Evidence map›Paper›PMID 42856448›Full record

ArticlemedRxiv : the preprint server for health sciences2026

Preindex Social Context and Cardiovascular Events Across Blood Pressure, LDL Cholesterol, and A1c Cohorts.

Sana Mirza, Joshua Moen

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Sana MirzaPhysician Assistant Program, UC Davis Health, Sacramento, California, USA.
Joshua MoenPhysician Assistant Program, UC Davis Health, Sacramento, California, USA.ORCID 0000-0002-6808-2239

Funding

Technology to Empower Changes in Health (TECH) Network Participant Technologies CenterU24OD023176 · OD · SCRIPPS RESEARCH INSTITUTE, THE · PI TOPOL, ERIC JEFFREY · 2016 to 2022
$204.7M
Precision Medicine Initiative Cohort Program BiobankU24OD023121 · OD · MAYO CLINIC ROCHESTER · PI CEKANOVA, MARIA, CICEK, MINE · 2016 to 2024
$185.5M
Enhancing All of Us Data Resources for Nutrition Precision Health: the All of Us Data and Research CenterU2COD023196 · OD · VANDERBILT UNIVERSITY MEDICAL CENTER · PI GLAZER, DAVID, HARRIS, PAUL A. · 2016 to 2022
$143.7M
Adaptive Platform for Personalized EngagementU24OD023163 · OD · VIGNET, INC. · PI JAIN, PRADUMAN · 2017 to 2020
$102.6M
University of Arizona-Banner Health All of Us Research Program OT2OD026549 · OD · UNIVERSITY OF ARIZONA · PI MORENO, FRANCISCO A, REIMAN, ERIC MICHAEL · 2018 to 2023
$78.9M
California Precision Medicine Research Program ConsortiumOT2OD026552 · OD · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ANTON-CULVER, HODA A, OHNO-MACHADO, LUCILA · 2018 to 2023
$73.4M
All of Us PennsylvaniaOT2OD026554 · OD · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI REIS, STEVEN E, VISWESWARAN, SHYAM · 2018 to 2023
$72.1M
New York City Consortium for Precision MedicineOT2OD026556 · OD · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BIER, LOUISE E, GHARAVI, ALI G · 2018 to 2023
$67.3M
SouthEast Enrollment Center (SEEC) OT2OD026551 · OD · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI CARRASQUILLO, OLVEEN, COLON, VIVIAN · 2018 to 2023
$62.8M
Southern All of Us NetworkOT2OD026548 · OD · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI FOUAD, MONA N., KORF, BRUCE R · 2018 to 2023
$60.5M
Illinois Precision Medicine Consortium OT2OD026557 · OD · NORTHWESTERN UNIVERSITY AT CHICAGO · PI AHSAN, HABIBUL, ARGOS, MARIA · 2018 to 2023
$60.5M
The New England Precision Medicine Consortium of the All of Us Research ProgramOT2OD026553 · OD · MASSACHUSETTS GENERAL HOSPITAL · PI CLARK, CHERYL RENEE, KARLSON, ELIZABETH W · 2018 to 2023
$58.8M
NIH HHS OT2 OD023205NIH HHS OT2 OD023206NIH HHS OT2 OD025276NIH HHS OT2 OD025277NIH HHS OT2 OD025315NIH HHS OT2 OD025337NIH HHS OT2 OD026548NIH HHS OT2 OD026549NIH HHS OT2 OD026550NIH HHS OT2 OD026551NIH HHS OT2 OD026552NIH HHS OT2 OD026553NIH HHS OT2 OD026554NIH HHS OT2 OD026555NIH HHS OT2 OD026556NIH HHS OT2 OD026557NIH HHS U24 OD023121NIH HHS U24 OD023163NIH HHS U24 OD023176NIH HHS U2C OD023196
6 · The paper itself

Abstract

Background: Cardiovascular prevention is organized around target metrics. Blood pressure, LDL cholesterol, and A1c define treatment targets, quality metrics, and, implicitly, who is considered safe. Yet a value below a cutoff is a correlative measurement, not an outcome. We tested whether social context recorded before the measurement separated subsequent cardiovascular events among people whose numbers already met the cutoff. Methods: In this retrospective cohort study, we analyzed All of Us participants without recorded cardiovascular disease and with low comorbidity in three separately indexed cohorts: systolic blood pressure (SBP), LDL cholesterol, and A1c. A preindex score combined household income, education, food security, physical activity, and ZIP-linked deprivation, standardized against a fixed pooled reference of 325,652 eligible people. Cox models adjusted for the measured biomarker value, its square, a score-by-biomarker term, medication exposure, and clinical covariates. The outcome was first myocardial infarction, ischemic stroke, or heart failure. A sensitivity analysis indexed participants at the first measurement after the score became computable and counted only events recorded in inpatient or emergency settings. Results: The SBP, LDL, and A1c cohorts included 19,525, 16,578, and 18,539 participants with 144, 129, and 191 events. Each one-SD more favorable score was associated with lower hazard: HR 0.611 (95% CI 0.498-0.749), 0.631 (0.512-0.778), and 0.544 (0.457-0.648), respectively. Below each study cutoff (SBP <140 mmHg, LDL <100 mg/dL, A1c <8%), less favorable context had event rates of 3.39 versus 1.68, 7.16 versus 2.05, and 8.67 versus 2.34 per 1,000 person-years. In a one-year analysis restricted to 2019-2023 index dates, the corresponding adjusted HRs were 0.530, 0.336, and 0.365. In the sensitivity cohorts (54,338, 41,451, and 33,607 participants; 493, 288, and 316 events), HRs were 0.732 (0.657-0.815), 0.660 (0.573-0.761), and 0.631 (0.552-0.722). Conclusions: A biomarker below the study cutoff did not identify a uniform, low-risk population. Social context recorded before the measurement separated cardiovascular events after adjustment for the measured value and clinical factors. Risk assessment that stops at the number misses this variation.

Indexed as

All of Usblood pressurecardiovascular eventshemoglobin A1cLDL cholesterolsocial determinants of health

Identifiers

PMID42856448
PMCPMC13648813

What OpenQuestion holds

Textmetadata
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.