The obese brain as a heritable phenotype: a combined morphometry and twin study.

BACKGROUND: Body weight and adiposity are heritable traits. To date, it remains unknown whether obesity-associated brain structural alterations are under a similar level of genetic control. METHODS: For this study, we utilized magnetic resonance imaging data from the Human Connectome Project. Voxel-based morphometry was used to investigate associations between body mass index (BMI) and regional gray matter volume (GMV) in a sample of 875 young adults with a wide BMI range (386 males/489 females; age 28.8±3.7 years; BMI 26.6±5.3 kg m-2) that included 86 pairs of monozygotic twins and 82 pairs of dizygotic twins. Twin data were analyzed by applying the additive genetic, common environmental and residual effects model to determine heritability of brain regions that were associated with BMI. RESULTS: We observed positive associations between BMI and GMV in the ventromedial prefrontal cortex and the right cerebellum and widespread negative associations within the prefrontal cortex, cerebellum, temporal lobes and distinct subcortical structures. Varying degrees of heritability were found for BMI-associated brain regions, with the highest heritability estimates for cerebellar GMV and subcortical structures. CONCLUSIONS: These data indicate that brain regions associated with obesity are subject to differing levels of genetic control and environmental influences. Specific brain regions with high heritability might represent an inherent vulnerability factor for obesity.

Deactivation of the left dorsolateral prefrontal cortex in Prader-Willi syndrome after meal consumption.

BACKGROUND/OBJECTIVES: Prader-Willi syndrome (PWS) is a type of human genetic obesity that may give us information regarding the physiology of non-syndromic obesity. The objective of this study was to investigate the functional correlates of hunger and satiety in individuals with PWS in comparison with healthy controls with obesity, hypothesizing that we would see significant differences in activation in the left dorsolateral prefrontal cortex (DLPFC) based on prior findings. SUBJECTS/METHODS: This study compared the central effects of food consumption in nine individuals with PWS (7 men, 2 women; body fat 35.3±10.0%) and seven controls (7 men; body fat 28.8±7.6%), matched for percentage body fat. H2(15)O-PET (positron emission tomography) scans were performed before and after consumption of a standardized liquid meal to obtain quantitative measures of regional cerebral blood flow (rCBF), a marker of neuronal activity. RESULTS: Compared with obese controls, PWS showed altered (P<0.05 family-wise error cluster-level corrected; voxelwise P<0.001) rCBF before and after meal consumption in multiple brain regions. There was a significant differential rCBF response within the left DLPFC after meal ingestion with decreases in DLPFC rCBF in PWS; in controls, DLPFC rCBF tended to remain unchanged. In more liberal analyses (P<0.05 family-wise error cluster-level corrected; voxelwise P<0.005), rCBF of the right orbitofrontal cortex (OFC) increased in PWS and decreased in controls. In PWS, ΔrCBF of the right OFC was associated with changes in appetite ratings. CONCLUSIONS: The pathophysiology of eating behavior in PWS is characterized by a paradoxical meal-induced deactivation of the left DLPFC and activation in the right OFC, brain regions implicated in the central regulation of eating behavior.

Body composition and energy expenditure predict ad-libitum food and macronutrient intake in humans.

BACKGROUND: Obesity is the result of chronic positive energy balance. The mechanisms underlying the regulation of energy homeostasis and food intake are not understood. Despite large increases in fat mass (FM), recent evidence indicates that fat-free mass (FFM) rather than FM is positively associated with intake in humans. METHODS: In 184 humans (73 females/111 males; age 34.5±8.8 years; percentage body fat: 31.6±8.1%), we investigated the relationship of FFM index (FFMI, kg m(-2)), FM index (FMI, kg m(-2)); and 24-h energy expenditure (EE, n=127) with ad-libitum food intake using a 3-day vending machine paradigm. Mean daily calories (CAL) and macronutrient intake (PRO, CHO, FAT) were determined and used to calculate the relative caloric contribution of each (%PRO, %CHO, %FAT) and percent of caloric intake over weight maintaining energy needs (%WMENs). RESULTS: FFMI was positively associated with CAL (P<0.0001), PRO (P=0.0001), CHO (P=0.0075) and FAT (P<0.0001). This remained significant after adjusting for FMI. Total EE predicted CAL and macronutrient intake (all P<0.0001). FMI was positively associated with CAL (P=0.019), PRO (P=0.025) and FAT (P=0.0008). In models with both FFMI and FMI, FMI was negatively associated with CAL (P=0.019) and PRO (P=0.033). Both FFMI and FMI were negatively associated with %CHO and positively associated with %FAT (all P<0.001). EE and FFMI (adjusted for FMI) were positively (EE P=0.0085; FFMI P=0.0018) and FMI negatively (P=0.0018; adjusted for FFMI) associated with %WMEN. CONCLUSION: Food and macronutrient intake are predicted by FFMI and to a lesser degree by FMI. FFM and FM may have opposing effects on energy homeostasis.