Milk Consumption Patterns and Gut Microbiota Composition in Healthy Adults: Insights from a Propensity Score-Matched Cross-Sectional Study.

Aim: Milk consumption patterns may be associated with gut microbiota composition, but observational findings can be strongly influenced by demographic and body composition differences among consumers. This observational cross-sectional study evaluated whether questionnaire-derived habitual milk consumption patterns were associated with gut microbiota composition in healthy adults by comparing non-milk consumers, bovine milk consumers, and plant-based beverage consumers.

Methods

Baseline cross-sectional data from 265 participants were analyzed: 99 non-milk consumers, 139 bovine milk consumers, and 27 plant-based beverage consumers. Clinical, anthropometric, body composition, and microbiota data were integrated. Alpha diversity was assessed using standard diversity indices, whereas beta diversity was evaluated using Bray-Curtis distances and permutational multivariate analysis of variance. Taxon-level analyses were corrected using the false discovery rate, and pairwise propensity score matching was applied as a sensitivity analysis to evaluate the influence of measured demographic, anthropometric, and body composition imbalance.

Results

Crude analyses showed a small beta-diversity difference between bovine milk and plant-based beverage consumers (R2 = 0.0119, p = 0.014), alongside marked differences in age, body mass index, fat mass, and impedance-derived parameters. In matched sensitivity analyses, the crude beta-diversity difference between bovine milk consumers and plant-based beverage consumers was not reproduced in a smaller matched subset (pair-stratified PERMANOVA R2 = 0.0214, p = 0.506). No matched comparison showed significant alpha-diversity differences, and no taxa remained significant after false-discovery-rate correction.

Conclusions

Crude beta-diversity differences between bovine milk consumers and plant-based beverage consumers were small and were not reproduced in propensity score-matched sensitivity analyses. These findings should be interpreted cautiously because of categorical exposure assessment, residual imbalance, limited PBBC sample size, dispersion differences in crude analyses, and potential unmeasured confounding.