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 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 | 0.021 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.038 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".