Article in Journal of the National Cancer Institute, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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.
Maria-Eleni SyleouniDivision of Chronic Disease Epidemiology, Epidemiology Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.ORCID 0000-0003-2883-9679
Corinne E JoshuDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID 0000-0002-5100-172X
Josef CoreshDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID 0000-0002-4598-0669
Marc J GunterCancer Epidemiology and Prevention Research Unit, School of Public Health, Imperial College London, London, United Kingdom.
Kenneth R ButlerDepartment of Medicine and the Mind Center, University of Mississippi Medical Center, Jackson, MS, United States.ORCID 0000-0003-3198-8835
David J CouperDepartment of Biostatistics, University of North Carolina, Chapel Hill, NC, United States.ORCID 0000-0002-4313-9235
Marcela GuevaraInstituto de Salud Pública y Laboral de Navarra, Pamplona, Spain.ORCID 0000-0001-9242-6364
Jiayun LuDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID 0000-0002-5592-6658
Anna PrizmentDepartment of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, United States.ORCID 0000-0003-1932-6548
Elio RiboliCancer Epidemiology and Prevention Research Unit, School of Public Health, Imperial College London, London, United Kingdom.ORCID 0000-0001-6795-6080
Meng RuDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID 0000-0002-1439-6451
Yahya Mahamat SalehNutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France.ORCID 0000-0002-5892-8886
Karl Smith-ByrneCancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, UK.
Mehrnoosh SooriDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID 0009-0002-8921-332X
Kala VisvanathanDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID 0000-0002-0582-8005
Vernon A BurkDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
Ziqiao WangDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID 0000-0003-3383-8670
Nilanjan ChatterjeeDepartment of Oncology, Johns Hopkins University School of Medicine, and the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD, United States.ORCID 0000-0002-9060-008X
Sabine RohrmannDivision of Chronic Disease Epidemiology, Epidemiology Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.ORCID 0000-0002-2215-1200
Elizabeth A PlatzDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID 0000-0003-3676-8954
Funding
THE ATHEROSCLEROSIS RISK IN COMMUNITIES (ARIC) STUDY - COORDINATING CENTER - TASK AREA B.2 AND B.375N92022D00001 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI COUPER, DAVID · 2022 to 2025
$13.7M
THE ATHEROSCLEROSIS RISK IN COMMUNITIES (ARIC) STUDY - FIELD CENTER - TASK ORDER 01, TASK AREA A75N92022D00003 · NHLBI · UNIVERSITY OF MINNESOTA · PI LUTSEY, PAMELA L. · 2022 to 2025
$5.1M
THE ATHEROSCLEROSIS RISK IN COMMUNITIES (ARIC) STUDY - FIELD CENTER - TASK ORDER 01, TASK AREA A75N92022D00005 · NHLBI · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI WAGENKNECHT, LYNNE · 2022 to 2025
$5.0M
THE ATHEROSCLEROSIS RISK IN COMMUNITIES (ARIC) STUDY - FIELD CENTER - TASK ORDER 01, TASK AREA A75N92022D00004 · NHLBI · UNIVERSITY OF MISSISSIPPI MED CTR · PI WINDHAM, BEVERLY GWEN · 2022 to 2025
$4.8M
THE ATHEROSCLEROSIS RISK IN COMMUNITIES (ARIC) STUDY - FIELD CENTER - TASK ORDER 01, TASK AREA A75N92022D00002 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI CORESH, JOSEF · 2022 to 2025
$4.7M
Enhancing ARIC Infrastructure to Yield a New Cancer Epidemiology CohortU01CA164975 · NCI · JOHNS HOPKINS UNIVERSITY · PI PLATZ, ELIZABETH A. · 2012 to 2018
$3.8M
Profiling Cardiovascular Events and Biomarkers in the Very Old to Improve Personalized Approaches for the Prevention of Cardiac and Vascular DiseaseR01HL134320 · NHLBI · BAYLOR COLLEGE OF MEDICINE · PI BALLANTYNE, CHRISTIE MITCHELL, SELVIN, ELIZABETH · 2016 to 2019
$3.1M
Enhancing the Interpretability and Applicability of Polygenic Scores through Multi-Omics Integration and Analysis of Family-Based StudiesK99HG013674 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI WANG, ZIQIAO · 2024 to 2025
backgroundPlasma proteomic data can be used to discover breast cancer risk biomarkers beyond established risk factors. Discovery using a large-scale platform in a diverse population is needed.
methodsWe investigated 4712 plasma proteins and breast cancer risk among postmenopausal women in a prospective cohort analysis in the Atherosclerosis Risk in Communities study. Proteins were measured by SomaScan 5K Assay. Incident cases were ascertained primarily from state cancer registries. We estimated hazard ratios (HRs) and 95% confidence intervals (CIs) using Cox regression adjusting for risk factors and applied the Benjamini-Hochberg method to control false discovery. We determined whether the statistically significant proteins were confirmed in a case-cohort study within the European Prospective Investigation into Cancer and Nutrition.
resultsAfter median follow-up of 23.3 years, 340 of 4403 women had an incident breast cancer. Two proteins were statistically significantly associated after P value adjustment: per doubling, the hazard ratio of breast cancer was 1.45 (95% CI = 1.23 to 1.70; P = 7.47*10-6, Padjusted = .0370) for protein LEG1 homolog and 2.52 (95% CI = 1.66 to 3.83; P = 1.50*10-5, Padjusted = 0.0371) for adenosine diphosphate (ADP)-dependent glucokinase. Results were consistent in a lagged analysis and among Black (28.1%) and White women. In the European Prospective Investigation into Cancer and Nutrition, both associations were confirmed (protein LEG1 homolog: HR = 1.24, 95% CI = 1.14 to 1.35; P = 9.79*10-7; ADP-dependent glucokinase: HR = 1.13, 95% CI = 1.03 to 1.23; P = .01).
conclusionWe identified 2 plasma proteins associated with increased breast cancer risk over the longer term in postmenopausal women; both were confirmed in an independent cohort. If further validated, these plasma protein biomarkers could be considered for utility in current risk stratification tools.
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.
Discovering plasma proteins associated with breast cancer incidence in postmenopausal women in the Atherosclerosis Risk in Communities study. · full record | OpenQuestion