Evidence map›Paper›PMID 26166298›Full record

ArticleBMC medicine2015

Age-related frailty and its association with biological markers of ageing.

Arnold Mitnitski, Joanna Collerton, Carmen Martin-Ruiz, Carol Jagger, Thomas von Zglinicki, Kenneth Rockwood, Thomas B L Kirkwood

Open access · goldAbstract read
In one paragraph

Article in BMC medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 139 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
139citing papers in PubMed, 4 pooled it
15.8field-weighted citation impact, top 1% 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

139 citing papers in PubMed, 4 syntheses or guidelines pooled it, 286 citations in OpenAlex.

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  8. Functional, molecular, and digital measurements of biological age.The Journal of clinical investigation · 2026
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79 more citing papers are in PubMed but not listed here.

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

7 authors at 2 institutions in 2 countries.

Arnold MitnitskiDepartment of Medicine, Dalhousie University, Halifax, NS, B3H 2E1, Canada. arnold.mitnitski@dal.ca.
Joanna CollertonInstitute of Health and Society and Newcastle University Institute for Ageing, Newcastle upon Tyne, NE4 5PL, UK. joanna.collerton@ncl.ac.uk.
Carmen Martin-RuizInstitute of Neuroscience and Newcastle University Institute for Ageing, Newcastle upon Tyne, NE4 5PL, UK. carmen.martin-ruiz@ncl.ac.uk.
Carol JaggerInstitute of Health and Society and Newcastle University Institute for Ageing, Newcastle upon Tyne, NE4 5PL, UK. carol.jagger@ncl.ac.uk.
Thomas von ZglinickiInstitute for Cell and Molecular Biosciences and Newcastle University Institute for Ageing, Newcastle upon Tyne, NE4 5PL, UK. t.vonzglinicki@ncl.ac.uk.
Kenneth RockwoodDivision of Geriatric Medicine, Dalhousie University, Halifax, NS, B3H 2E1, Canada. kenneth.rockwood@dal.ca.
Thomas B L KirkwoodInstitute for Cell and Molecular Biosciences and Newcastle University Institute for Ageing, Newcastle upon Tyne, NE4 5PL, UK. tom.kirkwood@ncl.ac.uk.
Newcastle University · GBDalhousie University · CA

Funding

Medical Research Council G0500997Medical Research Council G0601333The Dunhill Medical Trust R124/0509
6 · The paper itself

Abstract

backgroundThe relationship between age-related frailty and the underlying processes that drive changes in health is currently unclear. Considered individually, most blood biomarkers show only weak relationships with frailty and ageing. Here, we examined whether a biomarker-based frailty index (FI-B) allowed examination of their collective effect in predicting mortality compared with individual biomarkers, a clinical deficits frailty index (FI-CD), and the Fried frailty phenotype.

methodsWe analyzed baseline data and up to 7-year mortality in the Newcastle 85+ Study (n = 845; mean age 85.5). The FI-B combined 40 biomarkers of cellular ageing, inflammation, haematology, and immunosenescence. The Kaplan-Meier estimator was used to stratify participants into FI-B risk strata. Stability of the risk estimates for the FI-B was assessed using iterative, random subsampling of the 40 FI-B items. Predictive validity was tested using Cox proportional hazards analysis and discriminative ability by the area under receiver operating characteristic (ROC) curves.

resultsThe mean FI-B was 0.35 (SD, 0.08), higher than the mean FI-CD (0.22; SD, 0.12); no participant had an FI-B score <0.12. Higher values of each FI were associated with higher mortality risk. In a sex-adjusted model, each one percent increase in the FI-B increased the hazard ratio by 5.4 % (HR, 1.05; CI, 1.04-1.06). The FI-B was more powerful for mortality prediction than any individual biomarker and was robust to biomarker substitution. The ROC analysis showed moderate discriminative ability for 7-year mortality (AUC for FI-CD = 0.71 and AUC for FI-B = 0.66). No individual biomarker's AUC exceeded 0.61. The AUC for combined FI-CD/FI-B was 0.75.

conclusionsMany biological processes are implicated in ageing. The systemic effects of these processes can be elucidated using the frailty index approach, which showed here that subclinical deficits increased the risk of death. In the future, blood biomarkers may indicate the nature of the underlying causal deficits leading to age-related frailty, thereby helping to expose targets for early preventative interventions.

Indexed as

AgedAged, 80 and overAgingBiomarkersBlood Chemical AnalysisFemaleFrail ElderlyGeriatric AssessmentHumansMaleReference ValuesBiomarkers

Identifiers

PMID26166298
PMCPMC4499935
OpenAlexW1554335923

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.