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Vivas AJ, Boumediene S, Tobón GJ. Predicting autoimmune diseases: A comprehensive review of classic biomarkers and advances in artificial intelligence. Autoimmun Rev 2024:103611. [PMID: 39209014 DOI: 10.1016/j.autrev.2024.103611] [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: 01/09/2024] [Revised: 08/26/2024] [Accepted: 08/26/2024] [Indexed: 09/04/2024]
Abstract
Autoimmune diseases comprise a spectrum of disorders characterized by the dysregulation of immune tolerance, resulting in tissue or organ damage and inflammation. Their prevalence has been on the rise, significantly impacting patients' quality of life and escalating healthcare costs. Consequently, the prediction of autoimmune diseases has recently garnered substantial interest among researchers. Despite their wide heterogeneity, many autoimmune diseases exhibit a consistent pattern of paraclinical findings that hold predictive value. From serum biomarkers to various machine learning approaches, the array of available methods has been continuously expanding. The emergence of artificial intelligence (AI) presents an exciting new range of possibilities, with notable advancements already underway. The ultimate objective should revolve around disease prevention across all levels. This review provides a comprehensive summary of the most recent data pertaining to the prediction of diverse autoimmune diseases and encompasses both traditional biomarkers and the latest innovations in AI.
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Affiliation(s)
| | - Synda Boumediene
- Department of Medical Microbiology, Immunology and Cell Biology, Southern Illinois University-School of Medicine, Springfield, IL, United States of America
| | - Gabriel J Tobón
- Department of Medical Microbiology, Immunology and Cell Biology, Southern Illinois University-School of Medicine, Springfield, IL, United States of America; Department of Internal Medicine, Division of Rheumatology, Southern Illinois University-School of Medicine, Springfield, IL, United States of America.
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Bell KJ, Brodie S, Couper JJ, Colman P, Davis E, Deed G, Hagopian W, Haynes A, Hendrieckx C, Henry A, Gordon A, Howard K, Huynh T, Kerr B, Mikler K, Nassar N, Norris S, Oram R, Pawlak D, Shand A, Sinnott RO, Wadling B, Wentworth JM, Craig ME. Protocol for the Australian Type 1 Diabetes National Screening Pilot: Assessing the feasibility and acceptability of three general population screening models in children. Diabet Med 2024:e15419. [PMID: 39129150 DOI: 10.1111/dme.15419] [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: 04/30/2024] [Revised: 07/18/2024] [Accepted: 07/23/2024] [Indexed: 08/13/2024]
Abstract
AIM One third of Australian children diagnosed with type 1 diabetes present with life-threatening diabetic ketoacidosis (DKA) at diagnosis. Screening for early-stage, presymptomatic type 1 diabetes, with ongoing follow-up, can substantially reduce this risk (<5% risk). Several screening models are being trialled internationally, without consensus on the optimal approach. This pilot study aims to assess three models for a routine, population-wide screening programme in Australia. METHODS An implementation science-guided pilot study to evaluate the feasibility, acceptability and costs of three screening models in children will be conducted between July 2022 and June 2024. These models are as follows: (1) Genetic risk-stratified screening using newborn heel prick dried bloodspots, followed by autoantibody testing from 11 months of age; (2) genetic risk-stratified screening of infant (6-12 months) saliva followed by autoantibody testing from 10 months of age; and (3) autoantibody screening using capillary dried bloodspots collected from children aged 2, 6 or 10 years. Cohorts for each model will be recruited from targeted geographic areas across Australia involving ≥2 states per cohort, with a recruitment target of up to 3000 children per cohort (total up to 9000 children). The primary outcome is screening uptake for each cohort. Secondary outcomes include programme feasibility, costs, parental anxiety, risk perception, satisfaction, well-being and quality of life, and health professional attitudes and satisfaction. CONCLUSIONS This pilot is the first direct comparison of three screening implementation models for general population screening. Findings will provide evidence to inform a potential national screening programme for Australian children. TRIAL REGISTRATION ACTRN12622000381785.
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Affiliation(s)
- Kirstine J Bell
- Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
- Children's Hospital at Westmead Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Shannon Brodie
- Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
- Children's Hospital at Westmead Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Jennifer J Couper
- Diabetes and Endocrinology, Women's and Children's Hospital, Adelaide, South Australia, Australia
- Robinson Research Institute, University of Adelaide, Adelaide, South Australia, Australia
| | - Peter Colman
- Department of Diabetes and Endocrinology, Royal Melbourne Hospital, Parkville, Victoria, Australia
- Royal Melbourne Hospital, University of Melbourne Department of Medicine, Parkville, Victoria, Australia
| | - Elizabeth Davis
- Children's Diabetes Centre, Telethon Kids Institute, The University of Western Australia, Nedlands, Western Australia, Australia
- Department of Endocrinology and Diabetes, Perth Children's Hospital, Nedlands, Western Australia, Australia
| | - Gary Deed
- Monash University, Melbourne, Victoria, Australia
| | - William Hagopian
- University of Washington, Seattle, Washington, USA
- Indiana University, Indianapolis, Indiana, USA
| | - Aveni Haynes
- Children's Diabetes Centre, Telethon Kids Institute, The University of Western Australia, Nedlands, Western Australia, Australia
- UWA Medical School, Paediatrics, the University of Western Australia, Nedlands, Western Australia, Australia
| | - Christel Hendrieckx
- School of Psychology, Deakin University, Geelong, Victoria, Australia
- Institute for Health Transformation, Deakin University, Geelong, Victoria, Australia
- The Australian Centre for Behavioural Research in Diabetes, Diabetes Victoria, Melbourne, Victoria, Australia
| | - Amanda Henry
- Discipline of Women's Health, School of Clinical Medicine, UNSW Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Adrienne Gordon
- Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
- Sydney Institute for Women, Children and Families, Sydney Local Health District, Camperdown, New South Wales, Australia
| | - Kirsten Howard
- Menzies Centre for Health Policy and Economics, School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Tony Huynh
- Department of Endocrinology and Diabetes, Queensland Children's Hospital, South Brisbane, Queensland, Australia
- Children's Health Research Centre, Faculty of Medicine, The University of Queensland, South Brisbane, Queensland, Australia
- Department of Chemical Pathology, Mater Pathology, South Brisbane, Queensland, Australia
| | - Bernadette Kerr
- Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
- Children's Hospital at Westmead Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Kara Mikler
- Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
- Children's Hospital at Westmead Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Natasha Nassar
- Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
- Child Population and Translational Health Research, Children's Hospital at Westmead Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Sarah Norris
- Menzies Centre for Health Policy and Economics, School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Richard Oram
- University of Exeter College of Medicine and Health, Exeter, UK
- Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK
| | | | - Antonia Shand
- Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
- Royal Hospital for Women, Randwick, New South Wales, Australia
| | - Richard O Sinnott
- School of Computing and Information Systems, The University of Melbourne, Melbourne, Victoria, Australia
| | - Bethany Wadling
- Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
- Children's Hospital at Westmead Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - John M Wentworth
- Department of Diabetes and Endocrinology, Royal Melbourne Hospital, Parkville, Victoria, Australia
- Royal Melbourne Hospital, University of Melbourne Department of Medicine, Parkville, Victoria, Australia
- Population Health and Immunity Division, Walter and Eliza Hall Institute, Parkville, Victoria, Australia
| | - Maria E Craig
- Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
- Children's Hospital at Westmead Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
- Institute of Endocrinology and Diabetes, The Children's Hospital at Westmead, Sydney, New South Wales, Australia
- Discipline of Paediatrics and Child Health, School of Clinical Medicine, UNSW Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
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You L, Ferrat LA, Oram RA, Parikh HM, Steck AK, Krischer J, Redondo MJ. Identification of type 1 diabetes risk phenotypes using an outcome-guided clustering analysis. Diabetologia 2024:10.1007/s00125-024-06246-w. [PMID: 39103721 DOI: 10.1007/s00125-024-06246-w] [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: 03/18/2024] [Accepted: 06/18/2024] [Indexed: 08/07/2024]
Abstract
AIMS/HYPOTHESIS Although statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal the heterogeneity of the at-risk population by identifying clinically meaningful clusters are lacking. We aimed to identify and characterise clusters of islet autoantibody-positive individuals who share similar characteristics and type 1 diabetes risk. METHODS We tested a novel outcome-guided clustering method in initially non-diabetic autoantibody-positive relatives of individuals with type 1 diabetes, using the TrialNet Pathway to Prevention study data (n=1123). The outcome of the analysis was the time to development of type 1 diabetes, and variables in the model included demographic characteristics, genetics, metabolic factors and islet autoantibodies. An independent dataset (the Diabetes Prevention Trial of Type 1 Diabetes Study) (n=706) was used for validation. RESULTS The analysis revealed six clusters with varying type 1 diabetes risks, categorised into three groups based on the hierarchy of clusters. Group A comprised one cluster with high glucose levels (median for glucose mean AUC 9.48 mmol/l; IQR 9.16-10.02) and high risk (2-year diabetes-free survival probability 0.42; 95% CI 0.34, 0.51). Group B comprised one cluster with high IA-2A titres (median 287 DK units/ml; IQR 250-319) and elevated autoantibody titres (2-year diabetes-free survival probability 0.73; 95% CI 0.67, 0.80). Group C comprised four lower-risk clusters with lower autoantibody titres and glucose levels (with 2-year diabetes-free survival probability ranging from 0.84-0.99 in the four clusters). Within group C, the clusters exhibit variations in characteristics such as glucose levels, C-peptide levels and age. A decision rule for assigning individuals to clusters was developed. Use of the validation dataset confirmed that the clusters can identify individuals with similar characteristics. CONCLUSIONS/INTERPRETATION Demographic, metabolic, immunological and genetic markers may be used to identify clusters of distinctive characteristics and different risks of progression to type 1 diabetes among autoantibody-positive individuals with a family history of type 1 diabetes. The results also revealed the heterogeneity in the population and complex interactions between variables.
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Affiliation(s)
- Lu You
- Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.
| | - Lauric A Ferrat
- Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
- Faculty of Medicine, Department of Genetic Medicine and Development, University of Geneva, Geneva, Switzerland
| | - Richard A Oram
- Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Hemang M Parikh
- Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | - Andrea K Steck
- Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Jeffrey Krischer
- Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | - Maria J Redondo
- Baylor College of Medicine, Texas Children's Hospital, Houston, TX, USA
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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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Abel ED, Gloyn AL, Evans-Molina C, Joseph JJ, Misra S, Pajvani UB, Simcox J, Susztak K, Drucker DJ. Diabetes mellitus-Progress and opportunities in the evolving epidemic. Cell 2024; 187:3789-3820. [PMID: 39059357 PMCID: PMC11299851 DOI: 10.1016/j.cell.2024.06.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 06/21/2024] [Accepted: 06/24/2024] [Indexed: 07/28/2024]
Abstract
Diabetes, a complex multisystem metabolic disorder characterized by hyperglycemia, leads to complications that reduce quality of life and increase mortality. Diabetes pathophysiology includes dysfunction of beta cells, adipose tissue, skeletal muscle, and liver. Type 1 diabetes (T1D) results from immune-mediated beta cell destruction. The more prevalent type 2 diabetes (T2D) is a heterogeneous disorder characterized by varying degrees of beta cell dysfunction in concert with insulin resistance. The strong association between obesity and T2D involves pathways regulated by the central nervous system governing food intake and energy expenditure, integrating inputs from peripheral organs and the environment. The risk of developing diabetes or its complications represents interactions between genetic susceptibility and environmental factors, including the availability of nutritious food and other social determinants of health. This perspective reviews recent advances in understanding the pathophysiology and treatment of diabetes and its complications, which could alter the course of this prevalent disorder.
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Affiliation(s)
- E Dale Abel
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
| | - Anna L Gloyn
- Department of Pediatrics, Division of Endocrinology & Diabetes, Department of Genetics, Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Carmella Evans-Molina
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Joshua J Joseph
- Division of Endocrinology, Diabetes and Metabolism, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Shivani Misra
- Department of Metabolism, Digestion and Reproduction, Imperial College London, and Imperial College NHS Trust, London, UK
| | - Utpal B Pajvani
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Judith Simcox
- Howard Hughes Medical Institute, Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Katalin Susztak
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Daniel J Drucker
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada; Department of Medicine, University of Toronto, Toronto, ON, Canada
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Lopes EW, Turpin W, Croitoru K, Colombel JF, Torres J. Prediction and Prevention of Inflammatory Bowel Disease. Clin Gastroenterol Hepatol 2024:S1542-3565(24)00597-4. [PMID: 38996831 DOI: 10.1016/j.cgh.2024.05.047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 05/01/2024] [Accepted: 05/15/2024] [Indexed: 07/14/2024]
Affiliation(s)
- Emily W Lopes
- Division of Gastroenterology, Massachusetts General Hospital, Boston, Massachusetts; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Williams Turpin
- Zane Cohen Centre for Digestive Diseases, Mount Sinai Hospital, Division of Gastroenterology & Hepatology, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Kenneth Croitoru
- Zane Cohen Centre for Digestive Diseases, Mount Sinai Hospital, Division of Gastroenterology & Hepatology, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Jean-Frederic Colombel
- The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Joana Torres
- Division of Gastrenterology, Hospital da Luz, Lisboa, Portugal; Division of Gastroenterology, Hospital Beatriz Ângelo, Loures, Portugal; Faculdade de Medicina, Universidade de Lisboa, Portugal.
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Fung A, Loutet M, Roth DE, Wong E, Gill PJ, Morris SK, Beyene J. Clinical prediction models in children that use repeated measurements with time-varying covariates: a scoping review. Acad Pediatr 2024; 24:728-740. [PMID: 38561061 DOI: 10.1016/j.acap.2024.03.016] [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: 09/18/2023] [Revised: 02/29/2024] [Accepted: 03/27/2024] [Indexed: 04/04/2024]
Abstract
BACKGROUND Emerging evidence suggests that clinical prediction models that use repeated (time-varying) measurements within each patient may have higher predictive accuracy than models that use patient information from a single measurement. OBJECTIVE To determine the breadth of the published literature reporting the development of clinical prediction models in children that use time-varying predictors. DATA SOURCES MEDLINE, EMBASE and Cochrane databases. ELIGIBILITY CRITERIA We included studies reporting the development of a multivariable clinical prediction model in children, with or without validation, to predict a repeatedly measured binary or time-to-event outcome and utilizing at least one repeatedly measured predictor. SYNTHESIS METHODS We categorized included studies by the method used to model time-varying predictors. RESULTS Of 99 clinical prediction model studies that had a repeated measurements data structure, only 27 (27%) used methods that incorporated the repeated measurements as time-varying predictors in a single model. Among these 27 time-varying prediction model studies, we grouped model types into nine categories: time-dependent Cox regression, generalized estimating equations, random effects model, landmark model, joint model, neural network, K-nearest neighbor, support vector machine and tree-based algorithms. Where there was comparison of time-varying models to single measurement models, using time-varying predictors improved predictive accuracy. CONCLUSIONS Various methods have been used to develop time-varying prediction models in children, but there is a paucity of pediatric time-varying models in the literature. Incorporating time-varying covariates in pediatric prediction models may improve predictive accuracy. Future research in pediatric prediction model development should further investigate whether incorporation of time-varying covariates improves predictive accuracy.
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Affiliation(s)
- Alastair Fung
- Division of Paediatric Medicine (A Fung, DE Roth, and PJ Gill), Hospital for Sick Children, Toronto, Ontario, Canada; Dalla Lana School of Public Health (A Fung, M Loutet, DE Roth, PJ Gill, SK Morris, and J Beyene), University of Toronto, Toronto, Ontario, Canada; Centre for Global Child Health (A Fung, M Loutet, DE Roth, and SK Morris), Hospital for Sick Children, Toronto, Ontario, Canada.
| | - Miranda Loutet
- Dalla Lana School of Public Health (A Fung, M Loutet, DE Roth, PJ Gill, SK Morris, and J Beyene), University of Toronto, Toronto, Ontario, Canada; Centre for Global Child Health (A Fung, M Loutet, DE Roth, and SK Morris), Hospital for Sick Children, Toronto, Ontario, Canada
| | - Daniel E Roth
- Division of Paediatric Medicine (A Fung, DE Roth, and PJ Gill), Hospital for Sick Children, Toronto, Ontario, Canada; Dalla Lana School of Public Health (A Fung, M Loutet, DE Roth, PJ Gill, SK Morris, and J Beyene), University of Toronto, Toronto, Ontario, Canada; Centre for Global Child Health (A Fung, M Loutet, DE Roth, and SK Morris), Hospital for Sick Children, Toronto, Ontario, Canada; Temerty Faculty of Medicine (DE Roth, E Wong, PJ Gill, and SK Morris), University of Toronto, Toronto, Ontario, Canada; Child Health Evaluative Sciences (DE Roth, PJ Gill, and SK Morris), Hospital for Sick Children Research Institute, Toronto, Ontario, Canada
| | - Elliott Wong
- Temerty Faculty of Medicine (DE Roth, E Wong, PJ Gill, and SK Morris), University of Toronto, Toronto, Ontario, Canada
| | - Peter J Gill
- Division of Paediatric Medicine (A Fung, DE Roth, and PJ Gill), Hospital for Sick Children, Toronto, Ontario, Canada; Dalla Lana School of Public Health (A Fung, M Loutet, DE Roth, PJ Gill, SK Morris, and J Beyene), University of Toronto, Toronto, Ontario, Canada; Temerty Faculty of Medicine (DE Roth, E Wong, PJ Gill, and SK Morris), University of Toronto, Toronto, Ontario, Canada; Child Health Evaluative Sciences (DE Roth, PJ Gill, and SK Morris), Hospital for Sick Children Research Institute, Toronto, Ontario, Canada
| | - Shaun K Morris
- Dalla Lana School of Public Health (A Fung, M Loutet, DE Roth, PJ Gill, SK Morris, and J Beyene), University of Toronto, Toronto, Ontario, Canada; Centre for Global Child Health (A Fung, M Loutet, DE Roth, and SK Morris), Hospital for Sick Children, Toronto, Ontario, Canada; Temerty Faculty of Medicine (DE Roth, E Wong, PJ Gill, and SK Morris), University of Toronto, Toronto, Ontario, Canada; Child Health Evaluative Sciences (DE Roth, PJ Gill, and SK Morris), Hospital for Sick Children Research Institute, Toronto, Ontario, Canada; Division of Infectious Diseases (SK Morris), Hospital for Sick Children, Toronto, Ontario, Canada
| | - Joseph Beyene
- Dalla Lana School of Public Health (A Fung, M Loutet, DE Roth, PJ Gill, SK Morris, and J Beyene), University of Toronto, Toronto, Ontario, Canada; Department of Health Research Methods, Evidence and Impact (J Beyene), Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
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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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Pribitzer S, O'Rourke C, Ylescupidez A, Smithmyer M, Bender C, Speake C, Lord S, Greenbaum CJ. Beyond Stages: Predicting Individual Time Dependent Risk for Type 1 Diabetes. J Clin Endocrinol Metab 2024:dgae292. [PMID: 38712386 DOI: 10.1210/clinem/dgae292] [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: 01/26/2024] [Revised: 04/05/2024] [Accepted: 05/01/2024] [Indexed: 05/08/2024]
Abstract
BACKGROUND Essentially all individuals with multiple autoantibodies will develop clinical type 1 diabetes. Multiple AABs and normal glucose tolerance define Stage 1 diabetes; abnormal glucose tolerance defines Stage 2. However, the rate of progression within these stages is heterogeneous, necessitating personalized risk calculators to improve clinical implementation. METHODS We developed 3 models using TrialNet's Pathway to Prevention data to accommodate the reality that not all risk variables are clinically available. The Small model included AAB status, fasting glucose, HbA1c and age, while the Medium and Large models added predictors of disease progression measured via oral glucose tolerance testing. FINDINGS All models markedly improved granularity regarding personalized risk missing from current categories of stages of T1D. Model derived risk calculations are consistent with the expected reduction of risk with increasing age and increase in risk with higher glucose and lower insulin secretion, illustrating the suitability of the models. Adding glucose and insulin secretion data altered model predicted probabilities within Stages. In those with high 2-hour glucose, a high C-peptide markedly decreased predicted risk; lower C-peptide obviated the age-dependent risk of 2-hour glucose alone, providing a more nuanced estimate of rate of disease progression within Stage 2. CONCLUSIONS While essentially all those with multiple AABs will develop type 1 diabetes, the rate of progression is heterogeneous and not explained by any individual single risk variable. The model-based probabilities developed here provide an adaptable personalized risk calculator to better inform decisions about how and when to monitor disease progression in clinical practice.
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Affiliation(s)
- Stephan Pribitzer
- Center for Interventional Immunology, Benaroya Research Institute, Seattle, WA
| | - Colin O'Rourke
- Center for Interventional Immunology, Benaroya Research Institute, Seattle, WA
| | - Alyssa Ylescupidez
- Center for Interventional Immunology, Benaroya Research Institute, Seattle, WA
| | - Megan Smithmyer
- Center for Interventional Immunology, Benaroya Research Institute, Seattle, WA
| | - Christine Bender
- Center for Interventional Immunology, Benaroya Research Institute, Seattle, WA
| | - Cate Speake
- Center for Interventional Immunology, Benaroya Research Institute, Seattle, WA
| | - Sandra Lord
- Center for Interventional Immunology, Benaroya Research Institute, Seattle, WA
| | - Carla J Greenbaum
- Center for Interventional Immunology, Benaroya Research Institute, Seattle, WA
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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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11
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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: 124] [Impact Index Per Article: 124.0] [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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Xiang R, Kelemen M, Xu Y, Harris LW, Parkinson H, Inouye M, Lambert SA. Recent advances in polygenic scores: translation, equitability, methods and FAIR tools. Genome Med 2024; 16:33. [PMID: 38373998 PMCID: PMC10875792 DOI: 10.1186/s13073-024-01304-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 02/07/2024] [Indexed: 02/21/2024] Open
Abstract
Polygenic scores (PGS) can be used for risk stratification by quantifying individuals' genetic predisposition to disease, and many potentially clinically useful applications have been proposed. Here, we review the latest potential benefits of PGS in the clinic and challenges to implementation. PGS could augment risk stratification through combined use with traditional risk factors (demographics, disease-specific risk factors, family history, etc.), to support diagnostic pathways, to predict groups with therapeutic benefits, and to increase the efficiency of clinical trials. However, there exist challenges to maximizing the clinical utility of PGS, including FAIR (Findable, Accessible, Interoperable, and Reusable) use and standardized sharing of the genomic data needed to develop and recalculate PGS, the equitable performance of PGS across populations and ancestries, the generation of robust and reproducible PGS calculations, and the responsible communication and interpretation of results. We outline how these challenges may be overcome analytically and with more diverse data as well as highlight sustained community efforts to achieve equitable, impactful, and responsible use of PGS in healthcare.
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Affiliation(s)
- Ruidong Xiang
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Martin Kelemen
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Yu Xu
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Laura W Harris
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Helen Parkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK.
| | - Samuel A Lambert
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
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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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Wang D, Hou X, Huang J, Sun J, Kadowaki T, Lee MK, Jenkins AJ, Ji L. Incidence and trends of type 1 diabetes before and after 2000 in the Western Pacific Region: A systematic review and meta-analysis. Diabetes Res Clin Pract 2024; 207:111055. [PMID: 38104899 DOI: 10.1016/j.diabres.2023.111055] [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: 08/20/2023] [Revised: 12/09/2023] [Accepted: 12/13/2023] [Indexed: 12/19/2023]
Abstract
OBJECTIVES To undertake a systematic review of publications describing Type 1 diabetes (T1DM) incidence, trends over time and associated factors in the Western Pacific Region (WPR). METHODS As per the PROSPERO-registered (CRD42019122646) protocol English (MEDLINE, Embase, Global Health) and Chinese data-bases (China National Knowledge Infrastructure, VIP, Wanfang) from onset to 31/12/2019 were searched for T1DM incidence in the WPR. Country level data extracted included annual crude incidence rates by sex, number of new cases per annum (p.a.) and cumulatively, and the population at-risk. A meta-analysis for T1DM incidence was performed (by region and narrow age-bands, where possible) with subgroup analyses by time and by region. FINDINGS Forty-five population-based studies (21 from China), published 1973-2017, estimated T1DM incidence, mostly in youth, in 11 WPR countries. After 2000, mean annual T1DM incidence/100,000 person years aged 0-14 years ranged from 0.9 (95 % confidence intervals (CI), 0.6-1.3) in Fiji to 23.2 (95 % CI, 21.3-25.2) in Australia. The mean annual increase over time ranged from 2.8 % in Australia (1990-2002) to 14.2 % in Shanghai (1997-2011). T1DM incidence increased most in China (2.7-fold over 30-years) then Thailand (2-fold over 15-years). Most studies documented increasing incidence with age, though only two studies included people aged ≥ 20 years. Many, but not all studies reported significantly higher T1DM incidence in females vs. males. CONCLUSION T1DM incidence in the WPR is generally increasing, varying by age, sex, time and country. Results increase understanding of regional T1DM incidence and inform research and healthcare strategies.
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Affiliation(s)
- Du Wang
- The George Institute for Global Health, People's Republic of China
| | - Xiaoli Hou
- Department of Endocrinology and Metabolism, The Fifth Affiliated Hospital of Xinxiang Medical College, Xin Xiang 453100, People's Republic of China
| | - Juan Huang
- Department of Endocrinology and Metabolism, The First Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, People's Republic of China
| | - Jianjing Sun
- Department of Endocrinology, Jining No.1 People's Hospital, Jining 272 011, Shandong, People's Republic of China
| | - Takashi Kadowaki
- Toranomon Hospital, The University of Tokyo, Minato-ku, Tokyo 105-8470, Japan
| | - Moon-Kyu Lee
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Uijeongbu Eulji Medical Center, Eulji University School of Medicine, Uijeongbu, Republic of Korea
| | | | - Linong Ji
- Department of Endocrinology and Metabolism, Peking University People's Hospital, No. 11, Xizhimen Nan Da Jie, Xicheng District, Beijing 100044, People's Republic of China.
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15
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Galderisi A, Carr ALJ, Martino M, Taylor P, Senior P, Dayan C. Quantifying beta cell function in the preclinical stages of type 1 diabetes. Diabetologia 2023; 66:2189-2199. [PMID: 37712956 PMCID: PMC10627950 DOI: 10.1007/s00125-023-06011-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 08/08/2023] [Indexed: 09/16/2023]
Abstract
Clinically symptomatic type 1 diabetes (stage 3 type 1 diabetes) is preceded by a pre-symptomatic phase, characterised by progressive loss of functional beta cell mass after the onset of islet autoimmunity, with (stage 2) or without (stage 1) measurable changes in glucose profile during an OGTT. Identifying metabolic tests that can longitudinally track changes in beta cell function is of pivotal importance to track disease progression and measure the effect of disease-modifying interventions. In this review we describe the metabolic changes that occur in the early pre-symptomatic stages of type 1 diabetes with respect to both insulin secretion and insulin sensitivity, as well as the measurable outcomes that can be derived from the available tests. We also discuss the use of metabolic modelling to identify insulin secretion and sensitivity, and the measurable changes during dynamic tests such as the OGTT. Finally, we review the role of risk indices and minimally invasive measures such as those derived from the use of continuous glucose monitoring.
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Affiliation(s)
| | - Alice L J Carr
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB, Canada
| | - Mariangela Martino
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
| | - Peter Taylor
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
| | - Peter Senior
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB, Canada
| | - Colin Dayan
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK.
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16
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Simmons KM, Sims EK. Screening and Prevention of Type 1 Diabetes: Where Are We? J Clin Endocrinol Metab 2023; 108:3067-3079. [PMID: 37290044 DOI: 10.1210/clinem/dgad328] [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: 02/09/2023] [Revised: 05/10/2023] [Accepted: 05/31/2023] [Indexed: 06/10/2023]
Abstract
A diagnosis of type 1 diabetes (T1D) and the subsequent requirement for exogenous insulin treatment is associated with considerable acute and chronic morbidity and a substantial effect on patient quality of life. Importantly, a large body of work suggests that early identification of presymptomatic T1D can accurately predict clinical disease, and when paired with education and monitoring, can yield improved health outcomes. Furthermore, a growing cadre of effective disease-modifying therapies provides the potential to alter the natural history of early stages of T1D. In this mini review, we highlight prior work that has led to the current landscape of T1D screening and prevention, as well as challenges and next steps moving into the future of these rapidly evolving areas of patient care.
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Affiliation(s)
- Kimber M Simmons
- Barbara Davis Center for Diabetes, Division of Pediatrics, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Emily K Sims
- Division of Pediatric Endocrinology and Diabetology, Herman B Wells Center for Pediatric Research; Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN 46202, USA
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17
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Leslie RD, Ma RCW, Franks PW, Nadeau KJ, Pearson ER, Redondo MJ. Understanding diabetes heterogeneity: key steps towards precision medicine in diabetes. Lancet Diabetes Endocrinol 2023; 11:848-860. [PMID: 37804855 DOI: 10.1016/s2213-8587(23)00159-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/30/2023] [Accepted: 05/27/2023] [Indexed: 10/09/2023]
Abstract
Diabetes is a highly heterogeneous condition; yet, it is diagnosed by measuring a single blood-borne metabolite, glucose, irrespective of aetiology. Although pragmatically helpful, disease classification can become complex and limit advances in research and medical care. Here, we describe diabetes heterogeneity, highlighting recent approaches that could facilitate management by integrating three disease models across all forms of diabetes, namely, the palette model, the threshold model and the gradient model. Once diabetes has developed, further worsening of established diabetes and the subsequent emergence of diabetes complications are kept in check by multiple processes designed to prevent or circumvent metabolic dysfunction. The impact of any given disease risk factor will vary from person-to-person depending on their background, diabetes-related propensity, and environmental exposures. Defining the consequent heterogeneity within diabetes through precision medicine, both in terms of diabetes risk and risk of complications, could improve health outcomes today and shine a light on avenues for novel therapy in the future.
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Affiliation(s)
| | - Ronald Ching Wan Ma
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, 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 SAR, China; Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Paul W Franks
- Novo Nordisk Foundation, Hellerup, Denmark; Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmo, Sweden; Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Kristen J Nadeau
- Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Ewan R Pearson
- Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
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18
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Xie QY, Oh S, Wong A, Yau C, Herold KC, Danska JS. Immune responses to gut bacteria associated with time to diagnosis and clinical response to T cell-directed therapy for type 1 diabetes prevention. Sci Transl Med 2023; 15:eadh0353. [PMID: 37878676 DOI: 10.1126/scitranslmed.adh0353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 10/02/2023] [Indexed: 10/27/2023]
Abstract
Immune-targeted therapies have efficacy for treatment of autoinflammatory diseases. For example, treatment with the T cell-specific anti-CD3 antibody teplizumab delayed disease onset in participants at high risk for type 1 diabetes (T1D) in the TrialNet 10 (TN-10) trial. However, heterogeneity in therapeutic responses in TN-10 and other immunotherapy trials identifies gaps in understanding disease progression and treatment responses. The intestinal microbiome is a potential source of biomarkers associated with future T1D diagnosis and responses to immunotherapy. We previously reported that antibody responses to gut commensal bacteria were associated with T1D diagnosis, suggesting that certain antimicrobial immune responses may help predict disease onset. Here, we investigated anticommensal antibody (ACAb) responses against a panel of taxonomically diverse intestinal bacteria species in sera from TN-10 participants before and after teplizumab or placebo treatment. We identified IgG2 responses to three species that were associated with time to T1D diagnosis and with teplizumab treatment responses that delayed disease onset. These antibody responses link human intestinal bacteria with T1D progression, adding predictive value to known T1D risk factors. ACAb analysis provides a new approach to elucidate heterogeneity in responses to immunotherapy and identify individuals who may benefit from teplizumab, recently approved by the U.S. Food and Drug Administration for delaying T1D onset.
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Affiliation(s)
- Quin Yuhui Xie
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5T2S8, Canada
- Genetics and Genome Biology, Hospital for Sick Children, Toronto, Ontario M5G1X8, Canada
| | - Sean Oh
- Genetics and Genome Biology, Hospital for Sick Children, Toronto, Ontario M5G1X8, Canada
| | - Anthony Wong
- Genetics and Genome Biology, Hospital for Sick Children, Toronto, Ontario M5G1X8, Canada
| | - Christopher Yau
- Genetics and Genome Biology, Hospital for Sick Children, Toronto, Ontario M5G1X8, Canada
- Department of Immunology, University of Toronto, Toronto, Ontario M5T2S8, Canada
| | - Kevan C Herold
- Department of Immunobiology, Yale University, New Haven, CT 06520, USA
- Department of Internal Medicine, Yale University, New Haven, CT 06520, USA
| | - Jayne S Danska
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5T2S8, Canada
- Genetics and Genome Biology, Hospital for Sick Children, Toronto, Ontario M5G1X8, Canada
- Department of Immunology, University of Toronto, Toronto, Ontario M5T2S8, Canada
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19
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You L, Ferrat LA, Oram RA, Parikh HM, Steck AK, Krischer J, Redondo MJ. Type 1 Diabetes Risk Phenotypes Using Cluster Analysis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.10.10.23296375. [PMID: 37873281 PMCID: PMC10593014 DOI: 10.1101/2023.10.10.23296375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Background Although statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal clinically meaningful clusters in the at-risk population and allow for non-linear relationships between predictors are lacking. We aimed to identify and characterize clusters of islet autoantibody-positive individuals that share similar characteristics and type 1 diabetes risk. Methods We tested a novel outcome-guided clustering method in initially non-diabetic autoantibody-positive relatives of individuals with type 1 diabetes, using the TrialNet Pathway to Prevention (PTP) study data (n=1127). The outcome of the analysis was time to type 1 diabetes and variables in the model included demographics, genetics, metabolic factors and islet autoantibodies. An independent dataset (Diabetes Prevention Trial of Type 1 Diabetes, DPT-1 study) (n=704) was used for validation. Findings The analysis revealed 8 clusters with varying type 1 diabetes risks, categorized into three groups. Group A had three clusters with high glucose levels and high risk. Group B included four clusters with elevated autoantibody titers. Group C had three lower-risk clusters with lower autoantibody titers and glucose levels. Within the groups, the clusters exhibit variations in characteristics such as glucose levels, C-peptide levels, age, and genetic risk. A decision rule for assigning individuals to clusters was developed. The validation dataset confirms that the clusters can identify individuals with similar characteristics. Interpretation Demographic, metabolic, immunological, and genetic markers can be used to identify clusters of distinctive characteristics and different risks of progression to type 1 diabetes among autoantibody-positive individuals with a family history of type 1 diabetes. The results also revealed the heterogeneity in the population and complex interactions between variables.
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Affiliation(s)
- Lu You
- Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | | | | | - Hemang M Parikh
- Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | - Andrea K Steck
- Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Jeffrey Krischer
- Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | - Maria J Redondo
- Baylor College of Medicine, Texas Children's Hospital, Houston, TX, USA
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20
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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: 0] [Impact Index Per Article: 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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21
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Liao WL, Huang YN, Chang YW, Liu TY, Lu HF, Tiao ZY, Su PH, Wang CH, Tsai FJ. Combining polygenic risk scores and human leukocyte antigen variants for personalized risk assessment of type 1 diabetes in the Taiwanese population. Diabetes Obes Metab 2023; 25:2928-2936. [PMID: 37455666 DOI: 10.1111/dom.15187] [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: 03/17/2023] [Revised: 06/06/2023] [Accepted: 06/06/2023] [Indexed: 07/18/2023]
Abstract
AIMS To analyse the genome-wide association study (GWAS) data of patients with type 1 diabetes mellitus (T1D) in order to develop a risk score for the genetic effects on T1D risk and age at diagnosis in the Taiwanese population. MATERIALS AND METHODS We selected 610 patients with T1D and 2511 healthy individuals from an electronic medical record database of more than 300 000 individuals with genetic information, analysed their GWAS data, and developed a polygenic risk score (PRS). RESULTS The PRS, based on 149 selected single-nucleotide polymorphisms, could effectively predict T1D risk. A PRS increase was associated with increased T1D risk (odds ratio [OR] 2.09, 95% confidence interval [CI] 1.72-2.55). Moreover, a 1-unit increase in standardized T1D PRS decreased the age at diagnosis by 0.74 years. Combined PRS and human leukocyte antigen (HLA) DQA1*03:02-DQA1*05:01 genotypes could accurately predict T1D risk. In multivariable models, HLA variants and PRS were independent risk factors for T1D risk (OR 3.76 [95% CI 1.54-9.16] and 1.71 [95% CI 1.37-2.13] for HLA DQA1*03:02-DQA1*05:01 and PRS, respectively). In a limited study population of those aged ≤18 years, PRS remained significantly associated with T1D risk. The association between T1D PRS and age at diagnosis was more obvious among males and patients aged ≤18 years. CONCLUSIONS Polygenic risk score and HLA variations enable personalized risk estimates, enhance newborn screening efficiency for ketoacidosis prevention, and addresses the gap in data on T1D prediction in isolated Asian populations.
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Affiliation(s)
- Wen-Ling Liao
- Graduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan
- Center for Personalized Medicine, China Medical University Hospital, Taichung, Taiwan
| | - Yu-Nan Huang
- Division of Genetics and Metabolism, Children's Hospital of China Medical University, Taichung, Taiwan
- Department of Pediatrics, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Ya-Wen Chang
- Graduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan
- Center for Personalized Medicine, China Medical University Hospital, Taichung, Taiwan
- Department of Medical Research, Genetic Center, China Medical University Hospital, Taichung, Taiwan
| | - Ting-Yuan Liu
- Center for Precision Medicine, China Medical University Hospital, Taichung, Taiwan
| | - Hsing-Fang Lu
- Center for Precision Medicine, China Medical University Hospital, Taichung, Taiwan
| | - Zih-Yu Tiao
- School of Chinese Medicine, China Medical University, Taichung, Taiwan
| | - Pen-Hua Su
- Department of Pediatrics, Chung Shan Medical University Hospital, Taichung, Taiwan
- School of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Chung-Hsing Wang
- Division of Genetics and Metabolism, Children's Hospital of China Medical University, Taichung, Taiwan
- School of Medicine, China Medical University, Taichung, Taiwan
| | - Fuu-Jen Tsai
- Department of Medical Research, Genetic Center, China Medical University Hospital, Taichung, Taiwan
- School of Chinese Medicine, China Medical University, Taichung, Taiwan
- Division of Medical Genetics, China Medical University Children's Hospital, Taichung, Taiwan
- Department of Biotechnology and Bioinformatics, Asia University, Taichung, Taiwan
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22
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Sacks DB, Arnold M, Bakris GL, Bruns DE, Horvath AR, Lernmark Å, Metzger BE, Nathan DM, Kirkman MS. Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus. Diabetes Care 2023; 46:e151-e199. [PMID: 37471273 PMCID: PMC10516260 DOI: 10.2337/dci23-0036] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 05/11/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND Numerous laboratory tests are used in the diagnosis and management of diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially. APPROACH An expert committee compiled evidence-based recommendations for laboratory analysis in screening, diagnosis, or monitoring of diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association for Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association. CONTENT Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (HbA1c) in the blood. Glycemic control is monitored by the people with diabetes measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring (CGM) devices and also by laboratory analysis of HbA1c. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed. SUMMARY The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.
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Affiliation(s)
- David B. Sacks
- Department of Laboratory Medicine, National Institutes of Health, Bethesda, MD
| | - Mark Arnold
- Department of Chemistry, University of Iowa, Iowa City, IA
| | - George L. Bakris
- Department of Medicine, American Heart Association Comprehensive Hypertension Center, Section of Endocrinology, Diabetes and Metabolism, University of Chicago Medicine, Chicago, IL
| | - David E. Bruns
- Department of Pathology, University of Virginia Medical School, Charlottesville, VA
| | - Andrea R. Horvath
- New South Wales Health Pathology Department of Chemical Pathology, Prince of Wales Hospital, Sydney, New South Wales, Australia
| | - Åke Lernmark
- Department of Clinical Sciences, Lund University/CRC, Skane University Hospital Malmö, Malmö, Sweden
| | - Boyd E. Metzger
- Division of Endocrinology, Northwestern University, The Feinberg School of Medicine, Chicago, IL
| | - David M. Nathan
- Massachusetts General Hospital Diabetes Center and Harvard Medical School, Boston, MA
| | - M. Sue Kirkman
- Department of Medicine, University of North Carolina, Chapel Hill, NC
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23
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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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24
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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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25
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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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26
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Lernmark Å, Akolkar B, Hagopian W, Krischer J, McIndoe R, Rewers M, Toppari J, Vehik K, Ziegler AG. Possible heterogeneity of initial pancreatic islet beta-cell autoimmunity heralding type 1 diabetes. J Intern Med 2023; 294:145-158. [PMID: 37143363 PMCID: PMC10524683 DOI: 10.1111/joim.13648] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
The etiology of type 1 diabetes (T1D) foreshadows the pancreatic islet beta-cell autoimmune pathogenesis that heralds the clinical onset of T1D. Standardized and harmonized tests of autoantibodies against insulin (IAA), glutamic acid decarboxylase (GADA), islet antigen-2 (IA-2A), and ZnT8 transporter (ZnT8A) allowed children to be followed from birth until the appearance of a first islet autoantibody. In the Environmental Determinants of Diabetes in the Young (TEDDY) study, a multicenter (Finland, Germany, Sweden, and the United States) observational study, children were identified at birth for the T1D high-risk HLA haploid genotypes DQ2/DQ8, DQ2/DQ2, DQ8/DQ8, and DQ4/DQ8. The TEDDY study was preceded by smaller studies in Finland, Germany, Colorado, Washington, and Sweden. The aims were to follow children at increased genetic risk to identify environmental factors that trigger the first-appearing autoantibody (etiology) and progress to T1D (pathogenesis). The larger TEDDY study found that the incidence rate of the first-appearing autoantibody was split into two patterns. IAA first peaked already during the first year of life and tapered off by 3-4 years of age. GADA first appeared by 2-3 years of age to reach a plateau by about 4 years. Prior to the first-appearing autoantibody, genetic variants were either common or unique to either pattern. A split was also observed in whole blood transcriptomics, metabolomics, dietary factors, and exposures such as gestational life events and early infections associated with prolonged shedding of virus. An innate immune reaction prior to the adaptive response cannot be excluded. Clarifying the mechanisms by which autoimmunity is triggered to either insulin or GAD65 is key to uncovering the etiology of autoimmune T1D.
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Affiliation(s)
- Åke Lernmark
- Department of Clinical Sciences, Lund University CRC, Skåne University Hospital, Malmö, Sweden
| | - Beena Akolkar
- National Institute of Diabetes & Digestive & Kidney Diseases, Bethesda, MD USA
| | | | - Jeffrey Krischer
- Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL USA
| | - Richard McIndoe
- Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, Augusta, GA USA
| | - Marian Rewers
- Barbara Davis Center for Diabetes, University of Colorado, Aurora, Colorado USA
| | - Jorma Toppari
- Department of Pediatrics, Turku University Hospital, and Institute of Biomedicine, Research Centre for Integrated Physiology and Pharmacology, University of Turku, Turku, Finland
| | - Kendra Vehik
- Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL USA
| | - Anette-G. Ziegler
- Institute of Diabetes Research, Helmholtz Zentrum München, and Klinikum rechts der Isar, Technische Universität München, and Forschergruppe Diabetes e.V., Neuherberg, Germany
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27
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Hattersley AT. Laboratory Guidelines Are Needed for Diagnostic Genetic Testing for Monogenic Diabetes. Clin Chem 2023:hvad093. [PMID: 37473454 DOI: 10.1093/clinchem/hvad093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 06/07/2023] [Indexed: 07/22/2023]
Affiliation(s)
- Andrew T Hattersley
- University of Exeter Medical School, University of Exeter, Exeter, United Kingdom
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Sacks DB, Arnold M, Bakris GL, Bruns DE, Horvath AR, Lernmark Å, Metzger BE, Nathan DM, Kirkman MS. Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus. Clin Chem 2023:hvad080. [PMID: 37473453 DOI: 10.1093/clinchem/hvad080] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 05/12/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND Numerous laboratory tests are used in the diagnosis and management of diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially. APPROACH An expert committee compiled evidence-based recommendations for laboratory analysis in screening, diagnosis, or monitoring of diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association of Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association. CONTENT Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (Hb A1c) in the blood. Glycemic control is monitored by the people with diabetes measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring (CGM) devices and also by laboratory analysis of Hb A1c. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed. SUMMARY The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.
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Affiliation(s)
- David B Sacks
- Department of Laboratory Medicine, National Institutes of Health, Bethesda, MD, United States
| | - Mark Arnold
- Department of Chemistry, University of Iowa, Iowa City, IA, United States
| | - George L Bakris
- Department of Medicine, American Heart Association Comprehensive Hypertension Center, Section of Endocrinology, Diabetes and Metabolism, University of Chicago Medicine, Chicago, ILUnited States
| | - David E Bruns
- Department of Pathology, University of Virginia Medical School, Charlottesville, VA, United States
| | - Andrea R Horvath
- New South Wales Health Pathology Department of Chemical Pathology, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Åke Lernmark
- Department of Clinical Sciences, Lund University/CRC, Skane University Hospital Malmö, Malmö, Sweden
| | - Boyd E Metzger
- Division of Endocrinology, Northwestern University, The Feinberg School of Medicine, Chicago, IL, United States
| | - David M Nathan
- Massachusetts General Hospital Diabetes Center and Harvard Medical School, Boston, MA, United States
| | - M Sue Kirkman
- Department of Medicine, University of North Carolina, Chapel Hill, NC, United States
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Schirle L, Samuels DC, Faucon A, Cox NJ, Bruehl S. Polygenic Contributions to Chronic Overlapping Pain Conditions in a Large Electronic Health Record Sample. THE JOURNAL OF PAIN 2023; 24:1056-1068. [PMID: 36736868 PMCID: PMC10257768 DOI: 10.1016/j.jpain.2023.01.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 01/11/2023] [Accepted: 01/23/2023] [Indexed: 02/05/2023]
Abstract
Chronic overlapping pain conditions (COPCs) are believed to share common etiological mechanisms involving central sensitization. Genetic and environmental factors putatively combine to influence susceptibility to central sensitization and COPCs. This study employed a genome-wide polygenic risk score approach to evaluate genetic influences on 8 common COPCs. COPCs were identified by International Classification of Disease codes in Vanderbilt's deidentified clinical biorepository (BioVU), with each COPC condition empirically weighted for the level of central sensitization based on prior work. A centralized pain score (CPS) was calculated for 55,340 individuals by summing the weighted number of COPCs. Overall, 12,502 individuals (22.6%) were diagnosed with at least 1 COPC, with females exhibiting nearly twice the mean CPS as males. To assess the genetic influence on centralized pain in COPCs, 6 pain polygenic risk scores (PRSs) were developed using UK Biobank data to predict 6 pain criteria (no pain, neck/shoulder, abdomen, hip, knee, low back pain). These PRSs were then deployed in the BioVU cohort to test for association with CPS. In regression models adjusted for age, sex, and BMI, all pain PRSs except hip pain were significantly associated with CPS. Our findings support a shared polygenic influence across COPCs potentially involving central sensitization mechanisms. PERSPECTIVE: This study used a polygenic risk score approach to investigate genetic influences on chronic overlapping pain conditions. Significant findings in this study provide evidence supporting previous hypotheses that a shared polygenic influence involving central sensitization may underly chronic overlapping pain conditions and can guide future biomarker and risk assessment research.
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Affiliation(s)
- Lori Schirle
- Vanderbilt University School of Nursing, Nashville, Tennessee.
| | - David C Samuels
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, Tennessee; Vanderbilt Genetics Institute, Nashville, Tennessee
| | | | - Nancy J Cox
- Vanderbilt Genetics Institute, Nashville, Tennessee; Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Stephen Bruehl
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, Tennessee
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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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Papadimitriou DT, Dermitzaki E, Christopoulos P, Papagianni M, Kleanthous K, Marakaki C, Papadimitriou A, Mastorakos G. Secondary Prevention of Diabetes Type 1 with Oral Calcitriol and Analogs, the PRECAL Study. CHILDREN (BASEL, SWITZERLAND) 2023; 10:children10050862. [PMID: 37238410 DOI: 10.3390/children10050862] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 05/04/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023]
Abstract
Screening for Type 1 Diabetes (T1D, incidence 1:300) with T1D autoantibodies (T1Ab) at ages 2 and 6, while sensitive, lacks a preventive strategy. Cholecalciferol 2000 IU daily since birth reduced T1D by 80% at 1 year. T1D-associated T1Ab negativized within 0.6 years with oral calcitriol in 12 children. To further investigate secondary prevention of T1D with calcitriol and its less calcemic analog, paricalcitol, we initiated a prospective interventional non-randomized clinical trial, the PRECAL study (ISRCTN17354692). In total, 50 high-risk children were included: 44 were positive for T1Ab, and 6 had predisposing for T1D HLA genotypes. Nine T1Ab+ patients had variable impaired glucose tolerance (IGT), four had pre-T1D (3 T1Ab+, 1 HLA+), nine had T1Ab+ new-onset T1D not requiring insulin at diagnosis. T1Ab, thyroid/anti-transglutaminase Abs, glucose/calcium metabolism were determined prior and q3-6 months on calcitriol, 0.05 mcg/Kg/day, or paricalcitol 1-4 mcg × 1-3 times/day p.o. while on cholecalciferol repletion. Available data on 42 (7 dropouts, 1 follow-up < 3 months) patients included: all 26 without pre-T1D/T1D followed for 3.06 (0.5-10) years negativized T1Ab (15 +IAA, 3 IA2, 4 ICA, 2 +GAD, 1 +IAA/+GAD, 1 +ICA/+GAD) within 0.57 (0.32-1.3) years or did not develop to T1D (5 +HLA, follow-up 3 (1-4) years). From four pre-T1D cases, one negativized T1Ab (follow-up 1 year), one +HLA did not progress to T1D (follow-up 3.3 years) and two +T1Ab patients developed T1D in 6 months/3 years. Three out of nine T1D cases progressed immediately to overt disease, six underwent complete remission for 1 year (1 month-2 years). Five +T1Ab patients relapsed and negativized again after resuming therapy. Four (aged <3 years) negativized anti-TPO/TG, and two anti-transglutaminase-IgA. Eight presented mild hypercalciuria/hypercalcemia, resolving with dose titration/discontinuation. Secondary prevention of T1D with calcitriol and paricalcitol seems possible and reasonably safe, if started soon enough after seroconversion.
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Affiliation(s)
- Dimitrios T Papadimitriou
- Second Department of Obstetrics and Gynecology, Aretaieion University Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece
- Department of Pediatric-Adolescent Endocrinology and Diabetes, Athens Medical Center, 15125 Marousi, Greece
| | - Eleni Dermitzaki
- Department of Pediatric-Adolescent Endocrinology and Diabetes, Athens Medical Center, 15125 Marousi, Greece
| | - Panagiotis Christopoulos
- Second Department of Obstetrics and Gynecology, Aretaieion University Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Maria Papagianni
- Department of Nutrition and Dietetics, University of Thessaly, 42132 Trikala, Greece
- Unit of Endocrinology, Diabetes and Metabolism, Third Department of Pediatrics, Aristotle University of Thessaloniki, Hippokrateion Hospital of Thessaloniki, 54642 Thessaloniki, Greece
| | - Kleanthis Kleanthous
- Department of Pediatric-Adolescent Endocrinology and Diabetes, Athens Medical Center, 15125 Marousi, Greece
| | - Chrysanthi Marakaki
- Department of Pediatric-Adolescent Endocrinology and Diabetes, Athens Medical Center, 15125 Marousi, Greece
| | - Anastasios Papadimitriou
- Pediatric Endocrinology Unit, Attikon University Hospital, National and Kapodistrian University of Athens, 12462 Haidari, Greece
| | - George Mastorakos
- Second Department of Obstetrics and Gynecology, Aretaieion University Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece
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Abstract
Autoimmune diseases display a high degree of comorbidity within individuals and families, suggesting shared risk factors. Over the past 15 years, genome-wide association studies have established the polygenic basis of these common conditions and revealed widespread sharing of genetic effects, indicative of a shared immunopathology. Despite ongoing challenges in determining the precise genes and molecular consequences of these risk variants, functional experiments and integration with multimodal genomic data are providing valuable insights into key immune cells and pathways driving these diseases, with potential therapeutic implications. Moreover, genetic studies of ancient populations are shedding light on the contribution of pathogen-driven selection pressures to the increased prevalence of autoimmune disease. This Review summarizes the current understanding of autoimmune disease genetics, including shared effects, mechanisms, and evolutionary origins.
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Affiliation(s)
- Adil Harroud
- Department of Neurology and Neurosurgery, McGill University, Montréal, Quebec, Canada
- Department of Human Genetics, McGill University, Montréal, Quebec, Canada
- The Neuro (Montreal Neurological Institute and Hospital), McGill University, Montréal, Quebec, Canada
| | - David A Hafler
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA
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Ghalwash M, Anand V, Lou O, Martin F, Rewers M, Ziegler AG, Toppari J, Hagopian WA, Veijola R. Islet autoantibody screening in at-risk adolescents to predict type 1 diabetes until young adulthood: a prospective cohort study. THE LANCET. CHILD & ADOLESCENT HEALTH 2023; 7:261-268. [PMID: 36681087 PMCID: PMC10038928 DOI: 10.1016/s2352-4642(22)00350-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 11/21/2022] [Accepted: 11/23/2022] [Indexed: 01/20/2023]
Abstract
BACKGROUND Screening for islet autoantibodies in children and adolescents identifies individuals who will later develop type 1 diabetes, allowing patient and family education to prevent diabetic ketoacidosis at onset and to enable consideration of preventive therapies. We aimed to assess whether islet autoantibody screening is effective for predicting type 1 diabetes in adolescents aged 10-18 years with an increased risk of developing type 1 diabetes. METHODS Data were harmonised from prospective studies from Finland (the Diabetes Prediction and Prevention study), Germany (the BABYDIAB study), and the USA (Diabetes Autoimmunity Study in the Young and the Diabetes Evaluation in Washington study). Autoantibodies against insulin, glutamic acid decarboxylase, and insulinoma-associated protein 2 were measured at each follow-up visit. Children who were lost to follow-up or diagnosed with type 1 diabetes before 10 years of age were excluded. Inverse probability censoring weighting was used to include data from remaining participants. Sensitivity and the positive predictive value of these autoantibodies, tested at one or two ages, to predict type 1 diabetes by the age of 18 years were the main outcomes. FINDINGS Of 20 303 children with an increased type 1 diabetes risk, 8682 were included for the analysis with inverse probability censoring weighting. 1890 were followed up to 18 years of age or developed type 1 diabetes between the ages of 10 years and 18 years, and their median follow-up was 18·3 years (IQR 14·5-20·3). 442 (23·4%) of 1890 adolescents were positive for at least one islet autoantibody, and 262 (13·9%) developed type 1 diabetes. Time from seroconversion to diabetes diagnosis increased by 0·64 years (95% CI 0·34-0·95) for each 1-year increment of diagnosis age (Pearson's correlation coefficient 0·88, 95% CI 0·50-0·97, p=0·0020). The median interval between the last prediagnostic sample and diagnosis was 0·3 years (IQR 0·1-1·3) in the 227 participants who were autoantibody positive and 6·8 years (1·6-9·9) for the 35 who were autoantibody negative. Single screening at the age of 10 years was 90% (95% CI 86-95) sensitive, with a positive predictive value of 66% (60-72) for clinical diabetes. Screening at two ages (10 years and 14 years) increased sensitivity to 93% (95% CI 89-97) but lowered the positive predictive value to 55% (49-60). INTERPRETATION Screening of adolescents at risk for type 1 diabetes only once at 10 years of age for islet autoantibodies was highly effective to detect type 1 diabetes by the age of 18 years, which in turn could enable prevention of diabetic ketoacidosis and participation in secondary prevention trials. FUNDING JDRF International.
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Affiliation(s)
- Mohamed Ghalwash
- Center for Computational Health, IBM Research, Yorktown Heights, NY, USA; Faculty of Science, Ain Shams University, Cairo, Egypt
| | - Vibha Anand
- Center for Computational Health, IBM Research, Cambridge, MA, USA
| | | | | | - Marian Rewers
- Barbara Davis Center for Diabetes, University of Colorado, Denver, CO, USA
| | - Anette-G Ziegler
- Forschergruppe Diabetes and Institute of Diabetes Research, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich-Neuherberg, Germany der TU München, Munich, Germany
| | - Jorma Toppari
- Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, and Centre for Population Health Research, University of Turku, Turku, Finland; Department of Pediatrics, Turku University Hospital, Turku, Finland
| | | | - Riitta Veijola
- Department of Pediatrics, Research Unit of Clinical Medicine, Medical Research Center Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland.
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Michalek DA, Onengut-Gumuscu S, Repaske DR, Rich SS. Precision Medicine in Type 1 Diabetes. J Indian Inst Sci 2023; 103:335-351. [PMID: 37538198 PMCID: PMC10393845 DOI: 10.1007/s41745-023-00356-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 01/04/2023] [Indexed: 03/09/2023]
Abstract
Type 1 diabetes is a complex, chronic disease in which the insulin-producing beta cells in the pancreas are sufficiently altered or impaired to result in requirement of exogenous insulin for survival. The development of type 1 diabetes is thought to be an autoimmune process, in which an environmental (unknown) trigger initiates a T cell-mediated immune response in genetically susceptible individuals. The presence of islet autoantibodies in the blood are signs of type 1 diabetes development, and risk of progressing to clinical type 1 diabetes is correlated with the presence of multiple islet autoantibodies. Currently, a "staging" model of type 1 diabetes proposes discrete components consisting of normal blood glucose but at least two islet autoantibodies (Stage 1), abnormal blood glucose with at least two islet autoantibodies (Stage 2), and clinical diagnosis (Stage 3). While these stages may, in fact, not be discrete and vary by individual, the format suggests important applications of precision medicine to diagnosis, prevention, prognosis, treatment and monitoring. In this paper, applications of precision medicine in type 1 diabetes are discussed, with both opportunities and barriers to global implementation highlighted. Several groups have implemented components of precision medicine, yet the integration of the necessary steps to achieve both short- and long-term solutions will need to involve researchers, patients, families, and healthcare providers to fully impact and reduce the burden of type 1 diabetes.
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Affiliation(s)
- Dominika A. Michalek
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA USA
| | - Suna Onengut-Gumuscu
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA USA
| | - David R. Repaske
- Division of Endocrinology, Department of Pediatrics, University of Virginia, Charlottesville, VA USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA USA
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Karpen SR, Dunne JL, Frohnert BI, Marinac M, Richard C, David SE, O'Doherty IM. Consortium-based approach to receiving an EMA qualification opinion on the use of islet autoantibodies as enrichment biomarkers in type 1 diabetes clinical studies. Diabetologia 2023; 66:415-424. [PMID: 35867129 PMCID: PMC10024532 DOI: 10.1007/s00125-022-05751-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 04/25/2022] [Indexed: 02/04/2023]
Abstract
The development of medical products that can delay or prevent progression to stage 3 type 1 diabetes faces many challenges. Of note, optimising patient selection for type 1 diabetes prevention clinical trials is hindered by significant patient heterogeneity and a lack of characterisation of the time-varying probability of progression to stage 3 type 1 diabetes in individuals positive for two or more islet autoantibodies. To meet these needs, the Critical Path Institute's Type 1 Diabetes Consortium was launched in 2017 as a pre-competitive public-private partnership between stakeholders from the pharmaceutical industry, patient advocacy groups, philanthropic organisations, clinical researchers, the National Institutes of Health and the Food and Drug Administration. The Type 1 Diabetes Consortium acquired and aggregated data from three longitudinal observational studies, Environmental Determinants of Diabetes in the Young (TEDDY), Diabetes Autoimmunity Study in the Young (DAISY) and TrialNet Pathway to Prevention (TN01), and used analysis subsets of these data to support the model-based qualification of islet autoantibodies as enrichment biomarkers for patient selection in type 1 diabetes prevention trials, including registration studies. The Type 1 Diabetes Consortium has now received a qualification opinion from the European Medicines Agency for the use of these biomarkers, a major success for the field of type 1 diabetes. This endorsement will improve product developers' ability to design clinical trials of agents intended to prevent or delay type 1 diabetes that are reduced in size and/or length, while being adequately powered.
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Affiliation(s)
| | | | - Brigitte I Frohnert
- Barbara Davis Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
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Tsao CW, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, Baker-Smith CM, Beaton AZ, Boehme AK, Buxton AE, Commodore-Mensah Y, Elkind MSV, Evenson KR, Eze-Nliam C, Fugar S, Generoso G, Heard DG, Hiremath S, Ho JE, Kalani R, Kazi DS, Ko D, Levine DA, Liu J, Ma J, Magnani JW, Michos ED, Mussolino ME, Navaneethan SD, Parikh NI, Poudel R, Rezk-Hanna M, Roth GA, Shah NS, St-Onge MP, Thacker EL, Virani SS, Voeks JH, Wang NY, Wong ND, Wong SS, Yaffe K, Martin SS. Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association. Circulation 2023; 147:e93-e621. [PMID: 36695182 DOI: 10.1161/cir.0000000000001123] [Citation(s) in RCA: 1325] [Impact Index Per Article: 1325.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
BACKGROUND The American Heart Association, 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, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year's worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association 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 COVID-19 (coronavirus disease 2019) publications, 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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Xiao Q, Mao C, Gao Y, Huang H, Yu B, Yu L, Li X, Mao X, Zhang W, Yin J, Liu Z. Establishing a Prediction Model for the Efficacy of Platinum-Based Chemotherapy in NSCLC Based on a Two Cohorts GWAS Study. J Clin Med 2023; 12:jcm12041318. [PMID: 36835855 PMCID: PMC9958581 DOI: 10.3390/jcm12041318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 01/27/2023] [Accepted: 02/03/2023] [Indexed: 02/10/2023] Open
Abstract
Platinum drugs combined with other agents have been the first-line treatment for non-small cell lung cancer (NSCLC) in the past decades. To better evaluate the efficacy of platinum-based chemotherapy in NSCLC, we establish a platinum chemotherapy response prediction model. Here, a total of 217 samples from Xiangya Hospital of Central South University were selected as the discovery cohort for a genome-wide association analysis (GWAS) to select SNPs. Another 216 samples were genotyped as a validation cohort. In the discovery cohort, using linkage disequilibrium (LD) pruning, we extract a subset that does not contain correlated SNPs. The SNPs with p < 10-3 and p < 10-4 are selected for modeling. Subsequently, we validate our model in the validation cohort. Finally, clinical factors are incorporated into the model. The final model includes four SNPs (rs7463048, rs17176196, rs527646, and rs11134542) as well as two clinical factors that contributed to the efficacy of platinum chemotherapy in NSCLC, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.726.
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Affiliation(s)
- Qi Xiao
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha 410008, China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, 110 Xiangya Road, Changsha 410078, China
- National Clinical Research Center for Geriatric Disorders, 87 Xiangya Road, Changsha 410008, China
- Institute of Clinical Pharmacology, Engineering Research Center for Applied Technology of Pharmacogenomics of Ministry of Education, Central South University, Changsha 410078, China
| | - Chenxue Mao
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha 410008, China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, 110 Xiangya Road, Changsha 410078, China
- National Clinical Research Center for Geriatric Disorders, 87 Xiangya Road, Changsha 410008, China
- Institute of Clinical Pharmacology, Engineering Research Center for Applied Technology of Pharmacogenomics of Ministry of Education, Central South University, Changsha 410078, China
| | - Ying Gao
- Department of Geriatric Respiratory and Critical Care Medicine, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Hanxue Huang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha 410008, China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, 110 Xiangya Road, Changsha 410078, China
- National Clinical Research Center for Geriatric Disorders, 87 Xiangya Road, Changsha 410008, China
- Institute of Clinical Pharmacology, Engineering Research Center for Applied Technology of Pharmacogenomics of Ministry of Education, Central South University, Changsha 410078, China
| | - Bing Yu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha 410008, China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, 110 Xiangya Road, Changsha 410078, China
- National Clinical Research Center for Geriatric Disorders, 87 Xiangya Road, Changsha 410008, China
- Institute of Clinical Pharmacology, Engineering Research Center for Applied Technology of Pharmacogenomics of Ministry of Education, Central South University, Changsha 410078, China
| | - Lulu Yu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha 410008, China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, 110 Xiangya Road, Changsha 410078, China
- National Clinical Research Center for Geriatric Disorders, 87 Xiangya Road, Changsha 410008, China
- Institute of Clinical Pharmacology, Engineering Research Center for Applied Technology of Pharmacogenomics of Ministry of Education, Central South University, Changsha 410078, China
| | - Xi Li
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha 410008, China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, 110 Xiangya Road, Changsha 410078, China
- National Clinical Research Center for Geriatric Disorders, 87 Xiangya Road, Changsha 410008, China
- Institute of Clinical Pharmacology, Engineering Research Center for Applied Technology of Pharmacogenomics of Ministry of Education, Central South University, Changsha 410078, China
| | - Xiaoyuan Mao
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha 410008, China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, 110 Xiangya Road, Changsha 410078, China
- National Clinical Research Center for Geriatric Disorders, 87 Xiangya Road, Changsha 410008, China
- Institute of Clinical Pharmacology, Engineering Research Center for Applied Technology of Pharmacogenomics of Ministry of Education, Central South University, Changsha 410078, China
| | - Wei Zhang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha 410008, China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, 110 Xiangya Road, Changsha 410078, China
- National Clinical Research Center for Geriatric Disorders, 87 Xiangya Road, Changsha 410008, China
- Institute of Clinical Pharmacology, Engineering Research Center for Applied Technology of Pharmacogenomics of Ministry of Education, Central South University, Changsha 410078, China
| | - Jiye Yin
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha 410008, China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, 110 Xiangya Road, Changsha 410078, China
- National Clinical Research Center for Geriatric Disorders, 87 Xiangya Road, Changsha 410008, China
- Institute of Clinical Pharmacology, Engineering Research Center for Applied Technology of Pharmacogenomics of Ministry of Education, Central South University, Changsha 410078, China
- Correspondence: (J.Y.); (Z.L.); Tel.: +86-731-84805380 (J.Y.); +86-731-82655012 (Z.L.); Fax: +86-731-82354476 (J.Y. & Z.L.)
| | - Zhaoqian Liu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha 410008, China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, 110 Xiangya Road, Changsha 410078, China
- National Clinical Research Center for Geriatric Disorders, 87 Xiangya Road, Changsha 410008, China
- Institute of Clinical Pharmacology, Engineering Research Center for Applied Technology of Pharmacogenomics of Ministry of Education, Central South University, Changsha 410078, China
- Correspondence: (J.Y.); (Z.L.); Tel.: +86-731-84805380 (J.Y.); +86-731-82655012 (Z.L.); Fax: +86-731-82354476 (J.Y. & Z.L.)
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38
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Andreassen OA, Hindley GFL, Frei O, Smeland OB. New insights from the last decade of research in psychiatric genetics: discoveries, challenges and clinical implications. World Psychiatry 2023; 22:4-24. [PMID: 36640404 PMCID: PMC9840515 DOI: 10.1002/wps.21034] [Citation(s) in RCA: 43] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/07/2022] [Indexed: 01/15/2023] Open
Abstract
Psychiatric genetics has made substantial progress in the last decade, providing new insights into the genetic etiology of psychiatric disorders, and paving the way for precision psychiatry, in which individual genetic profiles may be used to personalize risk assessment and inform clinical decision-making. Long recognized to be heritable, recent evidence shows that psychiatric disorders are influenced by thousands of genetic variants acting together. Most of these variants are commonly occurring, meaning that every individual has a genetic risk to each psychiatric disorder, from low to high. A series of large-scale genetic studies have discovered an increasing number of common and rare genetic variants robustly associated with major psychiatric disorders. The most convincing biological interpretation of the genetic findings implicates altered synaptic function in autism spectrum disorder and schizophrenia. However, the mechanistic understanding is still incomplete. In line with their extensive clinical and epidemiological overlap, psychiatric disorders appear to exist on genetic continua and share a large degree of genetic risk with one another. This provides further support to the notion that current psychiatric diagnoses do not represent distinct pathogenic entities, which may inform ongoing attempts to reconceptualize psychiatric nosology. Psychiatric disorders also share genetic influences with a range of behavioral and somatic traits and diseases, including brain structures, cognitive function, immunological phenotypes and cardiovascular disease, suggesting shared genetic etiology of potential clinical importance. Current polygenic risk score tools, which predict individual genetic susceptibility to illness, do not yet provide clinically actionable information. However, their precision is likely to improve in the coming years, and they may eventually become part of clinical practice, stressing the need to educate clinicians and patients about their potential use and misuse. This review discusses key recent insights from psychiatric genetics and their possible clinical applications, and suggests future directions.
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Affiliation(s)
- Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Guy F L Hindley
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Oleksandr Frei
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Olav B Smeland
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
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39
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Jia X, Yu L. Effective assay technologies fit for large-scale population screening of type 1 diabetes. FRONTIERS IN CLINICAL DIABETES AND HEALTHCARE 2023; 3:1034698. [PMID: 36992730 PMCID: PMC10012058 DOI: 10.3389/fcdhc.2022.1034698] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 12/30/2022] [Indexed: 01/24/2023]
Abstract
While worldwide prevention efforts for type 1 diabetes (T1D) are underway to abrogate or slow progression to diabetes, mass screening of islet autoantibodies (IAbs) in the general population is urgently needed. IAbs, the most reliable biomarkers, play an essential role in prediction and clinical diagnosis of T1D. Through laboratory proficiency programs and harmonization efforts, a radio-binding assay (RBA) has been well established as the current 'gold' standard assay for all four IAbs. However, in view of the need for large-scale screening in the non-diabetic population, RBA consistently faces two fundamental challenges, cost-efficiency and disease specificity. While all four IAbs are important for disease prediction, the RBA platform, with a separate IAb test format is laborious, inefficient and expensive. Furthermore, the majority of IAb positivity in screening, especially from individuals with single IAb were found to be low risk with low affinity. It is well documented from multiple clinical studies that IAbs with low affinity are low risk with less or no disease relevance. At present, two non-radioactive multiplex assays, a 3-assay ELISA combining three IAbs and a multiplex ECL assay combining all four IAbs, have been successfully used as the primary methods for general population screenings in Germany and the US, respectively. Recently, the TrialNet Pathway to Prevention study has been organizing an IAb workshop which aims to analyze the 5-year T1D predictive values of IAbs. A T1D-specific assay with high efficiency, low cost and requiring low volume of sample will definitely be necessary to benefit general population screening.
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Affiliation(s)
| | - Liping Yu
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO, United States
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40
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Long SA, Buckner JH. Clinical and experimental treatment of type 1 diabetes. Clin Exp Immunol 2022; 210:105-113. [PMID: 35980300 PMCID: PMC9750829 DOI: 10.1093/cei/uxac077] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 07/26/2022] [Accepted: 08/17/2022] [Indexed: 01/25/2023] Open
Abstract
Type 1 diabetes (T1D) is an autoimmune disease resulting in the destruction of the insulin-producing pancreatic beta cells. Disease progression occurs along a trajectory from genetic risk, the development of islet autoantibodies, and autoreactive T cells ultimately progressing to clinical disease. Natural history studies and mechanistic studies linked to clinical trials have provided insight into the role of the immune system in disease pathogenesis. Here, we review our current understanding of the underlying etiology of T1D, focusing on the immune cell types that have been implicated in progression from pre-symptomatic T1D to clinical diagnosis and established disease. This knowledge has been foundational for the development of immunotherapies aimed at the prevention and treatment of T1D.
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Affiliation(s)
- S Alice Long
- Center for Translational Immunology, Benaroya Research Institute at Virginia Mason, Seattle, WA, USA
| | - Jane H Buckner
- Center for Translational Immunology, Benaroya Research Institute at Virginia Mason, Seattle, WA, USA
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41
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Tanvir Ahmed K, Cheng S, Li Q, Yong J, Zhang W. Incomplete time-series gene expression in integrative study for islet autoimmunity prediction. Brief Bioinform 2022; 24:6895461. [PMID: 36513375 PMCID: PMC9851333 DOI: 10.1093/bib/bbac537] [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: 08/05/2022] [Revised: 10/27/2022] [Accepted: 11/08/2022] [Indexed: 12/15/2022] Open
Abstract
Type 1 diabetes (T1D) outcome prediction plays a vital role in identifying novel risk factors, ensuring early patient care and designing cohort studies. TEDDY is a longitudinal cohort study that collects a vast amount of multi-omics and clinical data from its participants to explore the progression and markers of T1D. However, missing data in the omics profiles make the outcome prediction a difficult task. TEDDY collected time series gene expression for less than 6% of enrolled participants. Additionally, for the participants whose gene expressions are collected, 79% time steps are missing. This study introduces an advanced bioinformatics framework for gene expression imputation and islet autoimmunity (IA) prediction. The imputation model generates synthetic data for participants with partially or entirely missing gene expression. The prediction model integrates the synthetic gene expression with other risk factors to achieve better predictive performance. Comprehensive experiments on TEDDY datasets show that: (1) Our pipeline can effectively integrate synthetic gene expression with family history, HLA genotype and SNPs to better predict IA status at 2 years (sensitivity 0.622, AUC 0.715) compared with the individual datasets and state-of-the-art results in the literature (AUC 0.682). (2) The synthetic gene expression contains predictive signals as strong as the true gene expression, reducing reliance on expensive and long-term longitudinal data collection. (3) Time series gene expression is crucial to the proposed improvement and shows significantly better predictive ability than cross-sectional gene expression. (4) Our pipeline is robust to limited data availability. Availability: Code is available at https://github.com/compbiolabucf/TEDDY.
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Affiliation(s)
| | - Sze Cheng
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota Twin Cities, Minneapolis, MN 55455, USA
| | - Qian Li
- Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Jeongsik Yong
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota Twin Cities, Minneapolis, MN 55455, USA
| | - Wei Zhang
- Corresponding author. Wei Zhang, Computer Science Department, University of Central Florida. Tel.: 407-823-2763;
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42
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Libman I, Haynes A, Lyons S, Pradeep P, Rwagasor E, Tung JYL, Jefferies CA, Oram RA, Dabelea D, Craig ME. ISPAD Clinical Practice Consensus Guidelines 2022: Definition, epidemiology, and classification of diabetes in children and adolescents. Pediatr Diabetes 2022; 23:1160-1174. [PMID: 36537527 DOI: 10.1111/pedi.13454] [Citation(s) in RCA: 51] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/09/2022] [Indexed: 12/24/2022] Open
Affiliation(s)
- Ingrid Libman
- Division of Pediatric Endocrinology, UPMC Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Aveni Haynes
- Children's Diabetes Centre, Telethon Kids Institute, Perth, Western Australia, Australia
| | - Sarah Lyons
- Pediatric Diabetes and Endocrinology, Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA
| | - Praveen Pradeep
- Department of Endocrinology, All India Institute of Medical Sciences, New Delhi, India
| | - Edson Rwagasor
- Rwanda Biomedical Center, Rwanda Ministry of Health, Kigali, Rwanda
| | - Joanna Yuet-Ling Tung
- Department of Paediatrics and Adolescent Medicine, Hong Kong Children's Hospital, Hong Kong, Hong Kong
| | - Craig A Jefferies
- Starship Children's Health, Te Whatu Ora Health New Zealand, Auckland, New Zealand
| | - Richard A Oram
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Dana Dabelea
- Department of Epidemiology, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Maria E Craig
- The Children's Hospital at Westmead, Sydney, New South Wales (NSW), Australia.,University of Sydney Children's Hospital Westmead Clinical School, Sydney, NEW, Australia.,Discipline of Paediatrics & Child Health, School of Clinical Medicine, University of NSW Medicine & Health, Sydney, NSW, Australia
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43
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Besser REJ, Bell KJ, Couper JJ, Ziegler AG, Wherrett DK, Knip M, Speake C, Casteels K, Driscoll KA, Jacobsen L, Craig ME, Haller MJ. ISPAD Clinical Practice Consensus Guidelines 2022: Stages of type 1 diabetes in children and adolescents. Pediatr Diabetes 2022; 23:1175-1187. [PMID: 36177823 DOI: 10.1111/pedi.13410] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 08/29/2022] [Indexed: 12/29/2022] Open
Affiliation(s)
- Rachel E J Besser
- Wellcome Centre for Human Genetics, NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Kirstine J Bell
- Charles Perkins Centre and Faculty Medicine and Health, University of Sydney, Sydney, Australia
| | - Jenny J Couper
- Department of Pediatrics, University of Adelaide, South Australia, Australia.,Robinson Research Institute, University of Adelaide, Adelaide, Australia
| | - Anette-G Ziegler
- Institute of Diabetes Research, Helmholtz Zentrum München, and Forschergruppe Diabetes, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Diane K Wherrett
- Division of Endocrinology, Department of Pediatrics, Hospital for Sick Children, University of Toronto, Toronto, Canada
| | - Mikael Knip
- Children's Hospital, University of Helsinki, Helsinki, Finland
| | - Cate Speake
- Center for Interventional Immunology, Benaroya Research Institute at Virginia Mason, Seattle, Washington, USA
| | - Kristina Casteels
- Department of Pediatrics, University Hospitals Leuven, Leuven, Belgium.,Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Kimberly A Driscoll
- Department of Clinical and Health Psychology, University of Florida, Gainesville, Florida, USA
| | - Laura Jacobsen
- Division of Endocrinology, Department of Pediatrics, University of Florida, Gainesville, Florida, USA
| | - Maria E Craig
- Department of Pediatrics, The Children's Hospital at Westmead, University of Sydney, Sydney, Australia
| | - Michael J Haller
- Division of Endocrinology, Department of Pediatrics, University of Florida, Gainesville, Florida, USA
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Abstract
First envisioned by early diabetes clinicians, a person-centred approach to care was an aspirational goal that aimed to match insulin therapy to each individual's unique requirements. In the 100 years since the discovery of insulin, this goal has evolved to include personalised approaches to type 1 diabetes diagnosis, treatment, prevention and prediction. These advances have been facilitated by the recognition of type 1 diabetes as an autoimmune disease and by advances in our understanding of diabetes pathophysiology, genetics and natural history, which have occurred in parallel with advancements in insulin delivery, glucose monitoring and tools for self-management. In this review, we discuss how these personalised approaches have improved diabetes care and how improved understanding of pathogenesis and human biology might inform precision medicine in the future.
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Affiliation(s)
- Alice L J Carr
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK.
| | - Carmella Evans-Molina
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Anatomy, Cell Biology, and Physiology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
- Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA
- Richard L. Roudebush VA Medical Center, Indianapolis, IN, USA
| | - Richard A Oram
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK.
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45
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Besser REJ, Ng SM, Gregory JW, Dayan CM, Randell T, Barrett T. General population screening for childhood type 1 diabetes: is it time for a UK strategy? Arch Dis Child 2022; 107:790-795. [PMID: 34740879 DOI: 10.1136/archdischild-2021-321864] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Accepted: 10/18/2021] [Indexed: 12/21/2022]
Abstract
Type 1 diabetes (T1D) is a chronic autoimmune disease of childhood affecting 1:500 children aged under 15 years, with around 25% presenting with life-threatening diabetic ketoacidosis (DKA). While first-degree relatives have the highest risk of T1D, more than 85% of children who develop T1D do not have a family history. Despite public health awareness campaigns, DKA rates have not fallen over the last decade. T1D has a long prodrome, and it is now possible to identify children who go on to develop T1D with a high degree of certainty. The reasons for identifying children presymptomatically include prevention of DKA and related morbidities and mortality, reducing the need for hospitalisation, time to provide emotional support and education to ensure a smooth transition to insulin treatment, and opportunities for new treatments to prevent or delay progression. Research studies of population-based screening strategies include using islet autoantibodies alone or in combination with genetic risk factors, both of which can be measured from a capillary sample. If found during screening, the presence of two or more islet autoantibodies has a high positive predictive value for future T1D in childhood (under 18 years), offering an opportunity for DKA prevention. However, a single time-point test will not identify all children who go on to develop T1D, and so combining with genetic risk factors for T1D may be an alternative approach. Here we discuss the pros and cons of T1D screening in the UK, the different strategies available, the knowledge gaps and why a T1D screening strategy is needed.
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Affiliation(s)
- Rachel Elizabeth Jane Besser
- Department of Paediatric Diabetes and Endocrinology, NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK .,Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Sze May Ng
- Paediatric Department, Southport and Ormskirk NHS Trust, Ormskirk, UK.,Department of Women's and Children's Health, University of Liverpool, Liverpool, UK
| | - John W Gregory
- Division of Population Health, School of Medicine, Cardiff University, Cardiff, UK
| | - Colin M Dayan
- Clinical Diabetes and Metabolism, Cardiff University School of Medicine, Cardiff, UK
| | | | - Timothy Barrett
- Diabetes Unit, Institute of Child Health, Birmingham Women's and Children's Hospital, Birmingham, UK
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46
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Ghalwash M, Dunne JL, Lundgren M, Rewers M, Ziegler AG, Anand V, Toppari J, Veijola R, Hagopian W. Two-age islet-autoantibody screening for childhood type 1 diabetes: a prospective cohort study. Lancet Diabetes Endocrinol 2022; 10:589-596. [PMID: 35803296 PMCID: PMC10040253 DOI: 10.1016/s2213-8587(22)00141-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 04/25/2022] [Accepted: 04/27/2022] [Indexed: 01/08/2023]
Abstract
BACKGROUND Early prediction of childhood type 1 diabetes reduces ketoacidosis at diagnosis and provides opportunities for disease prevention. However, only highly efficient approaches are likely to succeed in public health settings. We sought to identify efficient strategies for initial islet autoantibody screening in children younger than 15 years. METHODS We harmonised data from five prospective cohorts from Finland (DIPP), Germany (BABYDIAB), Sweden (DiPiS), and the USA (DAISY and DEW-IT) into the Type 1 Diabetes Intelligence (T1DI) cohort. 24 662 children at high risk of diabetes enrolled before age 2 years were included and followed up for islet autoantibodies and diabetes until age 15 years, or type 1 diabetes onset, whichever occurred first. Islet autoantibodies measured included those against glutamic acid decarboxylase, insulinoma antigen 2, and insulin. Main outcomes were sensitivity and positive predictive value (PPV) of detected islet autoantibodies, tested at one or two fixed ages, for diagnosis of clinical type 1 diabetes. FINDINGS Of the 24 662 participants enrolled in the Type 1 Diabetes Intelligence cohort, 6722 total were followed up to age 15 years or until onset of type 1 diabetes. Type 1 diabetes developed by age 15 years in 672 children, but did not develop in 6050 children. Optimal screening ages for two measurements were 2 years and 6 years, yielding sensitivity of 82% (95% CI 79-86) and PPV of 79% (95% CI 75-80) for diabetes by age 15 years. Autoantibody positivity at the beginning of each test age was highly predictive of diagnosis in the subsequent 2-5·99 year or 6-15-year age intervals. Autoantibodies usually appeared before age 6 years even in children diagnosed with diabetes much later in childhood. INTERPRETATION Our results show that initial screening for islet autoantibodies at two ages (2 years and 6 years) is sensitive and efficient for public health translation but might require adjustment by country on the basis of population-specific disease characteristics. FUNDING Juvenile Diabetes Research Foundation.
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Affiliation(s)
- Mohamed Ghalwash
- Center for Computational Health, IBM Research, Yorktown Heights, NY, USA; Faculty of Science, Ain Shams University, Cairo, Egypt
| | | | - Markus Lundgren
- Department of Clinical Sciences Malmö, Lund University/Clinical Research Centre, Skåne University Hospital, Malmö, Sweden
| | - Marian Rewers
- Barbara Davis Center for Diabetes, University of Colorado, Denver, CO, USA
| | - Anette-G Ziegler
- Forschegruppe Diabetes and Institute of Diabetes Research, Helmholtz Zentrum München, German Research Centre for Environmental Health, Munich-Neuherberg, Germany der TU München, Munich, Germany
| | - Vibha Anand
- Center for Computational Health, IBM Research, Yorktown Heights, NY, USA
| | - Jorma Toppari
- Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, and Centre for Population Health Research, University of Turku, Turku, Finland; Department of Paediatrics, Turku University Hospital, Turku, Finland
| | - Riitta Veijola
- Department of Paediatrics, PEDEGO Research Unit, University of Oulu and Oulu University Hospital, Oulu, Finland
| | - William Hagopian
- Pacific Northwest Research Institute, Seattle, WA, USA; Department of Medicine, University of Washington, Seattle, WA, USA.
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47
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Hocking AM, Buckner JH. Genetic basis of defects in immune tolerance underlying the development of autoimmunity. Front Immunol 2022; 13:972121. [PMID: 35979360 PMCID: PMC9376219 DOI: 10.3389/fimmu.2022.972121] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 07/14/2022] [Indexed: 12/15/2022] Open
Abstract
Genetic variants associated with susceptibility to autoimmune disease have provided important insight into the mechanisms responsible for the loss of immune tolerance and the subsequent development of autoantibodies, tissue damage, and onset of clinical disease. Here, we review how genetic variants shared across multiple autoimmune diseases have contributed to our understanding of global tolerance failure, focusing on variants in the human leukocyte antigen region, PTPN2 and PTPN22, and their role in antigen presentation and T and B cell homeostasis. Variants unique to a specific autoimmune disease such as those in PADI2 and PADI4 that are associated with rheumatoid arthritis are also discussed, addressing their role in disease-specific immunopathology. Current research continues to focus on determining the functional consequences of autoimmune disease-associated variants but has recently expanded to variants in the non-coding regions of the genome using novel approaches to investigate the impact of these variants on mechanisms regulating gene expression. Lastly, studying genetic risk variants in the setting of autoimmunity has clinical implications, helping predict who will develop autoimmune disease and also identifying potential therapeutic targets.
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48
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Redondo MJ, Gignoux CR, Dabelea D, Hagopian WA, Onengut-Gumuscu S, Oram RA, Rich SS. Type 1 diabetes in diverse ancestries and the use of genetic risk scores. Lancet Diabetes Endocrinol 2022; 10:597-608. [PMID: 35724677 PMCID: PMC10024251 DOI: 10.1016/s2213-8587(22)00159-0] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 04/16/2022] [Accepted: 05/06/2022] [Indexed: 02/06/2023]
Abstract
Over 75 genetic loci within and outside of the HLA region influence type 1 diabetes risk. Genetic risk scores (GRS), which facilitate the integration of complex genetic information, have been developed in type 1 diabetes and incorporated into models and algorithms for classification, prognosis, and prediction of disease and response to preventive and therapeutic interventions. However, the development and validation of GRS across different ancestries is still emerging, as is knowledge on type 1 diabetes genetics in populations of diverse genetic ancestries. In this Review, we provide a summary of the current evidence on the evolutionary genetic variation in type 1 diabetes and the racial and ethnic differences in type 1 diabetes epidemiology, clinical characteristics, and preclinical course. We also discuss the influence of genetics on type 1 diabetes with differences across ancestries and the development and validation of GRS in various populations.
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Affiliation(s)
- Maria J Redondo
- Division of Diabetes and Endocrinology, Texas Children's Hospital, Baylor College of Medicine, Houston, TX, USA.
| | - Christopher R Gignoux
- Department of Medicine and Colorado Center for Personalized Medicine, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Dana Dabelea
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - William A Hagopian
- Division of Diabetes Programs, Pacific Northwest Research Institute, Seattle, WA, USA
| | - Suna Onengut-Gumuscu
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Richard A Oram
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, University of Exeter, Exeter, UK; The Academic Kidney Unit, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - Stephen S Rich
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
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49
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Redondo MJ. On the road to universal screening for risk of type 1 diabetes. Lancet Diabetes Endocrinol 2022; 10:554-555. [PMID: 35803297 PMCID: PMC10031625 DOI: 10.1016/s2213-8587(22)00166-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 05/19/2022] [Indexed: 10/17/2022]
Affiliation(s)
- Maria J Redondo
- Baylor College of Medicine, Texas Children's Hospital, Houston, TX, USA.
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50
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Scherm MG, Wyatt RC, Serr I, Anz D, Richardson SJ, Daniel C. Beta cell and immune cell interactions in autoimmune type 1 diabetes: How they meet and talk to each other. Mol Metab 2022; 64:101565. [PMID: 35944899 PMCID: PMC9418549 DOI: 10.1016/j.molmet.2022.101565] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 07/08/2022] [Accepted: 07/27/2022] [Indexed: 10/31/2022] Open
Abstract
Background Scope of review Major conclusions
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