Calculated Non-HDL Cholesterol Includes Cholesterol in Larger Triglyceride-Rich Lipoproteins in Hypertriglyceridemia
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Bibliographic record
Abstract
CONTEXT: Calculated non-high-density lipoprotein (HDL) cholesterol (non-HDLC) should selectively include cholesterol from atherogenic lipoproteins to be a reliable risk marker of cardiovascular disease. In hypertriglyceridemia (HTG), there is increased abundance of larger and less atherogenic triglyceride-rich lipoproteins (TRL), namely, larger very-low-density lipoproteins (VLDL), and chylomicrons. OBJECTIVE: We aim to demonstrate that serum triglyceride (TG) level has a substantial impact on non-HDLC's ability to represent cholesterol from atherogenic lipoproteins, even though TG is not part of the calculation for non-HDLC. DESIGN: Analysis of lipid profile data. SETTINGS: Lipid Clinic patient cohort, and Biochemistry Laboratory patient cohort. PATIENTS OR OTHER PARTICIPANTS: 7,492 patients in the Lipid Clinic cohort with baseline lipid profiles documented prior to starting lipid-lowering medications and 156,311 lipid profiles from The Ottawa Hospital Biochemistry Laboratory cohort. INTERVENTION: None. MAIN OUTCOME MEASURE: Our modeling process includes derivation of TG-interval-specific lipoprotein composition factor (LCF) for TRL, which represents the mass ratio of cholesterol to TG in TRL. A high LCF indicates that the TRLs are mainly the cholesterol-rich atherogenic remnant lipoproteins. A low LCF indicates that the TRLs are mainly the TG-rich larger VLDL and chylomicrons. RESULTS: As serum TG increases, there is progressive decline in the LCF for TRL, which indicates that the calculated non-HDLC level reflects progressive inclusion of cholesterol from larger TRL. This is shown in both cohorts. CONCLUSIONS: Calculated non-HDLC is influenced by TG level. As TG increases, non-HDLC gradually includes more cholesterol from larger TRL, which are less atherogenic than LDL and remnant lipoproteins.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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