Proton Pump Inhibitors and Likelihood of Colorectal Cancer in the Korean Population: Insights from a Nested Case-Control Study Using National Health Insurance Data.
Mi Jung KwonKyeong Min HanJoo-Hee KimJi Hee KimMin-Jeong KimNan Young KimHyo Geun ChoiHo Suk KangPublished in: Cancers (2023)
The potential connection between proton pump inhibitors (PPIs) and colorectal cancer (CRC) risk remains unclear, with specific ethnic genetic backgrounds playing a role in PPI-induced adverse effects. In this nested case-control study, we investigated the risk of CRC in relation to preceding PPI use and the duration of use using data from the Korean National Health Insurance Service-National Sample Cohort database, including 9374 incident CRC patients and 37,496 controls. To assess the impact of preceding PPI exposure (past vs. current) and use duration (days: <30, 30-90, and ≥90) on incident CRC, we conducted propensity score overlap-weighted multivariate logistic regression analyses, adjusted for confounding factors. Our findings revealed that past and current PPI users had an increased likelihood of developing CRC. Regardless of duration, individuals who used PPIs also had higher odds of developing CRC. Subgroup analyses revealed that CRC occurrence increased independent of history or duration of prior PPI use, consistent across various factors such as age, sex, income level, and residential area. These findings suggest that PPI use, regardless of past or present use and duration of use, may be related to an increased risk of developing CRC in the Korean population.
Keyphrases
- health insurance
- protein protein
- quality improvement
- end stage renal disease
- cardiovascular disease
- small molecule
- mental health
- healthcare
- big data
- ejection fraction
- electronic health record
- newly diagnosed
- magnetic resonance
- prognostic factors
- risk assessment
- chronic kidney disease
- physical activity
- gene expression
- oxidative stress
- air pollution
- artificial intelligence
- genome wide
- diabetic rats
- dna methylation
- patient reported outcomes
- climate change
- human health
- deep learning
- copy number