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Corona-Meraz FI, Vázquez-Del Mercado M, Sandoval-García F, Robles-De Anda JA, Tovar-Cuevas AJ, Rosales-Gómez RC, Guzmán-Ornelas MO, González-Inostroz D, Peña-Nava M, Martín-Márquez BT. Biomarkers in Systemic Lupus Erythematosus along with Metabolic Syndrome. J Clin Med 2024; 13:1988. [PMID: 38610754 PMCID: PMC11012563 DOI: 10.3390/jcm13071988] [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: 02/29/2024] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 04/14/2024] Open
Abstract
Metabolic syndrome (MetS) is a group of physiological abnormalities characterized by obesity, insulin resistance (IR), and hypertriglyceridemia, which carry the risk of developing cardiovascular disease (CVD) and type 2 diabetes (T2D). Immune and metabolic alterations have been observed in MetS and are associated with autoimmune development. Systemic lupus erythematosus (SLE) is an autoimmune disease caused by a complex interaction of environmental, hormonal, and genetic factors and hyperactivation of immune cells. Patients with SLE have a high prevalence of MetS, in which elevated CVD is observed. Among the efforts of multidisciplinary healthcare teams to make an early diagnosis, a wide variety of factors have been considered and associated with the generation of biomarkers. This review aimed to elucidate some primary biomarkers and propose a set of assessments to improve the projection of the diagnosis and evolution of patients. These biomarkers include metabolic profiles, cytokines, cardiovascular tests, and microRNAs (miRs), which have been observed to be dysregulated in these patients and associated with outcomes.
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Affiliation(s)
- Fernanda Isadora Corona-Meraz
- Multidisciplinary Health Research Center, Department of Biomedical Sciences, University Center of Tonala, University of Guadalajara, Guadalajara 45425, Jalisco, Mexico; (A.-J.T.-C.); (R.-C.R.-G.); (M.-O.G.-O.)
- Department of Molecular Biology and Genomics, Institute of Rheumatology and Musculoskeletal System Research, University Center of Health Sciences, University of Guadalajara, Guadalajara 44340, Jalisco, Mexico; (M.V.-D.M.); (F.S.-G.); (J.-A.R.-D.A.); (D.G.-I.); (M.P.-N.)
| | - Mónica Vázquez-Del Mercado
- Department of Molecular Biology and Genomics, Institute of Rheumatology and Musculoskeletal System Research, University Center of Health Sciences, University of Guadalajara, Guadalajara 44340, Jalisco, Mexico; (M.V.-D.M.); (F.S.-G.); (J.-A.R.-D.A.); (D.G.-I.); (M.P.-N.)
- Rheumatology Service, Internal Medicine Division, Civil Hospital of Guadalajara “Dr. Juan I. Menchaca”, Guadalajara 44340, Jalisco, Mexico
- Academic Group UDG-CA-703, “Immunology and Rheumatology”, University Center of Health Sciences, University of Guadalajara, Guadalajara 44340, Jalisco, Mexico
| | - Flavio Sandoval-García
- Department of Molecular Biology and Genomics, Institute of Rheumatology and Musculoskeletal System Research, University Center of Health Sciences, University of Guadalajara, Guadalajara 44340, Jalisco, Mexico; (M.V.-D.M.); (F.S.-G.); (J.-A.R.-D.A.); (D.G.-I.); (M.P.-N.)
- Academic Group UDG-CA-703, “Immunology and Rheumatology”, University Center of Health Sciences, University of Guadalajara, Guadalajara 44340, Jalisco, Mexico
| | - Jesus-Aureliano Robles-De Anda
- Department of Molecular Biology and Genomics, Institute of Rheumatology and Musculoskeletal System Research, University Center of Health Sciences, University of Guadalajara, Guadalajara 44340, Jalisco, Mexico; (M.V.-D.M.); (F.S.-G.); (J.-A.R.-D.A.); (D.G.-I.); (M.P.-N.)
| | - Alvaro-Jovanny Tovar-Cuevas
- Multidisciplinary Health Research Center, Department of Biomedical Sciences, University Center of Tonala, University of Guadalajara, Guadalajara 45425, Jalisco, Mexico; (A.-J.T.-C.); (R.-C.R.-G.); (M.-O.G.-O.)
| | - Roberto-Carlos Rosales-Gómez
- Multidisciplinary Health Research Center, Department of Biomedical Sciences, University Center of Tonala, University of Guadalajara, Guadalajara 45425, Jalisco, Mexico; (A.-J.T.-C.); (R.-C.R.-G.); (M.-O.G.-O.)
| | - Milton-Omar Guzmán-Ornelas
- Multidisciplinary Health Research Center, Department of Biomedical Sciences, University Center of Tonala, University of Guadalajara, Guadalajara 45425, Jalisco, Mexico; (A.-J.T.-C.); (R.-C.R.-G.); (M.-O.G.-O.)
| | - Daniel González-Inostroz
- Department of Molecular Biology and Genomics, Institute of Rheumatology and Musculoskeletal System Research, University Center of Health Sciences, University of Guadalajara, Guadalajara 44340, Jalisco, Mexico; (M.V.-D.M.); (F.S.-G.); (J.-A.R.-D.A.); (D.G.-I.); (M.P.-N.)
| | - Miguel Peña-Nava
- Department of Molecular Biology and Genomics, Institute of Rheumatology and Musculoskeletal System Research, University Center of Health Sciences, University of Guadalajara, Guadalajara 44340, Jalisco, Mexico; (M.V.-D.M.); (F.S.-G.); (J.-A.R.-D.A.); (D.G.-I.); (M.P.-N.)
| | - Beatriz-Teresita Martín-Márquez
- Department of Molecular Biology and Genomics, Institute of Rheumatology and Musculoskeletal System Research, University Center of Health Sciences, University of Guadalajara, Guadalajara 44340, Jalisco, Mexico; (M.V.-D.M.); (F.S.-G.); (J.-A.R.-D.A.); (D.G.-I.); (M.P.-N.)
- Academic Group UDG-CA-703, “Immunology and Rheumatology”, University Center of Health Sciences, University of Guadalajara, Guadalajara 44340, Jalisco, Mexico
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Wu R, Zhang J, Zou G, Li S, Wang J, Li X, Xu J. Diabetes Mellitus and Thyroid Cancers: Risky Correlation, Underlying Mechanisms and Clinical Prevention. Diabetes Metab Syndr Obes 2024; 17:809-823. [PMID: 38380275 PMCID: PMC10878320 DOI: 10.2147/dmso.s450321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 02/08/2024] [Indexed: 02/22/2024] Open
Abstract
The incidences of thyroid cancer and diabetes are rapidly increasing worldwide. The relationship between thyroid cancer and diabetes is a popular topic in medicine. Increasing evidence has shown that diabetes increases the risk of thyroid cancer to a certain extent. This mechanism may be related to genetic factors, abnormal thyroid-stimulating hormone secretion, oxidative stress injury, hyperinsulinemia, elevated insulin-like growth factor-1 levels, abnormal secretion of adipocytokines, and increased secretion of inflammatory factors and chemokines. This article reviews the latest research progress on the relationship between thyroid cancer and diabetes, including the association between diabetes and the risk of developing thyroid cancer, its underlying mechanisms, and potential anti-thyroid cancer effects of hypoglycemic drugs. It providing novel strategies for the prevention, treatment, and improving the prognosis of thyroid cancer.
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Affiliation(s)
- Rongqian Wu
- Department of Endocrinology and Metabolism, The 1 Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, People’s Republic of China
- Jiangxi Clinical Research Center for Endocrine and Metabolic Disease, Nanchang, People’s Republic of China
- Jiangxi Branch of National Clinical Research Center for Metabolic Disease, Nanchang, People’s Republic of China
| | - Junping Zhang
- Department of Endocrinology and Metabolism, The 1 Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, People’s Republic of China
- Jiangxi Clinical Research Center for Endocrine and Metabolic Disease, Nanchang, People’s Republic of China
- Jiangxi Branch of National Clinical Research Center for Metabolic Disease, Nanchang, People’s Republic of China
| | - Guilin Zou
- Department of Endocrinology and Metabolism, The 1 Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, People’s Republic of China
- Jiangxi Clinical Research Center for Endocrine and Metabolic Disease, Nanchang, People’s Republic of China
- Jiangxi Branch of National Clinical Research Center for Metabolic Disease, Nanchang, People’s Republic of China
| | - Shanshan Li
- Department of Endocrinology and Metabolism, The 1 Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, People’s Republic of China
| | - Jinying Wang
- Department of Endocrinology and Metabolism, The 1 Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, People’s Republic of China
| | - Xiaoxinlei Li
- Department of Endocrinology and Metabolism, The 1 Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, People’s Republic of China
| | - Jixiong Xu
- Department of Endocrinology and Metabolism, The 1 Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, People’s Republic of China
- Jiangxi Clinical Research Center for Endocrine and Metabolic Disease, Nanchang, People’s Republic of China
- Jiangxi Branch of National Clinical Research Center for Metabolic Disease, Nanchang, People’s Republic of China
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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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Pigeyre M, Gerstein H, Ahlqvist E, Hess S, Paré G. Identifying blood biomarkers for type 2 diabetes subtyping: a report from the ORIGIN trial. Diabetologia 2023; 66:1045-1051. [PMID: 36854916 DOI: 10.1007/s00125-023-05887-7] [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: 10/05/2022] [Accepted: 01/18/2023] [Indexed: 03/02/2023]
Abstract
AIMS/HYPOTHESIS Individuals with diabetes can be clustered into five subtypes using up to six routinely measured clinical variables. We hypothesised that circulating protein levels might be used to distinguish between these subtypes. We recently used five of these six variables to categorise 7017 participants from the Outcome Reduction with an Initial Glargine Intervention (ORIGIN) trial into these subtypes: severe autoimmune diabetes (SAID, n=241), severe insulin-deficient diabetes (SIDD, n=1594), severe insulin-resistant diabetes (SIRD, n=914), mild obesity-related diabetes (MOD, n=1595) and mild age-related diabetes (MARD, n=2673). METHODS Forward-selection logistic regression models were used to identify a subset of 233 cardiometabolic protein biomarkers that were independent determinants of one subtype vs the others. We then assessed the performance of adding identified biomarkers (one after one, from the most discriminant to the least) to predict each subtype vs the others using area under the receiver operating characteristic curve (AUC ROC). Models were adjusted for age, sex, ethnicity, C-peptide level, diabetes duration and glucose-lowering medication usage at blood collection. RESULTS A total of 25 biomarkers were independent determinants of subtypes, including 13 for SIDD, 2 for SIRD, 7 for MOD and 11 for MARD (all p<4.3 × 10-5). The performance of the biomarker sets (comprising 1 to 25 biomarkers), assessed through the AUC ROC, ranged from 0.611 to 0.734, 0.723 to 0.861, 0.672 to 0.742, and 0.651 to 0.751, for SIDD, SIRD, MOD and MARD, respectively. No biomarkers other than GAD antibodies were determinants of SAID. CONCLUSIONS/INTERPRETATION We identified 25 serum biomarkers, as independent determinants of type 2 diabetes subtypes, that could be combined into a diagnostic test for subtyping. TRIAL REGISTRATION ORIGIN trial, ClinicalTrials.gov NCT00069784.
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Affiliation(s)
- Marie Pigeyre
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada.
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada.
- Department of Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, ON, Canada.
| | - Hertzel Gerstein
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada
- Department of Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, ON, Canada
- Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, ON, Canada
| | - Emma Ahlqvist
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Sibylle Hess
- Global Medical Diabetes, Sanofi, Frankfurt, Germany
| | - Guillaume Paré
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada
- Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, ON, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, ON, Canada
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