Kidney360 Publish Ahead of Print, published on April 9, 2021 as doi:10.34067/KID.0007632020

1

Association of Blood Pressure Genetic Risk Score with Cardiovascular Disease and

Chronic Kidney Disease Progression

Findings from the CRIC Study

Jovia L. Nierenberg,1 Amanda H. Anderson,1 Jiang He,1,2 Afshin Parsa,3 Anand

Srivastava,4 Jordana B. Cohen,5 Santosh L. Saraf,6 Mahboob Rahman,7 Sylvia E. Rosas,8

Tanika N. Kelly,1 and the CRIC Study Investigators*

Affiliations:

1. Department of Epidemiology, Tulane University School of Public Health and

Tropical Medicine, New Orleans, Louisiana, USA.

2. Department of Medicine, Tulane University School of Public Health and Tropical

Medicine, New Orleans, Louisiana, 70112, USA.

3. Division of Kidney, Urologic, and Hematologic Diseases, The National Institutes

of Health, National Institute of Diabetes and Digestive and Kidney Diseases,

Bethesda, Maryland, USA.

4. Center for Translational Metabolism and Health, Institute for Public Health and

Medicine, Division of Nephrology and Hypertension, Northwestern University

Feinberg School of Medicine, Chicago, Illinois, USA.

5. Renal-Electrolyte and Hypertension Division, Perelman School of Medicine,

University of Pennsylvania, Philadelphia, USA.

6. Division of Hematology & Oncology, Department of Medicine, Comprehensive

Sickle Cell Center, University of Illinois at Chicago, Chicago, Illinois, USA.

Copyright 2021 by American Society of Nephrology. 2

7. Division of Nephrology, Department of Medicine, Case Western Reserve

University, Cleveland, Ohio, USA.

8. Kidney and Hypertension Unit, Joslin Diabetes Center, Harvard Medical School,

Boston, Massachusetts, USA.

Corresponding author: Professor Tanika N. Kelly, Department of Epidemiology,

Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street,

Suite 2000, New Orleans, LA 70112, USA. Telephone: (504) 988-6972, Fax: (504) 988-

1568, Email: [email protected]

* Lawrence J. Appel, MD, MPH, Jing Chen, MD, MMSc, MSc, Harold I. Feldman, MD,

MSCE, Alan S. Go, MD, James P. Lash, MD, Robert G. Nelson, MD, PhD, MS,

Mahboob Rahman, MD, Panduranga S. Rao, MD, Vallabh O Shah, PhD, MS, Raymond

R. Townsend, MD, Mark L. Unruh, MD, MS 3

Key Points

 High blood pressure has been associated with increased risk of cardiovascular

disease and progression to end stage kidney disease.

 We identified an association of blood pressure genetic risk score for with

cardiovascular disease but not with end stage kidney disease.

 Our study supports the role of blood pressure in cardiovascular disease but not

kidney disease progression among those with kidney disease.

4

Abstract

Background: In the general population, genetic risk for high blood pressure has been associated with cardiovascular disease but not kidney function or incident chronic kidney disease. These relationships have not been studied longitudinally in participants with chronic kidney disease. We examined whether blood pressure genetic risk predicts cardiovascular disease and kidney disease progression in patients with chronic kidney disease.

Methods: We included 1,493 African and 1,581 European ancestry participants from the

Chronic Renal Insufficiency Cohort who were followed for 12 years. We examined associations of blood pressure genetic risk scores with development of cardiovascular disease (myocardial infarction, congestive heart failure, or stroke) and chronic kidney disease progression (incident end stage kidney disease or halving of estimated glomerular filtration rate) using Cox proportional hazards models. Analyses were stratified by race and included adjustment for age, sex, study site, and ancestry principal components.

Results: Each standard deviation increase in systolic blood pressure and pulse pressure genetic risk score conferred respective 15% [95% confidence interval: 4%, 27%] and

11% (95% confidence interval: 1%, 23%) higher risks of cardiovascular disease, with a similar marginally significant trend for diastolic blood pressure, among European ancestry participants. Among African ancestry participants, each standard deviation increase in systolic and diastolic blood pressure genetic risk score conferred 10% (95% confidence interval: 1%, 20%) and 9% (95% confidence interval: 0%, 18%) higher risk of cardiovascular disease. Higher genetic risk was not associated with chronic kidney disease progression. 5

Conclusions: Genetic risk for elevation in blood pressure was associated with increased risk of cardiovascular disease but not chronic kidney disease progression. 6

Introduction

Chronic kidney disease (CKD) is estimated to affect between 10.4% and 11.8% of

adults worldwide1 and is associated with high risks of cardiovascular disease (CVD), progression to end-stage kidney disease (ESKD), and all-cause mortality.2 Hypertension

is a common co-morbid condition that has been identified by observational studies as an

important risk factor for progression to ESKD and development of CVD in CKD

patients.3–5 Consistent with these data, randomized controlled trials (RCTs) in CKD

patients have documented reduced CVD events associated with antihypertension

medication use.6 The identified reductions in CVD events appear to be driven by the

common BP lowering effects of different classes of anti-hypertension medications.7–9 In contrast, RCTs have identified enhanced benefits of renin-angiotensin-aldosterone system

(RAAS) inhibitors compared to other anti-hypertension medications on CKD

progression,10–12 while findings from trials assessing the renoprotective effects of general

BP lowering have been inconclusive,6,13,14 suggesting that mechanisms independent of

BP lowering may be responsible for the beneficial effects observed.

Because genetic information should not be confounded by traditional disease risk

factors, studies using genetic data as a proxy for lifetime exposures have gained

popularity as another tool for etiologic inference.15,16 Indeed, associations of genetic risk

scores (GRSs) with disease outcomes have generally been consistent with findings from

RCTs. For example, genetically elevated BP has reproducibly been shown to predict

CVD endpoints in the general population.17–23 Interestingly, BP GRSs have not

consistently associated with kidney function or CKD in large and well-powered

population-based studies.17,18,20–22 While these findings have generated additional 7

uncertainty regarding the etiologic role of BP in kidney function decline and ESKD, no

study has investigated whether BP GRS predicts CKD progression and ESKD in patients

with CKD. Furthermore, the association between BP GRS and CVD has also not been

assessed among patients with CKD who are at substantially increased risks of this

condition. Thus, the purpose of the current study was to examine the associations of BP

GRS with both CKD progression and CVD events among patients with CKD of African

and European ancestry participating in the Chronic Renal Insufficiency Cohort (CRIC)

Study.

Methods

Study Participants

A total of 5,499 men and women were enrolled in the CRIC Study between 2003 and 2015. The study design details and participant characteristics at baseline have been

previously published.24,25 Briefly, participants were eligible for the study if they were

between 21 and 79 years of age with an estimated glomerular filtration rate (eGFR)

between 20 and 70 ml/min per 1.73 m2. Exclusion criteria included glomerulonephritis requiring immunosuppressive therapy, advanced heart failure, cirrhosis, and polycystic kidney disease. Among the 3,939 CRIC participants from the first two recruitment phases, 3,074 had available genotype, covariable, and phenotype data and were included

in the current study.

Data Collection and Study Covariables

A detailed description of data collection and study covariables is available in the

Supplementary Methods. In brief, standard questionnaires were used to ascertain 8

information on baseline demographic characteristics, medical history, and medication

use. A physical examination was conducted to measure BP in triplicate, with the average

of the three measures used to estimate BP. Pretreatment BP levels were imputed in

participants on antihypertensive medication,26 by adding 15 and 10 mm Hg to SBP and

DBP, respectively.27 Pulse pressure (PP) was then calculated as the difference between the adjusted SBP and DBP values. Institutional review boards at participating institutions

approved the CRIC study protocol, and all study participants provided written informed

consent, including specific consent for genetic investigations. This study adhered to the

Declaration of Helsinki.

Genetic Risk Score Construction

Genotyping and imputation methods in the CRIC study have been described

previously28 and are summarized in the Supplementary Methods. A total of 901

independent BP loci, identified in a primarily European ancestry general population

sample, explaining up to 5.7% of BP variability, were utilized for construction of SBP

(884 SNPs), DBP (885 SNPs), and PP GRSs (256 SNPs) among CRIC participants.19

Among them, SNPs from 879 and 883 loci were available in African and European ancestry CRIC participants, respectively. Weighted GRS were calculated for SBP, DPB, and PP using a two-step procedure, which included: 1.) for each SNP, multiplying the

participant’s dosage of the risk allele by its previously estimated effect size; and 2.)

summing the products across all SNPs included in the risk score. Participants were then

categorized into quartiles of SBP, DBP, and PP GRS. Annotation and effect estimates

used for weighting of each SNP included in the GRS are presented in the Supplementary

Data. 9

Primary Outcomes

The primary cardiovascular outcome of interest was a composite of the first of congestive heart failure, myocardial infarction, or stroke that occurred during follow-up.

Congestive heart failure was identified by hospital admission for new or worsening congestive heart failure signs and symptoms, in addition to diminished cardiac output.

Myocardial infarction was defined by characteristic changes in troponin and creatine kinase–MB levels, symptoms of myocardial ischemia, electrocardiogram changes, or new fixed profusion abnormalities. Stroke was defined as rapid onset of neurologic deficit, headache, or other nonvascular cause and clinically relevant lesion on brain imaging for longer than 24 hours or death within 24 hours. All events classified as probable or definite during adjudication were included in these analyses.

During CRIC follow-up, incident ESKD was defined as receipt of chronic dialysis or kidney transplant. Information on the initiation and maintenance of dialysis and kidney transplant was obtained by annual clinical follow-up visits and interim telephone interviews and confirmed by a dialysis unit or hospital chart review. Ascertainment of

ESKD in the CRIC study was supplemented by information from the US Renal Data

System. For the current analysis, CKD progression events were defined as halving of eGFR or incident ESKD defined as the occurrence of renal dialysis or kidney transplantation.

Statistical Analysis

The means and standard deviations or frequencies and percentages of baseline characteristics were calculated for participants in each ancestry group. Our main analyses were stratified by ancestry group, and adjusted for age, sex, CRIC study site, and 10 principal components for ancestry (ten for participants of African ancestry and three for participants of European ancestry). GRSs were tested for association with baseline SBP,

DBP, and PP using linear regression models. In sensitivity analyses, GRSs were tested for association with unimputed baseline BP using linear regression models and changes in BP over follow up using mixed regression models. The BP GRSs were then tested for associations with each of our primary outcomes, time to CVD and CKD progression events, using Cox proportional hazards models. To account for additional possible confounding by clinical factors, we conducted sensitivity analyses on the GRS-primary outcome associations with additional adjustment for BMI, low density lipoprotein, and lipid lowering medications. To increase our statistical power for the assessment of CKD progression, we also examined the associations between the GRSs and continuous measures of kidney function decline over time using mixed models and two untransformed measures of decline – eGFR slope and UPCR slope. Finally, we performed a sensitivity analysis to examine the independent associations of measured

SBP and our SBP GRSs with the primary CVD and CKD endpoints. Analyses were conducted using SAS 9.4 (SAS Institute, Cary, NC) and R 3.6.1 (The R Foundation) statistical software.

Results

Study Participants

Baseline characteristics of CRIC participants, stratified by ancestry, are presented in Table 1. On average, participants had moderate CKD [mean (SD) eGFR, 48.4 (18.6)].

Most participants had hypertension and were using antihypertensive medication. BP, 11

BMI, low density lipoprotein, proportion with diabetes, and number of antihypertensive

medications were higher among African Ancestry participants than European ancestry

participants. African ancestry participants were generally similar across SBP GRS

quartiles (Supplementary Table 1). European ancestry participants had increasing BP

traits across SBP GRS quartiles (Supplementary Table 2).

Associations between BP GRS & Baseline Measures

GRSs for SBP, DBP, and PP were associated with baseline SBP, DBP, and PP,

respectively, among European ancestry participants (Supplementary Table 3). No

associations were found between BP GRSs and respective BP measures among African

ancestry participants. Models examining the relationship between per-quartile BP GRS

and baseline SBP, DBP, and PP, showed overall consistency with our per-SD models

(Supplementary Table 4). In addition, European ancestry participants in higher SBP,

DBP, and PP GRS quartiles and African ancestry participants in higher SBP GRS

quartiles were using a greater number of antihypertensive medications than those in lower

quartiles (Supplementary Table 5). As expected, GRS-BP associations were attenuated in models using unimputed BP measurements with number of antihypertensive medications as a covariate (Supplementary Table 6). BP GRSs were not associated with

baseline eGFR or UPCR in either ancestry (Supplementary Table 1) nor were they

associated with annual change in BP over follow up (data not shown).

Associations between BP GRS and Prospective CVD and CKD Endpoints

SBP GRSs among both African and European ancestry participants and PP GRSs

among European ancestry participants were associated with significantly increased risks

of CVD. A similar trend for DBP GRSs among both African and European ancestry 12

participants was also observed. Findings were similar when comparing the top GRS

quartile to the bottom GRS quartile (Table 2). Although no association with CVD was

observed for the PP GRS among African ancestry participants, the trend was similar to

the other BP GRSs. Results were similar in models that additionally adjusted for BMI, low density lipoprotein, and lipid lowering medication (Supplementary Table 7). We

did not find any associations between the BP GRSs and CKD progression events (Table

2 and Supplementary Table 7). Furthermore, BP GRSs did not associate with annual

kidney function decline over follow up, using either eGFR slope or UPCR slope, in either

continuous or quartile models (Table 3).

The Figure (A and B) depicts the relationships between each GRS and the CVD

composite, stratified by ancestry, per quartile of SBP, DBP, and PP GRS. Our quartile

models demonstrated overall consistency with our per-SD models. We found that risk of

CVD increased per quartile SBP GRS among European ancestry participants (p=0.03),

with similar trends for SBP and DPB GRS among African ancestry participants (P=0.10

and P=0.11, respectively). In addition, compared to participants in the lowest quartiles

SBP, DBP, and PP GRS, European ancestry participants in the highest quartiles had 1.41-

[95% confidence interval (CI): 1.06, 1.88; P=0.02], 1.18- (95%CI: 0.89, 1.56; P=0.25),

and 1.33-fold (95% CI: 1.00, 1.76; P=0.05) higher risks of CVD, respectively. The

Figure (C and D) also shows the relationships between each GRS and incident ESKD or halving of eGFR, stratified by ancestry, per quartile of SBP, DBP, and PP GRS. Similar

to the results for our continuous models, there were no associations between BP GRS and

this endpoint in either ancestry group. 13

We also examined two components of the CVD composite separately, congestive

heart failure and a composite of myocardial infarction and stroke. Among African

ancestry participants, we observed significant increases in risk of congestive heart failure

associated with our SBP and PP GRSs (Supplementary Figure 1) and consistent but

non-significant increases in risk of MI and stroke. Among European ancestry

participants, we observed a consistently positive effect direction in the relationships

between the GRSs and each of the components of the CVD composite (Supplementary

Figure 2), although these hazard ratios did not meet the threshold for statistical

significance.

Comparison of Effect Sizes on Clinical Outcomes

In our adjusted models, each standard deviation increase in measured SBP

conferred significant 1.3- and 1.4-fold increased risks of CVD events in African and

European ancestry participants, respectively. Effect sizes of measured SBP on CKD progression events were larger, conferring 1.6- and 1.7- fold increased risks for this

endpoint. Our SBP GRS associated with a significant 10% increased risk of CVD events in European ancestry participants after adjusting for measured BP (P=0.05),

demonstrating an independent association of this variable in Europeans and suggesting

that BP GRS provides information somewhat distinct from measured BP

(Supplementary Table 8). Although the effect size for SBP GRS on CVD events in

African ancestry participants was only modestly attenuated by the inclusion of measured

SBP in the model, the association was only marginally significant (P=0.07). As expected

based on our main analyses, hazard ratios for CKD events conferred by SBP GRSs were 14

not significant before or after adjustment for measured SBP in either ancestry group

(Supplementary Table 6).

Discussion

In the current study, we observed significant associations between BP GRS and

CVD among patients with CKD of both African and European ancestries. Those with

higher BP GRS had increased risk of CVD events over an average of 12 years of follow

up. Furthermore, effect sizes were generally consistent among the two components of the

CVD composite endpoints, congestive heart failure and MI/stroke. In contrast, there was

no association between BP GRS and CKD progression events or annual declines in

kidney function, based on eGFR and UPCR. While clinical trials show that BP lowering

medication reduces CVD and CKD progression events, these data suggest that the

mechanism of action for these two health conditions may be distinct.

In the current study, BP GRSs associated with increased risk of CVD events in both African and European ancestry CRIC participants. Effect sizes for SBP GRSs tended to be larger in magnitude and achieve smaller p-values than those for DBP and

PP. The relatively larger effects of SBP GRS on CVD events likely reflects the larger

effects of measured SBP on this phenotype.29 With 9% to 15% increased risks of CVD

events conferred by each standard deviation increase in SBP GRS across ancestry groups,

our findings are similar to previous research in the general population that showed 11%

increased risk of CVD30 or 11% increased prevalence of CVD17,23 associated with BP

GRSs. Our results are also consistent with RCTs demonstrating reductions in CVD

events associated with BP lowering. For example, a 2013 meta-analysis of RCTs 15

conducted by the Blood Pressure Lowering Treatment Trialists’ Collaboration reported

reduced risk of major cardiovascular events associated with reductions in SBP, with

similar associations identified across eGFR subgroups and antihypertension medication

classes.31 Another meta-analysis of RCTs, by Xie and colleagues from 2016, similarly

found reductions in risk of CVD events with intensive blood pressure lowering treatment

when compared to less intensive blood pressure lowering, again demonstrating consistent

reductions in CVD events across intervention types and patient subgroups, including

patients with kidney disease.6 Our findings add genetic evidence to the robust findings of

previous RCTs supporting the role of high BP in occurrence of CVD events in patients

with CKD.

We did not find a relationship between BP GRSs and worsening kidney disease

using three kidney disease progression measures – CKD progression events, eGFR change over time, and UPCR change over time. These findings are generally consistent

with studies of BP GRSs and kidney function in the general population.17,18,20–22 Our

results are further supported by a recent general population Mendelian randomization

study by Yu and colleagues,32 where no association between a blood pressure GRS and

kidney function was found. The authors did, however, find a causal relationship between

their kidney function GRS and BP, suggesting that the public health burden of hypertension can be reduced through preventing kidney function decline. These general

population findings are consistent with clinical trial evidence in CKD populations, which

did not demonstrate conclusive renoprotective effects of antihypertensive medication, but

strongly showed protective effects of RAAS inhibition in reducing CKD progression, independent of BP lowering.12,33,34 Our findings further build on previous work, 16

providing genetic evidence that lifetime burden of high BP may relate to CKD outcomes

differently than it does to CVD outcomes.

We generally found attenuated associations between BP GRSs and BP when

compared with previous BP GRS studies17,19, both in terms of effect estimates and statistical significance. These findings were consistent when we imputed BP measurements for antihypertension medication and when we adjusted for number of antihypertension medications. BP GRS serves as a proxy for lifetime burden of high BP.

Since 92% of CRIC participants were taking antihypertension medication and medication

use was higher among participants with increased BP genetic risk, baseline BP measures

may more so reflect the acute BP lowering effects of pharmaceutical intervention rather

than long-term BP measures. As expected, BP GRS was strongly associated with the

intermediate BP phenotype, antihypertension medication use, which further validates its

use as a proxy for high lifetime BP. In this context, our findings of attenuated associations compared to a general population with much lower frequencies of antihypertension medication use are unsurprising. Results were particularly attenuated in

African ancestry participants, likely reflecting the construction of a GRS using loci from

a majority European ancestry population.19 Although there is substantial evidence that

causal variants driving complex human trait associations are largely shared across diverse

populations,35,36 and that variants identified in specific ancestry groups may be relevant

to populations with distinct linkage disequilibrium structures,37 previous BP GRS studies

have often shown attenuated effects in non-European ancestry populations.18,19,22 Our

findings similarly highlight the disparity introduced by the historical Eurocentric biases in genomics research,38 providing further support for the importance of conducting 17

genetics research in diverse populations. African ancestry participants were also more

likely to use antihypertensive medications and were on a greater number of

antihypertensive medications than their European ancestry counterparts, which may have

also contributed to the relatively attenuated BP findings in this ancestry group.

This study has several important strengths. To our knowledge, it is the first study

to examine the relationship between BP GRSs and CKD progression among CKD patients. The CRIC study provides a unique opportunity to study this relationship in a

well phenotyped multi-ancestry cohort with over a decade of follow up and multiple

measures of kidney function decline. This study also has several important limitations.

Our findings have not been replicated in an independent population; however, they are

consistent with most previous BP GRS studies in the general population. Our study also

had fewer participants than many of the previous studies that examined the relationship between BP GRS and BP-related phenotypes, raising concerns related to statistical

power. However, CKD events were more frequent than CVD events and demonstrate

associations with SBP that are larger in magnitude. Hence, power for detecting GRS-

CKD associations was better than that of GRS-CVD associations. Even with our limited

sample size, we were able to identify compelling genetic evidence of the effects of BP on

CVD in CKD patients. BP GRS did not display strong associations with measured BP in

our sample, likely due to the high frequency of medication use. Therefore, the observed

findings are necessarily more reflective of the associations of lifetime burden of high BP

with CVD and CKD progression rather than BP measured at baseline or subsequent

changes in BP during follow up. 18

In the first study to examine the effects of aggregated BP loci on cardiovascular

and renal phenotypes in patients with CKD, we observed an association between

genetically elevated BP and cardiovascular endpoints. We did not find significant

associations between genetic risk for high BP and CKD progression, adding more

uncertainty to the etiologic role of BP in CKD.

Disclosures

J. Cohen reports Honoraria: UpToDate; Other Interests/Relationships: Research grant funding from the National Institutes of Health (NIH-NHLBI K23-HL133843 and R01-

HL153646, NIH-NCATS U01-TR003734) and the American Heart Association (Bugher

Award). A. Parsa reports Scientific Advisor or Membership: National Institutes of Health

– NIDDK. M. Rahman reports Consultancy Agreements: Barologics; Research Funding:

Bayer Pharmaceuticals, Duke Clinical Research Institute; Honoraria: Relypsa, Reata,

Bayer; Scientific Advisor or Membership: Associate Editor - CJASN, Editorial Board

member - American Journal of Nephrology. S. Rosas reports Consultancy Agreements:

Fibrogen, Astra Zeneca, Astellas- Consultant for event adjudication for a study, Reata;

Research Funding: NIH-NIDDK; Bayer; Ironwood, Astra Zeneca; Honoraria: 2019

Reata, Bayer- honorarium; Scientific Advisor or Membership: CJASN, ACKD-Editorial

Board, NKF-NE Medical Adivisory Board, NKF Scientific Advisory Board. S. Saraf

reports Consultancy Agreements: Novartis, Global Blood Therapeutics; Research

Funding: Pfizer, Novartis, Global Blood Therapeutics; Honoraria: Novartis, Global Blood

Therapeutics; Scientific Advisor or Membership: Novartis; Speakers Bureau: Global

Blood Therapeutics. A. Srivastava reports Consultancy Agreements: CVS Caremark, 19

Tate & Latham (Medicolegal consulting); Honoraria: Horizon Therapeutics PLC,

AstraZeneca, Bayer; Speakers Bureau: AstraZeneca. All remaining authors have nothing to disclose.

Funding

Funding for the CRIC Study was obtained under a cooperative agreement from National

Institute of Diabetes and Digestive and Kidney Diseases (U01DK060990,

U01DK060984, U01DK061022, U01DK061021, U01DK061028, U01DK060980,

U01DK060963, U01DK060902 and U24DK060990). In addition, this work was supported in part by: the Perelman School of Medicine at the University of Pennsylvania

Clinical and Translational Science Award NIH/NCATS UL1TR000003, Johns Hopkins

University UL1 TR-000424, University of Maryland GCRC M01 RR-16500, Clinical and

Translational Science Collaborative of Cleveland, UL1TR000439 from the National

Center for Advancing Translational Sciences (NCATS) component of the National

Institutes of Health and NIH roadmap for Medical Research, Michigan Institute for

Clinical and Health Research (MICHR) UL1TR000433, University of Illinois at Chicago

CTSA UL1RR029879, Tulane COBRE for Clinical and Translational Research in

Cardiometabolic Diseases P20 GM109036, Kaiser Permanente NIH/NCRR UCSF-CTSI

UL1 RR-024131, Department of Internal Medicine, University of New Mexico School of

Medicine Albuquerque, NM R01DK119199.

A.S. is supported by NIH grant K23DK120811, NIDDK Kidney Precision Medicine

Project Opportunity Pool grant under U2CDK114886, and core resources from the 20

George M. O’Brien Kidney Research Center at Northwestern University (NU-

GoKIDNEY) P30DK114857.

Acknowledgments

The authors thank the participants, investigators and staff of the CRIC study for their time and commitment. CRIC Study Investigators: Lawrence J. Appel, Jing Chen, Harold

I. Feldman, Alan S. Go, James P. Lash, Robert G. Nelson, Mahboob Rahman,

Panduranga S. Rao, Vallabh O Shah, Raymond R. Townsend, Mark L. Unruh.

Author Contributions

J Nierenberg: Conceptualization; Data curation; Formal analysis; Investigation;

Methodology; Project administration; Visualization; Writing - original draft; Writing - review and editing

A Anderson: Conceptualization; Supervision; Writing - review and editing

J He: Conceptualization; Supervision; Writing - review and editing

A Parsa: Data curation; Funding acquisition; Investigation; Methodology; Project administration; Writing - review and editing

A Srivastava: Writing - review and editing

J Cohen: Writing - review and editing

S Saraf: Writing - review and editing

M Rahman: Writing - review and editing

S Rosas: Writing - review and editing 21

T Kelly: Conceptualization; Formal analysis; Supervision; Writing - original draft;

Writing - review and editing

All authors approved the final version of the manuscript.

Supplemental Materials

Supplementary Methods

Supplementary Table 1. Baseline Characteristics of African Ancestry CRIC Participants

According to SBP GRS Quartile

Supplementary Table 2. Baseline Characteristics of European Ancestry CRIC

Participants According to SBP GRS Quartile

Supplementary Table 3. Associations between Blood Pressure GRS and Baseline Blood

Pressure and Kidney Function, per SD Increase in GRS and Comparing the Top and

Bottom GRS Quartiles

Supplementary Table 4. Median Baseline Blood Pressure, per Quartile Increase in

Respective Blood Pressure GRS

Supplementary Table 5. Median number of Antihypertensive Medications per Quartile

Increase in GRS

Supplementary Table 6. Associations between Blood Pressure GRS and Baseline

Unimputed Blood Pressure*, per SD Increase in GRS and Comparing the Top and

Bottom GRS Quartiles

Supplementary Table 7. Hazard Ratios for Primary Endpoints per SD Increase in GRS and Comparing the Top and Bottom GRS Quartiles, in Models with Additional

Adjustment for Baseline BMI, LDL, and Lipid Lowering Medication 22

Supplementary Figure 1. Hazard Ratios* for CVD Composite and its Component

Endpoints, per SD Increase in GRS, Among African Ancestry Participants

Supplementary Figure 2. Hazard Ratios* for CVD Composite and its Component

Endpoints per SD Increase in GRS, Among European Ancestry Participants

Supplementary Table 8. Comparison of Hazard Ratios per Standard Deviation of SBP and the SBP GRS on Clinical Outcomes

Supplemental References

Supplementary Data. Variant Effect Estimates on Blood Pressure Used in Genetic Risk

Score Construction

23

References

1. Mills KT, Xu Y, Zhang W, Bundy JD, Chen CS, Kelly TN, Chen J, He J: A

systematic analysis of worldwide population-based data on the global burden of

chronic kidney disease in 2010. Kidney Int. 88: 950–957, 2015

2. Webster AC, Nagler EV, Morton RL, Masson P: Chronic Kidney Disease. Lancet

389: 1238–1252, 2017

3. Klag MJ, Whelton PK, Randall BL, Neaton JD, Brancati FL, Ford CE, Shulman

NB, Stamler J: Blood pressure and end-stage renal disease in men. N. Engl. J.

Med. 334: 13–18, 1996

4. Bash LD, Astor BC, Coresh J: Risk of Incident ESRD: A Comprehensive Look at

Cardiovascular Risk Factors and 17 Years of Follow-up in the Atherosclerosis

Risk in Communities (ARIC) Study. Am. J. Kidney Dis. 55: 31–41, 2010

5. Lewington S, Clarke R, Qizilbash N, Peto R, Collins R, Prospective Studies

Collaboration: Age-specific relevance of blood pressure to cause-specific mortality

is best assessed by collaborative meta-analysis of individual participant data from

the separate prospective studies. Lancet 360: 1903–1913, 2002

6. Xie X, Atkins E, Lv J, Bennett A, Neal B, Ninomiya T, Woodward M, MacMahon

S, Turnbull F, Hillis GS, Chalmers J, Mant J, Salam A, Rahimi K, Perkovic V,

Rodgers A: Effects of intensive blood pressure lowering on cardiovascular and

renal outcomes: Updated systematic review and meta-analysis. Lancet 387: 435–

443, 2016

7. Ettehad D, Emdin CA, Kiran A, Anderson SG, Callender T, Emberson J, Chalmers

J, Rodgers A, Rahimi K: Blood pressure lowering for prevention of cardiovascular 24

disease and death: A systematic review and meta-analysis. Lancet 387: 957–967,

2016

8. Law MR, Morris JK, Wald NJ: Use of blood pressure lowering drugs in the

prevention of cardiovascular disease: Meta-analysis of 147 randomised trials in the

context of expectations from prospective epidemiological studies. BMJ 338: 1245,

2009

9. Thomopoulos C, Parati G, Zanchetti A: Effects of blood pressure-lowering

treatment on cardiovascular outcomes and mortality: 14-effects of different classes

of antihypertensive drugs in older and younger patients: Overview and meta-

analysis. J. Hypertens. 36: 1637–1647, 2018

10. Wright JT, Bakris G, Greene T, Agodoa LY, Appel LJ, Charleston J, Cheek DA,

Douglas-Baltimore JG, Gassman J, Glassock R, Hebert L, Jamerson K, Lewis J,

Phillips RA, Toto RD, Middleton JP, Rostand SG: Effect of blood pressure

lowering and antihypertensive drug class on progression of hypertensive kidney

disease: Results from the AASK trial. J. Am. Med. Assoc. 288: 2421–2431, 2002

11. Lewis EJ, Hunsicker LG, Bain RP, Rohde RD: The Effect of Angiotensin-

Converting-Enzyme Inhibition on Diabetic Nephropathy. N. Engl. J. Med. 329:

1456–1462, 1993

12. Sarafidis PA, Khosla N, Bakris GL: Antihypertensive Therapy in the Presence of

Proteinuria. Am. J. Kidney Dis. 49: 12–26, 2007

13. Casas JP, Chua W, Loukogeorgakis S, Vallance P, Smeeth L, Hingorani AD,

MacAllister RJ: Effect of inhibitors of the renin-angiotensin system and other

antihypertensive drugs on renal outcomes: Systematic review and meta-analysis. 25

Lancet 366: 2026–2033, 2005

14. Tsai WC, Wu HY, Peng Y Sen, Yang JY, Chen HY, Chiu YL, Hsu SP, Ko MJ, Pai

MF, Tu YK, Hung KY, Chien KL: Association of intensive blood pressure control

and kidney disease progression in nondiabetic patients with chronic kidney

disease: A systematic review and meta-analysis. JAMA Intern. Med. 177: 792–799,

2017

15. Nazarzadeh M, Pinho-Gomes AC, Smith Byrne K, Canoy D, Raimondi F, Ayala

Solares JR, Otto CM, Rahimi K: Systolic Blood Pressure and Risk of Valvular

Heart Disease: A Mendelian Randomization Study. JAMA Cardiol. 4: 788–795,

2019

16. Lyall DM, Celis-Morales C, Ward J, Iliodromiti S, Anderson JJ, Gill JMR, Smith

DJ, Ntuk UE, Mackay DF, Holmes M V., Sattar N, Pell JP: Association of body

mass index with cardiometabolic disease in the UK biobank: A mendelian

randomization study. JAMA Cardiol. 2: 882–889, 2017

17. Ehret GB, Munroe PB, Rice KM, Bochud M, Johnson AD, Chasman DI, Smith A

V, Tobin MD, Verwoert GC, Hwang SJ, Pihur V, Vollenweider P, O’Reilly PF,

Amin N, Bragg-Gresham JL, Teumer A, Glazer NL, Launer L, Zhao JH,

Aulchenko Y, Heath S, Sõber S, Parsa A, Luan J, Arora P, Dehghan A, Zhang F,

Lucas G, Hicks AA, Jackson AU, Peden JF, Tanaka T, Wild SH, Rudan I, Igl W,

Milaneschi Y, Parker AN, Fava C, Chambers JC, Fox ER, Kumari M, Go MJ, van

der Harst P, Kao WH, Sjögren M, Vinay DG, Alexander M, Tabara Y, Shaw-

Hawkins S, Whincup PH, Liu Y, Shi G, Kuusisto J, Tayo B, Seielstad M, Sim X,

Nguyen KD, Lehtimäki T, Matullo G, Wu Y, Gaunt TR, Onland-Moret NC, 26

Cooper MN, Platou CG, Org E, Hardy R, Dahgam S, Palmen J, Vitart V, Braund

PS, Kuznetsova T, Uiterwaal CS, Adeyemo A, Palmas W, Campbell H, Ludwig B,

Tomaszewski M, Tzoulaki I, Palmer ND, Aspelund T, Garcia M, Chang YP,

O’Connell JR, Steinle NI, Grobbee DE, Arking DE, Kardia SL, Morrison AC,

Hernandez D, Najjar S, McArdle WL, Hadley D, Brown MJ, Connell JM,

Hingorani AD, Day IN, Lawlor DA, Beilby JP, Lawrence RW, Clarke R,

Hopewell JC, Ongen H, Dreisbach AW, Li Y, Young JH, Bis JC, Kähönen M,

Viikari J, Adair LS, Lee NR, Chen MH, Olden M, Pattaro C, Bolton JA, Köttgen

A, Bergmann S, Mooser V, Chaturvedi N, Frayling TM, Islam M, Jafar TH,

Erdmann J, Kulkarni SR, Bornstein SR, Grässler J, Groop L, Voight BF, Kettunen

J, Howard P, Taylor A, Guarrera S, Ricceri F, Emilsson V, Plump A, Barroso I,

Khaw KT, Weder AB, Hunt SC, Sun Y V, Bergman RN, Collins FS, Bonnycastle

LL, Scott LJ, Stringham HM, Peltonen L, Perola M, Vartiainen E, Brand SM,

Staessen JA, Wang TJ, Burton PR, Soler Artigas M, Dong Y, Snieder H, Wang X,

Zhu H, Lohman KK, Rudock ME, Heckbert SR, Smith NL, Wiggins KL,

Doumatey A, Shriner D, Veldre G, Viigimaa M, Kinra S, Prabhakaran D, Tripathy

V, Langefeld CD, Rosengren A, Thelle DS, Corsi AM, Singleton A, Forrester T,

Hilton G, McKenzie CA, Salako T, Iwai N, Kita Y, Ogihara T, Ohkubo T,

Okamura T, Ueshima H, Umemura S, Eyheramendy S, Meitinger T, Wichmann

HE, Cho YS, Kim HL, Lee JY, Scott J, Sehmi JS, Zhang W, Hedblad B, Nilsson

P, Smith GD, Wong A, Narisu N, Stančáková A, Raffel LJ, Yao J, Kathiresan S,

O’Donnell CJ, Schwartz SM, Ikram MA, Longstreth WT, Mosley TH, Seshadri S,

Shrine NR, Wain L V, Morken MA, Swift AJ, Laitinen J, Prokopenko I, Zitting P, 27

Cooper JA, Humphries SE, Danesh J, Rasheed A, Goel A, Hamsten A, Watkins H,

Bakker SJ, van Gilst WH, Janipalli CS, Mani KR, Yajnik CS, Hofman A, Mattace-

Raso FU, Oostra BA, Demirkan A, Isaacs A, Rivadeneira F, Lakatta EG, Orru M,

Scuteri A, Ala-Korpela M, Kangas AJ, Lyytikäinen LP, Soininen P, Tukiainen T,

Würtz P, Ong RT, Dörr M, Kroemer HK, Völker U, Völzke H, Galan P, Hercberg

S, Lathrop M, Zelenika D, Deloukas P, Mangino M, Spector TD, Zhai G, Meschia

JF, Nalls MA, Sharma P, Terzic J, Kumar M V, Denniff M, Zukowska-

Szczechowska E, Wagenknecht LE, Fowkes FG, Charchar FJ, Schwarz PE,

Hayward C, Guo X, Rotimi C, Bots ML, Brand E, Samani NJ, Polasek O, Talmud

PJ, Nyberg F, Kuh D, Laan M, Hveem K, Palmer LJ, van der Schouw YT, Casas

JP, Mohlke KL, Vineis P, Raitakari O, Ganesh SK, Wong TY, Tai ES, Cooper RS,

Laakso M, Rao DC, Harris TB, Morris RW, Dominiczak AF, Kivimaki M,

Marmot MG, Miki T, Saleheen D, Chandak GR, Coresh J, Navis G, Salomaa V,

Han BG, Zhu X, Kooner JS, Melander O, Ridker PM, Bandinelli S, Gyllensten

UB, Wright AF, Wilson JF, Ferrucci L, Farrall M, Tuomilehto J, Pramstaller PP,

Elosua R, Soranzo N, Sijbrands EJ, Altshuler D, Loos RJ, Shuldiner AR, Gieger C,

Meneton P, Uitterlinden AG, Wareham NJ, Gudnason V, Rotter JI, Rettig R, Uda

M, Strachan DP, Witteman JC, Hartikainen AL, Beckmann JS, Boerwinkle E,

Vasan RS, Boehnke M, Larson MG, Järvelin MR, Psaty BM, Abecasis GR,

Chakravarti A, Elliott P, van Duijn CM, Newton-Cheh C, Levy D, Caulfield MJ,

Johnson T, International Consortium for Blood Pressure Genome-Wide

Association Studies, CHARGE-HF Consortium, CKDGen Consortium,

CARDIoGRAM, KidneyGen Consortium, EchoGen Consortium: Genetic variants 28

in novel pathways influence blood pressure and cardiovascular disease risk. Nature

478: 103–109, 2011

18. Ehret GB, Ferreira T, Chasman DI, Jackson AU, Schmidt EM, Johnson T,

Thorleifsson G, Luan J, Donnelly LA, Kanoni S, Petersen AK, Pihur V,

Strawbridge RJ, Shungin D, Hughes MF, Meirelles O, Kaakinen M, Bouatia-Naji

N, Kristiansson K, Shah S, Kleber ME, Guo X, Lyytikäinen LP, Fava C, Eriksson

N, Nolte IM, Magnusson PK, Salfati EL, Rallidis LS, Theusch E, Smith AJP,

Folkersen L, Witkowska K, Pers TH, Joehanes R, Kim SK, Lataniotis L, Jansen R,

Johnson AD, Warren H, Kim YJ, Zhao W, Wu Y, Tayo BO, Bochud M, Absher D,

Adair LS, Amin N, Arking DE, Axelsson T, Baldassarre D, Balkau B, Bandinelli

S, Barnes MR, Barroso I, Bevan S, Bis JC, Bjornsdottir G, Boehnke M,

Boerwinkle E, Bonnycastle LL, Boomsma DI, Bornstein SR, Brown MJ, Burnier

M, Cabrera CP, Chambers JC, Chang IS, Cheng CY, Chines PS, Chung RH,

Collins FS, Connell JM, Döring A, Dallongeville J, Danesh J, De Faire U, Delgado

G, Dominiczak AF, Doney ASF, Drenos F, Edkins S, Eicher JD, Elosua R, Enroth

S, Erdmann J, Eriksson P, Esko T, Evangelou E, Evans A, Fall T, Farrall M, Felix

JF, Ferrières J, Ferrucci L, Fornage M, Forrester T, Franceschini N, Franco OH,

Franco-Cereceda A, Fraser RM, Ganesh SK, Gao H, Gertow K, Gianfagna F,

Gigante B, Giulianini F, Goel A, Goodall AH, Goodarzi MO, Gorski M, Gräßler J,

Groves CJ, Gudnason V, Gyllensten U, Hallmans G, Hartikainen AL, Hassinen M,

Havulinna AS, Hayward C, Hercberg S, Herzig KH, Hicks AA, Hingorani AD,

Hirschhorn JN, Hofman A, Holmen J, Holmen OL, Hottenga JJ, Howard P,

Hsiung CA, Hunt SC, Ikram MA, Illig T, Iribarren C, Jensen RA, Kähönen M, 29

Kang HM, Kathiresan S, Keating BJ, Khaw KT, Kim YK, Kim E, Kivimaki M,

Klopp N, Kolovou G, Komulainen P, Kooner JS, Kosova G, Krauss RM, Kuh D,

Kutalik Z, Kuusisto J, Kvaløy K, Lakka TA, Lee NR, Lee I Te, Lee WJ, Levy D,

Li X, Liang KW, Lin H, Lin L, Lindström J, Lobbens S, Männistö S, Müller G,

Müller-Nurasyid M, Mach F, Markus HS, Marouli E, McCarthy MI, McKenzie

CA, Meneton P, Menni C, Metspalu A, Mijatovic V, Moilanen L, Montasser ME,

Morris AD, Morrison AC, Mulas A, Nagaraja R, Narisu N, Nikus K, O’Donnell

CJ, O’Reilly PF, Ong KK, Paccaud F, Palmer CD, Parsa A, Pedersen NL, Penninx

BW, Perola M, Peters A, Poulter N, Pramstaller PP, Psaty BM, Quertermous T,

Rao DC, Rasheed A, Rayner NW, Renström F, Rettig R, Rice KM, Roberts R,

Rose LM, Rossouw J, Samani NJ, Sanna S, Saramies J, Schunkert H, Sebert S,

Sheu WHH, Shin YA, Sim X, Smit JH, Smith A V., Sosa MX, Spector TD,

Stančáková A, Stanton A V., Stirrups KE, Stringham HM, Sundstrom J, Swift AJ,

Syvänen AC, Tai ES, Tanaka T, Tarasov K V., Teumer A, Thorsteinsdottir U,

Tobin MD, Tremoli E, Uitterlinden AG, Uusitupa M, Vaez A, Vaidya D, Van

Duijn CM, Van Iperen EPA, Vasan RS, Verwoert GC, Virtamo J, Vitart V, Voight

BF, Vollenweider P, Wagner A, Wain L V., Wareham NJ, Watkins H, Weder AB,

Westra HJ, Wilks R, Wilsgaard T, Wilson JF, Wong TY, Yang TP, Yao J, Yengo

L, Zhang W, Zhao JH, Zhu X, Bovet P, Cooper RS, Mohlke KL, Saleheen D, Lee

JY, Elliott P, Gierman HJ, Willer CJ, Franke L, Hovingh GK, Taylor KD,

Dedoussis G, Sever P, Wong A, Lind L, Assimes TL, Njølstad I, Schwarz PEH,

Langenberg C, Snieder H, Caulfield MJ, Melander O, Laakso M, Saltevo J,

Rauramaa R, Tuomilehto J, Ingelsson E, Lehtimäki T, Hveem K, Palmas W, März 30

W, Kumari M, Salomaa V, Chen YDI, Rotter JI, Froguel P, Jarvelin MR, Lakatta

EG, Kuulasmaa K, Franks PW, Hamsten A, Wichmann HE, Palmer CNA,

Stefansson K, Ridker PM, Loos RJF, Chakravarti A, Deloukas P, Morris AP,

Newton-Cheh C, Munroe PB: The genetics of blood pressure regulation and its

target organs from association studies in 342,415 individuals. Nat. Genet. 48:

1171–1184, 2016

19. Evangelou E, Warren HR, Mosen-Ansorena D, Mifsud B, Pazoki R, Gao H,

Ntritsos G, Dimou N, Cabrera CP, Karaman I, Ng FL, Evangelou M, Witkowska

K, Tzanis E, Hellwege JN, Giri A, Velez Edwards DR, Sun Y V., Cho K, Gaziano

JM, Wilson PWF, Tsao PS, Kovesdy CP, Esko T, Magi R, Milani L, Almgren P,

Boutin T, Debette S, Ding J, Giulianini F, Holliday EG, Jackson AU, Li-Gao R,

Lin W-Y, Luan J, Mangino M, Oldmeadow C, Prins B, Qian Y, Sargurupremraj

M, Shah N, Surendran P, Theriault S, Verweij N, Willems SM, Zhao J-H,

Amouyel P, Connell J, de Mutsert R, Doney ASF, Farrall M, Menni C, Morris

APD, Noordam R, Pare G, Poulter NR, Shields DC, Stanton A, Thom S, Abecasis

G, Amin N, Arking DE, Ayers KL, Barbieri CM, Batini C, Bis JC, Blake T,

Bochud M, Boehnke M, Boerwinkle E, Boomsma DI, Bottinger E, Braund PS,

Brumat M, Campbell A, Campbell H, Chakravarti A, Chambers JC, Chauhan G,

Ciullo M, Cocca M, Collins F, Cordell HJ, Davies G, de Borst MH, de Geus E,

Deary I, Deelen J, Del Greco FM, Demirkale CY, Dorr M, Ehret GB, Elosua R,

Enroth S, Erzurumluoglu AM, Ferreira T, Franberg M, Franco OH, Gandin I,

Gasparini P, Giedraitis V, Gieger C, Girotto G, Goel A, Gow AJ, Gudnason V,

Guo X, Gyllensten U, Hamsten A, Harris TB, Harris SE, Hartman CA, Havulinna 31

AS, Hicks AA, Hofer E, Hofman A, Hottenga J-J, Huffman JE, Hwang S-J,

Ingelsson E, James A, Jansen R, Jarvelin M-R, Joehanes R, Johansson A, Johnson

AD, Joshi P, Jousilahti P, Jukema JW, Jula AM, Kahonen M, Kathiresan S,

Keavney BD, Khaw K-T, Knekt P, Knight J, Kolcic I, Kooner JS, Koskinen S,

Kristiansson K, Kutalik Z, Laan M, Larson M, Launer LJ, Lehne B, Lehtimaki T,

Levy D, Liewald DCM, Lin L, Lind L, Lindgren CM, Liu Y, Loos RJF, Lopez

LM, Lu L, Lyytikainen L-P, Mahajan A, Mamasoula C, Marrugat J, Marten J,

Milaneschi Y, Morgan A, Morris APD, Morrison AC, Nalls MA, Nandakumar P,

Nelson CP, Newton-Cheh C, Niiranen T, Nolte IM, Nutile T, Oldehinkel AJ,

Oostra BA, O’Reilly PF, Org E, Padmanabhan S, Palmas W, Palotie A, Pattie A,

Penninx BWJH, Perola M, Munson PJ, Peters A, Polasek O, Pramstaller PP,

Quang Tri N, Raitakari OT, Ren M, Rettig R, Rice K, Ridker PM, Reid JS, Riese

H, Ripatti S, Robino A, Rose LM, Rotter JI, Rudan I, Ruggiero D, Saba Y, Sala

CF, Salomaa V, Samani NJ, Sarin A-P, Schmidt R, Schmidt H, Shrine N,

Siscovick D, Smith A V., Schneider H, Sober S, Sorice R, Starr JM, Stott DJ,

Strachan DP, Strawbridge RJ, Sundstrom J, Swertz MA, Taylor KD, Teumer A,

Tobin MD, Toniolo D, Traglia M, Trompet S, Tuomilehto J, Tzourio C,

Uitterlinden AG, Vaez A, van der Most PJ, van Duijn CM, Vergnaud A-C,

Verwoert GC, Vitart V, Volker U, Vollenweider P, Vuckovic D, Watkins H, Wild

SH, Willemsen G, Wilson JF, Wright AF, Yao J, Zemunik T, Zhang W, Attia JR,

Butterworth AS, Chasman DI, Conen D, Cucca F, Danesh J, Hayward C, Howson

JMM, Laakso M, Lakatta EG, Langenberg C, Melander O, Mook-Kanamori DO,

Munroe PB, Palmer CNA, Risch L, Scott RA, Scott RJ, Sever P, Spector TD, van 32 der Harst P, Wareham NJ, Zeggini E, Brown MJ, Metspalu A, Hung AM,

O’Donnell C, Edwards TL, Psaty BM, Tzoulaki I, Barnes MR, Wain LV, Elliott P,

Caulfield MJ, Edwards DRV, Sun Y V., Cho K, Gaziano JM, Wilson PWF, Tsao

PS, Kovesdy CP, Esko T, Magi R, Milani L, Almgren P, Boutin T, Debette S,

Ding J, Giulianini F, Holliday EG, Jackson AU, Li-Gao R, Lin W-Y, Luan J,

Mangino M, Oldmeadow C, Prins B, Qian Y, Sargurupremraj M, Shah N,

Surendran P, Theriault S, Verweij N, Willems SM, Zhao J-H, Amouyel P, Connell

J, Mutsert R de, Doney ASF, Farrall M, Menni C, Morris APD, Noordam R, Pare

G, Poulter NR, Shields DC, Stanton A, Thom S, Abecasis G, Amin N, Arking DE,

Ayers KL, Barbieri CM, Batini C, Bis JC, Blake T, Bochud M, Boehnke M,

Boerwinkle E, Boomsma DI, Bottinger E, Braund PS, Brumat M, Campbell A,

Campbell H, Chakravarti A, Chambers JC, Chauhan G, Ciullo M, Cocca M,

Collins F, Cordell HJ, Davies G, Borst MH de, Geus E de, Deary I, Deelen J,

Greco FM Del, Demirkale CY, Dorr M, Ehret GB, Elosua R, Enroth S,

Erzurumluoglu AM, Ferreira T, Franberg M, Franco OH, Gandin I, Gasparini P,

Giedraitis V, Gieger C, Girotto G, Goel A, Gow AJ, Gudnason V, Guo X,

Gyllensten U, Hamsten A, Harris TB, Harris SE, Hartman CA, Havulinna AS,

Hicks AA, Hofer E, Hofman A, Hottenga J-J, Huffman JE, Hwang S-J, Ingelsson

E, James A, Jansen R, Jarvelin M-R, Joehanes R, Johansson A, Johnson AD, Joshi

P, Jousilahti P, Jukema JW, Jula AM, Kahonen M, Kathiresan S, Keavney BD,

Khaw K-T, Knekt P, Knight J, Kolcic I, Kooner JS, Koskinen S, Kristiansson K,

Kutalik Z, Laan M, Larson M, Launer LJ, Lehne B, Lehtimaki T, Levy D, Liewald

DCM, Lin L, Lind L, Lindgren CM, Liu Y, Loos RJF, Lopez LM, Lu L, 33

Lyytikainen L-P, Mahajan A, Mamasoula C, Marrugat J, Marten J, Milaneschi Y,

Morgan A, Morris APD, Morrison AC, Nalls MA, Nandakumar P, Nelson CP,

Newton-Cheh C, Niiranen T, Nolte IM, Nutile T, Oldehinkel AJ, Oostra BA,

O’Reilly PF, Org E, Padmanabhan S, Palmas W, Palotie A, Pattie A, Penninx

BWJH, Perola M, Munson PJ, Peters A, Polasek O, Pramstaller PP, Tri NQ,

Raitakari OT, Ren M, Rettig R, Rice K, Ridker PM, Reid JS, Riese H, Ripatti S,

Robino A, Rose LM, Rotter JI, Rudan I, Ruggiero D, Saba Y, Sala CF, Salomaa V,

Samani NJ, Sarin A-P, Schmidt R, Schmidt H, Shrine N, Siscovick D, Smith A V.,

Schneider H, Sober S, Sorice R, Starr JM, Stott DJ, Strachan DP, Strawbridge RJ,

Sundstrom J, Swertz MA, Taylor KD, Teumer A, Tobin MD, Toniolo D, Traglia

M, Trompet S, Tuomilehto J, Tzourio C, Uitterlinden AG, Vaez A, Most PJ van

der, Duijn CM van, Vergnaud A-C, Verwoert GC, Vitart V, Volker U,

Vollenweider P, Vuckovic D, Watkins H, Wild SH, Willemsen G, Wilson JF,

Wright AF, Yao J, Zemunik T, Zhang W, Attia JR, Butterworth AS, Chasman DI,

Conen D, Cucca F, Danesh J, Hayward C, Howson JMM, Laakso M, Lakatta EG,

Langenberg C, Melander O, Mook-Kanamori DO, Munroe PB, Palmer CNA,

Risch L, Scott RJRARJRA, Scott RJRARJRA, Sever P, Spector TD, Harst P van

der, Wareham NJ, Zeggini E, Brown MJ, Metspalu A, Hung AM, O’Donnell C,

Edwards TL, Psaty BM, Tzoulaki I, Barnes MR, Wain L V., Elliott P, Caulfield

MJ: Genetic analysis of over 1 million people identifies 535 new loci associated

with blood pressure traits. Nat. Genet. 198234, 2017

20. Wain LV, Verwoert GC, O’Reilly PF, Shi G, Johnson T, Johnson AD, Bochud M,

Rice KM, Henneman P, Smith A V., Ehret GB, Amin N, Larson MG, Mooser V, 34

Hadley D, Dörr M, Bis JC, Aspelund T, Esko T, Janssens ACJW, Zhao JH, Heath

S, Laan M, Fu J, Pistis G, Luan J, Arora P, Lucas G, Pirastu N, Pichler I, Jackson

AU, Webster RJ, Zhang F, Peden JF, Schmidt H, Tanaka T, Campbell H, Igl W,

Milaneschi Y, Hottenga JJ, Vitart V, Chasman DI, Trompet S, Bragg-Gresham JL,

Alizadeh BZ, Chambers JC, Guo X, Lehtimäki T, Kühnel B, Lopez LM, Polašek

O, Boban M, Nelson CP, Morrison AC, Pihur V, Ganesh SK, Hofman A, Kundu

S, Mattace-Raso FUS, Rivadeneira F, Sijbrands EJG, Uitterlinden AG, Hwang SJ,

Vasan RS, Wang TJ, Bergmann S, Vollenweider P, Waeber G, Laitinen J, Pouta

A, Zitting P, McArdle WL, Kroemer HK, Völker U, Völzke H, Glazer NL, Taylor

KD, Harris TB, Alavere H, Haller T, Keis A, Tammesoo ML, Aulchenko Y,

Barroso IS, Khaw KT, Galan P, Hercberg S, Lathrop M, Eyheramendy S, Org E,

Sõber S, Lu X, Nolte IM, Penninx BW, Corre T, Masciullo C, Sala C, Groop L,

Voight BF, Melander O, O’Donnell CJ, Salomaa V, D’Adamo AP, Fabretto A,

Faletra F, Ulivi S, Del Greco M F, Facheris M, Collins FS, Bergman RN, Beilby

JP, Hung J, Musk AW, Mangino M, Shin SY, Soranzo N, Watkins H, Goel A,

Hamsten A, Gider P, Loitfelder M, Zeginigg M, Hernandez D, Najjar SS, Navarro

P, Wild SH, Corsi AM, Singleton A, De Geus EJC, Willemsen G, Parker AN,

Rose LM, Buckley B, Stott D, Orru M, Uda M, Van Der Klauw MM, Zhang W, Li

X, Scott J, Chen YDI, Burke GL, Kähönen M, Viikari J, Döring A, Meitinger T,

Davies G, Starr JM, Emilsson V, Plump A, Lindeman JH, Hoen PACT, König IR,

Felix JF, Clarke R, Hopewell JC, Ongen H, Breteler M, Debette S, Destefano AL,

Fornage M, Mitchell GF, Smith NL, Holm H, Stefansson K, Thorleifsson G,

Thorsteinsdottir U, Samani NJ, Preuss M, Rudan I, Hayward C, Deary IJ, 35

Wichmann HE, Raitakari OT, Palmas W, Kooner JS, Stolk RP, Jukema JW,

Wright AF, Boomsma DI, Bandinelli S, Gyllensten UB, Wilson JF, Ferrucci L,

Schmidt R, Farrall M, Spector TD, Palmer LJ, Tuomilehto J, Pfeufer A, Gasparini

P, Siscovick D, Altshuler D, Loos RJF, Toniolo D, Snieder H, Gieger C, Meneton

P, Wareham NJ, Oostra BA, Metspalu A, Launer L, Rettig R, Strachan DP,

Beckmann JS, Witteman JCM, Erdmann J, Van Dijk KW, Boerwinkle E, Boehnke

M, Ridker PM, Jarvelin MR, Chakravarti A, Abecasis GR, Gudnason V, Newton-

Cheh C, Levy D, Munroe PB, Psaty BM, Caulfield MJ, Rao DC, Tobin MD,

Elliott P, Van Duijn CM: Genome-wide association study identifies six new loci

influencing pulse pressure and mean arterial pressure. Nat. Genet. 43: 1005–1012,

2011

21. Liu C, Kraja AT, Smith JA, Brody JA, Franceschini N, Bis JC, Rice K, Morrison

AC, Lu Y, Weiss S, Guo X, Palmas W, Martin LW, Chen YDI, Surendran P,

Drenos F, Cook JP, Auer PL, Chu AY, Giri A, Zhao W, Jakobsdottir J, Lin LA,

Stafford JM, Amin N, Mei H, Yao J, Voorman A, Larson MG, Grove ML, Smith

A V., Hwang SJ, Chen H, Huan T, Kosova G, Stitziel NO, Kathiresan S, Samani

N, Schunkert H, Deloukas P, Li M, Fuchsberger C, Pattaro C, Gorski M,

Kooperberg C, Papanicolaou GJ, Rossouw JE, Faul JD, Kardia SLR, Bouchard C,

Raffel LJ, Uitterlinden AG, Franco OH, Vasan RS, O’Donnell CJ, Taylor KD, Liu

K, Bottinger EP, Gottesman O, Daw EW, Giulianini F, Ganesh S, Salfati E, Harris

TB, Launer LJ, Dörr M, Felix SB, Rettig R, Völzke H, Kim E, Lee WJ, Lee I Te,

Sheu WHH, Tsosie KS, Edwards DRV, Liu Y, Correa A, Weir DR, Völker U,

Ridker PM, Boerwinkle E, Gudnason V, Reiner AP, Van Duijn CM, Borecki IB, 36

Edwards TL, Chakravarti A, Rotter JI, Psaty BM, Loos RJF, Fornage M, Ehret

GB, Newton-Cheh C, Levy D, Chasman DI: Meta-analysis identifies common and

rare variants influencing blood pressure and overlapping with metabolic trait loci.

Nat. Genet. 48: 1162–1170, 2016

22. Giri A, Hellwege JN, Keaton JM, Park J, Qiu C, Warren HR, Torstenson ES,

Kovesdy CP, Sun Y V., Wilson OD, Robinson-Cohen C, Roumie CL, Chung CP,

Birdwell KA, Damrauer SM, DuVall SL, Klarin D, Cho K, Wang Y, Evangelou E,

Cabrera CP, Wain L V., Shrestha R, Mautz BS, Akwo EA, Sargurupremraj M,

Debette S, Boehnke M, Scott LJ, Luan J, Zhao JH, Willems SM, Thériault S, Shah

N, Oldmeadow C, Almgren P, Li-Gao R, Verweij N, Boutin TS, Mangino M,

Ntalla I, Feofanova E, Surendran P, Cook JP, Karthikeyan S, Lahrouchi N, Liu C,

Sepúlveda N, Richardson TG, Kraja A, Amouyel P, Farrall M, Poulter NR, Laakso

M, Zeggini E, Sever P, Scott RA, Langenberg C, Wareham NJ, Conen D, Palmer

CNA, Attia J, Chasman DI, Ridker PM, Melander O, Mook-Kanamori DO, van

der Harst P, Cucca F, Schlessinger D, Hayward C, Spector TD, Jarvelin MR,

Hennig BJ, Timpson NJ, Wei WQ, Smith JC, Xu Y, Matheny ME, Siew EE,

Lindgren C, Herzig KH, Dedoussis G, Denny JC, Psaty BM, Howson JMM,

Munroe PB, Newton-Cheh C, Caulfield MJ, Elliott P, Gaziano JM, Concato J,

Wilson PWF, Tsao PS, Velez Edwards DR, Susztak K, O’Donnell CJ, Hung AM,

Edwards TL: Trans-ethnic association study of blood pressure determinants in over

750,000 individuals. Nat. Genet. 51: 51–62, 2019

23. Warren HR, Evangelou E, Cabrera CP, Gao H, Ren M, Mifsud B, Ntalla I,

Surendran P, Liu C, Cook JP, Kraja AT, Drenos F, Loh M, Verweij N, Marten J, 37

Karaman I, Segura Lepe MP, O’Reilly PF, Knight J, Snieder H, Kato N, He J,

Shyong Tai E, Abdullah Said M, Porteous D, Alver M, Poulter N, Farrall M,

Gansevoort RT, Padmanabhan S, Mägi R, Stanton A, Connell J, Bakker SJL,

Metspalu A, Shields DC, Thom S, Brown M, Sever P, Esko T, Hayward C, van der

Harst P, Saleheen D, Chowdhury R, Chambers JC, Chasman DI, Chakravarti A,

Newton-Cheh C, Lindgren CM, Levy D, Kooner JS, Keavney B, Tomaszewski M,

Samani NJ, Howson JMM, Tobin MD, Munroe PB, Ehret GB, Wain L V., Barnes

MR, Tzoulaki I, Caulfield MJ, Elliott P, Wain V, Vaez A, Jansen R, Joehanes R, van der Most PJ, Mesut Erzurumluoglu A, O’Reilly P, Rose LM, Verwoert GC,

Hottenga JJ, Strawbridge RJ, Esko T, Arking DE, Hwang SJ, Guo X, Kutalik Z,

Trompet S, Shrine N, Teumer A, Ried JS, Bis JC, Smith A V., Amin N, Nolte IM,

Lyytikäinen LP, Mahajan A, Wareham NJ, Hofer E, Joshi PK, Kristiansson K,

Traglia M, Havulinna AS, Goel A, Nalls MA, Sõber S, Vuckovic D, Luan J,

Fabiola Del Greco M, Ayers KL, Marrugat J, Ruggiero D, Lopez LM, Niiranen T,

Enroth S, Jackson AU, Nelson CP, Huffman JE, Zhang W, Gandin I, Harris SE,

Zemonik T, Lu Y, Shah N, de Borst MH, Mangino M, Prins BP, Campbell A, Li-

Gao R, Chauhan G, Oldmeadow C, Abecasis G, Abedi M, Barbieri CM, Batini C,

Blake T, Boehnke M, Bottinger EP, Braund PS, Brumat M, Campbell H, Cocca M,

Collins F, Cordell HJ, Damman JJ, Davies G, de Geus EJ, de Mutsert R, Deelen J,

Demirkale Y, Doney ASF, Dörr M, Ferreira T, Frånberg M, Giedraitis V, Gieger

C, Giulianini F, Gow AJ, Hamsten A, Harris TB, Hofman A, Holliday EG,

Jarvelin MR, Johansson Å, Johnson AD, Jousilahti P, Jula A, Kähönen M,

Kathiresan S, Khaw KT, Kolcic I, Koskinen S, Langenberg C, Larson M, Launer 38

LJ, Lehne B, Liewald DCM, Lin L, Lind L, Mach F, Mamasoula C, Menni C,

Milaneschi Y, Morgan A, Morris AD, Morrison AC, Munson PJ, Nandakumar P,

Nguyen QT, Nutile T, Oldehinkel AJ, Oostra BA, Org E, Palotie A, Paré G, Pattie

A, Penninx BWJH, Pramstaller PP, Raitakari OT, Rice K, Ridker PM, Riese H,

Ripatti S, Robino A, Rotter JI, Rudan I, Saba Y, Saint Pierre A, Sala CF, Sarin AP,

Schmidt R, Scott R, Seelen MA, Siscovick D, Sorice R, Stott DJ, Sundström J,

Swertz M, Taylor KD, Tzourio C, Uitterlinden AG, Völker U, Vollenweider P,

Wild S, Willemsen G, Wright AF, Yao J, Thériault S, Conen D, John A, Debette

S, Mook-Kanamori DO, Zeggini E, Spector TD, Palmer CNA, Vergnaud AC,

Loos RJF, Polasek O, Starr JM, Girotto G, Lindgren CM, Vitart V, Tuomilehto J,

Gyllensten U, Knekt P, Deary IJ, Ciullo M, Elosua R, Keavney BD, Hicks AA,

Scott RA, Gasparini P, Laan M, Liu YM, Watkins H, Hartman CA, Salomaa V,

Toniolo D, Perola M, Wilson JF, Schmidt H, Zhao JH, Lehtimäki T, van Duijn

CM, Gudnason V, Psaty BM, Peters A, Rettig R, James A, Wouter Jukema J,

Strachan DP, Palmas W, Ingelsson E, Boomsma DI, Franco OH, Bochud M,

Morris AP: Genome-wide association analysis identifies novel blood pressure loci

and offers biological insights into cardiovascular risk. Nat. Genet. 49: 403–415,

2017

24. Lash JP, Go AS, Appel LJ, He J, Ojo A, Rahman M, Townsend RR, Xie D, Cifelli

D, Cohan J, Fink JC, Fischer MJ, Gadegbeku C, Hamm LL, Kusek JW, Landis JR,

Narva A, Robinson N, Teal V, Feldman HI, Chronic Renal Insufficiency Cohort

(CRIC) Study Group: Chronic renal insufficiency cohort (CRIC) study: Baseline

characteristics and associations with kidney function. Clin. J. Am. Soc. Nephrol. 4: 39

1302–1311, 2009

25. Feldman HI, Appel LJ, Chertow GM, Cifelli D, Cizman B, Daugirdas J, Fink JC,

Franklin-Becker ED, Go AS, Hamm LL, He J, Hostetter T, Hsu C-Y, Jamerson K,

Joffe M, Kusek JW, Landis JR, Lash JP, Miller ER, Mohler ER, Muntner P, Ojo

AO, Rahman M, Townsend RR, Wright JT, Chronic Renal Insufficiency Cohort

(CRIC) Study Investigators: The Chronic Renal Insufficiency Cohort (CRIC)

Study: Design and Methods. J. Am. Soc. Nephrol. 14: S148-53, 2003

26. Wu J, Kraja AT, Oberman A, Lewis CE, Ellison RC, Arnett DK, Heiss G, Lalouel

JM, Turner ST, Hunt SC, Province MA, Rao DC: A summary of the effects of

antihypertensive medications on measured blood pressure. Am. J. Hypertens. 18:

935–942, 2005

27. Tobin MD, Sheehan NA, Scurrah KJ, Burton PR: Adjusting for treatment effects

in studies of quantitative traits: Antihypertensive therapy and systolic blood

pressure. Stat. Med. 24: 2911–2935, 2005

28. Parsa A, Kanetsky PA, Xiao R, Gupta J, Mitra N, Limou S, Xie D, Xu H,

Anderson AH, Ojo A, Kusek JW, Lora CM, Hamm LL, He J, Sandholm N, Jeff J,

Raj DE, Böger CA, Bottinger E, Salimi S, Parekh RS, Adler SG, Langefeld CD,

Bowden DW, Groop P-H, Forsblom C, Freedman BI, Lipkowitz M, Fox CS,

Winkler CA, Feldman HI: Genome-Wide Association of CKD Progression: The

Chronic Renal Insufficiency Cohort Study. J. Am. Soc. Nephrol. 28: 923–934,

2017

29. Flint AC, Conell C, Ren X, Banki NM, Chan SL, Rao VA, Melles RB, Bhatt DL:

Effect of Systolic and Diastolic Blood Pressure on Cardiovascular Outcomes. N. 40

Engl. J. Med. 381: 243–251, 2019

30. Pazoki R, Dehghan A, Evangelou E, Warren H, Gao H, Caulfield M, Elliott P,

Tzoulaki I: Genetic predisposition to high blood pressure and lifestyle factors:

Associations with midlife blood pressure levels and cardiovascular events.

Circulation 137: 653–661, 2018

31. Blood Pressure Lowering Treatment Trialists’ Collaboration: Blood pressure

lowering and major cardiovascular events in people with and without chronic

kidney disease: Meta-analysis of randomised controlled trials. BMJ 347: 2013

32. Yu Z, Coresh J, Qi G, Grams M, Boerwinkle E, Snieder H, Teumer A, Pattaro C,

Köttgen A, Chatterjee N, Tin A: A bidirectional Mendelian randomization study

supports causal effects of kidney function on blood pressure. Kidney Int. 98: 708–

716, 2020

33. Zhang F, Liu H, Liu D, Liu Y, Li H, Tan X, Liu F, Peng Y, Zhang H: Effects of

RAAS Inhibitors in Patients with Kidney Disease. Curr. Hypertens. Rep. 19: 72,

2017

34. Taal MW, Brenner BM: Renoprotective benefits of RAS inhibition: From ACEI to

angiotensin II antagonists. Kidney Int. 57: 1803–1817, 2000

35. Li YR, Keating BJ: Trans-ethnic genome-wide association studies: Advantages

and challenges of mapping in diverse populations. Genome Med. 6: 1–14, 2014

36. Marigorta UM, Navarro A: High Trans-ethnic Replicability of GWAS Results

Implies Common Causal Variants. PLoS Genet. 9: 2013

37. Nierenberg JL, Li C, He J, Gu D, Chen J, Lu X, Li J, Wu X, Gu CC, Hixson JE,

Rao DC, Kelly TN: Blood Pressure Genetic Risk Score Predicts Blood Pressure 41

Responses to Dietary Sodium and Potassium: The GenSalt Study (Genetic

Epidemiology Network of Salt Sensitivity). Hypertens. (Dallas, Tex. 1979) 70:

1106–1112, 2017

38. Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ: Clinical use of

current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51:

584–591, 2019

42

Table 1. Baseline Characteristics of CRIC Participants According to Ancestry (N=3,074). Characteristic African Ancestry European Ancestry (n=1,493) (n=1,581) Age, years, mean (SD) 58 (11) 59 (11) Male, n (%) 726 (49) 945 (60) High school graduate, n (%) 1,095 (73) 1,493 (94) Current cigarette smokers, n (%) 285 (19) 148 (9) Ever cigarette smokers, n (%) 848 (57) 889 (56) Current alcohol use, n (%) 794 (53) 1,187 (75) Body mass index, kg/m2, mean (SD) 34 (8) 31 (7) Obesity*, n (%) 949 (64) 785 (50) Serum cholesterol, mg/dL, mean (SD) 186 (46) 180 (42) Low density lipoprotein, mg/dL, mean (SD) 106 (37) 99 (32) Uses lipid lowering medication, n (%) 830 (56) 995 (63) Diabetes, n (%) 771 (52) 632 (40) Systolic BP, mean (SD) 147 (24) 135 (20) Diastolic BP, mean (SD) 83 (14) 78 (12) Pulse pressure, mean (SD) 64 (20) 57 (17) Hypertension†, n (%) 1,454 (97) 1,435 (91) Uses antihypertensive medication, n (%) 1,415 (96) 1,391 (89) Number of antihypertensive medications, 3.0 (1.5) 2.3 (1.4) mean (SD) Serum creatinine, mg/dL, mean (SD) 2.0 (0.7) 1.7 (0.5) Estimated GFR, ml/min/1.73m2, mean (SD) 47 (18) 50 (19) Urinary protein to creatinine ratio† 1.0 (2.1) 0.7 (2.0) Systolic BP GRS, mean (SD)‡ 170 (3) 173 (4) Diastolic BP GRS, mean (SD)‡ 101 (2) 102 (2) Pulse pressure GRS, mean (SD)‡ 39 (1) 40 (2) * Body mass index ≥ 30 kg/m2; † BP ≥ 130/80 or use of antihypertensive medications † Ratio of urine protein concentration to urine creatinine concentration from 24-hour urine or a spot urine sample. When both 24-hour urine and spot urines are available, priority is given to the 24-hour urine. The ratio of urine total protein (mg/dL) to urine creatinine (mg/dL) is unitless. ‡ Weighted sum of risk alleles for SBP, DBP, and PP, respectively, using previously estimated effect sizes as weights. BP=blood pressure; GFR=glomerular filtration rate; GRS=genetic risk score; SD=standard deviation.

43

Table 2. Hazard Ratios for Primary Endpoints per SD Increase in GRS and Comparing the Top and Bottom GRS Quartiles African Ancestry European Ancestry Outcome / GRS Continuous Quartile Continuous Quartile HR (95% CI)* P HR (95% CI)† P HR (95% CI)* P HR (95% CI)† P CVD Event‡ SBP 1.10 (1.01-1.20) 0.03 1.21 (0.95-1.53) 0.12 1.15 (1.04-1.27) 6.3×10-3 1.41 (1.06-1.88) 0.02 DBP 1.09 (1.00-1.18) 0.05 1.15 (0.90-1.47) 0.25 1.09 (0.99-1.20) 0.09 1.18 (0.89-1.56) 0.25 PP 1.06 (0.97-1.15) 0.22 1.18 (0.92-1.52) 0.18 1.11 (1.01-1.23) 0.04 1.33 (1.00-1.76) 0.05 CKD Progression Event§ SBP 1.00 (0.92-1.08) 0.99 0.93 (0.75-1.16) 0.55 1.07 (0.97-1.18) 0.19 1.06 (0.80-1.40) 0.67 DBP 1.03 (0.95-1.11) 0.46 1.12 (0.90-1.40) 0.31 1.00 (0.90-1.10) 0.93 0.96 (0.74-1.25) 0.76 PP 0.97 (0.89-1.05) 0.40 0.91 (0.73-1.13) 0.40 1.07 (0.97-1.19) 0.17 1.18 (0.90-1.55) 0.24 * Results of Cox proportional hazards models, per SD increase in BP GRS, adjusted for age, sex, CRIC study site, and ancestry principal components. † Results of Cox proportional hazards models, comparing top quartile BP GRS to bottom quartile BP GRS, adjusted for age, sex, CRIC study site, and ancestry principal components. ‡ Composite of myocardial infarction, stroke, and congestive heart failure. § Halving of eGFR or ESKD CI=confidence interval; CVD=cardiovascular disease, DBP=diastolic blood pressure; eGFR=estimated glomerular filtration rate; ESKD=end stage kidney disease; GRS=genetic risk score; HR=hazard ratio; PP=pulse pressure; SBP=systolic blood pressure; SD=standard deviation.

44

Table 3. Kidney Disease Progression per SD Increase in GRS and Comparing the Top and Bottom GRS Quartiles African Ancestry European Ancestry Outcome / GRS Continuous Quartile Continuous Quartile β (SE)* P β (SE)* P β (SE)* P β (SE)* P eGFR slope SBP GRS -0.04 (0.32) 0.90 -0.21 (0.91) 0.82 0.10 (0.29) 0.73 0.32 (0.82) 0.69 DBP GRS -0.01 (0.32) 0.98 -0.14 (0.92) 0.88 0.10 (0.29) 0.73 0.40 (0.83) 0.63 PP GRS -0.03 (0.33) 0.92 -0.13 (0.92) 0.89 0.05 (0.29) 0.86 0.27 (0.83) 0.74 Urinary protein slope SBP GRS -0.02 (0.03) 0.56 -0.04 (0.09) 0.64 0.02 (0.03) 0.46 0.05 (0.09) 0.60 DBP GRS -0.02 (0.03) 0.57 -0.03 (0.09) 0.79 0.04 (0.03) 0.18 0.09 (0.09) 0.32 PP GRS -0.03 (0.03) 0.41 -0.07 (0.09) 0.46 0.01 (0.03) 0.75 0.04 (0.09) 0.67 * Results of linear models, per SD increase in BP GRS, adjusted for age, sex, CRIC study site, and ancestry principal components. † Results of linear models, comparing top quartile BP GRS to bottom quartile BP GRS, adjusted for age, sex, CRIC study site, and ancestry principal components. DBP=diastolic blood pressure; eGFR=estimated glomerular filtration rate; GRS=genetic risk score; PP=pulse pressure; SBP=systolic blood pressure; SE=standard error

45

Figure legend

Figure. Hazard Ratios for Primary Endpoints per Quartile Increase in GRS

Hazard ratios per quartile increase in GRS compared to quartile 1, for composite of cardiovascular disease and death and composite of ESKD or halving of eGFR, stratified by ancestry, and adjusted for age, sex, CRIC study site, and ancestry principal components. P values are for trend across quartiles.

CVD=cardiovascular disease; DBP=diastolic blood pressure; eGFR=estimated glomerular filtration rate; ESKD=end stage kidney disease; GRS=genetic risk score; PP= pulse pressure; SBP=systolic blood pressure.

Figure 1

Blood Pressure Genetic Risk Score Associates with Cardiovascular Disease but not Chronic Kidney Disease Progression: Findings from the CRIC Study

Jovia L. Nierenberg,1 Amanda H. Anderson,1 Jiang He,1,2 Afshin Parsa,3 Anand Srivastava,4 Jordana B. Cohen,5 Santosh L. Saraf,6 Mahboob Rahman,7 Sylvia E. Rosas,8 Tanika N. Kelly1, and the CRIC Study Investigators*

Affiliations: 1. Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA. 2. Department of Medicine, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, 70112, USA. 3. Division of Kidney, Urologic, and Hematologic Diseases, The National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, Maryland, USA. 4. Center for Translational Metabolism and Health, Institute for Public Health and Medicine, Division of Nephrology and Hypertension, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA. 5. Renal-Electrolyte and Hypertension Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA. 6. Division of Hematology & Oncology, Department of Medicine, Comprehensive Sickle Cell Center, University of Illinois at Chicago, Chicago, Illinois, USA. 7. Division of Nephrology, Department of Medicine, Case Western Reserve University, Cleveland, Ohio, USA. 8. Kidney and Hypertension Unit, Joslin Diabetes Center, Harvard Medical School, Boston, Massachusetts, USA.

* Lawrence J. Appel, MD, MPH, Jing Chen, MD, MMSc, MSc, Harold I. Feldman, MD, MSCE, Alan S. Go, MD, James P. Lash, MD, Robert G. Nelson, MD, PhD, MS, Mahboob Rahman, MD, Panduranga S. Rao, MD, Vallabh O Shah, PhD, MS, Raymond R. Townsend, MD, Mark L. Unruh, MD, MS

Supplementary Materials

2

Table of Contents Supplementary Methods ...... 3 Supplementary Table 1. Baseline Characteristics of African Ancestry CRIC Participants According to SBP GRS Quartile ...... 5 Supplementary Table 2. Baseline Characteristics of European Ancestry CRIC Participants According to SBP GRS Quartile ...... 7 Supplementary Table 3. Associations between Blood Pressure GRS and Baseline Blood Pressure and Kidney Function, per SD Increase in GRS and Comparing the Top and Bottom GRS Quartiles ...... 9 Supplementary Table 4. Median Baseline Blood Pressure, per Quartile Increase in Respective Blood Pressure GRS ...... 10 Supplementary Table 5. Median number of Antihypertensive Medications per Quartile Increase in GRS ...... 11 Supplementary Table 6. Associations between Blood Pressure GRS and Baseline Unimputed Blood Pressure*, per SD Increase in GRS and Comparing the Top and Bottom GRS Quartiles ...... 12 Supplementary Table 7. Hazard Ratios for Primary Endpoints per SD Increase in GRS and Comparing the Top and Bottom GRS Quartiles, in Models with Additional Adjustment for Baseline BMI, LDL, and Lipid Lowering Medication ...... 13 Supplementary Figure 1. Hazard Ratios* for CVD Composite and its Component Endpoints, per SD Increase in GRS, Among African Ancestry Participants ...... 14 Supplementary Figure 2. Hazard Ratios* for CVD Composite and its Component Endpoints per SD Increase in GRS, Among European Ancestry Participants ...... 15 Supplementary Table 8. Comparison of Hazard Ratios per Standard Deviation of SBP and the SBP GRS on Clinical Outcomes ...... 16 References ...... 17

3

Supplementary Methods

Data Collection and Study Covariables Information was obtained on demographic characteristics, medical history and medication use at the baseline CRIC examination. Three seated resting BP readings, obtained by study staff using a standardized method, were obtained at the baseline visit and the mean values of systolic BP (SBP) and diastolic BP (DBP) were calculated from these measures.1 To impute pretreatment BP levels in participants on antihypertensive medication,2 BP was adjusted by adding 15 and 10 mm Hg to SBP and DBP, respectively.3 Pulse pressure (PP) was calculated as the difference between the adjusted SBP and DBP values. Study participants attended annual clinic visits and underwent telephone interview every 6 months between in-person visits. Cardiovascular clinical outcomes were assessed using a standard medical event questionnaire at all follow-up contacts. Medical records were requested for event verification, and 2 physicians adjudicated each cardiovascular event and classified them as either probable or definite.4 Serum creatinine was measured from fasting blood samples at each in-person study visit. Serum creatinine was measured annually using an enzymatic method on an Ortho Vitros 950 (Ortho Biotech Products, Bridgewater, NJ) through October of 2008 and by the Jaffe method on a Beckman Synchron System (Beckman Coulter, Inc., Brea CA) thereafter. All serum creatinine measures were standardized to isotope dilution mass spectrometry traceable values. Serum cystatin C was measured using a particle–enhanced nephelometric immunoassay on a Dade-Behring BNII (Dade Behring, Inc., Newark, NJ). For the baseline examination and each annual in-person visit, eGFR was calculated according to a the 2012 CKD-EPI equation, which included serum creatinine, cystatin C, age, gender and race.5 Urinary protein-to-creatinine ratio (UPCR) was assessed using either 24-hour urine collections or spot urine samples, using previously described methods.6

Genotyping and Imputation Genotyping and imputation methods in the CRIC study have been described previously.7 In brief, DNA was extracted from buffy coat samples from 3,635 CRIC Study participants and genotyped using the Illumina HumanOmni 1-Quad array. A total of 3,527 samples passed quality control metrics, which included the removal of samples with sex discordance, reduced or excess genotypic heterozygosity, cryptic relatedness, or sample ID mismatches. Using principal components, subgroups of 1,493 and 1,581 participants were identified with African and European ancestry, respectively (with 453 participants of other ancestral groups excluded). Single nucleotide polymorphism (SNP) quality control was also performed in each subgroup, excluding variants with minor allele frequency < 0.03, deviations from Hardy-Weinberg equilibrium (P<1×10-7), and genotype call rate < 0.95. Imputation of 839,205 and 739,978 SNPs was then conducted, for African and European ancestry participants, respectively, using the 1000

4

Genomes mixed race/ethnicity (ALL) reference panel (NCBI build 37, release date March of 2012; n=1,093) with IMPUTE software.8,9 Following imputation, variants with quality score < 0.8 were further excluded from the genomics dataset.

5

Supplementary Table 1. Baseline Characteristics of African Ancestry CRIC Participants According to SBP GRS Quartile Q1 Q2 Q3 Q4 (N=374) (N=373) (N=373) (N=373) Age, years, mean (SD) 58 (11) 58 (10) 57 (12) 57 (11) Male, n (%) 188 (50) 174 (47) 189 (51) 175 (47) High school graduate, n (%) 257 (69) 279 (75) 295 (79) 264 (71) Current cigarette smokers, n (%) 77 (21) 69 (18) 72 (19) 67 (18) Ever cigarette smokers, n (%) 220 (59) 209 (56) 219 (59) 200 (54) Current alcohol use, n (%) 189 (51) 209 (56) 202 (54) 194 (52) Body mass index, kg/m2, mean (SD) 34 (9) 33 (8) 34 (8) 34 (8) Obesity*, n (%) 247 (66) 220 (59) 242 (65) 240 (64) Serum cholesterol, mg/dL, mean (SD) 186 (46) 185 (45) 185 (46) 187 (48) Low density lipoprotein, mg/dL, mean (SD) 107 (38) 105 (34) 104 (37) 108 (39) Uses lipid lowering medication, n (%) 207 (55) 197 (53) 223 (60) 203 (54) Diabetes, n (%) 211 (56) 178 (48) 193 (52) 189 (51) Systolic BP, mean (SD) 147 (25) 146 (22) 148 (23) 147 (25) Diastolic BP, mean (SD) 84 (14) 83 (14) 84 (14) 83 (15) Pulse pressure, mean (SD) 64 (21) 64 (19) 64 (20) 64 (21) Hypertension†, n (%) 364 (97) 362 (97) 363 (97) 365 (98) Uses antihypertensive medication, n (%) 357 (95) 350 (94) 352 (94) 356 (95) Number of antihypertensive medications, mean (SD) 2.9 (1.5) 2.9 (1.5) 3.1 (1.5) 3.1 (1.4) Serum creatinine, mg/dL, mean (SD) 2.0 (0.7) 1.9 (0.6) 1.9 (0.7) 2.0 (0.8) Estimated GFR, ml/min/1.73m2, mean (SD) 44 (17) 47 (17) 49 (20) 46 (19) Urinary protein to creatinine ratio† 1.0 (1.7) 0.9 (2.0) 0.9 (2.1) 1.1 (2.5) Systolic BP GRS, mean (SD)‡ 166 (2) 169 (1) 171 (1) 174 (2) Diastolic BP GRS, mean (SD)‡ 99 (1) 100 (1) 101 (1) 103 (1) Pulse pressure GRS, mean (SD)‡ 38 (1) 38 (1) 39 (1) 40 (1) * Body mass index ≥ 30 kg/m2; † BP ≥ 130/80 or use of antihypertensive medications † Ratio of urine protein concentration to urine creatinine concentration from 24-hour urine or a spot urine sample. When both 24-hour urine and spot urines are available,

6 priority is given to the 24-hour urine. The ratio of urine total protein (mg/dL) to urine creatinine (mg/dL) is unitless. ‡ Weighted sum of risk alleles for SBP, DBP, and PP, respectively, using previously estimated effect sizes as weights. BP=blood pressure; GFR=glomerular filtration rate; GRS=genetic risk score; Q1=first quartile; Q2=second quartile; Q3=third quartile; Q4=fourth quartile; SD=standard deviation.

7

Supplementary Table 2. Baseline Characteristics of European Ancestry CRIC Participants According to SBP GRS Quartile Q1 Q2 Q3 Q4 (N=396) (N=395) (N=395) (N=395) Age, years, mean (SD) 60 (10) 58 (11) 59 (11) 57 (12) Male, n (%) 234 (59) 248 (63) 219 (55) 244 (62) High school graduate, n (%) 373 (94) 370 (94) 374 (95) 376 (95) Current cigarette smokers, n (%) 50 (13) 35 (9) 29 (7) 34 (9) Ever cigarette smokers, n (%) 230 (58) 214 (54) 236 (60) 209 (53) Current alcohol use, n (%) 294 (74) 303 (77) 297 (75) 293 (74) Body mass index, kg/m2, mean (SD) 31 (8) 31 (7) 31 (8) 31 (7) Obesity*, n (%) 197 (50) 196 (50) 198 (50) 194 (49) Serum cholesterol, mg/dL, mean (SD) 178 (41) 184 (44) 180 (42) 178 (41) Low density lipoprotein, mg/dL, mean (SD) 99 (33) 103 (35) 99 (31) 97 (31) Uses lipid lowering medication, n (%) 247 (62) 238 (60) 252 (64) 258 (65) Diabetes, n (%) 163 (41) 156 (39) 150 (38) 163 (41) Systolic BP, mean (SD) 133 (20) 135 (20) 137 (20) 135 (20) Diastolic BP, mean (SD) 77 (12) 78 (12) 78 (12) 78 (11) Pulse pressure, mean (SD) 56 (17) 57 (17) 59 (18) 57 (17) Hypertension†, n (%) 341 (86) 352 (89) 369 (93) 373 (94) Uses antihypertensive medication, n (%) 334 (84) 338 (86) 356 (90) 363 (92) Number of antihypertensive medications, mean (SD) 2.1 (1.5) 2.2 (1.5) 2.4 (1.4) 2.5 (1.4) Serum creatinine, mg/dL, mean (SD) 1.7 (0.5) 1.7 (0.5) 1.7 (0.5) 1.7 (0.5) Estimated GFR, ml/min/1.73m2, mean (SD) 50 (19) 51 (19) 49 (18) 50 (19) Urinary protein to creatinine ratio† 0.6 (2.0) 0.7 (1.9) 0.7 (2.5) 0.6 (1.4) Systolic BP GRS, mean (SD)‡ 169 (2) 172 (1) 175 (1) 178 (2) Diastolic BP GRS, mean (SD)‡ 100 (2) 101 (1) 103 (1) 104 (2) Pulse pressure GRS, mean (SD)‡ 39 (1) 40 (1) 40 (1) 41 (1) * Body mass index ≥ 30 kg/m2; † BP ≥ 130/80 or use of antihypertensive medications † Ratio of urine protein concentration to urine creatinine concentration from 24-hour urine or a spot urine sample. When both 24-hour urine and spot urines are available,

8 priority is given to the 24-hour urine. The ratio of urine total protein (mg/dL) to urine creatinine (mg/dL) is unitless. ‡ Weighted sum of risk alleles for SBP, DBP, and PP, respectively, using previously estimated effect sizes as weights. BP=blood pressure; GFR=glomerular filtration rate; GRS=genetic risk score; Q1=first quartile; Q2=second quartile; Q3=third quartile; Q4=fourth quartile; SD=standard deviation.

9

Supplementary Table 3. Associations between Blood Pressure GRS and Baseline Blood Pressure and Kidney Function, per SD Increase in GRS and Comparing the Top and Bottom GRS Quartiles African Ancestry European Ancestry Outcome Continuous Quartile Continuous Quartile Beta (SE)* P Beta (SE)† P Beta (SE)* P Beta (SE)† P Blood Pressure SBP 0.02 (0.60) 0.97 -0.23 (1.70) 0.89 1.29 (0.48) 7.70×10-3 2.37 (1.36) 0.08 DBP 0.37 (0.35) 0.28 0.66 (0.98) 0.50 0.73 (0.28) 9.10×10-3 2.48 (0.78) 1.5×10-3 PP 0.49 (0.47) 0.30 1.60 (1.34) 0.23 1.38 (0.40) 6.10×10-4 3.35 (1.14) 3.2×10-3 eGFR SBP 0.47 (0.44) 0.29 1.88 (1.24) 0.13 -0.62 (0.43) 0.15 -0.18 (1.21) 0.88 DBP 0.18 (0.44) 0.68 0.01 (1.25) 1.00 -0.62 (0.43) 0.15 -1.79 (1.20) 0.13 PP 0.23 (0.44) 0.60 1.03 (1.26) 0.41 -0.41 (0.43) 0.34 -1.43 (1.21) 0.24 Urinary Protein to Creatinine Ratio SBP 0.03 (0.06) 0.62 0.08 (0.16) 0.62 -0.05 (0.05) 0.31 -0.18 (0.14) 0.20 DBP 0.11 (0.06) 0.05 0.24 (0.16) 0.13 -0.09 (0.05) 0.08 -0.22 (0.14) 0.13 PP 0.02 (0.06) 0.76 0.02 (0.16) 0.90 0.01 (0.05) 0.82 -0.06 (0.14) 0.67 * Results of linear models, per SD increase in BP GRS, adjusted for age, sex, CRIC study site, and ancestry principal components. † Results of linear models, comparing top quartile BP GRS to bottom quartile BP GRS, adjusted for age, sex, CRIC study site, and ancestry principal components. DBP=diastolic blood pressure; eGFR=estimated glomerular filtration rate; GRS=genetic risk score; PP=pulse pressure; SBP=systolic blood pressure; SD=standard deviation; SE=standard error.

10

Supplementary Table 4. Median Baseline Blood Pressure, per Quartile Increase in Respective Blood Pressure GRS Ancestry / GRS Q1 Q2 Q3 Q4 P African Ancestry SBP 146.82 145.9 148.31 146.58 0.55 DBP 83.21 83.22 82.51 83.87 0.59 PP 62.85 63.84 63.68 64.45 0.69

European Ancestry SBP 133.52 136.02 136.85 135.9 0.08 DBP 75.28 78.33 77.86 77.77 3.1×10-4 PP 56.81 57.45 58.62 60.16 0.02 Association per quartile increase in GRS, for baseline SBP, DBP, and PP, respectively, stratified by ancestry, and adjusted for age, sex, CRIC study site, and ancestry principal components. P values are for trend across quartiles. DBP=diastolic blood pressure; GRS=genetic risk score; PP=pulse pressure; Q1=first quartile; Q2=second quartile; Q3=third quartile; Q4=fourth quartile; SBP=systolic blood pressure.

11

Supplementary Table 5. Median number of Antihypertensive Medications per Quartile Increase in GRS Ancestry / GRS Q1 Q2 Q3 Q4 P African Ancestry SBP 2.91 2.83 3.14 3.11 6.2×10-3 DBP 2.87 3.02 3.04 3.06 0.29 PP 2.95 2.92 2.97 3.15 0.13

European Ancestry SBP 2.06 2.18 2.34 2.43 7.2×10-4 DBP 2.12 2.13 2.33 2.45 9.1×10-4 PP 2.05 2.19 2.26 2.52 2.2×10-5 Association per quartile increase in GRS, for baseline number of antihypertensive medications, stratified by ancestry, and adjusted for age, sex, CRIC study site, and ancestry principal components. P values are for trend across quartiles. DBP=diastolic blood pressure; GRS=genetic risk score; PP=pulse pressure; Q1=first quartile; Q2=second quartile; Q3=third quartile; Q4=fourth quartile; SBP=systolic blood pressure.

12

Supplementary Table 6. Associations between Blood Pressure GRS and Baseline Unimputed Blood Pressure*, per SD Increase in GRS and Comparing the Top and Bottom GRS Quartiles African Ancestry European Ancestry Outcome Continuous Quartile Continuous Quartile Beta (SE)* P Beta (SE)* P Beta (SE)* P Beta (SE)* P Unadjusted Blood Pressure SBP -0.28 (0.59) 0.63 -0.75 (1.65) 0.65 0.31 (0.46) 0.50 0.38 (1.28) 0.77 DBP 0.36 (0.34) 0.29 0.62 (0.95) 0.52 0.57 (0.27) 0.04 1.99 (0.76) 8.8×10-3 PP 0.28 (0.46) 0.54 1.04 (1.30) 0.43 0.78 (0.39) 0.04 1.54 (1.09) 0.16 * Blood pressure values were not adjusted for use of antihypertension medication, however, number of antihypertensive medications was included in the model as a covariate. † Results of linear models, per SD increase in BP GRS, adjusted for age, sex, CRIC study site, number of antihypertensive medications, and ancestry principal components. ‡ Results of linear models, comparing top quartile BP GRS to bottom quartile BP GRS, adjusted for age, sex, CRIC study site, number of antihypertensive medications, and ancestry principal components. DBP=diastolic blood pressure; GRS=genetic risk score; PP=pulse pressure; SBP=systolic blood pressure; SD=standard deviation; SE=standard error.

13

Supplementary Table 7. Hazard Ratios for Primary Endpoints per SD Increase in GRS and Comparing the Top and Bottom GRS Quartiles, in Models with Additional Adjustment for Baseline BMI, LDL, and Lipid Lowering Medication African Ancestry European Ancestry Outcome / GRS Continuous Quartile Continuous Quartile HR (95% CI)* P HR (95% CI)† P HR (95% CI)* P HR (95% CI)† P CVD Event‡ SBP 1.11 (1.02-1.21) 0.02 1.24 (0.97-1.57) 0.08 1.13 (1.02-1.25) 0.02 1.39 (1.05-1.86) 0.02 DBP 1.10 (1.01-1.20) 0.03 1.18 (0.92-1.51) 0.19 1.08 (0.98-1.19) 0.11 1.17 (0.89-1.56) 0.26 PP 1.06 (0.97-1.16) 0.19 1.20 (0.94-1.54) 0.15 1.10 (1.00-1.22) 0.06 1.26 (0.95-1.68) 0.11 CKD Progression Event§ SBP 1.00 (0.92-1.08) 0.92 0.94 (0.75-1.17) 0.56 1.04 (0.95-1.15) 0.39 1.06 (0.81-1.41) 0.66 DBP 1.03 (0.96-1.12) 0.41 1.12 (0.89-1.40) 0.33 0.98 (0.89-1.09) 0.74 0.96 (0.74-1.25) 0.76 PP 0.97 (0.90-1.05) 0.49 0.93 (0.74-1.15) 0.50 1.05 (0.95-1.16) 0.38 1.09 (0.83-1.44) 0.52 * Results of Cox proportional hazards models, per SD increase in BP GRS, adjusted for age, sex, CRIC study site, BMI, LDL, lipid lowering medications, and ancestry principal components. † Results of Cox proportional hazards models, comparing top quartile BP GRS to bottom quartile BP GRS, adjusted for age, sex, CRIC study site, and ancestry principal components. ‡ Composite of myocardial infarction, stroke, and congestive heart failure. § Halving of eGFR or ESKD BMI=body mass index; CI=confidence interval; CVD=cardiovascular disease, DBP=diastolic blood pressure; eGFR=estimated glomerular filtration rate; ESKD=end stage kidney disease; GRS=genetic risk score; HR=hazard ratio; LDL=low density lipoprotein; PP=pulse pressure; SBP=systolic blood pressure; SD=standard deviation.

14

Supplementary Figure 1. Hazard Ratios* for CVD Composite and its Component Endpoints, per SD Increase in GRS, Among African Ancestry Participants

* Results of Cox proportional hazards models, per SD increase in BP GRS, adjusted for age, sex, study cite and ancestry principal components. AA=African ancestry; CVD=cardiovascular disease; DBP=diastolic blood pressure; GRS=genetic risk score; MI=myocardial infarction; PP=pulse pressure; SBP=systolic blood pressure; SD=standard deviation.

15

Supplementary Figure 2. Hazard Ratios* for CVD Composite and its Component Endpoints per SD Increase in GRS, Among European Ancestry Participants

* Results of Cox proportional hazards models, per SD increase in BP GRS, adjusted for age, sex, study cite and ancestry principal components. CVD=cardiovascular disease; DBP=diastolic blood pressure; EA=European ancestry; GRS=genetic risk score; MI=myocardial infarction; PP=pulse pressure; SBP=systolic blood pressure; SD=standard deviation.

16

Supplementary Table 8. Comparison of Hazard Ratios per Standard Deviation of SBP and the SBP GRS on Clinical Outcomes African Ancestry European Ancestry HR (95% CI)* HR (95% CI)* CVD Composite SBP 1.30 (1.19, 1.42) 1.38 (1.25, 1.53) SBP, adjusted for SBP GRS 1.30 (1.19, 1.41) 1.36 (1.23, 1.51) SBP GRS 1.10 (1.01, 1.20) 1.15 (1.04, 1.27) SBP GRS, adjusted for SBP 1.08 (0.99, 1.18) 1.10 (1.00, 1.22) Halving of eGFR or ESKD SBP 1.60 (1.48, 1.73) 1.65 (1.50, 1.82) SBP, adjusted for SBP GRS 1.60 (1.48, 1.73) 1.65 (1.50, 1.83) SBP GRS 1.00 (0.92, 1.08) 1.07 (0.97, 1.18) SBP GRS, adjusted for SBP 0.99 (0.92, 1.07) 0.99 (0.89, 1.10) * Results of Cox proportional hazards models, adjusted for age, sex, study cite and ancestry principal components. CI=confidence interval; CVD=cardiovascular disease; eGFR=estimated glomerular filtration rate; ESKD=end stage kidney disease; GRS=genetic risk score; HR=hazard ratio; SBP=systolic blood pressure.

17

References

1. Muntner P, Anderson A, Charleston J, Chen Z, Ford V, Makos G, O’Connor A, Perumal K, Rahman M, Steigerwalt S, Teal V, Townsend R, Weir M, Wright JT: Hypertension Awareness, Treatment, and Control in Adults With CKD: Results From the Chronic Renal Insufficiency Cohort (CRIC) Study. Am. J. Kidney Dis. 55: 441–451, 2010 2. Wu J, Kraja AT, Oberman A, Lewis CE, Ellison RC, Arnett DK, Heiss G, Lalouel JM, Turner ST, Hunt SC, Province MA, Rao DC: A summary of the effects of antihypertensive medications on measured blood pressure. Am. J. Hypertens. 18: 935–942, 2005 3. Tobin MD, Sheehan NA, Scurrah KJ, Burton PR: Adjusting for treatment effects in studies of quantitative traits: Antihypertensive therapy and systolic blood pressure. Stat. Med. 24: 2911–2935, 2005 4. Lash JP, Go AS, Appel LJ, He J, Ojo A, Rahman M, Townsend RR, Xie D, Cifelli D, Cohan J, Fink JC, Fischer MJ, Gadegbeku C, Hamm LL, Kusek JW, Landis JR, Narva A, Robinson N, Teal V, Feldman HI, Chronic Renal Insufficiency Cohort (CRIC) Study Group: Chronic renal insufficiency cohort (CRIC) study: Baseline characteristics and associations with kidney function. Clin. J. Am. Soc. Nephrol. 4: 1302–1311, 2009 5. Inker LA, Schmid CH, Tighiouart H, Eckfeldt JH, Feldman HI, Greene T, Kusek JW, Manzi J, Van Lente F, Zhang YL, Coresh J, Levey AS: Estimating glomerular filtration rate from serum creatinine and cystatin C. N. Engl. J. Med. 367: 20–29, 2012 6. Hsu CY, Hsu RK, Liu KD, Yang J, Anderson A, Chen J, Chinchilli VM, Feldman HI, Garg AX, Hamm L, Himmelfarb J, Kaufman JS, Kusek JW, Parikh CR, Ricardo AC, Rosas SE, Saab G, Sha D, Siew ED, Sondheimer J, Taliercio JJ, Yang W, Go AS: Impact of AKI on urinary protein excretion: Analysis of two prospective cohorts. J. Am. Soc. Nephrol. 30: 1271–1281, 2019 7. Parsa A, Kanetsky PA, Xiao R, Gupta J, Mitra N, Limou S, Xie D, Xu H, Anderson AH, Ojo A, Kusek JW, Lora CM, Hamm LL, He J, Sandholm N, Jeff J, Raj DE, Böger CA, Bottinger E, Salimi S, Parekh RS, Adler SG, Langefeld CD, Bowden DW, Groop P-H, Forsblom C, Freedman BI, Lipkowitz M, Fox CS, Winkler CA, Feldman HI: Genome-Wide Association of CKD Progression: The Chronic Renal Insufficiency Cohort Study. J. Am. Soc. Nephrol. 28: 923–934, 2017 8. Howie BN, Donnelly P, Marchini J: A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5: 2009 9. Howie B, Marchini J, Stephens M: Genotype imputation with thousands of genomes. G3 Genes, Genomes, Genet. 1: 457–470, 2011