Race-Based Differences in Lipoprotein(a)-Associated Risk of Carotid Atherosclerosis
Bibliographic record
Abstract
Objective- Lp(a) [lipoprotein(a)] is a well-described risk factor for atherosclerosis, but Lp(a)-associated risk may vary by race/ethnicity. We aimed to determine whether race/ethnicity modifies Lp(a)-related risk of carotid atherosclerotic plaque outcomes among black, white, Chinese, and Hispanic individuals. Approach and Results- Carotid plaque presence and score were assessed by ultrasonography at baseline (n=5155) and following a median 9.4 year period (n=3380) in MESA (Multi-Ethnic Study of Atherosclerosis) participants. Lp(a) concentrations were measured by immunoassay and examined as a continuous and categorical variable using clinically-based cutoffs, 30 and 50 mg/dL. Lp(a) was related to greater risk of prevalent carotid plaque at baseline in whites alone (all P<0.001): per log unit (relative risk, 1.05); Lp(a)≥30 mg/dL (relative risk, 1.16); and Lp(a)≥50 mg/dL (relative risk, 1.20). Lp(a) levels over 50 mg/dL were associated with a higher plaque score at baseline in whites (all P<0.001) and Hispanics ( P=0.04). In prospective analyses, whites with Lp(a) ≥50 mg/dL were found to have greater risk of plaque progression (relative risk, 1.12; P=0.03) and higher plaque scores (all P<0.001) over the 9.4-year follow-up. Race-based differences between whites and black participants were significant for cross-sectional associations and for carotid plaque score following the 9.4 year study period. Conclusions- Race was found to be a modifying variable in Lp(a)-related risk of carotid plaque, and Lp(a) levels may have greater influence on plaque burden in whites than in black individuals. Borderline results in Hispanics suggest that elevated Lp(a) may increase the risk of carotid plaque, but follow-up studies are needed.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".