Aetiological and clinical heterogeneity is increasingly recognized as a common characteristic of Alzheimer’s disease and related dementias. This heterogeneity complicates diagnosis, treatment, and the design and testing of new drugs. An important line of research is discovery of multimodal biomarkers that will facilitate the targeting of subpopulations with homogeneous pathophysiological signatures. High-throughput ‘omics’ are unbiased data-driven techniques that probe the complex aetiology of Alzheimer’s disease from multiple levels (e.g. network, cellular, and molecular) and thereby account for pathophysiological heterogeneity in clinical populations. This review focuses on data reduction analyses that identify complementary disease-relevant perturbations for three omics techniques: neuroimaging-based subtypes, metabolomics-derived metabolite panels, and genomics-related polygenic risk scores. Neuroimaging can track accrued neurodegeneration and other sources of network impairments, metabolomics provides a global small-molecule snapshot that is sensitive to ongoing pathological processes, and genomics characterizes relatively invariant genetic risk factors representing key pathways associated with Alzheimer’s disease. Following this focused review, we present a roadmap for assembling these multiomics measurements into a diagnostic tool highly predictive of individual clinical trajectories, to further the goal of personalized medicine in Alzheimer’s disease.

Objective To profile the amine/phenol submetabolome to determine potential metabolite biomarkers associated with Parkinson’s disease (PD) and PD with incipient dementia. Methods At baseline of a 3-wave (18-month intervals) longitudinal study, serum samples were collected from 42 healthy controls and 43 PD patients. By wave 3 (year 3) 16 PD patients were diagnosed with dementia and were classified as PD with incipient dementia at baseline. Metabolomic profiling using dansylation isotope labeling liquid chromatography mass spectrometry was conducted to compare controls with full PD group, PD with no dementia and PD with incipient dementia. Results Metabolomic analyses detected 719 common metabolites in 80% of the samples. Some were significantly altered in pairwise comparison of different groups (fold-change of >1.2 or <0.83 with q<0.05). We discriminated PD and controls by using a 5-metabolite panel, vanillic acid, 3-hydroxykynurenine, isoleucyl-alanine, 5-acetylamino-6-amino-3-methyluracil and theophylline. The Receiver Operating Characteristic curve produced an Area-Under-the-Curve value of 0.955 with 87.5% sensitivity and 93.0% specificity. In comparing PD with no dementia with PD with incipient dementia we used an 8-metabolite panel, His-Asn-Asp-Ser, 3, 4-dihydroxyphenylacetone, desaminotyrosine, hydroxy-isoleucine, alanyl-alanine, putrescine [-2H], purine [+O] and its riboside. This produced an Area-Under-the-Curve value of 0.862 with 80.0% sensitivity and 77.0% specificity. Conclusions The significantly altered metabolites can be used to differentiate (1) PD patients from healthy controls with high accuracy and (2) the stable PD with no dementia group from those with incipient dementia. Following further validation in larger cohorts, these metabolites could be used for both discrimination and establishing prognosis in PD.

Genetic polymorphisms of Catechol-O-methyltransferase (COMT) and Brain-derived neurotrophic factor (BDNF) have shown promising but inconsistent linkages with executive function (EF) in normal aging. We tested (a) independent contributions of COMT and BDNF risk, (b) potential magnification by risk-related interactions or additive effects with age, and (c) effect modification through stratification by Apolipoprotein E (APOE; risk (ε4+)). Multiple linear regression models were applied with non-demented older adults (N = 634; range: 53–95 years) for an EF latent variable. No independent effects of BDNF or COMT on EF were observed. Additive (but not interactive) effects of COMT, BDNF, and age showed that older adults with a high-risk allelic combination performed differentially worse. Of two tested models of synergistic effects, the additive approach selectively supported a magnification hypothesis, which was qualified by the presence or absence of APOE ε4.

Using a non-invasive biofluid (saliva), we apply a powerful metabolomics workflow for unbiased biomarker discovery in Alzheimer's disease (AD). We profile and differentiate Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and AD groups. The workflow involves differential chemical isotope labeling liquid chromatography mass spectrometry using dansylation derivatization for in-depth profiling of the amine/phenol submetabolome. The total sample (N = 109) was divided in to the Discovery Phase (DP) (n = 82; 35 CN, 25 MCI, 22 AD) and a provisional Validation Phase (VP) (n = 27; 10 CN, 10 MCI, 7 AD). In DP we detected 6,230 metabolites. Pairwise analyses confirmed biomarkers for AD versus CN (63), AD versus MCI (47), and MCI versus CN (2). We then determined the top discriminating biomarkers and diagnostic panels. A 3-metabolite panel distinguished AD from CN and MCI (DP and VP: Area Under the Curve [AUC] = 1.000). The MCI and CN groups were best discriminated with a 2-metabolite panel (DP: AUC = 0.779; VP: AUC = 0.889). In addition, using positively confirmed metabolites, we were able to distinguish AD from CN and MCI with good diagnostic performance (AUC > 0.8). Saliva is a promising biofluid for both unbiased and targeted AD biomarker discovery and mechanism detection. Given its wide availability and convenient accessibility, saliva is a biofluid that can promote diversification of global AD biomarker research.

Recent studies have reported several genetic, health, and aging interaction effects in predicting cognitive performance and change. We used an accelerated longitudinal design to examine interactions among genetic, lifestyle, and aging for executive function (EF) in non-demented older adults (n=634; age range=53–95 years). The polymorphisms were Apolipoprotein E (APOE), Catechol-O-methyl transferase (COMT), and Brain-derived neurotrophic factor (BDNF). We tested (a) independent and additive effects of APOE, COMT, and BDNF and (b) APOE effect modification for COMT+ BDNF, on EF performance and 9-year change as separated by age and lifestyle activities. First, APOE ε4+ carriers had poorer EF performance and steeper 9-year decline. Second, APOE ε4+ carriers with (a) BDNF Met/Met genotype and (b) increasing allelic risk in the COMT+ BDNF risk panel had poorer EF performance; these effects were moderated by lifestyle activities (composite of everyday social, physical, cognitive activities). Examining APOE effect modification for COMT+ BDNF risk panel effects with other moderating factors may help identify complex neurobiological and genetic underpinnings of polygenic phenotypes such as EF in aging.

Background Trajectories of complex neurocognitive phenotypes in preclinical aging may be produced differentially through selective and interactive combinations of genetic risk. Objective We organize three possible combinations into a “network” of genetic risk indices derived from polymorphisms associated with normal and impaired cognitive aging, as well as Alzheimer’s disease (AD). Specifically, we assemble and examine three genetic clusters relevant to non-demented cognitive trajectories: (1) Apolipoprotein E (APOE), (2) a Cognitive Aging Genetic Risk Score (CA-GRS; Catechol-O-methyltransferase + Brain-derived neurotrophic factor), and (3) an AD-Genetic Risk Score (AD-GRS; Clusterin + Complement receptor 1 + Phosphatidylinositol-binding clathrin assembly protein). Method We use an accelerated longitudinal design (n = 634; age range = 55–95 years) to test whether AD-GRS (low versus high) moderates the effect of increasing CA-GRS risk on executive function (EF) performance and change as stratified by APOE status (ε4+ versus ε4-). Results APOE ε4 carriers with high AD-GRS had poorer EF performance at the centering age (75 years) and steeper 9-year decline with increasing CA-GRS but this association was not present in APOE ε4 carriers with low AD-GRS. Conclusions APOE ε4 carriers with high AD-GRS are at elevated risk of cognitive decline when they also possess higher CA-GRS risk. Genetic risk from both common cognitive aging and AD-related indices may interact in intensification networks to differential predict (1) level and trajectories of EF decline and (2) potential selective vulnerability for transitions into impairment and dementia.

We examined independent and cumulative effects of two Alzheimer's-related genetic polymorphisms, Apolipoprotein E (APOE) and Clusterin (CLU), in relation to the deleterious effects of poor vascular health (pulse pressure [PP]) on executive function (EF) performance and change in non-demented older adults. Using a sample (n = 593; age range = 53-95 years) from the Victoria Longitudinal Study, we applied latent growth modeling to test the effect of PP, as moderated by APOE and CLU, on an EF latent variable. EF was affected by higher levels of PP but differentially less so for carriers of low-risk alleles (APOE ε2+; CLU TT) than for moderate-or high-risk alleles (APOE ε2−; CLU C+). The cumulative genetic risk of APOE plus CLU provided similar moderation of PP level effects on EF. Future research may focus on how APOE and CLU might provide different but complementary contributions to predicting EF level and change. Vascular health risk in synergistic association with risk-related polymorphisms can elucidate the neurobiological underpinnings of cognitive trajectories in non-demented aging.

Background: Among the neurodegenerative diseases of aging, sporadic Alzheimer’s disease (AD) is the most prevalent and perhaps the most feared. With virtually no success at finding pharmaceutical therapeutics for altering progressive AD after diagnosis, research attention is increasingly directed at discovering biological and other markers that detect AD risk in the long asymptomatic phase. Both early detection and precision preclinical intervention require systematic investigation of multiple modalities and combinations of AD-related biomarkers and risk factors. We extend recent unbiased metabolomics research that produced a set of metabolite biomarker panels tailored to the discrimination of cognitively normal (CN), cognitively impaired and AD patients. Specifically, we compare the prediction importance of these panels with five other sets of modifiable and non-modifiable AD risk factors (genetic, lifestyle, cognitive, functional health and bio-demographic) in three clinical groups.Method: The three groups were: CN (n = 35), mild cognitive impairment (MCI; n = 25), and AD (n = 22). In a series of three pairwise comparisons, we used machine learning technology random forest analysis (RFA) to test relative predictive importance of up to 19 risk biomarkers from the six AD risk domains.Results: The three RFA multimodal prediction analyses produced significant discriminating risk factors. First, discriminating AD from CN was the AD metabolite panel and two cognitive markers. Second, discriminating AD from MCI was the AD/MCI metabolite panel and two cognitive markers. Third, discriminating MCI from CN was the MCI metabolite panel and seven markers from four other risk modalities: genetic, lifestyle, cognition and functional health.Conclusions: Salivary metabolomics biomarker panels, supplemented by other risk markers, were robust predictors of: (1) clinical differences in impairment and dementia and even; (2) subtle differences between CN and MCI. For the latter, the metabolite panel was supplemented by biomarkers that were both modifiable (e.g., functional) and non-modifiable (e.g., genetic). Comparing, integrating and identifying important multi-modal predictors may lead to novel combinations of complex risk profiles potentially indicative of neuropathological changes in asymptomatic or preclinical AD.