ABSTRACT The human skin microbiome is attracting increasing attention due to its association with health. However, comprehensive knowledge about the impact of various factors on skin microbiome variation and their comparative magnitudes remains to be investigated. Here, we profiled the human skin microbiome through an integrated analysis of public datasets. Skin microbiomes exhibit high diversity and possess distinct co‐occurrence patterns, based on which they have been classified into several modules. Key host and environmental factors were assessed for microbiome associations, and lifestyle habits (such as alcohol intake and physical activity) were notably found to be highly impactful. We thus conducted a local cohort for validation, and the results are highly consistent. Through machine learning‐based analysis, we further identified potential microbial biomarkers and demonstrated accurate bidirectional prediction based on either the skin microbiome or alcohol intake. Our study reveals that alcohol intake is a primary driver of skin microbial community structure, exhibiting an effect size comparable to traditional host factors. This work advances the skin microbiome field by revealing skin‐specific ecology and establishing it as a promising lifestyle mirror and physiological predictor. Clinical Trial Registration Clinical trial registration number: NCT05804851.
Xu et al. (Sun,) studied this question.