*2.6. Statistical Analysis*

We analyzed the study population stratified by sex. The data are expressed as median (interquartile range) for continuous variables or number (percentage) for categorical variables. The summary statistics for the characteristics of participants between the four categories based on BMI were calculated. The statistical significance of differences among the four categories was determined using analysis of variance for continuous variables and chi-squared tests for categorical variables. We conducted Cox regression analyses to identify the association between BMI categories and incident stroke. The hazard ratios (HRs) were calculated in an unadjusted model (Model 1), an age-adjusted model (Model 2), and after adjustment for age, hypertension, diabetes mellitus, dyslipidemia, cigarette smoking, alcohol consumption, and physical inactivity (Model 3). We performed three sensitivity analyses. First, we analyzed the relationship between BMI as a continuous variable and incident stroke. To detect any possible linear or non-linear dependency in regression models and to allow for a flexible interpretation of the relationship between BMI as continuous data and stroke events, continuous changes in BMI were assessed through shape-restricted cubic spline (RCS) regression models. We put three cut-off points for BMI (18.5, 25.0, and 30.0 kg/m2) as the knots. HRs and 95% confidence interval (CI) for incident stroke were calculated for each value of BMI with respect to the reference BMI value of 23.0 kg/m2. Second, we used multiple imputation for missing data, as previously described. [18,25] On the assumption of data missing at random, we imputed the missing data for covariates using the chained equation method with 20 iterations as described by Aloisio [26]. The HRs and standard errors were obtained using Rubin's rules [27]. Third, we analyzed the population after excluding hypertensive participants. The statistical significance was set at *p* < 0.05. The statistical analyses were performed using SPSS software (version 25, SPSS Inc., Chicago, IL, USA) and STATA (version 17; StataCorp LLC, College Station, TX, USA).

#### **3. Results**

#### *3.1. Baseline Clinical Characteristics*

The baseline clinical characteristics are shown in Table 1. Overall, the median (interquartile range) age was 45 (38–53) years, and 1,538,982 participants (56.2%) were men. The median (interquartile range) BMI was 23.2 (21.3–25.5) kg/m<sup>2</sup> in men and 21.0 (19.2–23.4) kg/m<sup>2</sup> in women. The prevalence of hypertension, diabetes mellitus, and dyslipidemia increased with BMI in both men and women.


#### **Table 1.** Clinical Characteristics of Study Population.


**Table 1.** *Cont.*

Data are reported as medians (interquartile range) and proportions (percentage). *p* values were calculated using chi-square tests for categorical variables and the analysis of variance for continuous variables. Participants were categorized into four groups based on body mass index (BMI); normal weight (BMI 18.5–24.9 kg/m2), underweight (BMI < 18.5 kg/m2), overweight (BMI 25.0–29.9 kg/m2), and obesity (BMI ≥ 30.0 kg/m2). SBP; systolic blood pressure, DBP; diastolic blood pressure, LDL-C; low-density lipoprotein cholesterol, HDL-C; high-density lipoprotein cholesterol.
