Abstract
Background and objectives The APOL1 risk variants (G1 and G2) are associated with kidney disease among Black adults, but the clinical presentation is heterogeneous. In mouse models and cell systems, increased gene expression of G1 and G2 confers cytotoxicity. How APOL1 risk variants relate to the circulating proteome warrants further investigation.
Design, setting, participants, & measurements Among 461 African American Study of Kidney Disease and Hypertension (AASK) participants (mean age: 54 years; 41% women; mean GFR: 46 ml/min per 1.73 m2), we evaluated associations of APOL1 risk variants with 6790 serum proteins (measured via SOMAscan) using linear regression models. Covariates included age, sex, percentage of European ancestry, and protein principal components 1–5. Associated proteins were then evaluated as mediators of APOL1-associated risk for kidney failure. Findings were replicated among 875 Atherosclerosis Risk in Communities (ARIC) study Black participants (mean age: 75 years; 66% women; mean eGFR: 67 ml/min per 1.73 m2).
Results In the AASK study, having two (versus zero or one) APOL1 risk alleles was associated with lower serum levels of APOL1 (P=3.11E-13; P=3.12E-06 [two aptamers]), APOL2 (P=1.45E-10), CLSTN2 (P=2.66E-06), MMP-2 (P=2.96E-06), SPOCK2 (P=2.57E-05), and TIMP-2 (P=2.98E-05) proteins. In the ARIC study, APOL1 risk alleles were associated with APOL1 (P=1.28E-11); MMP-2 (P=0.004) and TIMP-2 (P=0.007) were associated only in an additive model, and APOL2 was not available. APOL1 high-risk status was associated with a 1.6-fold greater risk of kidney failure in the AASK study; none of the identified proteins mediated this association. APOL1 protein levels were not associated with kidney failure in either cohort.
Conclusions APOL1 risk variants were strongly associated with lower circulating levels of APOL1 and other proteins, but none mediated the APOL1-associated risk for kidney failure. APOL1 protein level was also not associated with kidney failure.
- AASK (African American Study of Kidney Disease and Hypertension)
- chronic kidney disease
- end stage kidney disease
- epidemiology and outcomes
- genetic renal disease
- renal function decline
- proteomics
- apolipoprotein L1
Introduction
The APOL1 high-risk genotypes, present in 13% of Black individuals, have been associated with incident and progressive CKD (1⇓⇓⇓–5). In the African American Study of Kidney Disease and Hypertension (AASK), having two copies of the APOL1 risk variants (known as G1 and G2; high risk) was associated with a 1.88-fold higher risk of CKD progression, defined as a doubling of serum creatinine or kidney failure, compared with having one or no copies (low risk) (4). Similarly, in the Atherosclerosis Risk in Communities (ARIC) study, Black participants with APOL1 high-risk status had a significantly higher risk of incident kidney failure and faster rate of eGFR decline compared with their counterparts with the low-risk status (3).
Multiple cells in the kidney and body express APOL1, including podocytes, endothelial cells, and hepatocytes (6⇓–8). In vitro overexpression of risk-variant APOL1 in cultured human embryonic kidney cells and in podocytes has been associated with cytotoxicity (7). In mouse models, podocyte-specific (but not tubule- or liver-specific) transgenic expression of the APOL1 risk variants was also associated with albuminuria, glomerulosclerosis, and azotemia (7). In humans, graft survival seems to be related to donor rather than recipient APOL1 risk status (9,10). How APOL1 risk variants relate to the blood proteome and whether these circulating proteins contribute to the pathogenesis of APOL1-associated kidney disease are not known.
Using proteomics data from a cohort of Black adults with hypertension-attributed CKD (AASK) and a community-based general population cohort (ARIC), we sought to better understand the complex interplay of APOL1 risk variants, the blood proteome, and kidney disease.
Materials and Methods
Study Populations
AASK was a randomized controlled trial in which 1094 self-identified Black adults (ages 18–70 years) with CKD (GFR of 20–65 ml/min per 1.73 m2 by iothalamate clearance) attributed to hypertension were randomized to a BP drug (ramipril, metoprolol, or amlodipine) and a BP goal (mean arterial pressure ≤92 or 102–107 mm Hg). Participants were enrolled from 1995 to 1998. Exclusion criteria included diabetes mellitus and proteinuria >2.5 g (11,12). At the end of the trial phase, participants who had not developed kidney failure were invited to continue with the cohort phase, during which they had a common BP goal (<140/90 mm Hg; then, <130/80 mm Hg from 2004 onward) and received ramipril therapy (11). Our study population from AASK consisted of 461 participants who had both APOL1 genotyping and available baseline proteomics data (Supplemental Figure 1A).
ARIC is a community-based prospective cohort that recruited 15,792 adults (ages 45–64 years) from four communities in the United States. The baseline visit (Exam 1) occurred from 1987 to 1989 (5,13). To date, there have been seven follow-up examinations. Our study population from ARIC consisted of 875 self-identified Black participants who had APOL1 genotyping and available proteomics data from Exam 5 (2011–2013) (Supplemental Figure 1B).
For both AASK and ARIC, participants provided informed consent, and protocols were approved by institutional review boards at each study center (5,11⇓–13).
Proteomic Measurements
The SomaScan V4.1 platform (SomaLogic, Boulder, CO) was used to quantify proteins from stored serum samples collected at the baseline visit of the AASK trial (14). These assays utilize SOMAmer reagents, which are modified oligonucleotides designed to have fast off-rates for nonspecific binding proteins and much slower off-rates for targeted proteins. Protein levels are captured in relative fluorescence units (15,16). Similarly, proteins were measured from plasma samples collected at Exam 5 of ARIC using the SomaScan V4 platform. Measurements were performed in 2021 for AASK and in 2018 for ARIC as previously described (14). For AASK, only human proteins that passed an initial quality control check with a Bland–Altman coefficient of variation <50% were included, leaving 6790 proteins for analysis with a median blind duplicate coefficient of variation of 4%. For ARIC, the median interassay coefficients of variation were 4% for APOL1, 4% for calsyntenin-2 (CLSTN2), 5% for metalloproteinase inhibitor 2 (TIMP-2), 4% for 72-kD type IV collagenase (MMP-2), and 5% for testican-2 (SPOCK2) (14).
Genotyping
Participants in both studies previously underwent genotyping for the APOL1 G1 (rs73885319 and rs60910145) and G2 (rs71785313) risk variants using Taqman assays (4,5). We considered both additive and recessive genetic models. For the latter, high risk was defined as having two risk alleles (G1/G1, G2/G2, or G1/G2), and low risk was defined as having zero or one risk allele (G0/G0, G1/G0, or G2/G0) (1,2). Percentage of European ancestry was estimated using ANCESTRYMAP and on the basis of 140 and 1350 ancestry informative markers in AASK and ARIC, respectively (4,5). Among the 95 ARIC participants with missing data on ancestry, values were imputed with the mean.
Other Outcomes and Covariates
In AASK, kidney failure was defined as the initiation of maintenance dialysis or kidney transplantation (11,12). GFR was directly measured by 125I iothalamate clearance (12), and urine protein-creatinine ratio (UPCR) was determined from 24-hour urine collections processed at a central laboratory (11,12). In ARIC, the composite kidney outcome was defined as a 50% decline in eGFR on the basis of creatinine and cystatin C (eGFRCrCys) or kidney failure. GFR was estimated by the 2021 Chronic Kidney Disease Epidemiology Collaboration equation (17), with serum creatinine measured using the Roche enzymatic method and serum cystatin C measured using the Gentian immunoassay. Urine albumin-creatinine ratio (UACR) was calculated from urine albumin (measured using an immunoturbidometric method on the ProSpec nephelometric analyzer) and urine creatinine (measured using the Roche enzymatic method). Details regarding the other covariates have previously been described for both cohorts (3,11,12).
Statistical Analyses
In AASK, baseline characteristics by number of APOL1 risk alleles were compared using linear regression for continuous variables and logistic or multinomial logistic regression for categorical variables. Proteins were log base 2 (log2) transformed to achieve more normal distributions. In our primary analyses aimed at identifying proteins associated with APOL1, we constructed multivariable linear regression models, one for each protein, where the exposure was the number of APOL1 risk alleles and the outcome was the protein. Covariates included age, sex, percentage of European ancestry, and protein principal component 1 (PC1) to PC5. The approach of adjusting for protein PCs, which were calculated using the “prcomp” function in R, has been used in prior omics studies to account for technical variation related to batch effect (14) (J. Zhang et al., unpublished data). We considered both recessive (two versus zero/one risk alleles) and additive (per copy of risk allele) models. For the above analyses, a Bonferroni-corrected P value of <9.67 x 10-5 (i.e., <0.05 divided by 517) was considered statistically significant, where 517 was the number of PCs that explained 95% of the variance in proteins. For our top hits, effect modification by sex was assessed by including an interaction term between sex and APOL1 risk status (recessive model). Correlations with GFR and log2(UPCR) were then examined using Spearman correlation coefficients. Associations of the top proteins in AASK were then evaluated in ARIC, with a P value of 0.05 divided by the number of tested proteins being considered significant. Similar linear regression models (adjusted for age, sex, percentage of European ancestry, and protein PC1–PC5) were constructed relating each protein to number of APOL1 risk alleles (using both recessive and additive models). We also examined Spearman correlations between the top proteins in AASK with eGFRCrCys and log2(UACR).
To evaluate whether identified proteins attenuated the APOL1-associated risk for kidney failure in AASK, we first constructed a series of Weibull survival regression models where the exposure of interest was APOL1 risk status and the outcome was kidney failure, adjusting for baseline age, sex, percentage of European ancestry, and protein PC1–PC5 (demographic model), and with further adjustment for history of heart disease, smoking, systolic BP, body mass index, total cholesterol, HDL, GFR, log2(UPCR), and randomized treatment groups (full model). The association of APOL1 risk status with the composite kidney outcome was similarly assessed in ARIC using Weibull survival regression models, adjusting for age, sex, percentage of European ancestry, and protein PC1–PC5 (demographic model), as well as study center, prevalent cardiovascular disease, hypertension, diabetes mellitus, smoking, systolic BP, body mass index, total cholesterol, HDL, eGFRCrCys, and log2(UACR) (full model) at Exam 5, with the start of follow-up beginning at Exam 5 and administrative censoring occurring on December 31, 2019. In AASK, formal evaluation for mediation was assessed by modeling each protein as a linear function of APOL1 risk status (two versus zero/one risk alleles) and the associations of each protein as well as APOL1 risk status with kidney failure using Weibull survival regression models (full model). We then evaluated the proportion of the total effect of APOL1 risk status on kidney failure mediated through each protein. In sensitivity analyses, we repeated the above mediation studies without adjusting for baseline GFR and log2(UPCR).
Finally, we evaluated associations of APOL1 protein with kidney failure in AASK and the composite kidney outcome in ARIC using Cox proportional hazards models (full model). Data were analyzed using STATA/SE 15 software (StataCorp LLC, College Station, TX) and R version 4.0, package: mediation (18).
Results
At baseline, mean age of AASK participants was 54 (SD 11) years, 41% were women, mean GFR was 46 (SD 13) ml/min per 1.73 m2, and median UPCR was 80.6 (interquartile range, 27.5–348.1) mg/g creatinine (Table 1). Participants with more APOL1 risk alleles had a lower percentage of European ancestry, lower mean GFR, and higher median UPCR (Supplemental Table 1). In the recessive model, participants with two APOL1 risk alleles had lower levels of APOL1 (P=3.12E-06 for aptamer 1, P=3.11E-13 for aptamer 2), APOL2 (P=1.45E-10), CLSTN2 (P=2.66E-06), MMP-2 (P=2.96E-06), SPOCK2 (P=2.57E-05), and TIMP-2 (P=2.98E-05) compared with those with zero or one risk allele (Table 2). There was no effect modification by sex (P interaction=0.26 for APOL1 aptamer 1, P interaction=0.69 for APOL1 aptamer 2, P interaction=0.77 for APOL2, P interaction=0.90 for CLSTN2, P interaction=0.72 for MMP-2, P interaction=0.93 for SPOCK2, and P interaction=0.68 for TIMP-2). When considering an additive model, each additional APOL1 risk allele was strongly associated with lower levels of APOL1 (P=3.18E-09 for aptamer 1; P=4.33E-22 for aptamer 2) and APOL2 (P=2.15E-11). Correlations between these proteins with GFR and log2(UPCR) were weak (Spearman correlation coefficients [rs] ranging from −0.15 to 0.12) with the exception of SPOCK2, which was moderately correlated with GFR (rs=0.37) and log2(UPCR) (rs=−0.36) (Table 3). Given that a recent Mendelian randomization study demonstrated the positive causal effect of eGFR on SPOCK2 (19), we further explored the association of APOL1 high-risk status with SPOCK2 by additionally adjusting for baseline GFR and proteinuria and found that this resulted in a 31% reduction in effect size (β=−0.11; 95% confidence interval [95% CI], −0.18 to −0.04 for a recessive model adjusting for age, sex, percentage of European ancestry, protein PC1–PC5, GFR, and log2[UPCR]).
Baseline characteristics of study populations from the African American Study of Kidney Disease and Hypertension and the Atherosclerosis Risk in Communities study (Exam 5)
Top ten proteins associated with APOL1 risk alleles in the African American Study of Kidney Disease and Hypertension
Spearman correlations of top African American Study of Kidney Disease and Hypertension proteins with GFR and log2(urine protein-creatinine ratio) in the African American Study of Kidney Disease and Hypertension and eGFR on the basis of cystatin C and creatinine and log2(urine albumin-creatinine ratio) in the Atherosclerosis Risk in Communities study (Exam 5)
We sought to replicate the above findings in ARIC. Among 875 Black ARIC participants with available APOL1 genotyping and proteomics data, the mean age at Exam 5 was 75 (SD 5) years, 66% were women, mean eGFR was 67 (SD 21) ml/min per 1.73 m2, and median UACR was 11.1 (interquartile range, 6.3–31.1) mg/g (Table 1). Participants with more APOL1 risk alleles had a lower percentage of European ancestry and lower mean eGFRCrCys, but they were otherwise similar (Supplemental Table 1). Of the six proteins associated with APOL1 in AASK, five were available in ARIC (APOL1 [aptamer 1], CLSTN2, MMP-2, SPOCK2, and TIMP-2). In the recessive model, having two APOL1 risk alleles was associated with lower levels of APOL1 (P=1.28E-11) compared with having zero or one risk allele (Table 4). In the additive model, having more APOL1 risk alleles was significantly associated with lower levels of all of the proteins, with the exception of SPOCK2 and CLSTN2, which were borderline significant when accounting for multiple testing. The direction of associations was similar to those observed in AASK. SPOCK2 had a moderately positive correlation with eGFRCrCys (rs=0.49) and slightly negative correlation with log2(UACR) (rs=−0.13). Otherwise, correlations of the proteins with eGFRCrCys and log2(UACR) were relatively weak (Table 3).
Replication in the Atherosclerosis Risk in Communities study: Associations of top African American Study of Kidney Disease and Hypertension proteins with APOL1 risk alleles in the Atherosclerosis Risk in Communities study (Exam 5)
Next, we evaluated whether these proteins mediated the APOL1-associated risk of kidney disease progression. Over a mean follow-up of 8.3 (SD 3.0) years, 140 AASK participants developed kidney failure. In the demographic model, APOL1 high-risk status was associated with a 2.8-fold greater risk of developing kidney failure (hazard ratio [HR], 2.77; 95% CI, 1.94 to 3.97). With adjustment for additional covariates, including GFR and log2(UPCR), APOL1 high-risk status was associated with a 1.6-fold greater risk of kidney failure (HR, 1.62; 95% CI, 1.11 to 2.35). The association of APOL1 high-risk status was not mediated by any of the identified proteins in the fully adjusted model (Supplemental Table 2). However, when baseline GFR and UPCR were excluded from the models, SPOCK2 appeared to mediate 27% of the association between APOL1 high-risk status and kidney failure (Supplemental Table 3).
Over a mean follow-up of 5.1 (SD 1.6) years, 60 ARIC participants experienced a 50% decline in eGFRCrCys or kidney failure. In this older subset of the ARIC study, APOL1 high-risk status was not associated with the composite kidney outcome (demographic and fully adjusted models; HR, 1.14; 95% CI, 0.55 to 2.34 and HR, 1.04; 95% CI, 0.49 to 2.21, respectively). Given that there was no main effect, we did not pursue additional analyses to evaluate for mediation in ARIC. Finally, neither APOL1 aptamer was associated with kidney failure in AASK (HR, 1.03; 95% CI, 0.53 to 1.98 and HR, 1.00; 95% CI, 0.64 to 1.57 for aptamers 1 and 2, respectively) or the composite kidney outcome in ARIC (HR, 1.53; 95% CI, 0.70 to 3.38 for aptamer 1).
Discussion
In this study of APOL1 risk variants and the blood proteome, we report that having two APOL1 risk alleles conferred lower serum levels of six proteins (i.e., APOL1, APOL2, CLSTN2, MMP-2, SPOCK2, and TIMP-2) among individuals with CKD; the association of risk variants and APOL1 was replicated in a second cohort of community-dwelling adults with relatively preserved eGFR. However, none of the identified proteins mediated the APOL1-associated risk of kidney failure over and above the risk variants’ association with GFR. Moreover, APOL1 proteins were not associated with CKD progression. Taken together, our findings suggest that the APOL1 risk variants are associated with some changes in the blood proteome, but these alterations are unlikely to account for how the APOL1 risk variants lead to progressive kidney disease.
Circulating levels of APOL1 are produced primarily by the liver and released into circulation complexed with other proteins (20⇓–22). To date, the few studies that have examined associations of APOL1 risk variants with APOL1 protein levels have been relatively small (21,23). Bruggeman et al. (23) reported in a nested case-control study of Black individuals with HIV infection (n= approximately 270) that the number of APOL1 risk alleles was not associated with plasma APOL1 protein levels, which they measured by sandwich ELISA. Weckerle et al. (21) also found no association between APOL1 genotypes and serum APOL1 protein levels among healthy Black individuals without CKD (n=84). In this latter study, APOL1 protein was detected using a monoclonal anti-APOL1 antibody and quantified by enhanced chemiluminescence methods (21). In contrast, we discovered in AASK (n=461) and ARIC (n=875) that participants with more APOL1 risk alleles had significantly lower levels of APOL1 protein, as measured using an aptamer-based proteomics assay. We suspect that differences in sample size as well as protein measurement methods account for the incongruent findings across studies.
Our results are consistent with previous studies suggesting that circulating APOL1 may not play a role in kidney disease development. Bruggeman et al. (23) found that plasma levels of APOL1 did not correlate with eGFR or proteinuria. Among kidney transplant recipients, graft survival is associated with donor and not recipient APOL1 risk status, suggesting that only kidney-specific APOL1 may be pathologic (9,10). In human kidney samples, glomerular transcript levels of APOL1 showed negative correlation with eGFR (7). In contrast, histologic staining of diseased kidney sections of FSGS and HIV-associated nephropathy suggested reduced APOL1 presence in podocytes and tubules (6).
We also report that the APOL1 risk variants were associated with lower levels of other proteins. APOL2, another member of the apoL family, is thought to play a role in lipid transport within the cytoplasm and in the binding of lipids to organelles. It is highly expressed in the kidneys, urinary bladder, brain, stomach, male and female reproductive tissues, bone marrow, lung, pancreas, and adrenal gland (24). Given that APOL2 was not available in ARIC, we were unable to evaluate this association further in our replication cohort. The remaining proteins did not replicate but nonetheless are interesting. SPOCK2 is found primarily in the testis and brain, but also in the kidney and urinary bladder (24). Although its exact role is unclear, SPOCK2 has been shown to have positive correlations with eGFR, with higher levels being associated with slower eGFR decline among participants of the Jackson Heart Study and the Framingham Heart Study (25). In a recent Mendelian randomization study of 2882 individuals from four European cohorts, eGFR was found to have a positive causal effect on SPOCK2, suggesting that SPOCK2 could be a physiologic marker of kidney health (19). In this study, we demonstrated that further adjustment for baseline kidney measures resulted in a 31% reduction in effect size of APOL1 high-risk status with SPOCK2. These findings suggest that at least part of the association between APOL1 and SPOCK2 is mediated by the variants’ effect on GFR and/or proteinuria. MMP-2, a member of the matrix metalloproteinase family, has been found within the glomerulus (24) and may be involved in the development of interstitial fibrosis (26,27). TIMP-2, an endogenous inhibitor of metalloproteinases, may promote pathways of tubulointerstitial fibrosis and injury within the kidney (28). CLSTN2 is thought to modify neuronal postsynaptic signals (24,29).
None of the identified proteins mediated the association of APOL1 high-risk status with kidney failure in the fully adjusted model (AASK). However, when we did not account for baseline GFR and proteinuria, SPOCK2 emerged as a potential mediator. We hypothesize that this difference reflects its correlation with GFR rather than true mediation. SPOCK2 did not appear to mediate the APOL1-associated risk for kidney failure above and beyond the variants’ known associations with GFR or proteinuria (4,30,31).
Strengths of our study include the use of both a discovery cohort and a replication cohort, wherein data were collected prospectively. We also had several limitations. First, the sample size was relatively small. Second, the SOMAscan platform may not distinguish between protein products of the G0, G1, and G2 variants. The G1 variant consists of two nonsynonymous single-nucleotide polymorphisms leading to amino acid substitutions, whereas the G2 variant consists of a six–base pair deletion that results in the loss of two amino acids (1,2). These changes in amino acids might reduce the ability of the aptamer to bind to APOL1 protein. Third, despite adjusting for multiple baseline covariates, the possibility of residual confounding remains. Fourth, the study populations of AASK and ARIC were notably different, perhaps making it more difficult to replicate associations present only in specific populations. However, we also view this as a strength, as it provides support for the generalizability of our results. Finally, given that APOL1 high-risk status was not associated with CKD progression in ARIC, perhaps due to limited power from reduced sample size or the study population being older, we were unable to evaluate for mediation in the replication cohort.
In summary, the APOL1 risk variants are associated with lower levels of some proteins within the proteome, but these changes did not explain the higher risk of kidney failure among individuals with the high-risk status.
Disclosures
D.E. Arking reports serving on the Association for the Eradication of Heart Attach Scientific Advisory Board. C.M. Ballantyne reports consultancy agreements with Abbott Diagnostics, Althera, Amarin, Amgen, Arrowhead, AstraZeneca, Denka Seiken, Esperion, Genentech, Gilead, Illumina, Matinas BioPharma Inc., Merck, New Amsterdam, Novartis, Novo Nordisk, Pfizer, Regeneron, Roche Diagnostic, and Sanofi-Synthelabo; research funding from Abbott Diagnostic, Akcea, Amgen, Arrowhead, Esperion, Ionis, Novartis, Regeneron, and Roche Diagnostic; and serving in an advisory or leadership role for Arrowhead, Merck, and Pfizer. E. Boerwinkle reports ownership interest in Codified Genomics. T.K. Chen reports research funding from the National Institutes of Health (NIH)/the National Institute of Diabetes and Digestive and Kidney Diseases and Yale University. J. Coresh reports consultancy agreements with Healthy.io; ownership interest in Healthy.io; research funding from NIH and the National Kidney Foundation (NKF; which receives industry support); and serving in an advisory or leadership role for Healthy.io and NKF. M.E. Grams reports honoraria from academic institutions for giving grand rounds and American Diabetes Association for reviewing abstracts; serving in an advisory or leadership role for American Journal of Kidney Diseases, CJASN, the JASN Editorial Fellowship Committee, the Kidney Disease Improving Global Outcomes Executive Committee, the NKF Scientific Advisory Board, and the United States Renal Data System Scientific Advisory Board; and grant funding from NKF, which receives funding from multiple pharmaceutical companies. A. Köttgen reports honoraria from Sanofi Genzyme and serving in an advisory or leadership role for American Journal of Kidney Diseases, the American Kidney Fund, JASN, Kidney International, and Nature Reviews Nephrology. K. Susztak reports consultancy agreements with AstraZeneca, Bayer, Jnana, Maze, and Pfizer; ownership interest in Jnana; research funding from Bayer, Boehringer Ingelheim, Calico, Gilead, GSK, Lilly, Maze, Merck, Novartis, Novo Nordisk, and Regeneron; honoraria from AstraZeneca, Bayer, Jnana, and Maze; serving on the editorial boards of Cell Metabolism, eBioMedicine, JASN, Journal of Clinical Investigation, Kidney International, and Med; and serving in an advisory or leadership role for Jnana. All remaining authors have nothing to disclose.
Funding
T.K. Chen is supported by a Yale University George M. O’Brien Center for Kidney Research Pilot and Feasibility Grant (National Institute of Diabetes and Digestive and Kidney Diseases award P30DK079310) and National Institute of Diabetes and Digestive and Kidney Diseases grant K08DK117068. M.E. Grams is supported by National Institute of Diabetes and Digestive and Kidney Diseases grants R01DK108803 and R01DK24399 and National Heart, Lung, and Blood Institute grant K24HL155861. The work of A. Köttgen was supported by Deutsche Forschungsgemeinschaft project 431984000 SFB 1453. This work was supported in part by National Heart, Lung, and Blood Institute grant R01 HL134320.
Acknowledgments
The authors thank the staff and participants of AASK and the ARIC study for their important contributions. SomaLogic Inc. conducted the SomaScan assays in exchange for use of ARIC data.
AASK was conducted by the AASK investigators and supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). The AASK trial and cohort were supported by National Institutes of Health (NIH) and NIDDK institutional grants M01 RR-00080, M01 RR-00071, M0100032, P20-RR11145, M01 RR00827, M01 RR00052, 2P20 RR11104, RR029887, DK 2818-02, DK057867, and DK048689 and the following pharmaceutical companies: AstraZeneca, Forest Laboratories, GlaxoSmithKline, King Pharmaceuticals, Pfizer, Pharmacia, and Upjohn. The ARIC study was funded in whole or in part with federal funds from National Heart, Lung, and Blood Institute, NIH, Department of Health and Human Services contracts HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700005I, and HHSN268201700004I. Some of the data reported here were supplied by the United States Renal Data System.
This manuscript was not prepared in collaboration with the investigators of AASK and does not necessarily reflect the opinions or views of AASK, NIH, or NIDDK.
The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as an official policy or interpretation of the US Government.
Author Contributions
T.K. Chen, M.E. Grams, and A.L. Surapaneni conceptualized the study; J. Chen and A.L. Surapaneni were responsible for formal analysis; T.K. Chen, M.E. Grams, and A.L. Surapaneni were responsible for methodology; T.K. Chen was responsible for project administration; J. Coresh and M.E. Grams were responsible for resources; T.K. Chen, J. Coresh, M.E. Grams, and A.L. Surapaneni were responsible for validation; M.E. Grams and J. Coresh were responsible for funding acquisition; M.E. Grams provided supervision; T.K. Chen wrote the original draft; and D.E. Arking, C.M. Ballantyne, E. Boerwinkle, J. Chen, T.K. Chen, J. Coresh, M.E. Grams, A. Köttgen, A.L. Surapaneni, K. Susztak, A. Tin, and B. Yu reviewed and edited the manuscript.
Data Sharing Statement
The ARIC study follows National Institutes of Health data sharing guidelines; the steering committee will review any research requests, and the coordinating center will release data to BioLINCC (Biologic Specimen and Data Repository Information Coordinating Center) 1 year after quality control procedures have been completed. Preexisting data access policies for each of the parent cohort studies specify that research data requests can be submitted to each steering committee; these will be promptly reviewed for confidentiality or intellectual property restrictions and will not unreasonably be refused. Please refer to the data sharing policies of these studies. Individual-level patient or protein data may further be restricted by consent, confidentiality, or privacy. These policies apply to both clinical and proteomics data.
Supplemental Material
This article contains the following supplemental material online at http://cjasn.asnjournals.org/lookup/suppl/doi:10.2215/CJN.14701121/-/DCSupplemental.
Supplemental Figure 1. Flow chart of study populations in AASK and ARIC (Exam 5).
Supplemental Table 1. Baseline characteristics by number of APOL1 risk alleles in AASK and ARIC (Exam 5).
Supplemental Table 2. Results of mediation analyses of APOL1 risk alleles (two versus zero or one) and kidney failure by top APOL1-associated proteins in AASK.
Supplemental Table 3. Results of mediation analyses of APOL1 risk alleles (two versus zero or one) and kidney failure by top APOL1-associated proteins in AASK without adjustment for baseline GFR or UPCR.
Footnotes
Published online ahead of print. Publication date available at www.cjasn.org.
See related editorial, “Functional Assessment of High-Risk APOL1 Genetic Variants,” on pages 626–627.
- Received November 11, 2021.
- Accepted March 17, 2022.
- Copyright © 2022 by the American Society of Nephrology