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Drug–gene interactions and the risk of diabetic microvascular complications : A population-based cohort study

Li, Ning ; Sun, Jiao ; Li, Haibin ; Li, Changwei ; Wang, Xiao LU ; Ji, Jianguang LU orcid ; Ren, Tianmin ; Wen, Yalu and Zheng, Deqiang LU (2026) In Diabetes, Obesity and Metabolism
Abstract

Aims: Drug–gene interactions (DGIs) modify drug response and safety, yet their influence on diabetic microvascular complications remains unclear. This study aimed to elucidate the role of DGIs in these complications. Materials and Methods: Using UK Biobank (UKB) data, we identified medications frequently prescribed to individuals with diabetes and defined DGIs based on the Food and Drug Administration (FDA) and the Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines. Associations between DGIs and diabetic microvascular complications were evaluated using Cox proportional hazards models, which are suited for longitudinal time-to-event data. Two complementary analyses were performed: (1) a therapeutic class-level analysis... (More)

Aims: Drug–gene interactions (DGIs) modify drug response and safety, yet their influence on diabetic microvascular complications remains unclear. This study aimed to elucidate the role of DGIs in these complications. Materials and Methods: Using UK Biobank (UKB) data, we identified medications frequently prescribed to individuals with diabetes and defined DGIs based on the Food and Drug Administration (FDA) and the Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines. Associations between DGIs and diabetic microvascular complications were evaluated using Cox proportional hazards models, which are suited for longitudinal time-to-event data. Two complementary analyses were performed: (1) a therapeutic class-level analysis among medication users, and (2) a genotype-level analysis among individuals with non-normal metabolizer phenotypes who used the corresponding medications. Results: We identified 368 medications preferentially used among participants with diabetes, primarily cardiovascular agents and detected 55 clinically relevant DGIs implicating 30 medications and 7 genes. Among users of antithrombotic agents, the presence of DGIs was associated with diabetic kidney disease (DKD) (hazard ratio [HR]: 1.44, 95% confidence interval [CI]: 1.12–1.86) and diabetic neuropathy (DN) (HR: 2.13, 95% CI: 1.39–3.28). Likewise, among individuals with non-normal metabolizer status for CYP2C19 or CYP2D6, DGIs conferred elevated risks for DKD and DN (HR range: 1.26–2.11). However, no significant association was found between DGI and DR. Conclusion: This study provides the first comprehensive assessment of DGIs and diabetic microvascular complications. DGIs involving antithrombotic agents and non-normal CYP2C19 or CYP2D6 metabolizers were significantly linked to higher risks of DKD and DN. These findings underscore the potential of pharmacogenomic-guided prescribing to enhance drug safety.

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author
; ; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
epub
subject
keywords
cohort study, database research, diabetes complications, real-world evidence
in
Diabetes, Obesity and Metabolism
publisher
John Wiley & Sons Inc.
external identifiers
  • pmid:41582657
  • scopus:105029049689
ISSN
1462-8902
DOI
10.1111/dom.70501
language
English
LU publication?
yes
id
e3ad2fbc-b625-4fcc-9566-614f7c2f6436
date added to LUP
2026-02-20 16:00:45
date last changed
2026-02-21 02:48:31
@article{e3ad2fbc-b625-4fcc-9566-614f7c2f6436,
  abstract     = {{<p>Aims: Drug–gene interactions (DGIs) modify drug response and safety, yet their influence on diabetic microvascular complications remains unclear. This study aimed to elucidate the role of DGIs in these complications. Materials and Methods: Using UK Biobank (UKB) data, we identified medications frequently prescribed to individuals with diabetes and defined DGIs based on the Food and Drug Administration (FDA) and the Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines. Associations between DGIs and diabetic microvascular complications were evaluated using Cox proportional hazards models, which are suited for longitudinal time-to-event data. Two complementary analyses were performed: (1) a therapeutic class-level analysis among medication users, and (2) a genotype-level analysis among individuals with non-normal metabolizer phenotypes who used the corresponding medications. Results: We identified 368 medications preferentially used among participants with diabetes, primarily cardiovascular agents and detected 55 clinically relevant DGIs implicating 30 medications and 7 genes. Among users of antithrombotic agents, the presence of DGIs was associated with diabetic kidney disease (DKD) (hazard ratio [HR]: 1.44, 95% confidence interval [CI]: 1.12–1.86) and diabetic neuropathy (DN) (HR: 2.13, 95% CI: 1.39–3.28). Likewise, among individuals with non-normal metabolizer status for CYP2C19 or CYP2D6, DGIs conferred elevated risks for DKD and DN (HR range: 1.26–2.11). However, no significant association was found between DGI and DR. Conclusion: This study provides the first comprehensive assessment of DGIs and diabetic microvascular complications. DGIs involving antithrombotic agents and non-normal CYP2C19 or CYP2D6 metabolizers were significantly linked to higher risks of DKD and DN. These findings underscore the potential of pharmacogenomic-guided prescribing to enhance drug safety.</p>}},
  author       = {{Li, Ning and Sun, Jiao and Li, Haibin and Li, Changwei and Wang, Xiao and Ji, Jianguang and Ren, Tianmin and Wen, Yalu and Zheng, Deqiang}},
  issn         = {{1462-8902}},
  keywords     = {{cohort study; database research; diabetes complications; real-world evidence}},
  language     = {{eng}},
  publisher    = {{John Wiley & Sons Inc.}},
  series       = {{Diabetes, Obesity and Metabolism}},
  title        = {{Drug–gene interactions and the risk of diabetic microvascular complications : A population-based cohort study}},
  url          = {{http://dx.doi.org/10.1111/dom.70501}},
  doi          = {{10.1111/dom.70501}},
  year         = {{2026}},
}