GBD Causes of Death Collaborators. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 403, 2100–2132 (2024).
Goh, R. S. J. et al. The burden of cardiovascular disease in Asia from 2025 to 2050: a forecast analysis for East Asia, South Asia, South-East Asia, Central Asia, and high-income Asia Pacific regions. Lancet Reg. Health West Pac. 49, 101138 (2024).
Joynt Maddox, K. E. et al. Forecasting the burden of cardiovascular disease and stroke in the United States through 2050—prevalence of risk factors and disease: a presidential advisory from the American Heart Association. Circulation 150, e65–e88 (2024).
Bonaventura, A. & Abbate, A. Colchicine for cardiovascular prevention: the dawn of a new era has finally come. Eur. Heart J. 44, 3303–3304 (2023).
Nelson, K., Fuster, V. & Ridker, P. M. Low-dose colchicine for secondary prevention of coronary artery disease: JACC review topic of the week. J. Am. Coll. Cardiol. 82, 648–660 (2023).
Engelen, S. E., Robinson, A. J. B., Zurke, Y. X. & Monaco, C. Therapeutic strategies targeting inflammation and immunity in atherosclerosis: how to proceed? Nat. Rev. Cardiol. 19, 522–542 (2022).
Potere, N., Bonaventura, A. & Abbate, A. Novel therapeutics and upcoming clinical trials targeting inflammation in cardiovascular diseases. Arterioscler. Thromb. Vasc. Biol. 44, 2371–2395 (2024).
Ridker, P. M. & Rane, M. Interleukin-6 signaling and anti-interleukin-6 therapeutics in cardiovascular disease. Circ. Res. 128, 1728–1746 (2021).
Ridker, P. M. et al. IL-6 inhibition with ziltivekimab in patients at high atherosclerotic risk (RESCUE): a double-blind, randomised, placebo-controlled, phase 2 trial. Lancet 397, 2060–2069 (2021).
ZEUS—effects of ziltivekimab versus placebo on cardiovascular outcomes in participants with established atherosclerotic cardiovascular disease, chronic kidney disease and systemic inflammation. Clinicaltrials.gov https://clinicaltrials.gov/study/NCT05021835 (2021).
Chertow, G. M. et al. IL-6 inhibition with clazakizumab in patients receiving maintenance dialysis: a randomized phase 2b trial. Nat. Med. 30, 2328–2336 (2024).
A phase 2, randomized, double-blind, placebo-controlled study to evaluate quarterly and monthly TOUR006 in participants with chronic kidney disease and elevated high-sensitivity C-reactive protein. Clinicaltrials.gov https://www.clinicaltrials.gov/study/NCT06362759 (2024).
IL6R Genetics Consortium Emerging Risk Factors Collaboration Interleukin-6 receptor pathways in coronary heart disease: a collaborative meta-analysis of 82 studies. Lancet 379, 1205–1213 (2012).
Interleukin-6 Receptor Mendelian Randomisation Analysis Consortium. The interleukin-6 receptor as a target for prevention of coronary heart disease: a mendelian randomisation analysis. Lancet 379, 1214–1224 (2012).
Georgakis, M. K., Malik, R., Burgess, S. & Dichgans, M. Additive effects of genetic interleukin-6 signaling downregulation and low-density lipoprotein cholesterol lowering on cardiovascular disease: a 2×2 factorial Mendelian randomization analysis. J. Am. Heart Assoc. 11, e023277 (2022).
Georgakis, M. K. et al. Genetically downregulated interleukin-6 signaling is associated with a favorable cardiometabolic profile: a phenome-wide association study. Circulation 143, 1177–1180 (2021).
Georgakis, M. K. et al. Associations of genetically predicted IL-6 signaling with cardiovascular disease risk across population subgroups. BMC Med. 20, 245 (2022).
Levin, M. G. et al. A missense variant in the IL-6 receptor and protection from peripheral artery disease. Circ. Res. 129, 968–970 (2021).
Georgakis, M. K. et al. Interleukin-6 signaling effects on ischemic stroke and other cardiovascular outcomes: a Mendelian randomization study. Circ. Genom. Precis. Med. 13, e002872 (2020).
Prapiadou, S. et al. Proteogenomic data integration reveals CXCL10 as a potentially downstream causal mediator for IL-6 signaling on atherosclerosis. Circulation 149, 669–683 (2024).
Ferreira, R. C. et al. Functional IL6R 358Ala allele impairs classical IL-6 receptor signaling and influences risk of diverse inflammatory diseases. PLoS Genet. 9, e1003444 (2013).
Kang, S., Tanaka, T., Narazaki, M. & Kishimoto, T. Targeting interleukin-6 signaling in clinic. Immunity 50, 1007–1023 (2019).
Rose-John, S., Jenkins, B. J., Garbers, C., Moll, J. M. & Scheller, J. Targeting IL-6 trans-signalling: past, present and future prospects. Nat. Rev. Immunol. 23, 666–681 (2023).
Nishimoto, N. et al. Mechanisms and pathologic significances in increase in serum interleukin-6 (IL-6) and soluble IL-6 receptor after administration of an anti-IL-6 receptor antibody, tocilizumab, in patients with rheumatoid arthritis and Castleman disease. Blood 112, 3959–3964 (2008).
Heink, S. et al. Trans-presentation of IL-6 by dendritic cells is required for the priming of pathogenic TH17 cells. Nat. Immunol. 18, 74–85 (2017).
Ridker, P. M. et al. Modulation of the interleukin-6 signalling pathway and incidence rates of atherosclerotic events and all-cause mortality: analyses from the Canakinumab Anti-Inflammatory Thrombosis Outcomes Study (CANTOS). Eur. Heart J. 39, 3499–3507 (2018).
Ridker, P. M. et al. Relationship of C-reactive protein reduction to cardiovascular event reduction following treatment with canakinumab: a secondary analysis from the CANTOS randomised controlled trial. Lancet 391, 319–328 (2018).
Ridker, P. M. et al. Antiinflammatory therapy with canakinumab for atherosclerotic disease. N. Engl. J. Med. 377, 1119–1131 (2017).
Ridker, P. M. From C-reactive protein to interleukin-6 to interleukin-1: moving upstream to identify novel targets for atheroprotection. Circ. Res. 118, 145–156 (2016).
Garbers, C., Heink, S., Korn, T. & Rose-John, S. Interleukin-6: designing specific therapeutics for a complex cytokine. Nat. Rev. Drug Discov. 17, 395–412 (2018).
Stone, J. H. et al. Trial of tocilizumab in giant-cell arteritis. N. Engl. J. Med. 377, 317–328 (2017).
Said, S. et al. Genetic analysis of over half a million people characterises C-reactive protein loci. Nat. Commun. 13, 2198 (2022).
Gill, D. et al. Mendelian randomization for studying the effects of perturbing drug targets. Wellcome Open Res. 6, 16 (2021).
Zhao, S. et al. Adjusting for genetic confounders in transcriptome-wide association studies improves discovery of risk genes of complex traits. Nat. Genet. 56, 336–347 (2024).
Kerimov, N. et al. A compendium of uniformly processed human gene expression and splicing quantitative trait loci. Nat. Genet. 53, 1290–1299 (2021).
Konieczny, M. J. et al. The genomic architecture of circulating cytokine levels points to drug targets for immune-related diseases. Commun. Biol. 8, 34 (2025).
Smolen, J. S. et al. Effect of interleukin-6 receptor inhibition with tocilizumab in patients with rheumatoid arthritis (OPTION study): a double-blind, placebo-controlled, randomised trial. Lancet 371, 987–997 (2008).
Spiera, R. F. et al. Sarilumab for relapse of polymyalgia rheumatica during glucocorticoid taper. N. Engl. J. Med. 389, 1263–1272 (2023).
Rosa, M. et al. A Mendelian randomization study of IL6 signaling in cardiovascular diseases, immune-related disorders and longevity. npj Genom. Med. 4, 23 (2019).
Zhao, S. S. & Gill, D. Genetically proxied IL-6 receptor inhibition and risk of polymyalgia rheumatica. Ann. Rheum. Dis. 81, 1480–1882 (2022).
Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).
Kanai, M. et al. Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. Nat. Genet. 50, 390–400 (2018).
Ishigaki, K. et al. Large-scale genome-wide association study in a Japanese population identifies novel susceptibility loci across different diseases. Nat. Genet. 52, 669–679 (2020).
Burmester, G. R. et al. Efficacy and safety of sarilumab monotherapy versus adalimumab monotherapy for the treatment of patients with active rheumatoid arthritis (MONARCH): a randomised, double-blind, parallel-group phase III trial. Ann. Rheum. Dis. 76, 840–847 (2017).
Gabay, C. et al. Comparison of lipid and lipid-associated cardiovascular risk marker changes after treatment with tocilizumab or adalimumab in patients with rheumatoid arthritis. Ann. Rheum. Dis. 75, 1806–1812 (2016).
Wada, Y., Jensen, C., Meyer, A. S. P., Zonoozi, A. A. M. & Honda, H. Efficacy and safety of interleukin-6 inhibition with ziltivekimab in patients at high risk of atherosclerotic events in Japan (RESCUE-2): a randomized, double-blind, placebo-controlled, phase 2 trial. J. Cardiol. 82, 279–285 (2023).
Connelly, M. A., Otvos, J. D., Shalaurova, I., Playford, M. P. & Mehta, N. N. GlycA, a novel biomarker of systemic inflammation and cardiovascular disease risk. J. Transl. Med. 15, 219 (2017).
Ritchie, S. C. et al. The biomarker GlycA is associated with chronic inflammation and predicts long-term risk of severe infection. Cell Syst. 1, 293–301 (2015).
Campbell, L., Chen, C., Bhagat, S. S., Parker, R. A. & Ostor, A. J. Risk of adverse events including serious infections in rheumatoid arthritis patients treated with tocilizumab: a systematic literature review and meta-analysis of randomized controlled trials. Rheumatology 50, 552–562 (2011).
Fleischmann, R. et al. Long-term safety of sarilumab in rheumatoid arthritis: an integrated analysis with up to 7 years’ follow-up. Rheumatology 59, 292–302 (2020).
Kurki, M. I. et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 613, 508–518 (2023).
Huber, S. A., Sakkinen, P., Conze, D., Hardin, N. & Tracy, R. Interleukin-6 exacerbates early atherosclerosis in mice. Arterioscler. Thromb. Vasc. Biol. 19, 2364–2367 (1999).
Mehta, N. N., deGoma, E. & Shapiro, M. D. IL-6 and cardiovascular risk: a narrative review. Curr. Atheroscler. Rep. 27, 12 (2024).
Cupido, A. J. et al. Dissecting the IL-6 pathway in cardiometabolic disease: a Mendelian randomization study on both IL6 and IL6R. Br. J. Clin. Pharmacol. 88, 2875–2884 (2022).
Soehnlein, O., Lutgens, E. & Doring, Y. Distinct inflammatory pathways shape atherosclerosis in different vascular beds. Eur. Heart J. 27, ehaf054 (2025).
Wang, Q. et al. Epicardial adipose tissue in patients with coronary artery disease: a meta-analysis. J. Cardiovasc. Dev. Dis. 9, 253 (2022).
Slysz, J. et al. Single-cell profiling reveals inflammatory polarization of human carotid versus femoral plaque leukocytes. JCI Insight 8, e171359 (2023).
Otsuka, Y. et al. Effects of tumor necrosis factor inhibitors and tocilizumab on the glycosylated hemoglobin levels in patients with rheumatoid arthritis; an observational study. PLoS ONE 13, e0196368 (2018).
Ogata, A. et al. Improvement of HbA1c during treatment with humanised anti-interleukin 6 receptor antibody, tocilizumab. Ann. Rheum. Dis. 70, 1164–1165 (2011).
Liu, C. et al. Adiponectin, TNF-alpha and inflammatory cytokines and risk of type 2 diabetes: a systematic review and meta-analysis. Cytokine 86, 100–109 (2016).
Broca, F., Souchaud-Debouverie, O., Liuu, E., Roblot, P. & Martin, M. Severe infections in patients treated with tocilizumab for systemic diseases other than rheumatoid arthritis: a retrospective multicenter observational study. Eur. J. Rheumatol. 10, 18–22 (2023).
Duchen, D. et al. Pathogen exposure misclassification can bias association signals in GWAS of infectious diseases when using population-based common control subjects. Am. J. Hum. Genet. 110, 336–348 (2023).
Khandaker, G. M., Zammit, S., Burgess, S., Lewis, G. & Jones, P. B. Association between a functional interleukin 6 receptor genetic variant and risk of depression and psychosis in a population-based birth cohort. Brain Behav. Immun. 69, 264–272 (2018).
Kappelmann, N. et al. Dissecting the association between inflammation, metabolic dysregulation, and specific depressive symptoms: a genetic correlation and 2-sample Mendelian randomization study. JAMA Psychiatry 78, 161–170 (2021).
Baghdadi, L. R. Tocilizumab reduces depression risk in rheumatoid arthritis patients: a systematic review and meta-analysis. Psychol. Res. Behav. Manag. 17, 3419–3441 (2024).
Khandaker, G. M. et al. Protocol for the insight study: a randomised controlled trial of single-dose tocilizumab in patients with depression and low-grade inflammation. BMJ Open 8, e025333 (2018).
Foley, E. M. & Khandaker, G. M. The insight study: a randomised controlled trial of single-dose tocilizumab in patients with depression and low-grade inflammation. In Proc. 37th ECNP Congress Vol. 3, 305 (Neuroscience Applied, 2024).
Miller, A. H. & Raison, C. L. The role of inflammation in depression: from evolutionary imperative to modern treatment target. Nat. Rev. Immunol. 16, 22–34 (2016).
Roohi, E., Jaafari, N. & Hashemian, F. On inflammatory hypothesis of depression: what is the role of IL-6 in the middle of the chaos? J. Neuroinflammation 18, 45 (2021).
Liu, Z. et al. Association of circulating inflammation proteins and gallstone disease. J. Gastroenterol. Hepatol. 33, 1920–1924 (2018).
Ference, B. A. et al. Variation in PCSK9 and HMGCR and risk of cardiovascular disease and diabetes. N. Engl. J. Med. 375, 2144–2153 (2016).
Burgess, S., Davies, N. M. & Thompson, S. G. Bias due to participant overlap in two-sample Mendelian randomization. Genet. Epidemiol. 40, 597–608 (2016).
Zhang, W. et al. SharePro: an accurate and efficient genetic colocalization method accounting for multiple causal signals. Bioinformatics 40, btae295 (2024).
Zhang, W., Sladek, R., Li, Y., Najafabadi, H. & Dupuis, J. Accounting for genetic effect heterogeneity in fine-mapping and improving power to detect gene-environment interactions with SharePro. Nat. Commun. 15, 9374 (2024).
Zuber, V. et al. Combining evidence from Mendelian randomization and colocalization: review and comparison of approaches. Am. J. Hum. Genet. 109, 767–782 (2022).
Parisinos, C. A. et al. Variation in interleukin 6 receptor gene associates with risk of Crohn’s disease and ulcerative colitis. Gastroenterology 155, 303–306 e302 (2018).
Nishimoto, N. et al. Treatment of rheumatoid arthritis with humanized anti-interleukin-6 receptor antibody: a multicenter, double-blind, placebo-controlled trial. Arthritis Rheum. 50, 1761–1769 (2004).
The 1000 Genomes Project Consortium A global reference for human genetic variation. Nature 526, 68–74 (2015).
Palmer, T. M. et al. Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat. Methods Med. Res. 21, 223–242 (2012).
Codd, V. et al. Identification of seven loci affecting mean telomere length and their association with disease. Nat. Genet. 45, 422–427 (2013).
He, Y., Koido, M., Shimmori, Y. & Kamatani, Y. GWASLab: a Python package for processing and visualizing GWAS summary statistics v.3.6.6. GitHub https://github.com/Cloufield/gwaslab (2023).
Consortium, G. T. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).
Oelen, R. et al. Single-cell RNA-sequencing of peripheral blood mononuclear cells reveals widespread, context-specific gene expression regulation upon pathogenic exposure. Nat. Commun. 13, 3267 (2022).
Vosa, U. et al. Large-scale cis– and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat. Genet. 53, 1300–1310 (2021).
Western, D. et al. Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and implicates causal proteins for Alzheimer’s disease. Nat. Genet. 56, 2672–2684 (2024).
Sudlow, C. 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 Med. 12, e1001779 (2015).
Choi, S. W., Mak, T. S. & O’Reilly, P. F. Tutorial: a guide to performing polygenic risk score analyses. Nat. Protoc. 15, 2759–2772 (2020).
Aragam, K. G. et al. Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants. Nat. Genet. 54, 1803–1815 (2022).
Nikpay, M. et al. A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat. Genet. 47, 1121–1130 (2015).
Mishra, A. et al. Stroke genetics informs drug discovery and risk prediction across ancestries. Nature 611, 115–123 (2022).
Georgakis, M. K. et al. Genetic architecture of stroke of undetermined source: overlap with known stroke etiologies and associations with modifiable risk factors. Ann. Neurol. 91, 640–651 (2022).
Klarin, D. et al. Genome-wide association study of peripheral artery disease in the Million Veteran Program. Nat. Med. 25, 1274–1279 (2019).
Franceschini, N. et al. GWAS and colocalization analyses implicate carotid intima-media thickness and carotid plaque loci in cardiovascular outcomes. Nat. Commun. 9, 5141 (2018).
Ishigaki, K. et al. Multi-ancestry genome-wide association analyses identify novel genetic mechanisms in rheumatoid arthritis. Nat. Genet. 54, 1640–1651 (2022).
Zhao, S. S., Mackie, S. L., Larsson, S. C., Burgess, S. & Yuan, S. Modifiable risk factors and inflammation-related proteins in polymyalgia rheumatica: genome-wide meta-analysis and Mendelian randomisation. Rheumatology 64, 3012–3018 (2024).
Suzuki, K. et al. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology. Nature 627, 347–357 (2024).
Lagou, V. et al. GWAS of random glucose in 476,326 individuals provide insights into diabetes pathophysiology, complications and treatment stratification. Nat. Genet. 55, 1448–1461 (2023).
Chen, J. et al. The trans-ancestral genomic architecture of glycemic traits. Nat. Genet. 53, 840–860 (2021).
Williamson, A. et al. Genome-wide association study and functional characterization identifies candidate genes for insulin-stimulated glucose uptake. Nat. Genet. 55, 973–983 (2023).
Pulit, S. L. et al. Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum. Mol. Genet. 28, 166–174 (2019).
Agrawal, S. et al. Inherited basis of visceral, abdominal subcutaneous and gluteofemoral fat depots. Nat. Commun. 13, 3771 (2022).
Tambets, R. et al. Genome-wide association study for circulating metabolites in 619,372 individuals. Preprint at medRxiv https://doi.org/10.1101/2024.10.15.24315557 (2024).
Butler-Laporte, G. et al. Increasing serum iron levels and their role in the risk of infectious diseases: a Mendelian randomization approach. Int. J. Epidemiol. 52, 1163–1174 (2023).
Hamilton, F. W. et al. Therapeutic potential of IL6R blockade for the treatment of sepsis and sepsis-related death: a Mendelian randomisation study. PLoS Med. 20, e1004174 (2023).
Rogne, T. et al. GWAS identifies LINC01184/SLC12A2 as a risk locus for skin and soft tissue infections. J. Invest. Dermatol. 141, 2083–2086 e2088 (2021).
Mapping the human genetic architecture of COVID-19. Results release 7. COVID-19 Host Genetics Initiative https://www.covid19hg.org/results/r7/ (2022).
Verma, A. et al. Diversity and scale: genetic architecture of 2068 traits in the VA Million Veteran Program. Science 385, eadj1182 (2024).
Zhou, W. et al. Global Biobank Meta-analysis Initiative: powering genetic discovery across human disease. Cell Genom. 2, 100192 (2022).
Budu-Aggrey, A. et al. European and multi-ancestry genome-wide association meta-analysis of atopic dermatitis highlights importance of systemic immune regulation. Nat. Commun. 14, 6172 (2023).
Chen, M. H. et al. Trans-ethnic and ancestry-specific blood-cell genetics in 746,667 individuals from 5 global populations. Cell 182, 1198–1213 e1114 (2020).
Burgess, S., Butterworth, A. & Thompson, S. G. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet. Epidemiol. 37, 658–665 (2013).
Bowden, J., Davey Smith, G., Haycock, P. C. & Burgess, S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet. Epidemiol. 40, 304–314 (2016).
Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44, 512–525 (2015).
Slob, E. A. W. & Burgess, S. A comparison of robust Mendelian randomization methods using summary data. Genet. Epidemiol. 44, 313–329 (2020).
Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48 (2010).
Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 7, e34408 (2018).
Yavorska, O. O. & Burgess, S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int. J. Epidemiol. 46, 1734–1739 (2017).
Didelez, V., Meng, S. & Sheehan, N. A. Assumptions of IV methods for observational epidemiology. Stat. Sci. 25, 22–40 (2010).
Source link
