25 August 2020 In Phenolic compounds

BACKGROUND: Few studies have investigated the effect of dietary polyphenols on the complex human gut microbiota, and they focused mainly on single polyphenol molecules and select bacterial populations.

OBJECTIVE: The objective was to evaluate the effect of a moderate intake of red wine polyphenols on select gut microbial groups implicated in host health benefits.

DESIGN: Ten healthy male volunteers underwent a randomized, crossover, controlled intervention study. After a washout period, all of the subjects received red wine, the equivalent amount of de-alcoholized red wine, or gin for 20 d each. Total fecal DNA was submitted to polymerase chain reaction(PCR)-denaturing gradient gel electrophoresis and real-time quantitative PCR to monitor and quantify changes in fecal microbiota. Several biochemical markers were measured.

RESULTS: The dominant bacterial composition did not remain constant over the different intake periods. Compared with baseline, the daily consumption of red wine polyphenol for 4 wk significantly increased the number of Enterococcus, Prevotella, Bacteroides, Bifidobacterium, Bacteroides uniformis, Eggerthella lenta, and Blautia coccoides-Eubacterium rectale groups (P < 0.05). In parallel, systolic and diastolic blood pressures and triglyceride, total cholesterol, HDL cholesterol, and C-reactive protein concentrations decreased significantly (P < 0.05). Moreover, changes in cholesterol and C-reactive protein concentrations were linked to changes in the bifidobacteria number.

CONCLUSION: This study showed that red wine consumption can significantly modulate the growth of select gut microbiota in humans, which suggests possible prebiotic benefits associated with the inclusion of red wine polyphenols in the diet. This trial was registered at controlled-trials.com as ISRCTN88720134

25 August 2020 In General Health

OBJECTIVES: To examine the association between alcohol drinking patterns and health-related quality of life (HRQL).

METHODS: Population-based cross-sectional study was conducted in 2008-2010 among 12,715 adult individuals in Spain. HRQL was assessed with the SF-12 questionnaire and alcohol intake with a diet history. The threshold between average moderate drinking and average heavy drinking was >/= 40 g/day of alcohol in men and >/= 24 g/day in women. Binge drinking was defined as the intake of >/= 80 g in men and >/= 60 g in women at any drinking session during the preceding 30 days. Analyses were performed with linear regression and adjusted for the main confounders.

RESULTS: Compared to non-drinkers, all types of average drinkers reported better scores on the SF-12 physical component: beta=1.42 (95% confidence interval 1.03 to 1.81) in moderate drinkers and beta=1.86 (1.07 to 2.64) in heavy drinkers. In contrast, average alcohol consumption was not associated with the mental component of the SF-12. The number of binge drinking episodes and most types of beverage preference showed no association with physical or mental HRQL.

CONCLUSIONS: Alcohol drinkers, including those with heavy drinking, reported better physical HRQL than non-drinkers.

25 August 2020 In Drinking 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

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.

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