Statistical analysis
Data were analyzed using R (Version 3.6.2). Continuous variables were presented as mean ± SE and baseline differences among two exposure groups were evaluated using a t-test. Frequencies were presented as percentages and 95% confidence intervals (CI) and exposure groups compared using a Z-test. Multiple linear regression analysis was used to investigate the relationship between exposure groups and changes for each outcome between Q4 and Q1 for weight, BMI, SBP, DBP, CMRF, and between last and first measure for A1C measurements. Other confounding variables were included in the model, the confounders were determined based on the 10% rule [11]. To account for the effect modification, a variable was also included if a significant interaction term was observed in a model consisting of that variable and the exposure group. To explore exposure effects on clinically significant changes in cardiometabolic outcomes, a binary logistic regression analysis (reduction of at least vs no reduction of weight [5%], BMI [1 kg/m2], SBP [2 mmHg], of A1C [0.3%]) was performed. Confounders and effect modifiers were included, following the same rule as the multiple linear regression. A p-value of <0.05 was considered statistically significant.