Evidence map›Paper›PMID 42630308›Full record

ArticleFrontiers in pharmacology2026

Association between exposure to proton pump inhibitors and hyperuricemia: risk prediction model establishment and nomogram construction.

Rong-Zhong Wang, Si-Qin Sun, Ming-Xing Mao, Jia-Li Peng, Jing-Shu Dong, Xiao-Fang Xiong, Si-Jia Liu

Abstract read
In one paragraph

Article in Frontiers in pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Rong-Zhong WangWest China Hospital, Sichuan University, Chengdu, China.
Si-Qin SunWest China Hospital, Sichuan University, Chengdu, China.
Ming-Xing MaoWest China Hospital, Sichuan University, Chengdu, China.
Jia-Li PengReproductive and Maternal and Child Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Jing-Shu DongReproductive and Maternal and Child Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Xiao-Fang XiongWest China Hospital, Sichuan University, Chengdu, China.
Si-Jia LiuWest China Hospital, Sichuan University, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: This study aims to explore whether the exposure to proton pump inhibitor (PPI) is a potential independent risk factor for the development of new-onset hyperuricemia in patients who were hospitalized due to gastrointestinal bleeding, and to establish a validated clinical prediction indicator. Methods: We conducted a retrospective cohort study involving inpatients of the gastroenterology department admitted to West China Hospital between August 2022 and August 2025. LASSO and logistic regression were utilized to determine potential risk factors, and a nomogram prediction model was subsequently established accordingly. The model's discriminative ability was assessed using the area under the receiver operating characteristic (ROC) curve (AUC). Calibration curves and decision curve analysis (DCA) were utilized to evaluate the model's accuracy and clinical utility. Results: Of the 1900 patients enrolled, 10.5% developed new-onset hyperuricemia. Multivariate analysis identified PPI as a significant independent risk factor for new-onset hyperuricemia with adjusted odds ratio (OR) (95% CI) of 4.86 (3.04, 7.78). The nomogram incorporated ten predictors: gender, age, hospital time, BMI, smoking, triglyceride levels, PPIs, CCBs, diuretics, and hyperlipidemia. The model exhibited good discrimination, with AUCs of 0.759 (95% CI: 0.718∼0.800) and 0.727 (95% CI: 0.654∼0.799) in the training and validation set, respectively. The Hosmer-Lemeshow test demonstrated the model was well-calibrated ( Conclusion: PPI exposure may be a potential independent risk factor for new-onset hyperuricemia in patients who were hospitalized due to gastrointestinal bleeding. The developed nomogram demonstrates good predictive performance and may assist clinicians in assessing hyperuricemia risk and optimizing individualized patient management.

Indexed as

hyperuricemianomogram modelPPIretrospective studyrisk prediction

Identifiers

PMID42630308
PMCPMC13493512

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.