ArticleObesity (Silver Spring, Md.)2021
Multiomic Predictors of Short-Term Weight Loss and Clinical Outcomes During a Behavioral-Based Weight Loss Intervention.
Article in Obesity (Silver Spring, Md.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 18 citations in OpenAlex.
- Fasting appetite-related gut hormone responses after weight loss induced by calorie restriction, exercise, or both in people with overweight or obesity: a meta-analysis.International journal of obesity (2005) · 2025Pooled it
- Impact of Food-Based Weight Loss Interventions on Gut Microbiome in Individuals with Obesity: A Systematic Review.Nutrients · 2022Pooled it
- The Gut Microbiota during a Behavioral Weight Loss Intervention.Nutrients · 2021Trial
- Multi-omic modelling of body mass index response to a dietary weight loss intervention.Gut microbes · 2026Article
- Gut microbiome mediates the association between dietary quality and metabolic risk in a heterogeneous adult population.Nutrition & metabolism · 2026Article
- Multiomics to Predict Individual Responses to Weight Loss Interventions: A Promising Strategy to Enable Precision Nutrition.Diabetes care · 2026Article
- Metaflammation's Role in Systemic Dysfunction in Obesity: A Comprehensive Review.International journal of molecular sciences · 2025Review
- Response Regarding: Using Machine Learning to Predict Weight Gain in Adults: An Observational Analysis From the All of Us Research Program.The Journal of surgical research · 2025Article
- Precision or Personalized Nutrition: A Bibliometric Analysis.Nutrients · 2024Article
- The Beneficial Effects of Dietary Interventions on Gut Microbiota-An Up-to-Date Critical Review and Future Perspectives.Nutrients · 2023Review
- The Microbiome, Epigenome, and Diet in Adults with Obesity during Behavioral Weight Loss.Nutrients · 2023Article
- Proteomics, dietary intake, and changes in cardiometabolic health within a behavioral weight-loss intervention: A pilot study.Obesity (Silver Spring, Md.) · 2022Article
- The microbiota composition drives personalized nutrition: Gut microbes as predictive biomarkers for the success of weight loss diets.Frontiers in nutrition · 2022Review
- CANTARE: finding and visualizing network-based multi-omic predictive models.BMC bioinformatics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveIdentifying 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.
methodsHealthy adults with overweight or obesity (n = 62, age 18-55 years, BMI 27-45 kg/m
resultsA network of multiomic relationships informed predictive models for 10 clinical outcomes (body weight, waist circumference, fat mass, hemoglobin A
conclusionsThese data support the feasibility of using baseline multiomic features to provide insight for precision nutrition-based weight loss interventions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.