Thrombo‐inflammatory biomarkers and D‐dimer in a biracial cohort study
Bibliographic record
Abstract
BACKGROUND: Higher D-dimer is a risk factor for cardiovascular diseases and venous thromboembolism. In the general population, D-dimer and other thrombo-inflammatory biomarkers are higher among Black individuals, who also have higher risk of these conditions compared to White people. OBJECTIVE: To assess whether Black individuals have an exaggerated correlation between D-dimer and thrombo-inflammatory biomarkers characteristic of cardiovascular diseases. METHODS: Linear regression was used to assess correlations of 11 thrombo-inflammatory biomarkers with D-dimer in a cross-sectional study of 1068 participants of the biracial Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohort. RESULTS: Adverse levels of most biomarkers, especially fibrinogen, factor VIII, C-reactive protein, N-terminal pro-B-type natriuretic peptide, and interleukin (IL)-6, were associated with higher D-dimer. Several associations with D-dimer differed significantly by race. For example, the association of factor VIII with D-dimer was more than twice as large in Black compared to White participants. Specifically, D-dimer was 26% higher per standard deviation (SD) higher factor VIII in Black adults and was only 11% higher per SD higher factor VIII in White adults. In Black but not White adults, higher IL-10 and soluble CD14 were associated with higher D-dimer. CONCLUSIONS: Findings suggest that D-dimer might relate to Black/White differences in cardiovascular diseases and venous thromboembolism because it is a marker of amplified thrombo-inflammatory response in Black people. Better understanding of contributors to higher D-dimer in the general population is needed.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".