Matrix metalloproteinase 10 is linked to the risk of progression to dementia of the Alzheimer's type.

Alzheimer's disease has a long asymptomatic phase that offers a substantial time window for intervention. Using this window of opportunity will require early diagnostic and prognostic biomarkers to detect Alzheimer's disease pathology at predementia stages, thus allowing identification of patients who will most probably progress to dementia of the Alzheimer's type and benefit from specific disease-modifying therapies. Consequently, we searched for CSF proteins associated with disease progression along with the clinical disease staging. We measured the levels of 184 proteins in CSF samples from 556 subjective cognitive decline and mild cognitive impairment patients from three independent memory clinic longitudinal studies (Spanish ACE, n = 410; German DCN, n = 93; German Mannheim, n = 53). We evaluated the association between protein levels and clinical stage, and the effect of protein levels on the progression from mild cognitive impairment to dementia of the Alzheimer's type. Mild cognitive impairment subjects with increased CSF level of matrix metalloproteinase 10 (MMP-10) showed a higher probability of progressing to dementia of the Alzheimer's type and a faster cognitive decline. CSF MMP-10 increased the prediction accuracy of CSF amyloid-ß 42 (Aß42), phospho-tau 181 (P-tau181) and total tau (T-tau) for conversion to dementia of the Alzheimer's type. Including MMP-10 to the [A/T/(N)] scheme improved considerably the prognostic value in mild cognitive impairment patients with abnormal Aß42, but normal P-tau181 and T-tau, and in mild cognitive impairment patients with abnormal Aß42, P-tau181 and T-tau. MMP-10 was correlated with age in subjects with normal Aß42, P-tau181 and T-tau levels. Our findings support the use of CSF MMP-10 as a prognostic marker for dementia of the Alzheimer's type and its inclusion in the [A/T/(N)] scheme to incorporate pathologic aspects beyond amyloid and tau. CSF level of MMP-10 may reflect ageing and neuroinflammation.

Plasma extracellular vesicles reveal early molecular differences in amyloid positive patients with early-onset mild cognitive impairment.

In the clinical course of Alzheimer's disease (AD) development, the dementia phase is commonly preceded by a prodromal AD phase, which is mainly characterized by reaching the highest levels of Aß and p-tau-mediated neuronal injury and a mild cognitive impairment (MCI) clinical status. Because of that, most AD cases are diagnosed when neuronal damage is already established and irreversible. Therefore, a differential diagnosis of MCI causes in these prodromal stages is one of the greatest challenges for clinicians. Blood biomarkers are emerging as desirable tools for pre-screening purposes, but the current results are still being analyzed and much more data is needed to be implemented in clinical practice. Because of that, plasma extracellular vesicles (pEVs) are gaining popularity as a new source of biomarkers for the early stages of AD development. To identify an exosome proteomics signature linked to prodromal AD, we performed a cross-sectional study in a cohort of early-onset MCI (EOMCI) patients in which 184 biomarkers were measured in pEVs, cerebrospinal fluid (CSF), and plasma samples using multiplex PEA technology of Olink© proteomics. The obtained results showed that proteins measured in pEVs from EOMCI patients with established amyloidosis correlated with CSF p-tau181 levels, brain ventricle volume changes, brain hyperintensities, and MMSE scores. In addition, the correlations of pEVs proteins with different parameters distinguished between EOMCI Aß( +) and Aß(-) patients, whereas the CSF or plasma proteome did not. In conclusion, our findings suggest that pEVs may be able to provide information regarding the initial amyloidotic changes of AD. Circulating exosomes may acquire a pathological protein signature of AD before raw plasma, becoming potential biomarkers for identifying subjects at the earliest stages of AD development.

The Synergic Effect of AT(N) Profiles and Depression on the Risk of Conversion to Dementia in Patients with Mild Cognitive Impairment.

Few studies have addressed the impact of the association between Alzheimer's disease (AD) biomarkers and NPSs in the conversion to dementia in patients with mild cognitive impairment (MCI), and no studies have been conducted on the interaction effect of these two risk factors. AT(N) profiles were created using AD-core biomarkers quantified in cerebrospinal fluid (CSF) (normal, brain amyloidosis, suspected non-Alzheimer pathology (SNAP) and prodromal AD). NPSs were assessed using the Neuropsychiatric Inventory Questionnaire (NPI-Q). A total of 500 individuals with MCI were followed-up yearly in a memory unit. Cox regression analysis was used to determine risk of conversion, considering additive and multiplicative interactions between AT(N) profile and NPSs on the conversion to dementia. A total of 224 participants (44.8%) converted to dementia during the 2-year follow-up study. Pathologic AT(N) groups (brain amyloidosis, prodromal AD and SNAP) and the presence of depression and apathy were associated with a higher risk of conversion to dementia. The additive combination of the AT(N) profile with depression exacerbates the risk of conversion to dementia. A synergic effect of prodromal AD profile with depressive symptoms is evidenced, identifying the most exposed individuals to conversion among MCI patients.

Genome-wide meta-analysis for Alzheimer's disease cerebrospinal fluid biomarkers.

Amyloid-beta 42 (Aß42) and phosphorylated tau (pTau) levels in cerebrospinal fluid (CSF) reflect core features of the pathogenesis of Alzheimer's disease (AD) more directly than clinical diagnosis. Initiated by the European Alzheimer & Dementia Biobank (EADB), the largest collaborative effort on genetics underlying CSF biomarkers was established, including 31 cohorts with a total of 13,116 individuals (discovery n = 8074; replication n = 5042 individuals). Besides the APOE locus, novel associations with two other well-established AD risk loci were observed; CR1 was shown a locus for Aß42 and BIN1 for pTau. GMNC and C16orf95 were further identified as loci for pTau, of which the latter is novel. Clustering methods exploring the influence of all known AD risk loci on the CSF protein levels, revealed 4 biological categories suggesting multiple Aß42 and pTau related biological pathways involved in the etiology of AD. In functional follow-up analyses, GMNC and C16orf95 both associated with lateral ventricular volume, implying an overlap in genetic etiology for tau levels and brain ventricular volume.

Validation and Normative Data of the Spanish Version of the Face Name Associative Memory Exam (S-FNAME).

OBJECTIVE: The relevance of the episodic memory in the prediction of brain aging is well known. The Face Name Associative Memory Exam (FNAME) is a valued associative memory measure related to Alzheimer's disease (AD) biomarkers, such as amyloid-ß deposition preclinical AD individuals. Previous validation of the Spanish version of the FNAME test (S-FNAME) provided normative data and psychometric characteristics. The study was limited to subjects attending a memory clinic and included a reduced sample with gender inequality distribution. The purpose of this study was to assess S-FNAME psychometric properties and provide normative data in a larger independent sample of cognitively healthy individuals. METHOD: S-FNAME was administered to 511 cognitively healthy volunteers (242 women, aged 41-65 years) participating in the Barcelona Brain Health Initiative cohort study. RESULTS: Factor analysis supported construct validity revealing two underlying components: face-name and face-occupation and explaining 95.34% of the total variance, with satisfactory goodness of fit. Correlations between S-FNAME and Rey Auditory-Verbal Learning Test were statistically significant and confirmed its convergent validity. We also found weak correlations with non-memory tests supporting divergent validity. Women showed better scores, and S-FNAME was positively correlated with education and negatively with age. Finally, we generated normative data. CONCLUSIONS: The S-FNAME test exhibits good psychometric properties, consistent with previous findings, resulting in a valid and reliable tool to assess episodic memory in cognitively healthy middle-aged adults. It is a promising test for the early detection of subtle memory dysfunction associated with abnormal brain aging.

Neuropsychological, neuropsychiatric, and quality-of-life assessments in Alzheimer's disease patients treated with plasma exchange with albumin replacement from the randomized AMBAR study.

INTRODUCTION: We report the effects of plasma exchange (PE) with albumin replacement on neuropsychological, neuropsychiatric, and quality-of-life (QoL) outcomes in mild-to-moderate Alzheimer's disease (AD) patients in a phase 2b/3 trial (Alzheimer's Management by Albumin Replacement [AMBAR] study). METHODS: Three hundred forty-seven patients were randomized into placebo (sham-PE) and three PE-treatment arms with low/high doses of albumin, with/without intravenous immunoglobulin (IVIG). Specific test measurements were performed at baseline; month 2 (weekly conventional PE); months 6, 9, and 12 (monthly low-volume PE [LVPE]); and month 14. RESULTS: The PE-treated mild-AD cohort improved their language fluency and processing speed versus placebo at month 14 (effect sizes: >100%; P-values: .03 to .001). The moderate-AD cohort significantly improved short-term verbal memory (effect sizes: 94% to >100%; P-values: .02 to .003). The progression of the neuropsychiatric symptoms of PE-treated was similar to placebo. Mild-AD patients showed improved QoL (P-values: .04 to .008). DISCUSSION: PE-treated AD patients showed improvement in memory, language abilities, processing speed, and QoL-AD. No worsening of their psychoaffective status was observed.

Complementary pre-screening strategies to uncover hidden prodromal and mild Alzheimer's disease: Results from the MOPEAD project.

INTRODUCTION: The Models of Patient Engagement for Alzheimer's Disease (MOPEAD) project was conceived to explore innovative complementary strategies to uncover hidden prodromal and mild Alzheimer's disease (AD) dementia cases and to raise awareness both in the general public and among health professionals about the importance of early diagnosis. METHODS: Four different strategies or RUNs were used: (a) a web-based (WB) prescreening tool, (2) an open house initiative (OHI), (3) a primary care-based protocol for early detection of cognitive decline (PC), and (4) a tertiary care-based pre-screening at diabetologist clinics (DC). RESULTS: A total of 1129 patients at high risk of having prodromal AD or dementia were identified of 2847 pre-screened individuals (39.7%). The corresponding proportion for the different initiatives were 36.8% (WB), 35.6% (OHI), 44.4% (PC), and 58.3% (DC). CONCLUSION: These four complementary pre-screening strategies were useful for identifying individuals at high risk of having prodromal or mild AD.

Establishing In-House Cutoffs of CSF Alzheimer's Disease Biomarkers for the AT(N) Stratification of the Alzheimer Center Barcelona Cohort.

BACKGROUND: Clinical diagnosis of Alzheimer's disease (AD) increasingly incorporates CSF biomarkers. However, due to the intrinsic variability of the immunodetection techniques used to measure these biomarkers, establishing in-house cutoffs defining the positivity/negativity of CSF biomarkers is recommended. However, the cutoffs currently published are usually reported by using cross-sectional datasets, not providing evidence about its intrinsic prognostic value when applied to real-world memory clinic cases. METHODS: We quantified CSF Aß1-42, Aß1-40, t-Tau, and p181Tau with standard INNOTEST® ELISA and Lumipulse G® chemiluminescence enzyme immunoassay (CLEIA) performed on the automated Lumipulse G600II. Determination of cutoffs included patients clinically diagnosed with probable Alzheimer's disease (AD, n = 37) and subjective cognitive decline subjects (SCD, n = 45), cognitively stable for 3 years and with no evidence of brain amyloidosis in 18F-Florbetaben-labeled positron emission tomography (FBB-PET). To compare both methods, a subset of samples for Aß1-42 (n = 519), t-Tau (n = 399), p181Tau (n = 77), and Aß1-40 (n = 44) was analyzed. Kappa agreement of single biomarkers and Aß1-42/Aß1-40 was evaluated in an independent group of mild cognitive impairment (MCI) and dementia patients (n = 68). Next, established cutoffs were applied to a large real-world cohort of MCI subjects with follow-up data available (n = 647). RESULTS: Cutoff values of Aß1-42 and t-Tau were higher for CLEIA than for ELISA and similar for p181Tau. Spearman coefficients ranged between 0.81 for Aß1-40 and 0.96 for p181TAU. Passing-Bablok analysis showed a systematic and proportional difference for all biomarkers but only systematic for Aß1-40. Bland-Altman analysis showed an average difference between methods in favor of CLEIA. Kappa agreement for single biomarkers was good but lower for the Aß1-42/Aß1-40 ratio. Using the calculated cutoffs, we were able to stratify MCI subjects into four AT(N) categories. Kaplan-Meier analyses of AT(N) categories demonstrated gradual and differential dementia conversion rates (p = 9.815-27). Multivariate Cox proportional hazard models corroborated these findings, demonstrating that the proposed AT(N) classifier has prognostic value. AT(N) categories are only modestly influenced by other known factors associated with disease progression. CONCLUSIONS: We established CLEIA and ELISA internal cutoffs to discriminate AD patients from amyloid-negative SCD individuals. The results obtained by both methods are not interchangeable but show good agreement. CLEIA is a good and faster alternative to manual ELISA for providing AT(N) classification of our patients. AT(N) categories have an impact on disease progression. AT(N) classifiers increase the certainty of the MCI prognosis, which can be instrumental in managing real-world MCI subjects.

Identification of undiagnosed dementia cases using a web-based pre-screening tool: The MOPEAD project.

INTRODUCTION: Innovative patient engagement models are required to identify people with prodromal and mild Alzheimer's disease who are "hidden" in their communities and not normally found in a memory clinic setting. METHODS: A marketing campaign and a web-based pre-screening tool were used to identify individuals at risk of dementia in five European countries. Harmonized clinical evaluation of these patients was performed in participating memory clinics within the MOPEAD project. RESULTS: A total of 1487 individuals completed the pre-screening, with 547 of them found to be at risk of dementia (36.8%). Among the subset of 91 patients with a positive pre-screening result that underwent full clinical evaluation, 49 (53.8%) were diagnosed with either mild cognitive impairment or Alzheimer's disease. CONCLUSION: This novel web-based pre-screening tool showed to be a valid strategy to identify undiagnosed people with cognitive impairment.

Genome-wide association analysis of dementia and its clinical endophenotypes reveal novel loci associated with Alzheimer's disease and three causality networks: The GR@ACE project.

INTRODUCTION: Large variability among Alzheimer's disease (AD) cases might impact genetic discoveries and complicate dissection of underlying biological pathways. METHODS: Genome Research at Fundacio ACE (GR@ACE) is a genome-wide study of dementia and its clinical endophenotypes, defined based on AD's clinical certainty and vascular burden. We assessed the impact of known AD loci across endophenotypes to generate loci categories. We incorporated gene coexpression data and conducted pathway analysis per category. Finally, to evaluate the effect of heterogeneity in genetic studies, GR@ACE series were meta-analyzed with additional genome-wide association study data sets. RESULTS: We classified known AD loci into three categories, which might reflect the disease clinical heterogeneity. Vascular processes were only detected as a causal mechanism in probable AD. The meta-analysis strategy revealed the ANKRD31-rs4704171 and NDUFAF6-rs10098778 and confirmed SCIMP-rs7225151 and CD33-rs3865444. DISCUSSION: The regulation of vasculature is a prominent causal component of probable AD. GR@ACE meta-analysis revealed novel AD genetic signals, strongly driven by the presence of clinical heterogeneity in the AD series.

Exploring APOE genotype effects on Alzheimer's disease risk and amyloid ß burden in individuals with subjective cognitive decline: The FundacioACE Healthy Brain Initiative (FACEHBI) study baseline results.

INTRODUCTION: Subjective cognitive decline (SCD) has been proposed as a potential preclinical stage of Alzheimer's disease (AD). Nevertheless, the genetic and biomarker profiles of SCD individuals remain mostly unexplored. METHODS: We evaluated apolipoprotein E (APOE) ε4's effect in the risk of presenting SCD, using the Fundacio ACE Healthy Brain Initiative (FACEHBI) SCD cohort and Spanish controls, and performed a meta-analysis addressing the same question. We assessed the relationship between APOE dosage and brain amyloid burden in the FACEHBI SCD and Alzheimer's Disease Neuroimaging Initiative cohorts. RESULTS: Analysis of the FACEHBI cohort and the meta-analysis demonstrated SCD individuals presented higher allelic frequencies of APOE ε4 with respect to controls. APOE dosage explained 9% (FACEHBI cohort) and 11% (FACEHBI and Alzheimer's Disease Neuroimaging Initiative cohorts) of the variance of cerebral amyloid levels. DISCUSSION: The FACEHBI sample presents APOE ε4 enrichment, suggesting that a pool of AD patients is nested in our sample. Cerebral amyloid levels are partially explained by the APOE allele dosage, suggesting that other genetic or epigenetic factors are involved in this AD endophenotype.

The impact of automated hippocampal volumetry on diagnostic confidence in patients with suspected Alzheimer's disease: A European Alzheimer's Disease Consortium study.

INTRODUCTION: Hippocampal volume is a core biomarker of Alzheimer's disease (AD). However, its contribution over the standard diagnostic workup is unclear. METHODS: Three hundred fifty-six patients, under clinical evaluation for cognitive impairment, with suspected AD and Mini-Mental State Examination ≥20, were recruited across 17 European memory clinics. After the traditional diagnostic workup, diagnostic confidence of AD pathology (DCAD) was estimated by the physicians in charge. The latter were provided with the results of automated hippocampal volumetry in standardized format and DCAD was reassessed. RESULTS: An increment of one interquartile range in hippocampal volume was associated with a mean change of DCAD of -8.0% (95% credible interval: [-11.5, -5.0]). Automated hippocampal volumetry showed a statistically significant impact on DCAD beyond the contributions of neuropsychology, 18F-fluorodeoxyglucose positron emission tomography/single-photon emission computed tomography, and cerebrospinal fluid markers (-8.5, CrI: [-11.5, -5.6]; -14.1, CrI: [-19.3, -8.8]; -10.6, CrI: [-14.6, -6.1], respectively). DISCUSSION: There is a measurable effect of hippocampal volume on DCAD even when used on top of the traditional diagnostic workup.

Alzheimer's disease risk variants modulate endophenotypes in mild cognitive impairment.

INTRODUCTION: We evaluated the effect of Alzheimer's disease (AD) susceptibility loci on endophenotypes closely related with AD pathology in patients with mild cognitive impairment (MCI). METHODS: We selected 1730 MCI patients from four independent data sets. Weighted polygenic risk scores (PGS) were constructed of 18 non-apolipoprotein E (APOE) AD risk variants. In addition, we determined APOE genotype. AD endophenotypes were cognitive decline over time and cerebrospinal fluid (CSF) biomarkers (aß, tau, ptau). RESULTS: PGS was modestly associated with cognitive decline over time, as measured by mini-mental state examination (MMSE) (ß ± SE:-0.24 ± 0.10; P = .012), and with CSF levels of tau and ptau (tau: 1.38 ± 0.36, P = 1.21 × 10(-4); ptau: 1.40 ± 0.36, P = 1.02 × 10(-4)). DISCUSSION: In MCI, we observed a joint effect of AD susceptibility loci on nonamyloid endophenotypes, suggesting a link of these genetic loci with neuronal degeneration in general rather than with Alzheimer-related amyloid deposition.

Design of a comprehensive Alzheimer's disease clinic and research center in Spain to meet critical patient and family needs.

OBJECTIVES: Alzheimer's disease (AD) affects people worldwide, and the prevalence is increasing as the population ages. There is an international effort to understand the biology of AD to develop primary and secondary prevention strategies, and to develop effective therapeutic interventions for individuals who are already symptomatic. One of the critically important pieces of all national plans to address AD is the call for the development of service models to deliver quality, effective care based on the best evidence available. METHODS: We describe one type of care model developed by the Fundacio ACE, Institut Catala de Neurociencies Aplicades (Fundacio ACE, Barcelona, Spain) that integrates diagnosis, therapy, follow-up care, daycare, and a day hospital, and does so in the context of an active clinical research and educational program. RESULTS: There were 13,048 individuals newly evaluated and diagnosed in Fundacio ACE between 1996 and 2011. Of these, 6132 had AD (47.0%), 3871 had mild cognitive impairment (MCI) (29.7%), and 1958 had no cognitive impairment (15.0%). Follow-up information is available on 4735 (47.3%) AD and MCI patients, and these data indicate that MCI develops into dementia at a rate of 222.6/1000 person-years. Apolipoprotein E (APOE) genotyping was available in 22.4% of the patients. The ε4 allele occurred in 45.7% of the AD patients, in 37.8% of the MCI subjects, and in 31.6% of those without cognitive impairment. CONCLUSIONS: Fundació ACE can serve as a model system that can be adapted to other settings within their specific cultural, governmental, and legal constraints.

FACEmemory®, an Innovative Online Platform for Episodic Memory Pre-Screening: Findings from the First 3,000 Participants.

BACKGROUND: The FACEmemory® online platform comprises a complex memory test and sociodemographic, medical, and family questions. This is the first study of a completely self-administered memory test with voice recognition, pre-tested in a memory clinic, sensitive to Alzheimer's disease, using information and communication technologies, and offered freely worldwide. OBJECTIVE: To investigate the demographic and clinical variables associated with the total FACEmemory score, and to identify distinct patterns of memory performance on FACEmemory. METHODS: Data from the first 3,000 subjects who completed the FACEmemory test were analyzed. Descriptive analyses were applied to demographic, FACEmemory, and medical and family variables; t-test and chi-square analyses were used to compare participants with preserved versus impaired performance on FACEmemory (cut-off = 32); multiple linear regression was used to identify variables that modulate FACEmemory performance; and machine learning techniques were applied to identify different memory patterns. RESULTS: Participants had a mean age of 50.57 years and 13.65 years of schooling; 64.07% were women, and 82.10% reported memory complaints with worries. The group with impaired FACEmemory performance (20.40%) was older, had less schooling, and had a higher prevalence of hypertension, diabetes, dyslipidemia, and family history of neurodegenerative disease than the group with preserved performance. Age, schooling, sex, country, and completion of the medical and family history questionnaire were associated with the FACEmemory score. Finally, machine learning techniques identified four patterns of FACEmemory performance: normal, dysexecutive, storage, and completely impaired. CONCLUSIONS: FACEmemory is a promising tool for assessing memory in people with subjective memory complaints and for raising awareness about cognitive decline in the community.

Unveiling the sound of the cognitive status: Machine Learning-based speech analysis in the Alzheimer's disease spectrum.

BACKGROUND: Advancement in screening tools accessible to the general population for the early detection of Alzheimer's disease (AD) and prediction of its progression is essential for achieving timely therapeutic interventions and conducting decentralized clinical trials. This study delves into the application of Machine Learning (ML) techniques by leveraging paralinguistic features extracted directly from a brief spontaneous speech (SS) protocol. We aimed to explore the capability of ML techniques to discriminate between different degrees of cognitive impairment based on SS. Furthermore, for the first time, this study investigates the relationship between paralinguistic features from SS and cognitive function within the AD spectrum. METHODS: Physical-acoustic features were extracted from voice recordings of patients evaluated in a memory unit who underwent a SS protocol. We implemented several ML models evaluated via cross-validation to identify individuals without cognitive impairment (subjective cognitive decline, SCD), with mild cognitive impairment (MCI), and with dementia due to AD (ADD). In addition, we established models capable of predicting cognitive domain performance based on a comprehensive neuropsychological battery from Fundació Ace (NBACE) using SS-derived information. RESULTS: The results of this study showed that, based on a paralinguistic analysis of sound, it is possible to identify individuals with ADD (F1 = 0.92) and MCI (F1 = 0.84). Furthermore, our models, based on physical acoustic information, exhibited correlations greater than 0.5 for predicting the cognitive domains of attention, memory, executive functions, language, and visuospatial ability. CONCLUSIONS: In this study, we show the potential of a brief and cost-effective SS protocol in distinguishing between different degrees of cognitive impairment and forecasting performance in cognitive domains commonly affected within the AD spectrum. Our results demonstrate a high correspondence with protocols traditionally used to assess cognitive function. Overall, it opens up novel prospects for developing screening tools and remote disease monitoring.

Genome-wide association study and polygenic risk scores of retinal thickness across the cognitive continuum: data from the NORFACE cohort.

BACKGROUND: Several studies have reported a relationship between retinal thickness and dementia. Therefore, optical coherence tomography (OCT) has been proposed as an early diagnosis method for Alzheimer's disease (AD). In this study, we performed a genome-wide association study (GWAS) aimed at identifying genes associated with retinal nerve fiber layer (RNFL) and ganglion cell inner plexiform layer (GCIPL) thickness assessed by OCT and exploring the relationships between the spectrum of cognitive decline (including AD and non-AD cases) and retinal thickness. METHODS: RNFL and GCIPL thickness at the macula were determined using two different OCT devices (Triton and Maestro). These determinations were tested for association with common single nucleotide polymorphism (SNPs) using adjusted linear regression models and combined using meta-analysis methods. Polygenic risk scores (PRSs) for retinal thickness and AD were generated. RESULTS: Several genetic loci affecting retinal thickness were identified across the genome in accordance with previous reports. The genetic overlap between retinal thickness and dementia, however, was weak and limited to the GCIPL layer; only those observable with all-type dementia cases were considered. CONCLUSIONS: Our study does not support the existence of a genetic link between dementia and retinal thickness.

Macular vessel density in the superficial plexus is not a proxy of cerebrovascular damage in non-demented individuals: data from the NORFACE cohort.

INTRODUCTION: Optical coherence tomography angiography (OCT-A) is a novel tool that allows the detection of retinal vascular changes. We investigated the association of macular vessel density (VD) in the superficial plexus assessed by OCT-A with measures of cerebrovascular pathology and atrophy quantified by brain magnetic resonance imaging (MRI) in non-demented individuals. METHODS: Clinical, demographical, OCT-A, and brain MRI data from non-demented research participants were included. We analyzed the association of regional macular VD with brain vascular burden using the Fazekas scale assessed in a logistic regression analysis, and the volume of white matter hyperintensities (WMH) assessed in a multiple linear regression analysis. We also explored the associations of macular VD with hippocampal volume, ventricle volume and Alzheimer disease cortical signature (ADCS) thickness assessed in multiple linear regression analyses. All analyses were adjusted for age, sex, syndromic diagnosis and cardiovascular variables. RESULTS: The study cohort comprised 188 participants: 89 with subjective cognitive decline and 99 with mild cognitive impairment. No significant association of regional macular VD with the Fazekas categories (all, p > 0.111) and WMH volume (all, p > 0.051) were detected. VD in the nasal quadrant was associated to hippocampal volume (p = 0.007), but no other associations of macular VD with brain atrophy measures were detected (all, p > 0.05). DISCUSSION: Retinal vascular measures were not a proxy of cerebrovascular damage in non-demented individuals, while VD in the nasal quadrant was associated with hippocampal atrophy independently of the amyloid status.

Harnessing acoustic speech parameters to decipher amyloid status in individuals with mild cognitive impairment.

Alzheimer's disease (AD) is a neurodegenerative condition characterized by a gradual decline in cognitive functions. Currently, there are no effective treatments for AD, underscoring the importance of identifying individuals in the preclinical stages of mild cognitive impairment (MCI) to enable early interventions. Among the neuropathological events associated with the onset of the disease is the accumulation of amyloid protein in the brain, which correlates with decreased levels of Aß42 peptide in the cerebrospinal fluid (CSF). Consequently, the development of non-invasive, low-cost, and easy-to-administer proxies for detecting Aß42 positivity in CSF becomes particularly valuable. A promising approach to achieve this is spontaneous speech analysis, which combined with machine learning (ML) techniques, has proven highly useful in AD. In this study, we examined the relationship between amyloid status in CSF and acoustic features derived from the description of the Cookie Theft picture in MCI patients from a memory clinic. The cohort consisted of fifty-two patients with MCI (mean age 73 years, 65% female, and 57% positive amyloid status). Eighty-eight acoustic parameters were extracted from voice recordings using the extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS), and several ML models were used to classify the amyloid status. Furthermore, interpretability techniques were employed to examine the influence of input variables on the determination of amyloid-positive status. The best model, based on acoustic variables, achieved an accuracy of 75% with an area under the curve (AUC) of 0.79 in the prediction of amyloid status evaluated by bootstrapping and Leave-One-Out Cross Validation (LOOCV), outperforming conventional neuropsychological tests (AUC = 0.66). Our results showed that the automated analysis of voice recordings derived from spontaneous speech tests offers valuable insights into AD biomarkers during the preclinical stages. These findings introduce novel possibilities for the use of digital biomarkers to identify subjects at high risk of developing AD.