Metabolic Syndrome, a Cardiovascular Disease Risk Factor: Role of Adipocytokines and Impact of Diet and Physical Activity
Bibliographic record
Abstract
The metabolic syndrome comprises an array of cardiovascular disease (CVD) risk factors such as abdominal obesity, dyslipidemia, hypertension, and glucose intolerance. Insulin resistance and/or increased abdominal (visceral) obesity have been suggested as potential etiological factors. More recently, increasing evidence has associated insulin resistance and subclinical inflammation involving cytokines derived from adipose tissue, or adipocytokines. Despite the fact that precise mechanisms have yet to be established, there is a significant role for both diet and physical activity to improve the many factors associated with the metabolic syndrome, including modulation of various adipocytokines. Although both diet and physical activity have been studied for their ability to modify cytokines in more traditional inflammatory conditions, such as rheumatoid arthritis, they have been less studied in relation to inflammation as an underlying cause of the metabolic syndrome and/or CVD. A more thorough understanding of the clustering of metabolic abnormalities and their underlying etiology will help to define diet and physical activity guidelines for preventing and treating the metabolic syndrome, an important aspect of CVD prevention. This paper will address potential underlying causes of the metabolic syndrome, with a focus on the putative mechanistic role of adipocytokines, and will discuss the impact of diet and physical activity on the metabolic syndrome.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".