Evidence map›Paper›PMID 40439279›Full record

ReviewThe Journal of endocrinology2025

Tailoring cardiovascular risk prediction to females.

Thulani Ashcroft, Marie de Bakker, Atul Anand, Naveed Sattar, Jacqueline A Maybin, Dorien M Kimenai

Abstract readReview
In one paragraph

Review in The Journal of endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

6 authors.

Thulani Ashcroft
Marie de Bakker
Atul Anand
Naveed Sattar
Jacqueline A Maybin

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Atherosclerotic cardiovascular disease (ASCVD) is one of the leading causes of morbidity and mortality in females worldwide. In this review, we provide insights into how sex differences may affect traditional risk factors associated with ASCVD, and give an overview of non-traditional risk factors that have the potential to enhance cardiovascular risk prediction in females. We review clinically applied cardiovascular risk estimation systems, discussing the integration of promising risk factors within these systems. We also explore the role of novel approaches and future directions to refine primary prevention of ASCVD in females. The development of ASCVD varies by sex and age; therefore, cardiovascular risk estimation systems should incorporate both sex and age interactions with risk factors to improve ASCVD risk estimates. As the incidence of non-ASCVD (such as heart failure and arrhythmias) in females continues to rise, it is crucial to adopt a more holistic approach to risk assessment that extends beyond ASCVD outcomes. This review highlights the need for further studies on female-prevalent diseases and female-specific factors that may refine cardiovascular risk estimation in young females. Raising awareness is crucial to ensure studies include individuals from deprived areas and ethnic minorities, as more insights on the intersection between sex and social determinants of health will enhance understanding of the underlying mechanisms of ASCVD risk prevention in females. Finally, taking steps to improve and standardise data on female-specific risk factors throughout a female's life course could improve preventive cardiovascular care for females.

Indexed as

AtherosclerosisCardiovascular DiseasesFemaleHeart Disease Risk FactorsHumansRisk AssessmentRisk FactorsSex Factorscardiovascular diseasefemaleprimary carerisk assessmentrisk factors

Identifiers

PMID40439279
PMCPMC12152731

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.