Utility of non‐high‐density lipoprotein cholesterol in assessing incident type 2 diabetes risk
Bibliographic record
Abstract
AIMS: Traditional lipid indices have been associated with type 2 diabetes, but limited data are available regarding non-high-density lipoprotein (non-HDL) cholesterol. In view of recent guidelines for the clinical management of dyslipidemia recommending the monitoring of non-HDL cholesterol as a secondary target after achieving the low-density lipoprotein (LDL) cholesterol goal, we aimed to assess the association of non-HDL cholesterol with incident type 2 diabetes and compare its utility as a risk predictor with traditional lipid variables in Aboriginal Canadians. METHODS: Of 606 diabetes-free participants at baseline, 540 (89.1%) returned for 10-year follow-up assessments. Baseline anthropometry, blood pressure, fasting insulin and serum lipids were measured. Fasting and 2-h postload glucose were obtained at baseline and follow-up to determine the incidence of type 2 diabetes. RESULTS: The cumulative incidence of type 2 diabetes was 17.5%. Higher non-HDL cholesterol, total-to-HDL cholesterol ratio, apolipoprotein B, triglyceride and LDL cholesterol and lower HDL cholesterol concentrations were individually associated with incident type 2 diabetes in univariate analyses (all p < 0.05). Non-HDL cholesterol was a superior determinant of incident diabetes compared with LDL cholesterol (comparing C-statistics of univariate models p = 0.01) or HDL cholesterol (p = 0.004). With multivariate adjustment including waist circumference, non-HDL cholesterol remained associated with incident diabetes [odds ratio (OR) 1.42 (95% confidence interval, CI 1.07-1.88)], while LDL cholesterol and HDL cholesterol became non-significant. CONCLUSIONS: Non-HDL cholesterol was associated with incident type 2 diabetes and was superior to LDL cholesterol as a risk predictor in this population. Further studies are required to establish the utility of non-HDL cholesterol in non-Aboriginal populations.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".