The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Epigenome-wide association of DNA methylation markers in peripheral blood from Indian Asians and Europeans with incident type 2 diabetes: a nested case-control study
Open accessAbstract
Summary Background Indian Asians, who make up a quarter of the world’s population, are at high risk of developing type 2 diabetes. We investigated whether DNA methylation is associated with future type 2 diabetes incidence in Indian Asians and whether differences in methylation patterns between Indian Asians and Europeans are associated with, and could be used to predict, differences in the magnitude of risk of developing type 2 diabetes. Methods We did a nested case-control study of DNA methylation in Indian Asians and Europeans with incident type 2 diabetes who were identified from the 8-year follow-up of 25 372 participants in the London Life Sciences Prospective Population (LOLIPOP) study. Patients were recruited between May 1, 2002, and Sept 12, 2008. We did epigenome-wide association analysis using samples from Indian Asians with incident type 2 diabetes and age-matched and sex-matched Indian Asian controls, followed by replication testing of top-ranking signals in Europeans. For both discovery and replication, DNA methylation was measured in the baseline blood sample, which was collected before the onset of type 2 diabetes. Epigenome-wide significance was set at p<1 × 10−7. We compared methylation levels between Indian Asian and European controls without type 2 diabetes at baseline to estimate the potential contribution of DNA methylation to increased risk of future type 2 diabetes incidence among Indian Asians. Findings 1608 (11·9%) of 13 535 Indian Asians and 306 (4·3%) of 7066 Europeans developed type 2 diabetes over a mean of 8·5 years (SD 1·8) of follow-up. The age-adjusted and sex-adjusted incidence of type 2 diabetes was 3·1 times (95% CI 2·8–3·6; p<0·0001) higher among Indian Asians than among Europeans, and remained 2·5 times (2·1–2·9; p<0·0001) higher after adjustment for adiposity, physical activity, family history of type 2 diabetes, and baseline glycaemic measures. The mean absolute difference in methylation level between type 2 diabetes cases and controls ranged from 0·5% (SD 0·1) to 1·1% (0·2). Methylation markers at five loci were associated with future type 2 diabetes incidence; the relative risk per 1% increase in methylation was 1·09 (95% CI 1·07–1·11; p=1·3 × 10−17) for ABCG1, 0·94 (0·92–0·95; p=4·2 × 10−11) for PHOSPHO1, 0·94 (0·92–0·96; p=1·4 × 10−9) for SOCS3, 1·07 (1·04–1·09; p=2·1 × 10−10) for SREBF1, and 0·92 (0·90–0·94; p=1·2 × 10−17) for TXNIP. A methylation score combining results for the five loci was associated with future type 2 diabetes incidence (relative risk quartile 4 vs quartile 1 3·51, 95% CI 2·79–4·42; p=1·3 × 10−26), and was independent of established risk factors. Methylation score was higher among Indian Asians than Europeans (p=1 × 10−34). Interpretation DNA methylation might provide new insights into the pathways underlying type 2 diabetes and offer new opportunities for risk stratification and prevention of type 2 diabetes among Indian Asians. Funding The European Union, the UK National Institute for Health Research, the Wellcome Trust, the UK Medical Research Council, Action on Hearing Loss, the UK Biotechnology and Biological Sciences Research Council, the Oak Foundation, the Economic and Social Research Council, Helmholtz Zentrum Munchen, the German Research Center for Environmental Health, the German Federal Ministry of Education and Research, the German Center for Diabetes Research, the Munich Center for Health Sciences, the Ministry of Science and Research of the State of North Rhine-Westphalia, and the German Federal Ministry of Health.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
International Expert Committee report on the role of the A1c assay in the diagnosis of diabetes
The International Expert Committee (2009) · Diabetes Care · doi:10.2337/dc09-9033 · Cited by 3,185
DNA methylation arrays as surrogate measures of cell mixture distribution
Houseman, E. A., Accomando, W. P., Koestler, D. C., Christensen, B. C., Marsit, C. J., Nelson, H. H., Wiencke, J. K., & Kelsey, K. T. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-86 · Cited by 3,175
DNA methylation: roles in mammalian development
Smith, Z. D., & Meissner, A. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3354 · Cited by 3,045
the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Morris, A. P., Voight, B. F., Teslovich, T. M., Ferreira, T., Segrè, A. V., Steinthorsdottir, V., Strawbridge, R. J., Khan, H., Grallert, H., et al. (2012) · Nature Genetics · doi:10.1038/ng.2383 · Cited by 1,912
Self-citationDynamic DNA methylation programs persistent adverse effects of early-life stress
Murgatroyd, C., Patchev, A. V., Wu, Y., Micale, V., Bockmühl, Y., Fischer, D., Holsboer, F., Wotjak, C. T., Almeida, O. F. X., & Spengler, D. (2009) · Nature Neuroscience · doi:10.1038/nn.2436 · Cited by 1,162
Genomics, type 2 diabetes, and obesity
McCarthy, M. I. (2010) · New England Journal of Medicine · doi:10.1056/nejmra0906948 · Cited by 938
Self-citationWagner, J. R., Busche, S., Ge, B., Kwan, T., Pastinen, T., & Blanchette, M. (2014) · Genome biology · doi:10.1186/gb-2014-15-2-r37 · Cited by 446
Volkmar, M., Dedeurwaerder, S., Cunha, D. A., Ndlovu, M. N., Defrance, M., Deplus, R., Calonne, E., Volkmar, U., Igoillo‐Esteve, M., Naamane, N., et al. (2012) · The EMBO Journal · doi:10.1038/emboj.2011.503 · Cited by 420
Lehne, B., Drong, A. W., Loh, M., Zhang, W., Scott, W. R., Tan, S.-T., Afzal, U., Scott, J., Jarvelin, M.-R., Elliott, P., et al. (2015) · Genome biology · doi:10.1186/s13059-015-0600-x · Cited by 380
Self-citationThe genetic and epigenetic basis of type 2 diabetes and obesity
Drong, A. W., Lindgren, C. M., & McCarthy, M. I. (2012) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2012.149 · Cited by 212
Self-citation
Cited by
Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity
Wahl, S., Drong, A., Lehne, B., Loh, M., Scott, W. R., Kunze, S., Tsai, P.-C., Ried, J. S., Zhang, W., Yang, Y., et al. (2017) · Nature · doi:10.1038/nature20784 · Cited by 945
“…to asthma, eczema and cardiovascular disease.20 ABCG1 is involved in cholesterol and phospholipid transport, and regulates insulin secretion.17,21 Our observations thus provide insight into the regulatory pathways that may link adiposity to metabolic and cardiovascular disease, asthma and a…”
Self-citationIntegrative analysis of omics summary data reveals putative mechanisms underlying complex traits
Wu, Y., Zeng, J., Zhang, F., Zhu, Z., Qi, T., Zheng, Z., Lloyd-Jones, L. R., Marioni, R. E., Martin, N. G., Montgomery, G. W., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03371-0 · Cited by 685
Würtz, P., Kangas, A. J., Soininen, P., Lawlor, D. A., Davey Smith, G., & Ala-Korpela, M. (2017) · American Journal of Epidemiology · doi:10.1093/aje/kwx016 · Cited by 686
Self-citationBelsky, D. W., Caspi, A., Arseneault, L., Baccarelli, A., Corcoran, D. L., Gao, X., Hannon, E., Harrington, H. L., Rasmussen, L. J., Houts, R., et al. (2020) · eLife · doi:10.7554/elife.54870 · Cited by 652
DNA methylation signatures in peripheral blood strongly predict all-cause mortality
Zhang, Y., Wilson, R., Heiss, J., Breitling, L. P., Saum, K.-U., Schöttker, B., Holleczek, B., Waldenberger, M., Peters, A., & Brenner, H. (2017) · Nature Communications · doi:10.1038/ncomms14617 · Cited by 382
Metadata
- Authors
- Chambers, J. C., Loh, M., Lehne, B., Drong, A., Kriebel, J., Motta, V., Wahl, S., Elliott, H. R., Rota, F., Scott, W. R., Zhang, W., Tan, S.-T., Campanella, G., Chadeau-Hyam, M., Yengo, L., Richmond, R. C., Adamowicz-Brice, M., Afzal, U., Bozaoglu, K., Mok, Z. Y., Ng, H. K., Pattou, F., Prokisch, H., Rozario, M. A., Tarantini, L., Abbott, J., Ala-Korpela, M., Albetti, B., Ammerpohl, O., Bertazzi, P. A., Blancher, C., Caiazzo, R., Danesh, J., Gaunt, T. R., de Lusignan, S., Gieger, C., Illig, T., Jha, S., Jones, S., Jowett, J., Kangas, A. J., Kasturiratne, A., Kato, N., Kotea, N., Kowlessur, S., Pitkäniemi, J., Punjabi, P., Saleheen, D., Schafmayer, C., Soininen, P., Tai, E.-S., Thorand, B., Tuomilehto, J., Wickremasinghe, A. R., Kyrtopoulos, S. A., Aitman, T. J., Herder, C., Hampe, J., Cauchi, S., Relton, C. L., Froguel, P., Soong, R., Vineis, P., Jarvelin, M.-R., Scott, J., Grallert, H., Bollati, V., Elliott, P., McCarthy, M. I., & Kooner, J. S.
- Venue
- The Lancet Diabetes & Endocrinology
- Publisher
- Elsevier BV
- Published
- 2015-07
- Volume
- 3
- Issue
- 7
- Pages
- 526-534
- ISSN
- 2213-8587 (ISSN-L)
- OpenAlex
- W1808248494
- Cited by
- 510
- References
- 10
- FWCI
- 17.24
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Epigenome-wide association of DNA methylation markers in peripheral blood from Indian Asians and Europeans with incident type 2 diabetes: a nested case-control study. https://app.cochat.ai/papers/10.1016/s2213-8587(15)00127-8
- The paper
- Chambers, J. C., Loh, M., Lehne, B., Drong, A., Kriebel, J., Motta, V., Wahl, S., Elliott, H. R., Rota, F., Scott, W. R., Zhang, W., Tan, S.-T., Campanella, G., Chadeau-Hyam, M., Yengo, L., Richmond, R. C., Adamowicz-Brice, M., Afzal, U., Bozaoglu, K., . . . Kooner, J. S. (2015). Epigenome-wide association of DNA methylation markers in peripheral blood from Indian Asians and Europeans with incident type 2 diabetes: a nested case-control study. The Lancet Diabetes & Endocrinology. https://doi.org/10.1016/s2213-8587(15)00127-8
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.