A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals

A novel metabolomic aging score - better than conventional metrics in predicting short-term mortality

INTRODUCTION

Accurate prediction of short-term mortality is crucial for optimizing clinical prognosis and providing treatment decisions. Conventional metrics, including physiological indicators, laboratory indexes and scoring systems, suffer from limitations in comprehensiveness, accuracy, and dynamism. In contrast, the metabolomic aging score, as an emerging biomarker, offers substantial promise in short-term mortality prediction.

AREAS COVERED

By integrating multiple metabolites associated with aging and mortality, the score captures dynamic metabolic shifts, providing a real-time reflection of an individual's health status. This approach enables a more precise assessment of short-term mortality risk across diverse diseases, setting it apart from traditional, disease-specific biomarkers. In addition, the metabolic aging score also shows great application prospects in identifying susceptible populations and providing individualized precision medication. This article discusses the novel role of the metabolomic aging score in mortality prediction, highlighting its superior accuracy compared to conventional metrics.

EXPERT OPINION

This score has broad application prospects in the future and also faces challenges such as large-scale validation and standardization. Furthermore, the integration of artificial intelligence (AI) is poised to enhance the clinical utility of the metabolomic aging score, advancing its potential to transform healthcare practices.

Exploring geriatricians' perspectives on aging biomarkers: A reflexive thematic analysis

OBJECTIVES

Aging biomarkers have been developed to identify people at higher risk of age-related decline. The objective of this study is to understand geriatricians' perspectives on aging biomarkers in order to facilitate their integration into geriatric medicine.

STUDY DESIGN

Using reflexive thematic analysis, this qualitative study explores the views of geriatricians on the potential role of aging biomarkers in clinical practice based on thirteen semi-structured interviews.

MAIN OUTCOME MEASURES

Geriatricians' views on the role, utility, and challenges of the use of aging biomarkers in geriatric medicine.

RESULTS

Two main themes were developed: the complexity of geriatric medicine and the importance of trust in biomarkers. Clinicians highlighted the heterogeneity of the older patient population, noting that current assessments, such as the Comprehensive Geriatric Assessment (CGA), rely on the geriatricians' clinical judgment. While most participants saw potential for aging biomarkers to supplement the CGA in assessment of patients' resilience in recovery from invasive treatment, they emphasized the need to prove value beyond current treatment decisions. Furthermore, participants stressed the need for actionable, reliable, and context-specific tools. Concerns included the risk of oversimplifying the assessment of resilience, lack of applicability to the frail clinical population, and the ethical implications for both health care and society more broadly in the implementation of aging biomarkers.

CONCLUSIONS

This study emphasizes the importance of aligning biomarker development with the reality of geriatric medicine and clinicians' needs. Efforts from geriatricians, aging biomarker researchers, ethicists, and primary treating physicians are needed to successfully adopt aging biomarkers into geriatric medicine.

Multi-Omic Associations of Epigenetic Age Acceleration Are Heterogeneously Shaped by Genetic and Environmental Influences

Connections between the multi-ome and epigenetic age acceleration (EAA), and especially whether these are influenced by genetic or environmental factors, remain underexplored. We therefore quantified associations between the multi-ome comprising four layers-the proteome, metabolome, external exposome (here, sociodemographic factors), and specific exposome (here, lifestyle)-with six different EAA estimates. Two twin cohorts were used in a discovery-replication scheme, comprising, respectively, young (N = 642; mean age = 22.3) and older (N = 354; mean age = 62.3) twins. Within-pair twin designs were used to assess genetic and environmental effects on associations. We identified 40 multi-omic factors, of which 28 were proteins, associated with EAA in the young twins while adjusting for sex, smoking, and body mass index. Within-pair analyses revealed that genetic confounding influenced these associations heterogeneously, with six multi-omic factors -matrix metalloproteinase 9, complement component C6, histidine, glycoprotein acetyls, lactate, and neighborhood percentage of nonagenarians- remaining significantly associated with EAA, independent of genetic effects. Replication analyses showed that some associations assessed in young twins were consistent in older twins. Our study highlights the differential influence of genetic effects on the associations between the multi-ome and EAA and shows that some, but not all, of the associations persist into adulthood.

Advanced Glycation End-Products and Metabolomics Are Independently Associated With Frailty: The Longitudinal Doetinchem Cohort Study

Skin autofluorescence (SAF), reflecting advanced glycation endproducts' accumulation in tissue, has been proposed as a noninvasive aging biomarker. Yet, SAF has not been compared with well-established blood-based aging biomarkers such as MetaboHealth in association with frailty. Furthermore, no previous study determined the longitudinal association of SAF with frailty. We used 2 382 Doetinchem Cohort Study participants' (aged 46.0-85.4) cross-sectional data, of whom 1 654 had longitudinal SAF measurements. SAF was measured using the AGE Reader. MetaboHealth was calculated on 1H-NMR-metabolomics. Linear regressions were used for the associations of SAF and MetaboHealth on the 36-deficit frailty index and logistic regressions for being pre-frail or frail as determined by the frailty phenotype. Longitudinal associations were determined by an interaction term between age and SAF in a linear mixed model. SAF and MetaboHealth were associated with higher odds of pre-frailty (odd ratios per standard deviation SAF: 1.21 [1.10-1.32], MetaboHealth: 1.35 [1.24-1.49]) and frailty (SAF: 1.70 [1.41-2.06], MetaboHealth: 1.90 [1.57-2.32]). When mutually adjusted, both aging biomarkers remained associated with pre-frailty (SAF: 1.16 [1.05-1.27], MetaboHealth 1.33 [1.21-1.46]) and frailty (SAF: 1.52 [1.25-1.85], MetaboHealth: 1.75 [1.43-2.14]). Additionally, SAF and MetaboHealth were associated with higher frailty index scores (percentage increase per standard deviation SAF: 1.35 [1.00-1.70], MetaboHealth: 1.87 [1.54-2.20]), also after mutual adjustment (SAF: 1.02 [0.68-1.37], MetaboHealth: 1.69 [1.35-2.02]). SAF was also longitudinally associated with the frailty index (percentage per unit/year increase: 0.12 [0.07-0.16]). The mutual independence of SAF and MetaboHealth implies they capture distinct aspects of the aging process. Altogether, these findings emphasize SAF's clinical potential as an age-related decline biomarker, which could be further enhanced when combined with MetaboHealth.

Decoding immune cell interactions during cardiac allograft vasculopathy: insights derived from bioinformatic strategies

Chronic allograft vasculopathy (CAV) is a major cause of late graft failure in heart transplant recipients, characterized by progressive intimal thickening and diffuse narrowing of the coronary arteries. Unlike atherosclerosis, CAV exhibits a distinct cellular composition and lesion distribution, yet its pathogenesis remains incompletely understood. A major challenge in CAV research has been the limited application of advanced "-omics" technologies, which have revolutionized the study of other vascular diseases. Recent advancements in single-cell and spatial transcriptomics, proteomics, and metabolomics have begun to uncover the complex immune-endothelial-stromal interactions driving CAV progression. Notably, single-cell RNA sequencing has identified previously unrecognized immune cell populations and signaling pathways implicated in endothelial injury and vascular remodeling after heart transplantation. Despite these breakthroughs, studies applying these technologies to CAV remain sparse, limiting the translation of these insights into clinical practice. This review aims to bridge this gap by summarizing recent findings from single-cell and multi-omic approaches, highlighting key discoveries, and discussing their implications for understanding CAV pathogenesis.

Integrative analysis of blood biomarkers and clinical variables improves early detection of aggressive prostate cancer

Aggressive prostate cancer (PC) represents a significant health concern. Conventional screening methods, primarily based on prostate-specific antigen (PSA) levels, lack specificity, leading to an urgent need for more accurate diagnostic tools. This study investigates whether integrating clinical and routine blood laboratory parameters can improve the early non-invasive prediction of aggressive PC. In a pilot study of 578 patients with suspicion of PC, 28 laboratory values alongside data on family history, diet, and lifestyle were analyzed. A logistic regression classifier was developed, with model performance evaluated using repeated k-fold cross-validation on the complete dataset (n = 282). Participants were categorized into healthy, moderate PC (ISUP 1-2), and aggressive PC (ISUP 3-5). Significant associations were found between PC aggressiveness and lower levels of androstenedione, dehydroepiandrosterone-sulfate (DHEA-S) and free PSA%, as well as higher levels of sex hormone binding globulin (SHBG). The integration of these serum markers with clinical parameters into a new multi-stage risk classifier significantly improved the predictive accuracy for aggressive PC, outperforming PSA-only methods. The integration of DHEA-S, androstenedione, and SHBG as widely available and cost-effective novel blood biomarkers offers a more targeted, non-invasive prediction of aggressive PC. This approach could reduce reliance on invasive prostate biopsies and expensive magnetic resonance imaging.

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.

Periodic dietary restriction of animal products induces metabolic reprogramming in humans with effects on cardiometabolic health

Dietary interventions constitute powerful approaches for disease prevention and treatment. However, the molecular mechanisms through which diet affects health remain underexplored in humans. Here, we compare plasma metabolomic and proteomic profiles between dietary states for a unique group of individuals who alternate between omnivory and restriction of animal products for religious reasons. We find that short-term restriction drives reductions in levels of lipid classes and of branched-chain amino acids, not detected in a control group of individuals, and results in metabolic profiles associated with decreased risk for all-cause mortality. We show that 23% of proteins whose levels are affected by dietary restriction are druggable targets and reveal that pro-longevity hormone FGF21 and seven additional proteins (FOLR2, SUMF2, HAVCR1, PLA2G1B, OXT, SPP1, HPGDS) display the greatest magnitude of change. Through Mendelian randomization we demonstrate potentially causal effects of FGF21 and HAVCR1 on risk for type 2 diabetes, of HPGDS on BMI, and of OXT on risk for lacunar stroke. Collectively, we find that restriction-associated reprogramming improves metabolic health and emphasise high-value targets for pharmacological intervention.

Tissue-specific methylomic responses to a lifestyle intervention in older adults associate with metabolic and physiological health improvements

Across the lifespan, diet and physical activity profiles substantially influence immunometabolic health. DNA methylation, as a tissue-specific marker sensitive to behavioral change, may mediate these effects through modulation of transcription factor binding and subsequent gene expression. Despite this, few human studies have profiled DNA methylation and gene expression simultaneously in multiple tissues or examined how molecular levels react and interact in response to lifestyle changes. The Growing Old Together (GOTO) study is a 13-week lifestyle intervention in older adults, which imparted health benefits to participants. Here, we characterize the DNA methylation response to this intervention at over 750 thousand CpGs in muscle, adipose, and blood. Differentially methylated sites are enriched for active chromatin states, located close to relevant transcription factor binding sites, and associated with changing expression of insulin sensitivity genes and health parameters. In addition, measures of biological age are consistently reduced, with decreases in grimAge associated with observed health improvements. Taken together, our results identify responsive molecular markers and demonstrate their potential to measure progression and finetune treatment of age-related risks and diseases.

Increased 1H-NMR metabolomics-based health score associates with declined cognitive performance and functional independence in older adults at risk of cardiovascular disease

The 1-HMR metabolomics-based MetaboHealth score, comprised of 14 serum metabolic markers, associates with disease-specific mortality, but it is unclear whether the score also reflects cognitive changes and functional impairment. We aimed to assess the associations between the MetaboHealth score with cognitive function and functional decline in older adults at increased cardiovascular risk. A total of 5292 older adults free of dementia at baseline with mean age 75.3 years (SD = 3.4) from the Prospective Study of Pravastatin in the Elderly (PROSPER). MetaboHealth score were measured at baseline, and cognitive function and functional independence were measured at baseline and every 3 months during up to 2.5 years follow-up. Cognitive function was assessed using the Stroop test (selective attention), the Letter Digit Coding test (LDCT) (processing speed), and the two versions of the Picture Learning test (delayed and immediate; memory). Two tests of functional independence were used: Barthel Index (BI) and instrumental activities at daily living (IADL). A higher MetaboHealth score was associated with worse cognitive function (in all domains) and with worse functional independence. For example, after full adjustments, a 1-SD higher MetaboHealth score was associated with 9.02 s (95%CI 7.29, 10.75) slower performance on the Stroop test and 2.79 (2.21, 3.26) less digits coded on the LDCT. During follow-up, 1-SD higher MetaboHealth score was associated with an additional decline of 0.53 s (0.23, 0.83) on the Stroop test and - 0.08 (- 0.11, - 0.06) points on the IADL. Metabolic disturbance, as reflected by an increased metabolomics-based health score, may mark future cognitive and functional decline.

The metabolic vulnerability index as a novel tool for mortality risk stratification in a large-scale population-based cohort

Metabolic malnutrition and inflammation-key mechanism links to redox imbalance-are fundamental pathologies that accelerate aging and disease progression, ultimately leading to death. The recently proposed metabolic vulnerability index (MVX) integrates multiple circulatory biomarkers closely linked to both metabolic and inflammatory factors. This study aims to assess MVX's potential to predict mortality in community-based population. In this large community-based prospective study, we included UK Biobank participants who underwent plasma metabolomics analysis. Gender-specific MVX scores were calculated based on six established biomarkers of mortality. Linear and non-linear associations between MVX and mortality were assessed using Cox proportional hazards models and restricted cubic spline models, respectively. Among the 274,092 UKB participants, 24,241 all-cause deaths occurred during a median follow-up period of 13.7 years. A significant, graded positive association was observed between MVX quartiles and all-cause mortality risk (P for trend <0.05), with the highest MVX quartile exhibiting the greatest risk (HR = 1.21 and 95 % CI = 1.16-1.25 after full adjustment). Females had higher MVX score than males (P < 0.05), but males with the same MVX score faced a greater mortality risk. Baseline age and comorbidities interacted (P for interaction <0.05 and synergy index >1) with MVX on mortality risk. Longitudinal analyses showed that females with persistently high MVX score had a significantly increased risk of mortality (HR = 1.39 in fully adjusted model). Collectively, these findings highlight MVX as a novel tool that captures metabolic and potential redox vulnerabilities in community residents, and serves as a valuable resource for identifying high-risk individuals of mortality. Further research is warranted to investigate the underlying mechanisms and establish causal relationships.

Systematic analysis of relationships between serum lipids with all-cause and cause-specific mortality: Evidence from prospective cohort studies of UK Biobank and Women's Health Initiative

BACKGROUND & AIMS

Serum lipids, including lipoproteins, cholesterol, and triglycerides, are important modifiable factors influencing human health. However, the associations among different serum lipid profiles and mortality remain insufficiently understood, particularly regarding potential causality and population heterogeneity. This prospective study aims to systematically investigate the relationships between serum lipid concentrations of different densities and sizes with all-cause and cause-specific mortality.

METHODS

Cox proportional and Fine-Gray subdistribution hazard models were applied to investigate the associations of 54 lipid concentrations with all-cause and cause-specific mortality (including cardiovascular disease (CVD), cancer, and respiratory disease) in the UK Biobank cohort of 441,448 individuals with 17-year follow-up. Cohorts of 120,967 and 44,168 individuals from the Women's Health Initiative (WHI) with 16-year follow-up and a large-scale meta-analysis were utilized for external replication. We further assessed the underlying causality using Mendelian randomization (MR) and possible modifiers using multiple subgroup analyses.

RESULTS

During a median follow-up of 13.8 years, 39,290 deaths occurred, including 7399 from CVD, 18,928 from cancer, and 2707 from respiratory disease. We identified 160 significant associations between lipid concentrations and all-cause and cause-specific mortality. Importantly, most were inverse, with decreased lipid levels linked to increased risk of premature death [hazard ratios (HRs): 0.70-0.98 per standard deviation (SD)]. In contrast, positives were observed for HDL (large/very large) and triglyceride concentrations [HRs: 1.02-1.25 per SD], indicating increased mortality risk with higher levels. Most lipoproteins and cholesterol exhibited nonlinearly correlations with mortality, especially the significant U-shaped in total/HDL. However, MR showed that elevations in several lipids were associated with increased all-cause and CVD-specific mortality risk. Multiple subgroup analyses revealed that age, sex, and lipid-modifying drugs modified the lipid-mortality relationship; specifically, higher lipid concentrations increased mortality risk in younger adults not taking lipid-modifying drugs, but decreased mortality in older adults taking lipid-modifying drugs. The majority of associations were replicated in the WHI and external cohorts.

CONCLUSION

Our study systematically reported a large number of associations between serum lipid concentrations and mortality. Subgroup-based population heterogeneity analysis suggests that age, sex, and lipid-modifying drugs could be modifiers for the lipid-mortality relationship. These findings provide more guidance for lipid management and individualized prevention.

A proteomic signature of healthspan

The focus of aging research has shifted from increasing lifespan to enhancing healthspan to reduce the time spent living with disability. Despite significant efforts to develop biomarkers of aging, few studies have focused on biomarkers of healthspan. We developed a proteomics-based signature of healthspan (healthspan proteomic score (HPS)) using proteomic data from the Olink Explore 3072 assay in the UK Biobank Pharma Proteomics Project (53,018 individuals and 2920 proteins). A lower HPS was associated with higher mortality risk and several age-related conditions, such as COPD, diabetes, heart failure, cancer, myocardial infarction, dementia, and stroke. HPS showed superior predictive accuracy for these outcomes compared to other biological age measures. Proteins associated with HPS were enriched in hallmark pathways such as immune response, inflammation, cellular signaling, and metabolic regulation. The external validity was evaluated using the Essential Hypertension Epigenetics study with proteomic data also from the Olink Explore 3072 and complementary epigenetic data, making it a valuable tool for assessing healthspan and as a potential surrogate marker to complement existing proteomic and epigenetic biological age measures in geroscience-guided studies.

Rationale and design of the Dog Aging Project precision cohort: a multi-omic resource for longitudinal research in geroscience

A significant challenge in multi-omic geroscience research is the collection of high quality, fit-for-purpose biospecimens from a diverse and well-characterized study population with sufficient sample size to detect age-related changes in physiological biomarkers. The Dog Aging Project designed the precision cohort to study the mechanisms underlying age-related change in the metabolome, microbiome, and epigenome in companion dogs, an emerging model system for translational geroscience research. One thousand dog-owner pairs were recruited into cohort strata based on life stage, sex, size, and geography. We designed and built a novel implementation of the REDCap electronic data capture system to manage study participants, logistics, and biospecimen and survey data collection in a secure online platform. In collaboration with primary care veterinarians, we collected and processed blood, urine, fecal, and hair samples from 976 dogs. The resulting data include complete blood count, chemistry profile, immunophenotyping by flow cytometry, metabolite quantification, fecal microbiome characterization, epigenomic profile, urinalysis, and associated metadata characterizing sample conditions at collection and during lab processing. The project, which has already begun collecting second- and third-year samples from precision cohort dogs, demonstrates that scientifically useful biospecimens can be collected from a geographically dispersed population through collaboration with private veterinary clinics and downstream labs. The data collection infrastructure developed for the precision cohort can be leveraged for future studies. Most important, the Dog Aging Project is an open data project. We encourage researchers around the world to apply for data access and utilize this rich, constantly growing dataset in their own work.

Exploring Metabolic Shifts in Kidney Cancer and Non-Cancer Cells Under Pro- and Anti-Apoptotic Treatments Using NMR Metabolomics

This study investigates the metabolic responses of cancerous (RCC) and non-cancerous (HK2) kidney cells to treatment with Staurosporine (STAU), which has a pro-apoptotic effect, and Bongkrekic acid (BKA), which has an anti-apoptotic effect, individually and in combination, using 1H NMR metabolomics to identify metabolite markers linked to mitochondrial apoptotic pathways. BKA had minimal metabolic effects in RCC cells, suggesting its role in preserving mitochondrial function without significantly altering metabolic pathways. In contrast, STAU induced substantial metabolic reprogramming in RCC cells, disrupting energy production, redox balance, and biosynthesis, thereby triggering apoptotic pathways. The combined treatment of BKA and STAU primarily mirrored the effects of STAU alone, with BKA showing little capacity to counteract the pro-apoptotic effects. In non-cancerous HK2 cells, the metabolic alterations were far less pronounced, highlighting key differences in the metabolic responses of cancerous and non-cancerous cells. RCC cells displayed greater metabolic flexibility, while HK2 cells maintained a more regulated metabolic state. These findings emphasize the potential for targeting cancer-specific metabolic vulnerabilities while sparing non-cancerous cells, underscoring the value of metabolomics in understanding apoptotic and anti-apoptotic mechanisms. Future studies should validate these results in vivo and explore their potential for personalized treatment strategies.

VLDL triglycerides and cholesterol in non-alcoholic fatty liver disease and myocardial infarction

BACKGROUND AND AIMS

Myocardial infarction is a leading cause of death in individuals with non-alcoholic fatty liver disease (NAFLD). The two diseases share elevated very low-density lipoproteins (VLDL) carrying both triglycerides and cholesterol; however, in NAFLD mainly triglycerides accumulate in liver cells while in myocardial infarction mainly cholesterol accumulates in the atherosclerotic plaque. We hypothesized that VLDL triglycerides preferentially associate with risk of NAFLD, while VLDL cholesterol preferentially associates with risk of myocardial infarction.

METHODS

We examined 25,428 individuals without clinically diagnosed NAFLD or myocardial infarction at baseline, nested within 109,776 individuals from the prospective Copenhagen General Population Study and followed these individuals for a mean of 10 years. VLDL triglycerides, VLDL cholesterol, and low-density lipoprotein (LDL) cholesterol were determined using nuclear magnetic resonance spectrometry.

RESULTS

Continuously higher VLDL triglycerides were associated with continuously higher risk of NAFLD; however, this was not the case for VLDL cholesterol, LDL cholesterol, or apolipoprotein B. In contrast, continuously higher VLDL cholesterol, LDL cholesterol, and plasma apolipoprotein B were all associated with continuously higher risk of myocardial infarction. Compared to individuals with both VLDL triglycerides and VLDL cholesterol ≤66th percentile, the hazard ratios for NAFLD in individuals with VLDL triglycerides >66th percentile were 1.61(95 % confidence intervals:1.25-2.06) at high VLDL cholesterol and 1.41(0.90-2.21) at low VLDL cholesterol. Corresponding hazard ratios for myocardial infarction in individuals with VLDL cholesterol >66th percentile were 1.51(1.36-1.67) at high VLDL triglycerides and 1.42(1.18-1.69) at low VLDL triglycerides.

CONCLUSIONS

VLDL triglycerides predominated in NAFLD while VLDL cholesterol predominated in myocardial infarction; however, VLDL cholesterol was also elevated slightly in NAFLD while VLDL triglycerides was also elevated in myocardial infarction.

Photoreceptor metabolic window unveils eye-body interactions

Photoreceptors are specialized neurons at the core of the retina's functionality, with optical accessibility and exceptional sensitivity to systemic metabolic stresses. Here we show the ability of risk-free, in vivo photoreceptor assessment as a window into systemic health and identify shared metabolic underpinnings of photoreceptor degeneration and multisystem health outcomes. A thinner photoreceptor layer thickness is significantly associated with an increased risk of future mortality and 13 multisystem diseases, while systematic analyses of circulating metabolomics enable the identification of 109 photoreceptor-related metabolites, which in turn elevate or reduce the risk of these health outcomes. To translate these insights into a practical tool, we developed an artificial intelligence (AI)-driven photoreceptor metabolic window framework and an accompanying interpreter that comprehensively captures the metabolic landscape of photoreceptor-systemic health linkages and simultaneously predicts 16 multisystem health outcomes beyond established approaches while retaining interpretability. Our work, replicated across cohorts of diverse ethnicities, reveals the potential of photoreceptors to inform systemic health and advance a multisystem perspective on human health by revealing eye-body connections and shared metabolic influences.

Matrix effect evaluation using multi-component post-column infusion in untargeted hydrophilic interaction liquid chromatography-mass spectrometry plasma metabolomics

Metabolomics based on hydrophilic interaction liquid chromatography (HILIC) coupled with mass spectrometry (MS) is a powerful tool for polar metabolite identification and quantification to further contribute to biomarker discovery and disease mechanism elucidation. However, matrix effect (ME), which may lead to altered ionization efficiency due to co-eluting compounds, is a significant challenge during biological analysis. Therefore, ME evaluation plays a crucial role during method development. Two approaches to evaluate ME are using stable isotope labelled-internal standards (SIL-IS) and post-column infusion (PCI) of standards. In this study, we developed an untargeted HILIC-MS method by applying four PCI standards for ME evaluation. We found PCI is a compelling approach for ME assessment compared to SIL-IS method due to its advantage in untargeted analysis. Through the ME evaluation and chromatographic performance comparison of 18 SIL standards across three columns and three different mobile phase pH conditions, our findings revealed that the BEH-Z-HILIC column operated at pH 4 with 10 mM ammonium formate exhibited minimal ME and superior performance. The method showed exceptional linearity (R² > 0.98), reliable repeatability (RSD < 15 %), good inter-day precision (RSD < 30 %), and acceptable recovery (>75 %) for all SIL standards. Absolute matrix effect (AME) and relative matrix effect (RME) assessment in three plasma donors revealed a high consistency between PCI and SIL-IS approaches. Finally, this method coupled with the PCI approach was applied to 40 plasma samples. Fifty endogenous compounds were detected and their AME and RME were evaluated. Results showed that many compounds experienced severe ion suppression, though their ME variation between 40 samples is low. In conclusion, PCI method is a robust alternative for monitoring ME and evaluating ME of endogenous compounds during untargeted method optimization and biological analysis.

Decoding aging clocks: New insights from metabolomics

Chronological age is a crucial risk factor for diseases and disabilities among older adults. However, individuals of the same chronological age often exhibit divergent biological aging states, resulting in distinct individual risk profiles. Chronological age estimators based on omics data and machine learning techniques, known as aging clocks, provide a valuable framework for interpreting molecular-level biological aging. Metabolomics is an intriguing and rapidly growing field of study, involving the comprehensive profiling of small molecules within the body and providing the ultimate genome-environment interaction readout. Consequently, leveraging metabolomics to characterize biological aging holds immense potential. The aim of this review was to provide an overview of metabolomics approaches, highlighting the establishment and interpretation of metabolomic aging clocks while emphasizing their strengths, limitations, and applications, and to discuss their underlying biological significance, which has the potential to drive innovation in longevity research and development.

"Aging Clocks" Based on Cell-Free DNA

Aging is associated with systemic changes in the physiological and molecular parameters of the body. These changes are referred to as biomarkers of aging. Statistical models that link changes in individual biomarkers to biological age are called aging clocks. These tools facilitate a comprehensive evaluation of bodily health and permit the quantitative determination of the rate of aging. A particularly promising area for the development of aging clocks is the analysis of cell-free DNA (cfDNA), which is present in the blood and contains numerous potential biomarkers. This review explores in detail the fragmentomics, topology, and epigenetic landscape of cfDNA as possible biomarkers of aging. The review further underscores the potential of leveraging single-molecule sequencing of cfDNA in conjunction with long-read technologies to simultaneously profile multiple biomarkers, a strategy that could lead to the development of more precise aging clocks.

Mitochondrial DNA abundance and circulating metabolomic profiling: Multivariable-adjusted and Mendelian randomization analyses in UK Biobank

BACKGROUND

Low leukocyte mitochondrial DNA (mtDNA) abundance has been associated with a higher risk of atherosclerotic cardiovascular disease, but through unclear mechanisms. We aimed to investigate whether low mtDNA abundance is associated with worse metabolomic profiling, as being potential intermediate phenotypes, using cross-sectional and genetic studies.

METHODS

Among 61,186 unrelated European participants from UK Biobank, we performed multivariable-adjusted linear regression analyses to examine the associations between mtDNA abundance and 168 NMR-based circulating metabolomic measures and nine metabolomic principal components (PCs) that collectively covered 91.5% of the total variation of individual metabolomic measures. Subsequently, we conducted Mendelian randomization (MR) to approximate the causal effects of mtDNA abundance on the individual metabolomic measures and their metabolomic PCs.

RESULTS

After correction for multiple testing, low mtDNA abundance was associated with 130 metabolomic measures, predominantly lower concentrations of some amino acids and higher concentrations of lipids, lipoproteins and fatty acids; moreover, mtDNA abundance was associated with seven out of the nine metabolomic PCs. Using MR, genetically-predicted low mtDNA abundance was associated with lower lactate (standardized beta and 95% confidence interval: -0.17; -0.26, -0.08), and higher acetate (0.15; 0.07,0.23), and unsaturation degree (0.14; 0.08,0.20). Similarly, genetically-predicted low mtDNA abundance was associated with lower metabolomic PC2 (related to lower concentrations of lipids and fatty acids), and higher metabolomic PC9 (related to lower concentrations of glycolysis-related metabolites).

CONCLUSION

Low mtDNA abundance is associated with metabolomic perturbations, particularly reflecting a pro-atherogenic metabolomic profile, which potentially could link low mtDNA abundance to higher atherosclerosis risk.

Metabolomic age (MileAge) predicts health and life span: A comparison of multiple machine learning algorithms

Biological aging clocks produce age estimates that can track with age-related health outcomes. This study aimed to benchmark machine learning algorithms, including regularized regression, kernel-based methods, and ensembles, for developing metabolomic aging clocks from nuclear magnetic resonance spectroscopy data. The UK Biobank data, including 168 plasma metabolites from up to N = 225,212 middle-aged and older adults (mean age, 56.97 years), were used to train and internally validate 17 algorithms. Metabolomic age (MileAge) delta, the difference between metabolite-predicted and chronological age, from a Cubist rule-based regression model showed the strongest associations with health and aging markers. Individuals with an older MileAge were frailer, had shorter telomeres, were more likely to suffer from chronic illness, rated their health worse, and had a higher all-cause mortality hazard (HR = 1.51; 95% CI, 1.43 to 1.59; P < 0.001). This metabolomic aging clock (MileAge) can be applied in research and may find use in health assessments, risk stratification, and proactive health tracking.

Late-life protein or isoleucine restriction impacts physiological and molecular signatures of aging

Restricting the intake of protein or the branched-chain amino acid isoleucine promotes healthspan and extends lifespan in young or adult mice. However, their effects when initiated in aged animals are unknown. Here we investigate the consequences of consuming a diet with 67% reduction of all amino acids (low AA) or of isoleucine alone (low Ile), in male and female C57BL/6J.Nia mice starting at 20 months of age. Both dietary regimens effectively promote overall metabolic health without reducing calorie intake. Both low AA and low Ile diets improve aspects of frailty and slow multiple molecular indicators of aging rate; however, the low Ile diet reduces grip strength in both sexes and has mixed, sexually dimorphic effects on the heart. These results demonstrate that low AA and low Ile diets can promote aspects of healthy aging in aged mice and suggest that similar interventions might promote healthy aging in older adults.

Design of the VOILA-intervention study: A 12-week nutrition and resistance exercise intervention in metabolic or mobility compromised Dutch older adults and the response on immune-metabolic, gut and muscle health parameters

BACKGROUND

Exercise and nutrition interventions can slow ageing-induced decline in physiology. However, effects are heterogeneous and usually studied separately per outcome domain. In the VOILA study, we simultaneously study various health outcomes relevant for older adults and the inter-individual heterogeneity in response to a lifestyle intervention.

METHODS

VOILA is a 12-week lifestyle intervention in 3 groups of older adults (≥60 years), with compromised mobility (n=50), compromised metabolic health (n=50), or recovering from total knee replacement (TKR, n=70, of which 20 randomized to standard care only). The intervention includes high-intensity resistance exercise training thrice weekly, nutritional counselling, and nutritional supplements every morning and evening (including 20-25 g whey protein and (evening only) 5.5 g Biotis™ GOS). We measure immune-metabolic, gut health, muscle mass and physical functioning at baseline and after completion of the intervention/standard care. An additional reference group of healthy older adults (n=50) will undergo baseline measurements only.

DISCUSSION

Improvements in various physiological systems are expected, but with differences between groups/individuals. This study will provide insights into how the physiological state of older adults influences the extent of lifestyle-induced health improvements to create better tailored interventions to attenuate biological ageing and improve the health span of subgroups and individuals.

The performance of metabolomics-based prediction scores for mortality in older patients with solid tumors

Prognostic information is needed to balance benefits and risks of cancer treatment in older patients. Metabolomics-based scores were previously developed to predict 5- and 10-year mortality (MetaboHealth) and biological age (MetaboAge). This study aims to investigate the association of MetaboHealth and MetaboAge with 1-year mortality in older patients with solid tumors, and to study their predictive value for mortality in addition to established clinical predictors. This prospective cohort study included patients aged ≥ 70 years with a solid malignant tumor, who underwent blood sampling and a geriatric assessment before treatment initiation. The outcome was all-cause 1-year mortality. Of the 192 patients, the median age was 77 years. With each SD increase of MetaboHealth, patients had a 2.32 times increased risk of mortality (HR 2.32, 95% CI 1.59-3.39). With each year increase in MetaboAge, there was a 4% increased risk of mortality (HR 1.04, 1.01-1.07). MetaboHealth and MetaboAge showed an AUC of 0.66 (0.56-0.75) and 0.60 (0.51-0.68) for mortality prediction accuracy, respectively. The AUC of a predictive model containing age, primary tumor site, distant metastasis, comorbidity, and malnutrition was 0.76 (0.68-0.83). Addition of MetaboHealth increased AUC to 0.80 (0.74-0.87) (p = 0.09) and AUC did not change with MetaboAge (0.76 (0.69-0.83) (p = 0.89)). Higher MetaboHealth and MetaboAge scores were associated with 1-year mortality. The addition of MetaboHealth to established clinical predictors only marginally improved mortality prediction in this cohort with various types of tumors. MetaboHealth may potentially improve identification of older patients vulnerable for adverse events, but numbers were too small for definitive conclusions. The TENT study is retrospectively registered at the Netherlands Trial Register (NTR), trial number NL8107. Date of registration: 22-10-2019.

Plasma Metabolomics Identifies Key Metabolites and Improves Prediction of Diabetic Retinopathy: Development and Validation across Multinational Cohorts

PURPOSE

To identify longitudinal metabolomic fingerprints of diabetic retinopathy (DR) and to evaluate their usefulness in predicting DR development and progression.

DESIGN

Multicenter, multiethnic cohort study.

PARTICIPANTS

This study included 17 675 participants from the UK Biobank (UKB) who had baseline prediabetes or diabetes, identified in accordance with the 2021 American Diabetes Association guidelines, and were free of baseline DR and an additional 638 participants with type 2 diabetes mellitus from the Guangzhou Diabetic Eye Study (GDES) for external validation. Diabetic retinopathy was determined by ICD-10 codes in the UKB cohort and revised ETDRS grading criteria in the GDES cohort.

METHODS

Longitudinal DR metabolomic fingerprints were identified through nuclear magnetic resonance (NMR) assay in UKB participants. The predictive value of these fingerprints for predicting DR development were assessed in a fully withheld test set. External validation and extrapolation analyses of DR progression and microvascular damage were conducted in the GDES cohort using NMR technology. Model assessments included the concordance (C) statistic, net classification improvement (NRI), integrated discrimination improvement (IDI), calibration, and clinical usefulness in both cohorts.

MAIN OUTCOME MEASURES

DR development and progression and retinal microvascular damage.

RESULTS

Of 168 metabolites, 118 were identified as candidate metabolomic fingerprints for future DR development. These fingerprints significantly improved the predictability for DR development beyond traditional indicators (C statistic, 0.802 [95% confidence interval (CI), 0.760-0.843] vs. 0.751 [95% CI, 0.706-0.796]; P = 5.56 × 10-4). Glucose, lactate, and citrate were among the fingerprints validated in the GDES cohort. Using these parsimonious and replicable fingerprints yielded similar improvements for predicting DR development (C statistic, 0.807 [95% CI, 0.711-0.903] vs. 0.617 [95% CI, 0.494-0.740]; P = 1.68 × 10-4) and progression (C statistic, 0.797 [95% CI, 0.712-0.882] vs. 0.665 [95% CI, 0.545-0.784]; P = 0.003) in the external GDES cohort. Improvements in NRIs, IDIs, and clinical usefulness also were evident in both cohorts (all P < 0.05). In addition, lactate and citrate were associated with microvascular damage across macular and optic nerve head regions among Chinese GDES (all P < 0.05).

CONCLUSIONS

Metabolomic profiling may be effective in identifying robust fingerprints for predicting future DR development and progression, providing novel insights into the early and advanced stages of DR pathophysiology.

FINANCIAL DISCLOSURE(S)

The author(s) have no proprietary or commercial interest in any materials discussed in this article.

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.

Discriminating Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and comorbid conditions using metabolomics in UK Biobank

BACKGROUND

Diagnosing complex illnesses like Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is complicated due to the diverse symptomology and presence of comorbid conditions. ME/CFS patients often present with multiple health issues, therefore, incorporating comorbidities into research can provide a more accurate understanding of the condition's symptomatology and severity, to better reflect real-life patient experiences.

METHODS

We performed association studies and machine learning on 1194 ME/CFS individuals with blood plasma nuclear magnetic resonance (NMR) metabolomics profiles, and seven exclusive comorbid cohorts: hypertension (n = 13,559), depression (n = 2522), asthma (n = 6406), irritable bowel syndrome (n = 859), hay fever (n = 3025), hypothyroidism (n = 1226), migraine (n = 1551) and a non-diseased control group (n = 53,009).

RESULTS

We present a lipoprotein perspective on ME/CFS pathophysiology, highlighting gender-specific differences and identifying overlapping associations with comorbid conditions, specifically surface lipids, and ketone bodies from 168 significant individual biomarker associations. Additionally, we searched for, trained, and optimised a machine learning algorithm, resulting in a predictive model using 19 baseline characteristics and nine NMR biomarkers which could identify ME/CFS with an AUC of 0.83 and recall of 0.70. A multi-variable score was subsequently derived from the same 28 features, which exhibited ~2.5 times greater association than the top individual biomarker.

CONCLUSIONS

This study provides an end-to-end analytical workflow that explores the potential clinical utility that association scores may have for ME/CFS and other difficult to diagnose conditions.

Estimation of physiological aging based on routine clinical biomarkers: a prospective cohort study in elderly Chinese and the UK Biobank

BACKGROUND

Chronological age (CA) does not reflect individual variation in the aging process. However, existing biological age predictors are mostly based on European populations and overlook the widespread nonlinear effects of clinical biomarkers.

METHODS

Using data from the prospective Dongfeng-Tongji (DFTJ) cohort of elderly Chinese, we propose a physiological aging index (PAI) based on 36 routine clinical biomarkers to measure aging progress. We first determined the optimal level of each biomarker by restricted cubic spline Cox models. For biomarkers with a U-shaped relationship with mortality, we derived new variables to model their distinct effects below and above the optimal levels. We defined PAI as a weighted sum of variables predictive of mortality selected by a LASSO Cox model. To measure aging acceleration, we defined ΔPAI as the residual of PAI after regressing on CA. We evaluated the predictive value of ΔPAI on cardiovascular diseases (CVD) in the DFTJ cohort, as well as nine major chronic diseases in the UK Biobank (UKB).

RESULTS

In the DFTJ training set (n = 12,769, median follow-up: 10.38 years), we identified 25 biomarkers with significant nonlinear associations with mortality, of which 11 showed insignificant linear associations. By incorporating nonlinear effects, we selected CA and 17 clinical biomarkers to calculate PAI. In the DFTJ testing set (n = 15,904, 5.87 years), PAI predict mortality with a concordance index (C-index) of 0.816 (95% confidence interval, [0.796, 0.837]), better than CA (C-index = 0.771 [0.755, 0.788]) and PhenoAge (0.799 [0.784, 0.814]). ΔPAI was predictive of incident CVD and its subtypes, independent of traditional risk factors. In the external validation set of UKB (n = 296,931, 12.80 years), PAI achieved a C-index of 0.749 (0.746, 0.752) to predict mortality, remaining better than CA (0.706 [0.702, 0.709]) and PhenoAge (0.743 [0.739, 0.746]). In both DFTJ and UKB, PAI calibrated better than PhenoAge when comparing the predicted and observed survival probabilities. Furthermore, ΔPAI outperformed any single biomarker to predict incident risks of eight age-related chronic diseases.

CONCLUSIONS

Our results highlight the potential of PAI and ΔPAI as integrative biomarkers to evaluate aging acceleration and facilitate the development of targeted intervention strategies for healthy aging.

Nightingale Health Biobank Collaborative Group, Barrett JC, Esko T, Fischer K, Jostins-Dean L, Jousilahti P, Julkunen H, Jääskeläinen T, Kangas A, Kerimov N, Kerminen S, Kolde A, Koskela H, Kronberg J, Lundgren SN, Lundqvist A, Mäkelä V, Nybo K, Perola M, Salomaa V, Schut K, Soikkeli M, Soininen P, Tiainen M, Tillmann T, Würtz P. Metabolomic and genomic prediction of common diseases in 700,217 participants in three national biobanks

Identifying individuals at high risk of chronic diseases via easily measured biomarkers could enhance efforts to prevent avoidable illness and death. Using 'omic data can stratify risk for many diseases simultaneously from a single measurement that captures multiple molecular predictors of risk. Here we present nuclear magnetic resonance metabolomics in blood samples from 700,217 participants in three national biobanks. We built metabolomic scores that identify high-risk groups for diseases that cause the most morbidity in high-income countries and show consistent cross-biobank replication of the relative risk of disease for these groups. We show that these metabolomic scores are more strongly associated with disease onset than polygenic scores for most of these diseases. In a subset of 18,709 individuals with metabolomic biomarkers measured at two time points we show that people whose scores change have different risk of disease, suggesting that repeat measurements capture changes both to health status and disease risk possibly due to treatment, lifestyle changes or other factors. Lastly, we assessed the incremental predictive value of metabolomic scores over existing clinical risk scores for multiple diseases and found modest improvements in discrimination for several diseases whose clinical utility, while promising, remains to be determined.

Metabolomic Insights into COVID-19 Severity: A Scoping Review

BACKGROUND

In 2019, SARS-CoV-2, the novel coronavirus, entered the world scene, presenting a global health crisis with a broad spectrum of clinical manifestations. Recognizing the significance of metabolomics as the omics closest to symptomatology, it has become a useful tool for predicting clinical outcomes. Several metabolomic studies have indicated variations in the metabolome corresponding to different disease severities, highlighting the potential of metabolomics to unravel crucial insights into the pathophysiology of SARS-CoV-2 infection.

METHODS

The PRISMA guidelines were followed for this scoping review. Three major scientific databases were searched: PubMed, the Directory of Open Access Journals (DOAJ), and BioMed Central, from 2020 to 2024. Initially, 2938 articles were identified and vetted with specific inclusion and exclusion criteria. Of these, 42 articles were retrieved for analysis and summary.

RESULTS

Metabolites were identified that were repeatedly noted to change with COVID-19 and its severity. Phenylalanine, glucose, and glutamic acid increased with severity, while tryptophan, proline, and glutamine decreased, highlighting their association with COVID-19 severity. Additionally, pathway analysis revealed that phenylalanine, tyrosine and tryptophan biosynthesis, and arginine biosynthesis were the most significantly impacted pathways in COVID-19 severity.

CONCLUSIONS

COVID-19 severity is intricately linked to significant metabolic alterations that span amino acid metabolism, energy production, immune response modulation, and redox balance.

Sources of variation in the serum metabolome of female participants of the HUNT2 study

The aim of this study was to explore the intricate relationship between serum metabolomics and lifestyle factors, shedding light on their impact on health in the context of breast cancer risk. Detailed metabolic profiles of 2283 female participants in the Trøndelag Health Study (HUNT study) were obtained through nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS).We show that lifestyle-related variables can explain up to 30% of the variance in individual metabolites. Age and obesity were the primary factors affecting the serum metabolic profile, both associated with increased levels of triglyceride-rich very low-density lipoproteins (VLDL) and intermediate-density lipoproteins (IDL), amino acids and glycolysis-related metabolites, and decreased levels of high-density lipoproteins (HDL). Moreover, factors like hormonal changes associated with menstruation and contraceptive use or education level influence the metabolite levels.Participants were clustered into three distinct clusters based on lifestyle-related factors, revealing metabolic similarities between obese and older individuals, despite diverse lifestyle factors, suggesting accelerated metabolic aging with obesity. Our results show that metabolic associations to cancer risk may partly be explained by modifiable lifestyle factors.

Association between metabolomics-based biomarker scores and 10-year cognitive decline in men and women. The Doetinchem Cohort Study

BACKGROUND

Metabolomic scores based on age (MetaboAge) and mortality (MetaboHealth) are considered indicators of overall health, but their association with cognition in the general population is unknown. Therefore, the association between MetaboAge/MetaboHealth and level and decline in cognition was studied, as were differences between men and women.

METHODS

Data of 2821 participants (50% women, age range 45-75) from the Doetinchem Cohort Study was used. MetaboAge and MetaboHealth were calculated from 1H-NMR metabolomics data at baseline. Cognitive domain scores (memory, flexibility and processing speed) and global cognitive functioning were available over a 10-year period. The association between MetaboAge/MetaboHealth and level of cognitive functioning was studied using linear regressions while for the association between MetaboAge/MetaboHealth and cognitive decline longitudinal linear mixed models were used. Analyses were adjusted for demographics and lifestyle factors.

RESULTS

Higher MetaboAge, indicating poorer metabolomic ageing, was only associated with lower levels of processing speed in men. Higher MetaboHealth, indicating poorer immune-metabolic health, was associated with lower levels of cognitive functioning for all three domains and global cognitive functioning in both men and women. Only in men, MetaboHealth was also associated with 10-year decline in flexibility, processing speed and global cognition. Metabolites that contributed to the observed associations were in men mainly markers of protein metabolism, and in women mainly markers of lipid metabolism and inflammatory metabolites.

CONCLUSIONS

MetaboHealth, not MetaboAge, was associated with cognitive functioning independent of conventional risk factors. Individual metabolites affect cognitive functioning differently in men and women, suggesting sex-specific pathophysiological pathways underlying cognitive functioning.

Association of Glasgow Prognostic Score with frailty, mortality and adverse health outcomes in older patients with cancer: A prospective cohort study

INTRODUCTION

To balance benefits and risks of cancer treatment in older patients, prognostic information is needed. The Glasgow Prognostic Score (GPS), composed of albumin and C-reactive protein (CRP), might provide such information. This study first aims to investigate the association between GPS and frailty, functional decline, and health-related quality of life (HRQoL) decline as indicators of health problems in older patients with cancer. The second aim is to study the predictive value of GPS for mortality, in addition to clinical predictors.

MATERIALS AND METHODS

This prospective cohort study included patients aged ≥70 years with a solid malignant tumor who underwent a geriatric assessment and blood sampling before treatment initiation. GPS was calculated using serum albumin and CRP measured in batch, categorized into normal (0) and abnormal GPS (1-2). Outcomes were all-cause mortality and a composite outcome of decline in daily functioning and/or HRQoL, or mortality at one year follow-up. Daily functioning was assessed by Activities of Daily Living and Instrumental Activities of Daily Living questionnaires and HRQoL by the EQ-5D-3L and EQ-VAS questionnaires.

RESULTS

In total, 192 patients with a median age of 77 years (interquartile range 72.3-81.0) were included. Patients with abnormal GPS were more often frail compared to those with normal GPS (79 % vs. 63 %, p = 0.03). Patients with abnormal GPS had higher mortality rates after one year compared to those with normal GPS (48 % vs. 23 %, p < 0.01) in unadjusted analysis. Abnormal GPS was associated with increased mortality risk (hazard ratio 2.8, 95 % CI 1.7-4.8). The area under the receiver operating characteristics curve of age, distant metastasis, tumor site, comorbidity, and malnutrition combined was 0.73 (0.68-0.83) for mortality prediction, and changed to 0.78 (0.73-0.86) with GPS (p = 0.10). The composite outcome occurred in 88 % of patients with abnormal GPS versus 83 % with normal GPS (p = 0.44).

DISCUSSION

Abnormal GPS was associated with frailty and mortality. The addition of GPS to clinical predictors showed a numerically superior mortality prediction in this cohort of older patients with cancer, although not statistically significant. While GPS may improve the stratification of future older patients with cancer, larger studies including older patients with similar tumor types are necessary to evaluate its clinical usefulness.

TRIAL REGISTRATION

The TENT study is retrospectively registered at the Netherlands Trial Register (NTR), trial number NL8107. Date of registration: 22-10-2019.

Lipidomics identified novel cholesterol-independent predictors for risk of incident coronary heart disease: Mediation of risk from diabetes and aggravation of risk by ambient air pollution

INTRODUCTION

Previous lipidomics studies have identified various lipid predictors for cardiovascular risk, however, with limited predictive increment, sometimes using too many predictor variables at the expense of practical efficiency.

OBJECTIVES

To search for lipid predictors of future coronary heart disease (CHD) with stronger predictive power and efficiency to guide primary intervention.

METHODS

We conducted a prospective nested case-control study involving 1,621 incident CHD cases and 1:1 matched controls. Lipid profiling of 161 lipid species for baseline fasting plasma was performed by liquid chromatography-mass spectrometry.

RESULTS

In search of CHD predictors, seven lipids were selected by elastic-net regression during over 90% of 1000 cross-validation repetitions, and the derived composite lipid score showed an adjusted odds ratio of 3.75 (95% confidence interval: 3.15, 4.46) per standard deviation increase. Addition of the lipid score into traditional risk model increased c-statistic to 0.736 by an increment of 0.077 (0.063, 0.092). From the seven lipids, we found mediation of CHD risk from baseline diabetes through sphingomyelin (SM) 41:1b with a considerable mediation proportion of 36.97% (P < 0.05). We further found that the positive associations of phosphatidylcholine (PC) 36:0a, SM 41:1b, lysophosphatidylcholine (LPC) 18:0 and LPC 20:3 were more pronounced among participants with higher exposure to fine particulate matter or its certain components, also to ozone for LPC 18:0 and LPC 20:3, while the negative association of cholesteryl ester (CE) 18:2 was attenuated with higher black carbon exposure (P < 0.05).

CONCLUSION

We identified seven lipid species with greatest predictive increment so-far achieved for incident CHD, and also found novel biomarkers for CHD risk stratification among individuals with diabetes or heavy air pollution exposure.

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.

Metabolomic alterations in the plasma of patients with various clinical manifestations of COVID-19

BACKGROUND

The metabolomic profiles of individuals with different clinical manifestations of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have not been clearly characterized.

METHODS

We performed metabolomics analysis of 166 individuals, including 62 healthy controls, 16 individuals with asymptomatic SARS-CoV-2 infection, and 88 patients with moderate (n = 42) and severe (n = 46) symptomatic 2019 coronavirus disease (COVID-19; 17 with short-term and 34 with long-term nucleic-acid test positivity). By examining differential expression, we identified candidate metabolites associated with different SARS-CoV-2 infection presentations. Functional and machine learning analyses were performed to explore the metabolites' functions and verify their candidacy as biomarkers.

RESULTS

A total of 417 metabolites were detected. We discovered 70 differentially expressed metabolites that may help differentiate asymptomatic infections from healthy controls and COVID-19 patients with different disease severity. Cyclamic acid and N-Acetylneuraminic Acid were identified to distinguish symptomatic infected patients and asymptomatic infected patients. Shikimic Acid, Glycyrrhetinic acid and 3-Hydroxybutyrate can supply significant insights for distinguishing short-term and long-term nucleic-acid test positivity.

CONCLUSION

Metabolomic profiling may highlight novel biomarkers for the identification of individuals with asymptomatic SARS-CoV-2 infection and further our understanding of the molecular pathogenesis of COVID-19.

Inflammatory age and its impact on age-related health in older Chinese adults

INTRODUCTION

A standardized measure for inflammaging is lacking. We introduced the inflammatory age (iAge) as a quantification method and explored its associations with age-related traits and diseases in an older Chinese cohort.

METHODS

Inflammatory markers including white blood cell count (WBC), neutrophils, lymphocytes, monocytes, C-reactive protein, platelets and albumin were measured. Quantitative real-time polymerase chain reaction was used to measure telomere length. Traditional multivariable linear, partial least squares, and logistic regression were used.

RESULTS

iAge was constructed based on WBC, neutrophils, monocytes and albumin, which were associated with telomere length independently. A higher iAge indicated a heavier aging-related inflammation burden. Per 1-year increase in iAge was associated with higher body mass index (β 0.86 (95 % CI 0.67, 1.05) kg/m2), waist circumference (β 2.37 (95 % CI 1.85, 2.90) cm), glycosylated hemoglobin A1c (β 0.06 (95 % CI 0.02, 0.10) %), systolic blood pressure (β 1.06 (95 % CI 0.10, 2.03) mmHg), triglycerides (β 0.05 (95 % CI 0.01, 0.08) mmol/L), 10-year cardiovascular diseases risk (β 0.05 (95 % CI 0.02, 0.08) %), diabetes (OR 1.22 (95 % CI 1.02, 1.46)), hypertension (OR 1.21 (95 % CI 1.04, 1.42)) and metabolic syndrome risks (OR 1.25 (95 % CI 1.04, 1.51)), and lower fasting plasma glucose (β -0.016 (95 % CI -0.024, -0.007) mmol/L), total cholesterol (β -0.06 (95 % CI -0.12, -0.01) mmol/L) and high-density lipoprotein cholesterol (β -0.05 (95 % CI -0.07, -0.03) mmol/L).

CONCLUSION

The newly introduced iAge, derived from inflammatory markers and telomere length, aligns with various metabolic dysfunctions and age-related disease risks, underscoring its potential ability in identifying aging-related phenotypes.

A metabolomic profile of biological aging in 250,341 individuals from the UK Biobank

The metabolomic profile of aging is complex. Here, we analyse 325 nuclear magnetic resonance (NMR) biomarkers from 250,341 UK Biobank participants, identifying 54 representative aging-related biomarkers associated with all-cause mortality. We conduct genome-wide association studies (GWAS) for these 325 biomarkers using whole-genome sequencing (WGS) data from 95,372 individuals and perform multivariable Mendelian randomization (MVMR) analyses, discovering 439 candidate "biomarker - disease" causal pairs at the nominal significance level. We develop a metabolomic aging score that outperforms other aging metrics in predicting short-term mortality risk and exhibits strong potential for discriminating aging-accelerated populations and improving disease risk prediction. A longitudinal analysis of 13,263 individuals enables us to calculate a metabolomic aging rate which provides more refined aging assessments and to identify candidate anti-aging and pro-aging NMR biomarkers. Taken together, our study has presented a comprehensive aging-related metabolomic profile and highlighted its potential for personalized aging monitoring and early disease intervention.

NMR metabolomics-guided DNA methylation mortality predictors

BACKGROUND

1H-NMR metabolomics and DNA methylation in blood are widely known biomarkers predicting age-related physiological decline and mortality yet exert mutually independent mortality and frailty signals.

METHODS

Leveraging multi-omics data in four Dutch population studies (N = 5238, ∼40% of which male) we investigated whether the mortality signal captured by 1H-NMR metabolomics could guide the construction of DNA methylation-based mortality predictors.

FINDINGS

We trained DNA methylation-based surrogates for 64 metabolomic analytes and found that analytes marking inflammation, fluid balance, or HDL/VLDL metabolism could be accurately reconstructed using DNA-methylation assays. Interestingly, a previously reported multi-analyte score indicating mortality risk (MetaboHealth) could also be accurately reconstructed. Sixteen of our derived surrogates, including the MetaboHealth surrogate, showed significant associations with mortality, independent of relevant covariates.

INTERPRETATION

The addition of our metabolic analyte-derived surrogates to the well-established epigenetic clock GrimAge demonstrates that our surrogates potentially represent valuable mortality signal.

FUNDING

BBMRI-NL, X-omics, VOILA, Medical Delta, NWO, ERC.

A systematic review of phenotypic and epigenetic clocks used for aging and mortality quantification in humans

Aging is the leading driver of disease in humans and has profound impacts on mortality. Biological clocks are used to measure the aging process in the hopes of identifying possible interventions. Biological clocks may be categorized as phenotypic or epigenetic, where phenotypic clocks use easily measurable clinical biomarkers and epigenetic clocks use cellular methylation data. In recent years, methylation clocks have attained phenomenal performance when predicting chronological age and have been linked to various age-related diseases. Additionally, phenotypic clocks have been proven to be able to predict mortality better than chronological age, providing intracellular insights into the aging process. This review aimed to systematically survey all proposed epigenetic and phenotypic clocks to date, excluding mitotic clocks (i.e., cancer risk clocks) and those that were modeled using non-human samples. We reported the predictive performance of 33 clocks and outlined the statistical or machine learning techniques used. We also reported the most influential clinical measurements used in the included phenotypic clocks. Our findings provide a systematic reporting of the last decade of biological clock research and indicate possible avenues for future research.

The role of brown adipose tissue in branched-chain amino acid clearance in people

Brown adipose tissue (BAT) in rodents appears to be an important tissue for the clearance of plasma branched-chain amino acids (BCAAs) contributing to improved metabolic health. However, the role of human BAT in plasma BCAA clearance is poorly understood. Here, we evaluate patients with prostate cancer who underwent positron emission tomography-computed tomography imaging after an injection of 18F-fluciclovine (L-leucine analog). Supraclavicular adipose tissue (AT; primary location of human BAT) has a higher net uptake rate for 18F-fluciclovine compared to subcutaneous abdominal and upper chest AT. Supraclavicular AT 18F-fluciclovine net uptake rate is lower in patients with obesity and type 2 diabetes. Finally, the expression of genes involved in BCAA catabolism is higher in the supraclavicular AT of healthy people with high BAT volume compared to those with low BAT volume. These findings support the notion that BAT can potentially function as a metabolic sink for plasma BCAA clearance in people.

Western diets and chronic diseases

'Westernization', which incorporates industrial, cultural and dietary trends, has paralleled the rise of noncommunicable diseases across the globe. Today, the Western-style diet emerges as a key stimulus for gut microbial vulnerability, chronic inflammation and chronic diseases, affecting mainly the cardiovascular system, systemic metabolism and the gut. Here we review the diet of modern times and evaluate the threat it poses for human health by summarizing recent epidemiological, translational and clinical studies. We discuss the links between diet and disease in the context of obesity and type 2 diabetes, cardiovascular diseases, gut and liver diseases and solid malignancies. We collectively interpret the evidence and its limitations and discuss future challenges and strategies to overcome these. We argue that healthcare professionals and societies must react today to the detrimental effects of the Western diet to bring about sustainable change and improved outcomes in the future.

Lifestyle factors and metabolomic aging biomarkers: Meta-analysis of cross-sectional and longitudinal associations in three prospective cohorts

Biological age uses biophysiological information to capture a person's age-related risk of adverse outcomes. MetaboAge and MetaboHealth are metabolomics-based biomarkers of biological age trained on chronological age and mortality risk, respectively. Lifestyle factors contribute to the extent chronological and biological age differ. The association of lifestyle factors with MetaboAge and MetaboHealth, potential sex differences in these associations, and MetaboAge's and MetaboHealth's sensitivity to lifestyle changes have not been studied yet. Linear regression analyses and mixed-effect models were used to examine the cross-sectional and longitudinal associations of scaled lifestyle factors with scaled MetaboAge and MetaboHealth in 24,332 middle-aged participants from the Doetinchem Cohort Study, Rotterdam Study, and UK Biobank. Random-effect meta-analyses were performed across cohorts. Repeated metabolomics measurements had a ten-year interval in the Doetinchem Cohort Study and a five-year interval in the UK Biobank. In the first study incorporating longitudinal information on MetaboAge and MetaboHealth, we demonstrate associations between current smoking, sleeping ≥8 hours/day, higher BMI, and larger waist circumference were associated with higher MetaboHealth, the latter two also with higher MetaboAge. Furthermore, adhering to the dietary and physical activity guidelines were inversely associated with MetaboHealth. Lastly, we observed sex differences in the associations between alcohol use and MetaboHealth.

Peripheral inflammation is associated with brain atrophy and cognitive decline linked to mild cognitive impairment and Alzheimer's disease

Inflammation is an important factor in Alzheimer's disease (AD). An NMR measurement in plasma, glycoprotein acetyls (GlycA), captures the overall level of protein production and glycosylation implicated in systemic inflammation. With its additional advantage of reducing biological variability, GlycA might be useful in monitoring the relationship between peripheral inflammation and brain changes relevant to AD. However, the associations between GlycA and these brain changes have not been fully evaluated. Here, we performed Spearman's correlation analyses to evaluate these associations cross-sectionally and determined whether GlycA can inform AD-relevant longitudinal measurements among participants in the Alzheimer's Disease Neuroimaging Initiative (n = 1506), with additional linear models and stratification analyses to evaluate the influences of sex or diagnosis status and confirm findings from Spearman's correlation analyses. We found that GlycA was elevated in AD patients compared to cognitively normal participants. GlycA correlated negatively with multiple concurrent regional brain volumes in females diagnosed with late mild cognitive impairment (LMCI) or AD. Baseline GlycA level was associated with executive function decline at 3-9 year follow-up in participants diagnosed with LMCI at baseline, with similar but not identical trends observed in the future decline of memory and entorhinal cortex volume. Results here indicated that GlycA is an inflammatory biomarker relevant to AD pathogenesis and that the stage of LMCI might be relevant to inflammation-related intervention.

DNA methylation clocks for estimating biological age in Chinese cohorts

Epigenetic clocks are accurate predictors of human chronological age based on the analysis of DNA methylation (DNAm) at specific CpG sites. However, a systematic comparison between DNA methylation data and other omics datasets has not yet been performed. Moreover, available DNAm age predictors are based on datasets with limited ethnic representation. To address these knowledge gaps, we generated and analyzed DNA methylation datasets from two independent Chinese cohorts, revealing age-related DNAm changes. Additionally, a DNA methylation aging clock (iCAS-DNAmAge) and a group of DNAm-based multi-modal clocks for Chinese individuals were developed, with most of them demonstrating strong predictive capabilities for chronological age. The clocks were further employed to predict factors influencing aging rates. The DNAm aging clock, derived from multi-modal aging features (compositeAge-DNAmAge), exhibited a close association with multi-omics changes, lifestyles, and disease status, underscoring its robust potential for precise biological age assessment. Our findings offer novel insights into the regulatory mechanism of age-related DNAm changes and extend the application of the DNAm clock for measuring biological age and aging pace, providing the basis for evaluating aging intervention strategies.

Adiposity, type 2 diabetes and atherosclerotic cardiovascular disease risk: Use and abuse of the body mass index

The worldwide prevalence of individuals with an elevated body weight has increased steadily over the past five decades. Billions of research dollars have been invested to improve our understanding of the causes and consequences of having an elevated body weight. All this knowledge has, however, failed to influence populational body weight trajectories of most countries around the world. Research on the definition of "obesity" has also evolved. Body mass index (BMI), the most commonly used tool to make its diagnosis, has major limitations. In this review article, we will highlight evidence from observational studies, genetic association studies and randomized clinical trials that have shown the remarkable inter-individual differences in the way humans store energy as body fat. Increasing evidence also suggests that, as opposed to weight inclusive, lifestyle-based approaches, weight-centric approaches advising people to simply eat less and move more are not sustainable for most people for long-term weight loss and maintenance. It is time to recognize that this outdated approach may have produced more harm than good. On the basis of pathophysiological, genetic and clinical evidence presented in this review, we propose that it may be time to shift away from the traditional clinical approach, which is BMI-centric. Rather, emphasis should be placed on actionable lifestyle-related risk factors aiming at improving overall diet quality and increasing physical activity level in the general population.

Blood protein assessment of leading incident diseases and mortality in the UK Biobank

The circulating proteome offers insights into the biological pathways that underlie disease. Here, we test relationships between 1,468 Olink protein levels and the incidence of 23 age-related diseases and mortality in the UK Biobank (n = 47,600). We report 3,209 associations between 963 protein levels and 21 incident outcomes. Next, protein-based scores (ProteinScores) are developed using penalized Cox regression. When applied to test sets, six ProteinScores improve the area under the curve estimates for the 10-year onset of incident outcomes beyond age, sex and a comprehensive set of 24 lifestyle factors, clinically relevant biomarkers and physical measures. Furthermore, the ProteinScore for type 2 diabetes outperforms a polygenic risk score and HbA1c-a clinical marker used to monitor and diagnose type 2 diabetes. The performance of scores using metabolomic and proteomic features is also compared. These data characterize early proteomic contributions to major age-related diseases, demonstrating the value of the plasma proteome for risk stratification.