Background: Vildagliptin and pioglitazone/rosiglitazone are emerging Oral Hypoglycemic Agents (OHAs) which are used to treat patients suffering from Type 2 Diabetes Mellitus (T2DM). In this analysis, we aimed to systematically compare the adverse drug events which were observed with the use of vildagliptin versus pioglitazone or rosiglitazone respectively.
Methods: Online databases were searched for studies comparing vildagliptin with pioglitazone/rosiglitazone. Adverse drug events were considered as the clinical endpoints in this analysis. We calculated Odds Ratios (OR) with 95% Confidence Intervals (CIs) using the RevMan 5.3 software. All the authors had full access to the data which were used and approved the final version of the manuscript.
Results: A total number of 2396 patients were analyzed (1486 and 910 patients were treated with vildagliptin and pioglitazone/rosiglitazone respectively). Vildagliptin and pioglitazone/rosiglitazone were both associated with similar overall adverse drug events (OR: 1.00, 95% CI: 0.81-1.24; P = 1.00). Headache (OR: 0.88, 95% CI: 0.60-1.27; P = 0.49) and upper respiratory tract infection (OR: 0.95, 95% CI: 0.71-1.27; P = 0.75) were similarly observed. However, dizziness was significantly lower with pioglitazone/rosiglitazone (OR: 0.63, 95% CI: 0.43-0.92; P = 0.02). Back pain, diarrhea and nausea were insignificantly lower with pioglitazone/rosiglitazone (OR: 0.81, 95% CI: 0.49-1.33; P = 0.40), (OR: 0.83, 95% CI: 0.48-1.44; P = 0.52) and (OR: 0.52, 95% CI: 0.25-1.05; P = 0.07) respectively, whereas peripheral edema and weight gain were insignificantly higher (OR: 1.21, 95% CI: 0.56-2.62; P = 0.63) and (OR: 2.29, 95% CI: 0.51-10.34; P = 0.28) respectively. Nevertheless, when pioglitazone and rosiglitazone were separately compared with vildagliptin, peripheral edema and weight gain were significantly higher with rosiglitazone (OR: 2.36, 95% CI: 1.40-3.99; P = 0.001) and (OR: 5.20, 95% CI: 2.47-10.92; P = 0.0001) respectively.
Conclusion: Both vildagliptin and pioglitazone/rosiglitazone are acceptable for the treatment of patients with T2DM on the basis that they are not significantly different in terms of overall adverse drug events. However, weight gain and peripheral edema would have to be re-assessed in further larger randomized controlled trials.
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http://dx.doi.org/10.1186/s40360-017-0175-0 | DOI Listing |
Probiotics Antimicrob Proteins
January 2025
Faculty of Biotechnologies (BioTech), ITMO University, 9 Lomonosova Street, 191002, Saint Petersburg, Russia.
Antimicrobial peptides (AMPs) are small, positively charged biomolecules produced by various organisms such as animals, microbes, and plants. These AMPs play a significant role in defense mechanisms and protect from adverse conditions. The emerging problem of drug resistance in microbes poses a global health challenge in treating diseases.
View Article and Find Full Text PDFExpert Opin Drug Saf
January 2025
Shenzhen Institute of Pharmacovigilance and Risk Management, Shenzhen, Guangdong, China.
Background: The aims of this study were to promote the rational use and supervision of dextromethorphan (DM). This study analyzed serious adverse events such as addiction and mental disorders caused by DM in Shenzhen and the behavioral characteristics of people suspected of abusing DM on the Internet.
Methods: Adverse drug reaction/event (ADR/E) reports from 2017 to 2023 were extracted from the National Pharmaceutical Adverse Reaction Monitoring System database.
Clin Lung Cancer
December 2024
Georgetown University, Washington, DC. Electronic address:
Background: Thymic epithelial tumors (TETs), including thymoma and thymic carcinoma, are rare thoracic tumors of the anterior mediastinum. For those with advanced disease, platinum-based chemotherapy is used as first-line treatment. However, there is no standard regimen established for TET at progression after initial therapy, and treatment options for advanced/recurrent TETs are limited.
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January 2025
Psychiatry, Yale School of Medicine, New Haven, Connecticut, USA
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J Med Internet Res
December 2024
Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé - LIMICS, Inserm, Université Sorbonne Paris-Nord, Sorbonne Université, Paris, France.
Background: Artificial intelligence (AI) applied to real-world data (RWD; eg, electronic health care records) has been identified as a potentially promising technical paradigm for the pharmacovigilance field. There are several instances of AI approaches applied to RWD; however, most studies focus on unstructured RWD (conducting natural language processing on various data sources, eg, clinical notes, social media, and blogs). Hence, it is essential to investigate how AI is currently applied to structured RWD in pharmacovigilance and how new approaches could enrich the existing methodology.
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