{
  "abstract": "Background: Excessive weight gain after kidney transplantation adversely affects patient and graft outcomes. Machine Learning has been increasingly used to improve risk prediction across a range of transplant-related outcomes, yet its application to post-transplant weight trajectories remains unexplored. This study is the first to evaluate the feasibility of using Machine Learning algorithms to identify predictors of weight gain after kidney transplantation. Methods: This retrospective cohort study analyzed 374 adult kidney transplant recipients. We developed an Elastic Net regression model with 10-fold cross-validation to predict body weight variation at 12 months post-transplant. The analysis screened a comprehensive set of demographic and clinical variables, including recipient characteristics, donor type, dialysis duration, and pre-transplant hypertension, to identify predictors within the training (75%) and testing (25%) datasets. Results: Mean weight gain was 4.0 ± 7.4 kg. The Elastic Net model retained four predictors in the final model: female sex, deceased donor source, age, and pre-transplant body mass index. However, the model demonstrated limited predictive performance (R2 = 0.10; Mean Absolute Error = 8.7%), indicating that baseline variables explain only a small fraction of the observed variability in weight change. Conclusions: This pioneering study establishes an analytical benchmark and reveals the informational limits of standard clinical datasets for predicting post-transplant weight gain. While Machine Learning application is feasible, current demographic and clinical variables are insufficient for precise individual prediction. Future models must integrate more granular behavioral and pharmacological data to achieve clinically actionable risk stratification.",
  "abstract_shown": true,
  "affiliations": [
    "Post-Graduate Program in Medical Sciences: Endocrinology, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Su, Brazil.",
    "Post-Graduate Program in Neuroscience, McMaster University, Hamilton, Ontario, Canada.",
    "Nutrition and Dietetics Division, Hospital de Clínicas de Porto Alegre, Porto Alegre, Rio Grande do Su, Brazil.",
    "Post-Graduate Program in Health Sciences, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, Rio Grande do Su, Brazil.",
    "Department of Internal Medicine, School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.",
    "Endocrine Division, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil.",
    "Department of Nutrition, School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.",
    "Post-Graduate Program in Food, Nutrition and Health, School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil."
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Júlia de Melo Cardoso de Freitas"
    },
    {
      "affs": [
        1
      ],
      "name": "Pedro Lemos Ballester"
    },
    {
      "affs": [
        0
      ],
      "name": "Elis Forcellini Pedrollo"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Camila Corrêa"
    },
    {
      "affs": [
        3
      ],
      "name": "Rafaella Câmara Rocha Menezes",
      "orcid": "0000-0003-3176-3716"
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    {
      "affs": [
        4,
        5
      ],
      "name": "Cristiane Bauermann Leitão"
    },
    {
      "affs": [
        2,
        6,
        7
      ],
      "name": "Gabriela Corrêa Souza",
      "orcid": "0000-0002-3620-4334"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b9yjgs0hdm6kp.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0360501",
  "fulltext_url": "https://europepmc.org/articles/PMC13690245",
  "id": "b9yjgs0hdm6kp",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0360501",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2747|Renal Transplantation Outcomes and Treatments"
    ],
    [
      "plos",
      "/Biology and life sciences/Physiology/Physiological parameters/Body weight"
    ],
    [
      "plos",
      "/Biology and life sciences/Physiology/Physiological parameters/Body weight/Weight gain"
    ],
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      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
    ],
    [
      "plos",
      "/Medicine and health sciences/Nephrology/Medical dialysis"
    ],
    [
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      "/Medicine and health sciences/Pharmacology/Clinical pharmacology"
    ],
    [
      "plos",
      "/Medicine and health sciences/Pharmacology/Drugs/Immunosuppressives"
    ],
    [
      "plos",
      "/Medicine and health sciences/Surgical and invasive medical procedures/Transplantation/Organ transplantation/Renal transplantation"
    ],
    [
      "plos",
      "/Medicine and health sciences/Surgical and invasive medical procedures/Urinary system procedures/Renal transplantation"
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      "/Physical sciences/Mathematics/Statistics/Statistical methods/Forecasting"
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  "pdf_url": "https://europepmc.org/articles/PMC13690245?pdf=render",
  "pmcid": "PMC13690245",
  "pmid": "42911966",
  "published": "2026-10-22",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/b9yjgs0hdm6kp.ris",
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    {
      "first_seen": "2026-10-30T12:17:23Z",
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  "subjects": [
    {
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      "origin": "plos:pharmacology",
      "subfield": "pharmacology-toxicology"
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      "origin": "plos:physiology",
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    {
      "field": "medicine",
      "origin": "plos:surgical and invasive medical procedures",
      "subfield": "surgery"
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  ],
  "title": "Feasibility of a machine learning model for predicting weight gain after kidney transplantation machine learning and weight gain after kidney transplantation",
  "title_text": "Feasibility of a machine learning model for predicting weight gain after kidney transplantation machine learning and weight gain after kidney transplantation",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:10Z",
  "url": "https://research.draconitic.com/paper/b9yjgs0hdm6kp",
  "venue": "PLOS One",
  "versions": []
}