Unlocking Preclinical Alzheimer's: A Multi-Year Label-Free In Vitro Raman Spectroscopy Study Empowered by Chemometrics.

Alzheimer's disease is a progressive neurodegenerative disorder, the early detection of which is crucial for timely intervention and enrollment in clinical trials. However, the preclinical diagnosis of Alzheimer's encounters difficulties with gold-standard methods. The current definitive diagnosis of Alzheimer's still relies on expensive instrumentation and post-mortem histological examinations. Here, we explore label-free Raman spectroscopy with machine learning as an alternative to preclinical Alzheimer's diagnosis. A special feature of this study is the inclusion of patient samples from different cohorts, sampled and measured in different years. To develop reliable classification models, partial least squares discriminant analysis in combination with variable selection methods identified discriminative molecules, including nucleic acids, amino acids, proteins, and carbohydrates such as taurine/hypotaurine and guanine, when applied to Raman spectra taken from dried samples of cerebrospinal fluid. The robustness of the model is remarkable, as the discriminative molecules could be identified in different cohorts and years. A unified model notably classifies preclinical Alzheimer's, which is particularly surprising because of Raman spectroscopy's high sensitivity regarding different measurement conditions. The presented results demonstrate the capability of Raman spectroscopy to detect preclinical Alzheimer's disease for the first time and offer invaluable opportunities for future clinical applications and diagnostic methods.

GOIZ ZAINDU study: a FINGER-like multidomain lifestyle intervention feasibility randomized trial to prevent dementia in Southern Europe.

BACKGROUND: GOIZ ZAINDU ("caring early" in Basque) is a pilot study to adapt the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) methodology to the Basque population and evaluate the feasibility and adherence to a FINGER-like multidomain intervention program. Additional aims included the assessment of efficacy on cognition and data collection to design a large efficacy trial. METHOD: GOIZ ZAINDU is a 1-year, randomized, controlled trial of a multidomain intervention in persons aged 60+ years, with Cardiovascular Risk Factors, Aging and Dementia (CAIDE) risk score ≥ 6, no diagnosis of dementia, and below-than-expected performance in at least one of three cognitive screening tests. Randomization to a multidomain intervention (MD-Int) or regular health advice (RHA) was stratified by sex, age (>/≤ 75), and cognitive status (mild cognitive impairment (MCI)/normal cognition). MD-Int included cardiovascular risk factor control, nutritional counseling, physical activity, and cognitive training. The primary outcomes were retention rate and adherence to the intervention program. Exploratory cognitive outcomes included changes in the Neuropsychological Test Battery z-scores. Analyses were performed according to the intention to treat. RESULTS: One hundred twenty-five participants were recruited (mean age: 75.64 (± 6.46); 58% women). The MD-Int (n = 61) and RHA (n = 64) groups were balanced in terms of their demographics and cognition. Fifty-two (85%) participants from the RHA group and 56 (88%) from the MD-Int group completed the study. More than 70% of the participants had high overall adherence to the intervention activities. The risk of cognitive decline was higher in the RHA group than in the MD-Int group in terms of executive function (p =.019) and processing speed scores (p =.026). CONCLUSIONS: The GOIZ-ZAINDU study proved that the FINGER methodology is adaptable and feasible in a different socio-cultural environment. The exploratory efficacy results showed a lower risk of decline in executive function and processing speed in the intervention group. These results support the design of a large-scale efficacy trial. TRIAL REGISTRATION: GOIZ ZAINDU feasibility trial was approved and registered by the Euskadi Drug Research Ethics Committee (ID: PI2017134) on 23 January 2018. Retrospectively registered in ClinicalTrials.gov (NCT06163716) on 8 December 2023.

Multivariate GWAS of Alzheimer's disease CSF biomarker profiles implies GRIN2D in synaptic functioning.

BACKGROUND: Genome-wide association studies (GWAS) of Alzheimer's disease (AD) have identified several risk loci, but many remain unknown. Cerebrospinal fluid (CSF) biomarkers may aid in gene discovery and we previously demonstrated that six CSF biomarkers (ß-amyloid, total/phosphorylated tau, NfL, YKL-40, and neurogranin) cluster into five principal components (PC), each representing statistically independent biological processes. Here, we aimed to (1) identify common genetic variants associated with these CSF profiles, (2) assess the role of associated variants in AD pathophysiology, and (3) explore potential sex differences. METHODS: We performed GWAS for each of the five biomarker PCs in two multi-center studies (EMIF-AD and ADNI). In total, 973 participants (n = 205 controls, n = 546 mild cognitive impairment, n = 222 AD) were analyzed for 7,433,949 common SNPs and 19,511 protein-coding genes. Structural equation models tested whether biomarker PCs mediate genetic risk effects on AD, and stratified and interaction models probed for sex-specific effects. RESULTS: Five loci showed genome-wide significant association with CSF profiles, two were novel (rs145791381 [inflammation] and GRIN2D [synaptic functioning]) and three were previously described (APOE, TMEM106B, and CHI3L1). Follow-up analyses of the two novel signals in independent datasets only supported the GRIN2D locus, which contains several functionally interesting candidate genes. Mediation tests indicated that variants in APOE are associated with AD status via processes related to amyloid and tau pathology, while markers in TMEM106B and CHI3L1 are associated with AD only via neuronal injury/inflammation. Additionally, seven loci showed sex-specific associations with AD biomarkers. CONCLUSIONS: These results suggest that pathway and sex-specific analyses can improve our understanding of AD genetics and may contribute to precision medicine.

Whole-exome rare-variant analysis of Alzheimer's disease and related biomarker traits.

INTRODUCTION: Despite increasing evidence of a role of rare genetic variation in the risk of Alzheimer's disease (AD), limited attention has been paid to its contribution to AD-related biomarker traits indicative of AD-relevant pathophysiological processes. METHODS: We performed whole-exome gene-based rare-variant association studies (RVASs) of 17 AD-related traits on whole-exome sequencing (WES) data generated in the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD) study (n = 450) and whole-genome sequencing (WGS) data from ADNI (n = 808). RESULTS: Mutation screening revealed a novel probably pathogenic mutation (PSEN1 p.Leu232Phe). Gene-based RVAS revealed the exome-wide significant contribution of rare coding variation in RBKS and OR7A10 to cognitive performance and protection against left hippocampal atrophy, respectively. DISCUSSION: The identification of these novel gene-trait associations offers new perspectives into the role of rare coding variation in the distinct pathophysiological processes culminating in AD, which may lead to identification of novel therapeutic and diagnostic targets.

Estimation of the epidemiology of dementia and associated neuropsychiatric symptoms by applying machine learning to real-world data.

INTRODUCTION: Incidence rates of dementia-related neuropsychiatric symptoms (NPS) are not known and this hampers the assessment of their population burden. The objective of this study was to obtain an approximate estimate of the population incidence and prevalence of both dementia and NPS. METHODS: Given the dynamic nature of the population with dementia, a retrospective study was conducted within the database of the Basque Health Service (real-world data) at the beginning and end of 2019. Validated random forest models were used to identify separately depressive and psychotic clusters according to their presence in the electronic health records of all patients diagnosed with dementia. RESULTS: Among the 631,949 individuals over 60 years registered, 28,563 were diagnosed with dementia, of whom 15,828 (55.4%) showed psychotic symptoms and 19,461 (68.1%) depressive symptoms. The incidence of dementia in 2019 was 6.8/1000 person-years. Most incident cases of depressive (72.3%) and psychotic (51.9%) NPS occurred in cases of incident dementia. The risk of depressive-type NPS grows with years since dementia diagnosis, living in a nursing home, and female sex, but falls with older age. In the psychotic cluster model, the effects of male sex, and older age are inverted, both increasing the probability of this type of symptoms. CONCLUSIONS: The stigmatization factor conditions the social and attitudinal environment, delaying the diagnosis of dementia, preventing patients from receiving adequate care and exacerbating families' suffering. This study evidences the synergy between big data and real-world data for psychiatric epidemiological research.

Cerebrospinal fluid proteomic profiling of individuals with mild cognitive impairment and suspected non-Alzheimer's disease pathophysiology.

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 (Aß)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.

Genome-Wide Association Study of Alzheimer's Disease Brain Imaging Biomarkers and Neuropsychological Phenotypes in the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery Dataset.

Alzheimer's disease (AD) is the most frequent neurodegenerative disease with an increasing prevalence in industrialized, aging populations. AD susceptibility has an established genetic basis which has been the focus of a large number of genome-wide association studies (GWAS) published over the last decade. Most of these GWAS used dichotomized clinical diagnostic status, i.e., case vs. control classification, as outcome phenotypes, without the use of biomarkers. An alternative and potentially more powerful study design is afforded by using quantitative AD-related phenotypes as GWAS outcome traits, an analysis paradigm that we followed in this work. Specifically, we utilized genotype and phenotype data from n = 931 individuals collected under the auspices of the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD) study to perform a total of 19 separate GWAS analyses. As outcomes we used five magnetic resonance imaging (MRI) traits and seven cognitive performance traits. For the latter, longitudinal data from at least two timepoints were available in addition to cross-sectional assessments at baseline. Our GWAS analyses revealed several genome-wide significant associations for the neuropsychological performance measures, in particular those assayed longitudinally. Among the most noteworthy signals were associations in or near EHBP1 (EH domain binding protein 1; on chromosome 2p15) and CEP112 (centrosomal protein 112; 17q24.1) with delayed recall as well as SMOC2 (SPARC related modular calcium binding 2; 6p27) with immediate recall in a memory performance test. On the X chromosome, which is often excluded in other GWAS, we identified a genome-wide significant signal near IL1RAPL1 (interleukin 1 receptor accessory protein like 1; Xp21.3). While polygenic score (PGS) analyses showed the expected strong associations with SNPs highlighted in relevant previous GWAS on hippocampal volume and cognitive function, they did not show noteworthy associations with recent AD risk GWAS findings. In summary, our study highlights the power of using quantitative endophenotypes as outcome traits in AD-related GWAS analyses and nominates several new loci not previously implicated in cognitive decline.

Rare variants in IFFO1, DTNB, NLRC3 and SLC22A10 associate with Alzheimer's disease CSF profile of neuronal injury and inflammation.

Alzheimer's disease (AD) biomarkers represent several neurodegenerative processes, such as synaptic dysfunction, neuronal inflammation and injury, as well as amyloid pathology. We performed an exome-wide rare variant analysis of six AD biomarkers (ß-amyloid, total/phosphorylated tau, NfL, YKL-40, and Neurogranin) to discover genes associated with these markers. Genetic and biomarker information was available for 480 participants from two studies: EMIF-AD and ADNI. We applied a principal component (PC) analysis to derive biomarkers combinations, which represent statistically independent biological processes. We then tested whether rare variants in 9576 protein-coding genes associate with these PCs using a Meta-SKAT test. We also tested whether the PCs are intermediary to gene effects on AD symptoms with a SMUT test. One PC loaded on NfL and YKL-40, indicators of neuronal injury and inflammation. Four genes were associated with this PC: IFFO1, DTNB, NLRC3, and SLC22A10. Mediation tests suggest, that these genes also affect dementia symptoms via inflammation/injury. We also observed an association between a PC loading on Neurogranin, a marker for synaptic functioning, with GABBR2 and CASZ1, but no mediation effects. The results suggest that rare variants in IFFO1, DTNB, NLRC3, and SLC22A10 heighten susceptibility to neuronal injury and inflammation, potentially by altering cytoskeleton structure and immune activity disinhibition, resulting in an elevated dementia risk. GABBR2 and CASZ1 were associated with synaptic functioning, but mediation analyses suggest that the effect of these two genes on synaptic functioning is not consequential for AD development.

Sex-Specific Metabolic Pathways Were Associated with Alzheimer's Disease (AD) Endophenotypes in the European Medical Information Framework for AD Multimodal Biomarker Discovery Cohort.

BACKGROUND: physiological differences between males and females could contribute to the development of Alzheimer's Disease (AD). Here, we examined metabolic pathways that may lead to precision medicine initiatives. METHODS: We explored whether sex modifies the association of 540 plasma metabolites with AD endophenotypes including diagnosis, cerebrospinal fluid (CSF) biomarkers, brain imaging, and cognition using regression analyses for 695 participants (377 females), followed by sex-specific pathway overrepresentation analyses, APOE ε4 stratification and assessment of metabolites' discriminatory performance in AD. RESULTS: In females with AD, vanillylmandelate (tyrosine pathway) was increased and tryptophan betaine (tryptophan pathway) was decreased. The inclusion of these two metabolites (area under curve (AUC) = 0.83, standard error (SE) = 0.029) to a baseline model (covariates + CSF biomarkers, AUC = 0.92, SE = 0.019) resulted in a significantly higher AUC of 0.96 (SE = 0.012). Kynurenate was decreased in males with AD (AUC = 0.679, SE = 0.046). CONCLUSIONS: metabolic sex-specific differences were reported, covering neurotransmission and inflammation pathways with AD endophenotypes. Two metabolites, in pathways related to dopamine and serotonin, were associated to females, paving the way to personalised treatment.

TMEM106B and CPOX are genetic determinants of cerebrospinal fluid Alzheimer's disease biomarker levels.

INTRODUCTION: Neurofilament light (NfL), chitinase-3-like protein 1 (YKL-40), and neurogranin (Ng) are biomarkers for Alzheimer's disease (AD) to monitor axonal damage, astroglial activation, and synaptic degeneration, respectively. METHODS: We performed genome-wide association studies (GWAS) using DNA and cerebrospinal fluid (CSF) samples from the EMIF-AD Multimodal Biomarker Discovery study for discovery, and the Alzheimer's Disease Neuroimaging Initiative study for validation analyses. GWAS were performed for all three CSF biomarkers using linear regression models adjusting for relevant covariates. RESULTS: We identify novel genome-wide significant associations between DNA variants in TMEM106B and CSF levels of NfL, and between CPOX and YKL-40. We confirm previous work suggesting that YKL-40 levels are associated with DNA variants in CHI3L1. DISCUSSION: Our study provides important new insights into the genetic architecture underlying interindividual variation in three AD-related CSF biomarkers. In particular, our data shed light on the sequence of events regarding the initiation and progression of neuropathological processes relevant in AD.

Replication study of plasma proteins relating to Alzheimer's pathology.

INTRODUCTION: This study sought to discover and replicate plasma proteomic biomarkers relating to Alzheimer's disease (AD) including both the "ATN" (amyloid/tau/neurodegeneration) diagnostic framework and clinical diagnosis. METHODS: Plasma proteins from 972 subjects (372 controls, 409 mild cognitive impairment [MCI], and 191 AD) were measured using both SOMAscan and targeted assays, including 4001 and 25 proteins, respectively. RESULTS: Protein co-expression network analysis of SOMAscan data revealed the relation between proteins and "N" varied across different neurodegeneration markers, indicating that the ATN variants are not interchangeable. Using hub proteins, age, and apolipoprotein E ε4 genotype discriminated AD from controls with an area under the curve (AUC) of 0.81 and MCI convertors from non-convertors with an AUC of 0.74. Targeted assays replicated the relation of four proteins with the ATN framework and clinical diagnosis. DISCUSSION: Our study suggests that blood proteins can predict the presence of AD pathology as measured in the ATN framework as well as clinical diagnosis.

Estimation of the epidemiology of dementia and associated neuropsychiatric symptoms by applying machine learning to real-world data.

INTRODUCTION: Incidence rates of dementia-related neuropsychiatric symptoms (NPS) are not known and this hampers the assessment of their population burden. The objective of this study was to obtain an approximate estimate of the population incidence and prevalence of both dementia and NPS. METHODS: Given the dynamic nature of the population with dementia, a retrospective study was conducted within the database of the Basque Health Service (real-world data) at the beginning and end of 2019. Validated random forest models were used to identify separately depressive and psychotic clusters according to their presence in the electronic health records of all patients diagnosed with dementia. RESULTS: Among the 631,949 individuals over 60 years registered, 28,563 were diagnosed with dementia, of whom 15,828 (55.4%) showed psychotic symptoms and 19,461 (68.1%) depressive symptoms. The incidence of dementia in 2019 was 6.8/1000 person-years. Most incident cases of depressive (72.3%) and psychotic (51.9%) NPS occurred in cases of incident dementia. The risk of depressive-type NPS grows with years since dementia diagnosis, living in a nursing home, and female sex, but falls with older age. In the psychotic cluster model, the effects of male sex, and older age are inverted, both increasing the probability of this type of symptoms. CONCLUSIONS: The stigmatization factor conditions the social and attitudinal environment, delaying the diagnosis of dementia, preventing patients from receiving adequate care and exacerbating families' suffering. This study evidences the synergy between big data and real-world data for psychiatric epidemiological research.

Mediterranean Diet and Risk of Dementia and Alzheimer's Disease in the EPIC-Spain Dementia Cohort Study.

The Mediterranean diet (MD) has shown to reduce the occurrence of several chronic diseases. To evaluate its potential protective role on dementia incidence we studied 16,160 healthy participants from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Spain Dementia Cohort study recruited between 1992-1996 and followed up for a mean (±SD) of 21.6 (±3.4) years. A total of 459 incident cases of dementia were ascertained through expert revision of medical records. Data on habitual diet was collected through a validated diet history method to assess adherence to the relative Mediterranean Diet (rMED) score. Hazard ratios (HR) of dementia by rMED levels (low, medium and high adherence levels: ≤6, 7-10 and ≥11 points, respectively) were estimated using multivariable Cox models, whereas time-dependent effects were evaluated using flexible parametric Royston-Parmar (RP) models. Results of the fully adjusted model showed that high versus low adherence to the categorical rMED score was associated with a 20% (HR = 0.80, 95%CI: 0.60-1.06) lower risk of dementia overall and HR of dementia was 8% (HR = 0.92, 0.85-0.99, p = 0.021) lower for each 2-point increment of the continuous rMED score. By sub-types, a favorable association was also found in women for non-AD (HR per 2-points = 0.74, 95%CI: 0.62-0.89), while not statistically significant in men for AD (HR per 2-points = 0.88, 0.76-1.01). The association was stronger in participants with lower education. In conclusion, in this large prospective cohort study MD was inversely associated with dementia incidence after accounting for major cardiovascular risk factors. The results differed by dementia sub-type, sex, and education but there was no significant evidence of effect modification.

Plasma lipids are associated with white matter microstructural changes and axonal degeneration.

Dislipidemia is a risk factor for cognitive impairment. We studied the association between interindividual variability of plasma lipids and white matter (WM) microstructure, using diffusion tensor imaging (DTI) in 273 healthy adults. Special focus was placed on 7 regions of interest (ROI) which are structural components of cognitive neurocircuitry. We also investigated the effect of plasma lipids on cerebrospinal fluid (CSF) neurofilament light chain (NfL), an axonal degeneration marker. Low density lipoprotein (LDL) and triglyceride (TG) levels showed a negative association with axial diffusivity (AxD) in multiple regions. High density lipoproteins (HDL) showed a positive correlation. The association was independent of Apolipoprotein E (APOE) genotype, blood pressure or use of statins. LDL moderated the relation between NfL and AxD in the body of the corpus callosum (p = 0.041), right cingulum gyrus (p = 0.041), right fornix/stria terminalis (p = 0.025) and right superior longitudinal fasciculus (p = 0.020) and TG in the right inferior longitudinal fasciculus (p = 0.004) and left fornix/stria terminalis (p = 0.001). We conclude that plasma lipids are associated to WM microstructural changes and axonal degeneration and might represent a risk factor in the transition from healthy aging to disease.

Genome-wide association study of Alzheimer's disease CSF biomarkers in the EMIF-AD Multimodal Biomarker Discovery dataset.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Susceptibility to AD is considerably determined by genetic factors which hitherto were primarily identified using case-control designs. Elucidating the genetic architecture of additional AD-related phenotypic traits, ideally those linked to the underlying disease process, holds great promise in gaining deeper insights into the genetic basis of AD and in developing better clinical prediction models. To this end, we generated genome-wide single-nucleotide polymorphism (SNP) genotyping data in 931 participants of the European Medical Information Framework Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD) sample to search for novel genetic determinants of AD biomarker variability. Specifically, we performed genome-wide association study (GWAS) analyses on 16 traits, including 14 measures derived from quantifications of five separate amyloid-beta (Aß) and tau-protein species in the cerebrospinal fluid (CSF). In addition to confirming the well-established effects of apolipoprotein E (APOE) on diagnostic outcome and phenotypes related to Aß42, we detected novel potential signals in the zinc finger homeobox 3 (ZFHX3) for CSF-Aß38 and CSF-Aß40 levels, and confirmed the previously described sex-specific association between SNPs in geminin coiled-coil domain containing (GMNC) and CSF-tau. Utilizing the results from independent case-control AD GWAS to construct polygenic risk scores (PRS) revealed that AD risk variants only explain a small fraction of CSF biomarker variability. In conclusion, our study represents a detailed first account of GWAS analyses on CSF-Aß and -tau-related traits in the EMIF-AD MBD dataset. In subsequent work, we will utilize the genomics data generated here in GWAS of other AD-relevant clinical outcomes ascertained in this unique dataset.

Incidence of Dementia and Associated Factors in the EPIC-Spain Dementia Cohort.

BACKGROUND: Dementia has become a public health priority as the number of cases continues to grow worldwide. OBJECTIVE: To assess dementia incidence and determinants in the EPIC-Spain Dementia Cohort. METHODS: 25,015 participants (57% women) were recruited from three Spanish regions between 1992-1996 and followed-up for over 20 years. Incident cases were ascertained through individual revision of medical records of potential cases. Crude and age-adjusted incidence rates (IR) of dementia and sub-types (Alzheimer's disease (AD), and non-AD) were calculated by sex. Neelson-Aalen cumulative incidence estimates at 10, 15, and 20 years were obtained for each sex and age group. Multivariate Royston-Parmar models were used to assess independent determinants. RESULTS: Global IR were higher in women for dementia and AD, and similar by sex for non-AD. IR ranged from 0.09 cases of dementia (95% confidence interval: 0.06-0.13) and 0.05 (0.03-0.09) of AD per 1000 person-years (py) in participants below 60 years, to 23.2 (15.9-33.8) cases of dementia and 14.6 (9.1-33.5) of AD (per 1000 py) in those ≥85 years. Adjusted IR were consistently higher in women than men for overall dementia and AD. Up to 12.5% of women and 9.1% of men 60-65 years-old developed dementia within 20 years. Low education, diabetes, and hyperlipidemia were the main independent predictors of dementia risk, whereas alcohol showed an inverse association. CONCLUSION: Dementia incidence increased with age and was higher among women, but showed no geographical pattern. Dementia risk was higher among subjects with lower education, not drinking alcohol, and presenting cardiovascular risk factors.

Validation of Random Forest Machine Learning Models to Predict Dementia-Related Neuropsychiatric Symptoms in Real-World Data.

BACKGROUND: Neuropsychiatric symptoms (NPS) are the leading cause of the social burden of dementia but their role is underestimated. OBJECTIVE: The objective of the study was to validate predictive models to separately identify psychotic and depressive symptoms in patients diagnosed with dementia using clinical databases representing the whole population to inform decision-makers. METHODS: First, we searched the electronic health records of 4,003 patients with dementia to identify NPS. Second, machine learning (random forest) algorithms were applied to build separate predictive models for psychotic and depressive symptom clusters in the training set (N = 3,003). Third, calibration and discrimination were assessed in the test set (N = 1,000) to assess the performance of the models. RESULTS: Neuropsychiatric symptoms were noted in the electronic health record of 58% of patients. The area under the receiver operating curve reached 0.80 for the psychotic cluster model and 0.74 for the depressive cluster model. The Kappa index and accuracy also showed better discrimination in the psychotic model. Calibration plots indicated that both types of model had less predictive accuracy when the probability of neuropsychiatric symptoms was <25%. The most important variables in the psychotic cluster model were use of risperidone, level of sedation, use of quetiapine and haloperidol and the number of antipsychotics prescribed. In the depressive cluster model, the most important variables were number of antidepressants prescribed, escitalopram use, level of sedation, and age. CONCLUSION: Given their relatively good performance, the predictive models can be used to estimate prevalence of NPS in population databases.

Dickkopf-1 Overexpression in vitro Nominates Candidate Blood Biomarkers Relating to Alzheimer's Disease Pathology.

BACKGROUND: Previous studies suggest that Dickkopf-1 (DKK1), an inhibitor of Wnt signaling, plays a role in amyloid-induced toxicity and hence Alzheimer's disease (AD). However, the effect of DKK1 expression on protein expression, and whether such proteins are altered in disease, is unknown. OBJECTIVE: We aim to test whether DKK1 induced protein signature obtained in vitro were associated with markers of AD pathology as used in the amyloid/tau/neurodegeneration (ATN) framework as well as with clinical outcomes. METHODS: We first overexpressed DKK1 in HEK293A cells and quantified 1,128 proteins in cell lysates using aptamer capture arrays (SomaScan) to obtain a protein signature induced by DKK1. We then used the same assay to measure the DKK1-signature proteins in human plasma in two large cohorts, EMIF (n = 785) and ANM (n = 677). RESULTS: We identified a 100-protein signature induced by DKK1 in vitro. Subsets of proteins, along with age and apolipoprotein E ɛ4 genotype distinguished amyloid pathology (A + T-N-, A+T+N-, A+T-N+, and A+T+N+) from no AD pathology (A-T-N-) with an area under the curve of 0.72, 0.81, 0.88, and 0.85, respectively. Furthermore, we found that some signature proteins (e.g., Complement C3 and albumin) were associated with cognitive score and AD diagnosis in both cohorts. CONCLUSIONS: Our results add further evidence for a role of DKK regulation of Wnt signaling in AD and suggest that DKK1 induced signature proteins obtained in vitro could reflect theATNframework as well as predict disease severity and progression in vivo.

Cerebrospinal Fluid 7-Ketocholesterol Level is Associated with Amyloid-ß42 and White Matter Microstructure in Cognitively Healthy Adults.

BACKGROUND: Abnormal cholesterol metabolism changes the neuronal membrane and may promote amyloidogenesis. Oxysterols in cerebrospinal fluid (CSF) are related to Alzheimer's disease (AD) biomarkers in mild cognitive impairment and dementia. Cholesterol turnover is important for axonal and white matter (WM) microstructure maintenance. OBJECTIVE: We aim to demonstrate that the association of oxysterols, AD biomarkers, and WM microstructure occurs early in asymptomatic individuals. METHODS: We studied the association of inter-individual variability of CSF 24-hydroxycholesterol (24-OHC), 27-hydroxycholesterol (27-OHC), 7-ketocholesterol (7-KC), 7ß-hydroxycholesterol (7ß-OHC), amyloid-ß42 (Aß42), total-tau (t-tau), phosphorylated-tau (p-tau), neurofilament (NfL), and WM microstructure using diffusion tensor imaging, generalized linear models and moderation/mediation analyses in 153 healthy adults. RESULTS: Higher 7-KC levels were related to lower Aß42, indicative of greater AD pathology (p = 0.041) .  Higher 7-KC levels were related to lower fractional anisotropy (FA) and higher mean (MD), axial (AxD), and radial (RD) diffusivity. 7-KC modulated the association between AxD and NfL in the corpus callosum splenium (B = 39.39, p = 0.017), genu (B = 68.64, p = 0.000), and fornix (B = 10.97, p = 0.000). Lower Aß42 levels were associated to lower FA and higher MD, AxD, and RD in the fornix, corpus callosum, inferior longitudinal fasciculus, and hippocampus. The association between AxD and Aß42 was moderated by 7K-C (p = 0.048). CONCLUSION: This study adds clinical evidence to support the role of 7K-C on axonal integrity and the involvement of cholesterol metabolism in the Aß42 generation process.

Validation of Plasma Proteomic Biomarkers Relating to Brain Amyloid Burden in the EMIF-Alzheimer's Disease Multimodal Biomarker Discovery Cohort.

We have previously investigated, discovered, and replicated plasma protein biomarkers for use to triage potential trials participants for PET or cerebrospinal fluid measures of Alzheimer's disease (AD) pathology. This study sought to undertake validation of these candidate plasma biomarkers in a large, multi-center sample collection. Targeted plasma analyses of 34 proteins with prior evidence for prediction of in vivo pathology were conducted in up to 1,000 samples from cognitively healthy elderly individuals, people with mild cognitive impairment, and in patients with AD-type dementia, selected from the EMIF-AD catalogue. Proteins were measured using Luminex xMAP, ELISA, and Meso Scale Discovery assays. Seven proteins replicated in their ability to predict in vivo amyloid pathology. These proteins form a biomarker panel that, along with age, could significantly discriminate between individuals with high and low amyloid pathology with an area under the curve of 0.74. The performance of this biomarker panel remained consistent when tested in apolipoprotein E ɛ4 non-carrier individuals only. This blood-based panel is biologically relevant, measurable using practical immunocapture arrays, and could significantly reduce the cost incurred to clinical trials through screen failure.