Nuclear Magnetic Resonance Lipoprotein Abnormalities in Prediabetic Subjects in the Insulin Resistance Atherosclerosis Study
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Bibliographic record
Abstract
BACKGROUND: Subjects with type 2 diabetes have smaller LDL and HDL particles in addition to higher levels of triglycerides and lower HDL cholesterol. Elevated insulin resistance, blood pressure, and dyslipidemia (including small dense LDL) predicted incident diabetes. In the Insulin Resistance Atherosclerosis Study (IRAS) we studied nuclear magnetic resonance (NMR) lipoprotein particle measures in prediabetic individuals, considering potentially modifying covariates, including insulin resistance, as directly measured using a frequently sampled intravenous glucose tolerance test. METHODS AND RESULTS: Of 830 subjects who were nondiabetic at baseline, 130 (15.7%) developed diabetes after a mean follow-up of 5.2 years. Various lipoprotein abnormalities were found in prediabetic subjects compared with subjects who stayed nondiabetic at follow-up. In logistic regression analyses (demographically adjusted), VLDL particles, large VLDL, LDL particles, small LDL, large HDL, small HDL, VLDL size, LDL size, and HDL size were related to incident diabetes. The relation of VLDL size and small HDL to incident diabetes was independent of waist (odds ratio [OR] [95% CI], 1.43 [1.18 to 1.73] and 1.23 [1.01 to 1.51] for VLDL size and small HDL, respectively) and independent of conventionally (chemically) measured triglycerides and HDL cholesterol (OR [95% CI], 1.45 [1.18 to 1.78] and 1.30 [1.06 to 1.60], respectively). Insulin sensitivity attenuated the relation to incident diabetes of VLDL size (OR [95% CI], 1.25 [1.01 to 1.53]) but not of small HDL particles (OR [95% CI], 1.25 [1.02 to 1.54]). CONCLUSIONS: We have shown a range of lipoprotein abnormalities in prediabetic individuals, including compositional changes in HDL and VLDL. These findings extend previous work indicating a proatherogenic state in healthy, nondiabetic subjects who subsequently develop diabetes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it