ApoA-1 versus HDL-C as Markers of Cardiovascular Risk
Bibliographic record
Abstract
Abstract Background Conflicting results have been reported as to the relative importance of apoA-1 versus HDL-C as markers of ASCVD risk. Methods Residual discordance analysis with Cox proportional hazard models comparing apoA-1 and HDL-C as markers of ASCVD risk was applied to a sample of 291,995 UK Biobank, followed for a median of 11 years. Interaction test for the two markers and estimation of the effects of partitioning HDL-C into apoA-I, log-triglyceride and the remaining residual were also performed. Results ApoA-1 and HDL-C had similar associations with ASCVD risk (HRs of 0.85, p-value < 0.001 for both). The residual of HDL-C added significantly to the risk associated with apoA-1 as did the residual of apoA-1 to HDL-C. There was a statistically significant interaction between apoA-I and HDL-C ( HR = 1.05, 95% CI: (1.04, 1.06); p < 0.001 ). Decomposing HDL-C into the 3 components, apoA-I accounted for the largest portion of the effect with a HR of 0.85 95%CI: (0.83, 0.86) with smaller effects for lnTG: 1.04 (1.02, 1.06) and residual of HDL-C: 0.98 95%CI: (0.96, 0.995). Conclusions HDL-C and apoA-1 have associations of equivalent strength with ASCVD risk with significant interaction modifying the effect of one by the other. Upon decomposition, ApoA-I retained more of the effect of HDL-C as compared to log-triglycerides. While only observational, the results are consistent with the relation of HDL to risk not being determined by the concurrent level of triglyceride. Clinical Perspective The plasma levels of HDL-C and apoA-I are potent predictors of cardiovascular risk. However, there are few data comparing the relative precision of HDL-C and apoA-I for this purpose. Moreover, the risk of low HDL-C has been attributed to concurrent hypertriglyceridemia, consequently downgrading the potential importance of HDL in predicting or explaining risk. Novel Findings Based on residual discordance analysis, HDL-C and apoA-I have similar predictive precision for ASCVD risk. However, each adds significantly to the other and. Moreover, triglycerides account for only a small portion of the risk attributable to HDL with apoA-I accounting for the principal portion. Clinical Significance HDL, whether measured as HDL-C or apoA-I, is a potent predictor of ASCVD risk. It remains essential to search for the biological basis or bases for these relationships.
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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".