The Beneficial Effects of SesaVitaTM on Lipid Profiles and Blood Glucose Levels in Subjects With Prediabetes and Mild-To-Moderate Hyperlipidemia in India
Bibliographic record
Abstract
Individuals suffering from Type 2 diabetes develop prediabetes before progression of diabetes. In case of prediabetes people, the blood glucose levels are higher than normal but not sufficient to be diagnosed as diabetes. On the basis of existing reports on Sesame extract, SesaVitaTM which is an herbal food supplement containing Sesame seeds (Sesamum indicum L.) extract may provide an option for management of prediabetes. The objective of this study was to determine the beneficial effects of SesaVitaTM in prediabetes and mild to moderate hyperlipidemia subjects. This randomized, placebo-controlled, double-blind study comprised of 13 female and 07 male patients with prediabetes and mild to moderate hyperlipidemia, aged between 18 and 65 years. Twenty subjects were randomized to receive SesaVitaTM (500 mg/day) or placebo along with therapeutic lifestyle changes for 6 weeks. The primary outcome was the measure of efficacy in terms of change in serum lipid profile and glycaemic levels on week 3 and 6. Secondary outcome measures include safety and tolerability evaluated by physical examination and clinical laboratory evaluations. Improvements in lipid profile and glycaemic levels were observed in SesaVitaTM treated group when compared with placebo and baseline. A statistical significant reduction was observed in low density lipoprotein cholesterol (LDL-C), total cholesterol (TC), oral glucose tolerance test (OGTT) and fasting blood sugar (FBS) levels during week 3 and 6 when treated with SesaVitaTM extract. No adverse events occurred and all safety parameters were within normal ranges during the study. This study revealed that the treatment with SesaVitaTM was safe and well tolerated; may be beneficial in the management of prediabetes and mild-to-moderate hyperlipidemia.
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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".