Literature DB >> 21816975

Limited overlap between intermediate hyperglycemia as defined by A1C 5.7-6.4%, impaired fasting glucose, and impaired glucose tolerance.

Tuula Saukkonen1, Henna Cederberg, Jari Jokelainen, Mauri Laakso, Pirjo Härkönen, Sirkka Keinänen-Kiukaanniemi, Ulla Rajala.   

Abstract

OBJECTIVE: We compared the prevalences and overlap between intermediate hyperglycemia (IH), defined by a hemoglobin A(1c) (A1C) 5.7-6.4%, impaired fasting glucose (IFG), and impaired glucose tolerance (IGT). RESEARCH DESIGN AND METHODS: Oral glucose tolerance test results and A1C measurements were evaluated as markers of IH in an unselected cohort of 486 nondiabetic adults from Finland.
RESULTS: The overall prevalence of IH was 34%. Prevalences of isolated A1C 5.7-6.4%, IGT, and IFG were 8.0, 13.2, and 4.5%, respectively. Overlap between these three markers was uncommon. Isolated A1C 5.7-6.4% was associated with a higher BMI compared with isolated IFG and IGT and with a more adverse lipid profile compared with isolated IFG.
CONCLUSIONS: Prevalence of isolated IH was high, with limited overlap between the definitions. Differences in cardiovascular disease risk factors were observed among the groups. This study demonstrates that an A1C of 5.7-6.4% detects, in part, different individuals with IH compared with IFG and IGT.

Entities:  

Mesh:

Substances:

Year:  2011        PMID: 21816975      PMCID: PMC3177731          DOI: 10.2337/dc11-0183

Source DB:  PubMed          Journal:  Diabetes Care        ISSN: 0149-5992            Impact factor:   19.112


Hemoglobin A1c (A1C) at a range of 5.7–6.4% was proposed as an indicator of increased risk for type 2 diabetes (1) in addition to the currently used criteria of intermediate hyperglycemia (IH) as follows: impaired fasting glucose (IFG) and impaired glucose tolerance (IGT). The use of A1C in a nondiabetic range may detect a different prevalence of IH compared with IFG or IGT (2). However, the degree of overlap between these markers is not well reported. Recent studies from diabetic populations have yielded conflicting data regarding differences in cardiovascular disease (CVD) risk factor profiles among those diagnosed by A1C ≥6.5% and oral glucose tolerance test (OGTT) (3,4). Studies comparing CVD risk factor profiles among individuals with IH diagnosed by A1C 5.7–6.4% and OGTT are lacking. We hypothesized that these three different markers of IH, in part, detect different individuals and that individuals with A1C 5.7–6.4% would be characterized by a more unfavorable CVD risk profile than individuals diagnosed by OGTT. To this aim, we conducted a population-based observational study to compare 1) diagnosis of IH based on A1C 5.7–6.4% with OGTT and 2) differences in CVD risk factors among the three groups.

RESEARCH DESIGN AND METHODS

Between 1996 and 1998, 593 subjects (245 men, 348 women) born in 1935 were enrolled a longer follow-up study evaluating IH among inhabitants of Oulu, Finland (5,6). The study excluded 87 participants with previously known or screen-detected diabetes according to OGTT or A1C ≥6.5%, leaving 486 with complete data for analysis in the present report (5). The study protocol was approved by the ethics committee of the Faculty of Medicine, University of Oulu, Finland. Clinical data collection and anthropometric and glucose measurements are described in detail elsewhere (5,6). Standardized 75-g OGTT was performed according contemporary World Health Organization guidelines with fasting whole-blood glucose and 2-h glucose from a capillary sample (7,8). A1C was analyzed with the Bayer DCA 2000 Analyzer (Bayer Healthcare, Tarrytown, NY), calibrated to the Diabetes Control and Complications Trial standard (9). IH was classified as IFG (fasting blood glucose 5.6–6.0 mmol/L), IGT (2-h glucose ≥7.8 and <11.1 mmol/L), and elevated A1C (5.7–6.4%) (1,7,8). Isolated forms of IH are referred to as i-IGT, i-IFG, and i-A1C 5.7–6.4%. Differences in continuous variables were analyzed with two-way ANOVA and Kruskal-Wallis, as appropriate, and P values were adjusted for sex. Values of P < 0.05 were considered statistically significant. SAS software (SAS Institute, Cary, NC) was used for statistical analysis.

RESULTS

Characteristics of the participants have been presented previously (5). Prevalence of IH (IFG, IGT, or A1C 5.7–6.4%) was 34% (n = 165). Prevalences of i-A1C 5.7–6.4%, i-IGT, and i-IFG were 8.0, 13.2, and 4.5%, respectively. Overlap between these three markers was limited. Only 5 individuals (1%) fulfilled all three criteria (IFG, IGT, and A1C 5.7–6.4%), and 9 (2%), 8 (2%), and 17 (4%) subjects were included in the combination of IFG/IGT, IFG/A1C 5.7–6.4%, and IGT/A1C 5.7–6.4%, respectively. Mean BMI was higher in subjects with i-A1C 5.7–6.4% (29.7 kg/m2) compared with i-IFG (27.5 kg/m2, P = 0.034) and i-IGT (27.9 kg/m2, P = 0.022; Table 1). A trend was observed for higher waist circumference in the i-A1C 5.7–6.4% group compared with i-IGT (94.6 vs. 89.6 cm, P = 0.056).
Table 1

Cardiovascular risk factors of participants with i-A1C 5.7–6.4%, i-IFG, i-IGT, and normoglycemia

Variablei-A1C 5.7–6.4%
i-IFG
i-IGT
NG
P value*
n = 39n = 22n = 64n = 322i-A1C vs. i-IFGi-A1C vs. i-IGTi-IGT vs. i-IFG
Women56.445.560.961.5
BMI (kg/m2)29.7 (5.97)27.5 (3.00)27.9 (4.01)27.1 (3.61)0.0340.0220.686
Waist (cm)94.6 (13.9)91.5 (10.3)89.6 (12.0)86.9 (11.6)0.1640.0560.995
BP (mmHg)
 Systolic136 (16.9)145 (23.2)145 (18.4)141 (16.0)0.0710.0200.954
 Diastolic77.1 (6.69)79.5 (9.17)80.1 (5.89)78.6 (8.03)0.2900.0670.727
Triglycerides (mmol/L)1.38 (1.2–1.98)1.17 (0.76–1.64)1.34 (1–1.62)1.12 (0.88–1.5)0.0080.1830.073
Cholesterol (mmol/L)
 HDL1.33 (0.31)1.54 (0.38)1.45 (0.40)1.53 (0.38)0.0100.1680.096
 LDL4.01 (0.65)3.74 (0.73)3.73 (0.88)3.72 (0.80)0.2330.0550.771
 Total5.21 (0.94)5.34 (0.89)5.12 (1.02)5.28 (0.99)0.5420.6790.313
A1C (%)5.8 (5.8–6)5.2 (5.1–5.4)5.35 (5.1–5.5)5.2 (5–5.4)<0.0001<0.00010.215
FG (mmol/L)5.1 (4.8–5.3)5.7 (5.6–5.8)4.8 (4.65–5.2)4.8 (4.5–5.1)<0.00010.066<0.0001
2-h Glucose (mmol/L)6.5 (5.8–7.3)6.5 (5.6–7.2)8.5 (8–9.4)6.2 (5.6–6.9)0.955<0.0001<0.0001

Data are presented as medians (interquartile range) and means (SD).

BP, blood pressure; FG, fasting glucose; NG, normoglycemia.

*P values are adjusted for sex.

Cardiovascular risk factors of participants with i-A1C 5.7–6.4%, i-IFG, i-IGT, and normoglycemia Data are presented as medians (interquartile range) and means (SD). BP, blood pressure; FG, fasting glucose; NG, normoglycemia. *P values are adjusted for sex. Systolic blood pressure was lower in i-A1C 5.7–6.4% than in the i-IFG (136 vs. 145 mmHg, P = 0.071) and i-IGT (136 vs. 145 mmHg, P = 0.020) groups. Only 30.8% of subjects with i-A1C 5.7–6.4% were taking antihypertensive medication, compared with 48.4% in the i-IGT and 31.8% in the i-IFG groups. Values for HDL cholesterol were lower (P = 0.010) and triglycerides were higher (P = 0.008) in the i-A1C 5.7–6.4% group compared with the i-IFG group.

CONCLUSIONS

Comparison of the prevalences of IH as diagnosed by A1C 5.7–6.4%, IFG, and IGT, showed that isolated forms of prediabetes are common at the population level. Differences in CVD risk factors were observed between the i-A1C, i-IFG, and i-IGT groups. Individuals with A1C 5.7–6.4% with normal OGTT were characterized by higher BMI, central obesity, and a more adverse lipid profile. The comparability of A1C 5.7–6.4% with IFG and IGT as a marker of IH, particularly regarding associated CVD risk factors, is not well known (10). Data from previous studies using A1C and OGTT for the diagnosis of type 2 diabetes have been controversial regarding cardiovascular risk profile (3,4). In the Telde Study, Spanish subjects diagnosed with A1C were characterized by a higher BMI and waist circumference and lower levels of HDL cholesterol compared with the OGTT group (4). In contrast, in the Danish Inter99 study population, with a high prevalence of male smokers, individuals with A1C-defined diabetes were leaner and had a higher prevalence of lipid abnormalities compared with the OGTT group (3). Our results are in agreement with those of the Telde Study, showing that individuals classified with a prediabetic A1C of 5.7–6.4% were characterized by higher BMI, waist circumference, and triglycerides and lower HDL cholesterol levels. Further studies in different populations are required to evaluate the finding that the different markers seem to identify in part different individuals. The advantage of using A1C is less day-to-day variability compared with OGTT. The 2-h glucose levels have higher variability than fasting glucose levels, but both have higher variability than A1C (11,12). The day-to-day variability of the OGTT is a limitation that must be considered when interpreting these results, because OGTT was performed only once in this epidemiological study. Our study has several strengths. It was performed in an unselected nondiabetic population. We showed that differences in CVD risk profiles of type 2 diabetes are already present in the prediabetic stages of A1C 5.7–6.4%, IFG, and IGT. The study is limited because the OGTT was performed only once, and capillary samples were used for the 2-h glucose measurement, which may have given slightly higher values than venous plasma samples (7,8,13); however, this effect at the population level is marginal. Findings from our study are also limited to an aging white population. In conclusion, one in three participants in this population had a form of IH, with limited overlap between the three current definitions. Individuals with IH diagnosed by A1C 5.7–6.4% were characterized by higher BMI, central obesity, and a more adverse lipid profile compared with the OGTT groups.
  11 in total

1.  Comparing risk profiles of individuals diagnosed with diabetes by OGTT and HbA1c The Danish Inter99 study.

Authors:  R Borg; D Vistisen; D R Witte; K Borch-Johnsen
Journal:  Diabet Med       Date:  2010-08       Impact factor: 4.359

2.  Guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus.

Authors:  David B Sacks; David E Bruns; David E Goldstein; Noel K Maclaren; Jay M McDonald; Marian Parrott
Journal:  Clin Chem       Date:  2002-03       Impact factor: 8.327

3.  A1C between 5.7 and 6.4% as a marker for identifying pre-diabetes, insulin sensitivity and secretion, and cardiovascular risk factors: the Insulin Resistance Atherosclerosis Study (IRAS).

Authors:  Carlos Lorenzo; Lynne E Wagenknecht; Anthony J G Hanley; Marian J Rewers; Andrew J Karter; Steven M Haffner
Journal:  Diabetes Care       Date:  2010-06-23       Impact factor: 19.112

4.  Differences in cardiovascular risk profile of diabetic subjects discordantly classified by diagnostic criteria based on glycated hemoglobin and oral glucose tolerance test.

Authors:  Mauro Boronat; Pedro Saavedra; Laura López-Ríos; Marta Riaño; Ana M Wägner; Francisco J Nóvoa
Journal:  Diabetes Care       Date:  2010-08-31       Impact factor: 19.112

5.  Postchallenge glucose, A1C, and fasting glucose as predictors of type 2 diabetes and cardiovascular disease: a 10-year prospective cohort study.

Authors:  Henna Cederberg; Tuula Saukkonen; Mauri Laakso; Jari Jokelainen; Pirjo Härkönen; Markku Timonen; Sirkka Keinänen-Kiukaanniemi; Ulla Rajala
Journal:  Diabetes Care       Date:  2010-06-23       Impact factor: 17.152

6.  Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation.

Authors:  K G Alberti; P Z Zimmet
Journal:  Diabet Med       Date:  1998-07       Impact factor: 4.359

7.  Consensus statement on the worldwide standardization of the hemoglobin A1C measurement: the American Diabetes Association, European Association for the Study of Diabetes, International Federation of Clinical Chemistry and Laboratory Medicine, and the International Diabetes Federation.

Authors: 
Journal:  Diabetes Care       Date:  2007-09       Impact factor: 19.112

8.  Short-term variability in measures of glycemia and implications for the classification of diabetes.

Authors:  Elizabeth Selvin; Ciprian M Crainiceanu; Frederick L Brancati; Josef Coresh
Journal:  Arch Intern Med       Date:  2007-07-23

9.  Impact of A1C screening criterion on the diagnosis of pre-diabetes among U.S. adults.

Authors:  Devin M Mann; April P Carson; Daichi Shimbo; Vivian Fonseca; Caroline S Fox; Paul Muntner
Journal:  Diabetes Care       Date:  2010-07-13       Impact factor: 19.112

10.  Diagnosis and classification of diabetes mellitus.

Authors: 
Journal:  Diabetes Care       Date:  2010-01       Impact factor: 19.112

View more
  16 in total

Review 1.  Hypertension with diabetes mellitus: significance from an epidemiological perspective for Japanese.

Authors:  Yukako Tatsumi; Takayoshi Ohkubo
Journal:  Hypertens Res       Date:  2017-07-13       Impact factor: 3.872

Review 2.  Rational use of electronic health records for diabetes population management.

Authors:  Emma M Eggleston; Michael Klompas
Journal:  Curr Diab Rep       Date:  2014-04       Impact factor: 4.810

3.  PERFORMANCE OF A1C VERSUS OGTT FOR THE DIAGNOSIS OF PREDIABETES IN A COMMUNITY-BASED SCREENING.

Authors:  Jenny E Camacho; Vallabh O Shah; Ronald Schrader; Craig S Wong; Mark R Burge
Journal:  Endocr Pract       Date:  2016-08-02       Impact factor: 3.443

Review 4.  Innovative uses of electronic health records and social media for public health surveillance.

Authors:  Emma M Eggleston; Elissa R Weitzman
Journal:  Curr Diab Rep       Date:  2014-03       Impact factor: 4.810

5.  Not performing an OGTT results in significant underdiagnosis of (pre)diabetes in a high risk adult Caucasian population.

Authors:  A S Meijnikman; C E M De Block; E Dirinck; A Verrijken; I Mertens; B Corthouts; L F Van Gaal
Journal:  Int J Obes (Lond)       Date:  2017-07-19       Impact factor: 5.095

6.  Cardiometabolic risk profiles and carotid atherosclerosis in individuals with prediabetes identified by fasting glucose, postchallenge glucose, and hemoglobin A1c criteria.

Authors:  Maria A Marini; Elena Succurro; Ersilia Castaldo; Sabrina Cufone; Franco Arturi; Angela Sciacqua; Renato Lauro; Marta L Hribal; Francesco Perticone; Giorgio Sesti
Journal:  Diabetes Care       Date:  2012-03-07       Impact factor: 19.112

7.  Hormonal determinants of the severity of andropausal and depressive symptoms in middle-aged and elderly men with prediabetes.

Authors:  Michał Rabijewski; Lucyna Papierska; Roman Kuczerowski; Paweł Piątkiewicz
Journal:  Clin Interv Aging       Date:  2015-08-20       Impact factor: 4.458

8.  Circulating metabolite predictors of glycemia in middle-aged men and women.

Authors:  Peter Würtz; Mika Tiainen; Ville-Petteri Mäkinen; Antti J Kangas; Pasi Soininen; Juha Saltevo; Sirkka Keinänen-Kiukaanniemi; Pekka Mäntyselkä; Terho Lehtimäki; Markku Laakso; Antti Jula; Mika Kähönen; Mauno Vanhala; Mika Ala-Korpela
Journal:  Diabetes Care       Date:  2012-05-04       Impact factor: 19.112

9.  HbA1c alone is a poor indicator of cardiometabolic risk in middle-aged subjects with pre-diabetes but is suitable for type 2 diabetes diagnosis: a cross-sectional study.

Authors:  Seán R Millar; Ivan J Perry; Catherine M Phillips
Journal:  PLoS One       Date:  2015-08-12       Impact factor: 3.240

10.  Prediabetes is associated with an increased risk of testosterone deficiency, independent of obesity and metabolic syndrome.

Authors:  Chen-Hsun Ho; Hong-Jeng Yu; Chih-Yuan Wang; Fu-Shan Jaw; Ju-Ton Hsieh; Wan-Chung Liao; Yeong-Shiau Pu; Shih-Ping Liu
Journal:  PLoS One       Date:  2013-09-12       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.