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| Home > Publications database > Epigenome-wide association study of incident type 2 diabetes: a meta-analysis of five prospective European cohorts. > print |
| 001 | 178958 | ||
| 005 | 20240229143601.0 | ||
| 024 | 7 | _ | |a 10.1007/s00125-022-05652-2 |2 doi |
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| 037 | _ | _ | |a DKFZ-2022-00374 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Fraszczyk, Eliza |b 0 |
| 245 | _ | _ | |a Epigenome-wide association study of incident type 2 diabetes: a meta-analysis of five prospective European cohorts. |
| 260 | _ | _ | |a Heidelberg |c 2022 |b Springer |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1651579842_6246 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 500 | _ | _ | |a 2022 May;65(5):763-776 |
| 520 | _ | _ | |a Type 2 diabetes is a complex metabolic disease with increasing prevalence worldwide. Improving the prediction of incident type 2 diabetes using epigenetic markers could help tailor prevention efforts to those at the highest risk. The aim of this study was to identify predictive methylation markers for incident type 2 diabetes by combining epigenome-wide association study (EWAS) results from five prospective European cohorts.We conducted a meta-analysis of EWASs in blood collected 7-10 years prior to type 2 diabetes diagnosis. DNA methylation was measured with Illumina Infinium Methylation arrays. A total of 1250 cases and 1950 controls from five longitudinal cohorts were included: Doetinchem, ESTHER, KORA1, KORA2 and EPIC-Norfolk. Associations between DNA methylation and incident type 2 diabetes were examined using robust linear regression with adjustment for potential confounders. Inverse-variance fixed-effects meta-analysis of cohort-level individual CpG EWAS estimates was performed using METAL. The methylGSA R package was used for gene set enrichment analysis. Confirmation of genome-wide significant CpG sites was performed in a cohort of Indian Asians (LOLIPOP, UK).The meta-analysis identified 76 CpG sites that were differentially methylated in individuals with incident type 2 diabetes compared with control individuals (p values <1.1 × 10-7). Sixty-four out of 76 (84.2%) CpG sites were confirmed by directionally consistent effects and p values <0.05 in an independent cohort of Indian Asians. However, on adjustment for baseline BMI only four CpG sites remained genome-wide significant, and addition of the 76 CpG methylation risk score to a prediction model including established predictors of type 2 diabetes (age, sex, BMI and HbA1c) showed no improvement (AUC 0.757 vs 0.753). Gene set enrichment analysis of the full epigenome-wide results clearly showed enrichment of processes linked to insulin signalling, lipid homeostasis and inflammation.By combining results from five European cohorts, and thus significantly increasing study sample size, we identified 76 CpG sites associated with incident type 2 diabetes. Replication of 64 CpGs in an independent cohort of Indian Asians suggests that the association between DNA methylation levels and incident type 2 diabetes is robust and independent of ethnicity. Our data also indicate that BMI partly explains the association between DNA methylation and incident type 2 diabetes. Further studies are required to elucidate the underlying biological mechanisms and to determine potential causal roles of the differentially methylated CpG sites in type 2 diabetes development. |
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| 650 | _ | 7 | |a Biomarkers |2 Other |
| 650 | _ | 7 | |a DNA methylation |2 Other |
| 650 | _ | 7 | |a Epigenetics |2 Other |
| 650 | _ | 7 | |a Epigenome-wide association studies |2 Other |
| 650 | _ | 7 | |a Meta-analysis |2 Other |
| 650 | _ | 7 | |a Prediction |2 Other |
| 650 | _ | 7 | |a Prospective studies |2 Other |
| 650 | _ | 7 | |a Type 2 diabetes |2 Other |
| 700 | 1 | _ | |a Spijkerman, Annemieke M W |b 1 |
| 700 | 1 | _ | |a Zhang, Yan |0 P:(DE-He78)d19149dd97b17ce55e70abd2f9e64d3d |b 2 |
| 700 | 1 | _ | |a Brandmaier, Stefan |b 3 |
| 700 | 1 | _ | |a Day, Felix R |b 4 |
| 700 | 1 | _ | |a Zhou, Li |b 5 |
| 700 | 1 | _ | |a Wackers, Paul |b 6 |
| 700 | 1 | _ | |a Dollé, Martijn E T |b 7 |
| 700 | 1 | _ | |a Bloks, Vincent W |b 8 |
| 700 | 1 | _ | |a Gào, Xīn |0 P:(DE-He78)8218df9f6f41792399cd3a29b587e4e7 |b 9 |u dkfz |
| 700 | 1 | _ | |a Gieger, Christian |b 10 |
| 700 | 1 | _ | |a Kooner, Jaspal |b 11 |
| 700 | 1 | _ | |a Kriebel, Jennifer |b 12 |
| 700 | 1 | _ | |a Picavet, H Susan J |b 13 |
| 700 | 1 | _ | |a Rathmann, Wolfgang |b 14 |
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| 700 | 1 | _ | |a Loh, Marie |b 16 |
| 700 | 1 | _ | |a Verschuren, W M Monique |b 17 |
| 700 | 1 | _ | |a van Vliet-Ostaptchouk, Jana V |b 18 |
| 700 | 1 | _ | |a Wareham, Nicholas J |b 19 |
| 700 | 1 | _ | |a Chambers, John C |b 20 |
| 700 | 1 | _ | |a Ong, Ken K |b 21 |
| 700 | 1 | _ | |a Grallert, Harald |b 22 |
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| 700 | 1 | _ | |a Luijten, Mirjam |b 24 |
| 700 | 1 | _ | |a Snieder, Harold |b 25 |
| 773 | _ | _ | |a 10.1007/s00125-022-05652-2 |0 PERI:(DE-600)1458993-X |n 5 |p 763-776 |t Diabetologia |v 65 |y 2022 |x 0012-186X |
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