In the past decade, significant progress has been made in complex disease research across multiple omics layers from genome, transcriptome and proteome to metabolome. There is an increasing awareness of the importance of biological interconnections, and much success has been achieved using systems biology approaches. However, because of the typical focus on one single omics layer at a time, existing systems biology findings explain only a modest portion of complex disease. Recent advances in multi-omics data collection and sharing present us new opportunities for studying complex diseases in a more comprehensive fashion, and yet simultaneously create new challenges considering the unprecedented data dimensionality and diversity. Here, our goal is to review extant and emerging network approaches that can be applied across multiple biological layers to facilitate a more comprehensive and integrative multilayered omics analysis of complex diseases.

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Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data.

A major barrier to the effective conduct of clinical trials of new drug candidates against Alzheimer’s disease (AD) and to identifying patients for receiving future disease-modifying treatments is the limited capacity of the current health system to find and diagnose patients with early AD pathology. This may be related in part to the limited capacity of the current health systems to select those people likely to have AD pathology in order to confirm the diagnosis with available cerebrospinal fluid and imaging biomarkers at memory clinics. In the current narrative review, we summarize the literature on candidate blood tests for AD that could be implemented in primary care settings and used for the effective identification of individuals at increased risk of AD pathology, who could be referred for potential inclusion in clinical trials or future approved treatments following additional testing. We give an updated account of blood-based candidate biomarkers and biomarker panels for AD-related brain changes. Our analysis centres on biomarker candidates that have been replicated in more than one study and discusses the need of further studies to achieve the goal of a primary care-based screening algorithm for AD.

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Blood-based molecular biomarkers for Alzheimer's disease.

The neurodegenerative disorder Alzheimer's disease is characterised by the formation of β-amyloid plaques and neurofibrillary tangles in the brain parenchyma, which cause synapse and neuronal loss. This leads to clinical symptoms, such as progressive memory deficits. Clinically, these pathological changes can be detected in the cerebrospinal fluid and with brain imaging, although reliable blood tests for plaque and tangle pathologies remain to be developed. Plaques and tangles often co-exist with other brain pathologies, including aggregates of transactive response DNA-binding protein 43 and Lewy bodies, but the extent to which these contribute to the severity of Alzheimer's disease is currently unknown. In this 'At a glance' article and poster, we summarise the molecular biomarkers that are being developed to detect Alzheimer's disease and its related pathologies. We also highlight the biomarkers that are currently in clinical use and include a critical appraisal of the challenges associated with applying these biomarkers for diagnostic and prognostic purposes of Alzheimer's disease and related neurodegenerative disorders, also in their prodromal clinical phases.

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Molecular biomarkers of Alzheimer's disease: progress and prospects.

Importance Blood biomarkers able to diagnose Alzheimer disease (AD) at the preclinical stage would enable trial enrollment when the disease is potentially reversible. Plasma neuronal-enriched extracellular vesicles (nEVs) of patients with AD were reported to exhibit elevated levels of phosphorylated (p) tau, Aβ42, and phosphorylated insulin receptor substrate 1 (IRS-1). Objective To validate nEV biomarkers as AD predictors. Design, Setting, Participants This case-control study included longitudinal plasma samples from cognitively normal participants in the Baltimore Longitudinal Study of Aging (BLSA) cohort who developed AD up to January 2015 and age- and sex-matched controls who remained cognitively normal over a similar length of follow-up. Repeated samples were blindly analyzed over 1 year from participants with clinical AD and controls from the Johns Hopkins Alzheimer Disease Research Center (JHADRC). Data were collected from September 2016 to January 2018. Analyses were conducted in March 2019. Main Outcomes and Measures Neuronal-enriched extracellular vesicles were immunoprecipitated; tau, Aβ42, and IRS-1 biomarkers were quantified by immunoassays; and nEV concentration and diameter were determined by nanoparticle tracking analysis. Levels and longitudinal trajectories of nEV biomarkers between participants with future AD and control participants were compared. Results Overall, 887 longitudinal plasma samples from 128 BLSA participants who eventually developed AD and 222 age and sex-matched controls who remained cognitively normal were analyzed. Participants were followed up (from earliest sample to AD symptom onset) for a mean (SD) of 3.5 (2.31) years (range, 0-9.73 years). Overall, 161 participants were included in the training set, and 80 were in the test set. Participants in the BLSA cohort with future AD (mean [SD] age, 79.09 [7.02] years; 68 women [53.13%]) had longitudinally higher p-tau181, p-tau231, pSer312-IRS-1, pY-IRS-1, and nEV diameter than controls (mean [SD] age, 76.2 [7.36] years; 110 women [50.45%]) but had similar Aβ42, total tau, TSG101, and nEV concentration. In the training BLSA set, a model combining preclinical longitudinal data achieved 89.6% area under curve (AUC), 81.8% sensitivity, and 85.8% specificity for predicting AD. The model was validated in the test BLSA set (80% AUC, 55.6% sensitivity, 88.7% specificity). Preclinical levels of nEV biomarkers were associated with cognitive performance. In addition, 128 repeated samples over 1 year from 64 JHADRC participants with clinical AD and controls were analyzed. In the JHADRC cohort (35 participants with AD: mean [SD] age, 74.03 [8.73] years; 18 women [51.43%] and 29 controls: mean [SD] age, 72.14 [7.86] years; 23 women [79.31%]), nEV biomarkers achieved discrimination with 98.9% AUC, 100% sensitivity, and 94.7% specificity in the training set and 76.7% AUC, 91.7% sensitivity, and 60% specificity in the test set. Conclusions and Relevance We validated nEV biomarker candidates and further demonstrated that their preclinical longitudinal trajectories can predict AD diagnosis. These findings motivate further development of nEV biomarkers toward a clinical blood test for AD.

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Association of Extracellular Vesicle Biomarkers With Alzheimer Disease in the Baltimore Longitudinal Study of Aging.

Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data

With different approaches to finding prognostic or diagnostic biomarkers for Alzheimer’s disease (AD), many studies pursue only brief lists of biomarkers or disease specific pathways, potentially dismissing information from groups of correlated biomarkers. Using a novel Bayesian graphical network method, with data from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging, the aim of this study was to assess the biological connectivity between AD associated blood-based proteins. Briefly, three groups of protein markers (18, 37, and 48 proteins, respectively) were assessed for the posterior probability of biological connection both within and between clinical classifications. Clinical classification was defined in four groups: high performance healthy controls (hpHC), healthy controls (HC), participants with mild cognitive impairment (MCI), and participants with AD. Using the smaller group of proteins, posterior probabilities of network similarity between clinical classifications were very high, indicating no difference in biological connections between groups. Increasing the number of proteins increased the capacity to separate both hpHC and HC apart from the AD group (0 for complete separation, 1 for complete similarity), with posterior probabilities shifting from 0.89 for the 18 protein group, through to 0.54 for the 37 protein group, and finally 0.28 for the 48 protein group. Using this approach, we identified beta-2 microglobulin (β2M) as a potential master regulator of multiple proteins across all classifications, demonstrating that this approach can be used across many data sets to identify novel insights into diseases like AD.

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Bayesian graphical network analyses reveal complex biological interactions specific to Alzheimer's disease.

Objective: We assessed a blood-based signature, which previously demonstrated high accuracy at stratifying individuals with high or low neocortical β-amyloid burden (NAB), to determine whether it could also identify individuals at risk of progression to Alzheimer disease (AD) within 54 months. Methods: We generated the blood-based signature for 585 healthy controls (HCs) and 74 participants with mild cognitive impairment (MCI) from the Australian Imaging, Biomarkers and Lifestyle Study who underwent clinical reclassification (blinded to biomarker findings) at 54-month follow-up. The individuals were split into estimated high and low NAB groups based on a cutoff of 1.5 standardized uptake value ratio. We assessed the predictive accuracy of the high and low NAB groupings based on progression to mild cognitive impairment or AD according to clinical reclassification at 54-month follow-up. Results: Twelve percent of HCs with estimated high NAB progressed in comparison to 5% of HCs with estimated low NAB (odds ratio = 2.4). Forty percent of the participants with MCI who had estimated high NAB progressed in comparison to 5% of the participants with MCI who had estimated low NAB (odds ratio = 12.3). These ratios are in line with those reported for Pittsburgh compound B–PET results. Individuals with estimated high NAB had faster rates of memory decline than those with estimated low NAB. Conclusion: These findings suggest that a simple blood-based signature not only provides estimates of NAB but also predicts cognitive decline and disease progression, identifying individuals at risk of progressing toward AD at the prodromal and preclinical stages.

Predicting Alzheimer disease from a blood-based biomarker profile: A 54-month follow-up

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Bayesian graphical network analyses reveal complex biological interactions specific to Alzheimer's disease.

Predicting Alzheimer disease from a blood-based biomarker profile: A 54-month follow-up