Mahdi Akbarzadeh

Asian residents frequently have a higher percentage of body fat in comparison to individuals with European ancestry, which increases their susceptibility to metabolic diseases. This study aimed to explore the familial resemblance patterns of different body adiposity indicators among 16,983 Tehranian adults. The intraclass correlation coefficients (ICC) of first-degree pairs were estimated to verify the family resemblance of the anthropometric and adiposity-related traits(ARTs) between family members and spouses. The family-based heritability of ARTs was estimated using the classical likelihood-based approach. Results were obtained based upon two scenarios: the first measurement scenario(FM), analysis of the individuals’ ARTs in which they become ≥ 18 years for the first time, and the second scenario, which was based on the average of valid values of the ARTs for each individual(AM). There were 22,879 first-degree relative pairs (17,562 Parents/offspring and 5,137 siblings), 11,015 s-degree relative pairs, and 1,299 third-degree cousin pairs. The familial resemblance between sibling pairs of the same sex was significantly higher than those of other pairs, as for brother: brother pairs, ICC ranged between 19.6% (95%CI:0.118,0.274) for a body shape index(ABSI) to 35.6% (95%CI:0.280,0.432) for body mass index(BMI). Also, for sister: sister pairs, ICC varied from 19.4% (95%CI:0.116,0.272)(ABSI) to 36.6% (95%CI:0.280,0.432)(BMI). For spouses, ICC varied between 5.6% (95%CI:0.025,0.087) for waist-to-hip ratio(WHR) to 10.4% (95%CI:0.065,0.143) for waist circumference. Family-based heritability estimation ranged from 28% (SE = 0.026) for body adiposity index (BAI) to 43% (SE = 0.024) for BMI. The highest pairwise correlation between family members was shown to exist between siblings, and same-sex relative pairs have shown a relatively larger correlation than relative pairs with a different sex.

We aimed to investigate the familial resemblance of dietary intakes, including energy and nutrients, and the family-based heritability of dietary intake in different age-sex dyads of the Tehran cardiometabolic genetic study. This cross-sectional study was conducted on 9,798 participants, aged ≥ 18 years, with complete data in each of the third, fourth, fifth, and sixth surveys of the Tehran Cardiometabolic Genetic study, who were eligible to enter the current study based on inclusion and exclusion criteria. Nutrient intake was determined using a valid and reliable food frequency questionnaire (FFQ). FCOR command of the S.A.G.E. software was used to estimate the intra-class correlation coefficients of all relative pairs to verify the family resemblance of dietary nutrient intakes. Classical likelihood-based is used to assess the family-based heritability of dietary nutrient traits. There were 4338 families with a mean family size of 3.20 ± 2.89, including 1 to 32 members (2567 constituent pedigrees and 1572 singletons) and 3627 sibships. The mean ± SD age of participants was 42.0 ± 15.2 years, and 44.5% were males. The heritability of nutrient intake ranged from 3 to 21%. The resemblance degree of energy intake and most nutrients between spouses or between parents and children is weak to moderate; however, a high resemblance of intake was observed for some food components, especially among spouses, including trans fatty acids (TFAs) (r:0.70), chromium (r:0.44), fiber(r:0.35), pantothenic acid (r:0.31), and vitamin C(r:0.31). Based on our findings, the resemblance of nutrient intake in spouses was greater than in parent-offspring. The similarity in parent–offspring nutrient intake was different, and the correlation in mother-girls nutrient intakes was greater than other parent–child correlations. Also, the lowest resemblance in nutrient intake was observed among siblings. Our findings suggested a weak-to-moderate similarity between the nutrient intakes of parents and offspring. The resemblance degree in nutrient intake varied between different family pairs; the strongest correlation of nutrients was observed between spouses, which includes TFAs, chromium, fiber, pantothenic acid, and vitamin C. The lowest correlation of nutrients was between siblings, such as carbohydrates, thiamine, niacin, and vitamin K. An individual's nutrient intake can somewhat be influenced by genetics, family relationships, and the effects of parents, although the significant influence of environmental factors should not be ignored.

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.

Background: Metabolic syndrome (MetS) is a prevalent multifactorial disorder that can increase the risk of developing diabetes, cardiovascular diseases, and cancer. We aimed to compare different machine learning classification methods in predicting metabolic syndrome status as well as identifying influential genetic or environmental risk factors. Methods: This candidate gene study was conducted on 4756 eligible participants from the Tehran Cardio-metabolic Genetic study (TCGS). We compared predictive models using logistic regression (LR), Random Forest (RF), decision tree (DT), support vector machines (SVM), and discriminant analyses. Demographic and clinical features, as well as variables regarding common GCKR gene polymorphisms, were included in the models. We used a 10-repeated tenfold cross-validation to evaluate model performance. Results: 50.6% of participants had MetS. MetS was significantly associated with age, gender, schooling years, BMI, physical activity, rs780094, and rs780093 (P < 0.05) as indicated by LR. RF showed the best performance overall (AUC-ROC = 0.804, AUC-PR = 0.776, and Accuracy = 0.743) and indicated BMI, physical activity, and age to be the most influential model features. According to the DT, a person with BMI < 24 and physical activity < 8.8 possesses a 4% chance for MetS. In contrast, a person with BMI ≥ 25, physical activity < 2.7, and age ≥ 33, has 77% probability of suffering from MetS. Conclusion: Our findings indicated that, on average, machine learning models outperformed conventional statistical approaches for patient classification. These well-performing models may be used to develop future support systems that use a variety of data sources to identify persons at high risk of getting MetS.