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Misra S, Wagner R, Ozkan B, Schön M, Sevilla-Gonzalez M, Prystupa K, Wang CC, Kreienkamp RJ, Cromer SJ, Rooney MR, Duan D, Thuesen ACB, Wallace AS, Leong A, Deutsch AJ, Andersen MK, Billings LK, Eckel RH, Sheu WHH, Hansen T, Stefan N, Goodarzi MO, Ray D, Selvin E, Florez JC, Meigs JB, Udler MS. Precision subclassification of type 2 diabetes: a systematic review. COMMUNICATIONS MEDICINE 2023; 3:138. [PMID: 37798471 PMCID: PMC10556101 DOI: 10.1038/s43856-023-00360-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 09/15/2023] [Indexed: 10/07/2023] Open
Abstract
BACKGROUND Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We undertook a systematic review to determine whether strategies to subclassify type 2 diabetes were associated with high quality evidence, reproducible results and improved outcomes for patients. METHODS We searched PubMed and Embase for publications that used 'simple subclassification' approaches using simple categorisation of clinical characteristics, or 'complex subclassification' approaches which used machine learning or 'omics approaches in people with established type 2 diabetes. We excluded other diabetes subtypes and those predicting incident type 2 diabetes. We assessed quality, reproducibility and clinical relevance of extracted full-text articles and qualitatively synthesised a summary of subclassification approaches. RESULTS Here we show data from 51 studies that demonstrate many simple stratification approaches, but none have been replicated and many are not associated with meaningful clinical outcomes. Complex stratification was reviewed in 62 studies and produced reproducible subtypes of type 2 diabetes that are associated with outcomes. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into clinically meaningful subtypes. CONCLUSION Critical next steps toward clinical implementation are to test whether subtypes exist in more diverse ancestries and whether tailoring interventions to subtypes will improve outcomes.
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Affiliation(s)
- Shivani Misra
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.
- Department of Diabetes and Endocrinology, Imperial College Healthcare NHS Trust, London, UK.
| | - Robert Wagner
- Department of Endocrinology and Diabetology, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Moorenstr. 5, 40225, Düsseldorf, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf'm Hennekamp 65, 40225, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
| | - Bige Ozkan
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Martin Schön
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf'm Hennekamp 65, 40225, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Magdalena Sevilla-Gonzalez
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Katsiaryna Prystupa
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf'm Hennekamp 65, 40225, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
| | - Caroline C Wang
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Raymond J Kreienkamp
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit, Division of Endocrinology, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Pediatrics, Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA
| | - Sara J Cromer
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Diabetes Unit, Division of Endocrinology, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Mary R Rooney
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Daisy Duan
- Division of Endocrinology, Diabetes and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Anne Cathrine Baun Thuesen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Amelia S Wallace
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Aaron Leong
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Diabetes Unit, Division of Endocrinology, Massachusetts General Hospital, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, 100 Cambridge St 16th Floor, Boston, MA, USA
| | - Aaron J Deutsch
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Diabetes Unit, Division of Endocrinology, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Mette K Andersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Liana K Billings
- Division of Endocrinology, Diabetes and Metabolism, NorthShore University Health System, Skokie, IL, USA
- Department of Medicine, Pritzker School of Medicine, University of Chicago, Chicago, IL, USA
| | - Robert H Eckel
- Division of Endocrinology, Metabolism and Diabetes, University of Colorado School of Medicine, Aurora, CO, USA
| | - Wayne Huey-Herng Sheu
- Institute of Molecular and Genomic Medicine, National Health Research Institute, Miaoli County, Taiwan, ROC
- Division of Endocrinology and Metabolism, Taichung Veterans General Hospital, Taichung, Taiwan, ROC
- Division of Endocrinology and Metabolism, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Norbert Stefan
- German Center for Diabetes Research (DZD), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- University Hospital of Tübingen, Tübingen, Germany
- Institute of Diabetes Research and Metabolic Diseases (IDM), Helmholtz Center Munich, Neuherberg, Germany
| | - Mark O Goodarzi
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Debashree Ray
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Elizabeth Selvin
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jose C Florez
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Diabetes Unit, Division of Endocrinology, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - James B Meigs
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, 100 Cambridge St 16th Floor, Boston, MA, USA
| | - Miriam S Udler
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Diabetes Unit, Division of Endocrinology, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
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Jiang J, Li Y, Li F, He Y, Song L, Wang K, You W, Xia Z, Zuo Y, Su X, Zhai Q, Zhang Y, Gaisano H, Zheng D. Post-Load Insulin Secretion Patterns are Associated with Glycemic Status and Diabetic Complications in Patients with Type 2 Diabetes Mellitus. Exp Clin Endocrinol Diabetes 2023; 131:198-204. [PMID: 36796421 DOI: 10.1055/a-2018-4299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
BACKGROUND To examine whether the different patterns of post-load insulin secretion can identify the heterogeneity of type 2 diabetes mellitus (T2DM). METHODS Six hundred twenty-five inpatients with T2DM at Jining No. 1 People's Hospital were recruited from January 2019 to October 2021. The 140 g steamed bread meal test (SBMT) was conducted on patients with T2DM, and glucose, insulin, and C-peptide levels were recorded at 0, 60, 120, and 180 min. To avoid the effect of exogenous insulin, patients were categorized into three different classes by latent class trajectory analysis based on the post-load secretion patterns of C-peptide. The difference in short- and long-term glycemic status and prevalence of complications distributed among the three classes were compared by multiple linear regression and multiple logistic regression, respectively. RESULTS There were significant differences in long-term glycemic status (e. g., HbA1c) and short-term glycemic status (e. g., mean blood glucose, time in range) among the three classes. The difference in short-term glycemic status was similar in terms of the whole day, daytime, and nighttime. The prevalence of severe diabetic retinopathy and atherosclerosis showed a decreasing trend among the three classes. CONCLUSIONS The post-load insulin secretion patterns could well identify the heterogeneity of patients with T2DM in short- and long-term glycemic status and prevalence of complications, providing recommendations for the timely adjustment in treatment regimes of patients with T2DM and promotion of personalized treatment.
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Affiliation(s)
- Jiajia Jiang
- Department of Endocrinology, Jining No. 1 People's Hospital, Jining, Shandong, China.,Institute for Chronic Disease Management, Jining No. 1 People's Hospital, Jining, Shandong, China
| | - Yuhao Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China
| | - Feng Li
- Department of Endocrinology, Jining No. 1 People's Hospital, Jining, Shandong, China.,Institute for Chronic Disease Management, Jining No. 1 People's Hospital, Jining, Shandong, China
| | - Yan He
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.,Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Lijuan Song
- Department of Endocrinology, Jining No. 1 People's Hospital, Jining, Shandong, China
| | - Kun Wang
- Department of Endocrinology, Jining No. 1 People's Hospital, Jining, Shandong, China
| | - Wenjun You
- Department of Endocrinology, Jining No. 1 People's Hospital, Jining, Shandong, China
| | - Zhang Xia
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China
| | - Yingting Zuo
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China
| | - Xin Su
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China
| | - Qi Zhai
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China
| | - Yibo Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China
| | - Herbert Gaisano
- Departments of Medicine and Physiology, University of Toronto, Toronto, Ontario, Canada
| | - Deqiang Zheng
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.,Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
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O'Connor S, Blais C, Mésidor M, Talbot D, Poirier P, Leclerc J. Great diversity in the utilization and reporting of latent growth modeling approaches in type 2 diabetes: A literature review. Heliyon 2022; 8:e10493. [PMID: 36164545 PMCID: PMC9508412 DOI: 10.1016/j.heliyon.2022.e10493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 05/09/2022] [Accepted: 08/25/2022] [Indexed: 12/03/2022] Open
Abstract
Introduction The progression of complications of type 2 diabetes (T2D) is unique to each patient and can be depicted through individual temporal trajectories. Latent growth modeling approaches (latent growth mixture models [LGMM] or latent class growth analysis [LCGA]) can be used to classify similar individual trajectories in a priori non-observed groups (latent groups), sharing common characteristics. Although increasingly used in the field of T2D, many questions remain regarding the utilization of these methods. Objective To review the literature of longitudinal studies using latent growth modeling approaches to study T2D. Methods MEDLINE (Ovid), EMBASE, CINAHL and Wb of Science were searched through August 25th, 2021. Data was collected on the type of latent growth modeling approaches (LGMM or LCGA), characteristics of studies and quality of reporting using the GRoLTS-Checklist and presented as frequencies. Results From the 4,694 citations screened, a total of 38 studies were included. The studies were published beetween 2011 and 2021 and the length of follow-up ranged from 8 weeks to 14 years. Six studies used LGMM, while 32 studies used LCGA. The fields of research varied from clinical research, psychological science, healthcare utilization research and drug usage/pharmaco-epidemiology. Data sources included primary data (clinical trials, prospective/retrospective cohorts, surveys), or secondary data (health records/registries, medico-administrative). Fifty percent of studies evaluated trajectory groups as exposures for a subsequent clinical outcome, while 24% used predictive models of group membership and 5% used both. Regarding the quality of reporting, trajectory groups were adequately presented, however many studies failed to report important decisions made for the trajectory group identification. Conclusion Although LCGA were preferred, the contexts of utilization were diverse and unrelated to the type of methods. We recommend future authors to clearly report the decisions made regarding trajectory groups identification. There is a growing body of literature on trajectory modeling in type 2 diabetes. Latent class growth analysis can be used in many different contexts. The current reporting of methods used should be improved.
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Affiliation(s)
- Sarah O'Connor
- Research Centre, Institut universitaire de Cardiologie et Pneumologie de Québec-Université Laval (IUCPQ-UL), 2725 Ch. Ste-Foy, Quebec City, Quebec, G1V 4G5, Canada.,Faculty of Pharmacy, Université Laval, Ferdinand Vandry Pavillon, 1050 de La Médecine Avenue, Quebec City, Quebec, G1V 0A6, Canada
| | - Claudia Blais
- Faculty of Pharmacy, Université Laval, Ferdinand Vandry Pavillon, 1050 de La Médecine Avenue, Quebec City, Quebec, G1V 0A6, Canada.,Bureau D'information et D'études en Santé des Populations, Institut National de Santé Publique Du Québec, 945, Wolfe Avenue, Quebec City, Quebec, G1V 5B3, Canada
| | - Miceline Mésidor
- Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Ferdinand Vandry Pavillon, 1050 de La Médecine Avenue, Quebec City, Quebec, G1V 0A6, Canada.,Research Centre, CHU de Québec - Université Laval, 2400 D'Estimauville Avenue, Québec, QC, G1E 6W2, Canada
| | - Denis Talbot
- Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Ferdinand Vandry Pavillon, 1050 de La Médecine Avenue, Quebec City, Quebec, G1V 0A6, Canada.,Research Centre, CHU de Québec - Université Laval, 2400 D'Estimauville Avenue, Québec, QC, G1E 6W2, Canada
| | - Paul Poirier
- Research Centre, Institut universitaire de Cardiologie et Pneumologie de Québec-Université Laval (IUCPQ-UL), 2725 Ch. Ste-Foy, Quebec City, Quebec, G1V 4G5, Canada.,Faculty of Pharmacy, Université Laval, Ferdinand Vandry Pavillon, 1050 de La Médecine Avenue, Quebec City, Quebec, G1V 0A6, Canada
| | - Jacinthe Leclerc
- Research Centre, Institut universitaire de Cardiologie et Pneumologie de Québec-Université Laval (IUCPQ-UL), 2725 Ch. Ste-Foy, Quebec City, Quebec, G1V 4G5, Canada.,Faculty of Pharmacy, Université Laval, Ferdinand Vandry Pavillon, 1050 de La Médecine Avenue, Quebec City, Quebec, G1V 0A6, Canada.,Department of Nursing, Université Du Québec à Trois-Rivières, 3351 des Forges Boulevard, Trois-Rivières, Quebec, G8Z 4M3, Canada
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Tricò D, McCollum S, Samuels S, Santoro N, Galderisi A, Groop L, Caprio S, Shabanova V. Mechanistic Insights Into the Heterogeneity of Glucose Response Classes in Youths With Obesity: A Latent Class Trajectory Approach. Diabetes Care 2022; 45:1841-1851. [PMID: 35766976 PMCID: PMC9346992 DOI: 10.2337/dc22-0110] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 05/03/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVE In a large, multiethnic cohort of youths with obesity, we analyzed pathophysiological and genetic mechanisms underlying variations in plasma glucose responses to a 180 min oral glucose tolerance test (OGTT). RESEARCH DESIGN AND METHODS Latent class trajectory analysis was used to identify various glucose response profiles to a nine-point OGTT in 2,378 participants in the Yale Pathogenesis of Youth-Onset T2D study, of whom 1,190 had available TCF7L2 genotyping and 358 had multiple OGTTs over a 5 year follow-up. Insulin sensitivity, clearance, and β-cell function were estimated by glucose, insulin, and C-peptide modeling. RESULTS Four latent classes (1 to 4) were identified based on increasing areas under the curve for glucose. Participants in class 3 and 4 had the worst metabolic and genetic risk profiles, featuring impaired insulin sensitivity, clearance, and β-cell function. Model-predicted probability to be classified as class 1 and 4 increased across ages, while insulin sensitivity and clearance showed transient reductions and β-cell function progressively declined. Insulin sensitivity was the strongest determinant of class assignment at enrollment and of the longitudinal change from class 1 and 2 to higher classes. Transitions between classes 3 and 4 were explained only by changes in β-cell glucose sensitivity. CONCLUSIONS We identified four glucose response classes in youths with obesity with different genetic risk profiles and progressive impairment in insulin kinetics and action. Insulin sensitivity was the main determinant in the transition between lower and higher glucose classes across ages. In contrast, transitions between the two worst glucose classes were driven only by β-cell glucose sensitivity.
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Affiliation(s)
- Domenico Tricò
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Sarah McCollum
- Department of Pediatrics, Yale University School of Medicine, New Haven, CT
| | - Stephanie Samuels
- Department of Pediatrics, Yale University School of Medicine, New Haven, CT
| | - Nicola Santoro
- Department of Pediatrics, Yale University School of Medicine, New Haven, CT.,Department of Medicine and Health Sciences, "V. Tiberio" University of Molise, Campobasso, Italy
| | - Alfonso Galderisi
- Pediatric Endocrinology, Hôpital Necker-Enfants Malades, Paris, France
| | - Leif Groop
- Department of Clinical Sciences, Genomics, Diabetes and Endocrinology, Lund University, Malmö, Sweden
| | - Sonia Caprio
- Department of Pediatrics, Yale University School of Medicine, New Haven, CT
| | - Veronika Shabanova
- Department of Pediatrics, Yale University School of Medicine, New Haven, CT
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