Statistical analysis
death, hypercholesterolemia, inflammation, chronic diseases, weight gain, adiposity, diabetes
HYPERCHOLESTEROLEMIA, INFLAMMATION, EVENT, RECRUITMENT, CHRONIC DISEASES, ADIPOSITY, REGRESSION, HYPERTENSION, DIABETES
The analysis included all 6874 randomized PREDIMED-Plus participants. A preliminary cross-sectional exploration was undertaken to compare participant characteristics across tertiles of baseline body weight and categories of body weight change at the pre-censoring visit. For this purpose, we used chi-square and Kruskal–Wallis tests, as appropriate. These results are described using median and interquartile range (IQR) or count and percentages for continuous and categorical data, respectively.Because the exposures in this analysis were collected prior to outcome determination, we conducted a prospective analysis using the Cox proportional regression model, in which time to event for each participant began at randomization and ended at the time of COVID-19 diagnosis or the date of death or last contact on or prior to 31 December 2021, whichever occurred first.The results of the main Cox proportional regression model using baseline adiposity parameters or their changes from baseline at the pre-censoring visit as the exposure, and COVID-19 status (incident case or not-incident) as the outcome, are presented as Hazards Ratio (HR) with 95% confidence interval (CI). In addition to the crude model without adjustments, two other models were tested. Model 1 was adjusted for baseline age (years), sex (male/female), education (primary or less/secondary/university), marital status (single or divorced/married/widow(er)), and recruitment center (location). Model 2 additionally adjusted for the intervention group, baseline smoking status (never/former/current), Mediterranean diet adherence score (17-point scale), total physical activity (METs.min/week), alcohol intake (g/d as a quadratic term), previous diagnosis of chronic diseases (diabetes, hypertension, hypercholesterolemia (Yes/No), prior use of ACE-inhibitor (Yes/No), and having received at least one dose of COVID-19 vaccine (Yes/No).For modeling the linear association between absolute change in adiposity indicators (value at pre-censoring visit/value before 8 March 2020 − baseline value) and COVID-19 incidence, the respective baseline value was used as a covariate.A simplified supplementary analysis was carried out comparing the HR in those who experienced any amount of weight gain to those who remained weight stable or lost any amount of weight. In this analysis, we used a third model to additionally adjust for the total numbers of leucocytes that have been implicated in inflammation and a positive diagnosis of COVID-19 [An additional supplementary analysis using changes in adiposity parameters from baseline until 8 March 2020 (documented date of first community transmission of COVID-19 in Spain) as the exposure was performed using the same models as above to investigate their associations with COVID-19 incidence risk.Additionally, sensitivity analyses were carried out to verify the results after excluding participants (There were no missing data for age at trial entry, sex, education, intervention group, recruitment center, baseline physical activity, anthropometry, and prevalence of chronic conditions. Baseline smoking status and marital status had 0.4% missing data which were replaced with the mode of the variable for the cohort. Baseline alcohol consumption had 0.5% missing data, which was replaced with cohort mean consumption according to sex. Less than 0.1% of baseline Er MEDAS was missing and these were replaced with the cohort mean.STATA (Version 14.2) was used to perform all analyses with the statistical significance set at 5%. PREDIMED-Plus database updated until 10 March 2023 was used for this analysis. All analyses were conducted with robust estimates of the variance to correct for intra-cluster correlation. Assuming that 10% of PREDIMED-Plus participants were diagnosed with COVID-19, the sample size of the trial provided 80% power to identify a 20% reduction in HR from one level of the exposure category in comparison to the other, assuming similar numbers in each category and with the statistical significance set at 