Investigating misclassification of type 1 diabetes in a population-based cohort of British Pakistanis and Bangladeshis using polygenic risk scores
Abstract Correct classification of type 1 (T1D) and type 2 diabetes (T2D) is challenging due to overlapping clinical features and the increasingly early onset of T2D, particularly in South Asians. Polygenic risk scores (PRSs) for T1D and T2D have been shown to work relatively well in South Asians, d...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-80348-8 |
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| author | Timing Liu Alagu Sankareswaran Gordon Paterson Genes & Health Research Team Diane P. Fraser Sam Hodgson Qin Qin Huang Teng Hiang Heng Meera Ladwa Nick Thomas David A. van Heel Michael N. Weedon Chittaranjan S. Yajnik Richard A. Oram Giriraj R. Chandak Hilary C. Martin Sarah Finer |
| author_facet | Timing Liu Alagu Sankareswaran Gordon Paterson Genes & Health Research Team Diane P. Fraser Sam Hodgson Qin Qin Huang Teng Hiang Heng Meera Ladwa Nick Thomas David A. van Heel Michael N. Weedon Chittaranjan S. Yajnik Richard A. Oram Giriraj R. Chandak Hilary C. Martin Sarah Finer |
| author_sort | Timing Liu |
| collection | DOAJ |
| description | Abstract Correct classification of type 1 (T1D) and type 2 diabetes (T2D) is challenging due to overlapping clinical features and the increasingly early onset of T2D, particularly in South Asians. Polygenic risk scores (PRSs) for T1D and T2D have been shown to work relatively well in South Asians, despite being derived from largely European-ancestry samples. Here we used PRSs to investigate the rate of potential misclassification of diabetes amongst British Bangladeshis and Pakistanis. Using linked health records from the Genes & Health cohort (n = 38,344) we defined two reference groups meeting stringent diagnostic criteria: 31 T1D cases, 1842 T2D cases, and after excluding these, two further groups: 839 insulin-treated diabetic individuals with ambiguous features and 5174 non-diabetic controls. Combining these with 307 confirmed T1D cases and 307 controls from India, we calculated ancestry-corrected PRSs for T1D and T2D, with which we estimated the proportion of T1D cases within the ambiguous group at ~ 6%, dropping to ~ 4.5% within the subset who had T2D codes in their health records (and are thus most likely to have been misclassified). We saw no significant association between the T1D or T2D PRS and BMI at diagnosis, time to insulin, or the presence of T1D or T2D diagnostic codes amongst the T2D or ambiguous cases, suggesting that these clinical features are not particularly helpful for aiding diagnosis in ambiguous cases. Our results emphasise that robust identification of T1D cases and appropriate clinical care may require routine measurement of diabetes autoantibodies and C-peptide. |
| format | Article |
| id | doaj-art-7a37f0f10e5b48e8a38e858c66b30058 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-7a37f0f10e5b48e8a38e858c66b300582025-01-19T12:17:13ZengNature PortfolioScientific Reports2045-23222025-01-0115111210.1038/s41598-024-80348-8Investigating misclassification of type 1 diabetes in a population-based cohort of British Pakistanis and Bangladeshis using polygenic risk scoresTiming Liu0Alagu Sankareswaran1Gordon Paterson2Genes & Health Research TeamDiane P. Fraser3Sam Hodgson4Qin Qin Huang5Teng Hiang Heng6Meera Ladwa7Nick Thomas8David A. van Heel9Michael N. Weedon10Chittaranjan S. Yajnik11Richard A. Oram12Giriraj R. Chandak13Hilary C. Martin14Sarah Finer15Wellcome Trust Sanger InstituteGenomic Research on Complex diseases Group (GRC-Group), CSIR-Centre for Cellular and Molecular BiologyWolfson Institute of Population Health, Queen Mary University of LondonUniversity of ExeterWolfson Institute of Population Health, Queen Mary University of LondonWellcome Trust Sanger InstituteWellcome Trust Sanger InstituteWolfson Institute of Population Health, Queen Mary University of LondonUniversity of ExeterBlizard Institute, Queen Mary University of LondonUniversity of ExeterDiabetes Unit, King Edward Memorial Hospital and Research CentreUniversity of ExeterGenomic Research on Complex diseases Group (GRC-Group), CSIR-Centre for Cellular and Molecular BiologyWellcome Trust Sanger InstituteWolfson Institute of Population Health, Queen Mary University of LondonAbstract Correct classification of type 1 (T1D) and type 2 diabetes (T2D) is challenging due to overlapping clinical features and the increasingly early onset of T2D, particularly in South Asians. Polygenic risk scores (PRSs) for T1D and T2D have been shown to work relatively well in South Asians, despite being derived from largely European-ancestry samples. Here we used PRSs to investigate the rate of potential misclassification of diabetes amongst British Bangladeshis and Pakistanis. Using linked health records from the Genes & Health cohort (n = 38,344) we defined two reference groups meeting stringent diagnostic criteria: 31 T1D cases, 1842 T2D cases, and after excluding these, two further groups: 839 insulin-treated diabetic individuals with ambiguous features and 5174 non-diabetic controls. Combining these with 307 confirmed T1D cases and 307 controls from India, we calculated ancestry-corrected PRSs for T1D and T2D, with which we estimated the proportion of T1D cases within the ambiguous group at ~ 6%, dropping to ~ 4.5% within the subset who had T2D codes in their health records (and are thus most likely to have been misclassified). We saw no significant association between the T1D or T2D PRS and BMI at diagnosis, time to insulin, or the presence of T1D or T2D diagnostic codes amongst the T2D or ambiguous cases, suggesting that these clinical features are not particularly helpful for aiding diagnosis in ambiguous cases. Our results emphasise that robust identification of T1D cases and appropriate clinical care may require routine measurement of diabetes autoantibodies and C-peptide.https://doi.org/10.1038/s41598-024-80348-8 |
| spellingShingle | Timing Liu Alagu Sankareswaran Gordon Paterson Genes & Health Research Team Diane P. Fraser Sam Hodgson Qin Qin Huang Teng Hiang Heng Meera Ladwa Nick Thomas David A. van Heel Michael N. Weedon Chittaranjan S. Yajnik Richard A. Oram Giriraj R. Chandak Hilary C. Martin Sarah Finer Investigating misclassification of type 1 diabetes in a population-based cohort of British Pakistanis and Bangladeshis using polygenic risk scores Scientific Reports |
| title | Investigating misclassification of type 1 diabetes in a population-based cohort of British Pakistanis and Bangladeshis using polygenic risk scores |
| title_full | Investigating misclassification of type 1 diabetes in a population-based cohort of British Pakistanis and Bangladeshis using polygenic risk scores |
| title_fullStr | Investigating misclassification of type 1 diabetes in a population-based cohort of British Pakistanis and Bangladeshis using polygenic risk scores |
| title_full_unstemmed | Investigating misclassification of type 1 diabetes in a population-based cohort of British Pakistanis and Bangladeshis using polygenic risk scores |
| title_short | Investigating misclassification of type 1 diabetes in a population-based cohort of British Pakistanis and Bangladeshis using polygenic risk scores |
| title_sort | investigating misclassification of type 1 diabetes in a population based cohort of british pakistanis and bangladeshis using polygenic risk scores |
| url | https://doi.org/10.1038/s41598-024-80348-8 |
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