Development of risk models for early detection and prediction of chronic kidney disease in clinical settings.

Chronic kidney disease (CKD) imposes a high burden with high mortality and morbidity rates. Early detection of CKD is imperative in preventing the adverse outcomes attributed to the later stages. Therefore, this study aims to utilize machine learning techniques to predict CKD at early stages.

Association between serum hypertriglyceridemia and hematological indices: data mining approaches.

Background: High triglyceride (TG) affects and is affected of other hematological factors. The determination of serum fasted triglycerides concentrations, as part of a lipid profile, is crucial key point in hematological factors and significantly affect various systemic diseases. This study was carried out to assess the potential relation between the concentration of TG and hematological factors.

Predictive properties of novel anthropometric and biochemical indexes for prediction of cardiovascular risk.

Objective: Our aim was to examine the correlation between CVDs and various anthropometric and biochemical indices in the Iranian population.

Methods: 9704 healthy individuals without CVD aged 35-65 were enrolled in our study. The anthropometric indices including Body Adiposity Index (BAI), Abdominal Volume Index (AVI), Body Roundness Index (BRI), Waist to Hip Ratio (WHR), Weight-adjusted Waist Index (WWI), Conicity Index (C-Index), A Body Shape Index (ABSI), Waist to Height Ratio (WHtR), Body Surface Area (BSA), Body Mass Index (BMI), Lipid Accumulation Product (LAP) and Visceral Adiposity Index (VAI) were calculated.

The correlation between indirect calorimetry data and the metabolic syndrome development in men and women.

Objectives: To investigate the indirect calorimetric findings in subjects with Metabolic Syndrome (MetS) compared to those without MetS and explore the potential parameters for predicting MetS in susceptible individuals.

Methods: Data was collected from a cross-sectional study conducted during the second phase of The Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) study, which involved 1,014 participants. The presence of MetS was determined using the National Cholesterol Education Program Adult Treatment Panel III criteria.

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.

Multivariate linear regression to predict association of non-invasive arterial stiffness with cardiovascular events.

Background: Arterial stiffness is a crucial factor in determining an increase in systolic blood pressure and pulse pressure and can also predict the development of cardiovascular disease (CVD). The purpose of this study was to examine the relationship between arterial stiffness and future CVD.

Methods: Out of the original 9704 participants in the Mashhad stroke and heart atherosclerotic disorder (MASHAD) cohort study, we randomly selected 363 healthy participants, 226 normal subjects (who reported symptoms of CVD but were not confirmed) and 292 individuals who had experienced a major cardiovascular event.

A novel index for diagnosis of type 2 diabetes mellitus: Cholesterol, High density lipoprotein, and Glucose (CHG) index.

Background: Gold standard methods of type 2 diabetes mellitus are expensive and therefore not practical for large scale studies in low-income countries. We have investigated the total cholesterol, high density lipoprotein (HDL), and glucose (CHG) index for diagnosis of type 2 diabetes mellitus index which is derived from fasting state. In this study we aimed to compare the accuracy of with CHG index and triglycerides (TG) and glucose levels (TyG) as surrogates of type 2 diabetes mellitus.

Blood indices of inflammation and their association with hypertension in smokers: analysis using data mining approaches.

Although there have been reports on the association between smoking and increased level of inflammatory markers in hypertensive this has not been assessed prospectively in a large, modern cohort using data mining approaches. We conducted a cross-sectional analysis of the Mashad trial which was a prospective. 2085 smokers aged 35 to 65 years was studied.

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.

HTLV1 infection and long term association with liver function and lipid indices; 10 years' follow-up.

Background: Human T-cell leukemia virus type 1 (HTLV-1) is a well-known retrovirus, particularly prevalent in northeastern Iran, where it is associated with a range of disorders, including liver dysfunction. Previous studies have demonstrated that HTLV-1 infection can alter lipid profiles, yet no research has examined lipid indices and liver function tests in these patients in the long term.

Methods: This data is part of the Mashhad stroke and heart atherosclerotic disorder (MASHAD) study.

Identification of miR-20a as a Diagnostic and Prognostic Biomarker in Colorectal Cancer: MicroRNA Sequencing and Machine Learning Analysis.

Introduction: The differential expression of miRNAs, a key regulator in many cell signaling pathways, has been studied in various malignancies and may have an important role in cancer progression, including colorectal cancer (CRC).

Method: The present study used machine learning and gene interaction study tools to explore the prognostic and diagnostic value of miRNAs in CRC. Integrative analysis of 353 CRC samples and normal tissue data was obtained from the TCGA database and further analyzed by R packages to define the deferentially expressed miRNAs (DEMs).

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.

Oxidative balance and mental health: Exploring the link between prooxidant-antioxidant balance and depression in hypertensive and normotensive individuals, accounting for sex differences.

Background: Existing studies have suggested a link between oxidative stress levels and depression. Additionally, factors such as gender and conditions like hypertension have been shown to influence oxidative stress. This ten-year follow-up cohort study aims to examine the association between prooxidant-antioxidant balance (PAB) and the onset of depression and its symptoms in both hypertensive and normotensive individuals, while considering gender differences.

Investigation of the relationship between atherogenic index, anthropometric characteristics, and 10-year risk of metabolic syndrome: a population-based study.

Background: People with metabolic syndrome (MetS) are at a higher risk of coronary artery disease, diabetes mellitus, stroke, osteoarthritis, and some types of cancers. Finding markers which are available and inexpensive are most useful for the prediction of MetS. The present study aimed to determine the relationship between atherogenic index and anthropometric indicators and the 10-year risk of MetS.

Uric acid is associated with type 2 diabetes: data mining approaches.

Background: Several blood biomarkers have been related to the risk of type 2 diabetes mellitus (T2D); however, their predictive value has seldom been assessed using data mining algorithms.

Methods: This cohort study was conducted on 9704 participants recruited from the Mashhad Stroke and Heart Atherosclerotic disorders (MASHAD) study from 2010 to 2020. Individuals who were not between the ages of 35 and 65 were excluded.

Health Risk Assessment of Exposure to Heavy Metals in Wheat Flour from Iran Markets: Application of Monte Carlo Simulation Approach.

The significance of flour in the Iranian diet underscores the need to ensure its safety from chemical pollutants. This study aimed to evaluate the potential health risks posed by certain heavy metals, such as Fe, Zn, Cu, Al, Co, Hg, Cr, Ni, Pb, and Cd, in wheat flour available in the Iranian market. A total of 248 flour samples were collected from 11 provinces in Iran during the winter of 2021.

Effect of dairy products fortified with vitamin d on restless legs syndrome in women with premenstrual syndrome, abdominal obesity and vitamin d deficiency: a pilot study.

Backgrounds: Restless legs syndrome (RLS) is an unpleasant condition that affects the quality of life of patients. Its prevalence in increased in women with premenstrual syndrome (PMS). Vitamin D plays a key role in female reproduction through its impact on calcium homeostasis and neurotransmitters.

Association Between Serum Levels of Anti-heat Shock Protein 27 Antibody and Liver Cell Injury in Chronic Hepatitis B.

Heat Shock Protein 27 (HSP27), an anti-HBV factor, exists in the intracellular and extracellular spaces. As an inflammatory modulator, serum HSP27 (sHSP27) is associated with elevated pro-inflammatory cytokines and a higher likelihood of hepatocellular carcinoma in chronic hepatitis. SHSP27 results in natural antibody production (anti-HSP27-Ab) that is more stable and easily detectable compared to sHSP27.

The association of dietary indices for hyperinsulinemia and insulin resistance with the risk of metabolic syndrome: a population-based cross-sectional study.

We aimed to investigate the association between an empirical dietary index for hyperinsulinemia (EDIH), empirical dietary index for insulin resistance (EDIR), and MetS and its components in an adult Iranian population. In this cross-sectional study, a total of 6482 participants aged 35-65 years were recruited as part of the MASHAD cohort study. Dietary intakes were assessed using a validated food frequency questionnaire (FFQ).

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.

Hematological Indices and Genetic Variants of Premature Ovarian Insufficiency: Machine Learning Approaches.

Background: Premature Ovarian Insufficiency (POI) is associated with infertility. Little is known about the potential circulating biomarkers that could be used to predict POI. We have investigated the possible association between white and red blood cells, platelet indices, and eight established single nucleotide polymorphisms (SNPs) associated with POI risk.