Dataset Information

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

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/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 A_1c , 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.

SUBMITTER: Siebert JC

PROVIDER: S-EPMC8085074 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Siebert Janet C JC Stanislawski Maggie A MA Zaman Adnin A Ostendorf Danielle M DM Konigsberg Iain R IR Jambal Purevsuren P Ir Diana D Bing Kristen K Wayland Liza L Scorsone Jared J JJ Lozupone Catherine A CA Görg Carsten C Frank Daniel N DN Bessesen Daniel D MacLean Paul S PS Melanson Edward L EL Catenacci Victoria A VA Borengasser Sarah J SJ

Obesity (Silver Spring, Md.) 20210403 5

<h4>Objective</h4>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.<h4>Methods</h4>Healthy adults with overweight or obesity (n = 62, age 18-55 years, BMI 27-4 ...[more]

PMID: 33811477

Similar Datasets

Project description:BackgroundBehavioral weight loss interventions can lead to an average weight loss of 5%-10% of initial body weight, however there is wide individual variability in treatment response. Although built, social, and community food environments can have potential direct and indirect influences on body weight (through their influence on physical activity and energy intake), these environmental factors are rarely considered as predictors of variation in weight loss.ObjectiveEvaluate the association between built, social, and community food environments and changes in weight, moderate-to-vigorous physical activity (MVPA), and dietary intake among adults who completed an 18-month behavioral weight loss intervention.MethodsParticipants included 93 adults (mean ± SD; 41.5 ± 8.3 years, 34.4 ± 4.2 kg/m2, 82% female, 75% white). Environmental variables included urbanicity, walkability, crime, Neighborhood Deprivation Index (includes 13 social economic status factors), and density of convenience stores, grocery stores, and limited-service restaurants at the tract level. Linear regressions examined associations between environment and changes in body weight, waist circumference (WC), MVPA (SenseWear device), and dietary intake (3-day diet records) from baseline to 18 months.ResultsGrocery store density was inversely associated with change in weight (β = -0.95; p = 0.02; R 2 = 0.062) and WC (β = -1.23; p < 0.01; R 2 = 0.109). Participants living in tracts with lower walkability demonstrated lower baseline MVPA and greater increases in MVPA versus participants with higher walkability (interaction p = 0.03). Participants living in tracts with the most deprivation demonstrated greater increases in average daily steps (β = 2048.27; p = 0.02; R 2 = 0.039) versus participants with the least deprivation. Limited-service restaurant density was associated with change in % protein intake (β = 0.39; p = 0.046; R 2 = 0.051).ConclusionEnvironmental factors accounted for some of the variability (<11%) in response to a behavioral weight loss intervention. Grocery store density was positively associated with weight loss at 18 months. Additional studies and/or pooled analyses, encompassing greater environmental variation, are required to further evaluate whether environment contributes to weight loss variability.

Project description:ImportanceLifestyle interventions for weight loss are difficult to implement in clinical practice. Self-managed mobile health implementations without or with added support after unsuccessful weight loss attempts could offer effective population-level obesity management.ObjectiveTo test whether a wireless feedback system (WFS) yields noninferior weight loss vs WFS plus telephone coaching and whether participants who do not respond to initial treatment achieve greater weight loss with more vs less vigorous step-up interventions.Design, setting, and participantsIn this noninferiority randomized trial, 400 adults aged 18 to 60 years with a body mass index of 27 to 45 were randomized in a 1:1 ratio to undergo 3 months of treatment initially with WFS or WFS plus coaching at a US academic medical center between June 2017 and March 2021. Participants attaining suboptimal weight loss were rerandomized to undergo modest or vigorous step-up intervention.InterventionsThe WFS included a Wi-Fi activity tracker and scale transmitting data to a smartphone app to provide daily feedback on progress in lifestyle change and weight loss, and WFS plus coaching added 12 weekly 10- to 15-minute supportive coaching calls delivered by bachelor's degree-level health promotionists viewing participants' self-monitoring data on a dashboard; step-up interventions included supportive messaging via mobile device screen notifications (app-based screen alerts) without or with coaching or powdered meal replacement. Participants and staff were unblinded and outcome assessors were blinded to treatment randomization.Main outcomes and measuresThe primary outcome was the between-group difference in 6-month weight change, with the noninferiority margin defined as a difference in weight change of -2.5 kg; secondary outcomes included between-group differences for all participants in weight change at 3 and 12 months and between-group 6-month weight change difference among nonresponders exposed to modest vs vigorous step-up interventions.ResultsAmong 400 participants (mean [SD] age, 40.5 [11.2] years; 305 [76.3%] women; 81 participants were Black and 266 were White; mean [SD] body mass index, 34.4 [4.3]) randomized to undergo WFS (n = 199) vs WFS plus coaching (n = 201), outcome data were available for 342 participants (85.5%) at 6 months. Six-month weight loss was -2.8 kg (95% CI, -3.5 to -2.0) for the WFS group and -4.8 kg (95% CI, -5.5 to -4.1) for participants in the WFS plus coaching group (difference in weight change, -2.0 kg [90% CI, -2.9 to -1.1]; P < .001); the 90% CI included the noninferiority margin of -2.5 kg. Weight change differences were comparable at 3 and 12 months and, among nonresponders, at 6 months, with no difference by step-up therapy.Conclusions and relevanceA wireless feedback system (Wi-Fi activity tracker and scale with smartphone app to provide daily feedback) was not noninferior to the same system with added coaching. Continued efforts are needed to identify strategies for weight loss management and to accurately select interventions for different individuals to achieve weight loss goals.Trial registrationClinicalTrials.gov Identifier: NCT02997943.

Dataset Information

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

Objective

Methods

Results

Conclusions

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets