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eHigh school graduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e766 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e679 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e87 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eLess than 9th grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e141 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e110 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e31 (9.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eSome college or AA degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e1,240 (32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e1,137 (32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e103 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist Circumference(cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e3,793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e97.60 (16.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e96.70 (15.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e113.30 (19.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI(kg/m^2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e3,820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e28.20 (6.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e27.70 (6.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e35.50 (8.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e3,820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eNormal weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e1,040 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e1,019 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e21 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e1,521 (39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e1,277 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e244 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e1,207 (34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e1,149 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e58 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e52 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e50 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e2 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol consumrtion group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e3,611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e1-5 drinks/month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e1,906 (52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e1,729 (53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e177 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e10+ drinks/month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e543 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e506 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e37 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e5-10 drinks/month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e331 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e311 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e20 (9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eNon-drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e831 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e752 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e79 (21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCigarette consumption group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e3,820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eCurrent somker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e663 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e622 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e41 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eFormer smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e854 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e771 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e83 (29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eNever smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e2,303 (59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e2,102 (59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e201 (58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal protein intake(gm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e3,661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e81.22 (36.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e81.45 (37.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e78.85 (34.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal energy intake(kcal)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e3,661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e2,073.50 (851.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e2,084.62 (863.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e1,929.97 (680.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Cholesterol(mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e3,681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e193.00 (42.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e193.00 (42.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e188.00 (38.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTriglyceride(mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e1,767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e101.00 (139.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e98.70 (140.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e131.07 (118.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDL-cholesterol(mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e1,725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e115.00 (35.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e114.00 (35.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e116.00 (35.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDL-Cholesterol (mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e3,681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e50.00 (15.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e50.00 (15.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e46.00 (13.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCalcium(mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e3,649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e9.40 (0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e9.40 (0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e9.30 (0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIron(ug/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e3,655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e82.00 (35.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e83.00 (35.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e73.13 (33.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhosphorus (mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e3,664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e3.80 (0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e3.80 (0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e3.70 (0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eKidney disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e3,820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e82 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e67 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e15 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eNon-CKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e3,738 (98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e3,428 (98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e310 (97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e3,820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e377 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e314 (6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e63 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eNon-diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e3,443 (92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e3,181 (93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e262 (82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCLM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e3,820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eNo medication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e3,103 (81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e2,883 (82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e220 (68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eUnder treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e473 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e396 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e77 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eWithout treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e244 (5.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e216 (5.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e28 (7.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSarcopenia index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e3,820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e0.80 (0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e0.83 (0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e0.69 (0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrip strength\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e3,820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e36.54 (11.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e37.15 (11.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e32.22 (10.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003eBMI, Body mass index;\u0026nbsp;CLM,\u0026nbsp;Cholesterol-lowering medications;\u0026nbsp;CKD,\u0026nbsp;Chronic kidney disease\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eN not Missing (unweighted)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e2\u003c/sup\u003eN(weighted)(%);n (unweighted)(%); Median (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e3\u003c/sup\u003echi-squared test with Rao \u0026amp; Scott\u0026apos;s second-order correction; Wilcoxon rank-sum test for complex survey samples\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eComparison of Variables between Cholesterol-lowering Medication Users and Non-users\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"125%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eN = 90422607 (100%)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-treatment\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eN = 90422608 (87%)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnder treatment\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eN = 90422608 (13%)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep Value\u003c/strong\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e3,820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1,892 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e1,634 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e258 (56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e3,820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e43.0 (11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e41.0 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e53.0 (7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e3,820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e20-30 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e639 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e633 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e6 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e30-40 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e936 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e898 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e38 (7.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e40-50 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1,137 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e1,021 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e116 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e50-60 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1,108 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e795 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e313 (69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist Circumference(cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e3,793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e97.60 (16.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e96.50 (16.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e105.70 (15.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m^2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e3,820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e28.20 (6.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e27.80 (6.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e30.60 (6.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e3,820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e52 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e51 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e1 (\u0026lt;0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003eNormal weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1,040 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e977 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e63 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1,207 (34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e1,060 (34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e147 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1,521 (39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e1,259 (37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e262 (55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Cholesterol(mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e3,681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e193.00 (42.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e194.00 (41.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e184.60 (44.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTriglyceride(mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e1,767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e101.00 (139.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e98.68 (107.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e122.00 (253.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDL-cholesterol(mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e1,725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e115.00 (35.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e116.00 (35.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e106.00 (36.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDL-Cholesterol (mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e3,681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e50.00 (15.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e50.00 (16.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e48.00 (12.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCalcium(mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e3,649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e9.40 (0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e9.40 (0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e9.40 (0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e3,820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e377 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e190 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e187 (32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003eNon-diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e3,443 (92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e3,157 (96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e286 (68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSarcopenia index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e3,820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.80 (0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.80 (0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.79 (0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003eBMI, Body mass index;\u0026nbsp;CKD,\u0026nbsp;Chronic kidney disease\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eN not Missing (unweighted)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e2\u003c/sup\u003eN(weighted)(%);n (unweighted)(%); Median (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e3\u003c/sup\u003echi-squared test with Rao \u0026amp; Scott\u0026apos;s second-order correction; Wilcoxon rank-sum test for complex survey samples\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Multivariable Logistic Regression Analysis of Variables Associated with Sarcopenia\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"567\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\" valign=\"top\"\u003e\n \u003cp\u003e0.94, 1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\" valign=\"top\"\u003e\n \u003cp\u003e20-30 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\" valign=\"top\"\u003e\n \u003cp\u003e30-40 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\" valign=\"top\"\u003e\n \u003cp\u003e0.27, 2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\" valign=\"top\"\u003e\n \u003cp\u003e40-50 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\" valign=\"top\"\u003e\n \u003cp\u003e0.30, 3.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\" valign=\"top\"\u003e\n \u003cp\u003e50-60 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e2.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\" valign=\"top\"\u003e\n \u003cp\u003e0.33, 13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist Circumference(cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\" valign=\"top\"\u003e\n \u003cp\u003e0.95, 1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m^2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\" valign=\"top\"\u003e\n \u003cp\u003e1.05, 1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\" valign=\"top\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\" valign=\"top\"\u003e\n \u003cp\u003eNormal weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\" valign=\"top\"\u003e\n \u003cp\u003e0.05, 8.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\" valign=\"top\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\" valign=\"top\"\u003e\n \u003cp\u003e0.08, 16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\" valign=\"top\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\" valign=\"top\"\u003e\n \u003cp\u003e0.13, 34.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Cholesterol(mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\" valign=\"top\"\u003e\n \u003cp\u003e1.00, 1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTriglyceride(mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\" valign=\"top\"\u003e\n \u003cp\u003e1.00, 1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDL-cholesterol(mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\" valign=\"top\"\u003e\n \u003cp\u003e0.96, 1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.026\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCalcium(mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\" valign=\"top\"\u003e\n \u003cp\u003e0.67, 2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetic group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\" valign=\"top\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\" valign=\"top\"\u003e\n \u003cp\u003eNon-diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\" valign=\"top\"\u003e\n \u003cp\u003e0.56, 3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eOR = Odds Ratio, CI = Confidence Interval;\u0026nbsp;BMI, Body mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Differences in Variables between Sarcopenia and Non-sarcopenia Groups stratified by Obesity Status\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"117%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.3265306122449%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.83673469387755%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eObese group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.83673469387755%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-obese group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.02127659574468%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-sarcopenia\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eN = 1277 (83%)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSarcopenia\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;N = 244 (17%)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep Value\u003c/strong\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-sarcopenia\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;N = 2218 (97%)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSarcopenia\u003c/strong\u003e,\u003c/p\u003e\n \u003cp\u003eN = 81 (2.6%)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep Value\u003c/strong\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.02127659574468%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e1,521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e2,299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.02127659574468%\" valign=\"top\"\u003e\n \u003cp\u003e20-30 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e176 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e26 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e428 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e9 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.02127659574468%\" valign=\"top\"\u003e\n \u003cp\u003e30-40 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e329 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e43 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e545 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e19 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.02127659574468%\" valign=\"top\"\u003e\n \u003cp\u003e40-50 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e396 (32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e62 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e666 (29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e13 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.02127659574468%\" valign=\"top\"\u003e\n \u003cp\u003e50-60 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e376 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e113 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e579 (29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e40 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.02127659574468%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist Circumference(cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e1,505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e111.40 (12.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e120.63 (16.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e2,288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e90.40 (9.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e93.98 (10.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.02127659574468%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m^2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e1,521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e33.90 (5.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e37.36 (7.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e2,299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e25.40 (2.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e27.39 (3.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.02127659574468%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTriglyceride(mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e114.00 (185.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e137.17 (121.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e1,062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e91.00 (104.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e98.43 (101.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.02127659574468%\" valign=\"top\"\u003e\n \u003cp\u003eCLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e1,521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e2,299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.02127659574468%\" valign=\"top\"\u003e\n \u003cp\u003eNo medication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e976 (76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e159 (66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e1,907 (86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e61 (74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.02127659574468%\" valign=\"top\"\u003e\n \u003cp\u003eUnder treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e198 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e64 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e198 (9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e13 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.02127659574468%\" valign=\"top\"\u003e\n \u003cp\u003eWithout treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e103 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e21 (7.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e113 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e7 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.02127659574468%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSarcopenia index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e1,521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e0.74 (0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e0.67 (0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e2,299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e0.86 (0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e0.72 (0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.02127659574468%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrip strength\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e1,521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e37.75 (11.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e33.17 (10.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e2,299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e36.50 (10.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e31.12 (8.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003eBMI, Body mass index;\u0026nbsp;CLM,\u0026nbsp;Cholesterol-lowering medications\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eN not Missing (unweighted)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e2\u003c/sup\u003eN(unweighted)(%);n (unweighted)(%); Median (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e3\u003c/sup\u003echi-squared test with Rao \u0026amp; Scott\u0026apos;s second-order correction; Wilcoxon rank-sum test for complex survey samples\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Multivariable Logistic Regression Analysis of Variables Associated with Sarcopenia\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"101%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.02040816326531%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.02040816326531%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e0.92, 1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.02040816326531%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.02040816326531%\" valign=\"top\"\u003e\n \u003cp\u003e20-30 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.02040816326531%\" valign=\"top\"\u003e\n \u003cp\u003e30-40 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e0.31, 3.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.02040816326531%\" valign=\"top\"\u003e\n \u003cp\u003e40-50 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e0.49, 8.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.02040816326531%\" valign=\"top\"\u003e\n \u003cp\u003e50-60 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e4.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e0.59, 29.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.02040816326531%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist Circumference(cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e0.96, 1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.02040816326531%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m^2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e1.01, 1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.036\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.02040816326531%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Cholesterol(mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e0.25, 1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.02040816326531%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTriglyceride(mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e0.96, 1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.02040816326531%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDL-cholesterol(mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e0.82, 3.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.02040816326531%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDL-Cholesterol (mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e0.84, 4.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.02040816326531%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCalcium(mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e0.75, 2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.02040816326531%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eKidney disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.02040816326531%\" valign=\"top\"\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.02040816326531%\" valign=\"top\"\u003e\n \u003cp\u003eNon-CKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e0.04, 2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.02040816326531%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.02040816326531%\" valign=\"top\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.02040816326531%\" valign=\"top\"\u003e\n \u003cp\u003eNon-diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e0.71, 7.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.02040816326531%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCLM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.02040816326531%\" valign=\"top\"\u003e\n \u003cp\u003eNo medication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.02040816326531%\" valign=\"top\"\u003e\n \u003cp\u003eUnder treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e0.63, 4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.02040816326531%\" valign=\"top\"\u003e\n \u003cp\u003eWithout treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e0.23, 2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.02040816326531%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrip strength\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e0.93, 1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eOR = Odds Ratio, CI = Confidence Interval;\u0026nbsp;BMI, Body mass index;\u0026nbsp;CLM,\u0026nbsp;Cholesterol-lowering medications;\u0026nbsp;CKD,\u0026nbsp;Chronic kidney disease\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"sarcopenia, Cholesterol-lowering medications, obesity, risk factor, grip strength","lastPublishedDoi":"10.21203/rs.3.rs-3968474/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3968474/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eCholesterol-lowering medications, especially statins, are widely prescribed for primary and secondary prevention of cardiovascular diseases. The association between those medications and sarcopenia has garnered attention and remains a topic of contention. Our aim is to investigate whether cholesterol-lowering medications are a risk factor for sarcopenia.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe utilized data from the National Health and Nutrition Examination Survey (NHANES) database, extracting data from the 2011\u0026ndash;2014 survey cycles. By constructed univariate and multivariate regression models, we elucidated the relationship between the X variable and the Y variable. By conducted predictive models by the ROC curve to assess the average predictive value based on AUC.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe ratio of usage of cholesterol-lowering medication showed a significant difference between the sarcopenia group and non-sarcopenia group (77 (25.0%) vs. 396 (12.0%), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but when further analyzing the subgroups of obese and non-obese individuals, this difference disappeared. In the multivariable logistic regression analysis BMI demonstrated a significant and independent impact on sarcopenia (OR\u0026thinsp;=\u0026thinsp;1.13, 95%CI 1.01\u0026ndash;1.27, p\u0026thinsp;=\u0026thinsp;0.036). The ROC curve analysis of the model incorporating age, grip strength, and BMI as predictors yielded an area under the curve (AUC) of 0.7433.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThere is no direct correlation between cholesterol-lowering medications and sarcopenia. Instead, obesity emerges as an independent risk factor for sarcopenia. Additionally, the combination of BMI, age, and grip strength demonstrates good predictive value for identifying the risk of sarcopenia in clinical patients.\u003c/p\u003e","manuscriptTitle":"Cholesterol-lowering medications and sarcopenia: Large cross- sectional Study ：NHANES 2011-2014","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-06 19:23:13","doi":"10.21203/rs.3.rs-3968474/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4986fa19-538c-46f8-8ffd-b4b33ff57b7a","owner":[],"postedDate":"March 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-02T06:44:47+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-06 19:23:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3968474","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3968474","identity":"rs-3968474","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}