Proton Pump Inhibitor Use, CYP2C19 genotypes, and Subsequent Incidence of Chronic Kidney Disease

Proton Pump Inhibitor Use, CYP2C19 genotypes, and Subsequent Incidence of Chronic Kidney Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Proton Pump Inhibitor Use, CYP2C19 genotypes, and Subsequent Incidence of Chronic Kidney Disease Xu Wang, Ying Shan, Yanling Wei, Yujian He, Pengpeng wang, Guang Yang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5788578/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The widespread use of proton pump inhibitors (PPIs) has generated concerns about side effects Numerous studies have suggested that PPIs use may increase the risk of kidney disease. In particular, it is unclear whether genotype of the CYP2C19 enzyme system, the main metabolic pathway of PPIs, modifies the association between PPIs and the development of chronic kidney disease (CKD) or end-stage kidney disease (ESKD). Methods This was a prospective cohort study of white European participants from the UK Biobank. CKD and ESKD were determined by ICD-10 coding. Self-reported PPI use was recorded using an electronic questionnaire and confirmed by trained staff. The CYP2C19 genotype is defined by two of the most common CYP2C19 functional polymorphisms (rs4244285 and rs12248560). Multivariable Cox proportional hazards regression models were used to assess the associations of PPIs with incident CKD or ESKD. Results A total of 419670 participants were included in the study, of which 39,493 participants were PPIs users. Compared with non-user, PPI users had a significantly higher risk of developing CKD (HR 1.408, CI 1.352–1.467) and ESKD (HR 1.288, CI 1.03–1.609). This association persisted in sensitivity analyses. However, there was no significant difference in the risk of developing CKD among participants taking PPIs between the different CYP2C19 genotypes. Conclusion The use of PPIs was associated with the risks of developing CKD and ESKD. There was no evidence supporting the CYP2C19 genotype as a moderator on the relationship between PPIs and kidney diseases. proton pump inhibitors chronic kidney disease end-stage kidney disease CYP2C19 genotype Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Chronic kidney disease (CKD) is a major cause of premature morbidity and mortality worldwide. [ 1 , 2 ] Several factors, including age, gender, a history of cardiovascular disease, diabetes, obesity, metabolic disorders such as hypertension and hyperuricemia, as well as regional location and socioeconomic status, have been identified as independent risk factors for the development of CKD. [ 3 , 4 ] Proton pump inhibitors (PPIs) are commonly prescribed medications widely used in clinical practice for the treatment of acid-related gastrointestinal disorders, such as gastroesophageal reflux disease, peptic ulcers, Barrett's esophagus, and other related conditions, owing to their proven safety and efficacy. [ 5 – 8 ] However, a growing body of research suggests that long-term PPI use may have adverse effects on kidney function, potentially contributing to the development of CKD and end-stage kidney disease (ESKD). [ 9 – 13 ] For example, a study by Guedes et al. (2020) found that PPI use was associated with a 7.34-fold increased risk of CKD . [ 14 ] All PPIs are metabolized in the liver, primarily by the CYP2C19 enzyme (> 80%) and to a lesser extent by CYP3A4 enzymes. The Clinical Pharmacogenetics Implementation Consortium (CPIC) highlights that genetic polymorphisms in the CYP2C19 enzyme system can affect the metabolism of PPIs, leading to significant variability in individual pharmacokinetics, which may contribute to renal damage. [ 15 , 16 ] Additionally, the nephrotoxicity of PPIs may be influenced by co-morbid conditions and the use of concomitant medications. It remains unclear whether variations in CYP2C19 genotypes influence the development of kidney disease, and there is a lack of international studies addressing this issue. The aim of this study was to explore the potential association between PPIs and the development of CKD and ESKD. Additionally, we sought to assess whether CYP2C19 genotypes act as an effect modifier in the relationship between PPI use and incident CKD or ESKD. To achieve this, we conducted a prospective cohort study utilizing the UK Biobank dataset, which provides comprehensive data on comorbidities, concomitant medications, and genetic information for the study population. 2. Material and methods 2.1. UK Biobank The data for our study came from UK Biobank, one of the world's most accessible, largest and in-depth cohort studies, which following the lives of 500,000 people in England, Scotland and Wales, aged 40–69 years. Between 2006 and 2010, participants were asked to arrive at one of 22 assessment centers in the United Kingdom to complete questionnaires on sociodemographic and lifestyle factors, and underwent physical measurements and clinical assessments. Participants provided consent for the long-term follow-up of their health via medical records, such as illnesses and medications taken, cancer and death records. [ 17 , 18 ] The UK Biobank study has received ethical approval from the North West Multi-centre Research Ethics Committee (MREC) (REC reference 21/NW/0157). 2.2. Variables and measures Among UK Biobank 502,367 participants, we excluded 34,299 with a malignancy before baseline, 528 with a diagnosis of ESKD before baseline and 712 who were hospitalized with a diagnosis of acute renal failure (FID 41270) or unspecified renal failure before or within 90 days of baseline participants. For the analyses in which the outcome factor was the onset of CKD we excluded those who already had CKD at baseline and 90 days and less after baseline. Ultimately our study included 419,670 white participants, all of whom had complete clinical data and genetic data at baseline.(Fig. 1 ) Detailed information about the medication behavior of PPIs and H 2 receptor antagonists(H 2 RAs), including whether they were used and when they were used, was collected through a touchscreen questionnaire at the time of recruitment. Participants' medication use was collected using this question: do you regularly take any of the following medications? Or enter the name of the medication you take regularly (that is, most days of the week for the last four weeks). Those who used PPIs were defined as the exposed group; those who used H 2 RAs were defined as the control group; those who had not used either drug were defined as the blank control group; also, we excluded those who were taking both types of drugs. Our study included two study endpoints, CKD and ESKD. CKD and ESKD were determined by ICD-10 coding. The circumstances of the death were determined through a link to the death registry. Follow-up was calculated from the date of visit to the assessment center until the reported diagnosis of a CKD event, ESKD event, death, loss to follow-up, or end of follow-up (November 12, 2021), whichever occurred first. 2.3 Genotyping and definition of CYP2C19 genotypes CYP2C19 genotypes comprise poor metabolizers (PM), intermediate and intermediate + metabolizers (IM; IM+), extensive and extensive + metabolizers (EM [wild type]; EM+) and ultra-rapid metabolizers (UM) defined by the two-most common CYP2C19 functional polymorphisms (rs4244285 and rs12248560) which capture the CYP2C19 *1, *2 and *17 functional alleles (Supplementary Table 1). [ 19 , 20 ] 2.4 Covariates Covariate information included sociodemographic and lifestyle factors (age, gender, body mass index, smoking, alcohol consumption, education level, economic status, physical activity), comorbidities (acute pancreatitis, chronic lung disease, cerebrovascular disease, gastroesophageal reflux disease, upper gastrointestinal hemorrhage, peptic ulcer, Helicobacter pylori infection, Barrett's esophagus, gastritis and duodenitis, functional gastrointestinal), and concomitant medications (clopidogrel, warfarin, nonsteroidal anti-inflammatory drug, angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, labeled diuretics, citalopram, escitalopram, Valium). Specific data can be viewed in Table 1 . Table 1 Baseline characteristics of a cohort of new users of H 2 receptor antagonists, and new users of PPI Characteristic, n (%) All patients (n = 419670) PPI group (n = 39493) H 2 RAs group (n = 6003) Blank group (n = 374174) P Baseline age, years 58.0 [50.0, 63.0] 61.0 [55.0, 65.0] 59.0 [51.0, 64.0] 57.0 [50.0, 63.0] < 0.001 Man, n (%) 193443 (46.1) 17904 (45.3) 2743 (45.7) 172796 (46.2) 0.005 Education, n (%) 1 71412 (17.0) 11304 (28.6) 1399 (23.3) 58709 (15.7) < 0.001 2 114493 (27.3) 10658 (27.0) 1635 (27.2) 102200 (27.3) < 0.001 3 97229 (23.2) 9205 (23.3) 1442 (24.0) 86582 (23.1) < 0.001 4 136536 (32.5) 8326 (21.1) 1527 (25.4) 126683 (33.9) < 0.001 BMI (median [IQR]), kg/m 2 26.71[24.12,29.84] 28.56[25.80,31.93] 28.20[25.46,31.58] 26.50[23.97,29.57] < 0.001 Smoking status, n (%) never 227534 (54.2) 18123 (45.9) 2703 (45.0) 206708 (55.2) < 0.001 previous 147791 (35.2) 16950 (42.9) 2431 (40.5) 128410 (34.3) < 0.001 current 44345 (10.6) 4420 (11.2) 869 (14.5) 39056 (10.4) < 0.001 Alcohol consumption, n (%) never 13244 (3.2) 1844 (4.7) 254 (4.2) 11146 (3.0) < 0.001 previous 14262 (3.4) 2430 (6.2) 297 (4.9) 11535 (3.1) < 0.001 current 392164 (93.4) 35219 (89.2) 5452 (90.8) 351493 (93.9) < 0.001 Summed MET (median [IQR]) 1,790[815,3,592.88] 1,611 [678,3,444] 1,666 [693,3,448] 1,815 [834,3,612] < 0.001 TDI (median [IQR]) -2.26 [-3.70, 0.26] -1.90 [-3.52, 1.08] -1.74[-3.40,1.26] -2.30[-3.71,0.16] < 0.001 Acute pancreatitis, n (%) 1581 (0.4) 436 (1.1) 45 (0.7) 1100 (0.3) < 0.001 Chronic lung disease, n (%) 78249 (18.6) 10811 (27.4) 1406 (23.4) 66032 (17.6) < 0.001 Cerebrovascular disease, n (%) 5506 (1.3) 1236 (3.1) 132 (2.2) 4138 (1.1) < 0.001 Gastroesophageal reflux disease, n (%) 29708 (7.1) 17891 (45.3) 1911 (31.8) 9906 (2.6) < 0.001 Upper gastrointestinal tract bleeding, n (%) 4712 (1.1) 1426 (3.6) 126 (2.1) 3160 (0.8) < 0.001 Peptic ulcer, n (%) 10229 (2.4) 4227 (10.7) 564(9.4) 5438(1.5) < 0.001 H pylori infection, n (%) 535 (0.1) 113 (0.3) 10 (0.2) 412 (0.1) < 0.001 Barrett esophagus, n (%) 5273 (1.3) 3361 (8.5) 128 (2.1) 1784 (0.5) < 0.001 Gastritis and duodenitis, n (%) 18405 (4.4) 7840 (19.9) 599 (10.0) 9966 (2.7) < 0.001 Functional gastrointestinal, n (%) 6291 (1.5) 1390 (3.5) 135 (2.2) 4766 (1.3) < 0.001 Clopidogrel, n (%) 2678 (0.6) 1113 (2.8) 129 (2.1) 1436 (0.4) < 0.001 Warfarin, n (%) 4203 (1.0) 778 (2.0) 78 (1.3) 3347 (0.9) < 0.001 NSAIDs, n (%) 91862 (21.9) 11165 (28.3) 1908 (31.8) 78789 (21.1) < 0.001 ACEI/ARB, n (%) 64314 (15.3) 11510 (29.1) 1324 (22.1) 51480 (13.8) < 0.001 Loop diuretic, n (%) 3732 (0.9) 1198 (3.0) 110 (1.8) 2424 (0.6) < 0.001 Antidepressant, n (%) 7170 (1.7) 1389 (3.5) 163 (2.7) 5618 (1.5) < 0.001 Escitalopram, n (%) 1110 (0.3) 208 (0.5) 23 (0.4) 879 (0.2) < 0.001 Benzodiazepines, n (%) 1136 (0.3) 315 (0.8) 34 (0.6) 787 (0.2) < 0.001 Values are numbers (percentages) unless otherwise stated; education: level 1, “uneducated”; level 2, “O levels/GCSEs or equivalent” or “CSEs or equivalent”; level 3, “A levels/AS levels or equivalent” or “NVQ or HND or HNC or equivalent” or “Other professional qualifications”; level 4, “College or University degree” Abbreviations: H 2 RAs, histamine-2 receptor antagonists; NSAIDs, non-steroidal anti-inflammatory drugs; PPI, proton pump inhibitor; TDI, Townsend deprivation index; ACEI, angiotensin converting enzyme inhibitor; ARB, angiotensin receptor blocker; IQR, inter quartile range. MET, metabolic equivalent. 2.5 Statistical analysis The baseline characteristics of patients in the cohort for PPI and H 2 RAs users were reported utilizing frequency, percentage, mean along with standard deviation, or median accompanied by interquartile range, depending on the appropriateness of each measure. Furthermore, a comparison of baseline characteristics between the exposure group and the active comparator was conducted using the absolute standardized difference between the two groups for both continuous and categorical data. Incidence rates per 100,000 person-years were computed, and confidence intervals were estimated based on the normal distribution. Cox proportional hazard regression models were employed to evaluate the association between PPI exposure and renal risk. Various models were developed to assess this relationship using both univariate and multivariable analyses, while controlled for sociodemographic and lifestyle factors, comorbidities, and concomitant medications. Additionally, we performed additional stratified analyses to assess the association between differences in CYP2C19 genotypes and renal risk in PPI users. To ensure the robustness of our findings, several sensitivity analyses were performed. Firstly, individuals who experienced endpoint events within 1 year after baseline were excluded to mitigate the potential for reverse causality. Secondly, we further adjusted the baseline eGFR based on the final model to minimize the impact of confounding factors. Lastly, deaths occurring prior to the endpoint event were incorporated as a competing risk in the competing risk model. 3. Results This study ultimately enrolled a total of 419,670 participants, with 39,493 individuals in the PPI group and 6003 in the H 2 RAs group. Among PPI users, a higher proportion were female (54.70%), and they were more likely to use other medications, including non-steroidal anti-inflammatory drugs (NSAIDs), diuretics, anticoagulants, and antidepressants. Additionally, PPI users were more likely to have comorbidities such as chronic lung disease, cerebrovascular disease, upper gastrointestinal bleeding, and gastroesophageal reflux disease. The genotypes in the Caucasian population were as follows: EM 169,373 (40.46%), EM + 114,137 (27.27%), IM 79,372 (18.96%), IM + 27,010 (6.45%), UM 19,321 (4.62%), and PM 9,354 (2.23%). (Table 1 , Fig. 4 ) The findings indicated that, in the baseline model, PPI users had a significantly higher risk of CKD compared to non-users, with a hazard ratio (HR) of 2.50 (95% confidence interval CI 2.42–2.59). After adjusting for sociodemographic and lifestyle factors, comorbidities, and concomitant medications in the Cox proportional hazards regression models, the association between PPI use and CKD remained statistically significant, with an HR of 1.41 (95% CI 1.35–1.47). (Fig. 2 , 3 ) Given that PPI and H 2 RAs share similar indications, we also examined the relationship between H 2 RAs use and the risk of CKD. In the adjusted survival model, H 2 RAs users exhibited a 43.30% increased risk of CKD, with an HR of 1.43(95% CI: 1.31–1.57). Additionally, we observed a similar association between PPI use and the incidence of ESKD. In the baseline model, the risk of ESKD among PPI users was 2.53 times higher than that of non-users, with an HR of 2.53 (95% CI 2.11–3.03). However, in the adjusted survival model, the risk among PPI users was attenuated but still elevated, with an HR of 1.29(95% CI 1.03–1.61). Due to the lack of baseline eGFR data, it remains unclear whether PPI users had more advanced CKD at the outset. [ 21 , 22 ] To address this limitation, we conducted a sensitivity analysis, incorporating baseline eGFR as a correction factor. The results of this analysis were consistent with those from our initial analysis. (Table 2 , 3 ) Table 2 Sensitivity analyses of proton pump inhibitors and risk of chronic kidney disease HR [95%CI] Excluding the participants with acute pancreatitis or gastrointestinal bleeding within 2 months before baseline Blank group 1.00 [Reference] PPI group 1.42 [1.36, 1.48] H2RAs group 1.44 [1.32, 1.57] Calibration of eGFR at baseline Blank group 1.00 [Reference] PPI group 1.28 [1.23, 1.34] H2RAs group 1.28 [1.18, 1.40] Deaths occurring before the terminal event are analyzed as competing risks Blank group 1.00 [Reference] PPI group 1.40 [1.34, 1.45] H2RAs group 1.41 [1.29, 1.53] Multivariable-adjusted Cox model was fitted with adjustment for baseline covariates (see footnote in Table 2 ). Table 3 Sensitivity analyses of proton pump inhibitors and risk of end-stage kidney disease HR [95%CI] *† Excluding the participants with acute pancreatitis or gastrointestinal bleeding within 2 months before baseline Blank group 1.00 [Reference] PPI group 1.31 [1.05, 1.64] H2RAs group 1.19 [0.72, 1.97] Calibration of eGFR at baseline Blank group 1.00 [Reference] PPI group 0.92 [0.74, 1.15] H2RAs group 0.69 [0.41, 1.14] Deaths occurring before the terminal event are analyzed as competing risks Blank group 1.00 [Reference] PPI group 1.28 [1.02, 1.60] H2RAs group 1.15 [0.70, 1.90] Multivariable-adjusted Cox model was fitted with adjustment for baseline covariates (see footnote in Table 2 ) Furthermore, in a study examining the association between CYP2C19 genotypes and the development of CKD and ESKD in individuals using PPIs, we found that certain CYP2C19 genotypes were linked to a lower incidence of CKD and ESKD. However, in the adjusted model, this association was no longer statistically significant. (Table 4 , 5 ) Table 4 Association of the CYP2C19 genotypes with the development of CKD in a PPI-using population CYP2C19 metabolic genotype Univariate OR (95%CI) P values Multivariate OR (95%CI) P values EM 1.000 1.00 EM+ 1.005(0.973, 1.038) 0.758 1.005(0.972, 1.038) 0.777 IM 0.996(0.960, 1.034) 0.839 0.995(0.959, 1.032) 0.783 IM+ 0.969(0.916, 1.025) 0.275 0.966(0.912, 1.022) 0.277 PM 0.982(0.896, 1.076) 0.699 0.981(0.895, 1.075) 0.680 UM 1.007(0.944, 1.074) 0.837 0.998(0.935, 1.064) 0.942 PM = poor metabolizers: IM = intermediate metabolizers; IM + = intermediate metabolizers +; EM = extensive metabolizers; EM + = extensive metabolizers+; UM = ultrarapid metabolizers. EM was taken as reference group. Table 5 Association of the CYP2C19 genotypes with the development of ESKD in a PPI-using population PM = poor metabolizers: IM = intermediate metabolizers; IM + = intermediate metabolizers +; EM = extensive metabolizers; EM + = extensive metabolizers+; UM = ultrarapid metabolizers. EM was taken as reference group. CYP2C19 metabolic genotype Univariate OR (95%CI) P values Multivariate OR (95%CI) P values EM 1.000 1.000 EM+ 1.184 (0.803, 1.746) 0.394 1.190 (0.807, 1.755) 0.379 IM 0.955 (0.600, 1.518) 0.845 0.955 (0.601, 1.519) 0.846 IM+ 0.743 (0.339, 1.629) 0.459 0.748 (0.341, 1.640) 0.468 PM 0.613 (0.150, 2.509) 0.496 0.623 (0.152, 2.552) 0.511 UM 1.202 (0.573, 2.518) 0.627 1.186 (0.566, 2.487) 0.651 4. Discussion In this large observational cohort study of over 400,000 individuals, we found that long-term PPI use was associated with a higher risk of CKD and ESKD compared to non-users. This association remained significant in several sensitivity analyses. We also observed that H 2 RAs use was linked to an increased risk of developing CKD and ESKD. Epidemiological studies from the United States, Europe, and China have shown that PPI use is associated with an increased risk of CKD and progression to ESKD, and our results align with these findings. For example, Xie et al. used data from 173,321 participants in the U.S. Department of Veterans Affairs and found that PPI users had a 28% and 96% higher risk of CKD and ESKD, respectively. [ 12 ] Arora et al. using data from 99,269 participants in primary care VISN2 clinics, reported that PPI users had a higher likelihood of developing CKD (OR 1.10, 95% CI 1.05–1.16). [ 23 ] Several meta-analyses have also demonstrated a strong association between PPI use and the risk of CKD and ESRD. [ 24 – 27 ] However, these studies have some limitations, such as the lack of data on concomitant drug use and the inclusion of selective populations, which may introduce bias into the findings. The underlying mechanisms linking PPI use to CKD remain unclear. One possible explanation is that PPIs may increase the risk of acute interstitial nephritis (AIN), which could progress to chronic interstitial nephritis and eventually contribute to CKD development. [ 28 ] Additionally, PPI use has been associated with hypomagnesemia, which in turn can trigger oxidative stress and inflammatory responses, potentially acting as another contributing factor to CKD. [ 29 ] The 2021 Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines recommend that clinicians adjust PPI dosages based on patients' genotypes to improve therapeutic outcomes and minimize the risk of adverse effects. However, the impact of CYP2C19 genotypes on the incidence of renal diseases remains unclear. This study aimed to investigate the relationship between different CYP2C19 genotypes and the incidence of CKD and ESKD in a cohort of long-term PPI users. The results revealed no significant correlation between CYP2C19 genotypes and the incidence of CKD. A potential explanation for this finding is that during clinical practice, healthcare providers often adjust medication dosages based on the individual response of each patient. This personalized approach may reduce the influence of the CYP2C19 genotype on CKD progression and could introduce some bias into the study results. Our study has several strengths, including its prospective cohort design, large sample size, and detailed clinical data, which enabled us to adjust for potential confounders. However, there are also some limitations. First, as an observational study, we could not fully eliminate residual confounding. To address this, we made extensive adjustments for potential confounders and conducted sensitivity analyses. Additionally, the UK Biobank lacks information on the duration and dosage of PPI use, and we were unable to determine the exact date when the participants started to used PPIs. This limitation raises the possibility that some participants may have been misclassified as non-users, leading to potential misinterpretation and prevalent-user bias. We could not verify whether participants actually adhered to the medications they reported using. Finally, there may be monitoring bias. Patients using PPIs may be more health-conscious and, as a result, more likely to undergo tests that could indicate CKD. This could lead to an overestimation of renal risks in PPI users compared to non-users. 5. Conclusions In summary, this study found that PPI use was associated with an increased risk of CKD and ESKD. However, our results do not support a moderating role of the CYP2C19 gene phenotype in the development of kidney disease. Given the widespread use of PPIs and the public health implications of CKD and ESKD, healthcare providers should exercise caution when prescribing PPIs, particularly for patients requiring long-term use, to minimize the risk of adverse outcomes. Declarations 6.Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Ethics approval and consent to participate The studies involving human participants were reviewed and approved by the North West Multi-Center Research Ethics Committee, the England and Wales Patient Information Advisory Group. The patients/participants provided their written informed consent to participate in this study. Consent for publication All authors have read and approved the final version of the manuscript and have obtained written informed consent for publication. Funding This work was supported by Shenzhen Science and Technology Innovation Commission (JCYJ20220530150412026). Author Contribution WX wrote the main manuscript text, SY and W-YL were responsible for data control and analysis, Concept and design: H-YJ , W-PP and YG, H-XY and X-ZB were responsible for manuscript review and revision. Acquisition, analysis, or interpretation of the data: all authors. Data Availability The data analyzed in this study is subject to the following licenses/restrictions: The study was conducted using the UKB resource .The dataset can be obtained from the United Kingdom Biobank. Requests to access the dataset should be directed to https://www.ukbiobank.ac.uk. References Global regional, national burden of chronic kidney disease. 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017 [J]. Lancet. 2020;395(10225):709–33. HILL N R, FATOBA S T OKEJL, et al. 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Effect of CYP2C19 polymorphisms on antidepressant prescription patterns and treatment emergent mania in bipolar disorder [J]. Pharmacogenomics J. 2023;23(1):28–35. FABBRI C, TANSEY K E, PERLIS R H, et al. Effect of cytochrome CYP2C19 metabolizing activity on antidepressant response and side effects: Meta-analysis of data from genome-wide association studies [J]. Eur Neuropsychopharmacol. 2018;28(8):945–54. BRAGG-GRESHAM J PANIA, MASALA M, et al. Prevalence of CKD and its relationship to eGFR-related genetic loci and clinical risk factors in the SardiNIA study cohort [J]. J Am Soc Nephrol. 2014;25(7):1533–44. SHOU H, HSU J Y, XIE D, et al. Analytic Considerations for Repeated Measures of eGFR in Cohort Studies of CKD [J]. Clin J Am Soc Nephrol. 2017;12(8):1357–65. ARORA P, GUPTA A, GOLZY M, et al. Proton pump inhibitors are associated with increased risk of development of chronic kidney disease [J]. BMC Nephrol. 2016;17(1):112. WU C C, LIAO M H, KUNG W M et al. Proton Pump Inhibitors and Risk of Chronic Kidney Disease: Evidence from Observational Studies [J]. J Clin Med, 2023, 12(6). VENGRUS CS, DELFINO V D, BIGNARDI PR. Proton pump inhibitors use and risk of chronic kidney disease and end-stage renal disease [J]. Minerva Urol Nephrol. 2021;73(4):462–70. RAJAN P, IGLAY K, RHODES T, et al. Risk of bias in non-randomized observational studies assessing the relationship between proton-pump inhibitors and adverse kidney outcomes: a systematic review [J]. Th Adv Gastroenterol. 2022;15:17562848221074183. MORSCHEL C F, EDUARDO J C MAFRAD. The relationship between proton pump inhibitors and renal disease [J]. J Bras Nefrol. 2018;40(3):301–6. MOLEDINA D G, PERAZELLA MA. PPIs and kidney disease: from AIN to CKD [J]. J Nephrol. 2016;29(5):611–6. GOMMERS L M M, HOENDEROP J G J, DE BAAIJ J H. F. Mechanisms of proton pump inhibitor-induced hypomagnesemia [J]. Acta Physiol (Oxf). 2022;235(4):e13846. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5788578","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":401952577,"identity":"40e91b69-f4fb-47df-99a9-c94524b8fb8e","order_by":0,"name":"Xu Wang","email":"","orcid":"","institution":"Peking University Shenzhen Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xu","middleName":"","lastName":"Wang","suffix":""},{"id":401952578,"identity":"01af86a1-8f98-4bb5-ba1e-3ac0d0d913a6","order_by":1,"name":"Ying Shan","email":"","orcid":"","institution":"Peking University Shenzhen Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Shan","suffix":""},{"id":401952579,"identity":"940bd9ee-a02f-4769-804d-18c3a3f4129b","order_by":2,"name":"Yanling Wei","email":"","orcid":"","institution":"Peking University Shenzhen Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yanling","middleName":"","lastName":"Wei","suffix":""},{"id":401952580,"identity":"d8465ba7-4e9e-4365-acbd-cf68fe957353","order_by":3,"name":"Yujian He","email":"","orcid":"","institution":"Peking University Shenzhen Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yujian","middleName":"","lastName":"He","suffix":""},{"id":401952581,"identity":"3b84d09e-48eb-4615-a556-bdd68ed9c00b","order_by":4,"name":"Pengpeng wang","email":"","orcid":"","institution":"Peking University Shenzhen Hospital","correspondingAuthor":false,"prefix":"","firstName":"Pengpeng","middleName":"","lastName":"wang","suffix":""},{"id":401952582,"identity":"597e4806-8126-4867-b2ed-bbf57cf1dd17","order_by":5,"name":"Guang Yang","email":"","orcid":"","institution":"Peking University Shenzhen Hospital","correspondingAuthor":false,"prefix":"","firstName":"Guang","middleName":"","lastName":"Yang","suffix":""},{"id":401952583,"identity":"b56d2a7e-a5ae-4b8f-8480-5307f5365d8a","order_by":6,"name":"Xiaoyan Huang","email":"","orcid":"","institution":"Peking University Shenzhen Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyan","middleName":"","lastName":"Huang","suffix":""},{"id":401952584,"identity":"a3610a31-2e6c-4829-ab18-566e67f5d30c","order_by":7,"name":"Zibo Xiong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYDACZh4QKQFiHWBsAFIGJGhhSyBSCwMPnGFAnBb5dt6DDxjbLOTM+dd8ezizrc7enIH54aMbeLQwNvMlGzCckTC2nPF2u+HGtsPMlg1sxsY5+LzCzGMm/adCInHDjbPbJB+2HWAzOMDDJo1PCxtQiwSDAUjLmWdALXU8BLXwgLWAbDnfwya5sY1ZgqAWCWaoXwxusJkbzjh32MDgMAG/yPefBYVYnZzB+cPPHvaU1dkbHG9++BifFiT7EtggDGailIMA/wE2otWOglEwCkbByAIABT5ERJOhC+cAAAAASUVORK5CYII=","orcid":"","institution":"Peking University Shenzhen Hospital","correspondingAuthor":true,"prefix":"","firstName":"Zibo","middleName":"","lastName":"Xiong","suffix":""}],"badges":[],"createdAt":"2025-01-08 11:23:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5788578/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5788578/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73869679,"identity":"67ae04eb-8a20-448a-ae2e-8c093d2ccd83","added_by":"auto","created_at":"2025-01-15 12:09:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":120135,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of cohort assembly within the end stage kidney disease and chronic kidney disease cohort（abbreviated: ESRD, end stage renal disease; CKD, chronic kidney disease）\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5788578/v1/bee3c6e0815463c1c2774b4f.png"},{"id":73869672,"identity":"d5ff6c70-517a-427d-ad22-1e543bc41eb0","added_by":"auto","created_at":"2025-01-15 12:09:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":98342,"visible":true,"origin":"","legend":"\u003cp\u003eThe Full Model was derived using COX survival model conditional on sociodemographic and lifestyle factors (age, gender, BMI, smoking, alcohol consumption, education level, economic status, physical activity), Comorbid conditions (acute pancreatitis, chronic lung disease, cerebrovascular disease, gastroesophageal reflux disease, upper gastrointestinal hemorrhage, peptic ulcer, H pylori infection，Barrett esophagus，Gastritis and duodenitis，Functional gastrointestinal) and drug use status (clopidogrel, warfarin, NSAIDs, ACEI/ARB , labeled diuretics, citalopram, escitalopram, Valium).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5788578/v1/68484302f389544b671c4b70.png"},{"id":73869676,"identity":"c5fa5daf-9bb5-4cd2-998e-23b26947f5b6","added_by":"auto","created_at":"2025-01-15 12:09:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":153565,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier Survival\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5788578/v1/ed15d5a6e0c969aa17073136.png"},{"id":73869660,"identity":"52dd0e34-f7b3-4293-a743-9c2352e27f45","added_by":"auto","created_at":"2025-01-15 12:09:31","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":32679,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency of CYP2C19 genotypes\u003c/p\u003e\n\u003cp\u003ePM = poor metabolizers: IM = intermediate metabolizers; IM+=intermediate metabolizers +; EM = extensive metabolizers; EM+=extensive metabolizers+; UM=ultrarapid metabolizers. EM was taken as reference group.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5788578/v1/e77a2536f359e420740749e1.jpeg"},{"id":75495701,"identity":"6cf69089-0b28-418b-9bfa-462906c71393","added_by":"auto","created_at":"2025-02-05 08:09:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1435875,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5788578/v1/f3fd09be-cce6-4c63-abf9-30a8fbfe1e6d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Proton Pump Inhibitor Use, CYP2C19 genotypes, and Subsequent Incidence of Chronic Kidney Disease","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eChronic kidney disease (CKD) is a major cause of premature morbidity and mortality worldwide.\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e Several factors, including age, gender, a history of cardiovascular disease, diabetes, obesity, metabolic disorders such as hypertension and hyperuricemia, as well as regional location and socioeconomic status, have been identified as independent risk factors for the development of CKD. \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eProton pump inhibitors (PPIs) are commonly prescribed medications widely used in clinical practice for the treatment of acid-related gastrointestinal disorders, such as gastroesophageal reflux disease, peptic ulcers, Barrett's esophagus, and other related conditions, owing to their proven safety and efficacy.\u003csup\u003e[\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e However, a growing body of research suggests that long-term PPI use may have adverse effects on kidney function, potentially contributing to the development of CKD and end-stage kidney disease (ESKD). \u003csup\u003e[\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e For example, a study by Guedes et al. (2020) found that PPI use was associated with a 7.34-fold increased risk of CKD .\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAll PPIs are metabolized in the liver, primarily by the CYP2C19 enzyme (\u0026gt;\u0026thinsp;80%) and to a lesser extent by CYP3A4 enzymes. The Clinical Pharmacogenetics Implementation Consortium (CPIC) highlights that genetic polymorphisms in the CYP2C19 enzyme system can affect the metabolism of PPIs, leading to significant variability in individual pharmacokinetics, which may contribute to renal damage.\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e Additionally, the nephrotoxicity of PPIs may be influenced by co-morbid conditions and the use of concomitant medications.\u003c/p\u003e \u003cp\u003eIt remains unclear whether variations in CYP2C19 genotypes influence the development of kidney disease, and there is a lack of international studies addressing this issue. The aim of this study was to explore the potential association between PPIs and the development of CKD and ESKD. Additionally, we sought to assess whether CYP2C19 genotypes act as an effect modifier in the relationship between PPI use and incident CKD or ESKD. To achieve this, we conducted a prospective cohort study utilizing the UK Biobank dataset, which provides comprehensive data on comorbidities, concomitant medications, and genetic information for the study population.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. UK Biobank\u003c/h2\u003e \u003cp\u003eThe data for our study came from UK Biobank, one of the world's most accessible, largest and in-depth cohort studies, which following the lives of 500,000 people in England, Scotland and Wales, aged 40\u0026ndash;69 years. Between 2006 and 2010, participants were asked to arrive at one of 22 assessment centers in the United Kingdom to complete questionnaires on sociodemographic and lifestyle factors, and underwent physical measurements and clinical assessments. Participants provided consent for the long-term follow-up of their health via medical records, such as illnesses and medications taken, cancer and death records.\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e The UK Biobank study has received ethical approval from the North West Multi-centre Research Ethics Committee (MREC) (REC reference 21/NW/0157).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Variables and measures\u003c/h2\u003e \u003cp\u003eAmong UK Biobank 502,367 participants, we excluded 34,299 with a malignancy before baseline, 528 with a diagnosis of ESKD before baseline and 712 who were hospitalized with a diagnosis of acute renal failure (FID 41270) or unspecified renal failure before or within 90 days of baseline participants. For the analyses in which the outcome factor was the onset of CKD we excluded those who already had CKD at baseline and 90 days and less after baseline. Ultimately our study included 419,670 white participants, all of whom had complete clinical data and genetic data at baseline.(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDetailed information about the medication behavior of PPIs and H\u003csub\u003e2\u003c/sub\u003e receptor antagonists(H\u003csub\u003e2\u003c/sub\u003eRAs), including whether they were used and when they were used, was collected through a touchscreen questionnaire at the time of recruitment. Participants' medication use was collected using this question: do you regularly take any of the following medications? Or enter the name of the medication you take regularly (that is, most days of the week for the last four weeks).\u003c/p\u003e \u003cp\u003eThose who used PPIs were defined as the exposed group; those who used H\u003csub\u003e2\u003c/sub\u003eRAs were defined as the control group; those who had not used either drug were defined as the blank control group; also, we excluded those who were taking both types of drugs.\u003c/p\u003e \u003cp\u003eOur study included two study endpoints, CKD and ESKD. CKD and ESKD were determined by ICD-10 coding. The circumstances of the death were determined through a link to the death registry. Follow-up was calculated from the date of visit to the assessment center until the reported diagnosis of a CKD event, ESKD event, death, loss to follow-up, or end of follow-up (November 12, 2021), whichever occurred first.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Genotyping and definition of CYP2C19 genotypes\u003c/h2\u003e \u003cp\u003eCYP2C19 genotypes comprise poor metabolizers (PM), intermediate and intermediate\u0026thinsp;+\u0026thinsp;metabolizers (IM; IM+), extensive and extensive\u0026thinsp;+\u0026thinsp;metabolizers (EM [wild type]; EM+) and ultra-rapid metabolizers (UM) defined by the two-most common CYP2C19 functional polymorphisms (rs4244285 and rs12248560) which capture the CYP2C19 *1, *2 and *17 functional alleles (Supplementary Table\u0026nbsp;1).\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Covariates\u003c/h2\u003e \u003cp\u003eCovariate information included sociodemographic and lifestyle factors (age, gender, body mass index, smoking, alcohol consumption, education level, economic status, physical activity), comorbidities (acute pancreatitis, chronic lung disease, cerebrovascular disease, gastroesophageal reflux disease, upper gastrointestinal hemorrhage, peptic ulcer, Helicobacter pylori infection, Barrett's esophagus, gastritis and duodenitis, functional gastrointestinal), and concomitant medications (clopidogrel, warfarin, nonsteroidal anti-inflammatory drug, angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, labeled diuretics, citalopram, escitalopram, Valium). Specific data can be viewed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristics of a cohort of new users of H\u003csub\u003e2\u003c/sub\u003e receptor antagonists, and new users of PPI\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003eCharacteristic,\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003eAll patients\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;419670)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003ePPI group\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;39493)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 5.6795%;\"\u003e\n \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eRAs group\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;6003)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 5.6795%;\"\u003e\n \u003cp\u003eBlank group\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;374174)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 10.6683%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003eBaseline age, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e58.0 [50.0, 63.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e61.0 [55.0, 65.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e59.0 [51.0, 64.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e57.0 [50.0, 63.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003eMan, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e193443 (46.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e17904 (45.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e2743 (45.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e172796 (46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003eEducation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e71412 (17.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e11304 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e1399 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e58709 (15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e114493 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e10658 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e1635 (27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e102200 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e97229 (23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e9205 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e1442 (24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e86582 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e136536 (32.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e8326 (21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e1527 (25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e126683 (33.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003eBMI (median [IQR]), kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e26.71[24.12,29.84]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e28.56[25.80,31.93]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e28.20[25.46,31.58]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e26.50[23.97,29.57]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003eSmoking status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003enever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e227534 (54.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e18123 (45.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e2703 (45.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e206708 (55.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003eprevious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e147791 (35.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e16950 (42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e2431 (40.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e128410 (34.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003ecurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e44345 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e4420 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e869 (14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e39056 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003eAlcohol consumption, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003enever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e13244 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e1844 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e254 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e11146 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003eprevious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e14262 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e2430 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e297 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e11535 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003ecurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e392164 (93.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e35219 (89.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e5452 (90.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e351493 (93.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003eSummed MET (median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e1,790[815,3,592.88]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e1,611 [678,3,444]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e1,666 [693,3,448]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e1,815 [834,3,612]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003eTDI (median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e-2.26 [-3.70, 0.26]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e-1.90 [-3.52, 1.08]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e-1.74[-3.40,1.26]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e-2.30[-3.71,0.16]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003eAcute pancreatitis, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e1581 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e436 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e45 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e1100 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003eChronic lung disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e78249 (18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e10811 (27.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e1406 (23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e66032 (17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003eCerebrovascular disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e5506 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e1236 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e132 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e4138 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003eGastroesophageal reflux disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e29708 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e17891 (45.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e1911 (31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e9906 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003eUpper gastrointestinal tract bleeding, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e4712 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e1426 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e126 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e3160 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003ePeptic ulcer, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e10229 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e4227 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e564(9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e5438(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003eH pylori infection, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e535 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e113 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e10 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e412 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003eBarrett esophagus, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e5273 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e3361 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e128 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e1784 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003eGastritis and duodenitis, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e18405 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e7840 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e599 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e9966 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003eFunctional gastrointestinal, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e6291 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e1390 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e135 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e4766 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003eClopidogrel, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e2678 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e1113 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e129 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e1436 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003eWarfarin, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e4203 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e778 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e78 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e3347 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003eNSAIDs, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e91862 (21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e11165 (28.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e1908 (31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e78789 (21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003eACEI/ARB, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e64314 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e11510 (29.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e1324 (22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e51480 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003eLoop diuretic, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e3732 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e1198 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e110 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e2424 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003eAntidepressant, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e7170 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e1389 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e163 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e5618 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003eEscitalopram, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e1110 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e208 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e23 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e879 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.9798%;\"\u003e\n \u003cp\u003eBenzodiazepines, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.2846%;\"\u003e\n \u003cp\u003e1136 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e315 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 11.3591%;\"\u003e\n \u003cp\u003e34 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.6705%;\"\u003e\n \u003cp\u003e787 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.9933%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 52.881%;\"\u003eValues are numbers (percentages) unless otherwise stated;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 52.881%;\"\u003eeducation: level 1, \u0026ldquo;uneducated\u0026rdquo;; level 2, \u0026ldquo;O levels/GCSEs or equivalent\u0026rdquo; or \u0026ldquo;CSEs or equivalent\u0026rdquo;; level 3, \u0026ldquo;A levels/AS levels or equivalent\u0026rdquo; or \u0026ldquo;NVQ or HND or HNC or equivalent\u0026rdquo; or \u0026ldquo;Other professional qualifications\u0026rdquo;; level 4, \u0026ldquo;College or University degree\u0026rdquo;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 52.881%;\"\u003eAbbreviations: H\u003csub\u003e2\u003c/sub\u003eRAs, histamine-2 receptor antagonists; NSAIDs, non-steroidal anti-inflammatory drugs; PPI, proton pump inhibitor; TDI, Townsend deprivation index; ACEI, angiotensin converting enzyme inhibitor; ARB, angiotensin receptor blocker; IQR, inter quartile range. MET, metabolic equivalent.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe baseline characteristics of patients in the cohort for PPI and H\u003csub\u003e2\u003c/sub\u003eRAs users were reported utilizing frequency, percentage, mean along with standard deviation, or median accompanied by interquartile range, depending on the appropriateness of each measure. Furthermore, a comparison of baseline characteristics between the exposure group and the active comparator was conducted using the absolute standardized difference between the two groups for both continuous and categorical data. Incidence rates per 100,000 person-years were computed, and confidence intervals were estimated based on the normal distribution. Cox proportional hazard regression models were employed to evaluate the association between PPI exposure and renal risk. Various models were developed to assess this relationship using both univariate and multivariable analyses, while controlled for sociodemographic and lifestyle factors, comorbidities, and concomitant medications.\u003c/p\u003e \u003cp\u003eAdditionally, we performed additional stratified analyses to assess the association between differences in CYP2C19 genotypes and renal risk in PPI users. To ensure the robustness of our findings, several sensitivity analyses were performed. Firstly, individuals who experienced endpoint events within 1 year after baseline were excluded to mitigate the potential for reverse causality. Secondly, we further adjusted the baseline eGFR based on the final model to minimize the impact of confounding factors. Lastly, deaths occurring prior to the endpoint event were incorporated as a competing risk in the competing risk model.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThis study ultimately enrolled a total of 419,670 participants, with 39,493 individuals in the PPI group and 6003 in the H\u003csub\u003e2\u003c/sub\u003eRAs group. Among PPI users, a higher proportion were female (54.70%), and they were more likely to use other medications, including non-steroidal anti-inflammatory drugs (NSAIDs), diuretics, anticoagulants, and antidepressants. Additionally, PPI users were more likely to have comorbidities such as chronic lung disease, cerebrovascular disease, upper gastrointestinal bleeding, and gastroesophageal reflux disease. The genotypes in the Caucasian population were as follows: EM 169,373 (40.46%), EM\u0026thinsp;+\u0026thinsp;114,137 (27.27%), IM 79,372 (18.96%), IM\u0026thinsp;+\u0026thinsp;27,010 (6.45%), UM 19,321 (4.62%), and PM 9,354 (2.23%). (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe findings indicated that, in the baseline model, PPI users had a significantly higher risk of CKD compared to non-users, with a hazard ratio (HR) of 2.50 (95% confidence interval CI 2.42\u0026ndash;2.59). After adjusting for sociodemographic and lifestyle factors, comorbidities, and concomitant medications in the Cox proportional hazards regression models, the association between PPI use and CKD remained statistically significant, with an HR of 1.41 (95% CI 1.35\u0026ndash;1.47). (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGiven that PPI and H\u003csub\u003e2\u003c/sub\u003eRAs share similar indications, we also examined the relationship between H\u003csub\u003e2\u003c/sub\u003eRAs use and the risk of CKD. In the adjusted survival model, H\u003csub\u003e2\u003c/sub\u003eRAs users exhibited a 43.30% increased risk of CKD, with an HR of 1.43(95% CI: 1.31\u0026ndash;1.57).\u003c/p\u003e \u003cp\u003eAdditionally, we observed a similar association between PPI use and the incidence of ESKD. In the baseline model, the risk of ESKD among PPI users was 2.53 times higher than that of non-users, with an HR of 2.53 (95% CI 2.11\u0026ndash;3.03). However, in the adjusted survival model, the risk among PPI users was attenuated but still elevated, with an HR of 1.29(95% CI 1.03\u0026ndash;1.61).\u003c/p\u003e \u003cp\u003eDue to the lack of baseline eGFR data, it remains unclear whether PPI users had more advanced CKD at the outset. \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e To address this limitation, we conducted a sensitivity analysis, incorporating baseline eGFR as a correction factor. The results of this analysis were consistent with those from our initial analysis. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSensitivity analyses of proton pump inhibitors and risk of chronic kidney disease\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR [95%CI]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExcluding the participants with acute pancreatitis or gastrointestinal bleeding within 2 months before baseline\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlank group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPI group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42 [1.36, 1.48]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2RAs group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.44 [1.32, 1.57]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCalibration of eGFR at baseline\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlank group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPI group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28 [1.23, 1.34]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2RAs group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28 [1.18, 1.40]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeaths occurring before the terminal event are analyzed as competing risks\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlank group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPI group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40 [1.34, 1.45]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2RAs group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.41 [1.29, 1.53]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eMultivariable-adjusted Cox model was fitted with adjustment for baseline covariates (see footnote in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSensitivity analyses of proton pump inhibitors and risk of end-stage kidney disease\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR [95%CI]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cb\u003e*\u0026dagger;\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eExcluding the participants with acute pancreatitis or gastrointestinal bleeding within 2 months before baseline\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlank group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPI group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.31 [1.05, 1.64]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2RAs group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19 [0.72, 1.97]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCalibration of eGFR at baseline\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlank group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPI group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92 [0.74, 1.15]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2RAs group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69 [0.41, 1.14]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeaths occurring before the terminal event are analyzed as competing risks\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlank group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPI group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28 [1.02, 1.60]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2RAs group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15 [0.70, 1.90]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eMultivariable-adjusted Cox model was fitted with adjustment for baseline covariates (see footnote in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFurthermore, in a study examining the association between CYP2C19 genotypes and the development of CKD and ESKD in individuals using PPIs, we found that certain CYP2C19 genotypes were linked to a lower incidence of CKD and ESKD. However, in the adjusted model, this association was no longer statistically significant. (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of the CYP2C19 genotypes with the development of CKD in a PPI-using population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYP2C19 metabolic genotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnivariate OR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026nbsp;values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariate OR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026nbsp;values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEM+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.005(0.973, 1.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.005(0.972, 1.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.996(0.960, 1.034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.995(0.959, 1.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIM+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.969(0.916, 1.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.966(0.912, 1.022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.982(0.896, 1.076)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.981(0.895, 1.075)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.007(0.944, 1.074)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.998(0.935, 1.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003ePM\u0026thinsp;=\u0026thinsp;poor metabolizers: IM\u0026thinsp;=\u0026thinsp;intermediate metabolizers; IM\u0026thinsp;+\u0026thinsp;=\u0026thinsp;intermediate metabolizers +; EM\u0026thinsp;=\u0026thinsp;extensive metabolizers; EM\u0026thinsp;+\u0026thinsp;=\u0026thinsp;extensive metabolizers+; UM\u0026thinsp;=\u0026thinsp;ultrarapid metabolizers. EM was taken as reference group.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of the CYP2C19 genotypes with the development of ESKD in a PPI-using population PM\u0026thinsp;=\u0026thinsp;poor metabolizers: IM\u0026thinsp;=\u0026thinsp;intermediate metabolizers; IM\u0026thinsp;+\u0026thinsp;=\u0026thinsp;intermediate metabolizers +; EM\u0026thinsp;=\u0026thinsp;extensive metabolizers; EM\u0026thinsp;+\u0026thinsp;=\u0026thinsp;extensive metabolizers+; UM\u0026thinsp;=\u0026thinsp;ultrarapid metabolizers. EM was taken as reference group.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYP2C19 metabolic genotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnivariate OR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026nbsp;values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariate OR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026nbsp;values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEM+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.184 (0.803, 1.746)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.190 (0.807, 1.755)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.955 (0.600, 1.518)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.955 (0.601, 1.519)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIM+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.743 (0.339, 1.629)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.748 (0.341, 1.640)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.613 (0.150, 2.509)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.623 (0.152, 2.552)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.202 (0.573, 2.518)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.186 (0.566, 2.487)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this large observational cohort study of over 400,000 individuals, we found that long-term PPI use was associated with a higher risk of CKD and ESKD compared to non-users. This association remained significant in several sensitivity analyses. We also observed that H\u003csub\u003e2\u003c/sub\u003eRAs use was linked to an increased risk of developing CKD and ESKD.\u003c/p\u003e \u003cp\u003eEpidemiological studies from the United States, Europe, and China have shown that PPI use is associated with an increased risk of CKD and progression to ESKD, and our results align with these findings. For example, Xie et al. used data from 173,321 participants in the U.S. Department of Veterans Affairs and found that PPI users had a 28% and 96% higher risk of CKD and ESKD, respectively.\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e Arora et al. using data from 99,269 participants in primary care VISN2 clinics, reported that PPI users had a higher likelihood of developing CKD (OR 1.10, 95% CI 1.05\u0026ndash;1.16).\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e Several meta-analyses have also demonstrated a strong association between PPI use and the risk of CKD and ESRD.\u003csup\u003e[\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e However, these studies have some limitations, such as the lack of data on concomitant drug use and the inclusion of selective populations, which may introduce bias into the findings.\u003c/p\u003e \u003cp\u003eThe underlying mechanisms linking PPI use to CKD remain unclear. One possible explanation is that PPIs may increase the risk of acute interstitial nephritis (AIN), which could progress to chronic interstitial nephritis and eventually contribute to CKD development.\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e Additionally, PPI use has been associated with hypomagnesemia, which in turn can trigger oxidative stress and inflammatory responses, potentially acting as another contributing factor to CKD. \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e The 2021 Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines recommend that clinicians adjust PPI dosages based on patients' genotypes to improve therapeutic outcomes and minimize the risk of adverse effects. However, the impact of CYP2C19 genotypes on the incidence of renal diseases remains unclear. This study aimed to investigate the relationship between different CYP2C19 genotypes and the incidence of CKD and ESKD in a cohort of long-term PPI users. The results revealed no significant correlation between CYP2C19 genotypes and the incidence of CKD. A potential explanation for this finding is that during clinical practice, healthcare providers often adjust medication dosages based on the individual response of each patient. This personalized approach may reduce the influence of the CYP2C19 genotype on CKD progression and could introduce some bias into the study results.\u003c/p\u003e \u003cp\u003eOur study has several strengths, including its prospective cohort design, large sample size, and detailed clinical data, which enabled us to adjust for potential confounders. However, there are also some limitations. First, as an observational study, we could not fully eliminate residual confounding. To address this, we made extensive adjustments for potential confounders and conducted sensitivity analyses. Additionally, the UK Biobank lacks information on the duration and dosage of PPI use, and we were unable to determine the exact date when the participants started to used PPIs. This limitation raises the possibility that some participants may have been misclassified as non-users, leading to potential misinterpretation and prevalent-user bias. We could not verify whether participants actually adhered to the medications they reported using. Finally, there may be monitoring bias. Patients using PPIs may be more health-conscious and, as a result, more likely to undergo tests that could indicate CKD. This could lead to an overestimation of renal risks in PPI users compared to non-users.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn summary, this study found that PPI use was associated with an increased risk of CKD and ESKD. However, our results do not support a moderating role of the CYP2C19 gene phenotype in the development of kidney disease. Given the widespread use of PPIs and the public health implications of CKD and ESKD, healthcare providers should exercise caution when prescribing PPIs, particularly for patients requiring long-term use, to minimize the risk of adverse outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003e6.Conflict of interest\u003c/h2\u003e \u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003eThe studies involving human participants were reviewed and approved by the North West Multi-Center Research Ethics Committee, the England and Wales Patient Information Advisory Group. The patients/participants provided their written informed consent to participate in this study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003e All authors have read and approved the final version of the manuscript and have obtained written informed consent for publication.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by Shenzhen Science and Technology Innovation Commission (JCYJ20220530150412026).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eWX wrote the main manuscript text, SY and W-YL were responsible for data control and analysis, Concept and design: H-YJ , W-PP and YG, H-XY and X-ZB were responsible for manuscript review and revision. Acquisition, analysis, or interpretation of the data: all authors.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data analyzed in this study is subject to the following licenses/restrictions: The study was conducted using the UKB resource .The dataset can be obtained from the United Kingdom Biobank. Requests to access the dataset should be directed to https://www.ukbiobank.ac.uk.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGlobal regional, national burden of chronic kidney disease. 1990\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017 [J]. Lancet. 2020;395(10225):709\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHILL N R, FATOBA S T OKEJL, et al. Global Prevalence of Chronic Kidney Disease - A Systematic Review and Meta-Analysis [J]. PLoS ONE. 2016;11(7):e0158765.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZHANG L, WANG F, WANG L, et al. Prevalence of chronic kidney disease in China: a cross-sectional survey [J]. Lancet. 2012;379(9818):815\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLIN B, SHAO L, LUO Q, et al. Prevalence of chronic kidney disease and its association with metabolic diseases: a cross-sectional survey in Zhejiang province, Eastern China [J]. BMC Nephrol. 2014;15:36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNEHRA A K, ALEXANDER J A, LOFTUS C G, et al. Proton Pump Inhibitors: Review of Emerging Concerns [J]. Mayo Clin Proc. 2018;93(2):240\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLINSKY A, SIMON SR. Reversing gears: discontinuing medication therapy to prevent adverse events [J]. JAMA Intern Med. 2013;173(7):524\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLAU A N, TOMIZZA M, WONG-PACK M, et al. The relationship between long-term proton pump inhibitor therapy and skeletal frailty [J]. Endocrine. 2015;49(3):606\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAL-ALY Z, MADDUKURI G. Proton Pump Inhibitors and the Kidney: Implications of Current Evidence for Clinical Practice and When and How to Deprescribe [J]. Am J Kidney Dis. 2020;75(4):497\u0026ndash;507.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGUEDES J V M, AQUINO J A, CASTRO T L B, et al. Omeprazole use and risk of chronic kidney disease evolution [J]. PLoS ONE. 2020;15(3):e0229344.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHART E, DUNN T E, FEUERSTEIN S, et al. Proton Pump Inhibitors and Risk of Acute and Chronic Kidney Disease: A Retrospective Cohort Study [J]. Pharmacotherapy. 2019;39(4):443\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRODRIGUEZ-PONCELAS A, BARCELO M A, SAEZ M, et al. Duration and dosing of Proton Pump Inhibitors associated with high incidence of chronic kidney disease in population-based cohort [J]. PLoS ONE. 2018;13(10):e0204231.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXIE Y, BOWE B, LI T, et al. Proton Pump Inhibitors and Risk of Incident CKD and Progression to ESRD [J]. J Am Soc Nephrol. 2016;27(10):3153\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXIE Y, BOWE B, LI T, et al. Long-term kidney outcomes among users of proton pump inhibitors without intervening acute kidney injury [J]. Kidney Int. 2017;91(6):1482\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eREMUZZI G, GUEDES J V M, AQUINO JA et al. Omeprazole use and risk of chronic kidney disease evolution [J]. PLoS ONE, 2020, 15(3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHAGYM\u0026aacute;SI K, M\u0026uuml;LLNER K, HERSZ\u0026eacute;NYI L, et al. Update on the pharmacogenomics of proton pump inhibitors [J]. Pharmacogenomics. 2011;12(6):873\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSACHS G, SHIN J M, HOWDEN C W. Review article: the clinical pharmacology of proton pump inhibitors [J]. Aliment Pharmacol Ther. 2006;23(Suppl 2):2\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSUDLOW C, GALLACHER J, ALLEN N, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age [J]. PLoS Med. 2015;12(3):e1001779.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTREHEARNE A. Genetics, lifestyle and environment. UK Biobank is an open access resource following the lives of 500,000 participants to improve the health of future generations [J]. 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J Am Soc Nephrol. 2014;25(7):1533\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSHOU H, HSU J Y, XIE D, et al. Analytic Considerations for Repeated Measures of eGFR in Cohort Studies of CKD [J]. Clin J Am Soc Nephrol. 2017;12(8):1357\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eARORA P, GUPTA A, GOLZY M, et al. Proton pump inhibitors are associated with increased risk of development of chronic kidney disease [J]. BMC Nephrol. 2016;17(1):112.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWU C C, LIAO M H, KUNG W M et al. Proton Pump Inhibitors and Risk of Chronic Kidney Disease: Evidence from Observational Studies [J]. J Clin Med, 2023, 12(6).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVENGRUS CS, DELFINO V D, BIGNARDI PR. Proton pump inhibitors use and risk of chronic kidney disease and end-stage renal disease [J]. Minerva Urol Nephrol. 2021;73(4):462\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRAJAN P, IGLAY K, RHODES T, et al. Risk of bias in non-randomized observational studies assessing the relationship between proton-pump inhibitors and adverse kidney outcomes: a systematic review [J]. Th Adv Gastroenterol. 2022;15:17562848221074183.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMORSCHEL C F, EDUARDO J C MAFRAD. The relationship between proton pump inhibitors and renal disease [J]. J Bras Nefrol. 2018;40(3):301\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMOLEDINA D G, PERAZELLA MA. PPIs and kidney disease: from AIN to CKD [J]. J Nephrol. 2016;29(5):611\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGOMMERS L M M, HOENDEROP J G J, DE BAAIJ J H. F. Mechanisms of proton pump inhibitor-induced hypomagnesemia [J]. Acta Physiol (Oxf). 2022;235(4):e13846.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"proton pump inhibitors, chronic kidney disease, end-stage kidney disease, CYP2C19 genotype","lastPublishedDoi":"10.21203/rs.3.rs-5788578/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5788578/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe widespread use of proton pump inhibitors (PPIs) has generated concerns about side effects Numerous studies have suggested that PPIs use may increase the risk of kidney disease. In particular, it is unclear whether genotype of the CYP2C19 enzyme system, the main metabolic pathway of PPIs, modifies the association between PPIs and the development of chronic kidney disease (CKD) or end-stage kidney disease (ESKD).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis was a prospective cohort study of white European participants from the UK Biobank. CKD and ESKD were determined by ICD-10 coding. Self-reported PPI use was recorded using an electronic questionnaire and confirmed by trained staff. The CYP2C19 genotype is defined by two of the most common CYP2C19 functional polymorphisms (rs4244285 and rs12248560). Multivariable Cox proportional hazards regression models were used to assess the associations of PPIs with incident CKD or ESKD.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 419670 participants were included in the study, of which 39,493 participants were PPIs users. Compared with non-user, PPI users had a significantly higher risk of developing CKD (HR 1.408, CI 1.352\u0026ndash;1.467) and ESKD (HR 1.288, CI 1.03\u0026ndash;1.609). This association persisted in sensitivity analyses. However, there was no significant difference in the risk of developing CKD among participants taking PPIs between the different CYP2C19 genotypes.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe use of PPIs was associated with the risks of developing CKD and ESKD. There was no evidence supporting the CYP2C19 genotype as a moderator on the relationship between PPIs and kidney diseases.\u003c/p\u003e","manuscriptTitle":"Proton Pump Inhibitor Use, CYP2C19 genotypes, and Subsequent Incidence of Chronic Kidney Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-15 12:09:08","doi":"10.21203/rs.3.rs-5788578/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dcacabb7-3827-48ba-88db-a49d593bc4da","owner":[],"postedDate":"January 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-05T08:09:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-15 12:09:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5788578","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5788578","identity":"rs-5788578","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}