26 February 2019 In Cancer

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.

22 February 2019 In Cancer

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.

05 December 2018 In Drinking & Eating Patterns

BACKGROUND: Some of the previously reported health benefits of low-to-moderate alcohol consumption may derive from health status influencing alcohol consumption rather than the opposite. We examined whether health status changes influence changes in alcohol consumption, cessation included.

METHODS: Data came from 571 current drinkers aged >/=60 years participating in the Seniors-ENRICA cohort in Spain. Participants were recruited in 2008-2010 and followed-up for 8.2 years, with four waves of data collection. We assessed health status using a 52-item deficit accumulation (DA) index with four domains: functional, self-rated health and vitality, mental health, and morbidity and health services use. To minimise reverse causation, we examined how changes in health status over a 3-year period (wave 0-wave 1) influenced changes in alcohol consumption over the subsequent 5 years (waves 1-3) using linear/logistic regression, as appropriate.

RESULTS: Compared with participants in the lowest tertile of DA change (mean absolute 4.3% health improvement), those in the highest tertile (7.8% worsening) showed a reduction in alcohol intake (beta: -4.32 g/day; 95% CI -7.00 to -1.62; p trend=0.002) and were more likely to quit alcohol (OR: 2.80; 95% CI 1.54 to 5.08; p trend=0.001). The main contributors to decreasing drinking were increased functional impairment and poorer self-rated health, whereas worsening self-rated health, onset of diabetes or stroke and increased prevalence of hospitalisation influenced cessation.

CONCLUSIONS: Health deterioration is related to a subsequent reduction and cessation of alcohol consumption contributing to the growing evidence challenging the protective health effect previously attributed to low-to-moderate alcohol consumption.

06 September 2018 In General Health

OBJECTIVE: Premenstrual syndrome (PMS) is a very common disorder worldwide which carries an important economic burden. We conducted a systematic review and a meta-analysis to assess the role of alcohol in the occurrence of PMS.

METHODS: We searched MEDLINE, EMBASE, the five regional bibliographic databases of the WHO, the Proceedings database and the Open Access Thesis and Dissertations (OATD) from inception to May 2017. We also reviewed the references of every article retrieved and established personal contact with researchers to trace further publications or reports. We did not include any language limitations. Studies were included if: (1) they presented original data from cohort, case-control or cross-sectional studies, (2) PMS was clearly defined as the outcome of interest, (3) one of the exposure factors was alcohol consumption, (4) they provided estimates of odds ratios, relative risks, or any other effect measure and their confidence intervals, or enough data to calculate them.

RESULTS: We identified 39 studies of which 19 were eligible. Intake of alcohol was associated with a moderate increase in the risk of PMS (OR=1.45, 95% CI: 1.17 to 1.79). Heavy drinking yielded a larger increase in the risk than any drinking (OR=1.79, 95% CI: 1.39 to 2.32).

DISCUSSION: Our results suggest that alcohol intake presents a moderate association with PMS risk. Future studies should avoid cross-sectional designs and focus on determining whether there is a threshold of alcohol intake under which the harmful effect on PMS is non-existent.

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