Association between lean body mass and osteoarthritis: a cross-sectional study from the NHANES 2007-2018
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Abstract Background The prevention of osteoarthritis through controlling body measurements has received increasing attention in recent years, but the relationship between lean body mass (LBM) and osteoarthritis remains unclear. Hence, we used large-scale data to explore this association. Methods To explore the correlation between LBM and osteoarthritis, we utilized logistic regression equations to explore associations between covariates, exposures, and outcomes. We used multivariate regression models to further eliminate confounding factors. Restricted cubic splines were employed to investigate non-linear relationships. And the inflection point was identified by recursive algorithm. Furthermore, stratified analyses of gender and age were conducted. Results Osteoarthritis was negatively correlated with LBM [0.74 (0.67, 0.80)] in the model adjusting for all confounders. A further linear trend test was performed, and the OR (95% CI) were 0.72 (0.61, 0.85), 0.54 (0.44, 0.65), and 0.45 (0.36, 0.56) for Q2, Q3, and Q4, respectively, compared with Q1. Based on the restricted cubic spline curve, an inflection point of 52.26 kg was found to confirm the non-linear relationship. LBM was negatively correlated with osteoarthritis at 0.66 (0.50, 0.86) before the inflection point, and the association was not statistically significant afterward. Conclusion This large-scale study revealed that LBM was non-linearly correlated with osteoarthritis in the general American population. Differences in age and gender were further identified.
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Hence, we used large-scale data to explore this association. Methods To explore the correlation between LBM and osteoarthritis, we utilized logistic regression equations to explore associations between covariates, exposures, and outcomes. We used multivariate regression models to further eliminate confounding factors. Restricted cubic splines were employed to investigate non-linear relationships. And the inflection point was identified by recursive algorithm. Furthermore, stratified analyses of gender and age were conducted. Results Osteoarthritis was negatively correlated with LBM [0.74 (0.67, 0.80)] in the model adjusting for all confounders. A further linear trend test was performed, and the OR (95% CI) were 0.72 (0.61, 0.85), 0.54 (0.44, 0.65), and 0.45 (0.36, 0.56) for Q2, Q3, and Q4, respectively, compared with Q1. Based on the restricted cubic spline curve, an inflection point of 52.26 kg was found to confirm the non-linear relationship. LBM was negatively correlated with osteoarthritis at 0.66 (0.50, 0.86) before the inflection point, and the association was not statistically significant afterward. Conclusion This large-scale study revealed that LBM was non-linearly correlated with osteoarthritis in the general American population. Differences in age and gender were further identified. Large-scale Lean body mass Osteoarthritis Cross-sectional study Stratified analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 1.Introduction Among the elderly, osteoarthritis is the most common joint disease and a leading cause of disability[ 1 ]. Its clinical features include joint pain, stiffness, and restricted mobility, which in severe cases can lead to joint deformity and loss of function[ 2 ]. The trends of an aging population and increasing obesity are likely to compound this[ 3 ]. The prevalence of osteoarthritis is 10% in males and 18% in females aged over 60[ 4 ]. In various high-income countries, the medical cost of osteoarthritis is estimated to account for between 1% and 2.5% of the country's gross domestic product[ 5 ]. Osteoarthritis has a variety of moderate-to-strong risk factors, including female sex, obesity, and previous injury[ 6 ]. Obesity is a major risk factor for osteoarthritis. A longitudinal cohort study has demonstrated that a high body mass index (BMI) significantly increased the incidence of knee osteoarthritis and was positively associated with the risk of severe osteoarthritis[ 7 ]. Additionally, a Mendelian randomization study supported a causal relationship between BMI and osteoarthritis, which used genetic variants as instrumental variables to minimize the impact of confounding factors, thereby more clearly delineating the direct effects of BMI on osteoarthritis[ 8 ]. However, recent studies have questioned the efficacy of BMI as an indicator for assessing obesity[ 9 , 10 ]. While BMI is widely used to evaluate an individual's weight status, its limitations lie in that it only reflects the ratio of weight to height and does not accurately distinguish between the distribution of body fat and lean body[ 11 , 12 ]. Therefore, future research and clinical evaluations should place greater emphasis on comprehensive measurements of body composition to more accurately assess the impact of obesity on osteoarthritis. This not only aids in a better understanding of the etiology of osteoarthritis but also provides a scientific basis for developing more effective prevention and intervention strategies. Lean body mass (LBM) serves as a crucial indicator of body composition, more accurately reflecting the proportion of lean mass in the human body[ 13 ]. Higher levels of LBM are typically associated with better metabolic health and physical activity capacity[ 14 ]. Recent studies have discovered significant associations between LBM and various metabolic diseases, such as cardiovascular disease, lung cancer, and diabetes[ 15 , 16 ]. However, there are relatively few studies on the relationship between LBM and osteoarthritis, and the results of the available evidence are mostly inconsistent. Understanding the relationship between LBM and osteoarthritis may aid in better prevention of the disease through weight management. Therefore, we used the data from the National Health and Nutrition Examination Survey (NHANES) to examine the potential association between LBM and osteoarthritis. Our goal is to provide new evidence to support weight intervention strategies for reducing the risk of osteoarthritis. 2.Methods 2.1.Study population This study used NHANES dataset, a continuous health and nutrition survey conducted by the Centers for Disease Control and Prevention (CDC).To ensure national representativeness, stratified multistage probability samples were used. Its protocols were all approved by the National Center for Health Statistics Research Ethics Review Board, and the informed consents were provided by all participants. Based on NHANES data from six cycles, we conducted our study. The total number of study participants who finally completed demographic surveys, laboratory tests, and health status questionnaires was 59,842. The following are the exclusion criteria: (1) Age less than 20 years (n = 25072); (2)Missing LBM data (n = 3534); (3) Missing osteoarthritis data (n = 64). Ultimately, a total of 31,172 participants were recruited ( Fig. 1 ) . 2.2.Assessment of lean body mass Using Lee's anthropometric prediction equations, the LBM was calculated[ 17 ]. Dual-energy radiographic data were collected from 15,065 men and 16,107 women. We used multiple linear regression analysis and estimated LBM using sex, age, weight (kg), height (cm), and waist circumference (cm) as predictor variables, and the linear regression model had the highest consistency [LBM (females: R2 = 0.85; males: R2 = 0.91)]. Men LBM = 19.363 + 0.001 * age (year) + 0.064 * height (cm) + 0.756 * weight (kg) − 0.366 * waist circumference (cm) − 1.007 Women LBM = − 10.683 − 0.039 * age (year) + 0.186 * height (cm) + 0.383 * weight (kg) − 0.043 * waist circumference (cm) − 0.340 2.3.Diagnosis of osteoarthritis Osteoarthritis diagnostic data were obtained from the NHANES questionnaire, which assesses arthritis through the question in its codebook: “ Have you ever been told by a doctor or other health care professional that you suffered from arthritis?” The response options were “yes” or “no”, and if they answered “yes”, the staff member would further ask “What kind of arthritis”, with options of “osteoarthritis”, “rheumatoid arthritis”, “psoriatic arthritis”, “other”, “don't know”, and “refused”. If the participant answered “osteoarthritis”, then he/she was diagnosed with osteoarthritis[ 18 , 19 ]. 2.4.Covariates Based on previous literature, we used covariates including demographic data, laboratory data, examination data, and questionnaire data to ensure the validity and robustness of the findings. Demographic information included age, sex, race, education level, marital status, and income-to-poverty ratio. Anthropometric data included body mass index (BMI), and waist. Laboratory data included c-reactive protein (CRP), and uric acid. Finally, questionnaire data included smoking status, drinking status, vigorous recreational activities, stroke, cardiovascular disease, hypertension, diabetes, and cancer. 2.5.Statistical analysis According to the NCHS analysis guidelines, we weighted the data using sample weights in our analysis. Statistical analyses in this investigation were conducted using R software(Version 4.3.2). Significance was determined at a p-value of less than 0.05, utilizing a two-tailed test. The representation of continuous variables was in the form of mean values (standard error), while categorical variables were reported in percentage terms. The relationship between LBM and osteoarthritis was quantified using odds ratios (OR) and their 95% confidence intervals (CI). Multivariable logistic regression models were systematically developed to explore the independent influence of LBM on the incidence of osteoarthritis. Model 1 did not adjust for any covariates, model 2 adjusted only for age, sex, and race, while model 3 adjusted for all covariates. Moreover, we conducted linear trend tests across quartiles of LBM. The restricted cubic splines (RCS) were employed to examine the non-linear associations between LBM and osteoarthritis, further investigating the dose-response relationship between them. Upon detecting a nonlinear relationship, we employed a recursive algorithm to calculate inflection points and created a two-part binary logistic regression model on either side of the inflection point. 3.Results 3.1.Baseline Characterization The demographic characteristics of the study population based on LBM quartiles can be seen in Table 1 . The mean age of the study population was 47.19 (0.22) years, of which 48.33% were males and 51.67% were females. People who possessed higher LBM had a higher likelihood of being male, married or living with a partner. Moreover, LBM was positively associated with physical activity, BMI, waist circumference, uric acid, and CRP, and negatively associated with age, cancer and osteoarthritis. Table 1 Characteristics of the study population Variable Total Q1 Q2 Q3 Q4 P Age (years) 47.19(0.22) 50.03(0.36) 47.84(0.29) 46.18(0.32) 45.16(0.28) < 0.0001 Sex (%) < 0.0001 Men 48.33(0.01) 1.22(0.13) 19.43(0.63) 71.29(0.74) 92.20(0.40) Women 51.67(0.01) 98.78(0.13) 80.57(0.63) 28.71(0.74) 7.80(0.40) Race/ethnicity (%) < 0.0001 Mexican American 8.62(0.01) 9.63(0.84) 8.22(0.76) 9.53(0.86) 7.36(0.71) White 66.12(0.03) 63.90(1.49) 65.53(1.54) 63.66(1.61) 70.51(1.41) Black 11.17(0.01) 6.11(0.46) 12.34(0.84) 12.98(0.89) 12.81(0.86) Other 14.10(0.01) 20.36(1.02) 13.92(0.77) 13.84(0.76) 9.32(0.55) Education level (%) < 0.0001 Less than high school 5.27(0.00) 7.03(0.43) 5.84(0.34) 5.86(0.34) 2.86(0.26) High school 33.39(0.01) 31.15(0.92) 33.40(0.87) 35.91(1.00) 33.19(1.03) More than high school 61.28(0.02) 61.81(1.10) 60.76(1.00) 58.24(1.09) 63.95(1.12) Marital status (%) < 0.0001 Married /Living with partner 63.41(0.02) 60.14(0.86) 61.26(0.90) 63.02(0.80) 68.30(0.96) Divorced/separated/widowed 18.08(0.00) 24.58(0.58) 21.92(0.58) 14.78(0.49) 12.29(0.57) Never married 18.48(0.01) 15.27(0.73) 16.81(0.71) 22.20(0.81) 19.41(0.83) Smoking status (%) < 0.0001 Current 19.80(0.01) 17.10(0.80) 19.52(0.59) 23.71(0.75) 19.00(0.57) Former 24.40(0.01) 18.45(0.62) 21.91(0.82) 26.30(0.68) 29.85(0.81) Never 55.76(0.01) 64.45(0.88) 58.58(0.83) 49.99(0.95) 51.15(0.87) Drinking status (%) < 0.0001 Never 9.83(0.00) 17.31(0.71) 12.47(0.62) 8.56(0.54) 6.28(0.57) Former 11.40(0.00) 12.31(0.57) 12.68(0.51) 13.78(0.59) 11.65(0.57) Current 69.52(0.02) 70.38(1.01) 74.85(0.90) 77.67(0.87) 82.07(0.83) Physical activity (%) < 0.0001 Yes 78.54(0.02) 74.76(0.73) 75.38(0.79) 80.41(0.71) 82.77(0.61) No 21.46(0.01) 25.24(0.73) 24.62(0.79) 19.59(0.71) 17.23(0.61) Stroke (%) 0.002 Yes 2.79(0.00) 3.31(0.32) 2.92(0.22) 2.95(0.22) 2.11(0.17) No 97.12(0.02) 96.69(0.32) 97.08(0.22) 97.05(0.22) 97.89(0.17) CVD (%) 0.01 Yes 8.29(0.00) 7.35(0.47) 7.85(0.40) 9.38(0.43) 8.52(0.42) No 91.70(0.02) 92.65(0.47) 92.15(0.40) 90.62(0.43) 91.48(0.42) Hypertension (%) < 0.0001 Yes 37.40(0.01) 32.55(0.81) 36.12(0.81) 35.57(0.88) 44.00(0.86) No 62.60(0.01) 67.45(0.81) 63.88(0.81) 64.43(0.88) 56.00(0.86) Diabetes (%) < 0.0001 Yes 10.32(0.00) 6.75(0.34) 11.02(0.45) 10.78(0.39) 12.64(0.47) No 88.58(0.02) 93.25(0.34) 88.98(0.45) 89.22(0.39) 87.36(0.47) Cancer(%) < 0.0001 Yes 10.11(0.00) 12.18(0.50) 10.54(0.47) 9.83(0.41) 8.30(0.46) No 89.81(0.02) 87.82(0.50) 89.46(0.47) 90.17(0.41) 91.70(0.46) Osteoarthritis (%) < 0.0001 Yes 11.62(0.00) 13.81(0.54) 12.98(0.66) 10.06(0.49) 9.96(0.48) No 88.38(0.02) 86.19(0.54) 87.02(0.66) 89.94(0.49) 90.04(0.48) BMI(kg/m2) 28.94(0.08) 24.05(0.06) 28.57(0.09) 29.13(0.12) 33.12(0.13) < 0.0001 Waist(cm) 99.14(0.22) 85.44(0.20) 96.99(0.22) 99.97(0.26) 111.52(0.29) < 0.0001 Income-to-poverty ratio 3.00(0.04) 2.94(0.04) 2.86(0.04) 2.86(0.04) 3.26(0.04) < 0.0001 Uric acid (mg/dl) 5.41(0.01) 4.53(0.02) 4.99(0.02) 5.67(0.02) 6.25(0.02) < 0.0001 CRP(mg/dl) 2.14(0.07) 1.63(0.11) 2.18(0.11) 2.33(0.12) 2.36(0.12) < 0.0001 CVD, cardiovascular disease; BMI, body mass index; CRP, c-reactive protein. 3.2.Univariate analysis The association of covariates with osteoarthritis in the study is shown in Table 2 . Age, gender, education level, marital status, physical activity, income-to-poverty ratio, hypertension, cardiovascular disease, stroke, diabetes, cancer, waist, BMI,CRP, and uric acid were statistically associated with osteoarthritis. Compared to men, women were more likely to develop osteoarthritis [1.78(1.65,1.92)]. Osteoarthritis is more likely to occur in older individuals, those with higher levels of education, those who are divorced, separated, or widowed, and those with cancer, diabetes, hypertension, cardiovascular disease, or stroke. In addition, the development of osteoarthritis was positively associated with waist circumference, BMI, CRP, uric acid, and income-to-poverty ratio. Table 2 Association between covariates and osteoarthritis OR (95% CI) P Age 1.06 (1.06, 1.06) < 0.0001 Sex Men 1.0 Women 1.78 (1.65, 1.92) < 0.0001 Race/ethnicity Mexican American 1.0 White 3.08 (2.69, 3.51) < 0.0001 Black 1.39 (1.19, 1.63) < 0.0001 Other 1.26 (1.08, 1.47) 0.0029 Education level Less than high school 1.0 High school 1.33 (1.15, 1.53) 0.0001 More than high school 1.53 (1.33, 1.76) < 0.0001 Marital status Married/Living with partner 1.0 Divorced/separated/widowed 1.64 (1.52, 1.78) < 0.0001 Never married 0.39 (0.34, 0.45) <0.0001 Smoking status Current 1.0 Former 1.86 (1.67, 2.07) < 0.0001 Never 0.99 (0.89, 1.09) 0.8023 Drinking status Current 1.0 Former 1.45 (1.27, 1.65) < 0.0001 Never 0.95 (0.85, 1.07) 0.4094 Physical activity Yes 1.0 No 1.58 (1.46, 1.71) < 0.0001 CVD Yes 1.0 No 0.38 (0.35, 0.42) < 0.0001 Stroke Yes 1.0 No 0.47 (0.40, 0.54) < 0.0001 Diabetes Yes 1.0 No 0.53 (0.48, 0.57) < 0.0001 Hypertension Yes 1.0 No 0.32 (0.29, 0.34) < 0.0001 Cancer Yes 1.0 No 0.35 (0.32, 0.39) < 0.0001 BMI 1.04 (1.03, 1.04) < 0.0001 Waist 1.02 (1.02, 1.02) < 0.0001 Income-to-poverty ratio 1.09 (1.06, 1.11) < 0.0001 CRP 1.02 (1.01, 1.03) < 0.0001 Uric acid 1.04 (1.01, 1.07) 0.0028 CVD, cardiovascular disease; BMI, body mass index; CRP, c-reactive protein. 3.3.Relationship Between lean body mass and osteoarthritis We developed three models to explore the association between LBM and osteoarthritis ( Table 3 ) . The OR (95% CI) was 0.89 (0.86,0.93) and 1.00 (0.96,1.04) in Model 1 and Model 2. After correcting for all covariates, a negative relationship was found [0.74 (0.67,0.80)]. The linear trend test using LBM quartiles examined whether this relationship was stable.Using Q1 as a reference, the OR(95% CI) for Q2, Q3, and Q4 were 0.72 (0.61,0.85), 0.54 (0.44,0.65), and 0.45 (0.36,0.56), respectively. Table 3 Association between lean body mass and osteoarthritis Exposure Model 1 OR (95% CI) Model 2 OR (95% CI) Model 3 OR (95% CI) LBM. Z score 0.89 (0.86, 0.93) 1.00 (0.96, 1.04) 0.74 (0.67, 0.80) Q1 1.0 1.0 1.0 Q2 0.90 (0.82, 0.99) 1.00 (0.90, 1.12) 0.72 (0.61, 0.85) Q3 0.73 (0.66, 0.81) 0.84 (0.75, 0.94) 0.54 (0.44, 0.65) Q4 0.74 (0.67, 0.82) 0.96 (0.86, 1.08) 0.45 (0.36, 0.56) P for trend < 0.0001 0.0807 <0.0001 Sex Men 1.21 (1.13, 1.30) 1.15 (1.07, 1.23) 0.87 (0.71, 1.07) Women 1.10 (1.03, 1.18) 1.07 (1.00, 1.16) 0.43 (0.33, 0.55) Age < 60 1.13 (1.07, 1.20) 1.10 (1.03, 1.17) 0.82 (0.72, 0.94) ≥ 60 0.89 (0.84, 0.93) 0.88 (0.83, 0.93) 0.67 (0.60, 0.76) Model 1: no covariates were adjusted.. Model 2:.age, sex, race were adjusted.. Model 3:age, sex, race, education level, income-to-poverty ratio, marital status, smoking status, drinking status, vigorous recreational activity, BMI, waist, stroke, diabetes, CVD, cancer, hypertension, uric acid, and CRP were adjusted. In the subgroup analysis stratified by age or sex, the model is not adjusted for the stratification variable itself. The RCS was developed to better elucidate the dose-response relationship of LBM with osteoarthritis, and a non-linear correlation was observed between LBM and osteoarthritis ( Fig. 2 ) . We then calculated the inflection point 52.26 kg by recursive algorithm ( Table 4 ) . The effect value of the relationship was 0.56 (0.48,0.64) before the inflection point. After the inflection point, the difference was not statistically significant [0.90(0.80,1.01)]. In addition, we explored if there were similar associations existed in subgroups. After plotting stratified RCS, the non-linear association, consistent with the overall trend, was also evident among women and those over 60 years of age ( Figs. 3 , 4 ). Table 4 Threshold effect analysis of lean body mass on osteoarthritis using a two-piecewise linear regression model Osteoarthritis Adjust OR (95% CI) P -value LBM Fitting by standard linear model 0.74 (0.67, 0.80) < 0.0001 Fitting by two-piecewise linear model Inflection point 52.26 < 52.26 0.56 (0.48, 0.64) 52.26 0.90 (0.80, 1.01) 0.0679 Log-likelihood ratio < 0.001 Age, sex, race, education level, income-to-poverty ratio, marital status, smoking status, drinking status, vigorous recreational activity, BMI, waist, stroke, diabetes, CVD, cancer, hypertension, uric acid, and CRP were adjusted. 4. Discussion In this cross-sectional study, we explored the association between LBM and osteoarthritis. In regression models adjusted for all covariates, we observed a non-linear relationship between LBM and osteoarthritis, with an inflection point of 52.26 kg. Before the inflection point, LBM was negatively associated with osteoarthritis, whereas after the inflection point, the association was not statistically significant. In addition, stratified analysis by gender and age revealed that this non-linear relationship was also present among women and those over 60 years of age. Although the relationship between LBM and osteoarthritis has been explored, their results are not consistent and their small sample sizes have lacked further analysis of stratification. A meta-analysis of osteoarthritis and body composition suggested that LBM was positively associated with osteoarthritis[ 20 ]. Contradictory to this, a Swedish randomized clinical trial showed that patients with osteoarthritis of the knee phenotypically exhibited a low proportion of whole-body LBM[ 21 ]. Whereas, a study of middle-aged and older Korean adults showed that lower extremity LBM was negatively correlated with radiographic osteoarthritis of the knee[ 22 ]. In contrast, our study found a non-linear relationship and a negative correlation before the inflection point. Although the exact mechanisms between LBM and osteoarthritis are unknown, there are some studies that appear to provide a possible explanation for the correlation between LBM and osteoarthritis. One study suggests that this relationship between LBM and osteoarthritis may be due to the effect of muscle on oxidative stress in articular cartilage[ 23 ]. LBM gain enhances the body's antioxidant defense mechanisms, which in turn reduces damage to articular cartilage from oxidative stress[ 24 ]. Studies have shown that oxidative stress is one of the main causes of cartilage matrix degradation and chondrocyte apoptosis, whereas the secretion of antioxidant enzymes such as superoxide dismutase (SOD) and glutathione peroxidase (GPx) in muscle tissues is increased during exercise and these enzymes are effective in scavenging reactive oxygen species (ROS) in the body, protecting articular cartilage from oxidative stress damage[ 25 , 26 ]. Another study showed that LBM increase may affect chondrocyte metabolism and promote cartilage matrix synthesis[ 27 ]. Increased levels of insulin-like growth factor-1 (IGF-1) secreted by muscle during LBM gain promote chondrocyte proliferation and matrix synthesis and inhibit cartilage matrix degradation[ 28 , 29 ]. At the same time, LBM gain may imply an increase in muscle mass, and evidence suggests that enhanced muscle strength improves the distribution of mechanical loads on the joints, improves joint stability, and reduces wear and degradation of the articular cartilage[ 30 ]. In the joints of the lower extremity such as in the knee, muscles can help absorb impact forces and reduce cartilage damage. In summary, the relationship between lean body mass and osteoarthritis still has many unknown underlying mechanisms that need more research to further elucidate. There were several advantages of the present study over previous studies. First, this study included 31,172 individuals, which was the largest sample size to date. Second, NHANES included a representative population of non-institutionalized American civilians, so our findings would be broadly applicable to the entire American population. Third, NHANES has comprehensive health data collection, enabling researchers to conduct more sophisticated analyses of outcomes and associations among various factors. There are also some limitations to this study. First, some self-reported data may be subject to recall bias. Second, NHANES is a cross-sectional survey, and the data were collected at a specific point in time, limiting the ability to make causal inferences. Moreover, the dynamics of population health and nutritional status over time may not be adequately captured by cross-sectional data. In conclusion, we found a non-linear association between LBM and osteoarthritis in this cross-sectional study of the general U.S. adults. Differences in age and gender were further explored. Our study might provide information for public health policy makers and clinicians to intervene in osteoarthritis by controlling body measurements. Further randomized controlled trials are needed to verify this finding. Declarations Author contributions HL: Data curation, Writing-original draft, and Formal Analysis. ZZ and GW：Methodology, Visualization, and Validation. MZ and CY: Project administration, Writing-editing, and Visualization. DW: Supervision, Funding acquisition, Writing-review & editing. All authors reviewed the manuscript. Ethics approval The NHANES protocol was approved by the National Center for Health Statistics of the Institutional Review Board. Informed Consent Statement Informed consent was obtained from all subjects involved in the study Funding This work was supported by Construction of PhD Research and Innovation Team in Acupuncture and Moxibustion Discipline (grant numbers 2017sit01). Consent for publication Not applicable. Competing Interests The authors declare that there are no conflicts of interest regarding the publication of this paper. Availability of data and material The datasets used and/or analyzed during the current study are publicly available from the NHANES database, https://wwwn.cdc.gov/nchs/nhanes. Acknowledgements Not applicable References Hunter DJ, Bierma-Zeinstra S. Osteoarthritis. Lancet. 2019;393(10182):1745-59. doi: 10.1016/S0140-6736(19)30417-9. Yu H, Huang T, Lu WW, Tong L, Chen D. Osteoarthritis Pain. Int J Mol Sci. 2022;23(9). doi: 10.3390/ijms23094642. Hunter DJ, March L, Chew M. Osteoarthritis in 2020 and beyond: a Lancet Commission. Lancet. 2020;396(10264):1711-2. doi: 10.1016/S0140-6736(20)32230-3. Barnett R. Osteoarthritis. Lancet. 2018;391(10134):1985. doi: 10.1016/S0140-6736(18)31064-X. Hunter DJ, Schofield D, Callander E. The individual and socioeconomic impact of osteoarthritis. Nat Rev Rheumatol. 2014;10(7):437-41. doi: 10.1038/nrrheum.2014.44. Silverwood V, Blagojevic-Bucknall M, Jinks C, Jordan JL, Protheroe J, Jordan KP. Current evidence on risk factors for knee osteoarthritis in older adults: a systematic review and meta-analysis. Osteoarthritis Cartilage. 2015;23(4):507-15. doi: 10.1016/j.joca.2014.11.019. Mork PJ, Holtermann A, Nilsen TI. Effect of body mass index and physical exercise on risk of knee and hip osteoarthritis: longitudinal data from the Norwegian HUNT Study. J Epidemiol Community Health. 2012;66(8):678-83. doi: 10.1136/jech-2011-200834. He Y, Zheng C, He MH, Huang JR. The Causal Relationship Between Body Mass Index and the Risk of Osteoarthritis. Int J Gen Med. 2021;14:2227-37. doi: 10.2147/IJGM.S314180. Pischon T. Commentary: Use of the body mass index to assess the risk of health outcomes: time to say goodbye? Int J Epidemiol. 2010;39(2):528-9. doi: 10.1093/ije/dyp388. Javed A, Jumean M, Murad MH, Okorodudu D, Kumar S, Somers VK, et al. Diagnostic performance of body mass index to identify obesity as defined by body adiposity in children and adolescents: a systematic review and meta-analysis. Pediatr Obes. 2015;10(3):234-44. doi: 10.1111/ijpo.242. Stern L, Iqbal N, Seshadri P, Chicano KL, Daily DA, McGrory J, et al. The effects of low-carbohydrate versus conventional weight loss diets in severely obese adults: one-year follow-up of a randomized trial. Ann Intern Med. 2004;140(10):778-85. doi: 10.7326/0003-4819-140-10-200405180-00007. Willoughby D, Hewlings S, Kalman D. Body Composition Changes in Weight Loss: Strategies and Supplementation for Maintaining Lean Body Mass, a Brief Review. Nutrients. 2018;10(12). doi: 10.3390/nu10121876. Lee DH, Keum N, Hu FB, Orav EJ, Rimm EB, Willett WC, et al. Predicted lean body mass, fat mass, and all cause and cause specific mortality in men: prospective US cohort study. BMJ. 2018;362:k2575. doi: 10.1136/bmj.k2575. Bann D, Kuh D, Wills AK, Adams J, Brage S, Cooper R, et al. Physical activity across adulthood in relation to fat and lean body mass in early old age: findings from the Medical Research Council National Survey of Health and Development, 1946-2010. Am J Epidemiol. 2014;179(10):1197-207. doi: 10.1093/aje/kwu033. Cordoba-Rodriguez DP, Iglesia I, Gomez-Bruton A, Rodriguez G, Casajus JA, Morales-Devia H, et al. Fat-free/lean body mass in children with insulin resistance or metabolic syndrome: a systematic review and meta-analysis. BMC Pediatr. 2022;22(1):58. doi: 10.1186/s12887-021-03041-z. Jeong SM, Lee DH, Giovannucci EL. Predicted lean body mass, fat mass and risk of lung cancer: prospective US cohort study. Eur J Epidemiol. 2019;34(12):1151-60. doi: 10.1007/s10654-019-00587-2. Lee DH, Keum N, Hu FB, Orav EJ, Rimm EB, Sun Q, et al. Development and validation of anthropometric prediction equations for lean body mass, fat mass and percent fat in adults using the National Health and Nutrition Examination Survey (NHANES) 1999-2006. Br J Nutr. 2017;118(10):858-66. doi: 10.1017/S0007114517002665. Chen S, Han H, Jin J, Zhou G, Li Z. Osteoarthritis and sarcopenia-related traits: the cross-sectional study from NHANES 2011-2014 and Mendelian randomization study. J Orthop Surg Res. 2023;18(1):502. doi: 10.1186/s13018-023-03960-w. Xiao Q, Cai B, Yin A, Huo H, Lan K, Zhou G, et al. L-shaped association of serum 25-hydroxyvitamin D concentrations with cardiovascular and all-cause mortality in individuals with osteoarthritis: results from the NHANES database prospective cohort study. BMC Med. 2022;20(1):308. doi: 10.1186/s12916-022-02510-1. Long H, Xie D, Zeng C, Wei J, Wang Y, Yang T, et al. Association between body composition and osteoarthritis: A systematic review and meta-analysis. Int J Rheum Dis. 2019;22(12):2108-18. doi: 10.1111/1756-185X.13719. Karlsson MK, Magnusson H, Coster M, Karlsson C, Rosengren BE. Patients with knee osteoarthritis have a phenotype with higher bone mass, higher fat mass, and lower lean body mass. Clin Orthop Relat Res. 2015;473(1):258-64. doi: 10.1007/s11999-014-3973-3. Kim SR, Choi KH, Jung GU, Shin D, Kim K, Park SM. Associations Between Fat Mass, Lean Mass, and Knee Osteoarthritis: The Fifth Korean National Health and Nutrition Examination Survey (KNHANES V). Calcif Tissue Int. 2016;99(6):598-607. doi: 10.1007/s00223-016-0190-y. Powers SK, Jackson MJ. Exercise-induced oxidative stress: cellular mechanisms and impact on muscle force production. Physiol Rev. 2008;88(4):1243-76. doi: 10.1152/physrev.00031.2007. Dos Santos JM, Rodrigues Lacerda AC, Ribeiro VGC, Scheidt Figueiredo PH, Fonseca SF, da Silva Lage VK, et al. Oxidative Stress Biomarkers and Quality of Life Are Contributing Factors of Muscle Pain and Lean Body Mass in Patients with Fibromyalgia. Biology (Basel). 2022;11(6). doi: 10.3390/biology11060935. Henrotin Y, Kurz B, Aigner T. Oxygen and reactive oxygen species in cartilage degradation: friends or foes? Osteoarthritis Cartilage. 2005;13(8):643-54. doi: 10.1016/j.joca.2005.04.002. Riegger J, Schoppa A, Ruths L, Haffner-Luntzer M, Ignatius A. Oxidative stress as a key modulator of cell fate decision in osteoarthritis and osteoporosis: a narrative review. Cell Mol Biol Lett. 2023;28(1):76. doi: 10.1186/s11658-023-00489-y. Park S, Shin BK. Intermittent fasting with a high-protein diet mitigated osteoarthritis symptoms by increasing lean body mass and reducing inflammation in osteoarthritic rats with Alzheimer's disease-like dementia. Br J Nutr. 2022;127(1):55-67. doi: 10.1017/S0007114521000829. Ohlsson C, Mohan S, Sjogren K, Tivesten A, Isgaard J, Isaksson O, et al. The role of liver-derived insulin-like growth factor-I. Endocr Rev. 2009;30(5):494-535. doi: 10.1210/er.2009-0010. Wen C, Xu L, Xu X, Wang D, Liang Y, Duan L. Insulin-like growth factor-1 in articular cartilage repair for osteoarthritis treatment. Arthritis Res Ther. 2021;23(1):277. doi: 10.1186/s13075-021-02662-0. Andriacchi TP, Mundermann A. The role of ambulatory mechanics in the initiation and progression of knee osteoarthritis. Curr Opin Rheumatol. 2006;18(5):514-8. doi: 10.1097/01.bor.0000240365.16842.4e. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4635092","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":322962497,"identity":"2ca4c339-d4ba-468b-96df-15ac35cf6b8f","order_by":0,"name":"Hongrui Lu","email":"","orcid":"","institution":"The Second Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hongrui","middleName":"","lastName":"Lu","suffix":""},{"id":322962498,"identity":"0ad9f143-41b6-46ad-ab9d-f9637c8c802e","order_by":1,"name":"Zifan Zhuang","email":"","orcid":"","institution":"Guang'anmen Hospital, China Academy of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zifan","middleName":"","lastName":"Zhuang","suffix":""},{"id":322962505,"identity":"3235d248-228a-417e-9b81-2c8d7722fa73","order_by":2,"name":"Gengjian Wang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Gengjian","middleName":"","lastName":"Wang","suffix":""},{"id":322962508,"identity":"41f68b35-d41f-4f9e-a79a-550f1a9f36b0","order_by":3,"name":"Mengyun Zhang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Mengyun","middleName":"","lastName":"Zhang","suffix":""},{"id":322962512,"identity":"9f092406-d52c-4b2e-a664-ebcc4995f957","order_by":4,"name":"Chang Yang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chang","middleName":"","lastName":"Yang","suffix":""},{"id":322962513,"identity":"09bb3258-a4d6-4b8a-acee-efd836fd0bce","order_by":5,"name":"Dongyan Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqUlEQVRIiWNgGAWjYDCCAyCCjUGOjb39ANFaGBuAWoz5eM4kkKYlcZ6EgwFxOviONz9/zFN2OL1NgiGB4UfFNsJaJM8cM2zmOXc4t0268QBjz5nbhLUY3MhhbM5tA2qROZDAzNhGgpZ0NokEA9K0JBCvBeSX2X/OpRu2AQP5IFF+AYbYg48zyqzl5dvbDz74UUGEFihoBpMHiFYPBHWkKB4Fo2AUjIKRBgAxpUC5rt9g8QAAAABJRU5ErkJggg==","orcid":"","institution":"The Second Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Dongyan","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-06-25 09:06:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4635092/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4635092/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60680810,"identity":"88edeb97-0f4c-4590-8b69-29248e3cfa40","added_by":"auto","created_at":"2024-07-19 12:23:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":225735,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of population selection in this study.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4635092/v1/50d1f5001578f2aa23a4ba9b.png"},{"id":60680811,"identity":"f3cabdc6-e716-40b4-94ef-f5cd4bd5de1b","added_by":"auto","created_at":"2024-07-19 12:23:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":391734,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between lean body mass and osteoarthritis. Age, sex, race, education level, income-to-poverty ratio, marital status, smoking status, drinking status, vigorous recreational activity, BMI, waist, stroke, diabetes, CVD, cancer, hypertension, uric acid, and CRP were adjusted.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4635092/v1/06c9d5dc5d2cde4726ca8f61.png"},{"id":60680812,"identity":"e5ea7e4d-5f10-490a-a0d4-bf1dbc87fe90","added_by":"auto","created_at":"2024-07-19 12:23:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":633257,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between lean body mass and osteoarthritis stratified by sex (A: men; B: women).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4635092/v1/51c66fb150b6bdc3e0a132cb.png"},{"id":60680809,"identity":"623c1e56-ea49-4244-8244-e4ebdfc751a3","added_by":"auto","created_at":"2024-07-19 12:23:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":674734,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between lean body mass and osteoarthritis stratified by age (A: age \u0026lt; 60; B: age ≥ 60).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4635092/v1/00d074d1a21afde46e2b07b1.png"},{"id":62857932,"identity":"f44acc9b-d7b3-4a18-956a-11231f02e459","added_by":"auto","created_at":"2024-08-20 09:51:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2592659,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4635092/v1/52dadbe6-f50b-4ba1-9260-ed8a9ff3f2aa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between lean body mass and osteoarthritis: a cross-sectional study from the NHANES 2007-2018","fulltext":[{"header":"1.Introduction","content":"\u003cp\u003eAmong the elderly, osteoarthritis is the most common joint disease and a leading cause of disability[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Its clinical features include joint pain, stiffness, and restricted mobility, which in severe cases can lead to joint deformity and loss of function[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The trends of an aging population and increasing obesity are likely to compound this[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The prevalence of osteoarthritis is 10% in males and 18% in females aged over 60[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In various high-income countries, the medical cost of osteoarthritis is estimated to account for between 1% and 2.5% of the country's gross domestic product[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Osteoarthritis has a variety of moderate-to-strong risk factors, including female sex, obesity, and previous injury[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eObesity is a major risk factor for osteoarthritis. A longitudinal cohort study has demonstrated that a high body mass index (BMI) significantly increased the incidence of knee osteoarthritis and was positively associated with the risk of severe osteoarthritis[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Additionally, a Mendelian randomization study supported a causal relationship between BMI and osteoarthritis, which used genetic variants as instrumental variables to minimize the impact of confounding factors, thereby more clearly delineating the direct effects of BMI on osteoarthritis[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, recent studies have questioned the efficacy of BMI as an indicator for assessing obesity[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. While BMI is widely used to evaluate an individual's weight status, its limitations lie in that it only reflects the ratio of weight to height and does not accurately distinguish between the distribution of body fat and lean body[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Therefore, future research and clinical evaluations should place greater emphasis on comprehensive measurements of body composition to more accurately assess the impact of obesity on osteoarthritis. This not only aids in a better understanding of the etiology of osteoarthritis but also provides a scientific basis for developing more effective prevention and intervention strategies.\u003c/p\u003e \u003cp\u003eLean body mass (LBM) serves as a crucial indicator of body composition, more accurately reflecting the proportion of lean mass in the human body[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Higher levels of LBM are typically associated with better metabolic health and physical activity capacity[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Recent studies have discovered significant associations between LBM and various metabolic diseases, such as cardiovascular disease, lung cancer, and diabetes[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, there are relatively few studies on the relationship between LBM and osteoarthritis, and the results of the available evidence are mostly inconsistent. Understanding the relationship between LBM and osteoarthritis may aid in better prevention of the disease through weight management. Therefore, we used the data from the National Health and Nutrition Examination Survey (NHANES) to examine the potential association between LBM and osteoarthritis. Our goal is to provide new evidence to support weight intervention strategies for reducing the risk of osteoarthritis.\u003c/p\u003e"},{"header":"2.Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1.Study population\u003c/h2\u003e \u003cp\u003eThis study used NHANES dataset, a continuous health and nutrition survey conducted by the Centers for Disease Control and Prevention (CDC).To ensure national representativeness, stratified multistage probability samples were used. Its protocols were all approved by the National Center for Health Statistics Research Ethics Review Board, and the informed consents were provided by all participants. Based on NHANES data from six cycles, we conducted our study. The total number of study participants who finally completed demographic surveys, laboratory tests, and health status questionnaires was 59,842. The following are the exclusion criteria: (1) Age less than 20 years (n\u0026thinsp;=\u0026thinsp;25072); (2)Missing LBM data (n\u0026thinsp;=\u0026thinsp;3534); (3) Missing osteoarthritis data (n\u0026thinsp;=\u0026thinsp;64). Ultimately, a total of 31,172 participants were recruited\u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2.Assessment of lean body mass\u003c/h2\u003e \u003cp\u003eUsing Lee's anthropometric prediction equations, the LBM was calculated[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Dual-energy radiographic data were collected from 15,065 men and 16,107 women. We used multiple linear regression analysis and estimated LBM using sex, age, weight (kg), height (cm), and waist circumference (cm) as predictor variables, and the linear regression model had the highest consistency [LBM (females: R2\u0026thinsp;=\u0026thinsp;0.85; males: R2\u0026thinsp;=\u0026thinsp;0.91)].\u003c/p\u003e \u003cp\u003eMen\u003c/p\u003e \u003cp\u003eLBM\u0026thinsp;=\u0026thinsp;19.363\u0026thinsp;+\u0026thinsp;0.001 * age (year)\u0026thinsp;+\u0026thinsp;0.064 * height (cm)\u0026thinsp;+\u0026thinsp;0.756 * weight (kg)\u0026thinsp;\u0026minus;\u0026thinsp;0.366 * waist circumference (cm)\u0026thinsp;\u0026minus;\u0026thinsp;1.007\u003c/p\u003e \u003cp\u003eWomen\u003c/p\u003e \u003cp\u003eLBM\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;10.683\u0026thinsp;\u0026minus;\u0026thinsp;0.039 * age (year)\u0026thinsp;+\u0026thinsp;0.186 * height (cm)\u0026thinsp;+\u0026thinsp;0.383 * weight (kg)\u0026thinsp;\u0026minus;\u0026thinsp;0.043 * waist circumference (cm)\u0026thinsp;\u0026minus;\u0026thinsp;0.340\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3.Diagnosis of osteoarthritis\u003c/h2\u003e \u003cp\u003eOsteoarthritis diagnostic data were obtained from the NHANES questionnaire, which assesses arthritis through the question in its codebook: \u0026ldquo; Have you ever been told by a doctor or other health care professional that you suffered from arthritis?\u0026rdquo; The response options were \u0026ldquo;yes\u0026rdquo; or \u0026ldquo;no\u0026rdquo;, and if they answered \u0026ldquo;yes\u0026rdquo;, the staff member would further ask \u0026ldquo;What kind of arthritis\u0026rdquo;, with options of \u0026ldquo;osteoarthritis\u0026rdquo;, \u0026ldquo;rheumatoid arthritis\u0026rdquo;, \u0026ldquo;psoriatic arthritis\u0026rdquo;, \u0026ldquo;other\u0026rdquo;, \u0026ldquo;don't know\u0026rdquo;, and \u0026ldquo;refused\u0026rdquo;. If the participant answered \u0026ldquo;osteoarthritis\u0026rdquo;, then he/she was diagnosed with osteoarthritis[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4.Covariates\u003c/h2\u003e \u003cp\u003eBased on previous literature, we used covariates including demographic data, laboratory data, examination data, and questionnaire data to ensure the validity and robustness of the findings. Demographic information included age, sex, race, education level, marital status, and income-to-poverty ratio. Anthropometric data included body mass index (BMI), and waist. Laboratory data included c-reactive protein (CRP), and uric acid. Finally, questionnaire data included smoking status, drinking status, vigorous recreational activities, stroke, cardiovascular disease, hypertension, diabetes, and cancer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5.Statistical analysis\u003c/h2\u003e \u003cp\u003e According to the NCHS analysis guidelines, we weighted the data using sample weights in our analysis. Statistical analyses in this investigation were conducted using R software(Version 4.3.2). Significance was determined at a p-value of less than 0.05, utilizing a two-tailed test. The representation of continuous variables was in the form of mean values (standard error), while categorical variables were reported in percentage terms. The relationship between LBM and osteoarthritis was quantified using odds ratios (OR) and their 95% confidence intervals (CI). Multivariable logistic regression models were systematically developed to explore the independent influence of LBM on the incidence of osteoarthritis. Model 1 did not adjust for any covariates, model 2 adjusted only for age, sex, and race, while model 3 adjusted for all covariates. Moreover, we conducted linear trend tests across quartiles of LBM. The restricted cubic splines (RCS) were employed to examine the non-linear associations between LBM and osteoarthritis, further investigating the dose-response relationship between them. Upon detecting a nonlinear relationship, we employed a recursive algorithm to calculate inflection points and created a two-part binary logistic regression model on either side of the inflection point.\u003c/p\u003e \u003c/div\u003e"},{"header":"3.Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1.Baseline Characterization\u003c/h2\u003e \u003cp\u003eThe demographic characteristics of the study population based on LBM quartiles can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age of the study population was 47.19 (0.22) years, of which 48.33% were males and 51.67% were females. People who possessed higher LBM had a higher likelihood of being male, married or living with a partner. Moreover, LBM was positively associated with physical activity, BMI, waist circumference, uric acid, and CRP, and negatively associated with age, cancer and osteoarthritis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the study population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47.19(0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.03(0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.84(0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.18(0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45.16(0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48.33(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.22(0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.43(0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71.29(0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e92.20(0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.67(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98.78(0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80.57(0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.71(0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.80(0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/ethnicity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.62(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.63(0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.22(0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.53(0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.36(0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.12(0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.90(1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.53(1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63.66(1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70.51(1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.17(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.11(0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.34(0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.98(0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.81(0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.10(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.36(1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.92(0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.84(0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.32(0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.27(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.03(0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.84(0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.86(0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.86(0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.39(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.15(0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.40(0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.91(1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33.19(1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.28(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61.81(1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.76(1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58.24(1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63.95(1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried /Living with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63.41(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.14(0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.26(0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63.02(0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e68.30(0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced/separated/widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.08(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.58(0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.92(0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.78(0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.29(0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.48(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.27(0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.81(0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.20(0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.41(0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.80(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.10(0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.52(0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.71(0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.00(0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.40(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.45(0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.91(0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.30(0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.85(0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55.76(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.45(0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.58(0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49.99(0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51.15(0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking status (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.83(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.31(0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.47(0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.56(0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.28(0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.40(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.31(0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.68(0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.78(0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.65(0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.52(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.38(1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.85(0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77.67(0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e82.07(0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78.54(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74.76(0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.38(0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80.41(0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e82.77(0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.46(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.24(0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.62(0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.59(0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.23(0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.79(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.31(0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.92(0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.95(0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.11(0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97.12(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96.69(0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97.08(0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.05(0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.89(0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.29(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.35(0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.85(0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.38(0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.52(0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91.70(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92.65(0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92.15(0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90.62(0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e91.48(0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.40(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.55(0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.12(0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.57(0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e44.00(0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62.60(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.45(0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.88(0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e64.43(0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e56.00(0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.32(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.75(0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.02(0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.78(0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.64(0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88.58(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93.25(0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.98(0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89.22(0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e87.36(0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.11(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.18(0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.54(0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.83(0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.30(0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89.81(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.82(0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89.46(0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90.17(0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e91.70(0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteoarthritis (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.62(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.81(0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.98(0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.06(0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.96(0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88.38(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86.19(0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87.02(0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89.94(0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e90.04(0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.94(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.05(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.57(0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.13(0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33.12(0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99.14(0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85.44(0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.99(0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e99.97(0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e111.52(0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome-to-poverty ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.00(0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.94(0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.86(0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.86(0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.26(0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.41(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.53(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.99(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.67(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.25(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP(mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.14(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.63(0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.18(0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.33(0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.36(0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eCVD, cardiovascular disease; BMI, body mass index; CRP, c-reactive protein.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2.Univariate analysis\u003c/h2\u003e \u003cp\u003eThe association of covariates with osteoarthritis in the study is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Age, gender, education level, marital status, physical activity, income-to-poverty ratio, hypertension, cardiovascular disease, stroke, diabetes, cancer, waist, BMI,CRP, and uric acid were statistically associated with osteoarthritis. Compared to men, women were more likely to develop osteoarthritis [1.78(1.65,1.92)]. Osteoarthritis is more likely to occur in older individuals, those with higher levels of education, those who are divorced, separated, or widowed, and those with cancer, diabetes, hypertension, cardiovascular disease, or stroke. In addition, the development of osteoarthritis was positively associated with waist circumference, BMI, CRP, uric acid, and income-to-poverty ratio.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between covariates and osteoarthritis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.06 (1.06, 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.78 (1.65, 1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.08 (2.69, 3.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.39 (1.19, 1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.26 (1.08, 1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.33 (1.15, 1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.53 (1.33, 1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/Living with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced/separated/widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.64 (1.52, 1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39 (0.34, 0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.86 (1.67, 2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.89, 1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.45 (1.27, 1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.85, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.58 (1.46, 1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38 (0.35, 0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.47 (0.40, 0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.53 (0.48, 0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.32 (0.29, 0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.35 (0.32, 0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.04 (1.03, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.02, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome-to-poverty ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09 (1.06, 1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.01, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.04 (1.01, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eCVD, cardiovascular disease; BMI, body mass index; CRP, c-reactive protein.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3.Relationship Between lean body mass and osteoarthritis\u003c/h2\u003e \u003cp\u003eWe developed three models to explore the association between LBM and osteoarthritis \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The OR (95% CI) was 0.89 (0.86,0.93) and 1.00 (0.96,1.04) in Model 1 and Model 2. After correcting for all covariates, a negative relationship was found [0.74 (0.67,0.80)]. The linear trend test using LBM quartiles examined whether this relationship was stable.Using Q1 as a reference, the OR(95% CI) for Q2, Q3, and Q4 were 0.72 (0.61,0.85), 0.54 (0.44,0.65), and 0.45 (0.36,0.56), respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between lean body mass and osteoarthritis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1 OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2 OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3 OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLBM. Z score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.89 (0.86, 0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00 (0.96, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.74 (0.67, 0.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.90 (0.82, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00 (0.90, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72 (0.61, 0.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.73 (0.66, 0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84 (0.75, 0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.54 (0.44, 0.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.74 (0.67, 0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96 (0.86, 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45 (0.36, 0.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.21 (1.13, 1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.15 (1.07, 1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.87 (0.71, 1.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.10 (1.03, 1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.07 (1.00, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43 (0.33, 0.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.13 (1.07, 1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.10 (1.03, 1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.82 (0.72, 0.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.89 (0.84, 0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88 (0.83, 0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67 (0.60, 0.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 1: no covariates were adjusted..\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 2:.age, sex, race were adjusted..\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 3:age, sex, race, education level, income-to-poverty ratio, marital status, smoking status, drinking status, vigorous recreational activity, BMI, waist, stroke, diabetes, CVD, cancer, hypertension, uric acid, and CRP were adjusted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eIn the subgroup analysis stratified by age or sex, the model is not adjusted for the stratification variable itself.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe RCS was developed to better elucidate the dose-response relationship of LBM with osteoarthritis, and a non-linear correlation was observed between LBM and osteoarthritis \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. We then calculated the inflection point 52.26 kg by recursive algorithm \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The effect value of the relationship was 0.56 (0.48,0.64) before the inflection point. After the inflection point, the difference was not statistically significant [0.90(0.80,1.01)]. In addition, we explored if there were similar associations existed in subgroups. After plotting stratified RCS, the non-linear association, consistent with the overall trend, was also evident among women and those over 60 years of age\u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThreshold effect analysis of lean body mass on osteoarthritis using a two-piecewise linear regression model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteoarthritis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjust OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFitting by standard linear model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.74 (0.67, 0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFitting by two-piecewise linear model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflection point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 52.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.56 (0.48, 0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 52.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.90 (0.80, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog-likelihood ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAge, sex, race, education level, income-to-poverty ratio, marital status, smoking status, drinking status, vigorous recreational activity, BMI, waist, stroke, diabetes, CVD, cancer, hypertension, uric acid, and CRP were adjusted.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this cross-sectional study, we explored the association between LBM and osteoarthritis. In regression models adjusted for all covariates, we observed a non-linear relationship between LBM and osteoarthritis, with an inflection point of 52.26 kg. Before the inflection point, LBM was negatively associated with osteoarthritis, whereas after the inflection point, the association was not statistically significant. In addition, stratified analysis by gender and age revealed that this non-linear relationship was also present among women and those over 60 years of age.\u003c/p\u003e \u003cp\u003eAlthough the relationship between LBM and osteoarthritis has been explored, their results are not consistent and their small sample sizes have lacked further analysis of stratification. A meta-analysis of osteoarthritis and body composition suggested that LBM was positively associated with osteoarthritis[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Contradictory to this, a Swedish randomized clinical trial showed that patients with osteoarthritis of the knee phenotypically exhibited a low proportion of whole-body LBM[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Whereas, a study of middle-aged and older Korean adults showed that lower extremity LBM was negatively correlated with radiographic osteoarthritis of the knee[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In contrast, our study found a non-linear relationship and a negative correlation before the inflection point.\u003c/p\u003e \u003cp\u003eAlthough the exact mechanisms between LBM and osteoarthritis are unknown, there are some studies that appear to provide a possible explanation for the correlation between LBM and osteoarthritis. One study suggests that this relationship between LBM and osteoarthritis may be due to the effect of muscle on oxidative stress in articular cartilage[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. LBM gain enhances the body's antioxidant defense mechanisms, which in turn reduces damage to articular cartilage from oxidative stress[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Studies have shown that oxidative stress is one of the main causes of cartilage matrix degradation and chondrocyte apoptosis, whereas the secretion of antioxidant enzymes such as superoxide dismutase (SOD) and glutathione peroxidase (GPx) in muscle tissues is increased during exercise and these enzymes are effective in scavenging reactive oxygen species (ROS) in the body, protecting articular cartilage from oxidative stress damage[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Another study showed that LBM increase may affect chondrocyte metabolism and promote cartilage matrix synthesis[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Increased levels of insulin-like growth factor-1 (IGF-1) secreted by muscle during LBM gain promote chondrocyte proliferation and matrix synthesis and inhibit cartilage matrix degradation[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. At the same time, LBM gain may imply an increase in muscle mass, and evidence suggests that enhanced muscle strength improves the distribution of mechanical loads on the joints, improves joint stability, and reduces wear and degradation of the articular cartilage[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In the joints of the lower extremity such as in the knee, muscles can help absorb impact forces and reduce cartilage damage. In summary, the relationship between lean body mass and osteoarthritis still has many unknown underlying mechanisms that need more research to further elucidate.\u003c/p\u003e \u003cp\u003eThere were several advantages of the present study over previous studies. First, this study included 31,172 individuals, which was the largest sample size to date. Second, NHANES included a representative population of non-institutionalized American civilians, so our findings would be broadly applicable to the entire American population. Third, NHANES has comprehensive health data collection, enabling researchers to conduct more sophisticated analyses of outcomes and associations among various factors. There are also some limitations to this study. First, some self-reported data may be subject to recall bias. Second, NHANES is a cross-sectional survey, and the data were collected at a specific point in time, limiting the ability to make causal inferences. Moreover, the dynamics of population health and nutritional status over time may not be adequately captured by cross-sectional data.\u003c/p\u003e \u003cp\u003eIn conclusion, we found a non-linear association between LBM and osteoarthritis in this cross-sectional study of the general U.S. adults. Differences in age and gender were further explored. Our study might provide information for public health policy makers and clinicians to intervene in osteoarthritis by controlling body measurements. Further randomized controlled trials are needed to verify this finding.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHL: Data curation, Writing-original draft, and Formal Analysis. ZZ and GW：Methodology, Visualization, and Validation. MZ and CY: Project administration, Writing-editing, and Visualization. DW: Supervision, Funding acquisition, Writing-review \u0026amp; editing. \u0026nbsp;All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANES protocol was approved by the National Center for Health Statistics of the Institutional Review Board.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all subjects involved in the study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by\u0026nbsp;Construction of PhD Research and Innovation Team in Acupuncture and Moxibustion Discipline (grant numbers 2017sit01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest regarding the publication of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are publicly available from the NHANES database, https://wwwn.cdc.gov/nchs/nhanes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHunter DJ, Bierma-Zeinstra S. Osteoarthritis. Lancet. 2019;393(10182):1745-59. doi: 10.1016/S0140-6736(19)30417-9.\u003c/li\u003e\n\u003cli\u003eYu H, Huang T, Lu WW, Tong L, Chen D. Osteoarthritis Pain. Int J Mol Sci. 2022;23(9). doi: 10.3390/ijms23094642.\u003c/li\u003e\n\u003cli\u003eHunter DJ, March L, Chew M. Osteoarthritis in 2020 and beyond: a Lancet Commission. Lancet. 2020;396(10264):1711-2. doi: 10.1016/S0140-6736(20)32230-3.\u003c/li\u003e\n\u003cli\u003eBarnett R. Osteoarthritis. Lancet. 2018;391(10134):1985. doi: 10.1016/S0140-6736(18)31064-X.\u003c/li\u003e\n\u003cli\u003eHunter DJ, Schofield D, Callander E. The individual and socioeconomic impact of osteoarthritis. Nat Rev Rheumatol. 2014;10(7):437-41. doi: 10.1038/nrrheum.2014.44.\u003c/li\u003e\n\u003cli\u003eSilverwood V, Blagojevic-Bucknall M, Jinks C, Jordan JL, Protheroe J, Jordan KP. Current evidence on risk factors for knee osteoarthritis in older adults: a systematic review and meta-analysis. Osteoarthritis Cartilage. 2015;23(4):507-15. doi: 10.1016/j.joca.2014.11.019.\u003c/li\u003e\n\u003cli\u003eMork PJ, Holtermann A, Nilsen TI. Effect of body mass index and physical exercise on risk of knee and hip osteoarthritis: longitudinal data from the Norwegian HUNT Study. J Epidemiol Community Health. 2012;66(8):678-83. doi: 10.1136/jech-2011-200834.\u003c/li\u003e\n\u003cli\u003eHe Y, Zheng C, He MH, Huang JR. The Causal Relationship Between Body Mass Index and the Risk of Osteoarthritis. Int J Gen Med. 2021;14:2227-37. doi: 10.2147/IJGM.S314180.\u003c/li\u003e\n\u003cli\u003ePischon T. Commentary: Use of the body mass index to assess the risk of health outcomes: time to say goodbye? Int J Epidemiol. 2010;39(2):528-9. doi: 10.1093/ije/dyp388.\u003c/li\u003e\n\u003cli\u003eJaved A, Jumean M, Murad MH, Okorodudu D, Kumar S, Somers VK, et al. Diagnostic performance of body mass index to identify obesity as defined by body adiposity in children and adolescents: a systematic review and meta-analysis. Pediatr Obes. 2015;10(3):234-44. doi: 10.1111/ijpo.242.\u003c/li\u003e\n\u003cli\u003eStern L, Iqbal N, Seshadri P, Chicano KL, Daily DA, McGrory J, et al. The effects of low-carbohydrate versus conventional weight loss diets in severely obese adults: one-year follow-up of a randomized trial. Ann Intern Med. 2004;140(10):778-85. doi: 10.7326/0003-4819-140-10-200405180-00007.\u003c/li\u003e\n\u003cli\u003eWilloughby D, Hewlings S, Kalman D. Body Composition Changes in Weight Loss: Strategies and Supplementation for Maintaining Lean Body Mass, a Brief Review. Nutrients. 2018;10(12). doi: 10.3390/nu10121876.\u003c/li\u003e\n\u003cli\u003eLee DH, Keum N, Hu FB, Orav EJ, Rimm EB, Willett WC, et al. Predicted lean body mass, fat mass, and all cause and cause specific mortality in men: prospective US cohort study. BMJ. 2018;362:k2575. doi: 10.1136/bmj.k2575.\u003c/li\u003e\n\u003cli\u003eBann D, Kuh D, Wills AK, Adams J, Brage S, Cooper R, et al. Physical activity across adulthood in relation to fat and lean body mass in early old age: findings from the Medical Research Council National Survey of Health and Development, 1946-2010. Am J Epidemiol. 2014;179(10):1197-207. doi: 10.1093/aje/kwu033.\u003c/li\u003e\n\u003cli\u003eCordoba-Rodriguez DP, Iglesia I, Gomez-Bruton A, Rodriguez G, Casajus JA, Morales-Devia H, et al. Fat-free/lean body mass in children with insulin resistance or metabolic syndrome: a systematic review and meta-analysis. BMC Pediatr. 2022;22(1):58. doi: 10.1186/s12887-021-03041-z.\u003c/li\u003e\n\u003cli\u003eJeong SM, Lee DH, Giovannucci EL. Predicted lean body mass, fat mass and risk of lung cancer: prospective US cohort study. Eur J Epidemiol. 2019;34(12):1151-60. doi: 10.1007/s10654-019-00587-2.\u003c/li\u003e\n\u003cli\u003eLee DH, Keum N, Hu FB, Orav EJ, Rimm EB, Sun Q, et al. Development and validation of anthropometric prediction equations for lean body mass, fat mass and percent fat in adults using the National Health and Nutrition Examination Survey (NHANES) 1999-2006. Br J Nutr. 2017;118(10):858-66. doi: 10.1017/S0007114517002665.\u003c/li\u003e\n\u003cli\u003eChen S, Han H, Jin J, Zhou G, Li Z. Osteoarthritis and sarcopenia-related traits: the cross-sectional study from NHANES 2011-2014 and Mendelian randomization study. J Orthop Surg Res. 2023;18(1):502. doi: 10.1186/s13018-023-03960-w.\u003c/li\u003e\n\u003cli\u003eXiao Q, Cai B, Yin A, Huo H, Lan K, Zhou G, et al. L-shaped association of serum 25-hydroxyvitamin D concentrations with cardiovascular and all-cause mortality in individuals with osteoarthritis: results from the NHANES database prospective cohort study. BMC Med. 2022;20(1):308. doi: 10.1186/s12916-022-02510-1.\u003c/li\u003e\n\u003cli\u003eLong H, Xie D, Zeng C, Wei J, Wang Y, Yang T, et al. Association between body composition and osteoarthritis: A systematic review and meta-analysis. Int J Rheum Dis. 2019;22(12):2108-18. doi: 10.1111/1756-185X.13719.\u003c/li\u003e\n\u003cli\u003eKarlsson MK, Magnusson H, Coster M, Karlsson C, Rosengren BE. Patients with knee osteoarthritis have a phenotype with higher bone mass, higher fat mass, and lower lean body mass. Clin Orthop Relat Res. 2015;473(1):258-64. doi: 10.1007/s11999-014-3973-3.\u003c/li\u003e\n\u003cli\u003eKim SR, Choi KH, Jung GU, Shin D, Kim K, Park SM. Associations Between Fat Mass, Lean Mass, and Knee Osteoarthritis: The Fifth Korean National Health and Nutrition Examination Survey (KNHANES V). Calcif Tissue Int. 2016;99(6):598-607. doi: 10.1007/s00223-016-0190-y.\u003c/li\u003e\n\u003cli\u003ePowers SK, Jackson MJ. Exercise-induced oxidative stress: cellular mechanisms and impact on muscle force production. Physiol Rev. 2008;88(4):1243-76. doi: 10.1152/physrev.00031.2007.\u003c/li\u003e\n\u003cli\u003eDos Santos JM, Rodrigues Lacerda AC, Ribeiro VGC, Scheidt Figueiredo PH, Fonseca SF, da Silva Lage VK, et al. Oxidative Stress Biomarkers and Quality of Life Are Contributing Factors of Muscle Pain and Lean Body Mass in Patients with Fibromyalgia. Biology (Basel). 2022;11(6). doi: 10.3390/biology11060935.\u003c/li\u003e\n\u003cli\u003eHenrotin Y, Kurz B, Aigner T. Oxygen and reactive oxygen species in cartilage degradation: friends or foes? Osteoarthritis Cartilage. 2005;13(8):643-54. doi: 10.1016/j.joca.2005.04.002.\u003c/li\u003e\n\u003cli\u003eRiegger J, Schoppa A, Ruths L, Haffner-Luntzer M, Ignatius A. Oxidative stress as a key modulator of cell fate decision in osteoarthritis and osteoporosis: a narrative review. Cell Mol Biol Lett. 2023;28(1):76. doi: 10.1186/s11658-023-00489-y.\u003c/li\u003e\n\u003cli\u003ePark S, Shin BK. Intermittent fasting with a high-protein diet mitigated osteoarthritis symptoms by increasing lean body mass and reducing inflammation in osteoarthritic rats with Alzheimer\u0026apos;s disease-like dementia. Br J Nutr. 2022;127(1):55-67. doi: 10.1017/S0007114521000829.\u003c/li\u003e\n\u003cli\u003eOhlsson C, Mohan S, Sjogren K, Tivesten A, Isgaard J, Isaksson O, et al. The role of liver-derived insulin-like growth factor-I. Endocr Rev. 2009;30(5):494-535. doi: 10.1210/er.2009-0010.\u003c/li\u003e\n\u003cli\u003eWen C, Xu L, Xu X, Wang D, Liang Y, Duan L. Insulin-like growth factor-1 in articular cartilage repair for osteoarthritis treatment. Arthritis Res Ther. 2021;23(1):277. doi: 10.1186/s13075-021-02662-0.\u003c/li\u003e\n\u003cli\u003eAndriacchi TP, Mundermann A. The role of ambulatory mechanics in the initiation and progression of knee osteoarthritis. Curr Opin Rheumatol. 2006;18(5):514-8. doi: 10.1097/01.bor.0000240365.16842.4e.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Large-scale, Lean body mass, Osteoarthritis, Cross-sectional study, Stratified analysis","lastPublishedDoi":"10.21203/rs.3.rs-4635092/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4635092/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe prevention of osteoarthritis through controlling body measurements has received increasing attention in recent years, but the relationship between lean body mass (LBM) and osteoarthritis remains unclear. Hence, we used large-scale data to explore this association.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTo explore the correlation between LBM and osteoarthritis, we utilized logistic regression equations to explore associations between covariates, exposures, and outcomes. We used multivariate regression models to further eliminate confounding factors. Restricted cubic splines were employed to investigate non-linear relationships. And the inflection point was identified by recursive algorithm. Furthermore, stratified analyses of gender and age were conducted.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOsteoarthritis was negatively correlated with LBM [0.74 (0.67, 0.80)] in the model adjusting for all confounders. A further linear trend test was performed, and the OR (95% CI) were 0.72 (0.61, 0.85), 0.54 (0.44, 0.65), and 0.45 (0.36, 0.56) for Q2, Q3, and Q4, respectively, compared with Q1. Based on the restricted cubic spline curve, an inflection point of 52.26 kg was found to confirm the non-linear relationship. LBM was negatively correlated with osteoarthritis at 0.66 (0.50, 0.86) before the inflection point, and the association was not statistically significant afterward.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis large-scale study revealed that LBM was non-linearly correlated with osteoarthritis in the general American population. Differences in age and gender were further identified.\u003c/p\u003e","manuscriptTitle":"Association between lean body mass and osteoarthritis: a cross-sectional study from the NHANES 2007-2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-19 12:23:01","doi":"10.21203/rs.3.rs-4635092/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":"8ae12c24-2730-4839-b448-dea5b2518905","owner":[],"postedDate":"July 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-20T09:42:54+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-19 12:23:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4635092","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4635092","identity":"rs-4635092","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}
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