Pre-diagnostic cognitive and functional impairment in multiple sporadic neurodegenerative diseases

A therapeutic small molecule enhances γ-oscillations and improves cognition/memory in Alzheimer's disease model mice

Brain rhythms provide the timing for recruitment of brain activity required for linking together neuronal ensembles engaged in specific tasks. The γ-oscillations (30 to 120 Hz) orchestrate neuronal circuits underlying cognitive processes and working memory. These oscillations are reduced in numerous neurological and psychiatric disorders, including early cognitive decline in Alzheimer's disease (AD). Here, we report on a potent brain-permeable small molecule, DDL-920 that increases γ-oscillations and improves cognition/memory in a mouse model of AD, thus showing promise as a class of therapeutics for AD. We employed anatomical, in vitro and in vivo electrophysiological, and behavioral methods to examine the effects of our lead therapeutic candidate small molecule. As a novel in central nervous system pharmacotherapy, our lead molecule acts as a potent, efficacious, and selective negative allosteric modulator of the γ-aminobutyric acid type A receptors most likely assembled from α1β2δ subunits. These receptors, identified through anatomical and pharmacological means, underlie the tonic inhibition of parvalbumin (PV) expressing interneurons (PV+INs) critically involved in the generation of γ-oscillations. When orally administered twice daily for 2 wk, DDL-920 restored the cognitive/memory impairments of 3- to 4-mo-old AD model mice as measured by their performance in the Barnes maze. Our approach is unique as it is meant to enhance cognitive performance and working memory in a state-dependent manner by engaging and amplifying the brain's endogenous γ-oscillations through enhancing the function of PV+INs.

Early and selective localization of tau filaments to glutamatergic subcellular domains within the human anterodorsal thalamus

Widespread cortical accumulation of misfolded pathological tau proteins (ptau) in the form of paired helical filaments is a major hallmark of Alzheimer's disease. Subcellular localization of ptau at various stages of disease progression is likely to be informative of the cellular mechanisms involving its spread. Here, we found that the density of ptau within several distinct rostral thalamic nuclei in post-mortem human tissue (n = 25 cases) increased with the disease stage, with the anterodorsal nucleus (ADn) consistently being the most affected. In the ADn, ptau-positive elements were present already in the pre-cortical (Braak 0) stage. Tau pathology preferentially affected the calretinin-expressing subpopulation of glutamatergic neurons in the ADn. At the subcellular level, we detected ptau immunoreactivity in ADn cell bodies, dendrites, and in a specialized type of presynaptic terminal that expresses vesicular glutamate transporter 2 (vGLUT2) and likely originates from the mammillary body. The ptau-containing terminals displayed signs of degeneration, including endosomal/lysosomal organelles. In contrast, corticothalamic axon terminals lacked ptau. The data demonstrate the involvement of a specific cell population in ADn at the onset of the disease. The presence of ptau in subcortical glutamatergic presynaptic terminals supports hypotheses about the transsynaptic spread of tau selectively affecting specialized axonal pathways.

Extracellular vesicles as nanotheranostic platforms for targeted neurological disorder interventions

Central Nervous System (CNS) disorders represent a profound public health challenge that affects millions of people around the world. Diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), and traumatic brain injury (TBI) exemplify the complexities and diversities that complicate their early detection and the development of effective treatments. Amid these challenges, the emergence of nanotechnology and extracellular vesicles (EVs) signals a new dawn for treating and diagnosing CNS ailments. EVs are cellularly derived lipid bilayer nanosized particles that are pivotal in intercellular communication within the CNS and have the potential to revolutionize targeted therapeutic delivery and the identification of novel biomarkers. Integrating EVs with nanotechnology amplifies their diagnostic and therapeutic capabilities, opening new avenues for managing CNS diseases. This review focuses on examining the fascinating interplay between EVs and nanotechnology in CNS theranostics. Through highlighting the remarkable advancements and unique methodologies, we aim to offer valuable perspectives on how these approaches can bring about a revolutionary change in disease management. The objective is to harness the distinctive attributes of EVs and nanotechnology to forge personalized, efficient interventions for CNS disorders, thereby providing a beacon of hope for affected individuals. In short, the confluence of EVs and nanotechnology heralds a promising frontier for targeted and impactful treatments against CNS diseases, which continue to pose significant public health challenges. By focusing on personalized and powerful diagnostic and therapeutic methods, we might improve the quality of patients.

Clinical trajectories preceding incident dementia up to 15 years before diagnosis: a large prospective cohort study

BACKGROUND

Dementia has a long prodromal stage with various pathophysiological manifestations; however, the progression of pre-diagnostic changes remains unclear. We aimed to determine the evolutional trajectories of multiple-domain clinical assessments and health conditions up to 15 years before the diagnosis of dementia.

METHODS

Data was extracted from the UK-Biobank, a longitudinal cohort that recruited over 500,000 participants from March 2006 to October 2010. Each demented subject was matched with 10 healthy controls. We performed logistic regressions on 400 predictors covering a comprehensive range of clinical assessments or health conditions. Their evolutional trajectories were quantified using adjusted odds ratios (ORs) and FDR-corrected p-values under consecutive timeframes preceding the diagnosis of dementia.

FINDINGS

During a median follow-up of 13.7 [Interquartile range, IQR 12.9-14.2] years until July 2022, 7620 subjects were diagnosed with dementia. In general, upon approaching the diagnosis, demented subjects witnessed worse functional assessments and a higher prevalence of health conditions. Associations up to 15 years preceding the diagnosis comprised declined physical strength (hand grip strength, OR 0.65 [0.63-0.67]), lung dysfunction (peak expiratory flow, OR 0.78 [0.76-0.81]) and kidney dysfunction (cystatin C, OR 1.13 [1.11-1.16]), comorbidities of coronary heart disease (OR 1.78 [1.67-1.91]), stroke (OR 2.34 [2.1-1.37]), diabetes (OR 2.03 [1.89-2.18]) and a series of mental disorders. Cognitive functions in multiple tests also demonstrate decline over a decade before the diagnosis. Inadequate activity (3-5 year, overall time of activity, OR 0.82 [0.73-0.92]), drowsiness (3-5 year, sleep duration, OR 1.13 [1.04-1.24]) and weight loss (0-5 year, weight, OR 0.9 [0.83-0.98]) only exhibited associations within five years before the diagnosis. In addition, serum biomarkers of enriched endocrine, dysregulations of ketones, deficiency of brand-chain amino acids and polyunsaturated fatty acids were found in a similar prodromal time window and can be witnessed as the last pre-symptomatic conditions before the diagnosis.

INTERPRETATION

Our findings present a comprehensive temporal-diagnostic landscape preceding incident dementia, which could improve selection for preventive and early disease-modifying treatment trials.

Developing medicines for the pre-symptomatic stage of degenerative neurological conditions: Challenges and opportunities

Neurodegenerative disorders have a devastating disease course and an increasing prevalence due to prolonged life expectancy. In the last decades, it has become increasingly clear that these diseases often start much earlier, before the onset of symptoms, creating a potential window for pre-symptomatic treatment, a strategy that is desirable from both the biologic and the ethical point of view. However, studying treatments for a pre-symptomatic population presents objective difficulties. This article intends to give a perspective about opportunities and challenges of pre-symptomatic prevention of neurodegenerative diseases. Besides the requirement for biomarkers that would facilitate both the selection of study population and demonstrating a treatment effect, further considerations about balancing benefits, risks and uncertainties pertaining a pre-symptomatic population will be examined.

Plasma proteomic profiles predict future dementia in healthy adults

The advent of proteomics offers an unprecedented opportunity to predict dementia onset. We examined this in data from 52,645 adults without dementia in the UK Biobank, with 1,417 incident cases and a follow-up time of 14.1 years. Of 1,463 plasma proteins, GFAP, NEFL, GDF15 and LTBP2 consistently associated most with incident all-cause dementia (ACD), Alzheimer's disease (AD) and vascular dementia (VaD), and ranked high in protein importance ordering. Combining GFAP (or GDF15) with demographics produced desirable predictions for ACD (area under the curve (AUC) = 0.891) and AD (AUC = 0.872) (or VaD (AUC = 0.912)). This was also true when predicting over 10-year ACD, AD and VaD. Individuals with higher GFAP levels were 2.32 times more likely to develop dementia. Notably, GFAP and LTBP2 were highly specific for dementia prediction. GFAP and NEFL began to change at least 10 years before dementia diagnosis. Our findings strongly highlight GFAP as an optimal biomarker for dementia prediction, even more than 10 years before the diagnosis, with implications for screening people at high risk for dementia and for early intervention.

A therapeutic small molecule lead enhances γ-oscillations and improves cognition/memory in Alzheimer's disease model mice

Brain rhythms provide the timing and concurrence of brain activity required for linking together neuronal ensembles engaged in specific tasks. In particular, the γ-oscillations (30-120 Hz) orchestrate neuronal circuits underlying cognitive processes and working memory. These oscillations are reduced in numerous neurological and psychiatric disorders, including early cognitive decline in Alzheimer's disease (AD). Here we report on a potent brain permeable small molecule, DDL-920 that increases γ-oscillations and improves cognition/memory in a mouse model of AD, thus showing promise as a new class of therapeutics for AD. As a first in CNS pharmacotherapy, our lead candidate acts as a potent, efficacious, and selective negative allosteric modulator (NAM) of the γ-aminobutyric acid type A receptors (GABA A Rs) assembled from α1β2δ subunits. We identified these receptors through anatomical and pharmacological means to mediate the tonic inhibition of parvalbumin (PV) expressing interneurons (PV+INs) critically involved in the generation of γ-oscillations. Our approach is unique as it is meant to enhance cognitive performance and working memory in a state-dependent manner by engaging and amplifying the brain's endogenous γ-oscillations through enhancing the function of PV+INs.

Understanding ethnic diversity in open dementia neuroimaging data sets

Ethnic differences in dementia are increasingly recognized in epidemiological measures and diagnostic biomarkers. Nonetheless, ethnic diversity remains limited in many study populations. Here, we provide insights into ethnic diversity in open-access neuroimaging dementia data sets. Data sets comprising dementia populations with available data on ethnicity were included. Statistical analyses of sample and effect sizes were based on the Cochrane Handbook. Nineteen databases were included, with 17 studies of healthy groups or a combination of diagnostic groups if breakdown was unavailable and 12 of mild cognitive impairment and dementia groups. Combining all studies on dementia patients, the largest ethnic group was Caucasian (20 547 participants), with the next most common being Afro-Caribbean (1958), followed by Asian (1211). The smallest effect size detectable within the Caucasian group was 0.03, compared to Afro-Caribbean (0.1) and Asian (0.13). Our findings quantify the lack of ethnic diversity in openly available dementia data sets. More representative data would facilitate the development and validation of biomarkers relevant across ethnicities.

Patient-specific cognitive profiles in the detection of dementia subtypes: A proposal

Many physicians rely on sum score cognitive screening tests to evaluate patients for cognitive decline. Because the vast majority of cognitively impaired patients never receive more extensive testing, the results of these screening tests impact patients and their family members profoundly. No previous study has examined whether the metrics used by the popular Mini-Mental State Examination, Montreal Cognitive Assessment, and Saint Louis University Mental Status tests reliably identify single-domain deficits or allow clinicians to adequately track disease progression. We compare side by side the metrics used by these three tests to highlight the differences in the ways they measure domain impairments. We then contrast the sum score approach to cognitive screening with brief domain-specific tests that use extended metrics in each domain examined. Last, we suggest that moderate-to-severe domain-specific deficits on these tests should lead physicians to anticipate specific functional problems and alert family members.

Subjective Cognitive Decline in Women with Features Suggestive of Prodromal Parkinson's Disease

BACKGROUND

Cognitive deficits can be present in the prodromal phase of Parkinson's disease (PD). Subjective cognitive decline (SCD) may contribute to identifying individuals with prodromal PD.

OBJECTIVE

The objective of this study was to examine whether SCD is more likely to be present in women with features suggestive of prodromal PD compared with women without these features.

METHODS

The study population comprised 12,427 women from the Nurses' Health Study selected to investigate prodromal PD. Prodromal and risk markers of PD were assessed via self-administered questionnaires. We evaluated the association of hyposmia, constipation, and probable rapid eye movement sleep behavior disorder, three major features of prodromal PD, with SCD, adjusting for age, education, body mass index, physical activity, smoking, alcohol, caffeine intake, and depression. We also explored whether SCD was associated with the probability of prodromal PD and conducted additional analyses using data from neurocognitive tests.

RESULTS

Women experiencing the three examined nonmotor features had the worst mean SCD score and the highest odds of poor subjective cognition (odds ratio [OR] = 1.78; 95% confidence interval [CI], 1.29-2.47). This association persisted when women with objective cognitive deficits were excluded from analyses. SCD was also more common in women with a probability of prodromal PD ≥0.80, particularly among those aged younger than 75 years (OR of poor subjective cognition = 6.57 [95% CI, 2.43-17.77]). These observations were consistent with the results from analyses using neurocognitive tests, where a worse global cognitive performance was observed among women with three features.

CONCLUSIONS

Our study suggests that self-perceived cognitive decline can be present during the prodromal phase of PD. © 2023 International Parkinson and Movement Disorder Society.

Wearable movement-tracking data identify Parkinson's disease years before clinical diagnosis

Parkinson's disease is a progressive neurodegenerative movement disorder with a long latent phase and currently no disease-modifying treatments. Reliable predictive biomarkers that could transform efforts to develop neuroprotective treatments remain to be identified. Using UK Biobank, we investigated the predictive value of accelerometry in identifying prodromal Parkinson's disease in the general population and compared this digital biomarker with models based on genetics, lifestyle, blood biochemistry or prodromal symptoms data. Machine learning models trained using accelerometry data achieved better test performance in distinguishing both clinically diagnosed Parkinson's disease (n = 153) (area under precision recall curve (AUPRC) 0.14 ± 0.04) and prodromal Parkinson's disease (n = 113) up to 7 years pre-diagnosis (AUPRC 0.07 ± 0.03) from the general population (n = 33,009) compared with all other modalities tested (genetics: AUPRC = 0.01 ± 0.00, P = 2.2 × 10-3; lifestyle: AUPRC = 0.03 ± 0.04, P = 2.5 × 10-3; blood biochemistry: AUPRC = 0.01 ± 0.00, P = 4.1 × 10-3; prodromal signs: AUPRC = 0.01 ± 0.00, P = 3.6 × 10-3). Accelerometry is a potentially important, low-cost screening tool for determining people at risk of developing Parkinson's disease and identifying participants for clinical trials of neuroprotective treatments.

Progression of atypical parkinsonian syndromes: PROSPECT-M-UK study implications for clinical trials

The advent of clinical trials of disease-modifying agents for neurodegenerative disease highlights the need for evidence-based end point selection. Here we report the longitudinal PROSPECT-M-UK study of progressive supranuclear palsy (PSP), corticobasal syndrome (CBS), multiple system atrophy (MSA) and related disorders, to compare candidate clinical trial end points. In this multicentre UK study, participants were assessed with serial questionnaires, motor examination, neuropsychiatric and MRI assessments at baseline, 6 and 12 months. Participants were classified by diagnosis at baseline and study end, into Richardson syndrome, PSP-subcortical (PSP-parkinsonism and progressive gait freezing subtypes), PSP-cortical (PSP-frontal, PSP-speech and language and PSP-CBS subtypes), MSA-parkinsonism, MSA-cerebellar, CBS with and without evidence of Alzheimer's disease pathology and indeterminate syndromes. We calculated annual rate of change, with linear mixed modelling and sample sizes for clinical trials of disease-modifying agents, according to group and assessment type. Two hundred forty-three people were recruited [117 PSP, 68 CBS, 42 MSA and 16 indeterminate; 138 (56.8%) male; age at recruitment 68.7 ± 8.61 years]. One hundred and fifty-nine completed the 6-month assessment (82 PSP, 27 CBS, 40 MSA and 10 indeterminate) and 153 completed the 12-month assessment (80 PSP, 29 CBS, 35 MSA and nine indeterminate). Questionnaire, motor examination, neuropsychiatric and neuroimaging measures declined in all groups, with differences in longitudinal change between groups. Neuroimaging metrics would enable lower sample sizes to achieve equivalent power for clinical trials than cognitive and functional measures, often achieving N < 100 required for 1-year two-arm trials (with 80% power to detect 50% slowing). However, optimal outcome measures were disease-specific. In conclusion, phenotypic variance within PSP, CBS and MSA is a major challenge to clinical trial design. Our findings provide an evidence base for selection of clinical trial end points, from potential functional, cognitive, clinical or neuroimaging measures of disease progression.

Sleep movements and respiratory coupling as a biobehavioral metric for early Alzheimer's disease in independently dwelling adults

INTRODUCTION

Sleep disorder is often the first symptom of age-related cognitive decline associated with Alzheimer's disease (AD) observed in primary care. The relationship between sleep and early AD was examined using a patented sleep mattress designed to record respiration and high frequency movement arousals. A machine learning algorithm was developed to classify sleep features associated with early AD.

METHOD

Community-dwelling older adults (N = 95; 62-90 years) were recruited in a 3-h catchment area. Study participants were tested on the mattress device in the home bed for 2 days, wore a wrist actigraph for 7 days, and provided sleep diary and sleep disorder self-reports during the 1-week study period. Neurocognitive testing was completed in the home within 30-days of the sleep study. Participant performance on executive and memory tasks, health history and demographics were reviewed by a geriatric clinical team yielding Normal Cognition (n = 45) and amnestic MCI-Consensus (n = 33) groups. A diagnosed MCI group (n = 17) was recruited from a hospital memory clinic following diagnostic series of neuroimaging biomarker assessment and cognitive criteria for AD.

RESULTS

In cohort analyses, sleep fragmentation and wake after sleep onset duration predicted poorer executive function, particularly memory performance. Group analyses showed increased sleep fragmentation and total sleep time in the diagnosed MCI group compared to the Normal Cognition group. Machine learning algorithm showed that the time latency between movement arousals and coupled respiratory upregulation could be used as a classifier of diagnosed MCI vs. Normal Cognition cases. ROC diagnostics identified MCI with 87% sensitivity; 89% specificity; and 88% positive predictive value.

DISCUSSION

AD sleep phenotype was detected with a novel sleep biometric, time latency, associated with the tight gap between sleep movements and respiratory coupling, which is proposed as a corollary of sleep quality/loss that affects the autonomic regulation of respiration during sleep. Diagnosed MCI was associated with sleep fragmentation and arousal intrusion.

Capturing Measures That Matter: The Potential Value of Digital Measures of Physical Behavior for Alzheimer's Disease Drug Development

Alzheimer's disease (AD) is a devastating neurodegenerative disease and the primary cause of dementia worldwide. Despite the magnitude of AD's impact on patients, caregivers, and society, nearly all AD clinical trials fail. A potential contributor to this high rate of failure is that established clinical outcome assessments fail to capture subtle clinical changes, entail high burden for patients and their caregivers, and ineffectively address the aspects of health deemed important by patients and their caregivers. AD progression is associated with widespread changes in physical behavior that have impacts on the ability to function independently, which is a meaningful aspect of health for patients with AD and important for diagnosis. However, established assessments of functional independence remain underutilized in AD clinical trials and are limited by subjective biases and ceiling effects. Digital measures of real-world physical behavior assessed passively, continuously, and remotely using digital health technologies have the potential to address some of these limitations and to capture aspects of functional independence in patients with AD. In particular, measures of real-world gait, physical activity, and life-space mobility captured with wearable sensors may offer value. Additional research is needed to understand the validity, feasibility, and acceptability of these measures in AD clinical research.

Neurodegenerative disease of the brain: a survey of interdisciplinary approaches

Neurodegenerative diseases of the brain pose a major and increasing global health challenge, with only limited progress made in developing effective therapies over the last decade. Interdisciplinary research is improving understanding of these diseases and this article reviews such approaches, with particular emphasis on tools and techniques drawn from physics, chemistry, artificial intelligence and psychology.