Nuclear Magnetic Resonance Lipoprotein Abnormalities in Prediabetic Subjects in the Insulin Resistance Atherosclerosis Study
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".