METHODS: We used a panel of 34 putative susceptibility genes to perform sequencing on samples from 60,466 women with breast cancer and 53,461 controls. In separate analyses for protein-truncating variants and rare missense variants in these genes, we estimated odds ratios for breast cancer overall and tumor subtypes. We evaluated missense-variant associations according to domain and classification of pathogenicity.
RESULTS: Protein-truncating variants in 5 genes (ATM, BRCA1, BRCA2, CHEK2, and PALB2) were associated with a risk of breast cancer overall with a P value of less than 0.0001. Protein-truncating variants in 4 other genes (BARD1, RAD51C, RAD51D, and TP53) were associated with a risk of breast cancer overall with a P value of less than 0.05 and a Bayesian false-discovery probability of less than 0.05. For protein-truncating variants in 19 of the remaining 25 genes, the upper limit of the 95% confidence interval of the odds ratio for breast cancer overall was less than 2.0. For protein-truncating variants in ATM and CHEK2, odds ratios were higher for estrogen receptor (ER)-positive disease than for ER-negative disease; for protein-truncating variants in BARD1, BRCA1, BRCA2, PALB2, RAD51C, and RAD51D, odds ratios were higher for ER-negative disease than for ER-positive disease. Rare missense variants (in aggregate) in ATM, CHEK2, and TP53 were associated with a risk of breast cancer overall with a P value of less than 0.001. For BRCA1, BRCA2, and TP53, missense variants (in aggregate) that would be classified as pathogenic according to standard criteria were associated with a risk of breast cancer overall, with the risk being similar to that of protein-truncating variants.
CONCLUSIONS: The results of this study define the genes that are most clinically useful for inclusion on panels for the prediction of breast cancer risk, as well as provide estimates of the risks associated with protein-truncating variants, to guide genetic counseling. (Funded by European Union Horizon 2020 programs and others.).
METHODS: A total of 1875 community-dwelling climacteric women were included in this study. The Pittsburgh Sleep Quality Index (PSQI) and the Menopause Rating Scale (MRS) were adopted to assess sleep quality and menopausal symptoms, respectively. Data were collected 4 times from March 2019 to December 2019, at a 3-month interval.
RESULTS: The Cross-lagged analysis showed that worse sleep quality and more severe menopausal symptoms over time after controlling for specified covariates, and more severe menopausal symptoms were predicted by declined sleep quality. The Generalized estimation equation model showed that education level, marital status, chronic diseases, life events, income, and age were the influential factors of sleep quality, while menopausal symptoms were impacted by marital status and income.
CONCLUSIONS: Increasing negative sleep quality and more severe menopausal symptoms over time contribute to the health burden of climacteric women. Menopausal symptoms could be alleviated by sleep quality improvement, which is influenced by education level, marital status, chronic diseases, life events, age, and economic factors.