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Weight loss predictability by plasma metabolic signatures in adults with obesity and morbid obesity of the DiOGenes study

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Author: Stroeve, J.H.M. · Saccenti, E. · Bouwman, J. · Dane, A. · Strassburg, K. · Vervoort, J. · Hankemeier, T. · Astrup, A. · Smilde, A.K. · Ommen, B. van · Saris, W.H.M.
Source:Obesity, 2, 24, 379-388
Identifier: 530775
doi: doi:10.1002/oby.21361
Keywords: Nutrition · 3 hydroxy 2 methylpropionic acid · 3 hydroxybutyric acid · Acetic acid · Acetoacetic acid · Alanine · Cholesterol · Creatine · Creatinine · Fatty acid · Glucose · Glycine · High density lipoprotein cholesterol · Insulin · Isoleucine · Isovaleric acid · Lactic acid · Low density lipoprotein cholesterol · Phosphatidylcholine · Proline · Triacylglycerol · Tyrosine · Valine · Adult · Amino acid blood level · Analytical parameters · Cholesterol blood level · Controlled study · Creatinine blood level · Diastolic blood pressure · Energy metabolism · Fatty acid blood level · Female · Glucose blood level · Human · Insulin blood level · Liquid chromatography · Low calory diet · Male · Mass spectrometry · Morbid obesity · Nuclear magnetic resonance · Obesity · Phospholipid blood level · Protein blood level · Randomized controlled trial · Systolic blood pressure · Triacylglycerol blood level · Waist circumference · Weight reduction · Food and Nutrition · Healthy Living · Life · MSB - Microbiology and Systems Biology · ELSS - Earth, Life and Social Sciences


Objective: Aim is to predict successful weight loss by metabolic signatures at baseline and to identify which differences in metabolic status may underlie variations in weight loss success. Methods: In DiOGenes, a randomized, controlled trial, weight loss was induced using a low calorie diet (800 kcal) for 8-weeks. Men (N5236) and women (N5431) as well as groups with overweight/obesity and morbid obesity were studied separately. The relation between the metabolic status before weight loss and weight loss was assessed by stepwise regression on multiple datasets, including anthropometric parameters, NMR-based plasma metabolites, and LC-MS-based plasma lipid species. Results: Maximally, 57% of the variation in weight loss success can be predicted by baseline parameters. The most powerful predictive models were obtained in subjects with morbid obesity. In these models, the metabolites most predictive for weight loss were acetoacetate, triacylglycerols, phosphatidylcholines, specific amino acids, and creatine and creatinine. This metabolic profile suggests that high energy metabolism activity results in higher amounts of weight loss. Conclusions: Possible predictive (pre-diet) markers were found for amount of weight loss for specific subgroups.