Metabolomic markers reveal novel pathways of ageing and early development in human populations

Dietary protein and amino acid intakes for mitigating sarcopenia in humans

Adult humans generally experience a 0.5-1%/year loss in whole-body skeletal muscle mass and a reduction of muscle strength by 1.5-5%/year beginning at the age of 50 years. This results in sarcopenia (aging-related progressive losses of skeletal muscle mass and strength) that affects 10-16% of adults aged ≥ 60 years worldwide. Concentrations of some amino acids (AAs) such as branched-chain AAs, arginine, glutamine, glycine, and serine are reduced in the plasma of older than young adults likely due to insufficient protein intake, reduced protein digestibility, and increased AA catabolism by the portal-drained viscera. Acute, short-term, or long-term administration of some of these AAs or a mixture of proteinogenic AAs can enhance blood flow to skeletal muscle, activate the mechanistic target of rapamycin cell signaling pathway for the initiation of muscle protein synthesis, and modulate the metabolic activity of the muscle. In addition, some AA metabolites such as taurine, β-alanine, carnosine, and creatine have similar physiological effects on improving muscle mass and function in older adults. Long-term adequate intakes of protein and the AA metabolites can aid in mitigating sarcopenia in elderly adults. Appropriate combinations of animal- and plant-sourced foods are most desirable to maintain proper dietary AA balance.

Metabolomic age and risk of 50 chronic diseases in community-dwelling adults: A prospective cohort study

It is unclear how metabolomic age is associated with the risk of a wide range of chronic diseases. Our analysis included 110,692 participants (training: n = 27,673; testing: n = 27,673; validating: n = 55,346) aged 39-71 years at baseline (2006-2010) from the UK Biobank. Incident chronic diseases were identified using inpatient records, or death registers until January 2021. Predicted metabolomic age was trained and tested based on 168 metabolomics. Metabolomic age was linked to the risk of 50 diseases in the validation dataset. The median follow-up duration for individual diseases ranged from 11.2 years to 11.9 years. After controlling for false discovery rate, chronological age-adjusted age gap (CAAG) was significantly associated with the incidence of 25 out of 50 chronic diseases. After adjustment for full covariates, associations with 15 chronic diseases remained significant. Greater CAAG was associated with increased risk of eight cardiometabolic disorders (including cardiovascular diseases and diabetes), some cancers, alcohol use disorder, chronic obstructive pulmonary disease, chronic kidney disease, chronic liver disease and age-related macular degeneration. The association between CAAG and risk of peripheral vascular disease, other cardiac diseases, fracture, cataract and thyroid disorder was stronger among individuals with unhealthy diet than in those with healthy diet. The association between CAAG and risk of some conditions was stronger in younger individuals, those with metabolic disorders or low education. Metabolomic age plays an important role in the development of multiple chronic diseases. Healthy diet and high education may mitigate the risk for some chronic diseases due to metabolomic age acceleration.

Metabolomics profiling reveals distinct, sex-specific signatures in serum and brain metabolomes in mouse models of Alzheimer's disease

INTRODUCTION

Increasing evidence suggests that metabolic impairments contribute to early Alzheimer's disease (AD) mechanisms and subsequent dementia. Signals in metabolic pathways conserved across species can facilitate translation.

METHODS

We investigated differences in serum and brain metabolites between the early-onset 5XFAD and late-onset LOAD1 (APOE4.Trem2*R47H) mouse models of AD to C57BL/6J controls at 6 months of age.

RESULTS

We identified sex differences for several classes of metabolites, such as glycerophospholipids, sphingolipids, and amino acids. Metabolic signatures were notably different between brain and serum in both mouse models. The 5XFAD mice exhibited stronger differences in brain metabolites, whereas LOAD1 mice showed more pronounced differences in serum.

DISCUSSION

Several of our findings were consistent with results in humans, showing glycerophospholipids reduction in serum of apolipoprotein E (apoE) ε4 carriers and replicating the serum metabolic imprint of the APOE ε4 genotype. Our work thus represents a significant step toward translating metabolic dysregulation from model organisms to human AD.

HIGHLIGHTS

This was a metabolomic assessment of two mouse models relevant to Alzheimer's disease. Mouse models exhibit broad sex-specific metabolic differences, similar to human study cohorts. The early-onset 5XFAD mouse model primarily alters brain metabolites while the late-onset LOAD1 model primarily changes serum metabolites. Apolipoprotein E (apoE) ε4 mice recapitulate glycerophospolipid signatures of human APOE ε4 carriers in both brain and serum.

NMR metabolomic modeling of age and lifespan: A multicohort analysis

Metabolomic age models have been proposed for the study of biological aging, however, they have not been widely validated. We aimed to assess the performance of newly developed and existing nuclear magnetic resonance spectroscopy (NMR) metabolomic age models for prediction of chronological age (CA), mortality, and age-related disease. Ninety-eight metabolic variables were measured in blood from nine UK and Finnish cohort studies (N ≈31,000 individuals, age range 24-86 years). We used nonlinear and penalized regression to model CA and time to all-cause mortality. We examined associations of four new and two previously published metabolomic age models, with aging risk factors and phenotypes. Within the UK Biobank (N ≈102,000), we tested prediction of CA, incident disease (cardiovascular disease (CVD), type-2 diabetes mellitus, cancer, dementia, and chronic obstructive pulmonary disease), and all-cause mortality. Seven-fold cross-validated Pearson's r between metabolomic age models and CA ranged between 0.47 and 0.65 in the training cohort set (mean absolute error: 8-9 years). Metabolomic age models, adjusted for CA, were associated with C-reactive protein, and inversely associated with glomerular filtration rate. Positively associated risk factors included obesity, diabetes, smoking, and physical inactivity. In UK Biobank, correlations of metabolomic age with CA were modest (r = 0.29-0.33), yet all metabolomic model scores predicted mortality (hazard ratios of 1.01 to 1.06/metabolomic age year) and CVD, after adjustment for CA. While metabolomic age models were only moderately associated with CA in an independent population, they provided additional prediction of morbidity and mortality over CA itself, suggesting their wider applicability.

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.

Application of AI in biological age prediction

The development of anti-aging interventions requires quantitative measurement of biological age. Machine learning models, known as "aging clocks," are built by leveraging diverse aging biomarkers that vary across lifespan to predict biological age. In addition to traditional aging clocks harnessing epigenetic signatures derived from bulk samples, emerging technologies allow the biological age estimating at single-cell level to dissect cellular diversity in aging tissues. Moreover, imaging-based aging clocks are increasingly employed with the advantage of non-invasive measurement, making it suitable for large-scale human cohort studies. To fully capture the features in the ever-growing multi-modal and high-dimensional aging-related data and uncover disease associations, deep-learning based approaches, which are effective to learn complex and non-linear relationships without relying on pre-defined features, are increasingly applied. The use of big data and AI-based aging clocks has achieved high accuracy, interpretability and generalizability, guiding clinical applications to delay age-related diseases and extend healthy lifespans.

Metabolic Reprogramming of Immune Cells Following Vaccination: From Metabolites to Personalized Vaccinology

Identifying metabolic signatures induced by the immune response to vaccines allows one to discriminate vaccinated from non-vaccinated subjects and decipher the molecular mechanisms associated with the host immune response. This review illustrates and discusses the results of metabolomics-based studies on the innate and adaptive immune response to vaccines, long-term functional reprogramming (immune memory), and adverse reactions. Glycolysis is not overexpressed by vaccines, suggesting that the immune cell response to vaccinations does not require rapid energy availability as necessary during an infection. Vaccines strongly impact lipids metabolism, including saturated or unsaturated fatty acids, inositol phosphate, and cholesterol. Cholesterol is strategic for synthesizing 25-hydroxycholesterol in activated macrophages and dendritic cells and stimulates the conversion of macrophages and T cells in M2 macrophage and Treg, respectively. In conclusion, the large-scale application of metabolomics enables the identification of candidate predictive biomarkers of vaccine efficacy/tolerability.

Metabolomics profiling reveals distinct, sex-specific signatures in the serum and brain metabolomes in the mouse models of Alzheimer's disease

INTRODUCTION

Increasing evidence suggests that metabolic impairments contribute to early Alzheimer's disease (AD) mechanisms and subsequent dementia. Signals in metabolic pathways conserved across species provides a promising entry point for translation. METHODS: We investigated differences of serum and brain metabolites between the early-onset 5XFAD and late-onset LOAD1 (APOE4.Trem2*R47H) mouse models of AD to C57BL/6J controls at six months of age.

RESULTS

We identified sex differences for several classes of metabolites, such as glycerophospholipids, sphingolipids, and amino acids. Metabolic signatures were notably different between brain and serum in both mouse models. The 5XFAD mice exhibited stronger differences in brain metabolites, whereas LOAD1 mice showed more pronounced differences in serum.

DISCUSSION

Several of our findings were consistent with results in humans, showing glycerophospholipids reduction in serum of APOE4 carriers and replicating the serum metabolic imprint of the APOE4 genotype. Our work thus represents a significant step towards translating metabolic dysregulation from model organisms to human AD.

Targeted metabolomics reveals plasma biomarkers and metabolic alterations of the aging process in healthy young and older adults

With the exponential growth in the older population in the coming years, many studies have aimed to further investigate potential biomarkers associated with the aging process and its incumbent morbidities. Age is the largest risk factor for chronic disease, likely due to younger individuals possessing more competent adaptive metabolic networks that result in overall health and homeostasis. With aging, physiological alterations occur throughout the metabolic system that contribute to functional decline. In this cross-sectional analysis, a targeted metabolomic approach was applied to investigate the plasma metabolome of young (21-40y; n = 75) and older adults (65y + ; n = 76). A corrected general linear model (GLM) was generated, with covariates of gender, BMI, and chronic condition score (CCS), to compare the metabolome of the two populations. Among the 109 targeted metabolites, those associated with impaired fatty acid metabolism in the older population were found to be most significant: palmitic acid (p < 0.001), 3-hexenedioic acid (p < 0.001), stearic acid (p = 0.005), and decanoylcarnitine (p = 0.036). Derivatives of amino acid metabolism, 1-methlyhistidine (p = 0.035) and methylhistamine (p = 0.027), were found to be increased in the younger population and several novel metabolites were identified, such as cadaverine (p = 0.034) and 4-ethylbenzoic acid (p = 0.029). Principal component analysis was conducted and highlighted a shift in the metabolome for both groups. Receiver operating characteristic analyses of partial least squares-discriminant analysis models showed the candidate markers to be more powerful indicators of age than chronic disease. Pathway and enrichment analyses uncovered several pathways and enzymes predicted to underlie the aging process, and an integrated hypothesis describing functional characteristics of the aging process was synthesized. Compared to older participants, the young group displayed greater abundance of metabolites related to lipid and nucleotide synthesis; older participants displayed decreased fatty acid oxidation and reduced tryptophan metabolism, relative to the young group. As a result, we offer a better understanding of the aging metabolome and potentially reveal new biomarkers and predicted mechanisms for future study.

Multi-omics reveals aging-related pathway in natural aging mouse liver

Aging is associated with gradual changes in liver structure, altered metabolites and other physiological/pathological functions in hepatic cells. However, its characterized phenotypes based on altered metabolites and the underlying biological mechanism are unclear. Advancements in high-throughput omics technology provide new opportunities to understand the pathological process of aging. Here, in our present study, both metabolomics and phosphoproteomics were applied to identify the altered metabolites and phosphorylated proteins in liver of young (the WTY group) and naturally aged (the WTA group) mice, to find novel biomarkers and pathways, and uncover the biological mechanism. Analysis showed that the body weights, alanine aminotransferase (ALT) and aspartate aminotransferase (AST) increased in the WTA group. The grips decreased with age, while the triglyceride (TG) and cholesterol (TC) did not change significantly. The increase of fibrosis, accumulation of inflammatory cells, hepatocytes degeneration, the deposition of lipid droplets and glycogen, the damaged mitochondria, and deduction of endoplasmic reticulum were observed in the aging liver under optical and electron microscopes. In addition, a network of metabolites and phosphorylated proteomes of the aging liver was established. Metabolomics detected 970 metabolites in the positive ion mode and 778 metabolites in the negative ion mode. A total of 150 pathways were pooled. Phosphoproteomics identified 2618 proteins which contained 16621 phosphosites. A total of 164 pathways were detected. 65 common pathways were detected in two omics. Phosphorylated protein heat shock protein HSP 90-alpha (HSP90A) and v-raf murine viral oncogene homolog B1(BRAF), related to cancer pathway, were significantly upregulated in aged mice liver. Western blot verified that protein expression of MEK and ERK, downstream of BRAF pathway were elevated in the liver of aging mice. However, the protein expression of BRAF was not a significant difference. Overall, these findings revealed a close link between aging and cancer and contributed to our understanding of the multi-omics changes in natural aging.

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.

An epidemiological introduction to human metabolomic investigations

Metabolomics holds great promise for uncovering insights around biological processes impacting disease in human epidemiological studies. Metabolites can be measured across biological samples, including plasma, serum, saliva, urine, stool, and whole organs and tissues, offering a means to characterize metabolic processes relevant to disease etiology and traits of interest. Metabolomic epidemiology studies face unique challenges, such as identifying metabolites from targeted and untargeted assays, defining standards for quality control, harmonizing results across platforms that often capture different metabolites, and developing statistical methods for high-dimensional and correlated metabolomic data. In this review, we introduce metabolomic epidemiology to the broader scientific community, discuss opportunities and challenges presented by these studies, and highlight emerging innovations that hold promise to uncover new biological insights.

The HuMet Repository: Watching human metabolism at work

The human metabolism constantly responds to stimuli such as food intake, fasting, exercise, and stress, triggering adaptive biochemical processes across multiple metabolic pathways. To understand the role of these processes and disruptions thereof in health and disease, detailed documentation of healthy metabolic responses is needed but still scarce on a time-resolved metabolome-wide level. Here, we present the HuMet Repository, a web-based resource for exploring dynamic metabolic responses to six physiological challenges (exercise, 36 h fasting, oral glucose and lipid loads, mixed meal, cold stress) in healthy subjects. For building this resource, we integrated existing and newly derived metabolomics data measured in blood, urine, and breath samples of 15 young healthy men at up to 56 time points during the six highly standardized challenge tests conducted over four days. The data comprise 1.1 million data points acquired on multiple platforms with temporal profiles of 2,656 metabolites from a broad range of biochemical pathways. By embedding the dataset into an interactive web application, we enable users to easily access, search, filter, analyze, and visualize the time-resolved metabolomic readouts and derived results. Users can put metabolites into their larger context by identifying metabolites with similar trajectories or by visualizing metabolites within holistic metabolic networks to pinpoint pathways of interest. In three showcases, we outline the value of the repository for gaining biological insights and generating hypotheses by analyzing the wash-out of dietary markers, the complementarity of metabolomics platforms in dynamic versus cross-sectional data, and similarities and differences in systemic metabolic responses across challenges. With its comprehensive collection of time-resolved metabolomics data, the HuMet Repository, freely accessible at https://humet.org/, is a reference for normal, healthy responses to metabolic challenges in young males. It will enable researchers with and without computational expertise, to flexibly query the data for their own research into the dynamics of human metabolism.

Impact of acute stress on murine metabolomics and metabolic flux

Plasma metabolite concentrations and labeling enrichments are common measures of organismal metabolism. In mice, blood is often collected by tail snip sampling. Here, we systematically examined the effect of such sampling, relative to gold-standard sampling from an in-dwelling arterial catheter, on plasma metabolomics and stable isotope tracing. We find marked differences between the arterial and tail circulating metabolome, which arise from two major factors: handling stress and sampling site, whose effects were deconvoluted by taking a second arterial sample immediately after tail snip. Pyruvate and lactate were the most stress-sensitive plasma metabolites, rising ~14 and ~5-fold. Both acute handling stress and adrenergic agonists induce extensive, immediate production of lactate, and modest production of many other circulating metabolites, and we provide a reference set of mouse circulatory turnover fluxes with noninvasive arterial sampling to avoid such artifacts. Even in the absence of stress, lactate remains the highest flux circulating metabolite on a molar basis, and most glucose flux into the TCA cycle in fasted mice flows through circulating lactate. Thus, lactate is both a central player in unstressed mammalian metabolism and strongly produced in response to acute stress.

Human Metabolome Reference Database in a Biracial Cohort across the Adult Lifespan

As one of the OMICS in systems biology, metabolomics defines the metabolome and simultaneously quantifies numerous metabolites that are final or intermediate products and effectors of upstream biological processes. Metabolomics provides accurate information that helps determine the physiological steady state and biochemical changes during the aging process. To date, reference values of metabolites across the adult lifespan, especially among ethnicity groups, are lacking. The "normal" reference values according to age, sex, and race allow the characterization of whether an individual or a group deviates metabolically from normal aging, encompass a fundamental element in any study aimed at understanding mechanisms at the interface between aging and diseases. In this study, we established a metabolomics reference database from 20-100 years of age from a biracial sample of community-dwelling healthy men and women and examined metabolite associations with age, sex, and race. Reference values from well-selected healthy individuals can contribute to clinical decision-making processes of metabolic or related diseases.

Large-Scale metabolomics: Predicting biological age using 10,133 routine untargeted LC-MS measurements

Untargeted metabolomics is the study of all detectable small molecules, and in geroscience, metabolomics has shown great potential to describe the biological age-a complex trait impacted by many factors. Unfortunately, the sample sizes are often insufficient to achieve sufficient power and minimize potential biases caused by, for example, demographic factors. In this study, we present the analysis of biological age in ~10,000 toxicologic routine blood measurements. The untargeted screening samples obtained from ultra-high pressure liquid chromatography-quadruple time of flight mass spectrometry (UHPLC- QTOF) cover + 300 batches and + 30 months, lack pooled quality controls, lack controlled sample collection, and has previously only been used in small-scale studies. To overcome experimental effects, we developed and tested a custom neural network model and compared it with existing prediction methods. Overall, the neural network was able to predict the chronological age with an rmse of 5.88 years (r²  = 0.63) improving upon the 6.15 years achieved by existing normalization methods. We used the feature importance algorithm, Shapley Additive exPlanations (SHAP), to identify compounds related to the biological age. Most importantly, the model returned known aging markers such as kynurenine, indole-3-aldehyde, and acylcarnitines along with a potential novel aging marker, cyclo (leu-pro). Our results validate the association of tryptophan and acylcarnitine metabolism to aging in a highly uncontrolled large-s cale sample. Also, we have shown that by using robust computational methods it is possible to deploy large LC-MS datasets for metabolomics studies to reduce the risk of bias and empower aging studies.

Using mass spectrometry imaging to visualize age-related subcellular disruption

Metabolic homeostasis balances the production and consumption of energetic molecules to maintain active, healthy cells. Cellular stress, which disrupts metabolism and leads to the loss of cellular homeostasis, is important in age-related diseases. We focus here on the role of organelle dysfunction in age-related diseases, including the roles of energy deficiencies, mitochondrial dysfunction, endoplasmic reticulum (ER) stress, changes in metabolic flux in aging (e.g., Ca2+ and nicotinamide adenine dinucleotide), and alterations in the endoplasmic reticulum-mitochondria contact sites that regulate the trafficking of metabolites. Tools for single-cell resolution of metabolite pools and metabolic flux in animal models of aging and age-related diseases are urgently needed. High-resolution mass spectrometry imaging (MSI) provides a revolutionary approach for capturing the metabolic states of individual cells and cellular interactions without the dissociation of tissues. mass spectrometry imaging can be a powerful tool to elucidate the role of stress-induced cellular dysfunction in aging.

Metabolomics Profiling of Age-Associated Metabolites in Malay Population

Aging is a complex process characterized by progressive loss of functional abilities due to the accumulation of molecular damages. Metabolomics could offer novel insights into the predictors and mechanisms of aging. This cross-sectional study is aimed at identifying age-associated plasma metabolome in a Malay population. A total of 146 (90 females) healthy participants aged 28-69 were selected for the study. Untargeted metabolomics profiling was performed using liquid chromatography-tandem mass spectrometry. Association analysis was based on the general linear model. Gender-associated metabolites were adjusted for age, while age-associated metabolites were adjusted for gender or analyzed in a gender-stratified manner. Gender-associated metabolites such as 4-hydroxyphenyllactic acid, carnitine, cortisol, and testosterone sulfate showed higher levels in males than females. Deoxycholic acid and hippuric acid were among the metabolites with a positive association with age after being adjusted for gender, while 9(E),11(E)-conjugated linoleic acid, cortisol, and nicotinamide were negatively associated with age. In gender-stratified analysis, glutamine was one of the common metabolites that showed a direct association with age in both genders, while metabolites such as 11-deoxy prostaglandin F2β, guanosine monophosphate, and testosterone sulfate were inversely associated with age in males and females. This study reveals several age-associated metabolites in Malays that could reflect the changes in metabolisms during aging and may be used to discern the risk of geriatric syndromes and disorders later. Further studies are required to determine the interplay between these metabolites and environmental factors on the functional outcomes during aging.

PPI-Induced Changes in Plasma Metabolite Levels Influence Total Hip Bone Mineral Density in a UK Cohort

Proton pump inhibitors (PPIs) are among the most used drugs in the UK. PPI use has been associated with decreased bone mineral density (BMD) and increased fracture risk, although these results have been inconsistent. We hypothesized that PPI could modulate BMD by altering gut and/or host systemic metabolic environments. Using data from more than 5000 British male and female individuals, we confirmed that PPI use is associated with decreased lumbar spine and total hip BMD. This effect was not mediated through the gut microbiome. We suggest here that PPI use may influence total hip BMD, both directly and indirectly, via plasma metabolites involved in the sex hormone pathway. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Cross-sectionally calculated metabolic ageing does not relate to longitudinal metabolic changes - support for stratified ageing models

CONTEXT

Ageing varies between individuals with profound consequences for chronic diseases and longevity. One hypothesis to explain the diversity is a genetically regulated molecular clock that runs differently between individuals. Large and long enough human studies to test the hypothesis are rare due to practical challenges, but statistical models of ageing are built as proxies for the molecular clock by comparing young and old individuals cross-sectionally. These models remain untested against longitudinal data.

OBJECTIVE

We applied novel methodology to test if cross-sectional modelling can distinguish slow versus accelerated ageing in a human population.

DESIGN

We trained a machine learning model to predict age from 153 clinical and cardiometabolic traits. The model was tested against longitudinal data from another cohort.

PATIENTS OR OTHER PARTICIPANTS

The training data came from cross-sectional surveys of the Finnish population (n = 9,708; ages 25-74 years). The validation data included three time points across 10 years in the Young Finns Study (YFS; n = 1,009; ages 24-49 years).

INTERVENTION(S)

Predicted metabolic age in 2007 was compared against observed ageing rate from the 2001 visit to the 2011 visit in the YFS dataset.

MAIN OUTCOME MEASURE(S)

Correlation between predicted versus observed metabolic ageing.

RESULTS

The cross-sectional proxy failed to predict longitudinal observations (R2 = 0.018%, P = 0.67).

CONCLUSIONS

The finding is unexpected under the clock hypothesis that would produce a positive correlation between predicted and observed ageing. Our results are better explained by a stratified model where ageing rates per se are similar in adulthood but differences in starting points explain diverging metabolic fates.

Longitudinal profiling of metabolic ageing trends in two population cohorts of young adults

BACKGROUND

Quantification of metabolic changes over the human life course is essential to understanding ageing processes. Yet longitudinal metabolomics data are rare and long gaps between visits can introduce biases that mask true trends. We introduce new ways to process quantitative time-series population data and elucidate metabolic ageing trends in two large cohorts.

METHODS

Eligible participants included 1672 individuals from the Cardiovascular Risk in Young Finns Study and 3117 from the Northern Finland Birth Cohort 1966. Up to three time points (ages 24-49 years) were analysed by nuclear magnetic resonance metabolomics and clinical biochemistry (236 measures). Temporal trends were quantified as median change per decade. Sample quality was verified by consistency of shared biomarkers between metabolomics and clinical assays. Batch effects between visits were mitigated by a new algorithm introduced in this report. The results below satisfy multiple testing threshold of P < 0.0006.

RESULTS

Women gained more weight than men (+6.5% vs +5.0%) but showed milder metabolic changes overall. Temporal sex differences were observed for C-reactive protein (women +5.1%, men +21.1%), glycine (women +5.2%, men +1.9%) and phenylalanine (women +0.6%, men +3.5%). In 566 individuals with ≥+3% weight gain vs 561 with weight change ≤-3%, divergent patterns were observed for insulin (+24% vs -10%), very-low-density-lipoprotein triglycerides (+32% vs -6%), high-density-lipoprotein2 cholesterol (-6.5% vs +4.7%), isoleucine (+5.7% vs -6.0%) and C-reactive protein (+25% vs -22%).

CONCLUSION

We report absolute and proportional trends for 236 metabolic measures as new reference material for overall age-associated and specific weight-driven changes in real-world populations.

Epigenomic and Other Evidence for Cannabis-Induced Aging Contextualized in a Synthetic Epidemiologic Overview of Cannabinoid-Related Teratogenesis and Cannabinoid-Related Carcinogenesis

BACKGROUND

Twelve separate streams of empirical data make a strong case for cannabis-induced accelerated aging including hormonal, mitochondriopathic, cardiovascular, hepatotoxic, immunological, genotoxic, epigenotoxic, disruption of chromosomal physiology, congenital anomalies, cancers including inheritable tumorigenesis, telomerase inhibition and elevated mortality.

METHODS

Results from a recently published longitudinal epigenomic screen were analyzed with regard to the results of recent large epidemiological studies of the causal impacts of cannabis. We also integrate theoretical syntheses with prior studies into these combined epigenomic and epidemiological results.

RESULTS

Cannabis dependence not only recapitulates many of the key features of aging, but is characterized by both age-defining and age-generating illnesses including immunomodulation, hepatic inflammation, many psychiatric syndromes with a neuroinflammatory basis, genotoxicity and epigenotoxicity. DNA breaks, chromosomal breakage-fusion-bridge morphologies and likely cycles, and altered intergenerational DNA methylation and disruption of both the histone and tubulin codes in the context of increased clinical congenital anomalies, cancers and heritable tumors imply widespread disruption of the genome and epigenome. Modern epigenomic clocks indicate that, in cannabis-dependent patients, cannabis advances cellular DNA methylation age by 25-30% at age 30 years. Data have implications not only for somatic but also stem cell and germ line tissues including post-fertilization zygotes. This effect is likely increases with the square of chronological age.

CONCLUSION

Recent epigenomic studies of cannabis exposure provide many explanations for the broad spectrum of cannabis-related teratogenicity and carcinogenicity and appear to account for many epidemiologically observed findings. Further research is indicated on the role of cannabinoids in the aging process both developmentally and longitudinally, from stem cell to germ cell to blastocystoids to embryoid bodies and beyond.

Integrative perspective of the healthy aging process considering the metabolome, cardiac autonomic modulation and cardiorespiratory fitness evaluated in age groups

The aging process causes changes at all organic levels. Although metabolism, cardiac autonomic modulation (CAM), and cardiorespiratory fitness (CRF) are widely studied as a function of age, they are mainly studied in isolation, thus making it difficult to perceive their concomitant variations. This study aimed to investigate the integrated changes that occur in the metabolome, CAM, and CRF throughout aging in apparently healthy individuals. The subjects (n = 118) were divided into five groups according to age (20-29, 30-39, 40-49, 50-59, and 60-70 years old) and underwent blood collection, autonomic assessment, and a cardiopulmonary exercise test for metabolomics analysis using mass spectrometry and nuclear magnetic resonance, cardiac autonomic modulation analysis, and CRF by peak oxygen consumption analysis, respectively. The Tukey's post hoc and effect size with confidence interval were used for variables with a significant one-way ANOVA effect (P < 0.01). The main changes were in the oldest age group, where the CRF, valine, leucine, isoleucine, 3-hydroxyisobutyrate, and CAM reduced and hippuric acid increased. The results suggest significant changes in the metabolome, CAM, and CRF after the age of sixty as a consequence of aging impairments, but with some changes in the metabolic profile that may be favorable to mitigate the aging deleterious effects.

The Aging Process: A Metabolomics Perspective

Aging process is characterized by a progressive decline of several organic, physiological, and metabolic functions whose precise mechanism remains unclear. Metabolomics allows the identification of several metabolites and may contribute to clarifying the aging-regulated metabolic pathways. We aimed to investigate aging-related serum metabolic changes using a metabolomics approach. Fasting blood serum samples from 138 apparently healthy individuals (20−70 years old, 56% men) were analyzed by Proton Nuclear Magnetic Resonance spectroscopy (1H NMR) and Liquid Chromatography-High-Resolution Mass Spectrometry (LC-HRMS), and for clinical markers. Associations of the metabolic profile with age were explored via Correlations (r); Metabolite Set Enrichment Analysis; Multiple Linear Regression; and Aging Metabolism Breakpoint. The age increase was positively correlated (0.212 ≤ r ≤ 0.370, p < 0.05) with the clinical markers (total cholesterol, HDL, LDL, VLDL, triacylglyceride, and glucose levels); negatively correlated (−0.285 ≤ r ≤ −0.214, p < 0.05) with tryptophan, 3-hydroxyisobutyrate, asparagine, isoleucine, leucine, and valine levels, but positively (0.237 ≤ r ≤ 0.269, p < 0.05) with aspartate and ornithine levels. These metabolites resulted in three enriched pathways: valine, leucine, and isoleucine degradation, urea cycle, and ammonia recycling. Additionally, serum metabolic levels of 3-hydroxyisobutyrate, isoleucine, aspartate, and ornithine explained 27.3% of the age variation, with the aging metabolism breakpoint occurring after the third decade of life. These results indicate that the aging process is potentially associated with reduced serum branched-chain amino acid levels (especially after the third decade of life) and progressively increased levels of serum metabolites indicative of the urea cycle.

Measuring biological age using omics data

Age is the key risk factor for diseases and disabilities of the elderly. Efforts to tackle age-related diseases and increase healthspan have suggested targeting the ageing process itself to 'rejuvenate' physiological functioning. However, achieving this aim requires measures of biological age and rates of ageing at the molecular level. Spurred by recent advances in high-throughput omics technologies, a new generation of tools to measure biological ageing now enables the quantitative characterization of ageing at molecular resolution. Epigenomic, transcriptomic, proteomic and metabolomic data can be harnessed with machine learning to build 'ageing clocks' with demonstrated capacity to identify new biomarkers of biological ageing.

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 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.

A machine learning-based data mining in medical examination data: a biological features-based biological age prediction model

Background

Biological age (BA) has been recognized as a more accurate indicator of aging than chronological age (CA). However, the current limitations include: insufficient attention to the incompleteness of medical data for constructing BA; Lack of machine learning-based BA (ML-BA) on the Chinese population; Neglect of the influence of model overfitting degree on the stability of the association results.

Methods and results

Based on the medical examination data of the Chinese population (45–90 years), we first evaluated the most suitable missing interpolation method, then constructed 14 ML-BAs based on biomarkers, and finally explored the associations between ML-BAs and health statuses (healthy risk indicators and disease). We found that round-robin linear regression interpolation performed best, while AutoEncoder showed the highest interpolation stability. We further illustrated the potential overfitting problem in ML-BAs, which affected the stability of ML-Bas’ associations with health statuses. We then proposed a composite ML-BA based on the Stacking method with a simple meta-model (STK-BA), which overcame the overfitting problem, and associated more strongly with CA (r = 0.66, P < 0.001), healthy risk indicators, disease counts, and six types of disease.

Conclusion

We provided an improved aging measurement method for middle-aged and elderly groups in China, which can more stably capture aging characteristics other than CA, supporting the emerging application potential of machine learning in aging research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04966-7.

Machine learning integration of multimodal data identifies key features of blood pressure regulation

BACKGROUND

Association studies have identified several biomarkers for blood pressure and hypertension, but a thorough understanding of their mutual dependencies is lacking. By integrating two different high-throughput datasets, biochemical and dietary data, we aim to understand the multifactorial contributors of blood pressure (BP).

METHODS

We included 4,863 participants from TwinsUK with concurrent BP, metabolomics, genomics, biochemical measures, and dietary data. We used 5-fold cross-validation with the machine learning XGBoost algorithm to identify features of importance in context of one another in TwinsUK (80% training, 20% test). The features tested in TwinsUK were then probed using the same algorithm in an independent dataset of 2,807 individuals from the Qatari Biobank (QBB).

FINDINGS

Our model explained 39·2% [4·5%, MAE:11·32 mmHg (95%CI, +/- 0·65)] of the variance in systolic BP (SBP) in TwinsUK. Of the top 50 features, the most influential non-demographic variables were dihomo-linolenate, cis-4-decenoyl carnitine, lactate, chloride, urate, and creatinine along with dietary intakes of total, trans and saturated fat. We also highlight the incremental value of each included dimension. Furthermore, we replicated our model in the QBB [SBP variance explained = 45·2% (13·39%)] cohort and 30 of the top 50 features overlapped between cohorts.

INTERPRETATION

We show that an integrated analysis of omics, biochemical and dietary data improves our understanding of their in-between relationships and expands the range of potential biomarkers for blood pressure. Our results point to potentially key biological pathways to be prioritised for mechanistic studies.

FUNDING

Chronic Disease Research Foundation, Medical Research Council, Wellcome Trust, Qatar Foundation.

Air pollution, metabolites and respiratory health across the life-course

Previous studies have explored the relationships of air pollution and metabolic profiles with lung function. However, the metabolites linking air pollution and lung function and the associated mechanisms have not been reviewed from a life-course perspective. Here, we provide a narrative review summarising recent evidence on the associations of metabolic profiles with air pollution exposure and lung function in children and adults. Twenty-six studies identified through a systematic PubMed search were included with 10 studies analysing air pollution-related metabolic profiles and 16 studies analysing lung function-related metabolic profiles. A wide range of metabolites were associated with short- and long-term exposure, partly overlapping with those linked to lung function in the general population and with respiratory diseases such as asthma and COPD. The existing studies show that metabolomics offers the potential to identify biomarkers linked to both environmental exposures and respiratory outcomes, but many studies suffer from small sample sizes, cross-sectional designs, a preponderance on adult lung function, heterogeneity in exposure assessment, lack of confounding control and omics integration. The ongoing EXposome Powered tools for healthy living in urbAN Settings (EXPANSE) project aims to address some of these shortcomings by combining biospecimens from large European cohorts and harmonised air pollution exposure and exposome data.

Metabolomic and transcriptomic signatures of influenza vaccine response in healthy young and older adults

Seasonal influenza causes mild to severe respiratory infections and significant morbidity, especially in older adults. Transcriptomic analysis in populations across multiple flu seasons has provided insights into the molecular determinants of vaccine response. Still, the metabolic changes that underlie the immune response to influenza vaccination remain poorly characterized. We performed untargeted metabolomics to analyze plasma metabolites in a cohort of younger and older subjects before and after influenza vaccination to identify vaccine-induced molecular signatures. Metabolomic and transcriptomic data were combined to define networks of gene and metabolic signatures indicative of high and low antibody response in these individuals. We observed age-related differences in metabolic baselines and signatures of antibody response to influenza vaccination and the abundance of α-linolenic and linoleic acids, sterol esters, fatty-acylcarnitines, and triacylglycerol metabolism. We identified a metabolomic signature associated with age-dependent vaccine response, finding increased tryptophan and decreased polyunsaturated fatty acids (PUFAs) in young high responders (HRs), while fatty acid synthesis and cholesteryl esters accumulated in older HRs. Integrated metabolomic and transcriptomic analysis shows that depletion of PUFAs, which are building blocks for prostaglandins and other lipid immunomodulators, in young HR subjects at Day 28 is related to a robust immune response to influenza vaccination. Increased glycerophospholipid levels were associated with an inflammatory response in older HRs to flu vaccination. This multi-omics approach uncovered age-related molecular markers associated with influenza vaccine response and provides insight into vaccine-induced metabolic responses that may help guide development of more effective influenza vaccines.

Associations of the Lipidome with Ageing, Cognitive Decline and Exercise Behaviours

One of the most recognisable features of ageing is a decline in brain health and cognitive dysfunction, which is associated with perturbations to regular lipid homeostasis. Although ageing is the largest risk factor for several neurodegenerative diseases such as dementia, a loss in cognitive function is commonly observed in adults over the age of 65. Despite the prevalence of normal age-related cognitive decline, there is a lack of effective methods to improve the health of the ageing brain. In light of this, exercise has shown promise for positively influencing neurocognitive health and associated lipid profiles. This review summarises age-related changes in several lipid classes that are found in the brain, including fatty acyls, glycerolipids, phospholipids, sphingolipids and sterols, and explores the consequences of age-associated pathological cognitive decline on these lipid classes. Evidence of the positive effects of exercise on the affected lipid profiles are also discussed to highlight the potential for exercise to be used therapeutically to mitigate age-related changes to lipid metabolism and prevent cognitive decline in later life.

Large-scale analysis of circulating glutamate and adipose gene expression in relation to abdominal obesity

Circulating levels of the amino acid glutamate are associated with central fat accumulation, yet the pathophysiology of this relationship remains unknown. We aimed to (i) refine and validate the association between circulating glutamate and abdominal obesity in a large twin cohort, and (ii) investigate whether transcriptomic profiles in adipose tissue could provide insight into the biological mechanisms underlying the association. First, in a cohort of 4665 individuals from the TwinsUK resource, we identified individuals with abdominal obesity and compared prevalence of the latter across circulating glutamate quintiles. Second, we used transcriptomic signatures generated from adipose tissue, both subcutaneous and visceral, to investigate associations with circulating glutamate levels. Individuals in the top circulating glutamate quintile had a sevenfold higher prevalence of abdominal obesity compared to those in the bottom quintile. The adipose tissue transcriptomic analyses identified GLUL, encoding Glutamate-Ammonia Ligase, as being associated with circulating glutamate and abdominal obesity, with pronounced signatures in the visceral depot. In conclusion, circulating glutamate is positively associated with the prevalence of abdominal obesity which relates to dysregulated GLUL expression specifically in visceral adipose tissue.

Oral Senescence: From Molecular Biology to Clinical Research

Cellular senescence is an irreversible cell cycle arrest occurring following multiple rounds of cell division (replicative senescence) or in response to cellular stresses such as ionizing radiation, signaling imbalances and oxidative damage (stress-induced premature senescence). Even very small numbers of senescent cells can be deleterious and there is evidence that senescent cells are instrumental in a number of oral pathologies including cancer, oral sub mucous fibrosis and the side effects of cancer therapy. In addition, senescent cells are present and possibly important in periodontal disease and other chronic inflammatory conditions of the oral cavity. However, senescence is a double-edged sword because although it operates as a suppressor of malignancy in pre-malignant epithelia, senescent cells in the neoplastic environment promote tumor growth and progression. Many of the effects of senescent cells are dependent on the secretion of an array of diverse therapeutically targetable proteins known as the senescence-associated secretory phenotype. However, as senescence may have beneficial roles in wound repair, preventing fibrosis and stem cell activation the clinical exploitation of senescent cells is not straightforward. Here, we discuss biological mechanisms of senescence and we review the current approaches to target senescent cells therapeutically, including senostatics and senolytics which are entering clinical trials.

Metabolic Alterations in Cellular Senescence: The Role of Citrate in Ageing and Age-Related Disease

Recent mouse model experiments support an instrumental role for senescent cells in age-related diseases and senescent cells may be causal to certain age-related pathologies. A strongly supported hypothesis is that extranuclear chromatin is recognized by the cyclic GMP–AMP synthase-stimulator of interferon genes pathway, which in turn leads to the induction of several inflammatory cytokines as part of the senescence-associated secretory phenotype. This sterile inflammation increases with chronological age and age-associated disease. More recently, several intracellular and extracellular metabolic changes have been described in senescent cells but it is not clear whether any of them have functional significance. In this review, we highlight the potential effect of dietary and age-related metabolites in the modulation of the senescent phenotype in addition to discussing how experimental conditions may influence senescent cell metabolism, especially that of energy regulation. Finally, as extracellular citrate accumulates following certain types of senescence, we focus on the recently reported role of extracellular citrate in aging and age-related pathologies. We propose that citrate may be an active component of the senescence-associated secretory phenotype and via its intake through the diet may even contribute to the cause of age-related disease.

Age, Sex, Body Mass Index, Diet and Menopause Related Metabolites in a Large Homogeneous Alpine Cohort

Metabolomics in human serum samples provide a snapshot of the current metabolic state of an individuum. Metabolite concentrations are influenced by both genetic and environmental factors. Concentrations of certain metabolites can further depend on age, sex, menopause, and diet of study participants. A better understanding of these relationships is pivotal for the planning of metabolomics studies involving human subjects and interpretation of their results. We generated one of the largest single-site targeted metabolomics data sets consisting of 175 quantified metabolites in 6872 study participants. We identified metabolites significantly associated with age, sex, body mass index, diet, and menopausal status. While most of our results agree with previous large-scale studies, we also found novel associations including serotonin as a sex and BMI-related metabolite and sarcosine and C2 carnitine showing significantly higher concentrations in post-menopausal women. Finally, we observed strong associations between higher consumption of food items and certain metabolites, mostly phosphatidylcholines and lysophosphatidylcholines. Most, and the strongest, relationships were found for habitual meat intake while no significant relationships were found for most fruits, vegetables, and grain products. Summarizing, our results reconfirm findings from previous population-based studies on an independent cohort. Together, these findings will ultimately enable the consolidation of sets of metabolites which are related to age, sex, BMI, and menopause as well as to participants’ diet.

Tocotrienol Supplementation Led to Higher Serum Levels of Lysophospholipids but Lower Acylcarnitines in Postmenopausal Women: A Randomized Double-Blinded Placebo-Controlled Clinical Trial

Osteoporosis is a major health problem in postmenopausal women. Herein we evaluated the effects of 12-week tocotrienols (TT) supplementation on serum metabolites in postmenopausal, osteopenic women. Eighty-nine participants (59.7 ± 6.8 yr, BMI 28.7 ± 5.7 kg/m²) were assigned to 3 treatments: placebo (860 mg olive oil/day), 300mg TT (300 mg TT/day), and 600mg TT (600 mg TT/day) for 12 weeks. TT consisted of 90% δ-TT and 10% γ-TT. In this metabolomic study, we evaluated the placebo and 600mgTT at baseline and 12 weeks. As expected, TT and its metabolite levels were higher in the supplemented group after 12 weeks. At baseline, there were no differences in demographic parameters or comprehensive metabolic panels (CMP). Metabolomics analysis of serum samples revealed that 48 biochemicals were higher and 65 were lower in the 600mg TT group at 12 weeks, compared to baseline. The results confirmed higher serum levels of tocotrienols and lysophospholipids, but lower acylcarnitines and catabolites of tryptophan and steroids in subjects given 600mg TT. In summary, 12-week TT supplementation altered many serum metabolite levels in postmenopausal women. The present study supports our previous findings that TT supplementation helps reduce bone loss in postmenopausal osteopenic women by suppressing inflammation and oxidative stress. Furthermore, the body incorporates TT which restructures biomembranes and modifies phospholipid metabolism, a response potentially linked to reduced inflammation and oxidative stress.

Metabolic Signature of Leukocyte Telomere Length in Elite Male Soccer Players

Introduction: Biological aging is associated with changes in the metabolic pathways. Leukocyte telomere length (LTL) is a predictive marker of biological aging; however, the underlying metabolic pathways remain largely unknown. The aim of this study was to investigate the metabolic alterations and identify the metabolic predictors of LTL in elite male soccer players.

Methods: Levels of 837 blood metabolites and LTL were measured in 126 young elite male soccer players who tested negative for doping abuse at anti-doping laboratory in Italy. Multivariate analysis using orthogonal partial least squares (OPLS), univariate linear models and enrichment analyses were conducted to identify metabolites and metabolic pathways associated with LTL. Generalized linear model followed by receiver operating characteristic (ROC) analysis were conducted to identify top metabolites predictive of LTL.

Results: Sixty-seven metabolites and seven metabolic pathways showed significant associations with LTL. Among enriched pathways, lysophospholipids, benzoate metabolites, and glycine/serine/threonine metabolites were elevated with longer LTL. Conversely, monoacylglycerols, sphingolipid metabolites, long chain fatty acids and polyunsaturated fatty acids were enriched with shorter telomeres. ROC analysis revealed eight metabolites that best predict LTL, including glutamine, N-acetylglutamine, xanthine, beta-sitosterol, N2-acetyllysine, stearoyl-arachidonoyl-glycerol (18:0/20:4), N-acetylserine and 3-7-dimethylurate with AUC of 0.75 (0.64–0.87, p < 0.0001).

Conclusion: This study characterized the metabolic activity in relation to telomere length in elite soccer players. Investigating the functional relevance of these associations could provide a better understanding of exercise physiology and pathophysiology of elite athletes.

Statistical mediation of the relationships between chronological age and lipoproteins by nonessential amino acids in healthy men

Aging is a major risk factor for metabolic impairment that may lead to age-related diseases such as cardiovascular disease. Different mechanisms that may explain the interplay between aging and lipoproteins, and between aging and low-molecular-weight metabolites (LMWMs), in the metabolic dysregulation associated with age-related diseases have been described separately. Here, we statistically evaluated the possible mediation effects of LMWMs on the relationships between chronological age and lipoprotein concentrations in healthy men ranging from 19 to 75 years of age. Relative and absolute concentrations of LMWMs and lipoproteins, respectively, were assessed by nuclear magnetic resonance (NMR) spectroscopy. Multivariate linear regression and mediation analysis were conducted to explore the associations between age, lipoproteins and LMWMs. The statistical significance of the identified mediation effects was evaluated using the bootstrapping technique, and the identified mediation effects were validated on a publicly available dataset. Chronological age was statistically associated with five lipoprotein classes and subclasses. The mediation analysis showed that serine mediated 24.1% (95% CI: 22.9 – 24.7) of the effect of age on LDL-P, and glutamate mediated 17.9% (95% CI: 17.6 – 18.5) of the effect of age on large LDL-P. In the publicly available data, glutamate mediated the relationship between age and an NMR-derived surrogate of cholesterol. Our results suggest that the age-related increase in LDL particles may be mediated by a decrease in the nonessential amino acid glutamate. Future studies may contribute to a better understanding of the potential biological role of glutamate and LDL particles in aging mechanisms and age-related diseases.

Comparative Metabolomic Study of Drosophila Species with Different Lifespans

The increase in life expectancy, leading to a rise in the proportion of older people, is accompanied by a prevalence of age-related disorders among the world population, the fight against which today is one of the leading biomedical challenges. Exploring the biological insights concerning the lifespan is one of the ways to provide a background for designing an effective treatment for the increase in healthy years of life. Untargeted direct injection mass spectrometry-based metabolite profiling of 12 species of Drosophila with significant variations in natural lifespans was conducted in this research. A cross-comparison study of metabolomic profiles revealed lifespan signatures of flies. These signatures indicate that lifespan extension is associated with the upregulation of amino acids, phospholipids, and carbohydrate metabolism. Such information provides a metabolome-level view on longevity and may provide a molecular measure of organism age in age-related studies.

Untargeted Metabolomic Analysis of Human Milk from Mothers of Preterm Infants

The application of metabolomics in neonatology offers an approach to investigate the complex relationship between nutrition and infant health. Characterization of the metabolome of human milk enables an investigation into nutrients that affect the neonatal metabolism and identification of dietary interventions for infants at risk of diseases such as necrotizing enterocolitis (NEC). In this study, we aimed to identify differences in the metabolome of breast milk of 48 mothers with preterm infants with NEC and non-NEC healthy controls. A minimum significant difference was observed in the human milk metabolome between the mothers of infants with NEC and mothers of healthy control infants. However, significant differences in the metabolome related to fatty acid metabolism, oligosaccharides, amino sugars, amino acids, vitamins and oxidative stress-related metabolites were observed when comparing milk from mothers with control infants of ≤1.0 kg birth weight and >1.5 kg birth weight. Understanding the functional biological features of mothers’ milk that may modulate infant health is important in the future of tailored nutrition and care of the preterm newborn.

Metabolomic differences in lung function metrics: evidence from two cohorts

RATIONALE

The biochemical mechanisms underlying lung function are incompletely understood.

OBJECTIVES

To identify and validate the plasma metabolome of lung function using two independent adult cohorts: discovery-the European Prospective Investigation into Cancer-Norfolk (EPIC-Norfolk, n=10 460) and validation-the VA Normative Aging Study (NAS) metabolomic cohort (n=437).

METHODS

We ran linear regression models for 693 metabolites to identify associations with forced expiratory volume in one second (FEV₁) and the ratio of FEV₁ to forced vital capacity (FEV₁/FVC), in EPIC-Norfolk then validated significant findings in NAS. Significance in EPIC-Norfolk was denoted using an effective number of tests threshold of 95%; a metabolite was considered validated in NAS if the direction of effect was consistent and p<0.05.

MEASUREMENTS AND MAIN RESULTS

Of 156 metabolites that associated with FEV₁ in EPIC-Norfolk after adjustment for age, sex, body mass index, height, smoking and asthma status, 34 (21.8%) validated in NAS, including several metabolites involved in oxidative stress. When restricting the discovery sample to men only, a similar percentage, 18 of 79 significant metabolites (22.8%) were validated. A smaller number of metabolites were validated for FEV₁/FVC, 6 of 65 (9.2%) when including all EPIC-Norfolk as the discovery population, and 2 of 34 (5.9%) when restricting to men. These metabolites were characterised by involvement in respiratory track secretants. Interestingly, no metabolites were validated for both FEV₁ and FEV₁/FVC.

CONCLUSIONS

The validation of metabolites associated with respiratory function can help to better understand mechanisms of lung health and may assist the development of biomarkers.

A genetic model of methionine restriction extends Drosophila health- and lifespan

Loss of metabolic homeostasis is a hallmark of aging and is characterized by dramatic metabolic reprogramming. To analyze how the fate of labeled methionine is altered during aging, we applied ¹³C5-Methionine labeling to Drosophila and demonstrated significant changes in the activity of different branches of the methionine metabolism as flies age. We further tested whether targeted degradation of methionine metabolism components would "reset" methionine metabolism flux and extend the fly lifespan. Specifically, we created transgenic flies with inducible expression of Methioninase, a bacterial enzyme capable of degrading methionine and revealed methionine requirements for normal maintenance of lifespan. We also demonstrated that microbiota-derived methionine is an alternative and important source in addition to food-derived methionine. In this genetic model of methionine restriction (MetR), we also demonstrate that either whole-body or tissue-specific Methioninase expression can dramatically extend Drosophila health- and lifespan and exerts physiological effects associated with MetR. Interestingly, while previous dietary MetR extended lifespan in flies only in low amino acid conditions, MetR from Methioninase expression extends lifespan independently of amino acid levels in the food. Finally, because impairment of the methionine metabolism has been previously associated with the development of Alzheimer's disease, we compared methionine metabolism reprogramming between aging flies and a Drosophila model relevant to Alzheimer's disease, and found that overexpression of human Tau caused methionine metabolism flux reprogramming similar to the changes found in aged flies. Altogether, our study highlights Methioninase as a potential agent for health- and lifespan extension.

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.

Connecting the epigenome, metabolome and proteome for a deeper understanding of disease

Epigenome-wide association studies (EWAS) identify genes that are dysregulated by the studied clinical endpoints, thereby indicating potential new diagnostic biomarkers, drug targets and therapy options. Combining EWAS with deep molecular phenotyping, such as approaches enabled by metabolomics and proteomics, allows further probing of the underlying disease-associated pathways. For instance, methylation of the TXNIP gene is associated robustly with prevalent type 2 diabetes and further with metabolites that are short-term markers of glycaemic control. These associations reflect TXNIP's function as a glucose uptake regulator by interaction with the major glucose transporter GLUT1 and suggest that TXNIP methylation can be used as a read-out for the organism's exposure to glucose stress. Another case is the association between DNA methylation of the AHRR and F2RL3 genes with smoking and a protein that is involved in the reprogramming of the bronchial epithelium. These examples show that associations between DNA methylation and intermediate molecular traits can open new windows into how the body copes with physiological challenges. This knowledge, if carefully interpreted, may indicate novel therapy options and, together with monitoring of the methylation state of specific methylation sites, may in the future allow the early diagnosis of impending disease. It is essential for medical practitioners to recognize the potential that this field holds in translating basic research findings to clinical practice. In this review, we present recent advances in the field of EWAS with metabolomics and proteomics and discuss both the potential and the challenges of translating epigenetic associations, with deep molecular phenotypes, to biomedical applications.

Protein C-Mannosylation and C-Mannosyl Tryptophan in Chemical Biology and Medicine

C-Mannosylation is a post-translational modification of proteins in the endoplasmic reticulum. Monomeric α-mannose is attached to specific Trp residues at the first Trp in the Trp-x-x-Trp/Cys (W-x-x-W/C) motif of substrate proteins, by the action of C-mannosyltransferases, DPY19-related gene products. The acceptor substrate proteins are included in the thrombospondin type I repeat (TSR) superfamily, cytokine receptor type I family, and others. Previous studies demonstrated that C-mannosylation plays critical roles in the folding, sorting, and/or secretion of substrate proteins. A C-mannosylation-defective gene mutation was identified in humans as the disease-associated variant affecting a C-mannosylation motif of W-x-x-W of ADAMTSL1, which suggests the involvement of defects in protein C-mannosylation in human diseases such as developmental glaucoma, myopia, and/or retinal defects. On the other hand, monomeric C-mannosyl Trp (C-Man-Trp), a deduced degradation product of C-mannosylated proteins, occurs in cells and extracellular fluids. Several studies showed that the level of C-Man-Trp is upregulated in blood of patients with renal dysfunction, suggesting that the metabolism of C-Man-Trp may be involved in human kidney diseases. Together, protein C-mannosylation is considered to play important roles in the biosynthesis and functions of substrate proteins, and the altered regulation of protein C-manosylation may be involved in the pathophysiology of human diseases. In this review, we consider the biochemical and biomedical knowledge of protein C-mannosylation and C-Man-Trp, and introduce recent studies concerning their significance in biology and medicine.

Genetic and Environmental Contributions to Variation in the Stable Urinary NMR Metabolome over Time: A Classic Twin Study

Genes, sex, age, diet, lifestyle, gut microbiome, and multiple other factors affect human metabolomic profiles. Understanding metabolomic variation is critical in human nutrition research as metabolites that are sensitive to change versus those that are more stable might be more informative for a particular study design. This study aims to identify stable metabolomic regions and determine the genetic and environmental contributions to stability. Using a classic twin design, ¹H nuclear magnetic resonance (NMR) urinary metabolomic profiles were measured in 128 twins at baseline, 1 month, and 2 months. Multivariate mixed models identified stable urinary metabolites with intraclass correlation coefficients ≥0.51. Longitudinal twin modeling measured the contribution of genetic and environmental influences to variation in the stable urinary NMR metabolome, comprising stable metabolites. The conservation of an individual’s stable urinary NMR metabolome over time was assessed by calculating conservation indices. In this study, 20% of the urinary NMR metabolome is stable over 2 months (intraclass correlation (ICC) 0.51–0.65). Common genetic and shared environmental factors contributed to variance in the stable urinary NMR metabolome over time. Using the stable metabolome, 91% of individuals had good metabolomic conservation indices ≥0.70. To conclude, this research identifies 20% of the urinary NMR metabolome as stable, improves our knowledge of the sources of metabolomic variation over time, and demonstrates the conservation of an individual’s urinary NMR metabolome.

An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging

While many diseases of aging have been linked to the immunological system, immune metrics capable of identifying the most at-risk individuals are lacking. From the blood immunome of 1,001 individuals aged 8-96 years, we developed a deep-learning method based on patterns of systemic age-related inflammation. The resulting inflammatory clock of aging (iAge) tracked with multimorbidity, immunosenescence, frailty and cardiovascular aging, and is also associated with exceptional longevity in centenarians. The strongest contributor to iAge was the chemokine CXCL9, which was involved in cardiac aging, adverse cardiac remodeling and poor vascular function. Furthermore, aging endothelial cells in human and mice show loss of function, cellular senescence and hallmark phenotypes of arterial stiffness, all of which are reversed by silencing CXCL9. In conclusion, we identify a key role of CXCL9 in age-related chronic inflammation and derive a metric for multimorbidity that can be utilized for the early detection of age-related clinical phenotypes.

A Biomarker-based Biological Age in UK Biobank: Composition and Prediction of Mortality and Hospital Admissions

Background

Chronological age is the strongest risk factor for most chronic diseases. Developing a biomarker-based age and understanding its most important contributing biomarkers may shed light on the effects of age on later-life health and inform opportunities for disease prevention.

Methods

A subpopulation of 141 254 individuals healthy at baseline were studied, from among 480 019 UK Biobank participants aged 40–70 recruited in 2006–2010, and followed up for 6–12 years via linked death and secondary care records. Principal components of 72 biomarkers measured at baseline were characterized and used to construct sex-specific composite biomarker ages using the Klemera Doubal method, which derived a weighted sum of biomarker principal components based on their linear associations with chronological age. Biomarker importance in the biomarker ages was assessed by the proportion of the variation in the biomarker ages that each explained. The proportions of the overall biomarker and chronological age effects on mortality and age-related hospital admissions explained by the biomarker ages were compared using likelihoods in Cox proportional hazard models.

Results

Reduced lung function, kidney function, reaction time, insulin-like growth factor 1, hand grip strength, and higher blood pressure were key contributors to the derived biomarker age in both men and women. The biomarker ages accounted for >65% and >84% of the apparent effect of age on mortality and hospital admissions for the healthy and whole populations, respectively, and significantly improved prediction of mortality (p < .001) and hospital admissions (p < 1 × 10⁻¹⁰) over chronological age alone.

Conclusions

This study suggests that a broader, multisystem approach to research and prevention of diseases of aging warrants consideration.