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Engert, J. C.

Publications and source records attributed to Engert, J. C..

2 recordsLinked to original sources

Genetic inhibition of PCSK9, atherogenic lipoprotein concentrations, and calcific aortic valve stenosis

BackgroundProprotein convertase subtilisin/kexin type 9 (PCSK9) inhibition reduces plasma concentrations of low-density lipoprotein cholesterol (LDL-C), apolipoprotein B (apoB) and lipoprotein(a) [Lp(a)]. Atherogenic lipoprotein levels have been linked with calcific aortic valve stenosis (CAVS). Our objectives were to determine the association between variants in PCSK9 and lipoprotein-lipid levels, coronary artery disease (CAD) and CAVS, and to evaluate if PCSK9 could be implicated in aortic valve interstitial cells (VICs) calcification. MethodsWe built a genetic risk score weight for LDL-C levels (wGRS) using 10 independent PCSK9 single nucleotide polymorphisms and determined its association with lipoprotein-lipid levels in 9692 participants of the EPIC-Norfolk study. We investigated the association between the wGRS and CAD and CAVS in the UK Biobank, as well as the association between the PCSK9 R46L variant and CAVS in a meta-analysis of published prospective, population-based studies (Copenhagen studies, 1463 cases/101,620 controls) and unpublished studies (UK Biobank, 1350 cases/349,043 controls, Malmo Diet and Cancer study, 682 cases/5963 controls and EPIC-Norfolk study, 508 cases/20,421 controls). We evaluated PCSK9 expression and localization in explanted aortic valves by capillary Western blot and immunohistochemistry in patients with and without CAVS. Von Kossa staining was used to visualize aortic leaflet calcium deposits. PCSK9 expression under oxidative stress conditions in VICs was assessed. ResultsThe wGRS was significantly associated with lower LDL-C and apoB (p<0.001), but not with Lp(a). In the UK Biobank, the association of PCSK9 variants with CAD were positively correlated with their effects on apoB levels. CAVS was less prevalent in carriers of the PCSK9 R46L variant [odds ratio=0.71 (95% confidence interval, 0.57-0.88), p<0.001]. PCSK9 expression was elevated in the aortic valves of patients with aortic sclerosis and CAVS compared to controls. In calcified leaflets, PCSK9 co-localized with calcium deposits. PCSK9 expression was induced by oxidative stress in VICs. ConclusionGenetic inhibition of PCSK9 is associated with lifelong reductions in the levels of non-Lp(a) apoB-containing lipoproteins as well as lower odds of CAD and CAVS. PCSK9 is abundant in fibrotic and calcified aortic leaflets. Oxidative stress increases PCSK9 expression in VICs. These results provide a rationale for performing randomized clinical trials of PCSK9 inhibition in CAVS.

genetics↗

Penetrance of polygenic obesity susceptibility loci across the body mass index distribution: an update on scaling effects.

A growing number of single nucleotide polymorphisms (SNPs) have been associated with body mass index (BMI) and obesity, but whether the effect of these obesity susceptibility loci is uniform across the BMI distribution remains unclear. We studied the effects of 37 BMI/obesity-associated SNPs in 75,230 adults of European ancestry along BMI percentiles using conditional quantile regression (CQR) and meta-regression (MR) models. The effects of 9 SNPs (24%) increased significantly across the sample BMI distribution including, FTO (rs1421085, p=8.69x10-15), PCSK1 (rs6235, p=7.11x10-06), TCF7L2 (rs7903146, p=9.60x10-06), MC4R (rs11873305, p=5.08x10-05), FANCL (rs12617233, p=5.30x10-05), GIPR (rs11672660, p=1.64x-04), MAP2K5 (rs997295, p=3.25x10-04), FTO (rs6499653, p=6.23x10-04) and NT5C2 (rs3824755, p=7.90x10-04). We showed that such increases stem from unadjusted gene interactions that enhanced the effects of SNPs in persons with high BMI. When 125 height-associated were analyzed for comparison, only one (<1%), IGF1 (rs6219, p=1.80x10-04), showed effects that varied significantly across height percentiles. Cumulative gene scores of these SNPs (GS-BMI and GS-Height, respectively) showed that only GS-BMI had effects that increased significantly across the sample distribution (BMI: p=7.03x10-37, Height: p=0.499). Overall, these findings underscore the importance of gene-gene and gene-environment interactions in shaping the genetic architecture of BMI and advance a method to detect such interactions using only the sample outcome distribution.

genetics↗