Original Article

Molecular Psychiatry (2017) 22, 703–710; doi:10.1038/mp.2017.51 published online 28 March 2017

Prefrontal gray matter volume mediates genetic risks for obesity

N Opel1, R Redlich1, C Kaehler1,2, D Grotegerd1, K Dohm1, W Heindel3, H Kugel3, A Thalamuthu4, N Koutsouleris5, V Arolt1, A Teuber6, H Wersching6, B T Baune7, K Berger6 and U Dannlowski1

  1. 1Department of Psychiatry, University of Münster, Münster, Germany
  2. 2Department of Mathematics and Computer Science, University of Münster, Münster, Germany
  3. 3Department of Clinical Radiology, University of Münster, Münster, Germany
  4. 4Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
  5. 5Department of Psychiatry, University of Munich, Munich, Germany
  6. 6Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
  7. 7Discipline of Psychiatry, School of Medicine, University of Adelaide, Adelaide, SA, Australia

Correspondence: Professor U Dannlowski, Department of Psychiatry, University of Münster, Albert-Schweitzer Campus 1 A9, Münster 48149, Germany. E-mail: dannlow@uni-muenster.de

Received 19 October 2016; Revised 25 January 2017; Accepted 26 January 2017
Advance online publication 28 March 2017

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Abstract

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.