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Grace SL, Gillespie KM, Williams CL, Lampasona V, Achenbach P, Pearson ER, Williams AJK, Long AE, McDonald TJ, Jones AG. Autoantibodies to Truncated GAD(96-585) Antigen Stratify Risk of Early Insulin Requirement in Adult-Onset Diabetes. Diabetes 2024; 73:1583-1591. [PMID: 38976498 DOI: 10.2337/db23-0980] [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: 12/13/2023] [Accepted: 06/25/2024] [Indexed: 07/10/2024]
Abstract
We investigated whether characterization of full-length GAD (f-GADA) antibody (GADA) responses could identify early insulin requirement in adult-onset diabetes. In 179 f-GADA-positive participants diagnosed with type 2 diabetes, we assessed associations of truncated GADA (t-GADA) positivity, f-GADA IgG subclasses, and f-GADA affinity with early insulin requirement (<5 years), type 1 diabetes genetic risk score (T1D GRS), and C-peptide. t-GADA positivity was lower in f-GADA-positive without early insulin in comparison with f-GADA-positive type 2 diabetes requiring insulin within 5 years, and T1D (75% vs. 91% and 95% respectively, P < 0.0001). t-GADA positivity (in those f-GADA positive) identified a group with a higher T1D genetic susceptibility (mean T1D GRS 0.248 vs. 0.225, P = 0.003), lower C-peptide (1,156 pmol/L vs. 4,289 pmol/L, P = 1 × 10-7), and increased IA-2 antigen positivity (23% vs. 6%, P = 0.03). In survival analysis, t-GADA positivity was associated with early insulin requirement compared with those only positive for f-GADA, independently from age of diagnosis, f-GADA titer, and duration of diabetes (adjusted hazard ratio 5.7 [95% CI 1.4, 23.5], P = 0.017). The testing of t-GADA in f-GADA-positive individuals with type 2 diabetes identifies those who have genetic and clinical characteristics comparable to T1D and stratifies those at higher risk of early insulin requirement. ARTICLE HIGHLIGHTS
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Affiliation(s)
- Sian L Grace
- Department of Clinical and Biological Science, University of Exeter Medical School, Exeter, U.K
- School of Translational Sciences, Bristol Medical School, University of Bristol, Bristol, U.K
| | - Kathleen M Gillespie
- School of Translational Sciences, Bristol Medical School, University of Bristol, Bristol, U.K
| | - Claire L Williams
- School of Translational Sciences, Bristol Medical School, University of Bristol, Bristol, U.K
| | - Vito Lampasona
- San Raffaele Diabetes Research Institute, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Peter Achenbach
- Institute of Diabetes Research, Helmholtz Munich, German Center for Environmental Health, Munich, Germany
- Forschergruppe Diabetes, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Ewan R Pearson
- Biomedical Research Institute, University of Dundee, Dundee, U.K
| | - Alistair J K Williams
- School of Translational Sciences, Bristol Medical School, University of Bristol, Bristol, U.K
| | - Anna E Long
- School of Translational Sciences, Bristol Medical School, University of Bristol, Bristol, U.K
| | - Timothy J McDonald
- Department of Clinical and Biological Science, University of Exeter Medical School, Exeter, U.K
- Academic Department of Clinical Biochemistry, Royal Devon and Exeter National Health Service Foundation Trust, Exeter, U.K
| | - Angus G Jones
- Department of Clinical and Biological Science, University of Exeter Medical School, Exeter, U.K
- Macleod Diabetes and Endocrine Centre, Royal Devon and Exeter National Health Service Foundation Trust, Exeter, U.K
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Ribeiro AF, Fitas AL, Pires MO, Matoso P, Ligeiro D, Sobral D, Penha-Gonçalves C, Demengeot J, Caramalho Í, Limbert C. Whole Exome Sequencing in Children With Type 1 Diabetes Before Age 6 Years Reveals Insights Into Disease Heterogeneity. J Diabetes Res 2024; 2024:3076895. [PMID: 39364395 PMCID: PMC11449554 DOI: 10.1155/2024/3076895] [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: 02/28/2024] [Revised: 06/04/2024] [Accepted: 08/24/2024] [Indexed: 10/05/2024] Open
Abstract
Aims: This study is aimed at comparing whole exome sequencing (WES) data with the clinical presentation in children with type 1 diabetes onset ≤ 5 years of age (EOT1D). Methods: WES was performed in 99 unrelated children with EOT1D with subsequent analysis to identify potentially deleterious rare variants in MODY genes. High-resolution HLA class II haplotyping, SNP genotyping, and T1D-genetic risk score (T1D-GRS) were also evaluated. Results: Eight of the ninety-nine EOT1D participants carried a potentially deleterious rare variant in a MODY gene. Rare variants affected five genes: GCK (n = 1), HNF1B (n = 2), HNF4A (n = 1), PDX1 (n = 2), and RFX6 (n = 2). At diagnosis, these children had a mean age of 3.0 years, a mean HbA1c of 10.5%, a detectable C-peptide in 5/8, and a positive islet autoantibody in 6/7. Children with MODY variants tend to exhibit a lower number of pancreatic autoantibodies and a lower fasting C-peptide compared to EOT1D without MODY rare variants. They also carried at least one high-risk DR3-DQ2 or DR4-DQ8 haplotype and exhibited a T1D-GRS similar to the other individuals in the EOT1D cohort, but higher than healthy controls. Conclusions: WES found potentially deleterious rare variants in MODY genes in 8.1% of EOT1D, occurring in the context of a T1D genetic background. Such genetic variants may contribute to disease precipitation by a β-cell dysfunction mechanism. This supports the concept of different endotypes of T1D, and WES at T1D onset may be a prerequisite for the implementation of precision therapies in children with autoimmune diabetes.
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Affiliation(s)
- Andreia Fiúza Ribeiro
- Pediatric Endocrinology UnitHospital de Dona EstefâniaSão José Local Health Unit, Lisbon, Portugal
- Pediatric DepartmentHospital Prof. Doutor Fernando FonsecaAmadora Sintra Local Health Unit, Amadora, Portugal
| | - Ana Laura Fitas
- Pediatric Endocrinology UnitHospital de Dona EstefâniaSão José Local Health Unit, Lisbon, Portugal
- Comprehensive Health Research Centre (CHRC)NOVA Medical SchoolUniversidade NOVA de Lisboa, Lisbon, Portugal
| | - Marcela Oliveira Pires
- Pediatric Endocrinology UnitHospital de Dona EstefâniaSão José Local Health Unit, Lisbon, Portugal
- Pediatric DepartmentHospital de São Francisco XavierLisboa Ocidental Local Health Unit, Lisbon, Portugal
| | - Paula Matoso
- Instituto Gulbenkian de Ciência, Oeiras, Portugal
| | - Dário Ligeiro
- Blood and Transplantation Center of LisbonInstituto Português do Sangue e da Transplantação, Lisbon, Portugal
- Immunosurgery UnitChampalimaud Foundation, Lisbon, Portugal
| | | | | | | | - Íris Caramalho
- Instituto Gulbenkian de Ciência, Oeiras, Portugal
- Faculty of SciencesUniversity of Lisbon, Lisbon, Portugal
| | - Catarina Limbert
- Pediatric Endocrinology UnitHospital de Dona EstefâniaSão José Local Health Unit, Lisbon, Portugal
- Comprehensive Health Research Centre (CHRC)NOVA Medical SchoolUniversidade NOVA de Lisboa, Lisbon, Portugal
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Golden GJ, Wu VH, Hamilton JT, Amses KR, Shapiro MR, Japp AS, Liu C, Pampena MB, Kuri-Cervantes L, Knox JJ, Gardner JS, Atkinson MA, Brusko TM, Prak ETL, Kaestner KH, Naji A, Betts MR. Immune perturbations in human pancreas lymphatic tissues prior to and after type 1 diabetes onset. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.23.590798. [PMID: 39345402 PMCID: PMC11429609 DOI: 10.1101/2024.04.23.590798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/01/2024]
Abstract
Autoimmune destruction of pancreatic β cells results in type 1 diabetes (T1D), with pancreatic immune infiltrate representing a key feature in this process. Studies of human T1D immunobiology have predominantly focused on circulating immune cells in the blood, while mouse models suggest diabetogenic lymphocytes primarily reside in pancreas-draining lymph nodes (pLN). A comprehensive study of immune cells in human T1D was conducted using pancreas draining lymphatic tissues, including pLN and mesenteric lymph nodes, and the spleen from non-diabetic control, β cell autoantibody positive non-diabetic (AAb+), and T1D organ donors using complementary approaches of high parameter flow cytometry and CITEseq. Immune perturbations suggestive of a proinflammatory environment were specific for T1D pLN and AAb+ pLN. In addition, certain immune populations correlated with high T1D genetic risk independent of disease state. These datasets form an extensive resource for profiling human lymphatic tissue immune cells in the context of autoimmunity and T1D.
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Fuhri Snethlage CM, Balvers M, Ferwerda B, Rampanelli E, de Groen P, Roep BO, Herrema H, McDonald TJ, van Raalte DH, Weedon MN, Oram RA, Nieuwdorp M, Hanssen NMJ. Associations between diabetes-related genetic risk scores and residual beta cell function in type 1 diabetes: the GUTDM1 study. Diabetologia 2024; 67:1865-1876. [PMID: 38922416 PMCID: PMC11410997 DOI: 10.1007/s00125-024-06204-6] [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: 01/12/2024] [Accepted: 04/29/2024] [Indexed: 06/27/2024]
Abstract
AIMS/HYPOTHESIS Use of genetic risk scores (GRS) may help to distinguish between type 1 diabetes and type 2 diabetes, but less is known about whether GRS are associated with disease severity or progression after diagnosis. Therefore, we tested whether GRS are associated with residual beta cell function and glycaemic control in individuals with type 1 diabetes. METHODS Immunochip arrays and TOPMed were used to genotype a cross-sectional cohort (n=479, age 41.7 ± 14.9 years, duration of diabetes 16.0 years [IQR 6.0-29.0], HbA1c 55.6 ± 12.2 mmol/mol). Several GRS, which were originally developed to assess genetic risk of type 1 diabetes (GRS-1, GRS-2) and type 2 diabetes (GRS-T2D), were calculated. GRS-C1 and GRS-C2 were based on SNPs that have previously been shown to be associated with residual beta cell function. Regression models were used to investigate the association between GRS and residual beta cell function, assessed using the urinary C-peptide/creatinine ratio, and the association between GRS and continuous glucose monitor metrics. RESULTS Higher GRS-1 and higher GRS-2 both showed a significant association with undetectable UCPCR (OR 0.78; 95% CI 0.69, 0.89 and OR 0.84: 95% CI 0.75, 0.93, respectively), which were attenuated after correction for sex and age of onset (GRS-2) and disease duration (GRS-1). Higher GRS-C2 was associated with detectable urinary C-peptide/creatinine ratio (≥0.01 nmol/mmol) after correction for sex and age of onset (OR 6.95; 95% CI 1.19, 40.75). A higher GRS-T2D was associated with less time below range (TBR) (OR for TBR<4% 1.41; 95% CI 1.01 to 1.96) and lower glucose coefficient of variance (β -1.53; 95% CI -2.76, -0.29). CONCLUSIONS/INTERPRETATION Diabetes-related GRS are associated with residual beta cell function in individuals with type 1 diabetes. These findings suggest some genetic contribution to preservation of beta cell function.
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Affiliation(s)
- Coco M Fuhri Snethlage
- Department of (Experimental) Vascular and Internal Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
| | - Manon Balvers
- Department of (Experimental) Vascular and Internal Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Bart Ferwerda
- Department of Clinical Epidemiology and Biostatistics, Amsterdam UMC, Amsterdam, the Netherlands
| | - Elena Rampanelli
- Department of (Experimental) Vascular and Internal Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Pleun de Groen
- Department of (Experimental) Vascular and Internal Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Bart O Roep
- Leids Universitair Medisch Centrum, Internal Medicine, Leiden, the Netherlands
| | - Hilde Herrema
- Department of (Experimental) Vascular and Internal Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Timothy J McDonald
- Peninsula College of Medicine and Dentistry, Peninsula NIHR Clinical Research Facility, Exeter, Devon, UK
| | - Daniël H van Raalte
- Department of (Experimental) Vascular and Internal Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
- Department of Endocrinology and Metabolism, Amsterdam UMC, Amsterdam, the Netherlands
- Diabeter Center Amsterdam, Amsterdam, the Netherlands
| | - Michael N Weedon
- Peninsula College of Medicine and Dentistry, Peninsula NIHR Clinical Research Facility, Exeter, Devon, UK
| | - Richard A Oram
- Peninsula College of Medicine and Dentistry, Peninsula NIHR Clinical Research Facility, Exeter, Devon, UK
| | - Max Nieuwdorp
- Department of (Experimental) Vascular and Internal Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
- Diabeter Center Amsterdam, Amsterdam, the Netherlands
| | - Nordin M J Hanssen
- Department of (Experimental) Vascular and Internal Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
- Diabeter Center Amsterdam, Amsterdam, the Netherlands
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Billings LK, Shi Z, Mulford AJ, Wei J, Tran H, Ashworth A, Zheng SL, Dunnenberger HM, Hulick PJ, Sanders AR, Xu J. Validation of GenProb-T1D and its clinical utility for differentiating types of diabetes in a biobank from a US healthcare system. J Diabetes Investig 2024. [PMID: 39171755 DOI: 10.1111/jdi.14297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2024] [Revised: 07/29/2024] [Accepted: 08/06/2024] [Indexed: 08/23/2024] Open
Abstract
Atypical diabetes with overlapping clinical features of type 1 (T1D) and type 2 (T2D) is common and challenging diagnostically and for implementing effective treatment. Here, we validate a recently reported genetic probability of type 1 diabetes (GenProb-T1D) from the UK Biobank (UKB) for differentiating type 1 diabetes and type 2 diabetes in a diabetes patient cohort from a healthcare system-based biobank in the USA. Among 3,363 diabetes patients, we confirmed the performance of GenProb-T1D in differentiating typical type 1 diabetes vs type 2 diabetes. Furthermore, for 359 atypical diabetes patients, those with GenProb-T1D higher than the pre-defined cutoff derived from the UKB had clinical presentations more consistent with that of typical type 1 diabetes. Similar findings were found in participants of European and non-European ancestries. This study provides necessary validation to translate GenProb-T1D into genetic testing in a multi-ancestry cohort. Measuring underlying genetic susceptibility of type 1 diabetes and type 2 diabetes can supplement current clinical tools for earlier and more accurate diagnoses of diabetes.
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Affiliation(s)
- Liana K Billings
- Endeavor Health, Evanston, IL, USA
- University of Chicago Pritzker School of Medicine, Chicago, IL, USA
| | | | | | - Jun Wei
- Endeavor Health, Evanston, IL, USA
| | - Huy Tran
- Endeavor Health, Evanston, IL, USA
| | | | | | | | | | - Alan R Sanders
- Endeavor Health, Evanston, IL, USA
- University of Chicago Pritzker School of Medicine, Chicago, IL, USA
| | - Jianfeng Xu
- Endeavor Health, Evanston, IL, USA
- University of Chicago Pritzker School of Medicine, Chicago, IL, USA
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6
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Mănescu M, Mănescu IB, Grama A. A Review of Stage 0 Biomarkers in Type 1 Diabetes: The Holy Grail of Early Detection and Prevention? J Pers Med 2024; 14:878. [PMID: 39202069 PMCID: PMC11355657 DOI: 10.3390/jpm14080878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2024] [Revised: 08/16/2024] [Accepted: 08/19/2024] [Indexed: 09/03/2024] Open
Abstract
Type 1 diabetes mellitus (T1D) is an incurable autoimmune disease characterized by the destruction of pancreatic islet cells, resulting in lifelong dependency on insulin treatment. There is an abundance of review articles addressing the prediction of T1D; however, most focus on the presymptomatic phases, specifically stages 1 and 2. These stages occur after seroconversion, where therapeutic interventions primarily aim to delay the onset of T1D rather than prevent it. This raises a critical question: what happens before stage 1 in individuals who will eventually develop T1D? Is there a "stage 0" of the disease, and if so, how can we detect it to increase our chances of truly preventing T1D? In pursuit of answers to these questions, this narrative review aimed to highlight recent research in the field of early detection and prediction of T1D, specifically focusing on biomarkers that can predict T1D before the onset of islet autoimmunity. Here, we have compiled influential research from the fields of epigenetics, omics, and microbiota. These studies have identified candidate biomarkers capable of predicting seroconversion from very early stages to several months prior, suggesting that the prophylactic window begins at birth. As the therapeutic landscape evolves from treatment to delay, and ideally from delay to prevention, it is crucial to both identify and validate such "stage 0" biomarkers predictive of islet autoimmunity. In the era of precision medicine, this knowledge will enable early intervention with the potential for delaying, modifying, or completely preventing autoimmunity and T1D in at-risk children.
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Affiliation(s)
- Măriuca Mănescu
- Department of Pediatrics, Emergency County Clinical Hospital of Targu Mures, 50 Gheorghe Marinescu, 540136 Targu Mures, Romania;
| | - Ion Bogdan Mănescu
- Department of Laboratory Medicine, Faculty of Medicine, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 38 Gheorghe Marinescu, 540142 Targu Mures, Romania;
| | - Alina Grama
- Department of Pediatrics, Emergency County Clinical Hospital of Targu Mures, 50 Gheorghe Marinescu, 540136 Targu Mures, Romania;
- Department of Pediatrics, Faculty of Medicine, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 38 Gheorghe Marinescu, 540142 Targu Mures, Romania
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McGrail C, Sears TJ, Kudtarkar P, Carter H, Gaulton K. Genetic association and machine learning improves discovery and prediction of type 1 diabetes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.31.24311310. [PMID: 39132494 PMCID: PMC11312647 DOI: 10.1101/2024.07.31.24311310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Type 1 diabetes (T1D) has a large genetic component, and expanded genetic studies of T1D can lead to novel biological and therapeutic discovery and improved risk prediction. In this study, we performed genetic association and fine-mapping analyses in 817,718 European ancestry samples genome-wide and 29,746 samples at the MHC locus, which identified 165 independent risk signals for T1D of which 19 were novel. We used risk variants to train a machine learning model (named T1GRS) to predict T1D, which highly differentiated T1D from non-disease and type 2 diabetes (T2D) in Europeans as well as African Americans at or beyond the level of current standards. We identified extensive non-linear interactions between risk loci in T1GRS, for example between HLA-DQB1*57 and INS, coding and non-coding HLA alleles, and DEXI, INS and other beta cell loci, that provided mechanistic insight and improved risk prediction. T1D individuals formed distinct clusters based on genetic features from T1GRS which had significant differences in age of onset, HbA1c, and renal disease severity. Finally, we provided T1GRS in formats to enhance accessibility of risk prediction to any user and computing environment. Overall, the improved genetic discovery and prediction of T1D will have wide clinical, therapeutic, and research applications.
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Affiliation(s)
- Carolyn McGrail
- Biomedical sciences graduate program, University of California San Diego, La Jolla CA
| | - Timothy J. Sears
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla CA
| | - Parul Kudtarkar
- Department of Pediatrics, University of California San Diego, La Jolla CA
| | - Hannah Carter
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla CA
- Moore’s Cancer Center, University of California San Diego, La Jolla CA
- Department of Medicine, University of California San Diego, La Jolla CA
| | - Kyle Gaulton
- Department of Pediatrics, University of California San Diego, La Jolla CA
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla CA
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Robertson CC, Elgamal RM, Henry-Kanarek BA, Arvan P, Chen S, Dhawan S, Eizirik DL, Kaddis JS, Vahedi G, Parker SCJ, Gaulton KJ, Soleimanpour SA. Untangling the genetics of beta cell dysfunction and death in type 1 diabetes. Mol Metab 2024; 86:101973. [PMID: 38914291 PMCID: PMC11283044 DOI: 10.1016/j.molmet.2024.101973] [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: 03/14/2024] [Revised: 06/18/2024] [Accepted: 06/19/2024] [Indexed: 06/26/2024] Open
Abstract
BACKGROUND Type 1 diabetes (T1D) is a complex multi-system disease which arises from both environmental and genetic factors, resulting in the destruction of insulin-producing pancreatic beta cells. Over the past two decades, human genetic studies have provided new insight into the etiology of T1D, including an appreciation for the role of beta cells in their own demise. SCOPE OF REVIEW Here, we outline models supported by human genetic data for the role of beta cell dysfunction and death in T1D. We highlight the importance of strong evidence linking T1D genetic associations to bona fide candidate genes for mechanistic and therapeutic consideration. To guide rigorous interpretation of genetic associations, we describe molecular profiling approaches, genomic resources, and disease models that may be used to construct variant-to-gene links and to investigate candidate genes and their role in T1D. MAJOR CONCLUSIONS We profile advances in understanding the genetic causes of beta cell dysfunction and death at individual T1D risk loci. We discuss how genetic risk prediction models can be used to address disease heterogeneity. Further, we present areas where investment will be critical for the future use of genetics to address open questions in the development of new treatment and prevention strategies for T1D.
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Affiliation(s)
- Catherine C Robertson
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA; Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Ruth M Elgamal
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Belle A Henry-Kanarek
- Department of Internal Medicine and Division of Metabolism, Endocrinology, and Diabetes, University of Michigan, Ann Arbor, MI, USA
| | - Peter Arvan
- Department of Internal Medicine and Division of Metabolism, Endocrinology, and Diabetes, University of Michigan, Ann Arbor, MI, USA
| | - Shuibing Chen
- Department of Surgery, Weill Cornell Medicine, New York, NY, USA; Center for Genomic Health, Weill Cornell Medicine, New York, NY, USA
| | - Sangeeta Dhawan
- Department of Translational Research and Cellular Therapeutics, Arthur Riggs Diabetes and Metabolism Research Institute, City of Hope, Duarte, CA, USA
| | - Decio L Eizirik
- ULB Center for Diabetes Research, Université Libre de Bruxelles, Brussels, Belgium
| | - John S Kaddis
- Department of Diabetes and Cancer Discovery Science, Arthur Riggs Diabetes and Metabolism Research Institute, Beckman Research Institute, City of Hope, Duarte, CA, USA
| | - Golnaz Vahedi
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Stephen C J Parker
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA; Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA; Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
| | - Kyle J Gaulton
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.
| | - Scott A Soleimanpour
- Department of Internal Medicine and Division of Metabolism, Endocrinology, and Diabetes, University of Michigan, Ann Arbor, MI, USA.
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Joglekar MV, Kaur S, Pociot F, Hardikar AA. Prediction of progression to type 1 diabetes with dynamic biomarkers and risk scores. Lancet Diabetes Endocrinol 2024; 12:483-492. [PMID: 38797187 DOI: 10.1016/s2213-8587(24)00103-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 03/31/2024] [Accepted: 04/02/2024] [Indexed: 05/29/2024]
Abstract
Identifying biomarkers of functional β-cell loss is an important step in the risk stratification of type 1 diabetes. Genetic risk scores (GRS), generated by profiling an array of single nucleotide polymorphisms, are a widely used type 1 diabetes risk-prediction tool. Type 1 diabetes screening studies have relied on a combination of biochemical (autoantibody) and GRS screening methodologies for identifying individuals at high-risk of type 1 diabetes. A limitation of these screening tools is that the presence of autoantibodies marks the initiation of β-cell loss, and is therefore not the best biomarker of progression to early-stage type 1 diabetes. GRS, on the other hand, represents a static biomarker offering a single risk score over an individual's lifetime. In this Personal View, we explore the challenges and opportunities of static and dynamic biomarkers in the prediction of progression to type 1 diabetes. We discuss future directions wherein newer dynamic risk scores could be used to predict type 1 diabetes risk, assess the efficacy of new and emerging drugs to retard, or prevent type 1 diabetes, and possibly replace or further enhance the predictive ability offered by static biomarkers, such as GRS.
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Affiliation(s)
- Mugdha V Joglekar
- School of Medicine, Western Sydney University, Sydney, NSW, Australia
| | | | - Flemming Pociot
- Steno Diabetes Center Copenhagen, Herlev, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
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Yang PK, Jackson SL, Charest BR, Cheng YJ, Sun YV, Raghavan S, Litkowski EM, Legvold BT, Rhee MK, Oram RA, Kuklina EV, Vujkovic M, Reaven PD, Cho K, Leong A, Wilson PW, Zhou J, Miller DR, Sharp SA, Staimez LR, North KE, Highland HM, Phillips LS. Type 1 Diabetes Genetic Risk in 109,954 Veterans With Adult-Onset Diabetes: The Million Veteran Program (MVP). Diabetes Care 2024; 47:1032-1041. [PMID: 38608262 PMCID: PMC11116922 DOI: 10.2337/dc23-1927] [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: 10/13/2023] [Accepted: 03/11/2024] [Indexed: 04/14/2024]
Abstract
OBJECTIVE To characterize high type 1 diabetes (T1D) genetic risk in a population where type 2 diabetes (T2D) predominates. RESEARCH DESIGN AND METHODS Characteristics typically associated with T1D were assessed in 109,594 Million Veteran Program participants with adult-onset diabetes, 2011-2021, who had T1D genetic risk scores (GRS) defined as low (0 to <45%), medium (45 to <90%), high (90 to <95%), or highest (≥95%). RESULTS T1D characteristics increased progressively with higher genetic risk (P < 0.001 for trend). A GRS ≥90% was more common with diabetes diagnoses before age 40 years, but 95% of those participants were diagnosed at age ≥40 years, and their characteristics resembled those of individuals with T2D in mean age (64.3 years) and BMI (32.3 kg/m2). Compared with the low-risk group, the highest-risk group was more likely to have diabetic ketoacidosis (low GRS 0.9% vs. highest GRS 3.7%), hypoglycemia prompting emergency visits (3.7% vs. 5.8%), outpatient plasma glucose <50 mg/dL (7.5% vs. 13.4%), a shorter median time to start insulin (3.5 vs. 1.4 years), use of a T1D diagnostic code (16.3% vs. 28.1%), low C-peptide levels if tested (1.8% vs. 32.4%), and glutamic acid decarboxylase antibodies (6.9% vs. 45.2%), all P < 0.001. CONCLUSIONS Characteristics associated with T1D were increased with higher genetic risk, and especially with the top 10% of risk. However, the age and BMI of those participants resemble those of people with T2D, and a substantial proportion did not have diagnostic testing or use of T1D diagnostic codes. T1D genetic screening could be used to aid identification of adult-onset T1D in settings in which T2D predominates.
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Affiliation(s)
- Peter K. Yang
- Division for Heart Disease and Stroke Prevention, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA
- Atlanta Veterans Administration Medical Center, Atlanta, GA
- Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC
| | - Sandra L. Jackson
- Division for Heart Disease and Stroke Prevention, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA
| | - Brian R. Charest
- Massachusetts Veterans Epidemiology Research and Information Center, Boston, MA
| | - Yiling J. Cheng
- Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA
| | - Yan V. Sun
- Atlanta Veterans Administration Medical Center, Atlanta, GA
- Rollins School of Public Health, Emory University, Atlanta, GA
| | - Sridharan Raghavan
- Rocky Mountain Regional Veterans Affairs Medical Center, Aurora, CO
- University of Colorado School of Medicine, Denver, CO
| | - Elizabeth M. Litkowski
- Rocky Mountain Regional Veterans Affairs Medical Center, Aurora, CO
- University of Colorado School of Medicine, Denver, CO
| | - Brian T. Legvold
- Division of Endocrinology and Metabolism, Department of Medicine, Emory University School of Medicine, Atlanta, GA
| | - Mary K. Rhee
- Atlanta Veterans Administration Medical Center, Atlanta, GA
- Division of Endocrinology and Metabolism, Department of Medicine, Emory University School of Medicine, Atlanta, GA
| | - Richard A. Oram
- College of Medicine and Health, University of Exeter Medical School, Devon, U.K
| | - Elena V. Kuklina
- Division for Heart Disease and Stroke Prevention, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA
| | - Marijana Vujkovic
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | | | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center, Boston, MA
- Brigham and Women’s Hospital, Boston, MA
| | - Aaron Leong
- Harvard Medical School, Boston, MA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA
| | - Peter W.F. Wilson
- Atlanta Veterans Administration Medical Center, Atlanta, GA
- Rollins School of Public Health, Emory University, Atlanta, GA
- College of Medicine and Health, University of Exeter Medical School, Devon, U.K
| | - Jin Zhou
- Phoenix Veterans Affairs Health Care System, Phoenix, AZ
- UCLA Department of Medicine, University of California, Los Angeles, CA
| | | | - Seth A. Sharp
- Division of Endocrinology and Diabetes, Stanford University, Palo Alto, CA
| | - Lisa R. Staimez
- Rollins School of Public Health, Emory University, Atlanta, GA
| | - Kari E. North
- Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC
| | - Heather M. Highland
- Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC
| | - Lawrence S. Phillips
- Atlanta Veterans Administration Medical Center, Atlanta, GA
- Division of Endocrinology and Metabolism, Department of Medicine, Emory University School of Medicine, Atlanta, GA
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11
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Triolo TM, Parikh HM, Tosur M, Ferrat L, You L, Gottlieb PA, Oram RA, Onengut-Gumuscu S, Krischer JP, Rich SS, Steck AK, Redondo MJ. Genetic Associations with C-peptide Levels before Type 1 Diabetes Diagnosis in At-Risk Relatives. J Clin Endocrinol Metab 2024:dgae349. [PMID: 38767115 DOI: 10.1210/clinem/dgae349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 05/13/2024] [Accepted: 05/16/2024] [Indexed: 05/22/2024]
Abstract
OBJECTIVE We sought to determine whether the type 1 diabetes genetic risk score-2 (T1D-GRS2) and single nucleotide polymorphisms (SNPs) are associated with C-peptide preservation before type 1 diabetes diagnosis. METHODS We conducted a retrospective analysis of 713 autoantibody-positive participants who developed type 1 diabetes in the TrialNet Pathway to Prevention Study who had T1DExomeChip data. We evaluated the relationships of 16 known SNPs and T1D-GRS2 with area under the curve (AUC) C-peptide levels during oral glucose tolerance tests conducted in the 9 months before diagnosis. RESULTS Higher T1D-GRS2 was associated with lower C-peptide AUC in the 9 months before diagnosis in univariate (β=-0.06, P<0.0001) and multivariate (β=-0.03, P=0.005) analyses. Participants with the JAZF1 rs864745 T allele had lower C-peptide AUC in both univariate (β=-0.11, P=0.002) and multivariate (β=-0.06, P=0.018) analyses. CONCLUSIONS The type 2 diabetes-associated JAZF1 rs864745 T allele and higher T1D-GRS2 are associated with lower C-peptide AUC prior to diagnosis of type 1 diabetes, with implications for the design of prevention trials.
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Affiliation(s)
- Taylor M Triolo
- Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus
| | - Hemang M Parikh
- Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA
| | - Mustafa Tosur
- Department of Pediatrics, Division of Diabetes and Endocrinology, Baylor College of Medicine, Texas Children's Hospital, Houston, TX, USA
- USDA/ARS Children's Nutrition Research Center, Houston, TX, USA
| | | | - Lu You
- Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA
| | - Peter A Gottlieb
- Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus
| | | | | | - Jeffrey P Krischer
- Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA
| | | | - Andrea K Steck
- Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus
| | - Maria J Redondo
- Department of Pediatrics, Division of Diabetes and Endocrinology, Baylor College of Medicine, Texas Children's Hospital, Houston, TX, USA
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12
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Boullerne AI, Goudey B, Paganini J, Erlichster M, Gaitonde S, Feinstein DL. Validation of tag SNPs for multiple sclerosis HLA risk alleles across the 1000 genomes panel. Hum Immunol 2024; 85:110790. [PMID: 38575482 DOI: 10.1016/j.humimm.2024.110790] [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: 09/12/2023] [Revised: 02/10/2024] [Accepted: 03/21/2024] [Indexed: 04/06/2024]
Abstract
Currently, the genetic variants strongly associated with risk for Multiple Sclerosis (MS) are located in the Major Histocompatibility Complex. This includes DRB1*15:01 and DRB1*15:03 alleles at the HLA-DRB1 locus, the latter restricted to African populations; the DQB1*06:02 allele at the HLA-DQB1 locus which is in high linkage disequilibrium (LD) with DRB1*15:01; and protective allele A*02:01 at the HLA-A locus. HLA allele identification is facilitated by co-inherited ('tag') single nucleotide polymorphisms (SNPs); however, SNP validation is not typically done outside of the discovery population. We examined 19 SNPs reported to be in high LD with these alleles in 2,502 healthy subjects included in the 1000 Genomes panel having typed HLA data. Examination of 3 indices (LD R2 values, sensitivity and specificity, minor allele frequency) revealed few SNPs with high tagging performance. All SNPs examined that tag DRB1*15:01 were in perfect LD in the British population; three showed high tagging performance in 4 of the 5 European, and 2 of the 4 American populations. For DQB1*06:02, with no previously validated tag SNPs, we show that rs3135388 has high tagging performance in one South Asian, one American, and one European population. We identify for the first time that rs2844821 has high tagging performance for A*02:01 in 5 of 7 African populations including African Americans, and 4 of the 5 European populations. These results provide a basis for selecting SNPs with high tagging performance to assess HLA alleles across diverse populations, for MS risk as well as for other diseases and conditions.
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Affiliation(s)
- Anne I Boullerne
- Department of Anesthesiology, University Illinois, Chicago, IL, USA.
| | - Benjamin Goudey
- The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia; Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, Victoria, Australia
| | | | - Michael Erlichster
- MX3 Diagnostics, Melbourne, Victoria, Australia; Centre for Neural Engineering, University of Melbourne, Melbourne, Victoria, Australia; Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia
| | - Sujata Gaitonde
- Department of Pathology, University Illinois, Chicago, IL, USA
| | - Douglas L Feinstein
- Department of Anesthesiology, University Illinois, Chicago, IL, USA; Jesse Brown Veterans Affairs Medical Center, Chicago, IL, USA
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13
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Yu G, Tam HCH, Huang C, Shi M, Lim CKP, Chan JCN, Ma RCW. Lessons and Applications of Omics Research in Diabetes Epidemiology. Curr Diab Rep 2024; 24:27-44. [PMID: 38294727 PMCID: PMC10874344 DOI: 10.1007/s11892-024-01533-7] [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] [Accepted: 01/04/2024] [Indexed: 02/01/2024]
Abstract
PURPOSE OF REVIEW Recent advances in genomic technology and molecular techniques have greatly facilitated the identification of disease biomarkers, advanced understanding of pathogenesis of different common diseases, and heralded the dawn of precision medicine. Much of these advances in the area of diabetes have been made possible through deep phenotyping of epidemiological cohorts, and analysis of the different omics data in relation to detailed clinical information. In this review, we aim to provide an overview on how omics research could be incorporated into the design of current and future epidemiological studies. RECENT FINDINGS We provide an up-to-date review of the current understanding in the area of genetic, epigenetic, proteomic and metabolomic markers for diabetes and related outcomes, including polygenic risk scores. We have drawn on key examples from the literature, as well as our own experience of conducting omics research using the Hong Kong Diabetes Register and Hong Kong Diabetes Biobank, as well as other cohorts, to illustrate the potential of omics research in diabetes. Recent studies highlight the opportunity, as well as potential benefit, to incorporate molecular profiling in the design and set-up of diabetes epidemiology studies, which can also advance understanding on the heterogeneity of diabetes. Learnings from these examples should facilitate other researchers to consider incorporating research on omics technologies into their work to advance the field and our understanding of diabetes and its related co-morbidities. Insights from these studies would be important for future development of precision medicine in diabetes.
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Affiliation(s)
- Gechang Yu
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Henry C H Tam
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Chuiguo Huang
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Mai Shi
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Cadmon K P Lim
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Juliana C N Chan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Ronald C W Ma
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China.
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China.
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China.
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14
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Davis TME, Peters KE, Davis W. Use of a type 1 genetic risk score for classification of diabetes type in young Australian adults: the Fremantle Diabetes Study Phase II. Intern Med J 2024; 54:494-498. [PMID: 38224531 DOI: 10.1111/imj.16328] [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: 11/28/2023] [Accepted: 12/20/2023] [Indexed: 01/17/2024]
Abstract
The applicability of a UK-validated genetic risk score (GRS) was assessed in 158 participants in the Fremantle Diabetes Study Phase II diagnosed between 20 and <40 years of age with type 1 or type 2 diabetes or latent autoimmune diabetes of adults (LADA). For type 1 versus type 2/LADA, the area under the receiver operating characteristic curve (AUC) was highest for serum C-peptide (0.93) and lowest for the GRS (0.66). Adding age at diagnosis and body mass index to C-peptide increased the AUC minimally (0.96). The GRS appears of modest diabetes diagnostic value in young Australians.
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Affiliation(s)
- Timothy M E Davis
- Medical School, University of Western Australia, Fremantle Hospital, Fremantle, Western Australia, Australia
- Department of Endocrinology and Diabetes, Fiona Stanley and Fremantle Hospitals Group, Perth, Western Australia, Australia
| | - Kirsten E Peters
- Medical School, University of Western Australia, Fremantle Hospital, Fremantle, Western Australia, Australia
- Proteomics International, Perth, Western Australia, Australia
| | - Wendy Davis
- Medical School, University of Western Australia, Fremantle Hospital, Fremantle, Western Australia, Australia
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15
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Patel SK, Fourlanos S, Greenfield JR. Classification of type 1 diabetes: A pathogenic and treatment-based classification. Diabetes Metab Syndr 2024; 18:102986. [PMID: 38503115 DOI: 10.1016/j.dsx.2024.102986] [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] [Received: 06/01/2023] [Revised: 03/12/2024] [Accepted: 03/13/2024] [Indexed: 03/21/2024]
Abstract
AIM To improve the diagnosis and classification of patients who fail to satisfy current type 1 diabetes diagnostic criteria. METHODS Review of the literature and current diagnostic guidelines. DISCUSSION We propose a novel, clinically useful classification based on islet autoantibody status and non-fasting C-peptide levels. Notably, we discuss the subgroup of latent autoimmune diabetes in the young and propose a new subgroup classification of autoantibody negative type 1 diabetes in remission. CONCLUSION A novel classification system is proposed. Further work is needed to accurately diagnose and manage minority type 1 diabetes subgroups.
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Affiliation(s)
- Shivani K Patel
- Clinical Diabetes, Appetite and Metabolism Laboratory, Garvan Institute of Medical Research, Sydney, NSW, Australia; Department of Diabetes and Endocrinology, St Vincent's Hospital, Sydney, NSW, Australia; School of Clinical Medicine, UNSW Medicine & Health, St Vincent's Healthcare Clinical Campus, Faculty of Medicine, UNSW Sydney, Sydney, NSW, Australia.
| | - Spiros Fourlanos
- Department of Diabetes and Endocrinology, Royal Melbourne Hospital, Melbourne, VIC, Australia
| | - Jerry R Greenfield
- Clinical Diabetes, Appetite and Metabolism Laboratory, Garvan Institute of Medical Research, Sydney, NSW, Australia; Department of Diabetes and Endocrinology, St Vincent's Hospital, Sydney, NSW, Australia; School of Clinical Medicine, UNSW Medicine & Health, St Vincent's Healthcare Clinical Campus, Faculty of Medicine, UNSW Sydney, Sydney, NSW, Australia
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16
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Martin SS, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, Baker-Smith CM, Barone Gibbs B, Beaton AZ, Boehme AK, Commodore-Mensah Y, Currie ME, Elkind MSV, Evenson KR, Generoso G, Heard DG, Hiremath S, Johansen MC, Kalani R, Kazi DS, Ko D, Liu J, Magnani JW, Michos ED, Mussolino ME, Navaneethan SD, Parikh NI, Perman SM, Poudel R, Rezk-Hanna M, Roth GA, Shah NS, St-Onge MP, Thacker EL, Tsao CW, Urbut SM, Van Spall HGC, Voeks JH, Wang NY, Wong ND, Wong SS, Yaffe K, Palaniappan LP. 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association. Circulation 2024; 149:e347-e913. [PMID: 38264914 DOI: 10.1161/cir.0000000000001209] [Citation(s) in RCA: 182] [Impact Index Per Article: 182.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
BACKGROUND The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains. RESULTS Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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17
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Guertin KA, Repaske DR, Taylor JF, Williams ES, Onengut-Gumuscu S, Chen WM, Boggs SR, Yu L, Allen L, Botteon L, Daniel L, Keating KG, Labergerie MK, Lienhart TS, Gonzalez-Mejia JA, Starnowski MJ, Rich SS. Implementation of type 1 diabetes genetic risk screening in children in diverse communities: the Virginia PrIMeD project. Genome Med 2024; 16:31. [PMID: 38355597 PMCID: PMC10865687 DOI: 10.1186/s13073-024-01305-8] [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: 06/14/2023] [Accepted: 02/08/2024] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND Population screening for risk of type 1 diabetes (T1D) has been proposed to identify those with islet autoimmunity (presence of islet autoantibodies). As islet autoantibodies can be transient, screening with a genetic risk score has been proposed as an entry into autoantibody testing. METHODS Children were recruited from eight general pediatric and specialty clinics across Virginia with diverse community settings. Recruiters in each clinic obtained informed consent/assent, a medical history, and a saliva sample for DNA extraction in children with and without a history of T1D. A custom genotyping panel was used to define T1D genetic risk based upon associated SNPs in European- and African-genetic ancestry. Subjects at "high genetic risk" were offered a separate blood collection for screening four islet autoantibodies. A follow-up contact (email, mail, and telephone) in one half of the participants determined interest and occurrence of subsequent T1D. RESULTS A total of 3818 children aged 2-16 years were recruited, with 14.2% (n = 542) having a "high genetic risk." Of children with "high genetic risk" and without pre-existing T1D (n = 494), 7.0% (34/494) consented for autoantibody screening; 82.4% (28/34) who consented also completed the blood collection, and 7.1% (2/28) of them tested positive for multiple autoantibodies. Among children with pre-existing T1D (n = 91), 52% (n = 48) had a "high genetic risk." In the sample of children with existing T1D, there was no relationship between genetic risk and age at T1D onset. A major factor in obtaining islet autoantibody testing was concern over SARS-CoV-2 exposure. CONCLUSIONS Minimally invasive saliva sampling implemented using a genetic risk score can identify children at genetic risk of T1D. Consent for autoantibody screening, however, was limited largely due to the SARS-CoV-2 pandemic and need for blood collection.
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Affiliation(s)
- Kristin A Guertin
- Department of Public Health Sciences, University of Virginia, 1300 Jefferson Park Avenue, 3182 West Complex, Charlottesville, VA, 22903, USA
- Department of Public Health Sciences, UConn School of Medicine, UConn Health, 263 Farmington Avenue, MC 6325, Farmington, CT, 06030, USA
| | - David R Repaske
- Department of Pediatrics, Division of Pediatric Diabetes & Endocrinology, University of Virginia, UVAHealth, 1204 W Main Street, 6th Floor, Charlottesville, VA, 22903, USA
| | - Julia F Taylor
- Department of Pediatrics, Division of Pediatric Diabetes & Endocrinology, University of Virginia, UVAHealth, 1204 W Main Street, 6th Floor, Charlottesville, VA, 22903, USA
| | - Eli S Williams
- Department of Pathology, Division of Medical Genetics, UVAHealth, University of Virginia, 21 Hospital Drive, Charlottesville, VA, 22903, USA
| | - Suna Onengut-Gumuscu
- Department of Public Health Sciences, University of Virginia, 1300 Jefferson Park Avenue, 3182 West Complex, Charlottesville, VA, 22903, USA
- Center for Public Health Genomics, University of Virginia, 1335 Lee Street, 3235 West Complex, Charlottesville, VA, 22903, USA
| | - Wei-Min Chen
- Department of Public Health Sciences, University of Virginia, 1300 Jefferson Park Avenue, 3182 West Complex, Charlottesville, VA, 22903, USA
- Center for Public Health Genomics, University of Virginia, 1335 Lee Street, 3235 West Complex, Charlottesville, VA, 22903, USA
| | - Sarah R Boggs
- Department of Pediatrics, Division of Pediatric Diabetes & Endocrinology, University of Virginia, UVAHealth, 1204 W Main Street, 6th Floor, Charlottesville, VA, 22903, USA
| | - Liping Yu
- Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, 1774 Aurora Court, Suite A140, Aurora, CO, 80045, USA
| | - Luke Allen
- Center for Public Health Genomics, University of Virginia, 1335 Lee Street, 3235 West Complex, Charlottesville, VA, 22903, USA
| | - Lacey Botteon
- Center for Public Health Genomics, University of Virginia, 1335 Lee Street, 3235 West Complex, Charlottesville, VA, 22903, USA
| | - Louis Daniel
- Center for Public Health Genomics, University of Virginia, 1335 Lee Street, 3235 West Complex, Charlottesville, VA, 22903, USA
| | - Katherine G Keating
- Center for Public Health Genomics, University of Virginia, 1335 Lee Street, 3235 West Complex, Charlottesville, VA, 22903, USA
| | - Mika K Labergerie
- Center for Public Health Genomics, University of Virginia, 1335 Lee Street, 3235 West Complex, Charlottesville, VA, 22903, USA
| | - Tyler S Lienhart
- Center for Public Health Genomics, University of Virginia, 1335 Lee Street, 3235 West Complex, Charlottesville, VA, 22903, USA
| | - Jorge A Gonzalez-Mejia
- Center for Public Health Genomics, University of Virginia, 1335 Lee Street, 3235 West Complex, Charlottesville, VA, 22903, USA
| | - Matt J Starnowski
- Center for Public Health Genomics, University of Virginia, 1335 Lee Street, 3235 West Complex, Charlottesville, VA, 22903, USA
| | - Stephen S Rich
- Department of Public Health Sciences, University of Virginia, 1300 Jefferson Park Avenue, 3182 West Complex, Charlottesville, VA, 22903, USA.
- Center for Public Health Genomics, University of Virginia, 1335 Lee Street, 3235 West Complex, Charlottesville, VA, 22903, USA.
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Minniakhmetov I, Yalaev B, Khusainova R, Bondarenko E, Melnichenko G, Dedov I, Mokrysheva N. Genetic and Epigenetic Aspects of Type 1 Diabetes Mellitus: Modern View on the Problem. Biomedicines 2024; 12:399. [PMID: 38398001 PMCID: PMC10886892 DOI: 10.3390/biomedicines12020399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 02/06/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024] Open
Abstract
Omics technologies accumulated an enormous amount of data that advanced knowledge about the molecular pathogenesis of type 1 diabetes mellitus and identified a number of fundamental problems focused on the transition to personalized diabetology in the future. Among them, the most significant are the following: (1) clinical and genetic heterogeneity of type 1 diabetes mellitus; (2) the prognostic significance of DNA markers beyond the HLA genes; (3) assessment of the contribution of a large number of DNA markers to the polygenic risk of disease progress; (4) the existence of ethnic population differences in the distribution of frequencies of risk alleles and genotypes; (5) the infancy of epigenetic research into type 1 diabetes mellitus. Disclosure of these issues is one of the priorities of fundamental diabetology and practical healthcare. The purpose of this review is the systemization of the results of modern molecular genetic, transcriptomic, and epigenetic investigations of type 1 diabetes mellitus in general, as well as its individual forms. The paper summarizes data on the role of risk HLA haplotypes and a number of other candidate genes and loci, identified through genome-wide association studies, in the development of this disease and in alterations in T cell signaling. In addition, this review assesses the contribution of differential DNA methylation and the role of microRNAs in the formation of the molecular pathogenesis of type 1 diabetes mellitus, as well as discusses the most currently central trends in the context of early diagnosis of type 1 diabetes mellitus.
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Affiliation(s)
- Ildar Minniakhmetov
- Endocrinology Research Centre, Dmitry Ulyanov Street, 11, 117292 Moscow, Russia; (R.K.); (E.B.); (G.M.); (I.D.); (N.M.)
| | - Bulat Yalaev
- Endocrinology Research Centre, Dmitry Ulyanov Street, 11, 117292 Moscow, Russia; (R.K.); (E.B.); (G.M.); (I.D.); (N.M.)
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Liao K, Zöllner S. A Stacking Framework for Polygenic Risk Prediction in Admixed Individuals. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.31.24302103. [PMID: 38434717 PMCID: PMC10907988 DOI: 10.1101/2024.01.31.24302103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
Polygenic risk scores (PRS) are summaries of an individual's personalized genetic risk for a trait or disease. However, PRS often perform poorly for phenotype prediction when the ancestry of the target population does not match the population in which GWAS effect sizes were estimated. For many populations this can be addressed by performing GWAS in the target population. However, admixed individuals (whose genomes can be traced to multiple ancestral populations) lie on an ancestry continuum and are not easily represented as a discrete population. Here, we propose slaPRS (stacking local ancestry PRS), which incorporates multiple ancestry GWAS to alleviate the ancestry dependence of PRS in admixed samples. slaPRS uses ensemble learning (stacking) to combine local population specific PRS in regions across the genome. We compare slaPRS to single population PRS and a method that combines single population PRS globally. In simulations, slaPRS outperformed existing approaches and reduced the ancestry dependence of PRS in African Americans. In lipid traits from African British individuals (UK Biobank), slaPRS again improved on single population PRS while performing comparably to the globally combined PRS. slaPRS provides a data-driven and flexible framework to incorporate multiple population-specific GWAS and local ancestry in samples of admixed ancestry.
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Affiliation(s)
- Kevin Liao
- University of Michigan, Department of Biostatistics, Ann Arbor, MI, 48109, USA
| | - Sebastian Zöllner
- University of Michigan, Department of Biostatistics, Ann Arbor, MI, 48109, USA
- University of Michigan, Department of Psychiatry, Ann Arbor, MI, 48109, USA
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Subramanian S, Khan F, Hirsch IB. New advances in type 1 diabetes. BMJ 2024; 384:e075681. [PMID: 38278529 DOI: 10.1136/bmj-2023-075681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/28/2024]
Abstract
Type 1 diabetes is an autoimmune condition resulting in insulin deficiency and eventual loss of pancreatic β cell function requiring lifelong insulin therapy. Since the discovery of insulin more than 100 years ago, vast advances in treatments have improved care for many people with type 1 diabetes. Ongoing research on the genetics and immunology of type 1 diabetes and on interventions to modify disease course and preserve β cell function have expanded our broad understanding of this condition. Biomarkers of type 1 diabetes are detectable months to years before development of overt disease, and three stages of diabetes are now recognized. The advent of continuous glucose monitoring and the newer automated insulin delivery systems have changed the landscape of type 1 diabetes management and are associated with improved glycated hemoglobin and decreased hypoglycemia. Adjunctive therapies such as sodium glucose cotransporter-1 inhibitors and glucagon-like peptide 1 receptor agonists may find use in management in the future. Despite these rapid advances in the field, people living in under-resourced parts of the world struggle to obtain necessities such as insulin, syringes, and blood glucose monitoring essential for managing this condition. This review covers recent developments in diagnosis and treatment and future directions in the broad field of type 1 diabetes.
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Affiliation(s)
- Savitha Subramanian
- University of Washington Diabetes Institute, Division of Metabolism, Endocrinology and Nutrition, University of Washington, Seattle, WA, USA
| | - Farah Khan
- University of Washington Diabetes Institute, Division of Metabolism, Endocrinology and Nutrition, University of Washington, Seattle, WA, USA
| | - Irl B Hirsch
- University of Washington Diabetes Institute, Division of Metabolism, Endocrinology and Nutrition, University of Washington, Seattle, WA, USA
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21
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Billings LK, Shi Z, Wei J, Rifkin AS, Zheng SL, Helfand BT, Ilbawi N, Dunnenberger HM, Hulick PJ, Qamar A, Xu J. Utility of Polygenic Scores for Differentiating Diabetes Diagnosis Among Patients With Atypical Phenotypes of Diabetes. J Clin Endocrinol Metab 2023; 109:107-113. [PMID: 37560999 DOI: 10.1210/clinem/dgad456] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 07/10/2023] [Accepted: 08/08/2023] [Indexed: 08/11/2023]
Abstract
CONTEXT Misclassification of diabetes type occurs in people with atypical presentations of type 1 diabetes (T1D) or type 2 diabetes (T2D). Although current clinical guidelines suggest clinical variables and treatment response as ways to help differentiate diabetes type, they remain insufficient for people with atypical presentations. OBJECTIVE This work aimed to assess the clinical utility of 2 polygenic scores (PGSs) in differentiating between T1D and T2D. METHODS Patients diagnosed with diabetes in the UK Biobank were studied (N = 41 787), including 464 (1%) and 15 923 (38%) who met the criteria for classic T1D and T2D, respectively, and 25 400 (61%) atypical diabetes. The validity of 2 published PGSs for T1D (PGST1D) and T2D (PGST2D) in differentiating classic T1D or T2D was assessed using C statistic. The utility of genetic probability for T1D based on PGSs (GenProb-T1D) was evaluated in atypical diabetes patients. RESULTS The joint performance of PGST1D and PGST2D for differentiating classic T1D or T2D was outstanding (C statistic = 0.91), significantly higher than that of PGST1D alone (0.88) and PGST2D alone (0.70), both P less than .001. Using an optimal cutoff of GenProb-T1D, 23% of patients with atypical diabetes had a higher probability of T1D and its validity was independently supported by clinical presentations that are characteristic of T1D. CONCLUSION PGST1D and PGST2D can be used to discriminate classic T1D and T2D and have potential clinical utility for differentiating these 2 types of diseases among patients with atypical diabetes.
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Affiliation(s)
- Liana K Billings
- Department of Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA
- Department of Medicine, University of Chicago Pritzker School of Medicine, Chicago, IL 60637, USA
| | - Zhuqing Shi
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Jun Wei
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Andrew S Rifkin
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - S Lilly Zheng
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Brian T Helfand
- Department of Medicine, University of Chicago Pritzker School of Medicine, Chicago, IL 60637, USA
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL 60201, USA
- Department of Surgery, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Nadim Ilbawi
- Department of Family Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Henry M Dunnenberger
- Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Peter J Hulick
- Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Arman Qamar
- Department of Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Jianfeng Xu
- Department of Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA
- Department of Medicine, University of Chicago Pritzker School of Medicine, Chicago, IL 60637, USA
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL 60201, USA
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Squires S, Weedon MN, Oram RA. Exploring the application of deep learning methods for polygenic risk score estimation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.14.23299972. [PMID: 38168416 PMCID: PMC10760287 DOI: 10.1101/2023.12.14.23299972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Background Polygenic risk scores (PRS) summarise genetic information into a single number with multiple clinical and research uses. Machine learning (ML) has revolutionised a diverse set of fields, however, the impact of ML on genomics in general, and PRSs in particular, has been less significant. We explore how ML can improve the generation of PRSs. Methods We train ML models on known PRSs using UK Biobank data. We explore whether the models can recreate human programmed PRSs, including using a single model to generate multiple PRSs, and the difficulty in using ML for PRS generation. We also investigate how ML can compensate for missing data and the constraints on performance. Results We demonstrate almost perfect generation of PRSs, including when using one model to predict multiple scores, and with little loss of performance with reduced quantity of training data. For an example set of missing SNPs the MLP produces predictions that enable separation of cases from population samples with an area under the receiver operating characteristic curve of 0.847 (95% CI: 0.828-0.864) compared to 0.798 (95% CI: 0.779-0.818) for the PRS. We provide evidence that input information is the limiting factor of further improvement. Conclusions ML can accurately generate PRSs, including with one model for multiple PRSs. The models are transferable and have high longevity. With certain missing SNPs the ML models can statistically significantly improve on normal PRS generation. Models trained are probably at the edge of performance and further improvements likely require use of additional input data.
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Karpov DS, Sosnovtseva AO, Pylina SV, Bastrich AN, Petrova DA, Kovalev MA, Shuvalova AI, Eremkina AK, Mokrysheva NG. Challenges of CRISPR/Cas-Based Cell Therapy for Type 1 Diabetes: How Not to Engineer a "Trojan Horse". Int J Mol Sci 2023; 24:17320. [PMID: 38139149 PMCID: PMC10743607 DOI: 10.3390/ijms242417320] [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: 11/03/2023] [Revised: 12/04/2023] [Accepted: 12/07/2023] [Indexed: 12/24/2023] Open
Abstract
Type 1 diabetes mellitus (T1D) is an autoimmune disease caused by the destruction of insulin-producing β-cells in the pancreas by cytotoxic T-cells. To date, there are no drugs that can prevent the development of T1D. Insulin replacement therapy is the standard care for patients with T1D. This treatment is life-saving, but is expensive, can lead to acute and long-term complications, and results in reduced overall life expectancy. This has stimulated the research and development of alternative treatments for T1D. In this review, we consider potential therapies for T1D using cellular regenerative medicine approaches with a focus on CRISPR/Cas-engineered cellular products. However, CRISPR/Cas as a genome editing tool has several drawbacks that should be considered for safe and efficient cell engineering. In addition, cellular engineering approaches themselves pose a hidden threat. The purpose of this review is to critically discuss novel strategies for the treatment of T1D using genome editing technology. A well-designed approach to β-cell derivation using CRISPR/Cas-based genome editing technology will significantly reduce the risk of incorrectly engineered cell products that could behave as a "Trojan horse".
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Affiliation(s)
- Dmitry S. Karpov
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia; (D.S.K.); (A.O.S.); (M.A.K.); (A.I.S.)
| | - Anastasiia O. Sosnovtseva
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia; (D.S.K.); (A.O.S.); (M.A.K.); (A.I.S.)
| | - Svetlana V. Pylina
- Endocrinology Research Centre, 115478 Moscow, Russia; (S.V.P.); (A.N.B.); (D.A.P.); (A.K.E.)
| | - Asya N. Bastrich
- Endocrinology Research Centre, 115478 Moscow, Russia; (S.V.P.); (A.N.B.); (D.A.P.); (A.K.E.)
| | - Darya A. Petrova
- Endocrinology Research Centre, 115478 Moscow, Russia; (S.V.P.); (A.N.B.); (D.A.P.); (A.K.E.)
| | - Maxim A. Kovalev
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia; (D.S.K.); (A.O.S.); (M.A.K.); (A.I.S.)
| | - Anastasija I. Shuvalova
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia; (D.S.K.); (A.O.S.); (M.A.K.); (A.I.S.)
| | - Anna K. Eremkina
- Endocrinology Research Centre, 115478 Moscow, Russia; (S.V.P.); (A.N.B.); (D.A.P.); (A.K.E.)
| | - Natalia G. Mokrysheva
- Endocrinology Research Centre, 115478 Moscow, Russia; (S.V.P.); (A.N.B.); (D.A.P.); (A.K.E.)
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McGrail C, Chiou J, Elgamal R, Luckett AM, Oram RA, Benaglio P, Gaulton KJ. Genetic discovery and risk prediction for type 1 diabetes in individuals without high-risk HLA-DR3/DR4 haplotypes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.11.23298405. [PMID: 37986756 PMCID: PMC10659516 DOI: 10.1101/2023.11.11.23298405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Over 10% of type 1 diabetes (T1D) cases do not have high-risk HLA-DR3 or DR4 haplotypes with distinct clinical features such as later onset and reduced insulin dependence. To identify genetic drivers of T1D in the absence of DR3/DR4, we performed association and fine-mapping analyses in 12,316 non-DR3/DR4 samples. Risk variants at the MHC and other loci genome-wide had heterogeneity in effects on T1D dependent on DR3/DR4, and non-DR3/DR4 T1D had evidence for a greater polygenic burden. T1D-assocated variants in non-DR3/DR4 were more enriched for loci, regulatory elements, and pathways for antigen presentation, innate immunity, and beta cells, and depleted in T cells, compared to DR3/DR4. Non-DR3/DR4 T1D cases were poorly classified based on an existing genetic risk score GRS2, and we created a new GRS which highly discriminated non-DR3/DR4 T1D from both non-diabetes and T2D. In total we identified heterogeneity in T1D genetic risk and disease mechanisms dependent on high-risk HLA haplotype and which enabled accurate classification of T1D across HLA background.
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Affiliation(s)
- Carolyn McGrail
- Biomedical Sciences Graduate Program, UC San Diego, La Jolla, CA
| | - Joshua Chiou
- Biomedical Sciences Graduate Program, UC San Diego, La Jolla, CA
| | - Ruth Elgamal
- Biomedical Sciences Graduate Program, UC San Diego, La Jolla, CA
| | - Amber M Luckett
- University of Exeter College of Medicine and Health, Exeter, UK
| | - Richard A Oram
- University of Exeter College of Medicine and Health, Exeter, UK
- Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK
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Luterbacher F, Blouin JL, Schwitzgebel VM. Atypical diabetes with spontaneous remission associated with systemic lupus erythematosus in an adolescent girl of African ancestry, a case report. BMC Endocr Disord 2023; 23:228. [PMID: 37864241 PMCID: PMC10588024 DOI: 10.1186/s12902-023-01478-0] [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: 11/06/2022] [Accepted: 10/03/2023] [Indexed: 10/22/2023] Open
Abstract
BACKGROUND New-onset diabetes in youth encompasses type 1 diabetes, type 2 diabetes, monogenic diabetes, and rarer subtypes like Type B insulin resistance syndrome and ketosis-prone atypical diabetes in African populations. Some cases defy classification, posing management challenges. Here, we present a case of a unique, reversible diabetes subtype. CASE PRESENTATION We describe an adolescent African girl recently diagnosed with systemic lupus erythematosus. At age 15, she presented with ketoacidosis, HbA1c of 108.7 mmol/mol (12.1%), and positive anti-insulin antibodies. Initially diagnosed with type 1 diabetes, insulin was prescribed. Due to the presence of obesity and signs of insulin resistance, we added metformin. Concurrently, she received treatment for lupus with hydroxychloroquine, mycophenolate mofetil, and prednisone. After discharge, she stopped insulin due to cultural beliefs. Five months later, her glycemia and HbA1c normalized (37 mmol/mol or 5.5%) without insulin, despite corticosteroid therapy and weight gain. Autoantibodies normalized, and lupus activity decreased. Genetic testing for monogenic diabetes was negative, and the type 1 genetic risk score was exceptionally low. CONCLUSIONS We present a complex, reversible diabetes subtype. Features suggest an autoimmune origin, possibly influenced by overlapping HLA risk haplotypes with lupus. Lupus treatment or immunomodulation may have impacted diabetes remission. Ancestry-tailored genetic risk scores are currently designed to improve diagnostic accuracy.
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Affiliation(s)
- Fanny Luterbacher
- Pediatric Endocrinology and Diabetology, Department of Pediatrics, Gynecology and Obstetrics, University Hospitals of Geneva, Rue Willy-Donzé 6, 1205, Geneva, Switzerland
| | - Jean-Louis Blouin
- Department of Genetic Medicine and Development, Faculty of Medicine, University of Geneva, 1211, Geneva, Switzerland
- Department of Diagnostics, University Hospitals of Geneva, 1211, Geneva, Switzerland
| | - Valerie M Schwitzgebel
- Pediatric Endocrinology and Diabetology, Department of Pediatrics, Gynecology and Obstetrics, University Hospitals of Geneva, Rue Willy-Donzé 6, 1205, Geneva, Switzerland.
- Diabetes Center of the Faculty of Medicine, University of Geneva, 1211, Geneva, Switzerland.
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Urrutia I, Martínez R, Calvo B, Saso-Jiménez L, González P, Fernández-Rubio E, Martín-Nieto A, Aguayo A, Rica I, Gaztambide S, Castano L. Autoimmune Diabetes From Childhood to Adulthood: The Role of Pancreatic Autoantibodies and HLA-DRB1 Genotype. J Clin Endocrinol Metab 2023; 108:e1341-e1346. [PMID: 37207452 DOI: 10.1210/clinem/dgad277] [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] [Received: 02/18/2023] [Revised: 05/15/2023] [Accepted: 05/15/2023] [Indexed: 05/21/2023]
Abstract
CONTEXT Autoimmune diabetes can develop at any age, but unlike early-onset diabetes, adult onset is less well documented. We aimed to compare, over a wide age range, the most reliable predictive biomarkers for this pathology: pancreatic-autoantibodies and HLA-DRB1 genotype. METHODS A retrospective study of 802 patients with diabetes (aged 11 months to 66 years) was conducted. Pancreatic autoantibodies at diagnosis: insulin autoantibodies (IAA), glutamate decarboxylase autoantibodies (GADA), islet tyrosine phosphatase 2 autoantibodies (IA2A), and zinc transporter-8 autoantibodies (ZnT8A) and HLA-DRB1 genotype were analyzed. RESULTS Compared with early-onset patients, adults had a lower frequency of multiple autoantibodies, with GADA being the most common. At early onset, IAA was the most frequent in those younger than 6 years and correlated inversely with age; GADA and ZnT8A correlated directly and IA2A remained stable.The absence of HLA-DRB1 risk genotype was associated with higher age at diabetes onset (27.5 years; interquartile range [IQR], 14.3-35.7), whereas the high-risk HLA-DR3/DR4 was significantly more common at lower age (11.9 years; IQR, 7.1-21.6). ZnT8A was associated with DR4/non-DR3 (odds ratio [OR], 1.91; 95% CI, 1.15-3.17), GADA with DR3/non-DR4 (OR, 2.97; 95% CI, 1.55-5.71), and IA2A with DR4/non-DR3 and DR3/DR4 (OR, 3.89; 95% CI, 2.28-6.64, and OR, 3.08; 95% CI, 1.83-5.18, respectively). No association of IAA with HLA-DRB1 was found. CONCLUSION Autoimmunity and HLA-DRB1 genotype are age-dependent biomarkers. Adult-onset autoimmune diabetes is associated with lower genetic risk and lower immune response to pancreatic islet cells compared with early-onset diabetes.
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Affiliation(s)
- Inés Urrutia
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- CIBERDEM, CIBERER, UPV-EHU, Endo-ERN, 48903 Barakaldo, Spain
| | - Rosa Martínez
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- CIBERDEM, CIBERER, UPV-EHU, Endo-ERN, 48903 Barakaldo, Spain
| | - Begona Calvo
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- Department of Medical Oncology, Hospital Universitario Cruces, 48903 Barakaldo, Spain
| | - Laura Saso-Jiménez
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- CIBERDEM, CIBERER, UPV-EHU, Endo-ERN, 48903 Barakaldo, Spain
| | - Pedro González
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- Department of Endocrinology and Nutrition, Hospital Universitario Cruces, 48903 Barakaldo, Spain
| | - Elsa Fernández-Rubio
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- Department of Endocrinology and Nutrition, Hospital Universitario Cruces, 48903 Barakaldo, Spain
| | - Alicia Martín-Nieto
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- Department of Endocrinology and Nutrition, Hospital Universitario Cruces, 48903 Barakaldo, Spain
| | - Anibal Aguayo
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- CIBERDEM, CIBERER, UPV-EHU, Endo-ERN, 48903 Barakaldo, Spain
| | - Itxaso Rica
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- CIBERDEM, CIBERER, UPV-EHU, Endo-ERN, 48903 Barakaldo, Spain
- Department of Pediatric Endocrinology, Hospital Universitario Cruces, 48903 Barakaldo, Spain
| | - Sonia Gaztambide
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- CIBERDEM, CIBERER, UPV-EHU, Endo-ERN, 48903 Barakaldo, Spain
- Department of Endocrinology and Nutrition, Hospital Universitario Cruces, 48903 Barakaldo, Spain
| | - Luis Castano
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- CIBERDEM, CIBERER, UPV-EHU, Endo-ERN, 48903 Barakaldo, Spain
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Nureen L, Di Girolamo N. Limbal Epithelial Stem Cells in the Diabetic Cornea. Cells 2023; 12:2458. [PMID: 37887302 PMCID: PMC10605319 DOI: 10.3390/cells12202458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 10/10/2023] [Accepted: 10/11/2023] [Indexed: 10/28/2023] Open
Abstract
Continuous replenishment of the corneal epithelium is pivotal for maintaining optical transparency and achieving optimal visual perception. This dynamic process is driven by limbal epithelial stem cells (LESCs) located at the junction between the cornea and conjunctiva, which is otherwise known as the limbus. In patients afflicted with diabetes, hyperglycemia-induced impairments in corneal epithelial regeneration results in persistent epithelial and other defects on the ocular surface, termed diabetic keratopathy (DK), which progressively diminish vision and quality of life. Reports of delayed corneal wound healing and the reduced expression of putative stem cell markers in diabetic relative to healthy eyes suggest that the pathogenesis of DK may be associated with the abnormal activity of LESCs. However, the precise role of these cells in diabetic corneal disease is poorly understood and yet to be comprehensively explored. Herein, we review existing literature highlighting aberrant LESC activity in diabetes, focusing on factors that influence their form and function, and emerging therapies to correct these defects. The consequences of malfunctioning or depleted LESC stocks in DK and limbal stem cell deficiency (LSCD) are also discussed. These insights could be exploited to identify novel targets for improving the management of ocular surface complications that manifest in patients with diabetes.
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Affiliation(s)
| | - Nick Di Girolamo
- School of Biomedical Sciences, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia;
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28
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Srinivasan S, Wu P, Mercader JM, Udler MS, Porneala BC, Bartz TM, Floyd JS, Sitlani C, Guo X, Haessler J, Kooperberg C, Liu J, Ahmad S, van Duijn C, Liu CT, Goodarzi MO, Florez JC, Meigs JB, Rotter JI, Rich SS, Dupuis J, Leong A. A Type 1 Diabetes Polygenic Score Is Not Associated With Prevalent Type 2 Diabetes in Large Population Studies. J Endocr Soc 2023; 7:bvad123. [PMID: 37841955 PMCID: PMC10576255 DOI: 10.1210/jendso/bvad123] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Indexed: 10/17/2023] Open
Abstract
Context Both type 1 diabetes (T1D) and type 2 diabetes (T2D) have significant genetic contributions to risk and understanding their overlap can offer clinical insight. Objective We examined whether a T1D polygenic score (PS) was associated with a diagnosis of T2D in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium. Methods We constructed a T1D PS using 79 known single nucleotide polymorphisms associated with T1D risk. We analyzed 13 792 T2D cases and 14 169 controls from CHARGE cohorts to determine the association between the T1D PS and T2D prevalence. We validated findings in an independent sample of 2256 T2D cases and 27 052 controls from the Mass General Brigham Biobank (MGB Biobank). As secondary analyses in 5228 T2D cases from CHARGE, we used multivariable regression models to assess the association of the T1D PS with clinical outcomes associated with T1D. Results The T1D PS was not associated with T2D both in CHARGE (P = .15) and in the MGB Biobank (P = .87). The partitioned human leukocyte antigens only PS was associated with T2D in CHARGE (OR 1.02 per 1 SD increase in PS, 95% CI 1.01-1.03, P = .006) but not in the MGB Biobank. The T1D PS was weakly associated with insulin use (OR 1.007, 95% CI 1.001-1.012, P = .03) in CHARGE T2D cases but not with other outcomes. Conclusion In large biobank samples, a common variant PS for T1D was not consistently associated with prevalent T2D. However, possible heterogeneity in T2D cannot be ruled out and future studies are needed do subphenotyping.
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Affiliation(s)
- Shylaja Srinivasan
- Division of Pediatric Endocrinology, University of California at San Francisco, San Francisco, CA 94158, USA
| | - Peitao Wu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02215, USA
| | - Josep M Mercader
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Center for Genomic Medicine and Diabetes Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Miriam S Udler
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Center for Genomic Medicine and Diabetes Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Bianca C Porneala
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Traci M Bartz
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA 98195, USA
| | - James S Floyd
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA 98195, USA
- Department of Medicine, University of Washington, Seattle, WA 98195, USA
- Department of Epidemiology, University of Washington, Seattle, WA 98195, USA
| | - Colleen Sitlani
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA 98195, USA
- Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Xiquing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Jeffrey Haessler
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Jun Liu
- Department of Epidemiology, Erasmus Medical Center, 3015 GD Rotterdam, The Netherlands
- Nuffield Department of Population Health, University of Oxford, Oxford OX1 2JD, UK
| | - Shahzad Ahmad
- Department of Epidemiology, Erasmus Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Cornelia van Duijn
- Department of Epidemiology, Erasmus Medical Center, 3015 GD Rotterdam, The Netherlands
- Nuffield Department of Population Health, University of Oxford, Oxford OX1 2JD, UK
| | - Ching-Ti Liu
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Mark O Goodarzi
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Jose C Florez
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Center for Genomic Medicine and Diabetes Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - James B Meigs
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Stephen S Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22903, USA
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02215, USA
| | - Aaron Leong
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Center for Genomic Medicine and Diabetes Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
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29
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Koch S, Schmidtke J, Krawczak M, Caliebe A. Clinical utility of polygenic risk scores: a critical 2023 appraisal. J Community Genet 2023; 14:471-487. [PMID: 37133683 PMCID: PMC10576695 DOI: 10.1007/s12687-023-00645-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 03/31/2023] [Indexed: 05/04/2023] Open
Abstract
Since their first appearance in the context of schizophrenia and bipolar disorder in 2009, polygenic risk scores (PRSs) have been described for a large number of common complex diseases. However, the clinical utility of PRSs in disease risk assessment or therapeutic decision making is likely limited because PRSs usually only account for the heritable component of a trait and ignore the etiological role of environment and lifestyle. We surveyed the current state of PRSs for various diseases, including breast cancer, diabetes, prostate cancer, coronary artery disease, and Parkinson disease, with an extra focus upon the potential improvement of clinical scores by their combination with PRSs. We observed that the diagnostic and prognostic performance of PRSs alone is consistently low, as expected. Moreover, combining a PRS with a clinical score at best led to moderate improvement of the power of either risk marker. Despite the large number of PRSs reported in the scientific literature, prospective studies of their clinical utility, particularly of the PRS-associated improvement of standard screening or therapeutic procedures, are still rare. In conclusion, the benefit to individual patients or the health care system in general of PRS-based extensions of existing diagnostic or treatment regimens is still difficult to judge.
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Affiliation(s)
- Sebastian Koch
- Institut für Medizinische Informatik und Statistik, Christian-Albrechts-Universität zu Kiel, Universitätsklinikum Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Jörg Schmidtke
- Amedes MVZ Wagnerstibbe, Hannover, Germany
- Institut für Humangenetik, Medizinische Hochschule Hannover, Hannover, Germany
| | - Michael Krawczak
- Institut für Medizinische Informatik und Statistik, Christian-Albrechts-Universität zu Kiel, Universitätsklinikum Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Amke Caliebe
- Institut für Medizinische Informatik und Statistik, Christian-Albrechts-Universität zu Kiel, Universitätsklinikum Schleswig-Holstein Campus Kiel, Kiel, Germany.
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30
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Allen LA, Taylor PN, Gillespie KM, Oram RA, Dayan CM. Maternal type 1 diabetes and relative protection against offspring transmission. Lancet Diabetes Endocrinol 2023; 11:755-767. [PMID: 37666263 DOI: 10.1016/s2213-8587(23)00190-0] [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: 01/31/2023] [Revised: 06/12/2023] [Accepted: 06/16/2023] [Indexed: 09/06/2023]
Abstract
Type 1 diabetes is around twice as common in the offspring of men with type 1 diabetes than in the offspring of women with type 1 diabetes, but the reasons for this difference are unclear. This Review summarises the evidence on the rate of transmission of type 1 diabetes to the offspring of affected fathers compared with affected mothers. The findings of nine major studies are presented, describing the magnitude of the effect observed and the relative strengths and weaknesses of these studies. This Review also explores possible underlying mechanisms for this effect, such as genetic mechanisms (eg, the selective loss of fetuses with high-risk genes in mothers with type 1 diabetes, preferential transmission of susceptibility genes from fathers, and parent-of-origin effects influencing gene expression), environmental exposures (eg, exposure to maternal hyperglycaemia, exogenous insulin exposure, and transplacental antibody transfer), and maternal microchimerism. Understanding why type 1 diabetes is more common in the offspring of men versus women with type 1 diabetes will help in the identification of individuals at high risk of the disease and can pave the way in the development of interventions that mimic the protective elements of maternal type 1 diabetes to reduce the risk of disease in individuals at high risk.
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Affiliation(s)
- Lowri A Allen
- Diabetes Research Group, Cardiff University, University Hospital of Wales, Cardiff, UK.
| | - Peter N Taylor
- Diabetes Research Group, Cardiff University, University Hospital of Wales, Cardiff, UK
| | - Kathleen M Gillespie
- Diabetes and Metabolism, Bristol Medical School, University of Bristol, Southmead Hospital, Bristol, UK
| | - Richard A Oram
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, Royal Devon and Exeter Hospital, Exeter, UK
| | - Colin M Dayan
- Diabetes Research Group, Cardiff University, University Hospital of Wales, Cardiff, UK
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31
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Osafehinti D, Mulukutla SN, Hampe CS, Gaba R, Ram N, Weedon MN, Oram RA, Balasubramanyam A. Type 1 Diabetes Genetic Risk Score Differentiates Subgroups of Ketosis-Prone Diabetes. Diabetes Care 2023; 46:1778-1782. [PMID: 37506364 PMCID: PMC10516251 DOI: 10.2337/dc23-0622] [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: 04/09/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023]
Abstract
OBJECTIVE To determine whether genetic risk for type 1 diabetes (T1D) differentiates the four Aβ subgroups of ketosis-prone diabetes (KPD), where A+ and A- define the presence or absence of islet autoantibodies and β+ and β- define the presence or absence of β-cell function. RESEARCH DESIGN AND METHODS We compared T1D genetic risk scores (GRS) of patients with KPD across subgroups, race/ethnicity, β-cell function, and glycemia. RESULTS Among 426 patients with KPD (54% Hispanic, 31% African American, 11% White), rank order of GRS was A+β- > A+β+ = A-β- > A-β+. GRS of A+β- KPD was lower than that of a T1D cohort, and GRS of A-β+ KPD was higher than that of a type 2 diabetes cohort. GRS was lowest among African American patients, with a similar distribution across KPD subgroups. CONCLUSIONS T1D genetic risk delineates etiologic differences among KPD subgroups. Patients with A+β- KPD have the highest and those with A-β+ KPD the lowest GRS.
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Affiliation(s)
- Deborah Osafehinti
- Division of Diabetes, Endocrinology and Metabolism, Baylor College of Medicine, Houston, TX
| | | | | | - Ruchi Gaba
- Division of Diabetes, Endocrinology and Metabolism, Baylor College of Medicine, Houston, TX
| | - Nalini Ram
- Division of Diabetes, Endocrinology and Metabolism, Baylor College of Medicine, Houston, TX
| | - Michael N. Weedon
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, and The Academic Kidney Unit, Royal Devon and Exeter NHS Foundation Trust, Exeter, U.K
| | - Richard A. Oram
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, and The Academic Kidney Unit, Royal Devon and Exeter NHS Foundation Trust, Exeter, U.K
| | - Ashok Balasubramanyam
- Division of Diabetes, Endocrinology and Metabolism, Baylor College of Medicine, Houston, TX
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32
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Jones AG, Shields BM, Oram RA, Dabelea DM, Hagopian WA, Lustigova E, Shah AS, Knupp J, Mottl AK, DÀgostino RB, Williams A, Marcovina SM, Pihoker C, Divers J, Redondo MJ. Clinical prediction models combining routine clinical measures identify participants with youth-onset diabetes who maintain insulin secretion in the range associated with type 2 diabetes: The SEARCH for Diabetes in Youth Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.27.23296128. [PMID: 37808789 PMCID: PMC10557841 DOI: 10.1101/2023.09.27.23296128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Objective With the high prevalence of pediatric obesity and overlapping features between diabetes subtypes, accurately classifying youth-onset diabetes can be challenging. We aimed to develop prediction models that, using characteristics available at diabetes diagnosis, can identify youth who will retain endogenous insulin secretion at levels consistent with type 2 diabetes (T2D). Methods We studied 2,966 youth with diabetes in the prospective SEARCH study (diagnosis age ≤19 years) to develop prediction models to identify participants with fasting c-peptide ≥250 pmol/L (≥0.75ng/ml) after >3 years (median 74 months) of diabetes duration. Models included clinical measures at baseline visit, at a mean diabetes duration of 11 months (age, BMI, sex, waist circumference, HDL-C), with and without islet autoantibodies (GADA, IA-2A) and a Type 1 Diabetes Genetic Risk Score (T1DGRS). Results Models using routine clinical measures with or without autoantibodies and T1DGRS were highly accurate in identifying participants with c-peptide ≥0.75 ng/ml (17% of participants; 2.3% and 53% of those with and without positive autoantibodies) (area under receiver operator curve [AUCROC] 0.95-0.98). In internal validation, optimism was very low, with excellent calibration (slope=0.995-0.999). Models retained high performance for predicting retained c-peptide in older youth with obesity (AUCROC 0.88-0.96), and in subgroups defined by self-reported race/ethnicity (AUCROC 0.88-0.97), autoantibody status (AUCROC 0.87-0.96), and clinically diagnosed diabetes types (AUCROC 0.81-0.92). Conclusion Prediction models combining routine clinical measures at diabetes diagnosis, with or without islet autoantibodies or T1DGRS, can accurately identify youth with diabetes who maintain endogenous insulin secretion in the range associated with type 2 diabetes.
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Affiliation(s)
| | | | | | | | | | | | - Amy S Shah
- University of Cincinnati & Cincinnati Children's Hospital Medical Center
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33
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Park DK, Chen M, Kim S, Joo YY, Loving RK, Kim HS, Cha J, Yoo S, Kim JH. Overestimated prediction using polygenic prediction derived from summary statistics. BMC Genom Data 2023; 24:52. [PMID: 37710206 PMCID: PMC10500750 DOI: 10.1186/s12863-023-01151-4] [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: 02/12/2023] [Accepted: 08/16/2023] [Indexed: 09/16/2023] Open
Abstract
BACKGROUND When polygenic risk score (PRS) is derived from summary statistics, independence between discovery and test sets cannot be monitored. We compared two types of PRS studies derived from raw genetic data (denoted as rPRS) and the summary statistics for IGAP (sPRS). RESULTS Two variables with the high heritability in UK Biobank, hypertension, and height, are used to derive an exemplary scale effect of PRS. sPRS without APOE is derived from International Genomics of Alzheimer's Project (IGAP), which records ΔAUC and ΔR2 of 0.051 ± 0.013 and 0.063 ± 0.015 for Alzheimer's Disease Sequencing Project (ADSP) and 0.060 and 0.086 for Accelerating Medicine Partnership - Alzheimer's Disease (AMP-AD). On UK Biobank, rPRS performances for hypertension assuming a similar size of discovery and test sets are 0.0036 ± 0.0027 (ΔAUC) and 0.0032 ± 0.0028 (ΔR2). For height, ΔR2 is 0.029 ± 0.0037. CONCLUSION Considering the high heritability of hypertension and height of UK Biobank and sample size of UK Biobank, sPRS results from AD databases are inflated. Independence between discovery and test sets is a well-known basic requirement for PRS studies. However, a lot of PRS studies cannot follow such requirements because of impossible direct comparisons when using summary statistics. Thus, for sPRS, potential duplications should be carefully considered within the same ethnic group.
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Affiliation(s)
- David Keetae Park
- Department of Biomedical Engineering, Columbia University, New York, USA
| | - Mingshen Chen
- Department of Applied Mathematics & Statistics, Stony Brook University, New York, USA
| | - Seungsoo Kim
- Department of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, NY, USA
| | - Yoonjung Yoonie Joo
- Samsung Advanced Institute for Health Sciences & Technology (SAHIST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Rebekah K Loving
- Department of Biology, California Institute of Technology, Pasadena, USA
| | - Hyoung Seop Kim
- Department of Physical Medicine and Rehabilitation, Dementia Center, National Health Insurance Service Ilsan Hospital, Goyang, South Korea
| | - Jiook Cha
- Department of Psychology, Brain and Cognitive Sciences, AI Institute, Seoul National University, Seoul, South Korea
| | - Shinjae Yoo
- Computational Science Initiative, Brookhaven National Lab. Computer Science and Math, Building 725, Room 2-189, Upton, NY, 11973, USA.
| | - Jong Hun Kim
- Department of Neurology, Dementia Center, National Health Insurance Service Ilsan Hospital, 100 Ilsan-ro Ilsandong-gu, Goyang, Gyeonggi-Do, 10444, South Korea.
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34
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Shapiro MR, Dong X, Perry DJ, McNichols JM, Thirawatananond P, Posgai AL, Peters LD, Motwani K, Musca RS, Muir A, Concannon P, Jacobsen LM, Mathews CE, Wasserfall CH, Haller MJ, Schatz DA, Atkinson MA, Brusko MA, Bacher R, Brusko TM. Human immune phenotyping reveals accelerated aging in type 1 diabetes. JCI Insight 2023; 8:e170767. [PMID: 37498686 PMCID: PMC10544250 DOI: 10.1172/jci.insight.170767] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/19/2023] [Indexed: 07/29/2023] Open
Abstract
The proportions and phenotypes of immune cell subsets in peripheral blood undergo continual and dramatic remodeling throughout the human life span, which complicates efforts to identify disease-associated immune signatures in type 1 diabetes (T1D). We conducted cross-sectional flow cytometric immune profiling on peripheral blood from 826 individuals (stage 3 T1D, their first-degree relatives, those with ≥2 islet autoantibodies, and autoantibody-negative unaffected controls). We constructed an immune age predictive model in unaffected participants and observed accelerated immune aging in T1D. We used generalized additive models for location, shape, and scale to obtain age-corrected data for flow cytometry and complete blood count readouts, which can be visualized in our interactive portal (ImmScape); 46 parameters were significantly associated with age only, 25 with T1D only, and 23 with both age and T1D. Phenotypes associated with accelerated immunological aging in T1D included increased CXCR3+ and programmed cell death 1-positive (PD-1+) frequencies in naive and memory T cell subsets, despite reduced PD-1 expression levels on memory T cells. Phenotypes associated with T1D after age correction were predictive of T1D status. Our findings demonstrate advanced immune aging in T1D and highlight disease-associated phenotypes for biomarker monitoring and therapeutic interventions.
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Affiliation(s)
- Melanie R. Shapiro
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
| | - Xiaoru Dong
- Diabetes Institute and
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, Florida, USA
| | - Daniel J. Perry
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
| | - James M. McNichols
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
| | - Puchong Thirawatananond
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
| | - Amanda L. Posgai
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
| | - Leeana D. Peters
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
| | - Keshav Motwani
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
| | - Richard S. Musca
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
| | - Andrew Muir
- Department of Pediatrics, Emory University, Atlanta, Georgia, USA
| | - Patrick Concannon
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
- Genetics Institute and
| | - Laura M. Jacobsen
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Clayton E. Mathews
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
| | - Clive H. Wasserfall
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
| | - Michael J. Haller
- Diabetes Institute and
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Desmond A. Schatz
- Diabetes Institute and
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Mark A. Atkinson
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Maigan A. Brusko
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
| | - Rhonda Bacher
- Diabetes Institute and
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, Florida, USA
| | - Todd M. Brusko
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, Florida, USA
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Wibaek R, Andersen GS, Linneberg A, Hansen T, Grarup N, Thuesen ACB, Jensen RT, Wells JCK, Pilgaard KA, Brøns C, Vistisen D, Vaag AA. Low birthweight is associated with a higher incidence of type 2 diabetes over two decades independent of adult BMI and genetic predisposition. Diabetologia 2023; 66:1669-1679. [PMID: 37303008 PMCID: PMC10390608 DOI: 10.1007/s00125-023-05937-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 04/13/2023] [Indexed: 06/13/2023]
Abstract
AIMS/HYPOTHESIS Low birthweight is a risk factor for type 2 diabetes. Most previous studies are based on cross-sectional prevalence data, not designed to study the timing of onset of type 2 diabetes in relation to birthweight. We aimed to examine associations of birthweight with age-specific incidence rate of type 2 diabetes in middle-aged to older adults over two decades. METHODS Adults aged 30-60 years enrolled in the Danish Inter99 cohort in 1999-2001 (baseline examination), with information on birthweight from original birth records from 1939-1971 and without diabetes at baseline, were eligible. Birth records were linked with individual-level data on age at diabetes diagnosis and key covariates. Incidence rates of type 2 diabetes as a function of age, sex and birthweight were modelled using Poisson regression, adjusting for prematurity status at birth, parity, polygenic scores for birthweight and type 2 diabetes, maternal and paternal diabetes history, socioeconomic status and adult BMI. RESULTS In 4590 participants there were 492 incident type 2 diabetes cases during a mean follow-up of 19 years. Type 2 diabetes incidence rate increased with age, was higher in male participants, and decreased with increasing birthweight (incidence rate ratio [95% CI per 1 kg increase in birthweight] 0.60 [0.48, 0.75]). The inverse association of birthweight with type 2 diabetes incidence was statistically significant across all models and in sensitivity analysis. CONCLUSIONS/INTERPRETATION A lower birthweight was associated with increased risk of developing type 2 diabetes independent of adult BMI and genetic risk of type 2 diabetes and birthweight.
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Affiliation(s)
- Rasmus Wibaek
- Clinical Research, Steno Diabetes Center Copenhagen, Herlev, Denmark.
| | | | - Allan Linneberg
- Center for Clinical Research and Prevention, Copenhagen University Hospital - Bispebjerg and Frederiksberg, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Anne Cathrine B Thuesen
- Clinical Research, Steno Diabetes Center Copenhagen, Herlev, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Rasmus T Jensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jonathan C K Wells
- Childhood Nutrition Research Centre, Population Policy and Practice Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London, UK
| | - Kasper A Pilgaard
- Clinical Research, Steno Diabetes Center Copenhagen, Herlev, Denmark
| | - Charlotte Brøns
- Clinical Research, Steno Diabetes Center Copenhagen, Herlev, Denmark
| | - Dorte Vistisen
- Clinical Research, Steno Diabetes Center Copenhagen, Herlev, Denmark
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Allan A Vaag
- Clinical Research, Steno Diabetes Center Copenhagen, Herlev, Denmark.
- Lund University Diabetes Centre, Lund University, Malmö, Sweden.
- Department of Endocrinology, Skåne University Hospital, Malmö, Sweden.
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36
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Luckett AM, Weedon MN, Hawkes G, Leslie RD, Oram RA, Grant SFA. Utility of genetic risk scores in type 1 diabetes. Diabetologia 2023; 66:1589-1600. [PMID: 37439792 PMCID: PMC10390619 DOI: 10.1007/s00125-023-05955-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 05/23/2023] [Indexed: 07/14/2023]
Abstract
Iterative advances in understanding of the genetics of type 1 diabetes have identified >70 genetic regions associated with risk of the disease, including strong associations across the HLA class II region that account for >50% of heritability. The increased availability of genetic data combined with the decreased costs of generating these data, have facilitated the development of polygenic scores that aggregate risk variants from associated loci into a single number: either a genetic risk score (GRS) or a polygenic risk score (PRS). PRSs incorporate the risk of many possibly correlated variants from across the genome, even if they do not reach genome-wide significance, whereas GRSs estimate the cumulative contribution of a smaller subset of genetic variants that reach genome-wide significance. Type 1 diabetes GRSs have utility in diabetes classification, aiding discrimination between type 1 diabetes, type 2 diabetes and MODY. Type 1 diabetes GRSs are also being used in newborn screening studies to identify infants at risk of future presentation of the disease. Most early studies of type 1 diabetes genetics have been conducted in European ancestry populations, but, to develop accurate GRSs across diverse ancestries, large case-control cohorts from non-European populations are still needed. The current barriers to GRS implementation within healthcare are mainly related to a lack of guidance and knowledge on integration with other biomarkers and clinical variables. Once these limitations are addressed, there is huge potential for 'test and treat' approaches to be used to tailor care for individuals with type 1 diabetes.
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Affiliation(s)
- Amber M Luckett
- University of Exeter College of Medicine and Health, Exeter, UK
| | | | - Gareth Hawkes
- University of Exeter College of Medicine and Health, Exeter, UK
| | - R David Leslie
- Blizard Institute, Queen Mary University of London, London, UK.
| | - Richard A Oram
- University of Exeter College of Medicine and Health, Exeter, UK.
- Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK.
| | - Struan F A Grant
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Division of Diabetes and Endocrinology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Institute for Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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37
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Abstract
Despite major advances over the past decade, prevention and treatment of type 1 diabetes mellitus (T1DM) remain suboptimal, with large and unexplained variations in individual responses to interventions. The current classification schema for diabetes mellitus does not capture the complexity of this disease or guide clinical management effectively. One of the approaches to achieve the goal of applying precision medicine in diabetes mellitus is to identify endotypes (that is, well-defined subtypes) of the disease each of which has a distinct aetiopathogenesis that might be amenable to specific interventions. Here, we describe epidemiological, clinical, genetic, immunological, histological and metabolic differences within T1DM that, together, suggest heterogeneity in its aetiology and pathogenesis. We then present the emerging endotypes and their impact on T1DM prediction, prevention and treatment.
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Affiliation(s)
- Maria J Redondo
- Paediatric Diabetes & Endocrinology, Texas Children's Hospital, Baylor College of Medicine, Houston, TX, USA.
| | - Noel G Morgan
- Exeter Centre of Excellence for Diabetes Research (EXCEED), Department of Clinical and Biomedical and Science, University of Exeter Medical School, Exeter, UK
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38
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Jacobsen LM, Diggins K, Blanchfield L, McNichols J, Perry DJ, Brant J, Dong X, Bacher R, Gersuk VH, Schatz DA, Atkinson MA, Mathews CE, Haller MJ, Long SA, Linsley PS, Brusko TM. Responders to low-dose ATG induce CD4+ T cell exhaustion in type 1 diabetes. JCI Insight 2023; 8:e161812. [PMID: 37432736 PMCID: PMC10543726 DOI: 10.1172/jci.insight.161812] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 07/06/2023] [Indexed: 07/12/2023] Open
Abstract
BACKGROUNDLow-dose anti-thymocyte globulin (ATG) transiently preserves C-peptide and lowers HbA1c in individuals with recent-onset type 1 diabetes (T1D); however, the mechanisms of action and features of the response remain unclear. Here, we characterized the post hoc immunological outcomes of ATG administration and their potential use as biomarkers of metabolic response to therapy (i.e., improved preservation of endogenous insulin production).METHODSWe assessed gene and protein expression, targeted gene methylation, and cytokine concentrations in peripheral blood following treatment with ATG (n = 29), ATG plus granulocyte colony-stimulating factor (ATG/G-CSF, n = 28), or placebo (n = 31).RESULTSTreatment with low-dose ATG preserved regulatory T cells (Tregs), as measured by stable methylation of FOXP3 Treg-specific demethylation region (TSDR) and increased proportions of CD4+FOXP3+ Tregs (P < 0.001) identified by flow cytometry. While treatment effects were consistent across participants, not all maintained C-peptide. Responders exhibited a transient rise in IL-6, IP-10, and TNF-α (P < 0.05 for all) 2 weeks after treatment and a durable CD4+ exhaustion phenotype (increased PD-1+KLRG1+CD57- on CD4+ T cells [P = 0.011] and PD1+CD4+ Temra MFI [P < 0.001] at 12 weeks, following ATG and ATG/G-CSF, respectively). ATG nonresponders displayed higher proportions of senescent T cells (at baseline and after treatment) and increased methylation of EOMES (i.e., less expression of this exhaustion marker).CONCLUSIONAltogether in these exploratory analyses, Th1 inflammation-associated serum and CD4+ exhaustion transcript and cellular phenotyping profiles may be useful for identifying signatures of clinical response to ATG in T1D.TRIAL REGISTRATIONClinicalTrials.gov NCT02215200.FUNDINGThe Leona M. and Harry B. Helmsley Charitable Trust (2019PG-T1D011), the NIH (R01 DK106191 Supplement, K08 DK128628), NIH TrialNet (U01 DK085461), and the NIH NIAID (P01 AI042288).
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Affiliation(s)
- Laura M. Jacobsen
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, Florida, USA
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, Florida, USA
| | - Kirsten Diggins
- Benaroya Research Institute at Virginia Mason, Seattle, Washington, USA
| | - Lori Blanchfield
- Benaroya Research Institute at Virginia Mason, Seattle, Washington, USA
| | - James McNichols
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, Florida, USA
| | - Daniel J. Perry
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, Florida, USA
| | - Jason Brant
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, Florida, USA
| | - Xiaoru Dong
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, Florida, USA
- Department of Biostatistics, University of Florida, Gainesville, Florida, USA
| | - Rhonda Bacher
- Department of Biostatistics, University of Florida, Gainesville, Florida, USA
| | - Vivian H. Gersuk
- Benaroya Research Institute at Virginia Mason, Seattle, Washington, USA
| | - Desmond A. Schatz
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Mark A. Atkinson
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, Florida, USA
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, Florida, USA
| | - Clayton E. Mathews
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, Florida, USA
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, Florida, USA
| | - Michael J. Haller
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - S. Alice Long
- Benaroya Research Institute at Virginia Mason, Seattle, Washington, USA
| | - Peter S. Linsley
- Benaroya Research Institute at Virginia Mason, Seattle, Washington, USA
| | - Todd M. Brusko
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, Florida, USA
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, Florida, USA
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Goyal S, Rani J, Bhat MA, Vanita V. Genetics of diabetes. World J Diabetes 2023; 14:656-679. [PMID: 37383588 PMCID: PMC10294065 DOI: 10.4239/wjd.v14.i6.656] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 03/13/2023] [Accepted: 04/17/2023] [Indexed: 06/14/2023] Open
Abstract
Diabetes mellitus is a complicated disease characterized by a complex interplay of genetic, epigenetic, and environmental variables. It is one of the world's fastest-growing diseases, with 783 million adults expected to be affected by 2045. Devastating macrovascular consequences (cerebrovascular disease, cardiovascular disease, and peripheral vascular disease) and microvascular complications (like retinopathy, nephropathy, and neuropathy) increase mortality, blindness, kidney failure, and overall quality of life in individuals with diabetes. Clinical risk factors and glycemic management alone cannot predict the development of vascular problems; multiple genetic investigations have revealed a clear hereditary component to both diabetes and its related complications. In the twenty-first century, technological advancements (genome-wide association studies, next-generation sequencing, and exome-sequencing) have led to the identification of genetic variants associated with diabetes, however, these variants can only explain a small proportion of the total heritability of the condition. In this review, we address some of the likely explanations for this "missing heritability", for diabetes such as the significance of uncommon variants, gene-environment interactions, and epigenetics. Current discoveries clinical value, management of diabetes, and future research directions are also discussed.
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Affiliation(s)
- Shiwali Goyal
- Department of Ophthalmic Genetics and Visual Function Branch, National Eye Institute, Rockville, MD 20852, United States
| | - Jyoti Rani
- Department of Human Genetics, Guru Nanak Dev University, Amritsar 143005, Punjab, India
| | - Mohd Akbar Bhat
- Department of Ophthalmology, Georgetown University Medical Center, Washington DC, DC 20057, United States
| | - Vanita Vanita
- Department of Human Genetics, Guru Nanak Dev University, Amritsar 143005, Punjab, India
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40
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Shoaib M, Ye Q, IglayReger H, Tan MH, Boehnke M, Burant CF, Soleimanpour SA, Gagliano Taliun SA. Evaluation of polygenic risk scores to differentiate between type 1 and type 2 diabetes. Genet Epidemiol 2023; 47:303-313. [PMID: 36821788 PMCID: PMC10202843 DOI: 10.1002/gepi.22521] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 02/11/2023] [Accepted: 02/11/2023] [Indexed: 02/25/2023]
Abstract
Polygenic risk scores (PRS) quantify the genetic liability to disease and are calculated using an individual's genotype profile and disease-specific genome-wide association study (GWAS) summary statistics. Type 1 (T1D) and type 2 (T2D) diabetes both are determined in part by genetic loci. Correctly differentiating between types of diabetes is crucial for accurate diagnosis and treatment. PRS have the potential to address possible misclassification of T1D and T2D. Here we evaluated PRS models for T1D and T2D in European genetic ancestry participants from the UK Biobank (UKB) and then in the Michigan Genomics Initiative (MGI). Specifically, we investigated the utility of T1D and T2D PRS to discriminate between T1D, T2D, and controls in unrelated UKB individuals of European ancestry. We derived PRS models using external non-UKB GWAS. The T1D PRS model with the best discrimination between T1D cases and controls (area under the receiver operator curve [AUC] = 0.805) also yielded the best discrimination of T1D from T2D cases in the UKB (AUC = 0.792) and separation in MGI (AUC = 0.686). In contrast, the best T2D model did not discriminate between T1D and T2D cases (AUC = 0.527). Our analysis suggests that a T1D PRS model based on independent single nucleotide polymorphisms may help differentiate between T1D, T2D, and controls in individuals of European genetic ancestry.
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Affiliation(s)
- Muhammad Shoaib
- Montreal Heart Institute Research Centre, Montréal, Québec, Canada
- Université de Montréal, Université de Montréal, Montréal, Québec, Canada
| | - Qiang Ye
- Montreal Heart Institute Research Centre, Montréal, Québec, Canada
- Université de Montréal, Université de Montréal, Montréal, Québec, Canada
| | - Heidi IglayReger
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Meng H. Tan
- Division of Metabolism, Endocrinology & Diabetes, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Charles F. Burant
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Scott A. Soleimanpour
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Sarah A. Gagliano Taliun
- Montreal Heart Institute Research Centre, Montréal, Québec, Canada
- Department of Medicine and Department of Neurosciences, Université de Montréal, Montréal, Québec, Canada
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41
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Iakovliev A, McGurnaghan SJ, Hayward C, Colombo M, Lipschutz D, Spiliopoulou A, Colhoun HM, McKeigue PM. Genome-wide aggregated trans-effects on risk of type 1 diabetes: A test of the "omnigenic" sparse effector hypothesis of complex trait genetics. Am J Hum Genet 2023; 110:913-926. [PMID: 37164005 PMCID: PMC10257008 DOI: 10.1016/j.ajhg.2023.04.003] [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: 12/22/2022] [Accepted: 04/12/2023] [Indexed: 05/12/2023] Open
Abstract
The "omnigenic" hypothesis postulates that the polygenic effects of common SNPs on a typical complex trait are mediated through trans-effects on expression of a relatively sparse set of effector ("core") genes. We tested this hypothesis in a study of 4,964 cases of type 1 diabetes (T1D) and 7,497 controls by using summary statistics to calculate aggregated (excluding the HLA region) trans-scores for gene expression in blood. From associations of T1D with aggregated trans-scores, nine putative core genes were identified, of which three-STAT1, CTLA4 and FOXP3-are genes in which variants cause monogenic forms of autoimmune diabetes. Seven of these genes affect the activity of regulatory T cells, and two are involved in immune responses to microbial lipids. Four T1D-associated genomic regions could be identified as master regulators via trans-effects on gene expression. These results support the sparse effector hypothesis and reshape our understanding of the genetic architecture of T1D.
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Affiliation(s)
- Andrii Iakovliev
- Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Teviot Place, Edinburgh EH8 9AG, Scotland
| | - Stuart J McGurnaghan
- Institute of Genetics and Cancer, College of Medicine and Veterinary Medicine, University of Edinburgh, Western General Hospital Campus, Crewe Road, Edinburgh EH4 2XUC, Scotland
| | - Caroline Hayward
- Institute of Genetics and Cancer, College of Medicine and Veterinary Medicine, University of Edinburgh, Western General Hospital Campus, Crewe Road, Edinburgh EH4 2XUC, Scotland
| | - Marco Colombo
- University of Leipzig, Medical Faculty, University Hospital for Children and Adolescents, Center for Pediatric Research, Leipzig, Germany
| | - Debby Lipschutz
- Institute of Genetics and Cancer, College of Medicine and Veterinary Medicine, University of Edinburgh, Western General Hospital Campus, Crewe Road, Edinburgh EH4 2XUC, Scotland
| | - Athina Spiliopoulou
- Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Teviot Place, Edinburgh EH8 9AG, Scotland
| | - Helen M Colhoun
- Institute of Genetics and Cancer, College of Medicine and Veterinary Medicine, University of Edinburgh, Western General Hospital Campus, Crewe Road, Edinburgh EH4 2XUC, Scotland
| | - Paul M McKeigue
- Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Teviot Place, Edinburgh EH8 9AG, Scotland.
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Abstract
The number of older adults with type 1 diabetes (T1D) is increasing due to an overall increase in life expectancy and improvement in diabetes management and treatment of complications. They are a heterogeneous cohort due to the dynamic process of aging and the presence of comorbidities and diabetes-related complications. A high risk for hypoglycemia unawareness and severe hypoglycemia has been described. Periodic assessment of health status and adjustment of glycemic goals to mitigate hypoglycemia is imperative. Continuous glucose monitoring, insulin pump, and hybrid closed-loop systems are promising tools to improve glycemic control and mitigate hypoglycemia in this age group.
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Affiliation(s)
- Elena Toschi
- Joslin Diabetes Center; Beth Israel Deaconess Medical Center; Harvard Medical School, One Joslin Place, Boston, MA 02215, USA.
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43
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Perry DJ, Shapiro MR, Chamberlain SW, Kusmartseva I, Chamala S, Balzano-Nogueira L, Yang M, Brant JO, Brusko M, Williams MD, McGrail KM, McNichols J, Peters LD, Posgai AL, Kaddis JS, Mathews CE, Wasserfall CH, Webb-Robertson BJM, Campbell-Thompson M, Schatz D, Evans-Molina C, Pugliese A, Concannon P, Anderson MS, German MS, Chamberlain CE, Atkinson MA, Brusko TM. A genomic data archive from the Network for Pancreatic Organ donors with Diabetes. Sci Data 2023; 10:323. [PMID: 37237059 PMCID: PMC10219990 DOI: 10.1038/s41597-023-02244-6] [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: 12/24/2022] [Accepted: 05/16/2023] [Indexed: 05/28/2023] Open
Abstract
The Network for Pancreatic Organ donors with Diabetes (nPOD) is the largest biorepository of human pancreata and associated immune organs from donors with type 1 diabetes (T1D), maturity-onset diabetes of the young (MODY), cystic fibrosis-related diabetes (CFRD), type 2 diabetes (T2D), gestational diabetes, islet autoantibody positivity (AAb+), and without diabetes. nPOD recovers, processes, analyzes, and distributes high-quality biospecimens, collected using optimized standard operating procedures, and associated de-identified data/metadata to researchers around the world. Herein describes the release of high-parameter genotyping data from this collection. 372 donors were genotyped using a custom precision medicine single nucleotide polymorphism (SNP) microarray. Data were technically validated using published algorithms to evaluate donor relatedness, ancestry, imputed HLA, and T1D genetic risk score. Additionally, 207 donors were assessed for rare known and novel coding region variants via whole exome sequencing (WES). These data are publicly-available to enable genotype-specific sample requests and the study of novel genotype:phenotype associations, aiding in the mission of nPOD to enhance understanding of diabetes pathogenesis to promote the development of novel therapies.
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Affiliation(s)
- Daniel J Perry
- Department of Pathology, Immunology and Laboratory Medicine, Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL, 32611, USA
| | - Melanie R Shapiro
- Department of Pathology, Immunology and Laboratory Medicine, Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL, 32611, USA
| | - Sonya W Chamberlain
- Diabetes Center, School of Medicine, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Irina Kusmartseva
- Department of Pathology, Immunology and Laboratory Medicine, Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL, 32611, USA
| | - Srikar Chamala
- Department of Pathology, Immunology and Laboratory Medicine, Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL, 32611, USA
| | - Leandro Balzano-Nogueira
- Department of Pathology, Immunology and Laboratory Medicine, Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL, 32611, USA
| | - Mingder Yang
- Department of Pathology, Immunology and Laboratory Medicine, Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL, 32611, USA
| | - Jason O Brant
- Department of Pathology, Immunology and Laboratory Medicine, Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL, 32611, USA
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL, 32610, USA
| | - Maigan Brusko
- Department of Pathology, Immunology and Laboratory Medicine, Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL, 32611, USA
| | - MacKenzie D Williams
- Department of Pathology, Immunology and Laboratory Medicine, Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL, 32611, USA
| | - Kieran M McGrail
- Department of Pathology, Immunology and Laboratory Medicine, Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL, 32611, USA
| | - James McNichols
- Department of Pathology, Immunology and Laboratory Medicine, Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL, 32611, USA
| | - Leeana D Peters
- Department of Pathology, Immunology and Laboratory Medicine, Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL, 32611, USA
| | - Amanda L Posgai
- Department of Pathology, Immunology and Laboratory Medicine, Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL, 32611, USA
| | - John S Kaddis
- Department of Diabetes and Cancer Discovery Science, Arthur Riggs Diabetes and Metabolism Research Institute, Beckman Research Institute, City of Hope, Duarte, CA, 91010, USA
| | - Clayton E Mathews
- Department of Pathology, Immunology and Laboratory Medicine, Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL, 32611, USA
- Department of Pediatrics, Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL, 32610, USA
| | - Clive H Wasserfall
- Department of Pathology, Immunology and Laboratory Medicine, Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL, 32611, USA
| | - Bobbie-Jo M Webb-Robertson
- Department of Pathology, Immunology and Laboratory Medicine, Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL, 32611, USA
- Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Martha Campbell-Thompson
- Department of Pathology, Immunology and Laboratory Medicine, Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL, 32611, USA
- Department of Biomedical Engineering, College of Engineering, University of Florida, Gainesville, FL, 32611, USA
| | - Desmond Schatz
- Department of Pediatrics, Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL, 32610, USA
| | - Carmella Evans-Molina
- Center for Diabetes and Metabolic Diseases and the Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Alberto Pugliese
- Diabetes Research Institute, Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Department of Microbiology and Immunology, Miller School of Medicine, University of Miami, Miami, FL, 33021, USA
| | - Patrick Concannon
- Department of Pathology, Immunology and Laboratory Medicine, Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL, 32611, USA
- Genetics Institute, University of Florida, Gainesville, FL, 32601, USA
| | - Mark S Anderson
- Diabetes Center, School of Medicine, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Michael S German
- Diabetes Center, School of Medicine, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Chester E Chamberlain
- Diabetes Center, School of Medicine, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Mark A Atkinson
- Department of Pathology, Immunology and Laboratory Medicine, Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL, 32611, USA.
- Department of Pediatrics, Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL, 32610, USA.
| | - Todd M Brusko
- Department of Pathology, Immunology and Laboratory Medicine, Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL, 32611, USA.
- Department of Pediatrics, Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL, 32610, USA.
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Mameli C, Triolo TM, Chiarelli F, Rewers M, Zuccotti G, Simmons KM. Lessons and Gaps in the Prediction and Prevention of Type 1 Diabetes. Pharmacol Res 2023; 193:106792. [PMID: 37201589 DOI: 10.1016/j.phrs.2023.106792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 05/01/2023] [Accepted: 05/08/2023] [Indexed: 05/20/2023]
Abstract
Type 1 diabetes (T1D) is a serious chronic autoimmune condition. Even though the root cause of T1D development has yet to be determined, enough is known about the natural history of T1D pathogenesis to allow study of interventions that may delay or even prevent the onset of hyperglycemia and clinical T1D. Primary prevention aims to prevent the onset of beta cell autoimmunity in asymptomatic people at high genetic risk for T1D. Secondary prevention strategies aim to preserve functional beta cells once autoimmunity is present, and tertiary prevention aims to initiate and extend partial remission of beta cell destruction after the clinical onset of T1D. The approval of teplizumab in the United States to delay the onset of clinical T1D marks an impressive milestone in diabetes care. This treatment opens the door to a paradigm shift in T1D care. People with T1D risk need to be identified early by measuring T1D related islet autoantibodies. Identifying people with T1D before they have symptoms will facilitate better understanding of pre-symptomatic T1D progression and T1D prevention strategies that may be effective.
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Affiliation(s)
- Chiara Mameli
- Department of Pediatrics, V. Buzzi Children's Hospital, Milan, Italy; Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy.
| | - Taylor M Triolo
- Barbara Davis Center for Diabetes, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO 80045
| | | | - Marian Rewers
- Barbara Davis Center for Diabetes, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO 80045
| | - Gianvincenzo Zuccotti
- Department of Pediatrics, V. Buzzi Children's Hospital, Milan, Italy; Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - Kimber M Simmons
- Barbara Davis Center for Diabetes, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO 80045
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45
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Fyvie MJ, Gillespie KM. The importance of biomarker development for monitoring type 1 diabetes progression rate and therapeutic responsiveness. Front Immunol 2023; 14:1158278. [PMID: 37256143 PMCID: PMC10225507 DOI: 10.3389/fimmu.2023.1158278] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 03/28/2023] [Indexed: 06/01/2023] Open
Abstract
Type 1 diabetes (T1D) is an autoimmune condition of children and adults in which immune cells target insulin-producing pancreatic β-cells for destruction. This results in a chronic inability to regulate blood glucose levels. The natural history of T1D is well-characterized in childhood. Evidence of two or more autoantibodies to the islet antigens insulin, GAD, IA-2 or ZnT8 in early childhood is associated with high risk of developing T1D in the future. Prediction of risk is less clear in adults and, overall, the factors controlling the progression rate from multiple islet autoantibody positivity to onset of symptoms are not fully understood. An anti-CD3 antibody, teplizumab, was recently shown to delay clinical progression to T1D in high-risk individuals including adults and older children. This represents an important proof of concept for those at risk of future T1D. Given their role in risk assessment, islet autoantibodies might appear to be the most obvious biomarkers to monitor efficacy. However, monitoring islet autoantibodies in clinical trials has shown only limited effects, although antibodies to the most recently identified autoantigen, tetraspanin-7, have not yet been studied in this context. Measurements of beta cell function remain fundamental to assessing efficacy and different models have been proposed, but improved biomarkers are required for both progression studies before onset of diabetes and in therapeutic monitoring. In this mini-review, we consider some established and emerging predictive and prognostic biomarkers, including markers of pancreatic function that could be integrated with metabolic markers to generate improved strategies to measure outcomes of therapeutic intervention.
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Affiliation(s)
| | - Kathleen M. Gillespie
- Diabetes and Metabolism, Bristol Medical School, University of Bristol, Bristol, United Kingdom
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Podolakova K, Barak L, Jancova E, Tarnokova S, Podracka L, Dobiasova Z, Skopkova M, Gasperikova D, Stanik J. Complete remission in children and adolescents with type 1 diabetes mellitus-prevalence and factors. Sci Rep 2023; 13:6790. [PMID: 37100887 PMCID: PMC10133219 DOI: 10.1038/s41598-023-34037-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Accepted: 04/23/2023] [Indexed: 04/28/2023] Open
Abstract
Little is known about complete remission in Type 1 diabetes mellitus (T1D) with the discontinuance of insulin treatment for a period of time. In this retrospective study we analysed the frequency and factors of onset and duration of 1. remission and 2. complete remission in children and adolescents with T1D from the Children Diabetes Centre in Bratislava, Slovakia. A total of 529 individuals with T1D, aged < 19 years (8.5 ± 4.3 years) at diabetes onset were included in the study. Remission was defined by HbA1c < 7.0% (53 mmol/mol) and an insulin daily dose < 0.5 IU/kg (and 0 IU/kg for complete remission). Remission occurred in 210 (39.7%) participants, and 15 of them had complete remission (2.8% from all participants). We have identified a new independent factor of complete remission onset (higher C-peptide). Complete remitters had a longer duration of remission compared with other remitters and also differed in lower HbA1c levels. No association was seen with autoantibodies or genetic risk score for T1D. Thus, not only partial but also complete remission is influenced by factors pointing toward an early diagnosis of T1D, which is important for better patient outcome.
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Affiliation(s)
- Kristina Podolakova
- Department of Pediatrics, Medical Faculty of Comenius University and National Institute for Children's Diseases, Limbova 1, 833 40, Bratislava, Slovakia
| | - Lubomir Barak
- Department of Pediatrics, Medical Faculty of Comenius University and National Institute for Children's Diseases, Limbova 1, 833 40, Bratislava, Slovakia
| | - Emilia Jancova
- Department of Pediatrics, Medical Faculty of Comenius University and National Institute for Children's Diseases, Limbova 1, 833 40, Bratislava, Slovakia
| | - Simona Tarnokova
- Department of Laboratory Medicine, National Institute for Children's Diseases, Limbova 1, 833 40, Bratislava, Slovakia
| | - Ludmila Podracka
- Department of Pediatrics, Medical Faculty of Comenius University and National Institute for Children's Diseases, Limbova 1, 833 40, Bratislava, Slovakia
| | - Zuzana Dobiasova
- Department of Metabolic Disorders, Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Dubravska Cesta 9, 845 05, Bratislava, Slovakia
| | - Martina Skopkova
- Department of Metabolic Disorders, Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Dubravska Cesta 9, 845 05, Bratislava, Slovakia
| | - Daniela Gasperikova
- Department of Metabolic Disorders, Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Dubravska Cesta 9, 845 05, Bratislava, Slovakia
| | - Juraj Stanik
- Department of Pediatrics, Medical Faculty of Comenius University and National Institute for Children's Diseases, Limbova 1, 833 40, Bratislava, Slovakia.
- Department of Metabolic Disorders, Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Dubravska Cesta 9, 845 05, Bratislava, Slovakia.
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Mancuso G, Bechi Genzano C, Fierabracci A, Fousteri G. Type 1 diabetes and inborn errors of immunity: Complete strangers or 2 sides of the same coin? J Allergy Clin Immunol 2023:S0091-6749(23)00427-X. [PMID: 37097271 DOI: 10.1016/j.jaci.2023.03.026] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/31/2023] [Accepted: 03/31/2023] [Indexed: 04/26/2023]
Abstract
Type 1 diabetes (T1D) is a polygenic disease and does not follow a mendelian pattern. Inborn errors of immunity (IEIs), on the other hand, are caused by damaging germline variants, suggesting that T1D and IEIs have nothing in common. Some IEIs, resulting from mutations in genes regulating regulatory T-cell homeostasis, are associated with elevated incidence of T1D. The genetic spectrum of IEIs is gradually being unraveled; consequently, molecular pathways underlying human monogenic autoimmunity are being identified. There is an appreciable overlap between some of these pathways and the genetic variants that determine T1D susceptibility, suggesting that after all, IEI and T1D are 2 sides of the same coin. The study of monogenic IEIs with a variable incidence of T1D has the potential to provide crucial insights into the mechanisms leading to T1D. These insights contribute to the definition of T1D endotypes and explain disease heterogeneity. In this review, we discuss the interconnected pathogenic pathways of autoimmunity, β-cell function, and primary immunodeficiency. We also examine the role of environmental factors in disease penetrance as well as the circumstantial evidence of IEI drugs in preventing and curing T1D in individuals with IEIs, suggesting the repositioning of these drugs also for T1D therapy.
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Affiliation(s)
- Gaia Mancuso
- Unit of Immunology, Rheumatology, Allergy and Rare Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Camillo Bechi Genzano
- Columbia Center for Translational Immunology, Department of Medicine, Columbia University Irving Medical Center, New York, NY
| | | | - Georgia Fousteri
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy.
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Carrera P, Marzinotto I, Bonfanti R, Massimino L, Calzavara S, Favellato Μ, Jofra T, De Giglio V, Bonura C, Stabilini A, Favalli V, Bondesan S, Cicalese MP, Laurenzi A, Caretto A, Frontino G, Rigamonti A, Molinari C, Scavini M, Sandullo F, Zapparoli E, Caridi N, Bonfiglio S, Castorani V, Ungaro F, Petrelli A, Barera G, Aiuti A, Bosi E, Battaglia M, Piemonti L, Lampasona V, Fousteri G. Genetic determinants of type 1 diabetes in individuals with weak evidence of islet autoimmunity at disease onset. Diabetologia 2023; 66:695-708. [PMID: 36692510 DOI: 10.1007/s00125-022-05865-5] [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] [Received: 03/18/2022] [Accepted: 10/31/2022] [Indexed: 01/25/2023]
Abstract
AIMS/HYPOTHESIS Islet autoantibodies (AAbs) are detected in >90% of individuals with clinically suspected type 1 diabetes at disease onset. A single AAb, sometimes at low titre, is often detected in some individuals, making their diagnosis uncertain. Type 1 diabetes genetic risk scores (GRS) are a useful tool for discriminating polygenic autoimmune type 1 diabetes from other types of diabetes, particularly the monogenic forms, but testing is not routinely performed in the clinic. Here, we used a type 1 diabetes GRS to screen for monogenic diabetes in individuals with weak evidence of autoimmunity, i.e. with a single AAb at disease onset. METHODS In a pilot study, we genetically screened 142 individuals with suspected type 1 diabetes, 42 of whom were AAb-negative, 27 of whom had a single AAb (single AAb-positive) and 73 of whom had multiple AAbs (multiple AAb-positive) at disease onset. Next-generation sequencing (NGS) was performed in 41 AAb-negative participants, 26 single AAb-positive participants and 60 multiple AAb-positive participants using an analysis pipeline of more than 200 diabetes-associated genes. RESULTS The type 1 diabetes GRS was significantly lower in AAb-negative individuals than in those with a single and multiple AAbs. Pathogenetic class 4/5 variants in MODY or monogenic diabetes genes were identified in 15/41 (36.6%) AAb-negative individuals, while class 3 variants of unknown significance were identified in 17/41 (41.5%). Residual C-peptide levels at diagnosis were higher in individuals with mutations compared to those without pathogenetic variants. Class 3 variants of unknown significance were found in 11/26 (42.3%) single AAb-positive individuals, and pathogenetic class 4/5 variants were present in 2/26 (7.7%) single AAb-positive individuals. No pathogenetic class 4/5 variants were identified in multiple AAb-positive individuals, but class 3 variants of unknown significance were identified in 19/60 (31.7%) patients. Several patients across the three groups had more than one class 3 variant. CONCLUSIONS/INTERPRETATION These findings provide insights into the genetic makeup of patients who show weak evidence of autoimmunity at disease onset. Absence of islet AAbs or the presence of a single AAb together with a low type 1 diabetes GRS may be indicative of a monogenic form of diabetes, and use of NGS may improve the accuracy of diagnosis.
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Affiliation(s)
- Paola Carrera
- Unit of Genomics for Human Disease Diagnosis, IRCCS Ospedale San Raffaele, Milan, Italy
- Laboratory of Clinical Molecular Biology, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Ilaria Marzinotto
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Riccardo Bonfanti
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Luca Massimino
- Department of Gastroenterology and Digestive Endoscopy, IRCCS Ospedale San Raffaele Hospital, Milan, Italy
| | - Silvia Calzavara
- Laboratory of Clinical Molecular Biology, IRCCS Ospedale San Raffaele, Milan, Italy
| | | | - Tatiana Jofra
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy
| | | | - Clara Bonura
- Pediatric Department, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Angela Stabilini
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Valeria Favalli
- Pediatric Department, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Simone Bondesan
- Unit of Genomics for Human Disease Diagnosis, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Maria Pia Cicalese
- San Raffaele Telethon Institute for Gene Therapy, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Pediatric Immunohematology and Bone Marrow Transplantation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Andrea Laurenzi
- Department of Internal Medicine, Diabetology, Endocrinology and Metabolism, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Amelia Caretto
- Department of Internal Medicine, Diabetology, Endocrinology and Metabolism, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Giulio Frontino
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Andrea Rigamonti
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Chiara Molinari
- Department of Internal Medicine, Diabetology, Endocrinology and Metabolism, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Marina Scavini
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy
- Department of Internal Medicine, Diabetology, Endocrinology and Metabolism, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Federica Sandullo
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Ettore Zapparoli
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Nicoletta Caridi
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Silvia Bonfiglio
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Federica Ungaro
- Department of Gastroenterology and Digestive Endoscopy, IRCCS Ospedale San Raffaele Hospital, Milan, Italy
| | | | - Graziano Barera
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
- Pediatric Department, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Alessandro Aiuti
- Vita-Salute San Raffaele University, Milan, Italy
- San Raffaele Telethon Institute for Gene Therapy, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Pediatric Immunohematology and Bone Marrow Transplantation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Emanuele Bosi
- Department of Internal Medicine, Diabetology, Endocrinology and Metabolism, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Manuela Battaglia
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy
- Fondazione Telethon, Milan, Italy
| | - Lorenzo Piemonti
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Vito Lampasona
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy.
| | - Georgia Fousteri
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy.
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Mohan V, Uma Sankari G, Amutha A, Anjana RM, Jeba Rani S, Unnikrishnan R, Venkatesan U, Shanthi Rani CS. Clinical and biochemical profile of childhood-adolescent-onset type 1 diabetes and adult-onset type 1 diabetes among Asian Indians. Acta Diabetol 2023; 60:579-586. [PMID: 36700996 DOI: 10.1007/s00592-023-02034-x] [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] [Received: 11/11/2022] [Accepted: 01/09/2023] [Indexed: 01/27/2023]
Abstract
AIM To compare the clinical and biochemical profile and prevalence of complications among childhood/adolescent-onset (CAO; onset of diabetes< 20 years of age) and adult-onset (AO; onset of diabetes- ≥ 20 years of age) type 1 diabetes (T1D), seen at a tertiary care diabetes center in south India. METHOD Data of 5578 individuals with T1D, diagnosed based on a history of diabetic ketoacidosis or ketonuria, fasting C-peptide < 0.3 pmol/mL and stimulated C-peptide values < 0.6 pmol/mL, and requirement of insulin right from the time of diagnosis, presenting to our center between 1991 and 2021, were retrieved from our electronic medical records. Retinopathy was assessed by retinal photography, chronic kidney disease (CKD) by urinary albumin excretion ≥ 30 µg/mg of creatinine and/or eGFR < 60 mL/min, and neuropathy by vibration perception threshold >= 20v on biothesiometry. RESULTS Overall, 3559 (63.8%) of individuals with T1D, belonged to CAO group and 2019 (36.2%) to AO category. AO had higher prevalence of all microvascular complications compared to CAO at every diabetes duration interval, even after adjusting for A1c. Among the AO group, prevalence of retinopathy, CKD, and neuropathy was higher in the GAD negative group. Among CAO there were no differences between the GAD negative and GAD positive groups with respect to prevalence of complications of diabetes. CONCLUSION AO with T1D had higher prevalence of microvascular complications compared to CAO. Among AO, GAD negative individuals had higher percentage of retinopathy and CKD compared to GAD positive group.
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Affiliation(s)
- Viswanathan Mohan
- Madras Diabetes Research Foundation (ICMR Centre for Advanced Research On Diabetes) & Dr. Mohan's Diabetes Specialities Centre (IDF Centre of Excellence in Diabetes Care), No 4, Conran Smith Road, Gopalapuram, Chennai, 600 086, India.
| | - Ganesan Uma Sankari
- Madras Diabetes Research Foundation (ICMR Centre for Advanced Research On Diabetes) & Dr. Mohan's Diabetes Specialities Centre (IDF Centre of Excellence in Diabetes Care), No 4, Conran Smith Road, Gopalapuram, Chennai, 600 086, India
| | - Anandakumar Amutha
- Madras Diabetes Research Foundation (ICMR Centre for Advanced Research On Diabetes) & Dr. Mohan's Diabetes Specialities Centre (IDF Centre of Excellence in Diabetes Care), No 4, Conran Smith Road, Gopalapuram, Chennai, 600 086, India
| | - Ranjit Mohan Anjana
- Madras Diabetes Research Foundation (ICMR Centre for Advanced Research On Diabetes) & Dr. Mohan's Diabetes Specialities Centre (IDF Centre of Excellence in Diabetes Care), No 4, Conran Smith Road, Gopalapuram, Chennai, 600 086, India
| | - Saravanan Jeba Rani
- Madras Diabetes Research Foundation (ICMR Centre for Advanced Research On Diabetes) & Dr. Mohan's Diabetes Specialities Centre (IDF Centre of Excellence in Diabetes Care), No 4, Conran Smith Road, Gopalapuram, Chennai, 600 086, India
| | - Ranjit Unnikrishnan
- Madras Diabetes Research Foundation (ICMR Centre for Advanced Research On Diabetes) & Dr. Mohan's Diabetes Specialities Centre (IDF Centre of Excellence in Diabetes Care), No 4, Conran Smith Road, Gopalapuram, Chennai, 600 086, India
| | - Ulagamathesan Venkatesan
- Madras Diabetes Research Foundation (ICMR Centre for Advanced Research On Diabetes) & Dr. Mohan's Diabetes Specialities Centre (IDF Centre of Excellence in Diabetes Care), No 4, Conran Smith Road, Gopalapuram, Chennai, 600 086, India
| | - Coimbatore Subramanian Shanthi Rani
- Madras Diabetes Research Foundation (ICMR Centre for Advanced Research On Diabetes) & Dr. Mohan's Diabetes Specialities Centre (IDF Centre of Excellence in Diabetes Care), No 4, Conran Smith Road, Gopalapuram, Chennai, 600 086, India
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50
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Visvikis-Siest S, Stathopoulou MG, Sunder-Plassmann R, Alizadeh BZ, Barouki R, Chatzaki E, Dagher G, Dedoussis G, Deloukas P, Haliassos A, Hiegel BB, Manolopoulos V, Masson C, Paré G, Paulmichl M, Petrelis AM, Sipeky C, Süsleyici B, Weryha G, Chenchik A, Diehl P, Everts RE, Haushofer A, Lamont J, Mercado R, Meyer H, Munoz-Galeano H, Murray H, Nhat F, Nofziger C, Schnitzel W, Kanoni S. The 10th Santorini conference: Systems medicine, personalised health and therapy. “The odyssey from hope to practice: Patient first. Keep Ithaca always in your mind”, santorini, Greece, 23–26 May 2022. Front Genet 2023; 14:1171131. [PMID: 37021002 PMCID: PMC10069673 DOI: 10.3389/fgene.2023.1171131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 02/28/2023] [Indexed: 03/12/2023] Open
Affiliation(s)
- Sophie Visvikis-Siest
- EA_1122 IGE-PCV, Université de Lorraine, Nancy, France
- *Correspondence: Sophie Visvikis-Siest, ; Stavroula Kanoni,
| | - Maria G. Stathopoulou
- Team 10: Control of Gene Expression, INSERM U, Centre Méditerranéen de Médecine Moléculaire C3M, Nice, France
| | | | - Behrooz Z. Alizadeh
- Unit of Personalized Medicine, Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, Netherland
| | - Robert Barouki
- Université de Paris, Inserm unit 1124 (T3S), Paris, France
| | - Ekaterina Chatzaki
- Laboratory of Pharmacology, Medical School, Democritus University of Thrace, Alexandroupolis, Greece
- Institute of Agri-Food and Life Sciences, Hellenic Mediterranean University Research Centre, Heraklion, Crete, Greece
| | - Georges Dagher
- Inserm, Paris, France
- Graz Medical University, Graz, Austria
- Milano-Bicocca University, Milan, Italy
- Beijing Academy of Sciences, Beijing, China
| | - George Dedoussis
- Department of Nutrition and Dietetics, Harokopio University of Athens, Athens, Greece
| | - Panagiotis Deloukas
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Alexander Haliassos
- EurSpLM, ESEAP, The Greek Proficiency Testing Scheme for Clinical Laboratories Athens, Athens, Greece
| | | | - Vangelis Manolopoulos
- Laboratory of Pharmacology, Medical School, Democritus University of Thrace, Alexandroupolis, Greece
- Clinical Pharmacology and Pharmacogenetics Unit, Academic General Hospital of Alexandroupolis, Alexandroupolis, Greece
| | | | - Guillaume Paré
- Population Health Research Institute, Genetic and Molecular Epidemiology Laboratory, McMaster University, Hamilton, ON, Canada
| | | | | | - Csilla Sipeky
- UCB Pharma, Translational Medicine, Precision Medicine and Biomarkers, Genetics, Braine-l’Alleud, Belgium
| | - Belgin Süsleyici
- Marmara University, Faculty of Sciences and Letters, Department of Molecular Biology, Istanbul, Türkiye
| | | | | | - Paul Diehl
- Cellecta, Inc, Mountain View, CA, United States
| | | | - Alexander Haushofer
- Inst. f. Med. u. Chem. Labordiagnostik, Klinikum Wels-Grieskirchen GmbH, Wels, Austria
| | - John Lamont
- Randox Laboratories Limited, Crumlin, Co.Antrim, United Kingdom
| | | | | | | | - Helena Murray
- Randox Laboratories Limited, Crumlin, Co.Antrim, United Kingdom
| | - Ferrier Nhat
- Thermo Fisher Scientific, San Francisco, CA, United States
| | | | | | - Stavroula Kanoni
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
- *Correspondence: Sophie Visvikis-Siest, ; Stavroula Kanoni,
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