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Zhang R, Feng Y, Nie P, Wang W, Wu H, Wan X, Xu H, Fu F. Polystyrene microplastics disturb maternal glucose homeostasis and induce adverse pregnancy outcomes. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 279:116492. [PMID: 38795415 DOI: 10.1016/j.ecoenv.2024.116492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 05/13/2024] [Accepted: 05/20/2024] [Indexed: 05/28/2024]
Abstract
Pregnant women are a special group that is sensitive to adverse external stimuli, causing metabolic abnormalities and adverse pregnancy outcomes. Microplastics (MPs), an environmental pollutant widely used in various fields, can induce a variety of toxic responses in mammals. Recent studies verified an association between MPs and metabolic disorders. Our research built a gestational mouse model in which polystyrene microplastics (PS-MPs) of 1 μm size were consumed at concentrations of 0.1, 1, and 10 mg/L during pregnancy. Results indicated that PS-MPs induced placental malfunction and fetal growth retardation. Significant glucose disorders, decreased liver function, hepatic inflammation, and oxidative stress were also observed after PS-MPs exposure. The hepatic SIRT1/IRS1/PI3K pathway was inhibited in the 10 mg/L PS-MPs exposure group. Our study found that PS-MPs activated inflammatory response and oxidative stress by increasing hepatic lipopolysaccharide (LPS) that inhibited the hepatic SIRT1/IRS1/PI3K pathway, ultimately leading to insulin resistance, glucose metabolism disorders, and adverse pregnancy outcomes. This study provides a basis for preventing environment-related gestational diabetes and concomitant adverse pregnancy outcomes.
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Affiliation(s)
- Ruiying Zhang
- The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330000, China; State Key Laboratory of Food Science and Resources, Nanchang University, Nanchang 330047, China
| | - Yueying Feng
- The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330000, China; State Key Laboratory of Food Science and Resources, Nanchang University, Nanchang 330047, China
| | - Penghui Nie
- State Key Laboratory of Food Science and Resources, Nanchang University, Nanchang 330047, China
| | - Wanzhen Wang
- The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330000, China
| | - Hua Wu
- The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330000, China; State Key Laboratory of Food Science and Resources, Nanchang University, Nanchang 330047, China
| | - Xianxian Wan
- The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330000, China
| | - Hengyi Xu
- State Key Laboratory of Food Science and Resources, Nanchang University, Nanchang 330047, China.
| | - Fen Fu
- The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330000, China.
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2
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Abedimanesh N, Asghari S, Mohammadnejad K, Daneshvar Z, Rahmani S, Shokoohi S, Farzaneh AH, Hosseini SH, Jafari Anarkooli I, Noubarani M, Andalib S, Eskandari MR, Motlagh B. The anti-diabetic effects of betanin in streptozotocin-induced diabetic rats through modulating AMPK/SIRT1/NF-κB signaling pathway. Nutr Metab (Lond) 2021; 18:92. [PMID: 34656137 PMCID: PMC8520181 DOI: 10.1186/s12986-021-00621-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 10/03/2021] [Indexed: 11/21/2022] Open
Abstract
Background In the last few years, the effects of bioactive food components have received much attention because of their beneficial effects including decreasing inflammation, scavenging free radicals, and regulating cell signaling pathways. Betanin as a potent antioxidant has been previously reported to exhibit anti diabetic effects. The present study aimed to evaluate the effects of betanin on glycemic control, lipid profile, hepatic function tests, as well as the gene expression levels of 5′ adenosine monophosphate‑activated protein kinase (AMPK), sirtuin-1 (SIRT1), and nuclear factor kappa B (NF‑κB) in streptozocin (STZ) induced diabetic rats.
Methods Diabetes was induced in male Sprague–Dawley rats by intraperitoneal administration of STZ. Different doses of betanin (10, 20 and 40 mg/kg.b.w) was administered to diabetic rats for 28 days. Fasting blood glucose and serum insulin were measured. The histopathology of liver and pancreas tissue evaluated. Real-time PCR was performed to assess gene expression levels. Results Treatment of diabetic rats with betanin (10 and 20 mg/kg.b.w) reduced FBG levels compared to the control diabetic rats (P < 0.001). Betanin at the dose of 20 mg/kg.b.w was most effective in increasing serum insulin levels (P < 0.001) improving glucose tolerance test (GTT) as well as improvement in lipid profile and liver enzymes levels. According to histopathologic assay, different damages induced by STZ to liver and pancreas tissues was largely eliminated by treatment with 10 and 20 mg/kg.b.w of betanin. Betanin also significantly upregulated the AMPK and SIRT1 and downregulated the NF-κB mRNA expression compared to the diabetic control rats (P < 0.05). Conclusion Betanin could modulate AMPK/SIRT1/NF-κB signaling pathway and this may be one of its anti-diabetic molecular mechanisms.
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Affiliation(s)
- Nasim Abedimanesh
- Department of Nutrition, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran
| | - Somayyeh Asghari
- Department of Clinical Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran
| | - Kosar Mohammadnejad
- Department of Pharmacology and Toxicology, School of Pharmacy, Zanjan University of Medical Sciences, Zanjan, Iran
| | - Zahra Daneshvar
- Department of Pharmacology and Toxicology, School of Pharmacy, Zanjan University of Medical Sciences, Zanjan, Iran
| | - Soudeh Rahmani
- Department of Pharmacology and Toxicology, School of Pharmacy, Zanjan University of Medical Sciences, Zanjan, Iran
| | - Samaneh Shokoohi
- Department of Pharmacology and Toxicology, School of Pharmacy, Zanjan University of Medical Sciences, Zanjan, Iran
| | - Amir Hasan Farzaneh
- Department of Pharmacology and Toxicology, School of Pharmacy, Zanjan University of Medical Sciences, Zanjan, Iran
| | - Seyed Hojjat Hosseini
- Department of Physiology and Pharmacology, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran
| | - Iraj Jafari Anarkooli
- Department of Anatomical Sciences, School of Medicine, Zanjan University of Medical Science, Zanjan, Iran
| | - Maryam Noubarani
- Department of Pharmacology and Toxicology, School of Pharmacy, Zanjan University of Medical Sciences, Zanjan, Iran
| | - Sina Andalib
- Department of Pharmacology and Toxicology, School of Pharmacy, Zanjan University of Medical Sciences, Zanjan, Iran
| | - Mohammad Reza Eskandari
- Department of Pharmacology and Toxicology, School of Pharmacy, Zanjan University of Medical Sciences, Zanjan, Iran. .,Zanjan Pharmaceutical Nanotechnology Research Center (ZPNRC), Zanjan University of Medical Science, Zanjan, Iran.
| | - Behrooz Motlagh
- Department of Clinical Biochemistry, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran.
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3
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Kuo FY, Cheng KC, Li Y, Cheng JT. Oral glucose tolerance test in diabetes, the old method revisited. World J Diabetes 2021; 12:786-793. [PMID: 34168728 PMCID: PMC8192259 DOI: 10.4239/wjd.v12.i6.786] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 02/24/2021] [Accepted: 05/19/2021] [Indexed: 02/06/2023] Open
Abstract
The oral glucose tolerance test (OGTT) has been widely used both in clinics and in basic research for a long time. It is applied to diagnose impaired glucose tolerance and/or type 2 diabetes mellitus in individuals. Additionally, it has been employed in research to investigate glucose utilization and insulin sensitivity in animals. The main aim of each was quite different, and the details are also somewhat varied. However, the time or duration of the OGTT was the same, using the 2-h post-glucose load glycemia in both, following the suggestions of the American Diabetes Association. Recently, the use of 30-min or 1-h post-glucose load glycemia in clinical practice has been recommended by several studies. In this review article, we describe this new view and suggest perspectives for the OGTT. Additionally, quantification of the glucose curve in basic research is also discussed. Unlike in clinical practice, the incremental area under the curve is not suitable for use in the studies involving animals receiving repeated treatments or chronic treatment. We discuss the potential mechanisms in detail. Moreover, variations between bench and bedside in the application of the OGTT are introduced. Finally, the newly identified method for the OGTT must achieve a recommendation from the American Diabetes Association or another official unit soon. In conclusion, we summarize the recent reports regarding the OGTT and add some of our own perspectives, including machine learning and others.
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Affiliation(s)
- Feng Yu Kuo
- Cardiovascular Center, Veterans General Hospital, Kaohsiung 82445, Taiwan
| | - Kai-Chun Cheng
- Department of Pharmacy, College of Pharmacy and Health Care, Tajen University, Pingtung 90741, Taiwan
- Pharmacological Department of Herbal Medicine and Department of Psychosomatic Internal Medicine, Graduate School of Medical and Dental Sciences, Kagoshima University, Kagoshima 890-8544, Japan
| | - Yingxiao Li
- Department of Nursing, Tzu Chi University of Science and Technology, Hualien 973302, Taiwan
| | - Juei-Tang Cheng
- Department of Medical Research, Chi-Mei Medical Center, Tainan 71004, Taiwan
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4
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Obura M, Beulens JWJ, Slieker R, Koopman ADM, Hoekstra T, Nijpels G, Elders P, Dekker JM, Koivula RW, Kurbasic A, Laakso M, Hansen TH, Ridderstråle M, Hansen T, Pavo I, Forgie I, Jablonka B, Ruetten H, Mari A, McCarthy MI, Walker M, McDonald TJ, Perry MH, Pearson ER, Franks PW, 't Hart LM, Rutters F. Clinical profiles of post-load glucose subgroups and their association with glycaemic traits over time: An IMI-DIRECT study. Diabet Med 2021; 38:e14428. [PMID: 33067862 DOI: 10.1111/dme.14428] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 09/10/2020] [Accepted: 10/14/2020] [Indexed: 12/11/2022]
Abstract
AIM To examine the hypothesis that, based on their glucose curves during a seven-point oral glucose tolerance test, people at elevated type 2 diabetes risk can be divided into subgroups with different clinical profiles at baseline and different degrees of subsequent glycaemic deterioration. METHODS We included 2126 participants at elevated type 2 diabetes risk from the Diabetes Research on Patient Stratification (IMI-DIRECT) study. Latent class trajectory analysis was used to identify subgroups from a seven-point oral glucose tolerance test at baseline and follow-up. Linear models quantified the associations between the subgroups with glycaemic traits at baseline and 18 months. RESULTS At baseline, we identified four glucose curve subgroups, labelled in order of increasing peak levels as 1-4. Participants in Subgroups 2-4, were more likely to have higher insulin resistance (homeostatic model assessment) and a lower Matsuda index, than those in Subgroup 1. Overall, participants in Subgroups 3 and 4, had higher glycaemic trait values, with the exception of the Matsuda and insulinogenic indices. At 18 months, change in homeostatic model assessment of insulin resistance was higher in Subgroup 4 (β = 0.36, 95% CI 0.13-0.58), Subgroup 3 (β = 0.30; 95% CI 0.10-0.50) and Subgroup 2 (β = 0.18; 95% CI 0.04-0.32), compared to Subgroup 1. The same was observed for C-peptide and insulin. Five subgroups were identified at follow-up, and the majority of participants remained in the same subgroup or progressed to higher peak subgroups after 18 months. CONCLUSIONS Using data from a frequently sampled oral glucose tolerance test, glucose curve patterns associated with different clinical characteristics and different rates of subsequent glycaemic deterioration can be identified.
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Affiliation(s)
- M Obura
- Epidemiology and Data Science, Amsterdam Public Health research institute, VU University Medical Center, Amsterdam, The Netherlands
| | - J W J Beulens
- Epidemiology and Data Science, Amsterdam Public Health research institute, VU University Medical Center, Amsterdam, The Netherlands
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - R Slieker
- Epidemiology and Data Science, Amsterdam Public Health research institute, VU University Medical Center, Amsterdam, The Netherlands
- Department of Cell and Chemical Biology, Leiden University Medical Centre, Leiden, The Netherlands
| | - A D M Koopman
- Epidemiology and Data Science, Amsterdam Public Health research institute, VU University Medical Center, Amsterdam, The Netherlands
| | - T Hoekstra
- Epidemiology and Data Science, Amsterdam Public Health research institute, VU University Medical Center, Amsterdam, The Netherlands
- Department of Health Sciences, Faculty of Earth and Life Sciences, VU University, Amsterdam, The Netherlands
| | - G Nijpels
- Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, VU University Medical Centre, Amsterdam, The Netherlands
| | - P Elders
- Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, VU University Medical Centre, Amsterdam, The Netherlands
| | - J M Dekker
- Epidemiology and Data Science, Amsterdam Public Health research institute, VU University Medical Center, Amsterdam, The Netherlands
| | - R W Koivula
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden
- Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), University of Oxford, Oxford, UK
| | - A Kurbasic
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden
| | - M Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Finland
| | - T H Hansen
- The Novo Nordisk Foundation Centre for Basic Metabolic Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Cardiology and Endocrinology, Slagelse Hospital, Slagelse, Denmark
| | - M Ridderstråle
- The Novo Nordisk Foundation Centre for Basic Metabolic Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - T Hansen
- The Novo Nordisk Foundation Centre for Basic Metabolic Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - I Pavo
- Eli Lilly Regional Operations GmbH, Vienna, Austria
| | - I Forgie
- Division of Cardiovascular & Diabetes Medicine, Medical Research Institute, University of Dundee, Dundee, UK
| | - B Jablonka
- Sanofi-Aventis Deutschland GmbH, R&D, Frankfurt am Main, Germany
| | - H Ruetten
- Sanofi-Aventis Deutschland GmbH, R&D, Frankfurt am Main, Germany
| | - A Mari
- Institute of Biomedical Engineering, National Research Council, Padova, Italy
| | - M I McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), University of Oxford, Oxford, UK
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - M Walker
- Institute of Cellular Medicine (Diabetes), Newcastle University, Newcastle upon Tyne, UK
| | - T J McDonald
- NIHR Exeter Clinical Research Facility, University of Exeter Medical School and Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - M H Perry
- Department of Blood Sciences, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - E R Pearson
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - P W Franks
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden
- Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), University of Oxford, Oxford, UK
- Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
| | - L M 't Hart
- Epidemiology and Data Science, Amsterdam Public Health research institute, VU University Medical Center, Amsterdam, The Netherlands
- Department of Cell and Chemical Biology, Leiden University Medical Centre, Leiden, The Netherlands
- Department of Biomedical Data Sciences, Molecular Epidemiology Section, Leiden University Medical Centre, Leiden, The Netherlands
| | - F Rutters
- Epidemiology and Data Science, Amsterdam Public Health research institute, VU University Medical Center, Amsterdam, The Netherlands
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5
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Jagannathan R, Neves JS, Dorcely B, Chung ST, Tamura K, Rhee M, Bergman M. The Oral Glucose Tolerance Test: 100 Years Later. Diabetes Metab Syndr Obes 2020; 13:3787-3805. [PMID: 33116727 PMCID: PMC7585270 DOI: 10.2147/dmso.s246062] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 09/24/2020] [Indexed: 12/15/2022] Open
Abstract
For over 100 years, the oral glucose tolerance test (OGTT) has been the cornerstone for detecting prediabetes and type 2 diabetes (T2DM). In recent decades, controversies have arisen identifying internationally acceptable cut points using fasting plasma glucose (FPG), 2-h post-load glucose (2-h PG), and/or HbA1c for defining intermediate hyperglycemia (prediabetes). Despite this, there has been a steadfast global consensus of the 2-h PG for defining dysglycemic states during the OGTT. This article reviews the history of the OGTT and recent advances in its application, including the glucose challenge test and mathematical modeling for determining the shape of the glucose curve. Pitfalls of the FPG, 2-h PG during the OGTT, and HbA1c are considered as well. Finally, the associations between the 30-minute and 1-hour plasma glucose (1-h PG) levels derived from the OGTT and incidence of diabetes and its complications will be reviewed. The considerable evidence base supports modifying current screening and diagnostic recommendations with the use of the 1-h PG. Measurement of the 1-h PG level could increase the likelihood of identifying high-risk individuals when the pancreatic ß-cell function is substantially more intact with the added practical advantage of potentially replacing the conventional 2-h OGTT making it more acceptable in the clinical setting.
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Affiliation(s)
- Ram Jagannathan
- Division of Hospital Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - João Sérgio Neves
- Department of Surgery and Physiology, Cardiovascular Research and Development Center, Faculty of Medicine, University of Porto, Porto, Portugal
- Department of Endocrinology, Diabetes and Metabolism, Sa˜o Joa˜ o University Hospital Center, Porto, Portugal
| | - Brenda Dorcely
- NYU Grossman School of Medicine, Division of Endocrinology, Diabetes, Metabolism, New York, NY10016, USA
| | - Stephanie T Chung
- Diabetes, Obesity, and Endocrinology Branch, National Institute of Diabetes & Digestive & Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Kosuke Tamura
- Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD20892, USA
| | - Mary Rhee
- Emory University School of Medicine, Department of Medicine, Division of Endocrinology, Metabolism, and Lipids, Atlanta VA Health Care System, Atlanta, GA30322, USA
| | - Michael Bergman
- NYU Grossman School of Medicine, NYU Diabetes Prevention Program, Endocrinology, Diabetes, Metabolism, VA New York Harbor Healthcare System, Manhattan Campus, New York, NY10010, USA
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6
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Bergman M, Abdul-Ghani M, DeFronzo RA, Manco M, Sesti G, Fiorentino TV, Ceriello A, Rhee M, Phillips LS, Chung S, Cravalho C, Jagannathan R, Monnier L, Colette C, Owens D, Bianchi C, Del Prato S, Monteiro MP, Neves JS, Medina JL, Macedo MP, Ribeiro RT, Filipe Raposo J, Dorcely B, Ibrahim N, Buysschaert M. Review of methods for detecting glycemic disorders. Diabetes Res Clin Pract 2020; 165:108233. [PMID: 32497744 PMCID: PMC7977482 DOI: 10.1016/j.diabres.2020.108233] [Citation(s) in RCA: 96] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 05/19/2020] [Indexed: 02/07/2023]
Abstract
Prediabetes (intermediate hyperglycemia) consists of two abnormalities, impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) detected by a standardized 75-gram oral glucose tolerance test (OGTT). Individuals with isolated IGT or combined IFG and IGT have increased risk for developing type 2 diabetes (T2D) and cardiovascular disease (CVD). Diagnosing prediabetes early and accurately is critical in order to refer high-risk individuals for intensive lifestyle modification. However, there is currently no international consensus for diagnosing prediabetes with HbA1c or glucose measurements based upon American Diabetes Association (ADA) and the World Health Organization (WHO) criteria that identify different populations at risk for progressing to diabetes. Various caveats affecting the accuracy of interpreting the HbA1c including genetics complicate this further. This review describes established methods for detecting glucose disorders based upon glucose and HbA1c parameters as well as novel approaches including the 1-hour plasma glucose (1-h PG), glucose challenge test (GCT), shape of the glucose curve, genetics, continuous glucose monitoring (CGM), measures of insulin secretion and sensitivity, metabolomics, and ancillary tools such as fructosamine, glycated albumin (GA), 1,5- anhydroglucitol (1,5-AG). Of the approaches considered, the 1-h PG has considerable potential as a biomarker for detecting glucose disorders if confirmed by additional data including health economic analysis. Whether the 1-h OGTT is superior to genetics and omics in providing greater precision for individualized treatment requires further investigation. These methods will need to demonstrate substantially superiority to simpler tools for detecting glucose disorders to justify their cost and complexity.
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Affiliation(s)
- Michael Bergman
- NYU School of Medicine, NYU Diabetes Prevention Program, Endocrinology, Diabetes, Metabolism, VA New York Harbor Healthcare System, Manhattan Campus, 423 East 23rd Street, Room 16049C, NY, NY 10010, USA.
| | - Muhammad Abdul-Ghani
- Division of Diabetes, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA.
| | - Ralph A DeFronzo
- Division of Diabetes, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA.
| | - Melania Manco
- Research Area for Multifactorial Diseases, Bambino Gesù Children Hospital, Rome, Italy.
| | - Giorgio Sesti
- Department of Clinical and Molecular Medicine, University of Rome Sapienza, Rome 00161, Italy
| | - Teresa Vanessa Fiorentino
- Department of Medical and Surgical Sciences, University Magna Græcia of Catanzaro, Catanzaro 88100, Italy.
| | - Antonio Ceriello
- Department of Cardiovascular and Metabolic Diseases, Istituto Ricerca Cura Carattere Scientifico Multimedica, Sesto, San Giovanni (MI), Italy.
| | - Mary Rhee
- Emory University School of Medicine, Department of Medicine, Division of Endocrinology, Metabolism, and Lipids, Atlanta VA Health Care System, Atlanta, GA 30322, USA.
| | - Lawrence S Phillips
- Emory University School of Medicine, Department of Medicine, Division of Endocrinology, Metabolism, and Lipids, Atlanta VA Health Care System, Atlanta, GA 30322, USA.
| | - Stephanie Chung
- Diabetes Endocrinology and Obesity Branch, National Institutes of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Celeste Cravalho
- Diabetes Endocrinology and Obesity Branch, National Institutes of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Ram Jagannathan
- Emory University School of Medicine, Department of Medicine, Division of Endocrinology, Metabolism, and Lipids, Atlanta VA Health Care System, Atlanta, GA 30322, USA.
| | - Louis Monnier
- Institute of Clinical Research, University of Montpellier, Montpellier, France.
| | - Claude Colette
- Institute of Clinical Research, University of Montpellier, Montpellier, France.
| | - David Owens
- Diabetes Research Group, Institute of Life Science, Swansea University, Wales, UK.
| | - Cristina Bianchi
- University Hospital of Pisa, Section of Metabolic Diseases and Diabetes, University Hospital, University of Pisa, Pisa, Italy.
| | - Stefano Del Prato
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.
| | - Mariana P Monteiro
- Endocrine, Cardiovascular & Metabolic Research, Unit for Multidisciplinary Research in Biomedicine (UMIB), University of Porto, Porto, Portugal; Institute of Biomedical Sciences Abel Salazar (ICBAS), University of Porto, Porto, Portugal.
| | - João Sérgio Neves
- Department of Surgery and Physiology, Cardiovascular Research and Development Center, Faculty of Medicine, University of Porto, Porto, Portugal; Department of Endocrinology, Diabetes and Metabolism, São João University Hospital Center, Porto, Portugal.
| | | | - Maria Paula Macedo
- CEDOC-Centro de Estudos de Doenças Crónicas, NOVA Medical School, Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Lisboa, Portugal; APDP-Diabetes Portugal, Education and Research Center (APDP-ERC), Lisboa, Portugal.
| | - Rogério Tavares Ribeiro
- Institute for Biomedicine, Department of Medical Sciences, University of Aveiro, APDP Diabetes Portugal, Education and Research Center (APDP-ERC), Aveiro, Portugal.
| | - João Filipe Raposo
- CEDOC-Centro de Estudos de Doenças Crónicas, NOVA Medical School, Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Lisboa, Portugal; APDP-Diabetes Portugal, Education and Research Center (APDP-ERC), Lisboa, Portugal.
| | - Brenda Dorcely
- NYU School of Medicine, Division of Endocrinology, Diabetes, Metabolism, NY, NY 10016, USA.
| | - Nouran Ibrahim
- NYU School of Medicine, Division of Endocrinology, Diabetes, Metabolism, NY, NY 10016, USA.
| | - Martin Buysschaert
- Department of Endocrinology and Diabetology, Université Catholique de Louvain, University Clinic Saint-Luc, Brussels, Belgium.
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7
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Meamar R, Amini M, Aminorroaya A, Nasri M, Abyar M, Feizi A. Severity of the metabolic syndrome as a predictor of prediabetes and type 2 diabetes in first degree relatives of type 2 diabetic patients: A 15-year prospective cohort study. World J Diabetes 2020; 11:202-212. [PMID: 32477456 PMCID: PMC7243485 DOI: 10.4239/wjd.v11.i5.202] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 03/16/2020] [Accepted: 03/24/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Type 2 diabetes mellitus (T2DM) has high morbidity and mortality worldwide, therefore there is of paramount importance to identify the risk factors in the populations at risk early in the course of illness. A strong correlation between severity of metabolic syndrome (MetS) and HbA1c, fasting insulin and insulin resistance has been reported. Accordingly, the MetS severity score (or MestS Z-score) can potentially be used to predict the risk of T2DM progression over time.
AIM To evaluate the association the of MestS Z-score in first degree relatives (FDRs) of T2DM with the risk of prediabetes and type 2 diabetes in future.
METHODS A prospective open cohort study was conducted between 2003-2018. At baseline, the sample comprised of 1766 FDRs of patients with T2DM who had a normal glucose tolerance test. Relative risk (RR) and 95% confidence interval were calculated based on logistic regression. The receiver-operator characteristic analysis and area under the curve based on MetS Z-score were used to evaluate the risk of prediabetes and diabetes among the FDR population.
RESULTS Baseline MetS Z-scores were associated with the its latest values (P < 0.0001). Compared with individuals who were T2DM free at the end of follow up, those who developed T2DM had higher MetS Z-score at baseline (P < 0.001). In multivariable logistic regression analyses for every unit elevation in MetS Z-score at the baseline, the RR for developing future T2DM and prediabetes was (RR = 1.94, RR = 3.84), (RR = 1.5, RR = 2.17) in total population and female group, respectively (P < 0.05). The associations remained significant after adjusting the potential confounding variables. A cut off value of 0.97 and 0.94 was defined in the receiver-operator characteristic curve based on the MetS Z-score for differentiating female patients with diabetes and prediabetes from the normal population, respectively.
CONCLUSION The MetS Z-score was associated with an increased risk of future T2DM. Appropriate interventions at earlier stages for preventing and attenuating MetS effects may be considered as an effective strategy for FDR as at-risk population.
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Affiliation(s)
- Rokhsareh Meamar
- Isfahan Endocrine and Metabolism Research Center, Isfahan Clinical Toxicology Research Center, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran
| | - Masoud Amini
- Isfahan Endocrine and Metabolism Research Center, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran
| | - Ashraf Aminorroaya
- Isfahan Endocrine and Metabolism Research Center, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran
| | - Maryam Nasri
- Grovemead Health Center, London NW4-3EB, United Kingdom
| | - Majid Abyar
- Isfahan Endocrine and Metabolism Research Center, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran
| | - Awat Feizi
- Isfahan Endocrine and Metabolism Research Center, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran
- Department of Biostatistics and Epidemiology, School of Health, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran
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Hulman A. Comment on Scholtens et al. Hyperglycemia and Adverse Pregnancy Outcome Follow-up Study (HAPO FUS): Maternal Glycemia and Childhood Glucose Metabolism. Diabetes Care 2019;42:381-392. Diabetes Care 2019; 42:e127. [PMID: 31221712 DOI: 10.2337/dc19-0650] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Affiliation(s)
- Adam Hulman
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
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