Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Peer-reviewed

Research Article

Mendelian randomization study of whole blood viscosity and cardiovascular diseases

Abstract

Aims

Association between whole blood viscosity (WBV) and an increased risk of cardiovascular disease (CVD) has been reported. However, the causal relationship between WBV and CVD remains not thoroughly investigated. The aim of this study was to investigate the causal relation between WBV and CVD.

Methods

Two-sample Mendelian randomization (MR) was employed, with inverse variance weighting (IVW) as the primary method, to investigate the casual relationship between WBV and CVD. The calculated WBV and medical records of 378,210 individuals participating in the UK Biobank study were divided into halves and analyzed.

Results

The means of calculated WBVs were 16.9 (standard deviation: 0.8) and 55.1 (standard deviation: 17.2) for high shear rate (HSR) and low shear rate (LSR), respectively. 37,859 (10.0%) major cardiovascular events (MACE) consisted of 23,894 (6.3%) cases of myocardial infarction (MI), 9,245 (2.4%) cases of ischemic stroke, 10,377 (2.7%) cases of revascularization, and 5,703 (1.5%) cases of coronary heart disease-related death. In the MR analysis, no evidence was found indicating a causal effect of WBV on MACE (IVW p-value for HSR = 0.81, IVW p-value for LSR = 0.47), MI (0.92, 0.83), ischemic stroke (0.52, 0.74), revascularization (0.71, 0.54), and coronary heart disease-related death (0.83, 0.70). The lack of sufficient evidence for causality persisted in other MR methods, including weighted median and MR-egger.

Conclusions

The Mendelian randomization analysis conducted in this study does not support a causal relationship between calculated WBV and CVD.

Citation: Bhak Y, Tenesa A (2024) Mendelian randomization study of whole blood viscosity and cardiovascular diseases. PLoS ONE 19(4): e0294095. https://doi.org/10.1371/journal.pone.0294095

Editor: Eyüp Serhat Çalık, Ataturk University Faculty of Medicine, TURKEY

Received: October 24, 2023; Accepted: February 4, 2024; Published: April 26, 2024

Copyright: © 2024 Bhak, Tenesa. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Our data is available as a part of the UK Biobank project. Details of procedures for accessing the UK Biobank data can be found here: https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access.

Funding: This project was funded by the National Institute for Health Research (NIHR) Artificial Intelligence and Multimorbidity: Clustering in Individuals, Space and Clinical Context (AIM-CISC) grant NIHR202639. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Cardiovascular diseases (CVD), including heart attack and stroke, are one of leading causes of morbidity and mortality globally [1]. Association between the risk of CVD and Whole blood viscosity (WBV), a measure of the thickness and flow resistance of bulk blood, have been reported [2–8]. However, establishing a causal relationship between WBV and CVD remains challenging due to the potential biases from confounding factors in traditional studies lacking randomized trial designs.

Mendelian randomization (MR), an epidemiological method, utilizes genetic variants robustly associated with an exposure of interest as instrumental variables (IVs) to investigate the causal effects of risk factors on specific outcomes [9]. The advantage of MR lies in the random assignment of these genetic variants at conception, rendering MR studies less susceptible to confounding factors compared to traditional observational studies [10]. Furthermore, MR is robust against reverse causality since the development of diseases does not alter individuals’ genotypes. MR have been utilized to investigate casual relationships of risk factors such as blood pressure [11,12], obesity [13], type 2 diabetes mellitus [14] and, profile of blood lipids [15–17] in and CVD. The objective of this study was to use MR to examine the causal relationship between WBV and CVD. WBV of individuals was calculated by applying the formula previously reported [18].

Methods

Participants

The UK Biobank (UKB) is a prospective research resource of population-based cohort study that include comprehensive phenotype and genotype data from approximately 500,000 participants recruited in 2006–2010 residing in England, Scotland, and Wales (www.ukbiobank.ac.uk). This an open-access resource was established to support investigations into the factors influencing various health outcomes [19].

Ethics statement

The UK Biobank project was approved by the National Research Ethics Service Committee North West-Haydock (REC reference: 11/NW/0382). Participants provided written informed consent to participate in the UK Biobank. An electronic signed consent was obtained from the participants. This research was conducted using the UK Biobank Resource under project 44986.

Extrapolation of whole blood viscosity

WBV was calculated for both low shear rate (LSR) (0.5 sec-1) and high shear rate (HSR) (208 sec-1) from hematocrit (HCT) and total plasma protein concentration (TP) using the validated formulation [18]. HCT was calculated by multiplying red blood cell count by the mean corpuscular volume.

Study outcomes

The data pertaining to each component of the participants’ outcomes in the present study was accessible through the UK Biobank study [19]. The primary outcome of the study was major cardiovascular events (MACE), which encompassed a composite outcome involving the occurrence of non-fatal MI, coronary revascularization (defined as “percutaneous transluminal coronary angioplasty, PTCA” or “coronary artery bypass grafting, CABG”), ischemic stroke, or death due to coronary heart disease (CHD).

These specific outcomes were defined and categorized as follow: non-fatal MI defined algorithmically by UK Biobank (ICD9: 410.X, 411.0.X, 412.X, 429.79; ICD10: I21.X, I22.X, I23.X, I24.1, I25.2; self-report 20002: 1075), PTCA or CABG (self-report 20004: 1070, 1095, 1523; Procedures (OPCS): K50.1, K40.X, K41.X, K42.X, K43.X, K44.X), ischemic stroke (ICD9: 434.X, 436.X; ICD10: I63.X, I64.X; self-report 20002: 1583), and death due to CHD (Death 40001, 40002: I21.X, I22.X, I23.X, I24.X, I25.1, I25.2, I25.3, I25.5, I25.6, I25.8, I25.9) [17].

Mendelian randomization

We conducted split sample approach in two-sample MR setting to avoid sample overlap [20].

The dataset was randomly split into two halves, and GWASs were performed to estimate both instrumental variable–exposure and instrument variable–outcome associations for each half.

To ensure homogeneity, we limited our analyses to unrelated individuals of White British ancestry. Additionally, individuals with more than 10% missing genotypes or those with discrepancies between recorded sex and genetically determined sex were excluded. Following these exclusions, the final dataset consisted of 378,210 participants (Table 1).

thumbnail
Download:
Table 1. Baseline characteristics.

https://doi.org/10.1371/journal.pone.0294095.t001

GWASs were conducted using genotypes of the unrelated White British individuals. White British individuals are inferred from UK Biobank records. The unrelated individuals were identified using the KING software with following options: --unrelated --degree 2 (version 2.2.8) [21]. Autosomal genotypes of unrelated White British were further filtered using PLINK software (version 1.90p) with the following options; --geno 0.01, --hwe 1e-15, --maf 0.01, and --mind 0.1 [22]. For the GWASs of WBVs, REGENIE software (version 3.2.2) was utilized with the following option; --apply-rint [23]. Covariates considered in the GWAS included age, age square, genetic principal components 1 to 20, sex, and genotyping array.

To select independent instrumental variants for Mendelian randomization, summary statistics were clumped to extract index variants using PLINK software with the following options; --clump, --clump-p1 0.00000005, --clump-r2 0.001, and --clump-kb 10000 [22]. To avoid the risk of weak instrument bias, variants with F-statistics > 10 were selected [24]. The variants were filtered out if a variant had a reported association with CVD and or factors related with blood viscosity or CVD. The associations were investigated by utilizing PhenoScanner database with the following options; catalogue: diseases & traits, p-value: 5x10-8, proxies—EUR, r2: 0.8, and build: 37 [25,26]. Such factors include obesity [13,27–29], blood pressure [11,12,30], lipid traits [15–17,31–35], type 2 diabetes mellitus [14,36,37], smoking and alcohol intake [38–40]. For HSR, 45 variants from one half split and 37 variants from the other half split passed the filters, respectively. For LSR, 49 variants from one half split and 41 variants from the other half split passed the filters, respectively (S1–S4 Tables).

We conducted MR analysis to derive causal estimates using the TwoSampleMR R package (version 0.5.7) [41]. The causal estimates were initially derived using the inverse-variance weighted (IVW) method [42], followed by weighted median (WM) [43] and, MR-egger methods [44]. We estimated intercept of MR-Egger to test horizontal pleiotropy [45] and Q statistics to test global heterogeneity of the genetic instruments [46,47]. The resulting estimates from each half were then combined with fixed-effect meta-analysis to give a single estimate.

Statistical analyses

Categorical variables were presented as counts and percentages, continuous variables were presented as mean and standard deviations (SD). All significance tests were two-tailed, and statistical significance was determined at p < 0.05. The statistical analyses were performed using the R version 4.2.1 (R Foundation for Statistical Computing, Vienna, Austria).

Results

A total of 378,210 individuals were included in the study. The mean age ± SD was 57.0 ± 7.9 years. 53.7% of participants were women. The mean ± SD of calculated WBV were 55.1 ± 17.2 and 16.9 ± 0.8 for LSR and HSR, respectively. 37,859 (10.0%) of major cardiovascular events, including 23,893 (6.3%) myocardial infarction, 9,245 (2.4%) ischemic stroke, 10,376 (2.7%) revascularization, and 5,703 (1.5%) death due to CHD were recorded.

None of MR analyses provided evidence for a causal effect of WBVs on the risk of CVDs (Fig 1 and S5 Table). HSR didn’t show causality for MACE (IVW estimates: -0.02, 95% Confidence interval: -0.16–0.13, P = 0.81), MI (0.01, -0.15–0.17, P = 0.92), stroke (0.07, -0.15–0.30, p = 0.52), revascularization (0.04, -0.18–0.26, P = 0.71), and death by CVD (0.03, -0.22–0.27, P = 0.83). The not significant causal estimate remained consistent with WM and MR-egger results (Fig 1A). LSR also didn’t show causality for MACE (-0.05, -0.17–0.08, P = 0.47), MI (-0.01, -0.15–0.12, P = 0.83), stroke (-0.04, -0.25–0.17, P = 0.74), revascularization (0.06, -0.13–0.25, P = 0.54), and death by CVD (0.05, -0.19–0.28, P = 0.70). The not significant causal estimate remained consistent with WM and MR-egger results (Fig 1B).

thumbnail
Download:
Fig 1. Mendelian randomisation analysis between WBV and the risk of cardiovascular events.

Panel A shows mendelian randomisation results for the causal effect of HSR to the risk of cardiovascular events, and panel B shows mendelian randomisation results for the causal effect of LSR to the risk of cardiovascular events. The boxes represent causal estimates and the lines represent 95% confidence intervals. MACE denotes major cardiovascular event, MI myocardial infarction, IVW inverse-variance weighted, WM weighted median.

https://doi.org/10.1371/journal.pone.0294095.g001

Discussion

This study used MR to investigate the potential causal association between WBV and CVD. The result from this study indicated insufficient evidence to substantiate a causal link between WBV and the risk of CVD.

WBV is susceptible to various influencing factors. Notably, WBV demonstrate non-Newtonian fluid behaviour. Under conditions of low shear rates, blood cells tend to aggregate, leading to an elevation in viscosity. Conversely at HSR, the opposite phenomenon occurs [48–50]. WBV has been noted to exhibit association with CVD and cardiovascular risk factors in previous studies [3,51,52]. However, upon adjustments for these risk factors, the association between WBV and CVD was found to be statistically non-significant in the subsequent study [53]. The not significant association following adjustments remained consistently evident in this study using MR.

However, the study result should be interpreted and considered carefully since we have not utilized directly measured WBV. While the formula employed in our study has undergone validation and has been applied in previous researches [18,54,55], it does not considered factors for WBV such as blood cell aggregability and deformability [56]. To establish a robust causal link between WBV and both CVD and CVD-related factors, future studies should incorporate measured WBV values, taking into account these critical variables.

Supporting information

S1 Table. Instrumental variants for HSR in Group 1.

https://doi.org/10.1371/journal.pone.0294095.s001

(XLSX)

S2 Table. Instrumental variants for HSR in Group 2.

https://doi.org/10.1371/journal.pone.0294095.s002

(XLSX)

S3 Table. Instrumental variants for LSR in Group 1.

https://doi.org/10.1371/journal.pone.0294095.s003

(XLSX)

S4 Table. Instrumental variants for LSR in Group 2.

https://doi.org/10.1371/journal.pone.0294095.s004

(XLSX)

S5 Table. Mendelian randomization estimates.

https://doi.org/10.1371/journal.pone.0294095.s005

(XLSX)

Acknowledgments

This work used the Edinburgh Compute and Data Facility (ECDF) (http://www.ecdf.ed.ac.uk/). This research has been conducted using the UK Biobank Resource project 44986. For the purpose of open access, the author has applied a CC-BY public copyright licence to any Author Accepted Manuscript version arising from this submission.

References

  1. 1. Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. The lancet. 2012;380(9859):2095–128. pmid:23245604
  2. 2. Lowe G, Lee A, Rumley A, Price J, Fowkes F. Blood viscosity and risk of cardiovascular events: the Edinburgh Artery Study. British journal of haematology. 1997;96(1):168–73. pmid:9012704
  3. 3. Woodward M, Rumley A, Tunstall‐Pedoe H, Lowe GD. Does sticky blood predict a sticky end? Associations of blood viscosity, haematocrit and fibrinogen with mortality in the West of Scotland. British journal of haematology. 2003;122(4):645–50. pmid:12899720
  4. 4. Gori T, Wild PS, Schnabel R, Schulz A, Pfeiffer N, Blettner M, et al. The distribution of whole blood viscosity, its determinants and relationship with arterial blood pressure in the community: cross-sectional analysis from the Gutenberg Health Study. Therapeutic Advances in Cardiovascular Disease. 2015;9(6):354–65. pmid:26082340
  5. 5. Cetin EHO, Cetin MS, Canpolat U, Aydin S, Aras D, Topaloglu S, et al. Prognostic significance of whole blood viscosity estimated by de Simone’s formula in ST-elevation myocardial infarction. Biomarkers in Medicine. 2016;10(5):495–511. pmid:27075858
  6. 6. Erdoğan G, Yenerçağ M, Arslan U. The relationship between blood viscosity and acute arterial occlusion. Journal of Cardiovascular Emergencies. 2020;6(1):7–12.
  7. 7. Yenerçağ M, Arslan U, Çoksevim M, Dereli S, Doğduş M, Erdoğan G. Relationship between whole blood viscosity and lower extremity peripheral artery disease severity. Acta Medica Alanya. 2021;5(1):66–74.
  8. 8. Çınar T, Hayıroğlu Mİ, Selçuk M, Çiçek V, Doğan S, Kılıç Ş, et al. Association of whole blood viscosity with thrombus presence in patients undergoing transoesophageal echocardiography. The International Journal of Cardiovascular Imaging. 2021:1–7.
  9. 9. Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Human molecular genetics. 2014;23(R1):R89–R98. pmid:25064373
  10. 10. Davies NM, Holmes MV, D Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. bmj. 2018;362. pmid:30002074
  11. 11. Chan II, Kwok MK, Schooling CM. The total and direct effects of systolic and diastolic blood pressure on cardiovascular disease and longevity using Mendelian randomisation. Scientific Reports. 2021;11(1):21799. pmid:34750372
  12. 12. Malik R, Georgakis MK, Vujkovic M, Damrauer SM, Elliott P, Karhunen V, et al. Relationship between blood pressure and incident cardiovascular disease: linear and nonlinear mendelian randomization analyses. Hypertension. 2021;77(6):2004–13. pmid:33813844
  13. 13. Sun Y-Q, Burgess S, Staley JR, Wood AM, Bell S, Kaptoge SK, et al. Body mass index and all cause mortality in HUNT and UK Biobank studies: linear and non-linear mendelian randomisation analyses. bmj. 2019;364. pmid:30957776
  14. 14. Peters TM, Holmes MV, Richards JB, Palmer T, Forgetta V, Lindgren CM, et al. Sex differences in the risk of coronary heart disease associated with type 2 diabetes: a Mendelian randomization analysis. Diabetes Care. 2021;44(2):556–62. pmid:33277303
  15. 15. Voight BF, Peloso GM, Orho-Melander M, Frikke-Schmidt R, Barbalic M, Jensen MK, et al. Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study. The Lancet. 2012;380(9841):572–80. pmid:22607825
  16. 16. Ference BA, Robinson JG, Brook RD, Catapano AL, Chapman MJ, Neff DR, et al. Variation in PCSK9 and HMGCR and risk of cardiovascular disease and diabetes. New England Journal of Medicine. 2016;375(22):2144–53. pmid:27959767
  17. 17. Ference BA, Ray KK, Catapano AL, Ference TB, Burgess S, Neff DR, et al. Mendelian randomization study of ACLY and cardiovascular disease. New England Journal of Medicine. 2019;380(11):1033–42. pmid:30865797
  18. 18. de Simone G, Devereux RB, Chien S, Alderman MH, Atlas SA, Laragh JH. Relation of blood viscosity to demographic and physiologic variables and to cardiovascular risk factors in apparently normal adults. Circulation. 1990;81(1):107–17. pmid:2297818
  19. 19. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS medicine. 2015;12(3):e1001779. pmid:25826379
  20. 20. Burgess S, Davies NM, Thompson SG. Bias due to participant overlap in two‐sample Mendelian randomization. Genetic epidemiology. 2016;40(7):597–608. pmid:27625185
  21. 21. Manichaikul A, Mychaleckyj JC, Rich SS, Daly K, Sale M, Chen W-M. Robust relationship inference in genome-wide association studies. Bioinformatics. 2010;26(22):2867–73. pmid:20926424
  22. 22. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. The American journal of human genetics. 2007;81(3):559–75. pmid:17701901
  23. 23. Mbatchou J, Barnard L, Backman J, Marcketta A, Kosmicki JA, Ziyatdinov A, et al. Computationally efficient whole-genome regression for quantitative and binary traits. Nature genetics. 2021;53(7):1097–103. pmid:34017140
  24. 24. Pierce BL, Ahsan H, VanderWeele TJ. Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. International journal of epidemiology. 2011;40(3):740–52. pmid:20813862
  25. 25. Staley JR, Blackshaw J, Kamat MA, Ellis S, Surendran P, Sun BB, et al. PhenoScanner: a database of human genotype–phenotype associations. Bioinformatics. 2016;32(20):3207–9. pmid:27318201
  26. 26. Kamat MA, Blackshaw JA, Young R, Surendran P, Burgess S, Danesh J, et al. PhenoScanner V2: an expanded tool for searching human genotype–phenotype associations. Bioinformatics. 2019;35(22):4851–3. pmid:31233103
  27. 27. Elagizi A, Kachur S, Lavie CJ, Carbone S, Pandey A, Ortega FB, et al. An overview and update on obesity and the obesity paradox in cardiovascular diseases. Progress in cardiovascular diseases. 2018;61(2):142–50. pmid:29981771
  28. 28. Khan SS, Ning H, Wilkins JT, Allen N, Carnethon M, Berry JD, et al. Association of body mass index with lifetime risk of cardiovascular disease and compression of morbidity. JAMA cardiology. 2018;3(4):280–7. pmid:29490333
  29. 29. Guiraudou M, Varlet-Marie E, Raynaud de Mauverger E, Brun J-F. Obesity-related increase in whole blood viscosity includes different profiles according to fat localization. Clinical Hemorheology and Microcirculation. 2013;55(1):63–73. pmid:23455838
  30. 30. Letcher RL, Chien S, Pickering TG, Sealey JE, Laragh JH. Direct relationship between blood pressure and blood viscosity in normal and hypertensive subjects: role of fibrinogen and concentration. The American journal of medicine. 1981;70(6):1195–202.
  31. 31. Ference BA, Ginsberg HN, Graham I, Ray KK, Packard CJ, Bruckert E, et al. Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel. European heart journal. 2017;38(32):2459–72. pmid:28444290
  32. 32. Jeong SM, Choi S, Kim K, Kim SM, Lee G, Park SY, et al. Effect of change in total cholesterol levels on cardiovascular disease among young adults. Journal of the American Heart Association. 2018;7(12):e008819. pmid:29899019
  33. 33. Laufs U, Parhofer KG, Ginsberg HN, Hegele RA. Clinical review on triglycerides. European heart journal. 2020;41(1):99–109c. pmid:31764986
  34. 34. Kosmas CE, Martinez I, Sourlas A, Bouza KV, Campos FN, Torres V, et al. High-density lipoprotein (HDL) functionality and its relevance to atherosclerotic cardiovascular disease. Drugs in context. 2018;7. pmid:29623098
  35. 35. Irace C, Carallo C, Scavelli F, Esposito T, De Franceschi MS, Tripolino C, et al. Influence of blood lipids on plasma and blood viscosity. Clinical Hemorheology and Microcirculation. 2014;57(3):267–74. pmid:23445635
  36. 36. Liu B, Mason AM, Sun L, Di Angelantonio E, Gill D, Burgess S. Genetically predicted type 2 diabetes mellitus liability, glycated hemoglobin and cardiovascular diseases: a wide-angled Mendelian randomization study. Genes. 2021;12(10):1644. pmid:34681038
  37. 37. Andrea V and Timan I. Relationship between diabetes mellitus and blood viscosity as measured by the digital microcapillary® system. Journal of Physics: Conference Series; 2018: IOP Publishing.
    • 38. Rosoff DB, Davey Smith G, Mehta N, Clarke T-K, Lohoff FW. Evaluating the relationship between alcohol consumption, tobacco use, and cardiovascular disease: a multivariable Mendelian randomization study. PLoS medicine. 2020;17(12):e1003410. pmid:33275596
    • 39. Lowe G, Drummond M, Forbes C, Barbenel J. The effects of age and cigarette-smoking on blood and plasma viscosity in men. Scottish medical journal. 1980;25(1):13–7. pmid:7209492
    • 40. Hamazaki T, Shishido H. Increase in blood viscosity due to alcohol drinking. Thrombosis Research. 1983;30(6):587–94. pmid:6612686
    • 41. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenome. elife. 2018;7:e34408. pmid:29846171
    • 42. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genetic epidemiology. 2013;37(7):658–65. pmid:24114802
    • 43. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genetic epidemiology. 2016;40(4):304–14. pmid:27061298
    • 44. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. International journal of epidemiology. 2015;44(2):512–25. pmid:26050253
    • 45. Lawlor DA, Wade K, Borges MC, Palmer T, Hartwig FP, Hemani G, et al. A Mendelian Randomization dictionary: Useful definitions and descriptions for undertaking, understanding and interpreting Mendelian Randomization studies. 2019.
    • 46. Greco M FD, Minelli C, Sheehan NA, Thompson JR. Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Statistics in medicine. 2015;34(21):2926–40. pmid:25950993
    • 47. Bowden J, Del Greco M F, Minelli C, Zhao Q, Lawlor DA, Sheehan NA, et al. Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption. International journal of epidemiology. 2019;48(3):728–42. pmid:30561657
    • 48. Pop G, Duncker D, Gardien M, Vranckx P, Versluis S, Hasan D, et al. The clinical significance of whole blood viscosity in (cardio) vascular medicine. Netherlands heart journal. 2002;10(12):512. pmid:25696056
    • 49. Naghedi-Baghdar H, Nazari S-M, Taghipour A, Nematy M, Shokri S, Mehri M-R, et al. Effect of diet on blood viscosity in healthy humans: a systematic review. Electronic physician. 2018;10(3):6563. pmid:29765583
    • 50. Nwose EU. Cardiovascular risk assessment and support techniques: whole blood viscosity assessment issues I: extrapolation chart and reference values. North American Journal of Medical Sciences. 2010;2(4):165. pmid:22624134
    • 51. Lowe G, Smith W, Tunstall-Pedoe H, Crombie I, Lennie S, Anderson J, et al. Cardiovascular risk and haemorheology–results from the Scottish Heart Health Study and the MONICA Project, Glasgow. Clinical Hemorheology and Microcirculation. 1988;8(3–4):517–24.
    • 52. Woodward M, Rumley A, Tunstall‐Pedoe H, Lowe GD. Associations of blood rheology and interleukin‐6 with cardiovascular risk factors and prevalent cardiovascular disease. British journal of haematology. 1999;104(2):246–57. pmid:10050704
    • 53. Peters SA, Woodward M, Rumley A, Tunstall-Pedoe HD, Lowe GD. Plasma and blood viscosity in the prediction of cardiovascular disease and mortality in the Scottish Heart Health Extended Cohort Study. European journal of preventive cardiology. 2017;24(2):161–7. pmid:27798361
    • 54. Høieggen A, Fossum E, Moan A, Enger E, Kjeldsen SE. Whole-blood viscosity and the insulin-resistance syndrome. Journal of hypertension. 1998;16(2):203–10. pmid:9535148
    • 55. Moan A, Nordby G, Os I, Birkeland KI, Kjeldsen SE. Relationship between hemorrheologic factors and insulin sensitivity in healthy young men. Metabolism. 1994;43(4):423–7. pmid:8159097
    • 56. Lowe G. 1 Blood rheology in vitro and in vivo. Bailliere’s clinical haematology. 1987;1(3):597–636.