A Pharmacovigilance Approach for Assessing Cardiovascular, Osteological, and Carcinogenic Risk Associated with Thiazolidinedione Drugs Used in the Treatment of Type 2 Diabetes Mellitus
Notice bibliographique
Résumé
Diabetes is a chronic and debilitating disease that affects nearly half a billion people worldwide with the vast majority of diabetics suffering from Type 2 diabetes mellitus (T2DM), a disease characterized by insulin insensitivity that often requires pharmacotherapy to effectively maintain target blood sugar levels. The thiazolidinedione (TZD) class of drugs consists of oral hypoglycaemic agents used alone or in combination with other antidiabetic drugs to treat T2DM. The drugs within this class, which include rosiglitazone and pioglitazone, were originally heralded as providing novel first and second-line treatment of T2DM with glycaemic control and physiological effects comparable to, and in some cases, better than, first-line treatments such as metformin. However, over time they have also been associated with adverse cardiovascular, osteological, and carcinogenic effects in some, but not all clinical trials, observational studies, and meta-analyses. Given the conflicting evidence to date on the safety of TZD drugs, their role in the treatment of T2DM continues to be debated and epidemiological gaps remain. The objectives of this doctoral research are fourfold: 1) to conduct an in-depth review of the epidemiology of TZD pharmacotherapy including pharmacokinetics and modes of action, the results of previous studies investigating health risks and benefits associated with TZD treatment, and new and future uses for this class of drugs; 2) to determine whether diabetic patients treated with TZDs are at increased risk of adverse cardiovascular outcomes; 3) to assess whether TZD pharmacotherapy is associated with an increased risk of bone fractures and whether risks differ depending on fracture site and patient sex; and, 4) to investigate associations between TZD use and risk of bladder cancer. Specific research questions were investigated using nested case-control analyses designed to capture incident users of antidiabetic drugs and electronic health data from Cerner Health Facts®, an electronic medical record database that stores time-stamped patient records from more than 480 contributing hospitals throughout the United States. Findings from this work are reported in a series of manuscripts, including a published review paper. Key findings include: 1) TZD use was associated with an increased risk of incident myocardial infarction and congestive heart failure compared to never use of TZD drugs with a trend towards a potential early treatment effect within the first year of exposure to pioglitazone; 2) TZD use was associated with an increased risk of closed bone fractures among Type 2 diabetics with use of pioglitazone or rosiglitazone associated with an increased risk across multiple fracture sites in women, but only rosiglitazone use in men and only at peripheral fracture sites; 3) use of either pioglitazone or rosiglitazone were associated with an increased risk of incident bladder cancer compared to never users, however, a low number of bladder cancer cases resulted in underpowered analyses; and, 4) insulin use in a hospital setting may replace a patient's normal course of antidiabetic therapy which, when combined with other potential sources of bias in traditional nested case-control studies using hospital-based data, may lead to overestimation or underestimation of adverse health risks associated with non-insulin antidiabetic therapies. Although these findings warrant replication, the results of the research contained within this dissertation suggest that caution should be exercised when prescribing diabetic patients TZD drugs if they have cardiovascular, osteological, or carcinogenic risk factors. Additional pharmacovigilance studies should also continue to strive to better understand the health risks related to TZD therapy, especially as new therapeutic roles for TZDs in the prevention and treatment of some cancers, inflammatory diseases, and other conditions in non-diabetic populations are being explored.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,020 | 0,055 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,006 |
| Bibliométrie | 0,011 | 0,007 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,004 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».