Measuring biological age using metabolomics

Robinson, Oliver; Lau, Chung-Ho E

doi:10.18632/aging.104216

Cited by 8 publications

(6 citation statements)

References 8 publications

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“…An alternative option would be to collect wound exudate from negative pressure therapy 5 as this would enable multiple collections and monitoring of metabolic changes in inflammatory and healing processes, as the wound heals. In addition, there have been some studies in other areas which indicate that the blood metabolome is associated with age, [46][47][48] which means the same person will have different metabolomic profiling at different ages. Therefore, our metabolomic results are limited to paediatric patients and may not be applicable to older burn patients.…”

Section: Discussionmentioning

confidence: 99%

Local burn wound environment versus systemic response: Comparison of proteins and metabolites

Zang

Heath

Etican

et al. 2022

Wound Repair Regeneration

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In this study, paired blood plasma (BP) and blister fluid (BF) samples from five paediatric burn patients were analysed using mass spectrometry to compare their protein and metabolite composition. The relative quantification of proteins was achieved through a label‐free data independent acquisition mode. The relative quantification of metabolites was achieved using a Shimadzu Smart Metabolite Database gas chromatography mass spectrometry (GCMS) targeted assay. In total, 562 proteins and 141 individual metabolites were identified in the samples. There was 81% similarity in the proteins present in the BP and BF, with 50 and 54 unique proteins found in each sample type respectively. BF contained keratinocyte proliferation‐related proteins and blood plasma contained abundant blood clotting proteins and apolipoproteins. BF contained more carbohydrates and less alpha‐hydroxy acid metabolites than the BP. In this study, there were unique proteins and metabolites in BF and BP which were reflective of the local wound environment and systemic environments respectively. The results from this study demonstrate that the biomolecule content of BF is mostly the same as blood, but it also contains information specific to the local wound environment.

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Section: Discussionmentioning

confidence: 99%

Local burn wound environment versus systemic response: Comparison of proteins and metabolites

Zang

Heath

Etican

et al. 2022

Wound Repair Regeneration

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“…The copyright holder for this preprint this version posted February 11, 2024. ; https://doi.org/10.1101/2024.02.10.24302617 doi: medRxiv preprint Although chronological age prediction is valuable in fields such as forensics (Vidaki et al, 2017), it is of limited use in population health and geroscience, given that chronological age is non-modifiable (Robinson & Lau, 2020). A perfect prediction model would merely tell us about chronological, not biological, age (Nakamura et al, 1988).…”

Section: (Which Was Not Certified By Peer Review)mentioning

confidence: 99%

Metabolomic Age (MileAge) predicts health and lifespan: a comparison of multiple machine learning algorithms

Mutz,

Iniesta,

Lewis

2024

Preprint

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BackgroundMolecular ageing clocks estimate an individual’s biological age. Our aim was to compare multiple machine learning algorithms for developing ageing clocks from nuclear magnetic resonance (NMR) spectroscopy metabolomics data. To validate how well each ageing clock predicted age-related morbidity and lifespan, we assessed their associations with multiple health indicators (e.g., telomere length and frailty) and all-cause mortality.MethodsThe UK Biobank is a multicentre observational health study of middle-aged and older adults. The Nightingale Health platform was used to quantify 168 circulating plasma metabolites at the baseline assessment from 2006 to 2010. We trained and internally validated 17 machine learning algorithms including regularised regression, kernel-based methods and ensembles. Metabolomic age (MileAge) delta was defined as the difference between predicted and chronological age.ResultsThe sample included 101,359 participants (mean age = 56.53 years, SD = 8.10). Most metabolite levels varied by chronological age. The nested cross-validation mean absolute error (MAE) ranged from 5.31 to 6.36 years. 31.76% of participants had an age-bias adjusted MileAge more than one standard deviation (3.75 years) above or below the mean. A Cubist rule-based regression model overall performed best at predicting health outcomes. The all-cause mortality hazard ratio (HR) comparing individuals with a MileAge delta more than one standard deviation above and below the mean was HR = 1.52 (95% CI 1.41-1.64,p< 0.001) over a median follow-up of 13.87 years. Individuals with an older MileAge were frailer, had shorter telomeres, were more likely to have a chronic illness and rated their health worse.ConclusionsMetabolomic ageing clocks derived from multiple machine learning algorithms were robustly associated with health indicators and mortality. Our metabolomic ageing clock (MileAge) derived from a Cubist rule-based regression model can be incorporated in research, and may find applications in health assessments, risk stratification and proactive health tracking.

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“…Molecular footprints of ageing include methylated DNA [44][45][46], gene expression profiles [47,48], circulating proteins [49,50], metabolites [51,52], biochemical markers [53,54] and microbiota [55]. In the last decade, these composite analyses have generated a multitude of ageing clocks that reflect different aspects of ageing and that identify organ-specific temporal signatures [56,57] and correlate with various health outcomes.…”

Section: Biological Assessment Of Ageing Effectsmentioning

confidence: 99%

Ageing and cancer: a research gap to fill

2022

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

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Measuring biological age using metabolomics

Cited by 8 publications

References 8 publications

Local burn wound environment versus systemic response: Comparison of proteins and metabolites

Local burn wound environment versus systemic response: Comparison of proteins and metabolites

Metabolomic Age (MileAge) predicts health and lifespan: a comparison of multiple machine learning algorithms

Ageing and cancer: a research gap to fill

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