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Figure 1.
Prevalence of Proton Pump Inhibitor (PPI) Ever Use Over Time in the Atherosclerosis Risk in Communities Study
Prevalence of Proton Pump Inhibitor (PPI) Ever Use Over Time in the Atherosclerosis Risk in Communities Study
Figure 2.
Association Between Proton Pump Inhibitor Use and Incident Kidney Disease Stratified By Subgroups
Association Between Proton Pump Inhibitor Use and Incident Kidney Disease Stratified By Subgroups

Young refers to an age that is below the cohort median (62 years in the Atherosclerosis Risk in Communities study and 50 years in the Geisinger Health System replication cohort). ACE-I indicates angiotensin-converting enzyme inhibitor; AKI, acute kidney injury; ARB, angiotensin receptor blocker; CKD, chronic kidney disease; and PPI, proton pump inhibitor.

Table 1.  
Baseline Characteristics of the Study Populations
Baseline Characteristics of the Study Populations
Table 2.  
Proton Pump Inhibitor Use and the Risk of Incident Chronic Kidney Diseasea
Proton Pump Inhibitor Use and the Risk of Incident Chronic Kidney Diseasea
Table 3.  
Proton Pump Inhibitor Use and the Risk of Incident Acute Kidney Injurya
Proton Pump Inhibitor Use and the Risk of Incident Acute Kidney Injurya
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United States Renal Data System.  2014 USRDS Annual Data Report: an overview of the epidemiology of kidney disease in the United States.http://www.usrds.org/2014/view/Default.aspx. Published 2014. Accessed April 3, 2015.
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Go  AS, Chertow  GM, Fan  D, McCulloch  CE, Hsu  CY.  Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization.  N Engl J Med. 2004;351(13):1296-1305.PubMedGoogle ScholarCrossref
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Grams  ME, Juraschek  SP, Selvin  E,  et al.  Trends in the prevalence of reduced GFR in the United States: a comparison of creatinine- and cystatin C–based estimates.  Am J Kidney Dis. 2013;62(2):253-260.PubMedGoogle ScholarCrossref
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Maher  RL, Hanlon  J, Hajjar  ER.  Clinical consequences of polypharmacy in elderly.  Expert Opin Drug Saf. 2014;13(1):57-65.PubMedGoogle ScholarCrossref
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Forgacs  I, Loganayagam  A.  Overprescribing proton pump inhibitors.  BMJ. 2008;336(7634):2-3.PubMedGoogle ScholarCrossref
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Grant  K, Al-Adhami  N, Tordoff  J, Livesey  J, Barbezat  G, Reith  D.  Continuation of proton pump inhibitors from hospital to community.  Pharm World Sci. 2006;28(4):189-193.PubMedGoogle ScholarCrossref
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Wilhelm  SM, Rjater  RG, Kale-Pradhan  PB.  Perils and pitfalls of long-term effects of proton pump inhibitors.  Expert Rev Clin Pharmacol. 2013;6(4):443-451.PubMedGoogle ScholarCrossref
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Barron  JJ, Tan  H, Spalding  J, Bakst  AW, Singer  J.  Proton pump inhibitor utilization patterns in infants.  J Pediatr Gastroenterol Nutr. 2007;45(4):421-427.PubMedGoogle ScholarCrossref
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De Bruyne  P, Christiaens  T, Vander Stichele  R, Van Winckel  M.  Changes in prescription patterns of acid-suppressant medications by Belgian pediatricians: analysis of the national database, [1997-2009].  J Pediatr Gastroenterol Nutr. 2014;58(2):220-225.PubMedGoogle ScholarCrossref
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Yang  YX, Lewis  JD, Epstein  S, Metz  DC.  Long-term proton pump inhibitor therapy and risk of hip fracture.  JAMA. 2006;296(24):2947-2953.PubMedGoogle ScholarCrossref
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Lambert  AA, Lam  JO, Paik  JJ, Ugarte-Gil  C, Drummond  MB, Crowell  TA.  Risk of community-acquired pneumonia with outpatient proton-pump inhibitor therapy: a systematic review and meta-analysis.  PLoS One. 2015;10(6):e0128004. doi:10.1371/journal.pone.0128004.PubMedGoogle ScholarCrossref
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Dial  S, Alrasadi  K, Manoukian  C, Huang  A, Menzies  D.  Risk of Clostridium difficile diarrhea among hospital inpatients prescribed proton pump inhibitors: cohort and case-control studies.  CMAJ. 2004;171(1):33-38.PubMedGoogle ScholarCrossref
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Blank  ML, Parkin  L, Paul  C, Herbison  P.  A nationwide nested case-control study indicates an increased risk of acute interstitial nephritis with proton pump inhibitor use.  Kidney Int. 2014;86(4):837-844.PubMedGoogle ScholarCrossref
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Sierra  F, Suarez  M, Rey  M, Vela  MF.  Systematic review: proton pump inhibitor–associated acute interstitial nephritis.  Aliment Pharmacol Ther. 2007;26(4):545-553.PubMedGoogle ScholarCrossref
16.
Antoniou  T, Macdonald  EM, Hollands  S,  et al.  Proton pump inhibitors and the risk of acute kidney injury in older patients: a population-based cohort study.  CMAJ Open. 2015;3(2):E166-E171. doi:10.9778/cmajo.20140074.PubMedGoogle ScholarCrossref
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Klepser  DG, Collier  DS, Cochran  GL.  Proton pump inhibitors and acute kidney injury: a nested case-control study.  BMC Nephrol. 2013;14:150.PubMedGoogle ScholarCrossref
18.
Leonard  CE, Freeman  CP, Newcomb  CW,  et al.  Proton pump inhibitors and traditional nonsteroidal anti-inflammatory drugs and the risk of acute interstitial nephritis and acute kidney injury.  Pharmacoepidemiol Drug Saf. 2012;21(11):1155-1172.PubMedGoogle ScholarCrossref
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Coca  SG, Singanamala  S, Parikh  CR.  Chronic kidney disease after acute kidney injury: a systematic review and meta-analysis.  Kidney Int. 2012;81(5):442-448.PubMedGoogle ScholarCrossref
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Härmark  L, van der Wiel  HE, de Groot  MC, van Grootheest  AC.  Proton pump inhibitor–induced acute interstitial nephritis.  Br J Clin Pharmacol. 2007;64(6):819-823.PubMedGoogle Scholar
21.
Park  CH, Kim  EH, Roh  YH, Kim  HY, Lee  SK.  The association between the use of proton pump inhibitors and the risk of hypomagnesemia: a systematic review and meta-analysis.  PLoS One. 2014;9(11):e112558. doi:10.1371/journal.pone.0112558.PubMedGoogle ScholarCrossref
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Tin  A, Grams  ME, Maruthur  NM,  et al.  Results from the Atherosclerosis Risk in Communities study suggest that low serum magnesium is associated with incident kidney disease.  Kidney Int. 2015;87(4):820-827.PubMedGoogle ScholarCrossref
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ARIC Investigators.  The Atherosclerosis Risk in Communities (ARIC) study: design and objectives.  Am J Epidemiol. 1989;129(4):687-702.PubMedGoogle ScholarCrossref
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Grams  ME, Rebholz  CM, McMahon  B,  et al.  Identification of incident CKD stage 3 in research studies.  Am J Kidney Dis. 2014;64(2):214-221.PubMedGoogle ScholarCrossref
25.
Rebholz  CM, Coresh  J, Ballew  SH,  et al.  Kidney failure and ESRD in the Atherosclerosis Risk in Communities (ARIC) study: comparing ascertainment of treated and untreated kidney failure in a cohort study.  Am J Kidney Dis. 2015;66(2):231-239.PubMedGoogle ScholarCrossref
26.
Grams  ME, Waikar  SS, MacMahon  B, Whelton  S, Ballew  SH, Coresh  J.  Performance and limitations of administrative data in the identification of AKI.  Clin J Am Soc Nephrol. 2014;9(4):682-689.PubMedGoogle ScholarCrossref
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Levey  AS, Stevens  LA, Schmid  CH,  et al; CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration).  A new equation to estimate glomerular filtration rate.  Ann Intern Med. 2009;150(9):604-612.PubMedGoogle ScholarCrossref
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Yoshida  K, Solomon  DH, Kim  SC.  Active-comparator design and new-user design in observational studies.  Nat Rev Rheumatol. 2015;11(7):437-441.PubMedGoogle ScholarCrossref
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IMS Institute for Healthcare Informatics.  Medicine use and shifting costs of healthcare: a review of the use of medicines in the United States in 2013. http://www.imshealth.com/cds/imshealth/Global/Content/Corporate/IMS%20Health%20Institute/Reports/Secure/IIHI_US_Use_of_Meds_for_2013.pdf. Published April 2014. Accessed June 19, 2015.
30.
Björnsson  E, Abrahamsson  H, Simrén  M,  et al.  Discontinuation of proton pump inhibitors in patients on long-term therapy: a double-blind, placebo-controlled trial.  Aliment Pharmacol Ther. 2006;24(6):945-954.PubMedGoogle ScholarCrossref
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Batuwitage  BT, Kingham  JG, Morgan  NE, Bartlett  RL.  Inappropriate prescribing of proton pump inhibitors in primary care.  Postgrad Med J. 2007;83(975):66-68.PubMedGoogle ScholarCrossref
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Danaei  G, Tavakkoli  M, Hernán  MA.  Bias in observational studies of prevalent users: lessons for comparative effectiveness research from a meta-analysis of statins.  Am J Epidemiol. 2012;175(4):250-262.PubMedGoogle ScholarCrossref
Original Investigation
February 2016

Proton Pump Inhibitor Use and the Risk of Chronic Kidney Disease

Author Affiliations
  • 1Department of Epidemiology, The Johns Hopkins University, Baltimore, Maryland
  • 2Department of Medicine, Royal Brisbane and Women’s Hospital, Brisbane, Australia
  • 3Department of Medicine, Yale University School of Medicine, New Haven, Connecticut
  • 4Division of Nephrology, Geisinger Health System, Danville, Pennsylvania
  • 5Department of Medicine, The Johns Hopkins University, Baltimore, Maryland
JAMA Intern Med. 2016;176(2):238-246. doi:10.1001/jamainternmed.2015.7193
Abstract

Importance  Proton pump inhibitors (PPIs) are among the most commonly used drugs worldwide and have been linked to acute interstitial nephritis. Less is known about the association between PPI use and chronic kidney disease (CKD).

Objective  To quantify the association between PPI use and incident CKD in a population-based cohort.

Design, Setting, and Participants  In total, 10 482 participants in the Atherosclerosis Risk in Communities study with an estimated glomerular filtration rate of at least 60 mL/min/1.73 m2 were followed from a baseline visit between February 1, 1996, and January 30, 1999, to December 31, 2011. The data was analyzed from May 2015 to October 2015. The findings were replicated in an administrative cohort of 248 751 patients with an estimated glomerular filtration rate of at least 60 mL/min/1.73 m2 from the Geisinger Health System.

Exposures  Self-reported PPI use in the Atherosclerosis Risk in Communities study or an outpatient PPI prescription in the Geisinger Health System replication cohort. Histamine2 (H2) receptor antagonist use was considered a negative control and active comparator.

Main Outcomes and Measures  Incident CKD was defined using diagnostic codes at hospital discharge or death in the Atherosclerosis Risk in Communities Study, and by a sustained outpatient estimated glomerular filtration rate of less than 60 mL/min/1.73 m2 in the Geisinger Health System replication cohort.

Results  Among 10 482 participants in the Atherosclerosis Risk in Communities study, the mean (SD) age was 63.0 (5.6) years, and 43.9% were male. Compared with nonusers, PPI users were more often of white race, obese, and taking antihypertensive medication. Proton pump inhibitor use was associated with incident CKD in unadjusted analysis (hazard ratio [HR], 1.45; 95% CI, 1.11-1.90); in analysis adjusted for demographic, socioeconomic, and clinical variables (HR, 1.50; 95% CI, 1.14-1.96); and in analysis with PPI ever use modeled as a time-varying variable (adjusted HR, 1.35; 95% CI, 1.17-1.55). The association persisted when baseline PPI users were compared directly with H2 receptor antagonist users (adjusted HR, 1.39; 95% CI, 1.01-1.91) and with propensity score–matched nonusers (HR, 1.76; 95% CI, 1.13-2.74). In the Geisinger Health System replication cohort, PPI use was associated with CKD in all analyses, including a time-varying new-user design (adjusted HR, 1.24; 95% CI, 1.20-1.28). Twice-daily PPI dosing (adjusted HR, 1.46; 95% CI, 1.28-1.67) was associated with a higher risk than once-daily dosing (adjusted HR, 1.15; 95% CI, 1.09-1.21).

Conclusions and Relevance  Proton pump inhibitor use is associated with a higher risk of incident CKD. Future research should evaluate whether limiting PPI use reduces the incidence of CKD.

Introduction

Chronic kidney disease (CKD) affects approximately 13.6% of adults in the United States,1 is associated with a substantially increased risk of death and cardiovascular events,2 and accounts for a disproportionately large burden on the financial resources of Medicare.1 The increasing prevalence of CKD among communities cannot be fully explained by trends in known risk factors, such as diabetes mellitus and hypertension, suggesting that other variables may contribute to the disease process.3,4 Medication use may be a potential factor, particularly given tendencies toward polypharmacy.5 Identifying iatrogenic risk factors for CKD may help to promote the rational use of medications and reduce the burden of CKD worldwide.

Proton pump inhibitors (PPIs) are one of the most commonly prescribed medications in the United States, and it has been estimated that between 25% and 70% of these prescriptions have no appropriate indication.6 The duration of use frequently extends beyond recommended guidelines.7,8 There is also a trend toward PPI use in infants and children.9,10 Since the introduction of PPIs to the US market in 1990, several observational studies have linked PPI use to uncommon but serious adverse health outcomes, including hip fracture,11 community-acquired pneumonia,12Clostridium difficile infection,13 acute interstitial nephritis,14,15 and acute kidney injury (AKI).1618 It is plausible that PPI use may also be a risk factor for CKD, potentially mediated by recurrent AKI,19,20 or by hypomagnesemia, which has been associated with PPI use21 and with incident CKD.22 To our knowledge, no population-based studies have evaluated the association between PPI use and the risk of CKD.

The objective of this study was to quantify the association between PPI use and incident kidney disease in the general population. We hypothesized that PPI use is an independent risk factor for CKD and that the use of Histamine2 (H2) receptor antagonists, another common class of medications used to treat gastroesophageal reflux disease, is not. As a secondary outcome, we also evaluated the association between PPI use and AKI. Analyses were performed in the Atherosclerosis Risk in Communities (ARIC) study, a long-running population-based cohort, and were replicated in patients receiving care in the Geisinger Health System, an integrated health system in rural Pennsylvania.

Methods
Study Design and Setting of the ARIC Study

The ARIC study is a prospective cohort study of 15 792 adults 45 to 64 years old who were recruited as a population-based sample from 4 US communities (Forsyth, North Carolina; Jackson, Mississippi; suburban Minneapolis, Minnesota; and Washington County, Maryland). Participants attended the first visit between January 12, 1987, and March 29, 1990, and attended subsequent visits at 3-year intervals until their fourth visit between February 1, 1996, and January 30, 1999. Visit 5 occurred between June 1, 2011, and August 30, 2013. The dates of our study analysis were from February 1, 1996 (ARIC study visit 4) to December 31, 2011. The ARIC study has been approved by the institutional review boards at the University of Minnesota (Minneapolis), The Johns Hopkins University (Baltimore, Maryland), Wake Forest University (Winston-Salem, North Carolina), University of North Carolina (Winston-Salem), University of Texas Health Sciences Center at Houston, and University of Mississippi Medical Center (Jackson). Participants provided written informed consent. All participants were followed up through an annual telephone survey and a review of community hospital discharge lists until December 31, 2011. Deaths were determined by a telephone survey of alternative contacts and surveillance of local newspaper obituaries, state death lists, and death certificates from the Department of Vital Statistics. Further details about the ARIC study cohort have been published previously.23

Participants in the ARIC Study

For the present study, we included the 11 656 participants who attended visit 4. The ratio of urinary albumin level to creatinine level, an important risk factor for CKD, was first obtained at this visit, and few participants reported PPI use before 1996. Participants who were missing data for the estimated glomerular filtration rate (eGFR) or the ratio of urinary albumin to creatinine (n = 215) or who had an eGFR of less than 60 mL/min/1.73 m2 (n = 725) were excluded. Participants with missing data for years of education, health insurance status, cigarette smoking, body mass index (BMI), mean resting systolic blood pressure, use of antihypertensive or anticoagulant medication, or prevalent hypertension, diabetes mellitus, or cardiovascular disease (n = 234) were also excluded, resulting in a study population of 10 482 participants. The use of the full data set with multiple imputation for missing variables did not change the inference; therefore, we used the complete case analysis. The study population for the secondary outcome of AKI excluded persons with known end-stage renal disease (ESRD) or an eGFR of less than 15 mL/min/1.73 m2 (n = 50). Therefore, it included some participants with an eGFR of less than 60 mL/min/1.73 m2 but was otherwise similarly constructed (n = 11 145).

Measurement of Incident Kidney Disease in the ARIC Study

Incident CKD was defined by diagnostic codes that indicated CKD at hospital discharge (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM]) or death (ICD-10-CM) or by incident ESRD, as determined through linkage with the United States Renal Data System registry.24,25 In an earlier validation study24 that used at least a 25% decline in the eGFR to less than 60 mL/min/1.73 m2 at a follow-up outpatient visit as a reference standard for CKD, the sensitivity of diagnostic codes for defining CKD was 35.5%, and the specificity was 95.7%. Incident AKI was defined by hospitalization or death, with ICD-9-CM or ICD-10-CM diagnostic codes of 584.x or N17.x, respectively.26 Participants who died before developing CKD, were lost to follow-up, or had disease-free survival to December 31, 2011, were censored.

Measurement of PPI Use and Other Covariates in the ARIC Study

The use of PPIs and H2 receptor antagonists was measured at the baseline study visit through direct visual inspection of pill bottles for all medications used during the preceding 2 weeks. Exposure to antihypertensive, anticoagulant, aspirin, statin, diuretic, and nonsteroidal anti-inflammatory medications was measured in the same way. Subsequent exposure to PPIs and H2 receptor antagonists was obtained as part of the annual telephone follow-up, which included questions about medication use starting in September 2006. At each telephone follow-up from 2006 onward, participants were asked to assemble all medications they were taking and to “read the names of all the medications prescribed by a doctor.”

Baseline plasma and urinary creatinine levels were measured by the modified kinetic Jaffé method.24 The equation developed by the Chronic Kidney Disease Epidemiology Collaboration was used to calculate the eGFR.27 The urinary albumin level was measured using a nephelometer (BN100; Dade Behring or IMMAGE; Beckman).24 Three domains of socioeconomic status were measured, including self-reported highest level of education, health insurance status, and annual household income in the previous 12 months. Cigarette smoking status was defined categorically as a current, former, or never smoker at baseline, and the BMI was derived. Prevalent hypertension was defined as a systolic blood pressure of at least 140 mm Hg, a diastolic blood pressure of at least 90 mm Hg, or self-reported use of antihypertensive medication within the past 2 weeks. Prevalent diabetes mellitus was defined by a fasting blood glucose concentration of at least 126 mg/dL, a random glucose concentration of at least 200 mg/dL, self-report of a physician diagnosis of diabetes mellitus, or reported use of medication for diabetes in the past 2 weeks (to convert glucose concentration to millimoles per liter, multiply by 0.0555). Prevalent cardiovascular disease was defined as a composite outcome of prevalent coronary heart disease or stroke at visit 4.

Geisinger Health System Replication Cohort

The replication cohort consisted of 248 751 patients with an outpatient eGFR of at least 60 mL/min/1.73 m2 receiving care between February 13, 1997, and October 9, 2014, in the Geisinger Health System, a large rural health care system in central and northeastern Pennsylvania. Participants were selected at the earliest time point when they had both creatinine level and systolic blood pressure available. Incident CKD was defined as the first outpatient eGFR of less than 60 mL/min/1.73 m2 that was sustained at all subsequent assessments of the eGFR or as the development of ESRD, which was ascertained through linkage to the United States Renal Data System registry. Incident AKI was defined as an ICD-9-CM code of 584.x, and death was ascertained through linkage to the National Death Index. Individuals who did not develop the outcome of interest were censored at their last follow-up or death. Medication use was determined by prescriber prescription within 90 days before baseline. The frequency of PPI use was categorized as once daily or twice daily according to the prescription and was assumed to be once daily if not specified. Comorbidities were captured by inpatient and outpatient billing codes.

Statistical Analysis

Baseline characteristics of PPI users and non–PPI users were compared using t tests for continuous variables and χ2 tests for categorical variables. The Wilcoxon rank sum test was used for continuous variables that were not normally distributed. Cox proportional hazards regression was used to estimate the hazard ratios (HRs) and 95% CIs of incident CKD associated with PPI use. The proportional hazards assumption was tested using Schoenfeld residuals. Exposure to PPIs was modeled as a binary variable at baseline and in secondary analyses as a time-varying ever-use variable, in which a participant was considered an ever user at the first instance of PPI use and at all time points thereafter. In the ARIC study, time-varying PPI use represented baseline use, with updates in 2006 and yearly thereafter; in the replication cohort, it was evaluated by assessing all health care professional prescriptions throughout the study period. In the ARIC study, adjustment was performed for demographic variables (age, sex, race, and study center), socioeconomic status (health insurance and highest level of education), clinical measurements (baseline eGFR, logarithm of the ratio of urinary albumin to creatinine, cigarette smoking, mean systolic blood pressure, and BMI), prevalent comorbidities (diabetes mellitus and cardiovascular disease), and concomitant use of medications (antihypertensive medication and anticoagulant medication). Annual household income and concomitant use of nonsteroidal anti-inflammatory drugs, aspirin, diuretics, or statin medications were considered possible confounders a priori; however, they did not affect the results of adjusted analyses and were not included in the final model. In the replication cohort, fewer comorbidities were available; therefore, analyses were adjusted for age, sex, race, baseline eGFR, cigarette smoking, BMI, systolic blood pressure, diabetes mellitus, history of cardiovascular disease, antihypertensive medication use, anticoagulant medication use, and statin, aspirin, and nonsteroidal anti-inflammatory drug use. Subgroup analyses were performed, stratified by the median age, sex, race (in the ARIC study only), diabetes mellitus, and concomitant medication use.

In the replication cohort, the risk of CKD was also evaluated in once-daily and twice-daily PPI users. Similar analyses were performed for the secondary outcome of AKI. Absolute risk differences were estimated as the difference between the expected 10-year risk among PPI users and the expected 10-year risk had they not used PPIs.

Five sensitivity analyses were performed. First, the study population was limited to participants using H2 receptor antagonists or PPIs, and the risk of kidney disease associated with PPI use was assessed using H2 receptor antagonists as the active comparator. Second, the association between PPI use and incident kidney disease was examined in a propensity score–matched cohort, in which logistic regression was used to estimate the probability of PPI use based on observable predictors of PPI use, and controls not using PPIs were selected using 1:1 nearest-neighbor matching. Third, a new-user design was used, whereby the risk associated with time-varying PPI ever use was assessed only among persons not using PPIs at baseline.28 Given that new use was not available until 2006 in the ARIC study, this analysis was performed only in the replication cohort. Fourth, the association between H2 receptor antagonist use and incident kidney disease was assessed as a negative control. Fifth, persons with a baseline ratio of urinary albumin to creatinine exceeding 30 mg/g (or 1+ protein on dipstick in the replication cohort) were excluded from the study population. All analyses were performed using statistical software (Stata/IC, version 13.1; StataCorp LP).

Results
Study Population

In the ARIC study, 10 482 participants were followed up for a median of 13.9 years. In the replication cohort, 248 751 participants were followed up for a median of 6.2 years. At baseline in both cohorts, PPI users were more likely than nonusers to have a higher BMI and take antihypertensive, aspirin, or statin medications (Table 1). The characteristics of H2 receptor antagonist users were similar to those of PPI users. The prevalence of ever use of PPIs increased substantially during the follow-up period (Figure 1).

Association Between PPI Use and Kidney Disease in the ARIC Study

In the ARIC study, there were 56 incident CKD events among the 322 baseline PPI users (14.2 per 1000 person-years), and 1382 events among 10 160 baseline nonusers (10.7 per 1000 person-years). In unadjusted analysis, participants who used PPIs at baseline had 1.45 (95% CI, 1.11-1.90; P = .006) times the risk of incident CKD relative to that of nonusers (Table 2). The risk was similar after adjustment for potential confounders, including demographics, socioeconomic status, clinical measurements, prevalent comorbidities, and concomitant use of medications (HR, 1.50; 95% CI, 1.14-1.96; P = .003), as was the association when PPI use was modeled as a time-varying ever-use variable (HR, 1.35; 95% CI, 1.17-1.55; P < .001). Subgroup analyses were consistent with the primary results (Figure 2). The 10-year estimated absolute risk of CKD among the 322 baseline PPI users was 11.8% while the expected risk had they not used PPIs was 8.5% (absolute risk difference, 3.3%).

A slightly stronger association was seen between PPI use and AKI (Table 3). For example, in unadjusted analysis, participants who used PPIs at baseline had 1.72 (95% CI, 1.28-2.30; P < .001) times the risk of incident AKI relative to those who did not report use. The corresponding risks were similar after adjustment for potential confounders (HR, 1.64; 95% CI, 1.22-2.21; P < .001) and when PPI use was analyzed as a time-varying ever-use variable (HR, 1.49; 95% CI, 1.25-1.77; P < .001).

Association Between PPI Use and Kidney Disease in the Replication Cohort

In the replication cohort, there were 1921 incident CKD events among 16 900 baseline PPI users (20.1 per 1000 person-years) and 28 226 events among 231 851 baseline nonusers (18.3 per 1000 person-years). Proton pump inhibitor use was significantly associated with incident CKD in unadjusted analyses (HR, 1.20; 95% CI, 1.15-1.26; P < .001), in adjusted analyses (adjusted HR, 1.17; 95% CI, 1.12-1.23; P < .001), and when estimated using a time-varying ever-use model (adjusted HR, 1.22; 95% CI, 1.19-1.25; P < .001) (Table 2). Twice-daily PPI dosing (adjusted HR, 1.46; 95% CI, 1.28-1.67; P < .001) was associated with a higher risk of CKD than once-daily dosing (adjusted HR, 1.15; 95% CI, 1.09-1.21; P < .001). The 10-year absolute risk of CKD among the 16 900 baseline PPI users was 15.6%, and the expected risk had they not used PPIs was 13.9% (absolute risk difference, 1.7%).

Similar associations were seen with incident AKI (Table 3). Proton pump inhibitor use resulted in a higher risk of incident AKI in unadjusted analysis (HR, 1.30; 95% CI, 1.21-1.40; P < .001), adjusted analysis (HR, 1.31; 95% CI, 1.22-1.42; P < .001), and time-varying ever-use analysis (adjusted HR, 1.54; 95% CI, 1.47-1.60; P < .001). Twice-daily PPI dosing (adjusted HR, 1.62; 95% CI, 1.32-1.98; P < .001) was associated with a higher risk of AKI than once-daily dosing (adjusted HR, 1.28; 95% CI, 1.18-1.39; P < .001).

Sensitivity Analyses

When compared directly with H2 receptor antagonist use, PPI use was associated with incident CKD in the ARIC study (adjusted HR, 1.39; 95% CI, 1.01-1.91; P = .05) and in the replication cohort (adjusted HR, 1.29; 95% CI, 1.19-1.40; P < .001). Baseline PPI use was also associated with incident CKD in propensity score–matched analyses (HR, 1.76; 95% CI, 1.13-2.74; P = .01 in the ARIC study and HR, 1.16; 95% CI, 1.09-1.24; P < .001 in the replication cohort) and in the new-user analysis (adjusted HR, 1.24; 95% CI, 1.20-1.28; P < .001). The use of H2 receptor antagonists was not associated with increased risk of incident CKD in either cohort (adjusted HR, 1.15; 95% CI, 0.98-1.36; P = .10 in the ARIC study and adjusted HR, 0.93; 95% CI, 0.88-0.99, P = .03 in the replication cohort). Similar results were obtained when persons with baseline albuminuria were excluded (adjusted HR, 1.45; 95% CI, 1.09-1.96; P = .01 in the ARIC study and adjusted HR, 1.19; 95% CI, 1.13-1.25; P < .001 in the replication cohort). Sensitivity analyses using AKI as an outcome were also consistent (Table 3).

Discussion

In a prospective community-based cohort of more than 10 000 adults, we found that baseline use of PPIs was independently associated with a 20% to 50% higher risk of incident CKD, after adjusting for several potential confounding variables, including demographics, socioeconomic status, clinical measurements, prevalent comorbidities, and concomitant use of medications. The observed association persisted when PPI exposure was modeled as a time-varying ever-use variable and was replicated in a separate administrative cohort of 248 751 individuals. The risk was specific to PPI medications because the use of H2 receptor antagonists, which are prescribed for the same indication as PPIs, was not independently associated with CKD. Similar findings were demonstrated for the outcome of AKI and collectively suggest that PPI use is an independent risk factor for CKD and for AKI.

Previous studies1418 have also identified an association between PPI use and AKI, most specifically in the form of acute interstitial nephritis. Our study adds to the existing literature by describing an association between PPI use and incident CKD. We note that our study is observational and does not provide evidence of causality. However, a causal relationship between PPI use and CKD could have a considerable public health effect given the widespread extent of use. More than 15 million Americans used prescription PPIs in 2013, costing more than $10 billion.29 Study findings suggest that up to 70% of these prescriptions are without indication6 and that 25% of long-term PPI users could discontinue therapy without developing symptoms.30 Indeed, there are already calls for the reduction of unnecessary use of PPIs.31

Observational cohort studies represent one of the best methods to study adverse effects of medications used in real-world settings. However, several limitations inherent in observational design must be considered. First, unlike a randomized clinical trial, participants who are prescribed PPIs may be at a higher risk of CKD for reasons unrelated to their PPI use. For example, PPI users in both the ARIC study and the replication cohort were more likely to be obese, have a diagnosis of hypertension, and carry a greater burden of prescribed medications. In recognition of this potential bias, we performed adjustment for multiple confounders, including BMI, hypertension, diabetes mellitus, and concomitant medication use, compared PPI users directly with H2 receptor antagonist users, and conducted propensity score–matched analyses. Each of these sensitivity analyses showed a consistent association between PPI use and a higher risk of CKD.

A second limitation of our study is the potential for surveillance bias, whereby outcome assessment might have occurred more often in persons using PPIs. In the ARIC study, incident CKD was detected using hospitalization discharge codes, while outpatient creatinine levels were used in the replication cohort. However, the association between PPI use and new CKD persisted after accounting for predictors of more frequent contact with the medical system such as insurance status and comorbid illness. A third limitation is the low sensitivity of hospital discharge codes for diagnosing CKD in the ARIC study. However, the study results were replicated in the Geisinger Health System cohort, in which CKD was defined by direct laboratory measurements. Fourth, the inclusion of baseline PPI users can invoke selection bias, whereby baseline users represent a special group of PPI users who tolerate the medication without the development of CKD. In our study, there were few prevalent PPI users at baseline, which should lead to less bias.32 In addition, the results were replicated in a new-user design in the replication cohort, in which baseline PPI users were excluded. A fifth potential limitation is that neither PPI nor H2 receptor antagonist use was captured as directly observed therapy. In recent years, both have become available over the counter in the United States. Therefore, medication exposure in the ARIC study and the replication cohort may have been misclassified.

Notable strengths of the ARIC study include a large representative community-based sample, baseline visits occurring soon after PPIs were introduced into the United States, visual confirmation of medications used, comprehensive data pertaining to potential confounders, and close monitoring for more than 13 years of follow-up. Sensitivity analyses, including a time-varying exposure model, propensity score matching, and replication in a large second cohort, showed robust results. We also demonstrated specificity to PPI use rather than H2 receptor antagonist use.

Conclusions

In summary, we found that PPI use is an independent risk factor for CKD and AKI, but H2 antagonist use is not. Further research is required to investigate whether PPI use itself causes kidney damage and, if so, the underlying mechanisms of this association.

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Article Information

Correction: This article was corrected on February 29, 2016, to fix a typographical error in Table 2.

Accepted for Publication: October 29, 2015.

Corresponding Author: Morgan E. Grams, MD, PhD, Department of Epidemiology, The Johns Hopkins University, 2024 E Monument St, Baltimore, MD 21205 (mgrams2@jhmi.edu).

Published Online: January 11, 2016. doi:10.1001/jamainternmed.2015.7193.

Author Contributions: Dr Grams had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Lazarus, Chen, Chang, Grams.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Lazarus, Grams.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Lazarus, Chen, Sang, Grams.

Administrative, technical, or material support: Grams.

Study supervision: Wilson, Grams.

Funding/Support: Dr Grams is supported by grant K08DK092287 from the National Institute of Diabetes and Digestive and Kidney Diseases. The Atherosclerosis Risk in Communities study is performed as a collaborative investigation supported by contracts HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C from the National Heart, Lung, and Blood Institute.

Role of the Funder/Sponsor: The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: 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.

Additional Contributions: Some of the data reported herein were supplied by the United States Renal Data System. We thank the staff and participants of the Atherosclerosis Risk in Communities study for their important contributions.

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