Predicting Obesity via Deep Learning: The Role of Chrononutrition

Abstract Background Obesity is a significant public health challenge, prompting the need for assessing effective tools for its monitoring and prevention. Chrononutrition investigates the role of timing, regularity and frequency of food intake on body metabolism and health. Here we aim to check if data on the distribution of calorie intake in the day may improve BMI prediction as compared to using only data on total day intake. Methods We apply deep learning on chrononutritional data on calorie intake over 6 daily timeslots from a nationally representative cross-sectional survey sample of the Italian population (INRAN-SCAI 2005/2006) comprising 2313 Italian adults 18-64 ys old with 3 day diet diaries. In particular, we implemented three deep neural network models, varying network configurations and feature selection. The 1st includes 18 (6 for each day diary) chrononutritional variables and the 3 day total intakes, beside age and sex; the 2nd includes age, sex and the 18 chrononutritional variables only; the 3rd uses age, sex and the 3 day totals only. We applied Early Stopping to limit overfitting. Optimization was conducted using the Adam algorithm, minimizing the mean squared error (RMSE), while mean absolute error (MAE) was the primary performance indicator. Results The results highlight that Model 3 outperformed the other models with MAE=2.514 and RMSE=3.476, indicating that for the purpose of predicting obesity, information on the time-of-day distribution of calorie intake is not increasing predictive ability of BMI on top of age, sex and total calorie intake in the day. Conclusions Additional use of day-time intake information worsened prediction of BMI via deep learning using this cross sectional Italian survey. However the error made is still sizeable and warrants the inclusion of other variables; our results don’t rule out the role of chrononutrition on metabolic health which may act through other biological mechanisms related to specific nutrients. Key messages • Chrononutrition data don’t improve BMI prediction over total calorie intake data in a cross-setional representative sample of the Italian population. • Deep learning approach to BMI prediction shows the importance of targeted selection of features in public health nutrition.