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Ajayi IO, Balogun WO, Olopade OB, Ajani GO, Soyoye DO, Bolarinwa OA, Olamoyegun MA, Alatishe-Muhammad BW, Odeniyi IA, Odukoya O, Fasanmade OA, Diyaolu FP, Otrofanowei E, Akase I, Agabi PO, Adejimi A, Ajetunmobi OA, Durowade KA, Gabriel-Alayode EO, Ibrahim AO, Ezekpo OO, Elegbede TO, Lamidi AO, Owolabi FA, Yusuf AO, Adetunji TA, Ogunmodede AJ, Ameen AH, Biliaminu AS, Nasiru S. Prevalence of haemoglobin A1c based dysglycaemia among adult community dwellers in selected states in Nigeria: a descriptive cross-sectional study. Front Endocrinol (Lausanne) 2023; 14:1192491. [PMID: 37547317 PMCID: PMC10399573 DOI: 10.3389/fendo.2023.1192491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 06/14/2023] [Indexed: 08/08/2023] Open
Abstract
Background Type 2 diabetes mellitus (T2DM) is a disease of public health importance globally with an increasing burden of undiagnosed pre-diabetes and diabetes in low- and middle-income countries, Nigeria in particular. Pre-diabetes and diabetes are established risk factors for cardiovascular complications. However, data are scanty on the current prevalence of these conditions in Nigeria, based on haemoglobin A1c (HbA1c) diagnosis as recommended by the WHO in 2009. We aimed to determine the prevalence of pre-diabetes, diabetes, and undiagnosed diabetes among the adult population of Nigeria using HbA1c. Methodology A cross-sectional, multi-site population study was carried out in selected states in Nigeria (namely, Ekiti, Lagos, Osun, Oyo, and Kwara states) involving 2,708 adults (≥18 years) in rural and urban community dwellers, without prior diagnosis of pre-diabetes or diabetes. Participants with ongoing acute or debilitating illnesses were excluded. Data were collected using an interviewer-administered pretested, semi-structured questionnaire. Socio-demographic, clinical (weight, height, blood pressure, etc.), and laboratory characteristics of participants including HbA1c were obtained. Data were analysed using STATA version 16. Results The mean age of participants was 48.1 ± 15.8 years, and 65.5% were female. The overall prevalence of pre-diabetes and undiagnosed diabetes was 40.5% and 10.7%, respectively, while the prevalence of high blood pressure was 36.7%. The prevalence of pre-diabetes was the highest in Lagos (48.1%) and the lowest in Ekiti (36.7%), while the prevalence of diabetes was the highest in Kwara (14.2%) and the lowest in Ekiti (10%). There was a significant association between age of the participants (p< 0.001), gender (p = 0.009), educational status (p = 0.008), occupation (p< 0.001), tribe (p = 0.004), marital status (p< 0.001), blood pressure (p< 0.001), and their diabetic or pre-diabetic status. Independent predictors of diabetes and pre-diabetes include excess weight gain, sedentary living, and ageing. Participants within the age group 45-54 years had the highest total prevalence (26.6%) of pre-diabetes and diabetes. Conclusion Over half of the respondents had pre-diabetes and diabetes, with a high prevalence of undiagnosed diabetes. A nationwide screening campaign will promote early detection of pre-diabetes and undiagnosed diabetes among adult Nigerians. Health education campaigns could be an effective tool in community settings to improve knowledge of the risk factors for diabetes to reduce the prevalence of dysglycaemia.
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Affiliation(s)
| | - William O. Balogun
- College of Medicine, University of Ibadan, Ibadan, Nigeria
- Department of Medicine, University College Hospital Ibadan, Ibadan, Nigeria
| | | | - Gbadebo O. Ajani
- College of Medicine and Health Sciences, Afe Babalola University, Ekiti, Nigeria
| | - David O. Soyoye
- College of Health Sciences, Obafemi Awolowo University, Ile-Ife, Nigeria
| | | | - Michael A. Olamoyegun
- Department of Medicine, Ladoke Akintola University of Technology, Ogbomosho, Nigeria
| | | | | | | | | | | | | | - Iorhen Akase
- College of Medicine, University of Lagos, Lagos, Nigeria
| | - Paul O. Agabi
- College of Medicine, University of Lagos, Lagos, Nigeria
| | | | | | - Kabir A. Durowade
- College of Medicine and Health Sciences, Afe Babalola University, Ekiti, Nigeria
| | | | - Azeez O. Ibrahim
- Department of Medicine, Federal Teaching Hospital Ido-Ekiti, Ido-Ekiti, Nigeria
| | - Okechukwu O. Ezekpo
- College of Medicine and Health Sciences, Afe Babalola University, Ekiti, Nigeria
| | - Toyin O. Elegbede
- College of Medicine and Health Sciences, Afe Babalola University, Ekiti, Nigeria
| | - Ayodeji O. Lamidi
- College of Health Sciences, Obafemi Awolowo University, Ile-Ife, Nigeria
| | | | - Adebimpe O. Yusuf
- College of Health Sciences, Obafemi Awolowo University, Ile-Ife, Nigeria
| | | | | | - Abolore H. Ameen
- College of Health Sciences, University of Ilorin, Ilorin, Nigeria
| | | | - Sanni Nasiru
- Department of Medicine, University of Ilorin Teaching Hospital, Ilorin, Nigeria
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Tura A, Grespan E, Göbl CS, Koivula RW, Franks PW, Pearson ER, Walker M, Forgie IM, Giordano GN, Pavo I, Ruetten H, Dermitzakis ET, McCarthy MI, Pedersen O, Schwenk JM, Adamski J, De Masi F, Tsirigos KD, Brunak S, Viñuela A, Mahajan A, McDonald TJ, Kokkola T, Vangipurapu J, Cederberg H, Laakso M, Rutters F, Elders PJM, Koopman ADM, Beulens JW, Ridderstråle M, Hansen TH, Allin KH, Hansen T, Vestergaard H, Mari A. Profiles of Glucose Metabolism in Different Prediabetes Phenotypes, Classified by Fasting Glycemia, 2-Hour OGTT, Glycated Hemoglobin, and 1-Hour OGTT: An IMI DIRECT Study. Diabetes 2021; 70:2092-2106. [PMID: 34233929 DOI: 10.2337/db21-0227] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 06/24/2021] [Indexed: 11/13/2022]
Abstract
Differences in glucose metabolism among categories of prediabetes have not been systematically investigated. In this longitudinal study, participants (N = 2,111) underwent a 2-h 75-g oral glucose tolerance test (OGTT) at baseline and 48 months. HbA1c was also measured. We classified participants as having isolated prediabetes defect (impaired fasting glucose [IFG], impaired glucose tolerance [IGT], or HbA1c indicative of prediabetes [IA1c]), two defects (IFG+IGT, IFG+IA1c, or IGT+IA1c), or all defects (IFG+IGT+IA1c). β-Cell function (BCF) and insulin sensitivity were assessed from OGTT. At baseline, in pooling of participants with isolated defects, they showed impairment in both BCF and insulin sensitivity compared with healthy control subjects. Pooled groups with two or three defects showed progressive further deterioration. Among groups with isolated defect, those with IGT showed lower insulin sensitivity, insulin secretion at reference glucose (ISRr), and insulin secretion potentiation (P < 0.002). Conversely, those with IA1c showed higher insulin sensitivity and ISRr (P < 0.0001). Among groups with two defects, we similarly found differences in both BCF and insulin sensitivity. At 48 months, we found higher type 2 diabetes incidence for progressively increasing number of prediabetes defects (odds ratio >2, P < 0.008). In conclusion, the prediabetes groups showed differences in type/degree of glucometabolic impairment. Compared with the pooled group with isolated defects, those with double or triple defect showed progressive differences in diabetes incidence.
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Affiliation(s)
- Andrea Tura
- CNR Institute of Neuroscience, Padova, Italy
| | | | - Christian S Göbl
- Division of Obstetrics and Feto-Maternal Medicine, Department of Obstetrics and Gynecology, Medical University of Vienna, Vienna, Austria
| | - Robert W Koivula
- Oxford Centre for Diabetes Endocrinology and Metabolism, University of Oxford, Oxford, U.K
- Genetic and Molecular Epidemiology, Department of Clinical Science, Lund University, Skåne University Hospital Malmö, Malmö, Sweden
| | - Paul W Franks
- Genetic and Molecular Epidemiology, Department of Clinical Science, Lund University, Skåne University Hospital Malmö, Malmö, Sweden
| | - Ewan R Pearson
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, Scotland, U.K
| | - Mark Walker
- Institute of Cellular Medicine, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, U.K
| | - Ian M Forgie
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, Scotland, U.K
| | - Giuseppe N Giordano
- Genetic and Molecular Epidemiology, Department of Clinical Science, Lund University, Skåne University Hospital Malmö, Malmö, Sweden
| | - Imre Pavo
- Eli Lilly Regional Operations Ges.m.b.H., Vienna, Austria
| | - Hartmut Ruetten
- CardioMetabolism & Respiratory Medicine, Boehringer Ingelheim International GmbH, Ingelheim/Rhein, Germany
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Mark I McCarthy
- Oxford Centre for Diabetes Endocrinology and Metabolism, University of Oxford, Oxford, U.K
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, U.K
| | - Oluf Pedersen
- Section of Metabolic Genetics, Novo Nordisk Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Jochen M Schwenk
- Affinity Proteomics, Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna, Sweden
| | - Jerzy Adamski
- Institute of Experimental Genetics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Federico De Masi
- Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
- Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Konstantinos D Tsirigos
- Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
- Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Søren Brunak
- Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
- Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ana Viñuela
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, U.K
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, U.K
| | - Timothy J McDonald
- Blood Sciences, Royal Devon and Exeter NHS Foundation Trust, Exeter, U.K
| | - Tarja Kokkola
- Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Jagadish Vangipurapu
- Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Henna Cederberg
- Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Markku Laakso
- Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Femke Rutters
- Department of Epidemiology and Data Science, Amsterdam Medical Centre, location VUMC, Amsterdam, the Netherlands
| | - Petra J M Elders
- Department of Epidemiology and Data Science, Amsterdam Medical Centre, location VUMC, Amsterdam, the Netherlands
| | - Anitra D M Koopman
- Department of Epidemiology and Data Science, Amsterdam Medical Centre, location VUMC, Amsterdam, the Netherlands
| | - Joline W Beulens
- Department of Epidemiology and Data Science, Amsterdam Medical Centre, location VUMC, Amsterdam, the Netherlands
| | - Martin Ridderstråle
- Department of Clinical Sciences, Diabetes & Endocrinology Unit, Lund University, Skåne University Hospital Malmö, Malmö, Sweden
| | - Tue H Hansen
- Section of Metabolic Genetics, Novo Nordisk Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Kristine H Allin
- Section of Metabolic Genetics, Novo Nordisk Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- Section of Metabolic Genetics, Novo Nordisk Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Henrik Vestergaard
- Section of Metabolic Genetics, Novo Nordisk Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
- Department of Medicine, Bornholms Hospital, Rønne, Denmark
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Jepson C, Hsu JY, Fischer MJ, Kusek JW, Lash JP, Ricardo AC, Schelling JR, Feldman HI. Incident Type 2 Diabetes Among Individuals With CKD: Findings From the Chronic Renal Insufficiency Cohort (CRIC) Study. Am J Kidney Dis 2019; 73:72-81. [PMID: 30177484 PMCID: PMC6309655 DOI: 10.1053/j.ajkd.2018.06.017] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Accepted: 06/12/2018] [Indexed: 01/15/2023]
Abstract
RATIONALE & OBJECTIVE Few studies have examined incident type 2 diabetes mellitus (T2DM) in chronic kidney disease (CKD). Our objective was to examine rates of and risk factors for T2DM in CKD, using several alternative measures of glycemic control. STUDY DESIGN Prospective cohort study. SETTING & PARTICIPANTS 1,713 participants with reduced glomerular filtration rates and without diabetes at baseline, enrolled in the Chronic Renal Insufficiency Cohort (CRIC) Study. PREDICTORS Measures of kidney function and damage, fasting blood glucose, hemoglobin A1c (HbA1c), HOMA-IR (homeostatic model assessment of insulin resistance), demographics, family history of diabetes mellitus (DM), smoking status, medication use, systolic blood pressure, triglyceride level, high-density lipoprotein cholesterol level, body mass index, and physical activity. OUTCOME Incident T2DM (defined as fasting blood glucose ≥ 126mg/dL or prescription of insulin or oral hypoglycemic agents). ANALYTICAL APPROACH Concordance between fasting blood glucose and HbA1c levels was assessed using κ. Cause-specific hazards modeling, treating death and end-stage kidney disease as competing events, was used to predict incident T2DM. RESULTS Overall T2DM incidence rate was 17.81 cases/1,000 person-years. Concordance between fasting blood glucose and HbA1c levels was low (κ for categorical versions of fasting blood glucose and HbA1c = 13%). Unadjusted associations of measures of kidney function and damage with incident T2DM were nonsignificant (P ≥ 0.4). In multivariable models, T2DM was significantly associated with fasting blood glucose level (P = 0.002) and family history of DM (P = 0.03). The adjusted association of HOMA-IR with T2DM was comparable to that of fasting blood glucose level; the association of HbA1c level was nonsignificant (P ≥ 0.1). Harrell's C for the models ranged from 0.62 to 0.68. LIMITATIONS Limited number of outcome events; predictors limited to measures taken at baseline. CONCLUSIONS The T2DM incidence rate among individuals with CKD is markedly higher than in the general population, supporting the need for greater vigilance in this population. Measures of glycemic control and family history of DM were independently associated with incident T2DM.
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Affiliation(s)
- Christopher Jepson
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA.
| | - Jesse Y Hsu
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA
| | - Michael J Fischer
- Department of Medicine, University of Illinois College of Medicine, Chicago, IL; Center of Innovation for Complex Chronic Healthcare, Edward Hines Jr VA Hospital, Hines, and Jesse Brown VAMC, Chicago, IL
| | - John W Kusek
- Division of Kidney, Urologic and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD
| | - James P Lash
- Department of Medicine, University of Illinois College of Medicine, Chicago, IL
| | - Ana C Ricardo
- Department of Medicine, University of Illinois College of Medicine, Chicago, IL
| | - Jeffrey R Schelling
- Division of Nephrology and Hypertension, Case Western Reserve University, Cleveland, OH
| | - Harold I Feldman
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA
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Richter B, Hemmingsen B, Metzendorf M, Takwoingi Y. Development of type 2 diabetes mellitus in people with intermediate hyperglycaemia. Cochrane Database Syst Rev 2018; 10:CD012661. [PMID: 30371961 PMCID: PMC6516891 DOI: 10.1002/14651858.cd012661.pub2] [Citation(s) in RCA: 78] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND Intermediate hyperglycaemia (IH) is characterised by one or more measurements of elevated blood glucose concentrations, such as impaired fasting glucose (IFG), impaired glucose tolerance (IGT) and elevated glycosylated haemoglobin A1c (HbA1c). These levels are higher than normal but below the diagnostic threshold for type 2 diabetes mellitus (T2DM). The reduced threshold of 5.6 mmol/L (100 mg/dL) fasting plasma glucose (FPG) for defining IFG, introduced by the American Diabetes Association (ADA) in 2003, substantially increased the prevalence of IFG. Likewise, the lowering of the HbA1c threshold from 6.0% to 5.7% by the ADA in 2010 could potentially have significant medical, public health and socioeconomic impacts. OBJECTIVES To assess the overall prognosis of people with IH for developing T2DM, regression from IH to normoglycaemia and the difference in T2DM incidence in people with IH versus people with normoglycaemia. SEARCH METHODS We searched MEDLINE, Embase, ClincialTrials.gov and the International Clinical Trials Registry Platform (ICTRP) Search Portal up to December 2016 and updated the MEDLINE search in February 2018. We used several complementary search methods in addition to a Boolean search based on analytical text mining. SELECTION CRITERIA We included prospective cohort studies investigating the development of T2DM in people with IH. We used standard definitions of IH as described by the ADA or World Health Organization (WHO). We excluded intervention trials and studies on cohorts with additional comorbidities at baseline, studies with missing data on the transition from IH to T2DM, and studies where T2DM incidence was evaluated by documents or self-report only. DATA COLLECTION AND ANALYSIS One review author extracted study characteristics, and a second author checked the extracted data. We used a tailored version of the Quality In Prognosis Studies (QUIPS) tool for assessing risk of bias. We pooled incidence and incidence rate ratios (IRR) using a random-effects model to account for between-study heterogeneity. To meta-analyse incidence data, we used a method for pooling proportions. For hazard ratios (HR) and odds ratios (OR) of IH versus normoglycaemia, reported with 95% confidence intervals (CI), we obtained standard errors from these CIs and performed random-effects meta-analyses using the generic inverse-variance method. We used multivariable HRs and the model with the greatest number of covariates. We evaluated the certainty of the evidence with an adapted version of the GRADE framework. MAIN RESULTS We included 103 prospective cohort studies. The studies mainly defined IH by IFG5.6 (FPG mmol/L 5.6 to 6.9 mmol/L or 100 mg/dL to 125 mg/dL), IFG6.1 (FPG 6.1 mmol/L to 6.9 mmol/L or 110 mg/dL to 125 mg/dL), IGT (plasma glucose 7.8 mmol/L to 11.1 mmol/L or 140 mg/dL to 199 mg/dL two hours after a 75 g glucose load on the oral glucose tolerance test, combined IFG and IGT (IFG/IGT), and elevated HbA1c (HbA1c5.7: HbA1c 5.7% to 6.4% or 39 mmol/mol to 46 mmol/mol; HbA1c6.0: HbA1c 6.0% to 6.4% or 42 mmol/mol to 46 mmol/mol). The follow-up period ranged from 1 to 24 years. Ninety-three studies evaluated the overall prognosis of people with IH measured by cumulative T2DM incidence, and 52 studies evaluated glycaemic status as a prognostic factor for T2DM by comparing a cohort with IH to a cohort with normoglycaemia. Participants were of Australian, European or North American origin in 41 studies; Latin American in 7; Asian or Middle Eastern in 50; and Islanders or American Indians in 5. Six studies included children and/or adolescents.Cumulative incidence of T2DM associated with IFG5.6, IFG6.1, IGT and the combination of IFG/IGT increased with length of follow-up. Cumulative incidence was highest with IFG/IGT, followed by IGT, IFG6.1 and IFG5.6. Limited data showed a higher T2DM incidence associated with HbA1c6.0 compared to HbA1c5.7. We rated the evidence for overall prognosis as of moderate certainty because of imprecision (wide CIs in most studies). In the 47 studies reporting restitution of normoglycaemia, regression ranged from 33% to 59% within one to five years follow-up, and from 17% to 42% for 6 to 11 years of follow-up (moderate-certainty evidence).Studies evaluating the prognostic effect of IH versus normoglycaemia reported different effect measures (HRs, IRRs and ORs). Overall, the effect measures all indicated an elevated risk of T2DM at 1 to 24 years of follow-up. Taking into account the long-term follow-up of cohort studies, estimation of HRs for time-dependent events like T2DM incidence appeared most reliable. The pooled HR and the number of studies and participants for different IH definitions as compared to normoglycaemia were: IFG5.6: HR 4.32 (95% CI 2.61 to 7.12), 8 studies, 9017 participants; IFG6.1: HR 5.47 (95% CI 3.50 to 8.54), 9 studies, 2818 participants; IGT: HR 3.61 (95% CI 2.31 to 5.64), 5 studies, 4010 participants; IFG and IGT: HR 6.90 (95% CI 4.15 to 11.45), 5 studies, 1038 participants; HbA1c5.7: HR 5.55 (95% CI 2.77 to 11.12), 4 studies, 5223 participants; HbA1c6.0: HR 10.10 (95% CI 3.59 to 28.43), 6 studies, 4532 participants. In subgroup analyses, there was no clear pattern of differences between geographic regions. We downgraded the evidence for the prognostic effect of IH versus normoglycaemia to low-certainty evidence due to study limitations because many studies did not adequately adjust for confounders. Imprecision and inconsistency required further downgrading due to wide 95% CIs and wide 95% prediction intervals (sometimes ranging from negative to positive prognostic factor to outcome associations), respectively.This evidence is up to date as of 26 February 2018. AUTHORS' CONCLUSIONS Overall prognosis of people with IH worsened over time. T2DM cumulative incidence generally increased over the course of follow-up but varied with IH definition. Regression from IH to normoglycaemia decreased over time but was observed even after 11 years of follow-up. The risk of developing T2DM when comparing IH with normoglycaemia at baseline varied by IH definition. Taking into consideration the uncertainty of the available evidence, as well as the fluctuating stages of normoglycaemia, IH and T2DM, which may transition from one stage to another in both directions even after years of follow-up, practitioners should be careful about the potential implications of any active intervention for people 'diagnosed' with IH.
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Affiliation(s)
- Bernd Richter
- Institute of General Practice, Medical Faculty of the Heinrich‐Heine‐University DüsseldorfCochrane Metabolic and Endocrine Disorders GroupPO Box 101007DüsseldorfGermany40001
| | - Bianca Hemmingsen
- Institute of General Practice, Medical Faculty of the Heinrich‐Heine‐University DüsseldorfCochrane Metabolic and Endocrine Disorders GroupPO Box 101007DüsseldorfGermany40001
| | - Maria‐Inti Metzendorf
- Institute of General Practice, Medical Faculty of the Heinrich‐Heine‐University DüsseldorfCochrane Metabolic and Endocrine Disorders GroupPO Box 101007DüsseldorfGermany40001
| | - Yemisi Takwoingi
- University of BirminghamInstitute of Applied Health ResearchEdgbastonBirminghamUKB15 2TT
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Christiansen AL, Bygum A, Hother-Nielsen O, Rasmussen LM. Diagnosing diabetes mellitus in patients with porphyria cutanea tarda. Int J Dermatol 2018. [DOI: 10.1111/ijd.13938] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Affiliation(s)
- Anne L. Christiansen
- Department of Clinical Biochemistry and Pharmacology; Odense University Hospital; Odense C Denmark
| | - Anette Bygum
- Department of Dermatology and Allergy Centre; Odense University Hospital; Odense C Denmark
| | - Ole Hother-Nielsen
- Department of Endocrinology; Odense University Hospital; Odense C Denmark
| | - Lars M. Rasmussen
- Department of Clinical Biochemistry and Pharmacology; Odense University Hospital; Odense C Denmark
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