O031 - WEIGHT-LOSS SUCCESS THROUGH A STRUCTURED PROGRAM IS PREDICTABLE BY THE GUT MICROBIOME AND SERUM METABOLOME: A STEP TOWARDS PRECISION NUTRITION

Linked sessions

O031

WEIGHT-LOSS SUCCESS THROUGH A STRUCTURED PROGRAM IS PREDICTABLE BY THE GUT MICROBIOME AND SERUM METABOLOME: A STEP TOWARDS PRECISION NUTRITION

B. Seethaler1,*, N. K. Nguyen2, M. Basrai1, N. M. Delzenne3, J. Walter4, S. C. Bischoff1

1University of Hohenheim, Stuttgart, Germany, 2Microbiome Insights Inc, Vancouver, Canada, 3Université catholique de Louvain, Brussels, Belgium, 4University College Cork, Cork, Ireland

 

Rationale: Lifestyle changes are effective treatment options for obesity; however, they are challenging and not always successful. We previously showed that baseline gut microbiota composition can predict the success of a one-year lifestyle intervention1. Here, we validated these findings in a larger cohort undergoing the same intervention while evaluating additional parameters. Our goal is to develop a machine learning-based personalized nutrition approach.

Methods: We analyzed data from 50 individuals (mean BMI 42 kg/m2) who participated in a one-year lifestyle intervention. The first 6 months (6M) consisted of a 3-month very-low-calorie formula diet (800 kcal/day) followed by a 3-month transition to a healthy diet. The second 6 months (12M) focused on healthy diet maintenance. We assessed anthropometric and clinical parameters, gut microbiota composition (16S), and targeted serum metabolomics.

Results: There was a significant reduction in mean BMI (6M: -21%; 12M: -17%), body fat mass, C-reactive protein, and HbA1c (all padj < 0.001; linear mixed models). Gut microbiota changes were strongly associated with changes in BMI and other clinical parameters, especially after 6 months of intervention (Procrustes analysis, p = 0.003). Baseline serum phosphatidylcholine aa 40:1 predicted BMI reduction, indicating its potential as a weight loss biomarker. Using unsupervised clustering, we identified major and minor responders based on clinical improvements and weight-loss, and a random forest model accurately predicted an individual’s response to the intervention based on metabolomic and microbiome data (ROC AUC: 0.89).

Conclusion: Our findings support the integration of microbiome and metabolomic profiling into future personalized obesity interventions, paving the way for precision nutrition strategies.

References: 1Bischoff et al., Nutrients, 2022

Disclosure of Interest: None declared