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Wang D, Wang Y, Madhu S, Liang H, Bray CL. Total hemoglobin count has significant impact on A1C - Data from National Health and Nutrition Examination Survey 1999-2014. Prim Care Diabetes 2019; 13:316-323. [PMID: 30718167 DOI: 10.1016/j.pcd.2019.01.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Revised: 01/11/2019] [Accepted: 01/13/2019] [Indexed: 11/25/2022]
Abstract
BACKGROUND Previous study showed A1C was affected by hemoglobin and gender in non-anemic Koreans. However, there was no other data to support those important findings. METHODS Participants from National Health and Nutrition Examination Survey (NHANES) - 1999 to 2014 with age over 12 years were examined. After excluding participants with hypoglycemia and moderate to severe anemia, 22,974 participants were included in analysis. Both female and male were divided into three groups based on their hemoglobin level. Linear regression and multivariate regression analyses were conducted to assess the relationship between A1C and hemoglobin in three fasting plasma glucose (FPG) intervals. RESULTS For both female and male, A1C was significantly negatively correlated with hemoglobin in the FPG interval range between 80 to 126mg/dl, p<0.0001. A1C was not significantly associated with hemoglobin over the other two intervals (70≤FPG<80mg/dl and FPG≥126mg/dl, p>0.05). In addition, over FPG interval between 80 to 126mg/dl, after adjusting age and fasting glucose level, A1C still significantly negatively correlated with hemoglobin (regression coefficient=-0.052, p<0.0001 in females, regression coefficient=-0.050, p<0.0001 in males). CONCLUSIONS A1C would decrease by 0.05% (about 0.5mmol/mol) as a result of each 1g/dl increase in total hemoglobin count when plasma glucose level in normal or prediabetes range. The mechanism behind this finding is unclear. Further study should be conducted and hemoglobin count may need be taken into consideration when applying A1C for the diagnosis of diabetes or to screen for prediabetes.
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Affiliation(s)
- Dong Wang
- North Florida Regional Medical Center, Department of Medicine and Graduate Medical Education, Gainesville, FL, United States; University of Central Florida, College of Medicine, Orlando, FL, United States.
| | - Yanning Wang
- North Florida Regional Medical Center, Department of Medicine and Graduate Medical Education, Gainesville, FL, United States; University of Central Florida, College of Medicine, Orlando, FL, United States
| | - Suneetha Madhu
- North Florida Regional Medical Center, Department of Medicine and Graduate Medical Education, Gainesville, FL, United States; University of Central Florida, College of Medicine, Orlando, FL, United States
| | - Hong Liang
- North Florida Regional Medical Center, Department of Medicine and Graduate Medical Education, Gainesville, FL, United States; University of Central Florida, College of Medicine, Orlando, FL, United States
| | - Christopher Lawrence Bray
- North Florida Regional Medical Center, Department of Medicine and Graduate Medical Education, Gainesville, FL, United States; University of Central Florida, College of Medicine, Orlando, FL, United States
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Yu H, Wang J, Shrestha Y, Hu Y, Ma Y, Ren L, Zhang J, Li J. Importance of early elevated maternal HbA1c levels in identifying adverse fetal and neonatal events. Placenta 2019; 86:28-34. [PMID: 31401007 DOI: 10.1016/j.placenta.2019.07.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Revised: 07/12/2019] [Accepted: 07/18/2019] [Indexed: 12/12/2022]
Abstract
AIMS The aims of this study were to explore factors that associated with gestational diabetes mellitus (GDM), and to determine the relationship between early maternal HbA1c levels and adverse fetal or neonatal events, and to determine an optimal maternal glucose testing method in order to decrease the potential health risk for their offspring. METHODS From December 2015 to May 2016, a total of 6744 pregnant women were enrolled from Shanghai First Maternal and Infant Hospital affiliated to Tongji University prospectively in the nested case-control study. Each GDM case was matched with a healthy pregnant woman and followed up. Outcome analyses were conducted between GDM case and control groups, as well as elevated and normal maternal HbA1c levels, respectively. RESULTS A total of 1836 women were included in the adverse fetal and neonatal events examination. For pregnant women with early HbA1c ≥ 5.2%, the adjusted risk ratios (RR) of respiratory distress syndrome (RDS), pneumonia and jaundice were 4.37 (95%CI 1.54-12.35), 2.03 (95%CI 1.24-3.33) and 1.49 (95%CI 1.01-2.20), respectively. After treatments, the frequency for the majority of events in GDM group was similar to that of healthy pregnant women. Moreover, the area under the curve (AUC) of early maternal HbA1c in predicting potential RDS is 0.734. HbA1c ≤ 4.9% excluded for RDS. CONCLUSIONS Compared with women with normal HbA1c, those with an early elevated HbA1c level were more likely to develop adverse events, including RDS, pneumonia and jaundice. Early HbA1c testing can be used as an auxiliary method identifying potential RDS.
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Affiliation(s)
- Han Yu
- Institute of Clinical Epidemiology and Evidence-based Medicine, Tongji University School of Medicine, China; Key Laboratory of Arrhythmias, Ministry of Education, China, Tongji University School of Medicine, China.
| | - Jing Wang
- Institute of Clinical Epidemiology and Evidence-based Medicine, Tongji University School of Medicine, China; Department of Gynecology and Obstetrics, Shanghai Tongji Hospital Affiliated to Tongji University School of Medicine, China
| | - Yeshaswi Shrestha
- Institute of Clinical Epidemiology and Evidence-based Medicine, Tongji University School of Medicine, China; Key Laboratory of Arrhythmias, Ministry of Education, China, Tongji University School of Medicine, China
| | - Yongjia Hu
- Institute of Clinical Epidemiology and Evidence-based Medicine, Tongji University School of Medicine, China; Key Laboratory of Arrhythmias, Ministry of Education, China, Tongji University School of Medicine, China
| | - Yuan Ma
- Institute of Clinical Epidemiology and Evidence-based Medicine, Tongji University School of Medicine, China; Key Laboratory of Arrhythmias, Ministry of Education, China, Tongji University School of Medicine, China
| | - Longbing Ren
- Institute of Clinical Epidemiology and Evidence-based Medicine, Tongji University School of Medicine, China; Key Laboratory of Arrhythmias, Ministry of Education, China, Tongji University School of Medicine, China
| | - Jun Zhang
- Department of Clinical Laboratory, Shanghai First Maternity and Infant Hospital Affiliated to Tongji University School of Medicine, China
| | - Jue Li
- Institute of Clinical Epidemiology and Evidence-based Medicine, Tongji University School of Medicine, China; Key Laboratory of Arrhythmias, Ministry of Education, China, Tongji University School of Medicine, China.
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Fang J, Zhang Z, Ayala C, Thompson‐Paul AM, Loustalot F. Cardiovascular Health Among Non-Hispanic Asian Americans: NHANES, 2011-2016. J Am Heart Assoc 2019; 8:e011324. [PMID: 31238768 PMCID: PMC6662346 DOI: 10.1161/jaha.118.011324] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Accepted: 04/24/2019] [Indexed: 12/15/2022]
Abstract
Background Asian Americans are the fastest growing population in the United States, but little is known about their cardiovascular health (CVH). The objective of this study was to assess CVH among non-Hispanic Asian Americans (NHAAs) and to compare these estimates to those of non-Hispanic white (NHW) participants. Methods and Results Merging NHANES (National Health and Nutrition Examination Survey) data from 2011 to 2016, we examined 7 metrics (smoking, weight, physical activity, diet, blood cholesterol, blood glucose, and blood pressure) to assess CVH among 5278 NHW and 1486 NHAA participants aged ≥20 years. We assessed (1) the percentage meeting 6 to 7 metrics (ideal CVH), (2) the percentage meeting only 0 to 2 metrics (poor CVH), and (3) the overall mean CVH score. We compared these estimates between NHAAs and NHWs and among foreign-born NHAAs by birthplace and number of years living in the United States. The adjusted prevalence of ideal CVH was 8.7% among NHAAs and 5.9% among NHWs ( P<0.001). NHAAs were significantly more likely to have ideal CVH (adjusted prevalence ratio: 1.42; 95% CI, 1.29-1.55) compared with NHWs. Among NHAAs, there was no significant difference in ideal CVH between US- and foreign-born participants, nor by number of years living in the United States. With lower body mass index thresholds (<23, normal weight) for NHAAs, there were no statistically significant differences in the adjusted prevalence of ideal CVH (6.5% versus 5.9%, P=0.216) between NHAAs and NHWs. Conclusions NHAAs had a higher prevalence of overall ideal CVH compared with NHWs. However, when using a lower body mass index threshold for NHAAs, there was no difference in ideal CVH between the groups.
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Affiliation(s)
- Jing Fang
- Division for Heart Disease and Stroke PreventionNational Center for Chronic Disease Prevention and Health PromotionCenters for Disease Control and PreventionAtlantaGA
| | - Zefeng Zhang
- Division for Heart Disease and Stroke PreventionNational Center for Chronic Disease Prevention and Health PromotionCenters for Disease Control and PreventionAtlantaGA
| | - Carma Ayala
- Division for Heart Disease and Stroke PreventionNational Center for Chronic Disease Prevention and Health PromotionCenters for Disease Control and PreventionAtlantaGA
| | - Angela M. Thompson‐Paul
- Division for Heart Disease and Stroke PreventionNational Center for Chronic Disease Prevention and Health PromotionCenters for Disease Control and PreventionAtlantaGA
| | - Fleetwood Loustalot
- Division for Heart Disease and Stroke PreventionNational Center for Chronic Disease Prevention and Health PromotionCenters for Disease Control and PreventionAtlantaGA
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Hostalek U. Global epidemiology of prediabetes - present and future perspectives. Clin Diabetes Endocrinol 2019; 5:5. [PMID: 31086677 PMCID: PMC6507173 DOI: 10.1186/s40842-019-0080-0] [Citation(s) in RCA: 185] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Accepted: 05/01/2019] [Indexed: 02/06/2023] Open
Abstract
Prediabetes is defined as an intermediate state of hyperglycaemia with glucose levels above the normal state but below the diagnostic levels of diabetes. It is increasingly recognised as an important metabolic state, as individuals with prediabetes are at high risk of developing overt diabetes and its associated complications. A better understanding of prediabetes could help with earlier identification, thereby allowing earlier intervention, potentially lowering the number of individuals who go on to develop diabetes. The definitions and screening criteria for prediabetes differ between guidelines published by different organisations, resulting in estimations of prevalence that can vary widely from one another. Despite these differences, these estimates suggest that the number of individuals affected by prediabetes is increasing rapidly in all areas of the world. This short review compares and contrasts the diagnostic criteria for screening of prediabetes, the impact of various glycaemic measures on prevalence estimates, and discusses current and future trends in the global prevalence estimates of prediabetes.
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Affiliation(s)
- Ulrike Hostalek
- Global Medical Affairs, Merck KGaA, Frankfurterstr. 250, 64293 Darmstadt, Germany
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105
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Reséndiz-Abarca CA, Flores-Alfaro E, Suárez-Sánchez F, Cruz M, Valladares-Salgado A, Del Carmen Alarcón-Romero L, Vázquez-Moreno MA, Wacher-Rodarte NA, Gómez-Zamudio JH. Altered Glycemic Control Associated With Polymorphisms in the SLC22A1 (OCT1) Gene in a Mexican Population With Type 2 Diabetes Mellitus Treated With Metformin: A Cohort Study. J Clin Pharmacol 2019; 59:1384-1390. [PMID: 31012983 DOI: 10.1002/jcph.1425] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2018] [Accepted: 03/28/2019] [Indexed: 01/06/2023]
Abstract
The organic cation transporters OCT1 and OCT2 and the multidrug and toxin extrusion transporter MATE1, encoded by the SLC22A1, SLC22A2, and SLC47A1 genes, respectively, are responsible for the absorption of metformin in enterocytes, hepatocytes, and kidney cells. The aim of this study was to evaluate whether genetic variations in the SLC22A1, SLC22A2, and SLC47A1 genes could be associated with an altered response to metformin in patients with type 2 diabetes mellitus. A cohort study was conducted in 308 individuals with a diagnosis of type 2 diabetes mellitus of less than 3 years and who had metformin monotherapy. Three measurements of blood glycated hemoglobin (HbA1c ) were obtained at the beginning of the study and after 6 and 12 months. Five polymorphisms were analyzed in the SLC22A1 (rs622342, rs628031, rs594709), SLC22A2 (rs316019), and SLC47A1 (rs2289669) genes by real-time polymerase chain reaction. The results showed a significant association among genotypes CC-rs622342 (β = 1.36; P < .001), AA-rs628031 (β = 0.98; P = .032), and GG-rs594709 (β = 1.21; P = .016) in the SLC22A1 gene with an increase in HbA1c levels during the follow-up period. Additionally, a significant association was found in the CGA and CAG haplotypes with an increase in HbA1c levels compared to the highest-frequency haplotype (AGA). In conclusion, the genetic variation in the SLC22A1 gene was significantly related to the variation of the HbA1c levels, an important indicator of glycemic control in diabetic patients. This information may contribute to identifying patients with an altered response to metformin before starting their therapy.
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Affiliation(s)
- Carlos Alberto Reséndiz-Abarca
- Laboratorio de Investigación en Epidemiología Clínica y Molecular, Facultad de Ciencias Químico-Biológicas, Universidad Autónoma de Guerrero, Chilpancingo, Guerrero, México.,Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades "Bernardo Sepúlveda," Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Ciudad de México, México
| | - Eugenia Flores-Alfaro
- Laboratorio de Investigación en Epidemiología Clínica y Molecular, Facultad de Ciencias Químico-Biológicas, Universidad Autónoma de Guerrero, Chilpancingo, Guerrero, México
| | - Fernando Suárez-Sánchez
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades "Bernardo Sepúlveda," Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Ciudad de México, México
| | - Miguel Cruz
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades "Bernardo Sepúlveda," Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Ciudad de México, México
| | - Adán Valladares-Salgado
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades "Bernardo Sepúlveda," Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Ciudad de México, México
| | - Luz Del Carmen Alarcón-Romero
- Laboratorio de Investigación en Epidemiología Clínica y Molecular, Facultad de Ciencias Químico-Biológicas, Universidad Autónoma de Guerrero, Chilpancingo, Guerrero, México
| | - Miguel Alexander Vázquez-Moreno
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades "Bernardo Sepúlveda," Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Ciudad de México, México
| | - Niels Agustín Wacher-Rodarte
- Unidad de Investigación en Epidemiología Clínica, Hospital de Especialidades "Bernardo Sepúlveda," Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Ciudad de México, México
| | - Jaime Héctor Gómez-Zamudio
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades "Bernardo Sepúlveda," Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Ciudad de México, México
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106
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Mañé L, Flores-Le Roux JA, Gómez N, Chillarón JJ, Llauradó G, Gortazar L, Payà A, Pedro-Botet J, Benaiges D. Association of first-trimester HbA1c levels with adverse pregnancy outcomes in different ethnic groups. Diabetes Res Clin Pract 2019; 150:202-210. [PMID: 30880095 DOI: 10.1016/j.diabres.2019.03.017] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Revised: 12/16/2018] [Accepted: 03/07/2019] [Indexed: 10/27/2022]
Abstract
AIM To determine, in a multi-ethnic cohort, the association of first-trimester HbA1c levels with the development of pregnancy complications. METHODS A prospective study between April 2013-October 2016. Participants were stratified in five ethnic groups. Women had an HbA1c measurement added to their first antenatal bloods. Primary outcome was macrosomia and secondary outcomes included preeclampsia and large-for-gestational age (LGA). A multivariate logistic regression analysis was performed to adjust for potential confounders in determining the association between different HbA1c cut-off points and obstetric outcomes on each ethnic group. RESULTS 1,882 pregnancies were included. Analysis was limited to the three main ethnic groups: Caucasian (54.3%), South-Central Asian (19%) and Latin-American (12.2%). There was no association between HbA1c levels and obstetric outcomes among Caucasians. In Latin-Americans, an HbA1c ≥ 5.8% (40 mmol/mol) was associated with higher risk of macrosomia, whereas an HbA1c ≥ 5.9% (41 mmol/mol) was associated with LGA. In South-Central Asian, an HbA1c ≥ 5.7% (39 mmol/mol) was associated with increased risk of macrosomia and a continuous graded relationship between HbA1c levels and preeclampsia and LGA was detected starting at HbA1c levels of 5.4% (36 mmol/mol). CONCLUSION First-trimester HbA1c levels perform as a suitable predictor of pregnancy complications in South-Central Asian and Latin-American women whereas in Caucasian no significant associations were found.
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Affiliation(s)
- Laura Mañé
- Department of Endocrinology and Nutrition, Hospital del Mar, E-08003 Barcelona, Spain; Department of Medicine, Universitat Autònoma de Barcelona, E-08003 Barcelona, Spain
| | - Juana Antonia Flores-Le Roux
- Department of Endocrinology and Nutrition, Hospital del Mar, E-08003 Barcelona, Spain; Department of Medicine, Universitat Autònoma de Barcelona, E-08003 Barcelona, Spain.
| | - Nàdia Gómez
- Department of Medicine, Universitat Autònoma de Barcelona, E-08003 Barcelona, Spain
| | - Juan José Chillarón
- Department of Endocrinology and Nutrition, Hospital del Mar, E-08003 Barcelona, Spain; Department of Medicine, Universitat Autònoma de Barcelona, E-08003 Barcelona, Spain.
| | - Gemma Llauradó
- Department of Endocrinology and Nutrition, Hospital del Mar, E-08003 Barcelona, Spain; Department of Medicine, Universitat Autònoma de Barcelona, E-08003 Barcelona, Spain.
| | - Lucía Gortazar
- Department of Endocrinology and Nutrition, Hospital del Mar, E-08003 Barcelona, Spain
| | - Antonio Payà
- Department of Medicine, Universitat Autònoma de Barcelona, E-08003 Barcelona, Spain; Department of Gynaecology and Obstetrics, Hospital del Mar, E-08003 Barcelona, Spain.
| | - Juan Pedro-Botet
- Department of Endocrinology and Nutrition, Hospital del Mar, E-08003 Barcelona, Spain; Department of Medicine, Universitat Autònoma de Barcelona, E-08003 Barcelona, Spain.
| | - David Benaiges
- Department of Endocrinology and Nutrition, Hospital del Mar, E-08003 Barcelona, Spain; Department of Medicine, Universitat Autònoma de Barcelona, E-08003 Barcelona, Spain.
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Lee J, Lee YA, Kim JH, Lee SY, Shin CH, Yang SW. Discrepancies between Glycosylated Hemoglobin and Fasting Plasma Glucose for Diagnosing Impaired Fasting Glucose and Diabetes Mellitus in Korean Youth and Young Adults. Diabetes Metab J 2019; 43:174-182. [PMID: 30398041 PMCID: PMC6470094 DOI: 10.4093/dmj.2018.0046] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2018] [Accepted: 08/10/2018] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Glycosylated hemoglobin (HbA1c) has been recommended as a diagnostic test for prediabetes and diabetes. Here, we evaluated the level of agreement between diagnoses based on fasting plasma glucose (FPG) versus HbA1c levels and determined optimal HbA1c cutoff values for these diseases in youth and young adults. METHODS The study included 7,332 subjects (n=4,129, aged 10 to 19 years in youth group; and n=3,203 aged 20 to 29 years in young adult group) from the 2011 to 2016 Korea National Health and Nutrition Examination Survey. Prediabetes and diabetes were defined as 100 to 125 mg/dL (impaired fasting glucose [IFG]) and ≥126 mg/dL for FPG (diabetes mellitus [DM] by FPG [DMFPG]), and 5.7% to 6.4% and ≥6.5% for HbA1c, respectively. RESULTS In the youth group, 32.5% with IFG had an HbA1c level of 5.7% to 6.4%, and 72.2% with DMFPG had an HbA1c ≥6.5%. In the young adult group, 27.5% with IFG had an HbA1c level of 5.7% to 6.4%, and 66.6% with DMFPG had an HbA1c ≥6.5%. Kappa coefficients for agreement between the FPG and HbA1c results were 0.12 for the youth group and 0.19 for the young adult group. In receiver operating characteristic curve analysis, the optimal HbA1c cutoff for IFG and DMFPG were 5.6% and 5.9% in youths and 5.5% and 5.8% in young adults, respectively. CONCLUSION Usefulness of HbA1c for diagnosis of IFG and DMFPG in Koreans aged <30 years remains to be determined due to discrepancies between the results of glucose- and HbA1c-based tests. Additional testing might be warranted at lower HbA1c levels to detect IFG and DMFPG in this age group.
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Affiliation(s)
- Jieun Lee
- Department of Pediatrics, Inje University Ilsan Paik Hospital, Inje University College of Medicine, Goyang, Korea
| | - Young Ah Lee
- Department of Pediatrics, Seoul National University Children's Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Jae Hyun Kim
- Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea.
| | - Seong Yong Lee
- Department of Pediatrics, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul National University College of Medicine, Seoul, Korea
| | - Choong Ho Shin
- Department of Pediatrics, Seoul National University Children's Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Sei Won Yang
- Department of Pediatrics, Seoul National University Children's Hospital, Seoul National University College of Medicine, Seoul, Korea
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108
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Alexopoulos AS, Jackson GL, Edelman D, Smith VA, Berkowitz TSZ, Woolson SL, Bosworth HB, Crowley MJ. Clinical factors associated with persistently poor diabetes control in the Veterans Health Administration: A nationwide cohort study. PLoS One 2019; 14:e0214679. [PMID: 30925177 PMCID: PMC6440639 DOI: 10.1371/journal.pone.0214679] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Accepted: 03/18/2019] [Indexed: 01/22/2023] Open
Abstract
OBJECTIVE Patients with persistent poorly-controlled diabetes mellitus (PPDM) despite engagement in clinic-based care are at particularly high risk for diabetes complications and costs. Understanding this population's demographics, comorbidities and care utilization could guide strategies to address PPDM. We characterized factors associated with PPDM in a large sample of Veterans with type 2 diabetes. METHODS We identified a cohort of Veterans with medically treated type 2 diabetes, who received Veterans Health Administration primary care during fiscal years 2012 and 2013. PPDM was defined by hemoglobin A1c levels uniformly >8.5% during fiscal year (FY) 2012, despite engagement with care during this period. We used FY 2012 demographic, comorbidity and medication data to describe PPDM in relation to better-controlled diabetes patients and created multivariable models to examine associations between clinical factors and PPDM. We also constructed multivariable models to explore the association between PPDM and FY 2013 care utilization. RESULTS In our cohort of diabetes patients (n = 435,820), 12% met criteria for PPDM. Patients with PPDM were younger than better-controlled patients, less often married, and more often Black/African-American and Hispanic or Latino/Latina. Of included comorbidities, only retinopathy (OR 1.68, 95% confidence interval (CI): 1.63,1.73) and nephropathy (OR 1.26, 95% CI: 1.19,1.34) demonstrated clinically significant associations with PPDM. Complex insulin regimens such as premixed (OR 10.80, 95% CI: 10.11,11.54) and prandial-containing regimens (OR 18.74, 95% CI: 17.73,19.81) were strongly associated with PPDM. Patients with PPDM had higher care utilization, particularly endocrinology care (RR 3.56, 95% CI: 3.47,3.66); although only 26.4% of patients saw endocrinology overall. CONCLUSION PPDM is strongly associated with complex diabetes regimens, although heterogeneity in care utilization exists. While there is evidence of underutilization, inadequacy of available care may also contribute to PPDM. Our findings should inform tailored approaches to meet the needs of PPDM, who are among the highest-risk, highest-cost patients with diabetes.
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Affiliation(s)
- Anastasia-Stefania Alexopoulos
- Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham Veterans Affairs Health Care System, Durham, NC, United States of America
- Division of Endocrinology, Duke University, Durham, NC, United States of America
| | - George L. Jackson
- Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham Veterans Affairs Health Care System, Durham, NC, United States of America
- Department of Population Health Sciences, Duke University, Durham NC, United States of America
- Division of General Internal Medicine, Duke University, Durham NC, United States of America
| | - David Edelman
- Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham Veterans Affairs Health Care System, Durham, NC, United States of America
- Division of General Internal Medicine, Duke University, Durham NC, United States of America
| | - Valerie A. Smith
- Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham Veterans Affairs Health Care System, Durham, NC, United States of America
- Department of Population Health Sciences, Duke University, Durham NC, United States of America
- Division of General Internal Medicine, Duke University, Durham NC, United States of America
| | - Theodore S. Z. Berkowitz
- Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham Veterans Affairs Health Care System, Durham, NC, United States of America
| | - Sandra L. Woolson
- Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham Veterans Affairs Health Care System, Durham, NC, United States of America
| | - Hayden B. Bosworth
- Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham Veterans Affairs Health Care System, Durham, NC, United States of America
- Department of Population Health Sciences, Duke University, Durham NC, United States of America
- Division of General Internal Medicine, Duke University, Durham NC, United States of America
- Department of Psychiatry & Behavioral Sciences, Duke University, Durham NC, United States of America
| | - Matthew J. Crowley
- Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham Veterans Affairs Health Care System, Durham, NC, United States of America
- Division of Endocrinology, Duke University, Durham, NC, United States of America
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109
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Cohen RM, Franco RS, Smith EP, Higgins JM. When HbA1c and Blood Glucose Do Not Match: How Much Is Determined by Race, by Genetics, by Differences in Mean Red Blood Cell Age? J Clin Endocrinol Metab 2019; 104:707-710. [PMID: 30445523 DOI: 10.1210/jc.2018-02409] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Accepted: 11/12/2018] [Indexed: 11/19/2022]
Abstract
Commentary placing genetic ancestry markers and racial difference in HbA1c in the context of more common variations in the HbA1c-average glucose relationship and their clinical implications.
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Affiliation(s)
- Robert M Cohen
- Division of Endocrinology, Department of Medicine, University of Cincinnati College of Medicine, Cincinnati, Ohio
- Cincinnati Veterans Affairs Medical Center, Cincinnati, Ohio
| | - Robert S Franco
- Division of Hematology, Department of Medicine, University of Cincinnati College of Medicine, Cincinnati, Ohio
| | - Eric P Smith
- Department of Medicine, University of Cincinnati College of Medicine, Cincinnati Ohio
| | - John M Higgins
- Center for Systems Biology, Massachusetts General Hospital, Boston, Massachusetts
- Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts
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Echouffo-Tcheugui JB, Sheng S, DeVore AD, Matsouaka RA, Hernandez AF, Yancy CW, Heidenreich PA, Bhatt DL, Fonarow GC. Glycated Hemoglobin and Outcomes of Heart Failure (from Get With the Guidelines-Heart Failure). Am J Cardiol 2019; 123:618-626. [PMID: 30553509 DOI: 10.1016/j.amjcard.2018.11.023] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2018] [Revised: 11/10/2018] [Accepted: 11/15/2018] [Indexed: 12/17/2022]
Abstract
Glycated hemoglobin (HbA1C) is a risk factor for new onset heart failure (HF). There is however a paucity of data evaluating its association with outcomes in patients with established HF. We assessed the relation of HbA1C with outcomes among hospitalized HF patients. Among 41,776 HF patients from 263 hospitals participating to the Get with the Guidelines-HF registry between January 2009 and March 2016, we related HbA1C to outcomes (in-hospital mortality, length of hospital stay, discharge to home, 30-day mortality, 30-day readmission, and 1-year mortality), using generalized estimating equation to account for within-hospital clustering and potential confounders. There were 68% of HF patients with diabetes and median HbA1C was 7.1%. Each percent change in HbA1C was associated with higher odds of discharge to home for HbA1C levels <6.5% (covariate-adjusted odds ratio [OR] 1.13 [95% confidence interval 1.04 to 1.12]) or ≥6.5% (OR 1.05 [1.02 to 1.07]). After stratification by diabetes status, this association remained significant only among patients with diabetes (ORs for HbA1C levels <6.5%: 1.17 [1.07 to 1.27]; and ≥6.5%: 1.06 [1.03 to 1.09]). Compared with the lowest HbA1C tertile (HbA1C ≤6.1%), patients in the highest HbA1C tertile (HbA1C 7.3% to 19%) were more likely to have a length of hospital stay >4 days (OR 1.10 [1.02 to 1.18]) and to be discharged home (OR 1.23 [1.14 to 1.33]). There were no significant association between HbA1C and the following outcomes: in-hospital mortality, 30-day mortality, 30-day readmission, and 1-year mortality. In conclusion, among hospitalized HF patients, HbA1C was associated with prolonged hospital stay and home discharge, but not with readmission, short-term, or intermediate-term mortality.
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Associations of dietary intake with cardiometabolic risk in a multi-ethnic cohort: a longitudinal analysis of the Determinants of Adolescence, now young Adults, Social well-being and Health (DASH) study. Br J Nutr 2019; 121:1069-1079. [DOI: 10.1017/s0007114519000291] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
AbstractUnfavourable dietary habits, such as skipping breakfast, are common among ethnic minority children and may contribute to inequalities in cardiometabolic disease. We conducted a longitudinal follow-up of a subsample of the UK multi-ethnic Determinants of Adolescent Social well-being and Health cohort, which represents the main UK ethnic groups and is now aged 21–23 years. We aimed to describe longitudinal patterns of dietary intake and investigate their impact on cardiometabolic risk in young adulthood. Participants completed a dietary behaviour questionnaire and a 24 h dietary intake recall; anthropometry, blood pressure, total cholesterol and HDL-cholesterol and HbA1c were measured. The cohort consisted of 107 White British, 102 Black Caribbean, 132 Black African, 98 Indian, 111 Bangladeshi/Pakistani and 115 other/mixed ethnicity. Unhealthful dietary behaviours such as skipping breakfast and low intake of fruits and vegetables were common (56, 57 and 63 %, respectively). Rates of skipping breakfast and low fruit and vegetable consumption were highest among Black African and Black Caribbean participants. BMI and cholesterol levels at 21–23 years were higher among those who regularly skipped breakfast at 11–13 years (BMI 1·41 (95 % CI 0·57, 2·26), P=0·001; cholesterol 0·15 (95 % CI –0·01, 0·31), P=0·063) and at 21–23 years (BMI 1·05 (95 % CI 0·22, 1·89), P=0·014; cholesterol 0·22 (95 % CI 0·06, 0·37), P=0·007). Childhood breakfast skipping is more common in certain ethnic groups and is associated with cardiometabolic risk factors in young adulthood. Our findings highlight the importance of targeting interventions to improve dietary behaviours such as breakfast consumption at specific population groups.
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Hivert MF, Christophi CA, Jablonski KA, Edelstein SL, Kahn SE, Golden SH, Dagogo-Jack S, Mather KJ, Luchsinger JA, Caballero AE, Barrett-Connor E, Knowler WC, Florez JC, Herman WH. Genetic Ancestry Markers and Difference in A1c Between African American and White in the Diabetes Prevention Program. J Clin Endocrinol Metab 2019; 104:328-336. [PMID: 30358859 PMCID: PMC6300069 DOI: 10.1210/jc.2018-01416] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 10/19/2018] [Indexed: 01/18/2023]
Abstract
PURPOSE HbA1c levels are higher in blacks than non-Hispanic whites (NHWs). We investigated whether genetics could explain this difference in Diabetes Prevention Program (DPP) participants. METHODS We tested (i) genetic variants causing hemoglobinopathies, (ii) a genetic risk score (GRS) based on 60 variants associated with HbA1c from genome-wide association meta-analysis, and (iii) principal component (PC) factors that capture continental ancestry derived from genetic markers distributed across the genome. RESULTS Of 2658 eligible DPP participants, 537 (20%) self-identified as black and 1476 (56%) as NHW. Despite comparable fasting and 2-hour glucose levels, blacks had higher HbA1c (mean ± SD = 6.2 ± 0.6%) compared with NHWs (5.8 ± 0.4%; P < 0.001). In blacks, the genetic variant causing sickle cell trait was associated with higher HbA1c [β (SE) = +0.44 (0.08)%; P = 2.1 × 10-4]. The GRS was associated with HbA1c in both blacks and NHWs. Self-identified blacks were distributed along the first PC axis, as expected in mixed ancestry populations. The first PC explained 60% of the 0.4% difference in HbA1c between blacks and NHWs, whereas the sickle cell variant explained 16% and GRS explained 14%. CONCLUSIONS A large proportion of HbA1c difference between blacks and NHWs was associated with the first PC factor, suggesting that unidentified genetic markers influence HbA1c in blacks in addition to nongenetic factors.
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Affiliation(s)
- Marie-France Hivert
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, Massachusetts
- Diabetes Research Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Department of Medicine, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | | | | | - Sharon L Edelstein
- The Biostatistics Center, George Washington University, Rockville, Maryland
| | - Steven E Kahn
- Division of Metabolism, Endocrinology and Nutrition, Department of Medicine, VA Puget Sound Health Care System and University of Washington, Seattle, Washington
| | - Sherita Hill Golden
- Department of Medicine, Division of Endocrinology, Diabetes, and Metabolism and Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland
| | - Samuel Dagogo-Jack
- Department of Medicine, Division of Endocrinology, Diabetes & Metabolism, The University of Tennessee Health Science Center, Memphis, Tennessee
| | - Kieren J Mather
- Indiana University School of Medicine, Indianapolis, Indiana
| | - José A Luchsinger
- Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, New York
| | | | | | - William C Knowler
- Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, Arizona
| | - Jose C Florez
- Diabetes Research Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, Massachusetts
| | - William H Herman
- Departments of Internal Medicine and Epidemiology, University of Michigan, Ann Arbor, Michigan
- Correspondence and Reprint Requests: William H. Herman, MD, MPH, c/o Diabetes Prevention Program Coordinating Center, George Washington University Biostatistics Center, 6110 Executive Boulevard, Suite 750, Rockville, Maryland 20852. E-mail:
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Annani-Akollor ME, Laing EF, Osei H, Mensah E, Owiredu EW, Afranie BO, Anto EO. Prevalence of metabolic syndrome and the comparison of fasting plasma glucose and HbA1c as the glycemic criterion for MetS definition in non-diabetic population in Ghana. Diabetol Metab Syndr 2019; 11:26. [PMID: 30949244 PMCID: PMC6431006 DOI: 10.1186/s13098-019-0423-0] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Accepted: 03/16/2019] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Glycated hemoglobin (HbA1c), owing to its ability to reflect glycemia over a relatively longer time span, is still been investigated as an adjunct test for fasting plasma glucose (FPG) to identify subjects at risk of metabolic syndrome (MetS) in some Caucasian populations. However, whether or not HbA1c can serve as an adjunct to FPG in the definition of MetS in the Ghanaian population remains unknown. This study determined the prevalence of MetS and evaluated HbA1c ≥ 5.6% and FPG ≥ 5.6 mmol/l as the glycemic component of MetS among non-diabetic population in Ghana. METHODS This was a case-control study conducted at St Francis Xavier Hospital, Assin Fosu, Central Region, Ghana. A total of 264 non-diabetic Ghanaian adults consisting of 158 newly diagnosed hypertensives and 106 normotensives, were recruited for the study. Fasting plasma insulin and glucose, HbA1c, and lipid profile was performed for each respondent. RESULTS Using the FPG as glycemic criterion, the overall MetS prevalence was 46.6%, 37.1%, and 12.5% according by the IDF, NCEP ATP III, and WHO criteria, respectively. The prevalence of MetS using the HbA1c criterion was 54.2%, 52.7%, and 42.4% by the IDF, NCEP ATP III and WHO criteria, respectively. The HbA1c criterion identified more participants with MetS compared to the FPG criterion with a good agreement between HbA1c and FPG using the IDF and NCEP ATP III criteria (κ = 0.484 to 0.899) respectively. However, the overlap between HbA1c and FPG based diagnosis of MetS was limited for the WHO criterion. CONCLUSION The prevalence of metabolic syndrome is high among non-diabetics in Ghana. Introduction of HbA1c in addition to FPG in the screening of MetS improves identification of more people with MetS who would otherwise have been missed when only FPG-based diagnosis of MetS is used; with a substantial agreement with FPG, except when using the WHO criteria.
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Affiliation(s)
- Max Efui Annani-Akollor
- Department of Molecular Medicine, School of Medical Sciences, College of Health Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Edwin Ferguson Laing
- Department of Molecular Medicine, School of Medical Sciences, College of Health Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Henry Osei
- Department of Molecular Medicine, School of Medical Sciences, College of Health Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Evans Mensah
- St Francis Xavier Hospital, Assin Fosu, Central Region Ghana
| | - Eddie-Williams Owiredu
- Department of Molecular Medicine, School of Medical Sciences, College of Health Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Bright Oppong Afranie
- Department of Molecular Medicine, School of Medical Sciences, College of Health Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Enoch Odame Anto
- Department of Molecular Medicine, School of Medical Sciences, College of Health Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
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Sameer AS, Banday MZ, Nissar S, Saeed SA. A Comparison of Biomarkers in the Assessment of Glycemic Control in Diabetes: Reviewing the Evidence. Curr Diabetes Rev 2019; 15:471-479. [PMID: 30961503 DOI: 10.2174/1389557519666190408197922] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Revised: 03/21/2019] [Accepted: 04/02/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND Diabetes Mellitus (DM) is a chronic life-long progressive multisystem heterogeneous metabolic disorder with complex pathogenesis. INTRODUCTION Hyperglycemia is not only one of the classical signs of DM, but it also serves as the pivotal prerequisite for the diagnosis of the disease. However, with the advancement in the field of analytical biochemistry, a number of alternative and specific biomarkers have been discovered which can be used for better diagnosis of the DM. In this review, we have discussed various aspects of DM and different biomarkers used in assessing glycemia. METHODOLOGY A thorough literature survey was conducted to identify various studies that reported the use of conventional and non-conventional markers for the assessment of glycemia in DM patients. CONCLUSION The accurate detection and hence diagnosis of DM has become easy and more specific with the use of various biomarkers.
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Affiliation(s)
- Aga S Sameer
- Department of Basic Medical Sciences, College of Medicine, King Saud Bin Abdul Aziz University for Health Sciences, National Guard Health Affairs, Jeddah, Saudi Arabia
- King Abdullah International Medical Research Centre (KAIMRC), King Abdulaziz Medical City, Jeddah, Saudi Arabia
| | - Mujeeb Z Banday
- Department of Biology, United Arab Emirates University (UAEU), Al Ain, Abu Dhabi, United Arab Emirates
| | - Saniya Nissar
- Department of Clinical Biochemistry, University of Kashmir, Hazratbal, Srinagar, Kashmir, India
| | - Sheikh A Saeed
- Department of Basic Medical Sciences, College of Medicine, King Saud Bin Abdul Aziz University for Health Sciences, National Guard Health Affairs, Jeddah, Saudi Arabia
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HbA1c: High in acute cerebral infarction and low in brain trauma. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2019; 162:293-306. [DOI: 10.1016/bs.pmbts.2019.01.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Abstract
The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.
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Vecera K, Luedtke S, Larumbe E. Correlation of maternal A1c with glucose infusion rate requirements in the newborn. J Neonatal Perinatal Med 2018; 11:137-143. [PMID: 29843266 DOI: 10.3233/npm-181749] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
OBJECTIVE Fetal hyperinsulinemia and neonatal hyperglycemia are complications of poor maternal glycemic control and may result in increased glucose infusion rate (GIR) requirements in infants of diabetic mothers (IDMs). The objectives of this study were to correlate maternal A1c levels with GIR requirements in IDMs, establish an A1c threshold predictive for GIR requirements, and identify associations between A1c levels and complications in IDMs. STUDY DESIGN A retrospective review of paired maternal A1c values and GIR requirements of IDMs were compared via logistic regression analysis. A likelihood ratio was calculated to correlate A1c levels with GIR requirements, and identify a maternal A1c threshold. RESULTS Increasing A1c values were significantly correlated with GIR≥5 mg/kg/min (OR, 1.37; 95% CI 1.04-1.79, p = 0.021). Macrosomia was the most frequent complication (OR, 1.31; 95% CI 1.04-1.67, p = 0.022) and A1c > 6.8% was predictive for increased GIR requirements. CONCLUSION Increased A1c values were significantly associated with GIR requirements≥5 mg/kg/min. Increased maternal A1c is significantly associated with complications in newborns, specifically macrosomia. A maternal A1c of 6.8% was identified as a threshold predictive of increased GIR requirements.
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Affiliation(s)
- K Vecera
- Department of Pharmacy, Mercy Children's Hospital, St. Louis, MO, USA
| | - S Luedtke
- Department of Pharmacy Practice, Texas Tech University Health Sciences Center, Amarillo, TX, USA
| | - E Larumbe
- Texas Tech University Health Sciences Center, Clinical Research Institute, Amarillo, TX, USA
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Lee B, Heo YJ, Lee YA, Lee J, Kim JH, Lee SY, Shin CH, Yang SW. Association between hemoglobin glycation index and cardiometabolic risk factors in Korean pediatric nondiabetic population. Ann Pediatr Endocrinol Metab 2018; 23:196-203. [PMID: 30599480 PMCID: PMC6312919 DOI: 10.6065/apem.2018.23.4.196] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Accepted: 11/28/2018] [Indexed: 12/18/2022] Open
Abstract
PURPOSE The hemoglobin glycation index (HGI) represents the degree of nonenzymatic glycation and has been positively associated with cardiometabolic risk factors (CMRFs) and cardiovascular disease in adults. This study aimed to investigate the association between HGI, components of metabolic syndrome (MS), and alanine aminotransferase (ALT) in a pediatric nondiabetic population. METHODS Data from 3,885 subjects aged 10-18 years from the Korea National Health and Nutrition Examination Survey (2011-2016) were included. HGI was defined as subtraction of predicted glycated hemoglobin (HbA1c) from measured HbA1c. Participants were divided into 3 groups according to HGI tertile. Components of MS (abdominal obesity, fasting glucose, triglycerides, high-density lipoprotein cholesterol, and blood pressure), and proportion of MS, CMRF clustering (≥2 of MS components), and elevated ALT were compared among the groups. RESULTS Body mass index (BMI) z-score, obesity, total cholesterol, ALT, abdominal obesity, elevated triglycerides, and CMRF clustering showed increasing HGI trends from lower-to-higher tertiles. Multiple logistic regression analysis showed the upper HGI tertile was associated with elevated triglycerides (odds ratio, 1.65; 95% confidence interval, 1.18-2.30). Multiple linear regression analysis showed HGI level was significantly associated with BMI z-score, HbA1c, triglycerides, and ALT. When stratified by sex, age group, and BMI category, overweight/obese subjects showed linear HGI trends for presence of CMRF clustering and ALT elevation. CONCLUSION HGI was associated with CMRFs in a Korean pediatric population. High HGI might be an independent risk factor for CMRF clustering and ALT elevation in overweight/obese youth. Further studies are required to establish the clinical relevance of HGI for cardiometabolic health in youth.
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Affiliation(s)
- Bora Lee
- Department of Pediatrics, Seoul National University Children’s Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - You Jung Heo
- Department of Pediatrics, Seoul National University Children’s Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Young Ah Lee
- Department of Pediatrics, Seoul National University Children’s Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Jieun Lee
- Department of Pediatrics, Inje University Ilsan Paik Hospital, Goyang, Korea
| | - Jae Hyun Kim
- Department of Pediatrics, Seoul National University Bundang Hospital, Seongnam, Korea,Address for correspondence: Jae Hyun Kim, MD Department of Pediatrics, Seoul National University Bundang Hospital, Seongnam 13620, Korea Tel: +82-31-787-7287 Fax: +82-31-787-4054 E-mail:
| | - Seong Yong Lee
- Department of Pediatrics, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Korea
| | - Choong Ho Shin
- Department of Pediatrics, Seoul National University Children’s Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Sei Won Yang
- Department of Pediatrics, Seoul National University Children’s Hospital, Seoul National University College of Medicine, Seoul, Korea
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Kim MK, Jeong JS, Yun JS, Kwon HS, Baek KH, Song KH, Ahn YB, Ko SH. Hemoglobin glycation index predicts cardiovascular disease in people with type 2 diabetes mellitus: A 10-year longitudinal cohort study. J Diabetes Complications 2018; 32:906-910. [PMID: 30121206 DOI: 10.1016/j.jdiacomp.2018.08.007] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 08/03/2018] [Accepted: 08/06/2018] [Indexed: 01/09/2023]
Abstract
BACKGROUND AND AIMS Previous studies have suggested that the hemoglobin glycation index (HGI) can be used as a predictor of diabetes-related complications. We examined the prognostic significance of a high HGI for cardiovascular disease (CVD) in an ongoing hospital-based cohort. METHODS From March 2003 to December 2004, 1302 consecutive patients with type 2 diabetes and without a prior history of CVD were enrolled. CVD was defined as the occurrence of coronary artery disease or ischemic stroke. The HGI was calculated as the measured glycated hemoglobin (HbA1c) minus predicted HbA1c. Predicted HbA1c were calculated for 1302 participants by inserting fasting blood glucose (FBG) into the equation, Predicted HbA1c level = 0.02106 × FBG [mg/dL] + 4.973. Cox proportional hazards models were used to identify the associations between the HGI and CVD after adjusting for confounding variables. RESULTS During 11.1 years of follow-up, 225 participants (17.2%) were newly diagnosed with CVD. The baseline HGI was significantly higher in subjects with incident CVD than in those without CVD, although the baseline FBG levels did not differ according to the occurrence of CVD. Compared with patients without CVD, those with CVD were older, had a longer duration of diabetes and hypertension, and used more insulin at baseline. A Cox hazard regression analysis revealed that the development of CVD was significantly associated with baseline HGI (hazard ratio [HR], 1.94; 95% confidence interval [CI], 1.31-2.87; p < 0.001, comparing the highest and lowest quartiles of HGI). This relationship was unchanged after additional adjustment for baseline HbA1c level (HR, 1.74; 95% CI, 1.08-2.81). The HRs of HbA1c in relation to outcomes were similar to or lower than those seen for HGI. After adjustment for HGI, the effect of the highest HbA1c on incident CVD disappeared. CONCLUSIONS High HGI was independently associated with incident CVD in patients with type 2 diabetes. Patients with high HGI at baseline had a higher inherent risk for CVD.
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Affiliation(s)
- Mee Kyoung Kim
- Division of Endocrinology and Metabolism, Department of Internal Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Jee Sun Jeong
- Division of Endocrinology and Metabolism, Department of Internal Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Jae-Seung Yun
- Division of Endocrinology and Metabolism, Department of Internal Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Hyuk-Sang Kwon
- Division of Endocrinology and Metabolism, Department of Internal Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Ki Hyun Baek
- Division of Endocrinology and Metabolism, Department of Internal Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Ki-Ho Song
- Division of Endocrinology and Metabolism, Department of Internal Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Yu-Bae Ahn
- Division of Endocrinology and Metabolism, Department of Internal Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Seung-Hyun Ko
- Division of Endocrinology and Metabolism, Department of Internal Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
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Punthakee Z, Goldenberg R, Katz P. Definition, Classification and Diagnosis of Diabetes, Prediabetes and Metabolic Syndrome. Can J Diabetes 2018; 42 Suppl 1:S10-S15. [PMID: 29650080 DOI: 10.1016/j.jcjd.2017.10.003] [Citation(s) in RCA: 369] [Impact Index Per Article: 61.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Indexed: 12/16/2022]
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Abstract
PURPOSE OF REVIEW Using a global perspective, this review collates evidence on the heterogeneity of prediabetes definitions and diagnostic methods, their clinical and public health implications, and discusses possible options for improvement. RECENT FINDINGS Our review notes that the concept of prediabetes is increasingly recognized worldwide, but against a background of non-uniform definition and diagnostic criteria. This results in widely varying burden estimation. Current evidence shows a variety of prediabetes phenotypes. This reflects biological and diagnostic heterogeneity, resulting from the use of different tests (glucose or HbA1C) and thresholds to define prediabetes. The biological and diagnostic variabilities have implications for the characterization of the burden of prediabetes, natural history, prognosis, screening, implementation of lifestyle or drug interventions to mitigate related health risks, and monitoring of the effects of such interventions.
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Affiliation(s)
- Justin B Echouffo-Tcheugui
- Department of Medicine, Division of Endocrinology, Diabetes and Hypertension, Brigham and Women's Hospital/Harvard Medical School, 221 Longwood Avenue, Boston, MA, 02115, USA.
| | - Andre P Kengne
- Non-Communicable Diseases Research Unit, South African Medical Research Council, Cape Town, South Africa
| | - Mohammed K Ali
- Department of Family and Preventive Medicine, Emory University, Atlanta, GA, USA
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
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Biological and socioeconomic determinants of prediabetes in youth: an analysis using 2007 to 2011 Canadian Health Measures Surveys. Pediatr Res 2018; 84:248-253. [PMID: 29899385 DOI: 10.1038/s41390-018-0025-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 03/24/2018] [Accepted: 04/01/2018] [Indexed: 12/22/2022]
Abstract
OBJECTIVES To describe rates of prediabetes among youth in Canada and the associated social and biological characteristics. METHODS We analyzed the cross-sectional data from the first (2007-2009) and second (2009-2011) cycles of the Canadian Health Measures Survey (CHMS) for youth aged 6-19 years. Prediabetes was defined using the glycated hemoglobin (A1C) guidelines set out by the American Diabetes Association (ADA) and the Canadian Diabetes Association (CDA) of A1C ranges 5.7-6.4% (38.8-46.4 mmol/mol) and 6.0-6.4% (42.1-46.4 mmol/mol), respectively. RESULTS An elevated A1C was observed in 22.8% of our sample (n = 3449) based on the ADA definition and 5.2% of youth using the CDA definition. Independent predictors in a fully adjusted model for prediabetes were non-White (odds ratio (OR) 2.62: 95% Confidence intervals 2.05-3.35), obese (OR 1.53: 1.19-1.96), less physically active youth (0.97: 0.95-0.99), and parents with high school education or less (1.34: 1.02-1.74). Moreover, significant regional variations were noted with higher rates for all regions except Ontario. CONCLUSION Prediabetes is relatively common in Canada and associated with common biologic and socioeconomic factors. Importantly, regular physical activity was significantly associated with reduced odds of prediabetes. Targeted screening and continued emphasis on physical activity may help curb the increasing rates of prediabetes.
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Impact of Patient-Centered Medical Home Implementation on Diabetes Control in the Veterans Health Administration. J Gen Intern Med 2018; 33:1276-1282. [PMID: 29611089 PMCID: PMC6082213 DOI: 10.1007/s11606-018-4386-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Revised: 01/04/2018] [Accepted: 02/27/2018] [Indexed: 10/17/2022]
Abstract
BACKGROUND Given its widespread dissemination across primary care, the Veterans Health Administration (VA) is an ideal setting to examine the impact of the patient-centered medical home (PCMH) on diabetes outcomes. OBJECTIVE To assess the impact of PCMH implementation on diabetes outcomes among patients receiving care in the Veterans Health Administration. DESIGN Retrospective cohort analysis and multilevel logistic regression. PATIENTS Twenty thousand eight hundred fifty-eight patients in one Midwest VA network who had a diabetes diagnosis in both 2009 and 2012 and who received primary care between October 1, 2008 and September 30, 2009. MAIN MEASURES Glycemic and lipid control using VA quality indicators [hemoglobin (Hb) A1c < 9%, low-density lipoprotein cholesterol (LDL-C) < 100 mg/dL]. KEY RESULTS Odds of glycemic control were lower in 2012 than 2009 (OR = 0.72, 95% CI = 0.67-0.77, p < 0.001), and this change in control over time varied by race (OR of the interaction between time and race = 1.18, 95% CI = 1.02-1.36, p = 0.028). While the disparity in glycemic control between white and black patients persisted post-PCMH, the magnitude of the disparity was smaller in 2012 compared to 2009 (2012: OR = 1.32, 95% CI = 1.18-1.47, p < 0.0001 and 2009: OR = 1.59, 95% CI = 1.39-1.82, p < 0.0001). Odds of lipid control did not significantly change between 2009 and 2012 and change did not vary by race and/or gender. CONCLUSIONS Although there were no significant improvements in odds of lipid control, and odds of glycemic control decreased following PCMH implementation, there was evidence of reduced racial disparities in glycemic control post-PCMH implementation.
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Menke A, Casagrande S, Cowie CC. Contributions of A1c, fasting plasma glucose, and 2-hour plasma glucose to prediabetes prevalence: NHANES 2011-2014. Ann Epidemiol 2018; 28:681-685.e2. [PMID: 30122354 DOI: 10.1016/j.annepidem.2018.07.012] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2017] [Revised: 07/19/2018] [Accepted: 07/23/2018] [Indexed: 10/28/2022]
Abstract
PURPOSE Our goal was to characterize the contributions of A1c, fasting plasma glucose, and 2-hour plasma glucose to prediabetes prevalence and to characterize how those contributions differ among U.S. population subgroups. METHODS In the 2011-2014 National Health and Nutrition Examination Survey, a nationally representative sample of the U.S. population, among participants without diabetes (N = 3387), we created area-proportional three-Venn diagrams showing the proportion above the prediabetes cutpoint for each of the three markers in the overall population and in subgroups defined by age, race/ethnicity, sex, and body mass index. RESULTS In the overall population, 28.3% had fasting plasma glucose above the prediabetes cutpoint, 21.7% had A1c above the prediabetes cutpoint, and 13.3% had 2-hour plasma glucose above the prediabetes cutpoint. Adolescents and young adults tended to have only one marker exceed the prediabetes cutpoint, while older age groups tended to have multiple markers above the prediabetes cutpoint. For non-Hispanic whites, non-Hispanic blacks, non-Hispanic Asians, and Mexican-Americans, the unadjusted total percent above the A1c cutpoint was 19.3%, 36.4%, 20.5%, and 21.4%, respectively. CONCLUSIONS We provide a graphic reference showing fasting plasma glucose was the largest contributor to prediabetes prevalence in the overall population, followed by A1c and then 2-hour plasma glucose.
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Affiliation(s)
- Andy Menke
- Social & Scientific Systems, Inc., Silver Spring, MD.
| | | | - Catherine C Cowie
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD
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Vajravelu ME, Lee JM. Identifying Prediabetes and Type 2 Diabetes in Asymptomatic Youth: Should HbA1c Be Used as a Diagnostic Approach? Curr Diab Rep 2018; 18:43. [PMID: 29868987 PMCID: PMC7799173 DOI: 10.1007/s11892-018-1012-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
PURPOSE OF REVIEW Because the incidence of type 2 diabetes and prediabetes in children is rising, routine screening of those at risk is recommended. In 2010, the ADA made the recommendation to include hemoglobin A1c (HbA1c) as a diagnostic test for diabetes, in addition to the oral glucose tolerance test or fasting plasma glucose. Our objective was to assess the pediatric literature with regard to HbA1c test performance and discuss advantages and disadvantages of use of the test for diagnostic purposes. RECENT FINDINGS HbA1c has a number of advantages, including elimination of the need for fasting, lower variability, assay standardization, and long-term association with future development of diabetes. It also has many drawbacks. It can be affected by a number of non-glycemic factors, including red blood cell turnover, hemoglobinopathies, medications, race, and age. In particular, it performs differently in children compared with adults, generally with lower sensitivity for prediabetes (as low as 0-5% in children vs 23-27% in adults) and lower area under the receiver operating characteristic curve (AUC) (0.53 vs 0.73 for prediabetes), and it has lower efficacy at a higher cost, compared with other tests of glycemia. Finally, HbA1c may perform very differently across diverse populations according to race/ethnicity; in Chinese populations, the proportion of individuals classified with prediabetes based on HbA1c predominates compared with IFG (77% for HbA1c vs 27.7% for IFG), whereas in US populations, it is the opposite (24.8% for HbA1c vs 80.1% for FPG). HbA1c is controversial because although it is convenient, it is not a true measure of glycemia. The interpretation of HbA1c results requires a nuanced understanding that many primary care physicians who are ordering the test in greater numbers do not possess. Alternative markers of glycemia may hold promise for the future but are not yet endorsed for use in practice. Further studies are needed to determine appropriate thresholds for screening tests and the long-term impact of screening and identification.
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Affiliation(s)
- Mary Ellen Vajravelu
- Division of Endocrinology and Diabetes, The Children's Hospital of Philadelphia, 11NW30, 3401 Civic Center Blvd, Philadelphia, PA, 19104, USA
| | - Joyce M Lee
- University of Michigan, 300 NIB, Room 6E14, Campus Box 5456, Ann Arbor, MI, 48109, USA.
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Brown SA, Perkison WB, García AA, Cuevas HE, Velasquez MM, Winter MA, Hanis CL. The Starr County Border Health Initiative: Focus Groups on Diabetes Prevention in Mexican Americans. THE DIABETES EDUCATOR 2018; 44:293-306. [PMID: 29644932 PMCID: PMC6349423 DOI: 10.1177/0145721718770143] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Purpose The purpose of the study was to conduct focus groups with Mexican Americans in an impoverished rural community on the Texas-Mexico border to identify current barriers to adopting healthier lifestyles and to obtain recommendations for diabetes prevention. Methods Three separate 2-hour focus groups were led by an experienced bilingual Mexican American moderator. Interviews included questions about cultural factors and barriers that influence lifestyle behaviors, aspects of previous diabetes self-management interventions that were helpful for motivating behavioral change, and recommendations for diabetes prevention. Results Twenty-seven participants attended a focus group session; each session involved 7 to 12 informants. Individuals were diagnosed with prediabetes or type 2 diabetes mellitus; most were female, foreign born, and Spanish speaking. Interviews documented the cultural importance of food. Informants raised priority issues for diabetes prevention, including the need to learn how to prepare healthier foods and track caloric intake. Major barriers to healthier lifestyles included high costs of healthy foods, fatigue from busy schedules and working multiple jobs, a cultural view that exercise is a waste of valuable time, and fear of deportation. Conclusions Cultural influences and barriers to implementing healthy lifestyles should be assessed regularly and strategies implemented to overcome them. Such factors may change as environmental, sociocultural, and political environments change.
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Affiliation(s)
| | - William B Perkison
- School of Public Health, The University of Texas Health Science Center at Houston
| | | | | | | | - Mary A Winter
- School of Nursing, The University of Texas at Austin
| | - Craig L Hanis
- School of Public Health, The University of Texas Health Science Center at Houston
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Sayed A, Alyafei F, De Sanctis V, Soliman A, Elgamal M. Translating the HbA1c assay into estimated average glucose values in children and adolescents with type 1 diabetes mellitus. ACTA BIO-MEDICA : ATENEI PARMENSIS 2018; 89:22-26. [PMID: 30049928 DOI: 10.23750/abm.v89is4.7357] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Indexed: 11/23/2022]
Abstract
OBJECTIVE The A1c assay, expressed as the percent of hemoglobin that is glycated, measures chronic glycemia and is widely used to judge the adequacy of diabetes treatment and adjust therapy. Day-to-day management is guided by self-monitoring of capillary glucose concentrations (milligrams per decilitre or millimoles per litter) as well as by using continuous glucose monitoring systems (CGMS). We found a mathematical relationship between A1c and average glucose (AG) levels measured by CGMS over 5 days and determined the correlation between the variable CGMS parameters and HbA1c in 50 children with type 1 diabetes mellitus (DM-1) on MDI therapy. RESEARCH DESIGN AND METHODS A total of 50 diabetic children randomly selected from a cohort of children with DM-1 were included in the analyses. A1c levels obtained at the end of 3 months and measured in a central laboratory were compared with the AG levels during the previous 5 days recorded by CGMS. AG was calculated by combining weighted results from 5 days of continuous glucose monitoring performed before measuring HbA1c, with 3-5 point daily self-monitoring of capillary (fingerstick) glucose. RESULTS Linear regression analysis between the A1c and AG values provided the tightest correlations HbA1c=0.0494 MG- 2E-14, R2=0.90, P<0.0001), allowing calculation of an estimated average glucose (eAG) for A1c values. CONCLUSIONS Our study showed a linear relationship between HbA1C and AG values measured by CGMS for 5 days before HbA1c measurement. The AG can be easily calculated using a formula derived from linear regression analysis of HbA1c data obtained in our diabetic children.
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Affiliation(s)
- Ahmed Sayed
- Department of Pediatrics, Hamad Medical Center, Doha, Qatar.
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Ding L, Xu Y, Liu S, Bi Y, Xu Y. Hemoglobin A1c and diagnosis of diabetes. J Diabetes 2018; 10:365-372. [PMID: 29292842 DOI: 10.1111/1753-0407.12640] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Revised: 12/11/2017] [Accepted: 12/27/2017] [Indexed: 02/06/2023] Open
Abstract
The prevalence of diabetes is increasing markedly worldwide, especially in China. Hemoglobin A1c is an indicator of mean blood glucose concentrations and plays an important role in the assessment of glucose control and cardiovascular risk. In 2010, the American Diabetes Association included HbA1c ≥6.5% into the revised criteria for the diagnosis of diabetes. However, the debate as to whether HbA1c should be used to diagnose diabetes is far from being settled and there are still unanswered questions regarding the cut-off value of HbA1c for diabetes diagnosis in different populations and ethnicities. This review briefly introduces the history of HbA1c from discovery to diabetes diagnosis, key steps towards using HbA1c to diagnose diabetes, such as standardization of HbA1c measurements and controversies regarding HbA1c cut-off points, and the performance of HbA1c compared with glucose measurements in the diagnosis of diabetes.
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Affiliation(s)
- Lin Ding
- State Key Laboratory of Medical Genomics, Key Laboratory for Endocrine and Metabolic Diseases of Ministry of Health, National Clinical Research Center for Metabolic Diseases, Collaborative Innovation Center of Systems Biomedicine, and Shanghai Clinical Center for Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Yu Xu
- State Key Laboratory of Medical Genomics, Key Laboratory for Endocrine and Metabolic Diseases of Ministry of Health, National Clinical Research Center for Metabolic Diseases, Collaborative Innovation Center of Systems Biomedicine, and Shanghai Clinical Center for Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Shanshan Liu
- State Key Laboratory of Medical Genomics, Key Laboratory for Endocrine and Metabolic Diseases of Ministry of Health, National Clinical Research Center for Metabolic Diseases, Collaborative Innovation Center of Systems Biomedicine, and Shanghai Clinical Center for Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Yufang Bi
- State Key Laboratory of Medical Genomics, Key Laboratory for Endocrine and Metabolic Diseases of Ministry of Health, National Clinical Research Center for Metabolic Diseases, Collaborative Innovation Center of Systems Biomedicine, and Shanghai Clinical Center for Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Yiping Xu
- Department of Research and Development, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
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Raghav A, Ahmad J. Glycated albumin in chronic kidney disease: Pathophysiologic connections. Diabetes Metab Syndr 2018; 12:463-468. [PMID: 29396251 DOI: 10.1016/j.dsx.2018.01.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Accepted: 01/24/2018] [Indexed: 12/15/2022]
Abstract
Nephropathy in diabetes patients is the most common etiology of end-stage kidney disease (ESKD). Strict glycemic control reduces the development and progression of diabetes-related complications, and there is evidence that improved metabolic control improves outcomes in subjects having diabetes mellitus with advanced chronic kidney disease (CKD). Glycemic control in people with kidney disease is complex. Changes in glucose and insulin homoeostasis may occur as a consequence of loss of kidney function and dialysis. The reliability of measures of long-term glycemic control is affected by CKD and the accuracy of glycated haemoglobin (HbA1c) in the setting of CKD and ESKD is questioned. Despite the altered character of diabetes in CKD, current guidelines for diabetes management are not specifically adjusted for this patient group. The validity of indicators of long-term glycemic control has been the focus of increased recent research. This review discusses the current understanding of commonly used indicators of metabolic control (HbA1c, fructosamine, glycated albumin) in the setting of advanced CKD.
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Affiliation(s)
- Alok Raghav
- Rajiv Gandhi Centre for Diabetes and Endocrinology, Aligarh Muslim University, Aligarh, India
| | - Jamal Ahmad
- Rajiv Gandhi Centre for Diabetes & Endocrinology, J.N Medical College, Aligarh Muslim University, Aligarh, 202002, India.
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van Steen SC, Woodward M, Chalmers J, Li Q, Marre M, Cooper ME, Hamet P, Mancia G, Colagiuri S, Williams B, Grobbee DE, DeVries JH. Haemoglobin glycation index and risk for diabetes-related complications in the Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation (ADVANCE) trial. Diabetologia 2018; 61:780-789. [PMID: 29308539 PMCID: PMC6448976 DOI: 10.1007/s00125-017-4539-1] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Accepted: 11/21/2017] [Indexed: 12/21/2022]
Abstract
AIMS/HYPOTHESIS Previous studies have suggested that the haemoglobin glycation index (HGI) can be used as a predictor of diabetes-related complications in individuals with type 1 and type 2 diabetes. We investigated whether HGI was a predictor of adverse outcomes of intensive glucose lowering and of diabetes-related complications in general, using data from the Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation (ADVANCE) trial. METHODS We studied participants in the ADVANCE trial with data available for baseline HbA1c and fasting plasma glucose (FPG) (n = 11,083). HGI is the difference between observed HbA1c and HbA1c predicted from a simple linear regression of HbA1c on FPG. Using Cox regression, we investigated the association between HGI, both categorised and continuous, and adverse outcomes, considering treatment allocation (intensive or standard glucose control) and compared prediction of HGI and HbA1c. RESULTS Intensive glucose control lowered mortality risk in individuals with high HGI only (HR 0.74 [95% CI 0.61, 0.91]; p = 0.003), while there was no difference in the effect of intensive treatment on mortality in those with high HbA1c. Irrespective of treatment allocation, every SD increase in HGI was associated with a significant risk increase of 14-17% for macrovascular and microvascular disease and mortality. However, when adjusted for identical covariates, HbA1c was a stronger predictor of these outcomes than HGI. CONCLUSIONS/INTERPRETATION HGI predicts risk for complications in ADVANCE participants, irrespective of treatment allocation, but no better than HbA1c. Individuals with high HGI have a lower risk for mortality when on intensive treatment. Given the discordant results and uncertain relevance beyond HbA1c, clinical use of HGI in type 2 diabetes cannot currently be recommended.
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Affiliation(s)
- Sigrid C van Steen
- Department of Endocrinology, Academic Medical Centre, University of Amsterdam, Postbus 22660, 1100 DD, Amsterdam, the Netherlands.
| | - Mark Woodward
- The George Institute for Global Health, University of Sydney, Sydney, NSW, Australia
- Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA
| | - John Chalmers
- The George Institute for Global Health, University of Sydney, Sydney, NSW, Australia
| | - Qiang Li
- The George Institute for Global Health, University of Sydney, Sydney, NSW, Australia
| | - Michel Marre
- Department of Endocrinology, Hôpital Bichat-Claude Bernard, Université Paris, Paris, France
| | - Mark E Cooper
- Diabetes Domain, Baker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Pavel Hamet
- Centre de Rechercher, Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Québec, Canada
| | - Giuseppe Mancia
- Department of Medicine and Surgery, University of Milan-Bicocca, Milan, Italy
- Istituto Auxologico Italiano, Milan, Italy
| | - Stephen Colagiuri
- Boden Institute of Obesity, Nutrition and Exercise, University of Sydney, Sydney, NSW, Australia
| | - Bryan Williams
- National Institute of Health Research UCL Hospitals Biomedical Research Centre, London, UK
| | - Diederick E Grobbee
- Julius Clinical, Zeist, the Netherlands
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, the Netherlands
| | - J Hans DeVries
- Department of Endocrinology, Academic Medical Centre, University of Amsterdam, Postbus 22660, 1100 DD, Amsterdam, the Netherlands
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Moore KJ, Dunn EC, Marcus EN, Koru-Sengul T. Glycaemic indices and haemoglobin A1c as predictors for non-healing ulcers. J Wound Care 2018; 27:S6-S11. [PMID: 29641344 DOI: 10.12968/jowc.2018.27.sup4.s6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
OBJECTIVE Non-healing lower extremity ulcers (NHLU) are a common podiatric complication of diabetes, with poor glycaemic control as a risk factor for development. Glycaemic indices, such as haemoglobin A1c (HbA1c) and fasting plasma glucose (FPG), are used to diagnose and to monitor diabetes. Using a population-based, nationally representative sample, we evaluate the relationship between glycaemic indices and NHLU (as defined by the patient) to propose glycaemic thresholds for clinical suspicion of patient NHLU status. METHOD Using data from the 1999-2004 National Health and Nutrition Examination Surveys (NHANES), a total of 9769 adults (≥40 years old) with available self-reported diabetes and NHLU status were analysed. Glycaemic index markers, including FPG and HbA1c, were assessed via laboratory analysis from serum blood samples. Logistic regression models were fitted to determine optimal thresholds for FPG and HbA1c to predict NHLU status. RESULTS Compared with those without NHLU, NHLU patients were older, male, had higher rates of diabetes, were more likely to take insulin, and had lower total cholesterol. Youden's Index for NHLU identified the optimal FPG threshold as 117.7mg/dl (sensitivity: 33.5%; specificity: 82.6%). The optimal HbA1c threshold was 5.9% (sensitivity: 43.2%; specificity: 77.3%). HbA1c (Odds ratio (OR) 2.44, 95% Confidence Interval (CI) 1.96-3.05; Area under curve (AUC) 0.62) was a stronger discriminator of NHLU compared to FPG (OR 2.19; 95%CI 1.57-3.05; AUC 0.60). CONCLUSION This study identified glycaemic thresholds for suspicion of NHLU development that are lower than the glucose goal levels recommended as optimal by the American Diabetes Association. Health professionals should be aware of these glycaemic indices when screening patients with diabetes for NHLU. Future longitudinal and validation studies are necessary to better discern the ideal glycaemic index thresholds to identify NHLU.
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Affiliation(s)
- Kevin J Moore
- University of Miami Miler School of Medicine, Clinical Research Center, Miami, FL
| | - Erin C Dunn
- Pediatrics/Psychiatry/Child and Adolescent Psychiatry Resident, Pediatric Residency Office of Floating Hospital for Children at Tufts Medical Center, Boston, MA
| | - Erin N Marcus
- Associate Professor of Clinical Medicine; Division of General Internal Medicine, University of Miami Miller School of Medicine, Miami, FL
| | - Tulay Koru-Sengul
- Associate Professor, University of Miami Miler School of Medicine, Clinical Research Center, Miami, FL
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Hong JW, Noh JH, Kim DJ. Association between White Blood Cell Counts within Normal Range and Hemoglobin A1c in a Korean Population. Endocrinol Metab (Seoul) 2018; 33:79-87. [PMID: 29388402 PMCID: PMC5874199 DOI: 10.3803/enm.2018.33.1.79] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Revised: 11/29/2017] [Accepted: 12/27/2017] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND We examined whether white blood cell (WBC) count levels within normal range, could be associated with hemoglobin A1c (HbA1c) levels. METHODS Among the 11,472 people (≥19 years of age) who participated in the 2011 to 2012 Korea National Health and Nutrition Examination, subjects with chronic disease or illness, including 807 patients with diabetes currently taking anti-diabetic medications and/or 1,149 subjects with WBC levels <4,000 or >10,000/μL were excluded. RESULTS Overall, adjusted HbA1c levels increased across the WBC quartiles (5.55%±0.01%, 5.58%±0.01%, 5.60%±0.01%, and 5.65%±0.01%, P<0.001) after adjusting for confounding factors, such as age, gender, fasting plasma glucose, college graduation, smoking history, waist circumference, presence of hypertension, serum total cholesterol, serum triglyceride, and presence of anemia. The adjusted proportions (%) of HbA1c levels of ≥5.7%, ≥6.1%, and ≥6.5% showed significant increases across WBC quartiles (P<0.001, P=0.002, and P=0.022, respectively). Logistic regression analyses of WBC quartiles for the risk of HbA1c levels of ≥5.7%, ≥6.1%, and ≥6.5%, using the variables above as covariates, showed that the odds ratios of the fourth quartile of WBCs were 1.59 (95% confidence interval [CI], 1.35 to 1.89; P<0.001), 1.78 (95% CI, 1.31 to 2.42; P<0.001), and 2.03 (95% CI, 1.13 to 3.64; P=0.018), using the first quartile of WBCs as the reference. CONCLUSION HbA1c levels were positively associated with WBC levels within normal range in a general adult population.
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Affiliation(s)
- Jae Won Hong
- Department of Internal Medicine, Inje University Ilsan Paik Hospital, Inje University College of Medicine, Goyang, Korea
| | - Jung Hyun Noh
- Department of Internal Medicine, Inje University Ilsan Paik Hospital, Inje University College of Medicine, Goyang, Korea
| | - Dong Jun Kim
- Department of Internal Medicine, Inje University Ilsan Paik Hospital, Inje University College of Medicine, Goyang, Korea.
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Abstract
PURPOSE OF REVIEW Diabetes is the leading cause of kidney disease globally. Diabetic kidney disease (DKD) is a heterogeneous disorder manifested as albuminuria and/or decreasing GFR. Hyperglycemic burden is the major contributor to the development of DKD. In this article, we review the evidence for the contribution of glycemic variability and the pitfalls associated with use of hemoglobin A1c (A1C), the gold standard for assessment of glucose control, in the setting of DKD. RECENT FINDINGS Glycemic variability, characterized by swings in blood glucose levels, can result in generation of mitochondrial reactive oxygen species, a putative inciting factor for hyperglycemia-induced alterations in intracellular metabolic pathways. While there is indirect evidence supporting the role of glycemic variability in the pathogenesis of DKD, definitive data are lacking. A1C has many limitations and is a particularly suboptimal measure in patients with kidney disease, because its accuracy is compromised by variables affecting RBC survival and other factors. Continuous glucose monitoring (CGM) technology has the potential to enable us to use glucose as a more important clinical tool, for a more definitive understanding of glucose variability and its role in DKD. Glycemic variability may be a factor in the development of DKD, but definitive evidence is lacking. Currently, all available glycemic biomarkers, including A1C, have limitations and in the setting of DKD and should be used cautiously. Emerging data suggest that personal and professional CGM will play an important role in managing diabetes in patients with DKD, where risk of hypoglycemia is high.
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Affiliation(s)
- Savitha Subramanian
- Division of Metabolism, Endocrinology, and Nutrition, University of Washington, 4245 Roosevelt Way NE, Box 354691, Seattle, WA, 98105, USA.
| | - Irl B Hirsch
- Division of Metabolism, Endocrinology, and Nutrition, University of Washington, 4245 Roosevelt Way NE, Box 354691, Seattle, WA, 98105, USA
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Distribution of glycated haemoglobin and its determinants in Korean youth and young adults: a nationwide population-based study. Sci Rep 2018; 8:1962. [PMID: 29386645 PMCID: PMC5792600 DOI: 10.1038/s41598-018-20274-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2017] [Accepted: 01/16/2018] [Indexed: 12/18/2022] Open
Abstract
The present study aimed to describe the distribution of and to investigate the factors associated with glycated haemoglobin (HbA1c) values in Korean youth (10–19 years old) and young adults (20–29 years old). Data from the Korea Health and Nutrition Examination Survey (2011–2015) were used. A total of 6,418 participants (male 3,140 [53.2%]) aged 10–29 years were included in the analysis. Percentiles of HbA1c were calculated and HbA1c values were compared according to age, sex, and associated factors. The mean HbA1c values (% [mmol/mol]) were 5.42 ± 0.01 (35.7 ± 0.1) for youths and 5.32 ± 0.01 (34.7 ± 0.1) for young adults (P < 0.001). Male participants showed significantly higher HbA1c level than females (P < 0.001). When age was grouped into 5-year intervals, HbA1c was the highest in those aged 10–14 years and the lowest in those aged 20–24 years. After controlling for confounding variables, the HbA1c values of youths and male participants were significantly higher than those of young adults and female participants. The present study provides nationally representative data on the distribution of HbA1c values in Korean youth and young adults. There were significant differences in the level of HbA1c according to age and sex.
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Sayed A, Alyafei F, De Sanctis V, Soliman A, Elgamal M. Translating the HbA1c assay into estimated average glucose values in children and adolescents with type 1 diabetes mellitus. ACTA BIO-MEDICA : ATENEI PARMENSIS 2018; 89. [PMID: 30049928 PMCID: PMC6179094 DOI: 10.23750/abm.v89i5.7357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
OBJECTIVE The A1c assay, expressed as the percent of hemoglobin that is glycated, measures chronic glycemia and is widely used to judge the adequacy of diabetes treatment and adjust therapy. Day-to-day management is guided by self-monitoring of capillary glucose concentrations (milligrams per decilitre or millimoles per litter) as well as by using continuous glucose monitoring systems (CGMS). We found a mathematical relationship between A1c and average glucose (AG) levels measured by CGMS over 5 days and determined the correlation between the variable CGMS parameters and HbA1c in 50 children with type 1 diabetes mellitus (DM-1) on MDI therapy. RESEARCH DESIGN AND METHODS A total of 50 diabetic children randomly selected from a cohort of children with DM-1 were included in the analyses. A1c levels obtained at the end of 3 months and measured in a central laboratory were compared with the AG levels during the previous 5 days recorded by CGMS. AG was calculated by combining weighted results from 5 days of continuous glucose monitoring performed before measuring HbA1c, with 3-5 point daily self-monitoring of capillary (fingerstick) glucose. RESULTS Linear regression analysis between the A1c and AG values provided the tightest correlations HbA1c=0.0494 MG- 2E-14, R2=0.90, P<0.0001), allowing calculation of an estimated average glucose (eAG) for A1c values. CONCLUSIONS Our study showed a linear relationship between HbA1C and AG values measured by CGMS for 5 days before HbA1c measurement. The AG can be easily calculated using a formula derived from linear regression analysis of HbA1c data obtained in our diabetic children.
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Affiliation(s)
- Ahmed Sayed
- Department of Pediatrics, Hamad Medical Center, Doha, Qatar
| | - Fawzia Alyafei
- Department of Pediatrics, Hamad Medical Center, Doha, Qatar
| | - Vincenzo De Sanctis
- Pediatric and Adolescent Outpatient Clinic, Quisisana Hospital, Ferrara, Italy
| | - Ashraf Soliman
- Department of Pediatrics, Hamad Medical Center, Doha, Qatar,Correspondence: Ashraf Soliman MD PhD FRCP Department of Pediatrics, Hamad General Hospital PO Box 3050, Doha, Qatar Tel. 0097455983874 E-mail:
| | - Mona Elgamal
- Department of Pediatrics, Hamad Medical Center, Doha, Qatar
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138
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Abstract
The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.
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139
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Fiorentino TV, Marini MA, Succurro E, Andreozzi F, Sciacqua A, Hribal ML, Perticone F, Sesti G. Association between hemoglobin glycation index and hepatic steatosis in non-diabetic individuals. Diabetes Res Clin Pract 2017; 134:53-61. [PMID: 28993156 DOI: 10.1016/j.diabres.2017.09.017] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2017] [Revised: 08/26/2017] [Accepted: 09/19/2017] [Indexed: 01/10/2023]
Abstract
AIMS Hemoglobin glycation index (HGI), which is the difference between the observed value of HbA1 and the predicted HbA1c based on plasma glucose levels, represents a measure of the degree of non-enzymatic glycation of hemoglobin and it has been found to be positively associated with diabetic complications. Herein we investigated whether HGI is associated with hepatic steatosis and related biomarkers in subjects without diabetes. METHODS 1120 White individuals without diabetes were stratified in quartiles according to HGI levels. Hepatic steatosis was diagnosed by ultrasonography. RESULTS As compared with subjects in the lowest quartile of HGI those in the intermediate and high HGI groups displayed an unfavorable cardio-metabolic risk profile having significantly higher values of body mass index (BMI), waist circumference, % fat mass, total cholesterol, triglycerides, inflammatory markers such as high sensitivity C reactive protein, erythrocytes sedimentation rate, complement C3, platelets and white blood cell count, hepatic insulin resistance assessed by the liver IR index and lower concentrations of high-density lipoprotein. HGI was positively associated with the biomarker of liver damage alanine aminotransferase, and fatty liver index, an indicator of hepatic steatosis. In a logistic regression analysis adjusted for age, gender and BMI individuals in the highest quartile of HGI exhibited a 1.6-fold increased odd of having hepatic steatosis (95% CI: 1.03-2.41; p=0.03) as compared with subjects in the lowest quartile of HGI. CONCLUSIONS Higher levels of HGI may identify subjects without diabetes at increased risk of having hepatic steatosis.
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Affiliation(s)
- Teresa Vanessa Fiorentino
- Department of Medical and Surgical Sciences, Viale Europa, University Magna-Græcia of Catanzaro, 88100 Catanzaro, Italy
| | | | - Elena Succurro
- Department of Medical and Surgical Sciences, Viale Europa, University Magna-Græcia of Catanzaro, 88100 Catanzaro, Italy
| | - Francesco Andreozzi
- Department of Medical and Surgical Sciences, Viale Europa, University Magna-Græcia of Catanzaro, 88100 Catanzaro, Italy
| | - Angela Sciacqua
- Department of Medical and Surgical Sciences, Viale Europa, University Magna-Græcia of Catanzaro, 88100 Catanzaro, Italy
| | - Marta Letizia Hribal
- Department of Medical and Surgical Sciences, Viale Europa, University Magna-Græcia of Catanzaro, 88100 Catanzaro, Italy
| | - Francesco Perticone
- Department of Medical and Surgical Sciences, Viale Europa, University Magna-Græcia of Catanzaro, 88100 Catanzaro, Italy
| | - Giorgio Sesti
- Department of Medical and Surgical Sciences, Viale Europa, University Magna-Græcia of Catanzaro, 88100 Catanzaro, Italy.
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140
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Ehehalt S, Wiegand S, Körner A, Schweizer R, Liesenkötter KP, Partsch CJ, Blumenstock G, Spielau U, Denzer C, Ranke MB, Neu A, Binder G, Wabitsch M, Kiess W, Reinehr T. Low association between fasting and OGTT stimulated glucose levels with HbA1c in overweight children and adolescents. Pediatr Diabetes 2017; 18:734-741. [PMID: 27873429 DOI: 10.1111/pedi.12461] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2016] [Revised: 08/25/2016] [Accepted: 09/22/2016] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Diabetes and prediabetes are defined based on different methods such as fasting glucose, glucose at 2-hour in oral glucose tolerance test (OGTT), and glycated hemoglobin A1c (HbA1c). These parameters probably describe different deteriorations in glucose metabolism limiting the exchange between each other in definitions of diabetes. OBJECTIVE To investigate the relationship between OGTT and HbA1c in overweight and obese children and adolescents living in Germany. METHODS Study population: Overweight and obese children and adolescents (n = 4848; 2668 female) aged 7 to 17 years without known diabetes. The study population was stratified into the following subgroups: normal glucose tolerance, prediabetes, diabetes according to OGTT and/or HbA1c categories, confirmed diagnosis of diabetes. RESULTS In the entire study group fasting plasma glucose (FPG) correlated weakly to 2-hour glucose (r = 0.26), FPG correlated weakly to HbA1c (r = 0.18), and 2-hour glucose correlated weakly to HbA1c (r = 0.17, all P < .001). Patients with confirmed diabetes showed a very high correlation between FPG and 2-hour glucose (r = 0.73, n = 50). Moderate correlations could be found for patients with impaired fasting glucose (2-hour glucose vs HbA1c: r = 0.30, n = 436), for patients with diabetes according to OGTT and/or HbA1c (FPG vs 2-hour glucose: r = 0.43; 2-hour glucose vs HbA1c: r = -0.30, n = 115) and for patients with confirmed diabetes (2-hour glucose vs HbA1c: r = -0.47, all P < .001). CONCLUSIONS Because FPG, 2-hour glucose, and HbA1c correlated only weakly we propose that these parameters, particularly in the normal range, might reflect distinct aspects of carbohydrate metabolism.
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Affiliation(s)
- Stefan Ehehalt
- Public Health Department of Stuttgart, Department of Pediatrics, Dental Health Care, Health Promotion and Social Services, Stuttgart, Germany.,Pediatric Endocrinology and Diabetes, University Children's Hospital, University of Tübingen, Tübingen, Germany
| | - Susanna Wiegand
- Department of Pediatric Endocrinology and Diabetes, Charité Children's Hospital, Universitätsmedizin Berlin, Berlin, Germany
| | - Antje Körner
- Hospital for Children and Adolescents, Department of Women and Child Health, University Hospitals, University of Leipzig, Leipzig, Germany
| | - Roland Schweizer
- Pediatric Endocrinology and Diabetes, University Children's Hospital, University of Tübingen, Tübingen, Germany
| | | | | | - Gunnar Blumenstock
- Department of Clinical Epidemiology and Applied Biometry, University of Tübingen, Tuebingen, Germany
| | - Ulrike Spielau
- Hospital for Children and Adolescents, Department of Women and Child Health, University Hospitals, University of Leipzig, Leipzig, Germany
| | - Christian Denzer
- Division of Pediatric Endocrinology and Diabetes, Interdisciplinary Obesity Unit, Department of Pediatrics and Adolescent Medicine, Ulm University, Ulm, Germany
| | - Michael B Ranke
- Pediatric Endocrinology and Diabetes, University Children's Hospital, University of Tübingen, Tübingen, Germany
| | - Andreas Neu
- Pediatric Endocrinology and Diabetes, University Children's Hospital, University of Tübingen, Tübingen, Germany
| | - Gerhard Binder
- Pediatric Endocrinology and Diabetes, University Children's Hospital, University of Tübingen, Tübingen, Germany
| | - Martin Wabitsch
- Division of Pediatric Endocrinology and Diabetes, Interdisciplinary Obesity Unit, Department of Pediatrics and Adolescent Medicine, Ulm University, Ulm, Germany
| | - Wieland Kiess
- Hospital for Children and Adolescents, Department of Women and Child Health, University Hospitals, University of Leipzig, Leipzig, Germany
| | - Thomas Reinehr
- Department of Pediatric Endocrinology, Diabetes and Nutrition Medicine, Vestische Children's Hospital, University Witten/Herdecke, Datteln, Germany
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141
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Unwin N, Howitt C, Rose AMC, Samuels TA, Hennis AJM, Hambleton IR. Prevalence and phenotype of diabetes and prediabetes using fasting glucose vs HbA1c in a Caribbean population. J Glob Health 2017; 7:020407. [PMID: 28959440 PMCID: PMC5604098 DOI: 10.7189/jogh.07.020407] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Both fasting plasma glucose (FPG) and HbA1c are recommended for the diagnosis of diabetes and prediabetes by the American Diabetes Association (ADA), and for diabetes by the World Health Organization. The ADA guidance is influential on clinical practice in many developing countries, including in the Caribbean and Latin America. We aimed to compare the prevalence and characteristics of individuals identified as having diabetes and prediabetes by FPG and HbA1c in a predominantly African ancestry Caribbean population. METHODS A representative population-based sample of 1234 adults (≥25 years of age) resident in Barbados was recruited. Standard methods with appropriate quality control were used to collect data on height, weight, blood pressure, fasting lipids and history of diagnosed diabetes, and to measure fasting glucose and HbA1c. Those with previously diagnosed diabetes (n = 192) were excluded from the analyses. Diabetes was defined as: FPG ≥7.0 mmol/L or HbA1c ≥6.5%; prediabetes as: FPG ≥5.6 to <7mmol/L or HbA1c ≥5.7 to <6.5%. RESULTS Complete data were available on 939 participants without previously diagnosed diabetes. The prevalence of undiagnosed diabetes was higher, but not significantly so, by HbA1c (4.9%, 95% CI 3.5, 6.8) vs FPG (3.5%, 2.4, 5.1). Overall 79 individuals had diabetes by either measure, but only 21 on both. The prevalence of prediabetes was higher by HbA1c compared to FPG: 41.7% (37.9, 45.6) vs 15.0% (12.8, 17.5). Overall 558 individuals had prediabetes by either measure, but only 107 on both. HbA1c, but not FPG, was significantly higher in women than men; and FPG, but not HbA1c, was significantly associated with raised triglycerides and low HDL cholesterol. CONCLUSION The agreement between FPG and HbA1c defined hyperglycaemia is poor. In addition, there are some differences in the phenotype of those identified, and HbA1c gives a much higher prevalence of prediabetes. The routine use of HbA1c for screening and diagnosis in this population would have major implications for clinical and public health policies and resources. Given the lack of robust evidence, particularly for prediabetes, on whether intervention in the individuals identified would improve outcomes, this approach to screening and diagnosis cannot be currently recommended for this population.
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Affiliation(s)
- Nigel Unwin
- George Alleyne Chronic Disease Research Centre, Caribbean Institute for Health Research, The University of the West Indies, Bridgetown, Barbados
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Christina Howitt
- George Alleyne Chronic Disease Research Centre, Caribbean Institute for Health Research, The University of the West Indies, Bridgetown, Barbados
| | - Angela MC Rose
- George Alleyne Chronic Disease Research Centre, Caribbean Institute for Health Research, The University of the West Indies, Bridgetown, Barbados
| | - T Alafia Samuels
- George Alleyne Chronic Disease Research Centre, Caribbean Institute for Health Research, The University of the West Indies, Bridgetown, Barbados
| | - Anselm JM Hennis
- George Alleyne Chronic Disease Research Centre, Caribbean Institute for Health Research, The University of the West Indies, Bridgetown, Barbados
| | - Ian R Hambleton
- George Alleyne Chronic Disease Research Centre, Caribbean Institute for Health Research, The University of the West Indies, Bridgetown, Barbados
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142
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Hughes RCE, Williman JA, Gullam JE. Antenatal haemoglobin A1c centiles: does one size fit all? Aust N Z J Obstet Gynaecol 2017; 58:411-416. [DOI: 10.1111/ajo.12738] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2017] [Accepted: 09/29/2017] [Indexed: 01/05/2023]
Affiliation(s)
- Ruth C. E. Hughes
- Department of Obstetrics and Gynaecology; University of Otago; Christchurch New Zealand
| | - Jonathan A. Williman
- Biostatistics and Computational Biology Unit; University of Otago; Christchurch New Zealand
| | - Joanna E. Gullam
- Department of Obstetrics and Gynaecology; University of Otago; Christchurch New Zealand
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143
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Benaiges D, Flores-Le Roux JA, Marcelo I, Mañé L, Rodríguez M, Navarro X, Chillarón JJ, Llauradó G, Gortazar L, Pedro-Botet J, Payà A. Is first-trimester HbA1c useful in the diagnosis of gestational diabetes? Diabetes Res Clin Pract 2017; 133:85-91. [PMID: 28918341 DOI: 10.1016/j.diabres.2017.08.019] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Revised: 07/17/2017] [Accepted: 08/22/2017] [Indexed: 10/19/2022]
Abstract
AIMS To evaluate the usefulness and efficacy of first-trimester HbA1c in the diagnosis of gestational diabetes (GDM). METHODS Prospective observational of consecutive pregnant women. All women had a first-trimester HbA1c determination and GDM screening at 24-28weeks of pregnancy using a two-step approach. A ROC curve was drawn to determine the sensitivity and specificity of HbA1c in detecting GDM and a rule-in rule-out diagnostic algorithm was proposed. The cost of the proposed algorithm was calculated. RESULTS 152 (13.1%) of 1195 women were diagnosed of GDM. The area under the ROC curve for HbA1c to detect GDM was 0.679 (95%CI 0.631-0.727). A rule-out threshold for HbA1c of 4.8% (29mmol/mol) had 96.7% sensitivity (95%CI 93.9-99.5), 10.1% specificity (95%CI 8.3-12.0) and a negative predictive value of 95.3% (95%CI 91.3-99.3). A rule-in value of 5.6% (38mmol/mol) had a positive predictive value of 31.6% (95%CI 24.4-38.9), 89.3% specificity (95%CI 87.4-91.2) and 32.9% sensitivity (95%CI 25.4-40.4). The low positive predictive value of the rule-in threshold precludes its use for GDM diagnosis, but could be used to identify women at high risk of GDM in whom the diagnosis can be established using a one-step approach. The overall saving of the proposed algorithm would be 6.5% of the total cost with the standard strategy. CONCLUSIONS A first-trimester HbA1c does not have sufficient sensitivity or specificity to diagnose GDM, although the use of a higher and lower threshold could simplify the diagnostic process by reducing the number of oral glucose tolerance test, associated costs and patient inconvenience.
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Affiliation(s)
- David Benaiges
- Department of Endocrinology and Nutrition, Hospital del Mar, Passeig Marítim, 25-29, 08003 Barcelona, Spain; Department of Medicine, Universitat Pompeu Fabra, Campus del Mar, Passeig Marítim, 25-29, 08003 Barcelona, Spain; Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Passeig Marítim, 25-29, 08003 Barcelona, Spain.
| | - Juana A Flores-Le Roux
- Department of Endocrinology and Nutrition, Hospital del Mar, Passeig Marítim, 25-29, 08003 Barcelona, Spain; Department of Medicine, Universitat Pompeu Fabra, Campus del Mar, Passeig Marítim, 25-29, 08003 Barcelona, Spain; Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Passeig Marítim, 25-29, 08003 Barcelona, Spain
| | - Irene Marcelo
- Department of Medicine, Universitat Pompeu Fabra, Campus del Mar, Passeig Marítim, 25-29, 08003 Barcelona, Spain
| | - Laura Mañé
- Department of Endocrinology and Nutrition, Hospital del Mar, Passeig Marítim, 25-29, 08003 Barcelona, Spain; Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Passeig Marítim, 25-29, 08003 Barcelona, Spain
| | - Marta Rodríguez
- Department of Medicine, Universitat Pompeu Fabra, Campus del Mar, Passeig Marítim, 25-29, 08003 Barcelona, Spain
| | - Xavier Navarro
- Laboratori de Referència de Catalunya, Carrer de la Selva, 10, 08820 Prat de Llobregat, Spain
| | - Juan J Chillarón
- Department of Endocrinology and Nutrition, Hospital del Mar, Passeig Marítim, 25-29, 08003 Barcelona, Spain; Department of Medicine, Universitat Pompeu Fabra, Campus del Mar, Passeig Marítim, 25-29, 08003 Barcelona, Spain; Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Passeig Marítim, 25-29, 08003 Barcelona, Spain
| | - Gemma Llauradó
- Department of Endocrinology and Nutrition, Hospital del Mar, Passeig Marítim, 25-29, 08003 Barcelona, Spain; Department of Medicine, Universitat Pompeu Fabra, Campus del Mar, Passeig Marítim, 25-29, 08003 Barcelona, Spain; Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Passeig Marítim, 25-29, 08003 Barcelona, Spain
| | - Lucia Gortazar
- Department of Endocrinology and Nutrition, Hospital del Mar, Passeig Marítim, 25-29, 08003 Barcelona, Spain; Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Passeig Marítim, 25-29, 08003 Barcelona, Spain
| | - Juan Pedro-Botet
- Department of Endocrinology and Nutrition, Hospital del Mar, Passeig Marítim, 25-29, 08003 Barcelona, Spain; Department of Medicine, Universitat Pompeu Fabra, Campus del Mar, Passeig Marítim, 25-29, 08003 Barcelona, Spain; Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Passeig Marítim, 25-29, 08003 Barcelona, Spain
| | - Antonio Payà
- Department of Medicine, Universitat Pompeu Fabra, Campus del Mar, Passeig Marítim, 25-29, 08003 Barcelona, Spain; Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Passeig Marítim, 25-29, 08003 Barcelona, Spain; Department of Gynecology and Obstetrics, Hospital del Mar, Passeig Marítim, 25-29, 08003 Barcelona, Spain
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144
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Tschanz MP, Watts SA, Colburn JA, Conlin PR, Pogach LM. Overview and Discussion of the 2017 VA/DoD Clinical Practice Guideline for the Management of Type 2 Diabetes Mellitus in Primary Care. Fed Pract 2017; 34:S14-S19. [PMID: 30766312 PMCID: PMC6375527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The 2017 diabetes mellitus guidelines emphasize shared decision making, dietary changes, and HbA1c target range for optimal control of diabetes mellitus.
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Affiliation(s)
- Mark P Tschanz
- is an associate program director at Naval Medical Center San Diego in California. is the VHA Office of Nursing Services metabolic syndrome & diabetes advisor at Louis Stokes Cleveland VA Medical Center in Ohio. is a staff endocrinologist at San Antonio Military Medical Center in Texas. is chief of the medical service for the VA Boston Healthcare System in Massachusetts. is the national director of medicine for the VHA Office of Specialty Care Services
| | - Sharon A Watts
- is an associate program director at Naval Medical Center San Diego in California. is the VHA Office of Nursing Services metabolic syndrome & diabetes advisor at Louis Stokes Cleveland VA Medical Center in Ohio. is a staff endocrinologist at San Antonio Military Medical Center in Texas. is chief of the medical service for the VA Boston Healthcare System in Massachusetts. is the national director of medicine for the VHA Office of Specialty Care Services
| | - Jeffrey A Colburn
- is an associate program director at Naval Medical Center San Diego in California. is the VHA Office of Nursing Services metabolic syndrome & diabetes advisor at Louis Stokes Cleveland VA Medical Center in Ohio. is a staff endocrinologist at San Antonio Military Medical Center in Texas. is chief of the medical service for the VA Boston Healthcare System in Massachusetts. is the national director of medicine for the VHA Office of Specialty Care Services
| | - Paul R Conlin
- is an associate program director at Naval Medical Center San Diego in California. is the VHA Office of Nursing Services metabolic syndrome & diabetes advisor at Louis Stokes Cleveland VA Medical Center in Ohio. is a staff endocrinologist at San Antonio Military Medical Center in Texas. is chief of the medical service for the VA Boston Healthcare System in Massachusetts. is the national director of medicine for the VHA Office of Specialty Care Services
| | - Leonard M Pogach
- is an associate program director at Naval Medical Center San Diego in California. is the VHA Office of Nursing Services metabolic syndrome & diabetes advisor at Louis Stokes Cleveland VA Medical Center in Ohio. is a staff endocrinologist at San Antonio Military Medical Center in Texas. is chief of the medical service for the VA Boston Healthcare System in Massachusetts. is the national director of medicine for the VHA Office of Specialty Care Services
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Wheeler E, Leong A, Liu CT, Hivert MF, Strawbridge RJ, Podmore C, Li M, Yao J, Sim X, Hong J, Chu AY, Zhang W, Wang X, Chen P, Maruthur NM, Porneala BC, Sharp SJ, Jia Y, Kabagambe EK, Chang LC, Chen WM, Elks CE, Evans DS, Fan Q, Giulianini F, Go MJ, Hottenga JJ, Hu Y, Jackson AU, Kanoni S, Kim YJ, Kleber ME, Ladenvall C, Lecoeur C, Lim SH, Lu Y, Mahajan A, Marzi C, Nalls MA, Navarro P, Nolte IM, Rose LM, Rybin DV, Sanna S, Shi Y, Stram DO, Takeuchi F, Tan SP, van der Most PJ, Van Vliet-Ostaptchouk JV, Wong A, Yengo L, Zhao W, Goel A, Martinez Larrad MT, Radke D, Salo P, Tanaka T, van Iperen EPA, Abecasis G, Afaq S, Alizadeh BZ, Bertoni AG, Bonnefond A, Böttcher Y, Bottinger EP, Campbell H, Carlson OD, Chen CH, Cho YS, Garvey WT, Gieger C, Goodarzi MO, Grallert H, Hamsten A, Hartman CA, Herder C, Hsiung CA, Huang J, Igase M, Isono M, Katsuya T, Khor CC, Kiess W, Kohara K, Kovacs P, Lee J, Lee WJ, Lehne B, Li H, Liu J, Lobbens S, Luan J, Lyssenko V, Meitinger T, Miki T, Miljkovic I, Moon S, Mulas A, Müller G, Müller-Nurasyid M, Nagaraja R, Nauck M, Pankow JS, Polasek O, Prokopenko I, Ramos PS, Rasmussen-Torvik L, Rathmann W, Rich SS, Robertson NR, Roden M, Roussel R, Rudan I, Scott RA, Scott WR, Sennblad B, Siscovick DS, Strauch K, Sun L, Swertz M, Tajuddin SM, Taylor KD, Teo YY, Tham YC, Tönjes A, Wareham NJ, Willemsen G, Wilsgaard T, Hingorani AD, Egan J, Ferrucci L, Hovingh GK, Jula A, Kivimaki M, Kumari M, Njølstad I, Palmer CNA, Serrano Ríos M, Stumvoll M, Watkins H, Aung T, Blüher M, Boehnke M, Boomsma DI, Bornstein SR, Chambers JC, Chasman DI, Chen YDI, Chen YT, Cheng CY, Cucca F, de Geus EJC, Deloukas P, Evans MK, Fornage M, Friedlander Y, Froguel P, Groop L, Gross MD, Harris TB, Hayward C, Heng CK, Ingelsson E, Kato N, Kim BJ, Koh WP, Kooner JS, Körner A, Kuh D, Kuusisto J, Laakso M, Lin X, Liu Y, Loos RJF, Magnusson PKE, März W, McCarthy MI, Oldehinkel AJ, Ong KK, Pedersen NL, Pereira MA, Peters A, Ridker PM, Sabanayagam C, Sale M, Saleheen D, Saltevo J, Schwarz PEH, Sheu WHH, Snieder H, Spector TD, Tabara Y, Tuomilehto J, van Dam RM, Wilson JG, Wilson JF, Wolffenbuttel BHR, Wong TY, Wu JY, Yuan JM, Zonderman AB, Soranzo N, Guo X, Roberts DJ, Florez JC, Sladek R, Dupuis J, Morris AP, Tai ES, Selvin E, Rotter JI, Langenberg C, Barroso I, Meigs JB. Impact of common genetic determinants of Hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: A transethnic genome-wide meta-analysis. PLoS Med 2017; 14:e1002383. [PMID: 28898252 PMCID: PMC5595282 DOI: 10.1371/journal.pmed.1002383] [Citation(s) in RCA: 270] [Impact Index Per Article: 38.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Accepted: 08/03/2017] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Glycated hemoglobin (HbA1c) is used to diagnose type 2 diabetes (T2D) and assess glycemic control in patients with diabetes. Previous genome-wide association studies (GWAS) have identified 18 HbA1c-associated genetic variants. These variants proved to be classifiable by their likely biological action as erythrocytic (also associated with erythrocyte traits) or glycemic (associated with other glucose-related traits). In this study, we tested the hypotheses that, in a very large scale GWAS, we would identify more genetic variants associated with HbA1c and that HbA1c variants implicated in erythrocytic biology would affect the diagnostic accuracy of HbA1c. We therefore expanded the number of HbA1c-associated loci and tested the effect of genetic risk-scores comprised of erythrocytic or glycemic variants on incident diabetes prediction and on prevalent diabetes screening performance. Throughout this multiancestry study, we kept a focus on interancestry differences in HbA1c genetics performance that might influence race-ancestry differences in health outcomes. METHODS & FINDINGS Using genome-wide association meta-analyses in up to 159,940 individuals from 82 cohorts of European, African, East Asian, and South Asian ancestry, we identified 60 common genetic variants associated with HbA1c. We classified variants as implicated in glycemic, erythrocytic, or unclassified biology and tested whether additive genetic scores of erythrocytic variants (GS-E) or glycemic variants (GS-G) were associated with higher T2D incidence in multiethnic longitudinal cohorts (N = 33,241). Nineteen glycemic and 22 erythrocytic variants were associated with HbA1c at genome-wide significance. GS-G was associated with higher T2D risk (incidence OR = 1.05, 95% CI 1.04-1.06, per HbA1c-raising allele, p = 3 × 10-29); whereas GS-E was not (OR = 1.00, 95% CI 0.99-1.01, p = 0.60). In Europeans and Asians, erythrocytic variants in aggregate had only modest effects on the diagnostic accuracy of HbA1c. Yet, in African Americans, the X-linked G6PD G202A variant (T-allele frequency 11%) was associated with an absolute decrease in HbA1c of 0.81%-units (95% CI 0.66-0.96) per allele in hemizygous men, and 0.68%-units (95% CI 0.38-0.97) in homozygous women. The G6PD variant may cause approximately 2% (N = 0.65 million, 95% CI 0.55-0.74) of African American adults with T2D to remain undiagnosed when screened with HbA1c. Limitations include the smaller sample sizes for non-European ancestries and the inability to classify approximately one-third of the variants. Further studies in large multiethnic cohorts with HbA1c, glycemic, and erythrocytic traits are required to better determine the biological action of the unclassified variants. CONCLUSIONS As G6PD deficiency can be clinically silent until illness strikes, we recommend investigation of the possible benefits of screening for the G6PD genotype along with using HbA1c to diagnose T2D in populations of African ancestry or groups where G6PD deficiency is common. Screening with direct glucose measurements, or genetically-informed HbA1c diagnostic thresholds in people with G6PD deficiency, may be required to avoid missed or delayed diagnoses.
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Affiliation(s)
- Eleanor Wheeler
- Department of Human Genetics, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Aaron Leong
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, United States of America
- Harvard Medical School, Boston, MA, United States of America
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States of America
| | - Marie-France Hivert
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, United States of America
- Massachusetts General Hospital, Boston, MA, United States of America
| | - Rona J. Strawbridge
- Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Centre for Molecular Medicine, Karolinska Universitetsjukhuset, Solna, Sweden
| | - Clara Podmore
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
- Department of Internal Medicine, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Man Li
- Department of Epidemiology, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- Division of Nephrology, University of Utah, Salt Lake City, UT, United States of America
- Department of Human Genetics, University of Utah, Salt Lake City, UT, United States of America
| | - Jie Yao
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, United States of America
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Jaeyoung Hong
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States of America
| | - Audrey Y. Chu
- National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States of America
- Division of Preventive Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- Department of Cardiology, Ealing Hospital NHS Trust, Southall, Middlesex, United Kingdom
| | - Xu Wang
- Life Sciences Institute, National University of Singapore, Singapore, Singapore
| | - Peng Chen
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ, United States of America
- Key Laboratory of Pathobiology, Ministry of Education, Jilin University, Changchun, Jilin, China
- College of Basic Medical Sciences, Jilin University, Changchun, Jilin, China
| | - Nisa M. Maruthur
- Department of Epidemiology, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- Division of General Internal Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
- Welch Center for Prevention, Epidemiology and Clinical Research, The Johns Hopkins University, Baltimore, MD, United States of America
| | - Bianca C. Porneala
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, United States of America
| | - Stephen J. Sharp
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Yucheng Jia
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, United States of America
| | - Edmond K. Kabagambe
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States of America
| | - Li-Ching Chang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Wei-Min Chen
- University of Virginia Center for Public Health Genomics, Charlottesville, VA, United States of America
| | - Cathy E. Elks
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
- Personalised Healthcare & Biomarkers, Innovative Medicines and Early Development Biotech Unit, AstraZeneca, Cambridge, United Kingdom
| | - Daniel S. Evans
- California Pacific Medical Center Research Institute, San Francisco, CA, United States of America
| | - Qiao Fan
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
| | - Franco Giulianini
- Division of Preventive Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America
| | - Min Jin Go
- Division of Structural and Functional Genomics, Center for Genome Science, Korean National Institute of Health, Osong, Chungchungbuk-do, South Korea
| | - Jouke-Jan Hottenga
- Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Yao Hu
- The Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - Anne U. Jackson
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, United States of America
| | - Stavroula Kanoni
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Young Jin Kim
- Division of Structural and Functional Genomics, Center for Genome Science, Korean National Institute of Health, Osong, Chungchungbuk-do, South Korea
| | - Marcus E. Kleber
- Vth Department of Medicine, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Claes Ladenvall
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- Lund University Diabetes Centre, Lund University, Lund, Sweden
| | - Cecile Lecoeur
- University of Lille, CNRS, Institut Pasteur of Lille, UMR 8199—EGID, Lille, France
| | - Sing-Hui Lim
- Singapore Eye Research Institute, The Academia Level 6, Discovery Tower, Singapore, Singapore
| | - Yingchang Lu
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
- The Genetics of Obesity and Related Metabolic Traits Program, The Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Anubha Mahajan
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Carola Marzi
- Institute of Epidemiology II, Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD e.V.), Partner Munich, Munich, Germany
| | - Mike A. Nalls
- Data Tecnica International, Glen Echo, MD, United States of America
- Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, United States of America
| | - Pau Navarro
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland
| | - Ilja M. Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Lynda M. Rose
- Division of Preventive Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America
| | - Denis V. Rybin
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States of America
- Data Coordinating Center, Boston University School of Public Health, Boston, MA, United States of America
| | - Serena Sanna
- Istituto di Ricerca Genetica e Biomedica (IRGB), CNR, Monserrato, Italy
| | - Yuan Shi
- Singapore Eye Research Institute, The Academia Level 6, Discovery Tower, Singapore, Singapore
| | - Daniel O. Stram
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA, United States of America
| | - Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Shu Pei Tan
- Singapore Eye Research Institute, The Academia Level 6, Discovery Tower, Singapore, Singapore
| | - Peter J. van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Jana V. Van Vliet-Ostaptchouk
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Andrew Wong
- MRC Unit for Lifelong Health & Ageing, London, United Kingdom
| | - Loic Yengo
- University of Lille, CNRS, Institut Pasteur of Lille, UMR 8199—EGID, Lille, France
| | - Wanting Zhao
- Singapore Eye Research Institute, The Academia Level 6, Discovery Tower, Singapore, Singapore
| | - Anuj Goel
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Maria Teresa Martinez Larrad
- Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain
| | - Dörte Radke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Perttu Salo
- National Institute for Health and Welfare (THL), Helsinki, Finland
- University of Helsinki, Institute for Molecular Medicine, Finland (FIMM) and Diabetes and Obesity Research Program, Helsinki, Finland
| | - Toshiko Tanaka
- Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, United States of America
| | - Erik P. A. van Iperen
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, University of Amsterdam, Amsterdam, Netherlands
- Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, Netherlands
| | - Goncalo Abecasis
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, United States of America
| | - Saima Afaq
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Behrooz Z. Alizadeh
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Alain G. Bertoni
- Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, NC, United States of America
| | - Amelie Bonnefond
- University of Lille, CNRS, Institut Pasteur of Lille, UMR 8199—EGID, Lille, France
| | - Yvonne Böttcher
- Integrated Research and Treatment (IFB) Center Adiposity Diseases, University of Leipzig, Leipzig, Germany
| | - Erwin P. Bottinger
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Harry Campbell
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, Scotland
| | - Olga D. Carlson
- Laboratory of Clinical Investigation, National Institute on Aging, Baltimore, MD, United States of America
| | - Chien-Hsiun Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
- School of Chinese Medicine, China Medical University, Taichung City, Taiwan
| | - Yoon Shin Cho
- Division of Structural and Functional Genomics, Center for Genome Science, Korean National Institute of Health, Osong, Chungchungbuk-do, South Korea
- Department of Biomedical Science, Hallym University, Chuncheon, Gangwon-do, South Korea
| | - W. Timothy Garvey
- Department of Nutrition Sciences, University of Alabama at Birmingham and the Birmingham Veterans Affairs Medical Center, Birmingham, AL, United States of America
| | - Christian Gieger
- Institute of Epidemiology II, Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Mark O. Goodarzi
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States of America
| | - Harald Grallert
- Institute of Epidemiology II, Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD e.V.), Partner Munich, Munich, Germany
| | - Anders Hamsten
- Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Centre for Molecular Medicine, Karolinska Universitetsjukhuset, Solna, Sweden
| | - Catharina A. Hartman
- Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Christian Herder
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Institute for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Chao Agnes Hsiung
- Division of Endocrinology, Diabetes, Metabolism, Department of Internal Medicine, Wexner Medical Center, The Ohio State University, Columbus, OH, United States of America
| | - Jie Huang
- Boston VA Research Institute, Inc., Boston, MA, United States of America
| | - Michiya Igase
- Department of Geriatric Medicine, Ehime University Graduate School of Medicine, Ehime, Japan
| | - Masato Isono
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Tomohiro Katsuya
- Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Geriatric Medicine and Nephrology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Chiea-Chuen Khor
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
| | - Wieland Kiess
- Center of Pediatric Research, University Hospital for Children & Adolescents, Dept. of Women's & Child Health, University of Leipzig, Leipzig, Germany
- LIFE Child, LIFE Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Katsuhiko Kohara
- Faculty of Collaborative Regional Innovation, Ehime University, Ehime, Japan
| | - Peter Kovacs
- Integrated Research and Treatment (IFB) Center Adiposity Diseases, University of Leipzig, Leipzig, Germany
| | - Juyoung Lee
- Division of Structural and Functional Genomics, Center for Genome Science, Korean National Institute of Health, Osong, Chungchungbuk-do, South Korea
| | - Wen-Jane Lee
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Benjamin Lehne
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Huaixing Li
- The Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - Jianjun Liu
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
| | - Stephane Lobbens
- University of Lille, CNRS, Institut Pasteur of Lille, UMR 8199—EGID, Lille, France
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | | | - Thomas Meitinger
- Institute of Human Genetics, Technische Universität München, Munich, Germany
- Institute of Human Genetics, Helmholtz Zentrum München, Neuherberg, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Tetsuro Miki
- Department of Geriatric Medicine, Ehime University Graduate School of Medicine, Ehime, Japan
| | - Iva Miljkovic
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Sanghoon Moon
- Division of Structural and Functional Genomics, Center for Genome Science, Korean National Institute of Health, Osong, Chungchungbuk-do, South Korea
| | - Antonella Mulas
- Istituto di Ricerca Genetica e Biomedica (IRGB), CNR, Monserrato, Italy
| | - Gabriele Müller
- Center for Evidence-based Healthcare, University Hospital and Medical Faculty Carl Gustav Carus, Dresden, Germany
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Department of Medicine I, University Hospital Grosshadern, Ludwig-Maximilians-Universität, Munich, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
| | - Ramaiah Nagaraja
- Laboratory of Genetics, National Institute on Aging, Baltimore, MD, United States of America
| | - Matthias Nauck
- Institute for Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - James S. Pankow
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, United States of America
| | - Ozren Polasek
- University of Split, Split, Croatia
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Inga Prokopenko
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
- Department of Genomics of Common Disease, School of Public Health, Imperial College London, London, United Kingdom
| | - Paula S. Ramos
- Department of Medicine, Medical University of South Carolina, Charleston, SC, United States of America
| | - Laura Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America
| | - Wolfgang Rathmann
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, United States of America
| | - Neil R. Robertson
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Michael Roden
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Institute for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Department of Endocrinology and Diabetology, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Ronan Roussel
- INSERM, UMR_S 1138, Centre de Recherche des Cordelier, Paris, France
- Université Paris Diderot, Sorbonne Paris Cité, UFR de Médecine, Paris, France
- Assistance Publique Hôpitaux de Paris, Bichat Hospital, DHU FIRE, Department of Diabetology, Endocrinology and Nutrition, Paris, France
| | - Igor Rudan
- University of Edinburgh, Edinburgh, United Kingdom
| | - Robert A. Scott
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - William R. Scott
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- Department of Cardiology, Ealing Hospital NHS Trust, Southall, Middlesex, United Kingdom
| | - Bengt Sennblad
- Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Centre for Molecular Medicine, Karolinska Universitetsjukhuset, Solna, Sweden
- Science for life laboratory, Karolinska Institutet, Solna, Sweden
| | - David S. Siscovick
- The New York Academy of Medicine, New York, NY, United States of America
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
| | - Liang Sun
- The Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - Morris Swertz
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Salman M. Tajuddin
- Health Disparities Unit, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America
| | - Kent D. Taylor
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, United States of America
| | - Yik-Ying Teo
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Life Sciences Institute, National University of Singapore, Singapore, Singapore
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
- Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore
- NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore, Singapore
| | - Yih Chung Tham
- Singapore Eye Research Institute, The Academia Level 6, Discovery Tower, Singapore, Singapore
| | - Anke Tönjes
- Department of Medicine; University of Leipzig, Leipzig, Germany
| | - Nicholas J. Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Gonneke Willemsen
- Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Tom Wilsgaard
- Dept of Community Medicine, Faculty of Health Sciences, University of Tromsø, Tromsø, Norway
| | - Aroon D. Hingorani
- Institute of Cardiovascular Science, University College London, London, United Kingdom
| | | | | | | | - Josephine Egan
- Laboratory of Clinical Investigation, National Institute on Aging, Baltimore, MD, United States of America
| | - Luigi Ferrucci
- Laboratory of Clinical Investigation, National Institute on Aging, Baltimore, MD, United States of America
| | - G. Kees Hovingh
- Department of Vascular Medicine, Academic Medical Center, Amsterdam, Netherlands
| | - Antti Jula
- National Institute for Health and Welfare (THL), Helsinki, Finland
| | - Mika Kivimaki
- Department of Epidemiology and Public Health, University College London, London, United Kingdom
| | - Meena Kumari
- Department of Epidemiology and Public Health, University College London, London, United Kingdom
- Institute for Social and Economic Research, University of Essex, Colchester, United Kingdom
| | - Inger Njølstad
- Dept of Community Medicine, Faculty of Health Sciences, University of Tromsø, Tromsø, Norway
| | - Colin N. A. Palmer
- Pat Macpherson Centre for Pharmacogenetics and Pharmacogenomics, Medical Research Institute, Ninewells Hospital and Medical School, Dundee, United Kingdom
| | - Manuel Serrano Ríos
- Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain
| | | | - Hugh Watkins
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Tin Aung
- Singapore Eye Research Institute, The Academia Level 6, Discovery Tower, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Singapore National Eye Centre, Singapore, Singapore
| | - Matthias Blüher
- Department of Medicine; University of Leipzig, Leipzig, Germany
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, United States of America
| | - Dorret I. Boomsma
- Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Stefan R. Bornstein
- Dept of Medicine III, University of Dresden, Medical Faculty Carl Gustav Carus, Dresden, Germany
| | - John C. Chambers
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- Department of Cardiology, Ealing Hospital NHS Trust, Southall, Middlesex, United Kingdom
- Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Daniel I. Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America
- Division of Genetics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America
- Broad Institute of MIT and Harvard, Cambridge, MA, United States of America
| | - Yii-Der Ida Chen
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, United States of America
| | - Yduan-Tsong Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, The Academia Level 6, Discovery Tower, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Singapore National Eye Centre, Singapore, Singapore
| | - Francesco Cucca
- Istituto di Ricerca Genetica e Biomedica (IRGB), CNR, Monserrato, Italy
- Dipartimento di Scienze Biomediche, Università di Sassari, Italy
| | - Eco J. C. de Geus
- Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Panos Deloukas
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
- Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University, Jeddah, Saudi Arabia
| | - Michele K. Evans
- Health Disparities Unit, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, Division of Epidemiology, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, United States of America
| | - Yechiel Friedlander
- Braun School of Public Health, Hebrew University-Hadassah Medical Center, Jerusalem, Israel
| | - Philippe Froguel
- Department of Genomics of Common Disease, School of Public Health, Imperial College London, London, United Kingdom
- CNRS 8199-Lille University, France
| | - Leif Groop
- Lund University Diabetes Centre, Lund University, Lund, Sweden
- Finnish Institute for Molecular Medicine (FIMM), Helsinki, Finland
| | - Myron D. Gross
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, United States of America
| | - Tamara B. Harris
- National Institute on Aging, Bethesda, MD, United States of America
| | - Caroline Hayward
- Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Chew-Kiat Heng
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Khoo Teck Puat-National University Children’s Medical Institute, National University Health System, Singapore, Singapore
| | - Erik Ingelsson
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, United States of America
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Bong-Jo Kim
- Division of Structural and Functional Genomics, Center for Genome Science, Korean National Institute of Health, Osong, Chungchungbuk-do, South Korea
| | - Woon-Puay Koh
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Duke-NUS Medical School Singapore, Singapore
| | - Jaspal S. Kooner
- Department of Cardiology, Ealing Hospital NHS Trust, Southall, Middlesex, United Kingdom
- Imperial College Healthcare NHS Trust, London, United Kingdom
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
| | - Antje Körner
- Center of Pediatric Research, University Hospital for Children & Adolescents, Dept. of Women's & Child Health, University of Leipzig, Leipzig, Germany
- LIFE Child, LIFE Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Diana Kuh
- MRC Unit for Lifelong Health & Ageing, London, United Kingdom
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Xu Lin
- The Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University, Winston-Salem, NC, United States of America
| | - Ruth J. F. Loos
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
- The Genetics of Obesity and Related Metabolic Traits Program, The Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
- The Mindich Child Health Development Institute, The Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Patrik K. E. Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Insitutet, Stockholm, Sweden
| | - Winfried März
- Vth Department of Medicine, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
- Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
- Synlab Academy, Synlab Services GmbH, Mannheim, Germany
| | - Mark I. McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, United Kingdom
| | - Albertine J. Oldehinkel
- Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Ken K. Ong
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Nancy L. Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Insitutet, Stockholm, Sweden
| | - Mark A. Pereira
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, United States of America
| | - Annette Peters
- Institute of Epidemiology II, Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Paul M. Ridker
- Division of Preventive Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America
- Division of Cardiovascular Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, The Academia Level 6, Discovery Tower, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Michele Sale
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, United States of America
| | - Danish Saleheen
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, United States of America
- Center for Non-Communicable Diseases, Karachi, Pakistan
| | - Juha Saltevo
- Department of Medicine, Central Hospital, Central Finland, Jyväskylä, Finland
| | - Peter EH. Schwarz
- Dept of Medicine III, University of Dresden, Medical Faculty Carl Gustav Carus, Dresden, Germany
| | - Wayne H. H. Sheu
- Division of Endocrine and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- School of Medicine, National Yang-Ming University, Taipei, Taiwan
- School of Medicine, National Defense Medical Center, Taipei, Taiwan
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Timothy D. Spector
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
| | - Yasuharu Tabara
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Jaakko Tuomilehto
- Chronic Disease Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland
- Dasman Diabetes Institute, Dasman, Kuwait
- Centre for Vascular Prevention, Danube-University Krems, Krems, Austria
- Saudi Diabetes Research Group, King Abdulaziz University, Fahd Medical Research Center, Jeddah, Saudi Arabia
| | - Rob M. van Dam
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - James G. Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, United States of America
| | - James F. Wilson
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, Scotland
| | - Bruce H. R. Wolffenbuttel
- Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Tien Yin Wong
- Singapore Eye Research Institute, The Academia Level 6, Discovery Tower, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Singapore National Eye Centre, Singapore, Singapore
| | - Jer-Yuarn Wu
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
- School of Chinese Medicine, China Medical University, Taichung City, Taiwan
| | - Jian-Min Yuan
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States of America
- Division of Cancer Control and Population Sciences,University of Pittsburgh Cancer Institute, Pittsburgh, PA, United States of America
| | - Alan B. Zonderman
- Laboratory of Epidemiology & Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America
| | - Nicole Soranzo
- Department of Human Genetics, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge, United Kingdom
- Department of Haematology, University of Cambridge, Cambridge, United Kingdom
- The National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics at the University of Cambridge, United Kingdom
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, United States of America
| | - David J. Roberts
- Biomedical Research Centre Oxford Haematology Theme and Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Headington, Oxford, United Kingdom
- NHS Blood and Transplant, Headington, Oxford, United Kingdom
| | - Jose C. Florez
- Harvard Medical School, Boston, MA, United States of America
- Diabetes Unit and Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, United States of America
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, United States of America
| | - Robert Sladek
- Department of Medicine, McGill University, Montreal, Quebec, Canada
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States of America
- National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States of America
| | - Andrew P. Morris
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Department of Biostatistics, University of Liverpool, Liverpool, United Kingdom
| | - E-Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Duke-NUS Medical School Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Elizabeth Selvin
- Department of Epidemiology, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- Division of General Internal Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
- Welch Center for Prevention, Epidemiology and Clinical Research, The Johns Hopkins University, Baltimore, MD, United States of America
| | - Jerome I. Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, United States of America
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Inês Barroso
- Department of Human Genetics, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge, United Kingdom
- Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - James B. Meigs
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, United States of America
- Harvard Medical School, Boston, MA, United States of America
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, United States of America
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146
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Full KM, Schmied EA, Parada H, Cherrington A, Horton LA, Ayala GX. The Relationship Between Sleep Duration and Glycemic Control Among Hispanic Adults With Uncontrolled Type 2 Diabetes. DIABETES EDUCATOR 2017; 43:519-529. [DOI: 10.1177/0145721717724564] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Purpose The purpose of this study was to examine the relationship between sleep duration and glycemic control in adult Hispanic patients with uncontrolled type 2 diabetes. Methods This cross-sectional study used baseline data from 317 Hispanic adults with uncontrolled type 2 diabetes who participated in a randomized controlled trial testing a peer support intervention to improve diabetes control. To be eligible, participants had to be 18 years or older and have A1C >7% in the 3 months prior to randomization. Glycemic control was assessed by A1C ascertained through medical chart review; higher A1C levels reflected poorer glycemic control. Sleep duration (hours/night), diabetes control behaviors, and demographics were obtained by interviewer-administered questionnaire. We used multivariable generalized linear models to estimate the association between sleep duration and glycemic control. Results Forty-three percent of participants reported sleeping fewer than 7 hours per night. Sleep duration (hours/night) was inversely associated with A1C levels; however, the relationship was no longer statistically significant after adjusting for insulin status. Conclusions Sleep duration was not significantly associated with glycemic control in this sample of Hispanic adults with uncontrolled type 2 diabetes when adjusting for insulin. Future research should continue to explore this relationship among Hispanic adults with diabetes using an objective measure of sleep duration and a larger sample of Hispanic adults with both controlled and uncontrolled type 2 diabetes to determine if these results hold true.
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Affiliation(s)
- Kelsie M. Full
- San Diego State University/University of California, San Diego Joint Doctoral Program in Public Health (Health Behavior), San Diego, California (Miss Full)
- Institute for Behavioral and Community Health, San Diego State University Research Foundation, San Diego, California (Miss Full, Dr Schmied, Miss Horton, Dr Ayala)
- University of North Carolina at Chapel Hill, Department of Epidemiology, Chapel Hill, North Carolina (Dr Parada)
- Department of Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama (Dr Cherrington)
- College of Health and Human Services, San Diego State University, San Diego, California (Dr Ayala)
| | - Emily A. Schmied
- San Diego State University/University of California, San Diego Joint Doctoral Program in Public Health (Health Behavior), San Diego, California (Miss Full)
- Institute for Behavioral and Community Health, San Diego State University Research Foundation, San Diego, California (Miss Full, Dr Schmied, Miss Horton, Dr Ayala)
- University of North Carolina at Chapel Hill, Department of Epidemiology, Chapel Hill, North Carolina (Dr Parada)
- Department of Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama (Dr Cherrington)
- College of Health and Human Services, San Diego State University, San Diego, California (Dr Ayala)
| | - Humberto Parada
- San Diego State University/University of California, San Diego Joint Doctoral Program in Public Health (Health Behavior), San Diego, California (Miss Full)
- Institute for Behavioral and Community Health, San Diego State University Research Foundation, San Diego, California (Miss Full, Dr Schmied, Miss Horton, Dr Ayala)
- University of North Carolina at Chapel Hill, Department of Epidemiology, Chapel Hill, North Carolina (Dr Parada)
- Department of Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama (Dr Cherrington)
- College of Health and Human Services, San Diego State University, San Diego, California (Dr Ayala)
| | - Andrea Cherrington
- San Diego State University/University of California, San Diego Joint Doctoral Program in Public Health (Health Behavior), San Diego, California (Miss Full)
- Institute for Behavioral and Community Health, San Diego State University Research Foundation, San Diego, California (Miss Full, Dr Schmied, Miss Horton, Dr Ayala)
- University of North Carolina at Chapel Hill, Department of Epidemiology, Chapel Hill, North Carolina (Dr Parada)
- Department of Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama (Dr Cherrington)
- College of Health and Human Services, San Diego State University, San Diego, California (Dr Ayala)
| | - Lucy A. Horton
- San Diego State University/University of California, San Diego Joint Doctoral Program in Public Health (Health Behavior), San Diego, California (Miss Full)
- Institute for Behavioral and Community Health, San Diego State University Research Foundation, San Diego, California (Miss Full, Dr Schmied, Miss Horton, Dr Ayala)
- University of North Carolina at Chapel Hill, Department of Epidemiology, Chapel Hill, North Carolina (Dr Parada)
- Department of Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama (Dr Cherrington)
- College of Health and Human Services, San Diego State University, San Diego, California (Dr Ayala)
| | - Guadalupe X. Ayala
- San Diego State University/University of California, San Diego Joint Doctoral Program in Public Health (Health Behavior), San Diego, California (Miss Full)
- Institute for Behavioral and Community Health, San Diego State University Research Foundation, San Diego, California (Miss Full, Dr Schmied, Miss Horton, Dr Ayala)
- University of North Carolina at Chapel Hill, Department of Epidemiology, Chapel Hill, North Carolina (Dr Parada)
- Department of Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama (Dr Cherrington)
- College of Health and Human Services, San Diego State University, San Diego, California (Dr Ayala)
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147
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Hellgren M, Hjörleifsdottir Steiner K, Bennet L. Haemoglobin A1c as a screening tool for type 2 diabetes and prediabetes in populations of Swedish and Middle-East ancestry. Prim Care Diabetes 2017; 11:337-343. [PMID: 28545842 DOI: 10.1016/j.pcd.2017.04.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Revised: 04/13/2017] [Accepted: 04/30/2017] [Indexed: 12/16/2022]
Abstract
AIMS To explore and compare sensitivity and specificity for HbA1c ≥48mmol/mol as a predictor for type 2 diabetes mellitus (T2DM) in two populations with different ethnicity and to examine the predictive value of two levels of HbA1c (≥42mmol/mol, ≥39mmol/mol) for prediabetes in these populations. METHODS Four cohorts were examined with an oral glucose tolerance test. (1) The MEDIM Study (n=1991 individuals of Swedish and Iraqi ancestry); (2) The Skaraborg Project (n=1327 individuals of Swedish ancestry); (3) The 4-D study (n=424 individuals of Swedish, Iraqi and Turkish ancestry); (4) The Flemingsberg study (n=212 participants of Turkish ancestry). RESULTS HbA1c ≥48mmol/mol had a sensitivity for T2DM of 31% and 25% respectively in individuals of Middle-East and Swedish ancestry. The positive and negative predictive value was high in both populations (70.3, 96.4 and 96.2, 97.6 respectively). Using HbA1c ≥42mmol/mol and ≥39mmol/mol as a predictor for prediabetes gave a sensitivity of 17% and 36% in individuals of Middle-East and 15% and 34% in individuals of Swedish ancestry. CONCLUSIONS Even if HbA1c ≥48mmol/mol is a valuable diagnostic tool, it is a blunt and insensitive tool for screening and would exclude most people with T2DM, independent of ancestry and age. HbA1c is an inefficient way to detect individuals with prediabetes.
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Affiliation(s)
- Margareta Hellgren
- Department of Public Health and Community Medicine/Primary Health Care, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
| | - Kristin Hjörleifsdottir Steiner
- Department of Neurobiology, Care Sciences and Society, Division of Family Medicine, Karolinska Institutet, Alfred Nobels allé 23, 141 83 Huddinge, Sweden
| | - Louise Bennet
- Center for Primary Health Care Research, Clinical Research Centre, Skåne University Hospital, Malmö, Sweden
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148
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Kim MK, Jeong JS, Kwon HS, Baek KH, Song KH. Concordance the hemoglobin glycation index with glycation gap using glycated albumin in patients with type 2 diabetes. J Diabetes Complications 2017; 31:1127-1131. [PMID: 28487124 DOI: 10.1016/j.jdiacomp.2017.04.015] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2017] [Revised: 03/24/2017] [Accepted: 04/12/2017] [Indexed: 02/06/2023]
Abstract
BACKGROUND The hemoglobin glycation index (HGI) is an index of differences in the glycation of hemoglobin according to blood glucose level. The glycation gap (G-gap) is an empiric measure of the extent of disagreement between hemoglobin A1C (HbA1C) and glycated albumin (GA). The aim of this study was to investigate the extent of agreement between the HGI and G-gap with respect to GA level, and to elucidate factors related to a high HGI. METHOD Data were obtained from 105 patients with type 2 diabetes, and fasting blood glucose (FBG), HbA1c, and GA values were measured simultaneously. The G-gap was calculated as the difference between the measured and GA-based predicted HbA1c levels. HGI was calculated as the difference between measured and FBG-based predicted HbA1c levels. RESULTS The HGI and G-gap were highly correlated according GA (r=0.722, P<0.001). In general, the two indices were similar in terms of both direction and magnitude. The classification of patients as high, moderate, or low glycators based on HGI versus G-gap was consistent for the majority of the population and only 5% of patients were reclassified from high to low or low to high. Fasting C-peptide levels decreased linearly, and the percentage of patients using insulin increased linearly, between the lowest and highest HGI tertile (both P<0.05). CONCLUSIONS There was 95% agreement between the HGI and G-gap using GA among type 2 diabetes patients. Furthermore, a high HGI was associated with a higher prevalence of insulin use among type 2 diabetes patients.
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Affiliation(s)
- Mee Kyoung Kim
- Department of Internal Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Jee Sun Jeong
- Department of Internal Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Hyuk-Sang Kwon
- Department of Internal Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Ki Hyun Baek
- Department of Internal Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Ki-Ho Song
- Department of Internal Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
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149
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Abstract
As intensive treatment to lower levels of HbA1c characteristically results in an increased risk of hypoglycaemia, patients with diabetes mellitus face a life-long optimization problem to reduce average levels of glycaemia and postprandial hyperglycaemia while simultaneously avoiding hypoglycaemia. This optimization can only be achieved in the context of lowering glucose variability. In this Review, I discuss topics that are related to the assessment, quantification and optimal control of glucose fluctuations in diabetes mellitus. I focus on markers of average glycaemia and the utility and/or shortcomings of HbA1c as a 'gold-standard' metric of glycaemic control; the notion that glucose variability is characterized by two principal dimensions, amplitude and time; measures of glucose variability that are based on either self-monitoring of blood glucose data or continuous glucose monitoring (CGM); and the control of average glycaemia and glucose variability through the use of pharmacological agents or closed-loop control systems commonly referred to as the 'artificial pancreas'. I conclude that HbA1c and the various available metrics of glucose variability reflect the management of diabetes mellitus on different timescales, ranging from months (for HbA1c) to minutes (for CGM). Comprehensive assessment of the dynamics of glycaemic fluctuations is therefore crucial for providing accurate and complete information to the patient, physician, automated decision-support or artificial-pancreas system.
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Affiliation(s)
- Boris P Kovatchev
- University of Virginia School of Medicine, 1215 Lee Street, Charlottesvile, Virginia 22908, USA
- The School of Engineering and Applied Sciences, University of Virginia, Thornton Hall, P.O. Box 400259, Charlottesville, Virginia 22904-4259, USA
- Center for Diabetes Technology, University of Virginia School of Medicine, Ivy Translational Research Building, 560 Ray C. Hunt Drive, Charlottesville, Virginia 22903-2981, USA
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150
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Marini MA, Fiorentino TV, Andreozzi F, Mannino GC, Succurro E, Sciacqua A, Perticone F, Sesti G. Hemorheological alterations in adults with prediabetes identified by hemoglobin A1c levels. Nutr Metab Cardiovasc Dis 2017; 27:601-608. [PMID: 28511905 DOI: 10.1016/j.numecd.2017.04.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Revised: 04/11/2017] [Accepted: 04/11/2017] [Indexed: 11/16/2022]
Abstract
BACKGROUND AND AIMS A link between increased blood viscosity and type 2 diabetes has been previously reported. Herein, we investigated the association of blood viscosity with prediabetes, identified by glycated hemoglobin A1c (HbA1c) according to the new American Diabetes Association criteria, and subclinical atherosclerosis. METHODS AND RESULTS The study cohort includes 1136 non-diabetic adults submitted to anthropometrical evaluation, an oral glucose tolerance test and ultrasound measurement of carotid intima-media thickness (IMT). Whole blood viscosity was estimated using a validated formula based on hematocrit and total plasma proteins. After adjusting for age, and gender, individuals with HbA1c-defined prediabetes (HbA1c 5.7-6.4% [39-47 mmol/mol]) exhibited significantly higher values of hematocrit, and predicted blood viscosity as compared with controls. Increased levels of IMT were observed in subjects with HbA1c-defined prediabetes in comparison to controls. Predicted blood viscosity was positively correlated with age, waist circumference, blood pressure, cholesterol, triglycerides, fibrinogen, white blood cell, HbA1c, fasting and 2-h post-load glucose levels, fasting insulin, IMT and inversely correlated with HDL and Matsuda index of insulin sensitivity. Of the three glycemic parameters, i.e. HbA1c, fasting and 2-h post-load glucose, only HbA1c showed a significant correlation with predicted blood viscosity (β = 0.054, P = 0.04) in a multivariate regression analysis model including multiple atherosclerosis risk factors. CONCLUSION The study shows that individuals with HbA1c-defined prediabetes have increased predicted blood viscosity and IMT. The HbA1c criterion may be helpful to capture individuals with an increased risk of diabetes and cardiovascular disease who may benefit from an intensive lifestyle intervention.
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Affiliation(s)
- M A Marini
- Department of Systems Medicine, University of Rome Tor Vergata, 00133 Rome, Italy
| | - T V Fiorentino
- Department of Medical and Surgical Sciences, University Magna-Græcia of Catanzaro, Viale Europa, 88100 Catanzaro, Italy
| | - F Andreozzi
- Department of Medical and Surgical Sciences, University Magna-Græcia of Catanzaro, Viale Europa, 88100 Catanzaro, Italy
| | - G C Mannino
- Department of Medical and Surgical Sciences, University Magna-Græcia of Catanzaro, Viale Europa, 88100 Catanzaro, Italy
| | - E Succurro
- Department of Medical and Surgical Sciences, University Magna-Græcia of Catanzaro, Viale Europa, 88100 Catanzaro, Italy
| | - A Sciacqua
- Department of Medical and Surgical Sciences, University Magna-Græcia of Catanzaro, Viale Europa, 88100 Catanzaro, Italy
| | - F Perticone
- Department of Medical and Surgical Sciences, University Magna-Græcia of Catanzaro, Viale Europa, 88100 Catanzaro, Italy
| | - G Sesti
- Department of Medical and Surgical Sciences, University Magna-Græcia of Catanzaro, Viale Europa, 88100 Catanzaro, Italy.
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