Portals to frailty? Data-driven analyses detect early frailty profiles

Emerging nano-derived therapy for the treatment of dementia: a comprehensive review

Dementia includes a variety of neurodegenerative diseases that affect and target the brain's fundamental cognitive functions. It is undoubtedly one of the diseases that affects people globally. The ameliorating the disease is still not known; the symptoms, however, can be prevented to an extent. Dementia encompasses Alzheimer's disease, Parkinson's disease, Huntington's disease, Lewy body dementia, mixed dementia, and various other diseases. The aggregation of β-amyloid protein plaques and the formation of neurofibrillary tangles have been concluded as the foremost cause for the onset of the disease. As the cases climb, new neuroprotective methods are being developed in the form of new drug delivery systems that provide targeted delivery. Herbal drugs like Ashwagandha, Brahmi, and Cannabis have shown satisfactory results by not only treating the symptoms but have also been shown to reduce and ameliorate the formation of amyloid plaque formation. This article explores the intricate possibilities of drug delivery and the absolute use of herbal drugs to target neurodegenerative diseases. The various possibilities of nanotechnology currently available with new emerging techniques are also discussed.

Noninvasive Assessment of β-Secretase Activity Through Click Chemistry-Mediated Enrichment of Neuronal Extracellular Vesicles to Detect Alzheimer's Disease

Alzheimer's disease (AD), the most prevalent type of dementia, is characterized by a biological process that begins with the development of AD neuropathologic change (ADNPC) while individuals remain asymptomatic. A key molecular hallmark of ADNPC is the accumulation of amyloid-β plaques. β-secretase plays a critical role in the upstream pathological cleavage of amyloid precursor protein (APP), producing amyloid-β peptides that are prone to misfolding, ultimately contributing to plaque formation. Neuronal extracellular vesicles (NEVs) in the blood transport β-secretase and preserve its activity, allowing for noninvasive profiling of β-secretase activity for detecting early onset of ADNPC. In this study, a novel approach is approached for noninvasive assessment of β-secretase activity in AD patients using an NEV β-secretase activity assay. This assay identifies NEVs exhibiting colocalization of NEV markers with AD-associated β-secretase, generating a β-secretase activity profile for each patient. The NEV β-secretase activity assay represents a significant advancement in leveraging the diagnostic potential of NEVs, offering a noninvasive, quantitative method for reliably assessing β-secretase activity to detect the early onset of ADNPC.

Frailty in motion: Amnestic mild cognitive impairment and Alzheimer's disease cohorts display heterogeneity in multimorbidity classification and longitudinal transitions

BackgroundData-driven examination of multiple morbidities and deficits are informative for clinical and research applications in aging and dementia. Resulting profiles may change longitudinally according to dynamic alterations in extent, duration, and pattern of risk accumulation. Do such frailty-related changes include not only progression but also stability and reversion?ObjectiveWith cognitively impaired and dementia cohorts, we employed data-driven analytics to (a) detect the extent of heterogeneity in frailty-related multimorbidity and deficit burden subgroups and (b) identify key person characteristics predicting differential transition patterns.MethodsWe assembled baseline and 2-year follow-up data from the National Alzheimer's Coordinating Center for amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD) cohorts. We applied factor analyses to 43 multimorbidity and deficit indicators. Latent Transition Analysis (LTA) was applied to the resulting domains in order to detect subgroups differing in transition patterns for multimorbidity and deficit burden. We characterized heterogeneity in change patterns by evaluating key person characteristics as differential predictors.ResultsFactor analyses revealed five domains at two time points. LTA showed that two latent burden subgroups at Time 1 (Low, Moderate) differentiated into an additional two subgroups at Time 2 (adding Mild, Severe). Transition analyses detected heterogeneous changes, including progression, stability, and reversion. Baseline classifications and transitions varied according to clinical cohort, global cognition, sex, age, and education.ConclusionsHeterogeneous frailty-related subgroup transitions can be (a) detected in aging adults living with aMCI and AD, (b) characterized as not only progression but also stability and reversion, and (c) predicted by precision characteristics.

A single-cell network approach to decode metabolic regulation in gynecologic and breast cancers

Cancer metabolism is characterized by significant heterogeneity, presenting challenges for treatment efficacy and patient outcomes. Understanding this heterogeneity and its regulatory mechanisms at single-cell resolution is crucial for developing personalized therapeutic strategies. In this study, we employed a single-cell network approach to characterize malignant heterogeneity in gynecologic and breast cancers, focusing on the transcriptional regulatory mechanisms driving metabolic alterations. By leveraging single-cell RNA sequencing (scRNA-seq) data, we assessed the metabolic pathway activities and inferred cancer-specific protein-protein interactomes (PPI) and gene regulatory networks (GRNs). We explored the crosstalk between these networks to identify key alterations in metabolic regulation. Clustering cells by metabolic pathways revealed tumor heterogeneity across cancers, highlighting variations in oxidative phosphorylation, glycolysis, cholesterol, fatty acid, hormone, amino acid, and redox metabolism. Our analysis identified metabolic modules associated with these pathways, along with their key transcriptional regulators. These findings provide insights into the complex interplay between metabolic rewiring and transcriptional regulation in gynecologic and breast cancers, paving the way for potential targeted therapeutic strategies in precision oncology. Furthermore, this pipeline for dissecting coregulatory metabolic networks can be broadly applied to decipher metabolic regulation in any disease at single-cell resolution.

The Biorepository and Integrative Genomics resource for inclusive genomics: insights from a diverse pediatric and admixed cohort

The Biorepository and Integrative Genomics (BIG) Initiative in Tennessee has developed a pioneering resource to address gaps in genomic research by linking genomic, phenotypic, and environmental data from a diverse Mid-South population, including underrepresented groups. We analyzed 13,152 exomes from BIG and found significant genetic diversity, with 50% of participants inferred to have non-European or several types of admixed ancestry. Ancestry within the BIG cohort is stratified, with distinct geographic and demographic patterns, as African ancestry is more common in urban areas, while European ancestry is more common in suburban regions. We observe ancestry-specific rates of novel genetic variants, which are enriched for functional or clinical relevance. Disease prevalence analysis linked ancestry and environmental factors, showing higher odds ratios for asthma and obesity in minority groups, particularly in the urban area. Finally, we observe discrepancies between self-reported race and genetic ancestry, with related individuals self-identifying in differing racial categories. These findings underscore the limitations of race as a biomedical variable. BIG has proven to be an effective model for community-centered precision medicine. We integrated genomics education, and fostered great trust among the contributing communities. Future goals include cohort expansion, and enhanced genomic analysis, to ensure equitable healthcare outcomes.

Pathophysiological role of high mobility group box-1 signaling in neurodegenerative diseases

Nucleocytoplasmic translocation of HMGB1 (high mobility group box-1) plays a significant role in disease progression. Several methods contribute to the translocation of HMGB1 from the nucleus to the cytoplasm, including inflammasome activation, TNF-α signaling, CRM1-mediated transport, reactive oxygen species (ROS), JAK/STAT pathway, RIP3-mediated p53 involvement, XPO-1-mediated transport, and calcium-dependent mechanisms. Due to its diverse functions at various subcellular locations, HMGB1 has been identified as a crucial factor in several Central Nervous System (CNS) disorders, including Huntington's disease (HD), Parkinson's disease (PD), and Alzheimer's disease (AD). HMGB1 displays a wide array of roles in the extracellular environment as it interacts with several receptors, including CXCR4, TLR2, TLR4, TLR8, and RAGE, by engaging in these connections, HMGB1 can effectively regulate subsequent signaling pathways, hence exerting an impact on the progression of brain disorders through neuroinflammation. Therefore, focusing on treating neuroinflammation could offer a common therapeutic strategy for several disorders. The objective of the current literature is to demonstrate the pathological role of HMGB1 in various neurological disorders. This review also offers insights into numerous therapeutic targets that promise to advance multiple treatments intended to alleviate brain illnesses.

DeepDrug as an expert guided and AI driven drug repurposing methodology for selecting the lead combination of drugs for Alzheimer's disease

Alzheimer's Disease (AD) significantly aggravates human dignity and quality of life. While newly approved amyloid immunotherapy has been reported, effective AD drugs remain to be identified. Here, we propose a novel AI-driven drug-repurposing method, DeepDrug, to identify a lead combination of approved drugs to treat AD patients. DeepDrug advances drug-repurposing methodology in four aspects. Firstly, it incorporates expert knowledge to extend candidate targets to include long genes, immunological and aging pathways, and somatic mutation markers that are associated with AD. Secondly, it incorporates a signed directed heterogeneous biomedical graph encompassing a rich set of nodes and edges, and node/edge weighting to capture crucial pathways associated with AD. Thirdly, it encodes the weighted biomedical graph through a Graph Neural Network into a new embedding space to capture the granular relationships across different nodes. Fourthly, it systematically selects the high-order drug combinations via diminishing return-based thresholds. A five-drug lead combination, consisting of Tofacitinib, Niraparib, Baricitinib, Empagliflozin, and Doxercalciferol, has been selected from the top drug candidates based on DeepDrug scores to achieve the maximum synergistic effect. These five drugs target neuroinflammation, mitochondrial dysfunction, and glucose metabolism, which are all related to AD pathology. DeepDrug offers a novel AI-and-big-data, expert-guided mechanism for new drug combination discovery and drug-repurposing across AD and other neuro-degenerative diseases, with immediate clinical applications.

Protection of Alzheimer's disease progression by a human-origin probiotics cocktail

Microbiome abnormalities (dysbiosis) significantly contribute to the progression of Alzheimer's disease (AD). However, the therapeutic efficacy of microbiome modulators in protecting against these ailments remains poorly studied. Herein, we tested a cocktail of unique probiotics, including 5 Lactobacillus and 5 Enterococcus strains isolated from infant gut with proven microbiome modulating capabilities. We aimed to determine the probiotics cocktail's efficacy in ameliorating AD pathology in a humanized AD mouse model of APP/PS1 strains. Remarkably, feeding mice with 1 × 1011 CFU per day in drinking water for 16 weeks significantly reduced cognitive decline (measured by the Morris Water Maze test) and AD pathology markers, such as Aβ aggregation, microglia activation, neuroinflammation, and preserved blood-brain barrier (BBB) tight junctions. The beneficial effects were linked to a reduced inflammatory microbiome, leading to decreased gut permeability and inflammation in both systemic circulation and the brain. Although both male and female mice showed overall improvements in cognition and biological markers, females did not exhibit improvements in specific markers related to inflammation and barrier permeability, suggesting that the underlying mechanisms may differ depending on sex. In conclusion, our results suggest that this unique probiotics cocktail could serve as a prophylactic agent to reduce the progression of cognitive decline and AD pathology. This is achieved by beneficially modulating the microbiome, improving intestinal tight junction proteins, reducing permeability in both gut and BBB, and decreasing inflammation in the gut, blood circulation, and brain, ultimately mitigating AD pathology and cognitive decline.

APOE4 Increases Susceptibility to Amyloid, Accelerating Episodic Memory Decline

Apolipoprotein E4 (APOE4) is the strongest genetic risk factor for sporadic Alzheimer's disease (AD). Individuals with one copy of APOE4 exhibit greater amyloid-beta (Aβ) deposition compared to noncarriers, an effect that is even more pronounced in APOE4 homozygotes. Interestingly, APOE4 carriers not only show more AD pathology but also experience more rapid cognitive decline, particularly in episodic memory. The underlying mechanisms driving this domain-specific vulnerability, however, remain unclear. In this study, we examined whether the accelerated decline in episodic memory among APOE4 carriers is due to increased Aβ deposition or heightened susceptibility to Aβ-related effects. Using data from the Alzheimer's Disease Research Initiative, we modeled amyloid duration, the estimated number of years an individual has been amyloid-positive, and its impact on cognitive trajectories. Our findings reveal that APOE4 is associated with more rapid episodic memory decline as a function of amyloid duration. This decline was dose-dependent, with APOE4 homozygotes declining more rapidly than heterozygotes, and it was consistently observed across multiple episodic memory tasks and measures. Importantly, this pattern was not observed in other cognitive domains, such as processing speed, executive function, visuospatial skills, language, or crystallized intelligence. These results suggest that cognitive trajectories in AD differ by APOE genotype, with APOE4 conferring increased vulnerability to hippocampal dysfunction early in the disease course. Future research should investigate whether these cognitive differences stem from distinct pathological cascades in APOE4 carriers.

Knowledge-guided learning methods for integrative analysis of multi-omics data

Integrative analysis of multi-omics data has the potential to yield valuable and comprehensive insights into the molecular mechanisms underlying complex diseases such as cancer and Alzheimer's disease. However, a number of analytical challenges complicate multi-omics data integration. For instance, -omics data are usually high-dimensional, and sample sizes in multi-omics studies tend to be modest. Furthermore, when genes in an important pathway have relatively weak signal, it can be difficult to detect them individually. There is a growing body of literature on knowledge-guided learning methods that can address these challenges by incorporating biological knowledge such as functional genomics and functional proteomics into multi-omics data analysis. These methods have been shown to outperform their counterparts that do not utilize biological knowledge in tasks including prediction, feature selection, clustering, and dimension reduction. In this review, we survey recently developed methods and applications of knowledge-guided multi-omics data integration methods and discuss future research directions.

Revolutionizing medicine: recent developments and future prospects in stem-cell therapy

Stem-cell therapy is a revolutionary frontier in modern medicine, offering enormous capacity to transform the treatment landscape of numerous debilitating illnesses and injuries. This review examines the revolutionary frontier of treatments utilizing stem cells, highlighting the distinctive abilities of stem cells to undergo regeneration and specialized cell differentiation into a wide variety of phenotypes. This paper aims to guide researchers, physicians, and stakeholders through the intricate terrain of stem-cell therapy, examining the processes, applications, and challenges inherent in utilizing stem cells across diverse medical disciplines. The historical journey from foundational contributions in the late 19th and early 20th centuries to recent breakthroughs, including ESC isolation and iPSC discovery, has set the stage for monumental leaps in medical science. Stem cells' regenerative potential spans embryonic, adult, induced pluripotent, and perinatal stages, offering unprecedented therapeutic opportunities in cancer, neurodegenerative disorders, cardiovascular ailments, spinal cord injuries, diabetes, and tissue damage. However, difficulties, such as immunological rejection, tumorigenesis, and precise manipulation of stem-cell behavior, necessitate comprehensive exploration and innovative solutions. This manuscript summarizes recent biotechnological advancements, critical trial evaluations, and emerging technologies, providing a nuanced understanding of the triumphs, difficulties, and future trajectories in stem cell-based regenerative medicine. Future directions, including precision medicine integration, immune modulation strategies, advancements in gene-editing technologies, and bioengineering synergy, offer a roadmap in stem cell treatment. The focus on stem-cell therapy's potential highlights its significant influence on contemporary medicine and points to a future in which individualized regenerative therapies will alleviate various medical disorders.

A Review of Datasets, Optimization Strategies, and Learning Algorithms for Analyzing Alzheimer's Dementia Detection

Alzheimer's Dementia (AD) is a progressive neurological disorder that affects memory and cognitive function, necessitating early detection for its effective management. This poses a significant challenge to global public health. The early and accurate detection of dementia is crucial for several reasons. First, timely detection facilitates early intervention and planning of treatment. Second, precise diagnostic methods are essential for distinguishing dementia from other cognitive disorders and medical conditions that may present with similar symptoms. Continuous analysis and improvements in detection methods have contributed to advancements in medical research. It helps to identify new biomarkers, refine existing diagnostic tools, and foster the development of innovative technologies, ultimately leading to more accurate and efficient diagnostic approaches for dementia. This paper presents a critical analysis of multimodal imaging datasets, learning algorithms, and optimisation techniques utilised in the context of Alzheimer's dementia detection. The focus is on understanding the advancements and challenges in employing diverse imaging modalities, such as MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography), and EEG (ElectroEncephaloGram). This study evaluated various machine learning algorithms, deep learning models, transfer learning techniques, and generative adversarial networks for the effective analysis of multi-modality imaging data for dementia detection. In addition, a critical examination of optimisation techniques encompassing optimisation algorithms and hyperparameter tuning strategies for processing and analysing images is presented in this study to discern their influence on model performance and generalisation. Thorough examination and enhancement of methods for dementia detection are fundamental for addressing the healthcare challenges posed by dementia, facilitating timely interventions, improving diagnostic accuracy, and advancing research in neurodegenerative diseases.

Age of onset moderates the effects of Vascular Risk Factors on Neurodegeneration, Blood-Brain-Barrier permeability, and cognitive decline in Alzheimer's Disease

BACKGROUND

The role of Vascular risk factors (VRFs) in the progression of Alzheimer's Disease (AD) and cognitive decline remains to be elucidated, with previous studies resulting in conflicting findings. The possible impact of age-specific mechanisms of resilience/vulnerability is an under addressed issue. We evaluated the association of VRFs with markers of amyloid deposition, neurodegeneration, and blood-brain-barrier (BBB) permeability (Albumin quotient, Qalb), stratifying patients into early-onset (< 65, EOAD), classic late-onset (65-75, cLOAD) and very late-onset (> 75, vLOAD), to evaluate the moderating effect of age of onset. Moreover, we explored the effects of VRFs on cognitive decline at one year follow-up (ΔMMSE).

METHODS

For 368 patients with biologically confirmed AD, we computed eight risk factors in a composite measure of cumulative vascular risk (vascular score, VS). Stratifying patients according to age of onset, we regressed VS and main individual VRFs on p-tau/Aβ42, t-tau and Qalb, and used bootstrapped mediation analysis to test direct and indirect associations of VS with t-tau, using Qalb as mediator. In a subset of 105 patients, we performed multivariate backward regressions to assess the effects of sex, APOE, Qalb, VS, p-tau/Aβ42 and t-tau on ΔMMSE.

RESULTS

VS was positively associated with CSF t-tau in more vulnerable groups burdened by more aggressive disease progression (EOAD: β = 0.256, p = 0.019) or aging (vLOAD: β = 0.007, p < 0.001). Conversely, in patients with classic age of onset VS was associated with higher BBB permeability (cLOAD: β = 0.173, p = 0.015), which simultaneously causes the decrease of CSF t-tau, as a possible resilience response. Cognitive decline was not associated with VS in any of the subgroups. Instead, it was affected by both higher CSF t-tau and increased Qalb values in those with very early or very late onset (EOAD and vLOAD), but by Qalb alone in patients with classic age of onset, where CSF t-tau levels might be buffered by BBB permeability.

CONCLUSIONS

Our results show that age of onset weighs on the heterogeneous effects played by VRFs in AD, which do not seem to have direct impact on cognitive decline. These findings stress the importance of a tailored patient-centered approach to the application of vascular prevention strategies in AD.

Deciphering the tissue-specific functional effect of Alzheimer risk SNPs with deep genome annotation

Alzheimer's disease (AD) is a highly heritable brain dementia, along with substantial failure of cognitive function. Large-scale genome-wide association studies (GWASs) have led to a set of SNPs significantly associated with AD and related traits. GWAS hits usually emerge as clusters where a lead SNP with the highest significance is surrounded by other less significant neighboring SNPs. Although functionality is not guaranteed even with the strongest associations in GWASs, lead SNPs have historically been the focus of the field, with the remaining associations inferred to be redundant. Recent deep genome annotation tools enable the prediction of function from a segment of a DNA sequence with significantly improved precision, which allows in-silico mutagenesis to interrogate the functional effect of SNP alleles. In this project, we explored the impact of top AD GWAS hits around APOE region on chromatin functions and whether it will be altered by the genetic context (i.e., alleles of neighboring SNPs). Our results showed that highly correlated SNPs in the same LD block could have distinct impacts on downstream functions. Although some GWAS lead SNPs showed dominant functional effects regardless of the neighborhood SNP alleles, several other SNPs did exhibit enhanced loss or gain of function under certain genetic contexts, suggesting potential additional information hidden in the LD blocks.

Synaptic changes in psychiatric and neurological disorders: state-of-the art of in vivo imaging

Synapses are implicated in many neuropsychiatric illnesses. Here, we provide an overview of in vivo techniques to index synaptic markers in patients. Several positron emission tomography (PET) tracers for synaptic vesicle glycoprotein 2 A (SV2A) show good reliability and selectivity. We review over 50 clinical studies including over 1700 participants, and compare findings in healthy ageing and across disorders, including addiction, schizophrenia, depression, posttraumatic stress disorder, and neurodegenerative disorders, including tauopathies, Huntington's disease and α-synucleinopathies. These show lower SV2A measures in cortical brain regions across most of these disorders relative to healthy volunteers, with the most well-replicated findings in tauopathies, whilst changes in Huntington's chorea, Parkinson's disease, corticobasal degeneration and progressive supranuclear palsy are predominantly subcortical. SV2A PET measures are correlated with functional connectivity across brain networks, and a number of other measures of brain function, including glucose metabolism. However, the majority of studies found no relationship between grey matter volume measured with magnetic resonance imaging and SV2A PET measures. Cognitive dysfunction, in domains including working memory and executive function, show replicated inverse relationships with SV2A measures across diagnoses, and initial findings also suggest transdiagnostic relationships with mood and anxiety symptoms. This suggests that synaptic abnormalities could be a common pathophysiological substrate underlying cognitive and, potentially, affective symptoms. We consider limitations of evidence and future directions; highlighting the need to develop postsynaptic imaging markers and for longitudinal studies to test causal mechanisms.

Mapping computational cognitive profiles of aging to dissociable brain and sociodemographic factors

Aging is associated with declines in cognition and brain structural integrity. However, there is equivocality over (1) the specificity of affected domains in different people, (2) the location of associated patterns of brain structural deterioration, and (3) the sociodemographic factors contributing to 'unhealthy' cognition. We aimed to identify cognitive profiles displayed by older adults and determine brain and sociodemographic features potentially shaping these profiles. A sample of Southeast-Asian older adults (N = 386) participated in a multi-session study comprising cognitive testing, neuroimaging, and a structured interview. We used computational models to extract latent mechanisms underlying cognitive flexibility and response inhibition. Data-driven methods were used to construct cognitive profiles based on standard performance measures and model parameters. We also investigated grey matter volume and machine-learning derived 'brain-ages'. A profile associated with poor set-shifting and rigid focusing was associated with widespread grey matter reduction in cognitive control regions. A slow responding profile was associated with advanced brain-age. Both profiles were correlated with poor socioeconomic standing and cognitive reserve. We found that the impact of sociodemographic factors on cognitive profiles was partially mediated by total grey and white matter, and dorsolateral prefrontal and cerebellar volumes. This study furthers understanding of how distinct aging profiles of cognitive impairment uniquely correspond to specific vs. global brain deterioration and the significance of socioeconomic factors in informing cognitive performance in older age.

A comprehensive review of advanced focused ultrasound (FUS) microbubbles-mediated treatment of Alzheimer's disease

Alzheimer's disease (AD) is characterized by progressive neurodegeneration, memory loss, and cognitive impairment leading to dementia and death. The blood-brain barrier (BBB) prevents the delivery of drugs into the brain, which can limit their therapeutic potential in the treatment of AD. Therefore, there is a need to develop new approaches to bypass the BBB for appropriate treatment of AD. Recently, focused ultrasound (FUS) has been shown to disrupt the BBB, allowing therapeutic agents to penetrate the brain. In addition, microbubbles (MBs) as lipophilic carriers can penetrate across the BBB and deliver the active drug into the brain tissue. Therefore, combined with FUS, the drug-encapsulated MBs can pass through the ultrasound-disrupted zone of the BBB and diffuse into the brain tissue. This review provides clear and concise statements on the recent advances of the various FUS-mediated MBs-based carriers developed for delivering AD-related drugs. In addition, the sonogenetics-based FUS/MBs approaches for the treatment of AD are highlighted. The future perspectives and challenges of ultrasound-based MBs drug delivery in AD are then discussed.

The Alzheimer's disease risk gene BIN1 regulates activity-dependent gene expression in human-induced glutamatergic neurons

Bridging Integrator 1 (BIN1) is the second most important Alzheimer's disease (AD) risk gene, but its physiological roles in neurons and its contribution to brain pathology remain largely elusive. In this work, we show that BIN1 plays a critical role in the regulation of calcium homeostasis, electrical activity, and gene expression of glutamatergic neurons. Using single-cell RNA-sequencing on cerebral organoids generated from isogenic BIN1 wild type (WT), heterozygous (HET) and homozygous knockout (KO) human-induced pluripotent stem cells (hiPSCs), we show that BIN1 is mainly expressed by oligodendrocytes and glutamatergic neurons, like in the human brain. Both BIN1 HET and KO cerebral organoids show specific transcriptional alterations, mainly associated with ion transport and synapses in glutamatergic neurons. We then demonstrate that BIN1 cell-autonomously regulates gene expression in glutamatergic neurons by using a novel protocol to generate pure culture of hiPSC-derived induced neurons (hiNs). Using this system, we also show that BIN1 plays a key role in the regulation of neuronal calcium transients and electrical activity via its interaction with the L-type voltage-gated calcium channel Cav1.2. BIN1 KO hiNs show reduced activity-dependent internalization and higher Cav1.2 expression compared to WT hiNs. Pharmacological blocking of this channel with clinically relevant doses of nifedipine, a calcium channel blocker, partly rescues electrical and gene expression alterations in BIN1 KO glutamatergic neurons. Further, we show that transcriptional alterations in BIN1 KO hiNs that affect biological processes related to calcium homeostasis are also present in glutamatergic neurons of the human brain at late stages of AD pathology. Together, these findings suggest that BIN1-dependent alterations in neuronal properties could contribute to AD pathophysiology and that treatment with low doses of clinically approved calcium blockers should be considered as an option to slow disease-onset and progression.

Dementia diagnostic and treatment services in the Western Pacific: challenges, preparedness and opportunities in the face of amyloid-targeting therapies

Here we first review the limited available literature addressing the current landscape of specialist assessment services for dementia and cognitive decline and the preparedness for new amyloid-targeting therapies for Alzheimer's disease across the Western Pacific region. Considering the scarcity of literature, as national representatives of Western Pacific nations we were then guided by the World Health Organization's Global Action Plan on Dementia to provide country-specific reviews. As a whole, we highlight that the existing diverse socioeconomic and cultural landscape across the region poses unique challenges, including varying access to services and marked differences among countries in their preparedness for upcoming amyloid-targeting therapies for Alzheimer's disease. Therefore, there is an urgent call for intergovernmental collaboration and investment across the Western Pacific to ensure that for all nations, citizens living with dementia and cognitive decline have access to effective and equitable methods of diagnosis, treatment and care.

Frailty or frailties: exploring frailty index subdimensions in the English Longitudinal Study of Ageing

BACKGROUND

Frailty, a state of increased vulnerability to adverse health outcomes, has garnered significant attention in research and clinical practice. Existing constructs aggregate clinical features or health deficits into a single score. While simple and interpretable, this approach may overlook the complexity of frailty and not capture the full range of variation between individuals.

METHODS

Exploratory factor analysis was used to infer latent dimensions of a frailty index constructed using survey data from the English Longitudinal Study of Ageing, wave 9. The dataset included 58 self-reported health deficits in a representative sample of community-dwelling adults aged 65+ (N=4971). Deficits encompassed chronic disease, general health status, mobility, independence with activities of daily living, psychological well-being, memory and cognition. Multiple linear regression examined associations with CASP-19 quality of life scores.

RESULTS

Factor analysis revealed four frailty subdimensions. Based on the component deficits with the highest loading values, these factors were labelled 'mobility impairment and physical morbidity', 'difficulties in daily activities', 'mental health' and 'disorientation in time'. The four subdimensions were a better predictor of quality of life than frailty index scores.

CONCLUSIONS

Distinct subdimensions of frailty can be identified from standard index scores. A decomposed approach to understanding frailty has a potential to provide a more nuanced understanding of an individual's state of health across multiple deficits.

Heavy metals toxicity on epigenetic modifications in the pathogenesis of Alzheimer's disease (AD)

Alzheimer's disease (AD) is a progressive decline in cognitive ability and behavior which eventually disrupts daily activities. AD has no cure and the progression rate varies unlikely. Among various causative factors, heavy metals are reported to be a significant hazard in AD pathogenesis. Metal-induced neurodegeneration has been focused globally with thorough research to unravel the mechanistic insights in AD. Recently, heavy metals suggested to play an important role in epigenetic alterations which might provide evidential results on AD pathology. Epigenetic modifications are known to play towards novel therapeutic approaches in treating AD. Though many studies focus on epigenetics and heavy metal implications in AD, there is a lack of research on heavy metal influence on epigenetic toxicity in neurological disorders. The current review aims to elucidate the plausible role of cadmium (Cd), iron (Fe), arsenic (As), copper (Cu), and lithium (Li) metals on epigenetic factors and the increase in amyloid beta and tau phosphorylation in AD. Also, the review discusses the common methods of heavy metal detection to implicate in AD pathogenesis. Hence, from this review, we can extend the need for future research on identifying the mechanistic behavior of heavy metals on epigenetic toxicity and to develop diagnostic and therapeutic markers in AD.

Effectiveness of sensor-based interventions in improving gait and balance performance in older adults: systematic review and meta-analysis of randomized controlled trials

BACKGROUND

Sensor-based interventions (SI) have been suggested as an alternative rehabilitation treatment to improve older adults' functional performance. However, the effectiveness of different sensor technologies in improving gait and balance remains unclear and requires further investigation.

METHODS

Ten databases (Academic Search Premier; Cumulative Index to Nursing and Allied Health Literature, Complete; Cochrane Central Register of Controlled Trials; MEDLINE; PubMed; Web of Science; OpenDissertations; Open grey; ProQuest; and Grey literature report) were searched for relevant articles published up to December 20, 2022. Conventional functional assessments, including the Timed Up and Go (TUG) test, normal gait speed, Berg Balance Scale (BBS), 6-Minute Walk Test (6MWT), and Falling Efficacy Scale-International (FES-I), were used as the evaluation outcomes reflecting gait and balance performance. We first meta-analyzed the effectiveness of SI, which included optical sensors (OPTS), perception sensors (PCPS), and wearable sensors (WS), compared with control groups, which included non-treatment intervention (NTI) and traditional physical exercise intervention (TPEI). We further conducted sub-group analysis to compare the effectiveness of SI (OPTS, PCPS, and WS) with TPEI groups and compared each SI subtype with control (NTI and TPEI) and TPEI groups.

RESULTS

We scanned 6255 articles and performed meta-analyses of 58 selected trials (sample size = 2713). The results showed that SI groups were significantly more effective than control or TPEI groups (p < 0.000) in improving gait and balance performance. The subgroup meta-analyses between OPTS groups and TPEI groups revealed clear statistically significant differences in effectiveness for TUG test (mean difference (MD) = - 0.681 s; p < 0.000), normal gait speed (MD = 4.244 cm/s; p < 0.000), BBS (MD = 2.325; p = 0.001), 6MWT (MD = 25.166 m; p < 0.000), and FES-I scores (MD = - 2.036; p = 0.036). PCPS groups also presented statistically significant differences with TPEI groups in gait and balance assessments for normal gait speed (MD = 4.382 cm/s; p = 0.034), BBS (MD = 1.874; p < 0.000), 6MWT (MD = 21.904 m; p < 0.000), and FES-I scores (MD = - 1.161; p < 0.000), except for the TUG test (MD = - 0.226 s; p = 0.106). There were no statistically significant differences in TUG test (MD = - 1.255 s; p = 0.101) or normal gait speed (MD = 6.682 cm/s; p = 0.109) between WS groups and control groups.

CONCLUSIONS

SI with biofeedback has a positive effect on gait and balance improvement among a mixed population of older adults. Specifically, OPTS and PCPS groups were statistically better than TPEI groups at improving gait and balance performance, whereas only the group comparison in BBS and 6MWT can reach the minimal clinically important difference. Moreover, WS groups showed no statistically or clinically significant positive effect on gait and balance improvement compared with control groups. More studies are recommended to verify the effectiveness of specific SI. Research registration PROSPERO platform: CRD42022362817. Registered on 7/10/2022.

Difference between subjective and objective cognitive decline confirmed by power spectral density

INTRODUCTION

The study aims to use power spectrum changes in subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI), preclinical stages of Alzheimer's disease (AD), for future biomarker studies in early AD diagnosis.

METHODS

We recruited 23 SCD and 32 aMCI subjects and conducted comparative analysis using relative power spectral density (PSD). Automated preprocessing and statistical analysis were performed using iSync Brain® (iMediSync Inc., Republic of Korea) (https://isyncbrain.com/).

RESULTS

Theta band power in the temporal region was 14.826 ± 7.2394 for the SCD group and 20.003 ± 10.1768 for the aMCI group. In the parietal region, theta band power was 13.614 ± 7.5689 for SCD and 19.894 ± 11.1387 for aMCI. Beta1 band power in the frontal region was 6.639 ± 2.2904 for SCD and 5.465 ± 1.8907 for aMCI, and in the temporal region it was 7.359 ± 2.5619 for SCD and 5.921 ± 2.1605 for aMCI.

CONCLUSION

PSD analysis of resting-state EEG predicted SCD, a preclinical stage of AD. This cross-sectional study observed electrical-physiological characteristics of preclinical AD; however, follow-up studies are needed to evaluate predictive value for future cognitive decline.

Multi-pathological contributions toward atrophy patterns in the Alzheimer's disease continuum

Introduction

Heterogeneity in downstream atrophy in Alzheimer's disease (AD) is predominantly investigated in relation to pathological hallmarks (Aβ, tau) and co-pathologies (cerebrovascular burden) independently. However, the proportional contribution of each pathology in determining atrophy pattern remains unclear. We assessed heterogeneity in atrophy using two recently conceptualized dimensions: typicality (typical AD atrophy at the center and deviant atypical atrophy on either extreme including limbic predominant to hippocampal sparing patterns) and severity (overall neurodegeneration spanning minimal atrophy to diffuse typical AD atrophy) in relation to Aβ, tau, and cerebrovascular burden.

Methods

We included 149 Aβ + individuals on the AD continuum (cognitively normal, prodromal AD, AD dementia) and 163 Aβ- cognitively normal individuals from the ADNI. We modeled heterogeneity in MRI-based atrophy with continuous-scales of typicality (ratio of hippocampus to cortical volume) and severity (total gray matter volume). Partial correlation models investigated the association of typicality/severity with (a) Aβ (global Aβ PET centiloid), tau (global tau PET SUVR), cerebrovascular (total white matter hypointensity volume) burden (b) four cognitive domains (memory, executive function, language, visuospatial composites). Using multiple regression, we assessed the association of each pathological burden and typicality/severity with cognition.

Results

(a) In the AD continuum, typicality (r = -0.31, p < 0.001) and severity (r = -0.37, p < 0.001) were associated with tau burden after controlling for Aβ, cerebrovascular burden and age. Findings imply greater tau pathology in limbic predominant atrophy and diffuse atrophy. (b) Typicality was associated with memory (r = 0.49, p < 0.001) and language scores (r = 0.19, p = 0.02). Severity was associated with memory (r = 0.26, p < 0.001), executive function (r = 0.24, p = 0.003) and language scores (r = 0.29, p < 0.001). Findings imply better cognitive performance in hippocampal sparing and minimal atrophy patterns. Beyond typicality/severity, tau burden but not Aβ and cerebrovascular burden explained cognition.

Conclusion

In the AD continuum, atrophy-based severity was more strongly associated with tau burden than typicality after accounting for Aβ and cerebrovascular burden. Cognitive performance in memory, executive function and language domains was explained by typicality and/or severity and additionally tau pathology. Typicality and severity may differentially reflect burden arising from tau pathology but not Aβ or cerebrovascular pathologies which need to be accounted for when investigating AD heterogeneity.

Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer's disease detection

Explainable artificial intelligence (XAI) has gained much interest in recent years for its ability to explain the complex decision-making process of machine learning (ML) and deep learning (DL) models. The Local Interpretable Model-agnostic Explanations (LIME) and Shaply Additive exPlanation (SHAP) frameworks have grown as popular interpretive tools for ML and DL models. This article provides a systematic review of the application of LIME and SHAP in interpreting the detection of Alzheimer's disease (AD). Adhering to PRISMA and Kitchenham's guidelines, we identified 23 relevant articles and investigated these frameworks' prospective capabilities, benefits, and challenges in depth. The results emphasise XAI's crucial role in strengthening the trustworthiness of AI-based AD predictions. This review aims to provide fundamental capabilities of LIME and SHAP XAI frameworks in enhancing fidelity within clinical decision support systems for AD prognosis.

Investigation of the effect of Ramadan fasting on serum levels of melatonin, cortisol, and serotonin: the case of Turkey

INTRODUCTION

The aim of this study is to examine the effects of Ramadan fasting on melatonin, cortisol, and serotonin levels.

METHODS

In this study, the blood of 19 healthy male individuals between the ages of 26 and 51, registered in Agri (Turkey) Family Health Center and fasting during Ramadan, was studied. The study was carried out in 2021-2022. The SPSS-22 package program was used in the analysis of the data. Wilcoxon analysis was used in the study.

RESULTS

It was determined that the pre-test-post-test melatonin and cortisol levels of the individuals were not statistically significant (p>0.05). It was determined that the pre-test-post-test serotonin difference of the individuals was statistically significant (p<0.05).

CONCLUSION

It has been determined that Ramadan fasting increases the serotonin level of individuals but does not change the levels of melatonin and cortisol. It was determined that the level of happiness of individuals increased after Ramadan fasting. Longitudinal studies on the effects of Ramadan fasting are recommended.

ADME profiling, molecular docking, DFT, and MEP analysis reveal cissamaline, cissamanine, and cissamdine from Cissampelos capensis L.f. as potential anti-Alzheimer's agents

The current pharmacotherapies for Alzheimer's disease (AD) demonstrate limited efficacy and are associated with various side effects, highlighting the need for novel therapeutic agents. Natural products, particularly from medicinal plants, have emerged as a significant source of potential neuroprotective compounds. In this context, Cissampelos capensis L.f., renowned for its medicinal properties, has recently yielded three new proaporphine alkaloids; cissamaline, cissamanine, and cissamdine. Despite their promising bioactive profiles, the biological targets of these alkaloids in the context of AD have remained unexplored. This study undertakes a comprehensive in silico examination of the binding affinity and molecular interactions of these alkaloids with human protein targets implicated in AD. The drug likeness and ADME analyses indicate favorable pharmacokinetic profiles for these compounds, suggesting their potential efficacy in targeting the central nervous system. Molecular docking studies indicate that cissamaline, cissamanine, and cissamdine interact with key AD-associated proteins. These interactions are comparable to, or in some aspects slightly less potent than, those observed with established AD drugs, highlighting their potential as novel therapeutic agents for Alzheimer's disease. Crucially, Density Functional Theory (DFT) calculations offer deep insights into the electronic and energetic characteristics of these alkaloids. These calculations reveal distinct electronic properties, with differences in total energy, binding energy, HOMO-LUMO gaps, dipole moments, and electrophilicity indices. Such variations suggest unique reactivity profiles and molecular stability, pertinent to their pharmacological potential. Moreover, Molecular Electrostatic Potential (MEP) analyses provide visual representations of the electrostatic characteristics of these alkaloids. The analyses highlight areas prone to electrophilic and nucleophilic attacks, indicating their potential for specific biochemical interactions. This combination of DFT and MEP results elucidates the intricate electronic, energetic, and electrostatic properties of these compounds, underpinning their promise as AD therapeutic agents. The in silico findings of this study shed light on the promising potential of cissamaline, cissamanine, and cissamdine as agents for AD treatment. However, further in vitro and in vivo studies are necessary to validate these theoretical predictions and to understand the precise mechanisms through which these alkaloids may exert their therapeutic effects.

Predicting early Alzheimer's with blood biomarkers and clinical features

Alzheimer's disease (AD) is an incurable neurodegenerative disorder that leads to dementia. This study employs explainable machine learning models to detect dementia cases using blood gene expression, single nucleotide polymorphisms (SNPs), and clinical data from Alzheimer's Disease Neuroimaging Initiative (ADNI). Analyzing 623 ADNI participants, we found that the Support Vector Machine classifier with Mutual Information (MI) feature selection, trained on all three data modalities, achieved exceptional performance (accuracy = 0.95, AUC = 0.94). When using gene expression and SNP data separately, we achieved very good performance (AUC = 0.65, AUC = 0.63, respectively). Using SHapley Additive exPlanations (SHAP), we identified significant features, potentially serving as AD biomarkers. Notably, genetic-based biomarkers linked to axon myelination and synaptic vesicle membrane formation could aid early AD detection. In summary, this genetic-based biomarker approach, integrating machine learning and SHAP, shows promise for precise AD diagnosis, biomarker discovery, and offers novel insights for understanding and treating the disease. This approach addresses the challenges of accurate AD diagnosis, which is crucial given the complexities associated with the disease and the need for non-invasive diagnostic methods.

Physical activity trajectories and their associations with health outcomes in older adults with mild cognitive impairment or dementia: a national cohort study

BACKGROUND

Physical activity (PA) is a promising non-pharmacological intervention for this population. However, few studies have investigated their PA trajectories, influencing factors, and their relationship with health outcomes.

AIMS

The aim was to identify latent trajectories in PA and their determinants in older adults with mild cognitive impairment (MCI) or dementia, as well as to assess the associations between PA trajectories and health outcomes based on the capability-opportunity-motivation behavior model.

METHODS

This is a cohort study. Data were obtained from a national cohort study and included participants aged 60 years and older with MCI or dementia. PA trajectories were identified using group-based trajectory modelling. Multinomial logistic regression was conducted to identify the predictors of PA trajectories. Linear regression models were used to assess the associations between PA trajectories and health outcomes. This study adhered to the STROBE checklist for reporting.

RESULTS

Three distinct PA trajectories were identified: high-decreasing and rebound class (9.34%), moderate-decreasing class (10.31%), and low-increasing class (80.34%). The logistic regression showed that age, sex, education level, body mass index, residence, depressive symptoms, mobility activities of daily life score, frequency of social activities score were PA predictors. Adjusting for sociodemographic variables, only the high-decreasing and rebound class remained significantly associated with worse self-rated health.

DISCUSSION

This study revealed three PA trajectories among older adults with MCI/dementia. Besides sociodemographic variables, addressing physical function and mental health, providing social support are vital for promoting PA in this population.

Modeling the neuroimmune system in Alzheimer's and Parkinson's diseases

Parkinson's disease (PD) and Alzheimer's disease (AD) are neurodegenerative disorders caused by the interaction of genetic, environmental, and familial factors. These diseases have distinct pathologies and symptoms that are linked to specific cell populations in the brain. Notably, the immune system has been implicated in both diseases, with a particular focus on the dysfunction of microglia, the brain's resident immune cells, contributing to neuronal loss and exacerbating symptoms. Researchers use models of the neuroimmune system to gain a deeper understanding of the physiological and biological aspects of these neurodegenerative diseases and how they progress. Several in vitro and in vivo models, including 2D cultures and animal models, have been utilized. Recently, advancements have been made in optimizing these existing models and developing 3D models and organ-on-a-chip systems, holding tremendous promise in accurately mimicking the intricate intracellular environment. As a result, these models represent a crucial breakthrough in the transformation of current treatments for PD and AD by offering potential for conducting long-term disease-based modeling for therapeutic testing, reducing reliance on animal models, and significantly improving cell viability compared to conventional 2D models. The application of 3D and organ-on-a-chip models in neurodegenerative disease research marks a prosperous step forward, providing a more realistic representation of the complex interactions within the neuroimmune system. Ultimately, these refined models of the neuroimmune system aim to aid in the quest to combat and mitigate the impact of debilitating neuroimmune diseases on patients and their families.

Evaluating In-home Assistive Technology for Dementia Caregivers

OBJECTIVES

Dementia caregivers (CGs) are at heightened risk for developing problems with anxiety and depression. Much attention has been directed toward developing and deploying interventions designed to protect CG health, but few have been supported by rigorous empirical evidence. Technology-based interventions that are effective, scalable, and do not add greatly to the CG burden are of particular interest.

METHODS

We conducted a nine-month randomized controlled trial in 63 homes evaluating People Power Caregiver (PPCg), a system of sensors in the home connected to cloud-based software that alerts CGs about worrisome deviations from normal patterns (e.g., falls, wandering).

RESULTS

CGs in the active condition had significantly less anxiety than those in the control condition at the six-month assessment. Greater anxiety reduction in the active condition at the six-month assessment was associated with greater interaction with PPCg via SMS text messages. There were no differences in anxiety at the three-month or nine-month assessments or in depression at any assessment.

CONCLUSIONS

PPCg shows promise for reducing anxiety associated with caring for a =person with dementia.

CLINICAL IMPLICATIONS

Technology-based interventions can help reduce CG anxiety, a major adverse consequence of caregiving that may be difficult to treat due to other demands on caregiver time and energy.

Machine learning analyses identify multi-modal frailty factors that selectively discriminate four cohorts in the Alzheimer's disease spectrum: a COMPASS-ND study

BACKGROUND

Frailty indicators can operate in dynamic amalgamations of disease conditions, clinical symptoms, biomarkers, medical signals, cognitive characteristics, and even health beliefs and practices. This study is the first to evaluate which, among these multiple frailty-related indicators, are important and differential predictors of clinical cohorts that represent progression along an Alzheimer's disease (AD) spectrum. We applied machine-learning technology to such indicators in order to identify the leading predictors of three AD spectrum cohorts; viz., subjective cognitive impairment (SCI), mild cognitive impairment (MCI), and AD. The common benchmark was a cohort of cognitively unimpaired (CU) older adults.

METHODS

The four cohorts were from the cross-sectional Comprehensive Assessment of Neurodegeneration and Dementia dataset. We used random forest analysis (Python 3.7) to simultaneously test the relative importance of 83 multi-modal frailty indicators in discriminating the cohorts. We performed an explainable artificial intelligence method (Tree Shapley Additive exPlanation values) for deep interpretation of prediction effects.

RESULTS

We observed strong concurrent prediction results, with clusters varying across cohorts. The SCI model demonstrated excellent prediction accuracy (AUC = 0.89). Three leading predictors were poorer quality of life ([QoL]; memory), abnormal lymphocyte count, and abnormal neutrophil count. The MCI model demonstrated a similarly high AUC (0.88). Five leading predictors were poorer QoL (memory, leisure), male sex, abnormal lymphocyte count, and poorer self-rated eyesight. The AD model demonstrated outstanding prediction accuracy (AUC = 0.98). Ten leading predictors were poorer QoL (memory), reduced olfaction, male sex, increased dependence in activities of daily living (n = 6), and poorer visual contrast.

CONCLUSIONS

Both convergent and cohort-specific frailty factors discriminated the AD spectrum cohorts. Convergence was observed as all cohorts were marked by lower quality of life (memory), supporting recent research and clinical attention to subjective experiences of memory aging and their potentially broad ramifications. Diversity was displayed in that, of the 14 leading predictors extracted across models, 11 were selectively sensitive to one cohort. A morbidity intensity trend was indicated by an increasing number and diversity of predictors corresponding to clinical severity, especially in AD. Knowledge of differential deficit predictors across AD clinical cohorts may promote precision interventions.

Hybrid Multimodality Fusion with Cross-Domain Knowledge Transfer to Forecast Progression Trajectories in Cognitive Decline

Magnetic resonance imaging (MRI) and positron emission tomography (PET) are increasingly used to forecast progression trajectories of cognitive decline caused by preclinical and prodromal Alzheimer's disease (AD). Many existing studies have explored the potential of these two distinct modalities with diverse machine and deep learning approaches. But successfully fusing MRI and PET can be complex due to their unique characteristics and missing modalities. To this end, we develop a hybrid multimodality fusion (HMF) framework with cross-domain knowledge transfer for joint MRI and PET representation learning, feature fusion, and cognitive decline progression forecasting. Our HMF consists of three modules: 1) a module to impute missing PET images, 2) a module to extract multimodality features from MRI and PET images, and 3) a module to fuse the extracted multimodality features. To address the issue of small sample sizes, we employ a cross-domain knowledge transfer strategy from the ADNI dataset, which includes 795 subjects, to independent small-scale AD-related cohorts, in order to leverage the rich knowledge present within the ADNI. The proposed HMF is extensively evaluated in three AD-related studies with 272 subjects across multiple disease stages, such as subjective cognitive decline and mild cognitive impairment. Experimental results demonstrate the superiority of our method over several state-of-the-art approaches in forecasting progression trajectories of AD-related cognitive decline.

On the comparability of frailty scores under the accumulation of deficits approach

BACKGROUND

While the cumulative deficit model is arguably the most popular instrument for population-level frailty screening, several questions remain unanswered regarding the comparability of the resulting scores across subpopulations.

METHODS

Based on data from the five waves of the Mexican Health and Aging Study (MHAS) we draw upon the alignment method to test for measurement invariance of frailty scores as per the accumulation of deficits approach.

RESULTS

Our results show that adjusting for measurement non-invariance not only improves predictive validity of our frailty measures, but resulting scores are more consistent with what is theoretically expected from them in longitudinal research.

CONCLUSIONS

There are clear potential benefits of measurement invariance testing as a general analytical framework from which to tackle with issues of comparability in frailty research.

Neurophysiological signatures of cortical micro-architecture

Systematic spatial variation in micro-architecture is observed across the cortex. These micro-architectural gradients are reflected in neural activity, which can be captured by neurophysiological time-series. How spontaneous neurophysiological dynamics are organized across the cortex and how they arise from heterogeneous cortical micro-architecture remains unknown. Here we extensively profile regional neurophysiological dynamics across the human brain by estimating over 6800 time-series features from the resting state magnetoencephalography (MEG) signal. We then map regional time-series profiles to a comprehensive multi-modal, multi-scale atlas of cortical micro-architecture, including microstructure, metabolism, neurotransmitter receptors, cell types and laminar differentiation. We find that the dominant axis of neurophysiological dynamics reflects characteristics of power spectrum density and linear correlation structure of the signal, emphasizing the importance of conventional features of electromagnetic dynamics while identifying additional informative features that have traditionally received less attention. Moreover, spatial variation in neurophysiological dynamics is co-localized with multiple micro-architectural features, including gene expression gradients, intracortical myelin, neurotransmitter receptors and transporters, and oxygen and glucose metabolism. Collectively, this work opens new avenues for studying the anatomical basis of neural activity.

Evolving prion-like tau conformers differentially alter postsynaptic proteins in neurons inoculated with distinct isolates of Alzheimer's disease tau

OBJECTIVES

Although accumulation of misfolded tau species has been shown to predict cognitive decline in patients with Alzheimer's disease (AD) and other tauopathies but with the remarkable diversity of clinical manifestations, neuropathology profiles, and time courses of disease progression remaining unexplained by current genetic data. We considered the diversity of misfolded tau conformers present in individual AD cases as an underlying driver of the phenotypic variations of AD and progressive loss of synapses.

METHODS

To model the mechanism of tau propagation and synaptic toxicity of distinct tau conformers, we inoculated wild-type primary mouse neurons with structurally characterized Sarkosyl-insoluble tau isolates from the frontal cortex of six AD cases and monitored the impact for fourteen days. We analyzed the accumulation rate, tau isoform ratio, and conformational characteristics of de novo-induced tau aggregates with conformationally sensitive immunoassays, and the dynamics of synapse formation, maintenance, and their loss using a panel of pre-and post-synaptic markers.

RESULTS

At the same concentrations of tau, the different AD tau isolates induced accumulation of misfolded predominantly 4-repeat tau aggregates at different rates in mature neurons, and demonstrated distinct conformational characteristics corresponding to the original AD brain tau. The time-course of the formation of misfolded tau aggregates and colocalization correlated with significant loss of synapses in tau-inoculated cell cultures and the reduction of synaptic connections implicated the disruption of postsynaptic compartment as an early event.

CONCLUSIONS

The data obtained with mature neurons expressing physiological levels and adult isoforms of tau protein demonstrate markedly different time courses of endogenous tau misfolding and differential patterns of post-synaptic alterations. These and previous biophysical data argue for an ensemble of various misfolded tau aggregates in individual AD brains and template propagation of their homologous conformations in neurons with different rates and primarily postsynaptic interactors. Modeling tau aggregation in mature differentiated neurons provides a platform for investigating divergent molecular mechanisms of tau strain propagation and for identifying common structural features of misfolded tau and critical interactors for new therapeutic targets and approaches in AD.

Assessing the transportability of clinical prediction models for cognitive impairment using causal models

BACKGROUND

Machine learning models promise to support diagnostic predictions, but may not perform well in new settings. Selecting the best model for a new setting without available data is challenging. We aimed to investigate the transportability by calibration and discrimination of prediction models for cognitive impairment in simulated external settings with different distributions of demographic and clinical characteristics.

METHODS

We mapped and quantified relationships between variables associated with cognitive impairment using causal graphs, structural equation models, and data from the ADNI study. These estimates were then used to generate datasets and evaluate prediction models with different sets of predictors. We measured transportability to external settings under guided interventions on age, APOE ε4, and tau-protein, using performance differences between internal and external settings measured by calibration metrics and area under the receiver operating curve (AUC).

RESULTS

Calibration differences indicated that models predicting with causes of the outcome were more transportable than those predicting with consequences. AUC differences indicated inconsistent trends of transportability between the different external settings. Models predicting with consequences tended to show higher AUC in the external settings compared to internal settings, while models predicting with parents or all variables showed similar AUC.

CONCLUSIONS

We demonstrated with a practical prediction task example that predicting with causes of the outcome results in better transportability compared to anti-causal predictions when considering calibration differences. We conclude that calibration performance is crucial when assessing model transportability to external settings.

SARS-CoV-2-associated organs failure and inflammation: a focus on the role of cellular and viral microRNAs

SARS-CoV-2 has been responsible for the recent pandemic all over the world, which has caused many complications. One of the hallmarks of SARS-CoV-2 infection is an induced immune dysregulation, in some cases resulting in cytokine storm syndrome, acute respiratory distress syndrome and many organs such as lungs, brain, and heart that are affected during the SARS-CoV-2 infection. Several physiological parameters are altered as a result of infection and cytokine storm. Among them, microRNAs (miRNAs) might reflect this poor condition since they play a significant role in immune cellular performance including inflammatory responses. Both host and viral-encoded miRNAs are crucial for the successful infection of SARS-CoV-2. For instance, dysregulation of miRNAs that modulate multiple genes expressed in COVID-19 patients with comorbidities (e.g., type 2 diabetes, and cerebrovascular disorders) could affect the severity of the disease. Therefore, altered expression levels of circulating miRNAs might be helpful to diagnose this illness and forecast whether a COVID-19 patient could develop a severe state of the disease. Moreover, a number of miRNAs could inhibit the expression of proteins, such as ACE2, TMPRSS2, spike, and Nsp12, involved in the life cycle of SARS-CoV-2. Accordingly, miRNAs represent potential biomarkers and therapeutic targets for this devastating viral disease. In the current study, we investigated modifications in miRNA expression and their influence on COVID-19 disease recovery, which may be employed as a therapy strategy to minimize COVID-19-related disorders.

Focused ultrasound mitigates pathology and improves spatial memory in Alzheimer's mice and patients

Rationale: Bilateral sonication with focused ultrasound (FUS) in conjunction with microbubbles has been shown to separately reduce amyloid plaques and hyperphosphorylated tau protein in the hippocampal formation and the entorhinal cortex in different mouse models of Alzheimer's disease (AD) without any therapeutic agents. However, the two pathologies are expressed concurrently in human disease. Therefore, the objective of this study is to investigate the effects of repeated bilateral sonications in the presence of both pathologies. Methods: Herein, we investigate its functional and morphological outcomes on brains bearing both pathologies simultaneously. Eleven transgenic mice of the 3xTg-AD line (14 months old) expressing human amyloid beta and human tau and eleven age-matched wild-type littermates received four weekly bilateral sonications covering the hippocampus followed by working memory testing. Afterwards, immunohistochemistry and immunoassays (western blot and ELISA) were employed to assess any changes in amyloid beta and human tau. Furthermore, we present preliminary data from our clinical trial using a neuronavigation-guided FUS system for sonications in AD patients (NCT04118764). Results: Interestingly, both wild-type and transgenic animals that received FUS experienced improved working memory and spent significantly more time in the escape platform-quadrant, with wild-type animals spending 43.2% (sham: 37.7%) and transgenic animals spending 35.3% (sham: 31.0%) of the trial in the target quadrant. Furthermore, this behavioral amelioration in the transgenic animals correlated with a 58.3% decrease in the neuronal length affected by tau and a 27.2% reduction in total tau levels. Amyloid plaque population, volume and overall load were also reduced overall. Consistently, preliminary data from a clinical trial involving AD patients showed a 1.8% decrease of amyloid PET signal 3-weeks after treatment in the treated hemisphere compared to baseline. Conclusion: For the first time, it is shown that bilateral FUS-induced BBB opening significantly and simultaneously ameliorates both coexistent pathologies, which translated to improvements in spatial memory of transgenic animals with complex AD, the human mimicking phenotype. The level of cognitive improvement was significantly correlated with the volume of BBB opening. Non-transgenic animals were also shown to exhibit similar memory amelioration for the first time, indicating that BBB opening results into benefits in the neuronal function regardless of the existence of AD pathology. A potential mechanism of action for the reduction of the both pathologies investigated was the cholesterol metabolism, specifically the LRP1b receptor, which exhibited increased expression levels in transgenic mice following FUS-induced BBB opening. Initial clinical evidence supported that the beta amyloid reduction shown in rodents could be translatable to humans with significant amyloid reduction shown in the treated hemisphere.

The role of peripheral inflammatory insults in Alzheimer's disease: a review and research roadmap

Peripheral inflammation, defined as inflammation that occurs outside the central nervous system, is an age-related phenomenon that has been identified as a risk factor for Alzheimer's disease. While the role of chronic peripheral inflammation has been well characterized in the context of dementia and other age-related conditions, less is known about the neurologic contribution of acute inflammatory insults that take place outside the central nervous system. Herein, we define acute inflammatory insults as an immune challenge in the form of pathogen exposure (e.g., viral infection) or tissue damage (e.g., surgery) that causes a large, yet time-limited, inflammatory response. We provide an overview of the clinical and translational research that has examined the connection between acute inflammatory insults and Alzheimer's disease, focusing on three categories of peripheral inflammatory insults that have received considerable attention in recent years: acute infection, critical illness, and surgery. Additionally, we review immune and neurobiological mechanisms which facilitate the neural response to acute inflammation and discuss the potential role of the blood-brain barrier and other components of the neuro-immune axis in Alzheimer's disease. After highlighting the knowledge gaps in this area of research, we propose a roadmap to address methodological challenges, suboptimal study design, and paucity of transdisciplinary research efforts that have thus far limited our understanding of how pathogen- and damage-mediated inflammatory insults may contribute to Alzheimer's disease. Finally, we discuss how therapeutic approaches designed to promote the resolution of inflammation may be used following acute inflammatory insults to preserve brain health and limit progression of neurodegenerative pathology.

Arterial hypertension and β-amyloid accumulation have spatially overlapping effects on posterior white matter hyperintensity volume: a cross-sectional study

BACKGROUND

White matter hyperintensities (WMH) in subjects across the Alzheimer's disease (AD) spectrum with minimal vascular pathology suggests that amyloid pathology-not just arterial hypertension-impacts WMH, which in turn adversely influences cognition. Here we seek to determine the effect of both hypertension and Aβ positivity on WMH, and their impact on cognition.

METHODS

We analysed data from subjects with a low vascular profile and normal cognition (NC), subjective cognitive decline (SCD), and amnestic mild cognitive impairment (MCI) enrolled in the ongoing observational multicentre DZNE Longitudinal Cognitive Impairment and Dementia Study (n = 375, median age 70.0 [IQR 66.0, 74.4] years; 178 female; NC/SCD/MCI 127/162/86). All subjects underwent a rich neuropsychological assessment. We focused on baseline memory and executive function-derived from multiple neuropsychological tests using confirmatory factor analysis-, baseline preclinical Alzheimer's cognitive composite 5 (PACC5) scores, and changes in PACC5 scores over the course of three years (ΔPACC5).

RESULTS

Subjects with hypertension or Aβ positivity presented the largest WMH volumes (pFDR < 0.05), with spatial overlap in the frontal (hypertension: 0.42 ± 0.17; Aβ: 0.46 ± 0.18), occipital (hypertension: 0.50 ± 0.16; Aβ: 0.50 ± 0.16), parietal lobes (hypertension: 0.57 ± 0.18; Aβ: 0.56 ± 0.20), corona radiata (hypertension: 0.45 ± 0.17; Aβ: 0.40 ± 0.13), optic radiation (hypertension: 0.39 ± 0.18; Aβ: 0.74 ± 0.19), and splenium of the corpus callosum (hypertension: 0.36 ± 0.12; Aβ: 0.28 ± 0.12). Elevated global and regional WMH volumes coincided with worse cognitive performance at baseline and over 3 years (pFDR < 0.05). Aβ positivity was negatively associated with cognitive performance (direct effect-memory: - 0.33 ± 0.08, pFDR < 0.001; executive: - 0.21 ± 0.08, pFDR < 0.001; PACC5: - 0.29 ± 0.09, pFDR = 0.006; ΔPACC5: - 0.34 ± 0.04, pFDR < 0.05). Splenial WMH mediated the relationship between hypertension and cognitive performance (indirect-only effect-memory: - 0.05 ± 0.02, pFDR = 0.029; executive: - 0.04 ± 0.02, pFDR = 0.067; PACC5: - 0.05 ± 0.02, pFDR = 0.030; ΔPACC5: - 0.09 ± 0.03, pFDR = 0.043) and WMH in the optic radiation partially mediated that between Aβ positivity and memory (indirect effect-memory: - 0.05 ± 0.02, pFDR = 0.029).

CONCLUSIONS

Posterior white matter is susceptible to hypertension and Aβ accumulation. Posterior WMH mediate the association between these pathologies and cognitive dysfunction, making them a promising target to tackle the downstream damage related to the potentially interacting and potentiating effects of the two pathologies.

TRIAL REGISTRATION

German Clinical Trials Register (DRKS00007966, 04/05/2015).

3D bioprinting patient-derived induced pluripotent stem cell models of Alzheimer's disease using a smart bioink

BACKGROUND

Alzheimer's disease (AD), a progressive neurodegenerative disorder, is becoming increasingly prevalent as our population ages. It is characterized by the buildup of amyloid beta plaques and neurofibrillary tangles containing hyperphosphorylated-tau. The current treatments for AD do not prevent the long-term progression of the disease and pre-clinical models often do not accurately represent its complexity. Bioprinting combines cells and biomaterials to create 3D structures that replicate the native tissue environment and can be used as a tool in disease modeling or drug screening.

METHODS

This work differentiated both healthy and diseased patient-derived human induced pluripotent stems cells (hiPSCs) into neural progenitor cells (NPCs) that were bioprinted using the Aspect RX1 microfluidic printer into dome-shaped constructs. The combination of cells, bioink, and puromorphamine (puro)-releasing microspheres were used to mimic the in vivo environment and direct the differentiation of the NPCs into basal forebrain-resembling cholinergic neurons (BFCN). These tissue models were then characterized for cell viability, immunocytochemistry, and electrophysiology to evaluate their functionality and physiology for use as disease-specific neural models.

RESULTS

Tissue models were successfully bioprinted and the cells were viable for analysis after 30- and 45-day cultures. The neuronal and cholinergic markers β-tubulin III (Tuj1), forkhead box G1 (FOXG1), and choline acetyltransferase (ChAT) were identified as well as the AD markers amyloid beta and tau. Further, immature electrical activity was observed when the cells were excited with potassium chloride and acetylcholine.

CONCLUSIONS

This work shows the successful development of bioprinted tissue models incorporating patient derived hiPSCs. Such models can potentially be used as a tool to screen promising drug candidates for treating AD. Further, this model could be used to increase the understanding of AD progression. The use of patient derived cells also shows the potential of this model for use in personalized medicine applications.

Is metformin neuroprotective against diabetes mellitus-induced neurodegeneration? An updated graphical review of molecular basis

Diabetes mellitus (DM) is a metabolic disease that activates several molecular pathways involved in neurodegenerative disorders. Metformin, an anti-hyperglycemic drug used for treating DM, has the potential to exert a significant neuroprotective role against the detrimental effects of DM. This review discusses recent clinical and laboratory studies investigating the neuroprotective properties of metformin against DM-induced neurodegeneration and the roles of various molecular pathways, including mitochondrial dysfunction, oxidative stress, inflammation, apoptosis, and its related cascades. A literature search was conducted from January 2000 to December 2022 using multiple databases including Web of Science, Wiley, Springer, PubMed, Elsevier Science Direct, Google Scholar, the Core Collection, Scopus, and the Cochrane Library to collect and evaluate peer-reviewed literature regarding the neuroprotective role of metformin against DM-induced neurodegenerative events. The literature search supports the conclusion that metformin is neuroprotective against DM-induced neuronal cell degeneration in both peripheral and central nervous systems, and this effect is likely mediated via modulation of oxidative stress, inflammation, and cell death pathways.

Network-level analysis of ageing and its relationship with diseases and tissue regeneration in the mouse liver

The liver plays a vital role in maintaining whole-body metabolic homeostasis, compound detoxification and has the unique ability to regenerate itself post-injury. Ageing leads to functional impairment of the liver and predisposes the liver to non-alcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC). Mapping the molecular changes of the liver with ageing may help to understand the crosstalk of ageing with different liver diseases. A systems-level analysis of the ageing-induced liver changes and its crosstalk with liver-associated conditions is lacking. In the present study, we performed network-level analyses of the ageing liver using mouse transcriptomic data and a protein-protein interaction (PPI) network. A sample-wise analysis using network entropy measure was performed, which showed an increasing trend with ageing and helped to identify ageing genes based on local entropy changes. To gain further insights, we also integrated the differentially expressed genes (DEGs) between young and different age groups with the PPI network and identified core modules and nodes associated with ageing. Finally, we computed the network proximity of the ageing network with different networks of liver diseases and regeneration to quantify the effect of ageing. Our analysis revealed the complex interplay of immune, cancer signalling, and metabolic genes in the ageing liver. We found significant network proximities between ageing and NAFLD, HCC, liver damage conditions, and the early phase of liver regeneration with common nodes including NLRP12, TRP53, GSK3B, CTNNB1, MAT1 and FASN. Overall, our study maps the network-level changes of ageing and their interconnections with the physiology and pathology of the liver.

Progression of frailty as measured by a cumulative deficit index: A systematic review

BACKGROUND

Frailty is a risk factor for adverse health outcomes. There is a paucity of literature on frailty progression defined by a cumulative deficit model among community dwelling older people. The objective of this review was to synthesise evidence on these changes in health and mortality among community-dwelling older people.

METHODS

Six databases (Medline, Embase, CINAHL, Cochrane, PsycInfo, Web of Science) and a clinical trials registry were searched in July 2021. The inclusion criteria were studies using a frailty index and providing information on transition between frailty states or to death in community-dwelling older people aged ≥ 50. Exclusion criteria were studies examining specific health conditions, conference abstracts and non-English studies. To standardise the follow-up period and facilitate comparison, we converted the transition probabilities to annual transition rates.

RESULTS

Two reviewers independently screened 5078 studies and 61 studies were included for analysis. Of these, only three used the same frailty state cut-points to facilitate cross-cohort comparison. This review found that frailty tends to increase with time, people who are frail at baseline have greater likelihood to progress in frailty and die, and the main factor that accelerates frailty progression is age. Other risk factors for progression are having chronic disease, smoking, obesity, low-income or/and low-education levels. A frailty index is an accurate predictor of adverse outcomes and death.

DISCUSSION

This systematic review demonstrated that worsening in frailty was a common frailty transition, and older people who are frail at baseline are more likely to die. A frailty index has significant power to predict adverse health outcomes. It is a useful tool for within-cohort comparison but there are challenges comparing different cohorts due to dependence of frailty progression on age and differences in how frailty index is defined and measured.

Ensemble feature selection with data-driven thresholding for Alzheimer's disease biomarker discovery

BACKGROUND

Feature selection is often used to identify the important features in a dataset but can produce unstable results when applied to high-dimensional data. The stability of feature selection can be improved with the use of feature selection ensembles, which aggregate the results of multiple base feature selectors. However, a threshold must be applied to the final aggregated feature set to separate the relevant features from the redundant ones. A fixed threshold, which is typically used, offers no guarantee that the final set of selected features contains only relevant features. This work examines a selection of data-driven thresholds to automatically identify the relevant features in an ensemble feature selector and evaluates their predictive accuracy and stability. Ensemble feature selection with data-driven thresholding is applied to two real-world studies of Alzheimer's disease. Alzheimer's disease is a progressive neurodegenerative disease with no known cure, that begins at least 2-3 decades before overt symptoms appear, presenting an opportunity for researchers to identify early biomarkers that might identify patients at risk of developing Alzheimer's disease.

RESULTS

The ensemble feature selectors, combined with data-driven thresholds, produced more stable results, on the whole, than the equivalent individual feature selectors, showing an improvement in stability of up to 34%. The most successful data-driven thresholds were the robust rank aggregation threshold and the threshold algorithm threshold from the field of information retrieval. The features identified by applying these methods to datasets from Alzheimer's disease studies reflect current findings in the AD literature.

CONCLUSIONS

Data-driven thresholds applied to ensemble feature selectors provide more stable, and therefore more reproducible, selections of features than individual feature selectors, without loss of performance. The use of a data-driven threshold eliminates the need to choose a fixed threshold a-priori and can select a more meaningful set of features. A reliable and compact set of features can produce more interpretable models by identifying the factors that are important in understanding a disease.

Enhanced delivery of a low dose of aducanumab via FUS in 5×FAD mice, an AD model

BACKGROUND

Aducanumab (Adu), which is a human IgG1 monoclonal antibody that targets oligomer and fibril forms of beta-amyloid, has been reported to reduce amyloid pathology and improve impaired cognition after administration of a high dose (10 mg/kg) of the drug in Alzheimer's disease (AD) clinical trials. The purpose of this study was to investigate the effects of a lower dose of Adu (3 mg/kg) with enhanced delivery via focused ultrasound (FUS) in an AD mouse model.

METHODS

The FUS with microbubbles opened the blood-brain barrier (BBB) of the hippocampus for the delivery of Adu. The combined therapy of FUS and Adu was performed three times in total and each treatment was performed biweekly. Y-maze test, Brdu labeling, and immunohistochemical experimental methods were employed in this study. In addition, RNA sequencing and ingenuity pathway analysis were employed to investigate gene expression profiles in the hippocampi of experimental animals.

RESULTS

The FUS-mediated BBB opening markedly increased the delivery of Adu into the brain by approximately 8.1 times in the brains. The combined treatment induced significantly less cognitive decline and decreased the level of amyloid plaques in the hippocampi of the 5×FAD mice compared with Adu or FUS alone. Combined treatment with FUS and Adu activated phagocytic microglia and increased the number of astrocytes associated with amyloid plaques in the hippocampi of 5×FAD mice. Furthermore, RNA sequencing identified that 4 enriched canonical pathways including phagosome formation, neuroinflammation signaling, CREB signaling and reelin signaling were altered in the hippocami of 5×FAD mice receiving the combined treatment.

CONCLUSION

In conclusion, the enhanced delivery of a low dose of Adu (3 mg/kg) via FUS decreases amyloid deposits and attenuates cognitive function deficits. FUS-mediated BBB opening increases adult hippocampal neurogenesis as well as drug delivery. We present an AD treatment strategy through the synergistic effect of the combined therapy of FUS and Adu.

Phenotype-based screening rediscovered benzopyran-embedded microtubule inhibitors as anti-neuroinflammatory agents by modulating the tubulin-p65 interaction

Neuroinflammation is one of the critical processes implicated in central nervous system (CNS) diseases. Therefore, alleviating neuroinflammation has been highlighted as a therapeutic strategy for treating CNS disorders. However, the complexity of neuroinflammatory processes and poor drug transport to the brain are considerable hurdles to the efficient control of neuroinflammation using small-molecule therapeutics. Thus, there is a significant demand for new chemical entities (NCEs) targeting neuroinflammation. Herein, we rediscovered benzopyran-embedded tubulin inhibitor 1 as an anti-neuroinflammatory agent via phenotype-based screening. A competitive photoaffinity labeling study revealed that compound 1 binds to tubulin at the colchicine-binding site. Structure-activity relationship analysis of 1's analogs identified SB26019 as a lead compound with enhanced anti-neuroinflammatory efficacy. Mechanistic studies revealed that upregulation of the tubulin monomer was critical for the anti-neuroinflammatory activity of SB26019. We serendipitously found that the tubulin monomer recruits p65, inhibiting its translocation from the cytosol to the nucleus and blocking NF-κB-mediated inflammatory pathways. Further in vivo validation using a neuroinflammation mouse model demonstrated that SB26019 suppressed microglial activation by downregulating lba-1 and proinflammatory cytokines. Intraperitoneal administration of SB26019 showed its therapeutic potential as an NCE for successful anti-neuroinflammatory regulation. Along with the recent growing demands on tubulin modulators for treating various inflammatory diseases, our results suggest that colchicine-binding site-specific modulation of tubulins can be a potential strategy for preventing neuroinflammation and treating CNS diseases.

GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies

Recent advances in genome sequencing and imputation technologies provide an exciting opportunity to comprehensively study the contribution of genetic variants to complex phenotypes. However, our ability to translate genetic discoveries into mechanistic insights remains limited at this point. In this paper, we propose an efficient knockoff-based method, GhostKnockoff, for genome-wide association studies (GWAS) that leads to improved power and ability to prioritize putative causal variants relative to conventional GWAS approaches. The method requires only Z-scores from conventional GWAS and hence can be easily applied to enhance existing and future studies. The method can also be applied to meta-analysis of multiple GWAS allowing for arbitrary sample overlap. We demonstrate its performance using empirical simulations and two applications: (1) a meta-analysis for Alzheimer's disease comprising nine overlapping large-scale GWAS, whole-exome and whole-genome sequencing studies and (2) analysis of 1403 binary phenotypes from the UK Biobank data in 408,961 samples of European ancestry. Our results demonstrate that GhostKnockoff can identify putatively functional variants with weaker statistical effects that are missed by conventional association tests.