Literature DB >> 21420057

Usefulness of hemoglobin A1c as a criterion to define the metabolic syndrome in a cohort of italian nondiabetic white subjects.

Elena Succurro1, Maria Adelaide Marini, Franco Arturi, Alessandro Grembiale, Teresa Vanessa Fiorentino, Francesco Andreozzi, Angela Sciacqua, Renato Lauro, Marta Letizia Hribal, Francesco Perticone, Giorgio Sesti.   

Abstract

We compared the performance of hemoglobin A1c (HbA1c) versus the fasting plasma glucose (FPG) in diagnosing the metabolic syndrome and assessed the diagnostic accuracy of the metabolic syndrome definition using HbA1c in identifying insulin-resistant subjects. The cardiometabolic risk factors, HbA1c, and glucose tolerance were analyzed in 774 nondiabetic white subjects. Insulin sensitivity was estimated with an oral glucose tolerance test-derived insulin sensitivity index. Insulin resistance was defined as the lower quartile of insulin sensitivity index. A 90.9% agreement existed between the use of HbA1c and the FPG for diagnosis of the metabolic syndrome (κ coefficient = 0.813); however, the proportion of subjects who met the metabolic syndrome criteria using the HbA1c was greater (42.1% vs 39.7%). Compared to the subjects who met the metabolic syndrome criteria using the FPG alone, those with the metabolic syndrome using the HbA1c-alone criterion were younger, had greater visceral adiposity, greater levels of inflammatory markers and liver enzymes, and lower blood pressure. In a logistic regression analysis with adjustment for age and gender, the subjects with the metabolic syndrome using the HbA1c criterion only had a 3.6-fold increase risk of having insulin resistance, defined as the lowest quartile of the insulin sensitivity index. A similar risk (3.8-fold) was observed in those who met the metabolic syndrome criteria using FPG alone. Insulin-resistant subjects who did not meet the criteria for the metabolic syndrome using the HbA1c had an unfavorable cardiovascular disease risk profile. In conclusion, although a good agreement existed between the HbA1c and FPG criteria for the diagnosis of the metabolic syndrome, appreciably different groups of subjects were classified using each method.
Copyright © 2011 Elsevier Inc. All rights reserved.

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Year:  2011        PMID: 21420057     DOI: 10.1016/j.amjcard.2011.01.055

Source DB:  PubMed          Journal:  Am J Cardiol        ISSN: 0002-9149            Impact factor:   2.778


  11 in total

1.  The effectiveness of IDF and ATP-III in identifying metabolic syndrome and the usefulness of these tools for health-promotion in older Taiwanese.

Authors:  M M Chen; A C Tsai
Journal:  J Nutr Health Aging       Date:  2013-04       Impact factor: 4.075

2.  Trace proteinuria by dipstick screening is associated with metabolic syndrome, hypertension, and diabetes.

Authors:  Rieko Okada; Yoshinari Yasuda; Kazuyo Tsushita; Kenji Wakai; Nobuyuki Hamajima; Seiichi Matsuo
Journal:  Clin Exp Nephrol       Date:  2018-06-22       Impact factor: 2.801

3.  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

4.  Supplementary use of HbA1c as hyperglycemic criterion to detect metabolic syndrome.

Authors:  Parco M Siu; Queenie S Yuen
Journal:  Diabetol Metab Syndr       Date:  2014-11-06       Impact factor: 3.320

5.  Deriving an optimal threshold of waist circumference for detecting cardiometabolic risk in sub-Saharan Africa.

Authors:  K Ekoru; G A V Murphy; E H Young; H Delisle; C S Jerome; F Assah; B Longo-Mbenza; J P D Nzambi; J B K On'Kin; F Buntix; M C Muyer; D L Christensen; C S Wesseh; A Sabir; C Okafor; I D Gezawa; F Puepet; O Enang; T Raimi; E Ohwovoriole; O O Oladapo; P Bovet; W Mollentze; N Unwin; W K Gray; R Walker; K Agoudavi; S Siziya; J Chifamba; M Njelekela; C M Fourie; S Kruger; A E Schutte; C Walsh; D Gareta; A Kamali; J Seeley; S A Norris; N J Crowther; D Pillay; P Kaleebu; A A Motala; M S Sandhu
Journal:  Int J Obes (Lond)       Date:  2017-10-03       Impact factor: 5.095

6.  Effect of Almond Supplementation on Glycemia and Cardiovascular Risk Factors in Asian Indians in North India with Type 2 Diabetes Mellitus: A 24-Week Study.

Authors:  Seema Gulati; Anoop Misra; Ravindra M Pandey
Journal:  Metab Syndr Relat Disord       Date:  2017-01-04       Impact factor: 1.894

7.  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.

Authors:  Max Efui Annani-Akollor; Edwin Ferguson Laing; Henry Osei; Evans Mensah; Eddie-Williams Owiredu; Bright Oppong Afranie; Enoch Odame Anto
Journal:  Diabetol Metab Syndr       Date:  2019-03-22       Impact factor: 3.320

8.  Glycated Hemoglobin, Fasting Insulin and the Metabolic Syndrome in Males. Cross-Sectional Analyses of the Aragon Workers' Health Study Baseline.

Authors:  Gabriela Saravia; Fernando Civeira; Yamilee Hurtado-Roca; Eva Andres; Montserrat Leon; Miguel Pocovi; Jose Ordovas; Eliseo Guallar; Antonio Fernandez-Ortiz; Jose Antonio Casasnovas; Martin Laclaustra
Journal:  PLoS One       Date:  2015-08-04       Impact factor: 3.240

9.  Impact of HbA1c criterion on the definition of glycemic component of the metabolic syndrome: the China health and nutrition survey 2009.

Authors:  Xingxing Sun; Tingting Du; Rui Huo; Xuefeng Yu; Lixian Xu
Journal:  BMC Public Health       Date:  2013-11-05       Impact factor: 3.295

10.  Insulin-like growth factor-1 is a negative modulator of glucagon secretion.

Authors:  Elettra Mancuso; Gaia C Mannino; Concetta Di Fatta; Anastasia Fuoco; Rosangela Spiga; Francesco Andreozzi; Giorgio Sesti
Journal:  Oncotarget       Date:  2017-06-16
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