Explore the vast range of titles available.

Due to the central role in insulin secretion, the potassium inwardly-rectifying channel subfamily J member 11 ( KCNJ11 ) gene is one of the essential genes for type 2 diabetes (T2D) predisposition. However, the relevance of this gene to T2D development is not consistent among diverse populations. In the current study, we aim to capture the possible association of common KCNJ11 variants across Iranian adults, followed by a meta-analysis. We found that the tested variants of KCNJ11 have not contributed to T2D incidence in Iranian adults, consistent with similar insulin secretion levels among individuals with different genotypes. The integration of our results with 72 eligible published case–control studies (41,372 cases and 47,570 controls) as a meta-analysis demonstrated rs5219 and rs5215 are significantly associated with the increased T2D susceptibility under different genetic models. Nevertheless, the stratified analysis according to ethnicity showed rs5219 is involved in the T2D risk among disparate populations, including American, East Asian, European, and Greater Middle Eastern, but not South Asian. Additionally, the meta-regression analysis demonstrated that the sample size of both case and control groups was significantly associated with the magnitude of pooled genetic effect size. The present study can expand our knowledge about the  KCNJ11  common variant's contributions to T2D incidence, which is valuable for designing SNP-based panels for potential clinical applications in precision medicine. It also highlights the importance of similar sample sizes for avoiding high heterogeneity and conducting a more precise meta-analysis.

Background Missing data is a pervasive problem in longitudinal data analysis. Several single-imputation (SI) and multiple-imputation (MI) approaches have been proposed to address this issue. In this study, for the first time, the function of the longitudinal regression tree algorithm as a non-parametric method after imputing missing data using SI and MI was investigated using simulated and real data. Method Using different simulation scenarios derived from a real data set, we compared the performance of cross, trajectory mean, interpolation, copy-mean, and MI methods (27 approaches) to impute missing longitudinal data using parametric and non-parametric longitudinal models and the performance of the methods was assessed in real data. The real data included 3,645 participants older than 18 years within six waves obtained from the longitudinal Tehran cardiometabolic genetic study (TCGS). The data modeling was conducted using systolic and diastolic blood pressure (SBP/DBP) as the outcome variables and included predictor variables such as age, gender, and BMI. The efficiency of imputation approaches was compared using mean squared error (MSE), root-mean-squared error (RMSE), median absolute deviation (MAD), deviance, and Akaike information criteria (AIC). Results The longitudinal regression tree algorithm outperformed based on the criteria such as MSE, RMSE, and MAD than the linear mixed-effects model (LMM) for analyzing the TCGS and simulated data using the missing at random (MAR) mechanism. Overall, based on fitting the non-parametric model, the performance of the 27 imputation approaches was nearly similar. However, the SI traj-mean method improved performance compared with other imputation approaches. Conclusion Both SI and MI approaches performed better using the longitudinal regression tree algorithm compared with the parametric longitudinal models. Based on the results from both the real and simulated data, we recommend that researchers use the traj-mean method for imputing missing values of longitudinal data. Choosing the imputation method with the best performance is widely dependent on the models of interest and the data structure.

The degree of maintaining nutrient intake patterns, conformed in the family, for offspring into adulthood is unknown. The aim of this study was to investigate the correlation between nutrient intakes in three younger-middle-older generations of Tehranian adults by sex. Of individuals who participated in 2012–15 phase of the Tehran Lipid and Glucose Study, 1286 families (4685 subjects), who had at least two members of the family with complete data in two or three generations were entered in this cross-sectional study. The energy and nutrient intakes of parents and their young or adult offspring or grandparents-grandson/granddaughter dyads were compared. The differences were estimated using pairwise t-test and partial correlation. Data of parents with their offspring were paired based on living arrangement. There were 857 fathers (mean age: 55.4±11.1) and 1394 mothers (mean age: 50.1±11.4). The mean age of grandfathers and grandmothers were 69.4±7.9 and 63.7±8.5 respectively. The significant correlation in fathers-sons and father-daughter (living with their parents) pairs were observed for 9 and 7 nutrients, respectively. Correlations for most nutrients were significant for mother-daughter or sons (living with their parents) dyads. The mean percentage of energy from total fat and trans-fatty acids of sons or daughters (living with their parents) were higher than their parents. For most nutrients, there were no significant adjusted correlations between parents-adult offspring (living independent of their parents) dyads. Also few nutrient intakes of grandparents-grandson or granddaughter dyads were correlated. The nutrient intakes of adult offspring were not associated with their parents; this correlation for younger and older generations disappeared. There were weak to moderate correlation between nutrient intakes of parent-offspring dyads that lived with their parents. The resemblance was higher for mother-offspring than father-offspring. Overall, total fat and trans-fatty acid intakes of young offspring were higher than their parents.

The genetic variations among individuals are one of the notable factors determining disease severity and drug response. Nowadays, COVID-19 pandemic has been adversely affecting many aspects of human life. We used the Tehran Cardio-Metabolic Genetic Study (TCGS) data that is an ongoing genetic study including the whole-genome sequencing of 1200 individuals and chip genotyping of more than 15,000 participants. Here, the effect of ACE2 variations by focusing on the receptor-binding site of SARS-CoV-2 and ACE2 cleavage by TMPRSS2 protease were investigated through simulations study. After analyzing TCGS data, 570 genetic variations on the ACE2 gene, including single nucleotide polymorphisms (SNP) and insertion/deletion (INDEL) were detected. Interestingly, two observed missense variants, K26R and S331F, which only the first one was previously reported, can reduce the receptor affinity for the viral Spike protein. Moreover, our bioinformatics simulation of 3D structures and docking of proteins explains important details of ACE2-Spike and ACE2-TMPRSS2 interactions, especially the critical role of Arg652 of ACE2 for protease function of TMPRSS2 was uncovered. As our results show that the genetic variation of ACE2 can at least influence the affinity of this receptor to its partners, we need to consider the genetic variations on ACE2 as well as other genes in the pathways that contribute to the pathogenesis of COVID-19 for designing efficient drugs and vaccines.

Background Polygenic risk scores (PRS) utilize genetic variants to evaluate susceptibility to diseases. While the performance of PRS has been extensively studied in European populations, its accuracy and validation in the Middle East, particularly in Iran, remain underexplored. We aimed to assess whether PRSs improve coronary artery disease (CAD) prediction beyond clinical risk factors using sex-stratified analyses. Methods This longitudinal study leveraged data from the Tehran cardiometabolic genetic study (TCGS) cohort ( N  = 16,226), a long-term population-based study in Iran, including participants free of baseline CAD. We evaluated three established CAD-PRSs (PRS241, PRS175, PRS161) derived from GWAS of European cohorts, alongside a meta-PRS and multi-ancestry data. Multivariable Cox proportional hazards regression models were employed to assess the association between each PRS and CAD incidence, adjusting for established clinical covariates. Sensitivity analyses were conducted by sequentially adjusting for clinical risk factors to evaluate the independent effect of the PRS. Incremental predictive value beyond clinical factors was evaluated using net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA).The association of clinical risk factors associated with PRS was also investigated. Results Over a median follow-up of 15 years in 4,594 individuals, 991 CAD events occured. PRS241 demonstrated consistent sex-stratified associations, with a 70% increased risk in females (HR = 1.70, 95% CI: 1.28–2.25) and 54% in males (HR = 1.54, 95% CI: 1.22–1.93) comparing highest versus lowest quartiles. In sensitivity analyses, adjustment for HDL, LDL, hypertension, and diabetes resulted in only modest attenuation of these associations, with PRS241 maintaining significant independent risk. PRS161 was significantly associated with CAD only in males (38% increased risk), while PRS175 showed no significant associations in either sex. Integrating PRS241 into clinical risk models yielded small improvements in CAD prediction, indicated by enhanced discrimination (female: ΔC = 0.004; male: ΔC = 0.004) and reclassification (males: relative IDI = 0.0255). DCA further confirmed small net benefit for PRS241. Also, PRS241 and PRS161 were linked to adverse HDL levels in both sexes (PRS161: β = − 0.0068, FDR P-value = 0.002 in females ; β = − 0.006, FDR P-value = 0.010 in males. PRS241: β = − 0.005, FDR P-value = 0.007 in females ; β = − 0.005, FDR P-value = 0.012 in males). Notably, PRS241 also showed sex-stratified associations with hypertension (OR = 1.11, FDR P-value = 0.04) and diabetes (OR = 1.66, FDR P-value = 0.008) exclusively in females. Conclusion These findings demonstrate that incorporation of PRS241 into established clinical risk factors provides small but complementary value for CAD risk prediction. This may facilitate improved risk stratification and support more targeted primary prevention strategies in both sexes. Graphical Abstract

Background Traditional observational studies have shown positive associations between c-reactive protein (CRP) and heart failure (HF) risk. However, this association has not been fully elucidated. Therefore, Mendelian randomization was used to examine CRP’s possible etiological roles with HF. Methods We implemented a two-sample Mendelian randomization framework to examine the causality of the association between CRP and HF based on summary statistics by large-scale genome-wide association studies (GWAS) datasets of European ancestry through inverse-variance weighted, weighted median, MREgger regression, and MR-PRESSO methods. The summary statistics dataset on the association of genetic variants with CRP was used from the published GWAS of European descent in UK Biobank participants (N = 427,367) and the CHARGE consortium (N = 575,531). The GWAS dataset used to identify genetic variants underlying HF from the HERMES consortium includes 977,323 participants (47,309 cases and 930,014 controls). The odds ratio (OR) with 95% confidence intervals (CIs) was employed to examine this association. Results The results of our IVW indicated that CRP was strongly associated with HF (OR = 4.18, 95% CI = 3.40–5.13, p < 0.001). The Cochran heterogeneity test showed significant heterogeneity among SNPs of CRP (Q = 317.55, p < 0.001; I 2  = 37.6%), and no considerable pleiotropy was detected for the association of CRP with HF [intercept = 0.003; p = 0.234]. This finding remained consistent using different Mendelian randomization methods and sensitivity analyses. Conclusion Our MR study did identify convincing evidence to support CRP associated with HF risk. Human genetic data suggest that CRP is a causative factor in HF. Hence, CRP assessment may offer additional prognostic information as an adjuvant to overall risk assessment in HF patients. These findings prompt significant questions about the function of inflammation in the progression of HF. More research into the role of inflammation in HF is needed to guide trials of anti-inflammation management.

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.

Maturity-onset diabetes of the young (MODY) is an uncommon monogenic type of diabetes mellitus. Detecting genetic variants for MODY is a necessity for precise diagnosis and treatment. The majority of MODY genetic predisposition has been documented in European populations and a lack of information is present in Iranians which leads to misdiagnosis as a consequence of defects in unknown variants. In this study, using genetic variant information of 20,002 participants from the family-based TCGS (Tehran Cardiometabolic Genetic Study) cohort, we evaluated the genetic spectrum of MODY in Iran. We concentrated on previously discovered MODY-causing genes. Genetic variants were evaluated for their pathogenicity. We discovered 6 variants that were previously reported in the ClinVar as pathogenic/likely pathogenic (P/LP) for MODY in 45 participants from 24 families ( INS in 21 cases, GCK in 13, HNF1B in 8, HNF4A, HNF1A, and CEL in 1 case). One potential MODY variant with Uncertain Risk Allele in ClinVar classification was also identified, which showed complete disease penetrance (100%) in four subjects from one family . This is the first family-based study to define the genetic spectrum and estimate the prevalence of MODY in Iran. The discovered variants need to be investigated by additional studies.

Background Among candidate genes related to type 2 diabetes (T2DM), one of the strongest genes is Transcription factor 7 like 2 (TCF7L2), regarding the Genome-Wide Association Studies. We aimed to conduct a systematic review of the literature on the modification effect of TCF7L2 on the relation between glycemic parameters and lifestyle factors. Methods A systematic literature search was done for relevant publications using electronic databases, including PubMed, EMBASE, Scopus, and Web of Science, from January 1, 2000, to November 2, 2021. Results Thirty-eight studies (16 observational studies, six meal test trials, and 16 randomized controlled trials (RCTs)) were included. Most observational studies had been conducted on participants with non-diabetes showing that TCF7L2 modified the association between diet (fatty acids and fiber) and insulin resistance. In addition, findings from meal test trials showed that, compared to non-risk-allele carriers, consumption of meals with different percentages of total dietary fat in healthy risk-allele carriers increased glucose concentrations and impaired insulin sensitivity. However, ten RCTs, with intervention periods of less than ten weeks and more than one year, showed that TCF7L2 did not modify glycemic parameters in response to a dietary intervention involving different macronutrients. However, two weight loss dietary RCTs with more than 1-year duration showed that serum glucose and insulin levels decreased and insulin resistance improved in non-risk allele subjects with overweight/obesity. Regarding artichoke extract supplementation (ALE), two RCTs observed that ALE supplementation significantly decreased insulin concentration and improved insulin resistance in the TT genotype of the rs7903146 variant of TCF7L2. In addition, four studies suggested that physical activity levels and smoking status modified the association between TCF7L2 and glycemic parameters. However, three studies observed no effect of TCF7L2 on glycemic parameters in participants with different levels of physical activity and smoking status. Conclusion The modification effects of TCF7L2 on the relation between the lifestyle factors (diet, physical activity, and smoking status) and glycemic parameters were contradictory. PROSPERO registration number CRD42020196327

We sought to investigate the familial aggregation and family-based heritability of dietary intakes among adults in a population-based longitudinal study of the Tehran Lipid and Glucose Study (TLSG). Total of 4359 males and 5439 females entered our study. We categorized foods into main groups based on the literature on main food groups and their subgroups among the Iranian dietary habits and food culture as follows: grains, fruits, vegetables, dairy, meats, legume, nuts, beverages, snacks, and fats. The intraclass correlation coefficients (ICC) are estimated to verify familial resemblance of dietary habits for all relative pairs and spouses. Family-based heritability is obtained using a mixed effect framework with likelihood-based approach. For almost all food groups, the correlation between parents and offsprings tended to be larger than those of siblings. Family-based heritability of food groups varies from the lowest 6.36% for snacks to the highest 25.67% for fruits, and 25.66% for legume. Our findings indicated weak-to-moderate similarities between parents' and offspring's food intakes; however, the similarity in parent–child food intakes was different, and the correlation in mother-daughter food intakes was stronger than other parent–child correlations, and almost all of dietary components showed strong family-based heritability.