posted on 2023-06-10, 00:38authored byZahra Amiri, Mina Nosrati, Payam Sharifan, Sara Saffar Soflaei, Susan Darroudi, Hamideh Ghazizadeh, Maryam Mohammadi Bajgiran, Fahimeh Moafian, Maryam Tayefi, Elahe Hasanzade, Mahdi Rafiee, Gordon FernsGordon Ferns, Habibollah Esmaily, Mahnaz Amini, Majid Ghayour-Mobarhan
Vitamin D supplementation has been shown to prevent vitamin D deficiency, but various factors can affect the response to supplementation. Data mining is a statistical method for pulling out information from large databases. We aimed to evaluate the factors influencing serum 25-hydroxyvitamin D levels in response to supplementation of vitamin D using a random forest (RF) model. Data were extracted from the survey of ultraviolet intake by nutritional approach study. Vitamin D levels were measured at baseline and at the end of study to evaluate the responsiveness. We examined the relationship between 76 potential influencing factors on vitamin D response using RF. We found several features that were highly correlated to the serum vitamin D response to supplementation by RF including anthropometric factors (body mass index [BMI], free fat mass [FFM], fat percentage, waist-to-hip ratio [WHR]), liver function tests (serum gamma-glutamyl transferase [GGT], total bilirubin, total protein), hematological parameters (mean corpuscular volume [MCV], mean corpuscular hemoglobin concentration [MCHC], hematocrit), and measurement of insulin sensitivity (homeostatic model assessment of insulin resistance). BMI, total bilirubin, FFM, and GGT were found to have a positive relationship and homeostatic model assessment for insulin resistance, MCV, MCHC, fat percentage, total protein, and WHR were found to have a negative correlation to vitamin D concentration in response to supplementation. The accuracy of RF in predicting the response was 93% compared to logistic regression, for which the accuracy was 40%, in the evaluation of the correlation of the components of the data set to serum vitamin D.