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Lower Risk of Gout in Sodium Glucose Cotransporter 2 (SGLT2) Inhibitors Versus Dipeptidyl Peptidase-4 (DPP4) Inhibitors in Type-2 Diabetes Patients: A Propensity Score-Matched Study with Competing Risk Analysis

27 Pages Posted: 24 Jan 2022

See all articles by Xuejin Liu

Xuejin Liu

Kaili University - School of Educational Science

Jiandong Zhou

University of Oxford - Nuffield Department of Medicine

Hou In Chou

Diabetes Research Unit, Cardiovascular Analytics Group, Hong Kong, China-UK Collaboration

Fengshi Jing

University of North Carolina (UNC) at Chapel Hill - Project-China; Guangdong Second Provincial General Hospital - Institute for Artificial Intelligence

Lifang Li

King’s College London

Sharen Lee

Diabetes Research Unit, Cardiovascular Analytics Group, Hong Kong, China-UK Collaboration

Wing Tak Wong

The Chinese University of Hong Kong (CUHK) - School of Life and Health Sciences

Qingpeng Zhang

City University of Hong Kong (CityU) - School of Data Science

Carlin Chang

The University of Hong Kong - Department of Medicine

Tong Liu

Tianjin Medical University - Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease

Gary Tse

Second Hospital of Tianjin Medical University - Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease; Diabetes Research Unit, Cardiovascular Analytics Group, Hong Kong, China-UK Collaboration

Bernard Man Yung Cheung

The University of Hong Kong - Division of Clinical Pharmacology and Therapeutics

More...

Abstract

Background: The effects of sodium-glucose cotransporter 2 inhibitors (SGLT2I) versus dipeptidyl peptidase-4 inhibitors (DPP4I) on the risk of new gout diagnosis have not been explored. This study aims to compare the effects of SGLT2I against DPP4I on gout risks in a Chinese population. 

Methods: This was a retrospective population-based cohort study of patients with type-2 diabetes mellitus treated with SGLT2I or DPP4I between January 1 st , 2015 and December 31 st , 2020 in Hong Kong. The study outcomes are new-onset gout and all-cause mortality. Propensity score matching (1:1 ratio) between SGLT2I and DPP4I was performed. Univariable and multivariable Cox regression analysis models were conducted. Competing risks models and multiple approaches based on the propensity score were applied. 

Patients: This study included 60996 patients (median age: 62.3 years old, 54.96% males; SGLTI group: n=21690; DPP4I group: n=39306). 

Results: In the matched cohort, 1096 developed gout (IR: 2.52%) and 2195 died (IR: 5.05%). Univariable Cox regression showed that SGLT2I use was associated with lower risks of new diagnosis of gout (hazard ratio [HR]: 0.34; 95% confidence interval [CI]: 0.30-0.39; P-value<0.0001) and all-cause mortality (HR: 0.35; 95% CI: 0.32-0.39; P-value<0.0001) compared to DPP4I. The associated remained for both new diagnosis of gout (HR: 0.46; 95% CI: 0.37-0.57; P-value<0.0001) and all-cause mortality (HR: 0.38; 95% CI: 0.33-0.44; P-value<0.0001) after adjusting for significant demographics, past comorbidities, and non-SGLT2I/DPP4I medications. The risks of gout were lowered in each types of SGLT2I. The results were consistent on competing risk and other propensity score approaches analyses. 

Conclusions: SGLT2I use was associated with lower risks of new gout diagnosis compared to DPP4I use.

Funding: None to declare.

Declaration of Interest: None to declare.

Ethical Approval: This study was approved by the Joint Chinese University of Hong Kong – New Territories East Cluster Clinical Research Ethics Committee (Reference No. 2018.309, 2018.643).

Keywords: SGLT2i, DPP4i, Gout, diabetes, Rheumatology, renal function

Suggested Citation

Liu, Xuejin and Zhou, Jiandong and Chou, Hou In and Jing, Fengshi and Li, Lifang and Lee, Sharen and Wong, Wing Tak and Zhang, Qingpeng and Chang, Carlin and Liu, Tong and Tse, Gary and Cheung, Bernard Man Yung, Lower Risk of Gout in Sodium Glucose Cotransporter 2 (SGLT2) Inhibitors Versus Dipeptidyl Peptidase-4 (DPP4) Inhibitors in Type-2 Diabetes Patients: A Propensity Score-Matched Study with Competing Risk Analysis. Available at SSRN: https://ssrn.com/abstract=4016531 or http://dx.doi.org/10.2139/ssrn.4016531

Xuejin Liu

Kaili University - School of Educational Science ( email )

Guizhou
China

Jiandong Zhou

University of Oxford - Nuffield Department of Medicine ( email )

Old Road Campus
Roosevelt Drive
Oxford, OX3 7FZ
United Kingdom

Hou In Chou (Contact Author)

Diabetes Research Unit, Cardiovascular Analytics Group, Hong Kong, China-UK Collaboration ( email )

China

Fengshi Jing

University of North Carolina (UNC) at Chapel Hill - Project-China ( email )

Guangzhou, NC
China

Guangdong Second Provincial General Hospital - Institute for Artificial Intelligence ( email )

Guangzhou
China

Lifang Li

King’s College London ( email )

London
United Kingdom

Sharen Lee

Diabetes Research Unit, Cardiovascular Analytics Group, Hong Kong, China-UK Collaboration ( email )

China

Wing Tak Wong

The Chinese University of Hong Kong (CUHK) - School of Life and Health Sciences ( email )

Shenzhen
China

Qingpeng Zhang

City University of Hong Kong (CityU) - School of Data Science ( email )

Kowloon
Hong Kong

Carlin Chang

The University of Hong Kong - Department of Medicine ( email )

Hong Kong

Tong Liu

Tianjin Medical University - Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease ( email )

Tianjin
China

Gary Tse

Second Hospital of Tianjin Medical University - Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease

Tianjin
China

Diabetes Research Unit, Cardiovascular Analytics Group, Hong Kong, China-UK Collaboration ( email )

China

Bernard Man Yung Cheung

The University of Hong Kong - Division of Clinical Pharmacology and Therapeutics ( email )

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