Longitudinal Transcriptomic Remodeling of Adipose Tissue After Bariatric Surgery Revealed by Differential Expression and Explainable Machine Learning.

Background

Bariatric surgery improves metabolic health, but long-term transcriptomic remodeling of white adipose tissue (WAT) after Roux-en-Y gastric bypass (RYGB) remains incompletely defined. This study aimed to characterize longitudinal WAT gene-expression patterns after RYGB and prioritize candidate signatures of post-surgical adaptation using a publicly available dataset.

Methods

We analyzed subcutaneous WAT transcriptomic data from women with obesity who underwent RYGB, with samples collected before surgery and at 2 and 5 years after surgery. Differential expression analysis was integrated with pathway enrichment, supervised machine-learning-based feature prioritization and classification, and SHAP-based model interpretation.

Results

Differential expression and machine-learning analyses showed clear separation between baseline and post-surgery transcriptomic states. Pathway-level findings indicated reduced inflammatory and immune-related signaling, particularly across pathways related to phagosome function, lysosomal activity, antigen presentation, and host-defense responses after surgery. Gene-level analyses additionally suggested extracellular-matrix and metabolic remodeling. Machine-learning models distinguished baseline from post-surgery samples, while SHAP analysis identified genes with the strongest contributions to model predictions. Importantly, several statistically prioritized genes also showed high SHAP attribution, demonstrating concordance between univariate statistical significance and multivariate predictive relevance. This convergence suggests that the models captured biologically meaningful surgery-associated signals rather than purely data-driven classification artifacts.

Conclusions

This study advances the interpretation of longitudinal adipose-tissue transcriptomic remodeling after RYGB by combining differential expression, pathway enrichment, supervised machine learning, and explainable AI within a unified framework. The integrated workflow prioritized candidate long-term remodeling genes, particularly immune/inflammatory and extracellular-matrix-related transcriptomic signatures, that warrant validation in independent cohorts.