Multiomic Predictors of Short‐Term Weight Loss and Clinical Outcomes During a Behavioral‐Based Weight Loss Intervention. Issue 5 (3rd April 2021)

• Record Type: Journal Article
• 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.
• Is Part Of: Obesity. Volume 29:Issue 5(2021)
• Page Start: 859
• Page End: 869
• Publication Date: 2021-04-03
• Subjects: Obesity -- Periodicals; 616.398005
• DOI: 10.1002/oby.23127
• Languages: English
• ISSNs: 1930-7381

Abstract

Abstract : 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/m 2, 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 R 2 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. Abstract :