What Twin Studies Tell Us About Brain Responses to Food Cues

Schur, Ellen A.; Carnell, Susan

doi:10.1007/s13679-017-0282-7

Cited by 9 publications

(5 citation statements)

References 100 publications

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“…These data provide evidence that the strength of SN connectivity has a polygenic inheritance component, which we identified consistently in both pre- and post-meal functional responses. In consequence, when we took genetic confounders into account via within-pair analyses(39,40) our results differed somewhat from when twins were considered as individuals. Twins with greater body adiposity than their identical co-twin maintained higher SN connectivity after the meal.…”

Section: Discussioncontrasting

confidence: 62%

“…Given the above evidence for inherited influences on SN connectivity and recognized inherited influence on adiposity, we next employed a Twin Association Study approach(40) among the MZ twin pairs to control for genetic confounding. We calculated the within-pair differences in continuous variables (higher fat mass twin minus lower fat mass twin) to control for inherited and familial factors; these analyses are also inherently controlled for age and sex.…”

Section: Resultsmentioning

confidence: 99%

“…The current study attempted to address these differences, and thus facilitate comparison and reproducibility(51) by utilized a resting-state acquisition, applied an independent previously-defined SN template(3) and accounted for genetics(26,27,40).…”

Section: Discussionmentioning

confidence: 99%

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Salience network connectivity is reduced by a meal and influenced by genetic background and hypothalamic gliosis

et al. 2019

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Background/Objectives:The salience network (SN) comprises brain regions that evaluate cues in the external environment in light of internal signals. We examined the SN response to meal intake and potential genetic and acquired influences on SN function.Subjects/Methods:Monozygotic (MZ; 40 pairs) and dizygotic (15 pairs) twins had body composition and plasma metabolic profile evaluated (glucose, insulin, leptin, ghrelin and GLP-1). Twins underwent resting-state functional magnetic resonance imaging (fMRI) scans before and after a standardized meal. The strength of SN connectivity was analyzed pre- and post-meal and the percentage change elicited by a meal was calculated. A multi-echo T2 MRI scan measured T2 relaxation time, a radiologic index of gliosis, in the mediobasal hypothalamus (MBH) and control regions. Statistical approaches included intraclass correlations (ICC) to investigate genetic influences and within-pair analyses to exclude genetic confounders.Results:SN connectivity was reduced by meal ingestion (β=−0.20; P<0.001). Inherited influences on both pre- and post-meal connectivity were present (ICC MZ twins 26%, P<0.05 and 47%, P<0.001, respectively), but not percentage change in response to the meal. SN connectivity in response to a meal did not differ between participants with obesity and of normal weight (χ2(1)=0.93; P=0.33). However, when participants were classified as having high or low signs of MBH gliosis, the high MBH gliosis group failed to reduce the connectivity in response to a meal (z=−1.32; P=0.19). Excluding genetic confounders, the percentage change in SN connectivity by a meal correlated to body fat percentage (r=0.24; P<0.01).Conclusions:SN connectivity was reduced by a meal, indicating potential participation of the SN in control of feeding. The strength of SN connectivity is inherited, but the degree to which SN connectivity is reduced by eating appears to be influenced by adiposity and the presence of hypothalamic gliosis.

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Section: Discussioncontrasting

confidence: 62%

Section: Resultsmentioning

confidence: 99%

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Salience network connectivity is reduced by a meal and influenced by genetic background and hypothalamic gliosis

et al. 2019

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“…fMRI studies show that obesity, binge eating behaviors, and intentional weight loss are associated with changes in functional connectivity and response to food cues (Contreras-Rodríguez, Martín-Pérez, Vilar-López, & Verdejo-Garcia, 2017; Kahathuduwa et al, 2016; Wonderlich et al, 2017). Persons with obesity exhibit greater connectivity among brain regions important for food cravings (Contreras-Rodríguez et al, 2017), suggesting individuals may be predisposed to excess food intake through their pattern of neural response or that neural changes occur in response to food intake over time (Schur & Carnell, 2017).…”

Section: Neural Response To Food and Food Cuesmentioning

confidence: 99%

Cognitive dysfunction is a risk factor for overeating and obesity.

Gunstad

Sanborn

Hawkins

2020

American Psychologist

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This article introduces the rapidly growing literature linking cognitive dysfunction to overeating and obesity. Though neural responses to food and food cues can predispose individuals to overeating, these processes are moderated by a series of cognitive factors. Deficits in attentional bias, delay discounting, and episodic memory have clear connections to overeating in both laboratory and real-world settings. New weight loss interventions target these deficits through strategies designed to either directly improve cognitive function or circumvent them by tailoring weight management strategies to individuals’ specific cognitive profile. Future iterations of these interventions should better account for the influence of obesity-related risk factors such as poor sleep, high stress, socioeconomic burden, and prevalent medical risk factors. In so doing, future work may lead to treatment strategies that promote healthy eating and weight for a lifetime.

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“…High‐resolution fMRI may be needed to achieve resolution on the order of 1 mm 3 . Finally, functional neuroimaging results can be subject to genetic confounding (84), which can lead to faulty interpretations that, for example, obesity itself is characterized by a particular CNS response, whereas the signal instead represents genetic predispositions to both obesity and the identified CNS response (85).…”

Section: Promise and Perils Of Human Neuroimaging To Understand The Wmentioning

confidence: 99%

Strategies to Understand the Weight‐Reduced State: Genetics and Brain Imaging

Loos

Burant

Schur

2021

Obesity

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Most individuals with obesity or overweight have difficulty maintaining weight loss. The weight‐reduced state induces changes in many physiological processes that appear to drive weight regain. Here, we review the use of cell biology, genetics, and imaging techniques that are being used to begin understanding why weight regain is the normal response to dieting. As with obesity itself, weight regain has both genetic and environmental drivers. Genetic drivers for “thinness” and “obesity” largely overlap, but there is evidence for specific genetic loci that are different for each of these weight states. There is only limited information regarding the genetics of weight regain. Currently, most genetic loci related to weight point to the central nervous system as the organ responsible for determining the weight set point. Neuroimaging tools have proved useful in studying the contribution of the central nervous system to the weight‐reduced state in humans. Neuroimaging technologies fall into three broad categories: functional, connectivity, and structural neuroimaging. Connectivity and structural imaging techniques offer unique opportunities for testing mechanistic hypotheses about changes in brain function or tissue structure in the weight‐reduced state.

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What Twin Studies Tell Us About Brain Responses to Food Cues

Cited by 9 publications

References 100 publications

Salience network connectivity is reduced by a meal and influenced by genetic background and hypothalamic gliosis

Salience network connectivity is reduced by a meal and influenced by genetic background and hypothalamic gliosis

Cognitive dysfunction is a risk factor for overeating and obesity.

Strategies to Understand the Weight‐Reduced State: Genetics and Brain Imaging

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