Inflammation contributes to atherosclerosis and coronary artery disease (CAD). In order to
help identify therapeutic targets, it is important to ascertain whether biomarkers associated
with CAD risk are causal. In a recent meta-analysis of clinical trials, neutrophil-to lymphocyte ratio (NLR) was associated with increased cardiovascular risk 1
. We investigate a
potential causal nature of this relationship by performing Mendelian randomization (MR)
analyse

Asselbergs, Folkert W

Burgess, Stephen

Cupido, Arjen J

Gill, Dipender

Hovingh, G Kees

Kraaijenhof, Jordan M

English

PubMed

Apollo (Cambridge)

Genetically Predicted Neutrophil-to-lymphocyte Ratio and Coronary Artery Disease: Evidence from Mendelian RandomizationArjen J. Cupido, MD1,2,3, Jordan M. Kraaijenhof, MD1, Stephen Burgess, PhD4,5, Prof F.W. Asselbergs, MD, PhD2,6,7, Prof G. Kees Hovingh, MD, PhD#1, Dipender Gill, MD, PhD#8,9,101Department of Vascular Medicine, Academic Medical Center, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands2Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands3Division of Cardiology, Department of Medicine, University of California, Los Angeles, Los Angeles, CA4Medical Research Council Biostatistics Unit, Cambridge Institute of Public Health, University of Cambridge, Cambridge, UK5Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK6Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK7Health Data Research UK and Institute of Health Informatics, University College London, London, UK8Novo Nordisk Research Centre Oxford, Old Road Campus, Oxford, UK9Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK10Clinical Pharmacology and Therapeutics Section, Institute for Infection and Immunity, St George’s, University of London, London, UK# These authors contributed equally to this work.Keywordscardiovascular disease; inflammation; causality; NLRThis work is licensed under a CC BY 4.0 International license.Correspondence to: Arjen J. Cupido.Address for correspondence: Arjen Cupido, 3525 MRL; 675 Charles E. Young Drive South, Los Angeles, CA 90095, a.j.cupido@amsterdamumc.nl, T: +1 424 385 6721. Disclosures: GKH reports institutional research support from Aegerion, Amgen, AstraZeneca, Eli Lilly, Genzyme, Ionis, Kowa, Pfizer, Regeneron, Roche, Sanofi, and The Medicines Company; speaker’s bureau and consulting fees from Amgen, Aegerion, Sanofi, and Regeneron (fees paid to the academic institution); and part-time employment at Novo Nordisk, outside of the submitted work. DG is employed part-time by Novo Nordisk, outside of the submitted work.Europe PMC Funders GroupAuthor ManuscriptCirc Genom Precis Med. Author manuscript; available in PMC 2022 February 16.Published in final edited form as:Circ Genom Precis Med. 2022 February 01; 15(1): e003553. doi:10.1161/CIRCGEN.121.003553. Europe PMC Funders Author Manuscripts Europe PMC Funders Author ManuscriptsInflammation contributes to atherosclerosis and coronary artery disease (CAD). In order to help identify therapeutic targets, it is important to ascertain whether biomarkers associated with CAD risk are causal. In a recent meta-analysis of clinical trials, neutrophil-to-lymphocyte ratio (NLR) was associated with increased cardiovascular risk 1 . We investigate a potential causal nature of this relationship by performing Mendelian randomization (MR) analyses.All participants provided prior consent for each study included, and the studies were approved by the relevant review committees. Non-public estimates are available from the corresponding author upon reasonable request. To identify genetic variants associated with NLR, we obtained published genome-wide association study (GWAS) summary data for neutrophil count and lymphocyte count in 361,194 European ancestry participants from http://www.nealelab.is/uk-biobank/, which were selected based on self-reported ancestry and genetic principal components. The propagation of error method was used to estimate the association of all available single-nucleotide polymorphisms (SNPs) with lymphocyte count subtracted from neutrophil count, resulting either in a positive value (which represents an increasing NLR) or a negative value (representing a decreasing NLR). Instruments were selected for Mendelian randomization by clumping all common SNPs (MAF > 0.01) at genome-wide significance (p<5×10-8) to pairwise linkage disequilibrium threshold r2 <0.01 (using European participants from the 1000Genomes project as reference). We estimated the association of each candidate instrument with NLR in individual participant data on 396,020 UK Biobank participants of white British descent with similar genetic ancestry (as reported by the UK Biobank resource), for whom neutrophil count, lymphocyte count and data on all selected SNPs were available and had no extreme NLR value (defined as either NLR = 0 or NLR = infinity) 2 . To estimate the association of each SNP with NLR, we used log-linear regression with adjustment for age, sex, the first 10 principal components of genetic ancestry, and measurement batch. We chose log-linear regression because NLR can only be positive, with some values close to zero, rendering normal linear regression inappropriate. Figure 1A shows the distribution of NLR, which is similar to the distributions from various clinical trials including CANTOS 1 .Statistical power calculations for the minimum detectable odds ratio were performed for a power of 80% and type 1 error rate of 0.05 3 . We performed two-sample MR analyses, where we considered the following outcomes: CAD, myocardial infarction (MI), circulating C-reactive protein (CRP), interleukin 6 (IL-6) and fibrinogen levels (Figure). CRP, IL-6 and fibrinogen were considered to investigate the effect of NLR on inflammatory biomarkers 4 . Finally, to investigate the validity of the NLR instruments, we performed analyses with neutrophil count and lymphocyte count as outcomes. If data on a SNP was unavailable in one specific GWAS, we searched for an available proxy in high LD (r 2 > 0.9) and if unavailable we omitted the SNPs for that specific analysis. All analyses were performed using the package TwoSampleMR in R v4.0.3. Results are presented per 1 unit increase in genetically-predicted log-transformed NLR.Power calculations showed that we had 80% power to detect a minimum odds ratio of 1.07 for CAD. For MI, power calculations showed 80% power to detect an odds ratio of 1.08. After clumping and omitting multi-allelic SNPs, a total of 182 uncorrelated SNPs Cupido et al. Page 2Circ Genom Precis Med. Author manuscript; available in PMC 2022 February 16. Europe PMC Funders Author Manuscripts Europe PMC Funders Author Manuscriptswere selected as potential instruments in MR analyses given their genome-wide significant association with lymphocyte count subtracted from neutrophil count. In primary analyses (Figure 1B), we observed strong evidence of an association between genetically-predicted NLR and neutrophil count (2.66 % of white blood cell count (WBC), 95% CI 2.42, 2.90) and lymphocyte count (-0.59 % of WBC, 95% CI -0.83, -0.35). We did not observe evidence supporting a causal effect of NLR on CAD (0.91, 95% CI 0.69, 1.19), MI (0.91, 95% CI 0.67, 1.22), CRP (0.02 natural-log(mg/l) units, 95% CI -0.34, 0.39), IL-6 (-0.16 SD units, 95% CI - 0.49;0.17) or fibrinogen (0.07 log(g/L), 95% CI -0.18;0.32). In sensitivity analyses, we observed similar results using the MR Egger method. Excluding outliers, MR-PRESSO showed similar results to the primary inverse-variance weighted results, with the exception of CRP (Figure 1B).This MR study did not identify evidence to support that NLR is causally related to risk of CAD and MI. Moreover, we did not find consistent evidence of a causal association of NLR on CRP, IL6 or fibrinogen levels. This contrasts with previous reports that have suggested a potential causal role for NLR in CAD, given the finding that both neutrophils and lymphocytes are involved in atherogenesi 1,5 . The discrepancy may be attributable to the associations identified in epidemiological studies arising due to confounding and reverse causation. However, even if not causally related to CAD, NLR could still be used as a predictive measure for cardiovascular disease risk. To what extent NLR has added value in future cardiovascular risk prediction over and above CRP levels remains to be fully explored.Most data are retrievable from the public domain. Summary data for the genetic instrument are available from the corresponding author.Sources of fundingAJC reports grants from the Prince Bernhard Culture fund and Stichting de Drie Lichten. SB is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (204623). FWA is supported by University College London Hospitals National Institute for Health Research Biomedical Research Centre. GKH: reports research grants from Netherlands Organization for Scientific Research (ViDi 016.156.445), het Klinkerpad fonds and the European Union (transcard). All others have none.Non-standard Abbreviations and AcronymsCAD Coronary artery diseaseCRP C-reactive proteinGWAS Genome-wide association studyIL-6 Interleukin-6MAF Minor allele frequencyMI Myocardial infarctionMR Mendelian RandomizationNLR Neutrophil – to – Lymphocyte ratioCupido et al. Page 3Circ Genom Precis Med. Author manuscript; available in PMC 2022 February 16. Europe PMC Funders Author Manuscripts Europe PMC Funders Author ManuscriptsSNP Single-nucleotide polymorphismsReferences1. Adamstein NH, MacFadyen JG, Rose LM, Glynn RJ, Dey AK, Libby P, Tabas IA, Mehta NN, Ridker PM. The neutrophil–lymphocyte ratio and incident atherosclerotic events: analyses from five contemporary randomized trials. Eur Heart J. 2021; 42: 896–903. [PubMed: 33417682] 2. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, Motyer A, Vukcevic D, Delaneau O, O’Connell J, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018; 562: 203–209. [PubMed: 30305743] 3. Burgess S. Sample size and power calculations in Mendelian randomization with a single instrumental variable and a binary outcome. Int J of Epidemiology. 2014; 43: 922–929. 4. Kalaoja M, Corbin LJ, Tan VY, Ahola-Olli Av, Havulinna AS, Santalahti K, Pitkänen N, Lehtimäki T, Lyytikäinen LP, Raitoharju E, et al. The Role of Inflammatory Cytokines as Intermediates in the Pathway from Increased Adiposity to Disease. Obesity. 2021; 29: 428–437. [PubMed: 33491305] 5. Soehnlein O, Libby P. Targeting inflammation in atherosclerosis — from experimental insights to the clinic. Nat Rev Drug Discovery. 2021. Cupido et al. Page 4Circ Genom Precis Med. Author manuscript; available in PMC 2022 February 16. Europe PMC Funders Author Manuscripts Europe PMC Funders Author ManuscriptsFigure 1. NLR distribution and forest plotA: Distribution of NLR in the UK Biobank. 767 values above 10 are not displayed for clarity purposes. B: Overview of included data and estimates for the Mendelian randomization association scaled to a 1 unit increase in genetically-predicted log-transformed NLR. Estimates for CAD and MI are depicted as odds ratio; neutrophil and lymphocyte counts as percentage of total white blood cell count; CRP as natural-log(mg/L); IL-6 as SD unit; and fibrinogen as log(g/L). n = number of SNPs of the instrument available in the outcome dataset and thus used for analysis. P intercept = Egger intercept p-value, Outliers = outliers Cupido et al. Page 5Circ Genom Precis Med. Author manuscript; available in PMC 2022 February 16. Europe PMC Funders Author Manuscripts Europe PMC Funders Author Manuscriptsremoved by MR PRESSO. IVW = Inverse Variance Weighted method. ieu-X-XXX = MRC integrative Epidemiology Unit open GWAS project dataset code for use in TwoSampleMR package (https://gwas.mrcieu.ac.uk/).Cupido et al. Page 6Circ Genom Precis Med. Author manuscript; available in PMC 2022 February 16. Europe PMC Funders Author Manuscripts Europe PMC Funders Author Manuscripts

Genetically Predicted Neutrophil-to-Lymphocyte Ratio and Coronary Artery Disease: Evidence From Mendelian Randomization.

Cupido, Arjen J.

Kraaijenhof, Jordan M.

Asselbergs, Folkert W.

Hovingh, G. Kees

NARCIS 

Genetically Predicted Neutrophil-to-Lymphocyte Ratio and Coronary Artery Disease: Evidence From Mendelian Randomization

Asselbergs, FW

UCL Discovery

80Circulation: Genomic and Precision MedicineCirculation: Genomic and Precision Medicine is available at www.ahajournals.org/journal/circgenCirc Genom Precis Med. 2022;15:e003553. DOI: 10.1161/CIRCGEN.121.003553 February 2022Key Words: cardiovascular diseases ◼ causality ◼ inflammation ◼ linear models ◼ neutrophils Correspondence to: Arjen J. Cupido, MD, University of California, Los Angeles, 3525 MRL, 675 Charles E Young Dr S, Los Angeles, CA 90095. Email a.j.cupido@amsterdamumc.nl*G.K. Hovingh and D. Gill contributed equally.For Sources of Funding and Disclosures, see page 80.© 2022 The Authors. Circulation: Genomic and Precision Medicine is published on behalf of the American Heart Association, Inc., by Wolters Kluwer Health, Inc. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution, and reproduction in any medium, provided that the original work is properly cited.RESEARCH LETTERGenetically Predicted Neutrophil-to-Lymphocyte Ratio and Coronary Artery Disease: Evidence From Mendelian RandomizationArjen J. Cupido , MD; Jordan M. Kraaijenhof , MD; Stephen Burgess , PhD; Folkert W. Asselbergs , MD, PhD;  G. Kees Hovingh , MD, PhD*; Dipender Gill , MD, PhD*Inflammation contributes to atherosclerosis and coro-nary artery disease (CAD). To help identify therapeutic targets, it is important to ascertain whether biomark-ers associated with CAD risk are causal. In a recent meta-analysis of clinical trials, neutrophil-to-lymphocyte ratio (NLR) was associated with increased cardiovas-cular risk.1 We investigate a potential causal nature of this relationship by performing Mendelian randomization (MR) analyses.All participants provided prior consent for each study included, and the studies were approved by the relevant review committees. Nonpublic estimates are available from the corresponding author upon reasonable request. To identify genetic variants associated with NLR, we obtained published genome-wide association study sum-mary data for neutrophil count and lymphocyte count in 361 194 European ancestry participants from http://www.nealelab.is/uk-biobank/, which were selected based on self-reported ancestry and genetic principal components. The propagation of error method was used to estimate the association of all available single-nucleo-tide polymorphisms (SNPs) with lymphocyte count sub-tracted from neutrophil count, resulting either in a positive value (which represents an increasing NLR) or a nega-tive value (representing a decreasing NLR). Instruments were selected for MR by clumping all common SNPs (minor allele frequency, >0.01) at genome-wide sig-nificance (P<5×10−8) to pairwise linkage disequilibrium threshold r2 <0.01 (using European participants from the 1000Genomes project as reference). We estimated the association of each candidate instrument with NLR in individual participant data on 396 020 UK Biobank participants of White British descent with similar genetic ancestry (as reported by the UK Biobank resource), for whom neutrophil count, lymphocyte count, and data on all selected SNPs were available and had no extreme NLR value (defined as either NLR=0 or NLR=infinity).2 To estimate the association of each SNP with NLR, we used log-linear regression with adjustment for age, sex, the first 10 principal components of genetic ancestry, and measurement batch. We chose log-linear regres-sion because NLR can only be positive, with some values close to zero, rendering normal linear regression inap-propriate. The Figure (A) shows the distribution of NLR, which is similar to the distributions from various clinical trials including CANTOS (Canakinumab Anti-Inflamma-tory Thrombosis Outcome Study).1Statistical power calculations for the minimum detectable odds ratio were performed for a power of 80% and type 1 error rate of 0.05.3 We performed 2-sample MR analyses, where we considered the fol-lowing outcomes: CAD, myocardial infarction, circulat-ing CRP (C-reactive protein), IL-6 (interleukin-6), and fibrinogen levels (Figure). CRP, IL-6, and fibrinogen were considered to investigate the effect of NLR on inflammatory biomarkers.4 Finally, to investigate the validity of the NLR instruments, we performed analy-ses with neutrophil count and lymphocyte count as outcomes. If data on a SNP were unavailable in one specific genome-wide association study, we searched Downloaded from http://ahajournals.org by on July 5, 202281Cupido et al Neutrophil-to-Lymphocyte Ratio and Heart DiseaseCirc Genom Precis Med. 2022;15:e003553. DOI: 10.1161/CIRCGEN.121.003553 February 2022for an available proxy in high linkage disequilibrium (r2 >0.9), and if unavailable, we omitted the SNPs for that specific analysis. All analyses were performed using the package TwoSampleMR in R v4.0.3. Results are presented per 1-unit increase in genetically predicted log-transformed NLR.Power calculations showed that we had 80% power to detect a minimum odds ratio of 1.07 for CAD. For myocardial infarction, power calculations showed 80% power to detect an odds ratio of 1.08. After clumping and omitting multiallelic SNPs, a total of 182 uncorre-lated SNPs were selected as potential instruments in MR analyses given their genome-wide significant asso-ciation with lymphocyte count subtracted from neutro-phil count. In primary analyses (Figure [B]), we observed strong evidence of an association between genetically predicted NLR and neutrophil count (2.66% of white blood cell count [95% CI, 2.42–2.90]) and lymphocyte count (−0.59% of white blood cell count [95% CI, −0.83 to −0.35]). We did not observe evidence supporting a causal effect of NLR on CAD (0.91 [95% CI, 0.69–1.19]), myocardial infarction (0.91 [95% CI, 0.67–1.22]), CRP (0.02 natural-log(mg/L) units [95% CI, −0.34 to 0.39]), IL-6 (−0.16 SD units [95% CI, −0.49 to 0.17]), or fibrinogen (0.07 log(g/L) [95% CI, −0.18 to 0.32]). In sensitivity analyses, we observed similar results using the MR Egger method. Excluding outliers,  MR-Pleiotropy Residual Sum and Outlier showed similar results to the primary inverse-variance weighted results, with the exception of CRP (Figure [B]).This MR study did not identify evidence to support that NLR is causally related to risk of CAD and myo-cardial infarction. Moreover, we did not find consistent evidence of a causal association of NLR on CRP, IL-6, or fibrinogen levels. This contrasts with previous reports that have suggested a potential causal role for NLR in CAD, given the finding that both neutrophils and lym-phocytes are involved in atherogenesis.1,5 The discrep-ancy may be attributable to the associations identified in epidemiological studies arising due to confounding and reverse causation. However, even if not causally related to CAD, NLR could still be used as a predictive measure for cardiovascular disease risk. To what extent NLR has added value in future cardiovascular risk prediction over and above CRP levels remains to be fully explored.Most data are retrievable from the public domain. Summary data for the genetic instrument are available from the corresponding author.ARTICLE INFORMATIONAffiliationsDepartment of Vascular Medicine, Academic Medical Center, Amsterdam Uni-versity Medical Centers, University of Amsterdam, the Netherlands (A.J.C., J.M.K., G.K.H.). Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, the Netherlands (A.J.C., F.W.A.). Division of Car-diology, Department of Medicine, University of California, Los Angeles, Los An-geles, United States of America. (A.J.C.). Medical Research Council Biostatistics Unit, Cambridge Institute of Public Health (S.B.) and Department of Public Health and Primary Care (S.B.), University of Cambridge, United Kingdom. Institute of Cardiovascular Science, Faculty of Population Health Sciences (F.W.A.) and Health Data Research UK and Institute of Health Informatics (F.W.A.), University College London, United Kingdom. Genetics Department, Novo Nordisk Research Centre Oxford, Old Road Campus, United Kingdom (D.G.). Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, United King-dom (D.G.). Department of Clinical Pharmacology and Therapeutics, Institute for Infection and Immunity, St. George’s, University of London, United Kingdom (D.G.).Sources of FundingDr Cupido reports grants from the Prince Bernhard Culture Fund and Sticht-ing de Drie Lichten. Dr Burgess is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (204623/Z/16/Z) and supported the National Institute for Health Research Cambridge Biomedical Research Centre (BRC-1215-20014). The views expressed are those of the authors and not necessarily those of the National Institute for Health Research or the Department of Health and Social Care. Dr Asselbergs is supported by Uni-versity College London Hospitals National Institute for Health Research Biomedi-cal Research Centre. Dr Hovingh reports research grants from the Netherlands Organization for Scientific Research (ViDi 016.156.445), het Klinkerpad fonds and the European Union (transcard). Dr Gill is supported by the British Heart Foundation Research Centre of Excellence (RE/18/4/34215) at Imperial Col-lege London and by a National Institute for Health Research Clinical Lectureship (CL-2020-16-001) at St. George's, University of London.DisclosuresDr Hovingh has received funding from Regeneron, Aegerion, Amgen, AstraZen-eca, Eli Lilly, Genzyme, Kowa, Pfizer, Regeneron Pharmaceuticals, Roche, Sanofi, The Medicines Company, and Ionis and is part-time employed by Novo Nordisk, Copenhagen, Denmark. Dr Gill is employed part-time by Novo Nordisk. The other authors report no conflicts.REFERENCES 1. Adamstein NH, MacFadyen JG, Rose LM, Glynn RJ, Dey AK, Libby P, Tabas IA, Mehta NN, Ridker PM. The neutrophil-lymphocyte ratio and incident atherosclerotic events: analyses from five contemporary randomized trials. Eur Heart J. 2021;42:896–903. doi: 10.1093/eurheartj/ehaa1034 2. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, Motyer A, Vukcevic D, Delaneau O, O’Connell J, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562:203–209. doi: 10.1038/s41586-018-0579-z 3. Burgess S. Sample size and power calculations in Mendelian randomization with a single instrumental variable and a binary outcome. Int J Epidemiol. 2014;43:922–929. doi: 10.1093/ije/dyu005 4. Kalaoja M, Corbin LJ, Tan VY, Ahola-Olli AV, Havulinna AS, Santalahti K, Pitkänen N, Lehtimäki T, Lyytikäinen LP, Raitoharju E, et al. The role of inflammatory cytokines as intermediates in the pathway from increased adiposity to disease. Obesity (Silver Spring). 2021;29:428–437. doi: 10.1002/oby.23060 5. Soehnlein O, Libby P. Targeting inflammation in atherosclerosis - from experimental insights to the clinic. Nat Rev Drug Discov. 2021;20:589–610. doi: 10.1038/s41573-021-00198-1Downloaded from http://ahajournals.org by on July 5, 202282Cupido et al Neutrophil-to-Lymphocyte Ratio and Heart DiseaseCirc Genom Precis Med. 2022;15:e003553. DOI: 10.1161/CIRCGEN.121.003553 February 2022Figure. Neutrophil-to-lymphocyte ratio (NLR) distribution and forest plot.A, Distribution of NLR in the UK Biobank. Seven hundred sixty-seven values above 10 are not displayed for clarity purposes. B, Overview of included data and estimates for the Mendelian randomization association scaled to a 1-unit increase in genetically predicted log-transformed NLR. Estimates for coronary artery disease and myocardial infarction are depicted as odds ratio (OR); neutrophil and lymphocyte counts as percentage of total white blood cell count; CRP (C-reactive protein) as natural-log(mg/L); IL-6 (interleukin-6) as SD unit; and fibrinogen as log(g/L). n: number of single-nucleotide polymorphisms of the instrument available in the outcome data set and thus used for analysis; P intercept: Egger intercept P; outliers: outliers removed by MR-PRESSO. ieu-X-XXX: MRC Integrative Epidemiology Unit open genome-wide association study (GWAS) project data set code for use in TwoSampleMR package (https://gwas.mrcieu.ac.uk/). BMI indicates body mass index; and IVW, inverse-variance weighted. CHARGE indicates Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium; MR-PRESSO, Mendelian randomization-Pleiotropy Residual Sum and Outlier; and YFS, The Cardiovascular risk in Young Finns Study.Downloaded from http://ahajournals.org by on July 5, 2022

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Genetically Predicted Neutrophil-to-Lymphocyte Ratio and Coronary Artery Disease: Evidence From Mendelian Randomization.

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