Limited utility of diet quality scores in anthropometric prediabetes and diabetes screening models
Abstract
Background: Undiagnosed prediabetes and type 2 diabetes are major public health challenges. Machine learning tools utilizing anthropometrics effectively predict dysglycemia, but poor diet is a fundamental driver of metabolic dysfunction. This study investigates whether incorporating granular diet quality data improves digital screening tools, or if physical measurements render such data redundant.
Methods: We analyzed 20,704 adults from the National Health and Nutrition Examination Survey (1999-2018). We evaluated 13 Healthy Eating Index (HEI-2020) components from 24-hour dietary recalls against a 7-parameter anthropometric model. After Boruta feature selection, we trained Model Averaged Neural Networks and assessed discrimination (ROCAUC) and calibration on a held-out test set.
Results: Boruta identified eight HEI components (e.g., Added Sugars, Dairy) as relevant predictors. The baseline 7-parameter anthropometric model achieved robust discrimination (ROCAUC 0.796, 95% CI 0.785-0.807). Integrating either the HEI Total Score or the eight selected dietary features yielded no significant improvement (ROCAUC 0.796 for both; p > 0.05). Conversely, a diet quality-only model performed poorly (ROCAUC 0.591, 95% CI 0.577-0.605). Calibration remained strong across all models including anthropometrics. However, the diet quality-only model exhibited a limited calibration range, failing entirely to predict probability extremes (<0.273 and >0.818).
Conclusions: While diet quality is biologically critical, its addition via HEI components provides no incremental discrimination or calibration utility to screening models based on robust anthropometrics. The physical phenotype acts as a sufficient downstream integrator of metabolic history. Scalable prediabetes public health screening can primarily rely on simple, objective physical measurements, without requirement for labor-intensive dietary questionnaires.
Key messages: • Detailed diet quality data adds no predictive value to anthropometric diabetes screening tools. Public health screening can rely entirely on simple, scalable physical measurements.
• The physical phenotype acts as a robust downstream integrator of metabolic history. Anthropometric markers fully capture the diabetes risk otherwise encoded by long-term dietary habits.
Related
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BibTeX
@article{yoo2026limited,
title = {{Limited utility of diet quality scores in anthropometric prediabetes and diabetes screening models}},
author = {D Yoo and T Rebelo and VK Nguyen and S Zimmerman and O Jolliet and M Nordman},
journal = {The European Journal of Public Health},
year = {2026},
month = oct,
doi = {10.1093/eurpub/ckag169.111},
url = {https://doi.org/10.1093/eurpub/ckag169.111},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - Limited utility of diet quality scores in anthropometric prediabetes and diabetes screening models
AU - D Yoo
AU - T Rebelo
AU - VK Nguyen
AU - S Zimmerman
AU - O Jolliet
AU - M Nordman
PY - 2026
DA - 2026/10/01
JO - The European Journal of Public Health
DO - 10.1093/eurpub/ckag169.111
UR - https://doi.org/10.1093/eurpub/ckag169.111
AB - Background: Undiagnosed prediabetes and type 2 diabetes are major public health challenges. Machine learning tools utilizing anthropometrics effectively predict dysglycemia, but poor diet is a fundamental driver of metabolic dysfunction. This study investigates whether incorporating granular diet quality data improves digital screening tools, or if physical measurements render such data redundant. Methods: We analyzed 20,704 adults from the National Health and Nutrition Examination Survey (1999-2018). We evaluated 13 Healthy Eating Index (HEI-2020) components from 24-hour dietary recalls against a 7-parameter anthropometric model. After Boruta feature selection, we trained Model Averaged Neural Networks and assessed discrimination (ROCAUC) and calibration on a held-out test set. Results: Boruta identified eight HEI components (e.g., Added Sugars, Dairy) as relevant predictors. The baseline 7-parameter anthropometric model achieved robust discrimination (ROCAUC 0.796, 95% CI 0.785-0.807). Integrating either the HEI Total Score or the eight selected dietary features yielded no significant improvement (ROCAUC 0.796 for both; p > 0.05). Conversely, a diet quality-only model performed poorly (ROCAUC 0.591, 95% CI 0.577-0.605). Calibration remained strong across all models including anthropometrics. However, the diet quality-only model exhibited a limited calibration range, failing entirely to predict probability extremes (<0.273 and >0.818). Conclusions: While diet quality is biologically critical, its addition via HEI components provides no incremental discrimination or calibration utility to screening models based on robust anthropometrics. The physical phenotype acts as a sufficient downstream integrator of metabolic history. Scalable prediabetes public health screening can primarily rely on simple, objective physical measurements, without requirement for labor-intensive dietary questionnaires. Key messages: • Detailed diet quality data adds no predictive value to anthropometric diabetes screening tools. Public health screening can rely entirely on simple, scalable physical measurements. • The physical phenotype acts as a robust downstream integrator of metabolic history. Anthropometric markers fully capture the diabetes risk otherwise encoded by long-term dietary habits.
SN - 1101-1262
SN - 1464-360X
ER -