(2024) 22:335
Kim et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03549-y

BMC Medicine

RESEARCH

Open Access

Impact of proton pump inhibitor use
on clinical outcomes in East Asian patients
receiving clopidogrel following drug-eluting
stent implantation
Ju Hyeon Kim1, Soon Jun Hong1*, Jung‑Joon Cha1, Subin Lim1, Hyung Joon Joo1, Jae Hyoung Park1,
Cheol Woong Yu1, Tae Hoon Ahn2, Young‑Hoon Jeong2, Byeong‑Keuk Kim3, Kiyuk Chang4, Yongwhi Park5,
Young Bin Song6, Sung Gyun Ahn7, Jung‑Won Suh8, Sang Yeub Lee2, Jung Rae Cho9, Ae‑Young Her10,
Hyo‑Soo Kim11, Moo Hyun Kim12, Eun‑Seok Shin13 and Do‑Sun Lim1

Abstract
Background Concomitant use of clopidogrel and proton pump inhibitor (PPI) is common, but PPI may reduce
the antiplatelet effects of clopidogrel in patients undergoing percutaneous coronary intervention (PCI). We evalu‑
ated the impact of PPI use on clinical outcomes in post-PCI patients, by incorporating P2Y12 reaction unit (PRU)
and CYP2C19 genotyping results.
Methods From a multicenter registry of patients who underwent PCI with drug-eluting stent implantation and received
clopidogrel-based dual antiplatelet therapy (DAPT), patients who were prescribed a PPI at the time of PCI (PPI users) werecompared to those who were not (non-users). The primary outcome included all-cause death, myocardial infarction, stent
thrombosis, or cerebrovascular accident at 12 months. Major bleeding (Bleeding Academic Research Consortium [BARC]
types 3–5) and gastrointestinal (GI) bleeding (BARC types 3–5) were important secondary outcomes. The adjusted out‑
comes were compared using a 1:1 propensity-score (PS) matching and competing risk analysis.
Results Of 13,160 patients, 2,235 (17.0%) were prescribed PPI, with an average age of 65.4 years. PPI users had higher
on-treatment PRU levels than non-users. After PS matching, the primary outcome occurred in 51 patients who were
PPI users (cumulative incidence, 4.7%) and 41 patients who were non-users (cumulative incidence, 3.7%; log-rank
p = 0.27). In carriers of both CYP2C19 loss-of-function alleles, PPI use was linked to an increased risk of the primary out‑
come (hazard ratio, 3.22; 95% confidence interval, 1.18–8.78). The incidence of major bleeding and GI bleeding (BARC
types 3–5) was comparable between PPI users and non-users in the PS-matched cohort.
Conclusions In post-PCI patients receiving clopidogrel-based DAPT, PPI use was not linked to an increased riskof adverse cardiac and cerebrovascular events, but there was a small but significant increase in on-treatment PRU.
Future research using a more individualized approach would further elucidate these interactions and guide evidencebased clinical practices.
Keywords Clopidogrel, Proton pump inhibitor, Platelet reactivity, Poor metabolizer, Myocardial infarction, Bleeding*Correspondence:
Soon Jun Hongpsyche94@hanmail.net
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
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regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecom‑mons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Kim et al. BMC Medicine(2024) 22:335

Background
Patients receiving dual antiplatelet therapy (DAPT)
after percutaneous coronary intervention (PCI) withdrug-eluting stent (DES) implantation are at risk of bothischemic and bleeding complications. Proton pumpinhibitors (PPIs) are commonly prescribed in thesepatients to treat or prevent gastrointestinal (GI) bleeding[1, 2]. Clopidogrel is a prodrug requiring bioactivationby the liver enzyme CYP2C19 (cytochrome P450, family 2, subfamily C, polypeptide 19) [3], whereas PPIs areprimarily metabolized by the same enzyme into inactive
metabolites [4]. This competitive inhibition has raised
concerns regarding clinically relevant drug-drug interactions that could diminish the effectiveness of clopidogrel[5, 6]. In addition, the presence of CYP2C19 loss-of-function (LoF) alleles increases the likelihood of PPI-clopidogrel interaction [7, 8].
Some randomized clinical trials (RCTs) and observational studies have shown contradictory results regarding the impact of PPI coadministration on clopidogrel
efficacy, with some studies indicating attenuated P2Y12
receptor inhibition and increased adverse clinical outcomes [8–12]. However, previous studies have predominantly focused on metabolic interactions assessedusing platelet function test in a limited patient population [13–16]. The largest RCT (PPI users; n = 1,876)
found no significant cardiovascular interaction between
clopidogrel and omeprazole, but the study’s predominantly white population (94%) suggests an underestimation of homozygosity for the CYP2C19 LoF allele[8]. No research has investigated drug-drug-gene interactions on platelet reactivity and clinical outcomes inpatients receiving clopidogrel-based DAPT after DES
implantation.
The East Asian population has a higher prevalenceof CYP2C19 LoF allele than that of the Western population [17, 18], but they are significantly underrepresented in landmark RCTs. East Asian patients exhibit a
distinct clinical profile characterized by fewer thromboembolic events and increased bleeding risk during
antithrombotic therapies [19]. Due to this East Asianparadox, clopidogrel remains the most commonly prescribed platelet inhibitor in clinical practice becauseof its lower bleeding profile [20–22]. To address thesegaps, a large-scale, observational, multicenter Platelet function and genoType-Related long-term proGnosis in DES-treated patients (PTRG-DES) study inSouth Korea included regular platelet reactivity testing(before and after clopidogrel loading) and CYP2C19
genotyping [23–25]. From the PTRG-DES study, wesought to evaluate the safety of the concomitant useof PPI and clopidogrel-based DAPT on ischemic andPage 2 of 13

bleeding outcomes using propensity-score (PS) matching analysis.

Methods
Source of data and study population

The PTRG-DES is a nationwide registry supportedby the Korean Society of Interventional Cardiology(NCT04734028). The detailed study design and complete
inclusion and exclusion criteria have been previously
described [23–25]. Between January 2003 and December 2018, 33 academic centers in South Korea enrolled
13,160 consecutive patients who underwent PCI with
DES implantation and were treated with clopidogrelbased DAPT. Patients who received P2Y12 inhibitors
other than clopidogrel and those who required oral anticoagulants were excluded from the study. The institutional review board of each participating center approved
the PTRG-DES registry (Korea University Anam Hospital; 2018AN0283) and waived the requirement for written
informed consent for access to an institutional registry.
Study measurements and procedures

After ensuring that the antiplatelet effects would last fora long enough time, the VerifyNow assay (Accriva, San
Diego, CA, USA) was performed to measure the P2Y12
reaction unit (PRU) during the peri-procedural time[26]. Aspirin was administered in either a 300 mg coatedoral dosage at least 2 h before PCI or a 100 mg dose atleast 5 days before PCI. Clopidogrel was administered indoses of 600 mg at least 6 h before PCI, 300 mg at least
12 h before PCI, or 75 mg at least 5 days before PCI. No
patient receiving abciximab was enrolled because of thelong washout period. If eptifibatide or tirofiban was usedduring PCI, a 24-h washout period was required before
VerifyNow testing [23]. High platelet reactivity (HPR)
was defined as an on-treatment PRU > 208 [27].
Pyrosequencing of each single nucleotide polymorphism was performed for genotyping using commerciallyavailable analyzers such as the PSQ 96MA Pyrosequencer(Pyrosequencing AB, Uppsala, Sweden), the ABI PRISM®
3100 genetic analyzer (Applied Biosystems, Foster City,
CA, USA), or the Spartan RXTM system (Spartan Bioscience, Ottawa, Canada) [28–30]. The major Korean alleles
include CYP2C19*2 (rs4244285), CYP2C19*3 (rs4986893),
and CYP2C19*17 (rs12248560). Extensive metabolizers
include CYP2C19*1/*1 and CYP2C19*1/*17. Intermediate metabolizers include CYP2C19*1/*2, CYP2C19*1/*3,
CYP2C19*2/*17, and CYP2C19*3/*17. Poor metabolizers
include CYP2C19*2/*2, CYP2C19*2/*3, and CYP2C19*3/*3.
The physicians and patients were not informed of the PRU
and CYP2C19 genotyping results.

Kim et al. BMC Medicine(2024) 22:335

All PCI procedures were performed according to current guidelines. During PCI, parenteral anticoagulation
was used to maintain an active clotting time of 250–300 s.
The operator selected the treatment method, stent type,
diameter, length, and drug use. The index PCI guidelines
recommend DAPT with aspirin and clopidogrel maintenance doses for at least 1 year. The DAPT duration was at
the discretion of the attending physician.
Proton pump inhibitors and study outcomes

PPIs were prescribed at the discretion of the treating physician and documented as a drug class in the case reportform at the time of the PCI. However, specific PPIs anddosages were not reported in detail. Anemia was defined
as a hemoglobin level < 13 g/dL in men and < 12 g/dL in
women. Chronic kidney disease (CKD) was defined as an
estimated glomerular filtration rate < 60 mL/min/1.73 ­m2.
The primary outcome was the incidence of major
adverse cardiac and cerebrovascular events (MACCE)
including all-cause death, myocardial infarction (MI),stent thrombosis, or cerebrovascular accident at
12 months after the index PCI. All-cause mortality, major
bleeding (Bleeding Academic Research Consortium[BARC] types 3–5), and GI bleeding (BARC types 3–5)
were important secondary outcomes. MI was defined as
an increase in creatine kinase-myoglobin binding above
the upper normal limit or a troponin T/I level > 99th
percentile of the upper normal limit accompanied byischemic symptoms, electrocardiographic abnormalities, or abnormal imaging findings indicative of ischemia.
Any new embolic, thrombotic, or hemorrhagic strokewith neurologic impairments lasting for at least 24 h wasconsidered a cerebrovascular accident. Unless an undeniable non-cardiovascular cause was discovered, all deaths
were categorized as cardiovascular deaths. Patient interviews and medical records were used to obtain the demographic, angiographic, and procedural data. All follow-up
visits were conducted in the form of office visits or telephone calls, where necessary. An independent committee
blinded to the genetics and PRU data examined and adjudicated all clinical events from each participating site.
Statistical analysis

Categorical variables are reported as counts and percentages, whereas continuous variables are presented
as means and standard deviations. Group comparisonswere performed using a parametric unpaired t-test or
non-parametric Mann–Whitney U test for continuous variables and χ2 or Fisher’s exact test for categorical variables. To reduce the effect of selection bias, we
conducted PS matching analysis to compare the adverse
clinical events between PPI users and non-users. Using
multivariable logistic regression, we assessed thePage 3 of 13

likelihood of receiving PPIs during the index hospitalization. The variables were presentation as acute MI, age,sex, obesity, dyslipidemia, smoking, CKD, anemia, congestive heart failure, peripheral arterial disease, previous MI, previous coronary artery bypass graft, previous
PCI, hemoglobin level, platelet count, glomerular filtration rate, baseline PRU, multivessel disease, bifurcation
lesion, chronic total occlusion, PCI of the left main orleft anterior descending artery, discontinuation of DAPT
within 1 year, and the use of renin-angiotensin system
inhibitor, statin, calcium channel blocker, and aspirin.
We matched each patient in PPI users with those in
non-users at a 1:1 ratio using the optimal method, witha caliper width equal to 0.2 of the standard deviation of
the logit PS. The balance of baseline features was examined, and a standardized mean difference < 0.1 indicateda negligible difference.
During the follow-up period, patients were censoredat the time of the event or date of their last follow-up.
Only the first event was counted in patients with multiple
events reported for the same outcome. Cumulative incidence rates were calculated based on Kaplan–Meier estimates, and intergroup comparisons were assessed using
the log-rank test. A multivariable Cox proportional hazard regression model was used to analyze the influence ofdifferent covariates on time-to-event outcomes by calculating hazard ratios (HRs) and 95% confidence intervals.
The sub-distribution HR for the primary outcome wasestimated using the Fine-Gray competing risk model [31],and the discontinuation of DAPT was modelled as a single competing event. Subgroup analyses were performed
according to the presence of hypertension, diabetes mellitus, CYP2C19 genotyping, and HPR. Statistical analyses
were performed using R version 4.1.2 (R Foundation for
Statistical Computing, Vienna, Austria) with a value ofp < 0.05 considered statistically significant.

Results
Study population

Of the 13,160 patients, 2,235 (17.0%) were prescribed
PPI, with an average age of 65.4 years, and 64.0% weremen (Additional file 1: Table S1). PPI users were older,
more frequently presented with acute MI, and had higher
rates of dyslipidemia, CKD, anemia, and multivessel disease. The PS matching analysis included 6,673 patients
who had both baseline PRU measurements and CYP2C19
genotyping (PPI users, n = 1,133; Additional file 1: Fig.
S1). After 1:1 PS matching, 2,266 patients were includedin the final analysis. The demographic and clinical parameters at baseline were well balanced, with a standardized
mean difference < 0.1 (Table 1). PPI users showed higher
on-treatment PRU levels (after clopidogrel loading)than non-users in both the unmatched and PS-matchedKim et al. BMC Medicine(2024) 22:335

Page 4 of 13

Table 1 Baseline demographics and clinical characteristics
Propensity-score matched cohort (n = 2266)
PPI users(n = 1133)

Non-users(n = 1133)

P valueSMD

363 (32.0%)367 (32.4%)

0.89-0.0076388 (34.2%)

429 (37.9%)

Index presentation
Presentation as acute MI, n (%)  Stable angina  Unstable angina

382 (33.7%)

337 (29.7%)   Non-ST-segment elevation MI

188 (16.6%)

231 (20.4%)   ST-segment elevation MI

175 (15.4%)

136 (12.0%)

Age (years)

65.1 ± 11.2

65.6 ± 11.2

0.35

-0.0391

Male, n (%)

754 (66.5%)745 (65.8%)

0.72-0.0168

Co-morbidities, n (%)
Hypertension

677 (59.8%)705 (62.2%)

0.25

NA

Diabetes mellitus

382 (33.7%)

398 (35.1%)

0.51

NA

Dyslipidemia

676 (59.7%)

693 (61.2%)

0.49-0.0306

Smoking

325 (28.7%)

320 (28.2%)

0.85

0.0098Chronic kidney disease

266 (23.5%)

266 (23.5%)

1.00

0.0000Anemia373 (32.9%)410 (36.2%)

0.11-0.0695

Obesity (BMI ≥ 25 kg/m2)

488 (43.1%)

463 (40.9%)

0.31

0.0446176 (15.5%)

195 (17.2%)

0.31-0.0463

Previous history, n (%)
History of PAD
History of CHF

83 (7.3%)

93 (8.2%)

0.48-0.0339

Previous MI

52 (4.6%)

47 (4.1%)

0.68

0.0211

Previous PCI

111 (9.8%)

131 (11.6%)

0.20-0.0594

Previous CABG

8 (0.7%)6 (0.5%)0.79

0.0211

Previous stroke

81 (7.1%)

85 (7.5%)

0.81

NA

Lab measurements
LV ejection fraction, %58.3 ± 11.5

58.6 ± 10.8

0.63

NA

Hemoglobin, g/dL

13.2 ± 2.0

13.1 ± 2.0

0.08

0.0727

Total cholesterol, mg/dL

173.5 ± 43.9

175.3 ± 45.9

0.35

NA

Triglyceride, mg/dL

142.3 ± 99.1

138.8 ± 107.1

0.43

NA

LDL-cholesterol, mg/dL

107.3 ± 39.6

105.0 ± 37.7

0.19

NA

HDL-cholesterol, mg/dL

42.6 ± 11.6

43.1 ± 11.8

0.30

NA
-0.0102

estimated GFR

76.7 ± 27.8

76.9 ± 27.6

0.81

Platelet, × ­103/mm3

236.2 ± 77.0

240.0 ± 80.5

0.26

-0.0489

VerifyNow PRU, baseline

307.3 ± 63.9

309.1 ± 60.6

0.48

-0.02890.08

NA407 (35.9%)459 (40.5%)

CYP2C19 genotyping  Extensive metabolizer  Intermediate metabolizer

566 (50.0%)521 (46.0%)  Poor metabolizer

160 (14.1%)

153 (13.5%)

Angiographic feature, n (%)
Multivessel disease

475 (41.9%)458 (40.4%)

0.50

0.0304

Bifurcation lesion

102 (9.0%)

94 (8.3%)

0.60

0.0247Chronic total occlusion lesion

63 (5.6%)

62 (5.5%)

1.00

0.0039PCI at LM and/or LAD

741 (65.4%)

733 (64.7%)

0.76

0.0148

Concomitant discharge medications, n (%)
Aspirin1128 (99.6%)1128 (99.6%)

1.00

0.0000

Beta blocker

738 (65.1%)

727 (64.2%)

0.66

NA

Angiotensin blockade

770 (68.0%)

770 (68.0%)

1.00

0.0000

Calcium channel blocker

438 (38.7%)

438 (38.7%)

1.00

0.0000Kim et al. BMC Medicine(2024) 22:335

Page 5 of 13

Table 1 (continued)
Propensity-score matched cohort (n = 2266)
PPI users(n = 1133)
StatinDiscontinuation of DAPT within 1 year

Non-users(n = 1133)

P value

SMD

1060 (93.6%)

1061 (93.6%)

1.00-0.0036248 (21.9%)

247 (21.8%)

1.00

0.0021

Aspirin monotherapy

173

166

Clopidogrel monotherapy

73

81

Others20

Values are presented as numbers (percentages) or means ± standard deviation
BMI Body mass index, CABG Coronary artery bypass graft surgery, CHF Congestive heart failure, DAPT Dual antiplatelet therapy, GFR Glomerular filtration rate, HbA1c
Hemoglobin A1c, HDL High-density lipoprotein, LDL Low-density lipoprotein, LAD Left anterior descending artery, LM Left main, LV Left ventricular, MI Myocardial
infarction, PAD Peripheral artery disease, NA Not applicable due to exclusion from propensity-score matching, PCI Percutaneous coronary intervention, PRU P2Y12
reaction unit, PPI Proton pump inhibitor, SMD Standardized mean difference

Table 2 Platelet reactivity and procedural characteristics
Propensity-score matched cohort (n = 2266)
PPI users(n = 1133)

Non-users(n = 1133)

P value

SMD

Platelet reactivity
On-treatment PRU    > 208

232.0 ± 82.9

224.4 ± 81.7

0.03

NA

723 (63.8%)670 (59.1%)

0.03

NA

    ≥ 230

614 (54.2%)

548 (48.4%)

0.01

NA

    ≥ 252475 (41.9%)435 (38.4%)

0.10

NA

482 (42.5%)458 (40.4%)

0.33

NA

Procedural data, n (%)
Multivessel PCI
Treated lesions   Left main coronary artery

67 (5.9%)70 (6.2%)

0.86

NA

   Left anterior descending artery

703 (62.0%)694 (61.3%)

0.73

NA

   Left circumflex artery

339 (29.9%)324 (28.6%)

0.52

NA

   Right coronary artery

464 (41.0%)433 (38.2%)

0.20

NA

Number of stents per patient, n1.64 ± 0.8

1.62 ± 0.8

0.47

NA

Total stent length per patient, mm

37.8 ± 23.7

36.2 ± 21.5

0.09

NA

Mean stent diameter per patient, mm

3.06 ± 0.5

2.97 ± 0.4

DES type  First-generation DES

34 (3.0%)59 (5.2%)  Newer-generation DES1099 (97.0%)1074 (94.8%)< 0.001

NA

0.01

NA

Values are presented as numbers (percentages) or means ± standard deviation
DES Drug-eluting stent, NA Not applicable due to exclusion from propensity-score matching, PCI Percutaneous coronary intervention, PRU P2Y12 reaction unit, PPI
Proton pump inhibitor, SMD Standardized mean difference

cohorts (Table 2 and Additional file 1: Table S2). Figure 1
presents both the baseline and on-treatment PRU values
in the PS-matched cohort, as a percentile plot. PPI users
had larger mean stent diameters and demonstrated procedural characteristics similar to those of non-users in
the PS-matched cohort.

Effect of PPIs on ischemic and bleeding events

Table 3 and Additional file 1: Table S3 summarizes theprimary and secondary outcomes at 12 months according to PPI use. Before PS matching, composite MACCE
occurred in 99 PPI users (cumulative incidence, 4.6%)
and 326 non-users (cumulative incidence, 3.1%; log-rank
p < 0.001). After PS matching, the incidence of MACCE
at 12 months was comparable between PPI users andnon-users (cumulative incidence, 4.7% vs. 3.7%; log-rank

Kim et al. BMC Medicine(2024) 22:335

Page 6 of 13

Fig. 1 Distribution of the PRU before and after clopidogrel loading in the PS-matched cohort. The VerifyNow PRU results showed that baseline
PRU values were similar between PPI users and non-users, with average values of 307.3 ± 63.9 and 309.1 ± 60.6, respectively (p = 0.48). However,after clopidogrel loading, on-treatment PRU values were significantly higher in PPI users (232.0 ± 82.9) than in non-users (224.4 ± 81.7). PPI, proton
pump inhibitor; PRU, P2Y12 reaction unit; PS, propensity-score

Table 3 Incidence of the primary and secondary outcomes at 12 months
Propensity-score matched cohort (n = 2266)
PPI users(n = 1133)

Non-users(n = 1133)

HR [95% CI]

Log-rank P

51 (4.7%)

41 (3.7%)

1.26 [0.84–1.90]

0.27

All-cause death

29 (2.6%)

20 (1.8%)

1.47 [0.83–2.60]

0.18

Major bleeding

39 (3.5%)

42 (3.8%)

0.93 [0.60–1.44]

0.76

GI bleeding (≥ BARC types 3)

17 (1.6%)

12 (1.1%)

1.43 [0.68–2.99]

0.34

Cardiovascular death

17 (1.5%)

14 (1.3%)

1.23 [0.61–2.49]

0.57

Myocardial infarction

17 (1.6%)

10 (0.9%)

1.72 [0.79–3.77]

0.17

Cerebrovascular accident

5 (0.5%)10 (0.9%)

0.51 [0.17–1.48]

0.20

Stent thrombosis

8 (0.7%)6 (0.5%)1.34 [0.46–3.86]

0.59

Any revascularization

45 (4.2%)

64 (6.1%)

0.71 [0.49–1.04]

0.08

The primary outcome
MACCE
Key secondary outcomes

Other secondary outcomes

Values are presented as numbers (an estimate of the cumulative incidence of events over time)
BARC​Bleeding Academic Research Consortium, CI Confidence interval, HR Hazard ratio, GI Gastrointestinal, MACCE Major adverse cardiac and cerebrovascular events,
PPI Proton pump inhibitor

Kim et al. BMC Medicine(2024) 22:335

Page 7 of 13

Fig. 2 Cumulative incidence of major adverse cardiac and cerebrovascular events before and after PS matching. Before matching, the 12-month
cumulative incidence of the primary outcome was higher in PPI users than in non-users (A). After 1:1 matching, the incidence rate was similar
between PPI users and non-users (B). CI, confidence interval; HR, hazard ratio; PPI, proton pump inhibitor; PS, propensity-score

p = 0.27; Fig. 2). Other secondary ischemic outcomes,
including MI, stent thrombosis, and cerebrovascular
accidents, were similar between the two groups in the
PS-matched cohort (Fig. 3). PPI users showed a numerically higher incidence of spontaneous MI than non-users(1.6% vs. 0.9%, p = 0.17). The Fine-Gray model further
supported these findings when the discontinuation of
DAPT was modelled as a single competing event (Additional file 1: Table S4).
In total, 257 major bleeding events occurred in 13,160
patients at 12 months, of whom 208 (80.9%) required
blood transfusions. The causes of major bleeding (BARC
types 3–5) included GI (42.0%, n = 108), central nervous
system (8.6%), PCI-access site (6.6%), surgery-related(5.4%), epistaxis (3.5%), genitourinary (3.1%), pulmonary (1.9%), peripheral (1.6%), traumatic (0.8%), vascular(0.8%), pericardial (0.8%), retroperitoneal (0.4%), others(6.2%), and unknown (18.3%). Before PS matching, GI
bleeding occurred in 28 PPI users (cumulative incidence,1.3%) and 90 non-users (cumulative incidence, 0.9%; logrank p = 0.05). Of these, 10 patients with GI bleeding
did not meet the criteria for major bleeding. In contrast,
the incidence of major bleeding and GI bleeding (BARC
types 3–5) was comparable between PPI users and nonusers in the PS-matched cohort. Overall, 81 major bleeding events were documented after PS matching andcategorized by BARC types as follows: type 3a, 70.4%;
type 3b, 18.5%; type 3c, 4.9%; type 4, 3.7%; types 5a and5b, 1.2%. GI bleeding (BARC types 3–5) occurred in 17
PPI users (cumulative incidence, 1.6%) and 12 non-users(cumulative incidence, 1.1%; log-rank p = 0.34). There
was only one case of non-major GI bleeding in the PSmatched cohort.

Subgroup analysis in the PS‑matched cohort

Subgroup analysis with p for interaction in the PSmatched cohort is shown in Table 4 (MACCE) and
Additional file 1: Table S5 (major bleeding). In a subgroup analysis based on CYP2C19 genotyping, PPI use
was associated with an increased risk of MACCE (HR,3.22; p = 0.02) in poor metabolizers (n = 313). The interaction between PPI use and CYP2C19 genotyping was
not statistically significant for MACCE (p for interaction = 0.28). No significant differences in MACCE wereobserved between PPI users and non-users in the subgroups according to hypertension, diabetes mellitus,and HPR. There were also no significant differences inmajor bleeding events between PPI users and non-users
across various subgroups. Additional file 1: Table S6(PS-matched cohort) and Additional file 1: Table S7(overall cohort) describe the on-treatment PRU levels according to subgroups of genotyping and platelet
reactivity.
On‑treatment PRU and clinical outcomesin the PS‑matched cohort

In the PS-matched cohort, the PRU was strongly linked
to the unadjusted risk of MACCE (Additional file 1:
Fig. S2), and HPR was associated with a higher risk of
MACCE at 12 months (Additional file 1: Fig. S3). Ontreatment PRU ≥ 252 was also linked to a higher risk of
MACCE at 12 months (Additional file 1: Fig. S4). The
relationship between on-treatment PRU and clinical outcomes in the overall cohort has been previously reported[24].

Kim et al. BMC Medicine(2024) 22:335

Page 8 of 13

Fig. 3 Cumulative incidence of secondary outcomes in the PS-matched cohort. Key secondary outcomes included all-cause death, with PPI users
experiencing a 2.6% incidence rate versus 1.8% in non-users (A). PPI users showed a numerically higher incidence of spontaneous myocardial
infarction than non-users (B). Major bleeding events were similar between groups, 3.5% for PPI users and 3.8% for non-users (C). Major GI bleeding
also showed no significant differences between the two groups (D). BARC, Bleeding Academic Research Consortium; CI, confidence interval; HR,hazard ratio; GI, gastrointestinal; PPI, proton pump inhibitor; PS, propensity-score

Predictors of 12‑month MACCE in the PS‑matched cohort

The independent predictors of the primary outcome were
HPR and poor metabolizer status in the PS-matchedcohort (Additional file 1: Table S8). Poor metabolizer was
the strongest predictor of the primary outcome (adjusted
HR 1.80, p = 0.05). In contrast, intermediate metabolizer
did not present a significant risk. HPR was a significant
predictor in both univariable and multivariable analyses(adjusted HR 1.73, p = 0.03). PPI use was not independently associated with the primary outcome (adjusted
HR 1.24, p = 0.31).

Discussion
This study examined the relationship among PPI usage,
PRU, CYP2C19 genotyping and adverse clinical outcomes in a large, real-world population of patients who
underwent PCI with DES implantation and received
clopidogrel-based DAPT. The main findings of our study

were as follows: PPI use at the time of PCI was associatedwith higher PRU values during clopidogrel treatment,
which were consistent across the CYP2C19 genotyping
subgroups; the incidence of MACCE and major bleeding events at 12 months was comparable between PPI
users and non-users after PS matching; and the concomitant use of PPI and clopidogrel was associatedwith an increased risk of composite MACCE in poor
metabolizers.
Post-discharge bleeding events are associated withincreased subsequent all-cause mortality in patients withacute coronary syndromes [32]. GI bleeding significantly
affects clinical outcomes in patients treated with prolonged DAPT or potent P2Y12 inhibitors after PCI [33].
PPIs are frequently co-prescribed and recommended
by current guidelines to reduce the risk of GI bleeding in patients receiving DAPT or other P2Y12 inhibitors [2]. Several observational studies have suggested

Kim et al. BMC Medicine(2024) 22:335

Page 9 of 13

Table 4 Subgroup analysis for MACCE in the propensity-score matched cohort
No. ofPatients

PPI users(n = 1133)

Non-users(n = 1133)

Log-rank P

Hazard Ratio(95% CI)P valueYes1382

37 (5.6%)

28 (4.1%)

0.18

1.40 [0.85–2.28]

0.18

No

884

14 (3.3%)

13 (3.2%)

0.95

1.03 [0.48–2.18]

0.95

Yes

780

19 (5.1%)

16 (4.1%)

0.53

1.24 [0.64–2.40]

0.53

No

1486

32 (4.4%)

25 (3.5%)

0.35

1.28 [0.76–2.16]

0.35

866

16 (4.1%)

13 (3.0%)

0.35

1.41 [0.68–2.94]

0.35

P for
interaction

No. of MACCE events (%)
Hypertension

0.50

Diabetes mellitus

0.93

CYP2C19 genotyping
Extensive metabolizer

0.28

Intermediate metabolizer1087

19 (3.5%)

23 (4.5%)

0.38

0.76 [0.41–1.40]

0.38

Poor metabolizer313

16 (10.2%)

5 (3.3%)0.02

3.22 [1.18–8.78]

0.02

On-treatment PRU > 208

1393

38 (5.4%)

31 (4.8%)

0.56

1.15 [0.72–1.85]

0.56

On-treatment PRU ≤ 208

873

13 (3.3%)

10 (2.3%)

0.34

1.49 [0.65–3.39]

0.35

Platelet reactivity

0.60

Values are presented as numbers (an estimate of the cumulative incidence of events over time)
CI Confidence interval, HR Hazard ratio, MACCE Major adverse cardiac and cerebrovascular events, PPI Proton pump inhibitor, PRU P2Y12 reaction unitthat coadministration of PPI can reduce the antiplatelet effects of clopidogrel [6, 9, 15, 34, 35]. However, itremains unclear whether the suggested metabolic interaction between PPI and clopidogrel leads to an increased
risk of adverse ischemic outcomes, and whether PPI use
is likely to be a surrogate marker of poor clinical outcomes [36, 37]. Omeprazole is the only PPI studied in
RCTs, which demonstrated no apparent interaction
between clopidogrel and omeprazole for cardiovascular
hard endpoints [8, 38, 39]. Nevertheless, the largest RCT
was prematurely terminated after a median duration of
106 days and a maximum of 341 days, and the expectedprevalence of poor metabolizers within this cohort wasestimated to be less than 3% [8]. To ascertain if PPIs significantly reduce clopidogrel’s active metabolite levels
to the extent of impairing its efficacy, a comprehensive
study involving both PRU and CYP2C19 genotyping isessential.
PPI, as a drug class, was independently associatedwith a PRU > 208 in patients treated with clopidogrel
after successful DES implantation (PPI users; n = 2697)[9]. Our study uniquely describes PRU levels before andafter clopidogrel loading in patients prescribed PPIs andprovides results according to CYP2C19 genotyping subgroups. The baseline PRU values were comparable inboth the unmatched and PS-matched cohorts, and ontreatment PRU was significantly higher among PPI users
than among non-users (232.0 ± 82.9 vs. 224.4 ± 81.7 in
the PS-matched cohort). However, ischemic and bleeding events were similar between the two groups after
PS matching. It is plausible that the mean difference inon-treatment PRU was too small to be clinically significant or that the interaction between PPI and clopidogrel
was too weak to translate into clinical hard endpoints.
Another consideration is that ischemic events appear tocluster in the higher tertile or quartile of on-treatment
PRU over a certain cutoff point [40]. Although the relationship between on-treatment PRU and MACCE waslargely linear in the PS-matched cohort, there was athreshold effect such that on-treatment PRU must be
over 252 before a patient’s risk is elevated in the PTRGDES cohort [23, 24]. The proportion of patients withon-treatment PRU ≥ 252 was comparable between PPI
users and non-users (41.9% vs. 38.4%, p = 0.10) in the PSmatched cohort.
Although European guidelines recommend the routine use of PPIs in all patients receiving DAPT, either
platelet function testing or genetic testing is not recommended and may only be considered in specific situations(e.g. recurrent stent thrombosis) [2]. The PPI-clopidogrelinteraction may only be clinically significant in patientswith LoF CYP2C19 alleles. To date, studies evaluating
the additive effects of drug-drug and drug-gene interactions have used limited sample sizes to detect clinicallymeaningful differences. A recent meta-analysis foundthat in patients with any CYP2C19 LoF allele, taking
clopidogrel with PPIs was associated with an increased
risk of adverse outcomes compared to taking clopidogrel
without PPIs (p < 0.0001) [7]. Our study demonstrated
that PPI use by poor metabolizers was associated withan increased risk of MACCE at 12 months. Meanwhile,
clopidogrel-treated patients with a single LoF allele

Kim et al. BMC Medicine(2024) 22:335(intermediate metabolizers) can safely take PPI without a clear increase in cardiovascular risks, as shown in
our data. Poor metabolizers are expected to have little
or no CYP2C19 enzyme activity at baseline, and PPI use
could lead to further complete inhibition of the CYP2C19
enzyme to an extent that would be clinically meaningful.
This is particularly important if patients at high risk of
bleeding are being considered for a de-escalation strategy
with clopidogrel monotherapy, as it may inadvertently
increase the risk of thrombotic events following early
discontinuation of DAPT [41]. Therefore, caution should
be exercised when prescribing PPI and clopidogrel without CYP2C19 genotyping, particularly when clopidogrelmonotherapy is planned.
Limitations

Our study had few limitations. First, selection bias andunmeasured confounding factors could not be excluded
despite the well-balanced PS matching results. Before PS
matching, only 17% of the patients received PPI treatment, and high-risk baseline features of bleeding, such as
CKD, were more frequent in PPI users. Despite performing a thorough PS matching analysis including 26 variables to adequately address any potential bias, there was a
numerically higher incidence of major GI bleeding in PPI
users (1.6%) than in non-users (1.1%) in the PS-matchedcohort. Given the well-established benefits of PPIs for GI
protection [42], those who were already susceptible tobleeding complications were more likely to receive PPIs
in our cohort. This is because within the Korean reimbursement system, the prescription of PPI during DAPT
is limited to patients who have other valid medical justifications, such as gastroesophageal reflux disease. The
overall findings should be interpreted with caution without randomization of PPI use. However, the decision to
use PPI in previous studies was also based on the clinical judgment of the physician rather than on random
assignment [9, 36, 43, 44]. Second, individual PPIs were
not specified, and indications for PPIs—such as dosage,
duration, interruption, and termination of PPIs—were
not assessed despite long-term follow-up. Most previous studies regarding this drug-drug interaction did not
specify individual PPIs [12], and studies that did specify
PPIs included a limited number of patients to evaluate
long-term adverse outcomes [15, 45–47]. In observational analyses of prospective trials [36, 43, 44], the use of
PPIs has also been evaluated as a drug class. In addition,
drug exposure is an important time-dependent covariatethat may affect clopidogrel-mediated platelet inhibition.
In our study, the therapeutic crossover in both PPI users
and non-users could have biased the results toward thenull hypothesis. Third, the PTRG-DES registry covers a

Page 10 of 13

substantial period of inclusion from 2003 to 2018, which
encompasses significant changes in clinical practice,including the evolution of drug therapy, DES technology,
and PCI techniques. Finally, our analysis was restricted
to the Korean population, limiting its relevance to otherethnicities with varied CYP2C19 genotype prevalences.
In the PS-matched cohort, 61.8% had any CYP2C19 LoF
allele (intermediate metabolizers, 48.0%; poor metabolizers, 13.8%), consistent with the higher frequency in East
Asian patients [48, 49]. In a recent prospective trial, 23%of the patients were East Asians, and among them, 59.7%
had CYP2C19 LoF alleles [50]. East Asian patients have ahigher rate of bleeding complications than white patientswith similar platelet reactivity [19]. In our PS-matchedcohort, major bleeding occurred in 81 patients (cumulative incidence, 3.6%) at 12 months, which is comparable
to that reported in previous studies [51, 52].

Conclusions
Despite a small but significant increase in on-treatment PRU values, the concomitant use of PPIs, whenclinically indicated, in patients receiving clopidogrelbased DAPT was not associated with an increased
risk of adverse cardiac and cerebrovascular events at
12 months. However, poor metabolizers who received
PPIs had a higher risk of composite ischemic outcomesthan that of non-users. Future research using a moreindividualized approach is needed to elucidate theseinteractions and assess the impact of alternative antiplatelet strategies in high-risk patients.
Abbreviations
BARC​	Bleeding Academic Research Consortium
CKD	Chronic kidney disease
DAPT	Dual antiplatelet therapy
DES	Drug-eluting stentGI	Gastrointestinal
HPR	High platelet reactivityHR	Hazard ratio
LoF	Loss of function
MACCE	Major adverse cardiac and cerebrovascular events
MI	Myocardial infarction
PCI	Percutaneous coronary intervention
PPI	Proton pump inhibitor
PRU	P2Y12 reaction unit
PS	Propensity score
RCT​	Randomized clinical trial

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03549-y.
Additional file 1: Table S1. Baseline demographics and clinical characteris‑tics in the unmatched cohort. Table S2. Platelet reactivity and proceduralcharacteristics in the unmatched cohort. Table S3. Incidence of clinicaloutcomes at 12 months in the unmatched cohort. Table S4. Multivari‑able adjusted model in the propensity-score matched cohort. Table S5.
Subgroup analysis for major bleeding in the propensity score-matchedKim et al. BMC Medicine(2024) 22:335

cohort. Table S6. On-treatment PRU levels in the propensity scorematched cohort. Table S7. On-treatment PRU levels in the unmatchedcohort. Table S8. Risk of the primary outcome in the propensity-scorematched cohort. Fig. S1. Flow chart of the study population. Fig. S2.
Spline curve for the association of the PRU as a continuous variable withthe unadjusted risk of 1-year major adverse cardiac and cerebrovascular
events. Fig. S3. Cumulative incidence of the primary outcome accord‑
ing to PRUat 12 months. Fig. S4. Cumulative incidence of the primary
outcome according to PRUat 12 months.
Acknowledgements
The PTRG-DES consortium investigators provided the database for this study.
The authors express their gratitude to all the participating institutions fortheir diligent efforts and valuable feedback. We express our gratitude to theindividuals who participated in this study for their cooperation and willingness
to provide responses. This study was conducted under clinical trial registration
number NCT04734028.
Authors’ contributions
All authors read and approved the final manuscript. Conceptualization: JHK, SJH,
JJC, SL; data curation: HJJ, JHP, CWY, THA; formal analysis: JHK, SJH, YHJ, JWS; inves‑tigation: BKK, KC, YP, YBS, SGA, SYL, JRC, AYH, HSK, MHK, ESS, DSL; methodology:
YHJ, JWS; validation: JHK, SJH, YHJ, JWS; visualization: JHK and SJH; writing—origi‑
nal draft: JHK and SJH; and writing—review and editing: JHK, SJH, THA, and DSL.
Funding
This research was supported by a grant from Korea University, Seoul, Republicof Korea (grant number: K2210731).
Availability of data and materials
No datasets were generated or analysed during the current study.

Declarations
Ethics approval and consent to participate
The institutional review boardInstitutional Review Board of each participat‑
ing center approved the PTRG-DES registry (Korea University Anam Hospital,;
2018AN0283) and waived the requirement forof written informed consent foraccess to an institutional registry.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Author details
1
Department of Cardiology, Korea University Anam Hospital, Korea University
College of Medicine, Cardiovascular Center 73, Goryeodae‑Ro, Seongbuk‑Gu,
Seoul 02841, Republic of Korea. 2 Department of Cardiology, Heart and Brain
Institute, Chung-Ang University Gwang-Myeong Hospital, Chung-Ang Uni‑
versity College of Medicine, Gwangmyeong‑Si, Republic of Korea. 3 Severance
Cardiovascular Hospital, Seoul, Republic of Korea. 4 Division of Cardiology,
Department of Internal Medicine, College of Medicine, Catholic Universityof Korea, Seoul, Republic of Korea. 5 Department of Internal Medicine, Schoolof Medicine and Cardiovascular Center, Gyeongsang National University,
Gyeongsang National University Changwon Hospital, Changwon, Republicof Korea. 6 Division of Cardiology, Department of Medicine, Samsung Medi‑
cal Center, Sungkyunkwan University School of Medicine, Seoul, Republicof Korea. 7 Department of Cardiology, Yonsei University Wonju Severance Chris‑tian Hospital, Wonju, Republic of Korea. 8 Department of Internal Medicine,
Department of Cardiology, Seoul National University College of MedicineandSeoul National University Bundang Hospital, Seongnam, Republic of Korea.
9
Cardiology Division, Department of Internal Medicine, Kangnam Sacred Heart
Hospital, Hallym University College of Medicine, Seoul, Republic of Korea.
10
Division of Cardiology, Department of Internal Medicine, Kangwon National
University School of Medicine, Chuncheon, Republic of Korea. 11 Department
of Internal Medicine and Cardiovascular Center, Seoul National University

Page 11 of 13

Hospital, Seoul, Republic of Korea. 12 Department of Cardiology, Dong-A
University Hospital, Busan, Republic of Korea. 13 Division of Cardiology, Ulsan
University Hospital, University of Ulsan College of Medicine, Ulsan, Republicof Korea.
Received: 8 March 2024 Accepted: 29 July 2024

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