||5R21CA220080-02 Interpret this number
||University Of Hawaii At Manoa
||Improving Breast Cancer Risk Prediction with Composite Measures of Obesity and Body Fat Distribution
Breast cancer is the most common cancer in women and is the second leading cause of cancer mortality
among women. Obesity is an important risk factor for breast cancer incidence among postmenopausal women.
Recent evidence points to the role of central and visceral adiposity in the link between obesity and breast
cancer, and to the differences in body fat distribution and the extent of its association with breast cancer by
race/ethnicity. Estimation of a woman's breast cancer risk is an important tool used in primary breast cancer
prevention efforts. Various models for predicting absolute risk of breast cancer have been developed for a
number of racial/ethnic groups in several locations, but their ability to discriminate women who will eventually
develop breast cancer from those who will not remains limited, with the area under receiver operating
characteristic curve (AUC) generally ranging between 0.54-0.68. Efforts to include BMI in breast cancer risk
models did not produce substantive improvement in the AUC. The effect of body fat distribution on predicted
breast cancer risk mostly remains unexplored but has the potential to improve breast cancer risk prediction
models. Thus, we propose to construct breast cancer risk models that would account for adiposity
characteristics through the development of composite adiposity prediction scores based on anthropometry, and
subsequent inclusion of these scores in breast cancer risk models. We will conduct these analyses among
female participants of the Multiethnic Cohort, which includes five major racial/ethnic groups: African Americans,
Japanese Americans, Latinos, Native Hawaiians and Whites. In Aim 1, in a cross-sectional study of 939
healthy women, we will examine the associations of imaging-based adiposity measures with clinically
measured anthropometric characteristics; calibrate the self-reported anthropometric characteristics based on
clinically measured anthropometry; and construct composite prediction models for each adiposity measure,
using calibrated self-reported anthropometric characteristics within each racial/ethnic group. In Aim 2, using
data on 51,336 female MEC participants, we will construct breast cancer risk models using different
combinations of known risk factors for postmenopausal breast cancer and composite predicted adiposity
measures from Aim 1. We will then evaluate predictive and discriminatory accuracy of these models, select the
best-performing model within each ethnic group, and test these ethnic-specific models across racial/ethnic
groups to examine the feasibility of a trans-ethnic breast cancer risk model. Breast cancer risk prediction
models that include adiposity measures will have greater accuracy in predicting breast cancer, which would
benefit prevention efforts by better informing patients of their risk level and guiding personalized prevention
strategies across or within five different racial/ethnic populations.
Prediction of breast cancer risk among women of the Mariana Islands: the BRISK retrospective case-control study.
, Wilkens L.R.
, White K.K.
, Chong M.
, Buyum A.
, Badowski G.
, Leon Guerrero R.T.
, Novotny R.
BMJ open, 2022-12-09; 12(12), p. e061205.