A Multiomics Approach to Heterogeneity in Alzheimer's Disease

Focused Review and Roadmap

AmanPreet Badhwar; G. Peggy McFall; Shraddha Sapkota; Sandra E. Black; Howard Chertkow; Simon Duchesne; Mario Masellis; Liang Li; Roger A. Dixon; Pierre Bellec

Disclosures

Brain. 2020;143(5):1315-1331. 

In This Article

Metabolomics Panels

Metabolite Panels

We reviewed six Alzheimer's disease studies that constructed metabolite panels from top discriminant metabolites in biofluids: two plasma, one serum, two saliva, and one CSF (Supplementary Table 2). Platforms used for metabolomics analysis, performed alone or in combination, were as follows: ultra-performance liquid chromatography mass spectrometry (MS) and gas MS (Wang et al., 2014), liquid chromatography MS (Mapstone et al., 2017), faster ultra-performance liquid chromatography MS (Liang et al., 2015, 2016), liquid chromatography-tandem (also with solid phase extraction) and gas MS (Czech et al., 2012), and nuclear magnetic resonance spectroscopy (Figueira et al., 2016) (Supplementary Table 2).

Using plasma metabolome data, Wang et al. (2014) constructed a six-metabolite panel to discriminate Alzheimer's disease dementia from cognitively normal, and a five-metabolite panel to discriminate amnestic MCI from cognitively normal. Arachidonic acid, N,N-dimethylglycine, and thymine were present in both panels. Association of these three panel metabolites with lipid, amino acid, and nucleic acid metabolism, respectively, suggested specific metabolic deregulations early in Alzheimer's disease, resulting largely in increased inflammation and oxidative stress. Arachidonic acid (a polyunsaturated fatty acid) is a known modulator of neuroinflammation (Wang et al., 2014), while perturbations in N,N-dimethylglycine, and thymine levels can lead to oxidative DNA damage (Wang et al., 2014). Mapstone et al. (2017) used a 12 plasma metabolite panel to discriminate the following cohorts from cognitively normal: older adults with superior memory; amnestic MCI + Alzheimer's disease dementia patients; and participants who converted to amnestic MCI or Alzheimer's disease dementia in ~2 years. Similar to Wang et al. (2014), several panel metabolites were associated with lipid or amino acid metabolism. Panel metabolites were also found to be constituents of pathways regulating oxidative stress, inflammation, and nitric oxide bioavailability. Using serum metabolome data, Liang et al. (2016) identified a panel of two upregulated lipid metabolites (7-ketocholesterol, sphinganine-1-phosphate) that discriminated MCI from Alzheimer's disease dementia. 7-Ketocholesterol, a major oxidation product of cholesterol, has been reported to increase with Alzheimer's disease development, with potential role in the inflammatory responses (Testa et al., 2016), and amyloid-β formation/accumulation and induced neurotoxicity (Phan et al., 2013). Sphinganine-1-phosphate is an intermediate of sphingolipid metabolism, and sphingolipids have also been suggested to modulate the inflammation-associated pathogenesis of Alzheimer's disease, amyloid-β levels, and oxidative stress-driven neuronal apoptosis (Jazvinšćak Jembrek et al., 2015; Mizuno et al., 2016; Lin et al., 2017). Sphinganine-1-phosphate was also present in a three-metabolite panel constructed from the salivary metabolome, and discriminated patients with Alzheimer's disease dementia from cognitively normal individuals (Liang et al., 2015). The other two panel members, ornithine and phenyllactic acid, were amino acid metabolites (Ogata et al., 1999) with links to the same oxidative stress pathway reported by Mapstone et al. (2017). A separate study using saliva reported a seven-metabolite panel that discriminated pre-dementia (i.e. 5 years prior to dementia onset) from cognitively normal (Figueira et al., 2016). Of these seven metabolites, the inflammatory modulator histamine was also included as a panel metabolite by Mapstone et al. (2017), as mentioned before. Overall, the metabolites included in the panel were associated with amino acid, lipid, or energy metabolism. Czech et al. (2012) assessed multiple combinations of 16 CSF metabolites to discriminate Alzheimer's disease dementia from cognitively normal. Highest discrimination was obtained with a five metabolite panel consisting of amino acids and cortisol (Czech et al., 2012). Our focused review of Alzheimer's disease-associated metabolite panels highlight that the majority of discriminant molecules detected in biofluids are involved in amino acid, lipid, or nucleic acid metabolism (Figure 2B).

Figure 2.

A typical Alzheimer's disease metabolomics biomarker discovery pipeline. Metabolomics is a relatively recent addition to the systems biology toolkit for the study of NDDs of ageing (Wilkins and Trushina, 2017). It encompasses the global study of small molecules (50–1500 Da in mass) that are substrates and products of metabolism. Together, these metabolites (e.g. amino acids, antioxidants, vitamins) represent the overall physiological status of the organism. An individual's metabolic activity is influenced by an individual's genotype and environment (Kaddurah-Daouk et al., 2011). Analysis of the metabolome, therefore, provides an opportunity to study the dynamic molecular phenotype of an individual. Untargeted metabolomics approaches are increasingly used to compare two or more groups (e.g. Alzheimer's disease dementia and cognitively normal participants) and identify metabolite profiles associated with a disease. These profiles provide insight into underlying disease mechanisms, as well as constitute candidates for biomarker discovery and drug development. In the field of Alzheimer's disease research, metabolomics studies (targeted and untargeted) over the past decade have examined several biofluids and tissues, including serum, plasma, CSF, saliva, urine, and brain tissue (Wilkins and Trushina, 2017). Technologies include NMR (nuclear magnetic resonance) spectroscopy and mass spectrometry. (A) A typical Alzheimer's disease metabolomics biomarker discovery pipeline using mass spectrometry (MS)-liquid chromatography (LC) is depicted. Subsequent to metabolite extraction, identification, and quantification, most studies apply multivariate statistical methods to the metabolome data to identify the top discriminant metabolites. These can be further combined into metabolite panels to increase discriminative power (i.e. sensitivity and specificity) in Alzheimer's disease prediction and progression (Liang et al., 2015, 2016; Huan et al., 2018). Significant discriminative power is commonly tested with the receiver operating characteristic curve analysis (AUC values). Discriminant metabolite panels are then validated in independent samples. Following discriminant metabolite(s) discovery, researchers conduct pathway and network analyses, which provide crucial mechanistic insights into the sequences of processes leading to the heterogeneous phenotypes of neurodegeneration. Pathway analysies focus on identifying sequences of processes that lead to the presence of a discriminant metabolite. Network analyses examine how discriminant metabolites are connected to each other within Alzheimer's disease and related dementias. (B) The three main metabolism pathways (namely, amino acid, lipid and nucleic acid) that 90 Alzheimer's disease-associated metabolites in our review (n = 11 publications, Supplementary Table 2) were found to belong. The text colour indicates the biofluid metabolome each metabolite was identified in: red = serum or plasma, purple = saliva, black = CSF. A larger font size indicates that the metabolite was identified in more than one study (Supplementary Table 3) The maximum number of studies a metabolite was detected in our review was four. aPresence in plasma or serum and saliva. bPresence in plasma or serum and CSF. AD = Alzheimer's disease; CN = cognitively normal.

Metabolomics Pathways and Networks

We reviewed five Alzheimer's disease studies that followed-up non-targeted metabolomics research in biofluids with pathway or network analyses: two plasma, one plasma plus CSF, and two serum (Supplementary Table 2). Platforms used for metabolomics analysis were as follows: ultra-performance liquid chromatography-tandem MS (de Leeuw et al., 2017), MS (Graham et al., 2015), liquid chromatography MS (Trushina et al., 2013), gas MS (González-Domínguez et al., 2015), and ultra-performance liquid chromatography and gas MS (Orešič et al., 2011) (Supplementary Table 2).

In plasma, de Leeuw et al. (2017) identified 26 metabolites composed of mainly amino acids and lipids with significantly altered levels in Alzheimer's disease dementia patients. Network analyses suggested a shift in Alzheimer's disease towards amine and oxidative stress compounds, known to cause imbalances in neurotransmitter production, amyloid-β generation, inflammation, and neurovascular health. Perturbations in amino acid metabolism (interlinked polyamine and L-arginine pathways) were also demonstrated in the plasma metabolome of MCI to Alzheimer's disease dementia converters (Graham et al., 2015). Changes in polyamine and L-arginine metabolism have been linked to neurotoxicity, and deregulations in genesis and/or death of neural cells and neurotransmitter production (Graham et al., 2015). Other metabolic pathways notably impacted were prostaglandin, glucose and cholesterol (Graham et al., 2015). As inflammation promoting cyclo-oxygenase enzymes are involved in prostaglandin synthesis, altered prostaglandin biosynthesis in converters suggests an underlying inflammatory response (Graham et al., 2015). Deregulated glucose metabolism in converters may be due to brain insulin resistance and microvascular disease (Graham et al., 2015). Moreover, perturbations in cholesterol metabolism have been linked to amyloid-β deposition and tau hyperphosphorylation (Gamba et al., 2012; Graham et al., 2015). Cholesterol metabolism (specifically cholesterol and sphingolipids transport) was also found to be abnormal in both plasma and CSF from patients with Alzheimer's disease dementia (Trushina et al., 2013). In serum, metabolism of amino acids dominated the top pathways altered in patients with Alzheimer's disease dementia in one study (González-Domínguez et al., 2015), a finding in line with plasma metabolome data (de Leeuw et al., 2017). A second study in serum reported a three-metabolite panel predictive of progression from MCI to Alzheimer's disease dementia (within 27 ± 18 months), with major contribution from upregulated 2,4-dihydroxybutanoic acid, a metabolite potentially overproduced during hypoperfusion-related hypoxia (Orešič et al., 2011). Upregulation of the pentose phosphate pathway in progressors further supported the involvement of secondary hypoxia in Alzheimer's disease pathogenesis. More glucose is metabolized via the pentose phosphate pathway in the brain under hypoxic conditions.

Summary

Overall, metabolite panels, and metabolomics pathway and network analyses provide the following insights: (i) discriminant Alzheimer's disease-associated metabolites may be narrowly or broadly interconnected (Wilkins and Trushina, 2017); (ii) metabolomes of different biofluids provide convergent and biofluid-related mechanistic insights into Alzheimer's disease pathology (Trushina et al., 2013); (iii) genotype-associated (e.g. APOE status) differences in preclinical and clinical groups suggest different routes to Alzheimer's disease (de Leeuw et al., 2017); and (iv) neurodegenerative disease subtypes can be characterized by metabolomics analyses (de Leeuw et al., 2017).

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