Metabolic Syndrome - Risk Factors for Atherosclerosis and Diabetes
Bibliographic record
Abstract
Objective: To evaluate the lipoprotein profiles, triglycerides and glycemia along with the abdominal fat to explore the risk factors associated with non-diabetic state to IGF, IGT and Type-2 diabetes in Canadian population. Methods: We examined 780 subjects using the ADA and WHO criteria to classify them into groups based on (1) normal glucose tolerance with FBS < 6.0 and 2hBS < 7.0 mmol/l), (2) IFG; FPG ≥6.1 mmol/l but 2hBS > 7.8-11.1 mmol/l; (3) combined IFG/IGT (FPG ≥7.0 mmol/l and 2hBS > 11.1 mmol/l). We compared the three groups for glycemia, insulin secretion and insulin sensitivity based on their WHR, abdominal and visceral fat measurements. Results: The subjects with higher 2 hrs glucose levels 5.2 for NGT vs. 9.1 for IGT and 13.4 mmol/l for NIDDM, p < 0.001, apo C-III level (12.8 (DM) vs. 8.9 mg/dl (normal), p < 0.001), waist to hip ratio (0.91 (IGT) vs. 0.89 (Normal), p < 0.01) and abdominal fat and were found to be highly insulin resistant. Conclusions: The higher apolipoproteins levels, BMI and abdominal and visceral fat accompanied by poor glycemia were shown to be associated strongly with the metabolic abnormalities. These factors led to the worsening of insulin secretory dysfunction and insulin resistance and were strong predictors of diabetes. Abbreviations: 2hBS, 2-hour blood sugar, • FBS, fasting blood glucose, •NGT, normal glucose tolerance, • IFG, impaired fasting glucose, • IGT, impaired glucose tolerance, • MS, metabolic syndrome, • IR, insulin resistance • ISI, insulin sensitivity index • OGTT, oral glucose tolerance test • WHR, waist-to-hip ratio, • BMI, body mass index, • VFA, visceral fat area, • SFA, subcutaneous fat area, • WHO, World Health Organization.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".