Document Type

Journal Article

Publication Title

Aging

Publisher

Impact Journals

School

School of Medical and Health Sciences

RAS ID

31740

Funders

This work was supported by grants from the National Natural Science Foundation of China (NSFC) (81673247, 81872682 and 81773527), the Joint Project of the NSFC and the Australian National Health and Medical Research Council (NHMRC) (NSFC 81561128020-NHMRC APP1112767).

Grant Number

NHMRC Number : APP1112767

Grant Link

http://purl.org/au-research/grants/nhmrc/1112767

Comments

Cao, W., Zheng, D., Wang, G., Zhang, J., Ge, S., Singh, M., ... & Xu, X. (2020). Modelling biological age based on plasma peptides in Han Chinese adults. Aging, 12(11). https://doi.org/10.18632/aging.103286

Abstract

Age-related disease burdens increased over time, and whether plasma peptides can be used to accurately predict age in order to explain the variation in biological indicators remains inadequately understood. Here we first developed a biological age model based on plasma peptides in 1890 Chinese Han adults. Based on mass spectrometry, 84 peptides were detected with masses in the range of 0.6-10.0 kDa, and 13 of these peptides were identified as known amino acid sequences. Five of these thirteen plasma peptides, including fragments of apolipoprotein A-I (m/z 2883.99), fibrinogen alpha chain (m/z 3060.13), complement C3 (m/z 2190.59), complement C4-A (m/z 1898.21), and breast cancer type 2 susceptibility protein (m/z 1607.84) were finally included in the final model by performing a multivariate linear regression with stepwise selection. This biological age model accounted for 72.3% of the variation in chronological age. Furthermore, the linear correlation between the actual age and biological age was 0.851 (95% confidence interval: 0.836-0.864) and 0.842 (95% confidence interval: 0.810-0.869) in the training and validation sets, respectively. The biological age based on plasma peptides has potential positive effects on primary prevention, and its biological meaning warrants further investigation.

DOI

10.18632/aging.103286

Creative Commons License

Creative Commons Attribution 3.0 License
This work is licensed under a Creative Commons Attribution 3.0 License.

Research Themes

Health

Priority Areas

Multidisciplinary biological approaches to personalised disease diagnosis, prognosis and management

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