Evidence map›Paper›PMID 37243694›Full record

ArticleJournal of the National Cancer Institute2023

Prediction of breast cancer risk for sisters of women attending screening.

Xinhe Mao, Wei He, Mikael Eriksson, Linda S Lindström, Natalie Holowko, Svetlana Bajalica-Lagercrantz, Mattias Hammarström, Felix Grassmann, Keith Humphreys, Douglas Easton and 2 more

Open access · hybridAbstract read
In one paragraph

Article in Journal of the National Cancer Institute, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 24% of its field
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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Review
  2. 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

12 authors at 3 institutions in 3 countries.

Xinhe MaoDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.ORCID 0000-0001-5012-0092
Wei HeDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.ORCID 0000-0003-0161-3274
Mikael ErikssonDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Linda S LindströmDepartment of Oncology-Pathology, Karolinska Institutet and Hereditary Cancer Unit, Karolinska University Hospital, Stockholm, Sweden.
Natalie HolowkoDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Svetlana Bajalica-LagercrantzDepartment of Oncology-Pathology, Karolinska Institutet and Hereditary Cancer Unit, Karolinska University Hospital, Stockholm, Sweden.
Mattias HammarströmDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Felix GrassmannDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.ORCID 0000-0003-1390-7528
Keith HumphreysDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Douglas EastonDepartment of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK.
Per HallDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.ORCID 0000-0002-5640-9126
Kamila CzeneDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.ORCID 0000-0002-3233-5695
Karolinska Institutet · SEKarolinska University Hospital · SEUniversity of Cambridge · GB

Funding

China Scholarship Council 201806210002FORTE 2018-00877Stockholm County Council 20200102Swedish Cancer Society 22 2207Swedish Research Council 2022-00584Zhejiang University
6 · The paper itself

Abstract

backgroundRisk assessment is important for breast cancer prevention and early detection. We aimed to examine whether common risk factors, mammographic features, and breast cancer risk prediction scores of a woman were associated with breast cancer risk for her sisters.

methodsWe included 53 051 women from the Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) study. Established risk factors were derived using self-reported questionnaires, mammograms, and single nucleotide polymorphism genotyping. Using the Swedish Multi-Generation Register, we identified 32 198 sisters of the KARMA women (including 5352 KARMA participants and 26 846 nonparticipants). Cox models were used to estimate the hazard ratios of breast cancer for both women and their sisters, respectively.

resultsA higher breast cancer polygenic risk score, a history of benign breast disease, and higher breast density in women were associated with an increased risk of breast cancer for both women and their sisters. No statistically significant association was observed between breast microcalcifications and masses in women and breast cancer risk for their sisters. Furthermore, higher breast cancer risk scores in women were associated with an increased risk of breast cancer for their sisters. Specifically, the hazard ratios for breast cancer per 1 standard deviation increase in age-adjusted KARMA, Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA), and Tyrer-Cuzick risk scores were 1.16 (95% confidence interval [CI] = 1.07 to 1.27), 1.23 (95% CI = 1.12 to 1.35), and 1.21 (95% CI = 1.11 to 1.32), respectively.

conclusionA woman's breast cancer risk factors are associated with her sister's breast cancer risk. However, the clinical utility of these findings requires further investigation.

Indexed as

Breast NeoplasmsBreastBreast DensityEarly Detection of CancerFemaleHumansMammographyRisk AssessmentRisk Factors

Identifiers

PMID37243694
PMCPMC10637039
OpenAlexW4378549096

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.