Association Between cardiometabolic risk factor and responsiveness to vitamin D supplementation: A New Approach using Artificial Neural Network analysis

Abstract BackgroundAccumulating data have highlighted the prominence of supplementation as an effective approach for vitamin D deficiency. But individuals vary in their response to vitamin D supplementation. In this study, the effect of cardiometabolic risk factors were evaluate on magnitude of response to vitamin D supplementation by using novel statistical analysis, artificial neural networks(ANNs).Methods608 participants aged between 12 to 19 years old were assed in this prospective interventional study. Nine vitamin D capsules containing 50000IU vitamin D/weekly were given to all participants over the 9 week period. The change in serum 25(OH)D level was calculated as the difference between post-supplementation and basal levels. Suitable ANNs model were selected between different algorithms in the hidden and output layers and different numbers of neurons in the hidden layer. Then, the major determinants in predicting response to vitamin D supplementations were identified (Trial registration: IRCT201509047117N7; 2015-11-25; Retrospectively registered)ResultsSigmoid in both hidden and output layers with 4 hidden neurons had acceptable sensitivity, specificity and accuracy area under the ROC curve in our study. Baseline serum vitamin D (30.4%), waist to hip ratio (10.5%), BMI (10.5%), systolic blood pressure (8%), heart rate (6.4%), and waist circumference (6.1%) were the greatest importance in predicting the response in serum vitamin D levels. ConclusionWe provide the first attempt to relate anthropometric specific recommendations to attain serum vitamin D targets. With the exception of cardiometabolic risk factor, the relative importance of other factors and the mechanisms by which these factors may affect the response requires further analysis in future studies.