{"paper_id":"3e42b6ca-3703-49a3-9198-d8a4ff8c6cd6","body_text":"Physical Activity Dose-Response and Long-Term Effects on Dyslipidemia in Chinese Adults: A CHARLS Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Physical Activity Dose-Response and Long-Term Effects on Dyslipidemia in Chinese Adults: A CHARLS Study Kang Wan, Ruwen Wang, Hongmei Yan, Wei Chen, Fuyi Ma, Yue Jin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6932333/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Sep, 2025 Read the published version in BMC Public Health → Version 1 posted 12 You are reading this latest preprint version Abstract Background: This longitudinal study aimed to investigate the dose-response relationship and long-term protective effects of physical activity (PA) on dyslipidemia among middle-aged and older Chinese adults, with a focus on identifying optimal PA thresholds for sustained improvements in lipid profiles. Methods: Utilizing data from the China Health and Retirement Longitudinal Study (CHARLS, 2011–2020), 3,719 participants aged ≥45 years were stratified into quartiles (Q1–Q4) based on weekly metabolic equivalent hours (MET-h/week). Restricted cubic spline (RCS) models and Cox proportional hazards regression adjusted for demographics, lifestyle factors, and physiological parameters were employed to assess nonlinear associations between PA dose and dyslipidemia incidence (defined by elevated TC, TG, LDL-C, or reduced HDL-C). Results: Higher PA levels demonstrated a graded reduction in dyslipidemia risk. Compared to Q1 (lowest activity), Q4 (highest activity) exhibited a 19% lower risk (fully adjusted HR=0.81, 95% CI:0.66–0.98). RCS analysis revealed a nonlinear dose-response curve, with maximal risk reduction at 63.88–163.66 MET-h/week. Subgroup analyses confirmed consistent protective effects across genders, age groups, and BMI categories. Notably, PA exerted heterogeneous effects on lipid subcomponents: HDL-C and TG showed the strongest improvements, while LDL-C reductions plateaued at higher PA doses. Conclusions: This longitudinal study advocates metabolically-tailored PA prescriptions for dyslipidemia, with a nonlinear dose-response curve refuting \"more is better\" assumptions. Lipid-specific mechanisms demand differentiated exercise regimens: dose-dependent HDL-C optimization versus moderate LDL-C control. Age- and region-specific PA responsiveness underscores demographically-informed guidelines. These findings provide evidence to inform precision exercise guidelines aimed at reducing cardiovascular risk in aging populations. Physical Activity Dyslipidemia CHARLS Dose-response Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Dyslipidemia—clinically characterized by elevated total cholesterol, low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG), alongside reduced high-density lipoprotein cholesterol (HDL-C) [ 1 ],[ 2 ] , represents a modifiable risk factor for global disease burden. As a primary driver of cardiovascular morbidity, it contributes significantly to atherosclerotic cardiovascular disease (ASCVD) pathogenesis [ 3 ]–[ 5 ] . In China alone, ASCVD accounted for 66% of cardiovascular-related deaths (2.4 million) in 2016 [ 6 ] , highlighting the urgent need for effective management. Its etiology reflects a complex interplay between genetic predisposition and modifiable factors such as sedentary behavior, poor diet, obesity, and metabolic dysfunction [ 7 ]–[ 10 ] . Current U.S. and European guidelines prioritize statins as first-line pharmacotherapy for LDL-C reduction [ 11 ],[ 12 ] . However, statin therapy presents limitations including contraindications in pregnancy, hepatotoxicity risks, and cost barriers [ 13 ] . These constraints have prompted clinical guidelines to emphasize early lifestyle modifications—particularly increased physical activity (PA), nutritional interventions, weight control, and smoking cessation—as foundational preventive measures [ 14 ]–[ 16 ] . PA, defined as skeletal muscle-mediated energy expenditure exceeding resting levels [ 17 ] , exerts multifaceted lipid-regulatory effects. Regular engagement reduces triglycerides by ≤ 50%, elevates HDL-C by 5–10%, and modifies LDL particle composition from atherogenic small-dense to less harmful large-buoyant subtypes [ 18 ]–[ 20 ] . Global recommendations endorse 3.5-7 hours per week of moderate-to-vigorous PA (30–60 minutes daily) [ 21 ] , supported by Zhang et al.'s cross-sectional study of 17,535 Chinese adults linking low leisure-time PA to adverse lipid profiles [ 22 ] . Although the lipid benefits of PA are well-established, its dose-response dynamics resemble those of pharmacotherapy, requiring careful calibration for both efficacy and safety [ 23 ] . Notably, high-intensity PA yields lipid improvements comparable to moderate levels [ 24 ] , but with heightened risks of cardiovascular strain, musculoskeletal injury, and gastrointestinal issues [ 25 ]–[ 27 ] . This underscores the need to define both the minimum effective and maximum safe thresholds of PA. Moreover, whether PA-induced lipid improvements are sustained long-term remains uncertain, given limited longitudinal evidence. Do different PA doses exert distinct effects or exhibit dose-gradient relationships across lipid components (TC, TG, HDL-C, LDL-C)? The literature remains inconclusive [ 22 ],[ 28 ]–[ 30 ] . In an effort to better understand these issues, this study conducted a 10-year longitudinal analysis (2011–2020) of Chinese adults aged 45 + using data from the China Health and Retirement Longitudinal Study (CHARLS). A restricted cubic spline (RCS) model explored the nuanced relationship between PA dose (measured in MET-hours/week) and dyslipidemia risk. Stratified regression models adjusted for age, gender, region, smoking/alcohol history, and BMI were used to analyze associations across PA quartiles (Q1–Q4) and dyslipidemia incidence. Distinct effects of PA emerged across lipid subtypes—TC, TG, LDL-C, and HDL-C—each displaying unique response patterns. The findings offer enhanced clinical evidence for developing personalized PA-based interventions targeting specific dyslipidemia profiles. Methods Study design and population This analysis utilized data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative cohort of adults ≥ 45 years approved by Peking University ’ s ethics board (IRB00001052-11015), with written consent from all participants. As a retrospective analysis of existing anonymized datasets, patients and the public were not involved in the design, implementation, reporting, or dissemination plans of this research. Methodological details, including sampling and eligibility criteria, were previously described [ 31 ] . Baseline data collection occurred from June 2011 to March 2012, capturing 17,708 participants from 10,257 households nationwide. These individuals participated in biennial follow-up assessments (2013, 2015, 2018, 2020) conducted via face-to-face interviews using Computer-Assisted Personal Interviewing (CAPI) systems. The analytical sample comprised 3,719 participants divided into quartiles according to their weekly metabolic equivalent of task hours (MET-h/week). As depicted in Fig. 1 , exclusion criteria eliminated 13,863 initial participants based on: (1) unavailable MET-h/week measurements (n = 10,910) stemming from randomized physical activity question modules in 2011–2015 survey waves; (2) incomplete baseline dyslipidemia records (n = 811); (3) age ineligibility or missing age data (n = 350); and (4) loss to follow-up (n = 1,918). Data Collection and Operational Definitions The CHARLS study employed standardized protocols for data collection, including blood pressure measurements (triplicate seated readings averaged after ≥ 5-min rest) and anthropometrics (height, weight, waist circumference in light clothing; BMI calculated as kg/m² with ≥ 25 defining overweight). Functional capacity was assessed using validated ADL (6-item self-care tasks, e.g., feeding, hygiene) and IADL (5-item societal skills, e.g., finances, cooking) scales, scoring 1 point per limitation (max 6 ADL/5 IADL; higher scores = greater impairment). Exercise adherence to medical advice and self-reported sleep duration were recorded. Dyslipidemia diagnosis combined healthcare-verified self-reports with clinical thresholds: elevated TG (≥ 150 mg/dL), TC (≥ 200 mg/dL), or LDL (≥ 130 mg/dL), or reduced HDL (< 40 mg/dL men/<50 mg/dL women). Chronic diseases were medically confirmed from 14 predefined conditions. These operationalized metrics enabled analysis of physical activity, functional status, and dyslipidemia risk linkages. Exposure Assessment and Outcome Determination This longitudinal investigation examined the temporal relationship between physical activity (PA) and dyslipidemia development using data spanning from the 2011 baseline survey through either the first documented dyslipidemia event or study termination in 2020. The primary exposure metric, weekly metabolic equivalent hours (MET-h/week), was derived from an enhanced International Physical Activity Questionnaire (IPAQ) [ 32 ] , that systematically evaluated three PA domains: (1) Intensity stratification (vigorous/moderate/walking); (2) Duration categorization (> 4hr, 2-4hr, 30min-2hr, < 30min); (3) Weekly frequency (1–7 days). To align with CHARLS methodology while preserving cross-study comparability, we implemented three protocol adaptations: (1) Interval standardization using midpoint values (e.g., 3hr for 2-4hr category); (2) Minimum duration threshold (≥ 10min/session per IPAQ guidelines); (3) Boundary adjustments (lower-bound truncation at 30min, upper-limit capping at 4hr). The MET calculation framework operationalized intensity-specific multipliers: (1) 3.3 METs for walking × daily minutes × weekly days (2) 4.0 METs for moderate activity × daily minutes × weekly days (3) 8.0 METs for vigorous activity × daily minutes × weekly days Summation of these components yielded the comprehensive MET-h/week metric [ 33 ] , calculated as: Total PA = Σ(Walking + Moderate + Vigorous MET-h/week). Statistical Analysis All analyses were performed in R statistical environment (version 4.3.0) with statistical significance defined as two-tailed p < 0.05. Continuous variables were characterized using appropriate measures of central tendency and dispersion: normally distributed parameters reported as mean ± standard deviation, and non-normally distributed variables expressed as median (interquartile range). Distribution normality was verified through Shapiro-Wilk testing, with parametric comparisons conducted via ANOVA and non-parametric analyses using Kruskal-Wallis H-test. Categorical data were presented as counts with proportions (%) and analyzed using χ² tests. Missing data were handled through multiple imputation techniques to maintain analytical integrity while reducing potential bias. Kaplan-Meier methodology generated cumulative incidence curves for dyslipidemia development, stratified by baseline physical activity quartiles. Three sequential Cox proportional hazards models were developed to examine MET-h/week-dyslipidemia associations: Crude Model: Unadjusted baseline analysis Demographic-Adjusted Model: Controlled for age, gender, marital status, residence, smoking/alcohol consumption, educational attainment, chronic disease status, and baseline exercise patterns Full Model: Further adjusted for physiological parameters (blood pressure, waist circumference, BMI), sleep duration, and functional capacity scores (ADL/IADL) Multicollinearity was systematically evaluated through variance inflation factors (VIF < 10 for all covariates). Stratified analyses examined effect modification across key subgroups: age dichotomization (< 60 vs ≥ 60 years), gender, smoking/drinking status, marital status, urban/rural residence, and BMI categories (< 25 vs ≥ 25 kg/m²). These stratification procedures enabled assessment of association robustness across population subsets. Restricted cubic spline (RCS) regression with four knots (5th, 35th, 65th, 95th percentiles) elucidated potential nonlinear dose-response relationships. To investigate the dose–response relationship between MET-h/week and the incidence of dyslipidemia, RCS based on Cox regression models was employed, adjusting covariates in model 3, and the MET-h/week value at HR = 1 was treated as the reference. All regression models satisfied proportional hazards assumptions and demonstrated adequate goodness-of-fit through diagnostic testing. Results Participants characteristics The analytical cohort comprised 3,719 subjects stratified into quartiles (Q1-Q4) according to weekly metabolic equivalent hours (MET-h/week). As detailed in Table 1 , significant interquartile differences emerged across demographic, physiological, and health status parameters (all P < 0.05 unless specified). Stratification by activity levels revealed substantial variations in baseline characteristics, with a consistent inverse relationship between MET-h/week quartiles and participant age ( P < 0.001). Notably, participants in the most active quartile (Q4) demonstrated the youngest mean age. Physiological profiling identified progressive improvements across activity quartiles in multiple cardiometabolic indicators: systolic blood pressure (129.98 ± 21.67 vs 128.19 ± 20.72 mmHg, Q1 vs Q4), diastolic blood pressure (76.02 ± 12.03 vs 74.18 ± 12.05 mmHg), body weight (60.06 ± 12.33 vs 57.48 ± 10.78 kg), waist circumference (86.60 ± 12.15vs82.14 ± 11.12 cm), and BMI (24.04 ± 4.10 vs 22.82 ± 3.50 kg/m²) all decreased significantly with increasing activity levels( P < 0.001–0.003). Sleep duration remained comparable across quartiles( P = 0.348). Population stratification showed distinct demographic patterns associated with activity intensity. Higher MET-h/week quartiles contained proportionally more males, alcohol consumers, smokers, married individuals, and rural residents (all P < 0.001). Conversely, educational attainment exhibited an inverse relationship with activity levels, showing decreasing proportions of highly educated participants in upper quartiles. Health outcome analysis revealed two divergent patterns: while chronic disease prevalence showed no interquartile variation ( P = 0.188), dyslipidemia rates demonstrated significant dose-dependent reduction across activity quartiles (34.71% vs 23.76%, Q1 vs Q4; P < 0.001). Functional capacity assessments through ADL/IADL scales indicated superior performance in higher activity groups, with Q4 participants reporting 87.79% ADL independence versus 80.10% in Q1 ( P < 0.001). Clinician-initiated exercise recommendations showed quartile-dependent adoption rates, increasing from 66.15% in Q1 to 100% in upper quartiles ( P < 0.001). Table 1 Baseline characteristics of participants stratified by quartiles of METs Characteristics Overall (n = 3719) Quartiles of Mets P value Q1 (n = 1040) Q2 (n = 841) Q3 (n = 929) Q4 (n = 909) Met, h/week 104.30 (28.88, 224.00) 7.70 (0.00,28.88) 69.30 (57.38,84.00) 153.30 (119.70,185.00) 328.30 (280.88,368.40) < .001 Dyslipidemia, n (%) 1100 (29.58) 361 (34.71) 270 (32.10) 253 (27.23) 216 (23.76) < .001 Age, years 58.21 ± 8.62 60.43 ± 9.60 58.53 ± 8.73 57.22 ± 7.82 56.37 ± 7.47 < .001 SBP, mmHg 127.98 ± 20.83 129.98 ± 21.67 128.19 ± 20.72 127.17 ± 20.61 126.33 ± 20.01 < .001 DBP, mmHg 74.91 ± 11.95 76.02 ± 12.03 74.85 ± 11.60 74.34 ± 12.02 74.28 ± 12.05 0.003 Weight, kg 58.91 ± 11.50 60.06 ± 12.33 59.58 ± 11.16 58.42 ± 11.34 57.48 ± 10.78 < .001 Waist, cm 84.28 ± 12.21 86.60 ± 12.15 84.53 ± 13.03 83.56 ± 12.08 82.14 ± 11.12 < .001 Bmi, kg/m 2 23.54 ± 3.84 24.04 ± 4.10 23.83 ± 3.76 23.41 ± 3.78 22.82 ± 3.5 < .001 Sleep, h 6.43 ± 1.84 6.36 ± 1.89 6.43 ± 1.84 6.42 ± 1.88 6.51 ± 1.75 0.348 Female, n (%) 2037 (54.77) 623 (59.90) 485 (57.67) 523 (56.30) 406 (44.66) < .001 Drinking, n (%) 1193 (32.08) 268 (25.77) 243 (28.89) 300 (32.29) 382 (42.02) < .001 Smoking, n (%) 1122 (29.18) 269 (25.19) 216 (24.63) 279 (29.00) 358 (38.17) < .001 Married, n (%) 3354 (90.19) 897 (86.25) 751 (89.30) 855 (92.03) 851 (93.62) < .001 Rural residence, n (%) 2437 (65.53) 573 (55.10) 482 (57.31) 652 (70.18) 730 (80.31) < .001 Education, n (%) < .001 Illiterate 1726 (46.41) 458 (44.04) 360 (42.81) 456 (49.09) 452 (49.72) Primary school 803 (21.59) 228 (21.92) 168 (19.98) 202 (21.74) 205 (22.55) Middle school 786 (21.13) 229 (22.02) 185 (22.00) 187 (20.13) 185 (20.35) High school and above 404 (10.86) 125 (12.02) 128 (15.22) 84 (9.04) 67 (7.37) Exercise, n (%) 3367 (90.54) 688 (66.15) 841 (100.00) 929 (100.00) 909 (100.00) < .001 Chronic, n (%) 2461 (64.01) 691(64.70) 560(63.85) 632(65.70) 578(61.62) 0.188 6 ADL diff, n (%) < .001 0 3164 (85.08) 833 (80.10) 731 (86.92) 802 (86.33) 798 (87.79) 1 289 (7.77) 85 (8.17) 58 (6.90) 81 (8.72) 65 (7.15) 2 122 (3.28) 40 (3.85) 28 (3.33) 26 (2.80) 28 (3.08) 3 52 (1.40) 23 (2.21) 14 (1.66) 7 (0.75) 8 (0.88) 4 44 (1.18) 29 (2.79) 5 (0.59) 6 (0.65) 4 (0.44) 5 33 (0.89) 19 (1.83) 5 (0.59) 6 (0.65) 3 (0.33) 6 15 (0.40) 11 (1.06) 0 (0.00) 1 (0.11) 3 (0.33) 5 IADL diff, n (%) < .001 0 3006 (80.83) 772 (74.23) 708 (84.19) 765 (82.35) 761 (83.72) 1 327 (8.79) 88 (8.46) 65 (7.73) 97 (10.44) 77 (8.47) 2 183 (4.92) 64 (6.15) 36 (4.28) 41 (4.41) 42 (4.62) 3 104 (2.80) 46 (4.42) 22 (2.62) 17 (1.83) 19 (2.09) 4 60 (1.61) 39 (3.75) 6 (0.71) 5 (0.54) 10 (1.10) 5 39 (1.05) 31 (2.98) 4 (0.48) 4 (0.43) 0 (0.00) BMI : body mass index; Sleep : self-reported average daily hours of sleep over the past month; SBP : systolic blood pressure; DBP : diastolic blood pressure; MET : metabolic equivalent of task; Recommended Exercise : ever received medical exercise advice? Functional limitations were quantified using: ADL (Activities of Daily Living) score (0–6 scale assessing six basic daily tasks) and IADL (Instrumental Activities of Daily Living) score (0–5 scale evaluating complex life-management activities). Associations of baseline MET with incident Dyslipidemia Survival curve analysis demonstrated a consistent inverse relationship between baseline physical activity levels (MET-h/week) and cumulative dyslipidemia incidence. Participants in the highest activity quartile (Q4) maintained the lowest cumulative risk throughout follow-up, contrasting sharply with Q1 (lowest activity group) which exhibited the highest risk profile. Curve divergence became evident shortly after follow-up initiation (As shown in Fig. 2 ), with risk gradients progressively widening over time. By 108 months, the Q1-Q4 disparity reached maximal differentiation, while intermediate quartiles (Q2-Q3) showed graduated protective effects, confirming activity-dependent risk reduction. Using restricted cubic spline (RCS) regression, we examined the nonlinear dose-response relationship between MET-h/week and dyslipidemia risk. Nodes were placed at the 5th (0 MET-h/week), 35th (63.88 MET-h/week), 65th (163.66 MET-h/week), and 95th (377.5 MET-h/week) percentiles of the MET-h/week distribution, with 104.3 MET-h/week (HR = 1) set as the reference value. The analysis revealed a significant overall negative association between total MET-h/week and dyslipidemia risk ( P for overall < 0.001). The strongest risk reductions were observed in the range of 63.88–163.66 MET-h/week, with the risk plateauing at higher activity levels and showing a minor resurgence (As shown in Fig. 3 A). After adjusting for all confounders, the negative association remained significant ( P for overall = 0.014), although nonlinearity was not statistically significant ( P for non−linearity = 0.153; As shown in Fig. 3 B). In the Cox proportional hazards models, higher physical activity levels (MET-h/week) demonstrated progressive protective effects against dyslipidemia. In the unadjusted model (Model 1), hazard ratios (HRs) for Q2, Q3, and Q4 compared to Q1 were 0.90 (95%CI 0.77–1.05, P = 0.186),0.73 (95%CI 0.63–0.86, P < 0.001), and 0.62 (95%CI 0.52–0.73, P < 0.001) respectively, showing significant risk reduction from Q3 onward. After adjusting for demographic and behavioral covariates (Model 2), the protective effects persisted with HRs of 0.80 (0.67–0.96, P = 0.002) for Q3 and 0.74 (0.61–0.90, P = 0.001) for Q4. In the fully adjusted model (Model 3) accounting for physiological parameters (SBP, DBP, anthropometrics) and functional status scores, the highest quartile (Q4) maintained significant protection with HR = 0.81 (0.66–0.98, P = 0.035). Although Q3 showed non-significant trend (HR = 0.85,0.71–1.02, P = 0.088) and Q2 demonstrated neutral effect (HR = 0.97,0.81–1.16, P = 0.714), the monotonic dose-response pattern across quartiles remained evident. Notably, per SD increase analysis revealed consistent inverse associations across all models, with fully adjusted HR = 0.92 (0.85–0.99, P = 0.019), suggesting every standard deviation increase in MET-h/week corresponds with 8% risk reduction independent of comprehensive covariates (As shown in Table 2 ). Table 2 Multivariate-adjusted hazard ratios of MET-h/week for dyslipidemia Variables Total N No. of events (Incident rate) Model 1 Model 2 Model 3 HR (95%CI) P HR (95%CI) P HR (95%CI) P Per SD increase 3719 1100(29.6) 0.83(0.77 ~ 0.88) < .001 0.89(0.83 ~ 0.95) 0.001 0.92 (0.85 ~ 0.99) 0.019 Q1 1040 361(34.7) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Q2 841 270(32.1) 0.90 (0.77 ~ 1.05) 0.186 0.92 (0.77 ~ 1.10) 0.37 0.97 (0.81 ~ 1.16) 0.714 Q3 929 253(27.2) 0.73 (0.63 ~ 0.86) < .001 0.80 (0.67 ~ 0.96) 0.002 0.85 (0.71 ~ 1.02) 0.088 Q4 909 216(23.8) 0.62(0.52 ~ 0.73) < .001 0.74 (0.61 ~ 0.90) 0.001 0.81 (0.66 ~ 0.98) 0.035 HR: Hazard Ratio, CI: Confidence Interval Model1: unadjusted Model2: Adjust: gender, age, marital status, rural residence, drinking, smoking, education, chronic, recommended exercise Model3: Adjust: gender, age, marital status, rural residence, drinking, smoking, education, chronic, recommended exercise, SBP, DBP, weight, waist, BMI, sleep, ADL difficulty Score, IADL difficulty score Subgroup analyses Using the threshold of 104.3 MET-h/week identified through restricted cubic spline (RCS) regression (As shown in Table 3 ), physical activity levels were dichotomized into Light Physical Activity (LPA: <104.3 MET-h/week) and Moderate-to-Vigorous Physical Activity (MVPA: ≥104.3 MET-h/week). The analysis demonstrated that sustained MVPA significantly reduced dyslipidemia risk, with a hazard ratio (HR) of 0.74 (95% CI: 0.66–0.83; P < 0.001). Stratified analyses revealed consistent protective effects of MVPA across most subgroups. Significant risk reductions were observed in both males (HR = 0.80, P = 0.015) and females (HR = 0.71, P < 0.001), participants aged < 60 years (HR = 0.74, P < 0.001) and ≥ 60 years (HR = 0.76, P = 0.003), as well as across drinking status, smoking status, and residential areas (urban/rural). No significant interaction effects were detected between MVPA and these stratification variables ( P interaction >0.05 for all), indicating robust protective associations regardless of demographic or behavioral characteristics. Intriguingly, the risk reduction associated with MVPA did not reach statistical significance in unmarried participants (HR = 0.85, P = 0.403). This observation may reflect limited statistical power due to smaller subgroup size (n = 368 unmarried vs. n = 3,477 married), or potential confounding by differential social support patterns and health behaviors between marital status groups. Further investigation is warranted to elucidate these relationships. Table 3 Subgroup analysis of the association between MET-h/week and dyslipidemia Variables n (%) LPA MVPA HR (95%CI) P P for interaction All patients 3719 (100.00) 616/1845 484/1874 0.73 (0.65 ~ 0.83) < .001 Gender 0.219 Female 2037 (54.77) 381/1085 245/952 0.69 (0.59 ~ 0.81) < .001 Male 1682 (45.23) 235/760 239/922 0.80 (0.67 ~ 0.96) 0.018 Age 0.751 < 60 2209 (59.40) 328/996 306/1213 0.73 (0.62 ~ 0.85) < .001 ≥ 60 1510 (40.60) 288/849 178/661 0.76 (0.63 ~ 0.91) 0.003 Drinking 0.848 No 2526 (67.92) 468/1344 323/1182 0.74 (0.64 ~ 0.85) < .001 Yes 1193 (32.08) 148/501 161/692 0.76 (0.61 ~ 0.95) 0.016 Smoking 0.681 No 2615 (70.31) 477/1380 330/1235 0.73 (0.64 ~ 0.84) < .001 Yes 1104 (29.69) 139/465 154/639 0.77 (0.61 ~ 0.97) 0.028 Residence 0.648 Rural 2437 (65.53) 317/1037 336/1400 0.75 (0.65 ~ 0.88) < .001 Urban 1282 (34.47) 299/808 148/474 0.81 (0.66 ~ 0.98) 0.033 Bmi 0.170 < 25 2563 (68.92) 344/1200 293/1363 0.71 (0.61 ~ 0.83) < .001 ≥ 25 1156 (31.08) 272/645 191/511 0.85 (0.71 ~ 1.02) 0.084 HR: Hazard Ratio, CI: Confidence Interval Dose-gradient modulation of dyslipidemia by physical activity Building on previous findings from 2011–2020 (N = 3,791), which demonstrated that physical activity exceeding guideline-recommended thresholds significantly reduces dyslipidemia risk—albeit with plateauing benefits at higher doses. To further elucidate the dose-dependent modulation of dyslipidemia by physical activity, this study investigates the differential effects of exercise intensity on lipid subcomponents (TC, TG, HDL-C, LDL-C). By narrowing the observational window to 2011–2015 (capturing two timepoints with complete lipid profiles in the CHARLS database), we analyzed 2,710 eligible participants after excluding 1,081 cases with incomplete or substandard data. This analysis involving 2,710 participants stratified by physical activity levels (MET-h/week) into quartiles (Q1-Q4, ascending activity) revealed biomarker-specific dose-response relationships in lipid metabolism. Total cholesterol (TC) demonstrated fluctuating reductions with increased activity, peaking in Q1 (189.29 ± 40.45 mg/dL) followed by Q2 (183.26 ± 34.60), Q3 (184.02 ± 34.43), and Q4 (183.67 ± 40.69 mg/dL) (P = 0.007). Post-hoc analysis confirmed significant differences exclusively between Q1 and other quartiles (all P < 0.05), with no inter-quartile variations among Q2-Q4 (P > 0.05). Triglycerides (TG) exhibited marginal decline across quartiles (Q1:146.50 ± 87.08; Q2:138.60 ± 81.32; Q3:138.72 ± 90.14; Q4:134.38 ± 94.87 mg/dL), showing borderline overall significance (P = 0.075) and isolated Q1-Q4 difference (P = 0.0128). Demonstrating graded biological response, HDL-C concentrations exhibited progressive elevation from Q1 (50.13 ± 10.66 mg/dL) to Q4 (54.47 ± 13.78 mg/dL) with robust dose-dependent significance (P < 0.001). All adjacent quartile comparisons reached significance except Q1-Q2 (P < 0.05). Conversely, low-density lipoprotein cholesterol (LDL-C) manifested inverse dose-response, with Q1 (107.40 ± 30.88 mg/dL) significantly exceeding Q2 (102.32 ± 27.30), Q3 (101.61 ± 26.61), and Q4 (100.08 ± 29.98 mg/dL) (all P < 0.05), while no differences emerged among higher quartiles (P > 0.05). These findings delineate differential lipid responsiveness: HDL-C and LDL-C exhibited graded sensitivity to physical activity, TC reductions primarily reflected baseline differences between sedentary (Q1) and active groups, while TG demonstrated limited responsiveness (As shown in Table 4 and Fig. 4 ). Table 4 Gradient trends in lipid abnormality prevalence across quartile groups Variables (mg/dL) Total (n = 2710) Q1 (n = 733) Q2 (n = 625) Q3 (n = 685) Q4 (n = 667) P TC ≥ 200, n (%) 833 (30.74) 254 (34.65) 186 (29.76) 206 (30.07) 187 (28.04) 0.046 TG ≥ 150, n (%) 822 (30.33) 256 (34.92) 202 (32.32) 197 (28.76) 167 (25.04) < .001 LDLC ≥ 130, n (%) 420 (15.50) 142 (19.37) 89 (14.24) 96 (14.01) 93 (13.94) 0.009 HDL-C<39/50, n (%) 861 (31.77) 281 (38.34) 217 (34.72) 209 (30.51) 154 (23.09) < .001 Further quartile-based analysis (Q1-Q4 by MET-h/week) demonstrated significant heterogeneity in dyslipidemia thresholds (TC ≥ 200, TG ≥ 150, LDL-C ≥ 130, HDL-C < 39/50 mg/dL; all P < 0.05) [ 34 ] . HDL-C deficiency prevalence declined stepwise from 38.34% (281/733) in Q1 to 23.09% (154/667) in Q4 (P < 0.001), paralleled by TG abnormalities decreasing from 34.92% (256/733) to 25.04% (167/667). Notably, TC abnormalities peaked in Q1 (34.65%, 254/733), declined in Q2 (29.76%, 186/625), rebounded slightly in Q3 (30.07%, 206/685), and reached 28.04% (187/667) in Q4, reflecting a fluctuating decline (P = 0.046). LDL-C abnormalities were markedly higher in Q1 (19.37%, 142/733) compared to subsequent quartiles (14.24–13.94%), suggesting threshold-dependent regulation. These findings underscore the gradient-specific risk modulation across lipid biomarkers, with HDL-C and TG showing the clearest dose-response patterns, while TC and LDL-C exhibited attenuated but statistically meaningful trends (As shown in Table 5 and Fig. 5 ). Table 5 Dose-response comparison of Q1- Q4 on lipid metabolic markers Variables Total (n = 2710) Q1 (n = 733) Q2 (n = 625) Q3 (n = 685) Q4 (n = 667) P TC,(mg/dL) 185.18 ± 37.82 189.29 ± 40.45 183.26 ± 34.60 184.02 ± 34.43 183.67 ± 40.69 0.007 TG,(mg/dL) 139.73 ± 88.64 146.50 ± 87.08 138.60 ± 81.32 138.72 ± 90.14 134.38 ± 94.87 0.075 HDL-C,(mg/dL) 51.93 ± 12.18 50.13 ± 10.66 50.75 ± 11.20 52.45 ± 12.48 54.47 ± 13.78 < .001 LDL-C,(mg/dL) 102.96 ± 28.93 107.40 ± 30.88 102.32 ± 27.30 101.61 ± 26.61 100.08 ± 29.98 < .001 Discussion The present research, leveraging data from the CHARLS project, examines the relationship between physical activity levels (measured in metabolic equivalent hours per week, MET-h/week) and dyslipidemia risk. The results demonstrate a significant inverse correlation between baseline physical activity levels and the incidence of dyslipidemia over a 10-year follow-up period, with higher activity intensity amplifying protective effects. These findings align with research by Qinpei Zou et al., which identified a negative correlation between total physical activity (TPA) and dyslipidemia, further highlighting the critical role of occupational activity in elevating high-density lipoprotein cholesterol (HDL-C) levels, particularly in lipid management [ 28 ] . Beyond dyslipidemia incidence, the high-activity group exhibited superior outcomes in blood pressure, body weight, waist circumference, and BMI (P < 0.001–0.002). This phenomenon may stem from skeletal muscle-derived IL-6 suppressing hypothalamic AgRP neuronal activity, thereby reducing ghrelin secretion and activating brown adipose tissue thermogenesis [ 35 ]–[ 37 ] . These mechanisms explain the gradient reduction in body weight observed in the Q4 group (Q1: 60.05 kg vs. Q4: 57.47 kg), which correlated dose-dependently with MET values (Q4 median MET: 328.3 h/week). Notably, no intergroup differences in sleep duration were observed (P = 0.531), potentially linked to activity type. Recent studies indicate that high-intensity interval training (HIIT) increases the proportion of slow-wave sleep without prolonging total sleep duration, which aligns with the observed 'activity-sleep decoupling' phenomenon [ 38 ]–[ 40 ] . Individuals with higher education levels were disproportionately represented in low-activity groups (high school or above: Q1 12.27% vs. Q4 7.14%, P < 0.001), suggesting the existence of a \"cognitive-physical trade-off effect\" in Chinese society [ 41 ] . Occupations with high cognitive demands (e.g., white-collar roles) over activate the default mode network (DMN), suppressing striatal dopamine D2 receptor expression and inducing \"decision fatigue,\" thereby diminishing exercise motivation [ 42 ]–[ 44 ] . his observation aligns with the empirical study by Loyen Anne et al. (year) on sedentary behavior in four European nations. Their findings indicate that individuals with higher educational attainment spend, on average, more than 530 minutes per day in sedentary activities [ 45 ] . Notably, Q2-Q4 groups received physician exercise recommendations more frequently (Q1: 65.73% vs. Q2-Q4: 100%, P < 0.001), reflecting a \"positive feedback loop\" in healthcare systems. However, this pattern may trap low-activity populations (Q1) in a \"guidance deficiency-risk accumulation\" cycle, particularly given that 60.49% of Q1 participants were female. The functional advantages in activities of daily living (ADL/IADL) observed in high-activity groups (Q4 ADL independence: 88.06% vs. Q1 80.52%) may arise from neuromuscular optimization, such as cerebellar-basal ganglia circuit remodeling [ 46 ],[ 47 ] . Animal studies indicate that regular exercise increases synaptophysin expression in cerebellar Purkinje cells by 30%, improving motor coordination precision and elevating gait stability in older adults by 22% [ 48 ],[ 49 ] . Survival curves, restricted cubic spline (RCS) regression, and Cox models systematically delineated the dynamic relationship between physical activity (MET-h/week) and dyslipidemia risk. Even after adjusting for multiple covariates, the significant negative relationship between baseline physical activity levels and dyslipidemia risk persisted over the long follow-up period, further confirming the long-term protective effect of physical activity. This corresponds with the results of Lewington S. et al., who performed a meta-analysis of 25 randomized controlled trials, revealing that aerobic exercise at an intensity of 5.3 MET significantly elevated HDL-C levels by 2.53 mg/dL, irrespective of drugs or other lifestyle modifications [ 50 ] . Dagogo-Jack and his team discovered a robust connection between people's own reports of their physical activity levels, their waist size, and blood fats like triglycerides and HDL-C, and this was true for both African Americans and Caucasians. Their research really underscored how important exercise is in forecasting who might develop prediabetes or diabetes. On the flip side, while eating habits did have some link to body fat and blood fats, they weren't as big a factor in predicting these health issues [ 51 ] . All in all, their results show that staying active is super key to keeping cholesterol levels in check, even when you consider other things like medication, diet, and waist size. RCS analysis revealed the most pronounced risk reduction within the 63.88–163.66 MET-h/week range, with plateauing effects at higher doses, indicative of metabolic \"saturation.\" This threshold may correspond to molecular switches in lipid metabolism regulation. Animal studies show that exercise intensities reaching 60–70% of maximal oxygen uptake (VO 2 max) trigger AMPK phosphorylation, suppressing acetyl – CoA carboxylase (ACC) to reduce fatty acid synthesis while upregulating LDL receptor expression for accelerated LDL-C clearance [ 52 ]–[ 54 ] . Additionally, exercise-induced DNA demethylation persistently enhances lipoprotein lipase (LPL) gene promoter activity, potentially amplifying metabolic benefits post-threshold through cumulative epigenetic modifications [ 55 ],[ 56 ] . Subgroup analyses confirmed that moderate-to-vigorous physical activity (MVPA) universally reduced dyslipidemia risk across demographics. By 2020, male and female dyslipidemia prevalences were 28.0% and 30.7%, respectively. In contrast, Mutalifu et al. reported higher overall prevalence (39.3%) in Xinjiang (males: 52.6%, females: 24.3%) [ 57 ] , likely due to dietary patterns, rural population dominance, and occupational activity disparities. Despite rural residents constituting 80.38% of MVPA groups, their protective effects mirrored urban populations ( P-interaction > 0.05). Multiple Chinese research teams have attributed this parity to rural MVPA’s reliance on sustained agricultural labor, which exhibits higher lipid oxidation efficiency than urban intermittent training, albeit with chronic inflammation risks from repetitive motions [ 58 ],[ 59 ] . This study reveals dynamic associations between physical activity levels and lipid profiles. Specifically, high-density lipoprotein cholesterol (HDL-C) demonstrated a dose-dependent elevation across activity quartiles (Q1-Q4: 50.13 vs. 54.47 mg/dL, P < 0.001), with significant interquartile differences except between Q1 and Q2. The prevalence of HDL-C deficiency decreased progressively (Q1:38.34% vs. Q4:23.09%, P < 0.001), aligning with dose-dependent ApoA1 upregulation and HDL functional enhancement [ 60 ]–[ 62 ] . For low-density lipoprotein cholesterol (LDL-C), a stratified declining pattern was observed (Q1-Q4: 107.40 vs. 100.08 mg/dL, P < 0.05), with maximal reduction occurring during the sedentary-to-moderate activity transition (Q2:102.32 mg/dL). This aligns with S. Aho et al.'s findings that moderate-intensity exercise sufficiently reduces atherosclerosis risk [ 63 ],[ 64 ] . Notably, LDL-C abnormalities peaked at Q1 (19.37%) before stabilizing at lower levels (13.94%-14.24%), indicating threshold effects. Total cholesterol (TC) showed significant reduction only during initial sedentary population intervention (Q1-Q2:189.29 vs. 183.26 mg/dL, P < 0.05), supporting Marques-Leandro R's hypothesis regarding \"early-phase sensitive biomarkers\" [ 65 ] . TC abnormalities displayed a fluctuating decline (Q1:34.65% vs. Q4:28.04%, P = 0.046) with a minor Q3 rebound, suggesting phased modulation. Triglycerides (TG) exhibited non-linear reduction with significance only between extreme quartiles, underscoring both the necessity of high-intensity training for TG modulation and the limitations of moderate exercise [ 28 ],[ 66 ] . TG abnormalities showed linear reduction (Q1:34.92% vs. Q4: 25.04%), potentially through exercise-induced lipoprotein lipase activation [ 30 ] . These findings collectively emphasize the imperative for personalized lipid management strategies that incorporate exercise modalities, genetic predisposition, and metabolic baselines. limitations Notwithstanding the novel findings, several methodological considerations warrant discussion. First, while leveraging the nationally representative CHARLS cohort, specific exclusions were necessary to ensure data completeness and longitudinal validity. Although standard multiple imputation techniques were applied, the potential for residual selection bias cannot be entirely excluded. Second, physical activity quantification relied on self-reported questionnaires-a methodology balancing feasibility and granularity in large-scale studies. While rigorously adapted from validated IPAQ protocols, this approach may have introduced some degree of measurement inaccuracy inherent to recall-based assessments. Notably, the study's focus on longitudinal design prioritized participant retention over device-based monitoring, which future investigations could address through complementary accelerometry. Finally, while comprehensive covariate adjustment was implemented across demographic, behavioral, and clinical domains, the observational nature precludes complete exclusion of unmeasured confounders such as dietary patterns or genetic predisposition. Conclusion This 10-year CHARLS cohort study of adults ≥ 45 years revealed a S-shaped association between physical activity (PA) and dyslipidemia risk. Optimal protection occurred at 63.88–163.66 MET-h/week, with plateaued benefits and slight risk elevation beyond this range. PA differentially modulated lipid components: higher doses enhanced HDL-C, while moderate PA sufficed for LDL-C reduction, indicating distinct lipid-specific regulatory mechanisms. Demographic analysis showed the highest PA quartile (Q4) with optimal lipid profiles had younger age, male predominance, and rural residency, underscoring sociodemographic considerations in exercise prescription. These findings advocate for metabolically tailored PA regimens over uniform intensity escalation. Clinically, this supports stratified interventions aligning PA dosage with individual functional capacity and lipid metabolism characteristics, particularly for precision prevention in aging populations. Declarations Author contributions Kang Wan and Ruwen Wang contributed equally to this work and share first authorship. They were responsible for study design, data analysis, interpretation of results, and manuscript drafting. Hongmei Yan provided clinical insights on dyslipidemia, contributed to result interpretation, and revised the manuscript for important intellectual content. Wei Chen supported data collection and preprocessing and contributed to the methodological framework. Fuyi Ma assisted with literature review and statistical modeling. Yue Jin contributed to visualization and figure preparation. Ru Wang conceived the study, supervised the research process, secured funding, and critically revised the manuscript. All authors read and approved the final version of the manuscript. Funding This research was supported by multiple funding sources. Specifically, it received financial support from the Research and Innovation Grant for Graduate Students, Shanghai University of Sport (Grant No. YJSCX-2023-035), the National Natural Science Foundation of China (Grant No. 32271226), and the National Key R&D Program of China (Grant No. 2020YFA0803800). The authors gratefully acknowledge these contributions, which supported the research, authorship, and publication of this article. Data Availability Statement The data analyzed in this study are from the China Health and Retirement Longitudinal Study (CHARLS), which is publicly available at http://charls.pku.edu.cn. Researchers can apply for access to the dataset through the official website. The authors do not have any special access privileges to the data and others can obtain it in the same manner. Ethics approval and consent to participate This study utilized secondary data from the China Health and Retirement Longitudinal Study (CHARLS), which received ethical approval from the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015). All participants in CHARLS provided written informed consent. The current analysis was conducted in accordance with relevant guidelines and regulations. No additional ethical approval was required for this secondary data analysis. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author details 1 School of Exercise and Health, Shanghai University of Sport, Shanghai, China, 2 Physical Education College, Henan Sport University, Zhengzhou, China, 3 Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China, 4 Yangpu Hospital Affiliated to Tongji University School of Medicine, Department of Endocrinology, Shanghai, China. References Liu S, Li Y, Zeng X, et al. Burden of Cardiovascular Diseases in China, 1990–2016: Findings From the 2016 Global Burden of Disease Study[J]. JAMA Cardiol. 2019;4(4):342. Mooradian AD. Dyslipidemia in type 2 diabetes mellitus[J]. Nat Reviews Endocrinol. 2009;5(3):150–9. Ference BA, Yoo W, Alesh I, et al. Effect of long-term exposure to lower low-density lipoprotein cholesterol beginning early in life on the risk of coronary heart disease: a Mendelian randomization analysis[J]. J Am Coll Cardiol. 2012;60(25):2631–9. Lipid-related markers. and cardiovascular disease prediction[J]. JAMA. 2012;307(23):2499–506. Feigin VL, Stark BA, Johnson CO, et al. Global, regional, and national burden of stroke and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019[J]. Lancet Neurol. 2021;20(10):795–820. Zhao D, Liu J, Wang M, et al. Epidemiology of cardiovascular disease in China: current features and implications[J]. Nat Rev Cardiol. 2019;16(4):203–12. Berberich AJ, Hegele RA. A Modern Approach to Dyslipidemia[J]. Endocr Rev. 2022;43(4):611–53. Srikanth S, Deedwania P. Management of Dyslipidemia in Patients with Hypertension, Diabetes, and Metabolic Syndrome[J]. Curr Hypertens Rep. 2016;18(10):76. Klop B, Elte J, Cabezas M. Dyslipidemia in obesity: mechanisms and potential targets[J]. Nutrients. 2013;5(4):1218–40. Jialal I, Singh G. Management of diabetic dyslipidemia: An update[J]. World J Diabetes. 2019;10(5):280–90. Cholesterol Treatment Trialists’ (Ctt) Collaboration. Efficacy and safety of more intensive lowering of LDL cholesterol: a meta-analysis of data from 170,000 participants in 26 randomised trials[J]. Lancet. 2010;376(9753):1670–81. Cholesterol Treatment Trialists’ (Ctt) Collaborators. The effects of lowering LDL cholesterol with statin therapy in people at low risk of vascular disease: meta-analysis of individual data from 27 randomised trials[J]. Lancet. 2012;380(9841):581–90. Ridker PM. LDL cholesterol: controversies and future therapeutic directions[J]. Lancet. 2014;384(9943):607–17. Arvanitis M, Lowenstein CJ. Dyslipidemia[J]. Ann Intern Med. 2023;176(6):ITC81–96. Artinian NT, Fletcher GF, Mozaffarian D, et al. Interventions to promote physical activity and dietary lifestyle changes for cardiovascular risk factor reduction in adults: a scientific statement from the American Heart Association[J]. Circulation. 2010;122(4):406–41. Pearson GJ, Thanassoulis G, Anderson TJ, et al. 2021 Canadian Cardiovascular Society guidelines for the management of dyslipidemia for the prevention of cardiovascular disease in adults[J]. Can J Cardiol. 2021;37(8):1129–50. Barton H. Land use planning and health and well-being[J]. Land Use Policy. 2009;26:S115–23. Vanhees L, Geladas N, Hansen D, et al. Importance of characteristics and modalities of physical activity and exercise in the management of cardiovascular health in individuals with cardiovascular risk factors: recommendations from the EACPR. Part II[J]. Eur J Prev Cardiol. 2012;19(5):1005–33. Managing abnormal blood. lipids: a collaborative approach - PubMed[EB/OL]. [2024-11-29]. https://pubmed.ncbi.nlm.nih.gov/16286609/ Kraus WE, Houmard JA, Duscha BD, et al. Effects of the amount and intensity of exercise on plasma lipoproteins[J]. N Engl J Med. 2002;347(19):1483–92. Pelliccia A, Sharma S, Gati S et al. 2020 ESC Guidelines on Sports Cardiology and Exercise in Patients with Cardiovascular Disease[J]. Revista Española de Cardiología (English Edition), 2021, 74(6): 545. Zhang Y, Yang J, Ye J, et al. Separate and combined associations of physical activity and obesity with lipid-related indices in non-diabetic and diabetic patients[J]. Lipids Health Dis. 2019;18(1):49. Chen H, Cheng MC, Sun Y et al. Dose-response relationship between physical activity and frailty: A systematic review and meta-analysis[J]. Heliyon, 2024. Hupin D, Roche F, Gremeaux V, et al. Even a low-dose of moderate-to-vigorous physical activity reduces mortality by 22% in adults aged ≥ 60 years: a systematic review and meta-analysis[J]. Br J Sports Med. 2015;49(19):1262–7. Chomistek AK, Cook NR, Flint AJ, et al. Vigorous-intensity leisure-time physical activity and risk of major chronic disease in men[J]. Med Sci Sports Exerc. 2012;44(10):1898. Loprinzi PD. Dose–response association of moderate-to-vigorous physical activity with cardiovascular biomarkers and all-cause mortality: considerations by individual sports, exercise and recreational physical activities[J]. Prev Med. 2015;81:73–7. Powell KE, Paluch AE, Blair SN. Physical activity for health: What kind? How much? How intense? On top of what?[J]. Annu Rev Public Health. 2011;32(1):349–65. Zou Q, Su C, Du W, et al. Longitudinal Association between Physical Activity, Blood Lipids, and Risk of Dyslipidemia among Chinese Adults: Findings from the China Health and Nutrition Surveys in 2009 and 2015[J]. Nutrients. 2023;15(2):341. Mach F, Baigent C, Catapano AL, et al. 2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk[J]. Eur Heart J. 2020;41(1):111–88. Wang Y, Xu D. Effects of aerobic exercise on lipids and lipoproteins[J]. Lipids Health Dis. 2017;16(1):132. Zhao Y, Hu Y, Smith JP, et al. Cohort profile: the China health and retirement longitudinal study (CHARLS)[J]. Int J Epidemiol. 2014;43(1):61–8. Craig CL, Marshall AL, Sjöström M, et al. International physical activity questionnaire: 12-country reliability and validity[J]. Med Sci Sports Exerc. 2003;35(8):1381–95. Li X, Zhang W, Zhang W, et al. Level of physical activity among middle-aged and older Chinese people: evidence from the China health and retirement longitudinal study[J]. BMC Public Health. 2020;20(1):1682. Su X, Cai X, Pan Y, et al. Discordance of apolipoprotein B with low-density lipoprotein cholesterol or non-high-density lipoprotein cholesterol and coronary atherosclerosis[J]. Eur J Prev Cardiol. 2022;29(18):2349–58. Hwang E, Portillo B, Grose K, et al. Exercise-induced hypothalamic neuroplasticity: Implications for energy and glucose metabolism[J]. Mol Metabolism. 2023;73:101745. Zagmutt S, Mera P, Soler-Vázquez MC, et al. Targeting AgRP neurons to maintain energy balance: Lessons from animal models[J]. Biochem Pharmacol. 2018;155:224–32. Della Guardia L, Codella R. Exercise restores hypothalamic health in obesity by reshaping the inflammatory network[J]. Antioxidants. 2023;12(2):297. Frimpong E, Mograss M, Zvionow T, et al. Acute evening high-intensity interval training may attenuate the detrimental effects of sleep restriction on long-term declarative memory[J]. Sleep. 2023;46(7):zsad119. Min L, Wang D, You Y, et al. Effects of high-intensity interval training on sleep: a systematic review and meta-analysis[J]. Int J Environ Res Public Health. 2021;18(20):10973. Xie W, Lu D, Liu S et al. The optimal exercise intervention for sleep quality in adults: A systematic review and network meta-analysis[J]. Prev Med, 2024: 107955. Vine CA, Runswick OR, Blacker SD, et al. Cognitive, psychophysiological, and perceptual responses to a repeated military-specific load carriage treadmill simulation[J]. Hum Factors. 2024;66(10):2379–92. Simpson EH, Gallo EF, Balsam PD, et al. How changes in dopamine D2 receptor levels alter striatal circuit function and motivation[J]. Mol Psychiatry. 2022;27(1):436–44. Beeler JA, Faust RP, Turkson S, et al. Low dopamine D2 receptor increases vulnerability to obesity via reduced physical activity, not increased appetitive motivation[J]. Biol Psychiatry. 2016;79(11):887–97. Robertson CL, Ishibashi K, Chudzynski J, et al. Effect of exercise training on striatal dopamine D2/D3 receptors in methamphetamine users during behavioral treatment[J]. Neuropsychopharmacology. 2016;41(6):1629–36. Loyen A, Clarke-Cornwell AM, Anderssen SA, et al. Sedentary time and physical activity surveillance through accelerometer pooling in four European countries[J]. Sports Med. 2017;47(7):1421–35. Zuo L, Lan X, Zhou Y, et al. Cerebellar-cerebral circuits functional connectivity in patients with cognitive impairment after basal ganglia stroke: a pilot study[J]. Front Aging Neurosci. 2025;17:1478891. Veenhuizen Y, Cup EH, Jonker MA, et al. Self-management program improves participation in patients with neuromuscular disease: A randomized controlled trial[J]. Neurology. 2019;93(18):e1720–31. Huang TY, Lin LS, Cho KC, et al. Chronic treadmill exercise in rats delicately alters the Purkinje cell structure to improve motor performance and toxin resistance in the cerebellum[J]. J Appl Physiol. 2012;113(6):889–95. Tang C, Liu M, Zhou Z, et al. Treadmill Exercise alleviates cognition disorder by activating the FNDC5: dual role of integrin αV/β5 in Parkinson’s Disease[J]. Int J Mol Sci. 2023;24(9):7830. Lewington S, Whitlock G, Clarke R, et al. Prospective Studies Collaboration Blood cholesterol and vascular mortality by age, sex, and blood pressure: a meta-analysis of individual data from 61 prospective studies with 55,000 vascular deaths[J]. Lancet. 2007;370(9602):1829–39. Boucher AB, Adesanya EAO, Owei I, et al. Dietary habits and leisure-time physical activity in relation to adiposity, dyslipidemia, and incident dysglycemia in the pathobiology of prediabetes in a biracial cohort study[J]. Metab Clin Exp. 2015;64(9):1060–7. Hudson GM. The effects of resveratrol supplementation on glucose/insulin kinetics and transcription of the AMPK and insulin signaling pathways at rest and following an oral glucose tolerance test and graded exercise test in overweight women[M]. Baylor University; 2010. Patrick-Melin AJ. Effect of 7 Days Aerobic Exercise on Insulin Sensitivity, Oxidative Stress, TLR2/TLR4 Cell Surface Expression and Cytokine Secretion in Sedentary Obese Adults[D]. Kent State University; 2011. Zordoky BN, Nagendran J, Pulinilkunnil T, et al. AMPK-dependent inhibitory phosphorylation of ACC is not essential for maintaining myocardial fatty acid oxidation[J]. Circul Res. 2014;115(5):518–24. Marques LR, Diniz TA, Antunes BM, et al. Reverse cholesterol transport: molecular mechanisms and the non-medical approach to enhance HDL cholesterol[J]. Front Physiol. 2018;9:526. Lundsgaard AM, Fritzen AM, Kiens B. The importance of fatty acids as nutrients during post-exercise recovery[J]. Nutrients. 2020;12(2):280. Mutalifu M, Zhao Q, Wang Y, et al. Joint association of physical activity and diet quality with dyslipidemia: a cross-sectional study in Western China[J]. Lipids Health Dis. 2024;23(1):46. Li J, Zhang X, Zhang M, et al. Urban-rural differences in the association between occupational physical activity and mortality in Chinese working population: evidence from a nationwide cohort study[J]. The Lancet Regional Health–Western Pacific; 2024. p. 46. Zhu W, Chi A, Sun Y. Physical activity among older Chinese adults living in urban and rural areas: a review[J]. J Sport Health Sci. 2016;5(3):281–6. Muscella A, Stefàno E, Marsigliante S. The effects of exercise training on lipid metabolism and coronary heart disease[J]. Am J Physiol Heart Circ Physiol. 2020;319(1):H76–88. Wood G, Taylor E, Ng V, et al. Determining the effect size of aerobic exercise training on the standard lipid profile in sedentary adults with three or more metabolic syndrome factors: A systematic review and meta-analysis of randomised controlled trials[J]. Br J Sports Med. 2022;56(18):1032–41. Martínez-Vizcaíno V, Amaro-Gahete FJ, Fernández-Rodríguez R et al. Effectiveness of fixed-dose combination therapy (polypill) versus exercise to improve the blood-lipid profile: A network meta-analysis[J]. Sports Med, 2022: 1–13. Aho S, Vuoristo MS, Raitanen J, et al. Higher number of steps and breaks during sedentary behaviour are associated with better lipid profiles[J]. BMC Public Health. 2021;21(1):629. Navarese EP, Robinson JG, Kowalewski M, et al. Association between baseline LDL-C level and total and cardiovascular mortality after LDL-C lowering: A systematic review and meta-analysis[J]. JAMA. 2018;319(15):1566–79. Lr M, Ta D, Bm A et al. Reverse cholesterol transport: Molecular mechanisms and the non-medical approach to enhance HDL cholesterol[J]. Front Physiol, 2018, 9. Mahley RW, Arteriosclerosis. Thromb Vascular Biology. 2016;36(7):1305–15. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6932333\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":484665452,\"identity\":\"8e7642ad-ca4b-4f9a-98cb-411befe4593e\",\"order_by\":0,\"name\":\"Kang Wan\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shanghai University of Sport\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Kang\",\"middleName\":\"\",\"lastName\":\"Wan\",\"suffix\":\"\"},{\"id\":484665453,\"identity\":\"3229289d-d410-44fa-a228-72ec32a24679\",\"order_by\":1,\"name\":\"Ruwen 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Wang\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYPACGwjFQ4KWNNK1HCZBi8Hxs4dfV7adtzc4f4Dxwds2BnlzglrO5KVZnm27nbjhRgKz4dw2BsOdDQS0mB3IMTNsbLudYHCDgU2at40hweAAIS3n34C0nAM5jP03cVpu5Bg/bGw7wLjhQAIbM1Fa7G+8MWNsOJecOPNGYrPknHMShhsIaZHszzH+2FBmZ893/vDBD2/KbOQJ2gIEbBIQmrEBSEgQVg8EzB+IUjYKRsEoGAUjFwAAM1lCHGzlSbwAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Shanghai University of Sport\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Ru\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-06-19 14:53:34\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6932333/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6932333/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1186/s12889-025-24568-1\",\"type\":\"published\",\"date\":\"2025-09-29T15:56:51+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":86784708,\"identity\":\"4e128da0-ea41-4c8c-8cfe-d4451858adc1\",\"added_by\":\"auto\",\"created_at\":\"2025-07-15 13:57:54\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":742553,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe flowchart of study participants\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6932333/v1/a7add79314efe38dc55c7bdb.png\"},{\"id\":86784711,\"identity\":\"bd6ba555-5518-407c-8a12-7d7a612a2bb8\",\"added_by\":\"auto\",\"created_at\":\"2025-07-15 13:57:55\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":226254,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eKaplan–Meier curves for the cumulative incidence of dyslipidemia by baseline physical activity quartiles\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6932333/v1/1db81028a03b844318e514d4.png\"},{\"id\":86784710,\"identity\":\"8cf74ffa-b45b-453b-a5f8-ad7a1fad16c3\",\"added_by\":\"auto\",\"created_at\":\"2025-07-15 13:57:54\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":345503,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eUnadjusted (Fig.3A): significant negative association (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026lt;0.001), strongest reduction at 63.88–163.66 MET-h/week, plateau with minor resurgence (\\u003cem\\u003eP\\u003c/em\\u003e=0.094 for nonlinearity). Adjusted (Fig.3A): retained significance(\\u003cem\\u003eP\\u003c/em\\u003e=0.014) but nonsignificant nonlinearity (\\u003cem\\u003eP\\u003c/em\\u003e=0.153)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6932333/v1/d2d4ca6c5e1683bc9825751e.png\"},{\"id\":86784714,\"identity\":\"b9ff4409-7358-4d44-bdc4-a6ba23606648\",\"added_by\":\"auto\",\"created_at\":\"2025-07-15 13:57:55\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":97934,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDose-response relationships between physical activity quartiles and lipid subcomponents\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6932333/v1/cb4641b719bda7a890d6a755.png\"},{\"id\":86784715,\"identity\":\"6c057af9-4304-45af-acf5-f15920db6ce3\",\"added_by\":\"auto\",\"created_at\":\"2025-07-15 13:57:55\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":166245,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eQuartile-specific distribution of dyslipidemia across metabolic equivalent of task (MET)-hours per week\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6932333/v1/de9bdb4443f93f35669d6e12.png\"},{\"id\":92883572,\"identity\":\"9132796f-b74f-4e42-8fe9-7f925261d600\",\"added_by\":\"auto\",\"created_at\":\"2025-10-06 16:00:57\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2832004,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6932333/v1/4bde16f1-c5b5-497d-b872-ae79240a8b04.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Physical Activity Dose-Response and Long-Term Effects on Dyslipidemia in Chinese Adults: A CHARLS Study\",\"fulltext\":[{\"header\":\"Background\",\"content\":\"\\u003cp\\u003eDyslipidemia\\u0026mdash;clinically characterized by elevated total cholesterol, low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG), alongside reduced high-density lipoprotein cholesterol (HDL-C)\\u003csup\\u003e[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e],[\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]\\u003c/sup\\u003e, represents a modifiable risk factor for global disease burden. As a primary driver of cardiovascular morbidity, it contributes significantly to atherosclerotic cardiovascular disease (ASCVD) pathogenesis\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR4\\\" citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]\\u0026ndash;[\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]\\u003c/sup\\u003e. In China alone, ASCVD accounted for 66% of cardiovascular-related deaths (2.4\\u0026nbsp;million) in 2016\\u003csup\\u003e[\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]\\u003c/sup\\u003e, highlighting the urgent need for effective management. Its etiology reflects a complex interplay between genetic predisposition and modifiable factors such as sedentary behavior, poor diet, obesity, and metabolic dysfunction\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR8 CR9\\\" citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]\\u0026ndash;[\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e\\u003cp\\u003eCurrent U.S. and European guidelines prioritize statins as first-line pharmacotherapy for LDL-C reduction\\u003csup\\u003e[\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e],[\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]\\u003c/sup\\u003e. However, statin therapy presents limitations including contraindications in pregnancy, hepatotoxicity risks, and cost barriers\\u003csup\\u003e[\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]\\u003c/sup\\u003e. These constraints have prompted clinical guidelines to emphasize early lifestyle modifications\\u0026mdash;particularly increased physical activity (PA), nutritional interventions, weight control, and smoking cessation\\u0026mdash;as foundational preventive measures\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR15\\\" citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]\\u0026ndash;[\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e\\u003cp\\u003ePA, defined as skeletal muscle-mediated energy expenditure exceeding resting levels\\u003csup\\u003e[\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]\\u003c/sup\\u003e, exerts multifaceted lipid-regulatory effects. Regular engagement reduces triglycerides by \\u0026le;\\u0026thinsp;50%, elevates HDL-C by 5\\u0026ndash;10%, and modifies LDL particle composition from atherogenic small-dense to less harmful large-buoyant subtypes\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR19\\\" citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]\\u0026ndash;[\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]\\u003c/sup\\u003e. Global recommendations endorse 3.5-7 hours per week of moderate-to-vigorous PA (30\\u0026ndash;60 minutes daily)\\u003csup\\u003e[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]\\u003c/sup\\u003e, supported by Zhang et al.'s cross-sectional study of 17,535 Chinese adults linking low leisure-time PA to adverse lipid profiles\\u003csup\\u003e[\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e\\u003cp\\u003eAlthough the lipid benefits of PA are well-established, its dose-response dynamics resemble those of pharmacotherapy, requiring careful calibration for both efficacy and safety\\u003csup\\u003e[\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]\\u003c/sup\\u003e. Notably, high-intensity PA yields lipid improvements comparable to moderate levels\\u003csup\\u003e[\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]\\u003c/sup\\u003e, but with heightened risks of cardiovascular strain, musculoskeletal injury, and gastrointestinal issues\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR26\\\" citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]\\u0026ndash;[\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]\\u003c/sup\\u003e. This underscores the need to define both the minimum effective and maximum safe thresholds of PA. Moreover, whether PA-induced lipid improvements are sustained long-term remains uncertain, given limited longitudinal evidence. Do different PA doses exert distinct effects or exhibit dose-gradient relationships across lipid components (TC, TG, HDL-C, LDL-C)? The literature remains inconclusive\\u003csup\\u003e[\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e],[\\u003cspan additionalcitationids=\\\"CR29\\\" citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]\\u0026ndash;[\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e\\u003cp\\u003eIn an effort to better understand these issues, this study conducted a 10-year longitudinal analysis (2011\\u0026ndash;2020) of Chinese adults aged 45\\u0026thinsp;+\\u0026thinsp;using data from the China Health and Retirement Longitudinal Study (CHARLS). A restricted cubic spline (RCS) model explored the nuanced relationship between PA dose (measured in MET-hours/week) and dyslipidemia risk. Stratified regression models adjusted for age, gender, region, smoking/alcohol history, and BMI were used to analyze associations across PA quartiles (Q1\\u0026ndash;Q4) and dyslipidemia incidence. Distinct effects of PA emerged across lipid subtypes\\u0026mdash;TC, TG, LDL-C, and HDL-C\\u0026mdash;each displaying unique response patterns. The findings offer enhanced clinical evidence for developing personalized PA-based interventions targeting specific dyslipidemia profiles.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eStudy design and population\\u003c/h2\\u003e\\u003cp\\u003eThis analysis utilized data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative cohort of adults\\u0026thinsp;\\u0026ge;\\u0026thinsp;45 years approved by Peking University\\u003csup\\u003e\\u0026rsquo;\\u003c/sup\\u003es ethics board (IRB00001052-11015), with written consent from all participants. As a retrospective analysis of existing anonymized datasets, patients and the public were not involved in the design, implementation, reporting, or dissemination plans of this research. Methodological details, including sampling and eligibility criteria, were previously described\\u003csup\\u003e[\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]\\u003c/sup\\u003e. Baseline data collection occurred from June 2011 to March 2012, capturing 17,708 participants from 10,257 households nationwide. These individuals participated in biennial follow-up assessments (2013, 2015, 2018, 2020) conducted via face-to-face interviews using Computer-Assisted Personal Interviewing (CAPI) systems.\\u003c/p\\u003e\\u003cp\\u003eThe analytical sample comprised 3,719 participants divided into quartiles according to their weekly metabolic equivalent of task hours (MET-h/week). As depicted in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, exclusion criteria eliminated 13,863 initial participants based on: (1) unavailable MET-h/week measurements (n\\u0026thinsp;=\\u0026thinsp;10,910) stemming from randomized physical activity question modules in 2011\\u0026ndash;2015 survey waves; (2) incomplete baseline dyslipidemia records (n\\u0026thinsp;=\\u0026thinsp;811); (3) age ineligibility or missing age data (n\\u0026thinsp;=\\u0026thinsp;350); and (4) loss to follow-up (n\\u0026thinsp;=\\u0026thinsp;1,918).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eData Collection and Operational Definitions\\u003c/h3\\u003e\\n\\u003cp\\u003eThe CHARLS study employed standardized protocols for data collection, including blood pressure measurements (triplicate seated readings averaged after \\u0026ge;\\u0026thinsp;5-min rest) and anthropometrics (height, weight, waist circumference in light clothing; BMI calculated as kg/m\\u0026sup2; with \\u0026ge;\\u0026thinsp;25 defining overweight). Functional capacity was assessed using validated ADL (6-item self-care tasks, e.g., feeding, hygiene) and IADL (5-item societal skills, e.g., finances, cooking) scales, scoring 1 point per limitation (max 6 ADL/5 IADL; higher scores\\u0026thinsp;=\\u0026thinsp;greater impairment). Exercise adherence to medical advice and self-reported sleep duration were recorded. Dyslipidemia diagnosis combined healthcare-verified self-reports with clinical thresholds: elevated TG (\\u0026ge;\\u0026thinsp;150 mg/dL), TC (\\u0026ge;\\u0026thinsp;200 mg/dL), or LDL (\\u0026ge;\\u0026thinsp;130 mg/dL), or reduced HDL (\\u0026lt;\\u0026thinsp;40 mg/dL men/\\u0026lt;50 mg/dL women). Chronic diseases were medically confirmed from 14 predefined conditions. These operationalized metrics enabled analysis of physical activity, functional status, and dyslipidemia risk linkages.\\u003c/p\\u003e\\n\\u003ch3\\u003eExposure Assessment and Outcome Determination\\u003c/h3\\u003e\\n\\u003cp\\u003eThis longitudinal investigation examined the temporal relationship between physical activity (PA) and dyslipidemia development using data spanning from the 2011 baseline survey through either the first documented dyslipidemia event or study termination in 2020. The primary exposure metric, weekly metabolic equivalent hours (MET-h/week), was derived from an enhanced International Physical Activity Questionnaire (IPAQ)\\u003csup\\u003e[\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]\\u003c/sup\\u003e, that systematically evaluated three PA domains:\\u003c/p\\u003e\\u003cp\\u003e(1) Intensity stratification (vigorous/moderate/walking);\\u003c/p\\u003e\\u003cp\\u003e(2) Duration categorization (\\u0026gt;\\u0026thinsp;4hr, 2-4hr, 30min-2hr, \\u0026lt;\\u0026thinsp;30min);\\u003c/p\\u003e\\u003cp\\u003e(3) Weekly frequency (1\\u0026ndash;7 days).\\u003c/p\\u003e\\u003cp\\u003eTo align with CHARLS methodology while preserving cross-study comparability, we implemented three protocol adaptations:\\u003c/p\\u003e\\u003cp\\u003e(1) Interval standardization using midpoint values (e.g., 3hr for 2-4hr category);\\u003c/p\\u003e\\u003cp\\u003e (2) Minimum duration threshold (\\u0026ge;\\u0026thinsp;10min/session per IPAQ guidelines);\\u003c/p\\u003e\\u003cp\\u003e(3) Boundary adjustments (lower-bound truncation at 30min, upper-limit capping at 4hr).\\u003c/p\\u003e\\u003cp\\u003eThe MET calculation framework operationalized intensity-specific multipliers:\\u003c/p\\u003e\\u003cp\\u003e(1) 3.3 METs for walking \\u0026times; daily minutes \\u0026times; weekly days\\u003c/p\\u003e\\u003cp\\u003e(2) 4.0 METs for moderate activity \\u0026times; daily minutes \\u0026times; weekly days\\u003c/p\\u003e\\u003cp\\u003e(3) 8.0 METs for vigorous activity \\u0026times; daily minutes \\u0026times; weekly days\\u003c/p\\u003e\\u003cp\\u003eSummation of these components yielded the comprehensive MET-h/week metric\\u003csup\\u003e[\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]\\u003c/sup\\u003e, calculated as: Total PA\\u0026thinsp;=\\u0026thinsp;Σ(Walking\\u0026thinsp;+\\u0026thinsp;Moderate\\u0026thinsp;+\\u0026thinsp;Vigorous MET-h/week).\\u003c/p\\u003e\\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eStatistical Analysis\\u003c/h2\\u003e\\u003cp\\u003eAll analyses were performed in R statistical environment (version 4.3.0) with statistical significance defined as two-tailed p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05. Continuous variables were characterized using appropriate measures of central tendency and dispersion: normally distributed parameters reported as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviation, and non-normally distributed variables expressed as median (interquartile range). Distribution normality was verified through Shapiro-Wilk testing, with parametric comparisons conducted via ANOVA and non-parametric analyses using Kruskal-Wallis H-test. Categorical data were presented as counts with proportions (%) and analyzed using χ\\u0026sup2; tests. Missing data were handled through multiple imputation techniques to maintain analytical integrity while reducing potential bias.\\u003c/p\\u003e\\u003cp\\u003eKaplan-Meier methodology generated cumulative incidence curves for dyslipidemia development, stratified by baseline physical activity quartiles. Three sequential Cox proportional hazards models were developed to examine MET-h/week-dyslipidemia associations:\\u003c/p\\u003e\\u003cp\\u003eCrude Model: Unadjusted baseline analysis\\u003c/p\\u003e\\u003cp\\u003eDemographic-Adjusted Model: Controlled for age, gender, marital status, residence, smoking/alcohol consumption, educational attainment, chronic disease status, and baseline exercise patterns\\u003c/p\\u003e\\u003cp\\u003eFull Model: Further adjusted for physiological parameters (blood pressure, waist circumference, BMI), sleep duration, and functional capacity scores (ADL/IADL)\\u003c/p\\u003e\\u003cp\\u003eMulticollinearity was systematically evaluated through variance inflation factors (VIF\\u0026thinsp;\\u0026lt;\\u0026thinsp;10 for all covariates). Stratified analyses examined effect modification across key subgroups: age dichotomization (\\u0026lt;\\u0026thinsp;60 vs\\u0026thinsp;\\u0026ge;\\u0026thinsp;60 years), gender, smoking/drinking status, marital status, urban/rural residence, and BMI categories (\\u0026lt;\\u0026thinsp;25 vs\\u0026thinsp;\\u0026ge;\\u0026thinsp;25 kg/m\\u0026sup2;). These stratification procedures enabled assessment of association robustness across population subsets.\\u003c/p\\u003e\\u003cp\\u003eRestricted cubic spline (RCS) regression with four knots (5th, 35th, 65th, 95th percentiles) elucidated potential nonlinear dose-response relationships. To investigate the dose\\u0026ndash;response relationship between MET-h/week and the incidence of dyslipidemia, RCS based on Cox regression models was employed, adjusting covariates in model 3, and the MET-h/week value at HR\\u0026thinsp;=\\u0026thinsp;1 was treated as the reference. All regression models satisfied proportional hazards assumptions and demonstrated adequate goodness-of-fit through diagnostic testing.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eParticipants characteristics\\u003c/h2\\u003e\\u003cp\\u003eThe analytical cohort comprised 3,719 subjects stratified into quartiles (Q1-Q4) according to weekly metabolic equivalent hours (MET-h/week). As detailed in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, significant interquartile differences emerged across demographic, physiological, and health status parameters (all \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 unless specified). Stratification by activity levels revealed substantial variations in baseline characteristics, with a consistent inverse relationship between MET-h/week quartiles and participant age (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Notably, participants in the most active quartile (Q4) demonstrated the youngest mean age.\\u003c/p\\u003e\\u003cp\\u003ePhysiological profiling identified progressive improvements across activity quartiles in multiple cardiometabolic indicators: systolic blood pressure (129.98\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;21.67 vs 128.19\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;20.72 mmHg, Q1 vs Q4), diastolic blood pressure (76.02\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;12.03 vs 74.18\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;12.05 mmHg), body weight (60.06\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;12.33 vs 57.48\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;10.78 kg), waist circumference (86.60\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;12.15vs82.14\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.12 cm), and BMI (24.04\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.10 vs 22.82\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.50 kg/m\\u0026sup2;) all decreased significantly with increasing activity levels(\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001\\u0026ndash;0.003). Sleep duration remained comparable across quartiles(\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.348).\\u003c/p\\u003e\\u003cp\\u003ePopulation stratification showed distinct demographic patterns associated with activity intensity. Higher MET-h/week quartiles contained proportionally more males, alcohol consumers, smokers, married individuals, and rural residents (all \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Conversely, educational attainment exhibited an inverse relationship with activity levels, showing decreasing proportions of highly educated participants in upper quartiles.\\u003c/p\\u003e\\u003cp\\u003eHealth outcome analysis revealed two divergent patterns: while chronic disease prevalence showed no interquartile variation (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.188), dyslipidemia rates demonstrated significant dose-dependent reduction across activity quartiles (34.71% vs 23.76%, Q1 vs Q4; \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Functional capacity assessments through ADL/IADL scales indicated superior performance in higher activity groups, with Q4 participants reporting 87.79% ADL independence versus 80.10% in Q1 (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Clinician-initiated exercise recommendations showed quartile-dependent adoption rates, increasing from 66.15% in Q1 to 100% in upper quartiles (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001).\\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 participants stratified by quartiles of METs\\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\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eCharacteristics\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eOverall\\u003c/p\\u003e\\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;3719)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c6\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003eQuartiles of Mets\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003evalue\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eQ1 (n\\u0026thinsp;=\\u0026thinsp;1040)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eQ2 (n\\u0026thinsp;=\\u0026thinsp;841)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eQ3 (n\\u0026thinsp;=\\u0026thinsp;929)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eQ4 (n\\u0026thinsp;=\\u0026thinsp;909)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMet, h/week\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e104.30\\u003c/p\\u003e\\u003cp\\u003e(28.88, 224.00)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e7.70\\u003c/p\\u003e\\u003cp\\u003e(0.00,28.88)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e69.30\\u003c/p\\u003e\\u003cp\\u003e(57.38,84.00)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e153.30\\u003c/p\\u003e\\u003cp\\u003e(119.70,185.00)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e328.30\\u003c/p\\u003e\\u003cp\\u003e(280.88,368.40)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDyslipidemia, n (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1100 (29.58)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e361 (34.71)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e270 (32.10)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e253 (27.23)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e216 (23.76)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAge, years\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e58.21\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8.62\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e60.43\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.60\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e58.53\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8.73\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e57.22\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7.82\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e56.37\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7.47\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSBP, mmHg\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e127.98\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;20.83\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e129.98\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;21.67\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e128.19\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;20.72\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e127.17\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;20.61\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e126.33\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;20.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDBP, mmHg\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e74.91\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.95\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e76.02\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;12.03\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e74.85\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.60\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e74.34\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;12.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e74.28\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;12.05\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.003\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eWeight, kg\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e58.91\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.50\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e60.06\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;12.33\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e59.58\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.16\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e58.42\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.34\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e57.48\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;10.78\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eWaist, cm\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e84.28\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;12.21\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e86.60\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;12.15\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e84.53\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;13.03\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e83.56\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;12.08\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e82.14\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.12\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eBmi, kg/m\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e23.54\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.84\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e24.04\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.10\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e23.83\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.76\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e23.41\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.78\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e22.82\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSleep, h\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e6.43\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.84\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e6.36\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.89\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e6.43\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.84\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e6.42\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.88\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e6.51\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.75\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.348\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFemale, n (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2037 (54.77)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e623 (59.90)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e485 (57.67)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e523 (56.30)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e406 (44.66)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDrinking, n (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1193 (32.08)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e268 (25.77)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e243 (28.89)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e300 (32.29)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e382 (42.02)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd 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colname=\\\"c2\\\"\\u003e\\u003cp\\u003e15 (0.40)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e11 (1.06)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0 (0.00)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1 (0.11)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e3 (0.33)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e5 IADL diff, 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\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3006 (80.83)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e772 (74.23)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e708 (84.19)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e765 (82.35)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e761 (83.72)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e327 (8.79)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e88 (8.46)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e65 (7.73)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e97 (10.44)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e77 (8.47)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e183 (4.92)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e64 (6.15)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e36 (4.28)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e41 (4.41)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e42 (4.62)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e104 (2.80)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e46 (4.42)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e22 (2.62)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e17 (1.83)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e19 (2.09)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e60 (1.61)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e39 (3.75)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e6 (0.71)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e5 (0.54)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e10 (1.10)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e39 (1.05)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e31 (2.98)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4 (0.48)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e4 (0.43)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0 (0.00)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eBMI\\u003c/b\\u003e: body mass index; \\u003cb\\u003eSleep\\u003c/b\\u003e: self-reported average daily hours of sleep over the past month; \\u003cb\\u003eSBP\\u003c/b\\u003e: systolic blood pressure; \\u003cb\\u003eDBP\\u003c/b\\u003e: diastolic blood pressure; \\u003cb\\u003eMET\\u003c/b\\u003e: metabolic equivalent of task; \\u003cb\\u003eRecommended Exercise\\u003c/b\\u003e: ever received medical exercise advice? Functional limitations were quantified using: \\u003cb\\u003eADL\\u003c/b\\u003e (Activities of Daily Living) score (0\\u0026ndash;6 scale assessing six basic daily tasks) and \\u003cb\\u003eIADL\\u003c/b\\u003e (Instrumental Activities of Daily Living) score (0\\u0026ndash;5 scale evaluating complex life-management activities).\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eAssociations of baseline MET with incident Dyslipidemia\\u003c/h3\\u003e\\n\\u003cp\\u003eSurvival curve analysis demonstrated a consistent inverse relationship between baseline physical activity levels (MET-h/week) and cumulative dyslipidemia incidence. Participants in the highest activity quartile (Q4) maintained the lowest cumulative risk throughout follow-up, contrasting sharply with Q1 (lowest activity group) which exhibited the highest risk profile. Curve divergence became evident shortly after follow-up initiation (As shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e), with risk gradients progressively widening over time. By 108 months, the Q1-Q4 disparity reached maximal differentiation, while intermediate quartiles (Q2-Q3) showed graduated protective effects, confirming activity-dependent risk reduction.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eUsing restricted cubic spline (RCS) regression, we examined the nonlinear dose-response relationship between MET-h/week and dyslipidemia risk. Nodes were placed at the 5th (0 MET-h/week), 35th (63.88 MET-h/week), 65th (163.66 MET-h/week), and 95th (377.5 MET-h/week) percentiles of the MET-h/week distribution, with 104.3 MET-h/week (HR\\u0026thinsp;=\\u0026thinsp;1) set as the reference value. The analysis revealed a significant overall negative association between total MET-h/week and dyslipidemia risk (\\u003cem\\u003eP\\u003c/em\\u003e \\u003csub\\u003efor overall\\u003c/sub\\u003e \\u0026lt; 0.001). The strongest risk reductions were observed in the range of 63.88\\u0026ndash;163.66 MET-h/week, with the risk plateauing at higher activity levels and showing a minor resurgence (As shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA). After adjusting for all confounders, the negative association remained significant (\\u003cem\\u003eP\\u003c/em\\u003e \\u003csub\\u003efor overall\\u003c/sub\\u003e = 0.014), although nonlinearity was not statistically significant (\\u003cem\\u003eP\\u003c/em\\u003e \\u003csub\\u003efor non\\u0026minus;linearity\\u003c/sub\\u003e = 0.153; As shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eIn the Cox proportional hazards models, higher physical activity levels (MET-h/week) demonstrated progressive protective effects against dyslipidemia. In the unadjusted model (Model 1), hazard ratios (HRs) for Q2, Q3, and Q4 compared to Q1 were 0.90 (95%CI 0.77\\u0026ndash;1.05, P\\u0026thinsp;=\\u0026thinsp;0.186),0.73 (95%CI 0.63\\u0026ndash;0.86, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), and 0.62 (95%CI 0.52\\u0026ndash;0.73, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) respectively, showing significant risk reduction from Q3 onward. After adjusting for demographic and behavioral covariates (Model 2), the protective effects persisted with HRs of 0.80 (0.67\\u0026ndash;0.96, P\\u0026thinsp;=\\u0026thinsp;0.002) for Q3 and 0.74 (0.61\\u0026ndash;0.90, P\\u0026thinsp;=\\u0026thinsp;0.001) for Q4.\\u003c/p\\u003e\\u003cp\\u003eIn the fully adjusted model (Model 3) accounting for physiological parameters (SBP, DBP, anthropometrics) and functional status scores, the highest quartile (Q4) maintained significant protection with HR\\u0026thinsp;=\\u0026thinsp;0.81 (0.66\\u0026ndash;0.98, P\\u0026thinsp;=\\u0026thinsp;0.035). Although Q3 showed non-significant trend (HR\\u0026thinsp;=\\u0026thinsp;0.85,0.71\\u0026ndash;1.02, P\\u0026thinsp;=\\u0026thinsp;0.088) and Q2 demonstrated neutral effect (HR\\u0026thinsp;=\\u0026thinsp;0.97,0.81\\u0026ndash;1.16, P\\u0026thinsp;=\\u0026thinsp;0.714), the monotonic dose-response pattern across quartiles remained evident.\\u003c/p\\u003e\\u003cp\\u003eNotably, per SD increase analysis revealed consistent inverse associations across all models, with fully adjusted HR\\u0026thinsp;=\\u0026thinsp;0.92 (0.85\\u0026ndash;0.99, P\\u0026thinsp;=\\u0026thinsp;0.019), suggesting every standard deviation increase in MET-h/week corresponds with 8% risk reduction independent of comprehensive covariates (As shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\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\\u003eMultivariate-adjusted hazard ratios of MET-h/week for dyslipidemia\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"11\\\"\\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\\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=\\\"2\\\" morerows=\\\"1\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eTotal N No. of events\\u003c/p\\u003e\\u003cp\\u003e(Incident rate)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e\\u003cp\\u003eModel 1\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u003cp\\u003eModel 2\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c9\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c11\\\" namest=\\\"c10\\\"\\u003e\\u003cp\\u003eModel 3\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eHR (95%CI)\\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=\\\"c7\\\"\\u003e\\u003cp\\u003eHR (95%CI)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003eHR (95%CI)\\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\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003ePer SD increase\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3719\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1100(29.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.83(0.77\\u0026thinsp;~\\u0026thinsp;0.88)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.89(0.83\\u0026thinsp;~\\u0026thinsp;0.95)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0.92 (0.85\\u0026thinsp;~\\u0026thinsp;0.99)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e0.019\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eQ1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1040\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e361(34.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.00 (Reference)\\u003c/p\\u003e\\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\\u003cp\\u003e1.00 (Reference)\\u003c/p\\u003e\\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\\u003cp\\u003e1.00 (Reference)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eQ2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e841\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e270(32.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.90 (0.77\\u0026thinsp;~\\u0026thinsp;1.05)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.186\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.92 (0.77\\u0026thinsp;~\\u0026thinsp;1.10)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.37\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0.97 (0.81\\u0026thinsp;~\\u0026thinsp;1.16)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e0.714\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eQ3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e929\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e253(27.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.73 (0.63\\u0026thinsp;~\\u0026thinsp;0.86)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.80 (0.67\\u0026thinsp;~\\u0026thinsp;0.96)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.002\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0.85 (0.71\\u0026thinsp;~\\u0026thinsp;1.02)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e0.088\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eQ4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e909\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e216(23.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.62(0.52\\u0026thinsp;~\\u0026thinsp;0.73)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.74 (0.61\\u0026thinsp;~\\u0026thinsp;0.90)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0.81 (0.66\\u0026thinsp;~\\u0026thinsp;0.98)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e0.035\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"11\\\" nameend=\\\"c11\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eHR: Hazard Ratio, CI: Confidence Interval\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"11\\\" nameend=\\\"c11\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eModel1: unadjusted\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"11\\\" nameend=\\\"c11\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eModel2: Adjust: gender, age, marital status, rural residence, drinking, smoking, education, chronic, recommended exercise\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"11\\\" nameend=\\\"c11\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eModel3: Adjust: gender, age, marital status, rural residence, drinking, smoking, education, chronic, recommended exercise, SBP, DBP, weight, waist, BMI, sleep, ADL difficulty Score, IADL difficulty score\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\n\\u003ch3\\u003eSubgroup analyses\\u003c/h3\\u003e\\n\\u003cp\\u003eUsing the threshold of 104.3 MET-h/week identified through restricted cubic spline (RCS) regression (As shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e), physical activity levels were dichotomized into Light Physical Activity (LPA: \\u0026lt;104.3 MET-h/week) and Moderate-to-Vigorous Physical Activity (MVPA: \\u0026ge;104.3 MET-h/week). The analysis demonstrated that sustained MVPA significantly reduced dyslipidemia risk, with a hazard ratio (HR) of 0.74 (95% CI: 0.66\\u0026ndash;0.83; \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001).\\u003c/p\\u003e\\u003cp\\u003eStratified analyses revealed consistent protective effects of MVPA across most subgroups. Significant risk reductions were observed in both males (HR\\u0026thinsp;=\\u0026thinsp;0.80, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.015) and females (HR\\u0026thinsp;=\\u0026thinsp;0.71, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), participants aged\\u0026thinsp;\\u0026lt;\\u0026thinsp;60 years (HR\\u0026thinsp;=\\u0026thinsp;0.74, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and \\u0026ge;\\u0026thinsp;60 years (HR\\u0026thinsp;=\\u0026thinsp;0.76, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.003), as well as across drinking status, smoking status, and residential areas (urban/rural). No significant interaction effects were detected between MVPA and these stratification variables (\\u003cem\\u003eP\\u003c/em\\u003e \\u003csub\\u003einteraction\\u003c/sub\\u003e\\u0026gt;0.05 for all), indicating robust protective associations regardless of demographic or behavioral characteristics.\\u003c/p\\u003e\\u003cp\\u003eIntriguingly, the risk reduction associated with MVPA did not reach statistical significance in unmarried participants (HR\\u0026thinsp;=\\u0026thinsp;0.85, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.403). This observation may reflect limited statistical power due to smaller subgroup size (n\\u0026thinsp;=\\u0026thinsp;368 unmarried vs. n\\u0026thinsp;=\\u0026thinsp;3,477 married), or potential confounding by differential social support patterns and health behaviors between marital status groups. Further investigation is warranted to elucidate these relationships.\\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\\u003eSubgroup analysis of the association between MET-h/week and dyslipidemia\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"8\\\"\\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\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eVariables\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003en (%)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eLPA\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eMVPA\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eHR (95%CI)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e for interaction\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c8\\\" namest=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAll patients\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3719 (100.00)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e616/1845\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e484/1874\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.73 (0.65\\u0026thinsp;~\\u0026thinsp;0.83)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\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\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eGender\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.219\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\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\\u003e2037 (54.77)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e381/1085\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e245/952\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.69 (0.59\\u0026thinsp;~\\u0026thinsp;0.81)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" 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align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eBmi\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.170\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\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\\u003e2563 (68.92)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e344/1200\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e293/1363\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.71 (0.61\\u0026thinsp;~\\u0026thinsp;0.83)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\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\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026ge;\\u0026thinsp;25\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1156 (31.08)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e272/645\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e191/511\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.85 (0.71\\u0026thinsp;~\\u0026thinsp;1.02)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.084\\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\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"7\\\" nameend=\\\"c7\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eHR: Hazard Ratio, CI: Confidence Interval\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eDose-gradient modulation of dyslipidemia by physical activity\\u003c/h2\\u003e\\u003cp\\u003e Building on previous findings from 2011\\u0026ndash;2020 (N\\u0026thinsp;=\\u0026thinsp;3,791), which demonstrated that physical activity exceeding guideline-recommended thresholds significantly reduces dyslipidemia risk\\u0026mdash;albeit with plateauing benefits at higher doses. To further elucidate the dose-dependent modulation of dyslipidemia by physical activity, this study investigates the differential effects of exercise intensity on lipid subcomponents (TC, TG, HDL-C, LDL-C). By narrowing the observational window to 2011\\u0026ndash;2015 (capturing two timepoints with complete lipid profiles in the CHARLS database), we analyzed 2,710 eligible participants after excluding 1,081 cases with incomplete or substandard data.\\u003c/p\\u003e\\u003cp\\u003eThis analysis involving 2,710 participants stratified by physical activity levels (MET-h/week) into quartiles (Q1-Q4, ascending activity) revealed biomarker-specific dose-response relationships in lipid metabolism. Total cholesterol (TC) demonstrated fluctuating reductions with increased activity, peaking in Q1 (189.29\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;40.45 mg/dL) followed by Q2 (183.26\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;34.60), Q3 (184.02\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;34.43), and Q4 (183.67\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;40.69 mg/dL) (P\\u0026thinsp;=\\u0026thinsp;0.007). Post-hoc analysis confirmed significant differences exclusively between Q1 and other quartiles (all P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), with no inter-quartile variations among Q2-Q4 (P\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05). Triglycerides (TG) exhibited marginal decline across quartiles (Q1:146.50\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;87.08; Q2:138.60\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;81.32; Q3:138.72\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;90.14; Q4:134.38\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;94.87 mg/dL), showing borderline overall significance (P\\u0026thinsp;=\\u0026thinsp;0.075) and isolated Q1-Q4 difference (P\\u0026thinsp;=\\u0026thinsp;0.0128).\\u003c/p\\u003e\\u003cp\\u003eDemonstrating graded biological response, HDL-C concentrations exhibited progressive elevation from Q1 (50.13\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;10.66 mg/dL) to Q4 (54.47\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;13.78 mg/dL) with robust dose-dependent significance (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). All adjacent quartile comparisons reached significance except Q1-Q2 (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Conversely, low-density lipoprotein cholesterol (LDL-C) manifested inverse dose-response, with Q1 (107.40\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;30.88 mg/dL) significantly exceeding Q2 (102.32\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;27.30), Q3 (101.61\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;26.61), and Q4 (100.08\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;29.98 mg/dL) (all P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), while no differences emerged among higher quartiles (P\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05).\\u003c/p\\u003e\\u003cp\\u003eThese findings delineate differential lipid responsiveness: HDL-C and LDL-C exhibited graded sensitivity to physical activity, TC reductions primarily reflected baseline differences between sedentary (Q1) and active groups, while TG demonstrated limited responsiveness (As shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eGradient trends in lipid abnormality prevalence across quartile groups\\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\\u003eVariables\\u003c/p\\u003e\\u003cp\\u003e(mg/dL)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eTotal\\u003c/p\\u003e\\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;2710)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eQ1\\u003c/p\\u003e\\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;733)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eQ2\\u003c/p\\u003e\\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;625)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eQ3\\u003c/p\\u003e\\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;685)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eQ4\\u003c/p\\u003e\\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;667)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\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\\u003eTC\\u0026thinsp;\\u0026ge;\\u0026thinsp;200,\\u003c/p\\u003e\\u003cp\\u003en (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e833 (30.74)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e254 (34.65)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e186 (29.76)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e206 (30.07)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e187 (28.04)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.046\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTG\\u0026thinsp;\\u0026ge;\\u0026thinsp;150,\\u003c/p\\u003e\\u003cp\\u003en (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e822 (30.33)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e256 (34.92)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e202 (32.32)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e197 (28.76)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e167 (25.04)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLDLC\\u0026thinsp;\\u0026ge;\\u0026thinsp;130,\\u003c/p\\u003e\\u003cp\\u003en (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e420 (15.50)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e142 (19.37)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e89 (14.24)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e96 (14.01)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e93 (13.94)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.009\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHDL-C\\u0026lt;39/50,\\u003c/p\\u003e\\u003cp\\u003en (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e861 (31.77)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e281 (38.34)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e217 (34.72)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e209 (30.51)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e154 (23.09)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eFurther quartile-based analysis (Q1-Q4 by MET-h/week) demonstrated significant heterogeneity in dyslipidemia thresholds (TC\\u0026thinsp;\\u0026ge;\\u0026thinsp;200, TG\\u0026thinsp;\\u0026ge;\\u0026thinsp;150, LDL-C\\u0026thinsp;\\u0026ge;\\u0026thinsp;130, HDL-C\\u0026thinsp;\\u0026lt;\\u0026thinsp;39/50 mg/dL; all P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05)\\u003csup\\u003e[\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]\\u003c/sup\\u003e. HDL-C deficiency prevalence declined stepwise from 38.34% (281/733) in Q1 to 23.09% (154/667) in Q4 (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), paralleled by TG abnormalities decreasing from 34.92% (256/733) to 25.04% (167/667). Notably, TC abnormalities peaked in Q1 (34.65%, 254/733), declined in Q2 (29.76%, 186/625), rebounded slightly in Q3 (30.07%, 206/685), and reached 28.04% (187/667) in Q4, reflecting a fluctuating decline (P\\u0026thinsp;=\\u0026thinsp;0.046). LDL-C abnormalities were markedly higher in Q1 (19.37%, 142/733) compared to subsequent quartiles (14.24\\u0026ndash;13.94%), suggesting threshold-dependent regulation. These findings underscore the gradient-specific risk modulation across lipid biomarkers, with HDL-C and TG showing the clearest dose-response patterns, while TC and LDL-C exhibited attenuated but statistically meaningful trends (As shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eDose-response comparison of Q1- Q4 on lipid metabolic markers\\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\\u003eVariables\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eTotal (n\\u0026thinsp;=\\u0026thinsp;2710)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eQ1 (n\\u0026thinsp;=\\u0026thinsp;733)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eQ2 (n\\u0026thinsp;=\\u0026thinsp;625)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eQ3 (n\\u0026thinsp;=\\u0026thinsp;685)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eQ4 (n\\u0026thinsp;=\\u0026thinsp;667)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\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\\u003eTC,(mg/dL)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e185.18\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;37.82\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e189.29\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;40.45\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e183.26\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;34.60\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e184.02\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;34.43\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e183.67\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;40.69\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.007\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTG,(mg/dL)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e139.73\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;88.64\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e146.50\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;87.08\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e138.60\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;81.32\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e138.72\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;90.14\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e134.38\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;94.87\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.075\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHDL-C,(mg/dL)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e51.93\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;12.18\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e50.13\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;10.66\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e50.75\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.20\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e52.45\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;12.48\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e54.47\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;13.78\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLDL-C,(mg/dL)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e102.96\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;28.93\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e107.40\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;30.88\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e102.32\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;27.30\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e101.61\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;26.61\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e100.08\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;29.98\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThe present research, leveraging data from the CHARLS project, examines the relationship between physical activity levels (measured in metabolic equivalent hours per week, MET-h/week) and dyslipidemia risk. The results demonstrate a significant inverse correlation between baseline physical activity levels and the incidence of dyslipidemia over a 10-year follow-up period, with higher activity intensity amplifying protective effects. These findings align with research by Qinpei Zou et al., which identified a negative correlation between total physical activity (TPA) and dyslipidemia, further highlighting the critical role of occupational activity in elevating high-density lipoprotein cholesterol (HDL-C) levels, particularly in lipid management\\u003csup\\u003e[\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e\\u003cp\\u003eBeyond dyslipidemia incidence, the high-activity group exhibited superior outcomes in blood pressure, body weight, waist circumference, and BMI (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001\\u0026ndash;0.002). This phenomenon may stem from skeletal muscle-derived IL-6 suppressing hypothalamic AgRP neuronal activity, thereby reducing ghrelin secretion and activating brown adipose tissue thermogenesis\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR36\\\" citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]\\u0026ndash;[\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]\\u003c/sup\\u003e. These mechanisms explain the gradient reduction in body weight observed in the Q4 group (Q1: 60.05 kg vs. Q4: 57.47 kg), which correlated dose-dependently with MET values (Q4 median MET: 328.3 h/week). Notably, no intergroup differences in sleep duration were observed (P\\u0026thinsp;=\\u0026thinsp;0.531), potentially linked to activity type. Recent studies indicate that high-intensity interval training (HIIT) increases the proportion of slow-wave sleep without prolonging total sleep duration, which aligns with the observed 'activity-sleep decoupling' phenomenon\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR39\\\" citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]\\u0026ndash;[\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e\\u003cp\\u003eIndividuals with higher education levels were disproportionately represented in low-activity groups (high school or above: Q1 12.27% vs. Q4 7.14%, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), suggesting the existence of a \\\"cognitive-physical trade-off effect\\\" in Chinese society\\u003csup\\u003e[\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e]\\u003c/sup\\u003e. Occupations with high cognitive demands (e.g., white-collar roles) over activate the default mode network (DMN), suppressing striatal dopamine D2 receptor expression and inducing \\\"decision fatigue,\\\" thereby diminishing exercise motivation\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR43\\\" citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e]\\u0026ndash;[\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e]\\u003c/sup\\u003e. his observation aligns with the empirical study by Loyen Anne et al. (year) on sedentary behavior in four European nations. Their findings indicate that individuals with higher educational attainment spend, on average, more than 530 minutes per day in sedentary activities\\u003csup\\u003e[\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e\\u003cp\\u003eNotably, Q2-Q4 groups received physician exercise recommendations more frequently (Q1: 65.73% vs. Q2-Q4: 100%, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), reflecting a \\\"positive feedback loop\\\" in healthcare systems. However, this pattern may trap low-activity populations (Q1) in a \\\"guidance deficiency-risk accumulation\\\" cycle, particularly given that 60.49% of Q1 participants were female. The functional advantages in activities of daily living (ADL/IADL) observed in high-activity groups (Q4 ADL independence: 88.06% vs. Q1 80.52%) may arise from neuromuscular optimization, such as cerebellar-basal ganglia circuit remodeling\\u003csup\\u003e[\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e],[\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e]\\u003c/sup\\u003e. Animal studies indicate that regular exercise increases synaptophysin expression in cerebellar Purkinje cells by 30%, improving motor coordination precision and elevating gait stability in older adults by 22%\\u003csup\\u003e[\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e],[\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e\\u003cp\\u003eSurvival curves, restricted cubic spline (RCS) regression, and Cox models systematically delineated the dynamic relationship between physical activity (MET-h/week) and dyslipidemia risk. Even after adjusting for multiple covariates, the significant negative relationship between baseline physical activity levels and dyslipidemia risk persisted over the long follow-up period, further confirming the long-term protective effect of physical activity. This corresponds with the results of Lewington S. et al., who performed a meta-analysis of 25 randomized controlled trials, revealing that aerobic exercise at an intensity of 5.3 MET significantly elevated HDL-C levels by 2.53 mg/dL, irrespective of drugs or other lifestyle modifications \\u003csup\\u003e[\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e]\\u003c/sup\\u003e. Dagogo-Jack and his team discovered a robust connection between people's own reports of their physical activity levels, their waist size, and blood fats like triglycerides and HDL-C, and this was true for both African Americans and Caucasians. Their research really underscored how important exercise is in forecasting who might develop prediabetes or diabetes. On the flip side, while eating habits did have some link to body fat and blood fats, they weren't as big a factor in predicting these health issues \\u003csup\\u003e[\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e]\\u003c/sup\\u003e. All in all, their results show that staying active is super key to keeping cholesterol levels in check, even when you consider other things like medication, diet, and waist size.\\u003c/p\\u003e\\u003cp\\u003eRCS analysis revealed the most pronounced risk reduction within the 63.88\\u0026ndash;163.66 MET-h/week range, with plateauing effects at higher doses, indicative of metabolic \\\"saturation.\\\" This threshold may correspond to molecular switches in lipid metabolism regulation. Animal studies show that exercise intensities reaching 60\\u0026ndash;70% of maximal oxygen uptake (VO\\u003csub\\u003e2\\u003c/sub\\u003emax) trigger AMPK phosphorylation, suppressing acetyl \\u0026ndash; CoA carboxylase (ACC) to reduce fatty acid synthesis while upregulating LDL receptor expression for accelerated LDL-C clearance\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR53\\\" citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e]\\u0026ndash;[\\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e]\\u003c/sup\\u003e. Additionally, exercise-induced DNA demethylation persistently enhances lipoprotein lipase (LPL) gene promoter activity, potentially amplifying metabolic benefits post-threshold through cumulative epigenetic modifications \\u003csup\\u003e[\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e],[\\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e56\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e\\u003cp\\u003eSubgroup analyses confirmed that moderate-to-vigorous physical activity (MVPA) universally reduced dyslipidemia risk across demographics. By 2020, male and female dyslipidemia prevalences were 28.0% and 30.7%, respectively. In contrast, Mutalifu et al. reported higher overall prevalence (39.3%) in Xinjiang (males: 52.6%, females: 24.3%) \\u003csup\\u003e[\\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e]\\u003c/sup\\u003e, likely due to dietary patterns, rural population dominance, and occupational activity disparities. Despite rural residents constituting 80.38% of MVPA groups, their protective effects mirrored urban populations (\\u003cem\\u003eP-interaction\\u003c/em\\u003e\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05). Multiple Chinese research teams have attributed this parity to rural MVPA\\u0026rsquo;s reliance on sustained agricultural labor, which exhibits higher lipid oxidation efficiency than urban intermittent training, albeit with chronic inflammation risks from repetitive motions \\u003csup\\u003e[\\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e58\\u003c/span\\u003e],[\\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e\\u003cp\\u003eThis study reveals dynamic associations between physical activity levels and lipid profiles. Specifically, high-density lipoprotein cholesterol (HDL-C) demonstrated a dose-dependent elevation across activity quartiles (Q1-Q4: 50.13 vs. 54.47 mg/dL, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), with significant interquartile differences except between Q1 and Q2. The prevalence of HDL-C deficiency decreased progressively (Q1:38.34% vs. Q4:23.09%, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), aligning with dose-dependent ApoA1 upregulation and HDL functional enhancement\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR61\\\" citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e60\\u003c/span\\u003e]\\u0026ndash;[\\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e62\\u003c/span\\u003e]\\u003c/sup\\u003e. For low-density lipoprotein cholesterol (LDL-C), a stratified declining pattern was observed (Q1-Q4: 107.40 vs. 100.08 mg/dL, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), with maximal reduction occurring during the sedentary-to-moderate activity transition (Q2:102.32 mg/dL). This aligns with S. Aho et al.'s findings that moderate-intensity exercise sufficiently reduces atherosclerosis risk\\u003csup\\u003e[\\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e63\\u003c/span\\u003e],[\\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e64\\u003c/span\\u003e]\\u003c/sup\\u003e. Notably, LDL-C abnormalities peaked at Q1 (19.37%) before stabilizing at lower levels (13.94%-14.24%), indicating threshold effects.\\u003c/p\\u003e\\u003cp\\u003eTotal cholesterol (TC) showed significant reduction only during initial sedentary population intervention (Q1-Q2:189.29 vs. 183.26 mg/dL, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), supporting Marques-Leandro R's hypothesis regarding \\\"early-phase sensitive biomarkers\\\" \\u003csup\\u003e[\\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e65\\u003c/span\\u003e]\\u003c/sup\\u003e. TC abnormalities displayed a fluctuating decline (Q1:34.65% vs. Q4:28.04%, P\\u0026thinsp;=\\u0026thinsp;0.046) with a minor Q3 rebound, suggesting phased modulation. Triglycerides (TG) exhibited non-linear reduction with significance only between extreme quartiles, underscoring both the necessity of high-intensity training for TG modulation and the limitations of moderate exercise\\u003csup\\u003e[\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e],[\\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e66\\u003c/span\\u003e]\\u003c/sup\\u003e. TG abnormalities showed linear reduction (Q1:34.92% vs. Q4: 25.04%), potentially through exercise-induced lipoprotein lipase activation\\u003csup\\u003e[\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]\\u003c/sup\\u003e. These findings collectively emphasize the imperative for personalized lipid management strategies that incorporate exercise modalities, genetic predisposition, and metabolic baselines.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003elimitations\\u003c/h2\\u003e\\u003cp\\u003eNotwithstanding the novel findings, several methodological considerations warrant discussion. First, while leveraging the nationally representative CHARLS cohort, specific exclusions were necessary to ensure data completeness and longitudinal validity. Although standard multiple imputation techniques were applied, the potential for residual selection bias cannot be entirely excluded. Second, physical activity quantification relied on self-reported questionnaires-a methodology balancing feasibility and granularity in large-scale studies. While rigorously adapted from validated IPAQ protocols, this approach may have introduced some degree of measurement inaccuracy inherent to recall-based assessments. Notably, the study's focus on longitudinal design prioritized participant retention over device-based monitoring, which future investigations could address through complementary accelerometry. Finally, while comprehensive covariate adjustment was implemented across demographic, behavioral, and clinical domains, the observational nature precludes complete exclusion of unmeasured confounders such as dietary patterns or genetic predisposition.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThis 10-year CHARLS cohort study of adults\\u0026thinsp;\\u0026ge;\\u0026thinsp;45 years revealed a S-shaped association between physical activity (PA) and dyslipidemia risk. Optimal protection occurred at 63.88\\u0026ndash;163.66 MET-h/week, with plateaued benefits and slight risk elevation beyond this range. PA differentially modulated lipid components: higher doses enhanced HDL-C, while moderate PA sufficed for LDL-C reduction, indicating distinct lipid-specific regulatory mechanisms. Demographic analysis showed the highest PA quartile (Q4) with optimal lipid profiles had younger age, male predominance, and rural residency, underscoring sociodemographic considerations in exercise prescription. These findings advocate for metabolically tailored PA regimens over uniform intensity escalation. Clinically, this supports stratified interventions aligning PA dosage with individual functional capacity and lipid metabolism characteristics, particularly for precision prevention in aging populations.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAuthor contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eKang Wan and Ruwen Wang contributed equally to this work and share first authorship. They were responsible for study design, data analysis, interpretation of results, and manuscript drafting. Hongmei Yan provided clinical insights on dyslipidemia, contributed to result interpretation, and revised the manuscript for important intellectual content. Wei Chen supported data collection and preprocessing and contributed to the methodological framework. Fuyi Ma assisted with literature review and statistical modeling. Yue Jin contributed to visualization and figure preparation. Ru Wang conceived the study, supervised the research process, secured funding, and critically revised the manuscript. All authors read and approved the final version of the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis research was supported by multiple funding sources. Specifically, it received financial support from the Research and Innovation Grant for Graduate Students, Shanghai University of Sport (Grant No. YJSCX-2023-035), the National Natural Science Foundation of China (Grant No. 32271226), and the National Key R\\u0026amp;D Program of China (Grant No. 2020YFA0803800). The authors gratefully acknowledge these contributions, which supported the research, authorship, and publication of this article.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Availability Statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe data analyzed in this study are from the China Health and Retirement Longitudinal Study (CHARLS), which is publicly available at http://charls.pku.edu.cn. Researchers can apply for access to the dataset through the official website. The authors do not have any special access privileges to the data and others can obtain it in the same manner.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study utilized secondary data from the China Health and Retirement Longitudinal Study (CHARLS), which received ethical approval from the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015). All participants in CHARLS provided written informed consent. The current analysis was conducted in accordance with relevant guidelines and regulations. No additional ethical approval was required for this secondary data analysis.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor details\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003e1\\u003c/sup\\u003eSchool of Exercise and Health, Shanghai University of Sport, Shanghai, China, \\u003csup\\u003e2\\u003c/sup\\u003ePhysical Education College, Henan Sport University, Zhengzhou, China, \\u003csup\\u003e3\\u003c/sup\\u003eDepartment of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China, \\u003csup\\u003e4\\u003c/sup\\u003eYangpu Hospital Affiliated to Tongji University School of Medicine, Department of Endocrinology, Shanghai, China.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eLiu S, Li Y, Zeng X, et al. Burden of Cardiovascular Diseases in China, 1990\\u0026ndash;2016: Findings From the 2016 Global Burden of Disease Study[J]. JAMA Cardiol. 2019;4(4):342.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMooradian AD. Dyslipidemia in type 2 diabetes mellitus[J]. Nat Reviews Endocrinol. 2009;5(3):150\\u0026ndash;9.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eFerence BA, Yoo W, Alesh I, et al. Effect of long-term exposure to lower low-density lipoprotein cholesterol beginning early in life on the risk of coronary heart disease: a Mendelian randomization analysis[J]. J Am Coll Cardiol. 2012;60(25):2631\\u0026ndash;9.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLipid-related markers. and cardiovascular disease prediction[J]. JAMA. 2012;307(23):2499\\u0026ndash;506.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eFeigin VL, Stark BA, Johnson CO, et al. Global, regional, and national burden of stroke and its risk factors, 1990\\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019[J]. Lancet Neurol. 2021;20(10):795\\u0026ndash;820.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eZhao D, Liu J, Wang M, et al. Epidemiology of cardiovascular disease in China: current features and implications[J]. Nat Rev Cardiol. 2019;16(4):203\\u0026ndash;12.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBerberich AJ, Hegele RA. A Modern Approach to Dyslipidemia[J]. Endocr Rev. 2022;43(4):611\\u0026ndash;53.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSrikanth S, Deedwania P. Management of Dyslipidemia in Patients with Hypertension, Diabetes, and Metabolic Syndrome[J]. Curr Hypertens Rep. 2016;18(10):76.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eKlop B, Elte J, Cabezas M. Dyslipidemia in obesity: mechanisms and potential targets[J]. Nutrients. 2013;5(4):1218\\u0026ndash;40.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eJialal I, Singh G. Management of diabetic dyslipidemia: An update[J]. World J Diabetes. 2019;10(5):280\\u0026ndash;90.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eCholesterol Treatment Trialists\\u0026rsquo; (Ctt) Collaboration. Efficacy and safety of more intensive lowering of LDL cholesterol: a meta-analysis of data from 170,000 participants in 26 randomised trials[J]. Lancet. 2010;376(9753):1670\\u0026ndash;81.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eCholesterol Treatment Trialists\\u0026rsquo; (Ctt) Collaborators. The effects of lowering LDL cholesterol with statin therapy in people at low risk of vascular disease: meta-analysis of individual data from 27 randomised trials[J]. Lancet. 2012;380(9841):581\\u0026ndash;90.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eRidker PM. LDL cholesterol: controversies and future therapeutic directions[J]. Lancet. 2014;384(9943):607\\u0026ndash;17.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eArvanitis M, Lowenstein CJ. Dyslipidemia[J]. Ann Intern Med. 2023;176(6):ITC81\\u0026ndash;96.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eArtinian NT, Fletcher GF, Mozaffarian D, et al. Interventions to promote physical activity and dietary lifestyle changes for cardiovascular risk factor reduction in adults: a scientific statement from the American Heart Association[J]. Circulation. 2010;122(4):406\\u0026ndash;41.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003ePearson GJ, Thanassoulis G, Anderson TJ, et al. 2021 Canadian Cardiovascular Society guidelines for the management of dyslipidemia for the prevention of cardiovascular disease in adults[J]. Can J Cardiol. 2021;37(8):1129\\u0026ndash;50.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBarton H. Land use planning and health and well-being[J]. Land Use Policy. 2009;26:S115\\u0026ndash;23.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eVanhees L, Geladas N, Hansen D, et al. Importance of characteristics and modalities of physical activity and exercise in the management of cardiovascular health in individuals with cardiovascular risk factors: recommendations from the EACPR. Part II[J]. Eur J Prev Cardiol. 2012;19(5):1005\\u0026ndash;33.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eManaging abnormal blood. lipids: a collaborative approach - PubMed[EB/OL]. [2024-11-29]. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://pubmed.ncbi.nlm.nih.gov/16286609/\\u003c/span\\u003e\\u003cspan address=\\\"https://pubmed.ncbi.nlm.nih.gov/16286609/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eKraus WE, Houmard JA, Duscha BD, et al. Effects of the amount and intensity of exercise on plasma lipoproteins[J]. N Engl J Med. 2002;347(19):1483\\u0026ndash;92.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003ePelliccia A, Sharma S, Gati S et al. 2020 ESC Guidelines on Sports Cardiology and Exercise in Patients with Cardiovascular Disease[J]. Revista Espa\\u0026ntilde;ola de Cardiolog\\u0026iacute;a (English Edition), 2021, 74(6): 545.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eZhang Y, Yang J, Ye J, et al. Separate and combined associations of physical activity and obesity with lipid-related indices in non-diabetic and diabetic patients[J]. Lipids Health Dis. 2019;18(1):49.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eChen H, Cheng MC, Sun Y et al. Dose-response relationship between physical activity and frailty: A systematic review and meta-analysis[J]. Heliyon, 2024.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eHupin D, Roche F, Gremeaux V, et al. Even a low-dose of moderate-to-vigorous physical activity reduces mortality by 22% in adults aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;60 years: a systematic review and meta-analysis[J]. Br J Sports Med. 2015;49(19):1262\\u0026ndash;7.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eChomistek AK, Cook NR, Flint AJ, et al. Vigorous-intensity leisure-time physical activity and risk of major chronic disease in men[J]. Med Sci Sports Exerc. 2012;44(10):1898.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLoprinzi PD. Dose\\u0026ndash;response association of moderate-to-vigorous physical activity with cardiovascular biomarkers and all-cause mortality: considerations by individual sports, exercise and recreational physical activities[J]. Prev Med. 2015;81:73\\u0026ndash;7.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003ePowell KE, Paluch AE, Blair SN. Physical activity for health: What kind? How much? How intense? On top of what?[J]. Annu Rev Public Health. 2011;32(1):349\\u0026ndash;65.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eZou Q, Su C, Du W, et al. Longitudinal Association between Physical Activity, Blood Lipids, and Risk of Dyslipidemia among Chinese Adults: Findings from the China Health and Nutrition Surveys in 2009 and 2015[J]. Nutrients. 2023;15(2):341.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMach F, Baigent C, Catapano AL, et al. 2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk[J]. Eur Heart J. 2020;41(1):111\\u0026ndash;88.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eWang Y, Xu D. Effects of aerobic exercise on lipids and lipoproteins[J]. Lipids Health Dis. 2017;16(1):132.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eZhao Y, Hu Y, Smith JP, et al. Cohort profile: the China health and retirement longitudinal study (CHARLS)[J]. Int J Epidemiol. 2014;43(1):61\\u0026ndash;8.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eCraig CL, Marshall AL, Sj\\u0026ouml;str\\u0026ouml;m M, et al. International physical activity questionnaire: 12-country reliability and validity[J]. Med Sci Sports Exerc. 2003;35(8):1381\\u0026ndash;95.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLi X, Zhang W, Zhang W, et al. Level of physical activity among middle-aged and older Chinese people: evidence from the China health and retirement longitudinal study[J]. BMC Public Health. 2020;20(1):1682.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSu X, Cai X, Pan Y, et al. Discordance of apolipoprotein B with low-density lipoprotein cholesterol or non-high-density lipoprotein cholesterol and coronary atherosclerosis[J]. Eur J Prev Cardiol. 2022;29(18):2349\\u0026ndash;58.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eHwang E, Portillo B, Grose K, et al. Exercise-induced hypothalamic neuroplasticity: Implications for energy and glucose metabolism[J]. Mol Metabolism. 2023;73:101745.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eZagmutt S, Mera P, Soler-V\\u0026aacute;zquez MC, et al. Targeting AgRP neurons to maintain energy balance: Lessons from animal models[J]. Biochem Pharmacol. 2018;155:224\\u0026ndash;32.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eDella Guardia L, Codella R. Exercise restores hypothalamic health in obesity by reshaping the inflammatory network[J]. Antioxidants. 2023;12(2):297.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eFrimpong E, Mograss M, Zvionow T, et al. Acute evening high-intensity interval training may attenuate the detrimental effects of sleep restriction on long-term declarative memory[J]. Sleep. 2023;46(7):zsad119.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMin L, Wang D, You Y, et al. Effects of high-intensity interval training on sleep: a systematic review and meta-analysis[J]. Int J Environ Res Public Health. 2021;18(20):10973.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eXie W, Lu D, Liu S et al. The optimal exercise intervention for sleep quality in adults: A systematic review and network meta-analysis[J]. Prev Med, 2024: 107955.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eVine CA, Runswick OR, Blacker SD, et al. Cognitive, psychophysiological, and perceptual responses to a repeated military-specific load carriage treadmill simulation[J]. Hum Factors. 2024;66(10):2379\\u0026ndash;92.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSimpson EH, Gallo EF, Balsam PD, et al. How changes in dopamine D2 receptor levels alter striatal circuit function and motivation[J]. Mol Psychiatry. 2022;27(1):436\\u0026ndash;44.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBeeler JA, Faust RP, Turkson S, et al. Low dopamine D2 receptor increases vulnerability to obesity via reduced physical activity, not increased appetitive motivation[J]. Biol Psychiatry. 2016;79(11):887\\u0026ndash;97.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eRobertson CL, Ishibashi K, Chudzynski J, et al. Effect of exercise training on striatal dopamine D2/D3 receptors in methamphetamine users during behavioral treatment[J]. Neuropsychopharmacology. 2016;41(6):1629\\u0026ndash;36.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLoyen A, Clarke-Cornwell AM, Anderssen SA, et al. Sedentary time and physical activity surveillance through accelerometer pooling in four European countries[J]. Sports Med. 2017;47(7):1421\\u0026ndash;35.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eZuo L, Lan X, Zhou Y, et al. Cerebellar-cerebral circuits functional connectivity in patients with cognitive impairment after basal ganglia stroke: a pilot study[J]. Front Aging Neurosci. 2025;17:1478891.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eVeenhuizen Y, Cup EH, Jonker MA, et al. Self-management program improves participation in patients with neuromuscular disease: A randomized controlled trial[J]. Neurology. 2019;93(18):e1720\\u0026ndash;31.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eHuang TY, Lin LS, Cho KC, et al. Chronic treadmill exercise in rats delicately alters the Purkinje cell structure to improve motor performance and toxin resistance in the cerebellum[J]. J Appl Physiol. 2012;113(6):889\\u0026ndash;95.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eTang C, Liu M, Zhou Z, et al. Treadmill Exercise alleviates cognition disorder by activating the FNDC5: dual role of integrin αV/β5 in Parkinson\\u0026rsquo;s Disease[J]. Int J Mol Sci. 2023;24(9):7830.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLewington S, Whitlock G, Clarke R, et al. Prospective Studies Collaboration Blood cholesterol and vascular mortality by age, sex, and blood pressure: a meta-analysis of individual data from 61 prospective studies with 55,000 vascular deaths[J]. Lancet. 2007;370(9602):1829\\u0026ndash;39.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBoucher AB, Adesanya EAO, Owei I, et al. Dietary habits and leisure-time physical activity in relation to adiposity, dyslipidemia, and incident dysglycemia in the pathobiology of prediabetes in a biracial cohort study[J]. Metab Clin Exp. 2015;64(9):1060\\u0026ndash;7.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eHudson GM. The effects of resveratrol supplementation on glucose/insulin kinetics and transcription of the AMPK and insulin signaling pathways at rest and following an oral glucose tolerance test and graded exercise test in overweight women[M]. Baylor University; 2010.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003ePatrick-Melin AJ. Effect of 7 Days Aerobic Exercise on Insulin Sensitivity, Oxidative Stress, TLR2/TLR4 Cell Surface Expression and Cytokine Secretion in Sedentary Obese Adults[D]. Kent State University; 2011.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eZordoky BN, Nagendran J, Pulinilkunnil T, et al. AMPK-dependent inhibitory phosphorylation of ACC is not essential for maintaining myocardial fatty acid oxidation[J]. Circul Res. 2014;115(5):518\\u0026ndash;24.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMarques LR, Diniz TA, Antunes BM, et al. Reverse cholesterol transport: molecular mechanisms and the non-medical approach to enhance HDL cholesterol[J]. Front Physiol. 2018;9:526.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLundsgaard AM, Fritzen AM, Kiens B. The importance of fatty acids as nutrients during post-exercise recovery[J]. Nutrients. 2020;12(2):280.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMutalifu M, Zhao Q, Wang Y, et al. Joint association of physical activity and diet quality with dyslipidemia: a cross-sectional study in Western China[J]. Lipids Health Dis. 2024;23(1):46.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLi J, Zhang X, Zhang M, et al. Urban-rural differences in the association between occupational physical activity and mortality in Chinese working population: evidence from a nationwide cohort study[J]. The Lancet Regional Health\\u0026ndash;Western Pacific; 2024. p. 46.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eZhu W, Chi A, Sun Y. Physical activity among older Chinese adults living in urban and rural areas: a review[J]. J Sport Health Sci. 2016;5(3):281\\u0026ndash;6.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMuscella A, Stef\\u0026agrave;no E, Marsigliante S. The effects of exercise training on lipid metabolism and coronary heart disease[J]. Am J Physiol Heart Circ Physiol. 2020;319(1):H76\\u0026ndash;88.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eWood G, Taylor E, Ng V, et al. Determining the effect size of aerobic exercise training on the standard lipid profile in sedentary adults with three or more metabolic syndrome factors: A systematic review and meta-analysis of randomised controlled trials[J]. Br J Sports Med. 2022;56(18):1032\\u0026ndash;41.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMart\\u0026iacute;nez-Vizca\\u0026iacute;no V, Amaro-Gahete FJ, Fern\\u0026aacute;ndez-Rodr\\u0026iacute;guez R et al. Effectiveness of fixed-dose combination therapy (polypill) versus exercise to improve the blood-lipid profile: A network meta-analysis[J]. Sports Med, 2022: 1\\u0026ndash;13.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eAho S, Vuoristo MS, Raitanen J, et al. Higher number of steps and breaks during sedentary behaviour are associated with better lipid profiles[J]. BMC Public Health. 2021;21(1):629.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eNavarese EP, Robinson JG, Kowalewski M, et al. Association between baseline LDL-C level and total and cardiovascular mortality after LDL-C lowering: A systematic review and meta-analysis[J]. JAMA. 2018;319(15):1566\\u0026ndash;79.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLr M, Ta D, Bm A et al. Reverse cholesterol transport: Molecular mechanisms and the non-medical approach to enhance HDL cholesterol[J]. Front Physiol, 2018, 9.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMahley RW, Arteriosclerosis. Thromb Vascular Biology. 2016;36(7):1305\\u0026ndash;15.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-public-health\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"pubh\",\"sideBox\":\"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/pubh/default.aspx\",\"title\":\"BMC Public Health\",\"twitterHandle\":\"@BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Physical Activity, Dyslipidemia, CHARLS, Dose-response\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6932333/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6932333/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground:\\u003c/strong\\u003e This longitudinal study aimed to investigate the dose-response relationship and long-term protective effects of physical activity (PA) on dyslipidemia among middle-aged and older Chinese adults, with a focus on identifying optimal PA thresholds for sustained improvements in lipid profiles.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods:\\u003c/strong\\u003e Utilizing data from the China Health and Retirement Longitudinal Study (CHARLS, 2011–2020), 3,719 participants aged ≥45 years were stratified into quartiles (Q1–Q4) based on weekly metabolic equivalent hours (MET-h/week). Restricted cubic spline (RCS) models and Cox proportional hazards regression adjusted for demographics, lifestyle factors, and physiological parameters were employed to assess nonlinear associations between PA dose and dyslipidemia incidence (defined by elevated TC, TG, LDL-C, or reduced HDL-C).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults:\\u003c/strong\\u003e Higher PA levels demonstrated a graded reduction in dyslipidemia risk. Compared to Q1 (lowest activity), Q4 (highest activity) exhibited a 19% lower risk (fully adjusted HR=0.81, 95% CI:0.66–0.98). RCS analysis revealed a nonlinear dose-response curve, with maximal risk reduction at 63.88–163.66 MET-h/week. Subgroup analyses confirmed consistent protective effects across genders, age groups, and BMI categories. Notably, PA exerted heterogeneous effects on lipid subcomponents: HDL-C and TG showed the strongest improvements, while LDL-C reductions plateaued at higher PA doses.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusions:\\u003c/strong\\u003eThis longitudinal study advocates metabolically-tailored PA prescriptions for dyslipidemia, with a nonlinear dose-response curve refuting \\\"more is better\\\" assumptions. Lipid-specific mechanisms demand differentiated exercise regimens: dose-dependent HDL-C optimization versus moderate LDL-C control. Age- and region-specific PA responsiveness underscores demographically-informed guidelines. These findings provide evidence to inform precision exercise guidelines aimed at reducing cardiovascular risk in aging populations.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Physical Activity Dose-Response and Long-Term Effects on Dyslipidemia in Chinese Adults: A CHARLS Study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-07-15 13:57:50\",\"doi\":\"10.21203/rs.3.rs-6932333/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-08-06T08:21:18+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-08-05T02:57:31+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-07-30T00:38:52+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"333205438413062869694516410563370139920\",\"date\":\"2025-07-29T14:59:41+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-07-29T11:35:25+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"259725899261940408640487431458612870195\",\"date\":\"2025-07-29T11:08:20+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"202066477954155741697508818068933961049\",\"date\":\"2025-07-12T00:41:30+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-07-11T07:41:00+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2025-06-23T10:20:17+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-06-23T01:34:26+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-06-23T01:34:09+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Public Health\",\"date\":\"2025-06-19T14:51:19+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-public-health\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"pubh\",\"sideBox\":\"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/pubh/default.aspx\",\"title\":\"BMC Public Health\",\"twitterHandle\":\"@BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"68ec9d6f-2446-44d6-b821-af2948111e91\",\"owner\":[],\"postedDate\":\"July 15th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-10-06T15:58:30+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-6932333\",\"link\":\"https://doi.org/10.1186/s12889-025-24568-1\",\"journal\":{\"identity\":\"bmc-public-health\",\"isVorOnly\":false,\"title\":\"BMC Public Health\"},\"publishedOn\":\"2025-09-29 15:56:51\",\"publishedOnDateReadable\":\"September 29th, 2025\"},\"versionCreatedAt\":\"2025-07-15 13:57:50\",\"video\":\"\",\"vorDoi\":\"10.1186/s12889-025-24568-1\",\"vorDoiUrl\":\"https://doi.org/10.1186/s12889-025-24568-1\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6932333\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6932333\",\"identity\":\"rs-6932333\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}