Body Mass Index Variability and Its Role in Cardiovascular Disease Risk Stratification : A large cohort study.

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Jin Hee Ahn, Eun Jung Oh, Jiyeon Park, Sung Hyun Lee, Jae-Geum Shim, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8537727/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Apr, 2026 Read the published version in European Journal of Medical Research → Version 1 posted 11 You are reading this latest preprint version Abstract Although body mass index (BMI) is a well-established predictor of cardiovascular disease (CVD), the prognostic significance of intra-individual BMI variability remains uncertain. This study investigated the association between long-term BMI variability and the risk of CVD, including stroke and coronary artery disease (CAD). We conducted a retrospective cohort study of adults aged ≥ 40 years who underwent three or more annual health examinations from 2002 to 2011 at a single center in South Korea. BMI variability was quantified using the coefficient of variation and categorized into tertiles. Participants were followed for incident CVD events (stroke or CAD) from 2012 to 2021. Cox proportional hazards models estimated hazard ratios and 95% confidence intervals for CVD outcomes, adjusting for demographic, clinical, and behavioral covariates, with subgroup analyses conducted according to baseline BMI categories. Among the 80,941 participants, higher BMI variability was associated with significantly lower risks of CVD (HR, 0.90; 95% CI, 0.85–0.96) and CAD (HR, 0.89; 95% CI, 0.83–0.95) in fully adjusted models, while no significant association was observed for stroke. The inverse association between BMI variability and CVD or CAD was most prominent among participants with a baseline BMI ≥ 23.0 kg/m². These findings suggest that higher BMI variability, potentially reflecting intentional lifestyle modifications, may confer cardiovascular benefit, particularly in individuals with elevated baseline BMI. BMI(Body mass index) BMI variability Cardiovascular disease Figures Figure 1 Figure 2 Introduction Body mass index (BMI) is a widely accepted indicator of adiposity and a well-established predictor of cardiovascular disease (CVD), one of the leading causes of morbidity and mortality worldwide. 1 – 3 While most research has focused on the absolute value of BMI, emerging interest has turned to the health implications of BMI fluctuations over time. Understanding how BMI variability influences cardiovascular health could provide new insights into risk stratification and prevention strategies. BMI variability refers to intra-individual changes in BMI over time, which may result from lifestyle factors, metabolic adaptations, or underlying health conditions. 4 Although prior studies have explored the effects of weight cycling or extreme weight changes, the impact of BMI variability on cardiovascular outcomes remains poorly understood. Recent evidence suggests that BMI variability may have distinct pathophysiological consequences—such as promoting systemic inflammation, impairing metabolic regulation, and influencing cardiovascular risk—beyond the effects of static BMI levels. 5 – 7 The existing literature on BMI variability and CVD has yielded mixed results. Several studies have reported increased risks of adverse cardiovascular outcomes and mortality associated with weight fluctuations, whereas others have shown no association or even suggested that lower weight variability may be linked to protective effects, particularly among people with obesity. 4 , 8 , 9 These discrepancies may stem from differences in study design, population characteristics, or definitions of variability. 10,11 Moreover, relatively few studies have examined this association across baseline BMI categories or stratified by specific cardiovascular outcomes such as coronary artery disease or stroke. We conducted a large retrospective cohort study to investigate the association between BMI variability and the risk of CVD in a well-characterized cohort. By evaluating both overall and stratified risks, this study aims to clarify whether BMI variability functions as an independent risk factor or a modifying factor for CVD. The study may provide new insights into the prognostic significance of BMI fluctuations and inform personalized strategies for cardiovascular risk. Method Study design and Ethics This retrospective cohort study received ethical approval from the Institutional Review Board of Kangbuk Samsung Hospital and was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Prior to the health examination, all patients were informed that their health screening information could be used for research purposes, and written informed consent was obtained from all participants. The study was reported in compliance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Study population From 2002 to 2021, a total of 110,331 people aged 40 years or older who underwent at least three health examinations at Kangbuk Samsung Hospital were initially considered for inclusion. Participants with a history of cardiovascular disease (CVD) at baseline (n = 9,075), those who were lost to follow-up (n = 18,138), and those with incomplete data on key covariates (n = 2,177) were excluded from the final analysis. Exposure: BMI variability This study was structured in two phases: Phase 1 (exposure period) and Phase 2 (follow-up period). Participants who underwent three or more annual health examinations between 2002 and 2011 were eligible for inclusion in the exposure assessment. BMI was calculated at each visit as weight in kilograms divided by height in meters squared (kg/m²). BMI variability was quantified using the coefficient of variation (CV), defined as the standard deviation of BMI divided by the mean BMI across the three or more annual measurements. 2 The baseline was defined as the date of the last BMI measurement used to calculate variability. To ensure temporality and minimize the potential for reverse causation, participants with any diagnosis of cardiovascular disease (CVD) prior to January 1, 2012 were excluded. Follow-up for incident CVD events began on January 1, 2012 and continued through December 31, 2021. Based on the distribution of CV values, participants were categorized into tertiles: tertile 1 (lowest variability), tertile 2 (moderate variability), and tertile 3 (highest variability) (Fig. 1 ). Measurement of variables Baseline covariates were defined as values collected at the date of the third BMI measurement. Demographic characteristics, anthropometric measurements, laboratory values, and health behavior information were collected during standardized health examinations conducted at Kangbuk Samsung Hospital. Height and weight were measured by trained staff using automated instruments, and BMI was calculated as weight in kilograms divided by height in meters squared (kg/m²). Systolic and diastolic blood pressure (SBP and DBP) were measured using an automated sphygmomanometer after a period of rest. Fasting blood samples were obtained and analyzed at a certified central laboratory. Fasting glucose, glycated hemoglobin (HbA1c), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) were measured using enzymatic or immunoturbidimetric methods. Estimated glomerular filtration rate (eGFR) was calculated using the CKD-EPI equation. Serum uric acid was also measured by enzymatic colorimetric assay. Information on health behaviors was obtained through self-administered questionnaires. Regular physical activity was defined as engaging in exercise at least twice per week. Smoking status was categorized as current smoker or not, based on self-report. Current alcohol consumption of ≥ 20 g/day was classified as heavy drinking. Education level was categorized as ≤ high school, ≥college graduate, or unknown. The presence of diabetes, hypertension, and family history of cardiovascular disease was identified based on physician diagnosis or self-report. Outcomes The primary outcome of the study was newly diagnosed CVD, defined as stroke(including ischemic stroke, intracerebral hemorrhage of subarachnoid hemorrhage) or coronary artery disease(CAD). 3 Participants without stroke or CAD during their follow-up were considered to have completed the study at the end of follow-up. Statistical analysis Baseline characteristics of participants were presented according to tertiles of BMI variability. Continuous variables are presented as mean ± standard deviation or median (Interquartile range) and are compared using Analysis of variance (ANOVA) or Kruskal-Wallis tests according to the normality and the nonnormality, respectively. Categorical variables were expressed as frequencies and percentages and compared using Pearson’s chi-square test. Incidence rates (IRs) of cardiovascular disease (CVD), stroke, and coronary artery disease were calculated per 1,000 person-years (PY) with 95% confidence intervals (CIs). Cox proportional hazards models were used to estimate hazard ratios (HRs) and corresponding 95% CIs for the association between BMI variability tertiles and clinical outcomes. Three models were constructed: Model 1 adjusted for age and sex; Model 2 further adjusted for metabolic and hemodynamic parameters (triglycerides, LDL-C, glucose, estimated glomerular filtration rate, uric acid, systolic blood pressure, and heart rate); and Model 3 additionally adjusted for health behaviors including smoking status, alcohol consumption, and regular physical activity (≥ 2 times/week). To explore nonlinear associations between BMI variability and outcome risk, restricted cubic spline regression analyses were performed with three knots, using the median BMI variability value (CV = 0.02) as the reference point. Subgroup analyses were conducted by stratifying participants into four baseline BMI categories (< 18.5, 18.5–22.9, 23.0–24.9, and ≥ 25 kg/m²), and HRs for CVD were estimated within each BMI category. All statistical analyses were performed using R software version 4.3.1.(The R Foundation for Statistical Computing, Vienna, Austria) and STATA version 18(StataCorp LLC, College Station, TX, USA) Two-sided p-values < 0.05 were considered statistically significant. Results A total of 80,941 participants were included in the analysis. The mean age was 42.6 ± 4.6 years, and 62.5% were male. Baseline characteristics are presented in Table 1 . Participants were divided into Tertile1, Tertile 2 and Tertile 3 based on BMI variability Participants were divided into tertiles of BMI variability: tertile 1 (0–0.014), tertile 2 (0.015–0.025), and tertile 3 (0.026–0.223). Significant differences were observed among the BMI variability tertiles in terms of age, sex, height, weight, body mass index (BMI), education level, lipid profiles, glucose metabolism markers, kidney function parameters, and lifestyle factors such as smoking and alcohol consumption. Table 1 Baseline patient characteristics Total (n = 80,941) BMI variability P-value Tertile 1 (n = 26,798) Tertile 2 (n = 26,917) Tertile 3 (n = 27,226) BMI variability range 0–0.223 0–0.014 0.015–0.025 0.026–0.223 < 0.001 BMI variability, mean ± SD 0.023 ± 0.016 0.009 ± 0.003 0.020 ± 0.003 0.040 ± 0.015 < 0.001 Age, years 42.5 ± 4.6 42.6 ± 4.7 42.5 ± 4.5 42.5 ± 4.6 0.01 Male, n(%) 50561 (62.5) 18217 (68.0) 17335 (64.4) 15009 (55.1) < 0.001 Height, cm 167.3 ± 8.1 168.1 ± 7.9 167.5 ± 8.1 166.3 ± 8.3 < 0.001 Weight, kg 66.7 ± 11.8 67.5 ± 11.7 66.8 ± 11.7 65.8 ± 11.9 < 0.001 Body mass index, 23.7 ± 3.0 23.8 ± 3.0 23.7 ± 3.0 23.6 ± 3.1 < 0.001 Education level < 0.001 ≤high school 13438 (16.6) 4120 (15.4) 4417 (16.4) 4901 (18.0) ≥college graduate 48631 (60.1) 16380 (61.1) 16359 (60.8) 15892 (58.4) Unknown 18872 (23.3) 6298 (23.5) 6141 (22.8) 6433 (23.6) Body mass index, n < 0.001 25 25513 (31.5) 8841 (33.0) 8351 (31.0) 8321 (30.1) HR 66.0 ± 9.1 65.7 ± 9.0 65.9 ± 9.1 66.5 ± 9.3 < 0.001 SBP 112.8 ± 13.8 113.2 ± 13.5 112.8 ± 13.7 112.4 ± 14.1 < 0.001 DBP 73.5 ± 10.2 73.8 ± 10.2 73.5 ± 10.3 73.0 ± 10.4 < 0.001 Glucose 95.9 ± 15.9 95.8 ± 14.2 95.9 ± 15.5 96.1 ± 17.7 0.036 HbA1C 5.6 ± 0.5 5.6 ± 0.5 5.6 ± 0.5 5.6 ± 0.6 < 0.001 TG 128.5 ± 85.6 132.7 ± 87.1 130.1 ± 86.7 122.9 ± 82.7 < 0.001 LDL-C 119.7 ± 30.5 120.6 ± 30.4 119.9 ± 30.3 118.7 ± 30.7 < 0.001 HDL-C 55.7 ± 13.5 55.0 ± 13.3 55.5 ± 13.4 56.6 ± 13.7 < 0.001 eGFR 89.7 ± 14.2 89.2 ± 14.0 89.5 ± 14.1 90.4 ± 14.4 < 0.001 Uric acid 5.3 ± 1.4 5.4 ± 1.4 5.4 ± 1.4 5.2 ± 1.4 < 0.001 Physical exercise( \(\:\ge\:\) 2/wk) 18354 (22.7) 6152 (23.0) 6069 (22.6) 6133 (22.5) 0.694 Current smoker 19872 (24.6) 6805 (25.4) 6780 (25.2) 6287 (23.1) < 0.001 Current drinker 15735 (19.4) 5771 (21.5) 5355 (19.9) 4609 (16.9) < 0.001 Diabetes 1904 (2.4) 589 (2.2) 650 (2.4) 665 (2.4) 0.372 Hypertension 6640 (8.2) 2180 (8.1) 2218 (8.2) 2242 (8.2) 0.989 Continuous variables are presented as mean ± standard deviation or median (Interquartile range) and are compared using Analysis of variance (ANOVA) or Kruskal-Wallis tests according to the normality and the nonnormality, respectively. Categorical variables are presented as the n(%) and compared using Pearson’s chi-square test. SBP: Systolic blood pressure; DBP: Diastolic blood pressure; TG: Triglycerides; LDL-C: Low-density lipoprotein cholesterol; HDL-C: High-density lipoprotein cholesterol; eGFR: estimated glomerular filtration rate. Current drinker was defined as an individual with ongoing alcohol consumption and an average daily intake of ≥ 20 g of alcohol. Current smoker was defined as an individual who has smoked at least 100 cigarettes in their lifetime and currently smokes. Association between BMI Variability and Cardiovascular Risk Table 2 presents hazard ratios (HRs) and 95% confidence intervals (CIs) for cardiovascular disease (CVD) including stroke and coronary artery disease(CAD), across BMI variability tertiles. Compared with tertile 1, the adjusted hazard ratio (HR) for CVD in tertile 3 was 0.90 (95% CI, 0.85–0.96; P for trend = .002), and for CAD was 0.89 (95% CI, 0.83–0.95; P for trend = .001), after adjustment for demographic, clinical, and lifestyle covariates (Model 3). No significant association was observed between BMI variability and stroke incidence. Table 2 Hazard ratios and 95% confidence intervals for the incidence of CVD. BMI variability Event IR (95% CI) per 1,000 PY Multivariable Model Model 1 Model 2 Model 3 CVD 5612 8.71 (8.48, 8.94) Tertile 1 1993 9.24 (8.84, 9.65) 1 (reference) 1 (reference) 1 (reference) Tertile 2 1903 8.86 (8.47, 9.27) 0.98 (0.92, 1.04) 0.98 (0.92, 1.04) 0.98 (0.92, 1.04) Tertile 3 1716 8.01 (7.64, 8.40) 0.91 (0.85, 0.97) 0.91 (0.85, 0.97) 0.90 (0.85, 0.96) P for trend 0.005 0.004 0.002 Stroke 797 1.2 (1.12–1.28) Tertile 1 278 1.25 (1.11, 1.40) 1 (reference) 1 (reference) 1 (reference) Tertile 2 262 1.18 (1.05, 1.33) 0.96 (0.81, 1.14) 0.95 (0.80, 1.13) 0.95 (0.80, 1.12) Tertile 3 257 1.17 (1.03, 1.32) 0.97 (0.82, 1.15) 0.95 (0.80, 1.13) 0.947 (0.80, 1.12) P for trend 0.706 0.579 0.526 Coronary artery disease 4986 7.72 (7.51–7.93) Tertile 1 1785 8.25 (7.88–8.65) 1 (reference) 1 (reference) 1 (reference) Tertile 2 1700 7.90 (7.53–8.28) 0.98 (0.91–1.04) 0.98 (0.92–1.05) 0.98 (0.92–1.05) Tertile 3 1501 6.99 (6.65–7.36) 0.89 (0.83–0.96) 0.89 (0.83–0.96) 0.89 (0.83–0.95) P for trend 0.001 0.001 0.001 Model 1. Sex, Age Model 2. Sex, Age, Tg, LDL, Glucose, EGFR, ckdepi, Uric acid, SBP, Heart rate Model 3. Sex, Age, Tg, LDL, Glucose, EGFR_ckdepi, Uric acid, SBP, Heart rate, Smoking, Alcohol, Regular exercise ( \(\:\ge\:\) 2 times /wk) Restricted cubic spline analysis (Fig. 2 ) demonstrated a significant inverse linear association between BMI variability and CVD risk (P = .012). While no significant link was identified with stroke risk, higher BMI variability was notably correlated with a reduced risk of CAD. CVD Incidence Rates within BMI Categories Table 3 presents the incidence rates of CVD, stroke, and CAD, stratified by BMI variability tertiles and baseline BMI categories. CVD was observed in 6.9% of participants overall, with incidence rates of 3.9%, 5.7%, 7.8%, and 8.1% across BMI categories of < 18.5, 18.5–22.9, 23.0–24.9, and ≥ 25.0 kg/m², respectively. Significant differences in incidence rates of CVD and CAD were noted across BMI variability tertiles (P < .001 for both), particularly among people in the higher BMI categories (23.0–24.9 and ≥ 25.0 kg/m²). In these subgroups, higher BMI variability was associated with lower incidence rates of CVD (P = .012 and P = .002, respectively) and CAD (P = .001 for both). Table 3 CVD incidence rate according to BMI variability within each BMI category Variable Total (n = 80,941) BMI( 25) (n = 25,513) Event , n(%) P value Event , n(%) P value Event , n(%) P value Event , n(%) P value Event , n(%) P value CVD, n 5612 (6.9) < 0.001 89 (3.9) 0.456 1826 (5.7) 0.149 1636 (7.8) 0.012 2061 (8.1) 0.002 Tertile 1 1993 (7.4) 31 (4.3) 599(5.9) 596 (8.3) 767(8.7) Tertile 2 1903 (7.1) 23 (3.2) 618 (5.8) 571 (8.0) 691 (8.3) Tertile 3 1716 (6.3) 35 (4.2) 609 (5.4) 469 (7.0) 603 (7.3) Stroke 797 (1.0) 0.532 9 (0.4) 0.601 243 (0.8) 0.015 253 (1.2) 0.205 292 (1.1) 0.244 Tertile 1 278 (1.0) 4 (0.6) 66 (0.7) 95 (1.3) 113 (1.3) Tertile 2 262 (1.0) 3 (0.4) 102 (1.0) 73 (1.0) 84 (1.0) Tertile 3 257 (0.9) 2 (0.2) 75 (0.7) 85 (1.3) 95 (1.1) Coronary artery disease 4986 (6.2) < 0.001 81 (3.5) 0.370 1637 (5.1) 0.132 1439 (6.9) 0.001 1829 (7.2) 0.001 Tertile 1 1785 (6.7) 28 (3.9) 548 (5.4) 528 (7.4) 681 (7.7) Tertile 2 1700 (6.3) 20 (2.7) 538 (5.0) 516 (7.2) 626 (7.5) Tertile 3 1501 (5.5) 33 (3.9) 551 (4.8) 395 (5.9) 522 (6.3) Data are presented as number and percentages (%). P-values were calculated using Pearson’s chi-square test to compare the incidence of cardiovascular disease, stroke, and coronary artery disease across BMI variability tertiles within each BMI category. No consistent or significant pattern of association was observed between BMI variability and stroke incidence across BMI categories (all P > .05), although a marginal trend was seen in the 18.5–22.9 kg/m² group (P = .015) Hazard Ratios for CVD Risk within BMI Categories Hazard ratios for CVD, stroke, and CAD stratified by BMI categories are detailed in Table 4 . The highest BMI variability tertile(Tertile 3) was significantly associated with reduced CVD risk among participants with BMI of 23–24.9 kg/m² (HR 0.85; 95% CI, 0.75–0.95, P = 0.006) and BMI > 25 kg/m² (HR 0.89; 95% CI, 0.80–0.99, P = 0.027). For stroke, a significant risk reduction was noted only in Tertile 2 within the BMI > 25 kg/m² category (HR 0.74; 95% CI, 0.56–0.98, P = 0.035); otherwise, no statistically significant results were observed for stroke across other categories or tertiles. CAD risk was significantly lower in the highest tertile of BMI variability within BMI categories of 23–24.9 kg/m² (HR 0.86; 95% CI, 0.74–0.99, P = 0.039) and > 25 kg/m² (HR 0.87; 95% CI, 0.76–0.99, P = 0.032). Overall, increased BMI variability was inversely associated with risks of CVD and CAD, especially among people with higher baseline BMI values. Table 4 Hazard ratios and 95% confidence intervals (CIs) for CVD risk by BMI variability. Variable Total BMI( 25) HR(95%CI) P value HR(95%CI) P value HR(95%CI) P value HR(95%CI) P value HR(95%CI) P value CVD Tertile 1 (reference) (reference) (reference) (reference) (reference) Tertile 2 0.98 (0.92, 1.04) 0.488 0.94 (0.55, 1.60) 0.809 1.05 (0.94, 1.17) 0.426 0.96 (0.86, 1.08) 0.509 0.93 (0.84, 1.03) 0.177 Tertile 3 0.90 (0.85, 0.91) 0.002 1.05 (0.62, 1.77) 0.871 0.97 (0.86, 1.09) 0.576 0.85 (0.75, 0.95) 0.006 0.89 (0.80, 0.99) 0.027 Stroke Tertile 1 (reference) (reference) (reference) (reference) (reference) Tertile 2 0.95 (0.80, 1.12) 0.536 1.28 (0.33, 4.99) 0.724 1.31 (0.95, 1.79) 0.096 0.92 (0.68, 1.25) 0.59 0.74 (0.56, 0.98) 0.035 Tertile 3 0.95 (0.80, 1.12) 0.531 0.39 (0.07, 2.29) 0.296 1.13 (0.82, 1.56) 0.467 0.90 (0.66, 1.22) 0.485 0.89 (0.67, 1.16) 0.376 Coronary artery disease Tertile 1 (reference) (reference) (reference) (reference) (reference) Tertile 2 1.04 (0.97, 1.12) 0.302 1.07 (0.54, 2.15) 0.841 1.10 (0.96, 1.27) 0.172 1.01 (0.88, 1.16) 0.884 1.00 (0.89, 1.14) 0.963 Tertile 3 0.92 (0.85, 0.99) 0.03 1.07 (0.53, 2.15) 0.861 1.01 (0.89, 1.17) 0.846 0.86 (0.74, 0.99) 0.039 0.87 (0.76, 0.99) 0.032 Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox proportional hazards regression models. P-values indicate the statistical significance of the difference in risk compared with the reference group (Tertile 1 of BMI variability) within each BMI category. Discussion This large cohort study investigated the association between BMI variability and the risk of cardiovascular disease (CVD). We found that people in the highest tertile of BMI variability (Tertile 3) had a significantly lower risk of incident CVD compared to those in the lowest tertile (Tertile 1). This inverse association was particularly evident among participants with a baseline BMI ≥ 23 kg/m². These findings challenge the traditional notion that stable BMI is always optimal for cardiovascular health and suggest that moderate fluctuations in BMI may have a protective effect in certain populations. The findings of this study align with some prior research indicating that fluctuations in weight or BMI may have metabolic benefits, such as improved insulin sensitivity or lipid profiles. 12 – 14 However, they contrast with other studies that have associated weight variability with adverse outcomes, including increased CVD risk. 10,15 These discrepancies may stem from differences in study populations, definitions of variability, or follow-up durations. For instance, studies focusing on severe weight cycling may reflect underlying disease processes or unhealthy behaviors, which differ from the moderate, possibly health-driven fluctuations observed in our cohort. Further research is needed to reconcile these differences and clarify the mechanisms underlying the relationship between BMI variability and CVD. The observed reduction in CVD risk among people with higher BMI variability may be explained by several mechanisms. First, periodic weight changes may promote metabolic flexibility, which refers to the body's ability to efficiently switch between fuel sources—primarily glucose and fatty acids—depending on energy availability and physiological demand. 16 , 17 Enhanced metabolic flexibility has been associated with improved mitochondrial function, insulin sensitivity, and overall cardiometabolic health. In this context, moderate fluctuations in BMI may stimulate adaptive metabolic responses that enhance energy utilization and vascular efficiency. Second, BMI variability may reflect intentional lifestyle changes, such as intermittent physical activity or dietary modifications, both of which are independently associated with reduced CVD risk. Third, higher BMI variability may be associated with improved adipose tissue function, including enhanced adipokine secretion, reduced chronic inflammation, and lower insulin resistance—all of which contribute to cardiovascular protection. 18 However, these hypotheses require further investigation through longitudinal studies and mechanistic research. While BMI variability showed a consistent inverse association with CAD, the findings for stroke were inconsistent and largely nonsignificant. The differential impact on stroke and CAD may reflect variations in pathophysiology and vascular susceptibility. Coronary vessels may be more responsive to metabolic adaptations related to weight variability than cerebral vasculature, warranting further mechanistic investigation. Stroke risk may also be more strongly influenced by nonmetabolic risk factors, such as atrial fibrillation or embolic sources, which may dilute any potential benefit from BMI variability. Lifestyle factors, such as physical activity and dietary patterns, may play a significant role in mediating the relationship between BMI variability and cardiovascular disease (CVD) risk. 19 People with higher BMI variability may engage in more frequent weight management behaviors—such as structured exercise, intermittent fasting, or dietary modifications—that confer cardioprotective benefits. 20 – 22 Conversely, unhealthy weight fluctuations resulting from extreme dieting, disordered eating, or inconsistent lifestyle habits may have adverse cardiovascular consequences. These observations may partially intersect with the concept of the obesity paradox, wherein people with higher BMI—particularly those with preserved metabolic reserves—demonstrate better cardiovascular outcomes in certain populations. 23 – 25 In this context, moderate BMI fluctuations in overweight or mildly people with obesity may reflect dynamic physiological adaptations or intentional weight control behaviors, rather than metabolic instability. This could help explain why higher BMI variability, especially in those with elevated baseline BMI, is associated with reduced CVD risk in some studies, including our own. The biological mechanisms underlying the relationship between BMI variability and CVD risk remain incompletely understood. One potential explanation is that moderate BMI variability may stimulate adaptive responses in the cardiovascular system, such as improved endothelial function or enhanced cardiac efficiency. Additionally, fluctuations in BMI could influence the release of adipokines and cytokines, which play a key role in inflammation and insulin sensitivity. 26 – 28 For instance, adiponectin, an anti-inflammatory adipokine, has been shown to increase with weight loss and may contribute to reduced CVD risk. 29 – 31 Further research is needed to explore these biological pathways and their potential protective effects, particularly in diverse populations and across different BMI categories. The results of this study have important implications for clinical practice. While maintaining a stable BMI is often recommended for cardiovascular health, these findings suggest that fluctuations in BMI may not be always harmful and could even be beneficial in certain populations. In particular, people with higher baseline BMI may experience favorable cardiovascular outcomes associated with BMI variability. Clinicians should consider individual patient characteristics, including baseline BMI and overall metabolic health, when formulating weight management strategies. Furthermore, public health messages may warrant refinement to acknowledge the potential benefits of moderate BMI variability, especially in populations with elevated BMI. This study has several limitations. First, the observational design precludes establishing causality, and residual confounding factors may have influenced the results. Second, the study population consisted of relatively health-conscious people undergoing regular health screenings at a single center, potentially limiting the generalizability of the findings to broader or more diverse populations. Future research should include diverse cohorts, longer follow-up periods, and more detailed assessments of weight change patterns. Additionally, studies incorporating biomarkers and imaging data could provide deeper insights into the mechanisms linking BMI variability to cardiovascular outcomes. In conclusion, BMI variability is associated with the risk of cardiovascular disease, with higher variability linked to a lower risk of CVD, especially in people with BMI ≥ 23. These results highlight the potential importance of BMI variability in cardiovascular health and call for further research to elucidate the mechanisms involved. Abbreviations ANOVA , analysis of variance BMI , body mass index CAD , coronary artery disease CI, confidence interval CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration CVD , cardiovascular disease CV, coefficient of variation DBP, diastolic blood pressure eGFR , estimated glomerular filtration rate HbA1c , hemoglobin A1c HDL-C, high-density lipoprotein cholesterol HR, hazard ratio IR, incidence rate IRB, Institutional Review Board KBSMC, Kangbuk Samsung Medical Center LDL-C, low-density lipoprotein cholesterol PY, person-years SBP, systolic blood pressure STROBE, Strengthening the Reporting of Observational Studies in Epidemiology TG, triglycerides Declarations Author’s contributions • Jin Hee Ahn and Eun Jung Oh helped with the study design, planning, conduct, data analysis, and drafting of the paper. • Jae-Geum Shim and Jiyeon Park designed and planned the study, analyzed the data, and drafted and revised the paper. • Mi-yeon Lee helped with the data analysis. • Eun Ah Cho and Sung Hyun Lee helped with the formal analysis. Prior Presentations : Not applicable Clinical trial number : Not applicable Acknowledgments: The authors used ChatGPT (OpenAI, San Francisco, CA, USA) to assist in language editing of the manuscript. The content and interpretation of the manuscript are solely the responsibility of the authors. Funding Statement: The authors declare no financial support for this study. Conflicts of Interest: The authors declare no competing interests. Ethics approval and consent to participate : This study was approved by the Institutional Review Board of Kangbuk Samsung Hospital, Seoul, Republic of Korea (KBSMC 2022-06-016, June 21, 2022]). Written informed consent was obtained from all participants. Data Statement : The data that support the findings of this study are not publicly available due to privacy reasons, but are available from the data sharing committee upon reasonable request. References Tsao, C. W. et al. 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Intern Emerg Med 2, 165–176, doi: 10.1007/s11739-007-0027-9 (2007). Orlando, A., Nava, E., Giussani, M. & Genovesi, S. Adiponectin and Cardiovascular Risk. From Pathophysiology to Clinic: Focus on Children and Adolescents. Int J Mol Sci 20, doi: 10.3390/ijms20133228 (2019). Nakamura, K., Fuster, J. J. & Walsh, K. Adipokines: a link between obesity and cardiovascular disease. J Cardiol 63, 250–259, doi: 10.1016/j.jjcc.2013.11.006 (2014). Additional Declarations No competing interests reported. Supplementary Files GraphicalAbstract.tif Cite Share Download PDF Status: Published Journal Publication published 07 Apr, 2026 Read the published version in European Journal of Medical Research → Version 1 posted Editorial decision: Revision requested 13 Mar, 2026 Reviews received at journal 20 Feb, 2026 Reviews received at journal 15 Feb, 2026 Reviewers agreed at journal 13 Feb, 2026 Reviews received at journal 13 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers invited by journal 15 Jan, 2026 Editor assigned by journal 13 Jan, 2026 Submission checks completed at journal 13 Jan, 2026 First submitted to journal 07 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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13:19:26","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":141817,"visible":true,"origin":"","legend":"","description":"","filename":"c2708af3e3e84825b4424cb37beb36f71structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8537727/v1/d88b7d4de084f1c1a829c171.xml"},{"id":100687757,"identity":"47ec024c-59a3-46be-8fc4-0548a8ea35e4","added_by":"auto","created_at":"2026-01-20 13:20:44","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":162051,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8537727/v1/1518121df42e9e71137b1849.html"},{"id":100687066,"identity":"a802f379-e830-4c68-8bec-79d9f5e565eb","added_by":"auto","created_at":"2026-01-20 13:13:37","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":671662,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy flow chart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8537727/v1/921981a2c6b3d642486706e7.jpg"},{"id":100687216,"identity":"3aaae527-2863-42fa-924c-58fbe8a06206","added_by":"auto","created_at":"2026-01-20 13:15:57","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":571605,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between BMI variability and cardiovascular outcomes based on restricted cubic spline models.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRestricted cubic spline curves illustrate the association between body mass index (BMI) variability (expressed as coefficient of variation, CV) and the hazard ratio (HR) for (a) cardiovascular disease (CVD), (b) stroke, and (c) coronary artery disease. The reference value (HR = 1.0) was set at the median CV (0.02). The solid blue line represents the estimated HR, and the shaded area denotes the 95% confidence interval. A significant inverse trend was observed between BMI variability and CVD risk (P for trend = 0.012). While the trend for stroke was not statistically significant, coronary artery disease showed significantly inverse association across the CV spectrum, with most of the 95% confidence intervals remaining below the null value (HR = 1.0).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8537727/v1/c18c891eb05d4b65ffd8d7b6.jpg"},{"id":106809959,"identity":"dae19340-c9ec-4d59-897d-b0138e830bce","added_by":"auto","created_at":"2026-04-13 16:13:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2508810,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8537727/v1/f8380820-be1d-4e6e-80ac-6850350d05f3.pdf"},{"id":100687617,"identity":"3c7772d5-0b8f-46f8-a64e-e980182dd36c","added_by":"auto","created_at":"2026-01-20 13:19:03","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1233920,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.tif","url":"https://assets-eu.researchsquare.com/files/rs-8537727/v1/fee224cf271789711141b6b2.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eBody Mass Index Variability and Its Role in Cardiovascular Disease Risk Stratification : A large cohort study. \u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBody mass index (BMI) is a widely accepted indicator of adiposity and a well-established predictor of cardiovascular disease (CVD), one of the leading causes of morbidity and mortality worldwide.\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e While most research has focused on the absolute value of BMI, emerging interest has turned to the health implications of BMI fluctuations over time. Understanding how BMI variability influences cardiovascular health could provide new insights into risk stratification and prevention strategies. BMI variability refers to intra-individual changes in BMI over time, which may result from lifestyle factors, metabolic adaptations, or underlying health conditions.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Although prior studies have explored the effects of weight cycling or extreme weight changes, the impact of BMI variability on cardiovascular outcomes remains poorly understood. Recent evidence suggests that BMI variability may have distinct pathophysiological consequences\u0026mdash;such as promoting systemic inflammation, impairing metabolic regulation, and influencing cardiovascular risk\u0026mdash;beyond the effects of static BMI levels.\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe existing literature on BMI variability and CVD has yielded mixed results. Several studies have reported increased risks of adverse cardiovascular outcomes and mortality associated with weight fluctuations, whereas others have shown no association or even suggested that lower weight variability may be linked to protective effects, particularly among people with obesity.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e These discrepancies may stem from differences in study design, population characteristics, or definitions of variability. \u003csup\u003e10,11\u003c/sup\u003e Moreover, relatively few studies have examined this association across baseline BMI categories or stratified by specific cardiovascular outcomes such as coronary artery disease or stroke.\u003c/p\u003e \u003cp\u003eWe conducted a large retrospective cohort study to investigate the association between BMI variability and the risk of CVD in a well-characterized cohort. By evaluating both overall and stratified risks, this study aims to clarify whether BMI variability functions as an independent risk factor or a modifying factor for CVD. The study may provide new insights into the prognostic significance of BMI fluctuations and inform personalized strategies for cardiovascular risk.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and Ethics\u003c/h2\u003e \u003cp\u003e This retrospective cohort study received ethical approval from the Institutional Review Board of Kangbuk Samsung Hospital and was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Prior to the health examination, all patients were informed that their health screening information could be used for research purposes, and written informed consent was obtained from all participants. The study was reported in compliance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eFrom 2002 to 2021, a total of 110,331 people aged 40 years or older who underwent at least three health examinations at Kangbuk Samsung Hospital were initially considered for inclusion. Participants with a history of cardiovascular disease (CVD) at baseline (n\u0026thinsp;=\u0026thinsp;9,075), those who were lost to follow-up (n\u0026thinsp;=\u0026thinsp;18,138), and those with incomplete data on key covariates (n\u0026thinsp;=\u0026thinsp;2,177) were excluded from the final analysis.\u003c/p\u003e\n\u003ch3\u003eExposure: BMI variability\u003c/h3\u003e\n\u003cp\u003eThis study was structured in two phases: Phase 1 (exposure period) and Phase 2 (follow-up period). Participants who underwent three or more annual health examinations between 2002 and 2011 were eligible for inclusion in the exposure assessment. BMI was calculated at each visit as weight in kilograms divided by height in meters squared (kg/m\u0026sup2;). BMI variability was quantified using the coefficient of variation (CV), defined as the standard deviation of BMI divided by the mean BMI across the three or more annual measurements.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e The baseline was defined as the date of the last BMI measurement used to calculate variability. To ensure temporality and minimize the potential for reverse causation, participants with any diagnosis of cardiovascular disease (CVD) prior to January 1, 2012 were excluded. Follow-up for incident CVD events began on January 1, 2012 and continued through December 31, 2021. Based on the distribution of CV values, participants were categorized into tertiles: tertile 1 (lowest variability), tertile 2 (moderate variability), and tertile 3 (highest variability) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eMeasurement of variables\u003c/h3\u003e\n\u003cp\u003eBaseline covariates were defined as values collected at the date of the third BMI measurement. Demographic characteristics, anthropometric measurements, laboratory values, and health behavior information were collected during standardized health examinations conducted at Kangbuk Samsung Hospital. Height and weight were measured by trained staff using automated instruments, and BMI was calculated as weight in kilograms divided by height in meters squared (kg/m\u0026sup2;). Systolic and diastolic blood pressure (SBP and DBP) were measured using an automated sphygmomanometer after a period of rest. Fasting blood samples were obtained and analyzed at a certified central laboratory. Fasting glucose, glycated hemoglobin (HbA1c), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) were measured using enzymatic or immunoturbidimetric methods. Estimated glomerular filtration rate (eGFR) was calculated using the CKD-EPI equation. Serum uric acid was also measured by enzymatic colorimetric assay. Information on health behaviors was obtained through self-administered questionnaires. Regular physical activity was defined as engaging in exercise at least twice per week. Smoking status was categorized as current smoker or not, based on self-report. Current alcohol consumption of \u0026ge;\u0026thinsp;20 g/day was classified as heavy drinking. Education level was categorized as \u0026le;\u0026thinsp;high school, \u0026ge;college graduate, or unknown. The presence of diabetes, hypertension, and family history of cardiovascular disease was identified based on physician diagnosis or self-report.\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcome of the study was newly diagnosed CVD, defined as stroke(including ischemic stroke, intracerebral hemorrhage of subarachnoid hemorrhage) or coronary artery disease(CAD).\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Participants without stroke or CAD during their follow-up were considered to have completed the study at the end of follow-up.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eBaseline characteristics of participants were presented according to tertiles of BMI variability. Continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (Interquartile range) and are compared using Analysis of variance (ANOVA) or Kruskal-Wallis tests according to the normality and the nonnormality, respectively. Categorical variables were expressed as frequencies and percentages and compared using Pearson\u0026rsquo;s chi-square test. Incidence rates (IRs) of cardiovascular disease (CVD), stroke, and coronary artery disease were calculated per 1,000 person-years (PY) with 95% confidence intervals (CIs). Cox proportional hazards models were used to estimate hazard ratios (HRs) and corresponding 95% CIs for the association between BMI variability tertiles and clinical outcomes. Three models were constructed: Model 1 adjusted for age and sex; Model 2 further adjusted for metabolic and hemodynamic parameters (triglycerides, LDL-C, glucose, estimated glomerular filtration rate, uric acid, systolic blood pressure, and heart rate); and Model 3 additionally adjusted for health behaviors including smoking status, alcohol consumption, and regular physical activity (\u0026ge;\u0026thinsp;2 times/week). To explore nonlinear associations between BMI variability and outcome risk, restricted cubic spline regression analyses were performed with three knots, using the median BMI variability value (CV\u0026thinsp;=\u0026thinsp;0.02) as the reference point. Subgroup analyses were conducted by stratifying participants into four baseline BMI categories (\u0026lt;\u0026thinsp;18.5, 18.5\u0026ndash;22.9, 23.0\u0026ndash;24.9, and \u0026ge;\u0026thinsp;25 kg/m\u0026sup2;), and HRs for CVD were estimated within each BMI category. All statistical analyses were performed using R software version 4.3.1.(The R Foundation for Statistical Computing, Vienna, Austria) and STATA version 18(StataCorp LLC, College Station, TX, USA) Two-sided p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 80,941 participants were included in the analysis. The mean age was 42.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6 years, and 62.5% were male. Baseline characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Participants were divided into Tertile1, Tertile 2 and Tertile 3 based on BMI variability Participants were divided into tertiles of BMI variability: tertile 1 (0\u0026ndash;0.014), tertile 2 (0.015\u0026ndash;0.025), and tertile 3 (0.026\u0026ndash;0.223). Significant differences were observed among the BMI variability tertiles in terms of age, sex, height, weight, body mass index (BMI), education level, lipid profiles, glucose metabolism markers, kidney function parameters, and lifestyle factors such as smoking and alcohol consumption.\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 patient characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;80,941)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eBMI variability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eTertile 1\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;26,798)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eTertile 2\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;26,917)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eTertile 3\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;27,226)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI variability range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u0026ndash;0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u0026ndash;0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI variability, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.023\u0026thinsp;\u0026plusmn;\u0026thinsp;0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.020\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.040\u0026thinsp;\u0026plusmn;\u0026thinsp;0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50561 (62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18217 (68.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17335 (64.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15009 (55.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e167.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e168.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e167.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e166.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.7\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13438 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4120 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4417 (16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4901 (18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;college graduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48631 (60.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16380 (61.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16359 (60.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15892 (58.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18872 (23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6298 (23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6141 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6433 (23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index, 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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2291 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e719 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e730 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e842 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.5\u0026ndash;22.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32157 (39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10080 (37.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10694 (39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11383 (41.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23-24.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20980 (25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7158 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7142 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6680 (24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25513 (31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8841 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8351 (31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8321 (30.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e112.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e113.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e112.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e112.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95.8\u0026thinsp;\u0026plusmn;\u0026thinsp;14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96.1\u0026thinsp;\u0026plusmn;\u0026thinsp;17.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.036\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e128.5\u0026thinsp;\u0026plusmn;\u0026thinsp;85.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e132.7\u0026thinsp;\u0026plusmn;\u0026thinsp;87.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e130.1\u0026thinsp;\u0026plusmn;\u0026thinsp;86.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e122.9\u0026thinsp;\u0026plusmn;\u0026thinsp;82.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e119.7\u0026thinsp;\u0026plusmn;\u0026thinsp;30.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120.6\u0026thinsp;\u0026plusmn;\u0026thinsp;30.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e119.9\u0026thinsp;\u0026plusmn;\u0026thinsp;30.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e118.7\u0026thinsp;\u0026plusmn;\u0026thinsp;30.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55.7\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.5\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56.6\u0026thinsp;\u0026plusmn;\u0026thinsp;13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89.7\u0026thinsp;\u0026plusmn;\u0026thinsp;14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89.5\u0026thinsp;\u0026plusmn;\u0026thinsp;14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical exercise(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e2/wk)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18354 (22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6152 (23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6069 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6133 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.694\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19872 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6805 (25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6780 (25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6287 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15735 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5771 (21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5355 (19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4609 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1904 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e589 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e650 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e665 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.372\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6640 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2180 (8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2218 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2242 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.989\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (Interquartile range) and are compared using Analysis of variance (ANOVA) or Kruskal-Wallis tests according to the normality and the nonnormality, respectively. Categorical variables are presented as the n(%) and compared using Pearson\u0026rsquo;s chi-square test. SBP: Systolic blood pressure; DBP: Diastolic blood pressure; TG: Triglycerides; LDL-C: Low-density lipoprotein cholesterol; HDL-C: High-density lipoprotein cholesterol; eGFR: estimated glomerular filtration rate. Current drinker was defined as an individual with ongoing alcohol consumption and an average daily intake of \u0026ge;\u0026thinsp;20 g of alcohol. Current smoker was defined as an individual who has smoked at least 100 cigarettes in their lifetime and currently smokes.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eAssociation between BMI Variability and Cardiovascular Risk\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents hazard ratios (HRs) and 95% confidence intervals (CIs) for cardiovascular disease (CVD) including stroke and coronary artery disease(CAD), across BMI variability tertiles. Compared with tertile 1, the adjusted hazard ratio (HR) for CVD in tertile 3 was 0.90 (95% CI, 0.85\u0026ndash;0.96; P for trend\u0026thinsp;=\u0026thinsp;.002), and for CAD was 0.89 (95% CI, 0.83\u0026ndash;0.95; P for trend\u0026thinsp;=\u0026thinsp;.001), after adjustment for demographic, clinical, and lifestyle covariates (Model 3). No significant association was observed between BMI variability and stroke incidence.\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\u003eHazard ratios and 95% confidence intervals for the incidence of CVD.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBMI variability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEvent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIR (95% CI) per 1,000 PY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eMultivariable Model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 3\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\u003eCVD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.71 (8.48, 8.94)\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTertile 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.24 (8.84, 9.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTertile 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.86 (8.47, 9.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.92, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98 (0.92, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98 (0.92, 1.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTertile 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.01 (7.64, 8.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91 (0.85, 0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91 (0.85, 0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90 (0.85, 0.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP for trend\u003c/em\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 \u003cp\u003e\u003cem\u003e0.005\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.004\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.002\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStroke\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.2 (1.12\u0026ndash;1.28)\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTertile 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.25 (1.11, 1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTertile 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.18 (1.05, 1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96 (0.81, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95 (0.80, 1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.95 (0.80, 1.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTertile 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.17 (1.03, 1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97 (0.82, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95 (0.80, 1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.947 (0.80, 1.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP for trend\u003c/em\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 \u003cp\u003e\u003cem\u003e0.706\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.579\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.526\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCoronary artery disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.72 (7.51\u0026ndash;7.93)\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTertile 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.25 (7.88\u0026ndash;8.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTertile 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.90 (7.53\u0026ndash;8.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.91\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98 (0.92\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98 (0.92\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTertile 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.99 (6.65\u0026ndash;7.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89 (0.83\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89 (0.83\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.89 (0.83\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP for trend\u003c/em\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 \u003cp\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel 1. Sex, Age\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel 2. Sex, Age, Tg, LDL, Glucose, EGFR, ckdepi, Uric acid, SBP, Heart rate\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel 3. Sex, Age, Tg, LDL, Glucose, EGFR_ckdepi, Uric acid, SBP, Heart rate, Smoking, Alcohol, Regular exercise (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e2 times /wk)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRestricted cubic spline analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) demonstrated a significant inverse linear association between BMI variability and CVD risk (P\u0026thinsp;=\u0026thinsp;.012). While no significant link was identified with stroke risk, higher BMI variability was notably correlated with a reduced risk of CAD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCVD Incidence Rates within BMI Categories\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the incidence rates of CVD, stroke, and CAD, stratified by BMI variability tertiles and baseline BMI categories. CVD was observed in 6.9% of participants overall, with incidence rates of 3.9%, 5.7%, 7.8%, and 8.1% across BMI categories of \u0026lt;\u0026thinsp;18.5, 18.5\u0026ndash;22.9, 23.0\u0026ndash;24.9, and \u0026ge;\u0026thinsp;25.0 kg/m\u0026sup2;, respectively. Significant differences in incidence rates of CVD and CAD were noted across BMI variability tertiles (P\u0026thinsp;\u0026lt;\u0026thinsp;.001 for both), particularly among people in the higher BMI categories (23.0\u0026ndash;24.9 and \u0026ge;\u0026thinsp;25.0 kg/m\u0026sup2;). In these subgroups, higher BMI variability was associated with lower incidence rates of CVD (P\u0026thinsp;=\u0026thinsp;.012 and P\u0026thinsp;=\u0026thinsp;.002, respectively) and CAD (P\u0026thinsp;=\u0026thinsp;.001 for both).\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\u003eCVD incidence rate according to BMI variability within each BMI category\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;80,941)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eBMI(\u0026lt;\u0026thinsp;18.5)\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2,291)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eBMI(18.5\u0026ndash;22.9)\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;32,157)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eBMI(23-24.9)\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;20,980)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eBMI(\u0026gt;\u0026thinsp;25)\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;25,513)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEvent\u003c/b\u003e, \u003cb\u003en(%)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eEvent\u003c/b\u003e, \u003cb\u003en(%)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eEvent\u003c/b\u003e, \u003cb\u003en(%)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eEvent\u003c/b\u003e, \u003cb\u003en(%)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eEvent\u003c/b\u003e, \u003cb\u003en(%)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5612 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1826 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.149\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1636 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e0.012\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2061 (8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e0.002\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1993 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e599(5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e596 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e767(8.7)\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\u003eTertile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1903 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e618 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e571 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e691 (8.3)\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\u003eTertile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1716 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e609 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e469 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e603 (7.3)\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\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e797 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.532\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e243 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.015\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e253 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e0.205\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e292 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e0.244\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e278 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e66 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e95 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e113 (1.3)\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\u003eTertile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e262 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e102 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e73 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e84 (1.0)\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\u003eTertile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e257 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e75 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e85 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e95 (1.1)\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\u003eCoronary artery disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4986 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1637 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.132\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1439 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1829 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1785 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e548 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e528 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e681 (7.7)\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\u003eTertile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1700 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e538 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e516 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e626 (7.5)\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\u003eTertile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1501 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e551 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e395 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e522 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eData are presented as number and percentages (%). P-values were calculated using Pearson\u0026rsquo;s chi-square test to compare the incidence of cardiovascular disease, stroke, and coronary artery disease across BMI variability tertiles within each BMI category.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNo consistent or significant pattern of association was observed between BMI variability and stroke incidence across BMI categories (all P\u0026thinsp;\u0026gt;\u0026thinsp;.05), although a marginal trend was seen in the 18.5\u0026ndash;22.9 kg/m\u0026sup2; group (P\u0026thinsp;=\u0026thinsp;.015)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eHazard Ratios for CVD Risk within BMI Categories\u003c/h2\u003e \u003cp\u003eHazard ratios for CVD, stroke, and CAD stratified by BMI categories are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The highest BMI variability tertile(Tertile 3) was significantly associated with reduced CVD risk among participants with BMI of 23\u0026ndash;24.9 kg/m\u0026sup2; (HR 0.85; 95% CI, 0.75\u0026ndash;0.95, P\u0026thinsp;=\u0026thinsp;0.006) and BMI\u0026thinsp;\u0026gt;\u0026thinsp;25 kg/m\u0026sup2; (HR 0.89; 95% CI, 0.80\u0026ndash;0.99, P\u0026thinsp;=\u0026thinsp;0.027). For stroke, a significant risk reduction was noted only in Tertile 2 within the BMI\u0026thinsp;\u0026gt;\u0026thinsp;25 kg/m\u0026sup2; category (HR 0.74; 95% CI, 0.56\u0026ndash;0.98, P\u0026thinsp;=\u0026thinsp;0.035); otherwise, no statistically significant results were observed for stroke across other categories or tertiles. CAD risk was significantly lower in the highest tertile of BMI variability within BMI categories of 23\u0026ndash;24.9 kg/m\u0026sup2; (HR 0.86; 95% CI, 0.74\u0026ndash;0.99, P\u0026thinsp;=\u0026thinsp;0.039) and \u0026gt;\u0026thinsp;25 kg/m\u0026sup2; (HR 0.87; 95% CI, 0.76\u0026ndash;0.99, P\u0026thinsp;=\u0026thinsp;0.032). Overall, increased BMI variability was inversely associated with risks of CVD and CAD, especially among people with higher baseline BMI values.\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\u003eHazard ratios and 95% confidence intervals (CIs) for CVD risk by BMI variability.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" 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\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eBMI(\u0026lt;\u0026thinsp;18.5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eBMI(18.5\u0026ndash;22.9)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eBMI(23-24.9)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eBMI(\u0026gt;\u0026thinsp;25)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHR(95%CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eHR(95%CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eHR(95%CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eHR(95%CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eHR(95%CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(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\u003eTertile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.92, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.488\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94 (0.55, 1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.809\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05 (0.94, 1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.426\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.96 (0.86, 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e0.509\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.93 (0.84, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e0.177\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90 (0.85, 0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.002\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05 (0.62, 1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.871\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97 (0.86, 1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.576\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.85 (0.75, 0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e0.006\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.89 (0.80, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e0.027\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(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\u003eTertile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.80, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.536\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.28 (0.33, 4.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.724\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.31 (0.95, 1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.096\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.92 (0.68, 1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e0.59\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.74 (0.56, 0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e0.035\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.80, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.531\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39 (0.07, 2.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.296\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.13 (0.82, 1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.467\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.90 (0.66, 1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e0.485\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.89 (0.67, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e0.376\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary artery disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(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\u003eTertile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04 (0.97, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.302\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07 (0.54, 2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.841\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.10 (0.96, 1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.172\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01 (0.88, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e0.884\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.00 (0.89, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e0.963\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.85, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.03\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07 (0.53, 2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.861\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01 (0.89, 1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.846\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.86 (0.74, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e0.039\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.87 (0.76, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e0.032\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eHazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox proportional hazards regression models. P-values indicate the statistical significance of the difference in risk compared with the reference group (Tertile 1 of BMI variability) within each BMI category.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis large cohort study investigated the association between BMI variability and the risk of cardiovascular disease (CVD). We found that people in the highest tertile of BMI variability (Tertile 3) had a significantly lower risk of incident CVD compared to those in the lowest tertile (Tertile 1). This inverse association was particularly evident among participants with a baseline BMI\u0026thinsp;\u0026ge;\u0026thinsp;23 kg/m\u0026sup2;. These findings challenge the traditional notion that stable BMI is always optimal for cardiovascular health and suggest that moderate fluctuations in BMI may have a protective effect in certain populations.\u003c/p\u003e \u003cp\u003eThe findings of this study align with some prior research indicating that fluctuations in weight or BMI may have metabolic benefits, such as improved insulin sensitivity or lipid profiles.\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e However, they contrast with other studies that have associated weight variability with adverse outcomes, including increased CVD risk. \u003csup\u003e10,15\u003c/sup\u003e These discrepancies may stem from differences in study populations, definitions of variability, or follow-up durations. For instance, studies focusing on severe weight cycling may reflect underlying disease processes or unhealthy behaviors, which differ from the moderate, possibly health-driven fluctuations observed in our cohort. Further research is needed to reconcile these differences and clarify the mechanisms underlying the relationship between BMI variability and CVD.\u003c/p\u003e \u003cp\u003eThe observed reduction in CVD risk among people with higher BMI variability may be explained by several mechanisms. First, periodic weight changes may promote metabolic flexibility, which refers to the body's ability to efficiently switch between fuel sources\u0026mdash;primarily glucose and fatty acids\u0026mdash;depending on energy availability and physiological demand.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Enhanced metabolic flexibility has been associated with improved mitochondrial function, insulin sensitivity, and overall cardiometabolic health. In this context, moderate fluctuations in BMI may stimulate adaptive metabolic responses that enhance energy utilization and vascular efficiency. Second, BMI variability may reflect intentional lifestyle changes, such as intermittent physical activity or dietary modifications, both of which are independently associated with reduced CVD risk. Third, higher BMI variability may be associated with improved adipose tissue function, including enhanced adipokine secretion, reduced chronic inflammation, and lower insulin resistance\u0026mdash;all of which contribute to cardiovascular protection.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e However, these hypotheses require further investigation through longitudinal studies and mechanistic research.\u003c/p\u003e \u003cp\u003eWhile BMI variability showed a consistent inverse association with CAD, the findings for stroke were inconsistent and largely nonsignificant. The differential impact on stroke and CAD may reflect variations in pathophysiology and vascular susceptibility. Coronary vessels may be more responsive to metabolic adaptations related to weight variability than cerebral vasculature, warranting further mechanistic investigation. Stroke risk may also be more strongly influenced by nonmetabolic risk factors, such as atrial fibrillation or embolic sources, which may dilute any potential benefit from BMI variability.\u003c/p\u003e \u003cp\u003eLifestyle factors, such as physical activity and dietary patterns, may play a significant role in mediating the relationship between BMI variability and cardiovascular disease (CVD) risk. \u003csup\u003e19\u003c/sup\u003e People with higher BMI variability may engage in more frequent weight management behaviors\u0026mdash;such as structured exercise, intermittent fasting, or dietary modifications\u0026mdash;that confer cardioprotective benefits.\u003csup\u003e\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Conversely, unhealthy weight fluctuations resulting from extreme dieting, disordered eating, or inconsistent lifestyle habits may have adverse cardiovascular consequences. These observations may partially intersect with the concept of the obesity paradox, wherein people with higher BMI\u0026mdash;particularly those with preserved metabolic reserves\u0026mdash;demonstrate better cardiovascular outcomes in certain populations.\u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e In this context, moderate BMI fluctuations in overweight or mildly people with obesity may reflect dynamic physiological adaptations or intentional weight control behaviors, rather than metabolic instability. This could help explain why higher BMI variability, especially in those with elevated baseline BMI, is associated with reduced CVD risk in some studies, including our own.\u003c/p\u003e \u003cp\u003eThe biological mechanisms underlying the relationship between BMI variability and CVD risk remain incompletely understood. One potential explanation is that moderate BMI variability may stimulate adaptive responses in the cardiovascular system, such as improved endothelial function or enhanced cardiac efficiency. Additionally, fluctuations in BMI could influence the release of adipokines and cytokines, which play a key role in inflammation and insulin sensitivity.\u003csup\u003e\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e For instance, adiponectin, an anti-inflammatory adipokine, has been shown to increase with weight loss and may contribute to reduced CVD risk.\u003csup\u003e\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e Further research is needed to explore these biological pathways and their potential protective effects, particularly in diverse populations and across different BMI categories.\u003c/p\u003e \u003cp\u003eThe results of this study have important implications for clinical practice. While maintaining a stable BMI is often recommended for cardiovascular health, these findings suggest that fluctuations in BMI may not be always harmful and could even be beneficial in certain populations. In particular, people with higher baseline BMI may experience favorable cardiovascular outcomes associated with BMI variability. Clinicians should consider individual patient characteristics, including baseline BMI and overall metabolic health, when formulating weight management strategies. Furthermore, public health messages may warrant refinement to acknowledge the potential benefits of moderate BMI variability, especially in populations with elevated BMI.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, the observational design precludes establishing causality, and residual confounding factors may have influenced the results. Second, the study population consisted of relatively health-conscious people undergoing regular health screenings at a single center, potentially limiting the generalizability of the findings to broader or more diverse populations. Future research should include diverse cohorts, longer follow-up periods, and more detailed assessments of weight change patterns. Additionally, studies incorporating biomarkers and imaging data could provide deeper insights into the mechanisms linking BMI variability to cardiovascular outcomes.\u003c/p\u003e \u003cp\u003eIn conclusion, BMI variability is associated with the risk of cardiovascular disease, with higher variability linked to a lower risk of CVD, especially in people with BMI\u0026thinsp;\u0026ge;\u0026thinsp;23. These results highlight the potential importance of BMI variability in cardiovascular health and call for further research to elucidate the mechanisms involved.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eANOVA\u003c/strong\u003e, analysis of variance\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e, body mass index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCAD\u003c/strong\u003e, coronary artery disease\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCI,\u003c/strong\u003e confidence interval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCKD-EPI,\u003c/strong\u003e Chronic Kidney Disease Epidemiology Collaboration\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCVD\u003c/strong\u003e, cardiovascular disease\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCV,\u003c/strong\u003e coefficient of variation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDBP,\u003c/strong\u003e diastolic blood pressure\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eeGFR\u003c/strong\u003e, estimated glomerular filtration rate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHbA1c\u003c/strong\u003e, hemoglobin A1c\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHDL-C,\u003c/strong\u003e high-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHR,\u003c/strong\u003e hazard ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIR,\u003c/strong\u003e incidence rate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIRB,\u003c/strong\u003e Institutional Review Board\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKBSMC,\u003c/strong\u003e Kangbuk Samsung Medical Center\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLDL-C,\u003c/strong\u003e low-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePY,\u003c/strong\u003e person-years\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSBP,\u003c/strong\u003e systolic blood pressure\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSTROBE,\u003c/strong\u003e Strengthening the Reporting of Observational Studies in Epidemiology\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTG,\u003c/strong\u003e triglycerides\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026bull; Jin Hee Ahn and Eun Jung Oh helped with the study design, planning, conduct, data analysis, and drafting of the paper.\u003c/p\u003e\n\u003cp\u003e\u0026bull; Jae-Geum Shim and Jiyeon Park designed and planned the study, analyzed the data, and drafted and revised the paper.\u003c/p\u003e\n\u003cp\u003e\u0026bull; Mi-yeon Lee helped with the data analysis.\u003c/p\u003e\n\u003cp\u003e\u0026bull; Eun Ah Cho and Sung Hyun Lee helped with the formal analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrior Presentations\u003c/strong\u003e: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e : Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThe authors used ChatGPT (OpenAI, San Francisco, CA, USA) to assist in language editing of the manuscript. The content and interpretation of the manuscript are solely the responsibility of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement:\u003c/strong\u003e The authors declare no financial support for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate :\u003c/strong\u003e This study was approved by the Institutional Review Board of Kangbuk Samsung Hospital, Seoul, Republic of Korea (KBSMC 2022-06-016, June 21, 2022]). Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Statement :\u0026nbsp;\u003c/strong\u003eThe data that support the findings of this study are not publicly available due to privacy reasons, but are available from the data sharing committee upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTsao, C. 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J. \u0026amp; Walsh, K. Adipokines: a link between obesity and cardiovascular disease. \u003cem\u003eJ Cardiol\u003c/em\u003e 63, 250\u0026ndash;259, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jjcc.2013.11.006\u003c/span\u003e\u003cspan address=\"10.1016/j.jjcc.2013.11.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\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":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"BMI(Body mass index), BMI variability, Cardiovascular disease","lastPublishedDoi":"10.21203/rs.3.rs-8537727/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8537727/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlthough body mass index (BMI) is a well-established predictor of cardiovascular disease (CVD), the prognostic significance of intra-individual BMI variability remains uncertain. This study investigated the association between long-term BMI variability and the risk of CVD, including stroke and coronary artery disease (CAD). We conducted a retrospective cohort study of adults aged\u0026thinsp;\u0026ge;\u0026thinsp;40 years who underwent three or more annual health examinations from 2002 to 2011 at a single center in South Korea. BMI variability was quantified using the coefficient of variation and categorized into tertiles. Participants were followed for incident CVD events (stroke or CAD) from 2012 to 2021. Cox proportional hazards models estimated hazard ratios and 95% confidence intervals for CVD outcomes, adjusting for demographic, clinical, and behavioral covariates, with subgroup analyses conducted according to baseline BMI categories. Among the 80,941 participants, higher BMI variability was associated with significantly lower risks of CVD (HR, 0.90; 95% CI, 0.85\u0026ndash;0.96) and CAD (HR, 0.89; 95% CI, 0.83\u0026ndash;0.95) in fully adjusted models, while no significant association was observed for stroke. The inverse association between BMI variability and CVD or CAD was most prominent among participants with a baseline BMI\u0026thinsp;\u0026ge;\u0026thinsp;23.0 kg/m\u0026sup2;. These findings suggest that higher BMI variability, potentially reflecting intentional lifestyle modifications, may confer cardiovascular benefit, particularly in individuals with elevated baseline BMI.\u003c/p\u003e","manuscriptTitle":"Body Mass Index Variability and Its Role in Cardiovascular Disease Risk Stratification : A large cohort study.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-20 11:14:35","doi":"10.21203/rs.3.rs-8537727/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-13T08:48:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-20T14:57:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-15T06:22:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99666987723429035200195220321536180164","date":"2026-02-13T09:05:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-13T08:31:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49255601508423586258768494300278014406","date":"2026-02-12T22:43:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149566949587208083046131589044749438820","date":"2026-02-12T21:03:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-15T11:17:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-13T05:33:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-13T05:33:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2026-01-07T06:35:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3f7cef7b-92d2-4bfd-8e3f-63781028bc17","owner":[],"postedDate":"January 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T16:09:18+00:00","versionOfRecord":{"articleIdentity":"rs-8537727","link":"https://doi.org/10.1186/s40001-026-04366-0","journal":{"identity":"european-journal-of-medical-research","isVorOnly":false,"title":"European Journal of Medical Research"},"publishedOn":"2026-04-07 15:58:39","publishedOnDateReadable":"April 7th, 2026"},"versionCreatedAt":"2026-01-20 11:14:35","video":"","vorDoi":"10.1186/s40001-026-04366-0","vorDoiUrl":"https://doi.org/10.1186/s40001-026-04366-0","workflowStages":[]},"version":"v1","identity":"rs-8537727","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8537727","identity":"rs-8537727","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}