The effect of highly absorbed curcumin on an oxidized LDL in patients with COPD
Bibliographic record
Abstract
Objective COPD is primarily caused by regular smoking and is associated with a high frequency of coronary artery disease. There is growing recognition that the inflammation in COPD is not only confined to the lungs but also involves the systemic circulation and can impact nonpulmonary organs, including blood vessels. α1-antitrypsin-low-density lipoprotein (AT-LDL) complex is an oxidized LDL that accelerates atherosclerosis. Curcumin, one of the best-investigated natural products, is a powerful antioxidant. However, the effects of curcumin on AT-LDL remain unknown. We hypothesized that Theracurmin®, a highly absorptive curcumin with improved bioavailability using a drug delivery system, ameliorates the inflammatory status in subjects with COPD. Methods This is a randomized, double-blind, parallel-group study. The subjects were men or women (age, 20–85 years) who met the following criteria: 1) patients with COPD at stage 0, I, or II according to the definition by the Japanese Respiratory Society and 2) patients who have never smoked at least for the past 4 months. Subjects were randomly assigned to receive 90 mg Theracurmin® or placebo twice a day for 24 weeks, and changes in inflammatory parameters were evaluated. Results There were no differences between the Theracurmin® and placebo groups in terms of age, male/female ratio, or body mass index in 39 evaluable subjects. The percent changes in blood pressure and hemoglobin A1c and LDL-cholesterol, triglyceride, or high-density lipoprotein-cholesterol levels after treatment were similar for the two groups. However, the percent change in the AT-LDL level was significantly (P=0.020) lower in the Theracurmin® group compared with the placebo group. However, LDL-C, IL-6 and TNF-α were not changed. Conclusions Theracurmin® reduced levels of atherosclerotic AT-LDL, which may lead to the prevention of future cardiovascular events in patients with COPD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".