Predicting high sensitivity C-reactive protein levels and their associations in a large population using decision tree and linear regression.

High-sensitivity C-reactive protein (hs-CRP) is a biomarker of inflammation predicting the incidence of different health pathologies. In this study, we aimed to evaluate the association between hematological and demographic factors with hs-CRP levels using decision tree (DT) and linear regression (LR) modeling. This study was conducted on a population of 9704 males and females aged 35 to 65 years recruited from the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) cohort study.

Comparison between statistical and machine learning methods to detect the hematological indices with the greatest influence on elevated serum levels of low-density lipoprotein cholesterol.

Introduction: Elevated levels of low-density lipoprotein-cholesterol (LDL-C) is a significant risk factor for the development of cardiovascular diseases (CVD)s. Furthermore, studies have revealed an association between indices of the complete blood count (CBC) and dyslipidemia. We aimed to investigate the relationship between CBC parameters and serum levels of LDL.

Association of high-sensitivity C-reactive protein and hematologic-inflammatory indices with risk of cardiovascular diseases: a population-based study with partial least squares structural equation modeling approach.

Partial least squares structural equation modeling is a simple approach that may be used to determine the factors associated with diseases. In the current study, we aimed to explore the most associated high-sensitivity C-reactive protein (hs-CRP) as well as hematologic-inflammatory indices for the risk of cardiovascular disease (CVD). A total of 7362 healthy (non-CVD) participants aged 35-65 years old from baseline investigation were evaluated in the Phase 2 follow-up.

Evaluating the Association of Anthropometric Indices With Total Cholesterol in a Large Population Using Data Mining Algorithms.

Background: Unbalanced levels of serum total cholesterol (TC) and its subgroups are called dyslipidemia. Several anthropometric indices have been developed to provide a more accurate assessment of body shape and the health risks associated with obesity. In this study, we used the random forest model (RF), decision tree (DT), and logistic regression (LR) to predict total cholesterol based on new anthropometric indices in a sex-stratified analysis.

Association of Anti-Müllerian Hormone on Oocyte Maturation, Fertilization, and Pregnancy Rates in Patients under Assisted Reproductive Technology Cycles: A Cross-Sectional Study.

Background: Ovarian reserve is one of the most important factors that influences the success of assisted reproductive technology (ART). Recently, the role of anti-müllerian hormone (AMH) in ART has been investigated as a marker for the prediction of ovarian response. We aim to examine this relationship within a large Iranian population.

Serum Zinc and Copper Concentrations and Dyslipidemia as Risk Factors of Cardiovascular Disease in Adults: Data Mining Techniques.

This study aimed to examine the relationship between serum cholesterol levels and the ratio of zinc (Zn) and copper (Cu) in the blood serum and the incidence of cardiovascular disease (CVD). In Phase I of the study, 9704 individuals between the age of 35 and 65 years were recruited. Phase II of the cohort study comprised 7561 participants who completed the 10-year follow-up.

Prediction of the 10-year incidence of type 2 diabetes mellitus based on advanced anthropometric indices using machine learning methods in the Iranian population.

Background: Type 2 diabetes mellitus (T2DM) is a growing chronic disease that can lead to disability and early death. This study aimed to establish a predictive model for the 10-year incidence of T2DM based on novel anthropometric indices.

Methods: This was a prospective cohort study comparing people with (n = 1256) and without (n = 5193) diabetes mellitus in phase II of the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) study.

Dietary mineral intakes predict Coronavirus-disease 2019 (COVID-19) incidence and hospitalization in older adults.

Background: The aim of this study was to determine the association between dietary mineral intake and Coronavirus-disease 2019 (COVID-19) infection and its associated hospitalization.

Methods: This cohort study utilized the MASHAD study population, which comprised individuals aged 35-65. Upon recruitment in 2007, dietary intake was documented using a validated 65-item food frequency questionnaire (FFQ).

Predicting the 10-year incidence of dyslipidemia based on novel anthropometric indices, using data mining.

Background: The aim was to establish a 10-year dyslipidemia incidence model, investigating novel anthropometric indices using exploratory regression and data mining.

Methods: This data mining study was conducted on people who were diagnosed with dyslipidemia in phase 2 (n = 1097) of the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) study, who were compared with healthy people in this phase (n = 679). The association of dyslipidemia with several novel anthropometric indices including Conicity Index (C-Index), Body Roundness Index (BRI), Visceral Adiposity Index (VAI), Lipid Accumulation Product (LAP), Abdominal Volume Index (AVI), Weight-Adjusted-Waist Index (WWI), A Body Shape Index (ABSI), Body Mass Index (BMI), Body Adiposity Index (BAI) and Body Surface Area (BSA) was evaluated.

The association between macronutrient intakes and coronavirus disease 2019 (COVID-19) in an Iranian population: applying a dynamical system model.

Aims: The possible role of lifestyle including diet on immunity led us to investigate the association between dietary macronutrient intake and COVID-19 in an Iranian population.

Methods: Dietary intakes were recorded in the first phase of the MASHAD cohort study (started in 2007), using a 24-h dietary recall. To determine the COVID-19 incidence, data from all PCR-positive patients in Mashhad were recorded between February 2020 and June 2022.

Evaluation of rs1748195 ANGPTL3 gene polymorphism in patients with angiographic coronary artery disease compared to healthy individuals.

Subject: The Angiopoietin-like 3 (ANGPTL3) gene has been reported to be associated with cardiovascular risk. This study is designed to compare the genetic variant (rs1748195) of the ANGPTL3 gene and the presence of a coronary artery occlusion of >50% in Iranian nation.

Method: In this study, 184 patients underwent angiography and 317 healthy individuals were evaluated for polymorphism of rs1748195 the ANGPTL3 gene using Tetra-ARMs PCR.

Dynamic model with factors of polycystic ovarian syndrome in infertile women.

Background: Previous studies present various methods for prediction disease based on statistics or neural networks.These models use statistics and results from past procedures to provide prediction through probability analysis.

Objective: In this article, the authors present a dynamic model aiming at predicting the treatment result of infertile women with the factor of polycystic ovary syndrome.