Dynamics of biomarkers in relation to aging and mortality

Arbeev, Konstantin G.; Ukraintseva, Svetlana V.; Yashin, Anatoliy I.

doi:10.1016/j.mad.2016.04.010

Cited by 32 publications

(27 citation statements)

References 115 publications

(163 reference statements)

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“…Biomarkers may have a variety of biomedical applications, including the identification of individuals at risk of clinical outcomes, improving the evaluation of interventions aimed at modifying outcomes, and the elucidation of biological pathways that contribute to health or disease. In the field of aging, there have been numerous studies aimed at the identification of prognostic biomarkers of mortality (Arbeev, Ukraintseva & Yashin, ; Barron, Lara, White & Mathers, ). Most have utilized assays to evaluate specific candidate biomarkers believed to reflect relevant biological events, including many related to inflammation (Enomoto et al., ; Kabagambe et al., ; Reuben et al., ; Schnabel et al., ; Zuo et al., ).…”

Section: Introductionmentioning

confidence: 99%

High‐throughput serum proteomics for the identification of protein biomarkers of mortality in older men

et al. 2018

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SummaryThe biological perturbations associated with incident mortality are not well elucidated, and there are limited biomarkers for the prediction of mortality. We used a novel high‐throughput proteomics approach to identify serum peptides and proteins associated with 5‐year mortality in community‐dwelling men age ≥65 years who participated in a longitudinal observational study of musculoskeletal aging (Osteoporotic Fractures in Men: MrOS). In a discovery phase, serum specimens collected at baseline in 2473 men were analyzed using liquid chromatography–ion mobility–mass spectrometry, and incident mortality in the subsequent 5 years was ascertained by tri‐annual questionnaire. Rigorous statistical methods were utilized to identify 56 peptides (31 proteins) that were associated with 5‐year mortality. In an independent replication phase, selected reaction monitoring was used to examine 21 of those peptides in baseline serum from 750 additional men; 81% of those peptides remained significantly associated with mortality. Mortality‐associated proteins included a variety involved in inflammation or complement activation; several have been previously linked to mortality (e.g., C‐reactive protein, alpha 1‐antichymotrypsin) and others are not previously known to be associated with mortality. Other novel proteins of interest included pregnancy‐associated plasma protein, VE‐cadherin, leucine‐rich α‐2 glycoprotein 1, vinculin, vitronectin, mast/stem cell growth factor receptor, and Saa4. A panel of peptides improved the predictive value of a commonly used clinical predictor of mortality. Overall, these results suggest that complex inflammatory pathways, and proteins in other pathways, are linked to 5‐year mortality risk. This work may serve to identify novel biomarkers for near‐term mortality.

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Section: Introductionmentioning

confidence: 99%

High‐throughput serum proteomics for the identification of protein biomarkers of mortality in older men

et al. 2018

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“…We included 8 of Levine's original 10 biomarkers in the algorithm: albumin, alkaline phosphatase (log), blood urea nitrogen, creatinine (log), C-reactive protein (log), HbA1C, systolic blood pressure, and forced expiratory volume in 1 second (FEV1). We omitted total cholesterol because of evidence this biomarker shows different directions of association with aging in younger and older adults (Arbeev et al, 2016). Cytomegalovirus optical density was not available in the Understanding Society database.…”

Section: Decline In Physical Functioningmentioning

confidence: 99%

Quantification of the pace of biological aging in humans through a blood test: The DunedinPoAm DNA methylation algorithm

Belsky

Caspi

Arseneault

et al. 2020

Preprint

143

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ABSTRACTBiological aging is the gradual, progressive decline in system integrity that occurs with advancing chronological age, causing morbidity and disability. Measurements of the pace of aging are needed to serve as surrogate endpoints in trials of therapies designed to prevent disease by slowing biological aging. We report a blood DNA-methylation measure that is sensitive to variation in the pace of biological aging among individuals born the same year. We first modeled longitudinal change in 18 biomarkers tracking organ-system integrity across 12 years of follow-up in the Dunedin birth cohort. Rates of change in each biomarker were composited to form a measure of aging-related decline, termed Pace of Aging. Elastic-net regression was used to develop a DNA-methylation predictor of Pace of Aging, called DunedinPoAm for Dunedin (P)ace (o)f (A)ging (m)ethylation. Validation analyses showed DunedinPoAm was associated with functional decline in the Dunedin Study and more advanced biological age in the Understanding Society Study, predicted chronic disease and mortality in the Normative Aging Study, was accelerated by early-life adversity in the E-risk Study, and DunedinPoAm prediction was disrupted by caloric restriction in the CALERIE trial. DunedinPoAm generally outperformed epigenetic clocks. Findings provide proof-of-principle for DunedinPoAm as a single-time-point measure of a person’s pace of biological aging.

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“…However, chronological age (CA) itself might not be a reliable indicator of the functional deterioration because aging is thought to have heterogeneity which are reliant upon a balance between exposure to damaging properties and resiliency. 1,2 To simply represent the actual status of individual aging, the concept of biological age (BA) have been developed. BA is estimated as a single variable which merged the complex equations for estimating age using multiple biomarkers.…”

Section: Introductionmentioning

confidence: 99%

Prediction of biological age and all-cause mortality by 12-lead electrocardiogram in patients without structural heart disease

Hirota

Suzuki

Arita

et al. 2020

Preprint

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Background: The 12-lead electrocardiogram (ECG) is affected by not only the cardiovascular but also the non-cardiovascular status. Whether ECG can be the determinant of biological age (BA) and the gap between chronological age (CA) and ECG-predicted BA reflect differences in prognosis are unclear. Methods: In the Shinken Database 2010 – 2017 (n = 19170), 12-lead ECG was analyzed in 13005 patients excluding those with structural heart disease or having pacing beats, atrial or ventricular tachyarrhythmia, and indeterminate axis (R axis > 180˚) on index ECG. The prediction model of BA was developed by principal component analysis with 438 ECG parameters. The gap between ECG-predicted BA and CA was calculated (AgeDiff = ECG-predicted BA − CA). Results: The ECG-predicted BA was significantly correlated with CA (r = 0.967). Patients with a positively wide AgeDiff had a higher incidence of all-cause mortality compared to those with a narrow AgeDiff or those with negative AgeDiff. The risk of AgeDiff > 0 for all-cause mortality compared with AgeDiff ≤ 0 was 1.78 (95%CI: 1.00 − 3.16), which increased according to the aging and became the highest in patients with CA of 71 − 80 years. Conclusion: Our data suggested that 12-lead ECG can be a tool to estimate BA. The gap between ECG-predicted BA and CA allowed estimation of increased risk of all-cause mortality in patients without structural heart disease.

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Dynamics of biomarkers in relation to aging and mortality

Cited by 32 publications

References 115 publications

High‐throughput serum proteomics for the identification of protein biomarkers of mortality in older men

High‐throughput serum proteomics for the identification of protein biomarkers of mortality in older men

Quantification of the pace of biological aging in humans through a blood test: The DunedinPoAm DNA methylation algorithm

Prediction of biological age and all-cause mortality by 12-lead electrocardiogram in patients without structural heart disease

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