Baseline gene expression in subcutaneous adipose tissue predicts diet-induced weight loss in individuals with obesity

PeerJ

article

Ali Oghabian¹  Birgitta W. van der Kolk¹  Pekka Marttinen²  Armand Valsesia³  Dominique Langin⁴  W. H. Saris⁶  Arne Astrup⁷  Ellen E. Blaak⁶  Kirsi H. Pietiläinen¹ 

[1] Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki;Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University;Nestlé Institute of Health Sciences;Department of Biochemistry, Toulouse University Hospitals;Institut des Maladies Métaboliques et Cardiovasculaires, I2MC, Université de Toulouse, Inserm, Université Toulouse III—Paul Sabatier;Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University;Healthy Weight Center;Healthy Weight Hub, Abdominal Center, Helsinki University Hospital and University of Helsinki

关键词: Obesity;    Weight loss;    Machine learning;    Classification;    Prediction;    Bioinformatics;    RNA sequencing;    Gene expression;    Transcriptomics;    Subcutaneous adipose tissue;   

DOI  :  10.7717/peerj.15100

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

Background Weight loss effectively reduces cardiometabolic health risks among people with overweight and obesity, but inter-individual variability in weight loss maintenance is large. Here we studied whether baseline gene expression in subcutaneous adipose tissue predicts diet-induced weight loss success. Methods Within the 8-month multicenter dietary intervention study DiOGenes, we classified a low weight-losers (low-WL) group and a high-WL group based on median weight loss percentage (9.9%) from 281 individuals. Using RNA sequencing, we identified the significantly differentially expressed genes between high-WL and low-WL at baseline and their enriched pathways. We used this information together with support vector machines with linear kernel to build classifier models that predict the weight loss classes. Results Prediction models based on a selection of genes that are associated with the discovered pathways ‘lipid metabolism’ (max AUC = 0.74, 95% CI [0.62–0.86]) and ‘response to virus’ (max AUC = 0.72, 95% CI [0.61–0.83]) predicted the weight-loss classes high-WL/low-WL significantly better than models based on randomly selected genes (P < 0.01). The performance of the models based on ‘response to virus’ genes is highly dependent on those genes that are also associated with lipid metabolism. Incorporation of baseline clinical factors into these models did not noticeably enhance the model performance in most of the runs. This study demonstrates that baseline adipose tissue gene expression data, together with supervised machine learning, facilitates the characterization of the determinants of successful weight loss.

【 授权许可】

CC BY