Global metabolic profiling to model biological processes of aging in twins

Towards Healthy Longevity: Comprehensive Insights from Molecular Targets and Biomarkers to Biological Clocks

Aging is a complex and time-dependent decline in physiological function that affects most organisms, leading to increased risk of age-related diseases. Investigating the molecular underpinnings of aging is crucial to identify geroprotectors, precisely quantify biological age, and propose healthy longevity approaches. This review explores pathways that are currently being investigated as intervention targets and aging biomarkers spanning molecular, cellular, and systemic dimensions. Interventions that target these hallmarks may ameliorate the aging process, with some progressing to clinical trials. Biomarkers of these hallmarks are used to estimate biological aging and risk of aging-associated disease. Utilizing aging biomarkers, biological aging clocks can be constructed that predict a state of abnormal aging, age-related diseases, and increased mortality. Biological age estimation can therefore provide the basis for a fine-grained risk stratification by predicting all-cause mortality well ahead of the onset of specific diseases, thus offering a window for intervention. Yet, despite technological advancements, challenges persist due to individual variability and the dynamic nature of these biomarkers. Addressing this requires longitudinal studies for robust biomarker identification. Overall, utilizing the hallmarks of aging to discover new drug targets and develop new biomarkers opens new frontiers in medicine. Prospects involve multi-omics integration, machine learning, and personalized approaches for targeted interventions, promising a healthier aging population.

Implications of metabolism on multi-systems healthy aging across the lifespan

Aging is increasingly thought to involve dysregulation of metabolism in multiple organ systems that culminate in decreased functional capacity and morbidity. Here, we seek to understand complex interactions among metabolism, aging, and systems-wide phenotypes across the lifespan. Among 2469 adults (mean age 74.7 years; 38% Black) in the Health, Aging and Body Composition study we identified metabolic cross-sectionally correlates across 20 multi-dimensional aging-related phenotypes spanning seven domains. We used LASSO-PCA and bioinformatic techniques to summarize metabolome-phenome relationships and derive metabolic scores, which were subsequently linked to healthy aging, mortality, and incident outcomes (cardiovascular disease, disability, dementia, and cancer) over 9 years. To clarify the relationship of metabolism in early adulthood to aging, we tested association of these metabolic scores with aging phenotypes/outcomes in 2320 participants (mean age 32.1, 44% Black) of the Coronary Artery Risk Development in Young Adults (CARDIA) study. We observed significant overlap in metabolic correlates across the seven aging domains, specifying pathways of mitochondrial/cellular energetics, host-commensal metabolism, inflammation, and oxidative stress. Across four metabolic scores (body composition, mental-physical performance, muscle strength, and physical activity), we found strong associations with healthy aging and incident outcomes, robust to adjustment for risk factors. Metabolic scores for participants four decades younger in CARDIA were related to incident cardiovascular, metabolic, and neurocognitive performance, as well as long-term cardiovascular disease and mortality over three decades. Conserved metabolic states are strongly related to domain-specific aging and outcomes over the life-course relevant to energetics, host-commensal interactions, and mechanisms of innate immunity.

Metabolomics and Risk of Dementia: A Systematic Review of Prospective Studies

BACKGROUND

The projected increase in the prevalence of dementia has sparked interest in understanding the pathophysiology and underlying causal factors in its development and progression. Identifying novel biomarkers in the preclinical or prodromal phase of dementia may be important for predicting early disease risk. Applying metabolomic techniques to prediagnostic samples in prospective studies provides the opportunity to identify potential disease biomarkers.

OBJECTIVE

The objective of this systematic review was to summarize the evidence on the associations between metabolite markers and risk of dementia and related dementia subtypes in human studies with a prospective design.

DESIGN

We searched PubMed, PsycINFO, and Web of Science databases from inception through December 8, 2023. Thirteen studies (mean/median follow-up years: 2.1-21.0 y) were included in the review.

RESULTS

Several metabolites detected in biological samples, including amino acids, fatty acids, acylcarnitines, lipid and lipoprotein variations, hormones, and other related metabolites, were associated with risk of developing dementia. Our systematic review summarized the adjusted associations between metabolites and dementia risk; however, our findings should be interpreted with caution because of the heterogeneity across the included studies and potential sources of bias. Further studies are warranted with well-designed prospective cohort studies that have defined study populations, longer follow-up durations, the inclusion of additional diverse biological samples, standardization of techniques in metabolomics and ascertainment methods for diagnosing dementia, and inclusion of other related dementia subtypes.

CONCLUSIONS

This study contributes to the limited systematic reviews on metabolomics and dementia by summarizing the prospective associations between metabolites in prediagnostic biological samples with dementia risk. Our review discovered additional metabolite markers associated with the onset of developing dementia and may help aid in the understanding of dementia etiology. The protocol is registered in the International Prospective Register of Systematic Reviews (PROSPERO) database (https://www.crd.york.ac.uk/prospero/; registration ID: CRD42022357521).

Untargeted metabolomic profiling in children identifies novel pathways in asthma and atopy

BACKGROUND

Asthma and other atopic disorders can present with varying clinical phenotypes marked by differential metabolomic manifestations and enriched biological pathways.

OBJECTIVE

We sought to identify these unique metabolomic profiles in atopy and asthma.

METHODS

We analyzed baseline nonfasted plasma samples from a large multisite pediatric population of 470 children aged <13 years from 3 different sites in the United States and France. Atopy positivity (At+) was defined as skin prick test result of ≥3 mm and/or specific IgE ≥ 0.35 IU/mL and/or total IgE ≥ 173 IU/mL. Asthma positivity (As+) was based on physician diagnosis. The cohort was divided into 4 groups of varying combinations of asthma and atopy, and 6 pairwise analyses were conducted to best assess the differential metabolomic profiles between groups.

RESULTS

Two hundred ten children were classified as At-As-, 42 as At+As-, 74 as At-As+, and 144 as At+As+. Untargeted global metabolomic profiles were generated through ultra-high-performance liquid chromatography-tandem mass spectroscopy. We applied 2 independent machine learning classifiers and short-listed 362 metabolites as discriminant features. Our analysis showed the most diverse metabolomic profile in the At+As+/At-As- comparison, followed by the At-As+/At-As- comparison, indicating that asthma is the most discriminant condition associated with metabolomic changes. At+As+ metabolomic profiles were characterized by higher levels of bile acids, sphingolipids, and phospholipids, and lower levels of polyamine, tryptophan, and gamma-glutamyl amino acids.

CONCLUSION

The At+As+ phenotype displays a distinct metabolomic profile suggesting underlying mechanisms such as modulation of host-pathogen and gut microbiota interactions, epigenetic changes in T-cell differentiation, and lower antioxidant properties of the airway epithelium.

Give and Take: The Reciprocal Control of Metabolism and Cell Cycle

Cell cycle is an ordered sequence of events that occur in a cell preparing for cell division . The cell cycle is a four-stage process in which the cell increases in size, copies its DNA , prepares to divide, and divides. All these stages require a coordination of signaling pathways as well as adequate levels of energy and building blocks. These specific signaling and metabolic switches are tightly orchestrated in order for the cell cycle to occur properly. In this book chapter, we will provide information on the basis of metabolism and cell cycle interplay, and we will finish by an unexhaustive list of metabolomics approaches available to study the reciprocal control of metabolism and cell cycle.

Metabolite Stability in Archived Neonatal Dried Blood Spots Used for Epidemiologic Research

Epidemiologic studies of low-frequency exposures or outcomes using metabolomics analyses of neonatal dried blood spots (DBS) often require assembly of samples with substantial differences in duration of storage. Independent assessment of stability of metabolites in archived DBS will enable improved design and interpretation of epidemiologic research utilizing DBS. Neonatal DBS routinely collected and stored as part of the California Genetic Disease Screening Program between 1983 and 2011 were used. The study population included 899 children without cancer before age 6 years, born in California. High-resolution metabolomics with liquid-chromatography mass spectrometry was performed, and the relative ion intensities of common metabolites and selected xenobiotic metabolites of nicotine (cotinine and hydroxycotinine) were evaluated. In total, we detected 26,235 mass spectral features across 2 separate chromatography methods (C18 hydrophobic reversed-phase chromatography and hydrophilic-interaction liquid chromatography). For most of the 39 metabolites related to nutrition and health status, we found no statistically significant annual trends across the years of storage. Nicotine metabolites were captured in the DBS with relatively stable intensities. This study supports the usefulness of DBS stored long-term for epidemiologic studies of the metabolome. -Omics-based information gained from DBS may also provide a valuable tool for assessing prenatal environmental exposures in child health research.

Sebacic Acid as a Potential Age-Related Biomarker of Liver Aging: Evidence Linking Mice and Human

The aging process is complicated and involves diverse organ dysfunction; furthermore, the biomarkers that are able to reflect biological aging are eagerly sought after to monitor the system-wide decline associated with the aging process. To address this, we performed a metabolomics analysis using a longitudinal cohort study from Taiwan (N = 710) and established plasma metabolomic age using a machine learning algorithm. The resulting estimation of age acceleration among the older adults was found to be correlated with HOMA-insulin resistance. In addition, a sliding window analysis was used to investigate the undulating decrease in hexanoic and heptanoic acids that occurs among the older adults at different ages. A comparison of the metabolomic alterations associated with aging between humans and mice implied that ω-oxidation of medium-chain fatty acids was commonly dysregulated in older subjects. Among these fatty acids, sebacic acid, an ω-oxidation product produced by the liver, was significantly decreased in the plasma of both older humans and aged mice. Notably, an increase in the production and consumption of sebacic acid within the liver tissue of aged mice was observed, along with an elevation of pyruvate-to-lactate conversion. Taken together, our study reveals that sebacic acid and metabolites of ω-oxidation are the common aging biomarkers in both humans and mice. The further analysis suggests that sebacic acid may play an energetic role in supporting the production of acetyl-CoA during liver aging, and thus its alteration in plasma concentration potentially reflects the aging process.

How do metabolic processes age: Evidence from human metabolomic studies

Metabolomics, the global profiling of small molecules in the body, has emerged as a promising analytical approach for assessing molecular changes associated with ageing at the population level. Understanding root metabolic ageing pathways may have important implications for managing age-related disease risk. In this short review, relevant studies published in the last few years that have made valuable contributions to this field will be discussed. These include large-scale studies investigating metabolic changes with age, metabolomic clocks, and metabolic pathways associated with ageing phenotypes. Recent significant advances include the use of longitudinal study designs, populations spanning the whole life course, standardised analytical platforms of enhanced metabolome coverage and development of multivariate analyses. While many challenges remain, recent studies have demonstrated the considerable promise of this field.

In Vitro Bioactivities of Commonly Consumed Cereal, Vegetable, and Legume Seeds as Related to Their Bioactive Components: An Untargeted Metabolomics Approach Using UHPLC-QTOF-MS2

We conducted a comprehensive evaluation of the antioxidant, anti-obesity, anti-diabetic, and anti-glycation activities associated with the consumption of broccoli, red cabbage, alfalfa, and buckwheat seeds. Additionally, we explored the relationship between these biological activities and the profiles of amino acids, polyphenols, and organic acids identified in the seeds. Our findings demonstrated that red cabbage, broccoli, and buckwheat extracts exhibited significantly higher antioxidant potential compared to the alfalfa extract. Moreover, buckwheat displayed the most significant capacity for inhibiting alpha-glucosidase. Remarkably, broccoli and red cabbage demonstrated substantial anti-glycation and lipase inhibitory potentials. We identified the presence of amino acids, polyphenols, and organic acids in the extracts through untargeted metabolomics analysis. Correlation analysis revealed that pyroglutamic acid positively correlated with all the investigated functional properties. Most polyphenols made positive contributions to the functional properties, with the exception of ferulic acid, which displayed a negative correlation with all tested biological activities. Furthermore, gluconic acid and arabinonic acid among the organic acids identified displayed a positive correlation with all the functional properties. These results strongly support the anti-diabetic, anti-obesity, and anti-glycation potential of red cabbage, broccoli, and buckwheat seeds.

Current Trends in Anti-Aging Strategies

The process of aging manifests from a highly interconnected network of biological cascades resulting in the degradation and breakdown of every living organism over time. This natural development increases risk for numerous diseases and can be debilitating. Academic and industrial investigators have long sought to impede, or potentially reverse, aging in the hopes of alleviating clinical burden, restoring functionality, and promoting longevity. Despite widespread investigation, identifying impactful therapeutics has been hindered by narrow experimental validation and the lack of rigorous study design. In this review, we explore the current understanding of the biological mechanisms of aging and how this understanding both informs and limits interpreting data from experimental models based on these mechanisms. We also discuss select therapeutic strategies that have yielded promising data in these model systems with potential clinical translation. Lastly, we propose a unifying approach needed to rigorously vet current and future therapeutics and guide evaluation toward efficacious therapies.

Longitudinal metabolomics of increasing body-mass index and waist-hip ratio reveals two dynamic patterns of obesity pandemic

BACKGROUND/OBJECTIVE

This observational study dissects the complex temporal associations between body-mass index (BMI), waist-hip ratio (WHR) and circulating metabolomics using a combination of longitudinal and cross-sectional population-based datasets and new systems epidemiology tools.

SUBJECTS/METHODS

Firstly, a data-driven subgrouping algorithm was employed to simplify high-dimensional metabolic profiling data into a single categorical variable: a self-organizing map (SOM) was created from 174 metabolic measures from cross-sectional surveys (FINRISK, n = 9708, ages 25-74) and a birth cohort (NFBC1966, n = 3117, age 31 at baseline, age 46 at follow-up) and an expert committee defined four subgroups of individuals based on visual inspection of the SOM. Secondly, the subgroups were compared regarding BMI and WHR trajectories in an independent longitudinal dataset: participants of the Young Finns Study (YFS, n = 1286, ages 24-39 at baseline, 10 years follow-up, three visits) were categorized into the four subgroups and subgroup-specific age-dependent trajectories of BMI, WHR and metabolic measures were modelled by linear regression.

RESULTS

The four subgroups were characterised at age 39 by high BMI, WHR and dyslipidemia (designated TG-rich); low BMI, WHR and favourable lipids (TG-poor); low lipids in general (Low lipid) and high low-density-lipoprotein cholesterol (High LDL-C). Trajectory modelling of the YFS dataset revealed a dynamic BMI divergence pattern: despite overlapping starting points at age 24, the subgroups diverged in BMI, fasting insulin (three-fold difference at age 49 between TG-rich and TG-poor) and insulin-associated measures such as triglyceride-cholesterol ratio. Trajectories also revealed a WHR progression pattern: despite different starting points at the age of 24 in WHR, LDL-C and cholesterol-associated measures, all subgroups exhibited similar rates of change in these measures, i.e. WHR progression was uniform regardless of the cross-sectional metabolic profile.

CONCLUSIONS

Age-associated weight variation in adults between 24 and 49 manifests as temporal divergence in BMI and uniform progression of WHR across metabolic health strata.

Metabolome profiling in the study of aging processes

Aging of a living organism is closely related to systemic metabolic changes. But due to the multilevel and network nature of metabolic pathways, it is difficult to understand these connections. Today, this problem is solved using one of the main approaches of metabolomics — untargeted metabolome profiling. The purpose of this publication is to systematize the results of metabolomic studies based on such profiling, both in animal models and in humans.

Metabolome Profiling in Aging Studies

Organism aging is closely related to systemic metabolic changes. However, due to the multilevel and network nature of metabolic pathways, it is difficult to understand these connections. Today, scientists are trying to solve this problem using one of the main approaches of metabolomics-untargeted metabolome profiling. The purpose of this publication is to review metabolomic studies based on such profiling, both in animal models and in humans. This review describes metabolites that vary significantly across age groups and include carbohydrates, amino acids, carnitines, biogenic amines, and lipids. Metabolic pathways associated with the aging process are also shown, including those associated with amino acid, lipid, and energy metabolism. The presented data reveal the mechanisms of aging and can be used as a basis for monitoring biological age and predicting age-related diseases in the early stages of their development.

The Age-Accompanied and Diet-Associated Remodeling of the Phospholipid, Amino Acid, and SCFA Metabolism of Healthy Centenarians from a Chinese Longevous Region: A Window into Exceptional Longevity

As centenarians provide a paradigm of healthy aging, investigating the comprehensive metabolic profiles of healthy centenarians is of utmost importance for the pursuit of health and longevity. However, relevant reports, especially studies considering the dietary influence on metabolism, are still limited, mostly lacking the guidance of a model of healthy aging. Therefore, exploring the signatures of the integrative metabolic profiles of the healthy centenarians from a famous longevous region, Bama County, China, should be an effective way. The global metabolome in urine and the short-chain fatty acids (SCFAs) in the feces of 30 healthy centenarians and 31 elderly people aged 60−70 from the longevous region were analyzed by non-targeted metabolomics combined with metabolic target analysis. The results showed that the characteristic metabolites related to longevity were mostly summarized into phosphatidylserine, lyso-phosphatidylethanolamine, phosphatidylcholine, phosphatidylinositol, bile acids, and amino acids (p < 0.05). Six metabolic pathways were found significant relevant to longevity. Furthermore, acetic acid, propionic acid, butyric acid, valeric acid, and total SCFA were significantly increased in the centenarian group (p < 0.05) and were also positively associated with the dietary fiber intake (p < 0.01). It was age-accompanied and diet-associated remodeling of phospholipid, amino acid, and SCFA metabolism that expressed the unique metabolic signatures related to exceptional longevity. This metabolic remodeling is suggestive of cognitive benefits, better antioxidant capacity, the attenuation of local inflammation, and health-span-promoting processes, which play a critical and positive role in shaping healthy aging.

The metabolomics of human aging: Advances, challenges, and opportunities

As the global population becomes older, understanding the impact of aging on health and disease becomes paramount. Recent advancements in multiomic technology have allowed for the high-throughput molecular characterization of aging at the population level. Metabolomics studies that analyze the small molecules in the body can provide biological information across a diversity of aging processes. Here, we review the growing body of population-scale metabolomics research on aging in humans, identifying the major trends in the field, implicated biological pathways, and how these pathways relate to health and aging. We conclude by assessing the main challenges in the research to date, opportunities for advancing the field, and the outlook for precision health applications.

Multi-omics analysis reveals the host–microbe interactions in aged rhesus macaques

Aging is a complex multifactorial process that greatly affects animal health. Multi-omics analysis is widely applied in evolutionary biology and biomedical research. However, whether multi-omics can provide sufficient information to reveal comprehensive changes in aged non-human primates remains unclear. Here, we explored changes in host–microbe interactions with aging in Chinese rhesus macaques (Macaca mulatta lasiota, CRs) using multi-omics analysis. Results showed marked changes in the oral and gut microbiomes between young and aged CRs, including significantly reduced probiotic abundance and increased pathogenic bacterial abundance in aged CRs. Notably, the abundance of Lactobacillus, which can metabolize tryptophan to produce aryl hydrocarbon receptor (AhR) ligands, was decreased in aged CRs. Consistently, metabolomics detected a decrease in the plasma levels of AhR ligands. In addition, free fatty acid, acyl carnitine, heparin, 2-(4-hydroxyphenyl) propionic acid, and docosahexaenoic acid ethyl ester levels were increased in aged CRs, which may contribute to abnormal fatty acid metabolism and cardiovascular disease. Transcriptome analysis identified changes in the expression of genes associated with tryptophan metabolism and inflammation. In conclusion, many potential links among different omics were found, suggesting that aged CRs face multiple metabolic problems, immunological disorders, and oral and gut diseases. We determined that tryptophan metabolism is critical for the physiological health of aged CRs. Our findings demonstrate the value of multi-omics analyses in revealing host–microbe interactions in non-human primates and suggest that similar approaches could be applied in evolutionary and ecological research of other species.

Detection of the Disorders of Glycerophospholipids and Amino Acids Metabolism in Lung Tissue From Male COPD Patients

Background: At present, few studies have reported the metabolic profiles of lung tissue in patients with COPD. Our study attempted to analyze the lung metabolome in male COPD patients and to screen the overlapping biomarkers of the lung and plasma metabolomes.

Methods: We performed untargeted metabolomic analysis of normal lung tissue from two independent sets (the discovery set: 20 male COPD patients and 20 controls and the replication set: 47 male COPD patients and 27 controls) and of plasma samples from 80 male subjects containing 40 COPD patients and 40 controls.

Results: We found glycerophospholipids (GPs) and Amino acids were the primary classes of differential metabolites between male COPD patients and controls. The disorders of GPs metabolism and the valine, leucine and isoleucine biosynthesis metabolism pathways were identified in lung discovery set and then also validated in the lung replication set. Combining lung tissue and plasma metabolome, Phytosphingosine and l-tryptophan were two overlapping metabolites biomarkers. Binary logistic regression suggested that phytosphingosine together with l-tryptophan was closely associated with male COPD and showed strong diagnostic power with an AUC of 0.911 (95% CI: 0.8460-0.9765).

Conclusion: Our study revealed the metabolic perturbations of lung tissues from male COPD patients. The detected disorders of GPs and amino acids may provide an insight into the pathological mechanism of COPD. Phytosphingosine and l-tryptophan were two novel metabolic biomarkers for differentiating COPD patients and controls.

The metabolome as a biomarker of aging in Drosophila melanogaster

Many biomarkers have been shown to be associated not only with chronological age but also with functional measures of biological age. In human populations, it is difficult to show whether variation in biological age is truly predictive of life expectancy, as such research would require longitudinal studies over many years, or even decades. We followed adult cohorts of 20 Drosophila Genetic Reference Panel (DGRP) strains chosen to represent the breadth of lifespan variation, obtain estimates of lifespan, baseline mortality, and rate of aging, and associate these parameters with age‐specific functional traits including fecundity and climbing activity and with age‐specific targeted metabolomic profiles. We show that activity levels and metabolome‐wide profiles are strongly associated with age, that numerous individual metabolites show a strong association with lifespan, and that the metabolome provides a biological clock that predicts not only sample age but also future mortality rates and lifespan. This study with 20 genotypes and 87 metabolites, while relatively small in scope, establishes strong proof of principle for the fly as a powerful experimental model to test hypotheses about biomarkers and aging and provides further evidence for the potential value of metabolomic profiles as biomarkers of aging.

Metabolic Rewiring by Human Placenta-Derived Mesenchymal Stem Cell Therapy Promotes Rejuvenation in Aged Female Rats

Aging is a degenerative process involving cell function deterioration, leading to altered metabolic pathways, increased metabolite diversity, and dysregulated metabolism. Previously, we reported that human placenta-derived mesenchymal stem cells (hPD-MSCs) have therapeutic effects on ovarian aging. This study aimed to identify hPD-MSC therapy-induced responses at the metabolite and protein levels and serum biomarker(s) of aging and/or rejuvenation. We observed weight loss after hPD-MSC therapy. Importantly, insulin-like growth factor-I (IGF-I), known prolongs healthy life spans, were markedly elevated in serum. Capillary electrophoresis-time-of-flight mass spectrometry (CE-TOF/MS) analysis identified 176 metabolites, among which the levels of 3-hydroxybutyric acid, glycocholic acid, and taurine, which are associated with health and longevity, were enhanced after hPD-MSC stimulation. Furthermore, after hPD-MSC therapy, the levels of vitamin B6 and its metabolite pyridoxal 5′-phosphate were markedly increased in the serum and liver, respectively. Interestingly, hPD-MSC therapy promoted serotonin production due to increased vitamin B6 metabolism rates. Increased liver serotonin levels after multiple-injection therapy altered the expression of mRNAs and proteins associated with hepatocyte proliferation and mitochondrial biogenesis. Changes in metabolites in circulation after hPD-MSC therapy can be used to identify biomarker(s) of aging and/or rejuvenation. In addition, serotonin is a valuable therapeutic target for reversing aging-associated liver degeneration.

The Association Between Fecal Short-Chain Fatty Acids, Gut Microbiota, and Visceral Fat in Monozygotic Twin Pairs

PURPOSE

To examine the association of short-chain fatty acids (SCFAs), gut microbiota and obesity in individual twins and to control for genetic and shared environmental effects by studying monozygotic intrapair differences.

PATIENTS AND METHODS

The study recruited 20 pairs of monozygotic twins. Body composition measurements were performed by using the multi-frequency bioelectrical impedance technique. SCFAs were extracted from feces and quantified by gas chromatography-mass spectrometer. Gut microbiota was evaluated by 16S rRNA gene sequencing.

RESULTS

Fecal SCFAs were negatively correlated with adiposity parameters including body mass index, visceral adipose tissue and waist circumference (all P < 0.05). Metastat analysis showed that the top 5 relatively abundant bacterial taxa of viscerally obese and non-obese groups were Bacteroides, Collinsella, Eubacterium rectale group, Lachnoclostridium, and Tyzzerella. Participants with visceral obesity had lower abundance of Bacteroides and Collinsella compared to non-obese patients (P < 0.05). Among them, the abundance of Collinsella was positively correlated with acetic acid concentrations (r = 0.63, P = 0.011). There were no significant intrapair differences in each SCFA concentrations between the twins in our study (P > 0.05).

CONCLUSION

Low fecal concentrations of SCFAs were associated with visceral obesity, and the gut microbiota might be involved in the underlying mechanism.

An Integrated Strategy for Mass Spectrometry-Based Multiomics Analysis of Single Cells

Single-cell-based genomics and transcriptomics analysis have revealed substantial cellular heterogeneity among seemingly identical cells. Knowledge of the cellular heterogeneity at multiomics levels is vital for a better understanding of tumor metastasis and drug resistance, stem cell differentiation, and embryonic development. However, unlike genomics and transcriptomics studies, single-cell characterization of metabolites, proteins, and post-translational modifications at the omics level remains challenging due to the lack of amplification methods and the wide diversity of these biomolecules. Therefore, new tools that are capable of investigating these unamplifiable "omes" from the same single cells are in high demand. In this work, a microwell chip was prepared and the internal surface was modified for hydrophilic interaction liquid chromatography-based tandem extraction of metabolites and proteins and subsequent protein digestion. Next, direct electrospray ionization mass spectrometry was adopted for single-cell metabolome identification, and a data-independent acquisition-mass spectrometry approach was established for simultaneous proteome profiling and phosphoproteome analysis without phosphopeptide enrichment. This integrated strategy resulted in 132 putatively annotated compounds, more than 1200 proteins, and the first large-scale phosphorylation data set from single-cell analysis. Application of this strategy in chemical perturbation studies provides a multiomics view of cellular changes, demonstrating its capability for more comprehensive investigation of cellular heterogeneity.

Metabolic drift in the aging nervous system is reflected in human cerebrospinal fluid

Chronic diseases affecting the central nervous system (CNS) like Alzheimer's or Parkinson's disease typically develop with advanced chronological age. Yet, aging at the metabolic level has been explored only sporadically in humans using biofluids in close proximity to the CNS such as the cerebrospinal fluid (CSF). We have used an untargeted liquid chromatography high-resolution mass spectrometry (LC-HRMS) based metabolomics approach to measure the levels of metabolites in the CSF of non-neurological control subjects in the age of 20 up to 74. Using a random forest-based feature selection strategy, we extracted 69 features that were strongly related to age (p_age < 0.001, r_age = 0.762, R²_{Boruta age} = 0.764). Combining an in-house library of known substances with in silico chemical classification and functional semantic annotation we successfully assigned putative annotations to 59 out of the 69 CSF metabolites. We found alterations in metabolites related to the Cytochrome P450 system, perturbations in the tryptophan and kynurenine pathways, metabolites associated with cellular energy (NAD+, ADP), mitochondrial and ribosomal metabolisms, neurological dysfunction, and an increase of adverse microbial metabolites. Taken together our results point at a key role for metabolites found in CSF related to the Cytochrome P450 system as most often associated with metabolic aging.

Aging-related markers in rat urine revealed by dynamic metabolic profiling using machine learning

The process of aging and metabolism is intimately intertwined; thus, developing biomarkers related to metabolism is critical for delaying aging. However, few studies have identified reliable markers that reflect aging trajectories based on machine learning. We generated metabolomic profiles from rat urine using ultra-performance liquid chromatography/mass spectrometry. This was dynamically collected at four stages of the rat's age (20, 50, 75, and 100 weeks) for both the training and test groups. Partial least squares-discriminant analysis score plots revealed a perfect separation trajectory in one direction with increasing age in the training and test groups. We further screened 25 aging-related biomarkers through the combination of four algorithms (VIP, time-series, LASSO, and SVM-RFE) in the training group. They were validated in the test group with an area under the curve of 1. Finally, six metabolites, known or novel aging-related markers, were identified, including epinephrine, glutarylcarnitine, L-kynurenine, taurine, 3-hydroxydodecanedioic acid, and N-acetylcitrulline. We also found that, except for N-acetylcitrulline (p < 0.05), the identified aging-related metabolites did not differ between tumor-free and tumor-bearing rats at 100 weeks (p > 0.05). Our findings reveal the metabolic trajectories of aging and provide novel biomarkers as potential therapeutic antiaging targets.

Simultaneous metabolomics and proteomics analysis of plasma-derived extracellular vesicles

Extracellular vesicles (EVs) are nanoscale vesicles with a phospholipid bilayer. In the past few decades, EVs have gained more and more attention, which is attributed to their important roles in cell-to-cell communication. They are regarded as promising sources for disease biomarkers and have been explored for applications in early-stage diagnostics, monitoring of disease status, therapeutics and drug delivery. Nevertheless, EVs are a heterogeneous group of vesicles, and include two predominant classes: exosomes and microvesicles. The origins of these vesicles are diverse, which determines their differences in features and functions. To study the diversity of these EV subpopulations, it is essential to elucidate their compositions including proteins, metabolites, etc. Here, we presented a tandem extraction method to obtain metabolites and proteins from the same batch of EVs simultaneously, enabling a multi-omics differential analysis of exosomes and microvesicles in human plasma. As a result, we found 112 different proteins and 50 different metabolites between exosomes and microvesicles, demonstrating the diversity of these EV subpopulations. Furthermore, compared with human plasma, these two major classes of EVs showed distinct metabolome features, which indicated the necessity of analysing the metabolites derived from EVs to obtain a more comprehensive profile of the plasma metabolome, and the potential of EVs as important sources for biomarker screening.

Caloric restriction creates a metabolic pattern of chronological aging delay that in budding yeast differs from the metabolic design established by two other geroprotectors

Caloric restriction and the tor1Δ mutation are robust geroprotectors in yeast and other eukaryotes. Lithocholic acid is a potent geroprotector in Saccharomycescerevisiae. Here, we used liquid chromatography coupled with tandem mass spectrometry method of non-targeted metabolomics to compare the effects of these three geroprotectors on the intracellular metabolome of chronologically aging budding yeast. Yeast cells were cultured in a nutrient-rich medium. Our metabolomic analysis identified and quantitated 193 structurally and functionally diverse water-soluble metabolites implicated in the major pathways of cellular metabolism. We show that the three different geroprotectors create distinct metabolic profiles throughout the entire chronological lifespan of S. cerevisiae. We demonstrate that caloric restriction generates a unique metabolic pattern. Unlike the tor1Δ mutation or lithocholic acid, it slows down the metabolic pathway for sulfur amino acid biosynthesis from aspartate, sulfate and 5-methyltetrahydrofolate. Consequently, caloric restriction significantly lowers the intracellular concentrations of methionine, S-adenosylmethionine and cysteine. We also noticed that the low-calorie diet, but not the tor1Δ mutation or lithocholic acid, decreases intracellular ATP, increases the ADP:ATP and AMP:ATP ratios, and rises intracellular ADP during chronological aging. We propose a model of how the specific remodeling of cellular metabolism by caloric restriction contributes to yeast chronological aging delay.

Metabolomics of Cerebrospinal Fluid from Healthy Subjects Reveal Metabolites Associated with Ageing

To increase our understanding of age-related diseases affecting the central nervous system (CNS) it is important to understand the molecular processes of biological ageing. Metabolomics of cerebrospinal fluid (CSF) is a promising methodology to increase our understanding of naturally occurring processes of ageing of the brain and CNS that could be reflected in CSF. In the present study the CSF metabolomes of healthy subjects aged 30-74 years (n = 23) were studied using liquid chromatography high-resolution mass spectrometry (LC-HRMS), and investigated in relation to age. Ten metabolites were identified with high confidence as significantly associated with ageing, eight with increasing levels with ageing: isoleucine, acetylcarnitine, pipecolate, methionine, glutarylcarnitine, 5-hydroxytryptophan, ketoleucine, and hippurate; and two decreasing with ageing: methylthioadenosine and 3-methyladenine. To our knowledge, this is the first time the CSF metabolomes of healthy subjects are assessed in relation to ageing. The present study contributes to the field of ageing metabolomics by presenting a number of metabolites present in CSF with potential relevance for ageing and the results motivate further studies.

Metabolomics: small molecules that matter more

Metabolomics, an analytical study with high-throughput profiling, helps to understand interactions within a biological system. Small molecules, called metabolites or metabolomes with the size of <1500 Da, depict the status of a biological system in a different manner. Currently, we are in need to globally analyze the metabolome and the pathways involved in healthy, as well as diseased conditions, for possible therapeutic applications. Metabolome analysis has revealed high-abundance molecules during different conditions such as diet, environmental stress, microbiota, and disease and treatment states. As a result, it is hard to understand the complete and stable network of metabolites of a biological system. This review helps readers know the available techniques to study metabolomics in addition to other major omics such as genomics, transcriptomics, and proteomics. This review also discusses the metabolomics in various pathological conditions and the importance of metabolomics in therapeutic applications.

Aging and CMV discordance are associated with increased immune diversity between monozygotic twins

Background

Broadly, much of variance in immune system phenotype has been linked to the influence of non-heritable factors rather than genetics. In particular, two non-heritable factors: aging and human cytolomegavirus (CMV) infection, have been known to account for significant inter-individual immune variance. However, many specific relationships between them and immune composition remain unclear, especially between individuals over narrower age ranges. Further exploration of these relationships may be useful for informing personalized intervention development.

Results

To address this need, we evaluated 41 different cell type frequencies by mass cytometry and identified their relationships with aging and CMV seropositivity. Analyses were done using 60 healthy individuals, including 23 monozygotic twin pairs, categorized into young (12–31 years) and middle-aged (42–59 years). Aging and CMV discordance were associated with increased immune diversity between monozygotic twins overall, and particularly strongly in various T cell populations. Notably, we identified 17 and 11 cell subset frequencies as relatively influenced and uninfluenced by non-heritable factors, respectively, with results that largely matched those from studies on older-aged cohorts. Next, CD4+ T cell frequency was shown to diverge with age in twins, but with lower slope than in demographically similar non-twins, suggesting that much inter-individual variance in this cell type can be attributed to interactions between genetic and environmental factors. Several cell frequencies previously associated with memory inflation, such as CD27- CD8+ T cells and CD161+ CD4+ T cells, were positively correlated with CMV seropositivity, supporting findings that CMV infection may incur rapid aging of the immune system.

Conclusions

Our study confirms previous findings that aging, even within a relatively small age range and by mid-adulthood, and CMV seropositivity, both contribute significantly to inter-individual immune diversity. Notably, we identify several key immune cell subsets that vary considerably with aging, as well as others associated with memory inflation which correlate with CMV seropositivity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12979-021-00216-1.

Effects of myocardial ischemia/reperfusion injury on plasma metabolomic profile during aging

Background

Heart disease is a frequent cause of hospitalization and mortality for elderly patients. A common feature of both heart disease and aging itself is the involvement of metabolic organ alterations ultimately leading to changes in circulating metabolite levels. However, the specific contribution of aging and ischemic injury to the metabolic dysregulation occurring in older adults with ischemic heart disease is still unknown.

Aim

To evaluate the effects of aging and ischemia/reperfusion (I/R) injury on plasma metabolomic profiling in mice.

Methods

Young and aged mice were subjected to a minimally invasive model of I/R injury or sham operation. Complete evaluation of cardiac function and untargeted plasma metabolomics analysis were performed.

Results

We confirmed that aged mice from the sham group had impaired cardiac function and augmented left ventricular (LV) dimensions compared to young sham‐operated mice. Further, we found that ischemic injury did not drastically reduce LV systolic/diastolic function and dyssynchrony in aged compared to young mice. Using an untargeted metabolomics approach focused on aqueous metabolites, we found that ischemic injury does not affect the plasma metabolomic profile either in young or old mice. Our data also demonstrate that age significantly affects circulating metabolite levels (predominantly amino acids, phospholipids and organic acids) and perturbs several pathways involved in amino acid, glucid and nucleic acid metabolism as well as pyridoxal‐5′‐phosphate salvage pathway in both sham and ischemic mice.

Conclusions

Our approach increases our understanding of age‐associated plasma metabolomic signatures in mice with and without heart disease excluding confounding factors related to metabolic comorbidities.

Can Blood-Circulating Factors Unveil and Delay Your Biological Aging?

According to the World Health Organization, the population of over 60 will double in the next 30 years in the developed countries, which will enforce a further raise of the retirement age and increase the burden on the healthcare system. Therefore, there is an acute issue of maintaining health and prolonging active working longevity, as well as implementation of early monitoring and prevention of premature aging and age-related disorders to avoid early disability. Traditional indicators of biological age are not always informative and often require extensive and expensive analysis. The study of blood factors is a simple and easily accessible way to assess individual health and supplement the traditional indicators of a person's biological age with new objective criteria. With age, the processes of growth and development, tissue regeneration and repair decline; they are gradually replaced by enhanced catabolism, inflammatory cell activity, and insulin resistance. The number of senescent cells supporting the inflammatory loop rises; cellular clearance by autophagy and mitophagy slows down, resulting in mitochondrial and cellular damage and dysfunction. Monitoring of circulated blood factors not only reflects these processes, but also allows suggesting medical intervention to prevent or decelerate the development of age-related diseases. We review the age-related blood factors discussed in recent publications, as well as approaches to slowing aging for healthy and active longevity.

Targeting metabolic pathways for extension of lifespan and healthspan across multiple species

Metabolism plays a significant role in the regulation of aging at different levels, and metabolic reprogramming represents a major driving force in aging. Metabolic reprogramming leads to impaired organismal fitness, an age-dependent increase in susceptibility to diseases, decreased ability to mount a stress response, and increased frailty. The complexity of age-dependent metabolic reprogramming comes from the multitude of levels on which metabolic changes can be connected to aging and regulation of lifespan. This is further complicated by the different metabolic requirements of various tissues, cross-organ communication via metabolite secretion, and direct effects of metabolites on epigenetic state and redox regulation; however, not all of these changes are causative to aging. Studies in yeast, flies, worms, and mice have played a crucial role in identifying mechanistic links between observed changes in various metabolic traits and their effects on lifespan. Here, we review how changes in the organismal and organ-specific metabolome are associated with aging and how targeting of any one of over a hundred different targets in specific metabolic pathways can extend lifespan. An important corollary is that restriction or supplementation of different metabolites can change activity of these metabolic pathways in ways that improve healthspan and extend lifespan in different organisms. Due to the high levels of conservation of metabolism in general, translating findings from model systems to human beings will allow for the development of effective strategies for human health- and lifespan extension.

Global metabolic profiling to model biological processes of aging in twins

Aging is intimately linked to system-wide metabolic changes that can be captured in blood. Understanding biological processes of aging in humans could help maintain a healthy aging trajectory and promote longevity. We performed untargeted plasma metabolomics quantifying 770 metabolites on a cross-sectional cohort of 268 healthy individuals including 125 twin pairs covering human lifespan (from 6 months to 82 years). Unsupervised clustering of metabolic profiles revealed 6 main aging trajectories throughout life that were associated with key metabolic pathways such as progestin steroids, xanthine metabolism, and long-chain fatty acids. A random forest (RF) model was successful to predict age in adult subjects (≥16 years) using 52 metabolites (R²  = .97). Another RF model selected 54 metabolites to classify pediatric and adult participants (out-of-bag error = 8.58%). These RF models in combination with correlation network analysis were used to explore biological processes of healthy aging. The models highlighted established metabolites, like steroids, amino acids, and free fatty acids as well as novel metabolites and pathways. Finally, we show that metabolic profiles of twins become more dissimilar with age which provides insights into nongenetic age-related variability in metabolic profiles in response to environmental exposure.