‘If it were not for the great variability among individuals medicine might as well be a science and not an art’.
William Osler, 1892.
Hypertension affects 30% of the adult population , and is directly responsible for 54% of stroke and 47% of ischaemic heart disease . Pharmacotherapy is the mainstay of hypertension management, but selection of antihypertensive therapy is essentially by trial-and-error. Despite the availability of numerous drugs, response rates to any given drug are approximately 50% and only one in three patients with hypertension have their blood pressure controlled to target . There are multiple reasons for variable response to antihypertensive drugs and the preeminent reason is nonadherence to medications, with studies showing that up to 53% of patients with uncontrolled hypertension were nonadherent to treatments [4,5]. Other factors include obesity, excessive sodium and alcohol consumption, sleep apnoea, chronic kidney disease and renal artery stenosis. Moreover, it is now well recognized that there is substantial intraindividual variability in blood pressure and this will have a major impact on the determination of response to drugs . Bell et al. crucially noted in an individual patient data meta-analysis of randomized, placebo-controlled trials that most of the apparent variation in response to antihypertensive drugs is not due to effect of treatment but rather to background within-person fluctuation in day-to-day blood pressure levels. Thus, studies attempting to discover the genetic basis of antihypertensive response face considerable challenges, many of which may be insurmountable especially when analysing historical data. Moreover, the magnitude of a genetic effect underlying true antihypertensive drug response is difficult to estimate.
The study by Chittani et al. in the current issue of the Journal of Hypertension is a pharmacogenomics study that identifies two suggestive loci associated with SBP response to hydrochlorothiazide. This is a further addition to a series of publications in the last few years identifying several potential genetic variants associated with antihypertensive drugs response. However, most candidate gene studies have been small and exploratory, with results frequently not independently validated  nor replicated in populations from different ethnicity or geographic region . Genome-wide association studies (GWAS) have identified single-nucleotide polymorphisms (SNPs)/plausible genes associated with thiazide diuretics, angiotensin-converting enzyme (ACE) inhibitors and beta-blockers [11–15], but most of these studies are underpowered to detect genome-wide significant associations.
In their GWAS  in two Italian cohorts of treatment-naive essential hypertensive individuals, Chittani et al.  did not identify any SNP that crossed the accepted threshold for genome-wide significance (P < 5 × 10–8 to adjust for multiple testing covering the whole human genome). A total of 141 SNPs were associated with SBP and 130 with DBP at a meta-analysis P value less than 10–5. After filtering SNPs in linkage disequilibrium or mapping to desert regions, they narrowed the list down to six SNPs for SBP and five SNPs for DBP that were tested in four independent randomized clinical trials of European ancestry. Using these approaches, they identified three candidate SNPs for blood pressure response to hydrochlorothiazide: rs12505746 on the intronic region of the TET2 gene on chromosome 4, an aldosterone-responsive mediator of aENaC gene transcription and SNPs rs7387065 and rs113031 mapping to the intronic region of the CSMD1 on chromosome 8, encoding a transmebrane protein that belongs to the vascular protein sorting-13 family.
The results of the current study will add to the growing list of SNPs with similar levels of association. However, the critical question is to determine whether these signals are indeed true and worthwhile investigating further. The lack of overlap between the results of this study and two previous studies of hydrochlorothiazide response [11–14] needs to be explained, though the GERA study  was one of the replication cohorts. None of the studies had measures of treatment adherence, which is a crucial metric that needs to be assessed in future studies. The only practical index of generalizability and certainty of a result is an independent validation in multiple populations. However, this is a major barrier for drug response phenotypes, as these will have to be generated de novo, and it is unclear whether this is a viable option. Another important question is whether pharmacogenetic GWAS SNPs are useful at an individual level, given the low predictive value of GWAS SNPs in general across all other traits studied. GWAS has had a good success rate at identifying novel pathways particularly for hypertension [16–18], and this may well be the case for drug response. The assumption that the use of GWAS SNPs may lead to a significant change in clinical practice is not likely imminently. High throughput technologies and the almost trivial cost of genotyping have democratized genetic association studies, but the challenge for hypertension specialists is to develop robust strategies to validate the plausibility and clinical utility of newly identified GWAS SNPs without really understanding their function.
Conflicts of interest
There are no conflicts of interest.
1. Kearney PM, Whelton M, Reynolds K, Muntner P, Whelton PK, He J. Global burden of hypertension: analysis of worldwide data. Lancet
2. Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet
3. Falaschetti E, Chaudhury M, Mindell J, Poulter N. Continued improvement in hypertension management in England: results from the Health Survey for England 2006. Hypertension
4. Tomaszewski M, White C, Patel P, Masca N, Damani R, Hepworth J, et al. High rates of nonadherence to antihypertensive treatment revealed by high-performance liquid chromatography-tandem mass spectrometry (HP LC-MS/MS) urine analysis. Heart
5. Jung O, Gechter JL, Wunder C, Paulke A, Bartel C, Geiger H, et al. Resistant hypertension? Assessment of adherence by toxicological urine analysis. J Hypertens
6. Parati G, Ochoa JE, Lombardi C, Bilo G. Assessment and management of blood-pressure variability. Nat Rev Cardiol
7. Bell KJ, Hayen A, Macaskill P, Craig JC, Neal BC, Fox KM, et al. Monitoring initial response to angiotensin-converting enzyme inhibitor-based regimens: an individual patient data meta-analysis from randomized, placebo-controlled trials. Hypertension
8. Chittani M, Zaninello R, Lanzani C, Frau F, Ortu MF, Salvi E, et al. TET2 and CSMD1 genes affect SBP response to hydrochlorothiazide in never-treated essential hypertensives. J Hypertens
9. Munroe PB, Barnes MR, Caulfield MJ. Advances in blood pressure genomics. Circ Res
10. Padmanabhan S, Newton-Cheh C, Dominiczak AF. Genetic basis of blood pressure and hypertension. Trends Genet
11. Turner ST, Bailey KR, Schwartz GL, Chapman AB, Chai HS, Boerwinkle E. Genomic association analysis identifies multiple loci influencing antihypertensive response to an angiotensin II receptor blocker. Hypertension
12. Duarte JD, Turner ST, Tran B, Chapman AB, Bailey KR, Gong Y, et al. Association of chromosome 12 locus with antihypertensive response to hydrochlorothiazide may involve differential YEATS4 expression. Pharmacogenom J
13. Turner ST, Bailey KR, Fridley BL, Chapman AB, Schwartz GL, Chai HS, et al. Genomic association analysis suggests chromosome 12 locus influencing antihypertensive response to thiazide diuretic. Hypertension
14. Turner ST, Boerwinkle E, O’Connell JR, Bailey KR, Gong Y, Chapman AB, et al. Genomic association analysis of common variants influencing antihypertensive response to hydrochlorothiazide. Hypertension
15. Frau F, Zaninello R, Salvi E, Ortu MF, Braga D, Velayutham D, et al. Genome-wide association study identifies CAMKID variants involved in blood pressure response to losartan: the SOPHIA study. Pharmacogenomics
16. Padmanabhan S, Menni C, Lee WK, Laing S, Brambilla P, Sega R, et al. The effects of sex and method of blood pressure measurement on genetic associations with blood pressure in the PAMELA study. J Hypertens
17. Graham LA, Padmanabhan S, Fraser NJ, Kumar S, Bates JM, Raffi HS, et al. Validation of uromodulin as a candidate gene for human essential hypertension. Hypertension
18. Trudu M, Janas S, Lanzani C, Debaix H, Schaeffer C, Ikehata M, et al. Common noncoding UMOD gene variants induce salt-sensitive hypertension and kidney damage by increasing uromodulin expression. Nat Med