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  3. Nutritional Characteristics Of Foods With Addictive Potential: A Machine-learning Approach.
  1. Home
  3. Nutritional Characteristics Of Foods With Addictive Potential: A Machine-learning Approach.

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Nutritional Characteristics of Foods With Addictive Potential: A Machine-Learning Approach.

Ashley N Gearhardt1, Zach Hutelin1, Emmanuel Nartey1

  • 1Ashley N. Gearhardt is with the Department of Psychology, University of Michigan, Ann Arbor. Zach Hutelin, Mary Elizabeth Baugh, and Alexandra G. DiFeliceantonio are with the Fralin Biomedical Research Institute, Virginia Tech Carilion, Roanoke. Emmanuel Nartey and Monica L. Ahrens are with the Center for Biostatistics and Health Data Science, Department of Statistics, Virginia Tech, Blacksburg. Tera L. Fazzino is with the Department of Psychology, University of Kansas, Lawrence. Erica M. LaFata is with the Oregon Research Institute, Eugene. Kendrin R. Sonneville is with the Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor.

American Journal of Public Health
|June 3, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Foods high in carbohydrates, glycemic load, energy density, and fat are perceived as more addictive, particularly ultraprocessed foods (UPFs). This research identifies key nutritional factors contributing to the addictive potential of foods.

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Area of Science:

  • Nutritional Science
  • Food Science
  • Public Health

Background:

  • Ultraprocessed foods (UPFs) dominate the US food supply.
  • The perceived addictive potential of foods is a growing public health concern.
  • Understanding the nutritional drivers of food addiction is crucial for dietary guidelines.

Purpose of the Study:

  • To pinpoint nutritional characteristics linked to the perceived addictive nature of common US foods.
  • To investigate the role of ultraprocessed foods (UPFs) in food addiction.
  • To develop a framework for identifying foods with high addictive potential.

Main Methods:

  • Surveyed US adults (n=1664) on the perceived addictiveness of 297 foods (74.4% UPFs).
  • Utilized machine learning to analyze nutritional predictors of perceived addictiveness.
  • Examined 15 standard Nutrition Facts label variables and 166 expanded nutrient characteristics.

    • Main Results:

    • Nonlinear associations were found between nutrient content and perceived addictiveness.
    • Foods rich in carbohydrates, glycemic load, energy density, and fat were rated as more addictive.
    • UPFs frequently exhibited multiple addictive nutrient thresholds, unlike minimally processed foods.

    Conclusions:

    • A distinct nutritional signature correlates with perceived food addictiveness.
    • Findings offer a data-driven approach to identifying foods promoting compulsive consumption.
    • Results can inform policies for a healthier, less addictive food environment.