{
  "abstract": "Objectives: Insulin-like growth factor-1 (IGF-1) is widely used to guide growth hormone (GH) therapy, yet its predictors during the maintenance phase and puberty remain incompletely understood. We aimed to predict IGF-1 standard deviation scores (SDS) at 3 and 12 months in GH-treated boys and to identify key predictors of IGF-1 dynamics using interpretable modeling approaches.\n\nMethods: Data from 72 boys with growth hormone deficiency or idiopathic short stature from a previous Swedish clinical trial were analyzed using early-life characteristics, GH treatment variables, and puberty-related measures. Linear regression, Elastic Net regression, and explainable boosting machine (EBM) models were evaluated with nested five-fold patient-grouped cross-validation. Performance estimates were patient-balanced, with 95% confidence intervals based on 2,000 patient-cluster bootstrap resamples.\n\nResults: Elastic Net had the highest point estimates at 3 months, but confidence intervals did not establish a difference between the primary models. No clear differences between models were found at 12 months. Current IGF-1 SDS was the most prominent predictive feature, while additional treatment- and puberty-related variables were also selected across cross-validation models.\n\nConclusion: Interpretable modeling approaches provided moderate predictive performance for future IGF-1 SDS during the maintenance phase and puberty. The findings indicate that prediction is informed by current IGF-1 SDS together with additional clinical variables, but further validation in independent cohorts is required before clinical application.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Pediatrics, Göteborg Pediatric Growth Research Center (GP-GRC), Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg",
    "Department of Pediatric Endocrinology, Sahlgrenska University Hospital, Queen Silvia Children’s Hospital",
    "AI Competence Center, Sahlgrenska University Hospital",
    "Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Helena-Jamin Ly"
    },
    {
      "affs": [
        2
      ],
      "name": "Juulia T. Suvilehto"
    },
    {
      "affs": [
        3
      ],
      "name": "Beatrice Skyman"
    },
    {
      "affs": [
        0
      ],
      "name": "Carina Ankarberg-Lindgren"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Hans Fors"
    },
    {
      "affs": [
        2
      ],
      "name": "Jesper Havsol"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Jovanna Dahlgren"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dqndkw4g5nxec.bib",
  "cited_by": 0,
  "doi": "10.3389/fendo.2026.1873245",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13719384/?tool=EBI",
  "id": "dqndkw4g5nxec",
  "issns": [
    "1664-2392"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fendo.2026.1873245/full",
  "language": "eng",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Original Research"
    ],
    [
      "openalex",
      "2712|Growth Hormone and Insulin-like Growth Factors"
    ]
  ],
  "openalex": "W7222687863",
  "pdf_url": "https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1873245/pdf",
  "pmcid": "PMC13719384",
  "pmid": "42964089",
  "published": "2026-10-16",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/dqndkw4g5nxec.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:20Z",
      "native_id": "10.3389/fendo.2026.1873245",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fendo.2026.1873245/full"
    },
    {
      "first_seen": "2026-10-31T12:21:36Z",
      "native_id": "PMC:PMC13719384",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13719384"
    },
    {
      "first_seen": "2026-11-02T12:23:21Z",
      "native_id": "PMC13719384",
      "source": "pmc",
      "url": "https://doi.org/10.3389/fendo.2026.1873245"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2712|Growth Hormone and Insulin-like Growth Factors",
      "subfield": "endocrinology-metabolism"
    }
  ],
  "title": "Interpretable machine learning identifies predictors of IGF-1 during growth hormone therapy in boys",
  "title_text": "Interpretable machine learning identifies predictors of IGF-1 during growth hormone therapy in boys",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:20:27Z",
  "url": "https://research.draconitic.com/paper/dqndkw4g5nxec",
  "venue": "Frontiers in Endocrinology",
  "versions": []
}