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Neuroinflammation is a hallmark of Alzheimer's disease (AD) and both positive and negative associations of individual inflammation-related markers with brain structure and cognitive function have been described. We aimed to identify inflammatory signatures of CSF immune-related markers that relate to changes of brain structure and cognition across the clinical spectrum ranging from normal aging to AD. A panel of 16 inflammatory markers, Aβ42/40 and p-tau181 were measured in CSF at baseline in the DZNE DELCODE cohort (n = 295); a longitudinal observational study focusing on at-risk stages of AD. Volumetric maps of gray and white matter (GM/WM; n = 261) and white matter hyperintensities (WMHs, n = 249) were derived from baseline MRIs. Cognitive decline (n = 204) and the rate of change in GM volume was measured in subjects with at least 3 visits (n = 175). A principal component analysis on the CSF markers revealed four inflammatory components (PCs). Of these, the first component PC1 (highly loading on sTyro3, sAXL, sTREM2, YKL-40, and C1q) was associated with older age and higher p-tau levels, but with less pathological Aβ when controlling for p-tau. PC2 (highly loading on CRP, IL-18, complement factor F/H and C4) was related to male gender, higher body mass index and greater vascular risk. PC1 levels, adjusted for AD markers, were related to higher GM and WM volumes, less WMHs, better baseline memory, and to slower atrophy rates in AD-related areas and less cognitive decline. In contrast, PC2 related to less GM and WM volumes and worse memory at baseline. Similar inflammatory signatures and associations were identified in the independent F.ACE cohort. Our data suggest that there are beneficial and detrimental signatures of inflammatory CSF biomarkers. While higher levels of TAM receptors (sTyro/sAXL) or sTREM2 might reflect a protective glia response to degeneration related to phagocytic clearance, other markers might rather reflect proinflammatory states that have detrimental impact on brain integrity., (© 2024. The Author(s).)

Increasing life expectancy has led to an aging population, which has consequently increased the prevalence of dementia. Alzheimer's disease (AD), the most common form of dementia worldwide, is estimated to make up 50-80% of all cases. AD cases are expected to reach 131 million by 2050, and this increasing prevalence will critically burden economies and health systems in the next decades. There is currently no treatment that can stop or reverse disease progression. In addition, the late diagnosis of AD constitutes a major obstacle to effective disease management. Therefore, improved diagnostic tools and new treatments for AD are urgently needed. In this review, we investigate and describe both well-established and recently discovered AD biomarkers that could potentially be used to detect AD at early stages and allow the monitoring of disease progression. Proteins such as NfL, MMPs, p-tau217, YKL-40, SNAP-25, VCAM-1, and Ng / BACE are some of the most promising biomarkers because of their successful use as diagnostic tools. In addition, we explore the most recent molecular strategies for an AD therapeutic approach and nanomedicine-based technologies, used to both target drugs to the brain and serve as devices for tracking disease progression diagnostic biomarkers. State-of-the-art nanoparticles, such as polymeric, lipid, and metal-based, are being widely investigated for their potential to improve the effectiveness of both conventional drugs and novel compounds for treating AD. The most recent studies on these nanodevices are deeply explained and discussed in this review.

Background: Mild cognitive impairment (MCI) due to Alzheimer's disease (AD) diagnosis is based on cerebrospinal fluid (CSF) or neuroimaging biomarkers. Currently, non-invasive and inexpensive blood-based biomarkers are being investigated, such as neuronal-derived plasma exosomes (NPEs). Neuroinflammation and early vascular changes have been described in AD pathogenesis and can be traced in plasma and NPEs. However, they have not been studied in early onset MCI (EOMCI)., Objective: To describe the rationale, design, and baseline characteristics of the participants from the BIOFACE cohort, a two-year observational study on EOMCI conducted at Fundació ACE. The study goal is to characterize the different phenotypes from a clinical, neuropsychological, and biomarker point of view and to investigate the CSF and plasma proteomics as well as the role of NPEs as early biomarkers of AD., Methods: Participants underwent extended neurological and neuropsychological batteries, multimodal biomarkers including brain MRI, blood, saliva, CSF, anthropometric, and neuro-ophthalmological examinations., Results: Ninety-seven patients with EOMCI were recruited. 59.8%were women. Mean age at symptom onset was 57 years; mean MMSE was 28. First degree and presenile family history of dementia was present in 60.8%and 15.5%, respectively. Depressive and anxiety disorders along with vascular risk factors were the most frequent comorbidities. 29%of participants were APOE ɛ4 carriers, and 67%showed a CSF normal ATN profile., Conclusion: BIOFACE is a two-year study of clinical, cognition, and biomarkers that will shed light on the physiopathology and the potential utility of plasma and NPEs as non-invasive early diagnostic and prognostic biomarkers in people younger than 65 years.

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 Aβ42 peptide in the cerebrospinal fluid (CSF). Consequently, the development of non-invasive, low-cost, and easy-to-administer proxies for detecting Aβ42 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. [ABSTRACT FROM AUTHOR]

Background: Decline in language has emerged as a new potential biomarker for the early detection of Alzheimer's disease (AD). It remains unclear how sensitive language measures are across different tasks, language domains, and languages, and to what extent changes can be reliably detected in early stages such as subjective cognitive decline (SCD) and mild cognitive impairment (MCI). Method: Using a scene construction task for speech elicitation in a new Spanish/Catalan speaking cohort (N = 119), we automatically extracted features across seven domains, three acoustic (spectral, cepstral, and voice quality), one prosodic, and three from text (morpholexical, semantic, and syntactic). They were forwarded to a random forest classifier to evaluate the discriminability of participants with probable AD dementia, amnestic and nonamnestic MCI, SCD, and cognitively healthy controls. Repeated-measures analyses of variance and paired-samples Wilcoxon signed-ranks test were used to assess whether and how performance differs significantly across groups and linguistic domains. Results: The performance scores of the machine learning classifier were generally satisfactorily high, with the highest scores over .9. Model performance was significantly different for linguistic domains (p < .001), and speech versus text (p = .043), with speech features outperforming textual features, and voice quality performing best. High diagnostic classification accuracies were seen even within both cognitively healthy (controls vs. SCD) and MCI (amnestic and nonamnestic) groups. Conclusion: Speech-based machine learning is powerful in detecting cognitive decline and probable AD dementia across a range of different feature domains, though important differences exist between these domains as well. [ABSTRACT FROM AUTHOR]

Building on previous studies that report thinning of the macula in Alzheimer's disease (AD) and mild cognitive impairment (MCI) patients, the use of optical coherence tomography (OCT) has been proposed as a potential biomarker for AD. However, other studies contradict these results. A total of 930 participants (414 cognitively healthy people, 192 with probable amnestic MCI, and 324 probable AD patients) from a memory clinic were consecutively included in this study and underwent a spectral domain OCT scan (Maestro, Topcon) to assess total macular volume and thickness. Macular width measurements were also taken in several subregions (central, inner, and outer rings) and in layers such as the retinal nerve fiber (RNFL) and ganglion cell (CGL). The study employed a design of high ecological validity, with adjustment by age, education, sex, and OCT image quality. AD, MCI, and control groups did not significantly vary with regard to volume and retinal thickness in different layers. When these groups were compared, multivariate-adjusted analysis disclosed no significant differences in total (p = 0.564), CGL (p = 0.267), RNFL (p = 0.574), and macular thickness and volume (p = 0.380). The only macular regions showing significant differences were the superior (p = 0.040) and nasal (p = 0.040) sectors of the inner macular ring. However, adjustment for multiple comparisons nullified this significance. These results are not supporting existing claims for the usefulness of macular thickness as a biomarker of cognitive impairment in a memory unit. OCT biomarkers for AD should be subject to further longitudinal testing. [ABSTRACT FROM AUTHOR]