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.

25 January 2019 In Dementia

BACKGROUND: Alcohol use has been identified as a risk factor for dementia and cognitive decline. However, some patterns of drinking have been associated with beneficial effects.

METHODS AND RESULTS: To clarify the relationship between alcohol use and dementia, we conducted a scoping review based on a systematic search of systematic reviews published from January 2000 to October 2017 by using Medline, Embase, and PsycINFO. Overall, 28 systematic reviews were identified: 20 on the associations between the level of alcohol use and the incidence of cognitive impairment/dementia, six on the associations between dimensions of alcohol use and specific brain functions, and two on induced dementias. Although causality could not be established, light to moderate alcohol use in middle to late adulthood was associated with a decreased risk of cognitive impairment and dementia. Heavy alcohol use was associated with changes in brain structures, cognitive impairments, and an increased risk of all types of dementia.

CONCLUSION: Reducing heavy alcohol use may be an effective dementia prevention strategy.

27 July 2018 In General Health

OBJECTIVE: To examine the association between an overall maternal healthy lifestyle (characterized by a healthy body mass index, high quality diet, regular exercise, no smoking, and light to moderate alcohol intake) and the risk of developing obesity in offspring.

DESIGN: Prospective cohort studies of mother-child pairs.

SETTING: Nurses' Health Study II (NHSII) and Growing Up Today Study (GUTS) in the United States.

PARTICIPANTS: 24 289 GUTS participants aged 9-14 years at baseline who were free of obesity and born to 16 945 NHSII women.

MAIN OUTCOME MEASURE: Obesity in childhood and adolescence, defined by age and sex specific cutoff points from the International Obesity Task Force. Risk of offspring obesity was evaluated by multivariable log-binomial regression models with generalized estimating equations and an exchangeable correlation structure.

RESULTS: 1282 (5.3%) offspring became obese during a median of five years of follow-up. Risk of incident obesity was lower among offspring whose mothers maintained a healthy body mass index of 18.5-24.9 (relative risk 0.44, 95% confidence interval 0.39 to 0.50), engaged in at least 150 min/week of moderate/vigorous physical activities (0.79, 0.69 to 0.91), did not smoke (0.69, 0.56 to 0.86), and consumed alcohol in moderation (1.0-14.9 g/day; 0.88, 0.79 to 0.99), compared with the rest. Maternal high quality diet (top 40% of the Alternate Healthy Eating Index 2010 diet score) was not significantly associated with the risk of obesity in offspring (0.97, 0.83 to 1.12). When all healthy lifestyle factors were considered simultaneously, offspring of women who adhered to all five low risk lifestyle factors had a 75% lower risk of obesity than offspring of mothers who did not adhere to any low risk factor (0.25, 0.14 to 0.47). This association was similar across sex and age groups and persisted in subgroups of children with various risk profiles defined by factors such as pregnancy complications, birth weight, gestational age, and gestational weight gain. Children's lifestyle did not significantly account for the association between maternal lifestyle and offspring obesity risk, but when both mothers and offspring adhered to a healthy lifestyle, the risk of developing obesity fell further (0.18, 0.09 to 0.37).

CONCLUSION: Our study indicates that adherence to a healthy lifestyle in mothers during their offspring's childhood and adolescence is associated with a substantially reduced risk of obesity in the children. These findings highlight the potential benefits of implementing family or parental based multifactorial interventions to curb the risk of childhood obesity.

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