Multi-omic modelling of body mass index response to a dietary weight loss intervention.
Multi-omic data can improve predictions of BMI responses to weight loss interventions, but the clinical significance of these findings remains uncertain.
Where it sits
this study against the rest of the matrixyl corpusSummary and findings
This study examined the relationship between multi-omic data and body mass index (BMI) response to a dietary weight loss intervention in adults with overweight or obesity. The analysis included 150 participants over a 12-month period, utilizing various modeling techniques. The study found that combined omic risk scores explained 20.5-26.0% of the variance in longitudinal BMI trajectories.
Abstract
Obesity is a multifactorial condition, and there is wide heterogeneity in responses to weight loss interventions. Although it remains challenging, modeling responses to weight loss interventions can help tailor treatments, increase weight loss success, or improve our understanding of underlying pathophysiology. We leveraged multi-omic (genetics; gut microbiota: taxonomy, inferred gene pathways and metabolite dynamics; blood metabolomics) and clinical data (e.g., lipids, blood glucose) from a 12-month behavioral weight loss trial of adults (<i>n</i> = 150) with overweight/obesity, to forecast longitudinal body mass index (BMI) and BMI change (ΔBMI) using Mixed Effects Random Forests (MERF) and GLMM-Lasso. Across modeling approaches and outcomes, routinely available clinical variables and blood metabolomics consistently improved prediction over basic demographics, and metabolomics added value beyond clinical information. Across models, the combined omic risk score most improved models of longitudinal BMI trajectories, explaining 20.5-26.0% marginal variance (R<sup>2</sup>m), whereas metabolomic risk scores most improved BMI change prediction (R<sup>2</sup>m = 52.9-59.3%). Gut microbial taxonomy and inferred gene pathways offered modest but significant gains for some models and outcomes, while metabolite dynamics consistently failed to enhance performance. The most important features in the models included insulin, glycoprotein acetyls, lipoprotein sizes, and certain amino acids, aligning with known inflammatory and metabolic mechanisms. These findings support that select blood-based biomarkers correlate with individual responses to weight loss efforts.
Background
This paper addresses the variability in individual responses to weight loss interventions, a known challenge in obesity management. Prior research has indicated that obesity is influenced by a range of factors, including genetics and metabolism. Understanding these factors can help tailor interventions to improve weight loss outcomes, making this study relevant in the context of personalized medicine.
Methods
The study utilized a behavioral weight loss trial design with a population of 150 adults with overweight or obesity. The intervention lasted for 12 months and involved the collection of multi-omic data, including genetics, gut microbiota, and blood metabolomics. Primary outcomes included longitudinal BMI and BMI change, analyzed using Mixed Effects Random Forests and GLMM-Lasso.
Results
The primary endpoint showed that the combined omic risk score explained 20.5-26.0% of the marginal variance in longitudinal BMI trajectories. Additionally, metabolomic risk scores improved BMI change prediction with an R2m of 52.9-59.3%. The study identified key features such as insulin and glycoprotein acetyls that correlated with weight loss responses.
Interpretation
The findings suggest that while multi-omic data can enhance predictions of BMI responses, the clinical significance of the explained variance may be limited. The effect sizes reported, while statistically significant, may not translate to clinically meaningful changes in weight loss outcomes. Confounding factors such as the sample size and the complexity of the data may limit the generalizability of these results.
Key findings
- 20.5-26.0% marginal variance explained in longitudinal BMI trajectories by combined omic risk score.
- R2m = 52.9-59.3% for BMI change prediction using metabolomic risk scores.
- Insulin, glycoprotein acetyls, lipoprotein sizes, and certain amino acids were identified as important features.
Limitations
- sample size of 150 may limit generalizability
- complexity of multi-omic data interpretation
- focus on surrogate endpoints rather than direct weight loss outcomes