Evidence map›Paper›PMID 32501290›Full record

ArticleAging2020

Modelling biological age based on plasma peptides in Han Chinese adults.

Weijie Cao, Deqiang Zheng, Guohua Wang, Jie Zhang, Siqi Ge, Manjot Singh, Hao Wang, Manshu Song, Dong Li, Wei Wang and 2 more

Open access · greenAbstract read
In one paragraph

Article in Aging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
0.6field-weighted citation impact, top 34% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 25 citations in OpenAlex.

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  14. Cross-platform proteomics signatures of extreme old age.bioRxiv : the preprint server for biology · 2024
    Article
  15. Review
  16. The Complement System, Aging, and Aging-Related Diseases.International journal of molecular sciences · 2022
    Review
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  18. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 4 institutions in 2 countries.

Weijie CaoDepartment of Epidemiology and Health Statistics, School of Public Health, Beijing Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China.
Deqiang ZhengDepartment of Epidemiology and Health Statistics, School of Public Health, Beijing Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China.
Guohua WangThe Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China.
Jie ZhangDepartment of Epidemiology and Health Statistics, School of Public Health, Beijing Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China.
Siqi GeBeijing Neurosurgical Institute, Beijing 100070, China.
Manjot SinghSchool of Medical and Health Sciences, Edith Cowan University, Perth 6027, Australia.
Hao WangDepartment of Epidemiology and Health Statistics, School of Public Health, Beijing Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China.
Manshu SongSchool of Medical and Health Sciences, Edith Cowan University, Perth 6027, Australia.
Dong LiSchool of Public Health, Shandong First Medical University and Academy of Medical Sciences of Shandong Province, Tai'an 271016, China.
Wei WangDepartment of Epidemiology and Health Statistics, School of Public Health, Beijing Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China.
Xizhu XuSchool of Public Health, Shandong First Medical University and Academy of Medical Sciences of Shandong Province, Tai'an 271016, China.
Youxin WangDepartment of Epidemiology and Health Statistics, School of Public Health, Beijing Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China.
Capital Medical University · CNShandong First Medical University · CNEdith Cowan University · AUBeijing Institute of Neurosurgery · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Models, BiologicalAdultAgingAmino Acid SequenceApolipoprotein A-IAsian PeopleBiomarkersBRCA2 ProteinComplement C3Complement C4aCross-Sectional StudiesFemaleFibrinogenHumansLinear ModelsMaleApolipoprotein A-IBiomarkersBRCA2 ProteinComplement C3Complement C4aFibrinogenPeptidesageingbiological agemultiple linear regressionplasma peptideprimary prevention

Identifiers

PMID32501290
PMCPMC7346055
OpenAlexW3094098027

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.