Predicting BMI in Early Childhood Using First 1000 Days of Life Data through Machine Learning

A recent study published in Scientific Reports Journal reveals a machine learning (ML)-based approach that predicts adulthood obesity in children between two and four years of age, by assessing risk factors and tracking Body Mass Index (BMI) values in the initial 1,000 days of life. The study suggests that early adiposity among pediatric individuals predicts adult obesity, which also predicts cardiometabolic risks and pediatric morbidities. Additionally, the study establishes that obesity is difficult to treat and is likely to persist after establishment; hence detecting individuals at a heightened risk of adiposity during adulthood could improve prevention efforts. Through the study, researchers used ML algorithms to identify children at an increased risk of obesity, which could aid in informing obesity prevention policymaking and strategy development. Furthermore, they devised a dynamic, predictive BMI tracker to be used during childhood to identify the risk of adulthood obesity. The findings highlight that modifiable factors related to higher childhood BMI were detected in the prenatal and initial infancy stages, including maternal risk factors during pregnancy and whether the infant wakes up at night and requires assistance to fall asleep, while factors such as the proportion of individuals residing in food deserts and Hispanic ethnicity protected against elevated BMI.