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Osei-Yeboah J, Kengne AP, Owusu-Dabo E, Schulze MB, Meeks KA, Klipstein-Grobusch K, Smeeth L, Bahendeka S, Beune E, Moll van Charante EP, Agyemang C. Validation of prevalent diabetes risk scores based on non-invasively measured predictors in Ghanaian migrant and non-migrant populations - The RODAM study. PUBLIC HEALTH IN PRACTICE 2023; 6:100453. [PMID: 38034345 PMCID: PMC10687695 DOI: 10.1016/j.puhip.2023.100453] [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: 09/15/2023] [Revised: 11/06/2023] [Accepted: 11/15/2023] [Indexed: 12/02/2023] Open
Abstract
Background Non-invasive diabetes risk models are a cost-effective tool in large-scale population screening to identify those who need confirmation tests, especially in resource-limited settings. Aims This study aimed to evaluate the ability of six non-invasive risk models (Cambridge, FINDRISC, Kuwaiti, Omani, Rotterdam, and SUNSET model) to identify screen-detected diabetes (defined by HbA1c) among Ghanaian migrants and non-migrants. Study design A multicentered cross-sectional study. Methods This analysis included 4843 Ghanaian migrants and non-migrants from the Research on Obesity and Diabetes among African Migrants (RODAM) Study. Model performance was assessed using the area under the receiver operating characteristic curves (AUC), Hosmer-Lemeshow statistics, and calibration plots. Results All six models had acceptable discrimination (0.70 ≤ AUC <0.80) for screen-detected diabetes in the overall/combined population. Model performance did not significantly differ except for the Cambridge model, which outperformed Rotterdam and Omani models. Calibration was poor, with a consistent trend toward risk overestimation for screen-detected diabetes, but this was substantially attenuated by recalibration through adjustment of the original model intercept. Conclusion Though acceptable discrimination was observed, the original models were poorly calibrated among populations of African ancestry. Recalibration of these models among populations of African ancestry is needed before use.
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Affiliation(s)
- James Osei-Yeboah
- Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Meibergdreef 9, Amsterdam, the Netherlands
- Department of Global and International Health, School of Public Health, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Andre-Pascal Kengne
- Non-communicable Disease Research Unit, South African Medical Research Council, Cape Town, South Africa
| | - Ellis Owusu-Dabo
- Department of Global and International Health, School of Public Health, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Matthias B. Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam‐Rehbruecke, Nuthetal, Germany
- German Center for Diabetes Research (DZD), Germany
- Institute of Nutritional Science, University of Potsdam, Germany
| | - Karlijn A.C. Meeks
- Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Meibergdreef 9, Amsterdam, the Netherlands
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Kerstin Klipstein-Grobusch
- Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Liam Smeeth
- Department of Non‐Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | | | - Erik Beune
- Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Meibergdreef 9, Amsterdam, the Netherlands
| | - Eric P. Moll van Charante
- Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Meibergdreef 9, Amsterdam, the Netherlands
- Department of General Practice, Amsterdam UMC, University of Amsterdam, Amsterdam Public health Research Institute, Meibergdreef 9, Amsterdam, the Netherlands
| | - Charles Agyemang
- Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Meibergdreef 9, Amsterdam, the Netherlands
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Simmons SS. Strikes and Gutters: Biomarkers and anthropometric measures for predicting diagnosed diabetes mellitus in adults in low- and middle-income countries. Heliyon 2023; 9:e19494. [PMID: 37810094 PMCID: PMC10558610 DOI: 10.1016/j.heliyon.2023.e19494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 08/22/2023] [Accepted: 08/24/2023] [Indexed: 10/10/2023] Open
Abstract
The management of diabetes necessitates the requirement of reliable health indices, specifically biomarkers and anthropometric measures, to detect the presence or absence of the disease. Nevertheless, limited robust empirical evidence exists regarding the optimal metrics for predicting diabetes in adults, particularly within low- and middle-income countries. This study investigates objective and subjective indices for screening diabetes in these countries. METHODS Data for this study was sourced from surveys conducted among adults (aged 18 years and above) in seventeen (17) countries. Self-reported diabetes status, fifty-four biomarkers, and twenty-six core and twenty-eight estimated anthropometric indices, including weight, waist circumference, body mass index, glycaemic triglycerides, and fasting blood glucose, were utilised to construct lasso regression models. RESULTS The study revealed variances in diabetes prediction outcomes across different countries. Central adiposity measures, fasting plasma glucose and glycaemic triglycerides demonstrated superior predictive capabilities for diabetes when compared to body mass index. Furthermore, fasting plasma or blood glucose, serving as a biomarker, emerged as the most accurate predictor of diabetes. CONCLUSIONS These findings offer critical insights into both general and context-specific tools for diabetes screening. The study proposes that fasting plasma glucose and central adiposity indices should be considered as routine screening tools for diabetes, both in policy interventions and clinical practice. By identifying adults with or at higher risk of developing diabetes and implementing appropriate interventions, these screening tools possess the potential to mitigate diabetes-related complications in low- and middle-income countries.
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Affiliation(s)
- Sally Sonia Simmons
- Department of Social Policy, London School of Economics and Political Science, London, WC2A 2AE, United Kingdom
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3
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Wentzel A, Patterson AC, Duhuze Karera MG, Waldman ZC, Schenk BR, DuBose CW, Sumner AE, Horlyck-Romanovsky MF. Non-invasive type 2 diabetes risk scores do not identify diabetes when the cause is β-cell failure: The Africans in America study. Front Public Health 2022; 10:941086. [PMID: 36211668 PMCID: PMC9537602 DOI: 10.3389/fpubh.2022.941086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 08/19/2022] [Indexed: 01/25/2023] Open
Abstract
Background Emerging data suggests that in sub-Saharan Africa β-cell-failure in the absence of obesity is a frequent cause of type 2 diabetes (diabetes). Traditional diabetes risk scores assume that obesity-linked insulin resistance is the primary cause of diabetes. Hence, it is unknown whether diabetes risk scores detect undiagnosed diabetes when the cause is β-cell-failure. Aims In 528 African-born Blacks living in the United States [age 38 ± 10 (Mean ± SE); 64% male; BMI 28 ± 5 kg/m2] we determined the: (1) prevalence of previously undiagnosed diabetes, (2) prevalence of diabetes due to β-cell-failure vs. insulin resistance; and (3) the ability of six diabetes risk scores [Cambridge, Finnish Diabetes Risk Score (FINDRISC), Kuwaiti, Omani, Rotterdam, and SUNSET] to detect previously undiagnosed diabetes due to either β-cell-failure or insulin resistance. Methods Diabetes was diagnosed by glucose criteria of the OGTT and/or HbA1c ≥ 6.5%. Insulin resistance was defined by the lowest quartile of the Matsuda index (≤ 2.04). Diabetes due to β-cell-failure required diagnosis of diabetes in the absence of insulin resistance. Demographics, body mass index (BMI), waist circumference, visceral adipose tissue (VAT), family medical history, smoking status, blood pressure, antihypertensive medication, and blood lipid profiles were obtained. Area under the Receiver Operator Characteristics Curve (AROC) estimated sensitivity and specificity of each continuous score. AROC criteria were: Outstanding: >0.90; Excellent: 0.80-0.89; Acceptable: 0.70-0.79; Poor: 0.50-0.69; and No Discrimination: 0.50. Results Prevalence of diabetes was 9% (46/528). Of the diabetes cases, β-cell-failure occurred in 43% (20/46) and insulin resistance in 57% (26/46). The β-cell-failure group had lower BMI (27 ± 4 vs. 31 ± 5 kg/m2 P < 0.001), lower waist circumference (91 ± 10 vs. 101 ± 10cm P < 0.001) and lower VAT (119 ± 65 vs. 183 ± 63 cm3, P < 0.001). Scores had indiscriminate or poor detection of diabetes due to β-cell-failure (FINDRISC AROC = 0.49 to Cambridge AROC = 0.62). Scores showed poor to excellent detection of diabetes due to insulin resistance, (Cambridge AROC = 0.69, to Kuwaiti AROC = 0.81). Conclusions At a prevalence of 43%, β-cell-failure accounted for nearly half of the cases of diabetes. All six diabetes risk scores failed to detect previously undiagnosed diabetes due to β-cell-failure while effectively identifying diabetes when the etiology was insulin resistance. Diabetes risk scores which correctly classify diabetes due to β-cell-failure are urgently needed.
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Affiliation(s)
- Annemarie Wentzel
- Section on Ethnicity and Health, Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States,Hypertension in Africa Research Team, North-West University, Potchefstroom, South Africa,South African Medical Research Council, Unit for Hypertension and Cardiovascular Disease, North-West University, Potchefstroom, South Africa,*Correspondence: Annemarie Wentzel
| | - Arielle C. Patterson
- Section on Ethnicity and Health, Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States
| | - M. Grace Duhuze Karera
- Section on Ethnicity and Health, Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States,National Institute of Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, United States,Institute of Global Health Equity Research, University of Global Health Equity, Kigali, Rwanda
| | - Zoe C. Waldman
- Section on Ethnicity and Health, Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States
| | - Blayne R. Schenk
- Section on Ethnicity and Health, Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States
| | - Christopher W. DuBose
- Section on Ethnicity and Health, Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States
| | - Anne E. Sumner
- Section on Ethnicity and Health, Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States,National Institute of Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, United States
| | - Margrethe F. Horlyck-Romanovsky
- Section on Ethnicity and Health, Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States,Department of Health and Nutrition Sciences, Brooklyn College, City University of New York, New York, NY, United States,Margrethe F. Horlyck-Romanovsky
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Khosla L, Bhat S, Fullington LA, Horlyck-Romanovsky MF. HbA 1c Performance in African Descent Populations in the United States With Normal Glucose Tolerance, Prediabetes, or Diabetes: A Scoping Review. Prev Chronic Dis 2021; 18:E22. [PMID: 33705304 PMCID: PMC7986971 DOI: 10.5888/pcd18.200365] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Introduction African descent populations in the United States have high rates of type 2 diabetes and are incorrectly represented as a single group. Current glycated hemoglobin A1c (HbA1c) cutoffs (5.7% to <6.5% for prediabetes; ≥6.5% for type 2 diabetes) may perform suboptimally in evaluating glycemic status among African descent groups. We conducted a scoping review of US-based evidence documenting HbA1c performance to assess glycemic status among African American, Afro-Caribbean, and African people. Methods A PubMed, Scopus, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) search (January 2020) yielded 3,238 articles published from January 2000 through January 2020. After review of titles, abstracts, and full texts, 12 met our criteria. HbA1c results were compared with other ethnic groups or validated against the oral glucose tolerance test (OGTT), fasting plasma glucose (FPG), or previous diagnosis. We classified study results by the risk of false positives and risk of false negatives in assessing glycemic status. Results In 5 studies of African American people, the HbA1c test increased risk of false positives compared with White populations, regardless of glycemic status. Three studies of African Americans found that HbA1c of 5.7% to less than 6.5% or HbA1c of 6.5% or higher generally increased risk of overdiagnosis compared with OGTT or previous diagnosis. In one study of Afro-Caribbean people, HbA1c of 6.5% or higher detected fewer type 2 diabetes cases because of a greater risk of false negatives. Compared with OGTT, HbA1c tests in 4 studies of Africans found that HbA1c of 5.7% to less than 6.5% or HbA1c of 6.5% or higher leads to underdiagnosis. Conclusion HbA1c criteria inadequately characterizes glycemic status among heterogeneous African descent populations. Research is needed to determine optimal HbA1c cutoffs or other test strategies that account for risk profiles unique to African American, Afro-Caribbean, and African people living in the United States.
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Affiliation(s)
- Lakshay Khosla
- Department of Health and Nutrition Sciences, Brooklyn College, City University of New York, Brooklyn, New York.,College of Medicine, SUNY Downstate Health Sciences University, Brooklyn, New York
| | - Sonali Bhat
- Department of Health and Nutrition Sciences, Brooklyn College, City University of New York, Brooklyn, New York.,College of Medicine, SUNY Downstate Health Sciences University, Brooklyn, New York
| | - Lee Ann Fullington
- Library Department, Brooklyn College, City University of New York, Brooklyn, New York
| | - Margrethe F Horlyck-Romanovsky
- Department of Health and Nutrition Sciences, Brooklyn College, City University of New York, Brooklyn, New York.,Center for Systems and Community Design, Graduate School of Public Health and Health Policy, City University of New York, New York, New York.,City University of New York, Brooklyn College, 2900 Bedford Ave, Brooklyn, NY 11210.
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Mugeni R, Hormenu T, Hobabagabo A, Shoup EM, DuBose CW, Sumner AE, Horlyck-Romanovsky MF. Identifying Africans with undiagnosed diabetes: Fasting plasma glucose is similar to the hemoglobin A1C updated Atherosclerosis Risk in Communities diabetes prediction equation. Prim Care Diabetes 2020; 14:501-507. [PMID: 32173292 DOI: 10.1016/j.pcd.2020.02.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Accepted: 02/24/2020] [Indexed: 12/15/2022]
Abstract
AIMS Seventy percent of Africans living with diabetes are undiagnosed. Identifying who should be referred for testing is critical. Therefore we evaluated the ability of the Atherosclerosis Risk in Communities (ARIC) diabetes prediction equation with A1C added (ARIC + A1C) to identify diabetes in 451 African-born blacks living in America (66% male; age 38 ± 10y (mean ± SD); BMI 27.5 ± 4.4 kg/m2). METHODS All participants denied a history of diabetes. OGTTs were performed. Diabetes diagnosis required 2-h glucose ≥200 mg/dL. The five non-invasive (Age, parent history of diabetes, waist circumference, height, systolic blood pressure) and four invasive variables (Fasting glucose (FPG), A1C, triglycerides (TG), HDL) were obtained. Four models were tested: Model-1: Full ARIC + A1C equation; Model-2: All five non-invasive variables with one invasive variable excluded at a time; Model-3: All five non-invasive variables with one invasive variable included at a time; Model-4: Each invasive variable singly. Area under the receiver operator characteristic curve (AROC) predicted diabetes. Youden Index identified optimal cut-points. RESULTS Diabetes occurred in 7% (30/451). Model-1, the full ARIC + A1C equation, AROC = 0.83. Model-2: With FPG excluded, AROC = 0.77 (P = 0.038), but when A1C, HDL or TG were excluded AROC remained unchanged. Model-3 with all non-invasive variables and FPG alone, AROC=0.87; but with A1C, TG or HDL included AROC declined to ≤0.76. Model-4: FPG as a single predictor, AROC = 0.87. A1C, TG, or HDL as single predictors all had AROC ≤ 0.74. Optimal cut-point for FPG was 100 mg/dL. CONCLUSIONS To detect diabetes, FPG performed as well as the nine-variable updated ARIC + A1C equation.
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Affiliation(s)
- Regine Mugeni
- National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, United States; National Institute of Minority Health and Health Disparities (NIMHD), National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, United States
| | - Thomas Hormenu
- National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, United States
| | - Arsène Hobabagabo
- National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, United States; National Institute of Minority Health and Health Disparities (NIMHD), National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, United States
| | - Elyssa M Shoup
- National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, United States
| | - Christopher W DuBose
- National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, United States
| | - Anne E Sumner
- National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, United States; National Institute of Minority Health and Health Disparities (NIMHD), National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, United States
| | - Margrethe F Horlyck-Romanovsky
- National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, United States; City University of New York, Brooklyn College, 2900 Bedford Avenue, Brooklyn, NY, United States.
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Ni L, Yuan C, Chen G, Zhang C, Wu X. SGLT2i: beyond the glucose-lowering effect. Cardiovasc Diabetol 2020; 19:98. [PMID: 32590982 PMCID: PMC7320582 DOI: 10.1186/s12933-020-01071-y] [Citation(s) in RCA: 104] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Accepted: 06/16/2020] [Indexed: 02/06/2023] Open
Abstract
Sodium/glucose cotransporter-2 inhibitors (SGLT2i) are a new type of glucose-lowering drug that can reduce blood glucose by inhibiting its reabsorption in proximal tubules and by promoting urinary glucose excretion. SGLT2i are widely used in the clinical treatment of type 2 diabetes mellitus (T2DM). In recent studies, SGLT2i were found to not only reduce blood glucose but also protect the heart and kidney, which can significantly reduce cardiovascular events, delay the progression of renal failure, greatly improve the quality of life of patients, and reduce medical expenses for families and society. As adverse cardiac and renal events are the most common and serious complications of T2DM, it is very important to understand the cardio- and renoprotective mechanisms of SGLT2i. This article reviews the historical development, pharmacological mechanism, heart and kidney protection and safety of SGLT2i. The information presented provides a theoretical basis for the clinical prevention and treatment of diabetes and its complications and for the development of new glucose-lowering drugs.
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Affiliation(s)
- Lihua Ni
- Department of Nephrology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuhan, Hubei, 430071, China
| | - Cheng Yuan
- Department of Gynecological Oncology, Zhongnan Hospital, Wuhan University, Wuhan, 430071, People's Republic of China
| | - Guopeng Chen
- Institute of Model Animal, Wuhan University, Wuhan, 430071, China.,School of Basic Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Changjiang Zhang
- Department of Cardiology, Renmin Hospital of Wuhan University, Zhang Road No. 99, Wuhan, Hubei, 430060, China. .,Cardiovascular Research Institute, Wuhan University, Wuhan, 430060, People's Republic of China. .,Hubei Key Laboratory of Cardiology, Wuhan, 430060, People's Republic of China. .,Cardiovascular Disease Center, Enshi Central Hospital, Enshi, 445000, People's Republic of China.
| | - Xiaoyan Wu
- Department of Nephrology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuhan, Hubei, 430071, China.
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