Association between Cardiometabolic risk factor and responsiveness to vitamin D supplementation a new approach using artificial neural network analysis
Creators: name: Hanachi, Parichehr
name: Allahyari, Elahe
name: Mirmoosavi, Seyed Jamal
name: A.Ferns, Gordon
name: Bahrami, Afsane
name: Ghayour-Mobarhan, Majid
Publisher: name: figshare
Year: 2021
Subjects: subject: 110199 Medical Biochemistry and Metabolomics not elsewhere classified
subject: FOS: Clinical medicine
subject: 100302 Bioprocessing, Bioproduction and Bioproducts
subject: FOS: Industrial biotechnology
subject: 60199 Biochemistry and Cell Biology not elsewhere classified
subject: FOS: Biological sciences
subject: Cell Biology
subject: Biochemistry
subject: 100499 Medical Biotechnology not elsewhere classified
subject: FOS: Medical biotechnology
subject: 100703 Nanobiotechnology
subject: FOS: Nanotechnology
subject: 60114 Systems Biology
subject: 60101 Analytical Biochemistry
subject: 111201 Cancer Cell Biology
subject: 30499 Medicinal and Biomolecular Chemistry not elsewhere classified
subject: FOS: Chemical sciences
subject: Molecular Biology
subject: Developmental Biology
subject: Biological Techniques
subject: 100202 Biological Control
subject: FOS: Environmental biotechnology
subject: 69999 Biological Sciences not elsewhere classified
subject: Biotechnology
subject: Cardiology

description: Abstract Background: There are increasing data highlighting the effectiveness of vitamin D supplementation in the treatment of vitamin D deficiency. But individuals vary in their responsiveness to vitamin D supplementation. In this study, the association between several cardiometabolic risk factors and the magnitude of response to vitamin D supplementation (change in vitamin D level) was investigated using a novel artificial neural networks (ANNs) approach. Methods: Six hundred eight participants aged between 12 to 19 years old were recruited to this prospective interventional study. Nine vitamin D capsules containing 50,000 IU 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 postsupplementation 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. The major determinants for predicting the response to vitamin D supplementation were identified. Results: The sigmoid in both the hidden and output layers with 4 hidden neurons had acceptable sensitivity, specificity and accuracy, assessed as the area under the ROC curve, was determined 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 most important factors in predicting the response to serum vitamin D levels. Conclusion: We provide the first attempt to relate anthropometric specific recommendations to attain serum vitamin D targets. With the exception of cardiometabolic risk factors, the relative importance of other factors and the mechanisms by which these factors may affect the response requires further analysis in future studies (Trial registration: IRCT201509047117N7; 2015-11-25; Retrospectively registered).