Plasma Metabolomic and Proteomic Signatures of Blood Pressure Management After Bariatric Surgery Among Adolescents

Emerging omics approaches, including metabolomics and proteomics, can be integrated into obesity treatment for better blood pressure management. We tested whether preoperative metabolomic and proteomic profiles predict long-term elevated blood pressure (EBP) changes better than known risk factors in adolescents undergoing bariatric surgery.

We included 108 participants from the Teen-LABS (Teen-Longitudinal Assessment of Bariatric Surgery). Plasma untargeted metabolomics (via liquid chromatography with high-resolution mass spectrometry) and Olink proteomics were assessed pre surgery. An elastic net model with stability selection was used to identify features predictive of EBP reductions 5 years post surgery. Models including identified metabolites, proteins, and known risk factors (age, sex, race, clinical sites, parents' education level, body mass index, systolic blood pressure, and diastolic blood pressure at baseline) were compared with known-risk-factors-only models. For biological relevance, the consistency of association directions was examined in a multiethnic non-interventional adolescent cohort (n=79).

Metabolite-based models and protein-based models with known risk factors each outperformed known-risk- factors-only models in predicting EBP reductions (P<0.01). Higher levels of 4 metabolites (uric acid, taurocholic acid, nonadecanoic acid, and cystine) were consistently associated with reduced likelihood of EBP improvement or blood pressure improvement across cohorts. SERPINA11 (serine protease inhibitor, clade A, member 11), ICAM5 (intercellular adhesion molecule 5), and TINAGL1 (tubulointerstitial nephritis antigen-like 1) demonstrated consistent associations in both cohorts.

This is the first study identifying potential preoperative biomarkers for EBP reductions in adolescents following bariatric surgery. Integrating metabolomics and proteomics with known risk factors substantially improved predictive models compared with known-risk-factors-only models. Larger and more diverse cohorts are needed to confirm these findings and whether identified features are associated with other blood pressure treatments.