Factors determining the serum 25‐hydroxyvitamin D response to vitamin D supplementation: Data mining approach

• Published in: BioFactors (Oxford) 2021-09, Vol.47 (5), p.828-836
• Main Authors: Amiri, Zahra, Nosrati, Mina, Sharifan, Payam, Saffar Soflaei, Sara, Darroudi, Susan, Ghazizadeh, Hamideh, Mohammadi Bajgiran, Maryam, Moafian, Fahimeh, Tayefi, Maryam, Hasanzade, Elahe, Rafiee, Mahdi, Ferns, Gordon A., Esmaily, Habibollah, Amini, Mahnaz, Ghayour‐Mobarhan, Majid
• Format: Article
• Language: English
• Subjects: Adult; Body Mass Index; Data Mining; Dietary Supplements; Female; Humans; Insulin Resistance; Male; Middle Aged; Obesity - blood; Obesity - complications; random forest; Treatment Outcome; vitamin D; Vitamin D - analogs & derivatives; Vitamin D - blood; Vitamin D - therapeutic use; Vitamin D Deficiency - blood; Vitamin D Deficiency - complications; Vitamin D Deficiency - drug therapy; Vitamins - blood; Vitamins - therapeutic use
• ISSN: 0951-6433; 1872-8081
• DOI: 10.1002/biof.1770

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.