To synthesize evidence on phenotypic and genomic determinants of variability in pharmacotherapy response and propose a pragmatic framework for precision pharmacotherapy in obesity.
Approach:
Review Design: Narrative review integrating data from randomized trials, post-hoc subgroup analyses, observational cohorts, neurobehavioral studies, pharmacogenomic investigations, and polygenic risk score research.
Key Findings:
Response heterogeneity in obesity pharmacotherapy is influenced by factors such as central satiety circuitry, metabolic status, sex, baseline adiposity, treatment indication, previous GLP-1RA exposure, adherence, and tolerability.
Early on-treatment weight change, usually assessed within 12–16 weeks, is a clinically actionable predictor of longer-term outcomes.
Phenotype-guided approaches can classify mechanisms affecting weight loss, such as impaired satiation, postprandial hunger, emotional or hedonic eating, and low energy expenditure, aiding in medication selection.
Emerging genomic signals are promising but not yet ready for routine clinical application.
Interpretation:
Precision pharmacotherapy in obesity is currently best achieved through phenotype-guided treatment, structured early-response monitoring, and proactive management of adherence and tolerability, with genomic tools requiring further validation.
Limitations:
Existing genomic tools are investigational and lack sufficient replication for clinical use.
Current treatment algorithms primarily rely on BMI and comorbidities rather than systematic phenotypic classification.
Conclusion:
A structured approach to obesity pharmacotherapy that incorporates phenotypic and genomic data is essential for improving treatment outcomes.
by Dario S. Lopez Delgado, Miriam Gabriela Reyes-Zermeño, Elian David Sanjuanelo Lemus, Catherine G. Acosta-Celis, María Amparo Kantún-Marín, Martín Gomez-Lujan, Kevin Gabriel Fallaza-Moya, Oscar Muñoz-Chuquilín, Sandra Trujillo-Levano, Giancarlo Gutierrez-Chavez, Cesar Bonilla-Asalde, Oriana Rivera-Lozada, Joshuan J. Barboza
