Abstract P417: Association of Breast Arterial Calcification Presence and Severity with Cognitive Function: The MINERVA Study
Bibliographic record
Abstract
Mammographic Breast arterial calcification (BAC) may be a novel subclinical cardiovascular disease (CVD) risk marker. Since subclinical and clinical CVD are associated with cognitive impairment, we set out to investigate whether there is a relation between BAC presence/severity and cognitive function (CF). We used data from the M ult I eth N ic study of br E ast a R terial calcium gradation and cardio VA scular disease (MINERVA), a multiethnic cohort of women aged 60-79 at baseline (10/2012 and 2/2015) who were free of symptomatic CVD, all recruited at Kaiser Permanente of Northern California. The sample available for analyses with complete data on BAC, cognitive function and covariates was 3,919 (mean ± SD age=67 ± 4, 52% white, 18% Asian, 15% African-American and 12% Latina). A BAC continuous mass score (mg) was obtained using a validated densitometry method. BAC presence was BAC score > 0 mg, and severe BAC was BAC score > 20 mg. CF was dichotomized as Montreal Cognitive Assessment (MoCA) score < 23 (first quartile) vs ≥ 23 (quartiles 2, 3 and 4). The unadjusted (Model 1) odds ratios (OR, 95% CI; p-value) of CF < 23 vs. ≥ 23 associated with BAC > 0 vs. BAC=0 was 1.15 (0.98-1.35; 0.08). However, adjustment for age, race and education (Model 2) abolished this association. Further adjustment for factors independently predicting CF (Model 3, with further inclusion of diabetes, HDL-C, CES depression score, breast feeding, multiple sclerosis and osteoarthitis) did not change the association. The unadjusted (Model 1) odds ratios (OR, 95% CI; p-value) of CF < 23 vs. ≥ 23 associated with BAC > 20 vs. BAC≤ 20 was 1.44 (1.02-2.01; 0.04). However, adjustment for age, race and education (Model 2) abolished this association and further adjustment for factors independently predicting CF did not alter the association. In conclusion, we found a statistically significant association between severe BAC and CF, but it was explained by covariation (confounding) by age, race and education, arguing that BAC may not play a role in cognitive impairment.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".