👤 Weaam Ebrahim

Systemic hypertension arises from the interplay of numerous common and rare genetic variants spanning vascular, renal, endocrine, metabolic, and immune pathways. Modern genomic approaches triangulate evidence from candidate gene studies, biobank-scale genome-wide association studies (GWAS), and whole-exome or whole-genome sequencing, enabling stronger mechanistic inference. In this narrative synthesis, we focused on recent human studies emphasizing candidate gene analyses, GWAS, and sequencing efforts in hypertension, extracting data on study design, populations, key variants, and implicated biological pathways. Across methodologies, genetic evidence consistently supported central roles for endothelial nitric-oxide biology (NOS3) and oxidative or tonic regulation of arteriolar resistance (PRKG1, CYBA, and CYP4A11), alongside contributions from lipid-handling genes (ApoB and PCSK9) and mitochondrial or smooth-muscle regulators (HSG and MFN2). GWAS conducted across diverse ancestries repeatedly mapped blood pressure variation to vascular calcium dynamics (ATP2B1 and CACN* loci), renal tubular transport mechanisms (UMOD and SLC4A7), renin-angiotensin-aldosterone system-related steroidogenesis (CYP17A1 and CYP11B2), and immune remodeling pathways (SH2B3), with several loci demonstrating sex- or ancestry-specific modulation and enrichment in resistant-hypertension cohorts, particularly within calcium-handling and steroidogenic pathways. Sequencing studies further identified rare, functional, and ancestry-specific variants, including large blood pressure-lowering alleles and signals enriched in Middle Eastern populations, that refine biological mechanisms and support population-tailored risk stratification. Overall, convergent evidence across genetic approaches highlights four translationally actionable systems, such as vascular calcium handling, renal salt and bicarbonate transport, adrenal steroidogenesis, and immune or inflammatory tone, supporting the development of ancestry-aware polygenic risk tools, genetic sub-phenotyping (including resistant hypertension), and mechanism-aligned therapeutics as key steps toward precision hypertension care. Show less

Dysregulation of the Wnt signaling pathway contributes to the development of many cancer types. Natural compounds produced with biotechnological systems have been the focus of research for being a new drug candidate both with unlimited resources and cost-effective production. In this study, it was aimed to reveal the effects of isopropylchaetominine on cytotoxic, cytostatic, apoptotic and Wnt signaling pathways in brain, pancreatic and prostate cancer. The IC Show less

Obesity is among the leading public health threats globally. Over the last few years, visceral adiposity index (VAI), and body adiposity index (BAI), derived from anthropometric, and biochemical measures, have gained importance as a measure of obesity. However, unlike other common indices like body mass index, and waist circumference, the genetic predisposition of VAI, and BAI under-examined. 2265 sib-pairs from Indian Migration Study were used for examining the association of genetic variants from the Cardio-Metabochip array with VAI, and BAI. Mixed linear regression models were run, and all inferences were based on the within-sib component of the Fulker's association models. Gene-environment/lifestyle interaction analyses were also undertaken. rs6659428 at LOC400796 | SEC16B (β = 0.26, SE = 0.05), and rs7611535 at DRD3 | LOC645180 (β = 0.18, SE = 0.04) were associated with VAI at suggestive significance value of <8.21 × 10 We report three novel genetic loci for VAI, and BAI in Indians that are important indicators of adiposity. These findings need to be replicated and validated with larger samples from different ethnicities. Further, functional studies for understanding the biological mechanisms of these adiposity indices need to be undertaken to understand the underlying pathophysiology. Show less

Genome wide association studies (GWAS), mostly in Europeans have identified several common variants as associated with key lipid traits. Replication of these genetic effects in South Asian populations is important since it would suggest wider relevance for these findings. Given the rising prevalence of metabolic disorders and heart disease in the Indian sub-continent, these studies could be of future clinical relevance. We studied seven common variants associated with a variety of lipid traits in previous GWASs. The study sample comprised of 3178 sib-pairs recruited as participants for the Indian Migration Study (IMS). Associations with various lipid parameters and quantitative traits were analyzed using the Fulker genetic association model. We replicated five of the 7 main effect associations with p-values ranging from 0.03 to 1.97x10(-7). We identified particularly strong association signals at rs662799 in APOA5 (beta=0.18 s.d, p=1.97 x 10(-7)), rs10503669 in LPL (beta =-0.18 s.d, p=1.0 x 10(-4)) and rs780094 in GCKR (beta=0.11 s.d, p=0.001) loci in relation to triglycerides. In addition, the GCKR variant was also associated with total cholesterol (beta=0.11 s.d, p=3.9x10(-4)). We also replicated the association of rs562338 in APOB (p=0.03) and rs4775041 in LIPC (p=0.007) with LDL-cholesterol and HDL-cholesterol respectively. We report associations of five loci with various lipid traits with the effect size consistent with the same reported in Europeans. These results indicate an overlap of genetic effects pertaining to lipid traits across the European and Indian populations. Show less

Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and ∼ 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 × 10⁻⁸), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation. Show less

Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes. Show less

Blood lipids are important cardiovascular disease (CVD) risk factors with both genetic and environmental determinants. The Whitehall II study (n=5592) was genotyped with the gene-centric HumanCVD BeadChip (Illumina). We identified 195 SNPs in 16 genes/regions associated with 3 major lipid fractions and 2 apolipoprotein components at p<10(-5), with the associations being broadly concordant with prior genome-wide analysis. SNPs associated with LDL cholesterol and apolipoprotein B were located in LDLR, PCSK9, APOB, CELSR2, HMGCR, CETP, the TOMM40-APOE-C1-C2-C4 cluster, and the APOA5-A4-C3-A1 cluster; SNPs associated with HDL cholesterol and apolipoprotein AI were in CETP, LPL, LIPC, APOA5-A4-C3-A1, and ABCA1; and SNPs associated with triglycerides in GCKR, BAZ1B, MLXIPL, LPL, and APOA5-A4-C3-A1. For 48 SNPs in previously unreported loci that were significant at p<10(-4) in Whitehall II, in silico analysis including the British Women's Heart and Health Study, BRIGHT, ASCOT, and NORDIL studies (total n>12,500) revealed previously unreported associations of SH2B3 (p<2.2x10(-6)), BMPR2 (p<2.3x10(-7)), BCL3/PVRL2 (flanking APOE; p<4.4x10(-8)), and SMARCA4 (flanking LDLR; p<2.5x10(-7)) with LDL cholesterol. Common alleles in these genes explained 6.1%-14.7% of the variance in the five lipid-related traits, and individuals at opposite tails of the additive allele score exhibited substantial differences in trait levels (e.g., >1 mmol/L in LDL cholesterol [approximately 1 SD of the trait distribution]). These data suggest that multiple common alleles of small effect can make important contributions to individual differences in blood lipids potentially relevant to the assessment of CVD risk. These genes provide further insights into lipid metabolism and the likely effects of modifying the encoded targets therapeutically. Show less

To identify genetic variants influencing plasma lipid concentrations, we first used genotype imputation and meta-analysis to combine three genome-wide scans totaling 8,816 individuals and comprising 6,068 individuals specific to our study (1,874 individuals from the FUSION study of type 2 diabetes and 4,184 individuals from the SardiNIA study of aging-associated variables) and 2,758 individuals from the Diabetes Genetics Initiative, reported in a companion study in this issue. We subsequently examined promising signals in 11,569 additional individuals. Overall, we identify strongly associated variants in eleven loci previously implicated in lipid metabolism (ABCA1, the APOA5-APOA4-APOC3-APOA1 and APOE-APOC clusters, APOB, CETP, GCKR, LDLR, LPL, LIPC, LIPG and PCSK9) and also in several newly identified loci (near MVK-MMAB and GALNT2, with variants primarily associated with high-density lipoprotein (HDL) cholesterol; near SORT1, with variants primarily associated with low-density lipoprotein (LDL) cholesterol; near TRIB1, MLXIPL and ANGPTL3, with variants primarily associated with triglycerides; and a locus encompassing several genes near NCAN, with variants strongly associated with both triglycerides and LDL cholesterol). Notably, the 11 independent variants associated with increased LDL cholesterol concentrations in our study also showed increased frequency in a sample of coronary artery disease cases versus controls. Show less