Introduction
Patients with ESKD have higher levels of IL-6.1 Observational studies have shown that higher levels of circulating IL-6 are associated with a higher risk of developing ESKD. Genetic studies suggest a causal role for IL-6 in the development of atherosclerosis, aneurysms, and hypertension.2,3 As a result, pharmacologic modulation of IL-6 levels is currently under investigation to decrease the risk of these downstream events. It is unclear, however, if IL-6 levels are causally associated with ESKD. We, therefore, conducted a Mendelian randomization study to assess whether genetically determined IL-6 levels were associated with ESKD.
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Figure 1: Forest plot showing the association between genetically determined IL-6 concentrations and ESKD.
Methods
After approval by the Institutional Review Board at the Tennessee Valley Health System VA Medical Center, we conducted a two-sample Mendelian randomization study combining summary statistics from genome-wide association studies (GWASs).4 The primary exposure was the genetically predicted circulating IL-6 concentration, and the outcome was ESKD status.
Data Sources
For the exposure variable of IL-6 levels, we used the largest two-stage GWASs available in the literature, which included 21,758 predominantly European individuals in the discovery phase and 9173 in the replication cohort.5 The summary statistics used come from the meta-analysis of these two cohorts. For the outcome of ESKD, we included 495,923 patients from the Million Veteran Program (MVP) cohort, release 4.6 ESKD was defined as having any one of the following: (1) eGFR<15 mL/min per 1.73 m2 at two separate occasions at least 3 months apart, (2) an inpatient or outpatient International Classification of Disease (ICD)-9 or -10 or Current Procedural Terminology (CPT) code for dialysis, or (3) an ICD-9, ICD-10, or CPT code for renal transplantation. This definition included all prevalent cases and a few incident cases captured during cohort enrollment. Controls were all patients who had normal kidney function, defined as a lack of proteinuria and an eGFR>60 mL/minute per 1.73 m2, throughout the study period.
Mendelian Randomization Analysis
The MendelianRandomization (MR) and TwoSampleMR packages in R v4.0.2 were used to conduct all analyses. Candidate risk alleles were those associated with circulating IL-6 levels at genome-wide significance, P<5×10−8. Palindromic SNPs with minor allele frequency (MAF)>0.4 were removed. The list was then pruned to remove those in linkage disequilibrium (LD, R2≥0.2, 0.4, and 0.6) using the clump_data function in TwoSampleMR. This method uses 1000 Genomes as its reference data set and includes only those biallelic SNPs with MAF>0.01. One SNP in an HLA gene was removed out of concerns for pleiotropy. Risk alleles for all remaining SNPs were then harmonized between the two cohorts.
Our primary measure was the result of the random-effects, inverse-variance weighted meta-analysis. A P<0.05 was considered statistically significant. Given our sample size and assuming a significance level of 0.05 with a coefficient of determination of exposure among genetic variants of 2%, we estimated the power to be 81.5% to detect an odds ratio (OR) of 1.23 (https://sb452.shinyapps.io/power/).7 We also considered fixed-effects and weighted median models. We assessed horizontal pleiotropy using the MR pleiotropy residual sum and outlier (MR-PRESSO) test.8 This method calculates inverse variance-weighted estimators after removal of pleiotropic outliers. We additionally conducted sensitivity analyses using MR-Egger and stratifying by harmonized ancestry and race/ethnicity (HARE) and the presence of at least two ICD-9 or -10 codes for diabetes separated by thirty days.9,10 We further assessed for differences between age at enrollment for cases and age at last eGFR for controls.
Results
Of the 495,923 Veterans included, there were 11,849 cases and 484,074 controls (Table 1). The cohort was predominantly men (96.8% cases, 89.5% controls) and with a mean age of 66 years for cases and 61 for controls. Nearly half of all cases were diagnosed before enrollment (5609/11,489, 47%). Among cases, there were more Black patients compared with controls (35.7% versus 19.8%) and a greater proportion of patients with diabetes (67.4% versus 23.0%).
Table 1 -
Characteristics of the cohort
| Variable |
ESKD (n=11,849),
5609 Prevalent |
Controls (n=484,074) |
P Value |
| Sex |
Male: 11,475 (96.8%)
Female: 374 (3.2%) |
Male: 433,305 (89.5%)
Female: 50,768 (10.5%) |
<0.001 |
| Racea
|
White: 6157 (52.0%)
Black: 4228 (35.7%)
Other: 1464 (12.3%) |
White: 330,547 (68.3%)
Black: 95,766 (19.8%)
Other: 57,761 (11.9%) |
<0.001 |
| Diabetes |
7983 (67.4%) |
111,543 (23.0%) |
<0.001 |
| Mean age (SD) |
65.8 (10.3) |
61.0 (14.5) |
<0.001 |
aSelf-reported race as White, Black, or other.
There were 219 SNPs associated with IL-6 levels at P<5×10−8, 217 of which were also present in the MVP cohort. After harmonization, LD pruning, and removal of palindromic SNPs and those with concerns for pleiotropy, there were five SNPs associated with IL-6 levels at the genome-wide significance level that were present in both cohorts at the most stringent LD threshold of 0.2 (Table 2, Supplemental Figure 1). Relaxing this threshold to 0.4 or 0.6 failed to identify additional pathways and so only SNPs passing the more stringent threshold were used. Overall, there was a 24% increased odds of ESKD per genetically predicted 1 pg/ml increase in serum IL-6 levels in a random-effects model (OR 1.24, 95% confidence interval, 1.01 to 1.52, P=0.023) (Figure 1). Both fixed-effects (P=0.001) and weighted median (P=0.015) models were similarly significant. There was no evidence of horizontal pleiotropy on the basis of MR-PRESSO (P=0.09) or MR-Egger (P=0.76). There was marginal evidence of heterogeneity (I2=91.9%, P=0.051). Stratified by HARE, the overall association was the same among both White and Black Veterans (OR 1.24). The association among Black Veterans was present among only those with diabetes compared with nondiabetics (OR 1.39 versus 1.03) while there was no such association among White Veterans. There was no significant pleiotropy detected within racial groups using MR-PRESSO (White P=0.18, Black P=0.12).
Table 2 -
SNPs used as instrumental variables
| SNP |
Nearest Gene |
Chr |
Position |
IL-6 Increasing Allele |
Other Allele |
IL-6 Increasing Allele Frequency |
β
|
SEM |
P Value |
F Statistic |
| rs10752641 |
IL6R
|
1 |
154432042 |
C |
G |
0.738 |
0.053 |
0.016 |
0.001 |
40.676 |
| rs2228145 |
IL6R
|
1 |
154426970 |
C |
A |
0.621 |
−0.032 |
0.015 |
0.034 |
198.492 |
| rs3766926 |
ADAR
|
1 |
154564417 |
C |
T |
0.781 |
0.015 |
0.019 |
0.443 |
32.505 |
| rs56047170 |
UBE2Q1
|
1 |
154528053 |
A |
G |
0.169 |
0.014 |
0.018 |
0.451 |
30.765 |
| rs9616 |
ADAR
|
1 |
154555733 |
T |
A |
0.724 |
−0.024 |
0.019 |
0.196 |
71.223 |
Discussion
In this two-sample Mendelian randomization study, we observed a significantly increased risk of ESKD among individuals with a genetically increased IL-6 level. The association was consistent among both White and Black Veterans, although differed by diabetic status. These results suggest that IL-6 levels may be causally related to the risk of ESKD and could be a promising therapeutic target for prevention of ESKD in patients with CKD.
We used five variants in or near the IL6R gene that were associated with circulating IL-6 levels at the genome-wide level.5 Only two of these were individually associated with ESKD, with the greatest weight attributed to rs2228145, which encodes a functional variant Asp358Ala that has been associated with multiple cardiovascular outcomes including abdominal aortic aneurysm, atherosclerosis, and ischemic heart disease in the MVP population and others.11–14 Moreover, this variant was predictive of response to IL-6 modulation in rheumatoid arthritis.15 Although these could be evidence of pleiotropy, our analyses show that instead there might be a common mechanism for these outcomes and ESKD mediated by IL-6. For example, elevated IL-6 can lead to increased vascular permeability and decreased endothelial barrier function while exogeneous IL-6 can increase atherosclerosis and plaque destabilization in murine models.2
Patients on both hemodialysis and peritoneal dialysis have elevated levels of IL-6, which is also predictive of mortality among those with ESKD.16 Our results suggest that modulation of IL-6, therefore, could be preventive both of ESKD development or potentially of mortality among those with ESKD.17 Pharmacologic reduction of IL-6 has recently been shown to reduce the risk of atherosclerosis and thrombosis, although renal outcomes have yet to be reported.17
Our study had a number of limitations. First, the exposure cohort was almost exclusively Northern European while we had a multiethnic cohort for the ESKD outcome. Next, we used a two-sample MR design and did not directly measure IL-6 levels in our cohort. We stratified our analyses by HARE and still found the same point estimates in both strata, indicating that population stratification by race had little impact on our overall findings. We did observe differences by diabetic status and race, suggesting potential pleiotropy, although this was nonsignificant upon MR-PRESSO testing among Black Veterans only (P = 0.12). Cases were older than controls and so there is the potential that with additional follow-up more controls would have developed ESKD. This discrepancy would tend to bias associations toward the null, so the overall association between genetically predicted IL-6 levels and ESKD is unlikely to be affected by the difference in age.
In this large study of Veterans, we observed an increased risk of ESKD among those with genetically predicted elevated levels of IL-6. With the known association with other cardiovascular outcomes, our findings suggest that there may be a common mechanism between these and ESKD. More importantly, modulation of IL-6 levels is a promising target for reduction of ESKD risk, although additional studies are needed to confirm this mechanism for chemoprevention.
Disclosures
A. Bick reports the following: Ownership Interest: TenSixteen Bio. C. Chung reports the following: Advisory or Leadership Role: Journals: Arthritis Care and Research, Clinical Pharmacology and Therapeutics, Clinical Rheumatology. A. Hung reports the following: Consultancy: NHLBI consultant for Gene and life interaction grant; Research Funding: Vertex Grant to VUMC and VHA CSR&D Merit “Genetics of Kidney Disease & Hypertension, Risk Prediction and Drug Response”; Advisory or Leadership Role: Co-Chair Million Veteran Program Publications & Presentation committee, Veterans Affairs. Co-chair Pharmacogenomics for COVID-19 Million Veteran Program. Journal of Renal nutrition; Section Editor, Clinical Nephrology; Standing member: Ad-hoc SRC NHLBI; Ad-hoc Scientific Review Committee CSR&D; AdScientific Review Committee KNOD, SRC HSR&D bioinformatics. C. Robinson-Cohen reports the following: Advisory or Leadership Role: Clinical Journal of the American Society of Nephrology Editorial Board Member; Clinical Nephrology, Genetics Section Editor. The remaining authors have nothing to disclose.
Funding
This research is based on data from the Million Veteran Program which is funded by the Office of Research and Development, Veterans Health Administration. Genetic studies were funded by the VA Clinical Science Research and Development investigator-initiated grants CX001897 (A.H.) titled “Genetic of Kidney Disease and Hypertension-Risk Prediction and Drug Response,” and IK2 CX002452 (L.W.).
Acknowledgments
The funding agencies had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. This publication does not represent the views of the Department of Veteran Affairs or the United States Government.
Author Contributions
A. Hung and C. Robinson-Cohen conceptualized the study; L. Wheless and Z. Yu were responsible for formal analysis; M.M. Pike, C. Robinson-Cohen, and Z. Yu were responsible for methodology; M.M. Pike was responsible for software; H.-C. Chen, C. Robinson-Cohen, R. Tao, and Z. Yu were responsible for data curation; A. Hung and C. Robinson-Cohen provided supervision; A. Hung was responsible for funding acquisition; A. Hung was responsible for project administration; A. Hung and L. Wheless wrote the original draft; and A. Bick, H.-C. Chen, C.P. Chung, A. Hung, M.M. Pike, C. Robinson-Cohen, R. Tao, L. Wheless, and Z. Yu reviewed and edited the manuscript.
Data Sharing Statement
Data cannot be shared: MVP data are access restricted and cannot be shared publicly.
Supplemental Material
This article contains the following supplemental material online at https://links.lww.com/KN9/A252.
Supplemental Figure 1. Flow chart showing steps to identify five SNPs used as instrumental variable.
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