AI Article Synopsis

  • Researchers have created a breast cancer risk model that combines both genetic and environmental factors, analyzing data from 1319 breast cancer cases and 2094 controls in Japan.
  • The model categorizes individuals into three risk groups based on the number of risk alleles present, with higher odds ratios indicating greater risk of developing breast cancer.
  • The study found that combining genetic data with environmental factors improved the model's performance, suggesting that this approach could effectively help in stratifying individuals for breast cancer prevention strategies.

Article Abstract

Personalized approaches to prevention based on genetic risk models have been anticipated, and many models for the prediction of individual breast cancer risk have been developed. However, few studies have evaluated personalized risk using both genetic and environmental factors. We developed a risk model using genetic and environmental risk factors using 1319 breast cancer cases and 2094 controls from three case-control studies in Japan. Risk groups were defined based on the number of risk alleles for 14 breast cancer susceptibility loci, namely low (0-10 alleles), moderate (11-16) and high (17+). Environmental risk factors were collected using a self-administered questionnaire and implemented with harmonization. Odds ratio (OR) and C-statistics, calculated using a logistic regression model, were used to evaluate breast cancer susceptibility and model performance. Respective breast cancer ORs in the moderate- and high-risk groups were 1.69 (95% confidence interval, 1.39-2.04) and 3.27 (2.46-4.34) compared with the low-risk group. The C-statistic for the environmental model of 0.616 (0.596-0.636) was significantly improved by combination with the genetic model, to 0.659 (0.640-0.678). This combined genetic and environmental risk model may be suitable for the stratification of individuals by breast cancer risk. New approaches to breast cancer prevention using the model are warranted.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8345053PMC
http://dx.doi.org/10.3390/cancers13153796DOI Listing

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