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      Atypical Diabetes: What Have We Learned and What Does the Future Hold?

      review-article
      Author(s): 1 , , 2 , 3
      Publication date (Electronic):
      Journal: Diabetes Care
      Publisher: American Diabetes Association

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          Abstract

          As our understanding of the pathophysiology of diabetes evolves, we increasingly recognize that many patients may have a form of diabetes that does not neatly fit with a diagnosis of either type 1 or type 2 diabetes. The discovery and description of these forms of “atypical diabetes” have led to major contributions to our collective understanding of the basic biology that drives insulin secretion, insulin resistance, and islet autoimmunity. These discoveries now pave the way to a better classification of diabetes based on distinct endotypes. In this review, we highlight the key biological and clinical insights that can be gained from studying known forms of atypical diabetes. Additionally, we provide a framework for identification of patients with atypical diabetes based on their clinical, metabolic, and molecular features. Helpful clinical and genetic resources for evaluating patients suspected of having atypical diabetes are provided. Therefore, appreciating the various endotypes associated with atypical diabetes will enhance diagnostic accuracy and facilitate targeted treatment decisions.

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          Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology

          Sue Richards, Nazneen Aziz, Sherri Bale … (2015)
          The American College of Medical Genetics and Genomics (ACMG) previously developed guidance for the interpretation of sequence variants. 1 In the past decade, sequencing technology has evolved rapidly with the advent of high-throughput next generation sequencing. By adopting and leveraging next generation sequencing, clinical laboratories are now performing an ever increasing catalogue of genetic testing spanning genotyping, single genes, gene panels, exomes, genomes, transcriptomes and epigenetic assays for genetic disorders. By virtue of increased complexity, this paradigm shift in genetic testing has been accompanied by new challenges in sequence interpretation. In this context, the ACMG convened a workgroup in 2013 comprised of representatives from the ACMG, the Association for Molecular Pathology (AMP) and the College of American Pathologists (CAP) to revisit and revise the standards and guidelines for the interpretation of sequence variants. The group consisted of clinical laboratory directors and clinicians. This report represents expert opinion of the workgroup with input from ACMG, AMP and CAP stakeholders. These recommendations primarily apply to the breadth of genetic tests used in clinical laboratories including genotyping, single genes, panels, exomes and genomes. This report recommends the use of specific standard terminology: ‘pathogenic’, ‘likely pathogenic’, ‘uncertain significance’, ‘likely benign’, and ‘benign’ to describe variants identified in Mendelian disorders. Moreover, this recommendation describes a process for classification of variants into these five categories based on criteria using typical types of variant evidence (e.g. population data, computational data, functional data, segregation data, etc.). Because of the increased complexity of analysis and interpretation of clinical genetic testing described in this report, the ACMG strongly recommends that clinical molecular genetic testing should be performed in a CLIA-approved laboratory with results interpreted by a board-certified clinical molecular geneticist or molecular genetic pathologist or equivalent.
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            Staging Presymptomatic Type 1 Diabetes: A Scientific Statement of JDRF, the Endocrine Society, and the American Diabetes Association

            Richard Insel, Jessica Dunne, Mark A L Atkinson … (2015)
            Insights from prospective, longitudinal studies of individuals at risk for developing type 1 diabetes have demonstrated that the disease is a continuum that progresses sequentially at variable but predictable rates through distinct identifiable stages prior to the onset of symptoms. Stage 1 is defined as the presence of β-cell autoimmunity as evidenced by the presence of two or more islet autoantibodies with normoglycemia and is presymptomatic, stage 2 as the presence of β-cell autoimmunity with dysglycemia and is presymptomatic, and stage 3 as onset of symptomatic disease. Adoption of this staging classification provides a standardized taxonomy for type 1 diabetes and will aid the development of therapies and the design of clinical trials to prevent symptomatic disease, promote precision medicine, and provide a framework for an optimized benefit/risk ratio that will impact regulatory approval, reimbursement, and adoption of interventions in the early stages of type 1 diabetes to prevent symptomatic disease.
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              Use of the oral glucose tolerance test to assess insulin release and insulin sensitivity.

              M. Stumvoll, A Mitrakou, W Pimenta … (2000)
              The oral glucose tolerance test (OGTT) has often been used to evaluate apparent insulin release and insulin resistance in various clinical settings. However, because insulin sensitivity and insulin release are interdependent, to what extent they can be predicted from an OGTT is unclear. We studied insulin sensitivity using the euglycemic-hyperinsulinemic clamp and insulin release using the hyperglycemic clamp in 104 nondiabetic volunteers who had also undergone an OGTT. Demographic parameters (BMI, waist-to-hip ratio, age) and plasma glucose and insulin values from the OGTT were subjected to multiple linear regression to predict the metabolic clearance rate (MCR) of glucose, the insulin sensitivity index (ISI), and first-phase (1st PH) and second-phase (2nd PH) insulin release as measured with the respective clamps. The equations predicting MCR and ISI contained BMI, insulin (120 min), and glucose (90 min) and were highly correlated with the measured MCR (r = 0.80, P < 0.00005) and ISI (r = 0.79, P < 0.00005). The equations predicting 1st PH and 2nd PH contained insulin (0 and 30 min) and glucose (30 min) and were also highly correlated with the measured 1st PH (r = 0.78, P < 0.00005) and 2nd PH (r = 0.79, P < 0.00005). The parameters predicted by our equations correlated better with the measured parameters than homeostasis model assessment for secretion and resistance, the delta30-min insulin/delta30-min glucose ratio for secretion and insulin (120 min) for insulin resistance taken from the OGTT. We thus conclude that predicting insulin sensitivity and insulin release with reasonable accuracy from simple demographic parameters and values obtained during an OGTT is possible. The derived equations should be used in various clinical settings in which the use of clamps or the minimal model would be impractical.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Diabetes Care
                Journal ID (iso-abbrev): Diabetes Care
                Journal ID (publisher-id): diabetes care
                Title: Diabetes Care
                Publisher: American Diabetes Association
                ISSN (Print): 0149-5992
                ISSN (Electronic): 1935-5548
                Publication date (Print): May 2024
                Publication date (Electronic): 08 February 2024
                Publication date PMC-release: 08 February 2024
                Volume: 47
                Issue: 5
                Pages: 770-781
                Affiliations
                [1 ]Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics, Washington University School of Medicine, St. Louis, MO
                [2 ]Division of Diabetes, Endocrinology and Metabolism, Baylor College of Medicine, Houston, TX
                [3 ]Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
                Author notes
                Corresponding author: Stephen I. Stone, sstone@ 123456wustl.edu
                Author information
                https://orcid.org/0000-0002-6128-9341
                https://orcid.org/0000-0003-2093-5201
                Article
                Publisher ID: i230038
                DOI: 10.2337/dci23-0038
                PMC ID: 11043229
                PubMed ID: 38329838
                SO-VID: d2c0fbd5-4729-4466-87a3-ec37bb103858
                Copyright © © 2024 by the American Diabetes Association
                License:

                Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/journals/pages/license.

                History
                Date received : 02 August 2023
                Date accepted : 21 November 2023
                Funding
                Funded by: NIH Rare and Atypical Diabetes Network;
                Award ID: RADIANT
                Funded by: NIH;
                Award ID: DK124574
                S.I.S. is supported by a K08 grant from the National Institutes of Health (NIH) (DK124574). The authors are also supported by the RADIANT study, funded by the National Institute of Diabetes and Digestive and Kidney Diseases.
                Categories
                Subject: Review

                ScienceOpen disciplines: Endocrinology & Diabetes
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                ScienceOpen disciplines: Endocrinology & Diabetes

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