Determinants of the magnitude of response to vitamin D supplementation in adolescent girls identified using a decision tree algorithm.

Vitamin D (VitD) supplementation is an inexpensive and effective approach for improving VitD insufficiency/deficiency. However, the response to supplementation, with respect to the increase in serum 25(OH)D level varies between individuals. In this study, we have assessed the factors associated with the response to VitD supplementation using a decision-tree algorithm. Serum VitD levels, pre- and post-VitD supplementation was used as the determinant of responsiveness. The model was validated by constructing a receiver operating characteristic curve. Serum VitD at baseline levels was at the apex of the tree in our model, followed by serum low-density lipoprotein cholesterol and triglyceride, age, waist-hip ratio, and high-density lipoprotein cholesterol. Our model suggests that these determinants of responsiveness to VitD supplementation had sensitivity, specificity, and accuracy, 59.4, 75.8 and 69.3%, respectively. The decision tree model appears to be a relatively accurate, specific, and sensitive approach for identifying the factors associated with response to VitD supplementation.

An assessment of the risk factors for vitamin D deficiency using a decision tree model.

BACKGROUND AND OBJECTIVES: Vitamin D (25-hydroxyvitamin D or 25OHD) has a key role in the pathogenesis of several chronic disorders. Vitamin D deficiency is a common global public health problem. We aimed to evaluate the risk factors associated with vitamin D deficiency using a decision tree algorithm. METHODS: A total of 988 adolescent girls, aged 12-18 years old, were recruited to the study. Demographic characteristics, serum biochemical factors, all blood count parameters and trace elements such as Zinc, Copper, Calcium and SOD were measured. Serum levels of vitamin D below 20 ng/ml were considered to be deficiency. 70% of these girls (618 cases) were randomly allocated to a training dataset for the constructing of the decision-tree. The remaining 30% (285 cases) were used as the testing dataset to evaluate the performance of decision-tree. In this model, 14 input variables were included: age, academic attainment of their father, waist circumference, waist to hip ratio, zinc, copper, calcium, SOD, FBG, HDL-C, RBC, MCV, MCHC, HCT. The validation of the model was assessed by constructing a receiver operating characteristic (ROC) curve. RESULTS: The results showed that serum Zn concentration was the most important associated risk factor for vitamin D deficiency. The sensitivity, specificity, accuracy and the area under the ROC curve (AUC) values were 79.3%, 64%, 77.8% and 0.72 respectively using the testing dataset. CONCLUSIONS: The results suggest that the serum levels of Zn is an important associated risk factor for identifying subjects with vitamin D deficiency among Iranian adolescent girls.

Blood Lead Levels in Asymptomatic Opium Addict Patients; a Case Control Study.

INTRODUCTION: One of the newest non-occupational sources of lead contamination is drug addiction, which has recently been addressed as a major source of lead poisoning in some countries. The present study aimed to investigate the blood lead level (BLL) of asymptomatic opium addicts. METHODS: This case-control study was conducted during a one-year period to compare BLL of three groups consisting of opium addicts, patients under methadone maintenance therapy (MMT), and healthy individuals. RESULTS: 99 participants with the mean age of 55.43±12.83 years were studied in three groups of 33 cases (53.5% male). The mean lead level in opium addicts, MMT and control groups were 80.30 ± 6.03 µg/L, 67.94 ± 4.42 µg/L, and 57.30±4.77 µg/L, respectively (p=0.008). There was no significant difference in BLL between MMT and healthy individuals (p=0.433) and also between opium addicts and MMT individuals (p=0.271).Oral opium abusers had significantly higher lead levels (p = 0.036). There was a significant correlation between BLL and duration of drug abuse in opium addict cases (r=0.398, p=0.022). The odds ratio of having BLL ≥ 100 in oral opium users was 2.1 (95% CI: 0.92 - 4.61; p = 0.43). CONCLUSION: Based on the result of present study, when compared to healthy individuals, opium addicts, especially those who took substance orally had significantly higher levels of blood lead, and their odds of having BLL ≥ 100 was two times. Therefore, screening for BLL in opium addicts, particularly those with non-specific complaints, could be useful.

The application of a decision tree to establish the parameters associated with hypertension.

INTRODUCTION: Hypertension is an important risk factor for cardiovascular disease (CVD). The goal of this study was to establish the factors associated with hypertension by using a decision-tree algorithm as a supervised classification method of data mining. METHODS: Data from a cross-sectional study were used in this study. A total of 9078 subjects who met the inclusion criteria were recruited. 70% of these subjects (6358 cases) were randomly allocated to the training dataset for the constructing of the decision-tree. The remaining 30% (2720 cases) were used as the testing dataset to evaluate the performance of decision-tree. Two models were evaluated in this study. In model I, age, gender, body mass index, marital status, level of education, occupation status, depression and anxiety status, physical activity level, smoking status, LDL, TG, TC, FBG, uric acid and hs-CRP were considered as input variables and in model II, age, gender, WBC, RBC, HGB, HCT MCV, MCH, PLT, RDW and PDW were considered as input variables. The validation of the model was assessed by constructing a receiver operating characteristic (ROC) curve. RESULTS: The prevalence rates of hypertension were 32% in our population. For the decision-tree model I, the accuracy, sensitivity, specificity and area under the ROC curve (AUC) value for identifying the related risk factors of hypertension were 73%, 63%, 77% and 0.72, respectively. The corresponding values for model II were 70%, 61%, 74% and 0.68, respectively. CONCLUSION: We have developed a decision tree model to identify the risk factors associated with hypertension that maybe used to develop programs for hypertension management.