Cognitive decline and alcohol consumption in the aging population-A longitudinal analysis of the Survey of Health, Ageing and Retirement in Europe

Alcohol, flexible behavior, and the prefrontal cortex: Functional changes underlying impaired cognitive flexibility

Cognitive flexibility enables individuals to alter their behavior in response to changing environmental demands, facilitating optimal behavior in a dynamic world. The inability to do this, called behavioral inflexibility, is a pervasive behavioral phenotype in alcohol use disorder (AUD), driven by disruptions in cognitive flexibility. Research has repeatedly shown that behavioral inflexibility not only results from alcohol exposure across species but can itself be predictive of future drinking. Like many high-level executive functions, flexible behavior requires healthy functioning of the prefrontal cortex (PFC). The scope of this review addresses two primary themes: first, we outline tasks that have been used to investigate flexibility in the context of AUD or AUD models. We characterize these based on the task features and underlying cognitive processes that differentiate them from one another. We highlight the neural basis of flexibility measures, focusing on the PFC, and how acute or chronic alcohol in humans and non-human animal models impacts flexibility. Second, we consolidate findings on the molecular, physiological and functional changes in the PFC elicited by alcohol, that may contribute to cognitive flexibility deficits seen in AUD. Collectively, this approach identifies several key avenues for future research that will facilitate effective treatments to promote flexible behavior in the context of AUD, to reduce the risk of alcohol related harm, and to improve outcomes following AUD.

Alcohol consumption and accelerated biological ageing in middle-aged and older people: A longitudinal study from two cohorts

BACKGROUND AND AIMS

The relationship between alcohol consumption and age-related diseases is inconsistent. Biological age (BA) serves as both a precursor and a predictor of age-related diseases; however, longitudinal associations between alcohol consumption and BA in middle-aged and older people remain unclear. We measured whether there was a longitudinal association between drinking frequency and pure alcohol intake with BA among middle-aged and older people.

DESIGN AND SETTING AND PARTICIPANTS

This study involved two prospective cohort studies, set in Southwestern China and the United Kingdom. A total of 8046 participants from the China Multi-Ethnic Cohort study (CMEC) and 5412 participants from the UK Biobank (UKB), aged 30-79 years, took part, with complete data from two waves of clinical biomarkers.

MEASUREMENTS

BA was calculated by the Klemera Doubal's method. Accelerated BA equalled BA minus chronological age. Drinking frequency and pure alcohol intake were obtained through self-reported questionnaires. Drinking frequency in the past year was classified as current non-drinking, occasional (monthly drinking) and regular (weekly drinking).

FINDINGS

Compared with consistent current non-drinkers, more frequent drinkers [CMEC: β = 0.46, 95% confidence interval (CI) = 0.13-0.80; UKB: β = 0.65, 95% CI = 0.01-1.29)], less frequent drinkers (CMEC: β = 0.62, 95% CI = 0.37-0.87; UKB: β = 0.54, 95% CI = -0.01-1.09), consistent occasional drinkers (CMEC: β = 0.51, 95% CI = 0.23-0.79; UKB: β = 0.63, 95% CI = 0.13-1.13) and consistent regular drinkers (CMEC: β = 0.56, 95% CI = 0.17-0.95; UKB: β = 0.46, 95% CI = 0.00-0.91) exhibited increased accelerated BA. A non-linear relationship between pure alcohol intake and accelerated BA was observed among consistent regular drinkers.

CONCLUSIONS

In middle-aged and older people, any change in drinking frequency and any amount of pure alcohol intake seem to be positively associated with acceleration of biological ageing, compared with maintaining abstinence.

Sex differences in frailty among older adults

By definition, aging is a natural, gradual and continuous process. On the other hand, frailty reflects the increase in vulnerability to stressors and shortens the time without disease (health span) while longevity refers to the length of life (lifespan). The average life expectancy has significantly increased during the last few decades. A longer lifespan has been accompanied by an increase in frailty and decreased independence in older adults, with major differences existing between men and women. For example, women tend to live longer than men but also experience higher rates of frailty and disability. Sex differences prevent optimization of lifestyle interventions and therapies to effectively prevent frailty. Sex differences in frailty and aging are rooted in a complex interplay between uncontrollable (genetic, epigenetic, physiological), and controllable factors (psychosocial and lifestyle factors). Thus, understanding the underlying causes of sex differences in frailty and aging is essential for developing personalized interventions to promote healthy aging and improve quality of life in older men and women. In this review, we have discussed the key contributors and knowledge gaps related to sex differences in aging and frailty.

Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review

BACKGROUND

Increasingly, patient medication adherence data are being consolidated from claims databases and electronic health records (EHRs). Such databases offer an indirect avenue to gauge medication adherence in our data-rich healthcare milieu. The surge in data accessibility, coupled with the pressing need for its conversion to actionable insights, has spotlighted data mining, with machine learning (ML) emerging as a pivotal technique. Nonadherence poses heightened health risks and escalates medical costs. This paper elucidates the synergistic interaction between medical database mining for medication adherence and the role of ML in fostering knowledge discovery.

METHODS

We conducted a comprehensive review of EHR applications in the realm of medication adherence, leveraging ML techniques. We expounded on the evolution and structure of medical databases pertinent to medication adherence and harnessed both supervised and unsupervised ML paradigms to delve into adherence and its ramifications.

RESULTS

Our study underscores the applications of medical databases and ML, encompassing both supervised and unsupervised learning, for medication adherence in clinical big data. Databases like SEER and NHANES, often underutilized due to their intricacies, have gained prominence. Employing ML to excavate patient medication logs from these databases facilitates adherence analysis. Such findings are pivotal for clinical decision-making, risk stratification, and scholarly pursuits, aiming to elevate healthcare quality.

CONCLUSION

Advanced data mining in the era of big data has revolutionized medication adherence research, thereby enhancing patient care. Emphasizing bespoke interventions and research could herald transformative shifts in therapeutic modalities.