Evidence map›Paper›PMID 41351063›Full record

ArticleBMC genomics2025

EnsembleAge clock: a reliable and robust epigenetic age clock service reveals epigenetic age acceleration in opioid-overdosed brains.

Akshay Anand, Yash Agarwal, Tanisha Gupta, Jason Lin, Mirna Ghemrawi, Glenn S Gerhard, Hayan Lee

Abstract read
In one paragraph

Article in BMC genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Akshay Anand *Cancer Epigenetics Institute, Nuclear Dynamics and Cancer Program, Fox Chase Cancer Center, Philadelphia, PA, USA.
Yash Agarwal *Cancer Epigenetics Institute, Nuclear Dynamics and Cancer Program, Fox Chase Cancer Center, Philadelphia, PA, USA.
Tanisha GuptaCollege of Computing, Georgia Institute of Technology, Atlanta, GA, USA.
Jason LinComputational Biology, Brown University, Providence, RI, USA.
Mirna GhemrawiCenter for Forensic Science Research and Education, Florida International University, Horsham, PA, USA.
Glenn S GerhardDepartment of Medical Genetics and Molecular Biochemistry, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA.
Hayan LeeCancer Epigenetics Institute, Nuclear Dynamics and Cancer Program, Fox Chase Cancer Center, Philadelphia, PA, USA. Hayan.Lee@fccc.edu.

Funding

WORD PROCESSING CENTER--COREP30CA006927 · NCI · RESEARCH INST OF FOX CHASE CAN CTR · PI Margaret von Mehren · 1985 to 2026
$138.8M
NCI NIH HHS P30 CA006927NIH HHS P30CA006927
6 · The paper itself

Abstract

Age is a major risk factor for various diseases, such as cancer, cardiovascular conditions, and neurodegenerative diseases. However, chronological age, the simple number of years one has lived, does not capture individual health differences, prompting the development of methods to accurately estimate biological age instead of relying on chronological age. One of the major molecular approaches exploits DNA methylation (DNAm), which is an essential epigenetic modifier for regulating gene expression, cell differentiation, and aging. DNAm-based aging clocks have been developed to predict biological age, but the prediction is highly dependent on training data, including organs and assay technologies. To address these clocks' high variance, we present two EnsembleAge clocks, leveraging eight previously developed DNAm clocks, harnessing the strengths of each methylome age clock, smoothing out individual variances, and providing a more robust estimation of biological age. We trained our EnsembleAge clock models using DNA methylation data from nine organs in the Genotype-Tissue Expression (GTEx) dataset. Our EnsembleNaive clock model achieved the lowest median absolute error (MeAE) of 4.04 years in whole blood. The EnsembleLR model demonstrated the lowest MeAE of 6.35 years across multiple tissues, including breast, lung, muscle, ovary, prostate, testis, and colon. The significant reduction in MeAE underscores its high practical value in clinical and forensic applications, especially in contexts where epigenetic changes are subtle. We further applied our models to four public datasets representing diverse biological applications, including administered short-term medical opioid use (GSE151485) and long-term opioid overdose (GSE164822). Our model reveals over 10 years of age acceleration in opioid-overdosed brains but no significant epigenetic age acceleration when opioid usage was well administered. Our EnsembleAge clock models are also implemented as a web service, allowing users to conveniently upload their DNA methylation data and receive predictions of their biological age. This empowers individuals to track their biological/epigenetic age over time, mitigating the effect of variance and promoting healthy aging and a beneficial lifestyle. Our EnsembleAge clock service is available at https://ensemble.epiclock.app/ .

Indexed as

AgingAnalgesics, OpioidBrainEpigenesis, GeneticEpigenomicsDNA MethylationFemaleHumansMaleAnalgesics, OpioidAging biomarkerDNA methylationEpigenetic age clockMachine learning

Identifiers

PMID41351063
PMCPMC12683782

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LicenceCC BY
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Registered trials

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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.