{
  "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.\n\nMethods: 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.\n\nResults: 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).\n\nConclusions: 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.\n\nKey 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.\n\n• 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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "abstract",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "D Yoo"
    },
    {
      "name": "T Rebelo"
    },
    {
      "name": "VK Nguyen"
    },
    {
      "name": "S Zimmerman"
    },
    {
      "name": "O Jolliet"
    },
    {
      "name": "M Nordman"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fqqnqf5dmk76q.bib",
  "cited_by": 0,
  "doi": "10.1093/eurpub/ckag169.111",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13697986/",
  "id": "fqqnqf5dmk76q",
  "issns": [
    "1101-1262",
    "1464-360X"
  ],
  "landing_url": "https://doi.org/10.1093/eurpub/ckag169.111",
  "language": "en",
  "licence": "CC-BY-NC-4.0",
  "licence_label": "CC BY-NC 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "3. PARALLEL PROGRAMME"
    ],
    [
      "heading",
      "AcademicSubjects/MED00860"
    ],
    [
      "heading",
      "AcademicSubjects/SOC01210"
    ],
    [
      "heading",
      "AcademicSubjects/SOC02610"
    ],
    [
      "openalex",
      "2739|Nutritional Studies and Diet"
    ]
  ],
  "openalex": "W7223666754",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13697986/pdf/?tool=EBI",
  "pmcid": "PMC13697986",
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "Oxford University Press",
  "ris": "https://research.draconitic.com/paper/fqqnqf5dmk76q.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:57Z",
      "native_id": "PMC13697986",
      "source": "pmc",
      "url": "https://doi.org/10.1093/eurpub/ckag169.111"
    },
    {
      "first_seen": "2026-10-30T12:21:47Z",
      "native_id": "PMC:PMC13697986",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13697986"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2739|Nutritional Studies and Diet",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Limited utility of diet quality scores in anthropometric prediabetes and diabetes screening models",
  "title_text": "Limited utility of diet quality scores in anthropometric prediabetes and diabetes screening models",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:34:56Z",
  "url": "https://research.draconitic.com/paper/fqqnqf5dmk76q",
  "venue": "The European Journal of Public Health",
  "versions": []
}