Academic literature on the topic 'Hypertension, genetics, epidemiology, mendelian randomization'

Abstract Background Vitamin D deficiency is highly prevalent across the globe. Existing studies suggest that a low vitamin D level is associated with more than 130 outcomes. Exploring the causal role of vitamin D in health outcomes could support or question vitamin D supplementation. Methods We carried out a systematic literature review of previous Mendelian-randomization studies on vitamin D. We then implemented a Mendelian Randomization–Phenome Wide Association Study (MR-PheWAS) analysis on data from 339 256 individuals of White British origin from UK Biobank. We first ran a PheWAS analysis to test the associations between a 25(OH)D polygenic risk score and 920 disease outcomes, and then nine phenotypes (i.e. systolic blood pressure, diastolic blood pressure, risk of hypertension, T2D, ischaemic heart disease, body mass index, depression, non-vertebral fracture and all-cause mortality) that met the pre-defined inclusion criteria for further analysis were examined by multiple MR analytical approaches to explore causality. Results The PheWAS analysis did not identify any health outcome associated with the 25(OH)D polygenic risk score. Although a selection of nine outcomes were reported in previous Mendelian-randomization studies or umbrella reviews to be associated with vitamin D, our MR analysis, with substantial study power (>80% power to detect an association with an odds ratio >1.2 for per standard deviation increase of log-transformed 25[OH]D), was unable to support an interpretation of causal association. Conclusions We investigated the putative causal effects of vitamin D on multiple health outcomes in a White population. We did not support a causal effect on any of the disease outcomes tested. However, we cannot exclude small causal effects or effects on outcomes that we did not have enough power to explore due to the small number of cases.

Background:Gout is the most common inflammatory arthritis worldwide associated with comorbidities that may impair well-being and reduce longevity. Epidemiological evidence generally supports that gout patients are at high risk of hypertension. However, the causality between gout and hypertension is uncertain since confounding and other types of bias are difficult to contain in the observational study.Objectives:To test the causal link between gout and hypertension using a Mendelian Randomization (MR) analysis.Methods:A mendelian randomization analysis was conducted using individual patient data from the Taiwan Biobank featured 2452 individuals with gout and 66527 controls. We selected 12 SNPs as instrumental variables (IVs) with p-values < 5 × 10−8 and the linkage disequilibrium (LD) R2 value less than 0.8. We conducted traditional MR analysis using the inversed weighted variance (IVW) and median methods with different settings as the primary analysis. Further IV assumption-free methods, the MR-Egger methods [1], Causal Analysis Using Summary Effect Estimates (CAUSE) model [2], and structural equation modeling (SEM) [3,4] were also performed as a sensitivity analysis.Results:The prevalence of hypertension was 0.15% (n = 9549) in the cohort. Table 1 shows causal effect estimates between gout and hypertension using different methods. The average causal effect β is estimated at 0.09 and the corresponding odds ratio (OR) at 1.09 using traditional methods across different settings. Similar estimates were observed in the MR-Egger method, SEM model, and the CAUSE model, demonstrating the robustness of the causal association between gout and hypertension considering pleiotropic effects (Table 1). Furthermore, the model fit of the hypothesized SEM model is excellent with a comparative fit index of 0.978 and Tucker-Lewis index of 0.968. The SEM model explains at least 32.70% variance of hypertension and 32.6% variance of gout (Figure 1).Table 1.Estimate the causal effect of MR analysis and sensitivity analysisMethodCausal effect estimate β95% lower bound95% upper boundp-valuePrimary analysisIVW with fixed effect, first order0.09000.0656a0.1145a<10-5IVW with fixed effect, second order0.08950.0647a0.1143a<10-5IVW with random effect, first order0.09000.0656a0.1145a<10-5IVW with random effect, second order0.08950.0647a0.1143a<10-5Median method, simple0.09890.0645a0.1332a<10-5Median method, weighted0.09020.0583a0.1220a<10-5Median method, penalized0.09020.0583a0.1220a<10-5Sensitivity analysisMR-Egger0.07860.0132a0.1440a0.0183SEM0.05190.0481a0.0697a<10-5CAUSE model0.07640.0176b0.1349b<10-5* a represents the 95% confidence interval; b represents the 95% credible interval.** For the random effect model, if the estimated residual standard error is less than 1, then the MendelianRandomization package will automatically set the value of residual standard error into 1.Figure 1.Pathway analysis for SEM study assessing the relationships between gout and hypertension.Conclusion:These results strongly suggest that the association between gout and hypertension has a causal basis.References:[1]Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015 Apr; 44(2):512-525.[2]Morrison J, Knoblauch N, Marcus JH, Stephens M, He X. Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics. Nat Genet. 2020 Jul; 52(7):740-747.[3]Streiner DL. Building a better model: an introduction to structural equation modelling. Can J Psychiatry. 2006 Apr; 51(5):317-324.[4]Stephen B, Rhian MD, Adam SB, Simon GT, and the EPIC-InterAct. Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways. Int J Epidemiol. 2015 Apr; 44(2): 484-495.Disclosure of Interests:None declared

Abstract Background There is considerable interest in estimating the causal effect of a range of maternal environmental exposures on offspring health-related outcomes. Previous attempts to do this using Mendelian randomization methodologies have been hampered by the paucity of epidemiological cohorts with large numbers of genotyped mother–offspring pairs. Methods We describe a new statistical model that we have created which can be used to estimate the effect of maternal genotypes on offspring outcomes conditional on offspring genotype, using both individual-level and summary-results data, even when the extent of sample overlap is unknown. Results We describe how the estimates obtained from our method can subsequently be used in large-scale two-sample Mendelian randomization studies to investigate the causal effect of maternal environmental exposures on offspring outcomes. This includes studies that aim to assess the causal effect of in utero exposures related to fetal growth restriction on future risk of disease in offspring. We illustrate our framework using examples related to offspring birthweight and cardiometabolic disease, although the general principles we espouse are relevant for many other offspring phenotypes. Conclusions We advocate for the establishment of large-scale international genetics consortia that are focused on the identification of maternal genetic effects and committed to the public sharing of genome-wide summary-results data from such efforts. This information will facilitate the application of powerful two-sample Mendelian randomization studies of maternal exposures and offspring outcomes.

Abstract Metabolic syndrome is a constellation of metabolic abnormalities predisposing to cardiovascular diseases (CVD), type 2 diabetes, and increased mortality. Due to the high prevalence and severe co-morbidities, metabolic syndrome constitutes a major burden for both public health and the global economy. Improved understanding of the detailed molecular mechanisms could provide novel strategies for the treatment and preferably prevention of the metabolic syndrome-related health issues. Recent advancements in ‘omics’ technologies have facilitated the development of novel tools to examine the links between genetic variation and human health. The new techniques allow determination of millions of genotypes or quantification of hundreds of metabolic measures from a single blood sample. In this thesis, genomics and metabolomics approaches are coupled to improve our understanding of the metabolic syndrome-related health issues. More precisely, my projects evaluate the metabolic effects of two lipid-lowering therapies and non-alcoholic fatty liver, as well as assess genetic determinants of chronic inflammation. The present results indicate generally consistent metabolic effects of statins and proprotein convertase subtilisin/kexin type 9 (PCSK9) genetic inhibition. The subtle discrepancies observed could potentially contribute to differences in the efficacy to lower CVD risk between statins and PCSK9 inhibitors. The dissimilar metabolic effects of the four genetic variants that increase the risk of non-alcoholic fatty liver disease (NAFLD) highlight the heterogeneity of the molecular mechanisms involved in NAFLD pathogenesis. The results further suggest that fatty liver by itself might not promote unfavourable metabolic aberrations associated with fatty liver on a population level. The newly identified loci associating with inflammatory phenotypes elucidate the genetic mechanisms contributing to the inflammatory load. In particular, the present results suggest the important role of the locus determining the ABO blood types in the regulation of the soluble adhesion molecule levels. To conclude, this thesis successfully complements the knowledge of the molecular mechanisms involved in metabolic syndrome-related traits and provides examples of how to couple omics technologies in the study of complex traits or in the evaluation of drug effects<br>Tiivistelmä Metabolinen oireyhtymä on tila, jossa useiden aineenvaihdunnallisten riskitekijöiden kasautuminen suurentaa riskiä sairastua tyypin 2 diabetekseen ja sydän- ja verisuonitauteihin sekä lisää kokonaiskuolleisuutta. Vakavista liitännäissairauksista ja suuresta esiintyvyydestä johtuen metabolinen oireyhtymä kuormittaa merkittävästi sekä terveydenhuoltoa että kansantaloutta. Jotta metabolisen oireyhtymän hoitoon ja ennaltaehkäisyyn voitaisiin kehittää uusia keinoja, on tärkeää ymmärtää paremmin oireyhtymän syntyyn vaikuttavat täsmälliset molekyylimekanismit. Niin sanottujen ’omiikka-tekniikoiden’ viimeaikainen kehitys tarjoaa uusia mahdollisuuksia tutkia geenimuutosten vaikutuksia terveyteen. Uusien tekniikoiden avulla voidaan määrittää miljoonia genotyyppejä tai satoja aineenvaihdunnan merkkiaineita yhdestä verinäytteestä. Tässä väitöskirjatyössä yhdistetään genomiikan ja metabolomiikan menetelmiä metaboliseen oireyhtymään liittyvien terveysongelmien tutkimiseksi. Väitöskirjani osatöissä arvioin kahden lipidilääkkeen sekä ei-alkoholiperäisen rasvamaksan aineenvaihdunnallisia vaikutuksia sekä pyrin tunnistamaan krooniseen tulehdukseen vaikuttavia geneettisiä tekijöitä. Tulosten mukaan statiinien ja PCSK9:n (engl. proprotein convertase subtilisin/kexin type 9) geneettisen eston aineenvaihduntavaikutukset ovat hyvin samankaltaiset. Kuitenkin havaitut pienet poikkeavuudet tietyissä merkkiaineissa voivat vaikuttaa eroavaisuuksiin siinä, kuinka tehokkaasti lääkeaineet alentavat sydäntautiriskiä. Suuret erot rasvamaksan riskiä lisäävien geenimuutosten vaikutuksissa aineenvaihduntaan korostavat rasvamaksaan liittyvien molekyylimekanismien monimuotoisuutta. Tulosten perusteella vaikuttaa siltä, että rasvan kertyminen maksaan ei luultavasti itsessään aiheuta suuria muutoksia verenkierron aineenvaihduntatuotteiden pitoisuuksiin. Tulehdusmerkkiaineisiin assosioituvat uudet geenialueet täydentävät tulehduksen molekyylimekanismeihin liittyvää tietoa. Tulokset korostavat ABO-veriryhmän määräävän geenin vaikutusta liukoisten adheesiomolekyylien pitoisuuksiin. Kaiken kaikkiaan väitöskirjan osatyöt tuovat uutta tietoa metaboliseen oireyhtymään liittyvien terveysongelmien molekyylimekanismeihin. Projektit havainnollistavat, miten omiikka-tekniikoita voidaan hyödyntää monitekijäisten fenotyyppien tutkimuksessa sekä lääkeaineiden aineenvaihduntavaikutusten arvioinnissa

The heritability of blood pressure (BP) and hypertension is mainly due to a polygenic background. Thanks to the advance of Genome-wide association studies (GWAS, over the last two decades, more than 1,000 single nucleotide polymorphisms (SNPs) have been identified to be associated with BP traits. These, taken singularly, have a small effect on BP, but when grouped, they can explain 30-50% of the heritability of BP. Different strategies, classified under the field of genetic epidemiology, have evolved to exploit and interpret the genetic determinants of complex diseases. These include the construction of genetic risk scores (GRS), summing up all the risk alleles of the SNPs associated with a trait, and Mendelian Randomization (MR) to assess if an association between a modifiable exposure and an outcome is causal. In this thesis, I am presenting four works in which these approaches were used to investigate the genetic determinants of blood pressure and hypertension. In the first study, a GRS was constructed including more than 800 SNPs associated with either systolic BP (SBP) or diastolic BP (DBP) and we evaluated how it is associated with BP traits, hypertension, and cardiovascular disease (CVD) risk, in two Swedish cohorts, the Malmö Diet and Cancer (MDC) study and the Malmö Preventive Project (MPP). In the following three studies, we used MR methods to assess the causal association of different exposures, i) different adiposity traits in study II, ii) thyroid hormones in study III, iii) serum calcium and calciotropic hormones in study IV, with BP traits, hypertension, and CVD in MDC and MPP. As a result, we found that the GRS built with more than 800 SNPs can discriminate clinically meaningful differences in mmHg between individuals classified as being low-risk versus high-risk based on their GRS. Moreover, the score was independently associated with hypertension risk and CVDs, and with the same order of magnitude as traditional non-genetic risk factors. In the first MR study, the four adiposity traits exhibited a different causal role on the risk of prevalence and incidence of hypertension. This result could reflect distinct pathological mechanisms linking obesity and hypertension. Then we found that low TSH is slightly inversely associated with SBP, while a strong association in the same direction was reported with atrial fibrillation. Finally, serum calcium level showed a causal association with DBP while fibroblast growth factor 23 (FGF23) was inversely associated with SBP. In conclusion, genetic epidemiology provides useful techniques that help in stratifying individuals based on their genetic risk and providing new insight into the pathophysiology of hypertension. Since genetic information is available from birth, it is possible to speculate that personalized prevention and intervention strategies could be developed in the future even early in life.