NMR metabolomic modeling of age and lifespan: A multicohort analysis

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.

Association of metabolomic aging acceleration and body mass index phenotypes with mortality and obesity-related morbidities

This study aims to investigate the association between metabolomic aging acceleration and body mass index (BMI) phenotypes with mortality and obesity-related morbidities (ORMs). 85,458 participants were included from the UK Biobank. Metabolomic age was determined using 168 metabolites. The Chronological Age-Adjusted Gap was used to define metabolomically younger (MY) or older (MO) status. BMI categories were defined as normal weight, overweight, and obese. Participants were categorized into MY normal weight (MY-NW, reference), MY overweight (MY-OW), MY obesity (MY-OB), MO normal weight (MO-NW), MO overweight (MO-OW), and MO obesity (MO-OB). Mortality and 43 ORMs were identified through death registries and hospitalization records. Compared with MY-NW phenotype, MO-OB phenotype yielded increased risk of mortality and 32 ORMs, followed by MO-OW with mortality and 27 ORMs, MY-OB with mortality and 26 ORMs, MY-OW with 21 ORMs, and MO-NW with mortality and 14 ORMs. Consistently, MO-OB phenotype showed the highest risk of developing obesity-related multimorbidities, followed by MY-OB phenotype, MO-OW phenotype, MY-OW phenotype, and MO-NW phenotype. Additive interactions were found between metabolomic aging acceleration and obesity on CVD-specific mortality and 10 ORMs. Additionally, individuals with metabolomic aging acceleration had higher mortality and cardiovascular risk, even within the same BMI category. These findings suggest that metabolomic aging acceleration could help stratify mortality and ORMs risk across different BMI categories. Weight management should also be extended to individuals with overweight or obesity even in the absence of accelerated metabolomic aging, as they face increased healthy risk compared with MY-NW individuals. Additionally, delaying metabolic aging acceleration is needed for all metabolomically older groups, including those with normal weight.

Assessing Metabolic Ageing via DNA Methylation Surrogate Markers: A Multicohort Study in Britain, Ireland and the USA

Metabolomics and epigenomics have been used to develop 'ageing clocks' that assess biological age and identify 'accelerated ageing'. While metabolites are subject to short-term variation, DNA methylation (DNAm) may capture longer-term metabolic changes. We aimed to develop a hybrid DNAm-metabolic clock using DNAm as metabolite surrogates ('DNAm-metabolites') for age prediction. Within the UK Airwave cohort (n = 820), we developed DNAm metabolites by regressing 594 metabolites on DNAm and selected 177 DNAm metabolites and 193 metabolites to construct 'DNAm-metabolic' and 'metabolic' clocks. We evaluated clocks in their age prediction and association with noncommunicable disease risk factors. We additionally validated the DNAm-metabolic clock for the prediction of age and health outcomes in The Irish Longitudinal Study of Ageing (TILDA, n = 488) and the Health and Retirement Study (HRS, n = 4018). Around 70% of DNAm metabolites showed significant metabolite correlations (Pearson's r: > 0.30, p < 10-4) in the Airwave test set and overall stronger age associations than metabolites. The DNAm-metabolic clock was enriched for metabolic traits and was associated (p < 0.05) with male sex, heavy drinking, anxiety, depression and trauma. In TILDA and HRS, the DNAm-metabolic clock predicted age (r = 0.73 and 0.69), disability and gait speed (p < 0.05). In HRS, it additionally predicted time to death, diabetes, cardiovascular disease, frailty and grip strength. DNAm metabolite surrogates may facilitate metabolic studies using only DNAm data. Clocks built from DNAm metabolites provided a novel approach to assess metabolic ageing, potentially enabling early detection of metabolic-related diseases for personalised medicine.

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.

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.

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.