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Record W4411754131 · doi:10.1101/2025.06.27.25330451

Heightened Risk of Myocardial Ischemia with Mental Stress Among Black Women Survivors of a Myocardial Infarction in Midlife

2025· preprint· en· W4411754131 on OpenAlexaff
Viola Vaccarino, Amit Shah, Tené T. Lewis, Marina Piccinelli, Lisa Elon, Hua She, Yi‐An Ko, Zachary T. Martin, Nancy Murrah, Lucy Shallenberger, Tatum Roberts, Lewam Stefanos, J. Douglas Bremner, Paolo Raggi, Arshed A. Quyyumi

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMyocardial infarctionCardiologyMyocardial ischemiaInternal medicineMedicineMental stressMental healthStress (linguistics)IschemiaPsychiatry

Abstract

fetched live from OpenAlex

ABSTRACT Background Stark disparities in the outcome of myocardial infarction (MI) persist, with large unexplained variations affecting younger Black women. Mental stress induced myocardial ischemia (MSIMI) is an emerging mechanistic pathway that may help explain excess risks in this group. Objectives To determine whether MSIMI is more common in Black women with recent premature MI than other demographic groups. Methods We studied 602 individuals ≤ 61 years of age who were hospitalized for MI in the previous 8 months. Participants underwent 99mTc-sestamibi myocardial perfusion imaging at rest and after mental stress (speech task). A summed difference score (SDS) was used to quantify ischemia. Clinically significant MSIMI was defined as an SDS ≥3. Log-binomial regression models adjusted for sociodemographic, lifestyle, clinical and psychosocial factors. Results The mean age was 51 years (range, 25-61), 46% were women and 59% self-identified as Black. Black women had a more adverse psychosocial profile and higher rates of obesity and diabetes, but a less severe index MI. The incidence of MSIMI was approximately doubled in Black women than the other groups (p<.001 for interaction). Clinical and psychosocial risk factors did not explain these differences. In a fully adjusted model, the risk ratio of MSIMI for Black women was 2.2 (95% CI, 2.0-2.5) compared to Black men, 2.3 (1.8-2.9) compared to non-Black women, and 1.8 (1.4-2.2) compared to non-Black men. Conclusions Among midlife individuals with a recent MI, Black women have a disproportionately higher risk of MSIMI. Targeted interventions for this high-risk group are needed. CLINICAL PERSPECTIVE What is new? We show for the first time that Black women in midlife who have recently experienced a myocardial infarction face a disproportionately high risk of developing myocardial ischemia when under mental stress. This elevated risk cannot be attributed to more severe coronary artery disease, suggesting a stress-related cardiovascular vulnerability that may help explain why Black women experience both higher rates of premature heart attacks and worse outcomes following these events. What are the clinical implications? The high rate of ischemia with mental stress in Black women highlights the need for new targeted risk assessment protocols and prevention strategies that go beyond the control of conventional risk factors to address stress-related risk pathways. This paradigm shift in cardiac care should help reduce cardiovascular disparities and improve outcomes in this historically underserved population. GRAPHICAL ABSTRACT Differences in Myocardial Ischemia with Mental Stress by Sex and Race Among 602 individuals ≤ 61 years of age who were hospitalized for a myocardial infarction in the previous 8 months, and who underwent myocardial perfusion imaging with mental stress, Black women had approximately a twofold higher risk of developing myocardial ischemia compared with other demographic groups, even after adjusting for clinical and psychosocial factors.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.010
GPT teacher head0.278
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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