The aging phenotype in humans has been thoroughly studied but a detailed metabolic profiling capable of shading light on the underpinning biological processes of longevity is still missing. Here using a combined metabonomics approach compromising holistic ¹H-NMR profiling and targeted MS approaches, we report for the first time the metabolic phenotype of longevity in a well characterized human aging cohort compromising mostly female centenarians, elderly, and young individuals. With increasing age, targeted MS profiling of blood serum displayed a marked decrease in tryptophan concentration, while an unique alteration of specific glycerophospholipids and sphingolipids are seen in the longevity phenotype. We hypothesized that the overall lipidome changes specific to longevity putatively reflect centenarians' unique capacity to adapt/respond to the accumulating oxidative and chronic inflammatory conditions characteristic of their extreme aging phenotype. Our data in centenarians support promotion of cellular detoxification mechanisms through specific modulation of the arachidonic acid metabolic cascade as we underpinned increased concentration of 8,9-EpETrE, suggesting enhanced cytochrome P450 (CYP) enzyme activity. Such effective mechanism might result in the activation of an anti-oxidative response, as displayed by decreased circulating levels of 9-HODE and 9-oxoODE, markers of lipid peroxidation and oxidative products of linoleic acid. Lastly, we also revealed that the longevity process deeply affects the structure and composition of the human gut microbiota as shown by the increased extrection of phenylacetylglutamine (PAG) and p-cresol sulfate (PCS) in urine of centenarians. Together, our novel approach in this representative Italian longevity cohort support the hypothesis that a complex remodeling of lipid, amino acid metabolism, and of gut microbiota functionality are key regulatory processes marking exceptional longevity in humans.

As centenarians well represent the model of healthy aging, there are many important implications in revealing the underlying molecular mechanisms behind such successful aging. By combining NMR metabonomics and shot-gun lipidomics in serum we analyzed metabolome and lipidome composition of a group of centenarians with respect to elderly individuals. Specifically, NMR metabonomics profiling of serum revealed that centenarians are characterized by a metabolic phenotype distinct from that of elderly subjects, in particular regarding amino acids and lipid species. Shot- gun lipidomics approach displays unique changes in lipids biosynthesis in centenarians, with 41 differently abundant lipid species with respect to elderly subjects. These findings reveal phospho/sphingolipids as putative markers and biological modulators of healthy aging, in humans. Considering the particular actions of these metabolites, these data are suggestive of a better counteractive antioxidant capacity and a well-developed membrane lipid remodelling process in the healthy aging phenotype.

Hyperhomocysteinemia is a risk factor for cognitive decline and dementia, including Alzheimer disease (AD). Homocysteine (Hcy) is a sulfur-containing amino acid and metabolite of the methionine pathway. The interrelated methionine, purine, and thymidylate cycles constitute the one-carbon metabolism that plays a critical role in the synthesis of DNA, neurotransmitters, phospholipids, and myelin. In this study, we tested the hypothesis that one-carbon metabolites beyond Hcy are relevant to cognitive function and cerebrospinal fluid (CSF) measures of AD pathology in older adults. Cross-sectional analysis was performed on matched CSF and plasma collected from 120 older community-dwelling adults with (n = 72) or without (n = 48) cognitive impairment. Liquid chromatography-mass spectrometry was performed to quantify one-carbon metabolites and their cofactors. Least absolute shrinkage and selection operator (LASSO) regression was initially applied to clinical and biomarker measures that generate the highest diagnostic accuracy of a priori-defined cognitive impairment (Clinical Dementia Rating-based) and AD pathology (i.e., CSF tau phosphorylated at threonine 181 [p-tau181]/β-Amyloid 1–42 peptide chain [Aβ1–42] >0.0779) to establish a reference benchmark. Two other LASSO-determined models were generated that included the one-carbon metabolites in CSF and then plasma. Correlations of CSF and plasma one-carbon metabolites with CSF amyloid and tau were explored. LASSO-determined models were stratified by apolipoprotein E (APOE) ε4 carrier status. The diagnostic accuracy of cognitive impairment for the reference model was 80.8% and included age, years of education, Aβ1–42, tau, and p-tau181. A model including CSF cystathionine, methionine, S-adenosyl-L-homocysteine (SAH), S-adenosylmethionine (SAM), serine, cysteine, and 5-methyltetrahydrofolate (5-MTHF) improved the diagnostic accuracy to 87.4%. A second model derived from plasma included cystathionine, glycine, methionine, SAH, SAM, serine, cysteine, and Hcy and reached a diagnostic accuracy of 87.5%. CSF SAH and 5-MTHF were associated with CSF tau and p-tau181. Plasma one-carbon metabolites were able to diagnose subjects with a positive CSF profile of AD pathology in APOE ε4 carriers. We observed significant improvements in the prediction of cognitive impairment by adding one-carbon metabolites. This is partially explained by associations with CSF tau and p-tau181, suggesting a role for one-carbon metabolism in the aggregation of tau and neuronal injury. These metabolites may be particularly critical in APOE ε4 carriers.

The concept of healthy ageing today is linked to the efforts of maintaining physical and mental independence, and to prevent the decline of physiological functions that are mandatory to guarantee a good quality of life. Yet, an individual’s ageing trajectory and underlying mechanisms are multifaceted and still far from understood. Hence, there is an urgent need for the further development of ageing phenotypes that can be utilized in the identification of biomarkers to characterize and predict biological age, taking into account the multiple dimensions of ageing. New technologies, often termed ‘systems biology’ approaches, are being increasingly applied to research focused on ageing populations. These technologies should enable investigators, and eventually clinicians, to (a) understand ageing metabolism in a more detailed and specific way; (b) enable the phenotypic characterization of many age-related disorders including chronic disease states; and (c) stratify older adults and the multiple comorbid conditions that may impact them.