Comparing the effects of insulin glargine and thiazolidinediones on plasma lipids in type 2 diabetes: a patient‐level pooled analysis
Bibliographic record
Abstract
BACKGROUND: The prevalence of dyslipidaemia and the risk of cardiovascular disease are elevated in patients with type 2 diabetes. This analysis compared the effects of insulin glargine versus thiazolidinediones (TZDs) on lipid profiles. METHODS: Patient-level data were pooled from two randomized clinical studies. The population included 552 men and women aged >18 years, diagnosed with type 2 diabetes for at least 6 months, on metformin and/or sulphonylurea, and with A(1C) ≥7.5% and <12.0% at screening. Lipid outcome measures included change from baseline in lipid levels [low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), non-high-density lipoprotein cholesterol (non-HDL-C), total cholesterol, triglycerides, and free fatty acids] and attainment of lipid goals for LDL-C, non-HDL-C, and triglycerides. RESULTS: Both insulin glargine and TZDs improved lipid profiles from baseline values. Compared with TZDs, treatment with insulin glargine led to 7.9% greater reduction in LDL-C (p < 0.0003), 7.5% greater reduction in non-HDL-C (p < 0.0001), and 7.8% greater reduction in total cholesterol (p < 0.0001), whereas the HDL-C increase with TZD was 7.6% greater than that with insulin glargine (p < 0.0001). The percentage of patients attaining the lipid goals was comparable between insulin glargine and pioglitazone, but lower for rosiglitazone. Insulin glargine improved glycaemic control more than TZDs; however, insulin glargine caused more hypoglycaemia. Treatment with TZDs caused more weight gain and peripheral oedema. CONCLUSION: These findings suggest that the favourable effects of insulin glargine on plasma lipid profiles should be considered among the advantages of treatment with insulin glargine as they are for TZDs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".