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Unraveling new factors associated with hypertension

the Mendelian randomization approach

Grau-Perez, Mariaa,b; Redon, Josepa,c

doi: 10.1097/HJH.0000000000002251
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aCardiovascular and Renal Risk Research Group, INCLIVA Research Institute, University of Valencia, Valencia

bDepartment of Preventive Medicine and Public Health, School of Medicine, Autonomous University of Madrid

cCIBER Physiopathology of Obesity and Nutrition (CIBEROBN), Institute of Health Carlos III, Madrid, Spain

Correspondence to Josep Redon, Hospital Clinico, INCLIVA Research Institute, University of Valencia, Avda Blasco Ibañez, 17, 46010 Valencia, Spain. Tel: +34 658909676; e-mail: josep.redon@uv.es

Hypertension (HTN) is a frequent condition with multifactorial cause in which genetic, fetal and environmental factors interact to result in the final phenotype of blood pressure (BP) elevation. Relevance in terms of cardiovascular and renal risk has been demonstrated in multiple epidemiological and intervention studies being one of the most powerful markers for survival. Concerned with the importance in public health and clinical settings, boosted a large body on research in to identify factors that directly or indirectly can impact BP levels. Despite the high percentage of heritability demonstrated in several studies, a few Mendelian genetic studies have been identified as a direct cause of HTN [1]. Likewise, genome-wide association studies (GWAS) and their meta-analyses have identified 120 loci that are associated with BP regulation, but together these only explain about 3.5% of the trait variance [2,3]. In contrast, obesity, salt intake and sedentary life have been identified as relevant environmental factors associated with HTN. However, other frequent conditions have been linked to the presence of HTN, although the real impact is difficult to assess due to the high level of sharing and interaction between the factors related to HTN and the potential conditions, as an example is the case of diabetes mellitus. One way that can contribute to dissect more precisely the potential link between a factor and BP levels is Mendelian randomization analysis.

As confounders impaired observational studies in HTN, in the last years, several have used Mendelian randomization for assessing association of HTN to several factors with disparate results. Anthropometrics, such as BMI [4–6], adiposity fat composition [4]; adiposity [7,8]; birth weight [9]; dietary dairy consumption [10], smoking [11], caffeine [12] and alcohol intake [13], and environmental factors such inorganic arsenic metabolism [14], have been evaluated. Likewise, several potential markers of risk have been tested using Mendelian randomization, uric acid [15], gamma-glutamyl transferase [16], total bilirubin [17], beta-2-microglobulin [18], vitamin D levels [19] and apolipoprotein E polymorphism [20]. In this issue of the Journal, Au Yeung et al. [21] analyzed the link between glycated hemoglobin (HbA1c) and HTN using Mendelian randomization in the UK Biobank. The study suggests that HbA1c may increase HTN risk.

Mendelian randomization is an epidemiologic method that uses genetic variants as proxy indicators for the modifiable risk factors to assess the causal effect of the risk factor on a health outcome using observational data. Briefly, this technique receives its name because it exploits the principle of the random assortment in genetic inheritance, first proposed by Gregor Mendel in 1865 [22]. In this way, the Mendelian randomization method can be understood as a randomized controlled trial, in which the genotypes from one or several genetic variants are used to separate the individuals into random subgroups, this is, groups that are not related with other confounder variables. To conduct a Mendelian randomization analysis, the researcher has to find one or several genetic variants that meet three main assumptions: first, it is related to the risk factor (relevance assumption); second, it is not associated with any confounder of the risk factor-outcome association (independence assumption); and third, it is associated with the outcome only via the risk factor of interest (exclusion restriction assumption) [23,24]. Single nucleotide polymorphisms (SNPs) are the most commonly used genetic variants in Mendelian randomization studies.

Although Mendelian randomization design is less susceptible to confounding and reverse causality compared with conventional observational studies it is not exempt of limitations that require careful assessment [25]. For instance, the Mendelian randomization assumptions may be violated in practice due to inadequate phenotype (e.g. measurement error), biological reasons (e.g. pleiotropy of the genetic variants), non-Mendelian inheritance (e.g. linkage disequilibrium) or population characteristics (e.g. population stratification), and this violation can lead to invalid conclusions. Due to increased availability of GWAS, it is common that studies applying a Mendelian randomization analysis make use of large numbers of genetic variants at the time. Given that it cannot fully be tested that all variants meet the required assumptions, several Mendelian randomization robust approaches have been developed [26]. Examples of these robust approaches are Mendelian randomization-Pleiotropy Residual Sum and Outlier (MR-PRESSO), Mendelian randomization-Egger, the weighted median method and Mendelian randomization-LASSO.

Traditional Mendelian randomization studies are conducted with individual-level data, this is, each individual having available data on the genetic variant, and on the risk factor and outcome of interest. However, available summary estimates on SNP-traits from already published GWAS made possible a new Mendelian randomization design: the two-sample summary data Mendelian randomization. With this approach, summary estimates from the SNP-exposure and SNP-outcome associations from previous GWAS are combined applying a Mendelian randomization method to obtain the causal effect of the exposure on the outcome. It is worthy to comment that, when performing a Mendelian randomization analysis with individual data for dichotomic outcomes, as is the case of HTN yes or not, the SNP-exposure association is usually conducted among controls only, especially if the disease is rare on the population or if the risk factor measurement was made postdisease. However, for the case of summary data Mendelian randomization design this consideration is not necessary as study samples for GWAS studies are usually disease-independent.

In this issue, Au Yeung et al.[21] assessed the causal effect of HbA1c on HTN and BP levels with a two-sample summary data Mendelian randomization design. The SNP-HTN association was evaluated in 376 644 UK Biobank participants with verified white-British genetic background. Among these participants, 54% were women, the prevalence of HTN was 78% and the mean SBP and DBP were 138 and 82 mmHg, respectively.

In addition, the authors selected 38 SNPs strongly associated with HbA1c in 123 665 individuals of European ancestry from the Meta-Analyses of Glucose and Insulin-related traits consortium. Then they applied an inverse-variance weighted Mendelian randomization approach as their main analysis to assess the causal effect of HbA1c on HTN and SBP and DBP levels, and evaluated the consistency of the results by applying the weighted median, the Mendelian randomization-Egger and the MR-PRESSO methods. They also repeated the analyses excluding SNPs with potentially pleiotropic effects. All the analyses were conducted among all participants and stratified by sex. The investigators found that HbA1c was consistently associated with increased risk of HTN after excluding the SNPs with potential pleiotropic effects. Regarding BP, HbA1c was associated also with increased SBP, but not with increased DBP. The study concluded that the relationship, although at present is not clearly understood, ‘… may be one underlying pathway explaining the positive relation between poorer glycemic profile and coronary artery disease risk’.

Mendelian randomization is a powerful tool useful to dissect complex associations in which confounders impaired observational studies, but validity requires carefully selection of the phenotype and to fulfill the assumptions of relevance, independence and exclusion restriction at the time to select the genetic markers. This hypothesis-generating method should be considered as a solid starting point for potential associations that should be tested in further research.

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ACKNOWLEDGEMENTS

Conflicts of interest

There are no conflicts of interest.

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