Serum Insulin and Inflammatory Markers in Overweight Individuals with and without Dyslipidemia
Bibliographic record
Abstract
CONTEXT: The worldwide epidemic of overweight and obesity is setting the scene for a new wave of premature cardiovascular disease. OBJECTIVE: The objective of this study was to define relationships between dyslipidemia and other metabolic abnormalities in overweight subjects. DESIGN: This study included comparison of overweight subjects with and without dyslipidemia. SETTING: The setting was an institutional practice. PATIENTS: Dyslipidemic subjects (n = 715) had plasma triglyceride greater than or equal to the 75th percentile in combination with high-density lipoprotein cholesterol (HDL-C) less than or equal to the 25th percentile. Unrelated, normolipidemic controls (n = 1073) had HDL-C higher than the median and triglyceride lower than the median. It was a requirement for the control subjects to have a body mass index (BMI) greater than 25 kg/m(2). MAIN OUTCOME MEASURES: The main outcome measures included BMI, inflammatory markers, adipokines, blood pressure, and fasting plasma glucose and insulin. RESULTS: The mean BMI in the subjects and controls was 28.7 and 28.2 kg/m(2), respectively. Subjects had higher levels of plasma high-sensitivity C-reactive protein (3.0 vs. 2.0 mg/liter; P < 0.001), lower levels of adiponectin (4.7 vs. 6.6 mg/liter; P < 0.001), and, after adjustment for age, BMI, gender, smoking, statin, and beta-blocker use, higher systolic (P = 0.001) and diastolic (P = 0.05) blood pressures. Fasting plasma glucose, insulin, and homeostasis model of assessment-insulin resistance were all significantly higher in subjects than controls (P < 0.0001). CONCLUSIONS: Identification of people solely on the basis of an elevated plasma triglyceride and a low HDL-C uncovers an overweight group of people who have a generalized metabolic disorder. In contrast, overweight people with normal plasma lipids have normal glucose and insulin metabolism, low levels of inflammatory markers, and normal blood pressure. Such people may thus be at relatively low risk of developing diabetes and cardiovascular disease despite being overweight.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".