Evidence map›Paper›PMID 39827095›Full record

ArticleCardiovascular diabetology2025

Longitudinal association between DNA methylation and type 2 diabetes: findings from the KORA F4/FF4 study.

Liye Lai, Dave Laurence Juntilla, Monica Del C Gomez-Alonso, Harald Grallert, Barbara Thorand, Aiman Farzeen, Wolfgang Rathmann, Juliane Winkelmann, Holger Prokisch, Christian Gieger and 3 more

Erratum issuedAbstract read
In one paragraph

Article in Cardiovascular diabetology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Metformin Alone and in Combinations Alter the Methylation Patterns ofEndocrine, metabolic & immune disorders drug targets · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Liye LaiResearch Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany. liye.lai@helmholtz-munich.de.
Dave Laurence JuntillaResearch Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany.
Monica Del C Gomez-AlonsoResearch Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany.
Harald GrallertResearch Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany.
Barbara ThorandInstitute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany.
Aiman FarzeenResearch Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany.
Wolfgang RathmannInstitute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Juliane WinkelmannInstitute of Neurogenomics, Computational Health Center, Helmholtz Zentrum München, Neuherberg, Germany.
Holger ProkischInstitute of Neurogenomics, Computational Health Center, Helmholtz Zentrum München, Neuherberg, Germany.
Christian GiegerResearch Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany.
Christian HerderGerman Center for Diabetes Research (DZD), Neuherberg, Germany.
Annette Peters *Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany.
Melanie Waldenberger *Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany. melanie.waldenberger@helmholtz-munich.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundType 2 diabetes (T2D) has been linked to changes in DNA methylation levels, which can, in turn, alter transcriptional activity. However, most studies for epigenome-wide associations between T2D and DNA methylation comes from cross-sectional design. Few large-scale investigations have explored these associations longitudinally over multiple time-points.

methodsIn this longitudinal study, we examined data from the Cooperative Health Research in the Region of Augsburg (KORA) F4 and FF4 studies, conducted approximately seven years apart. Leucocyte DNA methylation was assessed using the Illumina EPIC and 450K arrays. Linear mixed-effects models were employed to identify significant associations between methylation sites and diabetes status, as well as with fasting plasma glucose (FPG), hemoglobin A1c (HbA1c), homoeostasis model assessment of beta cell function (HOMA-B), and homoeostasis model assessment of insulin resistance (HOMA-IR). Interaction effects between diabetes status and follow-up time were also examined. Additionally, we explored CpG sites associated with persistent prediabetes or T2D, as well as the progression from normal glucose tolerance (NGT) to prediabetes or T2D. Finally, we assessed the associations between the identified CpG sites and their corresponding gene expression levels.

resultsA total of 3,501 observations from 2,556 participants, with methylation measured at least once across two visits, were included in the analyses. We identified 64 sites associated with T2D including 15 novel sites as well as known associations like those with the thioredoxin-interacting protein (TXNIP) and ATP-binding cassette sub-family G member 1 (ABCG1) genes. Of these, eight CpG sites exhibited different rates of annual methylation change between the NGT and T2D groups, and seven CpG sites were linked to the progression from NGT to prediabetes or T2D, including those annotated to mannosidase alpha class 2a member 2 (MAN2A2) and carnitine palmitoyl transferase 1 A (CPT1A). Longitudinal analysis revealed significant associations between methylation and FPG at 128 sites, HbA1c at 41 sites, and HOMA-IR at 57 sites. Additionally, we identified 104 CpG-transcript pairs in whole blood, comprising 40 unique CpG sites and 96 unique gene transcripts.

conclusionsOur study identified novel differentially methylated loci linked to T2D as well as to changes in diabetes status through a longitudinal approach. We report CpG sites with different rates of annual methylation change and demonstrate that DNA methylation associated with T2D is linked to following transcriptional differences. These findings provide new insights into the molecular mechanisms of diabetes development.

Indexed as

Diabetes Mellitus, Type 2DNA MethylationEpigenesis, GeneticPrediabetic StateAgedBiomarkersBlood GlucoseCarrier ProteinsCpG IslandsDisease ProgressionFemaleGenetic Predisposition to DiseaseGlycated HemoglobinHumansInsulin ResistanceLongitudinal StudiesBiomarkersBlood GlucoseCarrier ProteinsGlycated Hemoglobinhemoglobin A1c protein, humanTXNIP protein, humanDiabetes progressionDNA methylationGene expressionGlycemic traitsType 2 diabetes

Identifiers

PMID39827095
PMCPMC11748594

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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.