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  • Clinical Research Article
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Predicting rapid weight gain in six-month-old infants: an exploratory modeling study

Pediatric Research (2026)Cite this article

Abstract

Background

Rapid weight gain (RWG) in early life is a significant risk factor for childhood obesity. Its multifactorial etiology warrants exploratory statistical and machine-learning analysis to aid early prediction.

Methods

Data from a prospective infant study were used to compare four models for predicting RWG from birth to 6 months: two machine‑learning methods (SVM with a linear kernel and Naïve Bayes), one regularized regression (LASSO), and one traditional statistical model (Generalized Linear Model, GLM). Performance was evaluated using AUC, accuracy, precision, sensitivity, specificity, and F1‑score, each with 95% confidence intervals (CI).

Results

Precision was comparable across models (0.70–0.75). The GLM showed the highest point estimates for AUC (0.66, 95% CI 0.48–0.83), specificity (0.45, 95% CI 0.37–0.53), accuracy (0.72, 95% CI 0.53–0.86), and F1‑score (0.800), while LASSO achieved the highest sensitivity (0.91, 95% CI 0.84–0.95). However, all CIs overlapped, indicating no statistically significant differences.

Conclusion

Although the GLM had the highest point estimates, all models showed similar and modest discriminative ability. Consistent early‑life predictors emerged across approaches, highlighting the multifactorial nature of RWG. Larger cohorts are needed to improve predictive accuracy and fully assess machine‑learning methods.

Impact

  • Rapid weight gain (RWG) results from the dynamic interplay of biological, dietary, behavioral, and environmental factors. Developing robust models to identify key determinants is therefore essential.

  • In this study, the GLM yielded the highest point estimates across key metrics, while the machine‑learning models nonetheless demonstrated promising potential.

  • Predictive modeling in this context not only enables risk stratification but also provides insight into underlying mechanisms, thereby guiding future longitudinal research and informing preventive strategies to support healthy growth trajectories in early life.

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Fig. 1: Standardized SVM coefficients using a linear kernel for predictors of RWG at six months of age, Mexico 2023.
Fig. 2: Optimal lambda (λ) selection in LASSO regression via cross-validation, Mexico 2023.
Fig. 3: LASSO regression coefficients for predictors of RWG at six months of age, Mexico 2023.
Fig. 4: Conditional probability distributions from the Naïve Bayes model to categorize RWG at six months of age, Mexico 2023.
Fig. 5: Predictors of RWG identified by the log-binominal GLM model, Mexico 2023.

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Data availability

The analyzed dataset is publicly available via DOI 10.5281/zenodo.17210326, and the code used to generate the models can be accessed at https://github.com/murillozamora/RWG/blob/main/RWG_AI_models.txt.

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Acknowledgements

Funding: This research received no external funding.

Author information

Authors and Affiliations

  1. Facultad de Medicina, Universidad de Colima, Colima, México

    Ana Daniela Ortega-Ramírez, Carmen Alicia Sánchez-Ramírez, Benjamín Trujillo-Hernández & Efrén Murillo Zamora

  2. Unidad de Investigación en Epidemiología Clínica, Instituto Mexicano del Seguro Social, Villa de Álvarez, Colima, México

    Efrén Murillo Zamora

Authors
  1. Ana Daniela Ortega-Ramírez
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  2. Carmen Alicia Sánchez-Ramírez
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  3. Benjamín Trujillo-Hernández
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  4. Efrén Murillo Zamora
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Contributions

A.D.O.-R.: Conceptualization; writing- review and editing; investigation. C.A.S.-R.: Supervision; writing-review and editing. B.T.-H.: Writing-review and editing. E.M.-Z.: Conceptualization; writing-original draft; data curation; methodology.

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Correspondence to Efrén Murillo Zamora.

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The authors declare no competing interests.

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Written informed consent was obtained from parents or legal guardians of all participating infants. Patient consent was not required.

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Ortega-Ramírez, A.D., Sánchez-Ramírez, C.A., Trujillo-Hernández, B. et al. Predicting rapid weight gain in six-month-old infants: an exploratory modeling study. Pediatr Res (2026). https://doi.org/10.1038/s41390-026-04850-7

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