The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Genetics of the HLA Region in the Prediction of Type 1 Diabetes
Open accessAbstract
Type 1 diabetes (T1D) is one of the most widely studied complex genetic disorders, and the genes in HLA are reported to account for approximately 40–50% of the familial aggregation of T1D. The major genetic determinants of this disease are polymorphisms of class II HLA genes encoding DQ and DR. The DR-DQ haplotypes conferring the highest risk are DRB1*03:01-DQA1*05:01-DQB1*02:01 (abbreviated “DR3”) and DRB1*04:01/02/04/05/08-DQA1*03:01-DQB1*03:02/04 (or DQB1*02; abbreviated “DR4”). The risk is much higher for the heterozygote formed by these two haplotypes (OR = 16.59; 95% CI, 13.7–20.1) than for either of the homozygotes (DR3/DR3, OR = 6.32; 95% CI, 5.12–7.80; DR4/DR4, OR = 5.68; 95% CI, 3.91). In addition, some haplotypes confer strong protection from disease, such as DRB1*15:01-DQA1*01:02-DQB1*06:02 (abbreviated “DR2”; OR = 0.03; 95% CI, 0.01–0.07). After adjusting for the genetic correlation with DR and DQ, significant associations can be seen for HLA class II DPB1 alleles, in particular, DPB1*04:02, DPB1*03:01, and DPB1*02:02. Outside of the class II region, the strongest susceptibility is conferred by class I allele B*39:06 (OR =10.31; 95% CI, 4.21–25.1) and other HLA-B alleles. In addition, several loci in the class III region are reported to be associated with T1D, as are some loci telomeric to class I. Not surprisingly, current approaches for the prediction of T1D in screening studies take advantage of genotyping HLA-DR and HLA-DQ loci, which is then combined with family history and screening for autoantibodies directed against islet-cell antigens. Inclusion of additional moderate HLA risk haplotypes may help identify the majority of children with T1D before the onset of the disease.
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
Insulin needs after CD3-antibody therapy in new-onset type 1 diabetes
Keymeulen, B., Vandemeulebroucke, E., Ziegler, A. G., Mathieu, C., Kaufman, L., Hale, G., Gorus, F., Goldman, M., Walter, M., Candon, S., et al. (2005) · New England Journal of Medicine · doi:10.1056/nejmoa043980 · Cited by 1,099
Incidence of childhood type 1 diabetes worldwide. Diabetes Mondiale (DiaMond) Project Group
Karvonen, M., Viik-Kajander, M., Moltchanova, E., Libman, I., LaPorte, R., & Tuomilehto, J. (2000) · Diabetes Care · doi:10.2337/diacare.23.10.1516 · Cited by 1,072
Incidence of diabetes in youth in the United States
SEARCH for Diabetes in Youth Study Group*, T. W. G. F. T. (2007) · JAMA · doi:10.1001/jama.297.24.2716 · Cited by 991 · cited 2×
“The incidence of T1D has risen over the past 20 years [5], highlighting the importance of environmental triggers in its etiology.”
BackgroundHLA DR-DQ Haplotypes and Genotypes and Type 1 Diabetes Risk
Erlich, H., Valdes, A. M., Noble, J., Carlson, J. A., Varney, M., Concannon, P., Mychaleckyj, J. C., Todd, J. A., Bonella, P., Fear, A. L., et al. (2008) · Diabetes · doi:10.2337/db07-1331 · Cited by 790
Self-citationConcannon, P., Rich, S. S., & Nepom, G. T. (2009) · New England Journal of Medicine · doi:10.1056/nejmra0808284 · Cited by 510
Concordance for islet autoimmunity among monozygotic twins
Redondo, M. J., Jeffrey, J., Fain, P. R., Eisenbarth, G. S., & Orban, T. (2008) · New England Journal of Medicine · doi:10.1056/nejmc0805398 · Cited by 353
HLA Class I and Genetic Susceptibility to Type 1 Diabetes
Noble, J. A., Valdes, A. M., Varney, M. D., Carlson, J. A., Moonsamy, P., Fear, A. L., Lane, J. A., Lavant, E., Rappner, R., Louey, A., et al. (2010) · Diabetes · doi:10.2337/db10-0699 · Cited by 254
Self-citation
Cited by
Noble, J. A., & Erlich, H. A. (2012) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a007732 · Cited by 335
Self-citationThe Emerging Global Epidemic of Type 1 Diabetes
Tuomilehto, J. (2013) · Current Diabetes Reports · doi:10.1007/s11892-013-0433-5 · Cited by 292
Immunogenetics of type 1 diabetes: a comprehensive review
Noble, J. A. (2015) · Journal of Autoimmunity · doi:10.1016/j.jaut.2015.07.014 · Cited by 227
Self-citationA combined risk score enhances prediction of type 1 diabetes among susceptible children
Ferrat, L. A., Vehik, K., Sharp, S. A., Lernmark, Å., Rewers, M. J., She, J.-X., Ziegler, A.-G., Toppari, J., Akolkar, B., Krischer, J. P., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0930-4 · Cited by 148
Chae, Y. K., Chiec, L., Mohindra, N., Gentzler, R., Patel, J., & Giles, F. (2017) · Cancer Immunology, Immunotherapy · doi:10.1007/s00262-016-1913-7 · Cited by 121
Utility of genetic risk scores in type 1 diabetes
Luckett, A. M., Weedon, M. N., Hawkes, G., Leslie, R. D., Oram, R. A., & Grant, S. F. A. (2023) · Diabetologia · doi:10.1007/s00125-023-05955-y · Cited by 80
The epidemiology of type 1 diabetes mellitus in older adults
Tomic, D., Harding, J. L., Jenkins, A. J., Shaw, J. E., & Magliano, D. J. (2025) · Nature Reviews Endocrinology · doi:10.1038/s41574-024-01046-z · Cited by 58
“For example, in European populations, HLA alleles DRB1*04, DQA1:03:01 and DQB1:03:01 have been described as strong indicators of the disease 48,49 .”
Wallia, A., Prince, G., Touma, E., El Muayed, M., & Seley, J. J. (2020) · Current Diabetes Reports · doi:10.1007/s11892-020-01366-0 · Cited by 23
Metadata
- Authors
- Noble, J. A., & Valdes, A. M.
- Venue
- Current Diabetes Reports
- Published
- 2011-09-13
- Volume
- 11
- Issue
- 6
- Pages
- 533-542
- ISSN
- 1534-4827 (ISSN-L)
- 1539-0829
- OpenAlex
- W2031364732
- Cited by
- 460
- References
- 7
- FWCI
- 6.39
- 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
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.