Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland.
PeerJ. 2023 Mar 24;11:e15100. doi: 10.7717/peerj.15100. eCollection 2023.
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.
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.
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 ( < 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.
减肥可有效降低超重和肥胖人群的心血管代谢健康风险，但个体间减肥维持的差异较大。本研究旨在探讨皮下脂肪组织的基线基因表达是否可预测饮食诱导的减肥效果。
在为期 8 个月的多中心饮食干预研究 DiOGenes 中，我们根据 281 名个体的体重减轻中位数百分比（9.9%），将低体重减轻者（low-WL）组和高体重减轻者（high-WL）组进行分类。使用 RNA 测序，我们在基线时鉴定出 high-WL 和 low-WL 之间显著差异表达的基因及其富集途径。我们利用这些信息以及带有线性核的支持向量机，构建了可预测体重减轻类别的分类器模型。
基于与发现的“脂质代谢”（最大 AUC = 0.74，95%CI [0.62-0.86]）和“病毒反应”（最大 AUC = 0.72，95%CI [0.61-0.83]）途径相关的基因选择构建的预测模型可显著更好地预测体重减轻类别的 high-WL/low-WL（<0.01）。基于“病毒反应”基因的模型的性能高度依赖于与脂质代谢相关的基因。将基线临床因素纳入这些模型并不会在大多数情况下明显提高模型性能。本研究表明，基线脂肪组织基因表达数据与有监督的机器学习相结合，可促进对成功减肥决定因素的特征描述。
