Exploring food intake networks and anhedonia symptoms in a Chilean Adults sample

Appetite. 2023 Nov 1:190:107042. doi: 10.1016/j.appet.2023.107042. Epub 2023 Sep 12.

Abstract

Introduction: high-calorie and sugar-sweetened food is considered more pleasant food. People with anhedonia symptoms have difficulties experiencing pleasure in daily activities. However, is still unclear if anhedonia symptomatology increases palatable food consumption in the Chilean Adults sample.

Objective: to explore food networks in the Chilean Adults sample and in people with anhedonia symptom.

Methods: the sample was recruited through digital platforms. Pregnant or lactating women and subjects under pharmacological treatment or psychotherapy were excluded. A total of 1242 subjects, 76.6% women, with a mean age of 30.7 (SD 9.3) and who were highly educated, participated in the study. Data were collected through an online survey. A questionnaire on food consumption based on daily and weekly frequency was used, as well as the Snaith-Hamilton Pleasure Scale to measure anhedonia symptoms. We employed the Gaussian graph model (GGM) to analyze food consumption as networks. We started with the total sample, and then we repeated the analysis on a subsample with anhedonia symptoms, and next on a subsample with exclusively food-related anhedonia.

Results: in the total sample, a positive and strong relationship was observed between fruits and vegetables, as well as a negative association with the triad of sugar-sweetened beverages, fast food, and fried food. The network in anhedonic subjects shows that "pasta, rice & potatoes" and "bread" have a stronger association and a more central place in the network compared those without anhedonia symptoms.

Conclusions: Subjects with anhedonia symptoms have a more central consumption of foods with a high or medium glycemic index compared to subjects without anhedonia symptoms, which could trigger the development of chronic diet-related diseases.

Keywords: Anhedonia; Diet; Food network; Food preference; Gaussian graph model.