For the last two decades, significant innovations in MS-based metabolic profiling and analysis of disease-related alterations have been made and, in doing so, these efforts have borne highly sensitive and valuable diagnostic information [38–40]. In the current study, we explored a combination of targeted and untargeted metabolic profiling in addition to advanced multivariate statistical analysis for the discovery of sensitive and specific metabolite biomarkers for rapid AD classification post-mortem. To capture the diversity of metabolites involved in AD pathobiology, we have used this particular method to detect 2,080 metabolites of the superior frontal gyrus from many biologically relevant metabolic pathways. Our multi-step biomarker selection, model construction, and cross validation have demonstrated the robust diagnostic power of this metabolic profiling method in this study of 48 NC, HPC, MCI and AD subjects. Additionally, we have applied complementary LC/GC-MS approaches for enhanced monitoring of the metabolome related to AD and, cumulatively, our results show clinically relevant disturbances in energy metabolism and substrate utilization.
The metabolite profiling approach presented in this study determined 5 fatty acids capable of discriminating AD patients from NC and HPC samples with an average AUC of 97%. Recent metabolomics studies have also shown perturbations in fatty acid metabolism across differing Alzheimer pathologies. It was found that the dysregulation of sphingolipids and glycerophospholipids, long-chain fatty acids, and unsaturated fatty acids have been associated with AD [7, 24, 41]. Similarly, significant disturbances in fatty acid metabolism were also observed in the current study (p = 0.008). Medium-chain fatty acids like lauric acid, which is found in high levels in coconut oil, have been proposed as possible nutritional therapies for the treatment of cognitive decline [42–44], and a significant difference in lauric acid was also observed in the AD and NC groups in this study. The markedly reduced levels of these fatty acids observed in our AD subjects could be linked to the impaired glucose metabolism that is well-documented in AD patients [45–47]. Declines in the levels of the identified fatty acids in conjunction with decreased glucose metabolism might suggest that β-oxidation of fatty acids, which is generally low in the brain, is being upregulated to support the energy needs of the brain in AD patients. Supplementation of the fatty acids that can be rapidly metabolized might help support the energy needs of the brain, potentially ameliorating symptoms. This might account for the data cited in the above referenced reports which suggest that the addition of lauric acid to the diet (via coconut oil), may improve some symptoms in AD patients. Lauric acid is known to cross the blood-brain barrier , and dietary lauric acid might therefore be accessible as an energy source for the brain . The results of the current study warrant further investigation of the therapeutic potential of lauric acid for the treatment and prevention of AD.
Previous studies have shown evidence for brain glucose dysregulation in AD as characterized by higher brain tissue glucose concentration, reduced glycolytic flux, and lower GLUT3 expression as a function of increasing AD pathogenesis [45, 50, 51]. Interestingly, the literature has shown involvement of the Warburg effect in non-tumor disease processes  and, in the context of AD, loss of brain aerobic glycolysis as a function of normal human aging is associated with increased tau deposition in preclinical AD [53, 54]. In addition, previous results have shown impaired hypothalamic insulin signaling to be associated with elevated BCAA levels in a mouse model of AD , while defects in BCAA metabolism have in-turn been shown to drive primary AD neuropathology . A prospective cohort study of over 22,000 participants found significant associations between circulating BCAAs and risk of incident dementia and AD . It has been shown that defects in BCAA metabolism, and subsequent accumulation, can lead to the phosphorylation of tau proteins and the incidence of AD . Other studies have found post-translational modifications to the stabilizing tau proteins, which were induced by lysine residues. It has been proposed that these modifications may play an integral role in the pathobiology of tau protein . Our pathway analysis also revealed similar results with a significant degradation of lysine (p = 0.007) and BCAAs (p = 0.025), potentially signifying the underlying pathophysiology of AD. Given the recent failure of numerous billion-dollar clinical trials targeting traditionally hypothesized AD mechanisms such as reduced acetylcholine, Aβ plaques/neurofibrillary tangles, and tau protein , our enzyme and pathway enrichment results further corroborate previous evidence of widespread mitochondrial dysfunction concomitant with Aβ pathology and AD progression [59–61], providing compelling evidence for mitochondrial bioenergetics as a novel therapeutic target for preventing/slowing the onset/progression of AD.
Overall, our findings led to an integrated hypothesis describing the pathophysiology of AD in Fig. 9 and are conceptualized with respect to the widespread mitochondrial dysfunction observed in our results. As can be seen, with increased AD pathogenicity, significant metabolic reprogramming is observed. Specifically, a decrease in aerobic glycolysis is followed by a shift toward degradation of BCAA for energy production, mostly associated with HPC and MCI subgroups. With even greater disease progression, further metabolic reprogramming is observed; fatty acids are progressively utilized for generation of ATP via increased β-oxidation activity and generation of FADH2 and NADH for oxidative phosphorylation in the electron transport chain. Preference for fatty acid substrates was most pronounced in the MCI and AD subgroups.
Additionally, we evaluated levels of 4 unidentified features with p < 0.05 and FC > 2, which informed the construction of independent PLS-DA models for enhanced classification of AD from MCI samples. The combination of these 4 features had a diagnostic sensitivity and specificity of 84.1% and 86.3%, respectively (AUC = 0.917). Although accurate tests for AD pathology with high severity are currently available (i.e., PET amyloid and tau, CSF amyloid and tau, plasma tau), diagnostic tests useful for intermediate (MCI) and low (HPC) pathology levels are still lacking. In this study, diagnosis of HPC and MCI subgroups was achieved with more than 90% overall AUC. Realization of these findings in plasma or CSF may inform clinical trial selection and study stratification, enable mass screening, and indicate viable therapeutic targets.
Strengths and limitations
A major strength of the study lies in the well-characterized BSHRI cohort  with measures of cognitive status and neuropathological examination at death. Furthermore, inclusion of traditionally understudied HPC and MCI groups allowed for the metabolic characterization of asymptomatic individuals with AD-consistent pathology and non-AD individuals with cognitive decline, respectively. Cumulatively, our panel of candidate markers shows potential for classification of individuals with early brain pathology and other dementias, facilitating enhanced post-mortem diagnosis. Furthermore, if validated in readily available biospecimens with minimally invasive sample collection, this novel panel of candidate markers may enable early disease diagnosis and enhanced treatment options. Additionally, we applied six distinct metabolomics assays encompassing complementary GC and LC techniques to ensure maximal coverage of the brain metabolome and were able to monitor more than 2,000 metabolites and features. Given the known benefit of complementary MS platforms for elucidation of AD pathology [18, 62, 63], our large-scale multi-platform metabolomics approach utilizing both targeted and untargeted profiling enables comprehensive pathway and enzyme analysis, a key strength of this study to previous literature.
The main limitation of this study is the relatively small sample size. Moreover, our samples were taken cross-sectionally and therefore cannot infer longitudinal changes in metabolite information over time. Also, samples were only taken from a single brain region; inferences to other AD-associated brain structures are unknown. Nevertheless, conventional power was achieved for all biomarker analyses (β < 0.2), and models were internally validated (p < 0.01). Our results warrant further investigation in a larger sample with serial cognitive assessments taken during life as well as tissue samples collected from distinct brain regions both resistant and vulnerable to AD pathology in order to monitor possible differential changes between tissue types.