Evidence map›Paper›PMID 40778153›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Prevalence of Risk Factors and Established Cardiovascular Disease Among All of Us Participants: Benchmarking Against National Estimates.

Mengjing Yang, Tinsae Admassu, Harlan M Krumholz, Chenxi Huang, Karthik Murugiah

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

5 authors.

Mengjing YangYale School of Public Health, New Haven, CT.
Tinsae AdmassuYale School of Public Health, New Haven, CT.
Harlan M KrumholzYale School of Public Health, New Haven, CT.ORCID 0000-0003-2046-127X
Chenxi HuangSection of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT.
Karthik MurugiahSection of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT.ORCID 0000-0001-7932-382X

Funding

Automated ascertainment of bleeding and target lesion revascularization after percutaneous coronary intervention (PCI) using electronic health record (EHR) dataK08HL157727 · NHLBI · YALE UNIVERSITY · PI Karthik Murugiah · 2022 to 2026
$831k
NHLBI NIH HHS K08 HL157727
6 · The paper itself

Abstract

Introduction: Knowing how demographics, cardiovascular risk factors and established cardiovascular disease (CVD) of All of Us (AoU) Research Program participants differ from the U.S population is important for cardiovascular researchers using these data, and to inform the generalizability of findings. Methods: We included AoU participants ≥18 years enrolled between May 2017-June 2022. 'AoU Survey' cohort included participants who completed the Personal and Family Health History survey. 'AoU EHR' included participants with linked EHR data. Cardiovascular risk factors and established CVD overall and by demographic sub-groups were compared to a weighted sample of adults ≥18 years from the National Health and Nutrition Examination Survey (NHANES) 2017-March 2020 cycle. Cardiovascular risk factors and established CVD were identified through survey questions in 'AoU Survey' and NHANES, and through SNOMED codes for 'AoU EHR'. Results: 'AoU Survey' (N=185,232) was older than NHANES (N=9,683 representing 248 million) (56±17 vs 48±18 years), with more female (60.4% vs 51.8%) and Black (18.6% vs 11.5%, all P <0.001) participants. Prevalence of cardiovascular comorbidities in 'AoU Survey' was similar to NHANES overall, but lower when age stratified. However, this pattern was not observed among Black participants, who exhibited higher hypertension (42.2% vs 39%), diabetes (19.2% vs 12.5%), and congestive heart failure (3.8% vs 3.6%) rates than the U.S Black population. In contrast, female participants showed lower cardiovascular comorbidities. 'AoU EHR' with its code-based identification had higher prevalence of cardiovascular comorbidities. Conclusion: AoU includes more historically underrepresented groups, as intended. However, researchers should account for differences in disease prevalence and subgroup characteristics with the U.S. population when interpreting survey estimates.

Identifiers

PMID40778153
PMCPMC12330449

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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.