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.

中文翻译：

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决定血清 25-羟基维生素 D 对补充维生素 D 反应的因素：数据挖掘方法

维生素 D 补充剂已被证明可以预防维生素 D 缺乏症，但各种因素会影响对补充剂的反应。数据挖掘是一种从大型数据库中提取信息的统计方法。我们旨在使用随机森林 (RF) 模型评估影响血清 25-羟基维生素 D 水平的因素，以响应补充维生素 D。数据是通过营养方法研究从紫外线摄入量调查中提取的。在基线和研究结束时测量维生素 D 水平以评估反应性。我们使用 RF 检查了 76 个潜在影响因素对维生素 D 反应的关系。我们发现了几个与 RF 补充后血清维生素 D 反应高度相关的特征，包括人体测量因素（体重指数 [BMI]、游离脂肪量 [FFM]、脂肪百分比、腰臀比 [WHR]）、肝功能检查（血清 γ-谷氨酰转移酶 [GGT]、总胆红素、总蛋白）、血液学参数（平均红细胞体积 [MCV]、平均红细胞血红蛋白浓度 [MCHC] ]，血细胞比容）和胰岛素敏感性的测量（胰岛素抵抗的稳态模型评估）。BMI、总胆红素、FFM 和 GGT 与胰岛素抵抗、MCV、MCHC、脂肪百分比、总蛋白和 WHR 呈正相关，稳态模型评估与维生素 D 浓度呈负相关。补充。在评估数据集成分与血清维生素 D 的相关性时，RF 预测反应的准确度为 93%，而逻辑回归的准确度为 40%。