Computationally Modeling the Physiologic Impact of the Ratio of Fats to Carbohydrates in the Diet on Intake Among Metabolically Healthy Adults

Abstract

Background: When it comes to how effectively a diet can help reduce or maintain body weight, a key question is how that diet affects a person's hunger, satiety, and subsequent eating.

Objectives: This study aimed to analyze modeling, from a physiologic perspective, how varying the ratio of fats to carbohydrates in a diet impacts hunger, satiety, and subsequent eating among metabolically healthy adults.

Methods: We developed a model representing an adult, their dietary intake, gastrointestinal tract, hunger/satiety levels, and meal consumption. We simulated agents eating fixed ratios of macronutrients and measured their subsequent eating over 24 h driven by physiologic responses.

Results: When increasing the proportion of energy from fats relative to carbohydrates, daily calories decrease by on mean 149 and 110 calories per 10% increase in fats for males and females, respectively. Additionally, a simulated diet with a relative ratio of energy from fats:carbohydrates of 20%:80% results in individuals snacking after 21:00 for ∼93% of days in both sexes, whereas a relative fat:carbohydrate ratio of 80%:20% results in late-night snacking ∼55% and ∼60% of days for males and females, respectively. Agents consuming at least a 40%:60% relative ratio of energy from fat:carbohydrate ratio can achieve the largest reductions in total calories consumed and late-night snacking compared with consuming higher relative proportions of carbohydrates.

Conclusions: Eating a diet with ≥40% of its energy from fats relative to carbohydrates can achieve the largest reductions in total calories consumed and late-night snacking each day than consuming higher proportions of carbohydrates, with even further reductions as more fat is added to the diet, when considering the physiologic responses to dietary intake alone. Future research should layer in other strong contributing factors to eating such as stress, social context, palatability, physical activity, and types of macronutrients, and also represent other metabolic profiles and ages.

Keywords: computational modeling; dietary intake; hunger; precision nutrition; satiety; systems science.

FIGURE 1

Model of fat and carbohydrate consumption’s impact on hunger, satiety, and subsequent intake. CCK, cholecystokinin; GI, gastrointestinal; GLP, glucagon-like peptide; PYY, peptide tyrosine tyrosine.

FIGURE 2

Impact of varying ratio of fats to carbohydrates in diet on mean calories per largest eating event (A), number of eating events per day (B), time between meals (C), percentage of time late-night snacking (D), and total daily calories (E).