1
|
Lind A, Freyhult E, de Jesus Cortez F, Ramelius A, Bennet R, Robinson PV, Seftel D, Gebhart D, Tandel D, Maziarz M, Larsson HE, Lundgren M, Carlsson A, Nilsson AL, Fex M, Törn C, Agardh D, Tsai CT, Lernmark Å. Childhood screening for type 1 diabetes comparing automated multiplex Antibody Detection by Agglutination-PCR (ADAP) with single plex islet autoantibody radiobinding assays. EBioMedicine 2024; 104:105144. [PMID: 38723553 PMCID: PMC11090024 DOI: 10.1016/j.ebiom.2024.105144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 04/16/2024] [Accepted: 04/18/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Two or more autoantibodies against either insulin (IAA), glutamic acid decarboxylase (GADA), islet antigen-2 (IA-2A) or zinc transporter 8 (ZnT8A) denote stage 1 (normoglycemia) or stage 2 (dysglycemia) type 1 diabetes prior to stage 3 type 1 diabetes. Automated multiplex Antibody Detection by Agglutination-PCR (ADAP) assays in two laboratories were compared to single plex radiobinding assays (RBA) to define threshold levels for diagnostic specificity and sensitivity. METHODS IAA, GADA, IA-2A and ZnT8A were analysed in 1504 (54% females) population based controls (PBC), 456 (55% females) doctor's office controls (DOC) and 535 (41% females) blood donor controls (BDC) as well as in 2300 (48% females) patients newly diagnosed (1-10 years of age) with stage 3 type 1 diabetes. The thresholds for autoantibody positivity were computed in 100 10-fold cross-validations to separate patients from controls either by maximizing the χ2-statistics (chisq) or using the 98th percentile of specificity (Spec98). Mean and 95% CI for threshold, sensitivity and specificity are presented. FINDINGS The ADAP ROC curves of the four autoantibodies showed comparable AUC in the two ADAP laboratories and were higher than RBA. Detection of two or more autoantibodies using chisq showed 0.97 (0.95, 0.99) sensitivity and 0.94 (0.91, 0.97) specificity in ADAP compared to 0.90 (0.88, 0.95) sensitivity and 0.97 (0.94, 0.98) specificity in RBA. Using Spec98, ADAP showed 0.92 (0.89, 0.95) sensitivity and 0.99 (0.98, 1.00) specificity compared to 0.89 (0.77, 0.86) sensitivity and 1.00 (0.99, 1.00) specificity in the RBA. The diagnostic sensitivity and specificity were higher in PBC compared to DOC and BDC. INTERPRETATION ADAP was comparable in two laboratories, both comparable to or better than RBA, to define threshold levels for two or more autoantibodies to stage type 1 diabetes. FUNDING Supported by The Leona M. and Harry B. Helmsley Charitable Trust (grant number 2009-04078), the Swedish Foundation for Strategic Research (Dnr IRC15-0067) and the Swedish Research Council, Strategic Research Area (Dnr 2009-1039). AL was supported by the DiaUnion collaborative study, co-financed by EU Interreg ÖKS, Capital Region of Denmark, Region Skåne and the Novo Nordisk Foundation.
Collapse
Affiliation(s)
- Alexander Lind
- Department of Clinical Sciences, Lund University CRC, Malmö, Sweden
| | - Eva Freyhult
- Department of Cell and Molecular Biology, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | | | - Anita Ramelius
- Department of Clinical Sciences, Lund University CRC, Malmö, Sweden
| | - Rasmus Bennet
- Department of Clinical Sciences, Lund University CRC, Malmö, Sweden
| | | | - David Seftel
- Enable Biosciences Inc., South San Francisco, CA, USA
| | - David Gebhart
- Enable Biosciences Inc., South San Francisco, CA, USA
| | | | - Marlena Maziarz
- Department of Clinical Sciences, Lund University CRC, Malmö, Sweden
| | | | - Markus Lundgren
- Department of Clinical Sciences, Lund University CRC, Malmö, Sweden
| | | | | | - Malin Fex
- Department of Clinical Sciences, Lund University CRC, Malmö, Sweden
| | - Carina Törn
- Department of Clinical Sciences, Lund University CRC, Malmö, Sweden
| | - Daniel Agardh
- Department of Clinical Sciences, Lund University CRC, Malmö, Sweden
| | | | - Åke Lernmark
- Department of Clinical Sciences, Lund University CRC, Malmö, Sweden.
| |
Collapse
|
2
|
Limbert C, von dem Berge T, Danne T. Personalizing Early-Stage Type 1 Diabetes in Children. Diabetes Care 2023; 46:1747-1749. [PMID: 37729506 DOI: 10.2337/dci23-0025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 07/13/2023] [Indexed: 09/22/2023]
Affiliation(s)
- Catarina Limbert
- Unit of Paediatric Endocrinology and Diabetes, Hospital Dona Estefânia, Lisbon, Portugal
- Comprehensive Health Research Centre, NOVA Medical School, Universidade NOVA de Lisboa, Lisbon, Portugal
| | | | - Thomas Danne
- Children's Hospital AUF DER BULT, Hannover, Germany
- Hannover Medical School, Hannover, Germany
| |
Collapse
|
3
|
Lernmark Å, Akolkar B, Hagopian W, Krischer J, McIndoe R, Rewers M, Toppari J, Vehik K, Ziegler AG. Possible heterogeneity of initial pancreatic islet beta-cell autoimmunity heralding type 1 diabetes. J Intern Med 2023; 294:145-158. [PMID: 37143363 PMCID: PMC10524683 DOI: 10.1111/joim.13648] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
The etiology of type 1 diabetes (T1D) foreshadows the pancreatic islet beta-cell autoimmune pathogenesis that heralds the clinical onset of T1D. Standardized and harmonized tests of autoantibodies against insulin (IAA), glutamic acid decarboxylase (GADA), islet antigen-2 (IA-2A), and ZnT8 transporter (ZnT8A) allowed children to be followed from birth until the appearance of a first islet autoantibody. In the Environmental Determinants of Diabetes in the Young (TEDDY) study, a multicenter (Finland, Germany, Sweden, and the United States) observational study, children were identified at birth for the T1D high-risk HLA haploid genotypes DQ2/DQ8, DQ2/DQ2, DQ8/DQ8, and DQ4/DQ8. The TEDDY study was preceded by smaller studies in Finland, Germany, Colorado, Washington, and Sweden. The aims were to follow children at increased genetic risk to identify environmental factors that trigger the first-appearing autoantibody (etiology) and progress to T1D (pathogenesis). The larger TEDDY study found that the incidence rate of the first-appearing autoantibody was split into two patterns. IAA first peaked already during the first year of life and tapered off by 3-4 years of age. GADA first appeared by 2-3 years of age to reach a plateau by about 4 years. Prior to the first-appearing autoantibody, genetic variants were either common or unique to either pattern. A split was also observed in whole blood transcriptomics, metabolomics, dietary factors, and exposures such as gestational life events and early infections associated with prolonged shedding of virus. An innate immune reaction prior to the adaptive response cannot be excluded. Clarifying the mechanisms by which autoimmunity is triggered to either insulin or GAD65 is key to uncovering the etiology of autoimmune T1D.
Collapse
Affiliation(s)
- Åke Lernmark
- Department of Clinical Sciences, Lund University CRC, Skåne University Hospital, Malmö, Sweden
| | - Beena Akolkar
- National Institute of Diabetes & Digestive & Kidney Diseases, Bethesda, MD USA
| | | | - Jeffrey Krischer
- Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL USA
| | - Richard McIndoe
- Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, Augusta, GA USA
| | - Marian Rewers
- Barbara Davis Center for Diabetes, University of Colorado, Aurora, Colorado USA
| | - Jorma Toppari
- Department of Pediatrics, Turku University Hospital, and Institute of Biomedicine, Research Centre for Integrated Physiology and Pharmacology, University of Turku, Turku, Finland
| | - Kendra Vehik
- Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL USA
| | - Anette-G. Ziegler
- Institute of Diabetes Research, Helmholtz Zentrum München, and Klinikum rechts der Isar, Technische Universität München, and Forschergruppe Diabetes e.V., Neuherberg, Germany
| | | |
Collapse
|
4
|
Morales JF, Muse R, Podichetty JT, Burton J, David S, Lang P, Schmidt S, Romero K, O'Doherty I, Martin F, Campbell‐Thompson M, Haller MJ, Atkinson MA, Kim S. Disease progression joint model predicts time to type 1 diabetes onset: Optimizing future type 1 diabetes prevention studies. CPT Pharmacometrics Syst Pharmacol 2023; 12:1016-1028. [PMID: 37186151 PMCID: PMC10349195 DOI: 10.1002/psp4.12973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 04/07/2023] [Accepted: 04/11/2023] [Indexed: 05/17/2023] Open
Abstract
Clinical trials seeking type 1 diabetes prevention are challenging in terms of identifying patient populations likely to progress to type 1 diabetes within limited (i.e., short-term) trial durations. Hence, we sought to improve such efforts by developing a quantitative disease progression model for type 1 diabetes. Individual-level data obtained from the TrialNet Pathway to Prevention and The Environmental Determinants of Diabetes in the Young natural history studies were used to develop a joint model that links the longitudinal glycemic measure to the timing of type 1 diabetes diagnosis. Baseline covariates were assessed using a stepwise covariate modeling approach. Our study focused on individuals at risk of developing type 1 diabetes with the presence of two or more diabetes-related autoantibodies (AAbs). The developed model successfully quantified how patient features measured at baseline, including HbA1c and the presence of different AAbs, alter the timing of type 1 diabetes diagnosis with reasonable accuracy and precision (<30% RSE). In addition, selected covariates were statistically significant (p < 0.0001 Wald test). The Weibull model best captured the timing to type 1 diabetes diagnosis. The 2-h oral glucose tolerance values assessed at each visit were included as a time-varying biomarker, which was best quantified using the sigmoid maximum effect function. This model provides a framework to quantitatively predict and simulate the time to type 1 diabetes diagnosis in individuals at risk of developing the disease and thus, aligns with the needs of pharmaceutical companies and scientists seeking to advance therapies aimed at interdicting the disease process.
Collapse
Affiliation(s)
- Juan Francisco Morales
- Department of Pharmaceutics, Center for Pharmacometrics and Systems Pharmacology, College of PharmacyUniversity of FloridaFloridaOrlandoUSA
| | | | | | | | | | | | - Stephan Schmidt
- Department of Pharmaceutics, Center for Pharmacometrics and Systems Pharmacology, College of PharmacyUniversity of FloridaFloridaOrlandoUSA
| | | | | | | | - Martha Campbell‐Thompson
- Department of Pathology, Immunology, and Laboratory MedicineDiabetes Institute, College of Medicine, University of FloridaFloridaGainesvilleUSA
| | - Michael J. Haller
- Department of PediatricsDiabetes Institute, College of Medicine, University of FloridaFloridaGainesvilleUSA
| | - Mark A. Atkinson
- Department of Pathology, Immunology, and Laboratory MedicineDiabetes Institute, College of Medicine, University of FloridaFloridaGainesvilleUSA
- Department of PediatricsDiabetes Institute, College of Medicine, University of FloridaFloridaGainesvilleUSA
| | - Sarah Kim
- Department of Pharmaceutics, Center for Pharmacometrics and Systems Pharmacology, College of PharmacyUniversity of FloridaFloridaOrlandoUSA
| |
Collapse
|
5
|
Marzinotto I, Pittman DL, Williams AJK, Long AE, Achenbach P, Schlosser M, Akolkar B, Winter WE, Lampasona V. Islet Autoantibody Standardization Program: interlaboratory comparison of insulin autoantibody assay performance in 2018 and 2020 workshops. Diabetologia 2023; 66:897-912. [PMID: 36759347 PMCID: PMC10036445 DOI: 10.1007/s00125-023-05877-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 12/21/2022] [Indexed: 02/11/2023]
Abstract
AIMS/HYPOTHESIS The Islet Autoantibody Standardization Program (IASP) aims to improve the performance of immunoassays measuring autoantibodies in type 1 diabetes and the concordance of results across laboratories. IASP organises international workshops distributing anonymised serum samples to participating laboratories and centralises the collection and analysis of results. In this report, we describe the results of assays measuring IAA submitted to the IASP 2018 and 2020 workshops. METHODS The IASP distributed uniquely coded sera from individuals with new-onset type 1 diabetes, multiple islet autoantibody-positive individuals, and diabetes-free blood donors in both 2018 and 2020. Serial dilutions of the anti-insulin mouse monoclonal antibody HUI-018 were also included. Sensitivity, specificity, area under the receiver operating characteristic curve (ROC-AUC), partial ROC-AUC at 95% specificity (pAUC95) and concordance of qualitative/quantitative results were compared across assays. RESULTS Results from 45 IAA assays of seven different formats and from 37 IAA assays of six different formats were submitted to the IASP in 2018 and 2020, respectively. The median ROC-AUC was 0.736 (IQR 0.617-0.803) and 0.790 (IQR 0.730-0.836), while the median pAUC95 was 0.016 (IQR 0.004-0.021) and 0.023 (IQR 0.014-0.026) in the 2018 and 2020 workshops, respectively. Assays largely differed in AUC (IASP 2018 range 0.232-0.874; IASP 2020 range 0.379-0.924) and pAUC95 (IASP 2018 and IASP 2020 range 0-0.032). CONCLUSIONS/INTERPRETATION Assay formats submitted to this study showed heterogeneous performance. Despite the high variability across laboratories, the in-house radiobinding assay (RBA) remains the gold standard for IAA measurement. However, novel non-radioactive IAA immunoassays showed a good performance and, if further improved, might be considered valid alternatives to RBAs.
Collapse
Affiliation(s)
- Ilaria Marzinotto
- San Raffaele Diabetes Research Institute, San Raffaele Scientific Institute, Milan, Italy
| | - David L Pittman
- Department of Pathology, University of Florida, Gainesville, FL, USA
| | - Alistair J K Williams
- Diabetes and Metabolism, Translational Health Sciences, University of Bristol, Bristol, UK
| | - Anna E Long
- Diabetes and Metabolism, Translational Health Sciences, University of Bristol, Bristol, UK
| | - Peter Achenbach
- Institute of Diabetes Research, Helmholtz Munich, German Research Center for Environmental Health, Neuherberg, Germany.
| | - Michael Schlosser
- Department of General Surgery, Visceral, Thoracic and Vascular Surgery, University Medical Center Greifswald, Greifswald, Germany
- Institute of Pathophysiology, Research Group of Predictive Diagnostics, University Medical Center Greifswald, Karlsburg, Germany
| | - Beena Akolkar
- Division of Diabetes, Endocrinology, and Metabolic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA
| | - William E Winter
- Department of Pathology, University of Florida, Gainesville, FL, USA
| | - Vito Lampasona
- San Raffaele Diabetes Research Institute, San Raffaele Scientific Institute, Milan, Italy.
| |
Collapse
|
6
|
Penzenstadler J, Earp JC, Wood Heickman LK, Pluchino K. Utility of islet autoantibodies as enrichment biomarkers in type 1 diabetes clinical studies: a viewpoint from the FDA. Diabetologia 2023; 66:603-604. [PMID: 36542112 DOI: 10.1007/s00125-022-05853-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 12/02/2022] [Indexed: 12/24/2022]
Affiliation(s)
- Justin Penzenstadler
- Division of Diabetes, Lipid Disorders, and Obesity, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA.
| | - Justin C Earp
- Division of Pharmacometrics, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - Lauren K Wood Heickman
- Division of Diabetes, Lipid Disorders, and Obesity, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - Kristen Pluchino
- Division of Diabetes, Lipid Disorders, and Obesity, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA
| |
Collapse
|