Beyond the core features of Alzheimer’s disease (AD) pathology, i.e. amyloid pathology, tau-related neurodegeneration and microglia response, multiple other molecular alterations and pathway dysregulations have been observed in AD. Their inter-individual variations, complex interactions and relevance for clinical manifestation and disease progression remain poorly understood, however. Heterogeneity at both pathophysiological and clinical levels complicates diagnosis, prognosis, treatment and drug design and testing. High-throughput “omics” comprise unbiased and untargeted data-driven methods which allow the exploration of a wide spectrum of disease-related changes at different endophenotype levels without focussing a priori on specific molecular pathways or molecules. Crucially, new methodological and statistical advances now allow for the integrative analysis of data resulting from multiple and different omics methods. These multi-omics approaches offer the unique advantage of providing a more comprehensive characterisation of the AD endophenotype and to capture molecular signatures and interactions spanning various biological levels. These new insights can then help decipher disease mechanisms more deeply. In this review, we describe the different multi-omics tools and approaches currently available and how they have been applied in AD research so far. We discuss how multi-omics can be used to explore molecular alterations related to core features of the AD pathologies and how they interact with comorbid pathological alterations. We further discuss whether the identified pathophysiological changes are relevant for the clinical manifestation of AD, in terms of both cognitive impairment and neuropsychiatric symptoms, and for clinical disease progression over time. Finally, we address the opportunities for multi-omics approaches to help discover novel biomarkers for diagnosis and monitoring of relevant pathophysiological processes, along with personalised intervention strategies in AD.
Introduction Neuropsychiatric symptoms are important treatment targets in the management of dementia and can be present at very early clinical stages of neurodegenerative diseases. Increased cortisol has been reported in Alzheimer’s disease (AD) and has been associated with faster cognitive decline. Elevated cortisol output has been observed in relation to perceived stress, depression, and anxiety. Dehydroepiandrosterone sulfate (DHEAS) has known anti-glucocorticoid effects and may counter the effects of cortisol. Objectives We aimed to examine whether CSF cortisol and DHEAS levels were associated with (1) neuropsychiatric symptoms at baseline, (2) changes in neuropsychiatric symptoms over 3 years, and (3) whether these associations were related to or independent of AD pathology. Methods One hundred and eighteen participants on a prospective study in a memory clinic setting, including patients with cognitive impairment (n = 78), i.e., mild cognitive impairment or mild dementia, and volunteers with normal cognition (n = 40), were included. Neuropsychiatric symptoms were assessed using the Neuropsychiatric Inventory Questionnaire (NPI-Q). CSF cortisol and DHEAS, as well as CSF AD biomarkers, were obtained at baseline. Neuropsychiatric symptoms were re-assessed at follow-up visits 18 and 36 months from baseline. We constructed linear regression models to examine the links between baseline neuropsychiatric symptoms, the presence of AD pathology as indicated by CSF biomarkers, and CSF cortisol and DHEAS. We used repeated-measures mixed ANCOVA models to examine the associations between the neuropsychiatric symptoms’ changes over time, baseline CSF cortisol and DHEAS, and AD pathology. Results Higher CSF cortisol was associated with higher NPI-Q severity scores at baseline after controlling for covariates including AD pathology status (B = 0.085 [0.027; 0.144], p = 0.027; r = 0.277). In particular, higher CSF cortisol was associated with higher baseline scores of depression/dysphoria, anxiety, and apathy/indifference. Elevated CSF cortisol was also associated with more marked increase in NPI-Q scores over time regardless of AD status (p = 0.036, η2 = 0.207), but this association was no longer significant after controlling for BMI and the use of psychotropic medications. CSF DHEAS was associated neither with NPI-Q scores at baseline nor with their change over time. Cortisol did not mediate the association between baseline NPI-Q and changes in clinical dementia rating sum of boxes over 36 months. Conclusion Higher CSF cortisol may reflect or contribute to more severe neuropsychiatric symptoms at baseline, as well as more pronounced worsening over 3 years, independently of the presence of AD pathology. Our findings also suggest that interventions targeting the HPA axis may be helpful to treat neuropsychiatric symptoms in patients with dementia.
Neuropsychiatric symptoms (NPS) severely affect patients and their caregivers, and are associated with worse long‐term outcomes. This study tested the hypothesis that altered protein levels in blood plasma could serve as biomarkers of NPS; and that altered protein levels are associated with persisting NPS and cognitive decline over time. We performed a cross‐sectional and longitudinal study in older subjects with cognitive impairment and cognitively unimpaired in a memory clinic setting. NPS were recorded through the Neuropsychiatric Inventory Questionnaire (NPI‐Q) while cognitive and functional impairment was assessed using the clinical dementia rating sum of boxes (CDR‐SoB) score at baseline and follow‐up visits. Shotgun proteomic analysis based on liquid chromatography‐mass spectrometry was conducted in blood plasma samples, identifying 420 proteins. The presence of Alzheimer's Disease (AD) pathology was determined by cerebrospinal fluid biomarkers. Eighty‐five subjects with a mean age of 70 (±7.4) years, 62% female and 54% with mild cognitive impairment or mild dementia were included. We found 15 plasma proteins with altered baseline levels in participants with NPS (NPI‐Q score > 0). Adding those 15 proteins to a reference model based on clinical data (age, CDR‐SoB) significantly improved the prediction of NPS (from receiver operating characteristic area under the curve [AUC] 0.75 to AUC 0.91, p = 0.004) with a specificity of 89% and a sensitivity of 74%. The identified proteins additionally predicted both persisting NPS and cognitive decline at follow‐up visits. The observed associations were independent of the presence of AD pathology. Using proteomics, we identified a panel of specific blood proteins associated with current and future NPS, and related cognitive decline in older people. These findings show the potential of untargeted proteomics to identify blood‐based biomarkers of pathological alterations relevant for NPS and related clinical disease progression.
Readily accessible diagnostic tools are crucial for early detection of Alzheimer’s disease (AD). Here, we sought to identify peripheral metabolism biomarkers of cerebral AD pathology. Untargeted liquid chromatography-mass spectrometry was used to quantify 2286 serum metabolites in participants on a longitudinal memory clinic study. Unbiased between-group analysis using Orthogonal Partial Least Squares Discriminant Analysis, Linear Discriminant Analysis and Principal Component Analysis were performed to build a classifier for AD as indicated by CSF biomarkers. MetaboAnalyst was subsequently used for selection of the most relevant metabolites; pathway enrichment was performed to determine biological pathway alterations related to AD. No biomarker signature was found in the whole cohort. Stratification according to sex allowed building a classifier for AD using 14 metabolites in males and 9 in females that significantly improved the prediction of the presence of AD compared to a reference model. Thirteen enriched pathways were identified, including lipid and amino acid metabolisms. Compared to a reference model, the selected metabolites significantly improved the prediction of cognitive decline in females. Sex-specific peripheral metabolism biomarkers are useful to predict cerebral AD pathology and cognitive decline, and detect related pathway alterations. This highlights the need for personalised diagnostic and therapeutic approaches in AD.
BackgroundNeuropsychiatric symptoms (NPS) in older people worsen the patients’ and caregivers’ quality of life and are associated with cognitive decline. A better understanding of the biology underlying NPS may lead to novel ways for early detection and more targeted treatment. This study tested the hypothesis that NPS are associated with altered protein levels in blood plasma and these proteins improve prediction of NPS in a memory clinic setting. Additionally, we explored the association of the protein patterns with persistent NPS and cognitive decline.MethodWe performed a cross‐sectional and longitudinal study in older people with and without cognitive impairment. NPS were recorded through the Neuropsychiatric Inventory Questionnaire (NPI‐Q) while cognition was assessed through a comprehensive neuropsychological test battery collected at baseline and follow‐up visits. Shotgun proteomic analysis based on liquid chromatography‐mass spectrometry identifying 420 proteins were conducted in blood plasma samples of all participants. Multivariate linear regression and correlation analysis were used.ResultEighty‐five subjects with a mean age of 70 (± 7.4) years, 65% female and 54% with mild cognitive impairment or mild dementia were divided into groups with (NPI‐Q score > 0) and without NPS. We found 15 plasma proteins with altered baseline levels in participants with NPS. The model which best predicted the occurrence of NPS at baseline contained clinical data (age, sex, education years, cognitive impairment) and additionally the 15 altered proteins. Comparing this model with a reference model containing just the clinical data, the prediction of NPS significantly improved (from receiver operating characteristic area under the curve (AUC) 0.72 to AUC 0.89, p = 0.001) with a specificity of 87 % and a sensitivity of 72% (Fig. 1). Preliminary results indicate that five out of the 15 proteins were associated with persisting NPS and six with cognitive decline at follow‐up (mean: 3.4 ± 1.3 years).ConclusionUsing a proteomic approach, we identified a panel of blood proteins associated with NPS in older people. These findings showed the potential of this approach to identify protein biomarkers of pathological changes underlying NPS and to detect novel treatment targets to reduce NPS and related cognitive decline.
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