Metabolite patterns predicting sex and age in participants of the Karlsruhe Metabolomics and Nutrition (KarMeN) study

Unraveling sex-specific risks of knee osteoarthritis before menopause: Do sex differences start early in life?

OBJECTIVE

Sufficient evidence within the past two decades have shown that osteoarthritis (OA) has a sex-specific component. However, efforts to reveal the biological causes of this disparity have emerged more gradually. In this narrative review, we discuss anatomical differences within the knee, incidence of injuries in youth sports, and metabolic factors that present early in life (childhood and early adulthood) that can contribute to a higher risk of OA in females.

DESIGN

We compiled clinical data from multiple tissues within the knee joint-since OA is a whole joint disorder-aiming to reveal relevant factors behind the sex differences from different perspectives.

RESULTS

The data gathered in this review indicate that sex differences in articular cartilage, meniscus, and anterior cruciate ligament are detected as early as childhood and are not only explained by sex hormones. Aiming to unveil the biological causes of the uneven sex-specific risks for knee OA, we review the current knowledge of sex differences mostly in young, but also including old populations, from the perspective of (i) human anatomy in both healthy and pathological conditions, (ii) physical activity and response to injury, and (iii) metabolic signatures.

CONCLUSIONS

We propose that to close the gap in health disparities, and specifically regarding OA, we should address sex-specific anatomic, biologic, and metabolic factors at early stages in life, as a way to prevent the higher severity and incidence of OA in women later in life.

The HuMet Repository: Watching human metabolism at work

Metabolism oscillates between catabolic and anabolic states depending on food intake, exercise, or stresses that change a multitude of metabolic pathways simultaneously. We present the HuMet Repository for exploring dynamic metabolic responses to oral glucose/lipid loads, mixed meals, 36-h fasting, exercise, and cold stress in healthy subjects. Metabolomics data from blood, urine, and breath of 15 young, healthy men at up to 56 time points are integrated and embedded within an interactive web application, enabling researchers with and without computational expertise to search, visualize, analyze, and contextualize the dynamic metabolite profiles of 2,656 metabolites acquired on multiple platforms. With examples, we demonstrate the utility of the resource for research into the dynamics of human metabolism, highlighting differences and similarities in systemic metabolic responses across challenges and the complementarity of metabolomics platforms. The repository, providing a reference for healthy metabolite changes to six standardized physiological challenges, is freely accessible through a web portal.

Genetic Architecture and Analysis Practices of Circulating Metabolites in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program

Circulating metabolite levels partly reflect the state of human health and diseases, and can be impacted by genetic determinants. Hundreds of loci associated with circulating metabolites have been identified; however, most findings focus on predominantly European ancestry or single study analyses. Leveraging the rich metabolomics resources generated by the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program, we harmonized and accessibly cataloged 1,729 circulating metabolites among 25,058 ancestrally-diverse samples. We provided recommendations for outlier and imputation handling to process metabolite data, as well as a general analytical framework. We further performed a pooled analysis following our practical recommendations and discovered 1,778 independent loci associated with 667 metabolites. Among 108 novel locus - metabolite pairs, we detected not only novel loci within previously implicated metabolite associated genes, but also novel genes (such as GAB3 and VSIG4 located in the X chromosome) that have putative roles in metabolic regulation. In the sex-stratified analysis, we revealed 85 independent locus-metabolite pairs with evidence of sexual dimorphism, including well-known metabolic genes such as FADS2 , D2HGDH , SUGP1 , UTG2B17 , strongly supporting the importance of exploring sex difference in the human metabolome. Taken together, our study depicted the genetic contribution to circulating metabolite levels, providing additional insight into the understanding of human health.

Blood metabolomic shift links diet and gut microbiota to multiple health outcomes among Hispanic/Latino immigrants in the U.S

Immigrants from less industrialized countries who are living in the U.S. often bear an elevated risk of multiple disease due to the adoption of a U.S. lifestyle. Blood metabolome holds valuable information on environmental exposure and the pathogenesis of chronic diseases, offering insights into the link between environmental factors and disease burden. Analyzing 634 serum metabolites from 7,114 Hispanics (1,141 U.S.-born, 5,973 foreign-born) in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), we identified profound blood metabolic shift during acculturation. Machine learning highlighted the prominent role of non-genetic factors, especially food and gut microbiota, in these changes. Immigration-related metabolites correlated with plant-based foods and beneficial gut bacteria for foreign-born Hispanics, and with meat-based or processed food and unfavorable gut bacteria for U.S.-born Hispanics. Cardiometabolic traits, liver, and kidney function exhibited a link with immigration-related metabolic changes, which were also linked to increased risk of diabetes, severe obesity, chronic kidney disease, and asthma.

Graphical abstract

Highlights

A substantial proportion of identified blood metabolites differ between U.S.-born and foreign-born Hispanics/Latinos in the U.S.Food and gut microbiota are the major modifiable contributors to blood metabolomic difference between U.S.-born and foreign-born Hispanics/Latinos.U.S. nativity related metabolites collectively correlate with a spectrum of clinical traits and chronic diseases.

New insights into healthy ageing, inflammageing and frailty using metabolomics

Human ageing is a normal process and does not necessarily result in the development of frailty. A mix of genetic, environmental, dietary, and lifestyle factors can have an impact on ageing, and whether an individual develops frailty. Frailty is defined as the loss of physiological reserve both at the physical and cellular levels, where systemic processes such as oxidative stress and inflammation contribute to physical decline. The newest "omics" technology and systems biology discipline, metabolomics, enables thorough characterisation of small-molecule metabolites in biological systems at a particular time and condition. In a biological system, metabolites-cellular intermediate products of metabolic reactions-reflect the system's final response to genomic, transcriptomic, proteomic, epigenetic, or environmental alterations. As a relatively newer technique to characterise metabolites and biomarkers in ageing and illness, metabolomics has gained popularity and has a wide range of applications. We will give a comprehensive summary of what is currently known about metabolomics in studies of ageing, with a focus on biomarkers for frailty. Metabolites related to amino acids, lipids, carbohydrates, and redox metabolism may function as biomarkers of ageing and/or frailty development, based on data obtained from human studies. However, there is a complexity that underpins biological ageing, due to both genetic and environmental factors that play a role in orchestrating the ageing process. Therefore, there is a critical need to identify pathways that contribute to functional decline in people with frailty.

Identifying critical age and gender-based metabolomic shifts in a Japanese population of the Tohoku Medical Megabank cohort

Understanding the physiological changes associated with aging and the associated disease risks is essential to establish biomarkers as indicators of biological aging. This study used the NMR-measured plasma metabolome to calculate age-specific metabolite indices. In doing so, the scope of the study was deliberately simplified to capture general trends and insights into age-related changes in metabolic patterns. In addition, changes in metabolite concentrations with age were examined in detail, with the period from 55-59 to 60-64 years being a period of significant metabolic change, particularly in men, and from 45-49 to 50-54 years in females. These results illustrate the different variations in metabolite concentrations by sex and provide new insights into the relationship between age and metabolic diseases.

Multi-omics reveals the role of MCM2 and hnRNP K phosphorylation in mouse renal aging through genomic instability

The process of aging is characterized by structural degeneration and functional decline, as well as diminished adaptability and resistance. The aging kidney exhibits a variety of structural and functional impairments. In aging mice, thinning and graying of fur were observed, along with a significant increase in kidney indices compared to young mice. Biochemical indicators revealed elevated levels of creatinine, urea nitrogen and serum uric acid, suggesting impaired kidney function. Histological analysis unveiled glomerular enlargement and sclerosis, severe hyaline degeneration, capillary occlusion, lymphocyte infiltration, tubular and glomerular fibrosis, and increased collagen deposition. Observations under electron microscopy showed thickened basement membranes, altered foot processes, and increased mesangium and mesangial matrix. Molecular marker analysis indicated upregulation of aging-related β-galactosidase, p16-INK4A, and the DNA damage marker γH2AX in the kidneys of aged mice. In metabolomics, a total of 62 significantly different metabolites were identified, and 10 pathways were enriched. We propose that citrulline, dopamine, and indoxyl sulfate have the potential to serve as markers of kidney damage related to aging in the future. Phosphoproteomics analysis identified 6656 phosphosites across 1555 proteins, annotated to 62 pathways, and indicated increased phosphorylation at the Ser27 site of Minichromosome maintenance complex component 2 (Mcm2) and decreased at the Ser284 site of heterogeneous nuclear ribonucleoprotein K (hnRNP K), with these modifications being confirmed by western blotting. The phosphorylation changes in these molecules may contribute to aging by affecting genome stability. Eleven common pathways were detected in both omics, including arginine biosynthesis, purine metabolism and biosynthesis of unsaturated fatty acids, etc., which are closely associated with aging and renal insufficiency.

Multivariate curve resolution-based data fusion approaches applied in 1H NMR metabolomic analysis of healthy cohorts

BACKGROUND

Metabolomics plays a critical role in deciphering metabolic alterations within individuals, demanding the use of sophisticated analytical methodologies to navigate its intricate complexity. While many studies focus on single biofluid types, simultaneous analysis of multiple matrices enhances understanding of complex biological mechanisms. Consequently, the development of data fusion methods enabling multiblock analysis becomes essential for comprehensive insights into metabolic dynamics.

RESULTS

This study introduces a novel guideline for jointly analyzing diverse metabolomic datasets (serum, urine, metadata) with a focus on metabolic differences between groups within a healthy cohort. The guideline presents two fusion strategies, 'Low-Level data fusion' (LLDF) and 'Mid-Level data fusion' (MLDF), employing a sequential application of Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS), linking the outcomes of successive analyses. MCR-ALS is a versatile method for analyzing mixed data, adaptable at various stages of data processing-encompassing resonance integration, data compression, and exploratory analysis. The LLDF and MLDF strategies were applied to 1H NMR spectral data extracted from urine and serum samples, coupled with biochemical metadata sourced from 145 healthy volunteers.

SIGNIFICANCE

Both methodologies effectively integrated and analysed multiblock datasets, unveiling the inherent data structure and variables associated with discernible factors among healthy cohorts. While both approaches successfully detected sex-related differences, the MLDF strategy uniquely revealed components linked to age. By applying this analysis, we aim to enhance the interpretation of intricate biological mechanisms and uncover variations that may not be easily discernible through individual data analysis.

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.

Changes in Lipid Profiles with the Progression of Pregnancy in Black Women

Background/Objectives: Lipid metabolism plays an important role in maternal health and fetal development. There is a gap in the knowledge of how lipid metabolism changes during pregnancy for Black women who are at a higher risk of adverse outcomes. We hypothesized that the comprehensive lipidome profiles would show variation across pregnancy indicative of requirements during gestation and fetal development. Methods: Black women were recruited at prenatal clinics. Plasma samples were collected at 8-18 weeks (T1), 22-29 weeks (T2), and 30-36 weeks (T3) of pregnancy. Samples from 64 women who had term births (≥37 weeks gestation) were subjected to "shotgun" Orbitrap mass spectrometry. Mixed-effects models were used to quantify systematic changes and dimensionality reduction models were used to visualize patterns and identify reliable lipid signatures. Results: Total lipids and major lipid classes showed significant increases with the progression of pregnancy. Phospholipids and glycerolipids exhibited a gradual increase from T1 to T2 to T3, while sphingolipids and total sterol lipids displayed a more pronounced increase from T2 to T3. Acylcarnitines, hydroxy acylcarnitines, and Lyso phospholipid levels significantly decreased from T1 to T3. A deviation was that non-esterified fatty acids decreased from T1 to T2 and increased again from T2 to T3, suggestive of a potential role for these lipids during the later stages of pregnancy. The fatty acids showing this trend included key fatty acids-non-esterified Linoleic acid, Arachidonic acid, Alpha-linolenic acid, Eicosapentaenoic acid, Docosapentaenoic acid, and Docosahexaenoic acid. Conclusions: Mapping lipid patterns and identifying lipid signatures would help develop intervention strategies to reduce perinatal health disparities among pregnant Black women.

Sex-Associated Metabolites and Incident Stroke, Incident Coronary Heart Disease, Hypertension, and Chronic Kidney Disease in the REGARDS Cohort

BACKGROUND

Sex disparities exist in cardiometabolic diseases. Metabolomic profiling offers insight into disease mechanisms, as the metabolome is influenced by environmental and genetic factors. We identified metabolites associated with sex and determined if sex-associated metabolites are associated with incident stoke, incident coronary heart disease, prevalent hypertension, and prevalent chronic kidney disease.

METHODS AND RESULTS

Targeted metabolomics was conducted for 357 metabolites in the REGARDS (Reasons for Geographic and Racial Differences in Stroke) case-cohort substudy for incident stroke. Weighted logistic regression models were used to identify metabolites associated with sex in REGARDS. Sex-associated metabolites were replicated in the HyperGEN (Hypertension Genetic Epidemiology Network) and using the literature. Weighted Cox proportional hazard models were used to evaluate associations between metabolites and incident stroke. Cox proportional hazard models were used to evaluate associations between metabolites and incident coronary heart disease. Weighted logistic regression models were used to evaluate associations between metabolites and hypertension and chronic kidney disease. Fifty-one replicated metabolites were associated with sex. Higher levels of 6 phosphatidylethanolamines were associated with incident stroke. No metabolites were associated with incident coronary heart disease. Higher levels of uric acid and leucine and lower levels of a lysophosphatidylcholine were associated with hypertension. Higher levels of indole-3-lactic acid, 7 phosphatidylethanolamines, and uric acid, and lower levels of betaine and bilirubin were associated with chronic kidney disease.

CONCLUSIONS

These findings suggest that the sexual dimorphism of the metabolome may contribute to sex differences in stroke, hypertension, and chronic kidney disease.

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.

New perspectives on 'Breathomics': metabolomic profiling of non-volatile organic compounds in exhaled breath using DI-FT-ICR-MS

Breath analysis offers tremendous potential for diagnostic approaches, since it allows for easy and non-invasive sample collection. "Breathomics" as one major research field comprehensively analyses the metabolomic profile of exhaled breath providing insights into various (patho)physiological processes. Recent research, however, primarily focuses on volatile compounds. This is the first study that evaluates the non-volatile organic compounds (nVOCs) in breath following an untargeted metabolomic approach. Herein, we developed an innovative method utilizing a filter-based device for metabolite extraction. Breath samples of 101 healthy volunteers (female n = 50) were analysed using DI-FT-ICR-MS and biostatistically evaluated. The characterisation of the non-volatile core breathome identified more than 1100 metabolites including various amino acids, organic and fatty acids and conjugates thereof, carbohydrates as well as diverse hydrophilic and lipophilic nVOCs. The data shows gender-specific differences in metabolic patterns with 570 significant metabolites. Male and female metabolomic profiles of breath were distinguished by a random forest approach with an out-of-bag error of 0.0099. Additionally, the study examines how oral contraceptives and various lifestyle factors, like alcohol consumption, affect the non-volatile breathome. In conclusion, the successful application of a filter-based device combined with metabolomics-analyses delineate a non-volatile breathprint laying the foundation for discovering clinical biomarkers in exhaled breath.

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).

Relation of the kynurenine pathway with normal age: A systematic review

BACKGROUND

The kynurenine pathway (KP) is gaining more attention as a common pathway involved in age-related conditions. However, which changes in the KP occur due to normal ageing is still largely unclear. The aim of this systematic review was to summarize the available evidence for associations of KP metabolites with age.

METHODS

We used an broad search strategy and included studies up to October 2023.

RESULTS

Out of 8795 hits, 55 studies were eligible for the systematic review. These studies suggest that blood levels of tryptophan decrease with age, while blood and cerebrospinal fluid levels of kynurenine and its ratio with tryptophan increase. Studies investigating associations between cerebrospinal fluid and blood levels of kynurenic acid and quinolinic acid with age reported either positive or non-significant findings. However, there is a large heterogeneity across studies. Additionally, most studies were cross-sectional, and only few studies investigated associations with other downstream kynurenines.

CONCLUSIONS

This systematic review suggests that levels of kynurenines are positively associated with age. Larger and prospective studies are needed that also investigate a more comprehensive panel of KP metabolites and changes during the life-course.

Evaluating the Effect of Data Merging and Postacquisition Normalization on Statistical Analysis of Untargeted High-Resolution Mass Spectrometry Based Urinary Metabolomics Data

Urine is one of the most widely used biofluids in metabolomic studies because it can be collected noninvasively and is available in large quantities. However, it shows large heterogeneity in sample concentration and consequently requires normalization to reduce unwanted variation and extract meaningful biological information. Biological samples like urine are commonly measured with electrospray ionization (ESI) coupled to a mass spectrometer, producing data sets for positive and negative modes. Combining these gives a more complete picture of the total metabolites present in a sample. However, the effect of this data merging on subsequent data analysis, especially in combination with normalization, has not yet been analyzed. To address this issue, we conducted a neutral comparison study to evaluate the performance of eight postacquisition normalization methods under different data merging procedures using 1029 urine samples from the Food Chain plus (FoCus) cohort. Samples were measured with a Fourier transform ion cyclotron resonance mass spectrometer (FT-ICR-MS). Normalization methods were evaluated by five criteria capturing the ability to remove sample concentration variation and preserve relevant biological information. Merging data after normalization was generally favorable for quality control (QC) sample similarity, sample classification, and feature selection for most of the tested normalization methods. Merging data after normalization and the usage of probabilistic quotient normalization (PQN) in a similar setting are generally recommended. Relying on a single analyte to capture sample concentration differences, like with postacquisition creatinine normalization, seems to be a less preferable approach, especially when data merging is applied.

Untargeted Metabolomics Profiling Reveals Exercise Intensity-Dependent Alterations in Thoroughbred Racehorses' Plasma after Routine Conditioning Sessions

Thoroughbred (TB) racehorses undergo rigorous conditioning programs to optimize their physical and mental capabilities through varied exercise sessions. While conventional investigations focus on limited hematological and biochemical parameters, this field study employed untargeted metabolomics to comprehensively assess metabolic responses triggered by exercise sessions routinely used in TB conditioning. Blood samples were collected pre- and post-exercise from ten racehorses, divided into two groups based on exercise intensity: high intensity (n = 6, gallop at ± 13.38 m/s, 1400 m) and moderate intensity (n = 4, soft canter at ± 7.63 m/s, 2500 m). Intensity was evaluated through monitoring of the speed, heart rate, and lactatemia. Resting and 30 min post-exercise plasma samples were analyzed using ultraperformance liquid chromatography coupled with high-resolution mass spectrometry. Unsupervised principal component analysis revealed exercise-induced metabolome changes, with high-intensity exercise inducing greater alterations. Following high-intensity exercise, 54 metabolites related to amino acid, fatty acid, nucleic acid, and vitamin metabolism were altered versus 23 metabolites, primarily linked to fatty acid and amino acid metabolism, following moderate-intensity exercise. Metabolomics confirmed energy metabolism changes reported by traditional biochemistry studies and highlighted the involvement of lipid and amino acid metabolism during routine exercise and recovery, aspects that had previously been overlooked in TB racehorses.

A new statistical workflow (R-packages based) to investigate associations between one variable of interest and the metabolome

INTRODUCTION

In metabolomics, the investigation of associations between the metabolome and one trait of interest is a key research question. However, statistical analyses of such associations are often challenging. Statistical tools enabling resilient verification and clear presentation are therefore highly desired.

OBJECTIVES

Our aim is to provide a contribution for statistical analysis of metabolomics data, offering a widely applicable open-source statistical workflow, which considers the intrinsic complexity of metabolomics data.

METHODS

We combined selected R packages tailored for all properties of heterogeneous metabolomics datasets, where metabolite parameters typically (i) are analyzed in different matrices, (ii) are measured on different analytical platforms with different precision, (iii) are analyzed by targeted as well as non-targeted methods, (iv) are scaled variously, (v) reveal heterogeneous variances, (vi) may be correlated, (vii) may have only few values or values below a detection limit, or (viii) may be incomplete.

RESULTS

The code is shared entirely and freely available. The workflow output is a table of metabolites associated with a trait of interest and a compact plot for high-quality results visualization. The workflow output and its utility are presented by applying it to two previously published datasets: one dataset from our own lab and another dataset taken from the repository MetaboLights.

CONCLUSION

Robustness and benefits of the statistical workflow were clearly demonstrated, and everyone can directly re-use it for analysis of own data.

Theoretical modeling and machine learning-based data processing workflows in comprehensive two-dimensional gas chromatography-A review

In recent years, comprehensive two-dimensional gas chromatography (GC × GC) has been gradually gaining prominence as a preferred method for the analysis of complex samples due to its higher peak capacity and resolution power compared to conventional gas chromatography (GC). Nonetheless, to fully benefit from the capabilities of GC × GC, a holistic approach to method development and data processing is essential for a successful and informative analysis. Method development enables the fine-tuning of the chromatographic separation, resulting in high-quality data. While generating such data is pivotal, it does not necessarily guarantee that meaningful information will be extracted from it. To this end, the first part of this manuscript reviews the importance of theoretical modeling in achieving good optimization of the separation conditions, ultimately improving the quality of the chromatographic separation. Multiple theoretical modeling approaches are discussed, with a special focus on thermodynamic-based modeling. The second part of this review highlights the importance of establishing robust data processing workflows, with a special emphasis on the use of advanced data processing tools such as, Machine Learning (ML) algorithms. Three widely used ML algorithms are discussed: Random Forest (RF), Support Vector Machine (SVM), and Partial Least Square-Discriminate Analysis (PLS-DA), highlighting their role in discovery-based analysis.

Circulating metabolic signatures of rapid and slow progression to type 1 diabetes in islet autoantibody-positive children

Aims/hypothesis

Appearance of multiple islet cell autoantibodies in early life is indicative of future progression to overt type 1 diabetes, however, at varying rates. Here, we aimed to study whether distinct metabolic patterns could be identified in rapid progressors (RP, disease manifestation within 18 months after the initial seroconversion to autoantibody positivity) vs. slow progressors (SP, disease manifestation at 60 months or later from the appearance of the first autoantibody).

Methods

Longitudinal samples were collected from RP (n=25) and SP (n=41) groups at the ages of 3, 6, 12, 18, 24, or ≥ 36 months. We performed a comprehensive metabolomics study, analyzing both polar metabolites and lipids. The sample series included a total of 239 samples for lipidomics and 213 for polar metabolites.

Results

We observed that metabolites mediated by gut microbiome, such as those involved in tryptophan metabolism, were the main discriminators between RP and SP. The study identified specific circulating molecules and pathways, including amino acid (threonine), sugar derivatives (hexose), and quinic acid that may define rapid vs. slow progression to type 1 diabetes. However, the circulating lipidome did not appear to play a major role in differentiating between RP and SP.

Conclusion/interpretation

Our study suggests that a distinct metabolic profile is linked with the type 1 diabetes progression. The identification of specific metabolites and pathways that differentiate RP from SP may have implications for early intervention strategies to delay the development of type 1 diabetes.

Biological differences underlying sex and gender disparities in bladder cancer: current synopsis and future directions

Sex and gender disparities in bladder cancer have long been a subject of interest to the cancer research community, wherein men have a 4 times higher incidence rate than women, and female patients often present with higher-grade disease and experience worse outcomes. Despite the known differences in disease incidence and clinical outcomes between male and female bladder cancer patients, clinical management remains the same. In this review, we critically analyze studies that report on the biological differences between men and women and evaluate how these differences contribute to sex and gender disparities in bladder cancer. Distinct characteristics of the male and female immune systems, differences in circulating hormone levels and hormone receptor expression, and different genetic and epigenetic alterations are major biological factors that all likely contribute to disparate incidence rates and outcomes for male and female bladder cancer patients. Future preclinical and clinical studies in this area should employ experimental approaches that account for and consider sex and gender disparities in bladder cancer, thereby facilitating the development of precision medicine for the effective treatment of bladder cancer in all patients.

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.

Metabolomic profiling of CSF and blood serum elucidates general and sex-specific patterns for mild cognitive impairment and Alzheimer's disease patients

Background

Beta-amyloid (Abeta) and tau protein in cerebrospinal fluid (CSF) are established diagnostic biomarkers for Alzheimer's disease (AD). However, these biomarkers may not the only ones existing parameters that reflect Alzheimer's disease neuropathological change. The use of quantitative metabolomics approach could provide novel insights into dementia progression and identify key metabolic alterations in CSF and serum.

Methods

In the present study, we quantified a set of 45 metabolites in CSF (71 patients) and 27 in serum (76 patients) in patients with mild cognitive impairment (MCI), AD, and controls using nuclear magnetic resonance (NMR)-based metabolomics.

Results

We found significantly reduced CSF (1.32-fold, p = 0.0195) and serum (1.47-fold, p = 0.0484) levels of the ketone body acetoacetate in AD and MCI patients. Additionally, we found decreased levels (1.20-fold, p = 0.0438) of the branched-chain amino acid (BCAA) valine in the CSF of AD patients with increased valine degradation pathway metabolites (such as 3-hydroxyisobutyrate and α-ketoisovalerate). Moreover, we discovered that CSF 2-hydroxybutyrate is dramatically reduced in the MCI patient group (1.23-fold, p = 0.039). On the other hand, vitamin C (ascorbate) was significantly raised in CSF of these patients (p = 0.008). We also identified altered CSF protein content, 1,5-anhydrosorbitol and fructose as further metabolic shifts distinguishing AD from MCI. Significantly decreased serum levels of the amino acid ornithine were seen in the AD dementia group when compared to healthy controls (1.36-fold, p = 0.011). When investigating the effect of sex, we found for AD males the sign of decreased 2-hydroxybutyrate and acetoacetate in CSF while for AD females increased serum creatinine was identified.

Conclusion

Quantitative NMR metabolomics of CSF and serum was able to efficiently identify metabolic changes associated with dementia groups of MCI and AD patients. Further, we showed strong correlations between these changes and well-established metabolomic and clinical indicators like Abeta.

Sexual Dimorphism of the Mouse Plasma Metabolome Is Associated with Phenotypes of 30 Gene Knockout Lines

Although metabolic alterations are observed in many monogenic and complex genetic disorders, the impact of most mammalian genes on cellular metabolism remains unknown. Understanding the effect of mouse gene dysfunction on metabolism can inform the functions of their human orthologues. We investigated the effect of loss-of-function mutations in 30 unique gene knockout (KO) lines on plasma metabolites, including genes coding for structural proteins (11 of 30), metabolic pathway enzymes (12 of 30) and protein kinases (7 of 30). Steroids, bile acids, oxylipins, primary metabolites, biogenic amines and complex lipids were analyzed with dedicated mass spectrometry platforms, yielding 827 identified metabolites in male and female KO mice and wildtype (WT) controls. Twenty-two percent of 23,698 KO versus WT comparison tests showed significant genotype effects on plasma metabolites. Fifty-six percent of identified metabolites were significantly different between the sexes in WT mice. Many of these metabolites were also found to have sexually dimorphic changes in KO lines. We used plasma metabolites to complement phenotype information exemplified for Dhfr, Idh1, Mfap4, Nek2, Npc2, Phyh and Sra1. The association of plasma metabolites with IMPC phenotypes showed dramatic sexual dimorphism in wildtype mice. We demonstrate how to link metabolomics to genotypes and (disease) phenotypes. Sex must be considered as critical factor in the biological interpretation of gene functions.

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.

Application of novel AI-based algorithms to biobank data: uncovering of new features and linear relationships

We independently analyzed two large public domain datasets that contain ¹H-NMR spectral data from lung cancer and sex studies. The biobanks were sourced from the Karlsruhe Metabolomics and Nutrition (KarMeN) study and Bayesian Automated Metabolite Analyzer for NMR data (BATMAN) study. Our approach of applying novel artificial intelligence (AI)-based algorithms to NMR is an attempt to globalize metabolomics and demonstrate its clinical applications. The intention of this study was to analyze the resulting spectra in the biobanks via AI application to demonstrate its clinical applications. This technique enables metabolite mapping in areas of localized enrichment as a measure of true activity while also allowing for the accurate categorization of phenotypes.

Biomarkers of aging

Aging biomarkers are a combination of biological parameters to (i) assess age-related changes, (ii) track the physiological aging process, and (iii) predict the transition into a pathological status. Although a broad spectrum of aging biomarkers has been developed, their potential uses and limitations remain poorly characterized. An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research: How old are we? Why do we get old? And how can we age slower? This review aims to address this need. Here, we summarize our current knowledge of biomarkers developed for cellular, organ, and organismal levels of aging, comprising six pillars: physiological characteristics, medical imaging, histological features, cellular alterations, molecular changes, and secretory factors. To fulfill all these requisites, we propose that aging biomarkers should qualify for being specific, systemic, and clinically relevant.

Blood Biomarkers for Healthy Aging

Measuring the abundance of biological molecules and their chemical modifications in blood and tissues has been the cornerstone of research and medical diagnoses for decades. Although the number and variety of molecules that can be measured have expanded exponentially, the blood biomarkers routinely assessed in medical practice remain limited to a few dozen, which have not substantially changed over the last 30-40 years. The discovery of novel biomarkers would allow, for example, risk stratification or monitoring of disease progression or the effectiveness of treatments and interventions, improving clinical practice in myriad ways. In this review, we combine the biomarker discovery concept with geroscience. Geroscience bridges aging research and translation to clinical applications by combining the framework of medical gerontology with high-technology medical research. With the development of geroscience and the rise of blood biomarkers, there has been a paradigm shift from disease prevention and cure to promoting health and healthy aging. New -omic technologies have played a role in the development of blood biomarkers, including epigenetic, proteomic, metabolomic, and lipidomic markers, which have emerged as correlates or predictors of health status, from disease to exceptional health.

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.

Replication and mediation of the association between the metabolome and clinical markers of metabolic health in an adolescent cohort study

Metabolomics-derived metabolites (henceforth metabolites) may mediate the relationship between modifiable risk factors and clinical biomarkers of metabolic health (henceforth clinical biomarkers). We set out to study the associations of metabolites with clinical biomarkers and a potential mediation effect in a population of young adults. First, we conducted a systematic literature review searching for metabolites associated with 11 clinical biomarkers (inflammation markers, glucose, blood pressure or blood lipids). Second, we replicated the identified associations in a study population of n = 218 (88 males and 130 females, average age of 18 years) participants of the DONALD Study. Sex-stratified linear regression models adjusted for age and BMI and corrected for multiple testing were calculated. Third, we investigated our previously reported metabolites associated with anthropometric and dietary factors mediators in sex-stratified causal mediation analysis. For all steps, both urine and blood metabolites were considered. We found 41 metabolites in the literature associated with clinical biomarkers meeting our inclusion criteria. We were able to replicate an inverse association of betaine with CRP in women, between body mass index and C-reactive protein (CRP) and between body fat and leptin. There was no evidence of mediation by lifestyle-related metabolites after correction for multiple testing. We were only able to partially replicate previous findings in our age group and did not find evidence of mediation. The complex interactions between lifestyle factors, the metabolome, and clinical biomarkers warrant further investigation.

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.

Acute effects of moderate vs. vigorous endurance exercise on urinary metabolites in healthy, young, physically active men-A multi-platform metabolomics approach

Introduction: Endurance exercise alters whole-body as well as skeletal muscle metabolism and physiology, leading to improvements in performance and health. However, biological mechanisms underlying the body's adaptations to different endurance exercise protocols are not entirely understood. Methods: We applied a multi-platform metabolomics approach to identify urinary metabolites and associated metabolic pathways that distinguish the acute metabolic response to two endurance exercise interventions at distinct intensities. In our randomized crossover study, 16 healthy, young, and physically active men performed 30 min of continuous moderate exercise (CME) and continuous vigorous exercise (CVE). Urine was collected during three post-exercise sampling phases (U01/U02/U03: until 45/105/195 min post-exercise), providing detailed temporal information on the response of the urinary metabolome to CME and CVE. Also, fasting spot urine samples were collected pre-exercise (U00) and on the following day (U04). While untargeted two-dimensional gas chromatography-mass spectrometry (GC×GC-MS) led to the detection of 608 spectral features, 44 metabolites were identified and quantified by targeted nuclear magnetic resonance (NMR) spectroscopy or liquid chromatography-mass spectrometry (LC-MS). Results: 104 urinary metabolites showed at least one significant difference for selected comparisons of sampling time points within or between exercise trials as well as a relevant median fold change >1.5 or <0. 6 ¯ (NMR, LC-MS) or >2.0 or <0.5 (GC×GC-MS), being classified as either exercise-responsive or intensity-dependent. Our findings indicate that CVE induced more profound alterations in the urinary metabolome than CME, especially at U01, returning to baseline within 24 h after U00. Most differences between exercise trials are likely to reflect higher energy requirements during CVE, as demonstrated by greater shifts in metabolites related to glycolysis (e.g., lactate, pyruvate), tricarboxylic acid cycle (e.g., cis-aconitate, malate), purine nucleotide breakdown (e.g., hypoxanthine), and amino acid mobilization (e.g., alanine) or degradation (e.g., 4-hydroxyphenylacetate). Discussion: To conclude, this study provided first evidence of specific urinary metabolites as potential metabolic markers of endurance exercise intensity. Future studies are needed to validate our results and to examine whether acute metabolite changes in urine might also be partly reflective of mechanisms underlying the health- or performance-enhancing effects of endurance exercise, particularly if performed at high intensities.

Integrative Multi-omics Analysis of Childhood Aggressive Behavior

This study introduces and illustrates the potential of an integrated multi-omics approach in investigating the underlying biology of complex traits such as childhood aggressive behavior. In 645 twins (cases = 42%), we trained single- and integrative multi-omics models to identify biomarkers for subclinical aggression and investigated the connections among these biomarkers. Our data comprised transmitted and two non-transmitted polygenic scores (PGSs) for 15 traits, 78,772 CpGs, and 90 metabolites. The single-omics models selected 31 PGSs, 1614 CpGs, and 90 metabolites, and the multi-omics model comprised 44 PGSs, 746 CpGs, and 90 metabolites. The predictive accuracy for these models in the test (N = 277, cases = 42%) and independent clinical data (N = 142, cases = 45%) ranged from 43 to 57%. We observed strong connections between DNA methylation, amino acids, and parental non-transmitted PGSs for ADHD, Autism Spectrum Disorder, intelligence, smoking initiation, and self-reported health. Aggression-related omics traits link to known and novel risk factors, including inflammation, carcinogens, and smoking.

Machine learning model for predicting age in healthy individuals using age-related gut microbes and urine metabolites

Age-related gut microbes and urine metabolites were investigated in 568 healthy individuals using metataxonomics and metabolomics. The richness and evenness of the fecal microbiota significantly increased with age, and the abundance of 16 genera differed between the young and old groups. Additionally, 17 urine metabolites contributed to the differences between the young and old groups. Among the microbes that differed by age, Bacteroides and Prevotella 9 were confirmed to be correlated with some urine metabolites. The machine learning algorithm eXtreme gradient boosting (XGBoost) was shown to produce the best performing age predictors, with a mean absolute error of 5.48 years. The accuracy of the model improved to 4.93 years with the inclusion of urine metabolite data. This study shows that the gut microbiota and urine metabolic profiles can be used to predict the age of healthy individuals with relatively good accuracy.

The association between acylcarnitine and amino acids profile and metabolic syndrome and its components in Iranian adults: Data from STEPs 2016

BACKGROUND

Evidence, albeit with conflicting results, has suggested that cardiometabolic risk factors, including obesity, type 2 diabetes (T2D), dyslipidemia, and hypertension, are highly associated with changes in metabolic signature, especially plasma amino acids and acylcarnitines levels. Here, we aimed to evaluate the association of circulating levels of amino acids and acylcarnitines with metabolic syndrome (MetS) and its components in Iranian adults.

METHODS

This cross-sectional study was performed on 1192 participants from the large-scale cross-sectional study of Surveillance of Risk Factors of non-communicable diseases (NCDs) in Iran (STEP 2016). The circulating levels of amino acids and acylcarnitines were measured using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in individuals with MetS (n=529) and without MetS (n=663).

RESULTS

The higher plasma levels of branched-chain amino acids (Val, Leu), aromatic amino acids (Phe, Tyr), Pro, Ala, Glu, and the ratio of Asp to Asn were significantly associated with MetS, whereas lower circulating levels of Gly, Ser, His, Asn, and citrulline were significantly associated with MetS. As for plasma levels of free carnitine and acylcarnitines, higher levels of short-chain acylcarnitines (C2, C3, C4DC), free carnitine (C0), and long-chain acylcarnitines (C16, C18OH) were significantly associated with MetS. Principal component analysis (PCA) showed that factor 3 (Tyr, Leu, Val, Met, Trp, Phe, Thr) [OR:1.165, 95% CI: 1.121-1.210, P<0.001], factor 7 (C0, C3, C4) [OR:1.257, 95% CI: 1.150-1.374, P<0.001], factor 8 (Gly, Ser) [OR:0.718, 95% CI: 0.651-0.793, P< 0.001], factor 9 (Ala, Pro, C4DC) [OR:1.883, 95% CI: 1.669-2.124, P<0.001], factor 10 (Glu, Asp, C18:2OH) [OR:1.132, 95% CI: 1.032-1.242, P= 0.009], factor 11 (citrulline, ornithine) [OR:0.862, 95% CI: 0.778-0.955, P= 0.004] and 13 (C18OH, C18:1 OH) [OR: 1.242, 95% CI: 1.042-1.480, P= 0.016] were independently correlated with metabolic syndrome.

CONCLUSION

Change in amino acid, and acylcarnitines profiles were seen in patients with MetS. Moreover, the alteration in the circulating levels of amino acids and acylcarnitines is along with an increase in MetS component number. It also seems that amino acid and acylcarnitines profiles can provide valuable information on evaluating and monitoring MetS risk. However, further studies are needed to establish this concept.

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.

Plasma Metabolomic and Lipidomic Profiling of Metabolic Dysfunction-Associated Fatty Liver Disease in Humans Using an Untargeted Multiplatform Approach

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a complex disorder that is implicated in dysregulations in multiple biological pathways, orchestrated by interactions between genetic predisposition, metabolic syndromes and environmental factors. The limited knowledge of its pathogenesis is one of the bottlenecks in the development of prognostic and therapeutic options for MAFLD. Moreover, the extent to which metabolic pathways are altered due to ongoing hepatic steatosis, inflammation and fibrosis and subsequent liver damage remains unclear. To uncover potential MAFLD pathogenesis in humans, we employed an untargeted nuclear magnetic resonance (NMR) spectroscopy- and high-resolution mass spectrometry (HRMS)-based multiplatform approach combined with a computational multiblock omics framework to characterize the plasma metabolomes and lipidomes of obese patients without (n = 19) or with liver biopsy confirmed MAFLD (n = 63). Metabolite features associated with MAFLD were identified using a metabolome-wide association study pipeline that tested for the relationships between feature responses and MAFLD. A metabolic pathway enrichment analysis revealed 16 pathways associated with MAFLD and highlighted pathway changes, including amino acid metabolism, bile acid metabolism, carnitine shuttle, fatty acid metabolism, glycerophospholipid metabolism, arachidonic acid metabolism and steroid metabolism. These results suggested that there were alterations in energy metabolism, specifically amino acid and lipid metabolism, and pointed to the pathways being implicated in alerted liver function, mitochondrial dysfunctions and immune system disorders, which have previously been linked to MAFLD in human and animal studies. Together, this study revealed specific metabolic alterations associated with MAFLD and supported the idea that MAFLD is fundamentally a metabolism-related disorder, thereby providing new perspectives for diagnostic and therapeutic strategies.

Nuclear Magnetic Resonance Metabolomic Profiling and Urine Chemistries in Incident Kidney Stone Formers Compared with Controls

BACKGROUND

The urine metabolites and chemistries that contribute to kidney stone formation are not fully understood. This study examined differences between the urine metabolic and chemistries profiles of first-time stone formers and controls.

METHODS

High-resolution 1H-nuclear magnetic resonance (NMR) spectroscopy-based metabolomic analysis was performed in 24-hour urine samples from a prospective cohort of 418 first-time symptomatic kidney stone formers and 440 controls. In total, 48 NMR-quantified metabolites in addition to 12 standard urine chemistries were assayed. Analysis of covariance was used to determine the association of stone former status with urine metabolites or chemistries after adjusting for age and sex and correcting for the false discovery rate. Gradient-boosted machine methods with nested cross-validation were applied to predict stone former status.

RESULTS

Among the standard urine chemistries, stone formers had lower urine oxalate and potassium and higher urine calcium, phosphate, and creatinine. Among NMR urine metabolites, stone formers had lower hippuric acid, trigonelline, 2-furoylglycine, imidazole, and citrate and higher creatine and alanine. A cross-validated model using urine chemistries, age, and sex yielded a mean AUC of 0.76 (95% CI, 0.73 to 0.79). A cross-validated model using urine chemistries, NMR-quantified metabolites, age, and sex did not meaningfully improve the discrimination (mean AUC, 0.78; 95% CI, 0.75 to 0.81). In this combined model, among the top ten discriminating features, four were urine chemistries and six NMR-quantified metabolites.

CONCLUSIONS

Although NMR-quantified metabolites did not improve discrimination, several urine metabolic profiles were identified that may improve understanding of kidney stone pathogenesis.

Serum Metabolomic Profiling in Aging Mice Using Liquid Chromatography-Mass Spectrometry

BACKGROUND

The process of aging and metabolism are intricately linked, thus rendering the identification of reliable biomarkers related to metabolism crucial for delaying the aging process. However, research of reliable markers that reflect aging profiles based on machine learning is scarce.

METHODS

Serum samples were obtained from aged mice (18-month-old) and young mice (3-month-old). LC-MS was used to perform a comprehensive analysis of the serum metabolome and machine learning was used to screen potential aging-related biomarkers.

RESULTS

In total, aging mice were characterized by 54 different metabolites when compared to control mice with criteria: VIP ≥ 1, q-value < 0.05, and Fold-Change ≥ 1.2 or ≤0.83. These metabolites were mostly involved in fatty acid biosynthesis, cysteine and methionine metabolism, D-glutamine and D-glutamate metabolism, and the citrate cycle (TCA cycle). We merged the comprehensive analysis and four algorithms (LR, GNB, SVM, and RF) to screen aging-related biomarkers, leading to the recognition of oleic acid. In addition, five metabolites were identified as novel aging-related indicators, including oleic acid, citric acid, D-glutamine, trypophol, and L-methionine.

CONCLUSIONS

Changes in the metabolism of fatty acids and conjugates, organic acids, and amino acids were identified as metabolic dysregulation related to aging. This study revealed the metabolic profile of aging and provided insights into novel potential therapeutic targets for delaying the effects of aging.

Kynurenine Pathway Metabolites in the Blood and Cerebrospinal Fluid Are Associated with Human Aging

The kynurenine pathway is implicated in aging, longevity, and immune regulation, but longitudinal studies and assessment of the cerebrospinal fluid (CSF) are lacking. We investigated tryptophan (Trp) and downstream kynurenine metabolites and their associations with age and change over time in four cohorts using comprehensive, targeted metabolomics. The study included 1574 participants in two cohorts with repeated metabolite measurements (mean age at baseline 58 years ± 8 SD and 62 ± 10 SD), 3161 community-dwelling older adults (age range 71-74 years), and 109 CSF donors (mean age 73 years ± 7 SD). In the first two cohorts, age was associated with kynurenine (Kyn), quinolinic acid (QA), and the kynurenine to tryptophan ratio (KTR), and inversely with Trp. Consistent with these findings, Kyn, QA, and KTR increased over time, whereas Trp decreased. Similarly, QA and KTR were higher in community-dwelling older adults of age 74 compared to 71, whereas Trp was lower. Kyn and QA were more strongly correlated with age in the CSF compared to serum and increased in a subset of participants with repeated CSF sampling (n = 33) over four years. We assessed associations with frailty and mortality in two cohorts. QA and KTR were most strongly associated with mortality and frailty. Our study provides robust evidence of changes in tryptophan and kynurenine metabolism with human aging and supports links with adverse health outcomes. Our results suggest that aging activates the inflammation and stress-driven kynurenine pathway systemically and in the brain, but we cannot determine whether this activation is harmful or adaptive. We identified a relatively stronger age-related increase of the potentially neurotoxic end-product QA in brain.

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.

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.

Exploring the association between habitual food intake and the urine and blood metabolome in adolescents and young adults: a cohort study

SCOPE

Habitual diet may be reflected in metabolite profiles that can improve accurate assessment of dietary exposure and further enhance our understanding of their link to health conditions. We aimed to explore the relationship of habitual food intake with blood and urine metabolites in adolescents and young adults.

METHODS

The study population comprised 228 participants (94 male and 134 female) of the DONALD study. Dietary intake was assessed by yearly repeated 3d-food records. Habitual diet was estimated as the average consumption of 23 food groups in adolescence. Using an untargeted metabolomics approach, we quantified 2638 metabolites in plasma and 1407 metabolites in urine. In each sex, we determined unique diet-metabolite associations using orthogonal projection to latent structures (oPLS) and random forests (RF).

RESULTS

We observed 6 metabolites in agreement between oPLS and RF in urine, 1 in females (vanillylmandelate to processed/ other meat) and 5 in males (indole-3-acetamide, and N6-methyladenosine to eggs; hippurate, citraconate/glutaconate, and X - 12111 to vegetables). We observed no association in blood in agreement.

CONCLUSION

We observed a limited reflection of habitual food group intake by single metabolites in urine and not in blood. The explored biomarkers should be confirmed in additional studies. This article is protected by copyright. All rights reserved.

High-Coverage Quantitative Metabolomics of Human Urine: Effects of Freeze-Thaw Cycles on the Urine Metabolome and Biomarker Discovery

Urine sample storage after collection at ultra-low-temperature (e.g., -80 °C) is normally required for comparative metabolome analysis of many samples, and therefore, freeze-thaw cycles (FTCs) are unavoidable. However, the reported effects of FTCs on the urine metabolome are controversial. Moreover, there is no report on the study of how urine FTCs affect biomarker discovery. Herein, we present our study of the FTC effects on the urine metabolome and biomarker discovery using a high-coverage quantitative metabolomics platform. Our study involved two centers located in Hangzhou, China, and Edmonton, Canada, to perform metabolome analysis of two separate cohorts of urine samples. The same workflow of sample preparation and dansylation isotope labeling LC-MS was used for in-depth analysis of the amine/phenol submetabolome. The analysis of 320 samples from the Hangzhou cohort consisting of 80 healthy subjects with each urine being subjected to four FTCs resulted in relative quantification of 3682 metabolites with 3307 identified or mass-matched. The analysis of 176 samples from the Edmonton cohort of 44 subjects with four FTCs quantified 3516 metabolites with 3166 identified or mass-matched. Multivariate and univariate analyses indicated that significant variations (fold change ≥ 1.5 with q-value ≤ 0.05) from FTCs were only observed in a very small fraction of the metabolites (<0.3%). Moreover, various metabolites did not show a consistent pattern of concentration changes from one to four FTCs, allowing the use of two separate cohorts of samples to remove these randomly changed metabolites. Three metabolite biomarkers for separating males and females were discovered, and FTC did not influence their discovery.

Urinary Metabolomic Study in a Healthy Children Population and Metabolic Biomarker Discovery of Attention-Deficit/Hyperactivity Disorder (ADHD)

Objectives

Knowledge of the urinary metabolomic profiles of healthy children and adolescents plays a promising role in the field of pediatrics. Metabolomics has also been used to diagnose disease, discover novel biomarkers, and elucidate pathophysiological pathways. Attention-deficit/hyperactivity disorder (ADHD) is one of the most common psychiatric disorders in childhood. However, large-sample urinary metabolomic studies in children with ADHD are relatively rare. In this study, we aimed to identify specific biomarkers for ADHD diagnosis in children and adolescents by urinary metabolomic profiling.

Methods

We explored the urine metabolome in 363 healthy children aged 1-18 years and 76 patients with ADHD using high-resolution mass spectrometry.

Results

Metabolic pathways, such as arachidonic acid metabolism, steroid hormone biosynthesis, and catecholamine biosynthesis, were found to be related to sex and age in healthy children. The urinary metabolites displaying the largest differences between patients with ADHD and healthy controls belonged to the tyrosine, leucine, and fatty acid metabolic pathways. A metabolite panel consisting of FAPy-adenine, 3-methylazelaic acid, and phenylacetylglutamine was discovered to have good predictive ability for ADHD, with a receiver operating characteristic area under the curve (ROC-AUC) of 0.918. A panel of FAPy-adenine, N-acetylaspartylglutamic acid, dopamine 4-sulfate, aminocaproic acid, and asparaginyl-leucine was used to establish a robust model for ADHD comorbid tic disorders and controls with an AUC of 0.918.

Targeted metabolomics analysis of amino acids and acylcarnitines as risk markers for diabetes by LC-MS/MS technique

Diabetes is a common chronic disease affecting millions of people worldwide. It underlies various complications and imposes many costs on individuals and society. Discovering early diagnostic biomarkers takes excellent insight into preventive plans and the best use of interventions. Therefore, in the present study, we aimed to evaluate the association between the level of amino acids and acylcarnitines and diabetes to develop diabetes predictive models. Using the targeted LC-MS/MS technique, we analyzed fasting plasma samples of 206 cases and 206 controls that were matched by age, sex, and BMI. The association between metabolites and diabetes was evaluated using univariate and multivariate regression analysis with adjustment for systolic and diastolic blood pressure and lipid profile. To deal with multiple comparisons, factor analysis was used. Participants' average age and BMI were 61.6 years, 28.9 kg/m2, and 55% were female. After adjustment, Factor 3 (tyrosine, valine, leucine, methionine, tryptophan, phenylalanine), 5 (C3DC, C5, C5OH, C5:1), 6 (C14OH, C16OH, C18OH, C18:1OH), 8 (C2, C4OH, C8:1), 10 (alanine, proline) and 11 (glutamic acid, C18:2OH) were positively associated with diabetes. Inline, factor 9 (C4DC, serine, glycine, threonine) and 12 (citrulline, ornithine) showed a reverse trend. Some amino acids and acylcarnitines were found as potential risk markers for diabetes incidents that reflected the disturbances in the several metabolic pathways among the diabetic population and could be targeted to prevent, diagnose, and treat diabetes.

Ageing and cancer: a research gap to fill

The complex mechanisms of ageing biology are increasingly understood. Interventions to reduce or delay ageing‐associated diseases are emerging. Cancer is one of the diseases promoted by tissue ageing. A clockwise mutational signature is identified in many tumours. Ageing might be a modifiable cancer risk factor. To reduce the incidence of ageing‐related cancer and to detect the disease at earlier stages, we need to understand better the links between ageing and tumours. When a cancer is established, geriatric assessment and measures of biological age might help to generate evidence‐based therapeutic recommendations. In this approach, patients and caregivers would include the respective weight to give to the quality of life and survival in the therapeutic choices. The increasing burden of cancer in older patients requires new generations of researchers and geriatric oncologists to be trained, to properly address disease complexity in a multidisciplinary manner, and to reduce health inequities in this population of patients. In this review, we propose a series of research challenges to tackle in the next few years to better prevent, detect and treat cancer in older patients while preserving their quality of life.

Utilization of Host and Microbiome Features in Determination of Biological Aging

The term 'old age' generally refers to a period characterized by profound changes in human physiological functions and susceptibility to disease that accompanies the final years of a person's life. Despite the conventional definition of old age as exceeding the age of 65 years old, quantifying aging as a function of life years does not necessarily reflect how the human body ages. In contrast, characterizing biological (or physiological) aging based on functional parameters may better reflect a person's temporal physiological status and associated disease susceptibility state. As such, differentiating 'chronological aging' from 'biological aging' holds the key to identifying individuals featuring accelerated aging processes despite having a young chronological age and stratifying them to tailored surveillance, diagnosis, prevention, and treatment. Emerging evidence suggests that the gut microbiome changes along with physiological aging and may play a pivotal role in a variety of age-related diseases, in a manner that does not necessarily correlate with chronological age. Harnessing of individualized gut microbiome data and integration of host and microbiome parameters using artificial intelligence and machine learning pipelines may enable us to more accurately define aging clocks. Such holobiont-based estimates of a person's physiological age may facilitate prediction of age-related physiological status and risk of development of age-associated diseases.

In-depth characterisation of the urine metabolome in cats with and without urinary tract diseases

INTRODUCTION

Our understanding of the urine metabolome and its association with urinary tract disease is limited in cats.

OBJECTIVES

We conducted a case-control study to characterise the feline urine metabolome, investigate its association with chronic kidney disease (CKD) and feline idiopathic cystitis (FIC), and assess its compositional relationship with the urine microbiome.

METHODS

The urine metabolome of 45 owned cats, including 23 controls, 16 CKD, and 6 FIC cases, was characterised by an untargeted metabolomics approach using high-performance chemical isotope labelling liquid chromatography-mass spectrometry.

RESULTS

We detected 9411 unique compounds in the urine of controls and cases and identified 1037 metabolites with high confidence. Amino acids, peptides, and analogues dominated these metabolites (32.2%), followed by carbonyl compounds (7.1%) and carbohydrates (6.5%). Seven controls from one household showed a significant level of metabolome clustering, with a distinct separation from controls from other households (p value < 0.001). Owner surveys revealed that this cluster of cats was fed dry food only, whereas all but one other control had wet food in their diet. Accordingly, the diet type was significantly associated with the urine metabolome composition in our multivariate model (p value = 0.001). Metabolites significantly altered in this cluster included taurine, an essential amino acid in cats. Urine metabolome profiles were not significantly different in CKD and FIC cases compared with controls, and no significant compositional relationship was detected between the urine metabolome and microbiome.

CONCLUSION

Our study reveals in-depth diversity of the feline urine metabolome composition, and suggests that it can vary considerably depending on environmental factors.