Free fatty acid kinetics during long‐term treatment with pioglitazone added to sulfonylurea or metformin in Type 2 diabetes
Bibliographic record
Abstract
BACKGROUND: Free fatty acids (FFAs) are linked to impaired insulin action, but their role in mediating long-term insulin sensitization during diabetes treatment is unclear. OBJECTIVES: To examine the effect of pioglitazone addition to existing therapy on FFA dynamics and insulin action. DESIGN: Two 2-year, randomized, parallel-group, double-blind, double-dummy, clinical trials. SETTING: One hundred and seventy-one centres in Europe, Australia and Canada. SUBJECTS: Male and female patients with Type 2 diabetes inadequately managed with metformin or sulfonylurea. INTERVENTIONS: Patients were randomized to pioglitazone (15-45 mg day(-1); n=319) or metformin (850-2550 mg day(-1); n=320) as add-on therapy to gliclazide or pioglitazone (n=317) versus gliclazide (80-320 mg day(-1); n=313) as add-on therapy to metformin. OUTCOME MEASURE: Plasma FFA profiles during oral glucose tolerance tests in selected centres before and during treatment (n=588). RESULTS: At Week 104, pioglitazone treatment decreased fasting FFAs by 0.08 mmol L(-1) when added to sulfonylurea and by 0.11 mmol L(-1) when added to metformin versus the respective sulfonylurea + metformin groups (0.03 mmol L(-1), P=0.05 and 0.04 mmol L(-1), P<0.05), and this was accompanied by significant improvements in fasting adipose tissue insulin sensitivity. Changes in postchallenge FFAs were similar between groups and not related to changes in liver transaminases, insulin action and secretion. However, the sensitivity of FFA to insulin was affected by treatment (P<0.001) and visit (P<0.05). Insulin sensitivity of FFA rose when pioglitazone was added to sulfonylurea (P<0.05), but decreased for gliclazide + metformin (P<0.05). CONCLUSION: Long-term improvements in adipose tissue insulin sensitivity and reduction in fasting FFAs with pioglitazone may help to reduce lipotoxicity in Type 2 diabetes.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".