Cohort profile update: Tehran cardiometabolic genetic study.

The Tehran cardiometabolic genetic study (TCGS) is a large population-based cohort study that conducts periodic follow-ups. TCGS has created a comprehensive database comprising 20,367 participants born between 1911 and 2015 selected from four main ongoing studies in a family-based longitudinal framework. The study's primary goal is to identify the potential targets for prevention and intervention for non-communicable diseases that may develop in mid-life and late life. TCGS cohort focuses on cardiovascular, endocrine, metabolic abnormalities, cancers, and some inherited diseases. Since 2017, the TCGS cohort has augmented by encoding all health-related complications, including hospitalization outcomes and self-reports according to ICD11 coding, and verifying consanguineous marriage using genetic markers. This research provides an update on the rationale and design of the study, summarizes its findings, and outlines the objectives for precision medicine.

Genetic Identification for Non-Communicable Disease: Findings from 20 Years of the Tehran Lipid and Glucose Study.

CONTEXT: Tehran Lipid and Glucose Study (TLGS), a longitudinal family based cohort study, is the oldest and largest longitudinal family based study in Iran, aimed at investigating effects of environmental, social and biological factors on the health of Tehranians over time. Considering the importance of genetic studies in this aspect, here we present a summary of the important genetic findings, and the potentiality of their contributions to future related projects. EVIDENCE ACQUISITION: For all related studies during the past 20 years the search sources were all prominent search engines such as PubMed, Scopus, and Google Scholar with the most proper Medical Subject Headings (MeSH). RESULTS: This review summarizes associations of 6 binary phenotypes and 17 quantitative traits with genetic markers in 26 genes. Of the 47 genetic markers, studied most were related to cardio metabolic risk factors. Results of heritability and linkage analysis were also collected and the highest heritability was found to be related to HDL-C (0.5). CONCLUSION: Considering the opportunity provided by large-scale cohort studies to investigate molecular effects of genetic variants on causality and different omics' data, genetic studies conducted on TLGS population have had a remarkable success in identifying genetic variants that facilitating a unique genetic database on Iranian populations. The results of genome wide association studies in this population are currently facilitating investigations to define the Iranian genetic differences with other population.

Predicting hepatitis B monthly incidence rates using weighted Markov chains and time series methods.

BACKGROUND: Hepatitis B (HB) is a major global mortality. Accurately predicting the trend of the disease can provide an appropriate view to make health policy disease prevention. This paper aimed to apply three different to predict monthly incidence rates of HB. METHODS: This historical cohort study was conducted on the HB incidence data of Hamadan Province, the west of Iran, from 2004 to 2012. Weighted Markov Chain (WMC) method based on Markov chain theory and two time series models including Holt Exponential Smoothing (HES) and SARIMA were applied on the data. The results of different applied methods were compared to correct percentages of predicted incidence rates. RESULTS: The monthly incidence rates were clustered into two clusters as state of Markov chain. The correct predicted percentage of the first and second clusters for WMC, HES and SARIMA methods was (100, 0), (84, 67) and (79, 47) respectively. CONCLUSIONS: The overall incidence rate of HBV is estimated to decrease over time. The comparison of results of the three models indicated that in respect to existing seasonality trend and non-stationarity, the HES had the most accurate prediction of the incidence rates.