Evidence map›Paper›PMID 28396265›Full record

ReviewEBioMedicine2017

Biological Age Predictors.

Juulia Jylhävä, Nancy L Pedersen, Sara Hägg

Abstract readReview
In one paragraph

Review in EBioMedicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 687 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
687citing papers in PubMed, 3 pooled it
–field-weighted citation impact
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

687 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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627 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

3 authors.

Juulia JylhäväDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. Electronic address: juulia.jylhava@ki.se.
Nancy L PedersenDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. Electronic address: nancy.pedersen@ki.se.
Sara HäggDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. Electronic address: sara.hagg@ki.se.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The search for reliable indicators of biological age, rather than chronological age, has been ongoing for over three decades, and until recently, largely without success. Advances in the fields of molecular biology have increased the variety of potential candidate biomarkers that may be considered as biological age predictors. In this review, we summarize current state-of-the-art findings considering six potential types of biological age predictors: epigenetic clocks, telomere length, transcriptomic predictors, proteomic predictors, metabolomics-based predictors, and composite biomarker predictors. Promising developments consider multiple combinations of these various types of predictors, which may shed light on the aging process and provide further understanding of what contributes to healthy aging. Thus far, the most promising, new biological age predictor is the epigenetic clock; however its true value as a biomarker of aging requires longitudinal confirmation.

Indexed as

BiomarkersAgingAnimal ExperimentationAnimalsCellular SenescenceDisease SusceptibilityEpigenesis, GeneticHumansMetabolomicsProteomicsTelomereTelomere HomeostasisBiomarkersAgingBiomarkerEpigenetic clockPredictionTelomere length

Identifiers

PMID28396265
PMCPMC5514388

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.