AD is not a sign of normal aging, but with age, the probability of suffering from AD increases annually. Considering the dramatic aging of populations worldwide, it is of great importance to explore common mechanisms between AD and natural aging. The key finding of this study is the systematic comparison of the metabolic mechanisms of AD, MCI, and aging. We first revealed the metabolic mechanism and potential biomarkers of AD in a natural aging population.
Based on this independent metabolomics analysis in a two-stage cross-sectional study, we found metabolites and pathways associated with aging. Some metabolites were involved in elderly-related diseases, such as fatty acids associated with atherosclerosis, prostaglandins and related compounds associated with cardiovascular disease, and long-chain fatty acids associated with MCI and AD. Strikingly, pathways enriched by aging-related metabolites were consistent with the hypotheses of aging mechanisms in previous studies, such as oxidative stress and inflammation. The carnitine shuttle indicates a process in which long-chain fatty acids are converted to corresponding acylcarnitines (ACs) and transferred into the mitochondria for energy production. Yu et al. (22) investigated higher concentrations of ACs during aging, indicating that our bodies may protect us from oxidative stress through the carnitine shuttle, and lower levels of histidine reflected the body's response to oxidative stress. The ratio of lipoxin A4 to cysteinyl leukotrienes was found to be negatively associated with age and suggests reduced anti-inflammatory ability. Additionally, in our work, pathways altered in the elderly were also related to various amino acid pathways. Glutamate is the middle step from glutamine to α-ketoglutarate (αKG), and these two deamination steps are called glutaminolysis (26). Enhanced glutaminolysis controls both cell growth and autophagy, which is known to decline with age by simulating lysosomal translocation and activation of mTORC1. Previous detection of amino acids in the serum of normal healthy Japanese people revealed that the concentrations of aspartate, asparagine, and arginine increased with age in males, whereas the levels of tyrosine asparagine, arginine and proline increased with age in females, which together suggests that aspartate and asparagine metabolism and arginine and proline metabolism are related to aging (27). Furthermore, delayed degradation of plasma tyrosine (precursor of dopamine) in the elderly may influence cognition disruption during aging (28, 29).
Previous studies have proven that oxidative stress plays a vital role in the progression of AD (30, 31), as well as in MCI. The shared pathways between AD and MCI in our results reflected that MCI and AD have the same mechanisms. Carnosine, synthesized from ß-alanine, is elevated in AD patients as a result of oxidative stress (32). Glutathione, made up of cysteine, glycine and glutamate, was shown to have neuroprotective effects by reducing Aß-related oxidative stress via 4-hydroxynonenal (33) and attenuating amyloid fibrillation (34). Additional perturbed mechanisms associated with energy metabolism are pyruvate metabolism, the TCA cycle and glutathione metabolism (35–37). Furthermore, lipid metabolism is one of the most extensively implicated dysfunctions in the context of AD. Consistent with that, cholesterol and sphingolipid transport, saturated fatty acid metabolism and sphingolipid metabolism were disrupted in both AD and MCI. Moreover, disrupted amino acid pathways may be related to alterations of neurotransmitters. Arginine (38) and butanoate metabolism were shown to be related to the metabolism of an inhibitory neurotransmitter, gamma-amino butyric acid (GABA). Tryptophan can be converted to serotonin or participate in the kynurenine pathway (KP). In AD brains, upregulation of the KP may result in the depletion of serotonin, which is vital for cognition and learning (39). In our results, four pathways overlapped between AD VS. MCI and MCI_AD VS. MCI, including lysine metabolism, polyamine metabolism, catecholamine metabolism, and prostaglandin 2 biosynthesis and metabolism. Specific lysine residues within the microtubule-binding motif are the major sites of tau acetylation, which can inhibit tau function as a result of impaired tau–microtubule interactions and promote pathological tau aggregation (40). Changes of polyamine metabolism in the brain influence the progression of AD through several mechanisms, such as the regulation of cholinergic neurotransmission (41). These pathway alterations indicated progressive changes in the patients from MCI to AD.
As shown in Fig. 1, plasma presents the most complete metabolic changes compared with those of CSF and serum; in particular, plasma reflects all metabolic changes in the brain tissue of AD patients. Thus, plasma metabolites will most likely be the source of noninvasive diagnostic markers. Moreover, we still cannot ignore the fact that more than 50% of metabolic changes in plasma cannot be evidenced in brain tissue and may be caused by other dietary or environmental exposures. Thus, the uniqueness of plasma AD markers should be given more attention in clinical studies.
As illustrated in Table 3, some fatty acids were found to be altered in both retrospective and prospective studies. In astroglia, palmitic acid may stimulate ceramide synthesis by secreting signalling molecules such as cytokines and nitric oxide, resulting in Aβ accumulation and tau hyperphosphorylation (42). Similarly, stearic, linoleic, and oleic acids were proven to be related to the accumulation of both Aβ and tau in vitro (43, 44). Five metabolites (palmitic acid, stearic acid, linoleic acid, glutamine, and oleic acid) in serum/plasma have also been confirmed in brain tissue, which suggests their powerful potential for the noninvasive diagnosis of AD. Moreover, arginine, creatine and histidine were observed in both retrospective and prospective studies. Considering that arginine and histidine were altered in both CSF and plasma/serum, these two metabolites may act as noninvasive biomarkers for the MCI population to monitor the progression from MCI to AD.
The novel findings in our study are the metabolic pathways and biomarkers related to both aging and AD. Regarding the three shared pathways between aging and AD, the TCA cycle did not attract our attention because it is such an extensive metabolic pathway altered in diverse physiological and pathological processes, which include the preclinical stage of AD (36). Purine nucleoside phosphorylase (PNP) converts guanosine to guanine and inosine to hypoxanthine and is an important enzyme involved in purine metabolism. A study on astroglia reported a marked increase in PNP with aging (45), while another study observed increased PNP activity in patients with AD (46). Regarding arginine and proline metabolism, arginine is the central substance and serves as the only precursor of nitric oxide (NO). NO could react with superoxide (O2−) to produce peroxynitrite (ONOO−), and the latter is so active that it would experience ecleavage and generate reactive oxygen/nitrogen species (ROS/RNS) (47), which could occur in the process of natural aging. In addition, the brain is much more vulnerable to nitroxidative stress than other tissues due to its high oxygen demand, weakened antioxidative ability and low proliferative trait of neurons, indicating that oxidative stress is involved in the initiation of AD in healthy individuals (15). Arginine could be metabolized to agmatine, which is involved in memory decline processes and can be found in both elderly and AD brain tissue (48). Arginine and proline metabolism contains several metabolic pathways we mentioned in the AD-related pathways, such glutathione, glycine, and polyamine metabolism. Evidence has shown that these pathways are related to aging (49–51). We can see that arginine and proline metabolism has been shown to play a role in prospective studies of the progression from no disease to MCI and eventually AD; likewise, arginine and proline metabolism appears in the pathways related to aging.
From the results of direct comparison of metabolite lists of aging and AD, there are three metabolites that were found to be duplicated: 16-a-hydroxypregnenolone, stearic acid and PC (16:0/22:5(4Z,7Z,10Z,13Z,16Z)). 16-alpha-hydroxypregnenolone is classified as a gluco/mineralocorticoid, a progestogin or aprogestogin derivative. Although no cytological mechanism studies have confirmed the role of 16-a-hydroxypregnenolone in aging and dementia, we observed that it was significantly associated with aging in our metabolomics analysis and altered in AD and MCI patients in population studies. Thus, experimental confirmation based on in vitro studies is urgently needed.
Strikingly, studies have demonstrated the role of stearic acid and PC (16:0/22:5(4Z,7Z,10Z,13Z,16Z)) in the pathological process of AD. Together with a recent study demonstrating a close relationship between tau protein and inflammatory signalling in astrocytes (52), we assume a possible pathological process in astrocytes combining aging and AD via inflammatory and oxidative responses. Patil and Chan et al. (43) found that astroglia-mediated oxidative stress may be related to stearic and palmitic fatty acid-induced hyperphosphorylation of tau. Investigations have shown that in astrocytes, stearic acid promotes the release of inflammatory factors such as IL-6 and TNFα (53). PC(16:0/22:5(4Z,7Z,10Z,13Z,16Z)) can be classified as PtdCho, which can be synthesized from cytidine diphosphate choline (CDP-choline) and diacylglycerol and contains long-chain polyunsaturated fatty acids, which are important components of neuron membranes. Wurtman et al. (54) proposed that choline was used to synthesize both acetylcholine (Ach) and PtdCho. Therefore, PtdCho could be taken to maintain the level of Ach when the body experiences a shortage of choline. Choline deficiency could occur in the contexts of both aging and AD, resulting in depletion of PtdCho and death of cholinergic neurons. This “autocannibalism” hypothesis partially explained the selective vulnerability of the cholinergic system and provided clues regarding PtdCho as a shared metabolite of natural aging and AD.
The limitations of our study are that we did not conduct in vitro experiments to verify the overlapping mechanisms between aging and AD. Although we used training and testing sets to screen out the aging-related metabolites that can be stably detected, our metabolomics research was a non-targeted test. It is necessary to further verify and analyse the sensitivity and specificity of metabolic markers based on targeted quantitative detection of a larger sample in a cohort population.
In conclusion, this study is the first to comprehensively compare metabolites and pathways between aging and AD by utilizing metabolomic measurement and systematic review. We proposed potential noninvasive biomarkers for AD diagnosis and MCI monitoring based on retrospective and prospective population studies. More importantly, we revealed the key role of arginine and proline metabolism in the progression from a healthy status to MCI to AD in a natural aging population. In particular, we provided potential metabolic markers (16-a-hydroxypregnenolone, stearic acid, and PC(16:0/22:5(4Z,7Z,10Z,13Z,16Z)) of AD diagnosis for future validation in a natural aging population.