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Kernel Density Estimation as a Measure of Environmental Exposure Related to Insulin Resistance in Breast Cancer Survivors.

ABSTRACT: Background: Environmental factors may influence breast cancer; however, most studies have measured environmental exposure in neighborhoods around home residences (static exposure). We hypothesize that tracking environmental exposures over time and space (dynamic exposure) is key to assessing total exposure. This study compares breast cancer survivors' exposure to walkable and recreation-promoting environments using dynamic Global Positioning System (GPS) and static home-based measures of exposure in relation to insulin resistance.Methods: GPS data from 249 breast cancer survivors living in San Diego County were collected for one week along with fasting blood draw. Exposure to recreation spaces and walkability was measured for each woman's home address within an 800 m buffer (static), and using a kernel density weight of GPS tracks (dynamic). Participants' exposure estimates were related to insulin resistance (using the homeostatic model assessment of insulin resistance, HOMA-IR) controlled by age and body mass index (BMI) in linear regression models.Results: The dynamic measurement method resulted in greater variability in built environment exposure values than did the static method. Regression results showed no association between HOMA-IR and home-based, static measures of walkability and recreation area exposure. GPS-based dynamic measures of both walkability and recreation area were significantly associated with lower HOMA-IR (P < 0.05).Conclusions: Dynamic exposure measurements may provide important evidence for community- and individual-level interventions that can address cancer risk inequities arising from environments wherein breast cancer survivors live and engage.Impact: This is the first study to compare associations of dynamic versus static built environment exposure measures with insulin outcomes in breast cancer survivors. Cancer Epidemiol Biomarkers Prev; 26(7); 1078-84. ©2017 AACR.

SUBMITTER: Jankowska MM

PROVIDER: S-EPMC5500421 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Kernel Density Estimation as a Measure of Environmental Exposure Related to Insulin Resistance in Breast Cancer Survivors.

Jankowska Marta M MM Natarajan Loki L Godbole Suneeta S Meseck Kristin K Sears Dorothy D DD Patterson Ruth E RE Kerr Jacqueline J

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20170303 7

<b>Background:</b> Environmental factors may influence breast cancer; however, most studies have measured environmental exposure in neighborhoods around home residences (static exposure). We hypothesize that tracking environmental exposures over time and space (dynamic exposure) is key to assessing total exposure. This study compares breast cancer survivors' exposure to walkable and recreation-promoting environments using dynamic Global Positioning System (GPS) and static home-based measures of ...[more]

PMID: 28258052

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Project description:Background While digital breast tomosynthesis (DBT) is rapidly replacing digital mammography (DM) in breast cancer screening, the potential of DBT density measures for breast cancer risk assessment remains largely unexplored. Purpose To compare associations of breast density estimates from DBT and DM with breast cancer. Materials and Methods This retrospective case-control study used contralateral DM/DBT studies from women with unilateral breast cancer and age- and ethnicity-matched controls (September 19, 2011-January 6, 2015). Volumetric percent density (VPD%) was estimated from DBT using previously validated software. For comparison, the publicly available Laboratory for Individualized Breast Radiodensity Assessment software package, or LIBRA, was used to estimate area-based percent density (APD%) from raw and processed DM images. The commercial Quantra and Volpara software packages were applied to raw DM images to estimate VPD% with use of physics-based models. Density measures were compared by using Spearman correlation coefficients (r), and conditional logistic regression was performed to examine density associations (odds ratios [OR]) with breast cancer, adjusting for age and body mass index. Results A total of 132 women diagnosed with breast cancer (mean age ± standard deviation [SD], 60 years ± 11) and 528 controls (mean age, 60 years ± 11) were included. Moderate correlations between DBT and DM density measures (r = 0.32-0.75; all P < .001) were observed. Volumetric density estimates calculated from DBT (OR, 2.3 [95% CI: 1.6, 3.4] per SD for VPD%DBT) were more strongly associated with breast cancer than DM-derived density for both APD% (OR, 1.3 [95% CI: 0.9, 1.9] [P < .001] and 1.7 [95% CI: 1.2, 2.3] [P = .004] per SD for LIBRA raw and processed data, respectively) and VPD% (OR, 1.6 [95% CI: 1.1, 2.4] [P = .01] and 1.7 [95% CI: 1.2, 2.6] [P = .04] per SD for Volpara and Quantra, respectively). Conclusion The associations between quantitative breast density estimates and breast cancer risk are stronger for digital breast tomosynthesis compared with digital mammography. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Yaffe in this issue.

Project description:BackgroundBreast cancer survivors with excess weight are more likely to have negative breast cancer outcomes. Biomarkers related to insulin resistance may help explain this negative association. Weight loss is associated with improvements in insulin sensitivity. Our goal was to identify the impact of a behaviorally based weight loss intervention on indices of insulin resistance.MethodsOverweight, early stage breast cancer survivors who completed initial cancer therapy were enrolled in a 6 month behaviorally based weight loss intervention that included calorie reduction, exercise and behavior modification. Biomarkers related to insulin resistance were obtained at baseline and after the intervention. Results from participants who achieved ≥5% weight loss were compared to those who lost less weight.ResultsDespite not having diabetes as a preexisting diagnosis prior to the study, 69% of all participants were considered to have pre-diabetes or diabetes at baseline based on American Diabetes Association definitions. Participants who achieved ≥5% weight loss had significantly lower fasting insulin, AUC insulin, and insulin resistance as measured by HOMA-IR. Beta cell function decreased as anticipated when insulin resistance improved. Additionally, leptin levels declined.ConclusionsBreast cancer survivors who achieved ≥5% weight loss demonstrated significant improvements in indices of insulin resistance. Despite an exclusion criteria of diabetes at the time of enrolment, a high proportion met criteria for pre-diabetes or diabetes at baseline. Pre-diabetes appears to be under recognized in overweight breast cancer survivors. Behaviorally based weight loss interventions can result in weight loss and improvements in biomarkers related to breast cancer outcomes and additionally may decrease the chance of developing diabetes.Trial registrationNCT01482702 4/12/2010 (retrospectively registered). https://clinicaltrials.gov/ct2/show/NCT01482702?term=Dittus&rank=4.

Dataset Information

Kernel Density Estimation as a Measure of Environmental Exposure Related to Insulin Resistance in Breast Cancer Survivors.

Publications

Kernel Density Estimation as a Measure of Environmental Exposure Related to Insulin Resistance in Breast Cancer Survivors.

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