Effects of green tea supplementation on elements, total antioxidants, lipids, and glucose values in the serum of obese patients

Abstract The consumption of green tea has been associated with cardiovascular and metabolic diseases. There have been some studies on the influence of green tea on the mineral status of obese subjects, but they have not yielded conclusive results. The aim of the present study is to examine the effects of green tea extract on the mineral, body mass, lipid profile, glucose, and antioxidant status of obese patients. A randomized, double-blind, placebo-controlled study was conducted. Forty-six obese patients were randomly assigned to receive either 379 mg of green tea extract, or a placebo, daily for 3 months. At baseline, and after 3 months of treatment, the anthropometric parameters, blood pressure, and total antioxidant status were assessed, as were the levels of plasma lipids, glucose, calcium, magnesium, iron, zinc, and copper. We found that 3 months of green tea extract supplementation resulted in decreases in body mass index, waist circumference, and levels of total cholesterol, lowdensity cholesterol, and triglyceride. Increases in total antioxidant level and in zinc concentration in serum were also observed. Glucose and iron levels were lower in the green tea extract group than in the control, although HDLcholesterol and magnesium were higher in the green tea extract group than in the placebo group. At baseline, a positive correlation was found between calcium and body mass index, as was a negative correlation between copper and triglycerides. After 3 months, a positive correlation between iron and body mass index and between magnesium and HDL-cholesterol, as well as a negative correlation between magnesium and glucose, were observed. The present findings demonstrate that green tea influences the body's mineral status. Moreover, the results of this study confirm the beneficial effects of green tea extract supplementation on body mass index, lipid profile, and total antioxidant status in patients with obesity

Genetic imputation of kidney transcriptome, proteome and multi-omics illuminates new blood pressure and hypertension targets

Genetic mechanisms of blood pressure (BP) regulation remain poorly defined. Using kidney-specific epigenomic annotations and 3D genome information we generated and validated gene expression prediction models for the purpose of transcriptome-wide association studies in 700 human kidneys. We identified 889 kidney genes associated with BP of which 399 were prioritised as contributors to BP regulation. Imputation of kidney proteome and microRNAome uncovered 97 renal proteins and 11 miRNAs associated with BP. Integration with plasma proteomics and metabolomics illuminated circulating levels of myo-inositol, 4-guanidinobutanoate and angiotensinogen as downstream effectors of several kidney BP genes (SLC5A11, AGMAT, AGT, respectively). We showed that genetically determined reduction in renal expression may mimic the effects of rare loss-of-function variants on kidney mRNA/protein and lead to an increase in BP (e.g., ENPEP). We demonstrated a strong correlation (r = 0.81) in expression of protein-coding genes between cells harvested from urine and the kidney highlighting a diagnostic potential of urinary cell transcriptomics. We uncovered adenylyl cyclase activators as a repurposing opportunity for hypertension and illustrated examples of BP-elevating effects of anticancer drugs (e.g. tubulin polymerisation inhibitors). Collectively, our studies provide new biological insights into genetic regulation of BP with potential to drive clinical translation in hypertension.</p

Data from: Molecular insights into genome-wide association studies of chronic kidney disease-defining traits

Genome-wide association studies (GWAS) have identified >100 loci of chronic kidney disease-defining traits (CKD-dt). Molecular mechanisms underlying these associations remain elusive. Using 280 kidney transcriptomes and 9958 gene expression profiles from 44 non-renal tissues we uncover gene expression partners (eGenes) for 88.9% of CKD-dt GWAS loci. Through epigenomic chromatin segmentation analysis and variant effect prediction we annotate functional consequences to 74% of these loci. Our colocalisation analysis and Mendelian randomisation in >130,000 subjects demonstrate causal effects of three eGenes (NAT8B, CASP9 and MUC1) on estimated glomerular filtration rate. We identify a common alternative splice variant in MUC1 (a gene responsible for rare Mendelian form of kidney disease) and observe increased renal expression of a specific MUC1 mRNA isoform as a plausible molecular mechanism of the GWAS association signal. These data highlight the variants and genes underpinning the associations uncovered in GWAS of CKD-dt

Prevalence of uncoupling protein one genetic polymorphisms and their relationship with cardiovascular and metabolic health.

Contribution of UCP1 single nucleotide polymorphisms (SNPs) to susceptibility for cardiometabolic pathologies (CMP) and their involvement in specific risk factors for these conditions varies across populations. We tested whether UCP1 SNPs A-3826G, A-1766G, Ala64Thr and A-112C are associated with common CMP and their risk factors across Armenia, Greece, Poland, Russia and United Kingdom. This case-control study included genotyping of these SNPs, from 2,283 Caucasians. Results were extended via systematic review and meta-analysis. In Armenia, GA genotype and A allele of Ala64Thr displayed ~2-fold higher risk for CMP compared to GG genotype and G allele, respectively (p0.05). Concluding, the studied SNPs could be associated with the most common CMP and their risk factors in some populations

Summary statistics of UK Biobank blood pressure genome-wide association studies (GWAS) using 337,422 unrelated white European individuals

Three blood pressure traits were analysed: systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP; the difference between SBP and DBP). Mean SBP and DBP values from automated values were calculated. After calculating blood pressure values, SBP and DBP were adjusted for medication use by adding 15 and 10 mm Hg to their values, respectively, for individuals reported to be taking blood pressure–lowering medication.For the UK Biobank genome-wide association studies (GWAS), we performed linear mixed model (LMM) association testing under an additive genetic model of the three continuous, medication-adjusted blood pressure traits (SBP, DBP, PP) for all measured and imputed genetic variants (Data Field-22828) with minor allele frequency (MAF) &gt;=1% and imputation score&gt;=0.3 in dosage format using the BOLT-LMM (v2.4.1) software. Covariates were age, age2, sex, BMI, genotyping array and 10PCs. Genomic inflation was not applied to the GWAS summary statistics.Sample QC was described below:We included up to 337,422 individuals from UK Biobank for the purpose of this project. We followed UK Biobank sample-based quality control criteria (Nature 2018;562:203-209); excluded were samples/individuals based on the following criteria: (i) outliers in heterozygosity and missingness, (ii) self-reported gender not consistent with genetic data inferred gender (ii) sample call rate (computed using probesets internal to Affymetrix