On the relationships between epilepsy, sleep, and Alzheimer's disease: A narrative review

Clinical and research application of fluid biomarkers in autosomal dominant Alzheimer's disease and Down syndrome

Autosomal dominant Alzheimer's disease (ADAD) and Down syndrome (DS) constitute genetic forms of Alzheimer's disease (AD). The study of these forms has been crucial in understanding the biomarker changes and disease progression, notably in advancing our knowledge of the natural history of AD. However, some specific characteristics of biomarkers in genetically determined forms and, most importantly, the near full penetrance and predictability of disease onset lead to a very different context of use for biomarkers in these populations. This article delves into the similarities and differences in biomarker profiles between genetically determined AD and sporadic cases, discussing the implications for research and clinical practice. It also emphasizes the need to account for factors that may affect biomarker reliability differently in genetically determined AD. Enhancing our understanding of the disease will pave the way for more personalized therapeutic approaches for affected individuals.

Alzheimer's disease and epilepsy: Research hotspots for comorbidity in the era of global aging

Neurological conditions such as Alzheimer's disease (AD) and epilepsy share a significant clinical overlap, particularly in the elderly, with each disorder potentiating the risk of the other. This interplay is significant amidst an aging global demographic. The review explores the classical pathologies of AD, including amyloid-beta plaques and hyperphosphorylated tau, and their potential role in the genesis of epilepsy. It also delves into the imbalance of glutamate and gamma-amino butyric acid activities, a key mechanism in epilepsy that may be influenced by AD pathology. The impact of age of onset on comorbidity is examined, with early-onset AD and Down syndrome presenting higher risks of epilepsy. The review suggests that epilepsy might precede cognitive symptoms in AD, indicating a complex interaction. Sleep modulation is highlighted as a factor, with sleep disturbances potentially contributing to AD progression. The necessity for cautious medication management is emphasized due to the cognitive effects of certain antiepileptic drugs. Animal models are recognized for their importance in understanding the relationship between AD and epilepsy, though creating fully representative models presents a challenge. The review concludes by noting the efficacy of medications such as lamotrigine, levetiracetam, and memantine in managing both conditions and suggests the ketogenic diet and cannabidiol as emerging treatment options, warranting further investigation for comprehensive patient care strategies.

Mapping multimorbidity progression among 190 diseases

BACKGROUND

Current clustering of multimorbidity based on the frequency of common disease combinations is inadequate. We estimated the causal relationships among prevalent diseases and mapped out the clusters of multimorbidity progression among them.

METHODS

In this cohort study, we examined the progression of multimorbidity among 190 diseases among over 500,000 UK Biobank participants over 12.7 years of follow-up. Using a machine learning method for causal inference, we analyzed patterns of how diseases influenced and were influenced by others in females and males. We used clustering analysis and visualization algorithms to identify multimorbidity progress constellations.

RESULTS

We show the top influential and influenced diseases largely overlap between sexes in chronic diseases, with sex-specific ones tending to be acute diseases. Patterns of diseases that influence and are influenced by other diseases also emerged (clustering significance Pau > 0.87), with the top influential diseases affecting many clusters and the top influenced diseases concentrating on a few, suggesting that complex mechanisms are at play for the diseases that increase the development of other diseases while share underlying causes exist among the diseases whose development are increased by others. Bi-directional multimorbidity progress presents substantial clustering tendencies both within and across International Classification Disease chapters, compared to uni-directional ones, which can inform future studies for developing cross-specialty strategies for multimorbidity. Finally, we identify 10 multimorbidity progress constellations for females and 9 for males (clustering stability, adjusted Rand index >0.75), showing interesting differences between sexes.

CONCLUSION

Our findings could inform the future development of targeted interventions and provide an essential foundation for future studies seeking to improve the prevention and management of multimorbidity.

Alzheimer's Disease and Epilepsy: Exploring Shared Pathways and Promising Biomarkers for Future Treatments

Background: Alzheimer's disease (AD) and epilepsy represent two complex neurological disorders with distinct clinical manifestations, yet recent research has highlighted their intricate interplay. This review examines the association between AD and epilepsy, with particular emphasis on late-onset epilepsy of unknown etiology, increasingly acknowledged as a prodrome of AD. It delves into epidemiology, pathogenic mechanisms, clinical features, diagnostic characteristics, treatment strategies, and emerging biomarkers to provide a comprehensive understanding of this relationship. Methods: A comprehensive literature search was conducted, identifying 128 relevant articles published between 2018 and 2024. Results: Findings underscore a bidirectional relationship between AD and epilepsy, indicating shared pathogenic pathways that extend beyond traditional amyloid-beta and Tau protein pathology. These pathways encompass neuroinflammation, synaptic dysfunction, structural and network alterations, as well as molecular mechanisms. Notably, epileptic activity in AD patients may exacerbate cognitive decline, necessitating prompt detection and treatment. Novel biomarkers, such as subclinical epileptiform activity detected via advanced electroencephalographic techniques, offer promise for early diagnosis and targeted interventions. Furthermore, emerging therapeutic approaches targeting shared pathogenic mechanisms hold potential for disease modification in both AD and epilepsy. Conclusions: This review highlights the importance of understanding the relationship between AD and epilepsy, providing insights into future research directions. Clinical data and diagnostic methods are also reviewed, enabling clinicians to implement more effective treatment strategies.

Persistent ∆FosB expression limits recurrent seizure activity and provides neuroprotection in the dentate gyrus of APP mice

Recurrent seizures lead to accumulation of the activity-dependent transcription factor ∆FosB in hippocampal dentate granule cells in both mouse models of epilepsy and mouse models of Alzheimer's disease (AD), which is also associated with increased incidence of seizures. In patients with AD and related mouse models, the degree of ∆FosB accumulation corresponds with increasing severity of cognitive deficits. We previously found that ∆FosB impairs spatial memory in mice by epigenetically regulating expression of target genes such as calbindin that are involved in synaptic plasticity. However, the suppression of calbindin in conditions of neuronal hyperexcitability has been demonstrated to provide neuroprotection to dentate granule cells, indicating that ∆FosB may act over long timescales to coordinate neuroprotective pathways. To test this hypothesis, we used viral-mediated expression of ∆JunD to interfere with ∆FosB signaling over the course of several months in transgenic mice expressing mutant human amyloid precursor protein (APP), which exhibit spontaneous seizures and develop AD-related neuropathology and cognitive deficits. Our results demonstrate that persistent ∆FosB activity acts through discrete modes of hippocampal target gene regulation to modulate neuronal excitability, limit recurrent seizure activity, and provide neuroprotection to hippocampal dentate granule cells in APP mice.

Delineating mechanisms underlying parvalbumin neuron impairment in different neurological and neurodegenerative disorders: the emerging role of mitochondrial dysfunction

Given the current paucity of effective treatments in many neurological disorders, delineating pathophysiological mechanisms among the major psychiatric and neurodegenerative diseases may fuel the development of novel, potent treatments that target shared pathways. Recent evidence suggests that various pathological processes, including bioenergetic failure in mitochondria, can perturb the function of fast-spiking, parvalbumin-positive neurons (PV+). These inhibitory neurons critically influence local circuit regulation, the generation of neuronal network oscillations and complex brain functioning. Here, we survey PV+ cell vulnerability in the major neuropsychiatric, and neurodegenerative diseases and review associated cellular and molecular pathophysiological alterations purported to underlie disease aetiology.

Alzheimer's Disease: The Past, Present, and Future of a Globally Progressive Disease

Alzheimer's disease (AD) is a significant 21st-century public health challenge. This article delves into AD's neurodegenerative complexities, highlighting cognitive decline, memory impairment, and societal burdens. Mechanistically, protein misfolding, amyloid-beta (Aβ) pathway abnormalities, and genetic/environmental factors are discussed. The pivotal amyloid hypothesis is dissected, focusing on Aβ aggregation's role in synaptic dysfunction and neurodegeneration. The review showcases promising therapeutic strategies, including anti-amyloid antibodies and β/γ-secretase inhibitors targeting Aβ production. Notably, the FDA-approved Lecanemab signifies a breakthrough, slowing disease progression. Anti-Tau therapies' emergence is highlighted, addressing late-stage intervention. Tau aggregation blockers and anti-Tau antibodies offer potential against intracellular tau pathology. The review underscores collaborative efforts to uncover AD's secrets and pave the way for memory preservation.

Temporal Feature Extraction and Machine Learning for Classification of Sleep Stages Using Telemetry Polysomnography

Accurate sleep stage detection is crucial for diagnosing sleep disorders and tailoring treatment plans. Polysomnography (PSG) is considered the gold standard for sleep assessment since it captures a diverse set of physiological signals. While various studies have employed complex neural networks for sleep staging using PSG, our research emphasises the efficacy of a simpler and more efficient architecture. We aimed to integrate a diverse set of feature extraction measures with straightforward machine learning, potentially offering a more efficient avenue for sleep staging. We also aimed to conduct a comprehensive comparative analysis of feature extraction measures, including the power spectral density, Higuchi fractal dimension, singular value decomposition entropy, permutation entropy, and detrended fluctuation analysis, coupled with several machine-learning models, including XGBoost, Extra Trees, Random Forest, and LightGBM. Furthermore, data augmentation methods like the Synthetic Minority Oversampling Technique were also employed to rectify the inherent class imbalance in sleep data. The subsequent results highlighted that the XGBoost classifier, when used with a combination of all feature extraction measures as an ensemble, achieved the highest performance, with accuracies of 87%, 90%, 93%, 96%, and 97% and average F1-scores of 84.6%, 89%, 90.33%, 93.5%, and 93.5% for distinguishing between five-stage, four-stage, three-stage, and two distinct two-stage sleep configurations, respectively. This combined feature extraction technique represents a novel addition to the body of research since it achieves higher performance than many recently developed deep neural networks by utilising simpler machine-learning models.

The Worldwide Prevalence of Sleep Problems Among Medical Students by Problem, Country, and COVID-19 Status: a Systematic Review, Meta-analysis, and Meta-regression of 109 Studies Involving 59427 Participants

Abstract

Purpose of Review

Several studies have found that medical students have a significant prevalence of sleep issues, such as poor sleep quality, excessive daytime sleepiness, and inadequate sleep duration. The purpose of this review is to carefully evaluate the current research on sleep problems among medical students and, as a result, estimate the prevalence of these disturbances. The EMBASE, PsychINFO, PubMed/MEDLINE, ScienceDirect, Scopus, and Web of Science and retrieved article reference lists were rigorously searched and rated for quality. Random effects meta-analysis was performed to compute estimates.

Recent Findings

The current meta-analysis revealed an alarming estimated pooled prevalence of poor sleep quality (K = 95, N = 54894) of 55.64% [95%CI 51.45%; 59.74%]. A total of 33.32% [95%CI 26.52%; 40.91%] of the students (K = 28, N = 10122) experienced excessive sleepiness during the day. The average sleep duration for medical students (K = 35, N = 18052) is only 6.5 h per night [95%CI 6.24; 6.64], which suggests that at least 30% of them get less sleep than the recommended 7-9 h per night.

Summary

Sleep issues are common among medical students, making them a genuine problem. Future research should focus on prevention and intervention initiatives aimed at these groups.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40675-023-00258-5.

Age Group Differences in the Association Between Sleep Status and Frailty Among Community-Dwelling Older Adults: The SONIC Study

Objective: We aimed to determine whether the association of sleep status with frailty differs between age groups of older adults. Method: This cross-sectional study was part of the observational Septuagenarians, Octogenarians, Nonagenarians Investigation with Centenarians (SONIC) study. Subjects were community-dwelling older adults in their 70s and 80s. Frailty was evaluated using the Japanese version of the Cardiovascular Health Study criteria (J-CHS). Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep status. Poor sleep quality was defined as a PSQI global score ≥6. Sleep duration was categorized as short (<6 hr), normal (6-8), and long (>8). We performed multivariable logistic regression to investigate the association between sleep status and frailty separately for each age group adjusted for multiple covariates. Results: In those in their 70s, long sleep duration and sleep medication use were independently associated with frailty. In those in their 80s, poor sleep quality was independently associated with frailty. Conclusions: The association between sleep status and frailty was different between age groups. The findings underscore the importance of incorporating the evaluation of sleep quantity and non-pharmacological therapies in those in their 70s and the evaluation of sleep quality in those in their 80s to help prevent the onset of frailty.

Alterations of sleep oscillations in Alzheimer's disease: A potential role for GABAergic neurons in the cortex, hippocampus, and thalamus

Sleep abnormalities are widely reported in patients with Alzheimer's disease (AD) and are linked to cognitive impairments. Sleep abnormalities could be potential biomarkers to detect AD since they are often observed at the preclinical stage. Moreover, sleep could be a target for early intervention to prevent or slow AD progression. Thus, here we review changes in brain oscillations observed during sleep, their connection to AD pathophysiology and the role of specific brain circuits. Slow oscillations (0.1-1 Hz), sleep spindles (8-15 Hz) and their coupling during non-REM sleep are consistently reduced in studies of patients and in AD mouse models although the timing and magnitude of these alterations depends on the pathophysiological changes and the animal model studied. Changes in delta (1-4 Hz) activity are more variable. Animal studies suggest that hippocampal sharp-wave ripples (100-250 Hz) are also affected. Reductions in REM sleep amount and slower oscillations during REM are seen in patients but less consistently in animal models. Thus, changes in a variety of sleep oscillations could impact sleep-dependent memory consolidation or restorative functions of sleep. Recent mechanistic studies suggest that alterations in the activity of GABAergic neurons in the cortex, hippocampus and thalamic reticular nucleus mediate sleep oscillatory changes in AD and represent a potential target for intervention. Longitudinal studies of the timing of AD-related sleep abnormalities with respect to pathology and dysfunction of specific neural networks are needed to identify translationally relevant biomarkers and guide early intervention strategies to prevent or delay AD progression.

Alzheimer's Disease with Epileptiform EEG Activity: Abnormal Cortical Sources of Resting State Delta Rhythms in Patients with Amnesic Mild Cognitive Impairment

BACKGROUND

Patients with amnesic mild cognitive impairment due to Alzheimer's disease (ADMCI) typically show a "slowing" of cortical resting-state eyes-closed electroencephalographic (rsEEG) rhythms. Some of them also show subclinical, non-convulsive, and epileptiform EEG activity (EEA) with an unclear relationship with that "slowing."

OBJECTIVE

Here we tested the hypothesis that the "slowing" of rsEEG rhythms is related to EEA in ADMCI patients.

METHODS

Clinical and instrumental datasets in 62 ADMCI patients and 38 normal elderly (Nold) subjects were available in a national archive. No participant had received a clinical diagnosis of epilepsy. The eLORETA freeware estimated rsEEG cortical sources. The area under the receiver operating characteristic curve (AUROCC) indexed the accuracy of eLORETA solutions in the classification between ADMCI-EEA and ADMCI-noEEA individuals.

RESULTS

EEA was observed in 15% (N = 8) of the ADMCI patients. The ADMCI-EEA group showed: 1) more abnormal Aβ 42 levels in the cerebrospinal fluid as compared to the ADMCI-noEEA group and 2) higher temporal and occipital delta (<4 Hz) rsEEG source activities as compared to the ADMCI-noEEA and Nold groups. Those source activities showed moderate accuracy (AUROCC = 0.70-0.75) in the discrimination between ADMCI-noEEA versus ADMCI-EEA individuals.

CONCLUSION

It can be speculated that in ADMCI-EEA patients, AD-related amyloid neuropathology may be related to an over-excitation in neurophysiological low-frequency (delta) oscillatory mechanisms underpinning cortical arousal and quiet vigilance.