Identifying eating behavior phenotypes and their correlates: a novel direction toward improving weight management interventions

• Type of Material: Article
• Authors: Sofia Bouhlal, National Human Genome Research Institute; Colleen M. McBride, Emory University; Niraj S. Trivedi, National Human Genome Research Institute; Tanya Agurs-Collins, National Cancer Institute; Susan Persky, National Human Genome Research Institute
• Language: English
• Date: 2017-04-01
• Publisher: Elsevier
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2017
• Final Published Version (URL): http://dx.doi.org/10.1016/j.appet.2016.12.006
• Title of Journal or Parent Work: Appetite
• ISSN: 0195-6663
• Volume: 111
• Start Page: 142
• End Page: 150
• Grant/Funding Information: This work was supported by the Intramural Research Program of the National Human Genome Research Institute [grant number Z01 HG200396-01].
• Keywords: Heatmap; 6-N-PROPYLTHIOURACIL; Self-efficacy; ADOLESCENTS; Phenotypes; ASSOCIATION; HEDONIC HUNGER; Eating behaviors; APPETITE; BMI; TASTE; WOMEN; Nutrition & Dietetics; Science & Technology; SENSITIVITY; OBESITY; Behavioral Sciences; DIETARY RECOMMENDATIONS; Life Sciences & Biomedicine
• Research Categories: Health Sciences, Public Health

Abstract

Common reports of over-response to food cues, difficulties with calorie restriction, and difficulty adhering to dietary guidelines suggest that eating behaviors could be interrelated in ways that influence weight management efforts. The feasibility of identifying robust eating phenotypes (showing face, content, and criterion validity) was explored based on well-validated individual eating behavior assessments. Adults (n = 260; mean age 34 years) completed online questionnaires with measurements of nine eating behaviors including: appetite for palatable foods, binge eating, bitter taste sensitivity, disinhibition, food neophobia, pickiness and satiety responsiveness. Discovery-based visualization procedures that have the combined strengths of heatmaps and hierarchical clustering were used to investigate: 1) how eating behaviors cluster, 2) how participants can be grouped within eating behavior clusters, and 3) whether group clustering is associated with body mass index (BMI) and dietary self-efficacy levels. Two distinct eating behavior clusters and participant groups that aligned within these clusters were identified: one with higher drive to eat and another with food avoidance behaviors. Participants’ BMI (p = 0.0002) and dietary self-efficacy (p  <  0.0001) were associated with cluster membership. Eating behavior clusters showed content and criterion validity based on their association with BMI (asso ciated, but not entirely overlapping) and dietary self-efficacy. Identifying eating behavior phenotypes appears viable. These efforts could be expanded and ultimately inform tailored weight management interventions.