Platelet distribution widths and white blood cell are associated with cardiovascular diseases: data mining approaches.

OBJECTIVE: To investigate the association between cardiovascular diseases (CVDs) and haematologic factors in a cohort of Iranian adults. METHOD: For a total population of 9,704 aged 35 to 65, a prospective study was designed. Haematologic factors and demographic characteristics (such as gender, age, and smoking status) were completed for all participants. The association between haematologic factors and CVDs was assessed through logistic regression (LR) analysis, decision tree (DT), and bootstrap forest (BF). RESULTS: Almost all of the included factors were significantly associated with CVD (p<.001). Among the included factors, were: age, white blood cell (WBC), and platelet distribution width (PDW) had the strongest correlation with the development of CVD. For unit OR interpretation, WBC has been represented as the most remarkable risk factor for CVD (OR: 1.22 (CI 95% (1.18, 1.27))). Also, age is associated with an increase in the odds of CVD + occurrence (OR: 1.12 (CI 95% (1.11, 1.13))). Moreover, males are times more likely to develop CVD than females (OR: 1.39 (CI 95% (1.22, 1.58))). In DT model, age is the best classifier factor in CVD development, followed by WBC and PDW. Furthermore, based on the BF algorithm, the most crucial factors correlated with CVD are age, WBC, PDW, sex, and smoking status. CONCLUSION: The obtained result from LR, DT, and BF models confirmed that age, WBC, and PDW are the most crucial factors for the development of CVD.

Development of Data Mining Algorithms for Identifying the Best Anthropometric Predictors for Cardiovascular Disease: MASHAD Cohort Study.

INTRODUCTION: Many studies have been published to assess the best anthropometric measurements associated with cardiovascular diseases (CVDs), but controversies still exist. AIM: Investigating the association between CVDs and anthropometric measurements among Iranian adults. METHODS: For a total population of 9354 aged 35 to 65, a prospective study was designed. Anthropometric measurements including ABSI (A Body Shape Index), Body Adiposity Index (BAI), Body Mass Index (BMI), Waist to Height Ratio (WHtR), Body Round Index (BRI), HC (Hip Circumference), Demispan, Mid-arm circumference (MAC), Waist-to-hip (WH) and Waist Circumference (WC) were completed. The association between these parameters and CVDs were assessed through logistic regression (LR) and decision tree (DT) models. RESULTS: During the 6-year follow-up, 4596 individuals (49%) developed CVDs. According to the LR, age, BAI, BMI, Demispan, and BRI, in male and age, WC, BMI, and BAI in female had a significant association with CVDs (p-value < 0.03). Age and BRI for male and age and BMI for female represent the most appropriate estimates for CVDs (OR: 1.07, (95% CI: 1.06, 1.08), 1.36 (1.22, 1.51), 1.14 (1.13, 1.15), and 1.05 (1.02, 1.07), respectively). In the DT for male, those with BRI ≥ 3.87, age ≥ 46 years, and BMI ≥ 35.97 had the highest risk to develop CVDs (90%). Also, in the DT for female, those with age ≥ 54 years and WC ≥ 84 had the highest risk to develop CVDs (71%). CONCLUSION: BRI and age in male and age and BMI in female had the greatest association with CVDs. Also, BRI and BMI was the strongest indices for this prediction.

Prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysis.

Type 2 Diabetes Mellitus (T2DM) is a significant public health problem globally. The diagnosis and management of diabetes are critical to reduce the diabetes complications including cardiovascular disease and cancer. This study was designed to assess the potential association between T2DM and routinely measured hematological parameters. This study was a subsample of 9000 adults aged 35-65 years recruited as part of Mashhad stroke and heart atherosclerotic disorder (MASHAD) cohort study. Machine learning techniques including logistic regression (LR), decision tree (DT) and bootstrap forest (BF) algorithms were applied to analyze data. All data analyses were performed using SPSS version 22 and SAS JMP Pro version 13 at a significant level of 0.05. Based on the performance indices, the BF model gave high accuracy, precision, specificity, and AUC. Previous studies suggested the positive relationship of triglyceride-glucose (TyG) index with T2DM, so we considered the association of TyG index with hematological factors. We found this association was aligned with their results regarding T2DM, except MCHC. The most effective factors in the BF model were age and WBC (white blood cell). The BF model represented a better performance to predict T2DM. Our model provides valuable information to predict T2DM like age and WBC.

Data mining approaches for type 2 diabetes mellitus prediction using anthropometric measurements.

BACKGROUND: The aim of this study was to evaluate the anthropometric measurements most associated with type 2 diabetes mellitus (T2DM) using machine learning approaches. METHODS: A prospective study was designed for a total population of 9354 (43% men and 57% women) aged 35-65. Anthropometric measurements include weight, height, demispan, Hip Circumference (HC), Mid-arm Circumference (MAC), Waist Circumference (WC), Body Roundness Index (BRI), Body Adiposity Index (BAI), A Body Shape Index (ABSI), Body Mass Index (BMI), Waist-to-height Ratio (WHtR), and Waist-to-hip Ratio (WHR) were completed for all participants. The association was assessed using logistic regression (LR) and decision tree (DT) analysis. Receiver operating characteristic (ROC) curve was performed to evaluate the DT's accuracy, sensitivity, and specificity using R software. RESULTS: Traditionally, 1461 women and 875 men with T2DM (T2DM group). According to the LR, in males, WC and BIA (p-value < 0.001) and in females, demispan and WC (p-value < 0.001) had the highest correlation with T2DM development risk. The DT indicated that WC has the most crucial effect on T2DM development risk, followed by HC, and BAI. CONCLUSIONS: Our results showed that in both men and women, WC was the most important anthropometric factor to predict T2DM.

The visceral adiposity index and lipid accumulation product as predictors of cardiovascular events in normal weight subjects.

INTRODUCTION: Visceral adipose tissue (VAT) has an important role in the incidence of cardiovascular disease (CVD) than obesity by itself. The visceral adiposity index (VAI) and lipid accumulation product (LAP) are surrogate indices for measuring VAT. The aimed of this study was to investigate the association of these markers with cardiovascular events among populations with different BMI category in Mashhad, northeast of Iran. METHOD: The present study comprised a prospective cohort of 9685 men and women (35-65 years) who were recruited from MASHAD study. BMI category was defined as normal weight (BMI <25), over weight (25 ≤ BMI<30) and obese (BMI≥30). Demographic, laboratory evaluations, anthropometric and metabolic parameters were performed. Logistic and Cox regression analyses were used to determine the association and risk of cardiovascular events with VAT and LAP. RESULTS: The mean VAI and LAP in CVD patients were significantly higher than in healthy ones in all 3 groups. In terms of CVD event prediction, VAI and LAP had significant association with the incidence of CVD in the second (RR (95% CI): 2.132 (1.047-4.342) and 2.701 (1.397-5.222), respectively) and third tertiles (RR (95% CI): 2.541 (1.163-5.556) and 2.720 (1.159-6.386), respectively) in the normal group, but this association was only found in the third tertiles (RR (95% CI): 2.448 (1.205-4.971) and 2.376 (1.086-5.199), respectively) in the overweight group. The result couldn't find this association for the obese group. CONCLUSION: In this study, we found that there was a significant association between LAP and VAI and cardiovascular events in normal weight and over-weight groups; however, no significant relationship was found in the obese group.

An Exploratory Study of Units of Reporting Opium Usein Iran: Implications for Epidemiologic Studies.

BACKGROUND: Measuring the amount of opium use is a challenge in epidemiologic studies. Self-report of amount of opium use at each consumption, widely used in the literature, usually fails to provide a good estimate. The purpose of this study is to systematically study the perceived weight units of reported opium use in Iran, and compare them to the standardized units of weight measurement. METHODS: An exploratory descriptive study was conducted in six major cities of Iran. Study participants were interviewed and asked to use a Play-Doh-like material to demonstrate the amount of opium they use. To obtain an estimate of the weight of the material used, we multiplied the volume by the density of the opium product. We experimentally determined the density of the commonly used opium products. We used medians and inter-quartile ranges (IQRs) to report the typical amount of each unit. RESULTS: A total of 108 individuals participated in this study. The most frequently reported unit was "gram"; the median perceived weight for one gram (g) of opium was 0.24 (IQR: 0.16) g. The second most commonly used unit was nokhod with a median of 0.16 (IQR: 0.16) g, followed by mesghaal and hab/habeh, which were 1.28 (IQR: 0.81) and 0.16 (IQR: 0.16) g, respectively. The median perceived weight of mesghaal and gram in the studied cities was less than the expected standardized values. CONCLUSION: In conclusion, the reported amount of opium use is highly inaccurate and unreliable, and is mainly subject to underestimation.

Rare P376L variant in the SR-BI gene associates with HDL dysfunction and risk of cardiovascular disease.

BACKGROUND: Scavenger receptor class B type 1 (SR-BI) encoded by SCARB1 gene serves as a multifunctional HDL receptor, facilitating the uptake of cholesteryl esters from HDL to the liver. Recent studies have identified the association between the P376L missense mutation of the SCARB1 gene with increased serum HDL-Cholesterol level. However, the contribution of this variant to the development of cardiovascular disease (CVD) remains unclear. OBJECTIVE: We have investigated the association between the P376L polymorphism with the properties of HDL and CVD outcomes in a population sample recruited as part of the Mashhad-Stroke and Heart-Atherosclerotic-Disorders (MASHAD) cohort. METHODS: Six hundred and fifteen individuals who had a median follow-up period of 7 years were recruited as part of the MASHAD cohort. Anthropometric, biochemical parameters and HDL lipid peroxidation (HDLox) were assessed. Genotyping was performed using TaqMan-real-time-PCR based method. The association of P376L-rs74830766 with cardiovascular-risk-factors and CVD events were evaluated. RESULTS: Carriers of the P376L variant were significantly more likely than non-carriers to develop CVD using multivariate analyses adjusted for traditional CVD risk factors defined as: age, sex, BMI, presence of diabetes, or hypertension, positive smoking habit, and total cholesterol (OR: 3.75, 95%CI: 1.76-7.98, p = 0.001). In an adjusted model, there was a two fold increase in cardiovascular endpoints among individuals who were heterozygous for the P376L variant (hazard ratio, 2.08; 95% CI, 1.12-to 3.84, p = 0.02). Although there was no association between the presence of the P376L variant and HDL-C level, serum HDLox, measured as dysfunctional HDL, was 13% higher among carriers of the P376L variant than non-carriers. CONCLUSION: We have found that carriers of the P376L variant possessed higher HDLox and were at increased risk of CVD in a representative population-based cohort, as compared to non-carriers.