Evidence map›Paper›PMID 31727725›Full record

ArticleCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology2020

Association of Imaging-Based Body Fat Distribution and Mammographic Density in the Multiethnic Cohort Adiposity Phenotype Study.

Gertraud Maskarinec, Michelle Ciba, Dan Ju, John A Shepherd, Thomas Ernst, Anna H Wu, Kristine R Monroe, Unhee Lim, Lynne R Wilkens, Loïc Le Marchand

Open access · bronzeAbstract read
In one paragraph

Article in Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.3field-weighted citation impact, top 36% 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

4 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Body Mass Index and Mammographic Density in a Multiracial and Multiethnic Population-Based Study.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2022
    Article
  3. Article
  4. 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

10 authors at 2 institutions in 1 country.

Gertraud MaskarinecUniversity of Hawaii Cancer Center, Honolulu, Hawaii. gertraud@cc.hawaii.edu.
Michelle CibaUniversity of Hawaii Cancer Center, Honolulu, Hawaii.
Dan JuUniversity of Pennsylvania, Philadelphia, Pennsylvania.
John A ShepherdUniversity of Hawaii Cancer Center, Honolulu, Hawaii.
Thomas ErnstUniversity of Maryland, Baltimore, Maryland.
Anna H WuUniversity of Southern California, Los Angeles, California.
Kristine R MonroeUniversity of Southern California, Los Angeles, California.
Unhee LimUniversity of Hawaii Cancer Center, Honolulu, Hawaii.
Lynne R WilkensUniversity of Hawaii Cancer Center, Honolulu, Hawaii.
Loïc Le MarchandUniversity of Hawaii Cancer Center, Honolulu, Hawaii.
University of Southern California · USUniversity of Pennsylvania · US

Funding

Southern California Clinical and Translational Science InstituteUL1TR001855 · NCATS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Thomas A Buchanan, Michele D. Kipke · 2016 to 2026
$81.3M
University of Hawaii Cancer Center CCSGP30CA071789 · NCI · UNIVERSITY OF HAWAII AT MANOA · PI GERTRAUD MASKARINEC · 1996 to 2026
$56.2M
Understanding Population Differences in Cancer: The MEC StudyU01CA164973 · NCI · UNIVERSITY OF HAWAII AT MANOA · PI HAIMAN, CHRISTOPHER ALAN, LE MARCHAND, LOIC · 2015 to 2025
$37.4M
Southern California Clinical and Translational Science InstituteUL1TR000130 · NCATS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI BUCHANAN, THOMAS A · 2012 to 2015
$36.0M
Recruitment and Data CollectionP01CA168530 · NCI · UNIVERSITY OF HAWAII AT MANOA · PI LE MARCHAND, LOIC · 2012 to 2016
$19.9M
NCATS NIH HHS UL1 TR000130NCATS NIH HHS UL1 TR001855NCI NIH HHS P01 CA168530NCI NIH HHS P30 CA071789NCI NIH HHS U01 CA164973
6 · The paper itself

Abstract

backgroundAs the stronger association of obesity with postmenopausal breast cancer in Asian than white women may be due to body fat distribution, we examined the relation of adiposity measures with percent mammographic density (PMD), a strong predictor of breast cancer incidence.

methodsA total of 938 women from five ethnic groups (69.1 ± 2.7 years) in the Adiposity Phenotype Study (APS) underwent DXA and MRI imaging. PMD was assessed in routine mammograms using a computer-assisted method. Spearman correlation coefficients were computed and general linear models were applied to estimate regression coefficients (β) for PMD per 0.5 SD units of adiposity measures while adjusting for known confounders, including DXA total body fat.

resultsFor 701 (75%) of the participants (69.1 ± 2.7 years), valid mammograms were obtained. Whereas total body fat, the trunk-to-periphery fat ratio (TPFR), visceral fat (VAT), and subcutaneous fat (SAT) were inversely correlated with PMD (

conclusionsThis is the first study to show an association of a high VAT/SAT ratio with greater PMD, a marker of breast cancer risk after taking into account total body fat. IMPACT: The results indicate a link between the propensity to accumulate VAT and the amount of fat in the breast (1-PMD), which may influence the relation of obesity with breast cancer incidence.

Indexed as

Absorptiometry, PhotonAdiposityAgedBody Mass IndexBreastBreast DensityBreast NeoplasmsEthnicityFemaleHumansIncidenceIntra-Abdominal FatMagnetic Resonance ImagingMammographyMiddle AgedObesity

Identifiers

PMID31727725
PMCPMC7007361
OpenAlexW2983846951

What OpenQuestion holds

Textmetadata
LicenceTDM
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.