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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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Reintar S, Pöchhacker M, Obermayer A, Eberhard K, Zirlik A, Verheyen N, von Lewinski D, Scherr D, Hutz B, Haudum CW, Pieber TR, Sourij H, Obermayer-Pietsch B. Urinary C-Peptide to Creatinine Ratio (UCPCR) as Indicator for Metabolic Risk in Apparently Healthy Adults-A BioPersMed Cohort Study. Nutrients 2023; 15:2073. [PMID: 37432211 DOI: 10.3390/nu15092073] [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: 03/28/2023] [Revised: 04/21/2023] [Accepted: 04/22/2023] [Indexed: 07/12/2023] Open
Abstract
Background: C-peptide is produced in equimolar amounts with insulin from pancreatic beta cells, and thus is a fundamental biomarker for beta cell function. A non-invasive urinary C-peptide-to-creatinine ratio (UCPCR) has attracted attention as a biomarker for metabolic conditions. However, the UCPCR as an indicative risk predictor for prediabetes is still being investigated. Methods: We aimed to characterize UCPCRs in healthy people using American Diabetes Association (ADA) criteria and to evaluate their metabolic outcomes over time. A total of 1022 participants of the Biomarkers in Personalized Medicine cohort (BioPersMed) were screened for this study. Totals of 317 healthy with normal glucose metabolism, 87 prediabetic, and 43 diabetic subjects were included. Results: Prediabetic participants had a significantly higher UCPCR median value than healthy participants (p < 0.05). Dysglycaemia of healthy baseline participants was measured twice over 4.5 ± 0.9 years; 25% and 30% were detected with prediabetes during follow-ups, predicted by UCPCR both for the first (p < 0.05) and the second visit (p < 0.05), respectively. This is in good agreement with the negative predictive UCPCR value of 60.2% based on logistic regression. UCPCR levels were equal in both sexes. Conclusion: UCPCR measurements provide an indicative approach for metabolic risk, representing a potential use for prevention and monitoring of impaired glucose metabolism.
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Affiliation(s)
- Sharmaine Reintar
- Department of Internal Medicine, Division of Endocrinology and Diabetology and Endocrinology Lab Platform, Medical University of Graz, 8036 Graz, Austria
| | - Magdalena Pöchhacker
- Department of Food Chemistry and Toxicology, Faculty of Chemistry, University of Vienna, 1090 Vienna, Austria
| | - Anna Obermayer
- Department of Internal Medicine, Division of Endocrinology and Diabetology and Endocrinology Lab Platform, Medical University of Graz, 8036 Graz, Austria
| | - Katharina Eberhard
- Center for Medical Research, Core Facility Computational Bioanalytics, Medical University of Graz, 8010 Graz, Austria
| | - Andreas Zirlik
- Department of Internal Medicine, Division of Cardiology, Medical University of Graz, 8036 Graz, Austria
| | - Nicolas Verheyen
- Department of Internal Medicine, Division of Cardiology, Medical University of Graz, 8036 Graz, Austria
| | - Dirk von Lewinski
- Department of Internal Medicine, Division of Cardiology, Medical University of Graz, 8036 Graz, Austria
| | - Daniel Scherr
- Department of Internal Medicine, Division of Cardiology, Medical University of Graz, 8036 Graz, Austria
| | - Barbara Hutz
- Department of Internal Medicine, Division of Endocrinology and Diabetology and Endocrinology Lab Platform, Medical University of Graz, 8036 Graz, Austria
| | - Christoph W Haudum
- Department of Internal Medicine, Division of Endocrinology and Diabetology and Endocrinology Lab Platform, Medical University of Graz, 8036 Graz, Austria
| | - Thomas R Pieber
- Department of Internal Medicine, Division of Endocrinology and Diabetology and Endocrinology Lab Platform, Medical University of Graz, 8036 Graz, Austria
| | - Harald Sourij
- Department of Internal Medicine, Division of Endocrinology and Diabetology and Endocrinology Lab Platform, Medical University of Graz, 8036 Graz, Austria
| | - Barbara Obermayer-Pietsch
- Department of Internal Medicine, Division of Endocrinology and Diabetology and Endocrinology Lab Platform, Medical University of Graz, 8036 Graz, Austria
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5
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Irilouzadian R, Afaghi S, Esmaeili Tarki F, Rahimi F, Malekpour Alamadari N. Urinary c-peptide creatinine ratio (UCPCR) as a predictor of coronary artery disease in type 1 diabetes mellitus. Endocrinol Diabetes Metab 2023; 6:e413. [PMID: 36808709 PMCID: PMC10164436 DOI: 10.1002/edm2.413] [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: 12/14/2022] [Revised: 01/28/2023] [Accepted: 02/05/2023] [Indexed: 02/23/2023] Open
Abstract
BACKGROUND Elevated C-peptide has been suggested as a risk factor for coronary artery disease (CAD). Elevated urinary C-peptide to creatinine ratio (UCPCR) as an alternative measurement is shown to be related to insulin secretion dysfunction; however, data regarding UCPCR predictive value for CAD in diabetes mellitus (DM) are scarce. Therefore, we aimed to assess the UCPCR association with CAD in type 1 DM (T1DM) patients. METHODS 279 patients previously diagnosed with T1DM included and categorized into two groups of CAD (n = 84) and without-CAD (n = 195). Furthermore, each group was divided into obese (body mass index (BMI) ≥ 30) and non-obese (BMI < 30) groups. Four models utilizing the binary logistic regression were designed to evaluate the role of UCPCR in CAD adjusted for well-known risk factors and mediators. RESULTS Median level of UCPCR was higher in CAD group compared to non-CAD group (0.07 vs. 0.04, respectively). Also, the well-acknowledged risk factors including being active smoker, hypertension, duration of diabetes, and body mass index (BMI) as well as higher levels of haemoglobin A1C (HbA1C), total cholesterol (TC), low-density lipoprotein (LDL) and estimated glomeruli filtration rate (e-GFR) had more significant pervasiveness in CAD patients. Based on multiple adjustments by logistic regression, UCPCR was a strong risk factor of CAD among T1DM patients independent of hypertension, demographic variables (gender, age, smoking, alcohol consumption), diabetes-related factors (diabetes duration, FBS, HbA1C), lipid profile (TC, LDL, HDL, TG) and renal-related indicators (creatinine, e-GFR, albuminuria, uric acid) in both patients with BMI≥30 and BMI < 30. CONCLUSION UCPCR is associated with clinical CAD, independent of CAD classic risk factors, glycaemic control, insulin resistance and BMI in type 1 DM patients.
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Affiliation(s)
- Rana Irilouzadian
- Burn Research Center, Iran university of medical sciences, Tehran, Iran
| | - Siamak Afaghi
- Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Farzad Esmaeili Tarki
- Research institute of internal medicine, Shahid Modarres hospital, Shahid Beheshti university of medical sciences, Tehran, Iran
| | - Fatemehsadat Rahimi
- Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases, Masih Daneshvari Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Nasser Malekpour Alamadari
- Department of Surgery, Clinical Research and Development Center, Shahid Modarres Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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7
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Zhou W, Li J, Yuan X, Wang W, Zhou H, Zhang H, Ye S. Application of urine C-peptide creatinine ratio in type 2 diabetic patients with different levels of renal function. Front Endocrinol (Lausanne) 2022; 13:1052794. [PMID: 36465621 PMCID: PMC9712960 DOI: 10.3389/fendo.2022.1052794] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Accepted: 10/28/2022] [Indexed: 11/18/2022] Open
Abstract
OBJECTIVE This study aims to investigate the effect of single urine C peptide/creatinine (UCPCR) in assessing the islet β Cell function of type 2 diabetes mellitus (T2DM) patients with different renal function. METHODS A total of 85 T2DM patients were recruited in this study, all the patients were assigned to one group with estimated glomerular filtration rate (eGFR)≤60 ml·min-1·1.73 m-2 and another group complicated with eGFR>60 ml·min-1·1.73 m-2. Serum creatinine, urine creatinine, serum fasting C-peptide (FCP), fasting blood glucose (FBG), glycosylated hemoglobin (HbA1C) and 24-hour urinary C-peptide (24hUCP) were measured. The modified homeostasis model assessment-islet β cell function [HOMA-islet (CP-DM)], the modified homeostasis model assessment-insulin resistance [HOMA-IR(CP)] and UCPCR were calculated. RESULTS When compared with group eGFR ≤60 ml·min-1·1.73 m-2, the levels of UCPCR, FCP, the modified HOMA-IR(CP) and HOMA-islet (CP-DM) were promoted and the concentrations of HbA1C, FPG, creatinine were decreased in the patients of eGFR>60 ml·min-1·1.73 m-2 (P<0.05); FCP was uncorrelated with 24hUCP while associated with UCPCR in the patients of eGFR ≤ 60 ml·min-1·1.73 m-2; UCPCR was positively correlated with FCP and HOMA-IR(CP) in the T2DM patients with different levels of renal function; the cut-off (UCPCR ≤ 1.13 nmol/g) had 88.37% sensitivity and 95.24% specificity [95% confidence interval (CI):0.919-0.997] for identifying severe insulin deficiency in T2DM patients[area under the curve (AUC) 0.978]. CONCLUSION UCPCR can be used to evaluate islets β Cell function in T2DM patients with different renal function status.
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Affiliation(s)
- Wan Zhou
- Department of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- *Correspondence: Wan Zhou,
| | - Jie Li
- Anhui Provincial Hospital, Affiliated to Anhui Medical University, Hefei, China
| | - Xiaojing Yuan
- The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Wei Wang
- Department of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Huanran Zhou
- Department of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Haoqiang Zhang
- Department of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Shandong Ye
- Department of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
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