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Response by Siedlinski et al to Letters Regarding Article, “White Blood Cells and Blood Pressure: A Mendelian Randomization Study”

Originally published 2020;142:e191–e192

    In Response:

    We are grateful to Dr Gill as well as Drs Wang, Chen, and Yang for providing important statistical and epidemiological comments on our article1 and, in general, the pathogenesis of hypertension. In fact, our current research activity encompasses some of the additional analyses proposed. For example, we are interested in sex-related differences in the immune background of hypertension, and we agree that sex-stratified analyses would be interesting; however, we are aware of potential biases in such work, and the availability of sex-stratified genome-wide association studies (GWAS) hampers this at the moment.2 Our initial analyses in the UK Biobank cohort suggest that, although lymphocyte count robustly associates with blood pressure (BP) in both sexes, there may be important differences in the effect size between sexes. It should be noted that such analyses are susceptible to limitations on cross-sectional studies such as unobserved confounding or reverse causation. Moreover, we should keep in mind all aspects of blood tests in the UK Biobank (ie, lack of discrimination between lymphocyte subsets and, more generally, uncertainty on how blood cell count reflects their infiltration of target organs by inflammatory cells).

    The additional analyses and methods proposed by Dr Gill are valuable, especially because they have been developed and applied by the epidemiology community to address important questions in the rapidly growing field of Mendelian randomization (MR) in recent years.3 Indeed, we observed a nonlinear, observational effect of total lymphocyte count on BP in the UK Biobank cohort,1 which might reflect a true nature of this relationship but might also arise as a result of residual (nonlinear) confounding. We fully agree that assessing nonlinear effects in MR analysis is of interest in this context; however, we note that our MR analysis estimated a population-averaged causal effect,4 and we disagree that MR assumes a linear relationship across the physiological distribution. We do agree that multivariable MR analysis could add additional information on the direct effect of specific blood cell count on BP that is possibly independent on the other cell types. We noted that the correlation between total blood eosinophil and lymphocyte count is modest (R=0.23), and we therefore focused our analysis on univariable models to estimate a total rather than direct effect. We also emphasize that, although directions of observational and genetic effects of lymphocyte count on BP are concordant, this is not the case for blood eosinophil count.

    We also fully support the idea of MR mediation analysis, which may be used to estimate the proportion of the genetic effect of lymphocyte count on BP that is possibly mediated by albuminuria. As mentioned in our article, a bidirectional association between BP and albuminuria exists and makes this type of MR analysis challenging. MR mediation analysis can be used to characterize a relationship between lymphocyte count and coronary heart disease5 in more detail (ie, with use of BP as a potential mediator). This would address a question of great relevance and further characterize a network of causal relationships between the above traits and vascular disease.



    • 1. Siedlinski M, Jozefczuk E, Xu X, Teumer A, Evangelou E, Schnabel RB, Welsh P, Maffia P, Erdmann J, Tomaszewski M, et al. White blood cells and blood pressure: a Mendelian randomization study.Circulation. 2020; 141:1307–1317. doi: 10.1161/CIRCULATIONAHA.119.045102LinkGoogle Scholar
    • 2. Drummond GR, Vinh A, Guzik TJ, Sobey CG. Immune mechanisms of hypertension.Nat Rev Immunol. 2019; 19:517–532. doi: 10.1038/s41577-019-0160-5CrossrefMedlineGoogle Scholar
    • 3. Burgess S, Smith GD, Davies NM, Dudbridge F, Gill D, Glymour MM, Hartwig FP, Holmes MV, Minelli C, Relton CL, et al. Guidelines for performing Mendelian randomization investigations.Wellcome Open Research. 2020; 4:186. doi: 10.12688/wellcomeopenres.1555.2 Scholar
    • 4. Burgess S, Davies NM, Thompson SG; EPIC-InterAct Consortium. Instrumental variable analysis with a nonlinear exposure-outcome relationship.Epidemiology. 2014; 25:877–885. doi: 10.1097/EDE.0000000000000161CrossrefMedlineGoogle Scholar
    • 5. Astle WJ, Elding H, Jiang T, Allen D, Ruklisa D, Mann AL, Mead D, Bouman H, Riveros-Mckay F, Kostadima MA, et al. The allelic landscape of human blood cell trait variation and links to common complex disease.Cell. 2016; 167:1415–1429.e19. doi: 10.1016/j.cell.2016.10.042CrossrefMedlineGoogle Scholar


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