| Title |
Clinical and genetic correlates of islet-autoimmune signatures in juvenile-onset type 1 diabetes
|
|---|---|
| Published in |
Diabetologia, November 2019
|
| DOI | 10.1007/s00125-019-05032-3 |
| Pubmed ID | |
| Authors |
Laura A. Claessens, Joris Wesselius, Menno van Lummel, Sandra Laban, Flip Mulder, Dick Mul, Tanja Nikolic, Henk-Jan Aanstoot, Bobby P. C. Koeleman, Bart O. Roep |
| Abstract |
Heterogeneity in individuals with type 1 diabetes has become more generally appreciated, but has not yet been extensively and systematically characterised. Here, we aimed to characterise type 1 diabetes heterogeneity by creating immunological, genetic and clinical profiles for individuals with juvenile-onset type 1 diabetes in a cross-sectional study. Participants were HLA-genotyped to determine HLA-DR-DQ risk, and SNP-genotyped to generate a non-HLA genetic risk score (GRS) based on 93 type 1 diabetes-associated SNP variants outside the MHC region. Islet autoimmunity was assessed as T cell proliferation upon stimulation with the beta cell antigens GAD65, islet antigen-2 (IA-2), preproinsulin (PPI) and defective ribosomal product of the insulin gene (INS-DRIP). Clinical parameters were collected retrospectively. Of 80 individuals, 67 had proliferation responses to one or more islet antigens, with vast differences in the extent of proliferation. Based on the multitude and amplitude of the proliferation responses, individuals were clustered into non-, intermediate and high responders. High responders could not be characterised entirely by enrichment for the highest risk HLA-DR3-DQ2/DR4-DQ8 genotype. However, high responders did have a significantly higher non-HLA GRS. Clinically, high T cell responses to beta cell antigens did not reflect in worsened glycaemic control, increased complications, development of associated autoimmunity or younger age at disease onset. The number of beta cell antigens that an individual responded to increased with disease duration, pointing to chronic islet autoimmunity and epitope spreading. Collectively, these data provide new insights into type 1 diabetes disease heterogeneity and highlight the importance of stratifying patients on the basis of their genetic and autoimmune signatures for immunotherapy and personalised disease management. |
X Demographics
The data shown below were collected from the profiles of 34 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 12% |
| Spain | 3 | 9% |
| United Kingdom | 3 | 9% |
| Saudi Arabia | 2 | 6% |
| Italy | 1 | 3% |
| Serbia | 1 | 3% |
| Mexico | 1 | 3% |
| Japan | 1 | 3% |
| Germany | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 16 | 47% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 28 | 82% |
| Scientists | 4 | 12% |
| Practitioners (doctors, other healthcare professionals) | 2 | 6% |
Mendeley readers
The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 38 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 6 | 16% |
| Student > Ph. D. Student | 6 | 16% |
| Student > Doctoral Student | 4 | 11% |
| Student > Master | 3 | 8% |
| Other | 2 | 5% |
| Other | 3 | 8% |
| Unknown | 14 | 37% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 7 | 18% |
| Agricultural and Biological Sciences | 4 | 11% |
| Medicine and Dentistry | 4 | 11% |
| Nursing and Health Professions | 3 | 8% |
| Business, Management and Accounting | 1 | 3% |
| Other | 4 | 11% |
| Unknown | 15 | 39% |
Attention Score in Context
This research output has an Altmetric Attention Score of 27. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 January 2020.
All research outputs
#1,406,269
of 25,387,668 outputs
Outputs from Diabetologia
#761
of 5,346 outputs
Outputs of similar age
#33,285
of 471,620 outputs
Outputs of similar age from Diabetologia
#25
of 70 outputs
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