Exposure to dipeptidyl-peptidase-4 inhibitors and COVID-19 among people with type 2 diabetes: A case-control study.
Gian Poalo FadiniMario Luca MorieriEnrico LongatoBenedetta Maria BonoraSilvia PinelliElisa SelminGiacomo VoltanDaniele FalaguastaSilvia TressoGiorgia CostantiniGiovanni SparacinoBarbara Di CamilloLara TramontanAnna Maria CattelanAndrea VianelloPaola FiorettoRoberto VettorAngelo AvogaroPublished in: Diabetes, obesity & metabolism (2020)
Because other coronaviruses enter the cells by binding to dipeptidyl-peptidase-4 (DPP-4), it has been speculated that DPP-4 inhibitors (DPP-4is) may exert an activity against severe acute respiratory syndrome coronavirus 2. In the absence of clinical trial results, we analysed epidemiological data to support or discard such a hypothesis. We retrieved information on exposure to DPP-4is among patients with type 2 diabetes (T2D) hospitalized for COVID-19 at an outbreak hospital in Italy. As a reference, we retrieved information on exposure to DPP-4is among matched patients with T2D in the same region. Of 403 hospitalized COVID-19 patients, 85 had T2D. The rate of exposure to DPP-4is was similar between T2D patients with COVID-19 (10.6%) and 14 857 matched patients in the region (8.8%), or 793 matched patients in the local outpatient clinic (15.4%), 8284 matched patients hospitalized for other reasons (8.5%), and when comparing 71 patients hospitalized for COVID-19 pneumonia (11.3%) with 351 matched patients with pneumonia of another aetiology (10.3%). T2D patients with COVID-19 who were on DPP-4is had a similar disease outcome as those who were not. In summary, we found no evidence that DPP-4is might affect hospitalization for COVID-19.
Keyphrases
- sars cov
- end stage renal disease
- coronavirus disease
- ejection fraction
- clinical trial
- newly diagnosed
- respiratory syndrome coronavirus
- peritoneal dialysis
- prognostic factors
- primary care
- emergency department
- healthcare
- cell death
- patient reported outcomes
- study protocol
- artificial intelligence
- deep learning
- open label
- endoplasmic reticulum stress