ArticleScientific reports2020
Data integration for prediction of weight loss in randomized controlled dietary trials.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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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
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Identification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventions.Microbiome · 2023Pooled it
- Gut microbiota predicts body fat change following a low-energy diet: a PREVIEW intervention study.Genome medicine · 2022Trial
- Predicting Long-Term Weight Loss Using Self-Reported, Digitally Collected, Real-World Data After Initiation of Semaglutide for Overweight or Obesity.Advances in therapy · 2026Article
- Precision nutrition through diet-gut microbiome interactions: Emerging insights driven by artificial intelligence, microbiome health metrics, and mechanistic modeling.Gut microbes reports · 2026Review
- Stool Microbiome Features and Weight Change Response to Treatment for cancer cachexia.Journal of cachexia, sarcopenia and muscle · 2025Article
- Gut metagenomes reveal interactions between dietary restriction, ageing and the microbiome in genetically diverse mice.Nature microbiology · 2025Article
- A novel nomogram for predicting optimal weight loss response following diet and exercise intervention in patients with obesity.Scientific reports · 2024Article
- Machine Learning in Nutrition Research.Advances in nutrition (Bethesda, Md.) · 2022Article
- SourceFinder: a Machine-Learning-Based Tool for Identification of Chromosomal, Plasmid, and Bacteriophage Sequences from Assemblies.Microbiology spectrum · 2022Article
- Disentangling host-microbiota complexity through hologenomics.Nature reviews. Genetics · 2022Review
- Article
Corrections and comments
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Authors and funding
28 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diet is an important component in weight management strategies, but heterogeneous responses to the same diet make it difficult to foresee individual weight-loss outcomes. Omics-based technologies now allow for analysis of multiple factors for weight loss prediction at the individual level. Here, we classify weight loss responders (N = 106) and non-responders (N = 97) of overweight non-diabetic middle-aged Danes to two earlier reported dietary trials over 8 weeks. Random forest models integrated gut microbiome, host genetics, urine metabolome, measures of physiology and anthropometrics measured prior to any dietary intervention to identify individual predisposing features of weight loss in combination with diet. The most predictive models for weight loss included features of diet, gut bacterial species and urine metabolites (ROC-AUC: 0.84-0.88) compared to a diet-only model (ROC-AUC: 0.62). A model ensemble integrating multi-omics identified 64% of the non-responders with 80% confidence. Such models will be useful to assist in selecting appropriate weight management strategies, as individual predisposition to diet response varies.
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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.