Logistic LASSO Regression for Dietary Intakes and Breast Cancer

A multitude of dietary factors from dietary fat to macro and micronutrients intakes have been associated with breast cancer, yet data are still equivocal. Therefore, utilizing data from the large, multi-year, cross-sectional National Health and Nutrition Examination Survey (NHANES), we applied a novel, modern statistical shrinkage technique, logistic least absolute shrinkage and selection operator (LASSO) regression, to examine the association between dietary intakes in women, >= 50 years, with self-reported breast cancer (n= 286) compared with women without self-reported breast cancer (1144) from the 1999-2010 NHANES cycle. Logistic LASSO regression was used to examine the relationship between twenty-nine variables, including dietary variables from food, as well as well-established/known breast cancer risk factors, and to subsequently identify the most relevant variables associated with self-reported breast cancer. We observed that as the penalty factor (lambda) increased in the logistic LASSO regression, well-established breast cancer risk factors, including age (beta = 0.83) and parity (beta = -0.05) remained in the model. For dietary macro and micronutrient intakes, only vitamin B12 (beta = 0.07) was positively associated with self-reported breast cancer. Caffeine (beta = -0.01) and alcohol (beta = 0.03) use also continued to remain in the model. These data suggest that a diet high in vitamin B12, as well as alcohol use may be associated with self-reported breast cancer. Nonetheless, additional prospective studies should apply more recent statistical techniques to dietary data and cancer outcomes to replicate and confirm the present findings.