A prediction model for childhood obesity risk based on maternal thyroid status and related parameters using machine learning: a mother–newborn–offspring study in a mild-to-moderate iodine deficiency area

Background: Childhood obesity and iodine deficiency are prevalent in developed countries and are linked to adverse health outcomes in adulthood. Mild-to-moderate iodine deficiency and insufficient maternal iodine intake during pregnancy may increase the risk of large-for-gestational-age newborns, which are associated with childhood obesity. Despite this, predicting childhood obesity during pregnancy remains a challenge. We assessed and evaluated machine learning algorithms predicting childhood obesity risk using maternal anthropometrics, thyroid function and iodine intake; and identified key prenatal factors contributing to childhood obesity. Methods: A diagnostic accuracy study was conducted based on 87 parameters collected from a mother-newborn-offspring prospective cohort (N = 191) in a mild-to-moderate iodine deficiency region. Maternal iodine status and thyroid function, including serum free tri-iodo-thyronine (FT3) concentrations, were assessed during the second half of pregnancy. Iodine intake was evaluated using a semi-quantitative food frequency questionnaire. Anthropometric measurements were obtained from mothers during pregnancy, from newborns at birth, and from children at 2 years of age. An outcome of overweight at 2 years was defined as a gender-adjusted weight percentile >85%. The dataset was split into training (80%) and test (20%) sets. Synthetic datasets were created to evaluate the performance of six machine learning models, including artificial neural networks (Nnet) that trained and evaluated the model using 5-fold cross-validation. Results: The best-performing model was Nnet, which achieved the highest accuracy (1500 instances with a balanced predicted outcome). On the unseen test data, accuracy, Kappa, outcome F1-score and weighted F1 were 0.743, 0.347, 0.500 and 0.769 (respectively). Significant predictors included gravidity, maternal-newborn anthropometrics (height and head circumference, respectively), maternal consumption and dietary intake of iodine-rich foods (popsicle, selected fish, and yogurt) and FT3. Conclusions: Machine learning approaches show promise in predicting childhood obesity risk using maternal and dietary factors during pregnancy. If validated, these findings could support interventions to reduce childhood obesity rates. (Figure presented.).