Afsane Bahrami1, Majid Ghayour-Mobarhan2, Elahe Allahyari3, Parichehr Hanachi4, Seyed Jamal Mirmoosavi5, Gordon A Ferns6. 1. Cellular and Molecular Research Center, Birjand University of Medical Sciences, Birjand, Iran. Bahramia@bums.ac.ir. 2. Metabolic Syndrome Research Center, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran. Ghayourm@mums.ac.ir. 3. Department of Epidemiology and Biostatistics, School of Health, Social Determinants of Health Research Center, Birjand University of Medical Sciences, Birjand, Iran. 4. Department of Biology, Biochemistry Unit, Alzahra University, Tehran, Iran. 5. Community Medicine, Community Medicine Department, Medical School, Sabzevar University of Medical Sciences, Sabzevar, Iran. 6. Division of Medical Education, Brighton & Sussex Medical School, Falmer, Brighton, Sussex, BN1 9PH, UK.
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 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. 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).
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 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. 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).
Entities:
Keywords:
Adolescent girls; Artificial neural network; Waist circumference; Waist to hip ratio
Authors: Marjolijn D Akkermans; Judith M van der Horst-Graat; Simone R B M Eussen; Johannes B van Goudoever; Frank Brus Journal: J Pediatr Gastroenterol Nutr Date: 2016-04 Impact factor: 2.839
Authors: L S Greene-Finestone; C Berger; M de Groh; D A Hanley; N Hidiroglou; K Sarafin; S Poliquin; J Krieger; J B Richards; D Goltzman Journal: Osteoporos Int Date: 2010-08-21 Impact factor: 4.507
Authors: María Fernanda Carrillo-Vega; Carmen García-Peña; Luis Miguel Gutiérrez-Robledo; Mario Ulises Pérez-Zepeda Journal: Arch Osteoporos Date: 2016-12-27 Impact factor: 2.617
Authors: Ibrahim M Kaddam; Adnan M Al-Shaikh; Bahaa A Abaalkhail; Khalid S Asseri; Yousef M Al-Saleh; Ali A Al-Qarni; Ahmed M Al-Shuaibi; Waleed G Tamimi; Abdelmoneim M Mukhtar Journal: Saudi Med J Date: 2017-04 Impact factor: 1.484
Authors: David Feldman; Aruna V Krishnan; Srilatha Swami; Edward Giovannucci; Brian J Feldman Journal: Nat Rev Cancer Date: 2014-04-04 Impact factor: 60.716
Authors: Rebecca J Moon; Nicholas C Harvey; Cyrus Cooper; Stefania D'Angelo; Sarah R Crozier; Hazel M Inskip; Inez Schoenmakers; Ann Prentice; Nigel K Arden; Nicholas J Bishop; Andrew Carr; Elaine M Dennison; Richard Eastell; Robert Fraser; Saurabh V Gandhi; Keith M Godfrey; Stephen Kennedy; M Zulf Mughal; Aris T Papageorghiou; David M Reid; Sian M Robinson; M Kassim Javaid Journal: J Clin Endocrinol Metab Date: 2016-10-27 Impact factor: 5.958