Literature DB >> 35948913

Associations of genetically predicted IL-6 signaling with cardiovascular disease risk across population subgroups.

Marios K Georgakis1,2,3, Rainer Malik4, Tom G Richardson5, Joanna M M Howson5, Christopher D Anderson6,7,8, Stephen Burgess9,10, G Kees Hovingh11,12, Martin Dichgans4,13,14, Dipender Gill15,16,17,18.   

Abstract

BACKGROUND: Interleukin 6 (IL-6) signaling is being investigated as a therapeutic target for atherosclerotic cardiovascular disease (CVD). While changes in circulating high-sensitivity C-reactive protein (hsCRP) are used as a marker of IL-6 signaling, it is not known whether there is effect heterogeneity in relation to baseline hsCRP levels or other cardiovascular risk factors. The aim of this study was to explore the association of genetically predicted IL-6 signaling with CVD risk across populations stratified by baseline hsCRP levels and cardiovascular risk factors.
METHODS: Among 397,060 White British UK Biobank participants without known CVD at baseline, we calculated a genetic risk score for IL-6 receptor (IL-6R)-mediated signaling, composed of 26 variants at the IL6R gene locus. We then applied linear and non-linear Mendelian randomization analyses exploring associations with a combined endpoint of incident coronary artery disease, ischemic stroke, peripheral artery disease, aortic aneurysm, and cardiovascular death stratifying by baseline hsCRP levels and cardiovascular risk factors.
RESULTS: The study participants (median age 59 years, 53.9% females) were followed-up for a median of 8.8 years, over which time a total of 46,033 incident cardiovascular events occurred. Genetically predicted IL-6R-mediated signaling activity was associated with higher CVD risk (hazard ratio per 1-mg/dL increment in absolute hsCRP levels: 1.11, 95% CI: 1.06-1.17). The increase in CVD risk was linearly related to baseline absolute hsCRP levels. There was no evidence of heterogeneity in the association of genetically predicted IL-6R-mediated signaling with CVD risk when stratifying the population by sex, age, body mass index, estimated glomerular filtration rate, or systolic blood pressure, but there was evidence of greater associations in individuals with low-density lipoprotein cholesterol ≥ 160 mg/dL.
CONCLUSIONS: Any benefit of inhibiting IL-6 signaling for CVD risk reduction is likely to be proportional to absolute reductions in hsCRP levels. Therapeutic inhibition of IL-6 signaling for CVD risk reduction should therefore prioritize those individuals with the highest baseline levels of hsCRP.
© 2022. The Author(s).

Entities:  

Keywords:  Atherosclerosis; C-reactive protein; Cardiovascular disease; Cytokines; Human genetics; Inflammation; Interleukin-6; Mendelian randomization

Mesh:

Substances:

Year:  2022        PMID: 35948913      PMCID: PMC9367072          DOI: 10.1186/s12916-022-02446-6

Source DB:  PubMed          Journal:  BMC Med        ISSN: 1741-7015            Impact factor:   11.150


Background

Chronic inflammation is an emerging therapeutic target for cardiovascular disease (CVD) [1]. Among pharmacological candidates, agents impacting interleukin (IL)-6 signaling have attracted attention due to converging evidence supporting the relevance of IL-6 in atherosclerosis [2]. Data from the Canakinumab Anti-inflammatory Thrombosis Outcome Study (CANTOS) trial showed that the cardiovascular benefit of IL-1β inhibition with canakinumab was proportional to the reductions in IL-6 and high-sensitivity C-reactive protein (hsCRP) levels [3]. A recent phase 2 trial found that ziltivekimab, a monoclonal antibody directly inhibiting IL-6, effectively and safely reduces biomarkers of inflammation and thrombosis among patients with chronic kidney disease [4]. While indirectly targeting the IL-6 pathway with canakinumab led to hsCRP reductions of 35–40% [3], monthly subcutaneous administration of ziltivekimab resulted in a decrease of hsCRP by 77–92% [4]. However, it remains unknown whether larger hsCRP reductions will translate to greater reductions in CVD risk [4], and the ongoing phase 3 cardiovascular outcomes trial testing ziltivekimab will not be completed before 2025 [5]. Mendelian randomization (MR) leverages genetic variants to investigate the effect of exposures on outcomes and can be applied to explore the therapeutic potential of specific drug targets [6]. The random allocation of genetic variants at conception makes this approach less vulnerable to confounding and reverse causation that can impede causal inference in traditional epidemiological investigations [6, 7]. In this study, we performed MR analyses in 397,060 White British UK Biobank participants to investigate (i) the effect of IL-6 receptor (IL-6R)-mediated signaling on CVD risk in relation to baseline hsCRP levels and (ii) whether the effect of IL-6R-mediated signaling on CVD risk varies across population subgroups stratified by cardiovascular risk factors. These results may inform on patient subgroups for inclusion into interventional trials targeting IL-6 signaling for reducing CVD risk.

Methods

Study population

This study follows the reporting recommendations by the STROBE-MR Guidelines (Research Checklist) [8]. Analyses were performed in UK Biobank (application #2532), a prospective cohort study of 502,460 individuals aged 37–73 years recruited between 2006 and 2010. The UK Biobank obtained approval from the Northwest Multi-Center Research Ethics Committee. All participants provided written informed consent. The current analysis was based on White British individuals of European genetic ancestry without known CVD with available genetic, biomarker, and outcome data (Table 1).
Table 1

Baseline characteristics of the UK Biobank participants included in our analyses stratified by the median IL-6R-mediated signaling genetic score

VariableIL-6R-mediated signaling GRS < medianIL-6R-mediated signaling GRS > medianp-value
Age, years5951–645951–640.983
Sex, females114,38352.8114,56352.90.585
CRP, mg/dL1.280.63–2.661.450.71–2.98 < 2.2 × 10−16
SBP, mmHg137126–151137126–1510.218
DBP, mmHg7784–927784–920.755
BMI, kg/m226.824.2–30.026.824.2–30.00.847
eGFR, mL/min/1.73 m288.376.5–100.188.276.3–100.00.062
HbA1c, %5.365.15–5.615.385.15–5.624.1 × 10−07
LDL-cholesterol, mg/dL136.5114.1–159.9136.3113.8–159.80.038
HDL-cholesterol, mg/dL53.945.1–64.653.845.1–64.50.131
Lipid-lowering drug use37,29617.237,74217.40.044
Antidiabetic drug use6,6323.06,9133.20.014
Antihypertensive drug use47,58722.047,61522.00.920

The results represent median (interquartile range) or N (%). The p-values are derived from the Mann–Whitney U test for quantitative variables and the chi-square test for binary variables and test the null hypothesis that there is no difference in the listed phenotype by median IL6R signaling genetic risk score (GRS)

Baseline characteristics of the UK Biobank participants included in our analyses stratified by the median IL-6R-mediated signaling genetic score The results represent median (interquartile range) or N (%). The p-values are derived from the Mann–Whitney U test for quantitative variables and the chi-square test for binary variables and test the null hypothesis that there is no difference in the listed phenotype by median IL6R signaling genetic risk score (GRS)

Genetic instruments

The genetic risk score (GRS) for IL-6 receptor (IL-6R)-mediated signaling was created as previously described [9-11] and included 26 variants 300 kB within the IL6R gene (clumped at pairwise r < 0.1) that were associated with hsCRP, a downstream biomarker of IL-6 signaling (Additional file 1: Table S1, Additional file 2: Fig. S1). As previously described [12], we meta-analyzed a genome-wide association study (GWAS) for hsCRP levels in 204,402 European ancestry individuals (Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium) [13] with data from 318,279 White British individuals in the UK Biobank [14]. This meta-analysis was performed to maximize the number of genetic variants to be leveraged as instruments for IL-6 signaling, in turn optimizing the power of the MR analysis. We selected variants associated with hsCRP levels (p < 5 × 10−8) after clumping for linkage disequilibrium at r2 < 0.1 (1000G European reference panel). We then created a genetic risk score (GRS) for IL-6R-mediated signaling, using association estimates from the CHARGE GWAS as weights for the 26 identified variants (Additional file 1: Table S1). As weights for the GRS were taken from a population that did not overlap with UK Biobank, risk of weak instrument bias related to participant overlap was minimized [15]. This GRS is associated with other biomarkers of upregulated IL-6 signaling as well (lower circulating IL-6 and soluble IL-6R levels), as has been previously described (Additional file 2: Fig. S1) [12].

Study outcomes

Genetic data were linked to inpatient hospital episode records, primary care data and death registry. The outcome considered was a combined CVD endpoint of incident coronary artery disease, ischemic stroke, peripheral artery disease, aortic aneurysm, and cardiovascular death (codes used to define these outcomes in Additional file 1: Table S2). In sensitivity analyses, we examined an alternative outcome excluding aortic aneurysm, because the disease mechanisms might diverge from those of the other outcomes that are primarily related with atherosclerosis [16].

Statistical analysis

We used the ratio of coefficients method to perform MR analyses [17]. This represents the association of the GRS with the outcome divided by the association of the GRS with hsCRP [18]. Cox regression was used to estimate association of the score with outcomes, incorporating age, sex, principal components 1 to 10 of genetic ancestry, genotyping chip, kinship, and assessment center as covariates. Linear regression was used to estimate the association of the GRS with hsCRP, incorporating the same covariates. In sensitivity analyses, we excluded individuals with evidence of relatedness within the UK Biobank cohort (kinship coefficient < 0.0884). To explore the shape of the association between genetically predicted IL-6R-mediated and CVD risk across individuals with varying baseline hsCRP levels, we stratified the population into strata based on residual hsCRP levels, defined as a participant’s hsCRP minus the genetic contribution to hsCRP from the GRS. Stratifying on hsCRP directly would introduce collider bias to distort estimates [19]. For each stratum, we calculated the MR estimate for the association of genetically proxied IL-6R-mediated signaling with the outcome using the ratio of coefficients method [18]. Using a flexible semiparametric framework [20], we then performed a meta-regression of the linear MR estimates obtained for each decile against the median hsCRP value per decile. A fractional polynomial test was used to investigate whether a non-linear model fit this meta-regression better than a linear model. This analysis was performed for both absolute and ln-transformed hsCRP levels. In alternative analyses, we stratified the analyses in centiles of hsCRP rather than deciles. To investigate if the associations between genetically proxied IL-6R-mediated signaling and CVD vary depending on levels of other cardiovascular risk factors, we performed MR analyses stratified by sex and age, and residual values of body mass index (BMI), cystatin C-based estimated glomerular filtration (eGFR), glycated hemoglobin (HbA1c), low-density lipoprotein cholesterol (LDL-C), and systolic blood pressure (SBP) [19]. Statistical significance for all analyses was set at a two-sided p-value < 0.05. Statistical analyses were performed in R (v4.1.1).

Results

From 502,460 individuals enrolled to the UK Biobank, a total of 397,060 individuals were included in analyses (Additional file 2: Fig. S2, Table 1). Median age at recruitment was 59 years (interquartile range 51–64) and 53.9% of the participants were female. The median hsCRP levels among UK Biobank participants were 1.35 mg/dL (interquartile range 0.67–2.80, Table 1). Levels of hsCRP followed a right-skewed distribution (Additional file 2: Fig. S3) were higher among females and older individuals, correlated positively with BMI, LDL-C, SBP, and HbA1c, and correlated negatively with eGFR (Additional file 2: Fig. S4). The GRS for IL-6R-mediated signaling was associated with hsCRP levels among both female and male participants (Additional file 2: Fig. S5). Over a median follow-up of 8.8 years (interquartile range 8.1–9.5 years), there were a total of 46,033 incident CVD events. MR analyses identified significant associations between genetically predicted IL-6R-mediated signaling and risk of the composite CVD outcome (hazard ratio per 1-mg/dL increment in absolute hsCRP levels: 1.11, 95% CI: 1.06–1.17, p = 6.7 × 10−5). This association was similar across individuals with varying baseline hsCRP levels (Fig. 1A) and followed a linear dose–response pattern based with absolute, but not ln-transformed hsCRP levels (Fig. 1B, C). A similar dose–response pattern was observed when stratifying the analyzed UK Biobank population into centiles of hsCRP rather than deciles (Additional file 2: Fig. S6). We observed similar results when excluding aortic aneurysm cases from our main outcome (Additional file 2: Fig. S7). The results also remained materially unchanged when excluding individuals with evidence of relatedness within the UK Biobank (Additional file 2: Fig. S8). Associations were similar in both sexes and across age subgroups. There was no evidence of a trend when stratifying by BMI, eGFR, HbA1c, or SBP (Fig. 2). There was evidence of heterogeneity across subgroups stratified by HbA1c (p = 0.001) and LDL-C levels (p = 0.004), with estimates of greater magnitude in individuals with LDL-C levels ≥ 160 mg/dl (p = 0.03).
Fig. 1

Associations between genetically predicted IL-6R-mediated signaling and risk of incident cardiovascular disease across measured hsCRP levels. A Mendelian randomization analyses stratified by baseline hsCRP levels. The hazard ratios are scaled for 1 mg/dL increment in absolute hsCRP levels. The p-values for heterogeneity and for trend are derived from the Cochran Q statistic and linear meta-regression analyses across deciles of measured hsCRP. B, C Mendelian randomization analyses of genetically predicted IL6R-mediated signaling and CVD risk across B ln-transformed measured hsCRP levels and C absolute measured hsCRP levels. For B, C, results are obtained from fractional polynomial models across associations derived for deciles of measured hsCRP levels. The reference is set to the minimum hsCRP value in the UK Biobank sample (0.08 mg/dL). The p-values for non-linearity are 0.001 for ln-transformed hsCRP levels and 0.99 for absolute hsCRP levels. For all graphs, the residual values of hsCRP are used to stratify, as determined in models regressing the genetic risk score for IL-6 signaling on measured hsCRP levels

Fig. 2

Association between genetically predicted IL-6R-mediated signaling activity and risk of cardiovascular disease across clinically relevant subgroups. The hazard ratios are scaled on 1 mg/dL increment in absolute hsCRP levels. The p-values for heterogeneity and for trend are derived from the Cochran Q statistic and linear meta-regression analyses across strata of the different measured variables. For all variables except for age and sex, the residual values are used to stratify, as determined in models regressing the genetic score for IL-6 signaling on these variables

Associations between genetically predicted IL-6R-mediated signaling and risk of incident cardiovascular disease across measured hsCRP levels. A Mendelian randomization analyses stratified by baseline hsCRP levels. The hazard ratios are scaled for 1 mg/dL increment in absolute hsCRP levels. The p-values for heterogeneity and for trend are derived from the Cochran Q statistic and linear meta-regression analyses across deciles of measured hsCRP. B, C Mendelian randomization analyses of genetically predicted IL6R-mediated signaling and CVD risk across B ln-transformed measured hsCRP levels and C absolute measured hsCRP levels. For B, C, results are obtained from fractional polynomial models across associations derived for deciles of measured hsCRP levels. The reference is set to the minimum hsCRP value in the UK Biobank sample (0.08 mg/dL). The p-values for non-linearity are 0.001 for ln-transformed hsCRP levels and 0.99 for absolute hsCRP levels. For all graphs, the residual values of hsCRP are used to stratify, as determined in models regressing the genetic risk score for IL-6 signaling on measured hsCRP levels Association between genetically predicted IL-6R-mediated signaling activity and risk of cardiovascular disease across clinically relevant subgroups. The hazard ratios are scaled on 1 mg/dL increment in absolute hsCRP levels. The p-values for heterogeneity and for trend are derived from the Cochran Q statistic and linear meta-regression analyses across strata of the different measured variables. For all variables except for age and sex, the residual values are used to stratify, as determined in models regressing the genetic score for IL-6 signaling on these variables

Discussion

Our findings are consistent with a linear dose–response relationship between genetically predicted IL-6R signaling and CVD risk in relation to absolute baseline hsCRP levels. For pharmacological purposes, this translates to greater efficacy against CVD of IL-6 signaling inhibition that achieves larger hsCRP reductions. Our results expand previous genetic data supporting a causal role of IL-6 signaling on atherosclerotic CVD [9, 10, 21] and are consistent with the known effects of IL-6 signaling on increasing CRP generation [22]. In primary prevention cohorts, circulating levels of both IL-6 and CRP have been found to be independently associated with risk of incident CVD [23-25]. Furthermore, the magnitude of cardiovascular risk reduction in the CANTOS trial was directly related to the degree of IL-6 reduction achieved [3]. Taken together, our current genetic findings add to the body of epidemiological and trial evidence supporting a dose response-relationship between IL-6 signaling mediated CRP lowering and CVD risk reduction. In terms of mechanisms underlying such a dose–response relationship, IL-6 is involved in upregulating cellular adhesion molecules at the vessel wall [26], increasing vascular permeability and disrupting endothelial barrier function [27], and promoting vascular smooth muscle growth [28]. It follows that greater absolute reductions in IL-6 signaling, as measured by hsCRP reduction, would confer greater benefit in cardiovascular risk reduction. When stratifying on other cardiovascular risk factors, there was evidence of greater CVD risk reduction through IL-6R signaling inhibition in individuals with higher LDL-C. This aligns with the notion that inflammation is the result of lipid accumulation in atherosclerotic plaques, and as such, greater benefits may be expected among patients with high baseline LDL-C levels. However, previous genetic analyses have suggested no departure from additive effects on CHD risk when considering genetic proxies for inhibition of IL-6R signaling and pharmacological LDL-C-lowering [11]. A limitation of this work is that it considered European ancestry individuals and may not translate across other ethnic groups. This is particularly relevant as the risk factors and pathophysiological mechanisms underlying CVD may vary across populations of different ethnic ancestry. Furthermore, we considered individuals without CVD at baseline, and it is unclear how these findings will apply to individuals with established CVD, who are at greater absolute risk and therefore likely to be prioritized for treatment. We also did not consider potential adverse effects of perturbing IL-6 signaling, including implications for allergic, autoimmune and infectious disease [10]. Of note, observational and genetic evidence has supported an association of low CRP levels with increased risk of Alzheimer’s disease [29], and further work is required to delineate the dose–response relationship between IL-6 signaling mediated changes in CRP levels and potential adverse outcomes. Finally, these genetic analyses explore small lifelong effects, and may not be directly extrapolated to short-term clinical interventions [6].

Conclusions

In summary, we find genetic evidence to support that any benefit of pharmacologically inhibiting IL-6 signaling for CVD risk reduction is likely to be proportional to absolute reductions in hsCRP levels. Our results indicate that therapeutic inhibition of IL-6 signaling for CVD risk reduction should prioritize those individuals with the highest baseline levels of hsCRP. Additional file 1: Table S1. Genetic variants included in the genetic risk score for interleukin-6 receptor signaling downregulation and their associations with hsCRP. Table S2. Definition of outcomes in the current analysis. Additional file 2: Fig. S1. Associations of genetically predicted IL6 receptor-mediated signaling (measured in 1 unit increment in ln-transformed hsCRP levels) with circulating biomarkers. Fig. S2. Selection of study participants. Fig. S3. Distribution of (A) absolute and (B) ln-transformed hsCRP levels in the analyzed UK Biobank population. Fig. S4. Levels of hsCRP levels by vascular risk factors in the analyzed UK Biobank participants. Fig. S5. Levels of high sensitivity C-reactive protein (hsCRP) across deciles of genetic risk score for IL-6 receptor mediated signaling in (A) males and (B) females. Fig. S6. Associations between genetically predicted IL-6R-mediated signaling across centiles of measured hsCRP levels and risk of incident cardiovascular disease. Fig. S7. Associations between genetically predicted IL-6R-mediated signaling and risk of incident cardiovascular disease across measured hsCRP levels after excluding aortic aneurysm cases from the definition of the outcome. Fig. S8. Associations between genetically predicted IL-6R-mediated signaling and risk of incident cardiovascular disease across measured hsCRP levels after excluding individuals with evidence of relatedness within the cohort (kinship coefficient < 0.0884).
  26 in total

Review 1.  Mendelian Randomization Studies in Stroke: Exploration of Risk Factors and Drug Targets With Human Genetic Data.

Authors:  Marios K Georgakis; Dipender Gill
Journal:  Stroke       Date:  2021-08-17       Impact factor: 7.914

Review 2.  Interleukin-6 Signaling and Anti-Interleukin-6 Therapeutics in Cardiovascular Disease.

Authors:  Paul M Ridker; Manas Rane
Journal:  Circ Res       Date:  2021-05-17       Impact factor: 17.367

Review 3.  Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement.

Authors:  Veronika W Skrivankova; Rebecca C Richmond; Benjamin A R Woolf; James Yarmolinsky; Neil M Davies; Sonja A Swanson; Tyler J VanderWeele; Julian P T Higgins; Nicholas J Timpson; Niki Dimou; Claudia Langenberg; Robert M Golub; Elizabeth W Loder; Valentina Gallo; Anne Tybjaerg-Hansen; George Davey Smith; Matthias Egger; J Brent Richards
Journal:  JAMA       Date:  2021-10-26       Impact factor: 56.272

4.  Semiparametric methods for estimation of a nonlinear exposure-outcome relationship using instrumental variables with application to Mendelian randomization.

Authors:  James R Staley; Stephen Burgess
Journal:  Genet Epidemiol       Date:  2017-03-20       Impact factor: 2.135

5.  Additive Effects of Genetic Interleukin-6 Signaling Downregulation and Low-Density Lipoprotein Cholesterol Lowering on Cardiovascular Disease: A 2×2 Factorial Mendelian Randomization Analysis.

Authors:  Marios K Georgakis; Rainer Malik; Stephen Burgess; Martin Dichgans
Journal:  J Am Heart Assoc       Date:  2021-12-20       Impact factor: 5.501

6.  Genome Analyses of >200,000 Individuals Identify 58 Loci for Chronic Inflammation and Highlight Pathways that Link Inflammation and Complex Disorders.

Authors:  Symen Ligthart; Ahmad Vaez; Urmo Võsa; Maria G Stathopoulou; Paul S de Vries; Bram P Prins; Peter J Van der Most; Toshiko Tanaka; Elnaz Naderi; Lynda M Rose; Ying Wu; Robert Karlsson; Maja Barbalic; Honghuang Lin; René Pool; Gu Zhu; Aurélien Macé; Carlo Sidore; Stella Trompet; Massimo Mangino; Maria Sabater-Lleal; John P Kemp; Ali Abbasi; Tim Kacprowski; Niek Verweij; Albert V Smith; Tao Huang; Carola Marzi; Mary F Feitosa; Kurt K Lohman; Marcus E Kleber; Yuri Milaneschi; Christian Mueller; Mahmudul Huq; Efthymia Vlachopoulou; Leo-Pekka Lyytikäinen; Christopher Oldmeadow; Joris Deelen; Markus Perola; Jing Hua Zhao; Bjarke Feenstra; Marzyeh Amini; Jari Lahti; Katharina E Schraut; Myriam Fornage; Bhoom Suktitipat; Wei-Min Chen; Xiaohui Li; Teresa Nutile; Giovanni Malerba; Jian'an Luan; Tom Bak; Nicholas Schork; Fabiola Del Greco M; Elisabeth Thiering; Anubha Mahajan; Riccardo E Marioni; Evelin Mihailov; Joel Eriksson; Ayse Bilge Ozel; Weihua Zhang; Maria Nethander; Yu-Ching Cheng; Stella Aslibekyan; Wei Ang; Ilaria Gandin; Loïc Yengo; Laura Portas; Charles Kooperberg; Edith Hofer; Kumar B Rajan; Claudia Schurmann; Wouter den Hollander; Tarunveer S Ahluwalia; Jing Zhao; Harmen H M Draisma; Ian Ford; Nicholas Timpson; Alexander Teumer; Hongyan Huang; Simone Wahl; YongMei Liu; Jie Huang; Hae-Won Uh; Frank Geller; Peter K Joshi; Lisa R Yanek; Elisabetta Trabetti; Benjamin Lehne; Diego Vozzi; Marie Verbanck; Ginevra Biino; Yasaman Saba; Ingrid Meulenbelt; Jeff R O'Connell; Markku Laakso; Franco Giulianini; Patrik K E Magnusson; Christie M Ballantyne; Jouke Jan Hottenga; Grant W Montgomery; Fernando Rivadineira; Rico Rueedi; Maristella Steri; Karl-Heinz Herzig; David J Stott; Cristina Menni; Mattias Frånberg; Beate St Pourcain; Stephan B Felix; Tune H Pers; Stephan J L Bakker; Peter Kraft; Annette Peters; Dhananjay Vaidya; Graciela Delgado; Johannes H Smit; Vera Großmann; Juha Sinisalo; Ilkka Seppälä; Stephen R Williams; Elizabeth G Holliday; Matthijs Moed; Claudia Langenberg; Katri Räikkönen; Jingzhong Ding; Harry Campbell; Michele M Sale; Yii-Der I Chen; Alan L James; Daniela Ruggiero; Nicole Soranzo; Catharina A Hartman; Erin N Smith; Gerald S Berenson; Christian Fuchsberger; Dena Hernandez; Carla M T Tiesler; Vilmantas Giedraitis; David Liewald; Krista Fischer; Dan Mellström; Anders Larsson; Yunmei Wang; William R Scott; Matthias Lorentzon; John Beilby; Kathleen A Ryan; Craig E Pennell; Dragana Vuckovic; Beverly Balkau; Maria Pina Concas; Reinhold Schmidt; Carlos F Mendes de Leon; Erwin P Bottinger; Margreet Kloppenburg; Lavinia Paternoster; Michael Boehnke; A W Musk; Gonneke Willemsen; David M Evans; Pamela A F Madden; Mika Kähönen; Zoltán Kutalik; Magdalena Zoledziewska; Ville Karhunen; Stephen B Kritchevsky; Naveed Sattar; Genevieve Lachance; Robert Clarke; Tamara B Harris; Olli T Raitakari; John R Attia; Diana van Heemst; Eero Kajantie; Rossella Sorice; Giovanni Gambaro; Robert A Scott; Andrew A Hicks; Luigi Ferrucci; Marie Standl; Cecilia M Lindgren; John M Starr; Magnus Karlsson; Lars Lind; Jun Z Li; John C Chambers; Trevor A Mori; Eco J C N de Geus; Andrew C Heath; Nicholas G Martin; Juha Auvinen; Brendan M Buckley; Anton J M de Craen; Melanie Waldenberger; Konstantin Strauch; Thomas Meitinger; Rodney J Scott; Mark McEvoy; Marian Beekman; Cristina Bombieri; Paul M Ridker; Karen L Mohlke; Nancy L Pedersen; Alanna C Morrison; Dorret I Boomsma; John B Whitfield; David P Strachan; Albert Hofman; Peter Vollenweider; Francesco Cucca; Marjo-Riitta Jarvelin; J Wouter Jukema; Tim D Spector; Anders Hamsten; Tanja Zeller; André G Uitterlinden; Matthias Nauck; Vilmundur Gudnason; Lu Qi; Harald Grallert; Ingrid B Borecki; Jerome I Rotter; Winfried März; Philipp S Wild; Marja-Liisa Lokki; Michael Boyle; Veikko Salomaa; Mads Melbye; Johan G Eriksson; James F Wilson; Brenda W J H Penninx; Diane M Becker; Bradford B Worrall; Greg Gibson; Ronald M Krauss; Marina Ciullo; Gianluigi Zaza; Nicholas J Wareham; Albertine J Oldehinkel; Lyle J Palmer; Sarah S Murray; Peter P Pramstaller; Stefania Bandinelli; Joachim Heinrich; Erik Ingelsson; Ian J Deary; Reedik Mägi; Liesbeth Vandenput; Pim van der Harst; Karl C Desch; Jaspal S Kooner; Claes Ohlsson; Caroline Hayward; Terho Lehtimäki; Alan R Shuldiner; Donna K Arnett; Lawrence J Beilin; Antonietta Robino; Philippe Froguel; Mario Pirastu; Tine Jess; Wolfgang Koenig; Ruth J F Loos; Denis A Evans; Helena Schmidt; George Davey Smith; P Eline Slagboom; Gudny Eiriksdottir; Andrew P Morris; Bruce M Psaty; Russell P Tracy; Ilja M Nolte; Eric Boerwinkle; Sophie Visvikis-Siest; Alex P Reiner; Myron Gross; Joshua C Bis; Lude Franke; Oscar H Franco; Emelia J Benjamin; Daniel I Chasman; Josée Dupuis; Harold Snieder; Abbas Dehghan; Behrooz Z Alizadeh
Journal:  Am J Hum Genet       Date:  2018-11-01       Impact factor: 11.025

7.  Genetically Downregulated Interleukin-6 Signaling Is Associated With a Favorable Cardiometabolic Profile: A Phenome-Wide Association Study.

Authors:  Marios K Georgakis; Rainer Malik; Xue Li; Dipender Gill; Michael G Levin; Ha My T Vy; Renae Judy; Marylyn Ritchie; Shefali S Verma; Girish N Nadkarni; Scott M Damrauer; Evropi Theodoratou; Martin Dichgans
Journal:  Circulation       Date:  2021-03-15       Impact factor: 29.690

8.  C-reactive protein concentration and risk of coronary heart disease, stroke, and mortality: an individual participant meta-analysis.

Authors:  Stephen Kaptoge; Emanuele Di Angelantonio; Gordon Lowe; Mark B Pepys; Simon G Thompson; Rory Collins; John Danesh
Journal:  Lancet       Date:  2009-12-22       Impact factor: 79.321

9.  Use of allele scores as instrumental variables for Mendelian randomization.

Authors:  Stephen Burgess; Simon G Thompson
Journal:  Int J Epidemiol       Date:  2013-08       Impact factor: 7.196

10.  Interleukin-6 Signaling Effects on Ischemic Stroke and Other Cardiovascular Outcomes: A Mendelian Randomization Study.

Authors:  Marios K Georgakis; Rainer Malik; Dipender Gill; Nora Franceschini; Cathie L M Sudlow; Martin Dichgans
Journal:  Circ Genom Precis Med       Date:  2020-05-12
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.