Department of Psychiatry, University of Münster, Münster, Germany.
Department of Mathematics and Computer Science, University of Münster, Münster, Germany.
Mol Psychiatry. 2017 May;22(5):703-710. doi: 10.1038/mp.2017.51. Epub 2017 Mar 28.
Genetic and neuroimaging research has identified neurobiological correlates of obesity. However, evidence for an integrated model of genetic risk and brain structural alterations in the pathophysiology of obesity is still absent. Here we investigated the relationship between polygenic risk for obesity, gray matter structure and body mass index (BMI) by the use of univariate and multivariate analyses in two large, independent cohorts (n=330 and n=347). Higher BMI and higher polygenic risk for obesity were significantly associated with medial prefrontal gray matter decrease, and prefrontal gray matter was further shown to significantly mediate the effect of polygenic risk for obesity on BMI in both samples. Building on this, the successful individualized prediction of BMI by means of multivariate pattern classification algorithms trained on whole-brain imaging data and external validations in the second cohort points to potential clinical applications of this imaging trait marker.
遗传和神经影像学研究已经确定了肥胖的神经生物学相关性。然而，在肥胖的病理生理学中，关于遗传风险和大脑结构改变的综合模型的证据仍然缺乏。在这里，我们通过在两个大型独立队列（n=330 和 n=347）中使用单变量和多变量分析，研究了肥胖的多基因风险、灰质结构和体重指数（BMI）之间的关系。更高的 BMI 和更高的肥胖多基因风险与内侧前额叶灰质减少显著相关，并且前额叶灰质进一步显示在两个样本中显著介导肥胖多基因风险对 BMI 的影响。在此基础上，基于全脑成像数据训练的多元模式分类算法成功地对 BMI 进行个体化预测，并在第二队列中进行外部验证，这表明这种成像特征标记物具有潜在的临床应用价值。
