PURPOSE: Breast cancer (BC) risk prediction allows systematic identification of individuals at highest and lowest risk. We extend the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) risk model to incorporate the effects of polygenic risk scores (PRS) and other risk factors (RFs).
METHODS: BOADICEA incorporates the effects of truncating variants in BRCA1, BRCA2, PALB2, CHEK2, and ATM; a PRS based on 313 single-nucleotide polymorphisms (SNPs) explaining 20% of BC polygenic variance; a residual polygenic component accounting for other genetic/familial effects; known lifestyle/hormonal/reproductive RFs; and mammographic density, while allowing for missing information.
RESULTS: Among all factors considered, the predicted UK BC risk distribution is widest for the PRS, followed by mammographic density. The highest BC risk stratification is achieved when all genetic and lifestyle/hormonal/reproductive/anthropomorphic factors are considered jointly. With all factors, the predicted lifetime risks for women in the UK population vary from 2.8% for the 1st percentile to 30.6% for the 99th percentile, with 14.7% of women predicted to have a lifetime risk of >/=17-<30% (moderate risk according to National Institute for Health and Care Excellence [NICE] guidelines) and 1.1% a lifetime risk of >/=30% (high risk).
CONCLUSION: This comprehensive model should enable high levels of BC risk stratification in the general population and women with family history, and facilitate individualized, informed decision-making on prevention therapies and screening.
PURPOSE: Breast cancer (BC) risk prediction allows systematic identification of individuals at highest and lowest risk. We extend the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) risk model to incorporate the effects of polygenic risk scores (PRS) and other risk factors (RFs).
METHODS: BOADICEA incorporates the effects of truncating variants in BRCA1, BRCA2, PALB2, CHEK2, and ATM; a PRS based on 313 single-nucleotide polymorphisms (SNPs) explaining 20% of BC polygenic variance; a residual polygenic component accounting for other genetic/familial effects; known lifestyle/hormonal/reproductive RFs; and mammographic density, while allowing for missing information.
RESULTS: Among all factors considered, the predicted UK BC risk distribution is widest for the PRS, followed by mammographic density. The highest BC risk stratification is achieved when all genetic and lifestyle/hormonal/reproductive/anthropomorphic factors are considered jointly. With all factors, the predicted lifetime risks for women in the UK population vary from 2.8% for the 1st percentile to 30.6% for the 99th percentile, with 14.7% of women predicted to have a lifetime risk of >/=17-<30% (moderate risk according to National Institute for Health and Care Excellence [NICE] guidelines) and 1.1% a lifetime risk of >/=30% (high risk).
CONCLUSION: This comprehensive model should enable high levels of BC risk stratification in the general population and women with family history, and facilitate individualized, informed decision-making on prevention therapies and screening.
BACKGROUND AND AIMS: Epidemiological evidence on the impact of different alcohol drinking patterns on health-care systems or hospitalizations is sparse. We investigated how the different average volumes of alcohol consumed relate to all-cause and cause-specific hospitalizations.
DESIGN: Prospective cohort study (baseline 2005-10) linked to a registry of hospital discharge records to identify hospitalizations at follow-up (December 2013).
SETTING: Molise region, Italy.
PARTICIPANTS: A total of 20 682 individuals (48% men, age >/= 35 years) who participated in the Moli-sani Study and were free from cardiovascular disease or cancer at baseline.
MEASUREMENTS: The alcohol volume consumed in the year before enrolment was classified as: life-time abstainers, former drinkers, occasional drinkers and current drinkers who drank 1-12 (referent), 12.1-24, 24.1-48 and > 48 g/day of alcohol. Cause-specific hospitalizations were assigned by Italian Diagnosis Related Groups classification or by ICD-9 code of main admission diagnoses. Incidence rate ratios (IRR) of hospitalization were estimated by Poisson regression, taking into account the total number of admissions that occurred during the follow-up per person.
FINDINGS: During a median follow-up of 6.3 years, 12 996 multiple hospital admissions occurred. In multivariable analyses, life-time abstainers and former drinkers had higher rates of all-cause [IRR = 1.11, 95% confidence interval (CI) = 1.05-1.17 and IRR = 1.19, 95% CI = 1.02-1.31, respectively] and vascular (IRR = 1.14, 95% CI = 1.02-1.27 and IRR = 1.48, 95% CI = 1.24-1.76, respectively) hospitalizations compared with light alcohol consumers. Alcohol consumption > 48 g/day was associated with a higher rate of hospitalization for both alcohol-related diseases (IRR = 1.74, 95% CI = 1.32-2.29) and cancer (IRR = 1.36, 95% CI = 1.12-1.65). The magnitude of the association between heavier alcohol intake and hospitalization tended to be greater in smokers than non-smokers. No associations were observed with hospitalization for trauma or neurodegenerative diseases.
CONCLUSIONS: Moderate alcohol consumption appears to have a modest but complex impact on global hospitalization burden. Heavier drinkers have a higher rate of hospitalization for all causes, including alcohol-related diseases and cancer, a risk that appears to be further magnified by concurrent smoking.
PURPOSE: To study the association between coffee and alcoholic beverage consumption and alcoholic liver disease mortality.
METHODS: In total, 219,279 men and women aged 30-67 years attended cardiovascular screening in Norway from 1994 to 2003. Linkage to the Cause of Death Registry identified 93 deaths from alcoholic liver disease. Coffee consumption was categorized into four levels: 0, 1-4, 5-8, and greater than or equal to 9 cups/d and alcohol consumption as 0, greater than 0 to less than 1.0, 1.0 to less than 2.0, and greater than or equal to 2.0 units/d, for beer, wine, liquor, and total alcohol consumption.
RESULTS: The hazard ratios per one category of consumption were 2.06 (95% confidence interval 1.62-2.61), 0.68 (0.46-1.00), and 2.54 (1.92-3.36) for beer, wine, and liquor, respectively. Stratification at 5 cups/d (the mean) revealed a stronger association between alcohol consumption and alcoholic liver disease at less than 5 versus 5 or more cups/d. With less than 5 cups/d, 0 alcohol units/d as reference, the hazard ratio reached to 25.5 (9.2-70.5) for greater than or equal to 2 units/d, whereas with greater than or equal to 5 cups/d, it reached 5.8 (1.9-17.9) for greater than or equal to 2 units/d. A test for interaction was significant (P = .01).
CONCLUSIONS: Coffee and wine consumption were inversely associated with alcoholic liver disease death. Total alcohol consumption was adversely associated with alcoholic liver disease mortality and the strength of the association varied with the level of coffee consumption.