ArticleBMC medicine2020
Integration of an interpretable machine learning algorithm to identify early life risk factors of childhood obesity among preterm infants: a prospective birth cohort.
Article in BMC medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- Socioeconomic disparities in the postnatal growth of preterm infants: a systematic review.Pediatric research · 2025Pooled it
- Advancing Precision Nutrition Through Multimodal Data and Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Early Life Determinants of Overweight and Obesity in a Sample of Mexico City Preschoolers.Nutrients · 2025Article
- Using interpretable machine learning methods to identify the relative importance of lifestyle factors for overweight and obesity in adults: pooled evidence from CHNS and NHANES.BMC public health · 2024Article
- Enhancing type 2 diabetes mellitus prediction by integrating metabolomics and tree-based boosting approaches.Frontiers in endocrinology · 2024Article
- Prediction and classification of obesity risk based on a hybrid metaheuristic machine learning approach.Frontiers in big data · 2024Article
- The relative contributions of soft tissue mass components as risk or protective factors of non-alcoholic fatty liver disease in children.European journal of clinical nutrition · 2023Article
- The Application of Digital Technologies and Artificial Intelligence in Healthcare: An Overview on Nutrition Assessment.Diseases (Basel, Switzerland) · 2023Article
- Predicting Childhood Obesity Based on Single and Multiple Well-Child Visit Data Using Machine Learning Classifiers.Sensors (Basel, Switzerland) · 2023Article
- Applications of Artificial Intelligence to Obesity Research: Scoping Review of Methodologies.Journal of medical Internet research · 2022Article
- Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases.World journal of gastroenterology · 2022Review
- Risk factors for obesity and overweight in Chinese children: a nationwide survey.Obesity (Silver Spring, Md.) · 2022Article
- Interpretable machine learning-derived nomogram model for early detection of diabetic retinopathy in type 2 diabetes mellitus: a widely targeted metabolomics study.Nutrition & diabetes · 2022Article
- The Prediction of Preterm Birth Using Time-Series Technology-Based Machine Learning: Retrospective Cohort Study.JMIR medical informatics · 2022Article
- Review
- The relationship between socioeconomic status and childhood overweight/obesity is linked through paternal obesity and dietary intake: a cross-sectional study in Chongqing, China.Environmental health and preventive medicine · 2021Article
- Association of preterm birth with poor metabolic outcomes in schoolchildren.Jornal de pediatriaArticle
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Authors and funding
13 authors at 7 institutions in 1 country.
Funding
Abstract
backgroundThe early life risk factors of childhood obesity among preterm infants are unclear and little is known about the influence of the feeding practices. We aimed to identify early life risk factors for childhood overweight/obesity among preterm infants and to determine feeding practices that could modify the identified risk factors.
methodsA total of 338,413 mother-child pairs were enrolled in the Jiaxing Birth Cohort (1999 to 2013), and 2125 eligible singleton preterm born children were included for analyses. We obtained data on health examination, anthropometric measurement, lifestyle, and dietary habits of each participant at their visits to clinics. An interpretable machine learning-based analytic framework was used to identify early life predictors for childhood overweight/obesity, and Poisson regression was used to examine the associations between feeding practices and the identified leading predictor.
resultsOf the eligible 2125 preterm infants (863 [40.6%] girls), 274 (12.9%) developed overweight/obesity at age 4-7 years. We summarized early life variables into 25 features and identified two most important features as predictors for childhood overweight/obesity: trajectory of infant BMI (body mass index) Z-score change during the first year of corrected age and maternal BMI at enrollment. According to the impacts of different BMI Z-score trajectories on the outcome, we classified this feature into the favored and unfavored trajectories. Compared with early introduction of solid foods (≤ 3 months of corrected age), introducing solid foods after 6 months of corrected age was significantly associated with 11% lower risk (risk ratio, 0.89; 95% CI, 0.82 to 0.97) of being in the unfavored trajectory.
conclusionsThe trajectory of BMI Z-score change within the first year of life is the most important predictor for childhood overweight/obesity among preterm infants. Introducing solid foods after 6 months of corrected age is a recommended feeding practice for mitigating the risk of being in the unfavored trajectory.
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