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.