Thiazolidinedione insulin sensitizers and the heart: a tale of two organs?
Bibliographic record
Abstract
Thiazolidinediones (TZDs) are relatively new agents for the treatment of type 2 diabetes. They act as agonists at the PPAR-gamma nuclear receptor and their therapeutic effects include decreased insulin resistance and hyperglycaemia, an improved plasma lipid, inflammation and pro-coagulant profile, and amelioration of hypertension, microalbuminuria and hepatic steatosis. The most common side effects of TZDs include weight gain and oedema, with occasional reports of congestive heart failure (CHF). This review discusses the benefit-risk profile of TZDs in treating patients with type 2 diabetes, with particular reference to the heart. To provide context, we explore briefly the epidemiology and pathophysiology of heart failure in patients with type 2 diabetes, touch on the association of heart disease and cardiovascular mortality with antihyperglycaemic treatment modalities other than TZDs, and then focus on the effects of TZDs on the heart, cardiovascular risk factors and outcomes. We describe the cluster of host factors, which seems to predispose patients with type 2 diabetes to TZD-induced or TZD-exacerbated oedema and CHF and then provide an overview of the putative mechanisms of these TZD-related side effects. We also propose that certain diuretics (amiloride and spironolactone), by targeting the distal nephron that expresses PPARgamma in collecting duct cells, might be of benefit in ameliorating the fluid retention and oedema associated with TZDs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".