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The plasma metabolome as a predictor of biological aging in humans.

ABSTRACT: Chronological age is an important predictor of morbidity and mortality; however, it is unable to account for heterogeneity in the decline of physiological function and health with advancing age. Several attempts have been made to instead define a "biological age" using multiple physiological parameters in order to account for variation in the trajectory of human aging; however, these methods require technical expertise and are likely too time-intensive and costly to be implemented into clinical practice. Accordingly, we sought to develop a metabolomic signature of biological aging that could predict changes in physiological function with the convenience of a blood sample. A weighted model of biological age was generated based on multiple clinical and physiological measures in a cohort of healthy adults and was then applied to a group of healthy older adults who were tracked longitudinally over a 5-10-year timeframe. Plasma metabolomic signatures were identified that were associated with biological age, including some that could predict whether individuals would age at a faster or slower rate. Metabolites most associated with the rate of biological aging included amino acid, fatty acid, acylcarnitine, sphingolipid, and nucleotide metabolites. These results not only have clinical implications by providing a simple blood-based assay of biological aging, but also provide insight into the molecular mechanisms underlying human healthspan.

SUBMITTER: Johnson LC

PROVIDER: S-EPMC6925078 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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The plasma metabolome as a predictor of biological aging in humans.

Johnson Lawrence C LC Parker Keli K Aguirre Brandon F BF Nemkov Travis G TG D'Alessandro Angelo A Johnson Sarah A SA Seals Douglas R DR Martens Christopher R CR

GeroScience 20191109 6

Chronological age is an important predictor of morbidity and mortality; however, it is unable to account for heterogeneity in the decline of physiological function and health with advancing age. Several attempts have been made to instead define a "biological age" using multiple physiological parameters in order to account for variation in the trajectory of human aging; however, these methods require technical expertise and are likely too time-intensive and costly to be implemented into clinical ...[more]

PMID: 31707594

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Project description:Measuring individual circadian phase is important to diagnose and treat circadian rhythm sleep-wake disorders and circadian misalignment, inform chronotherapy, and advance circadian science. Initial findings using blood transcriptomics to predict the circadian phase marker dim-light melatonin onset (DLMO) show promise. Alternatively, there are limited attempts using metabolomics to predict DLMO and no known omics-based biomarkers predict dim-light melatonin offset (DLMOff). We analyzed the human plasma metabolome during adequate and insufficient sleep to predict DLMO and DLMOff using one blood sample. Sixteen (8 male/8 female) healthy participants aged 22.4 ± 4.8 years (mean ± SD) completed an in-laboratory study with 3 baseline days (9 h sleep opportunity/night), followed by a randomized cross-over protocol with 9-h adequate sleep and 5-h insufficient sleep conditions, each lasting 5 days. Blood was collected hourly during the final 24 h of each condition to independently determine DLMO and DLMOff. Blood samples collected every 4 h were analyzed by untargeted metabolomics and were randomly split into training (68%) and test (32%) sets for biomarker analyses. DLMO and DLMOff biomarker models were developed using partial least squares regression in the training set followed by performance assessments using the test set. At baseline, the DLMOff model showed the highest performance (0.91 R2 and 1.1 ± 1.1 h median absolute error ± interquartile range [MdAE ± IQR]), with significantly (p < 0.01) lower prediction error versus the DLMO model. When all conditions (baseline, 9 h, and 5 h) were included in performance analyses, the DLMO (0.60 R2; 2.2 ± 2.8 h MdAE; 44% of the samples with an error under 2 h) and DLMOff (0.62 R2; 1.8 ± 2.6 h MdAE; 51% of the samples with an error under 2 h) models were not statistically different. These findings show promise for metabolomics-based biomarkers of circadian phase and highlight the need to test biomarkers that predict multiple circadian phase markers under different physiological conditions.

Project description:The paradigm of chronobiology is based almost wholly upon the daily biological clock, or circadian rhythm, which has been the focus of intense molecular, cellular, pharmacological, and behavioral, research. However, the circadian rhythm does not explain biological timings related to fundamental aspects of life history such as rates of tissue/organ/body size development and control of the timing of life stages such as gestation length, age at maturity, and lifespan. This suggests that another biological timing mechanism is at work. Here we focus on a "many days" (multidien) chronobiological period first observed as enigmatic recurring growth lines in developing mammalian tooth enamel that is strongly associate with all adult tissue, organ, and body masses as well as life history attributes such as gestation length, age at maturity, weaning, and lifespan, particularly among the well studied primates. Yet, knowledge of the biological factors regulating the patterning of mammalian life, such as the development of body size and life history structure, does not exist. To identify underlying molecular mechanisms we performed metabolome and genome analyses from blood plasma in domestic pigs. We show that blood plasma metabolites and small non-coding RNA (sncRNA) drawn from 33 domestic pigs over a two-week period strongly oscillate on a 5-day multidien rhythm, as does the pig enamel rhythm. Metabolomics and genomics pathway analyses actually reveal two 5-day rhythms, one related to growth in which biological functions include cell proliferation, apoptosis, and transcription regulation/protein synthesis, and another 5-day rhythm related to degradative pathways that follows three days later. Our results provide experimental confirmation of a 5-day multidien rhythm in the domestic pig linking the periodic growth of enamel with oscillations of the metabolome and genome. This association reveals a new class of chronobiological rhythm and a snapshot of the biological bases that regulate mammalian growth, body size, and life history.

Dataset Information

The plasma metabolome as a predictor of biological aging in humans.

Publications

The plasma metabolome as a predictor of biological aging in humans.

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