DIET, EXERCISEREGIMENS AND MEDICATIONSTHAT ALTER BLOOD LEPTIN, ADIPONECTIN LEVELS AND ADIPONECTIN/LEPTIN RATIO TO PREVENT AND CONTROL CARDIO METABOLIC DISEASES DEVELOPMENT AND PROGRESSION
Bibliographic record
Abstract
Objectives: High adiponectin and low leptin levels in serum are associated with less risk of insulin resistance, hypertension, atherosclerosis and a favorable lipid profile. In this systematic review, our objective was to determine effective diet changes, exercise and medications to achieve a favorable adiponectin/leptin ratio that decrease the risk of cardio metabolic conditions. Methods: After searching the literature in PubMed, MEDLINE, Google scholar and Cochrane library, we included randomized controlled trials and observational studies done on human participants regardless of age, BMI, sex and co-morbid conditions and studies that compared or assessed effects of interventions like diet, exercise or medications for more than two weeks on blood leptin and adiponectin levels. Follow up studies, animal studies and the studies in which adiponectin and leptin levels were not specified were excluded from the review. We screened 118 studies, data was retrieved from 70 studies out of which 36 studies were eligible for quality assessment. We used Cochrane risk of bias tool for randomized controlled trials and new castle Ottawa scale for observational studies for their quality assessment. Finally, we included 22 randomized controlled trials and 2 observational studies. Findings: The supplements, diet and exercise regimens and medications that were studied, many of them showed desirable changes in adipokine levels. Some of these regimens have not shown changes from baseline. Conclusions: Exercise, diet modifications and medications like Orlistat, Resveratrol, Pioglitazone should be used to target the adipokine levels to reduce cardiometabolic disease risk and progression.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".