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Introduction: The established link between DNA methylation and pathophysiology of dementia, along with its potential role as a molecular mediator of lifestyle and environmental influences, positions blood-derived DNA methylation as a promising tool for early dementia risk detection., Methods: In conjunction with an extensive array of machine learning techniques, we employed whole blood genome-wide DNA methylation data as a surrogate for 14 modifiable and non-modifiable factors in the assessment of dementia risk in independent dementia cohorts., Results: We established a multivariate methylation risk score (MMRS) for identifying mild cognitive impairment cross-sectionally, independent of age and sex (P = 2.0 × 10 -3 ). This score significantly predicted the prospective development of cognitive impairments in independent studies of Alzheimer's disease (hazard ratio for Rey's Auditory Verbal Learning Test (RAVLT)-Learning = 2.47) and Parkinson's disease (hazard ratio for MCI/dementia   = 2.59)., Discussion: Our work shows the potential of employing blood-derived DNA methylation data in the assessment of dementia risk., Highlights: We used whole blood DNA methylation as a surrogate for 14 dementia risk factors. Created a multivariate methylation risk score for predicting cognitive impairment. Emphasized the role of machine learning and omics data in predicting dementia. The score predicts cognitive impairment development at the population level., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Background: Alzheimer's disease (AD) is a neurodegenerative condition for which there is currently no available medication that can stop its progression. Previous studies suggest that mild cognitive impairment (MCI) is a phase that precedes the disease. Therefore, a better understanding of the molecular mechanisms behind MCI conversion to AD is needed., Method: Here, we propose a machine learning-based approach to detect the key metabolites and proteins involved in MCI progression to AD using data from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery Study. Proteins and metabolites were evaluated separately in multiclass models (controls, MCI and AD) and together in MCI conversion models (MCI stable vs converter). Only features selected as relevant by 3/4 algorithms proposed were kept for downstream analysis., Results: Multiclass models of metabolites highlighted nine features further validated in an independent cohort (0.726 mean balanced accuracy). Among these features, one metabolite, oleamide, was selected by all the algorithms. Further in-vitro experiments in rodents showed that disease-associated microglia excreted oleamide in vesicles. Multiclass models of proteins stood out with nine features, validated in an independent cohort (0.720 mean balanced accuracy). However, none of the proteins was selected by all the algorithms. Besides, to distinguish between MCI stable and converters, 14 key features were selected (0.872 AUC), including tTau, alpha-synuclein (SNCA), junctophilin-3 (JPH3), properdin (CFP) and peptidase inhibitor 15 (PI15) among others., Conclusions: This omics integration approach highlighted a set of molecules associated with MCI conversion important in neuronal and glia inflammation pathways., Competing Interests: Declaration of competing interest SL is named as an inventor on biomarker intellectual property protected by Proteome Sciences and Kings College London unrelated to the current study and within the past five years has advised for Optum labs, Merck, SomaLogic and been the recipient of funding from AstraZeneca and other companies via the IMI funding scheme. HZ has served at scientific advisory boards and/or as a consultant for Abbvie, Alector, ALZPath, Annexon, Apellis, Artery Therapeutics, AZTherapies, CogRx, Denali, Eisai, Nervgen, Novo Nordisk, Pinteon Therapeutics, Red Abbey Labs, reMYND, Passage Bio, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures in symposia sponsored by Cellectricon, Fujirebio, Alzecure, Biogen, and Roche, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). AL has served at scientific advisory boards of Fujirebio Europe, Eli Lilly, Novartis, Nutricia and Otsuka and is the inventor of a patent on synaptic markers in CSF (all unrelated to this study). JP has served at scientific advisory boards of Fujirebio Europe, Eli Lilly and Nestle Institute of Health Sciences, all unrelated to this study. SE has received unrestricted research grants from Janssen Pharmaceutica and ADx Neurosciences and has served at scientific advisory boards of Biogen, Eisai, Novartis, Nutricia/Danone, all unrelated to this study. FB is a steering committee or iDMC member for Biogen, Merck, Roche, EISAI and Prothena. Consultant for Roche, Biogen, Merck, IXICO, Jansen, Combinostics. Research agreements with Merck, Biogen, GE Healthcare, Roche. Co-founder and shareholder of Queen Square Analytics LTD, all unrelated to this study. CLQ has received funding related to this study from Pfizer and via the IMI funding scheme, in the past five years has been the recipient of funding from Novo Nordisk, unrelated to this study., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Introduction: This study employed an integrative system and causal inference approach to explore molecular signatures in blood and CSF, the amyloid/tau/neurodegeneration [AT(N)] framework, mild cognitive impairment (MCI) conversion to Alzheimer's disease (AD), and genetic risk for AD., Methods: Using the European Medical Information Framework (EMIF)-AD cohort, we measured 696 proteins in cerebrospinal fluid (n = 371), 4001 proteins in plasma (n = 972), 611 metabolites in plasma (n = 696), and genotyped whole-blood (7,778,465 autosomal single nucleotide epolymorphisms, n = 936). We investigated associations: molecular modules to AT(N), module hubs with AD Polygenic Risk scores and APOE4 genotypes, molecular hubs to MCI conversion and probed for causality with AD using Mendelian randomization (MR)., Results: AT(N) framework associated with protein and lipid hubs. In plasma, Proprotein Convertase Subtilisin/Kexin Type 7 showed evidence for causal associations with AD. AD was causally associated with Reticulocalbin 2 and sphingomyelins, an association driven by the APOE isoform., Discussion: This study reveals multi-omics networks associated with AT(N) and causal AD molecular candidates., (© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Background: Suspected non-Alzheimer's disease pathophysiology (SNAP) is a biomarker concept that encompasses individuals with neuronal injury but without amyloidosis. We aim to investigate the pathophysiology of SNAP, defined as abnormal tau without amyloidosis, in individuals with mild cognitive impairment (MCI) by cerebrospinal fluid (CSF) proteomics., Methods: Individuals were classified based on CSF amyloid beta (Aβ)1-42 (A) and phosphorylated tau (T), as cognitively normal A-T- (CN), MCI A-T+ (MCI-SNAP), and MCI A+T+ (MCI-AD). Proteomics analyses, Gene Ontology (GO), brain cell expression, and gene expression analyses in brain regions of interest were performed., Results: A total of 96 proteins were decreased in MCI-SNAP compared to CN and MCI-AD. These proteins were enriched for extracellular matrix (ECM), hemostasis, immune system, protein processing/degradation, lipids, and synapse. Fifty-one percent were enriched for expression in the choroid plexus., Conclusion: The pathophysiology of MCI-SNAP (A-T+) is distinct from that of MCI-AD. Our findings highlight the need for a different treatment in MCI-SNAP compared to MCI-AD., (© 2022 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Objective: To examine whether hippocampal volume loss is primarily associated with cognitive status or pathologic β-amyloid 1-42 (Aβ42) levels, this study compared hippocampal subfield volumes between patients with Parkinson disease (PD) with mild cognitive impairment (PD-MCI) and without cognitive impairment (PD-CN) and between patients with low and high Aβ42 levels, in addition exploring the relationship among hippocampal subfield volumes, CSF biomarkers (Aβ42, phosphorylated and total tau), neuropsychological tests, and activities of daily living., Methods: Forty-five patients with PD without dementia underwent CSF analyses and MRI as well as comprehensive motor and neuropsychological examinations. Hippocampal segmentation was conducted using FreeSurfer image analysis suite 6.0. Regression models were used to compare hippocampal subfield volumes between groups, and partial correlations defined the association between variables while controlling for intracranial volume (ICV)., Results: Linear regressions revealed cognitive group as a statistically significant predictor of both the hippocampal-amygdaloid transition area (HATA; β = -0.23, 95% CI -0.44 to -0.02) and the cornu ammonis 1 region (CA1; β = -0.28, 95% confidence interval [CI] -0.56 to -0.02), independent of disease duration and ICV, with patients with PD-MCI showing significantly smaller volumes than PD-CN. In contrast, no subfields were predicted by Aβ42 levels. Smaller hippocampal volumes were associated with worse performance on memory, language, spatial working memory, and executive functioning tests. The subiculum was negatively correlated with total tau levels ( r = -0.37, 95% CI -0.60 to -0.09)., Conclusion: Cognitive status, but not CSF Aβ42, predicted hippocampal volumes, specifically the CA1 and HATA. Hippocampal subfields were associated with various cognitive domains, as well as with tau pathology., (© 2020 American Academy of Neurology.)

Objective: In Parkinson's disease (PD), nonmotor symptoms (NMS) considerably influence disease progression and cognitive decline. Depression, anxiety, sleep disturbances, and hallucinations (DASH), may indicate a risk for dementia (PDD). Mild impairments in activities of daily living (ADL) caused by cognitive dysfunction are also present in the prodromal stage of PDD. The association of both factors has been sparsely investigated. Aim was to evaluate these specific NMS in a large nondemented PD cohort and their co-occurrence with cognitive dysfunction and ADL impairments., Method: Data of 226 PD patients was analyzed. Using corresponding items, two DASH scores were constructed from the NMS-Scale and Parkinson's disease Questionnaire (PDQ-39). Correlations between DASH scores and PDD risk factors were examined. PD patients with mild cognitive impairment (PD-MCI) were additionally split into patients with low and high DASH burden, the latter group additionally stratified by presence of cognitive-driven ADL impairment., Results: DASH-NMS scores differed significantly between PD-MCI and cognitively normal (PD-CN) patients ( p = .04), while the DASH-PDQ did not ( p = .73). The only significant predictor of the DASH-NMS score was cognitive-driven ADL ( p = .01). PD-MCI patients with a high DASH burden and more cognitive ADL impairment presented with worse global cognition than patients with a low burden ( p = .045)., Conclusion: Our results show that the DASH-NMS is superior to the DASH-PDQ score, related to the severity of cognitive impairment, and strongly influenced by cognitive-driven ADL impairment. Presence of DASH symptoms and cognitive-ADL in PD-MCI patients may define a risk group for PDD conversion. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

The core criterion for Parkinson's disease dementia (PDD) is the impairment in activities of daily living (ADL) function primarily caused by cognitive, not motor symptoms. There is evidence to assume that mild ADL impairments in mild cognitive impairment (PD-MCI) characterize those patients at high risk for dementia. Data of 216 Parkinson's disease (PD) patients assessed with comprehensive motor and neuropsychological assessments were analysed. Based on linear regression models, subscores of the Functional Activities Questionnaire (FAQ) primarily reflecting patients' global cognitive status (FAQ C ) or PD-related motor severity (FAQ M ) were developed. A quotient (FAQ Q ) of both scores was calculated, with values >1 indicating more cognitive- compared to motor-driven ADL impairment. Both FAQ C and FAQ M scores were higher in PD-MCI than cognitively normal (PD-CN) patients, indicating more severe cognitive- and motor-driven ADL impairments in this group. One third (31.6%) of the PD-MCI group had a FAQ Q score >1, which was significantly different from patients with PD-CN (p = .02). PD-MCI patients with an FAQ Q score >1 were more impaired on tests assessing attention (p = .019) and language (p = .033) compared to PD-MCI patients with lower FAQ Q values. The differentiation between cognitive- and motor-driven ADL is important, as the loss of functional capacity is the defining factor for a diagnosis of PDD. We were able to differentiate the cognitive-driven from the motor-driven ADL impairments for the FAQ. PD-MCI patients with more cognitive- compared to motor-driven ADL impairments may pose a risk group for conversion to PDD and can be targeted for early treatments., (© 2018 The British Psychological Society.)

Introduction: Plasma biomarkers for Alzheimer's disease (AD) diagnosis/stratification are a "Holy Grail" of AD research and intensively sought; however, there are no well-established plasma markers., Methods: A hypothesis-led plasma biomarker search was conducted in the context of international multicenter studies. The discovery phase measured 53 inflammatory proteins in elderly control (CTL; 259), mild cognitive impairment (MCI; 199), and AD (262) subjects from AddNeuroMed., Results: Ten analytes showed significant intergroup differences. Logistic regression identified five (FB, FH, sCR1, MCP-1, eotaxin-1) that, age/APOε4 adjusted, optimally differentiated AD and CTL (AUC: 0.79), and three (sCR1, MCP-1, eotaxin-1) that optimally differentiated AD and MCI (AUC: 0.74). These models replicated in an independent cohort (EMIF; AUC 0.81 and 0.67). Two analytes (FB, FH) plus age predicted MCI progression to AD (AUC: 0.71)., Discussion: Plasma markers of inflammation and complement dysregulation support diagnosis and outcome prediction in AD and MCI. Further replication is needed before clinical translation., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Introduction: We investigated relations between amyloid-β (Aβ) status, apolipoprotein E (APOE) ε4, and cognition, with cerebrospinal fluid markers of neurogranin (Ng), neurofilament light (NFL), YKL-40, and total tau (T-tau)., Methods: We included 770 individuals with normal cognition, mild cognitive impairment, and Alzheimer's disease (AD)-type dementia from the EMIF-AD Multimodal Biomarker Discovery study. We tested the association of Ng, NFL, YKL-40, and T-tau with Aβ status (Aβ- vs. Aβ+), clinical diagnosis APOE ε4 carriership, baseline cognition, and change in cognition., Results: Ng and T-tau distinguished between Aβ+ from Aβ- individuals in each clinical group, whereas NFL and YKL-40 were associated with Aβ+ in nondemented individuals only. APOE ε4 carriership did not influence NFL, Ng, and YKL-40 in Aβ+ individuals. NFL was the best predictor of cognitive decline in Aβ+ individuals across the cognitive spectrum., Discussion: Axonal degeneration, synaptic dysfunction, astroglial activation, and altered tau metabolism are involved already in preclinical AD. NFL may be a useful prognostic marker., (Copyright © 2019 the Alzheimer's Association. All rights reserved.)

Introduction: The early diagnosis of mild cognitive impairment (PD-MCI) in Parkinson's disease (PD) is essential as it increases the future risk for PD dementia (PDD). Recently, a novel weighting algorithm for the Montreal Cognitive Assessment (MoCA) subtests has been reported, to best discriminate between those with and without cognitive impairment in PD. The aim of our study was to validate this scoring algorithm in a large sample of non-demented PD patients, hypothesizing that the weighted MoCA would have a higher diagnostic accuracy for PD-MCI than the original MoCA., Methods: In 202 non-demented PD patients, we evaluated cognitive status, clinical and demographic data, as well as the MoCA with a weighted and unweighted score. Receiver operating characteristic (ROC) curve analysis was used to evaluate discriminative ability of the MoCA. Group comparisons and ROC analysis were performed for PD-MCI classifications with a cut-off ≤ 1, 1.5, and 2 standard deviation (SD) below appropriate norms., Results: PD-MCI patients scored lower on the weighted than the original MoCA version (p < 0.001) compared to PD patients with normal cognitive function. Areas under the curve only differed significantly for the 2 SD cut-off, leading to an increased sensitivity of the weighted MoCA score (72.9% vs. 70.5%) and specificity compared to the original version (79.0% vs. 65.4%)., Conclusions: Our results indicate better discriminant power for the weighted MoCA compared to the original for more advanced stages of PD-MCI (2 SD cut-off). Future studies are needed to evaluate the predictive value of the weighted MoCA for PDD.

Distinguishing amnestic mild cognitive impairment (MCI) from Alzheimer's disease (AD) and healthy aging depends mainly on clinical evaluation, and, ultimately, on investigator's judgment. Clinical evaluation in vivo is based primarily on cognitive assessments. The present study explores the potential of volumetric magnetic resonance imaging of parietal and lateral temporal brain structures to support the diagnosis of AD and to distinguish AD patients from patients with MCI and healthy control subjects (HCS). 52 age-matched HCS, 18 patients with MCI, and 59 patients with probable late onset AD were investigated. Using computational, neuromorphometric procedures gray matter (GM) was automatically parcellated into 28 local regions of interest, the volumes of which were computed. The left hippocampus (sensitivity/specificity: 80.8-90.4%/55.6-86.4%) and the right hippocampus (73.1-90.4%/66.7-84.7%) provided highest diagnostic accuracy in separating all three diagnostic groups. Promising diagnostic values for distinguishing MCI from HCS were found for the left superior parietal gyrus (61.5%/55.6%) and left supramarginal gyrus (65.4%/66.7%), and for distinguishing subjects with MCI from AD patients for the right middle temporal gyrus (77.8%/79.7%), left inferior temporal gyrus (83.3%/72.9%), and right superior temporal gyrus (77.8%/71.2%). The left superior temporal pole (92.3%/84.7%), left parahippocampal gyrus (86.5%/81.4%), left Heschl's gyrus (86.5%/79.7%), and the right superior temporal pole (82.7%/78.0%) revealed most promising diagnostic values for distinguishing AD patients from HCS. Data revealed that lateral temporal and parietal GM volumes distinguish between HCS, MCI, and AD as accurate as hippocampal volumes do; hence, these volumes can be used in the diagnostic procedure. Results also suggest that cognitive functions associated with these brain regions, e.g., language and visuospatial abilities, may be tested more extensively to obtain additional information that might enhance the diagnostic accuracy further.