Evidence map›Paper›PMID 32580727›Full record

ArticleBMC biology2020

New targeted approaches for epigenetic age predictions.

Yang Han, Julia Franzen, Thomas Stiehl, Michael Gobs, Chao-Chung Kuo, Miloš Nikolić, Jan Hapala, Barbara Elisabeth Koop, Klaus Strathmann, Stefanie Ritz-Timme and 1 more

Open access · goldAbstract read
In one paragraph

Article in BMC biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
61citing papers in PubMed, 1 pooled it
4.5field-weighted citation impact, top 4% 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

61 citing papers in PubMed, 1 synthesis or guideline pooled it, 98 citations in OpenAlex.

  1. Pooled it
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  13. Epigenetic networks coordinate DNA methylation across the genome.Molecular therapy : the journal of the American Society of Gene Therapy · 2025
    Review
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1 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

11 authors at 3 institutions in 1 country.

Yang HanHelmholtz-Institute for Biomedical Engineering, Stem Cell Biology and Cellular Engineering, RWTH Aachen University Medical School, Pauwelsstraße 20, 52074, Aachen, Germany.
Julia FranzenHelmholtz-Institute for Biomedical Engineering, Stem Cell Biology and Cellular Engineering, RWTH Aachen University Medical School, Pauwelsstraße 20, 52074, Aachen, Germany.
Thomas StiehlInterdisciplinary Center for Scientific Computing (IWR), Institute of Applied Mathematics, University of Heidelberg, Heidelberg, Germany.
Michael GobsHelmholtz-Institute for Biomedical Engineering, Stem Cell Biology and Cellular Engineering, RWTH Aachen University Medical School, Pauwelsstraße 20, 52074, Aachen, Germany.
Chao-Chung KuoHelmholtz-Institute for Biomedical Engineering, Stem Cell Biology and Cellular Engineering, RWTH Aachen University Medical School, Pauwelsstraße 20, 52074, Aachen, Germany.
Miloš NikolićHelmholtz-Institute for Biomedical Engineering, Stem Cell Biology and Cellular Engineering, RWTH Aachen University Medical School, Pauwelsstraße 20, 52074, Aachen, Germany.
Jan HapalaHelmholtz-Institute for Biomedical Engineering, Stem Cell Biology and Cellular Engineering, RWTH Aachen University Medical School, Pauwelsstraße 20, 52074, Aachen, Germany.
Barbara Elisabeth KoopInstitute for Legal Medicine, Heinrich Heine University, Düsseldorf, Germany.
Klaus StrathmannInstitute for Transfusion Medicine, RWTH Aachen University Medical School, Aachen, Germany.
Stefanie Ritz-TimmeInstitute for Legal Medicine, Heinrich Heine University, Düsseldorf, Germany.
Wolfgang WagnerHelmholtz-Institute for Biomedical Engineering, Stem Cell Biology and Cellular Engineering, RWTH Aachen University Medical School, Pauwelsstraße 20, 52074, Aachen, Germany. wwagner@ukaachen.de.ORCID 0000-0002-1971-3217
RWTH Aachen University · DEHeinrich Heine University Düsseldorf · DEHeidelberg University · DE

Funding

Deutsche Forschungsgemeinschaft RI704/4-1Deutsche Forschungsgemeinschaft WA 1706/12-1Deutsche Forschungsgemeinschaft WA 1706/8-1Deutsche Krebshilfe TRACK-AMLFederal Ministry of Education and Research VIP + Epi-Blood-CountInterdisciplinary Center for Clinical Research within the faculty of Medicine at the RWTH Aachen University O3-3
6 · The paper itself

Abstract

backgroundAge-associated DNA methylation changes provide a promising biomarker for the aging process. While genome-wide DNA methylation profiles enable robust age-predictors by integration of many age-associated CG dinucleotides (CpGs), there are various alternative approaches for targeted measurements at specific CpGs that better support standardized and cost-effective high-throughput analysis.

resultsIn this study, we utilized 4647 Illumina BeadChip profiles of blood to select CpG sites that facilitate reliable age-predictions based on pyrosequencing. We demonstrate that the precision of DNA methylation measurements can be further increased with droplet digital PCR (ddPCR). In comparison, bisulfite barcoded amplicon sequencing (BBA-seq) gave slightly lower correlation between chronological age and DNA methylation at individual CpGs, while the age-predictions were overall relatively accurate. Furthermore, BBA-seq data revealed that the correlation of methylation levels with age at neighboring CpG sites follows a bell-shaped curve, often associated with a CTCF binding site. We demonstrate that within individual BBA-seq reads the DNA methylation at neighboring CpGs is not coherently modified, but reveals a stochastic pattern. Based on this, we have developed a new approach for epigenetic age predictions based on the binary sequel of methylated and non-methylated sites in individual reads, which reflects heterogeneity in epigenetic aging within a sample.

conclusionTargeted DNA methylation analysis at few age-associated CpGs by pyrosequencing, BBA-seq, and particularly ddPCR enables high precision of epigenetic age-predictions. Furthermore, we demonstrate that the stochastic evolution of age-associated DNA methylation patterns in BBA-seq data enables epigenetic clocks for individual DNA strands.

Indexed as

DNA MethylationHigh-Throughput Nucleotide SequencingAgingBloodEpigenesis, GeneticEpigenomicsGenetic MarkersHumansGenetic MarkersAgingAmplicon sequencingBloodBuccal swabsCTCFDNA methylationDroplet digital PCREpigeneticHuman

Identifiers

PMID32580727
PMCPMC7315536
OpenAlexW3037653560

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.