Multi-omics signature of brain amyloid deposition in asymptomatic individuals at-risk for Alzheimer's disease: The INSIGHT-preAD study

Tackling neurodegeneration in vitro with omics: a path towards new targets and drugs

Drug discovery is a generally inefficient and capital-intensive process. For neurodegenerative diseases (NDDs), the development of novel therapeutics is particularly urgent considering the long list of late-stage drug candidate failures. Although our knowledge on the pathogenic mechanisms driving neurodegeneration is growing, additional efforts are required to achieve a better and ultimately complete understanding of the pathophysiological underpinnings of NDDs. Beyond the etiology of NDDs being heterogeneous and multifactorial, this process is further complicated by the fact that current experimental models only partially recapitulate the major phenotypes observed in humans. In such a scenario, multi-omic approaches have the potential to accelerate the identification of new or repurposed drugs against a multitude of the underlying mechanisms driving NDDs. One major advantage for the implementation of multi-omic approaches in the drug discovery process is that these overarching tools are able to disentangle disease states and model perturbations through the comprehensive characterization of distinct molecular layers (i.e., genome, transcriptome, proteome) up to a single-cell resolution. Because of recent advances increasing their affordability and scalability, the use of omics technologies to drive drug discovery is nascent, but rapidly expanding in the neuroscience field. Combined with increasingly advanced in vitro models, which particularly benefited from the introduction of human iPSCs, multi-omics are shaping a new paradigm in drug discovery for NDDs, from disease characterization to therapeutics prediction and experimental screening. In this review, we discuss examples, main advantages and open challenges in the use of multi-omic approaches for the in vitro discovery of targets and therapies against NDDs.

Cerebral Amyloidosis in Individuals with Subjective Cognitive Decline: From Genetic Predisposition to Actual Cerebrospinal Fluid Measurements

The possible relationship between Subjective Cognitive Decline (SCD) and dementia needs further investigation. In the present study, we explored the association between specific biomarkers of Alzheimer's Disease (AD), amyloid-beta 42 (Aβ42) and Tau with the odds of SCD using data from two ongoing studies. In total, 849 cognitively normal (CN) individuals were included in our analyses. Among the participants, 107 had available results regarding cerebrospinal fluid (CSF) Aβ42 and Tau, while 742 had available genetic data to construct polygenic risk scores (PRSs) reflecting their genetic predisposition for CSF Aβ42 and plasma total Tau levels. The associations between AD biomarkers and SCD were tested using logistic regression models adjusted for possible confounders such as age, sex, education, depression, and baseline cognitive test scores. Abnormal values of CSF Aβ42 were related to 2.5-fold higher odds of SCD, while higher polygenic loading for Aβ42 was associated with 1.6-fold higher odds of SCD. CSF Tau, as well as polygenic loading for total Tau, were not associated with SCD. Thus, only cerebral amyloidosis appears to be related to SCD status, either in the form of polygenic risk or actual CSF measurements. The temporal sequence of amyloidosis being followed by tauopathy may partially explain our findings.

An integrated precision medicine approach in major depressive disorder: a study protocol to create a new algorithm for the prediction of treatment response

Major depressive disorder (MDD) is the most common psychiatric disease worldwide with a huge socio-economic impact. Pharmacotherapy represents the most common option among the first-line treatment choice; however, only about one third of patients respond to the first trial and about 30% are classified as treatment-resistant depression (TRD). TRD is associated with specific clinical features and genetic/gene expression signatures. To date, single sets of markers have shown limited power in response prediction. Here we describe the methodology of the PROMPT project that aims at the development of a precision medicine algorithm that would help early detection of non-responder patients, who might be more prone to later develop TRD. To address this, the project will be organized in 2 phases. Phase 1 will involve 300 patients with MDD already recruited, comprising 150 TRD and 150 responders, considered as extremes phenotypes of response. A deep clinical stratification will be performed for all patients; moreover, a genomic, transcriptomic and miRNomic profiling will be conducted. The data generated will be exploited to develop an innovative algorithm integrating clinical, omics and sex-related data, in order to predict treatment response and TRD development. In phase 2, a new naturalistic cohort of 300 MDD patients will be recruited to assess, under real-world conditions, the capability of the algorithm to correctly predict the treatment outcomes. Moreover, in this phase we will investigate shared decision making (SDM) in the context of pharmacogenetic testing and evaluate various needs and perspectives of different stakeholders toward the use of predictive tools for MDD treatment to foster active participation and patients' empowerment. This project represents a proof-of-concept study. The obtained results will provide information about the feasibility and usefulness of the proposed approach, with the perspective of designing future clinical trials in which algorithms could be tested as a predictive tool to drive decision making by clinicians, enabling a better prevention and management of MDD resistance.

Genetic propensity for cerebral amyloidosis and risk of mild cognitive impairment and Alzheimer's disease within a cognitive reserve framework

INTRODUCTION

We constructed a polygenic risk score (PRS) for β-amyloid (PRSAβ42) to proxy AD pathology and investigated its association with incident Alzheimer's disease (AD)/amnestic mild cognitive impairment (aMCI) and the influence of cognitive reserve (CR), proxied by educational years, on the relationship between PRSAβ42 and AD/aMCI risk.

METHODS

A total of 618 cognitive-normal participants were followed-up for 2.92 years. The association of PRSAβ42 and CR with AD/aMCI incidence was examined with COX models. Then we examined the additive interaction between PRSAβ42 and CR and the CR effect across participants with different PRSAβ42 levels.

RESULTS

Higher PRSAβ42 and CR were associated with a 33.9% higher risk and 8.3% less risk for AD/aMCI, respectively. An additive interaction between PRSAβ42 and CR was observed. High CR was associated with 62.6% less risk of AD/aMCI incidence only in the high-PRSAβ42 group.

DISCUSSION

A super-additive effect of PRSAβ42 and CR on AD/aMCI risk was observed. CR influence was evident in participants with high PRSAβ42.

Integrative metabolomics science in Alzheimer's disease: Relevance and future perspectives

Alzheimer's disease (AD) is determined by various pathophysiological mechanisms starting 10-25 years before the onset of clinical symptoms. As multiple functionally interconnected molecular/cellular pathways appear disrupted in AD, the exploitation of high-throughput unbiased omics sciences is critical to elucidating the precise pathogenesis of AD. Among different omics, metabolomics is a fast-growing discipline allowing for the simultaneous detection and quantification of hundreds/thousands of perturbed metabolites in tissues or biofluids, reproducing the fluctuations of multiple networks affected by a disease. Here, we seek to critically depict the main metabolomics methodologies with the aim of identifying new potential AD biomarkers and further elucidating AD pathophysiological mechanisms. From a systems biology perspective, as metabolic alterations can occur before the development of clinical signs, metabolomics - coupled with existing accessible biomarkers used for AD screening and diagnosis - can support early disease diagnosis and help develop individualized treatment plans. Presently, the majority of metabolomic analyses emphasized that lipid metabolism is the most consistently altered pathway in AD pathogenesis. The possibility that metabolomics may reveal crucial steps in AD pathogenesis is undermined by the difficulty in discriminating between the causal or epiphenomenal or compensatory nature of metabolic findings.

Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective

Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.

Multiomics and artificial intelligence enabled peripheral blood-based prediction of amnestic mild cognitive impairment

BACKGROUND

Since dementia is preventable with early interventions, biomarkers that assist in diagnosing early stages of dementia, such as mild cognitive impairment (MCI), are urgently needed.

METHODS

Multiomics analysis of amnestic MCI (aMCI) peripheral blood (n = 25) was performed covering the transcriptome, microRNA, proteome, and metabolome. Validation analysis for microRNAs was conducted in an independent cohort (n = 12). Artificial intelligence was used to identify the most important features for predicting aMCI.

FINDINGS

We found that hsa-miR-4455 is the best biomarker in all omics analyses. The diagnostic index taking a ratio of hsa-miR-4455 to hsa-let-7b-3p predicted aMCI patients against healthy subjects with 97% overall accuracy. An integrated review of multiomics data suggested that a subset of T cells and the GCN (general control nonderepressible) pathway are associated with aMCI.

INTERPRETATION

The multiomics approach has enabled aMCI biomarkers with high specificity and illuminated the accompanying changes in peripheral blood. Future large-scale studies are necessary to validate candidate biomarkers for clinical use.

Multi-omics analysis revealed the role of CYP1A2 in the induction of mechanical allodynia in type 1 diabetes

Background: Mechanical allodynia (MA) is one of the leading clinical symptoms of painful diabetic peripheral neuropathy (PDPN), which is a primary reason for non-traumatic amputations, foot ulceration, and gait abnormalities in patients with diabetes. However, the pathogenic mechanisms of MA have not yet been fully elucidated, and there is no effective treatment. This study aims to study the potential pathogenetic mechanisms of MA and to provide targets for the therapy of MA. Methods: A single intraperitoneal injection of streptozotocin induced type 1 diabetes in rat models. Subsequently, rats were divided into the control group, the diabetic group without MA, and the diabetic group with MA based on weekly behavioral assays. The differentially expressed lipids in the sciatic nerve of each group were detected using untargeted lipidomics, and the differentially expressed genes in the sciatic nerve of each group were detected by transcriptomics. The pathogenesis of MA was predicted using integrated analysis and validated by immunofluorescence staining and transmission electron microscopy. Results: Untargeted lipidomics revealed the accumulation of a more severe lipid in MA rats. Transcriptomics results suggested that differentially expressed genes in MA rats were primarily related to lipid droplets and myelin sheath. Integrated analysis results indicated that the downregulation of Cytochrome P450 1A2 (CYP1A2) expression was closely linked to lipid metabolism disorders. Immunofluorescence staining demonstrated that down-regulation of CYP1A2 expression occurred in MA rats. Transmission electron microscopy results showed that more severe lipid droplet accumulation and myelin sheath degeneration occurred in MA rats. Conclusion: Our findings imply that the downregulation of CYP1A2 expression leads to disorders of lipid metabolism and further leads to lipid droplet accumulation and myelin sheath degeneration, which might ultimately lead to the development of MA. Therefore, our study contributes to promoting the understanding of the molecular mechanisms of MA and providing potential targets for the clinical treatment of MA.

The promise of multi-omics approaches to discover biological alterations with clinical relevance in Alzheimer's disease

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.

A guide to multi-omics data collection and integration for translational medicine

The emerging high-throughput technologies have led to the shift in the design of translational medicine projects towards collecting multi-omics patient samples and, consequently, their integrated analysis. However, the complexity of integrating these datasets has triggered new questions regarding the appropriateness of the available computational methods. Currently, there is no clear consensus on the best combination of omics to include and the data integration methodologies required for their analysis. This article aims to guide the design of multi-omics studies in the field of translational medicine regarding the types of omics and the integration method to choose. We review articles that perform the integration of multiple omics measurements from patient samples. We identify five objectives in translational medicine applications: (i) detect disease-associated molecular patterns, (ii) subtype identification, (iii) diagnosis/prognosis, (iv) drug response prediction, and (v) understand regulatory processes. We describe common trends in the selection of omic types combined for different objectives and diseases. To guide the choice of data integration tools, we group them into the scientific objectives they aim to address. We describe the main computational methods adopted to achieve these objectives and present examples of tools. We compare tools based on how they deal with the computational challenges of data integration and comment on how they perform against predefined objective-specific evaluation criteria. Finally, we discuss examples of tools for downstream analysis and further extraction of novel insights from multi-omics datasets.

Machine learning for classification of hypertension subtypes using multi-omics: A multi-centre, retrospective, data-driven study

Background

Arterial hypertension is a major cardiovascular risk factor. Identification of secondary hypertension in its various forms is key to preventing and targeting treatment of cardiovascular complications. Simplified diagnostic tests are urgently required to distinguish primary and secondary hypertension to address the current underdiagnosis of the latter.

Methods

This study uses Machine Learning (ML) to classify subtypes of endocrine hypertension (EHT) in a large cohort of hypertensive patients using multidimensional omics analysis of plasma and urine samples. We measured 409 multi-omics (MOmics) features including plasma miRNAs (PmiRNA: 173), plasma catechol O-methylated metabolites (PMetas: 4), plasma steroids (PSteroids: 16), urinary steroid metabolites (USteroids: 27), and plasma small metabolites (PSmallMB: 189) in primary hypertension (PHT) patients, EHT patients with either primary aldosteronism (PA), pheochromocytoma/functional paraganglioma (PPGL) or Cushing syndrome (CS) and normotensive volunteers (NV). Biomarker discovery involved selection of disease combination, outlier handling, feature reduction, 8 ML classifiers, class balancing and consideration of different age- and sex-based scenarios. Classifications were evaluated using balanced accuracy, sensitivity, specificity, AUC, F1, and Kappa score.

Findings

Complete clinical and biological datasets were generated from 307 subjects (PA=113, PPGL=88, CS=41 and PHT=112). The random forest classifier provided ∼92% balanced accuracy (∼11% improvement on the best mono-omics classifier), with 96% specificity and 0.95 AUC to distinguish one of the four conditions in multi-class ALL-ALL comparisons (PPGL vs PA vs CS vs PHT) on an unseen test set, using 57 MOmics features. For discrimination of EHT (PA + PPGL + CS) vs PHT, the simple logistic classifier achieved 0.96 AUC with 90% sensitivity, and ∼86% specificity, using 37 MOmics features. One PmiRNA (hsa-miR-15a-5p) and two PSmallMB (C9 and PC ae C38:1) features were found to be most discriminating for all disease combinations. Overall, the MOmics-based classifiers were able to provide better classification performance in comparison to mono-omics classifiers.

Interpretation

We have developed a ML pipeline to distinguish different EHT subtypes from PHT using multi-omics data. This innovative approach to stratification is an advancement towards the development of a diagnostic tool for EHT patients, significantly increasing testing throughput and accelerating administration of appropriate treatment.

Funding

European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 633983, Clinical Research Priority Program of the University of Zurich for the CRPP HYRENE (to Z.E. and F.B.), and Deutsche Forschungsgemeinschaft (CRC/Transregio 205/1).

Brain lipidomics: From functional landscape to clinical significance

Lipids are crucial components of cellular function owing to their role in membrane formation, intercellular signaling, energy storage, and homeostasis maintenance. In the brain, lipid dysregulations have been associated with the etiology and progression of neurodegeneration and other neurological pathologies. Hence, brain lipids are emerging as important potential targets for the early diagnosis and prognosis of neurological diseases. This review aims to highlight the significance and usefulness of lipidomics in diagnosing and treating brain diseases. We explored lipid alterations associated with brain diseases, paying attention to organ-specific characteristics and the functions of brain lipids. As the recent advances in brain lipidomics would have been impossible without advances in analytical techniques, we provide up-to-date information on mass spectrometric approaches and integrative analysis with other omic approaches. Last, we present the potential applications of lipidomics combined with artificial intelligence techniques and interdisciplinary collaborative research for treating brain diseases with clinical heterogeneities.

Association of APOE-Independent Alzheimer Disease Polygenic Risk Score With Brain Amyloid Deposition in Asymptomatic Older Adults

BACKGROUND AND OBJECTIVES

Brain amyloid deposition, a major risk factor for Alzheimer disease (AD), is currently estimated by measuring CSF or plasma amyloid peptide levels or by PET imaging. Assessing genetic risks relating to amyloid deposition before any accumulation has occurred would allow for earlier intervention in persons at increased risk for developing AD. Previous work linking amyloid burden and genetic risk relied almost exclusively on APOE, a major AD genetic risk factor. Here, we ask whether a polygenic risk score (PRS) that incorporates an optimized list of common variants linked to AD and excludes APOE is associated with brain amyloid load in cognitively unimpaired older adults.

METHODS

We included 291 asymptomatic older participants from the INveStIGation of AlzHeimer's PredicTors (INSIGHT pre-AD) cohort who underwent amyloid imaging, including 83 amyloid-positive (+) participants. We used an Alzheimer's (A) PRS composed of 33 AD risk variants excluding APOE and selected the 17 variants that showed the strongest association with amyloid positivity to define an optimized (oA) PRS. Participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study (228 participants, 90 amyloid [+]) were tested as a validation cohort. Finally, 2,300 patients with AD and 6,994 controls from the European Alzheimer's Disease Initiative (EADI) were evaluated.

RESULTS

A-PRS was not significantly associated with amyloid burden in the INSIGHT or ADNI cohorts with or without correction for the APOE genotype. However, oA-PRS was significantly associated with amyloid status independently of APOE adjustment (INSIGHT odds ratio [OR]: 5.26 [1.71-16.88]; ADNI OR: 3.38 [1.02-11.63]). Of interest, oA-PRS accurately discriminated amyloid (+) and (-) APOE ε4 carriers (INSIGHT OR: 181.6 [7.53-10,674.6]; ADNI OR: 44.94 [3.03-1,277]). A-PRS and oA-PRS showed a significant association with disease status in the EADI cohort (OR: 1.68 [1.53-1.85] and 2.06 [1.73-2.45], respectively). Genes assigned to oA-PRS variants were enriched in ontologies related to β-amyloid metabolism and deposition.

DISCUSSION

PRSs relying on AD genetic risk factors excluding APOE may improve risk prediction for brain amyloid, allowing stratification of cognitively unimpaired individuals at risk of AD independent of their APOE status.

Multi-Omics-Based Autophagy-Related Untypical Subtypes in Patients with Cerebral Amyloid Pathology

Recent multi-omics analyses paved the way for a comprehensive understanding of pathological processes. However, only few studies have explored Alzheimer's disease (AD) despite the possibility of biological subtypes within these patients. For this study, unsupervised classification of four datasets (genetics, miRNA transcriptomics, proteomics, and blood-based biomarkers) using Multi-Omics Factor Analysis+ (MOFA+), along with systems-biological approaches following various downstream analyses are performed. New subgroups within 170 patients with cerebral amyloid pathology (Aβ+) are revealed and the features of them are identified based on the top-rated targets constructing multi-omics factors of both whole (M-TPAD) and immune-focused models (M-IPAD). The authors explored the characteristics of subtypes and possible key-drivers for AD pathogenesis. Further in-depth studies showed that these subtypes are associated with longitudinal brain changes and autophagy pathways are main contributors. The significance of autophagy or clustering tendency is validated in peripheral blood mononuclear cells (PBMCs; n = 120 including 30 Aβ- and 90 Aβ+), induced pluripotent stem cell-derived human brain organoids/microglia (n = 12 including 5 Aβ-, 5 Aβ+, and CRISPR-Cas9 apolipoprotein isogenic lines), and human brain transcriptome (n = 78). Collectively, this study provides a strategy for precision medicine therapy and drug development for AD using integrative multi-omics analysis and network modelling.

Multiomics technologies: role in disease biomarker discoveries and therapeutics

 

Medical research has been revolutionized after the publication of the full human genome. This was the major landmark that paved the way for understanding the biological functions of different macro and micro molecules. With the advent of different high-throughput technologies, biomedical research was further revolutionized. These technologies constitute genomics, transcriptomics, proteomics, metabolomics, etc. Collectively, these high-throughputs are referred to as multi-omics technologies. In the biomedical field, these omics technologies act as efficient and effective tools for disease diagnosis, management, monitoring, treatment and discovery of certain novel disease biomarkers. Genotyping arrays and other transcriptomic studies have helped us to elucidate the gene expression patterns in different biological states, i.e. healthy and diseased states. Further omics technologies such as proteomics and metabolomics have an important role in predicting the role of different biological molecules in an organism. It is because of these high throughput omics technologies that we have been able to fully understand the role of different genes, proteins, metabolites and biological pathways in a diseased condition. To understand a complex biological process, it is important to apply an integrative approach that analyses the multi-omics data in order to highlight the possible interrelationships of the involved biomolecules and their functions. Furthermore, these omics technologies offer an important opportunity to understand the information that underlies disease. In the current review, we will discuss the importance of omics technologies as promising tools to understand the role of different biomolecules in diseases such as cancer, cardiovascular diseases, neurodegenerative diseases and diabetes.

SUMMARY POINTS

Lipids as Early and Minimally Invasive Biomarkers for Alzheimer's Disease

Alzheimer's disease (AD) is the most common neurodegenerative disorder worldwide. Specifically, typical late-onset AD is a sporadic form with a complex etiology that affects over 90% of patients. The current gold standard for AD diagnosis is based on the determination of amyloid status by analyzing cerebrospinal fluid samples or brain positron emission tomography. These procedures can be used widely as they have several disadvantages (expensive, invasive). As an alternative, blood metabolites have recently emerged as promising AD biomarkers. Small molecules that cross the compromised AD blood-brain barrier could be determined in plasma to improve clinical AD diagnosis at early stages through minimally invasive techniques. Specifically, lipids could play an important role in AD since the brain has a high lipid content, and they are present ubiquitously inside amyloid plaques. Therefore, a systematic review was performed with the aim of identifying blood lipid metabolites as potential early AD biomarkers. In conclusion, some lipid families (fatty acids, glycerolipids, glycerophospholipids, sphingolipids, lipid peroxidation compounds) have shown impaired levels at early AD stages. Ceramide levels were significantly higher in AD subjects, and polyunsaturated fatty acids levels were significantly lower in AD. Also, high arachidonic acid levels were found in AD patients in contrast to low sphingomyelin levels. Consequently, these lipid biomarkers could be used for minimally invasive and early AD clinical diagnosis.

Blood-Based Biomarkers for Alzheimer's Disease Diagnosis and Progression: An Overview

Alzheimer’s Disease (AD) is a progressive neurodegenerative disease characterized by amyloid-β (Aβ) plaque deposition and neurofibrillary tangle accumulation in the brain. Although several studies have been conducted to unravel the complex and interconnected pathophysiology of AD, clinical trial failure rates have been high, and no disease-modifying therapies are presently available. Fluid biomarker discovery for AD is a rapidly expanding field of research aimed at anticipating disease diagnosis and following disease progression over time. Currently, Aβ_1–42, phosphorylated tau, and total tau levels in the cerebrospinal fluid are the best-studied fluid biomarkers for AD, but the need for novel, cheap, less-invasive, easily detectable, and more-accessible markers has recently led to the search for new blood-based molecules. However, despite considerable research activity, a comprehensive and up-to-date overview of the main blood-based biomarker candidates is still lacking. In this narrative review, we discuss the role of proteins, lipids, metabolites, oxidative-stress-related molecules, and cytokines as possible disease biomarkers. Furthermore, we highlight the potential of the emerging miRNAs and long non-coding RNAs (lncRNAs) as diagnostic tools, and we briefly present the role of vitamins and gut-microbiome-related molecules as novel candidates for AD detection and monitoring, thus offering new insights into the diagnosis and progression of this devastating disease.

Potential Mechanisms Underlying Resistance to Dementia in Non-Demented Individuals with Alzheimer's Disease Neuropathology

Alzheimer’s disease (AD) is the most common form of dementia and typically characterized by the accumulation of amyloid-β plaques and tau tangles. Intriguingly, there also exists a group of elderly which do not develop dementia during their life, despite the AD neuropathology, the so-called non-demented individuals with AD neuropathology (NDAN). In this review, we provide extensive background on AD pathology and normal aging and discuss potential mechanisms that enable these NDAN individuals to remain cognitively intact. Studies presented in this review show that NDAN subjects are generally higher educated and have a larger cognitive reserve. Furthermore, enhanced neural hypertrophy could compensate for hippocampal and cingulate neural atrophy in NDAN individuals. On a cellular level, these individuals show increased levels of neural stem cells and ‘von Economo neurons’. Furthermore, in NDAN brains, binding of Aβ oligomers to synapses is prevented, resulting in decreased glial activation and reduced neuroinflammation. Overall, the evidence stated here strengthens the idea that some individuals are more resistant to AD pathology, or at least show an elongation of the asymptomatic state of the disease compared to others. Insights into the mechanisms underlying this resistance could provide new insight in understanding normal aging and AD itself. Further research should focus on factors and mechanisms that govern the NDAN cognitive resilience in order to find clues on novel biomarkers, targets, and better treatments of AD.

Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach

The wealth of high-throughput data has opened up new opportunities to analyze and describe biological processes at higher resolution, ultimately leading to a significant acceleration of scientific output using high-throughput data from the different omics layers and the generation of databases to store and report raw datasets. The great variability among the techniques and the heterogeneous methodologies used to produce this data have placed meta-analysis methods as one of the approaches of choice to correlate the resultant large-scale datasets from different research groups. Through multi-study meta-analyses, it is possible to generate results with greater statistical power compared to individual analyses. Gene signatures, biomarkers and pathways that provide new insights of a phenotype of interest have been identified by the analysis of large-scale datasets in several fields of science. However, despite all the efforts, a standardized regulation to report large-scale data and to identify the molecular targets and signaling networks is still lacking. Integrative analyses have also been introduced as complementation and augmentation for meta-analysis methodologies to generate novel hypotheses. Currently, there is no universal method established and the different methods available follow different purposes. Herein we describe a new unifying, scalable and straightforward methodology to meta-analyze different omics outputs, but also to integrate the significant outcomes into novel pathways describing biological processes of interest. The significance of using proper molecular identifiers is highlighted as well as the potential to further correlate molecules from different regulatory levels. To show the methodology’s potential, a set of transcriptomic datasets are meta-analyzed as an example.

Advances in Proteomic and Metabolomic Profiling of Neurodegenerative Diseases

Proteomics and metabolomics are two emerging fields that hold promise to shine light on the molecular mechanisms causing neurodegenerative diseases. Research in this area may reveal and quantify specific metabolites and proteins that can be targeted by therapeutic interventions intended at halting or reversing the neurodegenerative process. This review aims at providing a general overview on the current status of proteomic and metabolomic profiling in neurodegenerative diseases. We focus on the most common neurodegenerative disorders, including Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. We discuss the relevance of state-of-the-art metabolomics and proteomics approaches and their potential for biomarker discovery. We critically review advancements made so far, highlighting how metabolomics and proteomics may have a significant impact in future therapeutic and biomarker development. Finally, we further outline technologies used so far as well as challenges and limitations, placing the current information in a future-facing context.

Integrating Transcriptomics, Genomics, and Imaging in Alzheimer's Disease: A Federated Model

Alzheimer's disease (AD) affects more than 1 in 9 people age 65 and older and becomes an urgent public health concern as the global population ages. In clinical practice, structural magnetic resonance imaging (sMRI) is the most accessible and widely used diagnostic imaging modality. Additionally, genome-wide association studies (GWAS) and transcriptomics-the study of gene expression-also play an important role in understanding AD etiology and progression. Sophisticated imaging genetics systems have been developed to discover genetic factors that consistently affect brain function and structure. However, most studies to date focused on the relationships between brain sMRI and GWAS or brain sMRI and transcriptomics. To our knowledge, few methods have been developed to discover and infer multimodal relationships among sMRI, GWAS, and transcriptomics. To address this, we propose a novel federated model, Genotype-Expression-Imaging Data Integration (GEIDI), to identify genetic and transcriptomic influences on brain sMRI measures. The relationships between brain imaging measures and gene expression are allowed to depend on a person's genotype at the single-nucleotide polymorphism (SNP) level, making the inferences adaptive and personalized. We performed extensive experiments on publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Experimental results demonstrated our proposed method outperformed state-of-the-art expression quantitative trait loci (eQTL) methods for detecting genetic and transcriptomic factors related to AD and has stable performance when data are integrated from multiple sites. Our GEIDI approach may offer novel insights into the relationship among image biomarkers, genotypes, and gene expression and help discover novel genetic targets for potential AD drug treatments.

Epigenomics and Lipidomics Integration in Alzheimer Disease: Pathways Involved in Early Stages

Background: Alzheimer Disease (AD) is the most prevalent dementia. However, the physiopathological mechanisms involved in its development are unclear. In this sense, a multi-omics approach could provide some progress. Methods: Epigenomic and lipidomic analysis were carried out in plasma samples from patients with mild cognitive impairment (MCI) due to AD (n = 22), and healthy controls (n = 5). Then, omics integration between microRNAs (miRNAs) and lipids was performed by Sparse Partial Least Squares (s-PLS) regression and target genes for the selected miRNAs were identified. Results: 25 miRNAs and 25 lipids with higher loadings in the sPLS regression were selected. Lipids from phosphatidylethanolamines (PE), lysophosphatidylcholines (LPC), ceramides, phosphatidylcholines (PC), triglycerides (TG) and several long chain fatty acids families were identified as differentially expressed in AD. Among them, several fatty acids showed strong positive correlations with miRNAs studied. In fact, these miRNAs regulated genes implied in fatty acids metabolism, as elongation of very long-chain fatty acids (ELOVL), and fatty acid desaturases (FADs). Conclusions: The lipidomic–epigenomic integration showed that several lipids and miRNAs were differentially expressed in AD, being the fatty acids mechanisms potentially involved in the disease development. However, further work about targeted analysis should be carried out in a larger cohort, in order to validate these preliminary results and study the proposed pathways in detail.

The Hitchhiker's Guide to Untargeted Lipidomics Analysis: Practical Guidelines

Lipidomics is a newly emerged discipline involving the identification and quantification of thousands of lipids. As a part of the omics field, lipidomics has shown rapid growth both in the number of studies and in the size of lipidome datasets, thus, requiring specific and efficient data analysis approaches. This paper aims to provide guidelines for analyzing and interpreting lipidome data obtained using untargeted methods that rely on liquid chromatography coupled with mass spectrometry (LC-MS) to detect and measure the intensities of lipid compounds. We present a state-of-the-art untargeted LC-MS workflow for lipidomics, from study design to annotation of lipid features, focusing on practical, rather than theoretical, approaches for data analysis, and we outline possible applications of untargeted lipidomics for biological studies. We provide a detailed R notebook designed specifically for untargeted lipidome LC-MS data analysis, which is based on xcms software.

Association of plasma Aβ40/Aβ42 ratio and brain Aβ accumulation: testing a whole-brain PLS-VIP approach in individuals at risk of Alzheimer's disease

Molecular and brain regional/network-wise pathophysiological changes at preclinical stages of Alzheimer's disease (AD) have primarily been found through knowledge-based studies conducted in late-stage mild cognitive impairment/dementia populations. However, such an approach may compromise the objective of identifying the earliest spatial-temporal pathophysiological processes. We investigated 261 individuals with subjective memory complaints, a condition at increased risk of AD, to test a whole-brain, non-a-priori method based on partial least squares in unraveling the association between plasma Aβ42/Aβ40 ratio and an extensive set of brain regions characterized through molecular imaging of Aβ accumulation and cortical metabolism. Significant associations were mapped onto large-scale networks, identified through an atlas and by knowledge, to elaborate on the reliability of the results. Plasma Aβ42/40 ratio was associated with Aβ-PET uptake (but not FDG-PET) in regions generally investigated in preclinical AD such as those belonging to the default mode network, but also in regions/networks normally not accounted - including the central executive and salience networks - which likely have a selective vulnerability to incipient Aβ accumulation. The present whole-brain approach is promising to investigate early pathophysiological changes of AD to fully capture the complexity of the disease, which is essential to develop timely screening, detection, diagnostic, and therapeutic interventions.

Omics Data and Their Integrative Analysis to Support Stratified Medicine in Neurodegenerative Diseases

Molecular and clinical heterogeneity is increasingly recognized as a common characteristic of neurodegenerative diseases (NDs), such as Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis. This heterogeneity makes difficult the development of early diagnosis and effective treatment approaches, as well as the design and testing of new drugs. As such, the stratification of patients into meaningful disease subgroups, with clinical and biological relevance, may improve disease management and the development of effective treatments. To this end, omics technologies-such as genomics, transcriptomics, proteomics and metabolomics-are contributing to offer a more comprehensive view of molecular pathways underlying the development of NDs, helping to differentiate subtypes of patients based on their specific molecular signatures. In this article, we discuss how omics technologies and their integration have provided new insights into the molecular heterogeneity underlying the most prevalent NDs, aiding to define early diagnosis and progression markers as well as therapeutic targets that can translate into stratified treatment approaches, bringing us closer to the goal of personalized medicine in neurology.

The search for a convenient procedure to detect one of the earliest signs of Alzheimer's disease: A systematic review of the prediction of brain amyloid status

INTRODUCTION

Convenient, cost-effective tests for amyloid beta (Aβ) are needed to identify those at higher risk for developing Alzheimer's disease (AD). This systematic review evaluates recent models that predict dichotomous Aβ. (PROSPERO: CRD42020144734).

METHODS

We searched Embase and identified 73 studies from 29,581 for review. We assessed study quality using established tools, extracted information, and reported results narratively.

RESULTS

We identified few high-quality studies due to concerns about Aβ determination and analytical issues. The most promising convenient, inexpensive classifiers consist of age, apolipoprotein E genotype, cognitive measures, and/or plasma Aβ. Plasma Aβ may be sufficient if pre-analytical variables are standardized and scalable assays developed. Some models lowered costs associated with clinical trial recruitment or clinical screening.

DISCUSSION

Conclusions about models are difficult due to study heterogeneity and quality. Promising prediction models used demographic, cognitive/neuropsychological, imaging, and plasma Aβ measures. Further studies using standardized Aβ determination, and improved model validation are required.

Multi-omics integration in biomedical research - A metabolomics-centric review

Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.

Considerations for mass spectrometry-based multi-omic analysis of clinical samples

Introduction: The role of mass spectrometry in biomolecule analysis has become paramount over the last several decades ranging in the analysis across model systems and human specimens. Accordingly, the presence of mass spectrometers in clinical laboratories has also expanded alongside the number of researchers investigating the protein, lipid, and metabolite composition of an array of biospecimens. With this increase in the number of omic investigations, it is important to consider the entire experimental strategy from sample collection and storage, data collection and analysis.Areas covered: In this short review, we outline considerations for working with clinical (e.g. human) specimens including blood, urine, and cerebrospinal fluid, with emphasis on sample handling, profiling composition, targeted measurements and relevance to disease. Discussions of integrated genomic or transcriptomic datasets are not included. A brief commentary is also provided regarding new technologies with clinical relevance.Expert opinion: The role of mass spectrometry to investigate clinically related specimens is on the rise and the ability to integrate multiple omics datasets from mass spectrometry measurements will be crucial to further understanding human health and disease.