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 <UnorderedList Mark="Bullet"> <ItemContent> 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. </ItemContent> <ItemContent> In this study, the GLM yielded the highest point estimates across key metrics, while the machine‑learning models nonetheless demonstrated promising potential. </ItemContent> <ItemContent> 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. </ItemContent> </UnorderedList>

Predicting rapid weight gain in six-month-old infants: an exploratory modeling study

期刊论文
发表日期：

2026年3月7日

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

摘要

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.