Potential Factors Associated with Cognitive Improvement of Individuals Diagnosed with Mild Cognitive Impairment or Dementia in Longitudinal Studies

Web-based artificial intelligence to predict cognitive impairment following stroke: A multicenter study

BACKGROUND AND PURPOSE

Post-stroke cognitive impairment (PSCI) is highly prevalent in modern society. However, there is limited study implying an accurate and explainable machine learning model to predict PSCI. The aim of this study is to develop and validate a web-based artificial intelligence (AI) tool for predicting PSCI.

METHODS

The retrospective cohort study design was conducted to develop and validate a web-based prediction model. Adults who experienced a stroke between January 1, 2004, and September 30, 2017, were enrolled, and patients with PSCI were followed up from the stroke index date until their last follow-up. The model's performance metrics, including accuracy, area under the curve (AUC), recall, precision, and F1 score, were compared.

RESULTS

A total of 3209 stroke patients were included in the study. The model demonstrated an accuracy of 0.8793, AUC of 0.9200, recall of 0.6332, precision of 0.9664, and F1 score of 0.7651. In the external validation phase, the accuracy improved to 0.9039, AUC to 0.9094, recall to 0.7457, precision to 0.9168, and F1 score to 0.8224. The final model can be accessed at https://psci-calculator.my.id/.

CONCLUSION

Our results are able to produce a user-friendly interface that is useful for health practitioners to perform early prediction on PSCI. These findings also suggest that the provided AI model is reliable and can serve as a roadmap for future studies using AI models in a clinical setting.

Post-Stroke Insomnia Increased the Risk of Cognitive Impairments: A Hospital-Based Retrospective Cohort Study

OBJECTIVES/BACKGROUND

Insomnia is a common sleep complaint among patients who had a stroke and has been recognized as an independent risk factor for cognitive impairment. However, the relationship between poststroke insomnia and cognitive impairment over time is under-researched. Therefore, we examined the association between poststroke insomnia and the risk of cognitive impairment.

PARTICIPANTS

Stroke participants who had a stroke and were 20 years and older.

METHODS

This multicenter hospital-based retrospective cohort study with a 13-year follow-up period (2004-2017). The diagnosis of stroke, insomnia, and cognitive impairment was based on the International Classification of Diseases. The study participants who experienced a stroke were divided into two cohorts: those who also had insomnia and those who did not have insomnia. A Cox proportional-hazards regression model was used.

RESULTS

A total of 1,775 patients with a mean age of 67.6 years were included. Of these patients, 146 and 75 patients were diagnosed with insomnia and cognitive impairment during the follow-up period, respectively. The cumulative incidence of cognitive impairment in the stroke with insomnia cohort was significantly lower than that in the stroke without insomnia cohort (log-rank test, P < .001). The adjusted hazard ratio and 95% confidence interval (CI) of the stroke with insomnia cohort indicated a higher risk of cognitive impairment compared with the stroke without insomnia cohort (adjusted hazard ratio: 2.38; 95% CI: 1.41-4.03).

CONCLUSIONS

Patients who had a stroke and were diagnosed with insomnia exhibited a substantial increased risk of cognitive impairment over time.

Cognitive Reserve and Mild Cognitive Impairment: Predictors and Rates of Reversion to Intact Cognition vs Progression to Dementia

Background and Objectives

Little is known about the effect of education or other indicators of cognitive reserve on the rate of reversion from mild cognitive impairment (MCI) to normal cognition (NC) or the relative rate (RR) of reversion from MCI to NC vs progression from MCI to dementia. Our objectives were to (1) estimate transition rates from MCI to NC and dementia and (2) determine the effect of age, APOE, and indicators of cognitive reserve on the RR of reversion vs progression using multistate Markov modeling.

Methods

We estimated instantaneous transition rates between NC, MCI, and dementia after accounting for transition to death across up to 12 assessments in the Nun Study, a cohort study of religious sisters aged 75+ years. We estimated RRs of reversion vs progression for age, APOE, and potential cognitive reserve indicators: education, academic performance (high school grades), and written language skills (idea density, grammatical complexity).

Results

Of the 619 participants, 472 were assessed with MCI during the study period. Of these 472, 143 (30.3%) experienced at least one reverse transition to NC, and 120 of the 143 (83.9%) never developed dementia (mean follow-up = 8.6 years). In models adjusted for age group and APOE, higher levels of education more than doubled the RR ratio of reversion vs progression. Novel cognitive reserve indicators were significantly associated with a higher adjusted RR of reversion vs progression (higher vs lower levels for English grades: RR ratio = 1.83; idea density: RR ratio = 3.93; and grammatical complexity: RR ratio = 5.78).

Discussion

Knowledge of frequent reversion from MCI to NC may alleviate concerns of inevitable cognitive decline in those with MCI. Identification of characteristics predicting the rate of reversion from MCI to NC vs progression from MCI to dementia may guide population-level interventions targeting these characteristics to prevent or postpone MCI and dementia. Research on cognitive trajectories would benefit from incorporating predictors of reverse transitions and competing events, such as death, into statistical modeling. These results may inform the design and interpretation of MCI clinical trials, given that a substantial proportion of participants may experience improvement without intervention.

The dynamics of biomarkers across the clinical spectrum of Alzheimer's disease

Background

Quantifying changes in the levels of biological and cognitive markers prior to the clinical presentation of Alzheimer’s disease (AD) will provide a template for understanding the underlying aetiology of the clinical syndrome and, concomitantly, for improving early diagnosis, clinical trial recruitment and treatment assessment. This study aims to characterise continuous changes of such markers and determine their rate of change and temporal order throughout the AD continuum.

Methods

The methodology is founded on the development of stochastic models to estimate the expected time to reach different clinical disease states, for different risk groups, and synchronise short-term individual biomarker data onto a disease progression timeline. Twenty-seven markers are considered, including a range of cognitive scores, cerebrospinal (CSF) and plasma fluid proteins, and brain structural and molecular imaging measures. Data from 2014 participants in the Alzheimer’s Disease Neuroimaging Initiative database is utilised.

Results

The model suggests that detectable memory dysfunction could occur up to three decades prior to the onset of dementia due to AD (ADem). This is closely followed by changes in amyloid-β CSF levels and the first cognitive decline, as assessed by sensitive measures. Hippocampal atrophy could be observed as early as the initial amyloid-β accumulation. Brain hypometabolism starts later, about 14 years before onset, along with changes in the levels of total and phosphorylated tau proteins. Loss of functional abilities occurs rapidly around ADem onset. Neurofilament light is the only protein with notable early changes in plasma levels. The rate of change varies, with CSF, memory, amyloid PET and brain structural measures exhibiting the highest rate before the onset of ADem, followed by a decline. The probability of progressing to a more severe clinical state increases almost exponentially with age. In accordance with previous studies, the presence of apolipoprotein E4 alleles and amyloid-β accumulation can be associated with an increased risk of developing the disease, but their influence depends on age and clinical state.

Conclusions

Despite the limited longitudinal data at the individual level and the high variability observed in such data, the study elucidates the link between the long asynchronous pathophysiological processes and the preclinical and clinical stages of AD.

Polyphenol Health Effects on Cardiovascular and Neurodegenerative Disorders: A Review and Meta-Analysis

Several studies have demonstrated that polyphenol-enriched diets may have beneficial effects against the development of degenerative diseases, including atherosclerosis and disorders affecting the central nervous system. This activity has been associated not only with antioxidant and anti-inflammatory properties, but also with additional mechanisms, such as the modulation of lipid metabolism and gut microbiota function. However, long-term studies on humans provided controversial results, making the prediction of polyphenol impact on health uncertain. The aim of this review is to provide an overview and critical analysis of the literature related to the effects of the principal dietary polyphenols on cardiovascular and neurodegenerative disorders. We critically considered and meta-analyzed randomized controlled clinical trials involving subjects taking polyphenol-based supplements. Although some polyphenols might improve specific markers of cardiovascular risk and cognitive status, many inconsistent data are present in literature. Therefore, definitive recommendations for the use of these compounds in the prevention of cardiovascular disease and cognitive decline are currently not applicable. Once pivotal aspects for the definition of polyphenol bioactivity, such as the characterization of pharmacokinetics and safety, are addressed, it will be possible to have a clear picture of the realistic potential of polyphenols for disease prevention.