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. 2025 Apr 29:16:1580090.
doi: 10.3389/fphar.2025.1580090. eCollection 2025.

Proton pump inhibitors use and risk of type 2 diabetes mellitus: correlation analysis, prediction model construction, and key genes identification

Cuilv Liang  1 , Yin Zhang  1
Affiliations

Proton pump inhibitors use and risk of type 2 diabetes mellitus: correlation analysis, prediction model construction, and key genes identification

Cuilv Liang et al. Front Pharmacol. .

Abstract

Introduction: Prior cohort studies reported paradoxical results between proton pump inhibitor (PPI) usage and the risk of type 2 diabetes mellitus (T2DM). We investigated the correlation between the use of PPIs and T2DM risk, constructed predictive models, and identified the key genes involved.

Methods: In the correlation analysis, we extracted and analyzed the data from the National Health and Nutrition Examination Survey (NHANES) database and the FDA Adverse Event Reporting System (FAERS) database to examine the relationship between the use of PPIs and T2DM risk. Then, a nomogram was constructed to estimate the T2DM risk probability in patients treated with PPIs by using the optimal predictors identified by the least absolute shrinkage and selection operator and logistic regression methods. Finally, we investigated the key genes modulated by PPI usage in patients with T2DM by combining various bioinformatics techniques such as network pharmacology, difference analysis, and weighted gene co-expression network analysis.

Results: In the NHANES database, regardless of whether PPI usage was merely included or used to adjust for covariates, the binomial regression models indicated a positive correlation between PPI usage and T2DM risk (all p < 0.001). In the FAERS database, the T2DM signal for patients using PPIs was significant (lower limit of the reporting odds ratio was greater than 1). Sex, race, age, educational level, obesity, hypertension, and high cholesterol were included in the nomogram to predict the probability of PPI usage-induced T2DM risk (all p < 0.05). By intersecting the key cluster and the intersection of PPI usage-related genes and T2DM-related genes, we finally identified two crucial genes, AGT and JAK2, that may be involved in PPI usage-induced T2DM risk.

Discussion: Our findings revealed that PPI treatment can increase the risk of T2DM. Additionally, we were successful in constructing a new nomogram to identify individuals at high risk of developing T2DM among patients using PPIs and completed a preliminary exploration of possible gene targets and mechanisms. Our study will be useful in alerting clinicians to the T2DM risk involved in PPI treatment and allowing them to take early prevention and intervention measures.

Keywords: correlation analysis; gene identification; prediction model; proton pump inhibitors; type 2 diabetes mellitus.

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Conflict of interest statement

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.

Figures

FIGURE 1
FIGURE 1
Flowchart of subject selection in NHANES. NHANES, The National Health and Nutrition Examination Survey.
FIGURE 2
FIGURE 2
Correlation analysis in NHANES (a) and FAERS (b) database. PPIs, proton pump inhibitors; T2DM, type 2 diabetes mellitus; NHANES, National Health and Nutrition Examination Survey; FAERS, FDA Adverse Events Reporting System; CI, confidence interval; OR, odds ratio; PSM, propensity score matching; HLT, high-level terms; PT, preferred terms; ROR, report odds ratio.
FIGURE 3
FIGURE 3
Construction of a PPI-induced T2DM risk prediction model. (a) Results of the LASSO regression. (b) Nomogram predicting T2DM risk among the people of PPI use. (c) Analysis of ROC curve for nomogram in the training group. (d) Calibration curves of the nomogram in the training group. PPIs, proton pump inhibitors; T2DM, type 2 diabetes mellitus; LASSO, least absolute shrinkage and selection operator; ROC, receiver operating characteristic; AUC, the area under the curve.
FIGURE 4
FIGURE 4
Expression of DEGs in the GSE7014 dataset. (a) Volcano plot of DEGs in the GSE7014 dataset. (b) Heatmap plot of DEGs in the GSE7014 dataset. (c,d) GSEA analysis of DEGs between the T2DM and normal groups DEGs, differentially expressed genes; T2DM, type 2 diabetes mellitus; GSEA, gene set enrichment analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes.
FIGURE 5
FIGURE 5
Enrichment levels in genomic WGCNA. (a) Sample dendrogram and trait heatmap. (b) Selection of soft thresholds. (c) Cluster dendrogram of WGCNA. (d) Correlations between gene modules and T2DM. (e) Correlation between modules. (f) Correlation between brown module memberships and gene significance. WGCNA, weighted correlation network analysis; T2DM, type 2 diabetes mellitus.
FIGURE 6
FIGURE 6
Identification of key genes. (a) Intersection of DEGs and WGCNA brown module genes, named T2DM-related genes. (b) Intersection of T2DM-related genes and PPI-related genes. (c) Cytoscape’s plugin code for all T2DM-related genes and PPI-related genes, named key cluster. Green: PPI-related genes; Red: T2DM-related genes; Green and red: both PPI-related genes and T2DM-related genes. (d) Intersection of key clusters, T2DM-related genes and PPI-related genes, named key genes. (e) ROC curve of two key genes. WGCNA, weighted correlation network analysis; DEGs, differentially expressed genes; T2DM, type 2 diabetes mellitus; PPIs, proton pump inhibitors; ROC, receiver operating characteristic.
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