Literature DB >> 18809301

Use of confirmatory factor analysis for the identification of new components of the metabolic syndrome: the role of plasminogen activator inhibitor-1 and Haemoglobin A1c.

M Boronat1, P Saavedra, V F Varillas, F J Nóvoa.   

Abstract

BACKGROUND AND AIM: This study was aimed to identify additional components of metabolic syndrome from a set of cardiovascular risk markers. METHODS AND
RESULTS: The homeostasis model assessment of insulin resistance (HOMA-IR), C-reactive protein, fibrinogen, plasminogen activator inhibitor-1 (PAI-1), von Willebrand factor, homocysteine, Haemoglobin A1c (HbA1c), and lipoprotein(a) were assessed in a population-based sample of 902 nondiabetic adult subjects. Those biomarkers that were associated with metabolic syndrome were evaluated by multiple regression analysis, along with other traditional cardiovascular risk factors. Confirmatory factor analysis (CFA) was used to test the hypothesis that both the established components of metabolic syndrome and the novel variables identified by the regression analysis were associated with a single underlying factor. HOMA-IR, PAI-1 and HbA1c were the only biomarkers independently related to metabolic syndrome. CFA validated a one-factor model that included these variables. Moreover, the indices of goodness of fit were better for this expanded model than those obtained for a previously validated one-factor model that was restricted to the conventional elements of the syndrome.
CONCLUSIONS: These findings show that PAI-1 and HbA1c are singularly linked to metabolic syndrome. Their elevation is presumably another manifestation of the same pathophysiological mechanism that underlies the recognized traits of the syndrome.

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Year:  2008        PMID: 18809301     DOI: 10.1016/j.numecd.2008.07.007

Source DB:  PubMed          Journal:  Nutr Metab Cardiovasc Dis        ISSN: 0939-4753            Impact factor:   4.222


  6 in total

1.  Insulin resistance explains the relationship between novel cardiovascular risk factors and hypertension. The Telde Study.

Authors:  A M Wägner; J C Wiebe; M Boronat; P Saavedra; D Marrero; F Varillas; F J Nóvoa
Journal:  J Endocrinol Invest       Date:  2010-10-27       Impact factor: 4.256

2.  Diabese youngsters have 3.7 more chances in developing metabolic syndrome compared with the obese.

Authors:  A Galli-Tsinopoulou; M G Grammatikopoulou; C Stylianou; E Emmanouilidou; P Kokka
Journal:  J Endocrinol Invest       Date:  2010-02-24       Impact factor: 4.256

3.  The factor structure of the metabolic syndrome in obese individuals with binge eating disorder.

Authors:  Tomoko Udo; Sherry A McKee; Marney A White; Robin M Masheb; Rachel D Barnes; Carlos M Grilo
Journal:  J Psychosom Res       Date:  2013-10-18       Impact factor: 3.006

4.  Adipocytokines as features of the metabolic syndrome determined using confirmatory factor analysis.

Authors:  Mark M Smits; Pier Woudstra; Kristina M Utzschneider; Jenny Tong; Fernando Gerchman; Mirjam Faulenbach; Darcy B Carr; Kathryn Aston-Mourney; Alan Chait; Robert H Knopp; James B Meigs; Edward J Boyko; Steven E Kahn
Journal:  Ann Epidemiol       Date:  2013-03-25       Impact factor: 3.797

5.  Metabolic syndrome model definitions predicting type 2 diabetes and cardiovascular disease.

Authors:  Cécile M Povel; Joline W Beulens; Yvonne T van der Schouw; Martijn E T Dollé; Annemieke M W Spijkerman; W M Monique Verschuren; Edith J M Feskens; Jolanda M A Boer
Journal:  Diabetes Care       Date:  2012-08-29       Impact factor: 19.112

6.  Association of adipokines and adhesion molecules with indicators of obesity in women undergoing mammography screening.

Authors:  Caroline Isoppo de Souza; Daniela Dornelles Rosa; Betina Ettrich; Gabriela Hermann Cibeira; Juliana Giacomazzi; Paloma Tusset; Patrícia Ashton-Prolla; Lidia Rosi Medeiros; Maira Caleffi; Eurico Camargo Neto; Emilio Hideyuki Moriguchi; Marcia Silveira Graudenz
Journal:  Nutr Metab (Lond)       Date:  2012-10-31       Impact factor: 4.169

  6 in total

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