Clustering of fasting glycemic biomarkers is related to levels of cardiovascular disease risk in adults
Bibliographic record
Abstract
Modifiable cardiovascular disease (CVD) risk factors include obesity, dyslipidemia and type 2 diabetes (T2D). Non‐modifiable CVD risk factors can include single‐nucleotide polymorphisms such as those within APOE and hepatic lipase (LIPC) genes. Comparison of risk factors across varying levels of CVD risk can help to elucidate the most relevant biomarkers for risk assessment. This study included data from men and postmenopausal women (n=39) with or without dysglycemia and obesity, and with diet‐controlled T2D, to examine multiple CVD risk factors using glycemic status and APOE and LIPC −514C>T polymorphisms as response variables. Predictor variables included 23 anthropometric, fasting glycemic and lipidemic biomarkers, oral glucose tolerance test area under the curve and dietary data. Relationships between CVD risk factors and levels were investigated via cluster analysis, logistic regression, ANOVA and Pearson's chi‐square test. Results identified 3 clusters among glucose, insulin and incretins that corresponded to distinct CVD risk levels. Age and fasting glucose in all participants and those with APOE E3/E3 genotype were significant and LIPC genotypes were related to CVD risk levels. This study highlights potential CVD risk factors through cluster analysis and supports current CVD risk screening. Grant Funding Source : Ontario Ministry of Agriculture, Food and Rural Affairs
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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 | 0.002 |
| 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 | 0.000 | 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".