DIET, EXERCISEREGIMENS AND MEDICATIONSTHAT ALTER BLOOD LEPTIN, ADIPONECTIN LEVELS AND ADIPONECTIN/LEPTIN RATIO TO PREVENT AND CONTROL CARDIO METABOLIC DISEASES DEVELOPMENT AND PROGRESSION
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it