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Peer-reviewedCC BYPLOS One

Feasibility of a machine learning model for predicting weight gain after kidney transplantation machine learning and weight gain after kidney transplantation

  • Júlia de Melo Cardoso de Freitas1
  • Pedro Lemos Ballester2
  • Elis Forcellini Pedrollo1
  • Camila Corrêa1,3
  • Rafaella Câmara Rocha Menezes4
  • Cristiane Bauermann Leitão5,6
  • Gabriela Corrêa Souza3,7,8
8 affiliations
  1. 1Post-Graduate Program in Medical Sciences: Endocrinology, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Su, Brazil.
  2. 2Post-Graduate Program in Neuroscience, McMaster University, Hamilton, Ontario, Canada.
  3. 3Nutrition and Dietetics Division, Hospital de Clínicas de Porto Alegre, Porto Alegre, Rio Grande do Su, Brazil.
  4. 4Post-Graduate Program in Health Sciences, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, Rio Grande do Su, Brazil.
  5. 5Department of Internal Medicine, School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
  6. 6Endocrine Division, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil.
  7. 7Department of Nutrition, School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
  8. 8Post-Graduate Program in Food, Nutrition and Health, School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
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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.

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Cite

BibTeXRIS
BibTeX
@article{freitas2026feasibility,
  title = {{Feasibility of a machine learning model for predicting weight gain after kidney transplantation machine learning and weight gain after kidney transplantation}},
  author = {Júlia de Melo Cardoso de Freitas and Pedro Lemos Ballester and Elis Forcellini Pedrollo and Camila Corrêa and Rafaella Câmara Rocha Menezes and Cristiane Bauermann Leitão and Gabriela Corrêa Souza},
  journal = {PLOS One},
  year = {2026},
  month = oct,
  doi = {10.1371/journal.pone.0360501},
  url = {https://doi.org/10.1371/journal.pone.0360501},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Feasibility of a machine learning model for predicting weight gain after kidney transplantation machine learning and weight gain after kidney transplantation
AU  - Júlia de Melo Cardoso de Freitas
AU  - Pedro Lemos Ballester
AU  - Elis Forcellini Pedrollo
AU  - Camila Corrêa
AU  - Rafaella Câmara Rocha Menezes
AU  - Cristiane Bauermann Leitão
AU  - Gabriela Corrêa Souza
PY  - 2026
DA  - 2026/10/22
JO  - PLOS One
DO  - 10.1371/journal.pone.0360501
UR  - https://doi.org/10.1371/journal.pone.0360501
AB  - 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.
SN  - 1932-6203
ER  -