A Metabolomic Aging Clock Using Human Cerebrospinal Fluid

Metabolite Predictors of Breast and Colorectal Cancer Risk in the Women's Health Initiative

Metabolomics has been used extensively to capture the exposome. We investigated whether prospectively measured metabolites provided predictive power beyond well-established risk factors among 758 women with adjudicated cancers [n = 577 breast (BC) and n = 181 colorectal (CRC)] and n = 758 controls with available specimens (collected mean 7.2 years prior to diagnosis) in the Women's Health Initiative Bone Mineral Density subcohort. Fasting samples were analyzed by LC-MS/MS and lipidomics in serum, plus GC-MS and NMR in 24 h urine. For feature selection, we applied LASSO regression and Super Learner algorithms. Prediction models were subsequently derived using logistic regression and Super Learner procedures, with performance assessed using cross-validation (CV). For BC, metabolites did not increase predictive performance over established risk factors (CV-AUCs~0.57). For CRC, prediction increased with the addition of metabolites (median CV-AUC across platforms increased from ~0.54 to ~0.60). Metabolites related to energy metabolism: adenosine, 2-hydroxyglutarate, N-acetyl-glycine, taurine, threonine, LPC (FA20:3), acetate, and glycerate; protein metabolism: histidine, leucic acid, isoleucine, N-acetyl-glutamate, allantoin, N-acetyl-neuraminate, hydroxyproline, and uracil; and dietary/microbial metabolites: myo-inositol, trimethylamine-N-oxide, and 7-methylguanine, consistently contributed to CRC prediction. Energy metabolism may play a key role in the development of CRC and may be evident prior to disease development.

Precious2GPT: the combination of multiomics pretrained transformer and conditional diffusion for artificial multi-omics multi-species multi-tissue sample generation

Synthetic data generation in omics mimics real-world biological data, providing alternatives for training and evaluation of genomic analysis tools, controlling differential expression, and exploring data architecture. We previously developed Precious1GPT, a multimodal transformer trained on transcriptomic and methylation data, along with metadata, for predicting biological age and identifying dual-purpose therapeutic targets potentially implicated in aging and age-associated diseases. In this study, we introduce Precious2GPT, a multimodal architecture that integrates Conditional Diffusion (CDiffusion) and decoder-only Multi-omics Pretrained Transformer (MoPT) models trained on gene expression and DNA methylation data. Precious2GPT excels in synthetic data generation, outperforming Conditional Generative Adversarial Networks (CGANs), CDiffusion, and MoPT. We demonstrate that Precious2GPT is capable of generating representative synthetic data that captures tissue- and age-specific information from real transcriptomics and methylomics data. Notably, Precious2GPT surpasses other models in age prediction accuracy using the generated data, and it can generate data beyond 120 years of age. Furthermore, we showcase the potential of using this model in identifying gene signatures and potential therapeutic targets in a colorectal cancer case study.

Principal component-based clinical aging clocks identify signatures of healthy aging and targets for clinical intervention

Clocks that measure biological age should predict all-cause mortality and give rise to actionable insights to promote healthy aging. Here we applied dimensionality reduction by principal component analysis to clinical data to generate a clinical aging clock (PCAge) identifying signatures (principal components) separating healthy and unhealthy aging trajectories. We found signatures of metabolic dysregulation, cardiac and renal dysfunction and inflammation that predict unsuccessful aging, and we demonstrate that these processes can be impacted using well-established drug interventions. Furthermore, we generated a streamlined aging clock (LinAge), based directly on PCAge, which maintains equivalent predictive power but relies on substantially fewer features. Finally, we demonstrate that our approach can be tailored to individual datasets, by re-training a custom clinical clock (CALinAge), for use in the Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE) study of caloric restriction. Our analysis of CALERIE participants suggests that 2 years of mild caloric restriction significantly reduces biological age. Altogether, we demonstrate that this dimensionality reduction approach, through integrating different biological markers, can provide targets for preventative medicine and the promotion of healthy aging.

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

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

Global metabolomics revealed deviations from the metabolic aging clock in colorectal cancer patients

Background: Markers of aging hold promise in the context of colorectal cancer (CRC) care. Utilizing high-resolution metabolomic profiling, we can unveil distinctive age-related patterns that have the potential to predict early CRC development. Our study aims to unearth a panel of aging markers and delve into the metabolomic alterations associated with aging and CRC. Methods: We assembled a serum cohort comprising 5,649 individuals, consisting of 3,002 healthy volunteers, 715 patients diagnosed with colorectal advanced precancerous lesions (APL), and 1,932 CRC patients, to perform a comprehensive metabolomic analysis. Results: We successfully identified unique age-associated patterns across 42 metabolic pathways. Moreover, we established a metabolic aging clock, comprising 9 key metabolites, using an elastic net regularized regression model that accurately estimates chronological age. Notably, we observed significant chronological disparities among the healthy population, APL patients, and CRC patients. By combining the analysis of circulative carcinoembryonic antigen levels with the categorization of individuals into the "hypo" metabolic aging subgroup, our blood test demonstrates the ability to detect APL and CRC with positive predictive values of 68.4% (64.3%, 72.2%) and 21.4% (17.8%, 25.9%), respectively. Conclusions: This innovative approach utilizing our metabolic aging clock holds significant promise for accurately assessing biological age and enhancing our capacity to detect APL and CRC.

Biological age as estimated by baseline circulating metabolites is associated with incident diabetes and mortality

OBJECTIVES

It is unclear how metabolomic assessment of biological aging performs in non-White populations and whether such an approach can predict future mortality. We aimed to evaluate the application of serum metabolomics combined with machine learning methodologies to predict incident diabetes and mortality in a Thai population.

DESIGN, SETTING AND PARTICIPANTS

We analyzed serum samples and mortality data over 11 years from among 454 participants with no previous history of diabetes and with a fasting plasma glucose ≥85th percentile (5.4 mmol/L) but <7 mmol/L.

MEASUREMENTS

Untargeted serum metabolomics were assessed using liquid chromatography/mass spectrometry. A deep artificial neural network was used to predict biological age based on serum metabolite profiles and chronological age.

RESULTS

The mean age of participants was 40.5 ± 6.4 years, and 70.8% were men. We found a significant positive correlation between metabolomic age and chronological age (r = 0.71, P < 0.001). After 5 years, 61 of 404 participants with available glycated hemoglobin status (15.1%) progressed to diabetes. Chronological age was associated with incident diabetes but was not significant (P = 0.08), after adjusting for BMI and sex. Metabolomic age was significantly related to incident diabetes after controlling for BMI and sex (P < 0.05). Over the 11-year follow-up, 10 participants died owing to non-accidental causes. When metabolomic age and chronological age were included together in the model, metabolomic age (but not chronological age) was associated with mortality, independent of age, sex, and BMI. Among all identifiable metabolites, beta-D-mannosylphosphodecaprenyl and phosphatidylserines were the five leading metabolites associated with mortality.

CONCLUSION

We concluded that serum metabolomic profile was associated with incident diabetes as well as mortality over our 11-year study period, which may render it potentially useful in assessing biological aging in humans.

The ticking of aging clocks

Computational models that measure biological age and aging rate regardless of chronological age are called aging clocks. The underlying counting mechanisms of the intrinsic timers of these clocks are still unclear. Molecular mediators and determinants of aging rate point to the key roles of DNA damage, epigenetic drift, and inflammation. Persistent DNA damage leads to cellular senescence and the senescence-associated secretory phenotype (SASP), which induces cytotoxic immune cell infiltration; this further induces DNA damage through reactive oxygen and nitrogen species (RONS). I discuss the possibility that DNA damage (or the response to it, including epigenetic changes) is the fundamental counting unit of cell cycles and cellular senescence, that ultimately accounts for cell composition changes and functional decline in tissues, as well as the key intervention points.

How do metabolic processes age: Evidence from human metabolomic studies

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

Small immunological clocks identified by deep learning and gradient boosting

Background

The aging process affects all systems of the human body, and the observed increase in inflammatory components affecting the immune system in old age can lead to the development of age-associated diseases and systemic inflammation.

Results

We propose a small clock model SImAge based on a limited number of immunological biomarkers. To regress the chronological age from cytokine data, we first use a baseline Elastic Net model, gradient-boosted decision trees models, and several deep neural network architectures. For the full dataset of 46 immunological parameters, DANet, SAINT, FT-Transformer and TabNet models showed the best results for the test dataset. Dimensionality reduction of these models with SHAP values revealed the 10 most age-associated immunological parameters, taken to construct the SImAge small immunological clock. The best result of the SImAge model shown by the FT-Transformer deep neural network model has mean absolute error of 6.94 years and Pearson ρ = 0.939 on the independent test dataset. Explainable artificial intelligence methods allow for explaining the model solution for each individual participant.

Conclusions

We developed an approach to construct a model of immunological age based on just 10 immunological parameters, coined SImAge, for which the FT-Transformer deep neural network model had proved to be the best choice. The model shows competitive results compared to the published studies on immunological profiles, and takes a smaller number of features as an input. Neural network architectures outperformed gradient-boosted decision trees, and can be recommended in the further analysis of immunological profiles.

Biomarkers of aging

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

Untargeted metabolomics analysis of cerebrospinal fluid in patients with leptomeningeal metastases from non-small cell lung cancer

OBJECTIVE

To explore and analyze the diagnostic value of metabolic markers in cerebrospinal fluid (CSF) in leptomeningeal metastases (LM) of non-small cell lung cancer (NSCLC).

METHODS

Forty-six CSF samples from patients with NSCLC-LM were collected. Another 48 CSF samples from patients with nonmalignant neurological diseases were selected as control group. Metabolomic analysis of CSF was performed by high-performance liquid chromatography-mass spectrometry. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were applied for modeling. A multi-criteria evaluation system (variable importance value >1, multiple of change >2 and P < 0.05 for univariate analysis) was used to find differential metabolites between two groups. The subject working characteristic curves and pathway enrichment analysis were used to screen metabolites and pathways associated with NSCLC-LM.

RESULTS

The PCA model and OPLS-DA model showed good overall data quality. Thirty endogenous differential metabolites were screened, and six potential biomarkers were further identified, including tyrosine (t = 3.37, P = 0.024, AUC = 0.967), phenylalanine (t = 3.98, P < 0.001, AUC = 0.992), pyruvate (t = 4.48, P < 0.001, AUC = 0.976), tryptophan (t = -2.5, P = 0.014, AUC = 0.935), adenosine monophosphate (t = -6.13, P < 0.001, AUC = 0.932) and glucose (t = -4.00, P < 0.001, AUC = 0.993). Thirty differential metabolites screened were subjected to metabolic pathway enrichment analysis and matched to 20 relevant metabolic pathways, of which the four most likely to cause metabolite changes were as follows: glycolysis and sugar metabolism synthesis, pyruvate metabolism, phenylalanine metabolism, and phenylalanine, tyrosine and tryptophan biosynthesis.

CONCLUSIONS

Untargeted metabolomics can effectively screen for CSF metabolites specific to NSCLC-LM patients, and six potential metabolites and their metabolic pathways might be involved in the pathogenesis of NSCLC-LM.

Serum vitamin levels in multiple system atrophy: A case-control study

Aim

There is increasing evidence suggesting that vitamins may play important roles in the pathogenesis of multiple system atrophy (MSA). The purpose of this study was to detect the changes of serum vitamin levels and investigate their correlation with disease severity in MSA patients.

Methods

In this cross-sectional study, 244 MSA patients, 200 Parkinson's disease (PD) patients and 244 age-gender matched healthy controls were recruited. Serum vitamin levels were measured, including vitamin A, B1, B2, B9 (folate), B12, C, D, and E. Relevant clinical scales were used to assess the disease severity of MSA patients.

Results

Compared with the healthy controls, decreased serum folate levels and increased serum vitamin A and C levels were detected in MSA patients. Similar differences were also observed in the gender-based subgroup analysis. There were no differences detected between MSA and PD patients. In MSA patients, significant correlation was found between vitamin A, folate, or vitamin C and relevant clinical scales or laboratory findings. In addition, ROC analysis showed potential diagnostic value of the combination of vitamin A, folate, and vitamin C in distinguishing MSA patients from healthy controls.

Conclusion

There were significant changes in the blood vitamin spectrums of MSA patients, suggesting that dysregulation of vitamins homeostasis might play an important role in the pathogenesis of MSA.

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

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

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

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

Recent Advances in Studying Age-Associated Lipids Alterations and Dietary Interventions in Mammals

Lipids are involved in a broad spectrum of canonical biological functions, from energy supply and storage by triacylglycerols to membrane formation by sphingolipids, phospholipids and glycolipids. Because of this wide range of functions, there is an overlap between age-associated processes and lipid pathways. Lipidome analysis revealed age-related changes in the lipid composition of various tissues in mice and humans, which were also influenced by diet and gender. Some changes in the lipid profile can be linked to the onset of age-related neurodegenerative diseases like Alzheimer’s disease. Furthermore, the excessive accumulation of lipid storage organelles, lipid droplets, has significant implications for the development of inflammaging and non-communicable age-related diseases. Dietary interventions such as caloric restriction, time-restrictive eating, and lipid supplementation have been shown to improve pertinent health metrics or even extend life span and thus modulate aging processes.

LipidClock: A Lipid-Based Predictor of Biological Age

Complexity is a fundamental feature of biological systems. Omics techniques like lipidomics can simultaneously quantify many thousands of molecules, thereby directly capturing the underlying biological complexity. However, this approach transfers the original biological complexity to the resulting datasets, posing challenges in data reduction and analysis. Aging is a prime example of a process that exhibits complex behaviour across multiple scales of biological organisation. The aging process is characterised by slow, cumulative and detrimental changes that are driven by intrinsic biological stochasticity and mediated through non-linear interactions and feedback within and between these levels of organization (ranging from metabolites, macromolecules, organelles and cells to tissue and organs). Only collectively and over long timeframes do these changes manifest as the exponential increases in morbidity and mortality that define biological aging, making aging a problem more difficult to study than the aetiologies of specific diseases. But aging's time dependence can also be exploited to extract key insights into its underlying biology. Here we explore this idea by using data on changes in lipid composition across the lifespan of an organism to construct and test a LipidClock to predict biological age in the nematode Caenorhabdits elegans. The LipidClock consist of a feature transformation via Principal Component Analysis followed by Elastic Net regression and yields and Mean Absolute Error of 1.45 days for wild type animals and 4.13 days when applied to mutant strains with lifespans that are substantially different from that of wild type. Gompertz aging rates predicted by the LipidClock can be used to simulate survival curves that are in agreement with those from lifespan experiments.

Predictive Modeling of Alzheimer's and Parkinson's Disease Using Metabolomic and Lipidomic Profiles from Cerebrospinal Fluid

In recent years, metabolomics has been used as a powerful tool to better understand the physiology of neurodegenerative diseases and identify potential biomarkers for progression. We used targeted and untargeted aqueous, and lipidomic profiles of the metabolome from human cerebrospinal fluid to build multivariate predictive models distinguishing patients with Alzheimer's disease (AD), Parkinson's disease (PD), and healthy age-matched controls. We emphasize several statistical challenges associated with metabolomic studies where the number of measured metabolites far exceeds sample size. We found strong separation in the metabolome between PD and controls, as well as between PD and AD, with weaker separation between AD and controls. Consistent with existing literature, we found alanine, kynurenine, tryptophan, and serine to be associated with PD classification against controls, while alanine, creatine, and long chain ceramides were associated with AD classification against controls. We conducted a univariate pathway analysis of untargeted and targeted metabolite profiles and find that vitamin E and urea cycle metabolism pathways are associated with PD, while the aspartate/asparagine and c21-steroid hormone biosynthesis pathways are associated with AD. We also found that the amount of metabolite missingness varied by phenotype, highlighting the importance of examining missing data in future metabolomic studies.

Utilization of Host and Microbiome Features in Determination of Biological Aging

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