Plasma metabolomic profiles associated with mortality and longevity in a prospective analysis of 13,512 individuals

MULTI consortium, Anagnostakis F, Ko S, Saadatinia M, Wang J, Davatzikos C, Wen J. Multi-organ metabolome biological age implicates cardiometabolic conditions and mortality risk

Multi-organ biological aging clocks across different organ systems have been shown to predict human disease and mortality. Here, we extend this multi-organ framework to plasma metabolomics, developing five organ-specific metabolome-based biological age gaps (MetBAGs) using 107 plasma non-derivatized metabolites from 274,247 UK Biobank participants. Our age prediction models achieve a mean absolute error of approximately 6 years (0.25 Collapse

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MESH Headings

• Humans
• Metabolome/genetics
• Male
• Female
• Aging/genetics
• Aging/metabolism
• Aged
• Middle Aged
• Genome-Wide Association Study
• Metabolomics/methods
• Cardiovascular Diseases/mortality
• Cardiovascular Diseases/genetics
• Cardiovascular Diseases/metabolism
• Mendelian Randomization Analysis
• Polymorphism, Single Nucleotide
• Metabolic Diseases/genetics
• Metabolic Diseases/mortality
• Metabolic Diseases/metabolism
• Adult
• Aged, 80 and over
• United Kingdom

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Affiliation(s)

MULTI consortium
Filippos Anagnostakis Laboratory of AI and Biomedical Science (LABS), Columbia University, New York, NY, USA
Sarah Ko Laboratory of AI and Biomedical Science (LABS), Columbia University, New York, NY, USA
Mehrshad Saadatinia Laboratory of AI and Biomedical Science (LABS), Columbia University, New York, NY, USA
Jingyue Wang Laboratory of AI and Biomedical Science (LABS), Columbia University, New York, NY, USA
Christos Davatzikos Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Junhao Wen Laboratory of AI and Biomedical Science (LABS), Columbia University, New York, NY, USA. Department of Radiology, Columbia University, New York, NY, USA. New York Genome Center (NYGC), New York, NY, USA. Department of Biomedical Engineering (BME), Columbia University, New York, NY, USA. Data Science Institute (DSI), Columbia University, New York, NY, USA. Zuckerman Institute, Columbia University, New York, NY, USA. Center for Innovation in Imaging Biomarkers and Integrated Diagnostics (CIMBID), Department of Radiology, Columbia University, New York, NY, USA.

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Collaborators

Andrew Zalesky, Ye Ella Tian, Luigi Ferrucci, Keenan A Walker, Wenjia Bai, Michael S Rafii, Paul Aisen,

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Recommendations for sample selection, collection and preparation for NMR-based metabolomics studies of blood

BACKGROUND

Metabolic profiling of blood metabolites, particularly in plasma and serum, is vital for studying human diseases, human conditions, drug interventions and toxicology. The clinical significance of blood arises from its close ties to all human cells and facile accessibility. However, patient-specific variables such as age, sex, diet, lifestyle and health status, along with pre-analytical conditions (sample handling, storage, etc.), can significantly affect metabolomic measurements in whole blood, plasma, or serum studies. These factors, referred to as confounders, must be mitigated to reveal genuine metabolic changes due to illness or intervention onset.

REVIEW OBJECTIVE

This review aims to aid metabolomics researchers in collecting reliable, standardized datasets for NMR-based blood (whole/serum/plasma) metabolomics. The goal is to reduce the impact of confounding factors and enhance inter-laboratory comparability, enabling more meaningful outcomes in metabolomics studies.

KEY CONCEPTS

This review outlines the main factors affecting blood metabolite levels and offers practical suggestions for what to measure and expect, how to mitigate confounding factors, how to properly prepare, handle and store blood, plasma and serum biosamples and how to report data in targeted NMR-based metabolomics studies of blood, plasma and serum.

Elucidating the role of fatty acid reprogramming in ovarian cancer: insights cross-talk between blood, subcutaneous fat, and ovarian cancer tissues

Introduction

Aberrant fatty acid (FA) metabolism is increasingly recognized as a significant factor in ovarian cancer (OC) progression, although the comprehensive metabolic alterations across different body tissues remain unclear.

Methods

In this study, sixteen OC patients and twenty-nine non-cancer (NC) patients were recruited for metabolic profiling using a global and targeted metabolomic strategy based on a gas chromatography-hydrogen flame ionization detector (GC-FID). The patient survival was followed up to 3 years, and PFS was calculated.

Results

Our findings revealed distinct metabolite profiles that differentiate OC from NC groups across all sample types. We found seven, nine, and thirteen significant metabolites in subcutaneous fat, plasma, and ovarian tissue respectively. In particular, docosahexaenoic acid (DHA) and arachidonic acid (AA) levels were notably elevated in all sample types of OC patients. Furthermore, receiver operating characteristic (ROC) analysis highlight that three plasma FA showed the best specificity and sensitivity in differentiating the OC group from the NC group (Area Under The Curve, AUC > 0.89), including caprylic acid, myristoleic acid, and tetracosaenoic acid. Most of the significant FA in subcutaneous fat and ovarian tissue showed a high risk of OC. However, caprylic acid and tetracosanoic acid were identified as protective factors in the plasma sample. We also found that high levels of linoelaidic acid in subcutaneous fat and palmitelaidic acid in ovarian tissue were associated with poor prognosis. Pathway analysis indicated upregulation of fatty acid synthesis, inflammatory signaling, and ferroptosis pathways in OC patients.

Discussion

This study reveals a coordinated reprogramming of FA metabolism across multiple biospecimens in OC patients. Our results suggest that specific fatty acids may contribute to OC progression through dysregulation of fatty acid synthesis, inflammatory signaling, and ferroptosis. These findings offer mechanistic insights into OC progression and highlighting potential biomarkers and targeted therapeutic interventions.

Metabolic profiling of the Chinese population with extreme longevity identifies Lysophospholipid species as potential biomarkers for the human lifespan

BACKGROUND

Metabolic regulation plays a crucial role in extending the healthspan and lifespan across multiple organisms, including humans. Although numerous studies have identified the characteristics of the metabolome and potential biomarkers in long-lived populations worldwide, the metabolome landscape of Chinese centenarians remains largely unknown. This study characterised the plasma metabolic profiles of Chinese centenarians and nonagenarians and identified potential biomarkers of longevity.

METHODS

A global untargeted metabolomics approach was used to analyze plasma samples from 65 centenarians (average age 101.72 ± 1.46 years), 53 nonagenarians (average age 98.92 ± 0.27 years), 47 older individuals (average age 64.66 ± 3.31 years), and 35 middle-aged participants (average age 33.91 ± 3.53 years) recruited from the Lishui region, an area of China well known for the longevity of its population.

RESULTS

The plasma metabolic profiles of centenarians and nonagenarians differed significantly from those of the two younger populations. Specifically, 211 and 114 differentially abundant metabolites (DAMs) were identified in the centenarian and nonagenarian groups, respectively. The majority of these DAMs were glycerophosphoethanolamines, glycerophosphocholines, fatty esters, fatty alcohols, fatty acyls, and fatty acids and conjugates. For example, the circulating levels of LysoPA (20:2), LysoPA (20:3), LysoPC (16:0), LysoPC (18:2), and LysoPE (20:4) were significantly lower in centenarians than in the older and middle-aged groups. A similar pattern was also observed in the nonagenarian population. Notably, the plasma levels of five DAMs - LysoPA (20:3), LysoPC (18:2), LysoPE (20:4), PG (18:0/18:1), and PG (18:1/18:2) - were significantly and continuously reduced with the ageing process. Pearson correlation analysis revealed that the reduced abundance of LysoPA (20:3), LysoPC (18:2), LysoPE (20:4), LysoPE (24:0), PG (18:0/18:1), and PG (18:1/18:2) was significantly and negatively associated with lifespan, from middle-age to centenarian. ROC analysis indicated that LysoPA (20:3), LysoPC (18:2), LysoPE (20:4), LysoPE (24:0), PG (18:0/18:1), and PG (18:1/18:2), as well as the combination of these six DAMs (AUC = 0.9074), had high predictive power for the human longevity phenotype.

CONCLUSION

This study elucidated the plasma metabolic landscape of centenarians and nonagenarians in China and identified several potential biomarkers for predicting human lifespan. Our findings will aid in understanding the metabolic regulation of longevity and may promote the clinical practice of gerontology in the future.

Metabolomic signature of retinal ageing, polygenetic susceptibility, and major health outcomes

BACKGROUND/AIMS

To identify the metabolic underpinnings of retinal aging and examine how it is related to mortality and morbidity of common diseases.

METHODS

The retinal age gap has been established as essential aging indicator for mortality and systemic health. We applied neural network to train the retinal age gap among the participants in UK Biobank and used nuclear magnetic resonance (NMR) to profile plasma metabolites. The metabolomic signature of retinal ageing (MSRA) was identified using an elastic network model. Multivariable Cox regressions were used to assess associations between the signature with 12 serious health conditions. The participants in Guangzhou Diabetic Eye Study (GDES) cohort were analyzed for validation.

RESULTS

This study included 110 722 participants (mean age 56.5±8.1 years at baseline, 53.8% female), and 28 plasma metabolites associated with retinal ageing were identified. The MSRA revealed significant correlations with each 12 serious health conditions beyond traditional risk factors and genetic predispositions. Each SD increase in MSRA was linked to a 24%-76% higher risk of mortality, cardiovascular diseases, dementia and diabetes mellitus. MSRA showed dose-response relationships with risks of these diseases, with seven showing non-linear and five showing linear increases. Validation in the GDES further established the relation between retinal ageing-related metabolites and increased risks of cardiovascular and chronic kidney diseases (all p<0.05).

CONCLUSIONS

The metabolic connections between ocular and systemic health offer a novel tool for identifying individuals at high risk of premature ageing, promoting a more holistic view of human health.

Potential non-invasive biomarkers of chronic sleep disorders identified by salivary metabolomic profiling among middle-aged Japanese men

Sleep disorders have become a global social problem that increases the risk of developing mental illnesses and metabolic diseases. We aimed to identify biomarkers with which to non-invasively and objectively evaluate chronic sleep disorders. We used capillary electrophoresis-Fourier transform mass spectrometry (CE-FTMS) to analyze metabolomes in saliva collected from 50 persons each with good (≤ 2) and poor (≥ 6) sleep quality scored according to the Japanese version of the Pittsburgh Sleep Quality Index (PSQI-J) self-report questionnaire. The levels of five metabolites including glycerol and hippuric acid and eight including 2-hydroxybutyric acid (2HB), were respectively decreased and increased in participants with poor sleep quality. We established a random forest model consisting of six metabolites, including glycerol and hippuric acid, with a prediction accuracy of 0.866. Correlations between metabolites and sleep satisfaction were assessed using the Oguri-Shirakawa-Azumi sleep inventory, middle-age and aged version (OSA-MA) questionnaire. The results showed that 2'-deoxyguanosine, N1-acetylspermine, and 2,4-dihydroxybenzoic acid correlated positively, whereas glucosamine 6-phosphate and trimethylamine N-oxide correlated negatively with sleep quality. These findings suggested that changes in salivary metabolites reflect pathophysiological mechanisms of chronic sleep disorders, and that saliva samples could serve as non-invasive and objective diagnostic targets for predicting habitual sleep quality.

A multi-metabolite signature robustly predicts long-term mortality in the PREDIMED trial and several US cohorts

Metabolome-based biomarkers contribute to identify mechanisms of disease and to a better understanding of overall mortality. In a long-term follow-up subsample (n = 1878) of the PREDIMED trial, among 337 candidate baseline plasma metabolites repeatedly assessed at baseline and after 1 year, 38 plasma metabolites were identified as predictors of all-cause mortality. Gamma-amino-butyric acid (GABA), homoarginine, serine, creatine, 1-methylnicotinamide and a set of sphingomyelins, plasmalogens, phosphatidylethanolamines and cholesterol esters were inversely associated with all-cause mortality, whereas plasma dimethylguanidino valeric acid (DMGV), choline, short and long-chain acylcarnitines, 4-acetamidobutanoate, pseudouridine, 7-methylguanine, N6-acetyllysine, phenylacetylglutamine and creatinine were associated with higher mortality. The multi-metabolite signature created as a linear combination of these selected metabolites, also showed a strong association with all-cause mortality using plasma samples collected at 1-year follow-up in PREDIMED. This association was subsequently confirmed in 4 independent American cohorts, validating the signature as a consistent predictor of all-cause mortality across diverse populations.

Recent advances in precision nutrition and cardiometabolic diseases

A growing body of research on nutrition omics has led to recent advances in cardiovascular disease epidemiology and prevention. Within the PREDIMED trial, significant associations between diet-related metabolites and cardiovascular disease were identified, which were subsequently replicated in independent cohorts. Some notable metabolites identified include plasma levels of ceramides, acyl-carnitines, branched-chain amino acids, tryptophan, urea cycle pathways, and the lipidome. These metabolites and their related pathways have been associated with incidence of both cardiovascular disease and type 2 diabetes. Future directions in precision nutrition research include: a) developing more robust multimetabolomic scores to predict long-term risk of cardiovascular disease and mortality; b) incorporating more diverse populations and a broader range of dietary patterns; and c) conducting more translational research to bridge the gap between precision nutrition studies and clinical applications.

Recent advances in precision nutrition and cardiometabolic diseases

A growing body of research on nutrition omics has led to recent advances in cardiovascular disease epidemiology and prevention. Within the PREDIMED trial, significant associations between diet-related metabolites and cardiovascular disease were identified, which were subsequently replicated in independent cohorts. Some notable metabolites identified include plasma levels of ceramides, acyl-carnitines, branched-chain amino acids, tryptophan, urea cycle pathways, and the lipidome. These metabolites and their related pathways have been associated with incidence of both cardiovascular disease and type 2 diabetes. Future directions in precision nutrition research include: a) developing more robust multimetabolomic scores to predict long-term risk of cardiovascular disease and mortality; b) incorporating more diverse populations and a broader range of dietary patterns; and c) conducting more translational research to bridge the gap between precision nutrition studies and clinical applications.

Persistent PTSD symptoms are associated with plasma metabolic alterations relevant to long-term health: A metabolome-wide investigation in women

BACKGROUND

Post-traumatic stress disorder (PTSD) is characterized by severe distress and associated with cardiometabolic diseases. Studies in military and clinical populations suggest that dysregulated metabolomic processes may be a key mechanism. Prior work identified and validated a metabolite-based distress score (MDS) linked with depression and anxiety and subsequent cardiometabolic diseases. Here, we assessed whether PTSD shares metabolic alterations with depression and anxiety and if additional metabolites are related to PTSD.

METHODS

We leveraged plasma metabolomics data from three subsamples nested within the Nurses' Health Study II, including 2835 women with 2950 blood samples collected across three time points (1996-2014) and 339 known metabolites assayed by mass spectrometry-based techniques. Trauma and PTSD exposures were assessed in 2008 and characterized as follows: lifetime trauma without PTSD, lifetime PTSD in remission, and persistent PTSD symptoms. Associations between the exposures and the MDS or individual metabolites were estimated within each subsample adjusting for potential confounders and combined in random-effects meta-analyses.

RESULTS

Persistent PTSD symptoms were associated with higher levels of the previously developed MDS. Out of 339 metabolites, we identified 29 metabolites (primarily elevated glycerophospholipids and glycerolipids) associated with persistent symptoms (false discovery rate < 0.05; adjusting for technical covariates). No metabolite associations were found with the other PTSD-related exposures.

CONCLUSIONS

As the first large-scale, population-based metabolomics analysis of PTSD, our study highlighted shared and distinct metabolic differences linked to PTSD versus depression or anxiety. We identified novel metabolite markers associated with PTSD symptom persistence, suggesting further connections with metabolic dysregulation that may have downstream consequences for health.

Serine metabolism in aging and age-related diseases

Non-essential amino acids are often overlooked in biomedical research; however, they are crucial components of organismal metabolism. One such metabolite that is integral to physiological function is serine. Serine acts as a pivotal link connecting glycolysis with one-carbon and lipid metabolism, as well as with pyruvate and glutathione syntheses. Interestingly, increasing evidence suggests that serine metabolism may impact the aging process, and supplementation with serine may confer benefits in safeguarding against aging and age-related disorders. This review synthesizes recent insights into the regulation of serine metabolism during aging and its potential to promote healthy lifespan and mitigate a spectrum of age-related diseases.

Impact of omega-3 fatty acids on hypertriglyceridemia, lipidomics, and gut microbiome in patients with type 2 diabetes

BACKGROUND

Fish oil (FO), a mixture of omega-3 fatty acids mainly comprising docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA), has been recommended for patients with type 2 diabetes (T2D) and hypertriglyceridemia. However, its effects on lipidomic profiles and gut microbiota and the factors influencing triglyceride (TG) reduction remain unclear.

METHODS

We conducted a 12-week, randomized, double-blind, placebo-controlled trial in 309 Chinese patients with T2D with hypertriglyceridemia (ClinicalTrials.gov: NCT03120299). Participants were randomly assigned (1:1) to receive either 4 g FO or corn oil for 12 weeks. The primary outcome was changes in serum TGs and the lipidomic profile, and the secondary outcome included changes in the gut microbiome and other metabolic variables.

FINDINGS

The FO group had significantly better TG reduction (mean [95% confidence interval (CI)]: -1.51 [-2.01, -1.01] mmol/L) compared to the corn oil group (-0.66 [-1.15, -0.16] mmol/L, p = 0.02). FO significantly altered the serum lipid profile by reducing low-unsaturated TG species and increasing those containing DHA or EPA. FO had minor effects on gut microbiota, while baseline microbial features predicted the TG response to FO better than phenotypic or lipidomic features, potentially mediated by specific lipid metabolites. A total of 9 lipid metabolites significantly mediated the link between 4 baseline microbial variables and the TG response to FO supplementation.

CONCLUSIONS

Our findings demonstrate differential impacts of omega-3 fatty acids on lipidomic and microbial profiles in T2D and highlight the importance of baseline gut microbiota characteristics in predicting the TG-lowering efficacy of FO.

FUNDING

This study was funded by the National Nature Science Foundation.

Metabolomics in atrial fibrillation - A review and meta-analysis of blood, tissue and animal models

BACKGROUND

Atrial fibrillation (AF) is a highly prevalent cardiac arrhythmia associated with severe cardiovascular complications. AF presents a growing global challenge, however, current treatment strategies for AF do not address the underlying pathophysiology. To advance diagnosis and treatment of AF, a deeper understanding of AF root causes is needed. Metabolomics is a fast approach to identify, quantify and analyze metabolites in a given sample, such as human serum or atrial tissue. In the past two decades, metabolomics have enabled research on metabolite biomarkers to predict AF, metabolic features of AF, and testing metabolic mechanisms of AF in animal models. Due to the field's rapid evolution, the methods of AF metabolomics studies have not always been optimal. Metabolomics research has lacked standardization and requires expertise to face methodological challenges.

PURPOSE OF THE REVIEW

We summarize and meta-analyze metabolomics research on AF in human plasma and serum, atrial tissue, and animal models. We present the current progress on metabolic biomarkers candidates, metabolic features of clinical AF, and the translation of metabolomics findings from animal to human. We additionally discuss strengths and weaknesses of the metabolomics method and highlight opportunities for future AF metabolomics research.

Metabolite signatures of chronological age, aging, survival, and longevity

Metabolites that mark aging are not fully known. We analyze 408 plasma metabolites in Long Life Family Study participants to characterize markers of age, aging, extreme longevity, and mortality. We identify 308 metabolites associated with age, 258 metabolites that change over time, 230 metabolites associated with extreme longevity, and 152 metabolites associated with mortality risk. We replicate many associations in independent studies. By summarizing the results into 19 signatures, we differentiate between metabolites that may mark aging-associated compensatory mechanisms from metabolites that mark cumulative damage of aging and from metabolites that characterize extreme longevity. We generate and validate a metabolomic clock that predicts biological age. Network analysis of the age-associated metabolites reveals a critical role of essential fatty acids to connect lipids with other metabolic processes. These results characterize many metabolites involved in aging and point to nutrition as a source of intervention for healthy aging therapeutics.

A Single-Cell Metabolic Profiling Characterizes Human Aging via SlipChip-SERS

Metabolic dysregulation is a key driver of cellular senescence, contributing to the progression of systemic aging. The heterogeneity of senescent cells and their metabolic shifts are complex and unexplored. A microfluidic SlipChip integrated with surface-enhanced Raman spectroscopy (SERS), termed SlipChip-SERS, is developed for single-cell metabolism analysis. This SlipChip-SERS enables compartmentalization of single cells, parallel delivery of saponin and nanoparticles to release intracellular metabolites and to realize SERS detection with simple slipping operations. Analysis of different cancer cell lines using SlipChip-SERS demonstrated its capability for sensitive and multiplexed metabolic profiling of individual cells. When applied to human primary fibroblasts of different ages, it identified 12 differential metabolites, with spermine validated as a potent inducer of cellular senescence. Prolonged exposure to spermine can induce a classic senescence phenotype, such as increased senescence-associated β-glactosidase activity, elevated expression of senescence-related genes and reduced LMNB1 levels. Additionally, the senescence-inducing capacity of spermine in HUVECs and WRL-68 cells is confirmed, and exogenous spermine treatment increased the accumulation and release of H2O2. Overall, a novel SlipChip-SERS system is developed for single-cell metabolic analysis, revealing spermine as a potential inducer of senescence across multiple cell types, which may offer new strategies for addressing ageing and ageing-related diseases.

A Novel Metabolomic Aging Clock Predicting Health Outcomes and Its Genetic and Modifiable Factors

Existing metabolomic clocks exhibit deficiencies in capturing the heterogeneous aging rates among individuals with the same chronological age. Yet, the modifiable and non-modifiable factors in metabolomic aging have not been systematically studied. Here, a new aging measure-MetaboAgeMort-is developed using metabolomic profiles from 239,291 UK Biobank participants for 10-year all-cause mortality prediction. The MetaboAgeMort showed significant associations with all-cause mortality, cause-specific mortality, and diverse incident diseases. Adding MetaboAgeMort to a conventional risk factors model improved the predictive ability of 10-year mortality. A total of 99 modifiable factors across seven categories are identified for MetaboAgeMort. Among these, 16 factors representing pulmonary function, body composition, socioeconomic status, dietary quality, smoking status, alcohol intake, and disease status showed quantitatively stronger associations. The genetic analyses revealed 99 genomic risk loci and 271 genes associated with MetaboAgeMort. The tissue-enrichment analysis showed significant enrichment in liver. While the external validation of the MetaboAgeMort is required, this study illuminates heterogeneous metabolomic aging across the same age, providing avenues for identifying high-risk individuals, developing anti-aging therapies, and personalizing interventions, thus promoting healthy aging and longevity.

Metabolomics-Based Machine Learning for Predicting Mortality: Unveiling Multisystem Impacts on Health

Reliable predictors of long-term all-cause mortality are needed for middle-aged and older populations. Previous metabolomics mortality studies have limitations: a low number of participants and metabolites measured, measurements mainly using nuclear magnetic spectroscopy, and the use only of conventional statistical methods. To overcome these challenges, we applied liquid chromatography-tandem mass spectrometry and measured >1000 metabolites in the METSIM study including 10,197 men. We applied the machine learning approach together with conventional statistical methods to identify metabolites associated with all-cause mortality. The three independent machine learning methods (logistic regression, XGBoost, and Welch's t-test) identified 32 metabolites having the most impactful associations with all-cause mortality (25 increasing and 7 decreasing the risk). From these metabolites, 20 were novel and encompassed various metabolic pathways, impacting the cardiovascular, renal, respiratory, endocrine, and central nervous systems. In the Cox regression analyses (hazard ratios and their 95% confidence intervals), clinical and laboratory risk factors increased the risk of all-cause mortality by 1.76 (1.60-1.94), the 25 metabolites by 1.89 (1.68-2.12), and clinical and laboratory risk factors combined with the 25 metabolites by 2.00 (1.81-2.22). In our study, the main causes of death were cancers (28%) and cardiovascular diseases (25%). We did not identify any metabolites associated with cancer but found 13 metabolites associated with an increased risk of cardiovascular diseases. Our study reports several novel metabolites associated with an increased risk of mortality and shows that these 25 metabolites improved the prediction of all-cause mortality beyond and above clinical and laboratory measurements.

Protein catabolites as blood-based biomarkers of aging physiology: Findings from the Dog Aging Project

Our understanding of age-related physiology and metabolism has grown through the study of systems biology, including transcriptomics, single-cell analysis, proteomics and metabolomics. Studies in lab organisms in controlled environments, while powerful and complex, fall short of capturing the breadth of genetic and environmental variation in nature. Thus, there is now a major effort in geroscience to identify aging biomarkers and to develop aging interventions that might be applied across the diversity of humans and other free-living species. To meet this challenge, the Dog Aging Project (DAP) is designed to identify cross-sectional and longitudinal patterns of aging in complex systems, and how these are shaped by the diversity of genetic and environmental variation among companion dogs. Here we surveyed the plasma metabolome from the first year of sampling of the Precision Cohort of the DAP. By incorporating extensive metadata and whole genome sequencing information, we were able to overcome the limitations inherent in breed-based estimates of genetic and physiological effects, and to probe the physiological and dietary basis of the age-related metabolome. We identified a significant effect of age on approximately 40% of measured metabolites. Among other insights, we discovered a potentially novel biomarker of age in the post-translationally modified amino acids (ptmAAs). The ptmAAs, which can only be generated by protein hydrolysis, covaried both with age and with other biomarkers of amino acid metabolism, and in a way that was robust to diet. Clinical measures of kidney function mediated about half of the higher ptmAA levels in older dogs. This work identifies ptmAAs as robust indicators of age in dogs, and points to kidney function as a physiological mediator of age-associated variation in the plasma metabolome.

Integration of epidemiological and blood biomarker analysis links haem iron intake to increased type 2 diabetes risk

Dietary haem iron intake is linked to an increased risk of type 2 diabetes (T2D), but the underlying plasma biomarkers are not well understood. We analysed data from 204,615 participants (79% females) in three large US cohorts over up to 36 years, examining the associations between iron intake and T2D risk. We also assessed plasma metabolic biomarkers and metabolomic profiles in subsets of 37,544 (82% females) and 9,024 (84% females) participants, respectively. Here we show that haem iron intake but not non-haem iron is associated with a higher T2D risk, with a multivariable-adjusted hazard ratio of 1.26 (95% confidence interval 1.20-1.33; P for trend <0.001) comparing the highest to the lowest quintiles. Haem iron accounts for significant proportions of the T2D risk linked to unprocessed red meat and specific dietary patterns. Increased haem iron intake correlates with unfavourable plasma profiles of insulinaemia, lipids, inflammation and T2D-linked metabolites. We also identify metabolites, including L-valine and uric acid, potentially mediating the haem iron-T2D relationship, highlighting their pivotal role in T2D pathogenesis.

Persistent PTSD symptoms are associated with plasma metabolic alterations relevant to long-term health: A metabolome-wide investigation in women

Background

Posttraumatic stress disorder (PTSD) is characterized by severe distress and associated with cardiometabolic diseases. Studies in military and clinical populations suggest dysregulated metabolomic processes may be a key mechanism. Prior work identified and validated a metabolite-based distress score (MDS) linked with depression and anxiety and subsequent cardiometabolic diseases. Here, we assessed whether PTSD shares metabolic alterations with depression and anxiety and also if additional metabolites are related to PTSD.

Methods

We leveraged plasma metabolomics data from three subsamples nested within the Nurses' Health Study II, including 2835 women with 2950 blood samples collected across three timepoints (1996-2014) and 339 known metabolites consistently assayed by mass spectrometrybased techniques. Trauma and PTSD exposures were assessed in 2008 and characterized as follows: lifetime trauma without PTSD, lifetime PTSD in remission, and persistent PTSD symptoms. Associations between the exposures and the MDS or individual metabolites were estimated within each subsample adjusting for potential confounders and combined in random-effects meta-analyses.

Results

Persistent PTSD symptoms were associated with higher levels of the previously developed MDS for depression and anxiety. Out of 339 metabolites, we identified nine metabolites (primarily elevated glycerophospholipids) associated with persistent symptoms (false discovery rate<0.05). No metabolite associations were found with the other PTSD-related exposures.

Conclusions

As the first large-scale, population-based metabolomics analysis of PTSD, our study highlighted shared and distinct metabolic differences linked to PTSD versus depression or anxiety. We identified novel metabolite markers associated with PTSD symptom persistence, suggesting further connections with metabolic dysregulation that may have downstream consequences for health.

Metabolic gene function discovery platform GeneMAP identifies SLC25A48 as necessary for mitochondrial choline import

Organisms maintain metabolic homeostasis through the combined functions of small-molecule transporters and enzymes. While many metabolic components have been well established, a substantial number remains without identified physiological substrates. To bridge this gap, we have leveraged large-scale plasma metabolome genome-wide association studies (GWAS) to develop a multiomic Gene-Metabolite Association Prediction (GeneMAP) discovery platform. GeneMAP can generate accurate predictions and even pinpoint genes that are distant from the variants implicated by GWAS. In particular, our analysis identified solute carrier family 25 member 48 (SLC25A48) as a genetic determinant of plasma choline levels. Mechanistically, SLC25A48 loss strongly impairs mitochondrial choline import and synthesis of its downstream metabolite betaine. Integrative rare variant and polygenic score analyses in UK Biobank provide strong evidence that the SLC25A48 causal effects on human disease may in part be mediated by the effects of choline. Altogether, our study provides a discovery platform for metabolic gene function and proposes SLC25A48 as a mitochondrial choline transporter.

Development and validation of a plasmalogen score as an independent modifiable marker of metabolic health: population based observational studies and a placebo-controlled cross-over study

BACKGROUND

Decreased levels of circulating ethanolamine plasmalogens [PE(P)], and a concurrent increase in phosphatidylethanolamine (PE) are consistently reported in various cardiometabolic conditions. Here we devised, a plasmalogen score (Pls Score) that mirrors a metabolic signal that encompasses the levels of PE(P) and PE and captures the natural variation in circulating plasmalogens and perturbations in their metabolism associated with disease, diet, and lifestyle.

METHODS

We utilised, plasma lipidomes from the Australian Obesity, Diabetes and Lifestyle study (AusDiab; n = 10,339, 55% women) a nationwide cohort, to devise the Pls Score and validated this in the Busselton Health Study (BHS; n = 4,492, 56% women, serum lipidome) and in a placebo-controlled crossover trial involving Shark Liver Oil (SLO) supplementation (n = 10, 100% men). We examined the association of the Pls Score with cardiometabolic risk factors, type 2 diabetes mellitus (T2DM), cardiovascular disease and all-cause mortality (over 17 years).

FINDINGS

In a model, adjusted for age, sex and BMI, individuals in the top quintile of the Pls Score (Q5) relative to Q1 had an OR of 0.31 (95% CI 0.21-0.43), 0.39 (95% CI 0.25-0.61) and 0.42 (95% CI 0.30-0.57) for prevalent T2DM, incident T2DM and prevalent cardiovascular disease respectively, and a 34% lower mortality risk (HR = 0.66; 95% CI 0.56-0.78). Significant associations between diet and lifestyle habits and Pls Score exist and these were validated through dietary supplementation of SLO that resulted in a marked change in the Pls Score.

INTERPRETATION

The Pls Score as a measure that captures the natural variation in circulating plasmalogens, was not only inversely related to cardiometabolic risk and all-cause mortality but also associate with diet and lifestyle. Our results support the potential utility of the Pls Score as a biomarker for metabolic health and its responsiveness to dietary interventions. Further research is warranted to explore the underlying mechanisms and optimise the practical implementation of the Pls Score in clinical and population settings.

FUNDING

National Health and Medical Research Council (NHMRC grant 233200), National Health and Medical Research Council of Australia (Project grant APP1101320), Health Promotion Foundation of Western Australia, and National Health and Medical Research Council of Australia Senior Research Fellowship (#1042095).

A lipidomic based metabolic age score captures cardiometabolic risk independent of chronological age

BACKGROUND

Metabolic ageing biomarkers may capture the age-related shifts in metabolism, offering a precise representation of an individual's overall metabolic health.

METHODS

Utilising comprehensive lipidomic datasets from two large independent population cohorts in Australia (n = 14,833, including 6630 males, 8203 females), we employed different machine learning models, to predict age, and calculated metabolic age scores (mAge). Furthermore, we defined the difference between mAge and age, termed mAgeΔ, which allow us to identify individuals sharing similar age but differing in their metabolic health status.

FINDINGS

Upon stratification of the population into quintiles by mAgeΔ, we observed that participants in the top quintile group (Q5) were more likely to have cardiovascular disease (OR = 2.13, 95% CI = 1.62-2.83), had a 2.01-fold increased risk of 12-year incident cardiovascular events (HR = 2.01, 95% CI = 1.45-2.57), and a 1.56-fold increased risk of 17-year all-cause mortality (HR = 1.56, 95% CI = 1.34-1.79), relative to the individuals in the bottom quintile group (Q1). Survival analysis further revealed that men in the Q5 group faced the challenge of reaching a median survival rate due to cardiovascular events more than six years earlier and reaching a median survival rate due to all-cause mortality more than four years earlier than men in the Q1 group.

INTERPRETATION

Our findings demonstrate that the mAge score captures age-related metabolic changes, predicts health outcomes, and has the potential to identify individuals at increased risk of metabolic diseases.

FUNDING

The specific funding of this article is provided in the acknowledgements section.

Influence of age and sex on longitudinal metabolic profiles and body weight trajectories in the UK Biobank

BACKGROUND

Accurate characterization of how age influences body weight and metabolism at different stages of life is important for understanding ageing processes. Here, we explore observational longitudinal associations between metabolic health and weight from the fifth to the seventh decade of life, using carefully adjusted statistical designs.

METHODS

Body measures and biochemical data from blood and urine (220 measures) across two visits were available from 10 104 UK Biobank participants. Participants were divided into stable (within ±4% per decade), weight loss and weight gain categories. Final subgroups were metabolically matched at baseline (48% women, follow-up 4.3 years, ages 41-70; n = 3368 per subgroup) and further stratified by the median age of 59.3 years and sex.

RESULTS

Pulse pressure, haemoglobin A1c and cystatin-C tracked ageing consistently (P < 0.0001). In women under 59, age-associated increases in citrate, pyruvate, alkaline phosphatase and calcium were observed along with adverse changes across lipoprotein measures, fatty acid species and liver enzymes (P < 0.0001). Principal component analysis revealed a qualitative sex difference in the temporal relationship between body weight and metabolism: weight loss was not associated with systemic metabolic improvement in women, whereas both age strata converged consistently towards beneficial (weight loss) or adverse (weight gain) phenotypes in men.

CONCLUSIONS

We report longitudinal ageing trends for 220 metabolic measures in absolute concentrations, many of which have not been described for older individuals before. Our results also revealed a fundamental dynamic sex divergence that we speculate is caused by menopause-driven metabolic deterioration in women.

Plasma metabolites of a healthy lifestyle in relation to mortality and longevity: Four prospective US cohort studies

BACKGROUND

A healthy lifestyle is associated with a lower premature mortality risk and with longer life expectancy. However, the metabolic pathways of a healthy lifestyle and how they relate to mortality and longevity are unclear. We aimed to identify and replicate a healthy lifestyle metabolomic signature and examine how it is related to total and cause-specific mortality risk and longevity.

METHODS

In four large cohorts with 13,056 individuals and 28-year follow-up, we assessed five healthy lifestyle factors, used liquid chromatography mass spectrometry to profile plasma metabolites, and ascertained deaths with death certificates. The unique healthy lifestyle metabolomic signature was identified using an elastic regression. Multivariable Cox regressions were used to assess associations of the signature with mortality and longevity.

FINDINGS

The identified healthy lifestyle metabolomic signature was reflective of lipid metabolism pathways. Shorter and more saturated triacylglycerol and diacylglycerol metabolite sets were inversely associated with the healthy lifestyle score, whereas cholesteryl ester and phosphatidylcholine plasmalogen sets were positively associated. Participants with a higher healthy lifestyle metabolomic signature had a 17% lower risk of all-cause mortality, 19% for cardiovascular disease mortality, and 17% for cancer mortality and were 25% more likely to reach longevity. The healthy lifestyle metabolomic signature explained 38% of the association between the self-reported healthy lifestyle score and total mortality risk and 49% of the association with longevity.

CONCLUSIONS

This study identifies a metabolomic signature that measures adherence to a healthy lifestyle and shows prediction of total and cause-specific mortality and longevity.

FUNDING

This work was funded by the NIH, CIHR, AHA, Novo Nordisk Foundation, and SciLifeLab.

Searching for New Biomarkers to Assess COVID-19 Patients: A Pilot Study

During the initial diagnosis of urgent medical conditions, which include acute infectious diseases, it is important to assess the severity of the patient's clinical state as quickly as possible. Unlike individual biochemical or physiological indicators, derived indices make it possible to better characterize a complex syndrome as a set of symptoms, and therefore quickly take a set of adequate measures. Recently, we reported on novel diagnostic indices containing butyrylcholinesterase (BChE) activity, which is decreased in COVID-19 patients. Also, in these patients, the secretion of von Willebrand factor (vWF) increases, which leads to thrombosis in the microvascular bed. The objective of this study was the determination of the concentration and activity of vWF in patients with COVID-19, and the search for new diagnostic indices. One of the main objectives was to compare the prognostic values of some individual and newly derived indices. Patients with COVID-19 were retrospectively divided into two groups: survivors (n = 77) and deceased (n = 24). According to clinical symptoms and computed tomography (CT) results, the course of disease was predominantly moderate in severity. The first blood sample (first point) was taken upon admission to the hospital, the second sample (second point)-within 4-6 days after admission. Along with the standard spectrum of biochemical indicators, BChE activity (BChEa or BChEb for acetylthiocholin or butyrylthiocholin, respectively), malondialdehyde (MDA), and vWF analysis (its antigen level, AGFW, and its activity, ActWF) were determined and new diagnostic indices were derived. The pooled sensitivity, specificity, and area under the receiver operating curve (AUC), as well as Likelihood ratio (LR) and Odds ratio (OR) were calculated. The level of vWF antigen in the deceased group was 1.5-fold higher than the level in the group of survivors. Indices that include vWF antigen levels are superior to indices using vWF activity. It was found that the index [Urea] × [AGWF] × 1000/(BChEb × [ALB]) had the best discriminatory power to predict COVID-19 mortality (AUC = 0.91 [0.83, 1.00], p < 0.0001; OR = 72.0 [7.5, 689], p = 0.0002). In addition, [Urea] × 1000/(BChEb × [ALB]) was a good predictor of mortality (AUC = 0.95 [0.89, 1.00], p < 0.0001; OR = 31.5 [3.4, 293], p = 0.0024). The index [Urea] × [AGWF] × 1000/(BChEb × [ALB]) was the best predictor of mortality associated with COVID-19 infection, followed by [Urea] × 1000/(BChEb × [ALB]). After validation in a subsequent cohort, these two indices could be recommended for diagnostic laboratories.