Frauke Beyer1,2, Rui Zhang3, Markus Scholz4,5, Kerstin Wirkner5, Markus Loeffler4,5, Michael Stumvoll6,7, Arno Villringer3,6,8, A Veronica Witte3,6. 1. Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103, Leipzig, Germany. fbeyer@cbs.mpg.de. 2. Collaborative Research Centre 1052 'Obesity Mechanisms', Subproject A1, Faculty of Medicine, Leipzig University, 04103, Leipzig, Germany. fbeyer@cbs.mpg.de. 3. Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103, Leipzig, Germany. 4. Faculty of Medicine, Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, 04103, Leipzig, Germany. 5. Leipzig Research Center for Civilization Diseases (LIFE), Leipzig University, 04103, Leipzig, Germany. 6. Collaborative Research Centre 1052 'Obesity Mechanisms', Subproject A1, Faculty of Medicine, Leipzig University, 04103, Leipzig, Germany. 7. Department of Endocrinology and Nephrology, Leipzig University Hospital, 04103, Leipzig, Germany. 8. Clinic of Cognitive Neurology, Leipzig University Hospital, 04103, Leipzig, Germany.
Abstract
BACKGROUND: Obesity is of complex origin, involving genetic and neurobehavioral factors. Genetic polymorphisms may increase the risk for developing obesity by modulating dopamine-dependent behaviors, such as reward processing. Yet, few studies have investigated the association of obesity, related genetic variants, and structural connectivity of the dopaminergic reward network. METHODS: We analyzed 347 participants (age range: 20-59 years, BMI range: 17-38 kg/m2) of the LIFE-Adult Study. Genotyping for the single nucleotid polymorphisms rs1558902 (FTO) and rs1800497 (near dopamine D2 receptor) was performed on a microarray. Structural connectivity of the reward network was derived from diffusion-weighted magnetic resonance imaging at 3 T using deterministic tractography of Freesurfer-derived regions of interest. Using graph metrics, we extracted summary measures of clustering coefficient and connectivity strength between frontal and striatal brain regions. We used linear models to test the association of BMI, risk alleles of both variants, and reward network connectivity. RESULTS: Higher BMI was significantly associated with lower connectivity strength for number of streamlines (β = -0.0025, 95%-C.I.: [-0.004, -0.0008], p = 0.0042), and, to lesser degree, fractional anisotropy (β = -0.0009, 95%-C.I. [-0.0016, -0.00008], p = 0.031), but not clustering coefficient. Strongest associations were found for left putamen, right accumbens, and right lateral orbitofrontal cortex. As expected, the polymorphism rs1558902 in FTO was associated with higher BMI (F = 6.9, p < 0.001). None of the genetic variants was associated with reward network structural connectivity. CONCLUSIONS: Here, we provide evidence that higher BMI correlates with lower reward network structural connectivity. This result is in line with previous findings of obesity-related decline in white matter microstructure. We did not observe an association of variants in FTO or near DRD2 receptor with reward network structural connectivity in this population-based cohort with a wide range of BMI and age. Future research should further investigate the link between genetics, obesity and fronto-striatal structural connectivity.
BACKGROUND: Obesity is of complex origin, involving genetic and neurobehavioral factors. Genetic polymorphisms may increase the risk for developing obesity by modulating dopamine-dependent behaviors, such as reward processing. Yet, few studies have investigated the association of obesity, related genetic variants, and structural connectivity of the dopaminergic reward network. METHODS: We analyzed 347 participants (age range: 20-59 years, BMI range: 17-38 kg/m2) of the LIFE-Adult Study. Genotyping for the single nucleotid polymorphisms rs1558902 (FTO) and rs1800497 (near dopamine D2 receptor) was performed on a microarray. Structural connectivity of the reward network was derived from diffusion-weighted magnetic resonance imaging at 3 T using deterministic tractography of Freesurfer-derived regions of interest. Using graph metrics, we extracted summary measures of clustering coefficient and connectivity strength between frontal and striatal brain regions. We used linear models to test the association of BMI, risk alleles of both variants, and reward network connectivity. RESULTS: Higher BMI was significantly associated with lower connectivity strength for number of streamlines (β = -0.0025, 95%-C.I.: [-0.004, -0.0008], p = 0.0042), and, to lesser degree, fractional anisotropy (β = -0.0009, 95%-C.I. [-0.0016, -0.00008], p = 0.031), but not clustering coefficient. Strongest associations were found for left putamen, right accumbens, and right lateral orbitofrontal cortex. As expected, the polymorphism rs1558902 in FTO was associated with higher BMI (F = 6.9, p < 0.001). None of the genetic variants was associated with reward network structural connectivity. CONCLUSIONS: Here, we provide evidence that higher BMI correlates with lower reward network structural connectivity. This result is in line with previous findings of obesity-related decline in white matter microstructure. We did not observe an association of variants in FTO or near DRD2 receptor with reward network structural connectivity in this population-based cohort with a wide range of BMI and age. Future research should further investigate the link between genetics, obesity and fronto-striatal structural connectivity.
Authors: Martin E Hess; Simon Hess; Kate D Meyer; Linda A W Verhagen; Linda Koch; Hella S Brönneke; Marcelo O Dietrich; Sabine D Jordan; Yogesh Saletore; Olivier Elemento; Bengt F Belgardt; Thomas Franz; Tamas L Horvath; Ulrich Rüther; Samie R Jaffrey; Peter Kloppenburg; Jens C Brüning Journal: Nat Neurosci Date: 2013-06-30 Impact factor: 24.884
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