Figure 4.

Associations of sex hormone features with stool microbiome functional pathways (n = 197). (A) Analysis of Compositions of Microbiomes 2 (ANCOM2) was used to identify functional pathways associated with each inverse-normal transformed hormone feature, adjusting for age, study site, race/ethnicity, income, smoking status, alcohol use, illicit drug use, HCV serostatus, estimated glomerular filtration rate, serum gamma-glutamyl transferase, waist circumference, fasting glucose, hemoglobin A1c, total cholesterol, triglycerides, HDL cholesterol, diabetes medication use, lipid-lowering medication use, hypertension medication use, and HIV serostatus. Effect estimates shown in heatmap were obtained from multivariable linear regression of clr-transformed functional pathways (outcomes) on inverse-normal transformed hormone features (predictors), adjusting for above-mentioned covariates. All pathways with ANCOM2 detection level ≥ 0.6 are shown. Pathways are annotated by MetaCyc database ontology. *ANCOM2 detection level = 0.6, **0.7, ***0.8, ****0.9. (B) Mean percent contribution of taxonomic classes to the functional pathways. Stratified pathway by species output from Woltka was used to determine the abundance of each pathway per species, and percent contribution was determined per species for each pathway within each sample, then averaged across samples.