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To identify common variants contributing to normal variation in two specific domains of cognitive functioning, we conducted a genome-wide association study (GWAS) of executive functioning and information processing speed in non-demented older adults from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) consortium. Neuropsychological testing was available for 5429-32,070 subjects of European ancestry aged 45 years or older, free of dementia and clinical stroke at the time of cognitive testing from 20 cohorts in the discovery phase. We analyzed performance on the Trail Making Test parts A and B, the Letter Digit Substitution Test (LDST), the Digit Symbol Substitution Task (DSST), semantic and phonemic fluency tests, and the Stroop Color and Word Test. Replication was sought in 1311-21860 subjects from 20 independent cohorts. A significant association was observed in the discovery cohorts for the single-nucleotide polymorphism (SNP) rs17518584 (discovery P-value=3.12 × 10(-8)) and in the joint discovery and replication meta-analysis (P-value=3.28 × 10(-9) after adjustment for age, gender and education) in an intron of the gene cell adhesion molecule 2 (CADM2) for performance on the LDST/DSST. Rs17518584 is located about 170 kb upstream of the transcription start site of the major transcript for the CADM2 gene, but is within an intron of a variant transcript that includes an alternative first exon. The variant is associated with expression of CADM2 in the cingulate cortex (P-value=4 × 10(-4)). The protein encoded by CADM2 is involved in glutamate signaling (P-value=7.22 × 10(-15)), gamma-aminobutyric acid (GABA) transport (P-value=1.36 × 10(-11)) and neuron cell-cell adhesion (P-value=1.48 × 10(-13)). Our findings suggest that genetic variation in the CADM2 gene is associated with individual differences in information processing speed.

Vitamin D has been investigated in association with cognitive function in older adults. It is unclear whether hypovitaminosis D could be associated with Alzheimer's disease (AD). Our objective was to systematically review and quantitatively synthesize the association of low serum 25-hydroxyvitamin D (25OHD) concentrations with AD in adults. A Medline and PsycINFO® search was conducted on May 2012, with no limit of date, using the MeSH terms "Vitamin D" OR "Hydroxycholecalciferols" combined with the MeSH terms "Alzheimer disease" OR "Dementia" OR "Cognition" OR "Cognition disorders" OR "Memory" OR "Memory Disorders" OR "Executive Function" OR "Attention" OR "Neuropsychological Tests". Of the 284 selected studies, 10 observational studies (including 9 case-controls and 1 cohort study) met the selection criteria. All were of good quality. The number of AD cases ranged from 20 to 211 (40%-100% female). Finally, 7 case-control studies were eligible for fixed and random-effects meta-analyses of bias-corrected effect size of the difference in serum 25OHD concentrations between AD cases and controls using an inverse-variance method. The pooled effect size in random-effects meta-analysis was 1.40 (95% CI: 0.26;2.54), a 'large' effect size that indicates that serum 25OHD concentrations were 1.4 standard deviation units lower in AD cases compared to cognitively healthy controls (p = 0.016). In conclusion, AD cases had lower serum vitamin D concentrations than matched controls. This reinforces the conceptualization of vitamin D as a 'neurosteroid hormone' and as a potential biomarker of AD. [ABSTRACT FROM AUTHOR]

Adjuvant-induced arthritis can be transferred to naive Dark Agouti (DA) strain (DA.CD45.1) rats by thoracic duct (TD) lymphocytes. Disease can be re-induced in convalescent rats by further transfer of arthritogenic cells, suggesting that resolution of the adoptive disease is not due to active regulation. To examine whether resolution is due to exhaustion of effector cells, we transferred the disease to DA.CD45.1 recipients, using CD4+ T cells from DA.CD45.2 donors. At the height of the adoptively transferred disease, donor cells comprised only 5–10% of recirculating CD4+ T cells but they accounted for ∼40% of the CD4+ T cells in synovium-rich tissues of the hind paws. Approximately 65% of the donor cells in the synovium expressed a marker of proliferation (Ki-67 antigen). Division of CD4+ T cells continued in shielded paws after suppression of the recirculating pool of lymphocytes by selective irradiation. Intravenously injected CD4+ TD T lymphoblasts from arthritic donors were recruited to normal paws and, in greater numbers, to paws of animals with existing arthritis. Survival of the [125I]iodo-deoxyuridine-labeled lymphoblasts was greater in animals with existing arthritis. We conclude that effector CD4+ T cells in target tissues can proliferate in response to autoantigens and exhibit enhanced survival. However, without a continuous supply, adoptively transferred effector cells do not produce autonomous local disease, due to limits to their lifespan and ability to replicate indefinitely. [ABSTRACT FROM AUTHOR]

Objective: Hypertension is a recognized risk factor for the development of cognitive impairment and dementia in older adults. Aortic stiffness and altered haemodynamics could promote the transmission of detrimental high pressure pulsatility into the cerebral circulation, potentially damaging brain microvasculature and leading to cognitive impairment. We determined whether reservoir-excess pressure parameters were associated with cognitive function in people with hypertension (HT) and normotension (NT)., Methods: We studied 35 middle-aged and older treatment-naïve stage II/III HT (office systolic BP 176 ± 17 mmHg) and 35 age-, sex- and body mass index-matched NT (office systolic BP 127 ± 8 mmHg). Parameters derived from reservoir-excess pressure analysis including reservoir pressure integral (INTPR), excess pressure integral (INTXSP), systolic rate constant (SRC), diastolic rate constant (DRC) and pulse wave velocity (PWV) were calculated from an ensemble-averaged aortic pressure waveform derived from radial artery tonometry. Cognitive function was assessed using the Addenbrooke's Cognitive Examination Revised (ACE-R), Trail Making Test Part A (TMT-A) and Part B (TMT-B)., Results: All reservoir-excess pressure parameters were greater in HT than NT (all P < 0.05). Greater INTXSP was associated with lower ACE-R score (rs = -0.31), longer TMT-A (r = 0.31) and TMT-B (r = 0.38). Likewise, greater DRC and PWV were also associated with lower ACE-R score (rs = -0.27 and rs = -0.33), longer TMT-A (r = 0.51 and r = 0.40) and TMT-B (r = 0.38 and r = 0.32). Greater INTXSP, DRC and PWV are consistently associated with worse cognitive function in this study., Conclusions: These observations support a potential mechanistic link between adverse haemodynamics and a heightened risk of cognitive impairment in older adults with hypertension., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Introduction: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. This study integrates common genetic association results from the latest ALS genome-wide association study (GWAS) summary statistics with functional genomic annotations with the aim of providing mechanistic insights into ALS risk loci, inferring drug repurposing opportunities, and enhancing prediction of ALS risk and clinical characteristics., Methods: Genes associated with ALS were identified using GWAS summary statistic methodology including SuSiE SNP-based fine-mapping, and transcriptome- and proteome-wide association study (TWAS/PWAS) analyses. Using several approaches, gene associations were integrated with the DrugTargetor drug-gene interaction database to identify drugs that could be repurposed for the treatment of ALS. Furthermore, ALS gene associations from TWAS were combined with observed blood expression in two external ALS case-control datasets to calculate polytranscriptomic scores and evaluate their utility for prediction of ALS risk and clinical characteristics, including site of onset, age at onset, and survival., Results: SNP-based fine-mapping, TWAS and PWAS identified 118 genes associated with ALS, with TWAS and PWAS providing novel mechanistic insights. Drug repurposing analyses identified six drugs significantly enriched for interactions with ALS associated genes, though directionality could not be determined. Additionally, drug class enrichment analysis showed gene signatures linked to calcium channel blockers may reduce ALS risk, whereas antiepileptic drugs may increase ALS risk. Across the two observed expression target samples, ALS polytranscriptomic scores significantly predicted ALS risk ( R 2  = 5.1 %; p -value = 3.2 × 10 -27 ) and clinical characteristics., Conclusions: Functionally-informed analyses of ALS GWAS summary statistics identified novel mechanistic insights into ALS aetiology, highlighted several therapeutic research avenues, and enabled statistically significant prediction of ALS risk., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: OP provides consultancy services for UCB pharma company. AAC reports consultancies or advisory boards for Amylyx, Apellis, Biogen, Brainstorm, Cytokinetics, GenieUs, GSK, Lilly, Mitsubishi Tanabe Pharma, Novartis, OrionPharma, Quralis, and Wave Pharmaceuticals. The other authors declare no competing interests., (© 2024 The Authors.)

Importance: There is limited information on modifiable risk factors for young-onset dementia (YOD)., Objective: To examine factors that are associated with the incidence of YOD., Design, Setting, and Participants: This prospective cohort study used data from the UK Biobank, with baseline assessment between 2006 and 2010 and follow-up until March 31, 2021, for England and Scotland, and February 28, 2018, for Wales. Participants younger than 65 years and without a dementia diagnosis at baseline assessment were included in this study. Participants who were 65 years and older and those with dementia at baseline were excluded. Data were analyzed from May 2022 to April 2023., Exposures: A total of 39 potential risk factors were identified from systematic reviews of late-onset dementia and YOD risk factors and grouped into domains of sociodemographic factors (education, socioeconomic status, and sex), genetic factors (apolipoprotein E), lifestyle factors (physical activity, alcohol use, alcohol use disorder, smoking, diet, cognitive activity, social isolation, and marriage), environmental factors (nitrogen oxide, particulate matter, pesticide, and diesel), blood marker factors (vitamin D, C-reactive protein, estimated glomerular filtration rate function, and albumin), cardiometabolic factors (stroke, hypertension, diabetes, hypoglycemia, heart disease, atrial fibrillation, and aspirin use), psychiatric factors (depression, anxiety, benzodiazepine use, delirium, and sleep problems), and other factors (traumatic brain injury, rheumatoid arthritis, thyroid dysfunction, hearing impairment, and handgrip strength)., Main Outcome and Measures: Multivariable Cox proportional hazards regression was used to study the association between the risk factors and incidence of YOD. Factors were tested stepwise first within domains and then across domains., Results: Of 356 052 included participants, 197 036 (55.3%) were women, and the mean (SD) age at baseline was 54.6 (7.0) years. During 2 891 409 person-years of follow-up, 485 incident YOD cases (251 of 485 men [51.8%]) were observed, yielding an incidence rate of 16.8 per 100 000 person-years (95% CI, 15.4-18.3). In the final model, 15 factors were significantly associated with a higher YOD risk, namely lower formal education, lower socioeconomic status, carrying 2 apolipoprotein ε4 allele, no alcohol use, alcohol use disorder, social isolation, vitamin D deficiency, high C-reactive protein levels, lower handgrip strength, hearing impairment, orthostatic hypotension, stroke, diabetes, heart disease, and depression., Conclusions and Relevance: In this study, several factors, mostly modifiable, were associated with a higher risk of YOD. These modifiable risk factors should be incorporated in future dementia prevention initiatives and raise new therapeutic possibilities for YOD.

With the increase in large multimodal cohorts and high-throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice. HIGHLIGHTS: Artificial intelligence and machine learning approaches may accelerate dementia biomarker discovery. Remaining challenges include data set suitability due to size and bias in cohort selection. Multimodal data, diverse data sets, improved machine learning approaches, real-world validation, and interdisciplinary collaboration are required., (© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Introduction: The use of applied modeling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefits, particularly as "deep phenotyping" cohorts with multi-omics health data become available., Methods: This narrative review synthesizes understanding of applied models and digital health technologies, in terms of dementia risk prediction, diagnostic discrimination, prognosis, and progression. Machine learning approaches show evidence of improved predictive power compared to standard clinical risk scores in predicting dementia, and the potential to decompose large numbers of variables into relatively few critical predictors., Results: This review focuses on key areas of emerging promise including: emphasis on easier, more transparent data sharing and cohort access; integration of high-throughput biomarker and electronic health record data into modeling; and progressing beyond the primary prediction of dementia to secondary outcomes, for example, treatment response and physical health., Discussion: Such approaches will benefit also from improvements in remote data measurement, whether cognitive (e.g., online), or naturalistic (e.g., watch-based accelerometry)., (© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care. HIGHLIGHTS: Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation., (© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Introduction: Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials., Methods: Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research., Results: Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross-model reproducibility and translation to human biology, while sustaining biological interpretability., Discussion: AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data., Highlights: There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross-species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi-omics analysis with AI offers exciting future possibilities in drug discovery., (© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high-dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia-related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine. HIGHLIGHTS: We have identified five key challenges in dementia genetics and omics studies. AI can enable detection of undiscovered patterns in dementia genetics and omics data. Enhanced and more diverse genetics and omics datasets are still needed. Multidisciplinary collaborative efforts using AI can boost dementia research., (© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Introduction: Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia., Methods: We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases., Results: A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort., Discussion: The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice., Highlights: There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias., (© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Introduction: A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding., Methods: ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field., Results: Risk-profiling tools may help identify high-risk populations for clinical trials; however, their performance needs improvement. New risk-profiling and trial-recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug-repurposing efforts and prioritization of disease-modifying therapeutics., Discussion: ML is not yet widely used but has considerable potential to enhance precision in dementia prevention., Highlights: Artificial intelligence (AI) is not widely used in the dementia prevention field. Risk-profiling tools are not used in clinical practice. Causal insights are needed to understand risk factors over the lifespan. AI will help personalize risk-management tools for dementia prevention. AI could target specific patient groups that will benefit most for clinical trials., (© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data analytics and machine learning tools for drug discovery and utilizing them to inform successful clinical trial design has the potential to accelerate progress. Opportunities arise at multiple stages in the therapy pipeline and the growing availability of large medical data sets opens possibilities for big data analyses to answer key questions in clinical and therapeutic challenges. However, before this goal is reached, several challenges need to be overcome and only a multi-disciplinary approach can promote data-driven decision-making to its full potential. Herein we review the current state of machine learning applications to clinical trial design and drug discovery, while presenting opportunities and recommendations that can break down the barriers to implementation., (© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)