Association Between Physical Activity and Risk of Non-Alcoholic Fatty Liver Disease: The Mediating Role of Insulin Resistance

Association Between Physical Activity and Risk of Non-Alcoholic Fatty Liver Disease: The Mediating Role of Insulin Resistance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association Between Physical Activity and Risk of Non-Alcoholic Fatty Liver Disease: The Mediating Role of Insulin Resistance Zhong Zheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9123706/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: This study aimed to investigate the association between physical activity and the risk of non-alcoholic fatty liver disease (NAFLD), with a specific focus on evaluating the potential mediating role of insulin resistance, quantified using the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR). Methods: Data for this analysis were derived from the National Health and Nutrition Examination Survey (NHANES), comprising 20,194 adults. To address the complex survey design and missing covariate data, analyses incorporated sampling weights and employed multiple imputation, respectively. Multivariable logistic regression models were employed to assess the association between physical activity levels and NAFLD risk. RCS analysis was conducted to examine potential nonlinear relationships. The mediating effect of HOMA-IR was evaluated using the SPSS PROCESS macro with the bootstrap method. Results: Compared to the low physical activity group, the high physical activity group exhibited a significantly reduced risk of NAFLD (adjusted OR = 0.84, P = 0.002). The RCS analysis suggested a nonlinear association between physical activity and NAFLD risk (P for nonlinearity < 0.05), with the lowest risk observed at moderate activity levels and a slight increase at very high levels. Nevertheless, after adjustment for confounders, the overall association trended toward a monotonic decrease. It is notable that the confidence intervals were wider in the high activity range. Mediation analysis revealed that HOMA-IR played a substantial mediating role in the inverse association between physical activity and NAFLD, accounting for 81.96% of the total effect (P < 0.001). Conclusion: Physical activity exerts an independent protective effect against NAFLD, an effect largely mediated by the amelioration of insulin resistance. Increasing physical activity levels, particularly to achieve and sustain a moderate-to-high intensity, may represent a significant public health strategy for mitigating the population-level risk of NAFLD. Non-alcoholic Fatty Liver Disease(NAFLD) Physical Activity HOMA-IR Mediation Analysis Figures Figure 1 Figure 2 1. Introduction Non-alcoholic fatty liver disease (NAFLD) is emerging as a predominant cause of chronic liver disease worldwide[1]. Epidemiological studies indicate a continuously rising prevalence, with the global pooled prevalence reaching approximately 30% (1991–2019) and showing an accelerated increase in recent years[2].This trend is largely attributed to the parallel rise in global rates of obesity and type 2 diabetes[3]. Without effective interventions to curb this trend, the prevalence of NAFLD is projected to increase further.The growing burden of NAFLD is associated not only with impaired health-related quality of life but also with significant socioeconomic costs due to reduced work productivity and increased healthcare utilization. Implementing therapeutic interventions in patients at risk for NAFLD progression is crucial for preventing or reversing the adverse outcomes associated with the transition from simple steatosis to non-alcoholic steatohepatitis (NASH)[4]. Among various lifestyle interventions, physical activity is widely regarded as a key strategy for both the primary prevention and management of NAFLD[5]. However, the underlying biological mechanisms through which physical activity confers its hepatoprotective effects remain incompletely elucidated. Patients with NAFLD are often obese and/or diabetic, and insulin resistance is considered a key pathogenic factor in the development and progression of NAFLD[6]. Furthermore, research has found that regular physical activity can effectively reduce insulin resistance[7]. This suggests that the amelioration of insulin resistance may constitute a critical mediating pathway linking physical activity to a reduced risk of NAFLD. Despite the biological plausibility of this hypothesis, studies that quantitatively assess this mediating pathway in nationally representative, large-scale population-based samples remain limited. Therefore, this study aimed to investigate the association between physical activity and NAFLD in adults and to examine the mediating role of the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) in this relationship. 2. Materials and Methods 2.1 Data Source This study utilized data from the National Health and Nutrition Examination Survey(NHANES). NHANES is an ongoing, cross-sectional survey designed to assess the health and nutritional status of a nationally representative sample of the non-institutionalized U.S. population[8]. Detailed survey design, protocols, and data are publicly available on its official website( https://www.cdc.gov/nchs/nhanes/index.htm ). The NHANES study protocol was approved by the National Center for Health Statistics Ethics Review Board, and written informed consent was obtained from all participants. 2.2 Study Participants Aiming to investigate the association between physical activity and NAFLD risk and the mediating role of HOMA-IR, this study initially pooled data from 119,555 participants across 11 NHANES cycles (1999–2023). Participants were excluded based on the following criteria: (1) missing data on NAFLD status; (2) missing data on the primary exposure (physical activity); (3) missing data required to calculate the mediator (HOMA-IR). After applying these exclusions, a total of 20,194 participants (10,209 men and 9,985 women) were included in the final analysis (Fig.S1). 2.3 Definitions of Key Variables Non-alcoholic Fatty Liver Disease: NAFLD was defined using the US Fatty Liver Index(USFLI). The USFLI was calculated as: e y /(1 + e y ) × 100, where y = − 0.8073 × non-Hispanic black + 0.3458 × Mexican American + 0.0093 × age + 0.6151 × log e (gamma glutamyltransferase) + 0.0249 × waist circumference + 1.1792 × log e (insulin) + 0.8242 × log e (glucose) − 14.7812[9, 10]. A USFLI score ≥ 30 indicated the presence of NAFLD. Furthermore, the following participants were excluded from the NAFLD definition: (1) excessive alcohol consumers (> 3 drinks/day for men, > 2 drinks/day for women); (2) those positive for hepatitis B surface antigen or hepatitis C virus antibody/RNA; (3) pregnant women; (4) individuals reporting long-term use (> 90 days) of medications associated with hepatic steatosis (e.g., methotrexate, amiodarone, corticosteroids, valproic acid, tamoxifen). Physical Activity: Physical activity levels were assessed via Physical Activity Questionnaire (PAQ). Participants reported the types, frequency, duration per session, and intensity of activities performed over the past 30 days. The metabolic equivalent of task (MET) value for each specific activity was assigned based on its type and intensity[11]. The monthly MET-minutes for each activity were calculated by multiplying its MET value by the average duration per session and the monthly frequency. The monthly MET-minutes from all activities were then summed and divided by 4.29 to derive the total weekly physical activity level (MET-min/week). According to established criteria, participants were categorized into two groups: the Low Physical Activity group (< 500 MET-min/week) and the High Physical Activity group (≥ 500 MET-min/week)[12]. 2.4. Covariate Assessment Data on study participants were extracted from the NHANES database, encompassing demographic and behavioral characteristics—including age, sex, body mass index (BMI), average daily energy intake (based on two-day dietary recalls), smoking status (defined as having smoked at least 100 cigarettes in one's lifetime), and histories of diabetes, hypertension, and dyslipidemia—as well as laboratory measurements: total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), alanine aminotransferase (ALT), aspartate aminotransferase (AST), glycated hemoglobin (HbA1c), fasting blood glucose (FBG), and fasting serum insulin (FSI). Insulin resistance was estimated using the Homeostasis Model Assessment of Insulin Resistance, calculated as: HOMA-IR = FBG × FSI/22.5[13, 14]. Hypertension was defined as a self-reported diagnosis, systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or current use of antihypertensive medication. Diabetes was defined as a self-reported diagnosis, current use of insulin or oral hypoglycemic agents, HbA1c ≥ 6.5%, fasting plasma glucose ≥ 126 mg/dL, or 2-hour postprandial glucose ≥ 200 mg/dL. Dyslipidemia was defined as meeting any of the following: total cholesterol ≥ 200 mg/dL, triglycerides ≥ 150 mg/dL, LDL-C ≥ 130 mg/dL, HDL-C < 40 mg/dL in men or < 50 mg/dL in women, or current use of lipid-lowering medication. 2.5 Statistical Analysis All statistical analyses were conducted using R software (version 4.4.3) and SPSS (version 25.0). In accordance with NHANES analytical guidelines, all estimates accounted for the complex survey design using sampling weights, and missing covariate data were addressed through multiple imputation. Continuous variables with a normal distribution are presented as the mean [standard error (SE)], and group differences were assessed using independent sample t-tests. Categorical variables are presented as percentages, with differences evaluated using the chi-square test. The association between physical activity and NAFLD was analyzed using multivariable logistic regression models to determine odds ratios (OR) and their corresponding 95% confidence intervals (CI). The potential nonlinear relationship between physical activity and NAFLD risk was explored using RCS regression analysis in R. The mediating effect of HOMA-IR was tested by constructing a mediation model executed via the PROCESS macro for SPSS, utilizing the bootstrap method. A significance level of α = 0.05 was set, P-value < 0.05 considered statistically significant. 3. Results 3.1 Baseline Characteristics of the Study Participants A total of 119,555 participants were initially considered for this study. After excluding individuals with missing data on key variables (NAFLD status, physical activity, and HOMA-IR), 20,194 participants were included in the final analysis. The baseline characteristics of the study population are summarized in Table 1 . The mean age of the participants was 44.67 ± 0.26 years. Among them, 5,663 individuals (28.04%) were identified as having NAFLD, while 14,531 (71.96%) were classified as non-NAFLD. Significant differences (P < 0.05) were observed between the NAFLD and non-NAFLD groups regarding age, sex, BMI, physical activity, HOMA-IR, TC, TG, LDL-C, HDL-C, ALT, AST, HbA1c, smoking status, hypertension, diabetes, and dyslipidemia. Table 1 Baseline Characteristics of Study Participants by Case-Control Status Variable Total (n = 20194) non-NAFLD (n = 14531) NAFLD (n = 5663) Statistic P Age(years), n(%) 44.67 ± 0.26 41.56 ± 0.28 52.05 ± 0.32 χ²=546.77 < 0.001 <60 14377 (72.96) 11152 (77.70) 3225 (61.70) ≥ 60 5817 (27.04) 3379 (22.30) 2438 (38.30) Gender, n(%) χ²=195.05 < 0.001 Male 10209 (51.18) 7027 (47.99) 3182 (58.74) Female 9985 (48.82) 7504 (52.01) 2481 (41.26) BMI, n(%) χ²=6401.48 < 0.001 < 25 8164 (36.48) 7850 (50.03) 314 (4.36) ≥ 25 and < 30 5953 (31.39) 4398 (33.99) 1555 (25.21) ≥ 30 6077 (32.14) 2283 (15.98) 3794 (70.43) Physical activity (MET-min/week), n(%) 3043.71 ± 59.88 3193.00 ± 62.41 2689.85 ± 110.28 χ²=302.14 < 0.001 Low physical activity 6655 (31.63) 4270 (27.93) 2385 (40.39) High physical activity 13539 (68.37) 10261 (72.07) 3278 (59.61) Energy intake (kcal/day) 2087.18 ± 9.47 2085.76 ± 10.23 2090.53 ± 15.43 t = 0.29 0.769 HOMA-IR 3.74 ± 0.07 2.04 ± 0.02 7.78 ± 0.19 t = 29.37 < 0.001 TC (mmol/L) 4.87 ± 0.01 4.83 ± 0.02 4.96 ± 0.02 t = 4.80 < 0.001 TG (mmol/L) 1.36 ± 0.01 1.12 ± 0.01 1.93 ± 0.03 t = 23.54 < 0.001 LDL-C (mmol/L) 2.85 ± 0.01 2.83 ± 0.01 2.91 ± 0.02 t = 3.50 < 0.001 HDL-C (mmol/L) 1.38 ± 0.00 1.46 ± 0.01 1.19 ± 0.01 t=-38.78 < 0.001 ALT(U/L) 23.27 ± 0.15 20.39 ± 0.14 30.07 ± 0.35 t = 24.95 < 0.001 AST(U/L) 24.05 ± 0.17 22.98 ± 0.13 26.58 ± 0.47 t = 7.45 < 0.001 HbA1c (%) 5.58 ± 0.01 5.40 ± 0.01 6.01 ± 0.02 t = 28.66 < 0.001 Smoking, n(%) χ²=128.11 < 0.001 Yes 7452 (37.53) 5017 (35.02) 2435 (43.46) No 12742 (62.47) 9514 (64.98) 3228 (56.54) Hypertension, n(%) χ²=1693.41 < 0.001 Yes 6955 (34.58) 3813 (25.63) 3142 (55.78) No 13239 (65.42) 10718 (74.37) 2521 (44.22) Diabetes, n(%) χ²=2246.24 < 0.001 Yes 3202 (14.21) 1151 (6.65) 2051 (32.14) No 16992 (85.79) 13380 (93.35) 3612 (67.86) Hyperlipidemia, n(%) χ²=1476.91 < 0.001 Yes 12739 (67.13) 7918 (58.87) 4821 (86.68) No 7455 (32.87) 6613 (41.13) 842 (13.32) Note: NAFLD:nonalcoholic fatty liver disease; BMI: body mass index; HOMA-IR༚Homeostasis Model Assessment of Insulin Resistance;TC: total cholesterol; TG: triglyceride; LDL-C: low-density lipoprotein cholesterol; HDL-C:high-density lipoprotein cholesterol;ALT: alanine aminotransferase; AST: asparate aminotransferase; HbA1c :Hemoglobin A1c. 3.2 Association Between Physical Activity and NAFLD Risk To examine the independent association between physical activity and NAFLD, variables showing statistical significance (P < 0.05) in the univariate analysis were included in the multivariable logistic regression models. It is important to note that HOMA-IR was not entered into these regression models to avoid potential over-adjustment or multicollinearity that could obscure the estimation of the main exposure effect, as the primary analytical focus was to delineate its mediating role in a separate pathway analysis. As presented in Table 2 , physical activity, age, sex, BMI, TC, TG, LDL-C, HDL-C, ALT, AST, HbA1c, smoking, hypertension, diabetes, and dyslipidemia were all significantly associated with NAFLD risk in the unadjusted model. After full adjustment for these potential confounders, physical activity level remained a significant predictor of NAFLD. Specifically, a high level of physical activity was identified as an independent protective factor against NAFLD (OR = 0.84, P = 0.002). Table 2 Prevalence of NAFLD among All Study Participants Variables Unadjusted model adjusted model β S.E t P OR (95%CI) β S.E t P OR (95%CI) Physical activity Low physical activity 1.00 (Reference) 1.00 (Reference) High physical activity -0.56 0.04 -13.57 < 0.001 0.57 (0.53 ~ 0.62) -0.18 0.06 -3.07 0.002 0.84 (0.75 ~ 0.94) Age, ≥ 60 0.77 0.05 16.66 < 0.001 2.16 (1.98 ~ 2.37) 0.57 0.07 8.43 < 0.001 1.77 (1.55 ~ 2.02) Gender,Female -0.43 0.04 -10.15 < 0.001 0.65 (0.60 ~ 0.70) -0.38 0.07 -5.21 < 0.001 0.68 (0.59 ~ 0.79) BMI ≥ 25 and < 30 2.14 0.08 26.43 < 0.001 8.51 (7.26 ~ 9.97) 1.58 0.09 16.67 < 0.001 4.84 (4.02 ~ 5.83) ≥ 30 3.92 0.09 44.29 < 0.001 50.58 (42.51 ~ 60.17) 3.33 0.10 32.46 < 0.001 28.01 (22.91 ~ 34.26) TC 0.11 0.02 4.84 < 0.001 1.11 (1.07 ~ 1.16) 0.24 0.28 0.87 0.388 1.27 (0.74 ~ 2.19) TG 1.04 0.06 17.51 < 0.001 2.84 (2.52 ~ 3.19) 0.26 0.18 1.47 0.145 1.30 (0.92 ~ 1.85) LDL 0.09 0.03 3.49 < 0.001 1.09 (1.04 ~ 1.15) -0.34 0.27 -1.26 0.211 0.71 (0.42 ~ 1.21) HDL -2.42 0.08 -29.46 < 0.001 0.09 (0.08 ~ 0.10) -1.31 0.30 -4.35 < 0.001 0.27 (0.15 ~ 0.49) ALT 0.06 0.00 17.41 < 0.001 1.06 (1.05 ~ 1.06) 0.04 0.00 9.05 < 0.001 1.04 (1.03 ~ 1.05) AST 0.03 0.00 6.05 < 0.001 1.03 (1.02 ~ 1.04) -0.01 0.00 -5.31 < 0.001 0.99 (0.98 ~ 0.99) HbA1c 0.97 0.06 17.33 < 0.001 2.63 (2.36 ~ 2.94) 0.26 0.04 5.99 < 0.001 1.29 (1.19 ~ 1.41) Smoking 0.36 0.05 7.50 < 0.001 1.43 (1.30 ~ 1.57) 0.16 0.06 2.65 0.009 1.17 (1.04 ~ 1.31) Hypertension 1.30 0.05 25.98 < 0.001 3.66 (3.32 ~ 4.04) 0.45 0.07 6.18 < 0.001 1.56 (1.36 ~ 1.80) Diabetes 1.89 0.05 37.71 < 0.001 6.65 (6.03 ~ 7.34) 0.70 0.09 7.96 < 0.001 2.00 (1.69 ~ 2.38) Hyperlipidemia 1.51 0.06 24.83 < 0.001 4.55 (4.04 ~ 5.12) 0.48 0.09 5.63 < 0.001 1.62 (1.37 ~ 1.92) Note: BMI: body mass index; HOMA-IR༚Homeostasis Model Assessment of Insulin Resistance;TC: total cholesterol; TG: triglyceride; LDL-C: low-density lipoprotein cholesterol; HDL-C:high-density lipoprotein cholesterol;ALT: alanine aminotransferase; AST: asparate aminotransferase; HbA1c :Hemoglobin A1c. 3.3 Nonlinear Association Between Physical Activity and NAFLD Risk The RCS analysis was applied to flexibly model the dose-response relationship between the level of physical activity (in MET-min/week) and the risk of NAFLD. In the model unadjusted for covariates (Fig. 1 A), a significant nonlinear association was observed (P for overall < 0.001; P for nonlinear < 0.001). Specifically, as physical activity increased from the baseline (0 MET-min/week), the risk of NAFLD decreased correspondingly, reaching its nadir at moderate activity levels. However, a modest rebound in risk was noted at the very high end of the activity spectrum, where the associated confidence intervals were notably wider. After adjusting for potential confounders, including age, sex, BMI, HDL-C, ALT, AST, HbA1c, smoking, hypertension, diabetes, and dyslipidemia (Fig. 1 B), the nonlinear relationship remained statistically significant (P for overall < 0.001; P for nonlinear < 0.001). In this adjusted model, the rebound trend at extreme activity levels was attenuated. The adjusted odds ratio curve exhibited a generally monotonic decreasing trend with increasing physical activity, although risk estimates in the high-activity range remained less precise, as indicated by relatively wider confidence intervals in this segment. 3.4 Mediating Role of HOMA-IR in the Association Between Physical Activity and NAFLD To investigate the potential mechanism through which physical activity influences NAFLD risk, HOMA-IR was introduced as a mediator. Path analysis was conducted using Model 4 of the SPSS PROCESS macro, and the mediating effect was tested using the bootstrap method as advocated by Hayes. The results of the path analysis are presented in Fig. 2 . Physical activity level significantly and negatively predicted HOMA-IR (β = -1.03, P < 0.001), suggesting that high physical activity may reduce insulin resistance. Concurrently, HOMA-IR exhibited a significant positive effect on NAFLD risk (β = 1.28, P < 0.001), indicating that elevated insulin resistance is associated with an increased likelihood of developing NAFLD. Taken together, these findings suggest that physical activity likely reduces the risk of NAFLD, at least in part, through the pathway of ameliorating insulin resistance. The detailed results of the mediation analysis are presented in Table 3 . A significant and substantial mediated effect of physical activity on NAFLD risk through HOMA-IR was observed (β = -0.06, 95% CI: -0.07 ~ − 0.04, p < 0.001). This indirect effect accounted for the majority (81.96%) of the total effect, underscoring the central mediating role of HOMA-IR in the relationship between physical activity and NAFLD. Concurrently, the direct effect of physical activity on NAFLD, though smaller in magnitude, remained statistically significant (β = -0.01, 95% CI༚-0.02 ~ − 0.01, p < 0.001), constituting 18.04% of the total effect. In summary, these findings strongly support HOMA-IR as a crucial mediator in the pathway through which physical activity reduces NAFLD risk, suggesting that the amelioration of insulin resistance is likely a key biological mechanism underlying the protective effect of physical activity on the liver. Table 3 Decomposition of Total, Direct, and Indirect Effects Effect Efficiency Value 95%CI P Effect size Indirect -0.06 -0.07 ~ − 0.04 < 0.001 81.96% Direct -0.01 -0.02 ~ − 0.01 < 0.001 18.04% Total -0.07 -0.09 ~ − 0.06 < 0.001 100.00% 4. Discussion Based on nationally representative cross-sectional data from the United States, this study confirms that higher levels of physical activity are independently associated with a reduced risk of NAFLD, a finding consistent with prior research[15]. More importantly, our refined dose-response analysis revealed a significant nonlinear association between physical activity and NAFLD risk. The modest rebound in risk at very high activity levels observed in the unadjusted model may be attributable to potential confounders associated with extreme exercise patterns, such as specific dietary habits or alterations in body composition. However, after thorough adjustment for major metabolic confounders, the association still trended toward a monotonic decrease. This underscores the importance of maintaining a certain volume of at least moderate-intensity physical activity weekly, while the effects at the highest activity levels require further evaluation with more data. The core contribution of this study lies in quantifying the pivotal mediating role of insulin resistance in the association between physical activity and NAFLD within a large population-based sample. Mediation analysis revealed that HOMA-IR explained over 80% of the total effect. This robust evidence strongly indicates that the amelioration of systemic insulin resistance serves as the primary biological pathway through which physical activity reduces NAFLD risk. This mechanism aligns well with established pathophysiological understanding: physical activity effectively mitigates insulin resistance by enhancing skeletal muscle glucose uptake, improving lipid metabolism, attenuating systemic inflammation, and enhancing glucose tolerance. In turn, the improvement in insulin resistance reduces hepatic de novo lipogenesis and promotes fatty acid oxidation, ultimately decreasing intrahepatic lipid deposition[16–18]. Research also indicates that physical activity induces adaptive changes in skeletal muscle—such as mitochondrial biogenesis and metabolic optimization—and stimulates the release of myokines, thereby enhancing insulin sensitivity and non-insulin-dependent glucose uptake, which collectively contribute to improved insulin resistance[19]. Notably, even increasing daily low-intensity physical activity has been shown to significantly ameliorate insulin resistance, highlighting the intrinsic importance of physical activity itself[20]. Therefore, by effectively improving insulin resistance, physical activity constitutes an independent protective factor for the prevention and management of NAFLD. Furthermore, the direct effect identified in this study (approximately 18%) suggests that physical activity may also confer hepatoprotective benefits through pathways independent of ameliorating classical insulin resistance. For instance, mechanisms such as modulating bile acid metabolism, improving gut barrier function and microbiota, or reducing oxidative stress could directly influence hepatic lipid metabolism[14, 21]. Currently, research on these direct pathways linking physical activity to NAFLD risk remains limited, and their specific mechanisms warrant further investigation. Several limitations of this study should be acknowledged. Firstly, the cross-sectional design precludes the establishment of causal relationships. Secondly, physical activity was assessed via self-reported questionnaires, which may introduce measurement error. Despite these limitations, the study’s large sample size, nationally representative nature, and the application of sophisticated weighting and mediation analyses lend robustness to the findings. In conclusion, our research provides strong epidemiological evidence supporting the hypothesis that physical activity helps prevent NAFLD by improving insulin resistance, and underscores the public health importance of promoting physical activity—particularly at moderate intensities—as a primary prevention strategy for metabolic liver disease. 5. Conclusion This nationally representative study confirms that higher levels of physical activity serve as an independent protective factor against non-alcoholic fatty liver disease (NAFLD), with a significant nonlinear dose-response relationship observed between the two. Furthermore, our quantitative mediation analysis reveals that the amelioration of insulin resistance constitutes the core mechanism underlying this protective effect, accounting for over 80% of the total association. These findings collectively provide critical mechanistic evidence and a robust public health rationale for promoting physical activity, particularly at moderate-to-vigorous intensities, as a key strategy for the primary prevention of NAFLD. Declarations Consent for publication Not applicable. Competing interests The authors declare that they have no conflicts of interest. Funding No funding support. Author Contribution ZZ collected the clinical data, reviewed the literature, preparation of the figures, and drafted the manuscript. Acknowledgements Not applicable. Availability of data and materials This study utilized data from the National Health and Nutrition Examination Survey( https://wwwn.cdc.gov/nchs/nhanes ). References M.H. Le, Y.H. Yeo, X. Li, J. Li, B. Zou, Y. Wu, Q. Ye, D.Q. Huang, C. Zhao, J. Zhang, C. Liu, N. Chang, F. Xing, S. Yan, Z.H. Wan, N.S.Y. Tang, M. Mayumi, X. Liu, C. Liu, F. Rui, H. Yang, Y. Yang, R. Jin, R.H.X. Le, Y. Xu, D.M. Le, S. Barnett, C.D. Stave, R. Cheung, Q. Zhu and M.H. Nguyen, 2019 Global NAFLD Prevalence: A Systematic Review and Meta-analysis, Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association , 20 (2022), 2809–2817.e2828. Z.M. Younossi, S. Zelber-Sagi, L. Henry and L.H. Gerber, Lifestyle interventions in nonalcoholic fatty liver disease, Nature reviews. Gastroenterology & hepatology , 20 (2023), 708–722. M.H. Le, Y.H. Yeo, B. Zou, S. Barnet, L. Henry, R. Cheung and M.H. Nguyen, Forecasted 2040 global prevalence of nonalcoholic fatty liver disease using hierarchical bayesian approach, Clinical and molecular hepatology , 28 (2022), 841–850. N. Chalasani, Z. Younossi, J.E. Lavine, A.M. Diehl, E.M. Brunt, K. Cusi, M. Charlton and A.J. Sanyal, The diagnosis and management of non-alcoholic fatty liver disease: practice guideline by the American Gastroenterological Association, American Association for the Study of Liver Diseases, and American College of Gastroenterology, Gastroenterology , 142 (2012), 1592–1609. M. Romero-Gómez, S. Zelber-Sagi and M. Trenell, Treatment of NAFLD with diet, physical activity and exercise, Journal of hepatology , 67 (2017), 829–846. E. Muzurović, D.P. Mikhailidis and C. Mantzoros, Non-alcoholic fatty liver disease, insulin resistance, metabolic syndrome and their association with vascular risk, Metabolism: clinical and experimental , 119 (2021), 154770. J. van der Velde, S.C. Boone, E. Winters-van Eekelen, M.K.C. Hesselink, V.B. Schrauwen-Hinderling, P. Schrauwen, H.J. Lamb, F.R. Rosendaal and R. de Mutsert, Timing of physical activity in relation to liver fat content and insulin resistance, Diabetologia , 66 (2023), 461–471. C.L. Johnson, S.M. Dohrmann, V.L. Burt and L.K. Mohadjer, National health and nutrition examination survey: sample design, 2011–2014, Vital Health Stat 2 (2014), 1–33. Y. Zhang, F. Wang, J. Tang, L. Shen, J. He and Y. Chen, Association of triglyceride glucose-related parameters with all-cause mortality and cardiovascular disease in NAFLD patients: NHANES 1999–2018, Cardiovascular diabetology , 23 (2024), 262. J. Pan, Y. Hu, N. Pang and L. Yang, Association between Dietary Niacin Intake and Nonalcoholic Fatty Liver Disease: NHANES 2003–2018, Nutrients , 15 (2023). B.E. Ainsworth, W.L. Haskell, M.C. Whitt, M.L. Irwin, A.M. Swartz, S.J. Strath, W.L. O'Brien, D.R. Bassett, Jr., K.H. Schmitz, P.O. Emplaincourt, D.R. Jacobs, Jr. and A.S. Leon, Compendium of physical activities: an update of activity codes and MET intensities, Medicine and science in sports and exercise , 32 (2000), S498-504. K.A. MacGregor, I.J. Gallagher and C.N. Moran, Relationship Between Insulin Sensitivity and Menstrual Cycle Is Modified by BMI, Fitness, and Physical Activity in NHANES, The Journal of clinical endocrinology and metabolism , 106 (2021), 2979–2990. X.Z. Hou, Y.F. Lv, Y.S. Li, Q. Wu, Q.Y. Lv, Y.T. Yang, L.L. Li, X.J. Ye, C.Y. Yang, M.S. Wang, L.L. Cao and S.H. Wang, Association between different insulin resistance surrogates and all-cause mortality in patients with coronary heart disease and hypertension: NHANES longitudinal cohort study, Cardiovascular diabetology , 23 (2024), 86. H. Guo, C. Wan, J. Zhu, X. Jiang and S. Li, Association of systemic immune-inflammation index with insulin resistance and prediabetes: a cross-sectional study, Frontiers in endocrinology , 15 (2024), 1377792. A. Henry, J.M. Paik, P. Austin, K.E. Eberly, P. Golabi, I. Younossi, L. Henry, L. Gerber and Z.M. Younossi, Vigorous physical activity provides protection against all-cause deaths among adults patients with nonalcoholic fatty liver disease (NAFLD), Alimentary pharmacology & therapeutics , 57 (2023), 709–722. M. Gutch, S. Kumar, S.M. Razi, K.K. Gupta and A. Gupta, Assessment of insulin sensitivity/resistance, Indian Journal of Endocrinology and Metabolism , 19 (2015). A. Sampath Kumar, A.G. Maiya, B.A. Shastry, K. Vaishali, N. Ravishankar, A. Hazari, S. Gundmi and R. Jadhav, Exercise and insulin resistance in type 2 diabetes mellitus: A systematic review and meta-analysis, Annals of physical and rehabilitation medicine , 62 (2019), 98–103. L. DiPietro, T.E. Seeman, N.S. Stachenfeld, L.D. Katz and E.R. Nadel, Moderate-intensity aerobic training improves glucose tolerance in aging independent of abdominal adiposity, Journal of the American Geriatrics Society , 46 (1998), 875–879. J. Myers, P. Kokkinos and E. Nyelin, Physical Activity, Cardiorespiratory Fitness, and the Metabolic Syndrome, Nutrients , 11 (2019). F. Al-Rashed, A. Alghaith, R. Azim, D. AlMekhled, R. Thomas, S. Sindhu and R. Ahmad, Increasing the Duration of Light Physical Activity Ameliorates Insulin Resistance Syndrome in Metabolically Healthy Obese Adults, Cells , 9 (2020). Y.M. Park, M. Myers and V.J. Vieira-Potter, Adipose tissue inflammation and metabolic dysfunction: role of exercise, Missouri medicine , 111 (2014), 65–72. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9123706","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616749976,"identity":"bdbc380c-0caa-43c1-91bf-d738afddfd9c","order_by":0,"name":"Zhong Zheng","email":"data:image/png;base64,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","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":true,"prefix":"","firstName":"Zhong","middleName":"","lastName":"Zheng","suffix":""}],"badges":[],"createdAt":"2026-03-14 15:38:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9123706/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9123706/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106459800,"identity":"37d48c3a-75a8-4453-b364-bdc25eeaf0a3","added_by":"auto","created_at":"2026-04-08 19:25:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":134631,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline plots for the association between physical activity and NAFLD. A: Unadjusted model; B: Model adjusted for age, sex, BMI, HDL-C, ALT, AST, HbA1c, smoking, hypertension, diabetes, and hyperlipidemia.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9123706/v1/0e98f665f5a89db681a68bf7.png"},{"id":106724229,"identity":"e8ea11dd-44bd-4099-afa2-cfcc20fab570","added_by":"auto","created_at":"2026-04-12 18:26:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":60620,"visible":true,"origin":"","legend":"\u003cp\u003eMediation analysis: path coefficients for physical activity, HOMA-IR, and NAFLD.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9123706/v1/c5a94f140db0b079788b77f2.png"},{"id":109003031,"identity":"4f023c63-bf9a-42f4-a43a-7968ec4e5d7b","added_by":"auto","created_at":"2026-05-11 15:16:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":620451,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9123706/v1/f45b2cd2-b10c-4361-afaa-a67b1c398892.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between Physical Activity and Risk of Non-Alcoholic Fatty Liver Disease: The Mediating Role of Insulin Resistance","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNon-alcoholic fatty liver disease (NAFLD) is emerging as a predominant cause of chronic liver disease worldwide[1]. Epidemiological studies indicate a continuously rising prevalence, with the global pooled prevalence reaching approximately 30% (1991\u0026ndash;2019) and showing an accelerated increase in recent years[2].This trend is largely attributed to the parallel rise in global rates of obesity and type 2 diabetes[3]. Without effective interventions to curb this trend, the prevalence of NAFLD is projected to increase further.The growing burden of NAFLD is associated not only with impaired health-related quality of life but also with significant socioeconomic costs due to reduced work productivity and increased healthcare utilization.\u003c/p\u003e \u003cp\u003eImplementing therapeutic interventions in patients at risk for NAFLD progression is crucial for preventing or reversing the adverse outcomes associated with the transition from simple steatosis to non-alcoholic steatohepatitis (NASH)[4]. Among various lifestyle interventions, physical activity is widely regarded as a key strategy for both the primary prevention and management of NAFLD[5].\u003c/p\u003e \u003cp\u003eHowever, the underlying biological mechanisms through which physical activity confers its hepatoprotective effects remain incompletely elucidated. Patients with NAFLD are often obese and/or diabetic, and insulin resistance is considered a key pathogenic factor in the development and progression of NAFLD[6]. Furthermore, research has found that regular physical activity can effectively reduce insulin resistance[7]. This suggests that the amelioration of insulin resistance may constitute a critical mediating pathway linking physical activity to a reduced risk of NAFLD. Despite the biological plausibility of this hypothesis, studies that quantitatively assess this mediating pathway in nationally representative, large-scale population-based samples remain limited.\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to investigate the association between physical activity and NAFLD in adults and to examine the mediating role of the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) in this relationship.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Source\u003c/h2\u003e \u003cp\u003eThis study utilized data from the National Health and Nutrition Examination Survey(NHANES). NHANES is an ongoing, cross-sectional survey designed to assess the health and nutritional status of a nationally representative sample of the non-institutionalized U.S. population[8]. Detailed survey design, protocols, and data are publicly available on its official website(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/index.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/index.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The NHANES study protocol was approved by the National Center for Health Statistics Ethics Review Board, and written informed consent was obtained from all participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study Participants\u003c/h2\u003e \u003cp\u003eAiming to investigate the association between physical activity and NAFLD risk and the mediating role of HOMA-IR, this study initially pooled data from 119,555 participants across 11 NHANES cycles (1999\u0026ndash;2023). Participants were excluded based on the following criteria: (1) missing data on NAFLD status; (2) missing data on the primary exposure (physical activity); (3) missing data required to calculate the mediator (HOMA-IR). After applying these exclusions, a total of 20,194 participants (10,209 men and 9,985 women) were included in the final analysis (Fig.S1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Definitions of Key Variables\u003c/h2\u003e \u003cp\u003eNon-alcoholic Fatty Liver Disease:\u003c/p\u003e \u003cp\u003eNAFLD was defined using the US Fatty Liver Index(USFLI). The USFLI was calculated as: e\u003csup\u003ey\u003c/sup\u003e/(1\u0026thinsp;+\u0026thinsp;e\u003csup\u003ey\u003c/sup\u003e) \u0026times; 100, where y\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.8073 \u0026times; non-Hispanic black\u0026thinsp;+\u0026thinsp;0.3458 \u0026times; Mexican American\u0026thinsp;+\u0026thinsp;0.0093 \u0026times; age\u0026thinsp;+\u0026thinsp;0.6151 \u0026times; log\u003csub\u003ee\u003c/sub\u003e (gamma glutamyltransferase)\u0026thinsp;+\u0026thinsp;0.0249 \u0026times; waist circumference\u0026thinsp;+\u0026thinsp;1.1792 \u0026times; log\u003csub\u003ee\u003c/sub\u003e (insulin)\u0026thinsp;+\u0026thinsp;0.8242 \u0026times; log\u003csub\u003ee\u003c/sub\u003e (glucose)\u0026thinsp;\u0026minus;\u0026thinsp;14.7812[9, 10]. A USFLI score\u0026thinsp;\u0026ge;\u0026thinsp;30 indicated the presence of NAFLD. Furthermore, the following participants were excluded from the NAFLD definition: (1) excessive alcohol consumers (\u0026gt;\u0026thinsp;3 drinks/day for men, \u0026gt;\u0026thinsp;2 drinks/day for women); (2) those positive for hepatitis B surface antigen or hepatitis C virus antibody/RNA; (3) pregnant women; (4) individuals reporting long-term use (\u0026gt;\u0026thinsp;90 days) of medications associated with hepatic steatosis (e.g., methotrexate, amiodarone, corticosteroids, valproic acid, tamoxifen).\u003c/p\u003e \u003cp\u003ePhysical Activity:\u003c/p\u003e \u003cp\u003ePhysical activity levels were assessed via Physical Activity Questionnaire (PAQ). Participants reported the types, frequency, duration per session, and intensity of activities performed over the past 30 days. The metabolic equivalent of task (MET) value for each specific activity was assigned based on its type and intensity[11]. The monthly MET-minutes for each activity were calculated by multiplying its MET value by the average duration per session and the monthly frequency. The monthly MET-minutes from all activities were then summed and divided by 4.29 to derive the total weekly physical activity level (MET-min/week). According to established criteria, participants were categorized into two groups: the Low Physical Activity group (\u0026lt;\u0026thinsp;500 MET-min/week) and the High Physical Activity group (\u0026ge;\u0026thinsp;500 MET-min/week)[12].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Covariate Assessment\u003c/h2\u003e \u003cp\u003eData on study participants were extracted from the NHANES database, encompassing demographic and behavioral characteristics\u0026mdash;including age, sex, body mass index (BMI), average daily energy intake (based on two-day dietary recalls), smoking status (defined as having smoked at least 100 cigarettes in one's lifetime), and histories of diabetes, hypertension, and dyslipidemia\u0026mdash;as well as laboratory measurements: total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), alanine aminotransferase (ALT), aspartate aminotransferase (AST), glycated hemoglobin (HbA1c), fasting blood glucose (FBG), and fasting serum insulin (FSI). Insulin resistance was estimated using the Homeostasis Model Assessment of Insulin Resistance, calculated as: HOMA-IR\u0026thinsp;=\u0026thinsp;FBG \u0026times; FSI/22.5[13, 14].\u003c/p\u003e \u003cp\u003eHypertension was defined as a self-reported diagnosis, systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg, diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, or current use of antihypertensive medication. Diabetes was defined as a self-reported diagnosis, current use of insulin or oral hypoglycemic agents, HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5%, fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL, or 2-hour postprandial glucose\u0026thinsp;\u0026ge;\u0026thinsp;200 mg/dL. Dyslipidemia was defined as meeting any of the following: total cholesterol\u0026thinsp;\u0026ge;\u0026thinsp;200 mg/dL, triglycerides\u0026thinsp;\u0026ge;\u0026thinsp;150 mg/dL, LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;130 mg/dL, HDL-C\u0026thinsp;\u0026lt;\u0026thinsp;40 mg/dL in men or \u0026lt;\u0026thinsp;50 mg/dL in women, or current use of lipid-lowering medication.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using R software (version 4.4.3) and SPSS (version 25.0). In accordance with NHANES analytical guidelines, all estimates accounted for the complex survey design using sampling weights, and missing covariate data were addressed through multiple imputation. Continuous variables with a normal distribution are presented as the mean [standard error (SE)], and group differences were assessed using independent sample t-tests. Categorical variables are presented as percentages, with differences evaluated using the chi-square test. The association between physical activity and NAFLD was analyzed using multivariable logistic regression models to determine odds ratios (OR) and their corresponding 95% confidence intervals (CI). The potential nonlinear relationship between physical activity and NAFLD risk was explored using RCS regression analysis in R. The mediating effect of HOMA-IR was tested by constructing a mediation model executed via the PROCESS macro for SPSS, utilizing the bootstrap method. A significance level of α\u0026thinsp;=\u0026thinsp;0.05 was set, P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline Characteristics of the Study Participants\u003c/h2\u003e \u003cp\u003eA total of 119,555 participants were initially considered for this study. After excluding individuals with missing data on key variables (NAFLD status, physical activity, and HOMA-IR), 20,194 participants were included in the final analysis. The baseline characteristics of the study population are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age of the participants was 44.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26 years. Among them, 5,663 individuals (28.04%) were identified as having NAFLD, while 14,531 (71.96%) were classified as non-NAFLD. Significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were observed between the NAFLD and non-NAFLD groups regarding age, sex, BMI, physical activity, HOMA-IR, TC, TG, LDL-C, HDL-C, ALT, AST, HbA1c, smoking status, hypertension, diabetes, and dyslipidemia.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of Study Participants by Case-Control Status\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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 (n\u0026thinsp;=\u0026thinsp;20194)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enon-NAFLD (n\u0026thinsp;=\u0026thinsp;14531)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNAFLD (n\u0026thinsp;=\u0026thinsp;5663)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\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), n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=546.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14377 (72.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11152 (77.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3225 (61.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e5817 (27.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3379 (22.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2438 (38.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=195.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10209 (51.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7027 (47.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3182 (58.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9985 (48.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7504 (52.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2481 (41.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=6401.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8164 (36.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7850 (50.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e314 (4.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;25 and \u0026lt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5953 (31.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4398 (33.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1555 (25.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6077 (32.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2283 (15.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3794 (70.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity (MET-min/week), n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3043.71\u0026thinsp;\u0026plusmn;\u0026thinsp;59.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3193.00\u0026thinsp;\u0026plusmn;\u0026thinsp;62.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2689.85\u0026thinsp;\u0026plusmn;\u0026thinsp;110.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=302.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow physical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6655 (31.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4270 (27.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2385 (40.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh physical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13539 (68.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10261 (72.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3278 (59.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy intake (kcal/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2087.18\u0026thinsp;\u0026plusmn;\u0026thinsp;9.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2085.76\u0026thinsp;\u0026plusmn;\u0026thinsp;10.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2090.53\u0026thinsp;\u0026plusmn;\u0026thinsp;15.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;29.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;4.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;23.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et=-38.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;24.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;7.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;28.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=128.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e7452 (37.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5017 (35.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2435 (43.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e12742 (62.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9514 (64.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3228 (56.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=1693.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e6955 (34.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3813 (25.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3142 (55.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e13239 (65.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10718 (74.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2521 (44.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=2246.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e3202 (14.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1151 (6.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2051 (32.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e16992 (85.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13380 (93.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3612 (67.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=1476.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e12739 (67.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7918 (58.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4821 (86.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e7455 (32.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6613 (41.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e842 (13.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: NAFLD:nonalcoholic fatty liver disease; BMI: body mass index; HOMA-IR༚Homeostasis Model Assessment of Insulin Resistance;TC: total cholesterol; TG: triglyceride; LDL-C: low-density lipoprotein cholesterol; HDL-C:high-density lipoprotein cholesterol;ALT: alanine aminotransferase; AST: asparate aminotransferase; HbA1c :Hemoglobin A1c.\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 Association Between Physical Activity and NAFLD Risk\u003c/h2\u003e \u003cp\u003eTo examine the independent association between physical activity and NAFLD, variables showing statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the univariate analysis were included in the multivariable logistic regression models. It is important to note that HOMA-IR was not entered into these regression models to avoid potential over-adjustment or multicollinearity that could obscure the estimation of the main exposure effect, as the primary analytical focus was to delineate its mediating role in a separate pathway analysis.\u003c/p\u003e \u003cp\u003eAs presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, physical activity, age, sex, BMI, TC, TG, LDL-C, HDL-C, ALT, AST, HbA1c, smoking, hypertension, diabetes, and dyslipidemia were all significantly associated with NAFLD risk in the unadjusted model. After full adjustment for these potential confounders, physical activity level remained a significant predictor of NAFLD. Specifically, a high level of physical activity was identified as an independent protective factor against NAFLD (OR\u0026thinsp;=\u0026thinsp;0.84, P\u0026thinsp;=\u0026thinsp;0.002).\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\u003ePrevalence of NAFLD among All Study Participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eUnadjusted model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c12\" namest=\"c8\"\u003e \u003cp\u003eadjusted model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eS.E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eOR (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\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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow physical 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 \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh physical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-13.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.57 (0.53\u0026thinsp;~\u0026thinsp;0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.84 (0.75\u0026thinsp;~\u0026thinsp;0.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, \u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.16 (1.98\u0026thinsp;~\u0026thinsp;2.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.77 (1.55\u0026thinsp;~\u0026thinsp;2.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender,Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-10.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65 (0.60\u0026thinsp;~\u0026thinsp;0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-5.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.68 (0.59\u0026thinsp;~\u0026thinsp;0.79)\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=\"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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;25 and \u0026lt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.51 (7.26\u0026thinsp;~\u0026thinsp;9.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.84 (4.02\u0026thinsp;~\u0026thinsp;5.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50.58 (42.51\u0026thinsp;~\u0026thinsp;60.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e32.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e28.01 (22.91\u0026thinsp;~\u0026thinsp;34.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.11 (1.07\u0026thinsp;~\u0026thinsp;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.27 (0.74\u0026thinsp;~\u0026thinsp;2.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.84 (2.52\u0026thinsp;~\u0026thinsp;3.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.30 (0.92\u0026thinsp;~\u0026thinsp;1.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.09 (1.04\u0026thinsp;~\u0026thinsp;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.71 (0.42\u0026thinsp;~\u0026thinsp;1.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-29.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09 (0.08\u0026thinsp;~\u0026thinsp;0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-4.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.27 (0.15\u0026thinsp;~\u0026thinsp;0.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.06 (1.05\u0026thinsp;~\u0026thinsp;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.04 (1.03\u0026thinsp;~\u0026thinsp;1.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03 (1.02\u0026thinsp;~\u0026thinsp;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-5.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.99 (0.98\u0026thinsp;~\u0026thinsp;0.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.63 (2.36\u0026thinsp;~\u0026thinsp;2.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.29 (1.19\u0026thinsp;~\u0026thinsp;1.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.43 (1.30\u0026thinsp;~\u0026thinsp;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.17 (1.04\u0026thinsp;~\u0026thinsp;1.31)\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 \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.66 (3.32\u0026thinsp;~\u0026thinsp;4.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.56 (1.36\u0026thinsp;~\u0026thinsp;1.80)\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 \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.65 (6.03\u0026thinsp;~\u0026thinsp;7.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.00 (1.69\u0026thinsp;~\u0026thinsp;2.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.55 (4.04\u0026thinsp;~\u0026thinsp;5.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.62 (1.37\u0026thinsp;~\u0026thinsp;1.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eNote: BMI: body mass index; HOMA-IR༚Homeostasis Model Assessment of Insulin Resistance;TC: total cholesterol; TG: triglyceride; LDL-C: low-density lipoprotein cholesterol; HDL-C:high-density lipoprotein cholesterol;ALT: alanine aminotransferase; AST: asparate aminotransferase; HbA1c :Hemoglobin A1c.\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 Nonlinear Association Between Physical Activity and NAFLD Risk\u003c/h2\u003e \u003cp\u003eThe RCS analysis was applied to flexibly model the dose-response relationship between the level of physical activity (in MET-min/week) and the risk of NAFLD. In the model unadjusted for covariates (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), a significant nonlinear association was observed (P for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001; P for nonlinear\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Specifically, as physical activity increased from the baseline (0 MET-min/week), the risk of NAFLD decreased correspondingly, reaching its nadir at moderate activity levels. However, a modest rebound in risk was noted at the very high end of the activity spectrum, where the associated confidence intervals were notably wider.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter adjusting for potential confounders, including age, sex, BMI, HDL-C, ALT, AST, HbA1c, smoking, hypertension, diabetes, and dyslipidemia (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), the nonlinear relationship remained statistically significant (P for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001; P for nonlinear\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In this adjusted model, the rebound trend at extreme activity levels was attenuated. The adjusted odds ratio curve exhibited a generally monotonic decreasing trend with increasing physical activity, although risk estimates in the high-activity range remained less precise, as indicated by relatively wider confidence intervals in this segment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Mediating Role of HOMA-IR in the Association Between Physical Activity and NAFLD\u003c/h2\u003e \u003cp\u003eTo investigate the potential mechanism through which physical activity influences NAFLD risk, HOMA-IR was introduced as a mediator. Path analysis was conducted using Model 4 of the SPSS PROCESS macro, and the mediating effect was tested using the bootstrap method as advocated by Hayes.\u003c/p\u003e \u003cp\u003eThe results of the path analysis are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Physical activity level significantly and negatively predicted HOMA-IR (β = -1.03, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that high physical activity may reduce insulin resistance. Concurrently, HOMA-IR exhibited a significant positive effect on NAFLD risk (β\u0026thinsp;=\u0026thinsp;1.28, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that elevated insulin resistance is associated with an increased likelihood of developing NAFLD. Taken together, these findings suggest that physical activity likely reduces the risk of NAFLD, at least in part, through the pathway of ameliorating insulin resistance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe detailed results of the mediation analysis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. A significant and substantial mediated effect of physical activity on NAFLD risk through HOMA-IR was observed (β = -0.06, 95% CI: -0.07\u0026thinsp;~\u0026thinsp;\u0026minus;\u0026thinsp;0.04, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This indirect effect accounted for the majority (81.96%) of the total effect, underscoring the central mediating role of HOMA-IR in the relationship between physical activity and NAFLD. Concurrently, the direct effect of physical activity on NAFLD, though smaller in magnitude, remained statistically significant (β = -0.01, 95% CI༚-0.02\u0026thinsp;~\u0026thinsp;\u0026minus;\u0026thinsp;0.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), constituting 18.04% of the total effect. In summary, these findings strongly support HOMA-IR as a crucial mediator in the pathway through which physical activity reduces NAFLD risk, suggesting that the amelioration of insulin resistance is likely a key biological mechanism underlying the protective effect of physical activity on the liver.\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\u003eDecomposition of Total, Direct, and Indirect Effects\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEfficiency Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEffect size\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.07\u0026thinsp;~\u0026thinsp;\u0026minus;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.96%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02\u0026thinsp;~\u0026thinsp;\u0026minus;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.04%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.09\u0026thinsp;~\u0026thinsp;\u0026minus;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eBased on nationally representative cross-sectional data from the United States, this study confirms that higher levels of physical activity are independently associated with a reduced risk of NAFLD, a finding consistent with prior research[15]. More importantly, our refined dose-response analysis revealed a significant nonlinear association between physical activity and NAFLD risk. The modest rebound in risk at very high activity levels observed in the unadjusted model may be attributable to potential confounders associated with extreme exercise patterns, such as specific dietary habits or alterations in body composition. However, after thorough adjustment for major metabolic confounders, the association still trended toward a monotonic decrease. This underscores the importance of maintaining a certain volume of at least moderate-intensity physical activity weekly, while the effects at the highest activity levels require further evaluation with more data.\u003c/p\u003e\n\u003cp\u003eThe core contribution of this study lies in quantifying the pivotal mediating role of insulin resistance in the association between physical activity and NAFLD within a large population-based sample. Mediation analysis revealed that HOMA-IR explained over 80% of the total effect. This robust evidence strongly indicates that the amelioration of systemic insulin resistance serves as the primary biological pathway through which physical activity reduces NAFLD risk. This mechanism aligns well with established pathophysiological understanding: physical activity effectively mitigates insulin resistance by enhancing skeletal muscle glucose uptake, improving lipid metabolism, attenuating systemic inflammation, and enhancing glucose tolerance. In turn, the improvement in insulin resistance reduces hepatic de novo lipogenesis and promotes fatty acid oxidation, ultimately decreasing intrahepatic lipid deposition[16\u0026ndash;18]. Research also indicates that physical activity induces adaptive changes in skeletal muscle\u0026mdash;such as mitochondrial biogenesis and metabolic optimization\u0026mdash;and stimulates the release of myokines, thereby enhancing insulin sensitivity and non-insulin-dependent glucose uptake, which collectively contribute to improved insulin resistance[19]. Notably, even increasing daily low-intensity physical activity has been shown to significantly ameliorate insulin resistance, highlighting the intrinsic importance of physical activity itself[20]. Therefore, by effectively improving insulin resistance, physical activity constitutes an independent protective factor for the prevention and management of NAFLD.\u003c/p\u003e\n\u003cp\u003eFurthermore, the direct effect identified in this study (approximately 18%) suggests that physical activity may also confer hepatoprotective benefits through pathways independent of ameliorating classical insulin resistance. For instance, mechanisms such as modulating bile acid metabolism, improving gut barrier function and microbiota, or reducing oxidative stress could directly influence hepatic lipid metabolism[14, 21]. Currently, research on these direct pathways linking physical activity to NAFLD risk remains limited, and their specific mechanisms warrant further investigation.\u003c/p\u003e\n\u003cp\u003eSeveral limitations of this study should be acknowledged. Firstly, the cross-sectional design precludes the establishment of causal relationships. Secondly, physical activity was assessed via self-reported questionnaires, which may introduce measurement error. Despite these limitations, the study\u0026rsquo;s large sample size, nationally representative nature, and the application of sophisticated weighting and mediation analyses lend robustness to the findings. In conclusion, our research provides strong epidemiological evidence supporting the hypothesis that physical activity helps prevent NAFLD by improving insulin resistance, and underscores the public health importance of promoting physical activity\u0026mdash;particularly at moderate intensities\u0026mdash;as a primary prevention strategy for metabolic liver disease.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis nationally representative study confirms that higher levels of physical activity serve as an independent protective factor against non-alcoholic fatty liver disease (NAFLD), with a significant nonlinear dose-response relationship observed between the two. Furthermore, our quantitative mediation analysis reveals that the amelioration of insulin resistance constitutes the core mechanism underlying this protective effect, accounting for over 80% of the total association. These findings collectively provide critical mechanistic evidence and a robust public health rationale for promoting physical activity, particularly at moderate-to-vigorous intensities, as a key strategy for the primary prevention of NAFLD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo funding support.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZZ collected the clinical data, reviewed the literature, preparation of the figures, and drafted the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThis study utilized data from the National Health and Nutrition Examination Survey(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwwn.cdc.gov/nchs/nhanes\u003c/span\u003e\u003cspan address=\"https://wwwn.cdc.gov/nchs/nhanes\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e M.H. Le, Y.H. Yeo, X. Li, J. Li, B. Zou, Y. Wu, Q. Ye, D.Q. Huang, C. Zhao, J. Zhang, C. Liu, N. Chang, F. Xing, S. Yan, Z.H. Wan, N.S.Y. Tang, M. Mayumi, X. Liu, C. Liu, F. Rui, H. Yang, Y. Yang, R. Jin, R.H.X. Le, Y. Xu, D.M. Le, S. Barnett, C.D. Stave, R. Cheung, Q. Zhu and M.H. Nguyen, 2019 Global NAFLD Prevalence: A Systematic Review and Meta-analysis, \u003cem\u003eClinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association\u003c/em\u003e, \u003cb\u003e20\u003c/b\u003e (2022), 2809\u0026ndash;2817.e2828.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Z.M. Younossi, S. Zelber-Sagi, L. Henry and L.H. Gerber, Lifestyle interventions in nonalcoholic fatty liver disease, \u003cem\u003eNature reviews. Gastroenterology \u0026amp; hepatology\u003c/em\u003e, \u003cb\u003e20\u003c/b\u003e (2023), 708\u0026ndash;722.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e M.H. Le, Y.H. Yeo, B. Zou, S. Barnet, L. Henry, R. Cheung and M.H. Nguyen, Forecasted 2040 global prevalence of nonalcoholic fatty liver disease using hierarchical bayesian approach, \u003cem\u003eClinical and molecular hepatology\u003c/em\u003e, \u003cb\u003e28\u003c/b\u003e (2022), 841\u0026ndash;850.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e N. Chalasani, Z. Younossi, J.E. Lavine, A.M. Diehl, E.M. Brunt, K. Cusi, M. Charlton and A.J. Sanyal, The diagnosis and management of non-alcoholic fatty liver disease: practice guideline by the American Gastroenterological Association, American Association for the Study of Liver Diseases, and American College of Gastroenterology, \u003cem\u003eGastroenterology\u003c/em\u003e, \u003cb\u003e142\u003c/b\u003e (2012), 1592\u0026ndash;1609.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e M. Romero-G\u0026oacute;mez, S. Zelber-Sagi and M. Trenell, Treatment of NAFLD with diet, physical activity and exercise, \u003cem\u003eJournal of hepatology\u003c/em\u003e, \u003cb\u003e67\u003c/b\u003e (2017), 829\u0026ndash;846.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e E. Muzurović, D.P. Mikhailidis and C. Mantzoros, Non-alcoholic fatty liver disease, insulin resistance, metabolic syndrome and their association with vascular risk, \u003cem\u003eMetabolism: clinical and experimental\u003c/em\u003e, \u003cb\u003e119\u003c/b\u003e (2021), 154770.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e J. van der Velde, S.C. Boone, E. Winters-van Eekelen, M.K.C. Hesselink, V.B. Schrauwen-Hinderling, P. Schrauwen, H.J. Lamb, F.R. Rosendaal and R. de Mutsert, Timing of physical activity in relation to liver fat content and insulin resistance, \u003cem\u003eDiabetologia\u003c/em\u003e, \u003cb\u003e66\u003c/b\u003e (2023), 461\u0026ndash;471.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e C.L. Johnson, S.M. Dohrmann, V.L. Burt and L.K. Mohadjer, National health and nutrition examination survey: sample design, 2011\u0026ndash;2014, \u003cem\u003eVital Health Stat 2\u003c/em\u003e (2014), 1\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Y. Zhang, F. Wang, J. Tang, L. Shen, J. He and Y. Chen, Association of triglyceride glucose-related parameters with all-cause mortality and cardiovascular disease in NAFLD patients: NHANES 1999\u0026ndash;2018, \u003cem\u003eCardiovascular diabetology\u003c/em\u003e, \u003cb\u003e23\u003c/b\u003e (2024), 262.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e J. Pan, Y. Hu, N. Pang and L. Yang, Association between Dietary Niacin Intake and Nonalcoholic Fatty Liver Disease: NHANES 2003\u0026ndash;2018, \u003cem\u003eNutrients\u003c/em\u003e, \u003cb\u003e15\u003c/b\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e B.E. Ainsworth, W.L. Haskell, M.C. Whitt, M.L. Irwin, A.M. Swartz, S.J. Strath, W.L. O'Brien, D.R. Bassett, Jr., K.H. Schmitz, P.O. Emplaincourt, D.R. Jacobs, Jr. and A.S. Leon, Compendium of physical activities: an update of activity codes and MET intensities, \u003cem\u003eMedicine and science in sports and exercise\u003c/em\u003e, \u003cb\u003e32\u003c/b\u003e (2000), S498-504.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e K.A. MacGregor, I.J. Gallagher and C.N. Moran, Relationship Between Insulin Sensitivity and Menstrual Cycle Is Modified by BMI, Fitness, and Physical Activity in NHANES, \u003cem\u003eThe Journal of clinical endocrinology and metabolism\u003c/em\u003e, \u003cb\u003e106\u003c/b\u003e (2021), 2979\u0026ndash;2990.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e X.Z. Hou, Y.F. Lv, Y.S. Li, Q. Wu, Q.Y. Lv, Y.T. Yang, L.L. Li, X.J. Ye, C.Y. Yang, M.S. Wang, L.L. Cao and S.H. Wang, Association between different insulin resistance surrogates and all-cause mortality in patients with coronary heart disease and hypertension: NHANES longitudinal cohort study, \u003cem\u003eCardiovascular diabetology\u003c/em\u003e, \u003cb\u003e23\u003c/b\u003e (2024), 86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e H. Guo, C. Wan, J. Zhu, X. Jiang and S. Li, Association of systemic immune-inflammation index with insulin resistance and prediabetes: a cross-sectional study, \u003cem\u003eFrontiers in endocrinology\u003c/em\u003e, \u003cb\u003e15\u003c/b\u003e (2024), 1377792.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e A. Henry, J.M. Paik, P. Austin, K.E. Eberly, P. Golabi, I. Younossi, L. Henry, L. Gerber and Z.M. Younossi, Vigorous physical activity provides protection against all-cause deaths among adults patients with nonalcoholic fatty liver disease (NAFLD), \u003cem\u003eAlimentary pharmacology \u0026amp; therapeutics\u003c/em\u003e, \u003cb\u003e57\u003c/b\u003e (2023), 709\u0026ndash;722.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e M. Gutch, S. Kumar, S.M. Razi, K.K. Gupta and A. Gupta, Assessment of insulin sensitivity/resistance, \u003cem\u003eIndian Journal of Endocrinology and Metabolism\u003c/em\u003e, \u003cb\u003e19\u003c/b\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e A. Sampath Kumar, A.G. Maiya, B.A. Shastry, K. Vaishali, N. Ravishankar, A. Hazari, S. Gundmi and R. Jadhav, Exercise and insulin resistance in type 2 diabetes mellitus: A systematic review and meta-analysis, \u003cem\u003eAnnals of physical and rehabilitation medicine\u003c/em\u003e, \u003cb\u003e62\u003c/b\u003e (2019), 98\u0026ndash;103.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e L. DiPietro, T.E. Seeman, N.S. Stachenfeld, L.D. Katz and E.R. Nadel, Moderate-intensity aerobic training improves glucose tolerance in aging independent of abdominal adiposity, \u003cem\u003eJournal of the American Geriatrics Society\u003c/em\u003e, \u003cb\u003e46\u003c/b\u003e (1998), 875\u0026ndash;879.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e J. Myers, P. Kokkinos and E. Nyelin, Physical Activity, Cardiorespiratory Fitness, and the Metabolic Syndrome, \u003cem\u003eNutrients\u003c/em\u003e, \u003cb\u003e11\u003c/b\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e F. Al-Rashed, A. Alghaith, R. Azim, D. AlMekhled, R. Thomas, S. Sindhu and R. Ahmad, Increasing the Duration of Light Physical Activity Ameliorates Insulin Resistance Syndrome in Metabolically Healthy Obese Adults, \u003cem\u003eCells\u003c/em\u003e, \u003cb\u003e9\u003c/b\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Y.M. Park, M. Myers and V.J. Vieira-Potter, Adipose tissue inflammation and metabolic dysfunction: role of exercise, \u003cem\u003eMissouri medicine\u003c/em\u003e, \u003cb\u003e111\u003c/b\u003e (2014), 65\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e\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":"Non-alcoholic Fatty Liver Disease(NAFLD), Physical Activity, HOMA-IR, Mediation Analysis","lastPublishedDoi":"10.21203/rs.3.rs-9123706/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9123706/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective: This study aimed to investigate the association between physical activity and the risk of non-alcoholic fatty liver disease (NAFLD), with a specific focus on evaluating the potential mediating role of insulin resistance, quantified using the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR).\u003c/p\u003e \u003cp\u003eMethods: Data for this analysis were derived from the National Health and Nutrition Examination Survey (NHANES), comprising 20,194 adults. To address the complex survey design and missing covariate data, analyses incorporated sampling weights and employed multiple imputation, respectively. Multivariable logistic regression models were employed to assess the association between physical activity levels and NAFLD risk. RCS analysis was conducted to examine potential nonlinear relationships. The mediating effect of HOMA-IR was evaluated using the SPSS PROCESS macro with the bootstrap method.\u003c/p\u003e \u003cp\u003eResults: Compared to the low physical activity group, the high physical activity group exhibited a significantly reduced risk of NAFLD (adjusted OR\u0026thinsp;=\u0026thinsp;0.84, P\u0026thinsp;=\u0026thinsp;0.002). The RCS analysis suggested a nonlinear association between physical activity and NAFLD risk (P for nonlinearity\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with the lowest risk observed at moderate activity levels and a slight increase at very high levels. Nevertheless, after adjustment for confounders, the overall association trended toward a monotonic decrease. It is notable that the confidence intervals were wider in the high activity range. Mediation analysis revealed that HOMA-IR played a substantial mediating role in the inverse association between physical activity and NAFLD, accounting for 81.96% of the total effect (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eConclusion: Physical activity exerts an independent protective effect against NAFLD, an effect largely mediated by the amelioration of insulin resistance. Increasing physical activity levels, particularly to achieve and sustain a moderate-to-high intensity, may represent a significant public health strategy for mitigating the population-level risk of NAFLD.\u003c/p\u003e","manuscriptTitle":"Association Between Physical Activity and Risk of Non-Alcoholic Fatty Liver Disease: The Mediating Role of Insulin Resistance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-08 19:25:27","doi":"10.21203/rs.3.rs-9123706/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":"04c93e97-961d-4d97-8e84-dd6ee4dc2d94","owner":[],"postedDate":"April 8th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-11T15:03:00+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T15:14:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-08 19:25:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9123706","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9123706","identity":"rs-9123706","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}