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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] [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.
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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.
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2
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Felton JL, Redondo MJ, Oram RA, Speake C, Long SA, Onengut-Gumuscu S, Rich SS, Monaco GSF, Harris-Kawano A, Perez D, Saeed Z, Hoag B, Jain R, Evans-Molina C, DiMeglio LA, Ismail HM, Dabelea D, Johnson RK, Urazbayeva M, Wentworth JM, Griffin KJ, Sims EK. Islet autoantibodies as precision diagnostic tools to characterize heterogeneity in type 1 diabetes: a systematic review. COMMUNICATIONS MEDICINE 2024; 4:66. [PMID: 38582818 PMCID: PMC10998887 DOI: 10.1038/s43856-024-00478-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 03/05/2024] [Indexed: 04/08/2024] Open
Abstract
BACKGROUND Islet autoantibodies form the foundation for type 1 diabetes (T1D) diagnosis and staging, but heterogeneity exists in T1D development and presentation. We hypothesized that autoantibodies can identify heterogeneity before, at, and after T1D diagnosis, and in response to disease-modifying therapies. METHODS We systematically reviewed PubMed and EMBASE databases (6/14/2022) assessing 10 years of original research examining relationships between autoantibodies and heterogeneity before, at, after diagnosis, and in response to disease-modifying therapies in individuals at-risk or within 1 year of T1D diagnosis. A critical appraisal checklist tool for cohort studies was modified and used for risk of bias assessment. RESULTS Here we show that 152 studies that met extraction criteria most commonly characterized heterogeneity before diagnosis (91/152). Autoantibody type/target was most frequently examined, followed by autoantibody number. Recurring themes included correlations of autoantibody number, type, and titers with progression, differing phenotypes based on order of autoantibody seroconversion, and interactions with age and genetics. Only 44% specifically described autoantibody assay standardization program participation. CONCLUSIONS Current evidence most strongly supports the application of autoantibody features to more precisely define T1D before diagnosis. Our findings support continued use of pre-clinical staging paradigms based on autoantibody number and suggest that additional autoantibody features, particularly in relation to age and genetic risk, could offer more precise stratification. To improve reproducibility and applicability of autoantibody-based precision medicine in T1D, we propose a methods checklist for islet autoantibody-based manuscripts which includes use of precision medicine MeSH terms and participation in autoantibody standardization workshops.
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Affiliation(s)
- Jamie L Felton
- Department of Pediatrics, Center for Diabetes and Metabolic Diseases, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Maria J Redondo
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
- Division of Pediatric Diabetes and Endocrinology, Texas Children's Hospital, Houston, TX, USA
| | - Richard A Oram
- NIHR Exeter Biomedical Research Centre (BRC), Academic Kidney Unit, University of Exeter, Exeter, UK
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
- Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK
| | - Cate Speake
- Center for Interventional Immunology, Benaroya Research Institute, Seattle, WA, USA
| | - S Alice Long
- Center for Translational Immunology, Benaroya Research Institute, Seattle, WA, USA
| | - Suna Onengut-Gumuscu
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Gabriela S F Monaco
- Department of Pediatrics, Center for Diabetes and Metabolic Diseases, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Arianna Harris-Kawano
- Department of Pediatrics, Center for Diabetes and Metabolic Diseases, Indianapolis, IN, USA
| | - Dianna Perez
- Department of Pediatrics, Center for Diabetes and Metabolic Diseases, Indianapolis, IN, USA
| | - Zeb Saeed
- Department of Endocrinology, Diabetes and Metabolism, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Benjamin Hoag
- Department of Pediatrics, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD, USA
| | - Rashmi Jain
- Department of Pediatrics, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD, USA
| | - Carmella Evans-Molina
- Department of Pediatrics, Center for Diabetes and Metabolic Diseases, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Endocrinology, Diabetes and Metabolism, Indiana University School of Medicine, Indianapolis, IN, USA
- Richard L. Roudebush VAMC, Indianapolis, IN, USA
| | - Linda A DiMeglio
- Department of Pediatrics, Center for Diabetes and Metabolic Diseases, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Heba M Ismail
- Department of Pediatrics, Center for Diabetes and Metabolic Diseases, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Dana Dabelea
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, Aurora, CO, USA
| | - Randi K Johnson
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA
| | | | - John M Wentworth
- Royal Melbourne Hospital Department of Diabetes and Endocrinology, Parkville, VIC, Australia
- Walter and Eliza Hall Institute, Parkville, VIC, Australia
- University of Melbourne Department of Medicine, Parkville, VIC, Australia
| | - Kurt J Griffin
- Department of Pediatrics, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD, USA
- Sanford Research, Sioux Falls, SD, USA
| | - Emily K Sims
- Department of Pediatrics, Center for Diabetes and Metabolic Diseases, Indianapolis, IN, USA.
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA.
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3
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Frohnert BI, Ghalwash M, Li Y, Ng K, Dunne JL, Lundgren M, Hagopian W, Lou O, Winkler C, Toppari J, Veijola R, Anand V. Refining the Definition of Stage 1 Type 1 Diabetes: An Ontology-Driven Analysis of the Heterogeneity of Multiple Islet Autoimmunity. Diabetes Care 2023; 46:1753-1761. [PMID: 36862942 PMCID: PMC10516254 DOI: 10.2337/dc22-1960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 01/30/2023] [Indexed: 03/04/2023]
Abstract
OBJECTIVE To estimate the risk of progression to stage 3 type 1 diabetes based on varying definitions of multiple islet autoantibody positivity (mIA). RESEARCH DESIGN AND METHODS Type 1 Diabetes Intelligence (T1DI) is a combined prospective data set of children from Finland, Germany, Sweden, and the U.S. who have an increased genetic risk for type 1 diabetes. Analysis included 16,709 infants-toddlers enrolled by age 2.5 years and comparison between groups using Kaplan-Meier survival analysis. RESULTS Of 865 (5%) children with mIA, 537 (62%) progressed to type 1 diabetes. The 15-year cumulative incidence of diabetes varied from the most stringent definition (mIA/Persistent/2: two or more islet autoantibodies positive at the same visit with two or more antibodies persistent at next visit; 88% [95% CI 85-92%]) to the least stringent (mIA/Any: positivity for two islet autoantibodies without co-occurring positivity or persistence; 18% [5-40%]). Progression in mIA/Persistent/2 was significantly higher than all other groups (P < 0.0001). Intermediate stringency definitions showed intermediate risk and were significantly different than mIA/Any (P < 0.05); however, differences waned over the 2-year follow-up among those who did not subsequently reach higher stringency. Among mIA/Persistent/2 individuals with three autoantibodies, loss of one autoantibody by the 2-year follow-up was associated with accelerated progression. Age was significantly associated with time from seroconversion to mIA/Persistent/2 status and mIA to stage 3 type 1 diabetes. CONCLUSIONS The 15-year risk of progression to type 1 diabetes risk varies markedly from 18 to 88% based on the stringency of mIA definition. While initial categorization identifies highest-risk individuals, short-term follow-up over 2 years may help stratify evolving risk, especially for those with less stringent definitions of mIA.
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Affiliation(s)
| | - Mohamed Ghalwash
- Center for Computational Health at IBM Research at IBM T.J. Watson Research Center, Yorktown Heights, NY
- Ain Shams University, Cairo, Egypt
| | - Ying Li
- Center for Computational Health at IBM Research at IBM T.J. Watson Research Center, Yorktown Heights, NY
| | - Kenney Ng
- Center for Computational Health at IBM Research at IBM T.J. Watson Research Center, Cambridge, MA
| | | | - Markus Lundgren
- Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
- Department of Pediatrics, Kristianstad Hospital, Kristianstad, Sweden
| | | | | | - Christiane Winkler
- Institute of Diabetes Research, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich-Neuherberg, Munich, Germany
- Forschergruppe Diabetes e.V. at Helmholtz Zentrum, Munich, Germany
| | - Jorma Toppari
- Institute of Biomedicine and Population Research Centre, University of Turku and Department of Pediatrics, Turku University Hospital, Turku, Finland
| | - Riitta Veijola
- Department of Pediatrics, PEDEGO Research Unit, University of Oulu and Oulu University Hospital, Oulu, Finland
| | - Vibha Anand
- Center for Computational Health at IBM Research at IBM T.J. Watson Research Center, Cambridge, MA
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4
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Sacks DB, Arnold M, Bakris GL, Bruns DE, Horvath AR, Lernmark Å, Metzger BE, Nathan DM, Kirkman MS. Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus. Diabetes Care 2023; 46:e151-e199. [PMID: 37471273 PMCID: PMC10516260 DOI: 10.2337/dci23-0036] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 05/11/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND Numerous laboratory tests are used in the diagnosis and management of diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially. APPROACH An expert committee compiled evidence-based recommendations for laboratory analysis in screening, diagnosis, or monitoring of diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association for Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association. CONTENT Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (HbA1c) in the blood. Glycemic control is monitored by the people with diabetes measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring (CGM) devices and also by laboratory analysis of HbA1c. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed. SUMMARY The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.
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Affiliation(s)
- David B. Sacks
- Department of Laboratory Medicine, National Institutes of Health, Bethesda, MD
| | - Mark Arnold
- Department of Chemistry, University of Iowa, Iowa City, IA
| | - George L. Bakris
- Department of Medicine, American Heart Association Comprehensive Hypertension Center, Section of Endocrinology, Diabetes and Metabolism, University of Chicago Medicine, Chicago, IL
| | - David E. Bruns
- Department of Pathology, University of Virginia Medical School, Charlottesville, VA
| | - Andrea R. Horvath
- New South Wales Health Pathology Department of Chemical Pathology, Prince of Wales Hospital, Sydney, New South Wales, Australia
| | - Åke Lernmark
- Department of Clinical Sciences, Lund University/CRC, Skane University Hospital Malmö, Malmö, Sweden
| | - Boyd E. Metzger
- Division of Endocrinology, Northwestern University, The Feinberg School of Medicine, Chicago, IL
| | - David M. Nathan
- Massachusetts General Hospital Diabetes Center and Harvard Medical School, Boston, MA
| | - M. Sue Kirkman
- Department of Medicine, University of North Carolina, Chapel Hill, NC
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5
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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
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6
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Hirvonen MK, Lietzén N, Moulder R, Bhosale SD, Koskenniemi J, Vähä-Mäkilä M, Nurmio M, Orešič M, Ilonen J, Toppari J, Veijola R, Hyöty H, Lähdesmäki H, Knip M, Cheng L, Lahesmaa R. Serum APOC1 levels are decreased in young autoantibody positive children who rapidly progress to type 1 diabetes. Sci Rep 2023; 13:15941. [PMID: 37743383 PMCID: PMC10518308 DOI: 10.1038/s41598-023-43039-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 09/18/2023] [Indexed: 09/26/2023] Open
Abstract
Better understanding of the early events in the development of type 1 diabetes is needed to improve prediction and monitoring of the disease progression during the substantially heterogeneous presymptomatic period of the beta cell damaging process. To address this concern, we used mass spectrometry-based proteomics to analyse longitudinal pre-onset plasma sample series from children positive for multiple islet autoantibodies who had rapidly progressed to type 1 diabetes before 4 years of age (n = 10) and compared these with similar measurements from matched children who were either positive for a single autoantibody (n = 10) or autoantibody negative (n = 10). Following statistical analysis of the longitudinal data, targeted serum proteomics was used to verify 11 proteins putatively associated with the disease development in a similar yet independent and larger cohort of children who progressed to the disease within 5 years of age (n = 31) and matched autoantibody negative children (n = 31). These data reiterated extensive age-related trends for protein levels in young children. Further, these analyses demonstrated that the serum levels of two peptides unique for apolipoprotein C1 (APOC1) were decreased after the appearance of the first islet autoantibody and remained relatively less abundant in children who progressed to type 1 diabetes, in comparison to autoantibody negative children.
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Affiliation(s)
- M Karoliina Hirvonen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Niina Lietzén
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Robert Moulder
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Santosh D Bhosale
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark
| | - Jaakko Koskenniemi
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
- Department of Pediatrics, University of Turku and Turku University Hospital, Turku, Finland
- Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, and Centre for Population Health Research, University of Turku, Turku, Finland
| | - Mari Vähä-Mäkilä
- Department of Pediatrics, University of Turku and Turku University Hospital, Turku, Finland
| | - Mirja Nurmio
- Department of Pediatrics, University of Turku and Turku University Hospital, Turku, Finland
| | - Matej Orešič
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- School of Medical Sciences, Örebro University, Örebro, Sweden
| | - Jorma Ilonen
- Immunogenetics Laboratory, University of Turku, Turku, Finland
| | - Jorma Toppari
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
- Department of Pediatrics, University of Turku and Turku University Hospital, Turku, Finland
- Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, and Centre for Population Health Research, University of Turku, Turku, Finland
| | - Riitta Veijola
- Department of Pediatrics, Research Unit of Clinical Medicine, Medical Research Center, University of Oulu, Oulu, Finland
- Department for Children and Adolescents, Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Heikki Hyöty
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Fimlab Laboratories, Tampere, Finland
| | - Harri Lähdesmäki
- Department of Computer Science, Aalto University School of Science, Aalto, Finland
| | - Mikael Knip
- Pediatric Research Center, New Children's Hospital, Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Pediatrics, Tampere University Hospital, Tampere, Finland
| | - Lu Cheng
- Department of Computer Science, Aalto University School of Science, Aalto, Finland.
| | - Riitta Lahesmaa
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland.
- Institute of Biomedicine, University of Turku, Turku, Finland.
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7
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Lamichhane S, Sen P, Dickens AM, Kråkström M, Ilonen J, Lempainen J, Hyöty H, Lahesmaa R, Veijola R, Toppari J, Hyötyläinen T, Knip M, Orešič M. Circulating metabolic signatures of rapid and slow progression to type 1 diabetes in islet autoantibody-positive children. Front Endocrinol (Lausanne) 2023; 14:1211015. [PMID: 37745723 PMCID: PMC10516565 DOI: 10.3389/fendo.2023.1211015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 08/18/2023] [Indexed: 09/26/2023] Open
Abstract
Aims/hypothesis Appearance of multiple islet cell autoantibodies in early life is indicative of future progression to overt type 1 diabetes, however, at varying rates. Here, we aimed to study whether distinct metabolic patterns could be identified in rapid progressors (RP, disease manifestation within 18 months after the initial seroconversion to autoantibody positivity) vs. slow progressors (SP, disease manifestation at 60 months or later from the appearance of the first autoantibody). Methods Longitudinal samples were collected from RP (n=25) and SP (n=41) groups at the ages of 3, 6, 12, 18, 24, or ≥ 36 months. We performed a comprehensive metabolomics study, analyzing both polar metabolites and lipids. The sample series included a total of 239 samples for lipidomics and 213 for polar metabolites. Results We observed that metabolites mediated by gut microbiome, such as those involved in tryptophan metabolism, were the main discriminators between RP and SP. The study identified specific circulating molecules and pathways, including amino acid (threonine), sugar derivatives (hexose), and quinic acid that may define rapid vs. slow progression to type 1 diabetes. However, the circulating lipidome did not appear to play a major role in differentiating between RP and SP. Conclusion/interpretation Our study suggests that a distinct metabolic profile is linked with the type 1 diabetes progression. The identification of specific metabolites and pathways that differentiate RP from SP may have implications for early intervention strategies to delay the development of type 1 diabetes.
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Affiliation(s)
- Santosh Lamichhane
- Turku Bioscience, University of Turku and Åbo Akademi University, Turku, Finland
| | - Partho Sen
- Turku Bioscience, University of Turku and Åbo Akademi University, Turku, Finland
| | - Alex M. Dickens
- Turku Bioscience, University of Turku and Åbo Akademi University, Turku, Finland
- Department of Chemistry, University of Turku, University, Turku, Finland
| | - Matilda Kråkström
- Turku Bioscience, University of Turku and Åbo Akademi University, Turku, Finland
| | - Jorma Ilonen
- Immunogenetics Laboratory, Institute of Biomedicine, University of Turku, Turku, Finland
| | - Johanna Lempainen
- Immunogenetics Laboratory, Institute of Biomedicine, University of Turku, Turku, Finland
- Department of Pediatrics and Adolescent Medicine, Turku University Hospital, Turku, Finland
- Clinical Microbiology, Turku University Hospital, Turku, Finland
| | - Heikki Hyöty
- Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland
- Fimlab Laboratories, Pirkanmaa Hospital District, Tampere, Finland
| | - Riitta Lahesmaa
- Turku Bioscience, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
| | - Riitta Veijola
- Department of Pediatrics, PEDEGO Research Unit, Medical Research Centre, University of Oulu, Oulu, Finland
- Department of Children and Adolescents, Oulu University Hospital, Oulu, Finland
| | - Jorma Toppari
- Department of Pediatrics and Adolescent Medicine, Turku University Hospital, Turku, Finland
- Institute of Biomedicine, Centre for Integrative Physiology and Pharmacology, and Centre for Population Health Research, University of Turku, Turku, Finland
| | | | - Mikael Knip
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Pediatrics, Tampere University Hospital, Tampere, Finland
| | - Matej Orešič
- Turku Bioscience, University of Turku and Åbo Akademi University, Turku, Finland
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
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8
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Cantley J, Eizirik DL, Latres E, Dayan CM. Islet cells in human type 1 diabetes: from recent advances to novel therapies - a symposium-based roadmap for future research. J Endocrinol 2023; 259:e230082. [PMID: 37493471 PMCID: PMC10502961 DOI: 10.1530/joe-23-0082] [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: 03/21/2023] [Accepted: 07/25/2023] [Indexed: 07/27/2023]
Abstract
There is a growing understanding that the early phases of type 1 diabetes (T1D) are characterised by a deleterious dialogue between the pancreatic beta cells and the immune system. This, combined with the urgent need to better translate this growing knowledge into novel therapies, provided the background for the JDRF-DiabetesUK-INNODIA-nPOD symposium entitled 'Islet cells in human T1D: from recent advances to novel therapies', which took place in Stockholm, Sweden, in September 2022. We provide in this article an overview of the main themes addressed in the symposium, pointing to both promising conclusions and key unmet needs that remain to be addressed in order to achieve better approaches to prevent or reverse T1D.
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Affiliation(s)
- J Cantley
- School of Medicine, University of Dundee, Dundee, United Kingdom of Great Britain and Northern Ireland
| | - D L Eizirik
- ULB Center for Diabetes Research, Université Libre de Bruxelles Faculté de Médecine, Bruxelles, Belgium
| | - E Latres
- JDRF International, New York, NY, USA
| | - C M Dayan
- Cardiff University School of Medicine, Cardiff, United Kingdom of Great Britain and Northern Ireland
| | - the JDRF-DiabetesUK-INNODIA-nPOD Stockholm Symposium 2022
- School of Medicine, University of Dundee, Dundee, United Kingdom of Great Britain and Northern Ireland
- ULB Center for Diabetes Research, Université Libre de Bruxelles Faculté de Médecine, Bruxelles, Belgium
- JDRF International, New York, NY, USA
- Cardiff University School of Medicine, Cardiff, United Kingdom of Great Britain and Northern Ireland
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Sacks DB, Arnold M, Bakris GL, Bruns DE, Horvath AR, Lernmark Å, Metzger BE, Nathan DM, Kirkman MS. Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus. Clin Chem 2023:hvad080. [PMID: 37473453 DOI: 10.1093/clinchem/hvad080] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 05/12/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND Numerous laboratory tests are used in the diagnosis and management of diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially. APPROACH An expert committee compiled evidence-based recommendations for laboratory analysis in screening, diagnosis, or monitoring of diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association of Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association. CONTENT Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (Hb A1c) in the blood. Glycemic control is monitored by the people with diabetes measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring (CGM) devices and also by laboratory analysis of Hb A1c. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed. SUMMARY The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.
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Affiliation(s)
- David B Sacks
- Department of Laboratory Medicine, National Institutes of Health, Bethesda, MD, United States
| | - Mark Arnold
- Department of Chemistry, University of Iowa, Iowa City, IA, United States
| | - George L Bakris
- Department of Medicine, American Heart Association Comprehensive Hypertension Center, Section of Endocrinology, Diabetes and Metabolism, University of Chicago Medicine, Chicago, ILUnited States
| | - David E Bruns
- Department of Pathology, University of Virginia Medical School, Charlottesville, VA, United States
| | - Andrea R Horvath
- New South Wales Health Pathology Department of Chemical Pathology, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Åke Lernmark
- Department of Clinical Sciences, Lund University/CRC, Skane University Hospital Malmö, Malmö, Sweden
| | - Boyd E Metzger
- Division of Endocrinology, Northwestern University, The Feinberg School of Medicine, Chicago, IL, United States
| | - David M Nathan
- Massachusetts General Hospital Diabetes Center and Harvard Medical School, Boston, MA, United States
| | - M Sue Kirkman
- Department of Medicine, University of North Carolina, Chapel Hill, NC, United States
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10
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Quattrin T, Mastrandrea LD, Walker LSK. Type 1 diabetes. Lancet 2023; 401:2149-2162. [PMID: 37030316 DOI: 10.1016/s0140-6736(23)00223-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 12/03/2022] [Accepted: 01/26/2023] [Indexed: 04/10/2023]
Abstract
Type 1 diabetes is a chronic disease caused by autoimmune destruction of pancreatic β cells. Individuals with type 1 diabetes are reliant on insulin for survival. Despite enhanced knowledge related to the pathophysiology of the disease, including interactions between genetic, immune, and environmental contributions, and major strides in treatment and management, disease burden remains high. Studies aimed at blocking the immune attack on β cells in people at risk or individuals with very early onset type 1 diabetes show promise in preserving endogenous insulin production. This Seminar will review the field of type 1 diabetes, highlighting recent progress within the past 5 years, challenges to clinical care, and future directions in research, including strategies to prevent, manage, and cure the disease.
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Affiliation(s)
- Teresa Quattrin
- Department of Pediatrics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA; Diabetes Center, John R Oishei Children's Hospital, Buffalo, NY, USA.
| | - Lucy D Mastrandrea
- Department of Pediatrics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA; Diabetes Center, John R Oishei Children's Hospital, Buffalo, NY, USA
| | - Lucy S K Walker
- Institute of Immunity and Transplantation, Division of Infection and Immunity, University College London, London, UK
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11
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Ghalwash M, Anand V, Lou O, Martin F, Rewers M, Ziegler AG, Toppari J, Hagopian WA, Veijola R. Islet autoantibody screening in at-risk adolescents to predict type 1 diabetes until young adulthood: a prospective cohort study. THE LANCET. CHILD & ADOLESCENT HEALTH 2023; 7:261-268. [PMID: 36681087 PMCID: PMC10038928 DOI: 10.1016/s2352-4642(22)00350-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 11/21/2022] [Accepted: 11/23/2022] [Indexed: 01/20/2023]
Abstract
BACKGROUND Screening for islet autoantibodies in children and adolescents identifies individuals who will later develop type 1 diabetes, allowing patient and family education to prevent diabetic ketoacidosis at onset and to enable consideration of preventive therapies. We aimed to assess whether islet autoantibody screening is effective for predicting type 1 diabetes in adolescents aged 10-18 years with an increased risk of developing type 1 diabetes. METHODS Data were harmonised from prospective studies from Finland (the Diabetes Prediction and Prevention study), Germany (the BABYDIAB study), and the USA (Diabetes Autoimmunity Study in the Young and the Diabetes Evaluation in Washington study). Autoantibodies against insulin, glutamic acid decarboxylase, and insulinoma-associated protein 2 were measured at each follow-up visit. Children who were lost to follow-up or diagnosed with type 1 diabetes before 10 years of age were excluded. Inverse probability censoring weighting was used to include data from remaining participants. Sensitivity and the positive predictive value of these autoantibodies, tested at one or two ages, to predict type 1 diabetes by the age of 18 years were the main outcomes. FINDINGS Of 20 303 children with an increased type 1 diabetes risk, 8682 were included for the analysis with inverse probability censoring weighting. 1890 were followed up to 18 years of age or developed type 1 diabetes between the ages of 10 years and 18 years, and their median follow-up was 18·3 years (IQR 14·5-20·3). 442 (23·4%) of 1890 adolescents were positive for at least one islet autoantibody, and 262 (13·9%) developed type 1 diabetes. Time from seroconversion to diabetes diagnosis increased by 0·64 years (95% CI 0·34-0·95) for each 1-year increment of diagnosis age (Pearson's correlation coefficient 0·88, 95% CI 0·50-0·97, p=0·0020). The median interval between the last prediagnostic sample and diagnosis was 0·3 years (IQR 0·1-1·3) in the 227 participants who were autoantibody positive and 6·8 years (1·6-9·9) for the 35 who were autoantibody negative. Single screening at the age of 10 years was 90% (95% CI 86-95) sensitive, with a positive predictive value of 66% (60-72) for clinical diabetes. Screening at two ages (10 years and 14 years) increased sensitivity to 93% (95% CI 89-97) but lowered the positive predictive value to 55% (49-60). INTERPRETATION Screening of adolescents at risk for type 1 diabetes only once at 10 years of age for islet autoantibodies was highly effective to detect type 1 diabetes by the age of 18 years, which in turn could enable prevention of diabetic ketoacidosis and participation in secondary prevention trials. FUNDING JDRF International.
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Affiliation(s)
- Mohamed Ghalwash
- Center for Computational Health, IBM Research, Yorktown Heights, NY, USA; Faculty of Science, Ain Shams University, Cairo, Egypt
| | - Vibha Anand
- Center for Computational Health, IBM Research, Cambridge, MA, USA
| | | | | | - Marian Rewers
- Barbara Davis Center for Diabetes, University of Colorado, Denver, CO, USA
| | - Anette-G Ziegler
- Forschergruppe Diabetes and Institute of Diabetes Research, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich-Neuherberg, Germany der TU München, Munich, Germany
| | - Jorma Toppari
- Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, and Centre for Population Health Research, University of Turku, Turku, Finland; Department of Pediatrics, Turku University Hospital, Turku, Finland
| | | | - Riitta Veijola
- Department of Pediatrics, Research Unit of Clinical Medicine, Medical Research Center Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland.
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12
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Mistry S, Gouripeddi R, Raman V, Facelli JC. Stratifying risk for onset of type 1 diabetes using islet autoantibody trajectory clustering. Diabetologia 2023; 66:520-534. [PMID: 36446887 PMCID: PMC10097474 DOI: 10.1007/s00125-022-05843-x] [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: 06/30/2022] [Accepted: 10/20/2022] [Indexed: 12/02/2022]
Abstract
AIMS/HYPOTHESIS Islet autoantibodies can be detected prior to the onset of type 1 diabetes and are important tools for aetiologic studies, prevention trials and disease screening. Current risk stratification models rely on the positivity status of islet autoantibodies alone, but additional autoantibody characteristics may be important for understanding disease onset. This work aimed to determine if a data-driven model incorporating characteristics of islet autoantibody development, including timing, type and titre, could stratify risk for type 1 diabetes onset. METHODS Data on autoantibodies against GAD (GADA), tyrosine phosphatase islet antigen-2 (IA-2A) and insulin (IAA) were obtained for 1,415 children enrolled in The Environmental Determinants of Diabetes in the Young study with at least one positive autoantibody measurement from years 1 to 12 of life. Unsupervised machine learning algorithms were trained to identify clusters of autoantibody development based on islet autoantibody timing, type and titre. Risk for type 1 diabetes across each identified cluster was evaluated using time-to-event analysis. RESULTS We identified 2-4 clusters in each year cohort that differed by autoantibody timing, titre and type. During the first 3 years of life, risk for type 1 diabetes onset was driven by membership in clusters with high titres of all three autoantibodies (1-year risk: 20.87-56.25%, 5-year risk: 67.73-69.19%). Type 1 diabetes risk transitioned to type-specific titres during ages 4 to 8, as clusters with high titres of IA-2A (1-year risk: 20.88-28.93%, 5-year risk: 62.73-78.78%) showed faster progression to diabetes compared with high titres of GADA (1-year risk: 4.38-6.11%, 5-year risk: 25.06-31.44%). The importance of high GADA titres decreased during ages 9 to 12, with clusters containing high titres of IA-2A alone (1-year risk: 14.82-30.93%) or both GADA and IA-2A (1-year risk: 8.27-25.00%) demonstrating increased risk. CONCLUSIONS/INTERPRETATION This unsupervised machine learning approach provides a novel tool for stratifying risk for type 1 diabetes onset using multiple autoantibody characteristics. These findings suggest that age-dependent changes in IA-2A titres modulate risk for type 1 diabetes onset across 12 years of life. Overall, this work supports incorporation of islet autoantibody timing, type and titre in risk stratification models for aetiologic studies, prevention trials and disease screening.
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Affiliation(s)
- Sejal Mistry
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Ramkiran Gouripeddi
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
- Clinical and Translational Science Institute, University of Utah, Salt Lake City, UT, USA
| | - Vandana Raman
- Division of Pediatric Endocrinology, Department of Pediatrics, University of Utah, Salt Lake City, UT, USA
| | - Julio C Facelli
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
- Clinical and Translational Science Institute, University of Utah, Salt Lake City, UT, USA.
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13
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Ferrannini E, Mari A, Monaco GSF, Skyler JS, Evans-Molina C. The effect of age on longitudinal measures of beta cell function and insulin sensitivity during the progression of early stage type 1 diabetes. Diabetologia 2023; 66:508-519. [PMID: 36459177 PMCID: PMC9716154 DOI: 10.1007/s00125-022-05836-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 10/04/2022] [Indexed: 12/04/2022]
Abstract
AIM/HYPOTHESIS The risk of progressing from autoantibody positivity to type 1 diabetes is inversely related to age. Separately, whether age influences patterns of C-peptide loss or changes in insulin sensitivity in autoantibody-positive individuals who progress to stage 3 type 1 diabetes is unclear. METHODS Beta cell function and insulin sensitivity were determined by modelling of OGTTs performed in 658 autoantibody-positive participants followed longitudinally in the Diabetes Prevention Trial-Type 1 (DPT-1). In this secondary analysis of DPT-1 data, time trajectories of beta cell function and insulin sensitivity were analysed in participants who progressed to type 1 diabetes (progressors) to address the impact of age on patterns of metabolic progression to diabetes. RESULTS Among the entire DPT-1 cohort, the highest discriminant age for type 1 diabetes risk was 14 years, with participants aged <14 years being twice as likely to progress to type 1 diabetes as those aged ≥14 years. At study entry, beta cell glucose sensitivity was impaired to a similar extent in progressors aged <14 years and progressors aged ≥14 years. From study entry to stage 3 type 1 diabetes onset, beta cell glucose sensitivity and insulin sensitivity declined in both progressor groups. However, there were no significant differences in the yearly rate of decline in either glucose sensitivity (-13.7 [21.2] vs -11.9 [21.5] pmol min-1 m-2 [mmol/l]-1, median [IQR], p=0.52) or insulin sensitivity (-22 [37] vs -14 [40] ml min-1 m-2, median [IQR], p=0.07) between progressors aged <14 years and progressors aged ≥14 years. CONCLUSIONS/INTERPRETATION Our data indicate that during progression to stage 3 type 1 diabetes, rates of change in declining glucose and insulin sensitivity are not significantly different between progressors aged <14 years and progressors aged ≥14 years. These data suggest there is a predictable course of declining metabolic function during the progression to type 1 diabetes that is not influenced by age.
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Affiliation(s)
| | | | - Gabriela S F Monaco
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
- The Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Jay S Skyler
- Diabetes Research Institute, University of Miami, Miami, FL, USA
| | - Carmella Evans-Molina
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA.
- The Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA.
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA.
- Roudebush VA Medical Center, Indianapolis, IN, USA.
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14
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Ballou C, Barton F, Payne EH, Berney T, Villard J, Meier RPH, Baidal D, Alejandro R, Robien M, Eggerman TL, Kamoun M, Muller YD. Matching for HLA-DR excluding diabetogenic HLA-DR3 and HLA-DR4 predicts insulin independence after pancreatic islet transplantation. Front Immunol 2023; 14:1110544. [PMID: 37026004 PMCID: PMC10070978 DOI: 10.3389/fimmu.2023.1110544] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 02/16/2023] [Indexed: 04/08/2023] Open
Abstract
Introduction In pancreatic islet transplantation, the exact contribution of human leukocyte antigen (HLA) matching to graft survival remains unclear. Islets may be exposed to allogenic rejection but also the recurrence of type 1 diabetes (T1D). We evaluated the HLA-DR matching, including the impact of diabetogenic HLA-DR3 or HLA-DR4 matches. Methods We retrospectively examined the HLA profile in 965 transplant recipients and 2327 islet donors. The study population was obtained from patients enrolled in the Collaborative Islet Transplant Registry. We then identified 87 recipients who received a single-islet infusion. Islet-kidney recipients, 2nd islet infusion, and patients with missing data were excluded from the analysis (n=878). Results HLA-DR3 and HLA-DR4 were present in 29.7% and 32.6% of T1D recipients and 11.6% and 15.8% of the donors, respectively. We identified 52 T1D islet recipients mismatched for HLA-DR (group A), 11 with 1 or 2 HLA-DR-matches but excluding HLA-DR3 and HLA- DR4 (group B), and 24 matched for HLA-DR3 or HLA-DR4 (group C). Insulin-independence was maintained in a significantly higher percentage of group B recipients from year one through five post-transplantation (p<0.01). At five-year post-transplantation, 78% of group B was insulin-independent compared to 24% (group A) and 35% (group C). Insulin-independence correlated with significantly better glycemic control (HbA1c <7%), fasting blood glucose, and reduced severe hypoglycemic events. Matching HLA-A-B-DR (≥3) independently of HLA- DR3 or HLA-DR4 matching did not improve graft survival. Conclusion This study suggests that matching HLA-DR but excluding the diabetogenic HLA-DR3 and/or 4 is a significant predictor for long-term islet survival.
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Affiliation(s)
- Cassandra Ballou
- Collaborative Islet Transplant Registry Coordinating Center, The EMMES Company, LLC, Rockville, MD, United States
- *Correspondence: Yannick D. Muller, ; Cassandra Ballou,
| | - Franca Barton
- Collaborative Islet Transplant Registry Coordinating Center, The EMMES Company, LLC, Rockville, MD, United States
| | - Elizabeth H. Payne
- Collaborative Islet Transplant Registry Coordinating Center, The EMMES Company, LLC, Rockville, MD, United States
| | - Thierry Berney
- Division of Transplantation, Department of Surgery, Geneva University Hospitals, Geneva, Switzerland
| | - Jean Villard
- Department of Genetic, Laboratory and Pathology Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Raphael P. H. Meier
- Department of Surgery, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - David Baidal
- Department of Medicine and the Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Rodolfo Alejandro
- Department of Medicine and the Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Mark Robien
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, United States
| | - Thomas L. Eggerman
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States
| | - Malek Kamoun
- Immunology and Histocompatibility Testing Laboratory, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Yannick D. Muller
- Division of Immunology and Allergy, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
- *Correspondence: Yannick D. Muller, ; Cassandra Ballou,
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15
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Ng K, Anand V, Stavropoulos H, Veijola R, Toppari J, Maziarz M, Lundgren M, Waugh K, Frohnert BI, Martin F, Lou O, Hagopian W, Achenbach P. Quantifying the utility of islet autoantibody levels in the prediction of type 1 diabetes in children. Diabetologia 2023; 66:93-104. [PMID: 36195673 PMCID: PMC9729160 DOI: 10.1007/s00125-022-05799-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 08/02/2022] [Indexed: 12/14/2022]
Abstract
AIMS/HYPOTHESIS The aim of this study was to explore the utility of islet autoantibody (IAb) levels for the prediction of type 1 diabetes in autoantibody-positive children. METHODS Prospective cohort studies in Finland, Germany, Sweden and the USA followed 24,662 children at increased genetic or familial risk of developing islet autoimmunity and diabetes. For the 1403 who developed IAbs (523 of whom developed diabetes), levels of autoantibodies against insulin (IAA), glutamic acid decarboxylase (GADA) and insulinoma-associated antigen-2 (IA-2A) were harmonised for analysis. Diabetes prediction models using multivariate logistic regression with inverse probability censored weighting (IPCW) were trained using 10-fold cross-validation. Discriminative power for disease was estimated using the IPCW concordance index (C index) with 95% CI estimated via bootstrap. RESULTS A baseline model with covariates for data source, sex, diabetes family history, HLA risk group and age at seroconversion with a 10-year follow-up period yielded a C index of 0.61 (95% CI 0.58, 0.63). The performance improved after adding the IAb positivity status for IAA, GADA and IA-2A at seroconversion: C index 0.72 (95% CI 0.71, 0.74). Using the IAb levels instead of positivity indicators resulted in even better performance: C index 0.76 (95% CI 0.74, 0.77). The predictive power was maintained when using the IAb levels alone: C index 0.76 (95% CI 0.75, 0.76). The prediction was better for shorter follow-up periods, with a C index of 0.82 (95% CI 0.81, 0.83) at 2 years, and remained reasonable for longer follow-up periods, with a C index of 0.76 (95% CI 0.75, 0.76) at 11 years. Inclusion of the results of a third IAb test added to the predictive power, and a suitable interval between seroconversion and the third test was approximately 1.5 years, with a C index of 0.78 (95% CI 0.77, 0.78) at 10 years follow-up. CONCLUSIONS/INTERPRETATION Consideration of quantitative patterns of IAb levels improved the predictive power for type 1 diabetes in IAb-positive children beyond qualitative IAb positivity status.
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Affiliation(s)
| | | | | | - Riitta Veijola
- Department of Pediatrics, PEDEGO Research Unit, University of Oulu and Oulu University Hospital, Oulu, Finland
| | - Jorma Toppari
- Institute of Biomedicine and Centre for Population Health Research, University of Turku, Turku, Finland
- Department of Pediatrics, Turku University Hospital, Turku, Finland
| | - Marlena Maziarz
- Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
| | - Markus Lundgren
- Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
- Department of Pediatrics, Kristianstad Hospital, Kristianstad, Sweden
| | - Kathy Waugh
- Barbara Davis Center for Diabetes, University of Colorado, Denver, CO, USA
| | | | | | | | | | - Peter Achenbach
- Institute of Diabetes Research, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich-Neuherberg, Germany.
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16
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Kwon BC, Achenbach P, Anand V, Frohnert BI, Hagopian W, Hu J, Koski E, Lernmark Å, Lou O, Martin F, Ng K, Toppari J, Veijola R. Islet Autoantibody Levels Differentiate Progression Trajectories in Individuals With Presymptomatic Type 1 Diabetes. Diabetes 2022; 71:2632-2641. [PMID: 36112006 PMCID: PMC9750947 DOI: 10.2337/db22-0360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 08/29/2022] [Indexed: 01/24/2023]
Abstract
In our previous data-driven analysis of evolving patterns of islet autoantibodies (IAb) against insulin (IAA), GAD (GADA), and islet antigen 2 (IA-2A), we discovered three trajectories, characterized according to multiple IAb (TR1), IAA (TR2), or GADA (TR3) as the first appearing autoantibodies. Here we examined the evolution of IAb levels within these trajectories in 2,145 IAb-positive participants followed from early life and compared those who progressed to type 1 diabetes (n = 643) with those remaining undiagnosed (n = 1,502). With use of thresholds determined by 5-year diabetes risk, four levels were defined for each IAb and overlaid onto each visit. In diagnosed participants, high IAA levels were seen in TR1 and TR2 at ages <3 years, whereas IAA remained at lower levels in the undiagnosed. Proportions of dwell times (total duration of follow-up at a given level) at the four IAb levels differed between the diagnosed and undiagnosed for GADA and IA-2A in all three trajectories (P < 0.001), but for IAA dwell times differed only within TR2 (P < 0.05). Overall, undiagnosed participants more frequently had low IAb levels and later appearance of IAb than diagnosed participants. In conclusion, while it has long been appreciated that the number of autoantibodies is an important predictor of type 1 diabetes, consideration of autoantibody levels within the three autoimmune trajectories improved differentiation of IAb-positive children who progressed to type 1 diabetes from those who did not.
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Affiliation(s)
- Bum Chul Kwon
- Center for Computational Health, IBM Research, Cambridge, MA
- Corresponding author: Bum Chul Kwon,
| | - Peter Achenbach
- Institute of Diabetes Research, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich-Neuherberg, Germany
| | - Vibha Anand
- Center for Computational Health, IBM Research, Cambridge, MA
| | | | | | - Jianying Hu
- Center for Computational Health, IBM Research, Yorktown Heights, NY
| | - Eileen Koski
- Center for Computational Health, IBM Research, Yorktown Heights, NY
| | - Åke Lernmark
- Department of Clinical Sciences Malmö, Lund University CRC, Skåne University Hospital, Malmö, Sweden
| | | | | | - Kenney Ng
- Center for Computational Health, IBM Research, Cambridge, MA
| | - Jorma Toppari
- Institute of Biomedicine and Centre for Population Health Research, University of Turku, and Department of Pediatrics, Turku University Hospital, Turku, Finland
| | - Riitta Veijola
- Medical Research Center, PEDEGO Research Unit, Department of Pediatrics, University of Oulu and Oulu University Hospital, Oulu, Finland
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17
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Besser REJ, Bell KJ, Couper JJ, Ziegler AG, Wherrett DK, Knip M, Speake C, Casteels K, Driscoll KA, Jacobsen L, Craig ME, Haller MJ. ISPAD Clinical Practice Consensus Guidelines 2022: Stages of type 1 diabetes in children and adolescents. Pediatr Diabetes 2022; 23:1175-1187. [PMID: 36177823 DOI: 10.1111/pedi.13410] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 08/29/2022] [Indexed: 12/29/2022] Open
Affiliation(s)
- Rachel E J Besser
- Wellcome Centre for Human Genetics, NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Kirstine J Bell
- Charles Perkins Centre and Faculty Medicine and Health, University of Sydney, Sydney, Australia
| | - Jenny J Couper
- Department of Pediatrics, University of Adelaide, South Australia, Australia.,Robinson Research Institute, University of Adelaide, Adelaide, Australia
| | - Anette-G Ziegler
- Institute of Diabetes Research, Helmholtz Zentrum München, and Forschergruppe Diabetes, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Diane K Wherrett
- Division of Endocrinology, Department of Pediatrics, Hospital for Sick Children, University of Toronto, Toronto, Canada
| | - Mikael Knip
- Children's Hospital, University of Helsinki, Helsinki, Finland
| | - Cate Speake
- Center for Interventional Immunology, Benaroya Research Institute at Virginia Mason, Seattle, Washington, USA
| | - Kristina Casteels
- Department of Pediatrics, University Hospitals Leuven, Leuven, Belgium.,Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Kimberly A Driscoll
- Department of Clinical and Health Psychology, University of Florida, Gainesville, Florida, USA
| | - Laura Jacobsen
- Division of Endocrinology, Department of Pediatrics, University of Florida, Gainesville, Florida, USA
| | - Maria E Craig
- Department of Pediatrics, The Children's Hospital at Westmead, University of Sydney, Sydney, Australia
| | - Michael J Haller
- Division of Endocrinology, Department of Pediatrics, University of Florida, Gainesville, Florida, USA
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18
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Krischer JP, Liu X, Lernmark Å, Hagopian WA, Rewers MJ, She JX, Toppari J, Ziegler AG, Akolkar B. Predictors of the Initiation of Islet Autoimmunity and Progression to Multiple Autoantibodies and Clinical Diabetes: The TEDDY Study. Diabetes Care 2022; 45:2271-2281. [PMID: 36150053 PMCID: PMC9643148 DOI: 10.2337/dc21-2612] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 06/16/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To distinguish among predictors of seroconversion, progression to multiple autoantibodies and from multiple autoantibodies to type 1 diabetes in young children. RESEARCH DESIGN AND METHODS Genetically high-risk newborns (n = 8,502) were followed for a median of 11.2 years (interquartile range 9.3-12.6); 835 (9.8%) developed islet autoantibodies and 283 (3.3%) were diagnosed with type 1 diabetes. Predictors were examined using Cox proportional hazards models. RESULTS Predictors of seroconversion and progression differed, depending on the type of first appearing autoantibody. Male sex, Finnish residence, having a sibling with type 1 diabetes, the HLA DR4 allele, probiotic use before age 28 days, and single nucleotide polymorphism (SNP) rs689_A (INS) predicted seroconversion to IAA-first (having islet autoantibody to insulin as the first appearing autoantibody). Increased weight at 12 months and SNPs rs12708716_G (CLEC16A) and rs2292239_T (ERBB3) predicted GADA-first (autoantibody to GAD as the first appearing). For those having a father with type 1 diabetes, the SNPs rs2476601_A (PTPN22) and rs3184504_T (SH2B3) predicted both. Younger age at seroconversion predicted progression from single to multiple autoantibodies as well as progression to diabetes, except for those presenting with GADA-first. Family history of type 1 diabetes and the HLA DR4 allele predicted progression to multiple autoantibodies but not diabetes. Sex did not predict progression to multiple autoantibodies, but males progressed more slowly than females from multiple autoantibodies to diabetes. SKAP2 and MIR3681HG SNPs are newly reported to be significantly associated with progression from multiple autoantibodies to type 1 diabetes. CONCLUSIONS Predictors of IAA-first versus GADA-first autoimmunity differ from each other and from the predictors of progression to diabetes.
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Affiliation(s)
- Jeffrey P. Krischer
- Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL
| | - Xiang Liu
- Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL
| | - Åke Lernmark
- Department of Clinical Sciences, Lund University Clinical Research Centre, Skåne University Hospital, Malmo, Sweden
| | | | - Marian J. Rewers
- Barbara Davis Center for Childhood Diabetes, University of Colorado, Aurora, CO
| | | | - Jorma Toppari
- Department of Pediatrics, Turku University Hospital, Turku, Finland
- Research Centre for Integrated Physiology and Pharmacology and Centre for Population Health Research, Institute of Biomedicine, University of Turku, Turku, Finland
| | - Anette-G. Ziegler
- Institute of Diabetes Research, Helmholtz Zentrum München, Klinikum rechts der Isar, Technische Universität München, and Forschergruppe Diabetes e.V., Neuherberg, Germany
| | - Beena Akolkar
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD
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19
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Kero J, Koskenniemi JJ, Karsikas S, Pokka T, Lou O, Toppari J, Veijola R. INnoVative trial design for testing the Efficacy, Safety and Tolerability of 6-month treatment with incretin-based therapy to prevent type 1 DIAbetes in autoantibody positive participants: A protocol for three parallel double-blind, randomised controlled trials (INVESTDIA). Diabet Med 2022; 39:e14913. [PMID: 35797241 PMCID: PMC9540026 DOI: 10.1111/dme.14913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 06/30/2022] [Accepted: 07/06/2022] [Indexed: 11/28/2022]
Abstract
AIMS β-cell stress and dysfunction may contribute to islet autoimmunity and progression to clinical type 1 diabetes. We present a protocol of three randomised controlled trials assessing the effects of glucagon-like peptide 1 (GLP - 1) analogue liraglutide in three early stages of type 1 diabetes. METHODS We will test 10- to 30-year-old people with multiple islet autoantibodies for their glucose metabolism and randomise participants with stage 1 (multiple islet autoantibodies and normoglycaemia), stage 2 (multiple islet autoantibodies and dysglycaemia) and early stage 3 (clinical diagnosis) type 1 diabetes, 10-14 persons in each, to a 6-month intervention with liraglutide or placebo with 6-month follow-up in the stage 2 and stage 3 trials and 18-month follow-up in the stage 1 trial. Primary efficacy outcome in the stage 1 and stage 2 trials is a first-phase insulin response in an intravenous glucose tolerance test and C-peptide area under the curve in a 2-h mixed-meal tolerance test in the stage 3 trial. In addition, safety and tolerability of liraglutide treatment will be assessed. CONCLUSIONS Most prevention trials of type 1 diabetes have targeted the immune system. Treatment with GLP-1 analogue liraglutide supports the pancreatic β-cells, which should likewise attenuate islet autoimmunity. Our innovative study design allows simultaneous investigation of an intervention in three groups of people who represent various early stages of type 1 diabetes and maximises the eligibility to participate. TRIAL REGISTRATION NCT02611232 (stage 1 trial), NCT02898506 (stage 2 trial), NCT02908087 (stage 3 trial).
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Affiliation(s)
- Jukka Kero
- Research Centre for Integrative Physiology and Pharmacology, Institute of BiomedicineUniversity of TurkuTurkuFinland
- Department of PaediatricsTurku University HospitalTurkuFinland
- Centre for Population Health ResearchUniversity of Turku and Turku University HospitalTurkuFinland
| | - Jaakko J. Koskenniemi
- Research Centre for Integrative Physiology and Pharmacology, Institute of BiomedicineUniversity of TurkuTurkuFinland
- Department of PaediatricsTurku University HospitalTurkuFinland
- Centre for Population Health ResearchUniversity of Turku and Turku University HospitalTurkuFinland
| | - Sara Karsikas
- Department of PaediatricsTurku University HospitalTurkuFinland
| | - Tytti Pokka
- Department for Children and AdolescentsOulu University HospitalOuluFinland
- Department of Paediatrics, PEDEGO Research UnitMRC Oulu, University of OuluOuluFinland
| | | | - Jorma Toppari
- Research Centre for Integrative Physiology and Pharmacology, Institute of BiomedicineUniversity of TurkuTurkuFinland
- Centre for Population Health ResearchUniversity of Turku and Turku University HospitalTurkuFinland
| | - Riitta Veijola
- Department for Children and AdolescentsOulu University HospitalOuluFinland
- Department of Paediatrics, PEDEGO Research UnitMRC Oulu, University of OuluOuluFinland
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20
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Kontola H, Alanko I, Koskenniemi JJ, Löyttyniemi E, Itoshima S, Knip M, Veijola R, Toppari J, Kero J. Exploring Minimally Invasive Approach to Define Stages of Type 1 Diabetes Remotely. Diabetes Technol Ther 2022; 24:655-665. [PMID: 35653748 DOI: 10.1089/dia.2021.0554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Objective: New methods are pivotal in accurately predicting, monitoring, and diagnosing the clinical manifestation of type 1 diabetes (T1D) in high-risk children. Continuous glucose monitoring (CGM) is a valuable tool for patients with T1D, but there is still a knowledge gap regarding its utility in the prediction of diabetes. The current study explored whether 10-day CGM or CGM during an oral glucose tolerance test (OGTT) performed in the laboratory or at home (home-OGTT) could be accurate in detecting stages of T1D. Research Design and Methods: Forty-six subjects 4-25 years of age carrying genetic risk for T1D were recruited and classified into the following groups: islet autoantibody (IAb) negative, one IAb, and stages 1-3 of T1D, based on the laboratory OGTT and IAb results at baseline. A 10-day CGM was initiated before the OGTT. Results: In this study, we showed that CGM was sensitive in detecting asymptomatic individuals at stage 3, and dysglycemic individuals in stage 2 of T1D both during OGTT and the 10-day period. CGM also showed significant differences in several variables during the 10-day sensoring among individuals at different stages of T1D. Furthermore, CGM showed different OGTT profiles and detected significantly more abnormal OGTT results when compared with plasma glucose. Conclusions: CGM together with home-OGTT could detect stages of T1D and offer an alternative method to confirm normoglycemia in high-risk individuals.
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Affiliation(s)
- Helena Kontola
- Department of Pediatrics, Turku University Hospital, Turku, Finland
| | - Inka Alanko
- Department of Pediatrics, Turku University Hospital, Turku, Finland
| | - Jaakko J Koskenniemi
- Department of Pediatrics, Turku University Hospital, Turku, Finland
- Research Center for Integrative Physiology and Pharmacology, and Center for Population Health Research, Institute of Biomedicine, University of Turku, Turku, Finland
| | | | - Saori Itoshima
- Department of Pediatrics, Turku University Hospital, Turku, Finland
- Research Center for Integrative Physiology and Pharmacology, and Center for Population Health Research, Institute of Biomedicine, University of Turku, Turku, Finland
| | - Mikael Knip
- Pediatric Research Center, New Children's Hospital, Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Center for Child Health Research, Tampere University Hospital, Tampere, Finland
| | - Riitta Veijola
- Department of Children and Adolescents, Oulu University Hospital, Oulu, Finland
- PEDEGO Research Unit, Department of Pediatrics, Medical Research Center, University of Oulu, Oulu, Finland
| | - Jorma Toppari
- Department of Pediatrics, Turku University Hospital, Turku, Finland
- Research Center for Integrative Physiology and Pharmacology, and Center for Population Health Research, Institute of Biomedicine, University of Turku, Turku, Finland
| | - Jukka Kero
- Department of Pediatrics, Turku University Hospital, Turku, Finland
- Research Center for Integrative Physiology and Pharmacology, and Center for Population Health Research, Institute of Biomedicine, University of Turku, Turku, Finland
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21
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Ghalwash M, Dunne JL, Lundgren M, Rewers M, Ziegler AG, Anand V, Toppari J, Veijola R, Hagopian W. Two-age islet-autoantibody screening for childhood type 1 diabetes: a prospective cohort study. Lancet Diabetes Endocrinol 2022; 10:589-596. [PMID: 35803296 PMCID: PMC10040253 DOI: 10.1016/s2213-8587(22)00141-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 04/25/2022] [Accepted: 04/27/2022] [Indexed: 01/08/2023]
Abstract
BACKGROUND Early prediction of childhood type 1 diabetes reduces ketoacidosis at diagnosis and provides opportunities for disease prevention. However, only highly efficient approaches are likely to succeed in public health settings. We sought to identify efficient strategies for initial islet autoantibody screening in children younger than 15 years. METHODS We harmonised data from five prospective cohorts from Finland (DIPP), Germany (BABYDIAB), Sweden (DiPiS), and the USA (DAISY and DEW-IT) into the Type 1 Diabetes Intelligence (T1DI) cohort. 24 662 children at high risk of diabetes enrolled before age 2 years were included and followed up for islet autoantibodies and diabetes until age 15 years, or type 1 diabetes onset, whichever occurred first. Islet autoantibodies measured included those against glutamic acid decarboxylase, insulinoma antigen 2, and insulin. Main outcomes were sensitivity and positive predictive value (PPV) of detected islet autoantibodies, tested at one or two fixed ages, for diagnosis of clinical type 1 diabetes. FINDINGS Of the 24 662 participants enrolled in the Type 1 Diabetes Intelligence cohort, 6722 total were followed up to age 15 years or until onset of type 1 diabetes. Type 1 diabetes developed by age 15 years in 672 children, but did not develop in 6050 children. Optimal screening ages for two measurements were 2 years and 6 years, yielding sensitivity of 82% (95% CI 79-86) and PPV of 79% (95% CI 75-80) for diabetes by age 15 years. Autoantibody positivity at the beginning of each test age was highly predictive of diagnosis in the subsequent 2-5·99 year or 6-15-year age intervals. Autoantibodies usually appeared before age 6 years even in children diagnosed with diabetes much later in childhood. INTERPRETATION Our results show that initial screening for islet autoantibodies at two ages (2 years and 6 years) is sensitive and efficient for public health translation but might require adjustment by country on the basis of population-specific disease characteristics. FUNDING Juvenile Diabetes Research Foundation.
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Affiliation(s)
- Mohamed Ghalwash
- Center for Computational Health, IBM Research, Yorktown Heights, NY, USA; Faculty of Science, Ain Shams University, Cairo, Egypt
| | | | - Markus Lundgren
- Department of Clinical Sciences Malmö, Lund University/Clinical Research Centre, Skåne University Hospital, Malmö, Sweden
| | - Marian Rewers
- Barbara Davis Center for Diabetes, University of Colorado, Denver, CO, USA
| | - Anette-G Ziegler
- Forschegruppe Diabetes and Institute of Diabetes Research, Helmholtz Zentrum München, German Research Centre for Environmental Health, Munich-Neuherberg, Germany der TU München, Munich, Germany
| | - Vibha Anand
- Center for Computational Health, IBM Research, Yorktown Heights, NY, USA
| | - Jorma Toppari
- Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, and Centre for Population Health Research, University of Turku, Turku, Finland; Department of Paediatrics, Turku University Hospital, Turku, Finland
| | - Riitta Veijola
- Department of Paediatrics, PEDEGO Research Unit, University of Oulu and Oulu University Hospital, Oulu, Finland
| | - William Hagopian
- Pacific Northwest Research Institute, Seattle, WA, USA; Department of Medicine, University of Washington, Seattle, WA, USA.
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22
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Li Z, Veijola R, Koski E, Anand V, Martin F, Waugh K, Hyöty H, Winkler C, Killian MB, Lundgren M, Ng K, Maziarz M, Toppari J. Childhood Height Growth Rate Association With the Risk of Islet Autoimmunity and Development of Type 1 Diabetes. J Clin Endocrinol Metab 2022; 107:1520-1528. [PMID: 35244713 PMCID: PMC9113806 DOI: 10.1210/clinem/dgac121] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Indexed: 12/26/2022]
Abstract
CONTEXT Rapid growth has been suggested to promote islet autoimmunity and progression to type 1 diabetes (T1D). Childhood growth has not been analyzed separately from the infant growth period in most previous studies, but it may have distinct features due to differences between the stages of development. OBJECTIVE We aimed to analyze the association of childhood growth with development of islet autoimmunity and progression to T1D diagnosis in children 1 to 8 years of age. METHODS Longitudinal data of childhood growth and development of islet autoimmunity and T1D were analyzed in a prospective cohort study including 10 145 children from Finland, Germany, Sweden, and the United States, 1-8 years of age with at least 3 height and weight measurements and at least 1 measurement of islet autoantibodies. The primary outcome was the appearance of islet autoimmunity and progression from islet autoimmunity to T1D. RESULTS Rapid increase in height (cm/year) was associated with increased risk of seroconversion to glutamic acid decarboxylase autoantibody, insulin autoantibody, or insulinoma-like antigen-2 autoantibody (hazard ratio [HR] = 1.26 [95% CI = 1.05, 1.51] for 1-3 years of age and HR = 1.48 [95% CI = 1.28, 1.73] for >3 years of age). Furthermore, height rate was positively associated with development of T1D (HR = 1.80 [95% CI = 1.15, 2.81]) in the analyses from seroconversion with insulin autoantibody to diabetes. CONCLUSION Rapid height growth rate in childhood is associated with increased risk of islet autoimmunity and progression to T1D. Further work is needed to investigate the biological mechanism that may explain this association.
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Affiliation(s)
- Zhiguo Li
- Center for Computational Health, IBM T.J. Watson Research Center, Yorktown Heights, 10598 NY, and Cambridge, MA, USA
- Zhiguo Li, PhD, Center for Computational Health, IBM T.J. Watson Research Center, Yorktown Heights, 10598 NY, USA.
| | - Riitta Veijola
- Department of Pediatrics, PEDEGO Research Unit, University of Oulu, 90014 Oulu, and Oulu University Hospital, Oulu, Finland
| | - Eileen Koski
- Center for Computational Health, IBM T.J. Watson Research Center, Yorktown Heights, 10598 NY, and Cambridge, MA, USA
| | - Vibha Anand
- Center for Computational Health, IBM T.J. Watson Research Center, Yorktown Heights, 10598 NY, and Cambridge, MA, USA
| | | | - Kathleen Waugh
- Barbara Davis Center for Diabetes, University of Colorado, Denver, CO, USA
| | - Heikki Hyöty
- Department of Virology, Faculty of Medicine and Health Technology, Tampere University, and Fimlab Laboratories, Pirkanmaa Hospital District, Tampere, Finland
| | - Christiane Winkler
- Institute of Diabetes Research, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich-Neuherberg, Germany
- Forschergruppe Diabetes e.V. at Helmholtz Zentrum, München, German Research Center for Environmental Health, Munich-Neuherberg, Germany
- Forschergruppe Diabetes, Technical UniversityMunich, at Klinikum rechts der Isar, Munich, Germany
| | | | - Markus Lundgren
- Department of Clinical Sciences, Lund University Diabetes Center, Malmö, Sweden
- Department of Pediatrics, Kristianstad Hospital, Kristianstad, Sweden
| | - Kenney Ng
- Center for Computational Health, IBM T.J. Watson Research Center, Yorktown Heights, 10598 NY, and Cambridge, MA, USA
| | - Marlena Maziarz
- Department of Clinical Sciences, Lund University Diabetes Center, Malmö, Sweden
| | - Jorma Toppari
- Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, and Centre for Population Health Research, University of Turku, and Department of Pediatrics, Turku University Hospital, Turku, Finland
- Correspondence: Jorma Toppari, MD, PhD, Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, and Centre for Population Health Research, University of Turku, and Department of Pediatrics, Turku University Hospital, 20520 Turku, Finland.
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23
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Progression of type 1 diabetes from latency to symptomatic disease is predicted by distinct autoimmune trajectories. Nat Commun 2022; 13:1514. [PMID: 35314671 PMCID: PMC8938551 DOI: 10.1038/s41467-022-28909-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 02/16/2022] [Indexed: 12/13/2022] Open
Abstract
Development of islet autoimmunity precedes the onset of type 1 diabetes in children, however, the presence of autoantibodies does not necessarily lead to manifest disease and the onset of clinical symptoms is hard to predict. Here we show, by longitudinal sampling of islet autoantibodies (IAb) to insulin, glutamic acid decarboxylase and islet antigen-2 that disease progression follows distinct trajectories. Of the combined Type 1 Data Intelligence cohort of 24662 participants, 2172 individuals fulfill the criteria of two or more follow-up visits and IAb positivity at least once, with 652 progressing to type 1 diabetes during the 15 years course of the study. Our Continuous-Time Hidden Markov Models, that are developed to discover and visualize latent states based on the collected data and clinical characteristics of the patients, show that the health state of participants progresses from 11 distinct latent states as per three trajectories (TR1, TR2 and TR3), with associated 5-year cumulative diabetes-free survival of 40% (95% confidence interval [CI], 35% to 47%), 62% (95% CI, 57% to 67%), and 88% (95% CI, 85% to 91%), respectively (p < 0.0001). Age, sex, and HLA-DR status further refine the progression rates within trajectories, enabling clinically useful prediction of disease onset. Presence of islet autoantibodies precedes the onset of type 1 diabetes but it does not predict whether and how fast symptomatic disease appears. Here authors present a model to predict and visualize progression to diabetes by using a large longitudinal data set on autoantibodies and clinical parameters as input.
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24
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Wherrett DK. Improving Prediction of Risk for the Development of Type 1 Diabetes-Insights From Populations at High Risk. Diabetes Care 2021; 44:dci210018. [PMID: 34548281 PMCID: PMC8740939 DOI: 10.2337/dci21-0018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Affiliation(s)
- Diane K Wherrett
- Department of Pediatrics, Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada
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