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Seidel-Jacobs E, Ptushkina V, Strassburger K, Icks A, Kuss O, Burkart V, Szendroedi J, Müssig K, Bódis K, Karusheva Y, Zaharia OP, Roden M, Rathmann W. Socio-economic inequalities in glycaemic control in recently diagnosed adults with type 1 and type 2 diabetes. Diabet Med 2022; 39:e14833. [PMID: 35324027 DOI: 10.1111/dme.14833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 03/22/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND AND AIMS It is unclear whether socio-economic status (SES) is associated with glycaemic control in people with recently diagnosed diabetes. The aim was to investigate whether SES is related to haemoglobin A1c (HbA1c) during the first year after diagnosis in people with type 1 and type 2 diabetes and if metabolic, quality of care or mental factors may explain the association. METHODS In the German Diabetes Study, people with type 1 (n = 274, median age 36 [25th; 75th percentile: 28; 48] years) and type 2 diabetes (n = 424, 54 [47; 60] years) underwent detailed metabolic characterisation within the first year after diagnosis. SES was documented using a standardised questionnaire. Associations between SES and HbA1c were assessed using multivariable linear regression and restricted cubic spline regression analyses. Additional covariables were patient characteristics, laboratory measurements, health behaviour, quality of care and depression variables. Models were separately fitted for diabetes type, SES and its dimensions (income, education, occupation). RESULTS Higher SES score was associated with lower HbA1c (-0.7 mmol/mol per unit increase in SES, 95% CI: -1.1; -0.2 mmol/mol [-0.1%, 95% CI: -0.1; 0.0%]) in people with type 1 diabetes. Included covariates did not attenuate this association. In people with type 2 diabetes, effect estimates were close to zero indicating no relevant difference. CONCLUSION Socio-economic inequalities in HbA1c already exist during the first year after diagnosis in people with type 1 diabetes. The absence of association between glycaemic control and SES in type 2 diabetes could be due to the lower complexity of diabetes therapy compared to type 1 diabetes.
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Affiliation(s)
- Esther Seidel-Jacobs
- Institute for Biometrics and Epidemiology, German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Germany
| | - Violetta Ptushkina
- Institute for Biometrics and Epidemiology, German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Germany
| | - Klaus Strassburger
- Institute for Biometrics and Epidemiology, German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Germany
| | - Andrea Icks
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Germany
- Institute for Health Service Research and Health Economics, German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute for Health Service Research and Health Economics, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Centre for Health and Society, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Oliver Kuss
- Institute for Biometrics and Epidemiology, German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Germany
- Centre for Health and Society, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Volker Burkart
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Julia Szendroedi
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Germany
- Department of Internal Medicine I and Clinical Chemistry, Heidelberg University Hospital, Heidelberg, Germany
| | - Karsten Müssig
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Internal Medicine and Gastroenterology, Niels Stensen Hospitals, Franziskus Hospital Harderberg, Georgsmarienhütte, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kálmán Bódis
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Yanislava Karusheva
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- University of Cambridge, Wellcome-MRC Institute of Metabolic Science, Cambridge, UK
| | - Oana-Patricia Zaharia
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Michael Roden
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Wolfgang Rathmann
- Institute for Biometrics and Epidemiology, German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Germany
- Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
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Alkhalidy H, Orabi A, Alnaser K, Al-Shami I, Alzboun T, Obeidat MD, Liu D. Obesity Measures as Predictors of Type 2 Diabetes and Cardiovascular Diseases among the Jordanian Population: A Cross-Sectional Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:12187. [PMID: 34831943 PMCID: PMC8618033 DOI: 10.3390/ijerph182212187] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 11/16/2021] [Accepted: 11/18/2021] [Indexed: 12/23/2022]
Abstract
Obesity is strongly associated with cardiovascular diseases (CVD) and type 2 diabetes (T2D). This study aimed to use obesity measures, body mass index (BMI) and waist circumference (WC) to predict the CVD and T2D risk and to determine the best predictor of these diseases among Jordanian adults. A cross-sectional study was conducted at the governmental and military hospitals across Jordan. The study participants were healthy or previously diagnosed with CVD or T2D. The continuous variables were compared using ANOVA, and the categorical variables were compared using the X2 test. The multivariate logistic regression was used to predict CVD and T2D risk through their association with BMI and WC. The final sample consisted of 6000 Jordanian adults with a mean age of 41.5 ± 14.7 years, 73.6% females. The BMI (OR = 1.7, CI: 1.30-2.30, p < 0.001) was associated with a higher risk of T2D compared to WC (OR = 1.3, CI: 1.04-1.52, p = 0.016). However, our results showed that BMI was not associated with CVD risk, while the WC was significantly and positively associated with CVD risk (OR = 1.9, CI: 1.47-2.47, p < 0.001). In conclusion, an elevated BMI predicts a higher risk of T2D, while WC is more efficient in predicting CVD risk. Our results can be used to construct a population-specific intervention to reduce the risk of CVD and T2D among adults in Jordan and other countries with similar backgrounds.
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Affiliation(s)
- Hana Alkhalidy
- Department of Nutrition and Food Technology, Faculty of Agriculture, Jordan University of Science and Technology, Irbid 22110, Jordan; (A.O.); (K.A.); (T.A.)
| | - Aliaa Orabi
- Department of Nutrition and Food Technology, Faculty of Agriculture, Jordan University of Science and Technology, Irbid 22110, Jordan; (A.O.); (K.A.); (T.A.)
| | - Khadeejah Alnaser
- Department of Nutrition and Food Technology, Faculty of Agriculture, Jordan University of Science and Technology, Irbid 22110, Jordan; (A.O.); (K.A.); (T.A.)
| | - Islam Al-Shami
- Department of Clinical Nutrition and Dietetics, Faculty of Applied Medical Sciences, The Hashemite University, Zarqa 13133, Jordan;
| | - Tamara Alzboun
- Department of Nutrition and Food Technology, Faculty of Agriculture, Jordan University of Science and Technology, Irbid 22110, Jordan; (A.O.); (K.A.); (T.A.)
| | - Mohammad D. Obeidat
- Department of Animal Production, Faculty of Agriculture, Jordan University of Science and Technology, Irbid 22110, Jordan;
| | - Dongmin Liu
- Department of Human Nutrition, Foods and Exercise, College of Agriculture and Life Sciences, Virginia Tech, Blacksburg, VA 24061, USA;
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4
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He Y, Lakhani CM, Rasooly D, Manrai AK, Tzoulaki I, Patel CJ. Comparisons of Polyexposure, Polygenic, and Clinical Risk Scores in Risk Prediction of Type 2 Diabetes. Diabetes Care 2021; 44:935-943. [PMID: 33563654 PMCID: PMC7985424 DOI: 10.2337/dc20-2049] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 01/13/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To establish a polyexposure score (PXS) for type 2 diabetes (T2D) incorporating 12 nongenetic exposures and examine whether a PXS and/or a polygenic risk score (PGS) improves diabetes prediction beyond traditional clinical risk factors. RESEARCH DESIGN AND METHODS We identified 356,621 unrelated individuals from the UK Biobank of White British ancestry with no prior diagnosis of T2D and normal HbA1c levels. Using self-reported and hospital admission information, we deployed a machine learning procedure to select the most predictive and robust factors out of 111 nongenetically ascertained exposure and lifestyle variables for the PXS in prospective T2D. We computed the clinical risk score (CRS) and PGS by taking a weighted sum of eight established clinical risk factors and >6 million single nucleotide polymorphisms, respectively. RESULTS In the study population, 7,513 had incident T2D. The C-statistics for the PGS, PXS, and CRS models were 0.709, 0.762, and 0.839, respectively. Individuals in the top 10% of PGS, PXS, and CRS had 2.00-, 5.90-, and 9.97-fold greater risk, respectively, compared to the remaining population. Addition of PGS and PXS to CRS improved T2D classification accuracy, with a continuous net reclassification index of 15.2% and 30.1% for cases, respectively, and 7.3% and 16.9% for controls, respectively. CONCLUSIONS For T2D, the PXS provides modest incremental predictive value over established clinical risk factors. However, the concept of PXS merits further consideration in T2D risk stratification and is likely to be useful in other chronic disease risk prediction models.
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Affiliation(s)
- Yixuan He
- Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - Chirag M Lakhani
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - Danielle Rasooly
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA.,Computational Health Informatics Program, Boston Children's Hospital, Boston, MA
| | - Arjun K Manrai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA.,Computational Health Informatics Program, Boston Children's Hospital, Boston, MA
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, Imperial College London School of Public Health, London, U.K.,Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Chirag J Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
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5
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Mishra R, Hodge KM, Cousminer DL, Leslie RD, Grant SFA. A Global Perspective of Latent Autoimmune Diabetes in Adults. Trends Endocrinol Metab 2018; 29:638-650. [PMID: 30041834 DOI: 10.1016/j.tem.2018.07.001] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Revised: 06/30/2018] [Accepted: 07/02/2018] [Indexed: 12/21/2022]
Abstract
Latent autoimmune diabetes in adults (LADA) is characterized by the presence of islet autoantibodies and initial insulin independence, which can lead to misdiagnosis of type 2 diabetes (T2D). As such, understanding the genetic etiology of LADA could aid in more accurate diagnosis. However, there is ongoing debate regarding the exact definition of LADA, so understanding its impact in different populations when contrasted with type 1 diabetes (T1D) and T2D is one potential strategy to gain insight into its etiology. Unfortunately, the lack of consistent and thorough autoantibody screening around the world has hampered well-powered genetic studies of LADA. This review highlights recent genetic and epidemiological studies of LADA in diverse populations as well as the importance of autoantibody screening in facilitating future research.
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Affiliation(s)
- Rajashree Mishra
- Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; These authors contributed equally
| | - Kenyaita M Hodge
- Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; These authors contributed equally
| | - Diana L Cousminer
- Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Richard D Leslie
- Department of Immunobiology, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, E1 2AD, UK
| | - Struan F A Grant
- Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Institute for Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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