{
  "abstract": "sO_ST_ABSImportanceC_ST_ABSPreventing substance use disorder (SUD) is a top public health priority, but clinical practice currently lacks validated prognostic tools to identify risk before the onset of heavy use/problems.\n\nObjectiveTo determine whether models can predict SUD using genetic, behavioral, and environmental factors and to evaluate performance across substance types (alcohol, cannabis, tobacco), sex, and genetic similarity groups.\n\nDesignThis prognostic study developed and validated a multi-domain risk model using data from four longitudinal cohorts (The National Longitudinal Study of Adolescent to Adult Health, Avon Longitudinal Study of Parents and Children, The Collaborative Study on the Genetics of Alcoholism, and FinnTwin12) with follow-up from early adolescence into young adulthood.\n\nSettingPopulation-based and family-based samples from the United States, United Kingdom, and Finland.\n\nParticipantsThe analysis included participants with available genomic and behavioral data. The cohorts represented a spectrum of risk, ranging from nationally representative community samples to high-risk populations.\n\nExposure(s) (for observational studies)Predictors included polygenic scores for substance use-related phenotypes and 10 established early-life behavioral and environmental risk factors.\n\nMain Outcome(s) and Measure(s)The outcomes were a diagnosis of moderate-to- severe SUD based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive value.\n\nResultsA total of 16,362 participants were included in the study (54.7% female; mean [SD] age 26.73 [3.36]). Genetic similarity group composition was 70.4% European, 16.8% African, 9.8% admixed American, 2.2% East Asian, and 0.7% South Asian. SUD prevalence was 22.0%. The integrated model achieved an AUC of 0.78 for predicting any SUD, with a sensitivity of 69% and a positive predictive value of 45%. Performance was comparable for specific substances (AUC range 0.74-0.82) and across genetic similarity groups (AUC range 0.61-0.82). The model demonstrated higher AUC in females compared with males.\n\nConclusions and RelevanceFindings highlight the potential utility of an SUD prediction model to inform clinicians and patients about risk for developing a SUD.\n\nKey pointsO_ST_ABSQuestionC_ST_ABSCan we develop prediction models to detect elevated risk for developing a substance use disorder (SUD) prior to onset.?\n\nFindingsUsing a harmonized dataset of four diverse longitudinal cohorts, we developed and evaluated a prediction model that integrates genetic, behavioral, and environmental predictors. The model demonstrated good discriminatory power to identify individuals at risk for future SUD.\n\nMeaningThese findings establish a framework for integrating multi-domain, multi{-}informant data into clinical workflows, offering a basis for personalized risk stratification and prevention of SUDs.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Psychiatry, Rutgers Robert Wood Johnson Medical School"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 16,
  "authors": [
    {
      "name": "I.-T. Hung"
    },
    {
      "name": "F. Aliev"
    },
    {
      "name": "S. I.-C. Kuo"
    },
    {
      "name": "A. Kim"
    },
    {
      "name": "G. Khan"
    },
    {
      "name": "H. E. Poore"
    },
    {
      "name": "G. F. Dash"
    },
    {
      "name": "M. Choi"
    },
    {
      "name": "H. Zhou"
    },
    {
      "name": "A. Latvala"
    },
    {
      "name": "S. Aaltonen"
    },
    {
      "name": "J. Kaprio"
    },
    {
      "name": "J. E. Salvatore"
    },
    {
      "name": "R. J. Rose"
    },
    {
      "name": "COGA Collaborators"
    },
    {
      "affs": [
        0
      ],
      "name": "D. M. Dick"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bgtyk76d8ckhr.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.16.26365759",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.16.26365759v1.full",
  "id": "bgtyk76d8ckhr",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.10.16.26365759v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "addiction medicine"
    ],
    [
      "openalex",
      "2713|Substance Abuse Treatment and Outcomes"
    ]
  ],
  "openalex": "W7222980505",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.10.16.26365759v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bgtyk76d8ckhr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:12Z",
      "native_id": "10.64898/2026.10.16.26365759",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.16.26365759v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:addiction medicine",
      "subfield": "psychiatry"
    }
  ],
  "title": "Building a Prediction Model to Detect Elevated Risk for Developing a Substance Use Disorder",
  "title_text": "Building a Prediction Model to Detect Elevated Risk for Developing a Substance Use Disorder",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:12Z",
  "url": "https://research.draconitic.com/paper/bgtyk76d8ckhr",
  "venue": "medRxiv",
  "versions": []
}