24 June 2019 In General Health

In this article, we critically evaluate the evidence relating to the effects of the Mediterranean diet (MD) on the risk of cardiovascular disease (CVD). Strong evidence indicating that the MD prevents CVD has come from prospective cohort studies. However, there is only weak supporting evidence from randomized controlled trials (RCTs) as none have compared subjects who follow an MD and those who do not. Instead, RCTs have tested the effect of 1 or 2 features of the MD. This was the case in the Prevenciomicronn con Dieta Mediterranea (PREDIMED) study: the major dietary change in the intervention groups was the addition of either extravirgin olive oil or nuts. Meta-analyses generally suggest that the MD causes small favorable changes in risk factors for CVD, including blood pressure, blood glucose, and waist circumference. However, the effect on blood lipids is generally weak. The MD may also decrease several biomarkers of inflammation, including C-reactive protein. The 7 key features of the MD can be divided into 2 groups. Some are clearly protective against CVD (olive oil as the main fat; high in legumes; high in fruits/vegetables/nuts; and low in meat/meat products and increased in fish). However, other features of the MD have a less clear relationship with CVD (low/moderate alcohol use, especially red wine; high in grains/cereals; and low/moderate in milk/dairy). In conclusion, the evidence indicates that the MD prevents CVD. There is a need for RCTs that test the effectiveness of the MD for preventing CVD. Key design features for such a study are proposed.

24 June 2019 In Diabetes

BACKGROUND & AIMS: Alcohol consumption correlates with type 2 diabetes through its effects on insulin resistance, changes in alcohol metabolite levels, and anti-inflammatory effects. We aim to clarify association between frequency of alcohol consumption and risk of diabetes in Taiwanese population.

METHODS: The National Health Interview Survey (NHIS) in 2001, 2005, and 2009 selected a representative sample of Taiwan population using a multistage sampling design. Information was collected by standardized face to face interview. Study subjects were connected to the Taiwan National Health Insurance claims dataset and National Register of Deaths Dataset from 2000 to 2013. Kaplan-Meier curve with log rank test was employed to assess the influence of alcohol drinking on incidence of diabetes. Univariate and multivariate Cox proportional regression were used to recognize risk factors of diabetes.

RESULTS: A total of 43,000 participants were included (49.65% male; mean age, 41.79 +/- 16.31 years). During the 9-year follow-up period, 3650 incident diabetes cases were recognized. Kaplan-Meier curves comparing the four groups of alcohol consumption frequency showed significant differences (p < 0.01). After adjustment for potentially confounding variables, compared to social drinkers, the risks of diabetes were significantly higher for non-drinkers (adjusted hazard ratio [AHR] = 1.21; 95% confidence interval [CI], 1.09-1.34; p < 0.01), regular drinkers (AHR = 1.19; 95% CI, 1.06-1.35; p < 0.01), and heavy drinkers (AHR = 2.21, 95% CI, 1.56-3.13, p < 0.01).

CONCLUSIONS: Social drinkers have a significantly decreased risk of new-onset diabetes compared with non-, regular, and heavy drinkers.

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.

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