Multiomic Predictors of Short‐Term Weight Loss and Clinical Outcomes During a Behavioral‐Based Weight Loss Intervention

• Published in: Obesity (Silver Spring, Md.) Vol. 29; no. 5; pp. 859 - 869
• Main Authors: Siebert, Janet C., Stanislawski, Maggie A., Zaman, Adnin, Ostendorf, Danielle M., Konigsberg, Iain R., Jambal, Purevsuren, Ir, Diana, Bing, Kristen, Wayland, Liza, Scorsone, Jared J., Lozupone, Catherine A., Görg, Carsten, Frank, Daniel N., Bessesen, Daniel, MacLean, Paul S., Melanson, Edward L., Catenacci, Victoria A., Borengasser, Sarah J.
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.05.2021
• Subjects: Adolescent; Adult; Behavior Therapy - methods; Clinical outcomes; DNA methylation; Female; Humans; Insulin resistance; Male; Metabolites; Middle Aged; Obesity; Obesity - therapy; Weight Loss - physiology; Weight Reduction Programs - methods; Young Adult
• ISSN: 1930-7381; 1930-739X
• DOI: 10.1002/oby.23127

Objective Identifying predictors of weight loss and clinical outcomes may increase understanding of individual variability in weight loss response. We hypothesized that baseline multiomic features, including DNA methylation (DNAme), metabolomics, and gut microbiome, would be predictive of short‐term changes in body weight and other clinical outcomes within a comprehensive weight loss intervention. Methods Healthy adults with overweight or obesity (n = 62, age 18‐55 years, BMI 27‐45 kg/m2, 75.8% female) participated in a 1‐year behavioral weight loss intervention. To identify baseline omic predictors of changes in clinical outcomes at 3 and 6 months, whole‐blood DNAme, plasma metabolites, and gut microbial genera were analyzed. Results A network of multiomic relationships informed predictive models for 10 clinical outcomes (body weight, waist circumference, fat mass, hemoglobin A1c, homeostatic model assessment of insulin resistance, total cholesterol, triglycerides, C‐reactive protein, leptin, and ghrelin) that changed significantly (P < 0.05). For eight of these, adjusted R2 ranged from 0.34 to 0.78. Our models identified specific DNAme sites, gut microbes, and metabolites that were predictive of variability in weight loss, waist circumference, and circulating triglycerides and that are biologically relevant to obesity and metabolic pathways. Conclusions These data support the feasibility of using baseline multiomic features to provide insight for precision nutrition–based weight loss interventions.