Format

Send to

Choose Destination
Breast Cancer Res. 2018 Dec 29;20(1):156. doi: 10.1186/s13058-018-1078-8.

Adjusting for BMI in analyses of volumetric mammographic density and breast cancer risk.

Author information

1
Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK. susan.hudson@lshtm.ac.uk.
2
Cancer Registry of Norway, Institute of Population-based Cancer Research, Oslo, Norway.
3
Division of Imaging and Technology, Ninewells Hospital Medical School, University of Dundee, Dundee, DD2 1SY, UK.
4
Royal Marsden NHS Foundation Trust, London, SW3 6JJ, UK.
5
Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
6
Faculty of Population Health Sciences, Institute of Child Health, University College London, London, WC1N 1EH, UK.

Abstract

BACKGROUND:

Fully automated assessment of mammographic density (MD), a biomarker of breast cancer risk, is being increasingly performed in screening settings. However, data on body mass index (BMI), a confounder of the MD-risk association, are not routinely collected at screening. We investigated whether the amount of fat in the breast, as captured by the amount of mammographic non-dense tissue seen on the mammographic image, can be used as a proxy for BMI when data on the latter are unavailable.

METHODS:

Data from a UK case control study (numbers of cases/controls: 414/685) and a Norwegian cohort study (numbers of cases/non-cases: 657/61059), both with volumetric MD measurements (dense volume (DV), non-dense volume (NDV) and percent density (%MD)) from screening-age women, were analysed. BMI (self-reported) and NDV were taken as measures of adiposity. Correlations between BMI and NDV, %MD and DV were examined after log-transformation and adjustment for age, menopausal status and parity. Logistic regression models were fitted to the UK study, and Cox regression models to the Norwegian study, to assess associations between MD and breast cancer risk, expressed as odds/hazard ratios per adjusted standard deviation (OPERA). Adjustments were first made for standard risk factors except BMI (minimally adjusted models) and then also for BMI or NDV. OPERA pooled relative risks (RRs) were estimated by fixed-effect models, and between-study heterogeneity was assessed by the I2 statistics.

RESULTS:

BMI was positively correlated with NDV (adjusted r = 0.74 in the UK study and r = 0.72 in the Norwegian study) and with DV (r = 0.33 and r = 0.25, respectively). Both %MD and DV were positively associated with breast cancer risk in minimally adjusted models (pooled OPERA RR (95% confidence interval): 1.34 (1.25, 1.43) and 1.46 (1.36, 1.56), respectively; I2 = 0%, P >0.48 for both). Further adjustment for BMI or NDV strengthened the %MD-risk association (1.51 (1.41, 1.61); I2 = 0%, P = 0.33 and 1.51 (1.41, 1.61); I2 = 0%, P = 0.32, respectively). Adjusting for BMI or NDV marginally affected the magnitude of the DV-risk association (1.44 (1.34, 1.54); I2 = 0%, P = 0.87 and 1.49 (1.40, 1.60); I2 = 0%, P = 0.36, respectively).

CONCLUSIONS:

When volumetric MD-breast cancer risk associations are investigated, NDV can be used as a measure of adiposity when BMI data are unavailable.

KEYWORDS:

BMI; Breast cancer; Breast density; Mammographic density; OPERA

PMID:
30594212
PMCID:
PMC6311032
DOI:
10.1186/s13058-018-1078-8
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center