Parent-child correlation in energy and macronutrient intakes: A meta-analysis and systematic review.

In the current study, we aimed to review the evidence from twin and family-based studies that have assessed the familial similarity in intakes of energy and macronutrients among various parent-child pairs. The online literature databases, including Web of Science, PubMed, and Scopus, were searched up to December 2022 to find potentially eligible studies. We converted Pearson's, Spearman's, or intra-class correlation coefficients to z's using Fisher's z transformation to obtain approximate normality and then calculated a mean and standard error (SE) of transformed correlation weighted by the sample sizes in the studies. We reported pooled r and 95% CI as our final results in five groups, including parent-child, mother-daughter, mother-son, father-daughter, and father-son. Twenty-one eligible studies were included in this meta-analysis, in which the sample size ranged from 33 and 4310. Our analysis showed that family resemblance in the intake of energy and macronutrients in various parent-offspring pairs was weak to moderate which could be different based on family pairs, nutrients, and studies. The highest similarity in dietary intakes was observed among the mother-daughter pair, which was for carbohydrate and protein intake, respectively. The lowest correlations in dietary intakes were found between mother-son or father-son pairs. Our meta-analysis suggested that family similarity for intakes of energy and macronutrients was not strong in parent-child pairs. The highest correlation in dietary intake was mostly found in mother-daughter pairs. The weak similarities in dietary intake among parent-child pairs indicate the noticeable effect of the environment outside the family on individuals' dietary choices.

Causal effect of serum 25 hydroxyvitamin D concentration on cardioembolic stroke: Evidence from two-sample Mendelian randomization.

BACKGROUND AND AIMS: The putative association between serum 25-hydroxyvitamin D concentration [25(OH)D] and the risk of cardioembolic stroke (CES) has been examined in observational studies, which indicate controversial findings. We performed Mendelian randomization (MR) analysis to determine the causal relationship of serum 25(OH)D with the risk of CES. METHODS AND RESULTS: The summary statistics dataset on the genetic variants related to 25(OH)D was used from the published GWAS of European descent participants in the UK Biobank, including 417,580 subjects, yielding 143 independent loci in 112 1-Mb regions. GWAS summary data of CES was obtained from GIGASTROKE Consortium, which included European individuals (10,804 cases, 1,234,808 controls). Our results unveiled a causal relationship between 25(OH)D and CES using IVW [OR = 0.82, 95% CI: 0.67-0.98, p = 0.037]. Horizontal pleiotropy was not seen [MR-Egger intercept = 0.001; p = 0.792], suggesting an absence of horizontal pleiotropy. Cochrane's Q [Q = 78.71, p-value = 0.924], Rucker's Q [Q = 78.64, p-value = 0.913], and I2 = 0.0% (95% CI: 0.0%, 24.6%) statistic suggested no heterogeneity. This result remained consistent using different MR methods and sensitivity analyses, including Maximum likelihood [OR = 0.82, 95%CI: 0.67-0.98, p-value = 0.036], Constrained maximum likelihood [OR = 0.76, 95%CI: 0.64-0.90, p-value = 0.002], Debiased inverse-variance weighted [OR = 0.82, 95%CI: 0.68-0.99, p-value = 0.002], MR-PRESSO [OR = 0.82, 95%CI 0.77-0.87, p-value = 0.022], RAPS [OR = 0.82, 95%CI 0.67-0.98, p-value = 0.038], MR-Lasso [OR = 0.82, 95%CI 0.68-0.99, p-value = 0.037]. CONCLUSION: Our MR analysis provides suggestive evidence that increased 25(OH)D levels may play a protective role in the development of cardioembolic stroke. Determining the role of 25(OH)D in stroke subtypes has important clinical and public health implications.

The Tehran longitudinal family-based cardiometabolic cohort study sheds new light on dyslipidemia transmission patterns.

Dyslipidemia, as a metabolic risk factor, with the strongest and most heritable independent cause of cardiovascular diseases worldwide. We investigated the familial transmission patterns of dyslipidemia through a longitudinal family-based cohort, the Tehran Cardiometabolic Genetic Study (TCGS) in Iran. We enrolled 18,729 individuals (45% were males) aged > 18 years (mean: 38.15 (15.82)) and observed them over five 3-year follow-up periods. We evaluated the serum concentrations of total cholesterol, triglyceride, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol with the first measurement among longitudinal measures and the average measurements (AM) of the five periods. Heritability analysis was conducted using a mixed-effect framework with likelihood-based and Bayesian approaches. The periodic prevalence and heritability of dyslipidemia were estimated to be 65.7 and 42%, respectively. The likelihood of an individual having at least one dyslipidemic parent reveals an OR = 6.94 (CI 5.28-9.30) compared to those who do not have dyslipidemic parents. The most considerable intraclass correlation of family members was for the same-sex siblings, with ICC ~ 25.5%. For serum concentrations, heritability ranged from 33.64 to 60.95%. Taken together, these findings demonstrate that familial transmission of dyslipidemia in the Tehran population is strong, especially within the same-gender siblings. According to previous reports, the heritability of dyslipidemia in this population is considerably higher than the global average.

Using the Bayesian variational spike and slab model in a genome-wide association study for finding associated loci with bipolar disorder.

OBJECTIVE: The genome-wide association studies (GWAS) analysis, the most successful technique for discovering disease-related genetic variation, has some statistical concerns, including multiple testing, the correlation among variants (single-nucleotide polymorphisms) based on linkage disequilibrium and omitting the important variants when fitting the model with just one variant. To eliminate these problems in a small sample-size study, we used a sparse Bayesian learning model for finding bipolar disorder (BD) genetic variants. METHODS: This study used the Wellcome Trust Case Control Consortium data set, including 1998 BD cases and 1500 control samples, and after quality control, 380,628 variants were analysed. In this GWAS, a Bayesian logistic model with hierarchical shrinkage spike and slab priors was used, with all variants considered simultaneously in one model. In order to decrease the computational burden, an alternative inferential method, Bayesian variational inference, has been used. RESULTS: Thirteen variants were selected as associated with BD. The three of them (rs7572953, rs1378850 and rs4148944) were reported in previous GWAS. Eight of which were related to hemogram parameters, such as lymphocyte percentage, plateletcrit and haemoglobin concentration. Among selected related genes, GABPA, ELF3 and JAM2 were enriched in the platelet-derived growth factor pathway. These three genes, along with APP, ARL8A, CDH23 and GPR37L1, could be differential diagnostic variants for BD. CONCLUSIONS: By reducing the statistical restrictions of GWAS analysis, the application of the Bayesian variational spike and slab models can offer insight into the genetic link with BD even with a small sample size. To uncover related variations with other traits, this model needs to be further examined.

Association of baseline and changes in adiponectin, homocysteine, high-sensitivity C-reactive protein, interleukin-6, and interleukin-10 levels and metabolic syndrome incidence: Tehran lipid and glucose study.

Background: Metabolic syndrome (MetS) is accompanied by chronic low-grade inflammation, and inflammatory markers like high-sensitivity C-reactive protein(hs-CRP), interleukin-6(IL-6), and homocysteine(Hcy) contribute to inflammation, obesity, and insulin resistance. Adiponectin(AdipoQ) and interleukin-10(IL-10) are anti-inflammatory markers that play protective roles in MetS. This study aimed to investigate the association between these biochemical marker changes and MetS in a sample of the Tehranian population during six years of follow-up. Methods: In this longitudinal study, 340 adults at baseline and after a six-year follow-up, aged ≥18 years, were selected randomly from the Tehran Lipid and Glucose Study (TLGS). MetS was defined according to the Joint Interim Statement (JIS) criteria. Individuals were categorized into four groups based on their MetS status at baseline and follow-up: 1) non-MetS: participants who did not have MetS at both baseline and follow-up; 2) incident MetS: participants who did not have MetS at baseline but developed MetS during the follow-up ; 3) recovery MetS: participants who had MetS at baseline but no longer had MetS during the follow-up; 4) persistent MetS: participants who had MetS both at baseline and follow-up. Results: The mean follow-up time was 6.1 years. There were 176 subjects in the non-MetS group, 35 in the incident MetS group, 41 in the recovery MetS group, and 88 in the persistent MetS group. Increases in the levels of both hs-CRP 1.40 (95% CI: 1.15, 1.71, p = 0.001) and IL-6 1.09 (95% CI: 1.03, 1.17, p = 0.004) significantly increased the odds of the incident and persistent MetS, respectively. The area under the ROC curve (AUC) was more than 0.69 (p < 0.000) for hs-CRP in predicting MetS incidence and more than 0.86 (p < 0.000) for IL-6 in predicting MetS persistence. Conclusion: After a six-year average follow-up, hs-CRP and IL-6 levels were deemed more reliable predictors of MetS incidence and persistence, respectively.

Familial resemblance and family-based heritability of nutrients intake in Iranian population: Tehran cardiometabolic genetic study.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Regularized Machine Learning Models for Prediction of Metabolic Syndrome Using GCKR, APOA5, and BUD13 Gene Variants: Tehran Cardiometabolic Genetic Study.

OBJECTIVE: Metabolic syndrome (MetS) is a complex multifactorial disorder that considerably burdens healthcare systems. We aim to classify MetS using regularized machine learning models in the presence of the risk variants of GCKR, BUD13 and APOA5, and environmental risk factors. MATERIALS AND METHODS: A cohort study was conducted on 2,346 cases and 2,203 controls from eligible Tehran Cardiometabolic Genetic Study (TCGS) participants whose data were collected from 1999 to 2017. We used different regularization approaches [least absolute shrinkage and selection operator (LASSO), ridge regression (RR), elasticnet (ENET), adaptive LASSO (aLASSO), and adaptive ENET (aENET)] and a classical logistic regression (LR) model to classify MetS and select influential variables that predict MetS. Demographics, clinical features, and common polymorphisms in the GCKR, BUD13 and APOA5 genes of eligible participants were assessed to classify TCGS participant status in MetS development. The models' performance was evaluated by 10-repeated 10-fold crossvalidation. Various assessment measures of sensitivity, specificity, classification accuracy, and area under the receiver operating characteristic curve (AUC-ROC) and AUC-precision-recall (AUC-PR) curves were used to compare the models. RESULTS: During the follow-up period, 50.38% of participants developed MetS. The groups were not similar in terms of baseline characteristics and risk variants. MetS was significantly associated with age, gender, schooling years, body mass index (BMI), and alternate alleles in all the risk variants, as indicated by LR. A comparison of accuracy, AUCROC, and AUC-PR metrics indicated that the regularization models outperformed LR. Regularized machine learning models provided comparable classification performances, whereas the aLASSO model was more parsimonious and selected fewer predictors. CONCLUSION: Regularized machine learning models provided more accurate and parsimonious MetS classifying models. These high-performing diagnostic models can lay the foundation for clinical decision support tools that use genetic and demographical variables to locate individuals at high risk for MetS.

A wide range of missing imputation approaches in longitudinal data: a simulation study and real data 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.

Evidence of familial resemblance and family-based heritability of food intakes derived from a longitudinal cohort study.

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.

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.

Longitudinal Associations Between TPO Gene Variants and Thyroid Peroxidase Antibody Seroconversion in a Population-Based Study: Tehran Thyroid Study.

Introduction: Autoimmune thyroid diseases (AITD) are usually accompanied by anti-thyroid antibodies which can serve as early predictive markers. This study was designed to investigate the relationship between thyroid peroxidase (TPO) gene variants and the presence of TPOAb and to evaluate the effect of environmental factors associated with seroconversion from TPOAb-negative to TPOAb-positive. Methods: Participants from phases 1 and 2 of the Tehran Thyroid Study in (n = 5327, ≥20 years) were evaluated in terms of TPOAb positivity, and its relationship with 53 single nucleotide polymorphisms (SNPs) from within the TPO gene (cross-sectional approach). TPOAb-negative participants (n = 4815) were followed up for seroconversion for 5.5 years. The relationship between the TPO gene variants and the TPOAb seroconversion was evaluated (longitudinal approach). Results: There were 521 TPOAb-positive participants in the cross-sectional phase and 266 new TPOAb-positive cases observed during the follow-up period. After quality control (Hardy-Weinberg equilibrium (p < 1 × 10-5) and minor allele frequency < 0.05), 49 SNPs were qualified for association analyses. From this set fourteen SNPs were identified that were associated with TPOAb positivity. rs6605278, located in the 3'UTR TPO gene, was the most highly significantly associated of the variant and remained associated after adjustment for age, gender, body mass index (BMI), smoking, number of parity, and oral contraceptive consumption in both cross-sectional and longitudinal analyses (p < 0.05). Conclusions: TPOAb-positivity can be partially explained by variants in the TPO gene. New TPOAb-associated SNPs were observed in Iranians as an ethnically diverse population.

Effect of C-reactive protein on the risk of Heart failure: a mendelian randomization study.

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; I2 = 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.

Elevated Plasma Levels of MT4-MMP and MT6-MMP; A New Observation in Patients with Thyroid Nodules.

BACKGROUND: Based on the critical role of MT4-MMP and MT6-MMP in carcinogenesis, we focused on MT4-MMP and MT6-MMP circulating levels in patients with thyroid nodules. METHODS: Plasma samples were collected from three groups, including papillary thyroid cancer (PTC; n=30), multinodular goiter (MNG; n=30), and healthy subjects (n=22). Enzyme-linked immunosorbent assay (ELISA) was used to obtain the concentration of MT4-MMP and MT6-MMP in the three groups. RESULTS: Analysis of data demonstrated increased levels of MT4-MMP (PTC: 4.90±1.35, MNG: 4.89±1.37, and healthy: 3.13±1.42) and MT6-MMP (PTC: 8.29±2.50, MNG: 7.34±2.09, and healthy:5.01±2.13) in thyroid nodules by comparison with healthy subjects (P<0.05). There were no significant differences in the levels of the two MT-MMPs between PTC and MNG (P>0.05). Increased plasma levels of MT4-MMP (odds ratio=2.48; 95% CI: 1.46-4.19; P=0.001) or MT6-MMP (odds ratio=1.81; 95% CI: 1.29-2.53; P=0.001) were associated with increased risk of PTC tumorigenesis. Interestingly, a strong positive association was observed between MT4-MMP and MT6-MMP in the three groups (PTC: r=0.766**, P=0.000; MNG: r=0.856**, P=0.000; healthy r=0.947**, P=0.000). Areas under the ROC curve for MT4-MMP and MT6-MMP were 0.82 and 0.96, respectively. At the cutoff value>4.7 (ng/mL), MT4-MMP and MT6-MMP showed a sensitivity of 63.3% and 90.0%, respectively, with 100% specificity. CONCLUSION: Our work has led us to imply that the higher levels of MT4-MMP and MT6-MMP are closely linked with both PTC and MNG tumorigenesis. They may probably promote the development of thyroid lesions; however, more research is needed to further clarify the current findings.

Evaluation of serum nitric oxide synthase levels in patients with coronary slow flow based on corrected TIMI frame count.

Introduction: The coronary slow flow phenomenon (CSFP) finding in angiography is characterized by the delayed filling of the terminal vessels without significant epicardial coronary disease. The endothelium performs a vital role in cardiovascular homeostasis by releasing vasoactive substances. Endothelial cells produce nitric oxide (NO) as one of these essential compounds. Three isoforms of nitric oxide synthase (NOS) are endothelial nitric oxide synthase (eNOS), neuronal nitric oxide synthase (nNOS), and induced nitric oxide synthase (iNOS). We aimed to determine the role of NOS in the development of CSFP as the first human study. Material and methods: A total of 129 patients who met the inclusion criteria were enrolled in the study. The patients were classified into five groups based on the results of coronary angiography: Group 1 without coronary artery disease (CAD) and without CSF, group 2 without CAD and with CSF, group 3 with CAD (< 50%) and without CSF, group 4 with CAD (50-90%) and without CSF, and group 5 with CAD and CSF. The serum level of NOS was determined in the participants. Coronary flow was quantified in patients with CSFP using the corrected TIMI frame count (CTFC) method, and the correlation between the levels of this biomarker and CTFC was investigated. Results: In this study, the NOS serum levels were not significantly correlated with the mean CTFC. Since the total amount of NOS was measured as a result of 3 isoforms of this enzyme, the lack of correlation could be related to increased iNOS level and decreased eNOS concentration. Conclusions: These results should be confirmed by more human studies.

Physical Activity and Exercise Promote Peroxisome Proliferator-Activated Receptor Gamma Expression in Adipose Tissues of Obese Adults.

Background: Peroxisome proliferator-activated receptor gamma (PPARγ) has recently been studied for its potential influence on the functional response of the human body to exercise. We aimed to investigate the association of habitual physical activity (PA) with PPARγ mRNA level in the visceral and subcutaneous adipose tissues (VAT and SAT) in non-obese and obese non-diabetic adults. Methods: VAT and SAT were obtained from 95 individuals, including 40 non-obese (BMI<30kg/m2) and 55 obese (BMI≥30kg/m2) who underwent elective abdominal surgery (Tehran, Iran, 2012-2015). The assessment of habitual PA was performed by a valid and reliable International PA Questionnaire-long form, and the metabolic equivalent of task (MET) was evaluated. Real-time quantitative reverse transcriptase-PCR evaluated the PPARγ expression in VAT and SAT. Results: PPARγ expression in both VAT (1.18 vs. 0.37 fold change, P<0.001) and SAT (2.07 vs. 0.29 fold change, P=0.004) among obese subjects was higher than the non-obese group. After controlling for age, sex, and total energy in-take, a positive association was found between total METs and PPARγ expression in both VAT and SAT among obese participants (ß=0.22, P=0.007 and ß=0.12, P<0.001, respectively). Among obese participants, there was a direct association between leisure time-related METs with VAT PPARγ expression (ß=0.05, P=0.026). Moreover, in this group, an association was observed between occupation-related METs with PPARγ in both fat tissues (ß=0.11, P=0.002 and ß=0.17, P=0.013, respectively), and household work-related METs with SAT PPARγ (ß=0.21, P=0.011). Conclusion: High PA as an indispensable part of a healthy lifestyle may exert its beneficial effect by regulating PPARγ expression.

The AGT epistasis pattern proposed a novel role for ZBED9 in regulating blood pressure: Tehran Cardiometabolic genetic study (TCGS).

INTRODUCTION: High blood pressure is widely regarded as the most important risk factor for cardiovascular diseases. Epistasis analysis may provide additional insight into the genetic basis of hypertension. METHODS: A nested case-control design was used on 4214 unrelated Tehran Cardiometabolic Genetic Study (TCGS) adults to evaluate 65 SNPs of previously associated genes, including ZBED9, AGT, and TNXB. The integrated effect of each gene was determined using the Sequence-based Kernel Association Test (SKAT). We used model-based multifactor dimension reduction (Mb-MDR) and entropy-based gene-gene interaction (IGENT) methods to determine interaction and epistasis patterns. RESULTS: The integrated effect of each gene has a statistically significant association with blood pressure traits (P-value < 0.05). The single-locus analysis identified two missense variants in ZBED9 (rs450630) and AGT (rs4762) associated with hypertension. In the ZBED9 gene, significant local interactions were discovered. The G allele in rs450630 showed an antagonistic effect on hypertension, but interestingly, IGENT analysis revealed significant epistasis effects for different combinations of ZBED9, AGT, and TNXB loci. CONCLUSION: We discovered a novel interaction effect between a significant variant in an essential gene for hypertension (AGT) and a missense variant in ZBED9, which has shifted our focus to ZBED9's role in blood pressure regulation.

Association of miR-34a and miR-143 levels with PPARγ gene expression in adipose tissues of non-diabetic adults.

BACKGROUND: Peroxisome proliferator-activated receptor gamma (PPARγ) is a promising therapeutic molecule. Epigenetic mechanisms, including non-coding RNAs, regulate the expression level of the PPARγ gene. OBJECTIVE: We aimed to examine the PPARγ expression in non-diabetic individuals in four body mass index (BMI) categories and its association with miR-34a and miR-143 expression. METHODS: Visceral and subcutaneous adipose tissues (VAT and SAT) samples were collected from patients undergoing bariatric or elective open abdominal surgeries. The subjects (mean age: 42±14.8 years) included 18 normal-weight, 19 overweight, 18 obese, and 19 morbidly obese individuals. The RNAs levels were determined by quantitative real-time PCR. RESULTS: The PPARγ expression was significantly upregulated in both adipose depots of the morbidly obese subjects compared to the normal group. SAT PPARγ level was significantly increased in the obese group compared to the normal-weight group (P<0.01); this increase was also significant in the SAT of morbidly obese subjects compared to the overweight cases (P=0.02). Differences in the regulation of PPARγ expression in both SAT and VAT were significant between the four groups (P<0.05). While miR-143 was overexpressed in the SAT of obese and morbidly obese individuals compared to the normal-weight group, the pairwise comparison showed no significant difference in the miR-34a expression of SAT between the four BMI groups (P>0.01). After controlling for the confounding factors, the expression of VAT PPARγ was directly associated with the miR-34a level in the normal-weight group (ß=0.311, P=0.010). A negative association was observed between the VAT PPARγ expression and miR-34a expression in obese cases (ß = - 0.594, P=0.039). CONCLUSION: The results also confirmed the regulatory function of microRNAs in the PPARγ expression and adipogenesis.

Evaluating machine learning-powered classification algorithms which utilize variants in the GCKR gene to predict metabolic syndrome: Tehran Cardio-metabolic Genetics Study.

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.

Resemblance of nutrient intakes in three generations of parent-offspring pairs: Tehran lipid and Glucose Study.

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.