Fig. 2: Omics-based BMI estimates captured the variance in BMI better than any single analyte.

a, The variables that were retained across all ten CombiBMI models (132 analytes: 77 metabolites, 51 proteins and four clinical laboratory tests). β-coefficient was obtained from the fitted CombiBMI model with LASSO linear regression (Supplementary Data 3). Each background color corresponds to the analyte category. Data: the standard box plot (Methods), n = 10 models. b–d, Univariate explained variance in BMI by each metabolite (b), protein (c) or clinical laboratory test (d). BMI was independently regressed on each of the analytes that were retained in at least one of the ten LASSO models (209 metabolites, 74 proteins and 41 clinical laboratory tests; Supplementary Data 5), using OLS linear regression with sex, age and ancestry principal components as covariates. Multiple testing was adjusted with the Benjamini–Hochberg method across the 210 (b), 75 (c) or 42 (d) regressions, including each omics-based BMI model as reference. Among the analytes that were significantly associated with BMI (180 metabolites, 63 proteins and 30 clinical laboratory tests), only the top 30 significant analytes are presented with their univariate variances. All exact values of test summaries are found in Supplementary Data 5.