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Data integration for prediction of weight loss in randomized controlled dietary trials.

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.

SUBMITTER: Nielsen RL

PROVIDER: S-EPMC7674420 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Data integration for prediction of weight loss in randomized controlled dietary trials.

Nielsen Rikke Linnemann RL Helenius Marianne M Garcia Sara L SL Roager Henrik M HM Aytan-Aktug Derya D Hansen Lea Benedicte Skov LBS Lind Mads Vendelbo MV Vogt Josef K JK Dalgaard Marlene Danner MD Bahl Martin I MI Jensen Cecilia Bang CB Muktupavela Rasa R Warinner Christina C Aaskov Vincent V Gøbel Rikke R Kristensen Mette M Frøkiær Hanne H Sparholt Morten H MH Christensen Anders F AF Vestergaard Henrik H Hansen Torben T Kristiansen Karsten K Brix Susanne S Petersen Thomas Nordahl TN Lauritzen Lotte L Licht Tine Rask TR Pedersen Oluf O Gupta Ramneek R

Scientific reports 20201118 1

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 ...[more]

PMID: 33208769

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Project description:Dietary composition can influence systemic inflammation; higher levels of circulating inflammatory biomarkers are associated with increased risk of breast and other cancers. A total of 438 overweight/obese, healthy, postmenopausal women were randomized to a caloric-restriction diet (goal: 10% weight-loss), aerobic-exercise (225 min/week moderate-to-vigorous activity), combined diet+exercise, or control. Dietary inflammatory index (DII) and energy-adjusted (E-DII) scores were derived from food frequency questionnaires (FFQ) and could be calculated for 365 participants with complete FFQs at baseline and 12 months. Changes from baseline to 12 months in E-DII scores in the intervention arms versus controls were analyzed using generalized estimating equations, adjusted for confounders. We examined associations between changes in previously measured biomarkers and E-DII at 12 months. Participants randomized to diet and diet+exercise arms had greater reductions in E-DII (-104.4% and -84.4%), versus controls (-34.8%, both P < 0.001). Weight change had a more marked effect than E-DII change on biomarkers at 12-months; associations between E-DII and biomarker changes were reduced after adjustment by weight change. Changes in E-DII at 12 months, adjusted for weight change, were negatively associated with changes in ghrelin [r = -0.19; P = 0.05 (diet), r = -0.29; P = 0.02 (diet+exercise)], and positively with VEGF [r = 0.22; P = 0.03 (diet+exercise)], and red blood cell counts [r = 0.30; P = 0.004 (exercise)]. C-reactive protein (CRP) and IL6 levels were not associated with E-DII changes at 12 months. In conclusion, a behavior change of low-calorie, low-fat diet significantly reduces dietary inflammatory potential, modulating biomarkers that are associated with tumorigenesis, such as VEGF, but not CRP or IL6. PREVENTION RELEVANCE: Diets high in saturated fats and low in fruit and vegetable intake are associated with increased inflammation, which increases cancer risk. This study showed that changes in diet quality had effects on factors associated with cancer; however, the majority of beneficial effects were associated with weight loss rather than diet quality.

Dataset Information

Data integration for prediction of weight loss in randomized controlled dietary trials.

Publications

Data integration for prediction of weight loss in randomized controlled dietary trials.

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