Clinical Efficacy of Orlistat Therapy in Overweight and Obese Patients With Insulin-Treated Type 2 Diabetes
Bibliographic record
Abstract
UNLABELLED: OBJECTIVE; Weight loss improves glycemic control, lipid profiles, and blood pressure in patients with type 2 diabetes. However, successful long-term weight loss is difficult for these patients, particularly those treated with insulin. The aim of this study was to assess the effect of orlistat, a gastrointestinal lipase inhibitor, on weight loss, glycemic control, and cardiovascular risk factors in overweight or obese insulin-treated type 2 diabetic patients. RESEARCH DESIGN AND METHODS: This study was a 1-year multicenter, randomized, double-blind, placebo-controlled trial of orlistat (120 mg three times a day) or placebo combined with a reduced-calorie diet in overweight or obese adults (BMI 28-40 kg/m(2)) with type 2 diabetes treated with insulin alone or combined with oral agents, but with suboptimal metabolic control (HbA(1c) 7.5-12.0%). Outcome measurements included changes in body weight, glycemic control, blood pressure, and serum lipids. RESULTS; After 1 year, the orlistat group lost significantly more weight (-3.89 +/- 0.3% of baseline body weight, means +/- SE) than the placebo group (-1.27 +/- 0.3%, P < 0.001). Orlistat treatment, compared with placebo, produced greater decreases in HbA(1c) (-0.62 +/- 0.08 vs. -0.27 +/- 0.08%, P = 0.002), fasting serum glucose (-1.63 +/- 0.3 vs. -1.08 +/- 0.3 mmol/l, P = 0.02), and the required doses of insulin and other diabetic medications. Orlistat also produced greater improvements than placebo in serum total cholesterol (P = 0.0002) and LDL cholesterol concentrations (P = 0.001) and LDL/HDL ratio (P = 0.01). CONCLUSIONS; Orlistat therapy produces clinically significant weight loss, with improvements in glycemic control and cardiovascular disease risk factors, in overweight or obese patients with type 2 diabetes who have suboptimal metabolic control with insulin therapy.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".