Literature DB >> 15451912

The metabolic syndrome defined by factor analysis and incident type 2 diabetes in a chinese population with high postprandial glucose.

Jian-Jun Wang1, Qing Qiao, Maija E Miettinen, Jani Lappalainen, Gang Hu, Jaakko Tuomilehto.   

Abstract

OBJECTIVE: The aim of this study was to examine how the major components of the metabolic syndrome relate to each other and to the development of diabetes using factor analysis. RESEARCH DESIGN AND METHODS: The screening survey for type 2 diabetes was conducted in 1994, and a follow-up study of nondiabetic individuals at baseline was carried out in 1999 in the Beijing area. Among 934 nondiabetic and 305 diabetic subjects at baseline, factor analysis was performed using the principle components analysis with varimax orthogonal rotation of continuously distributed variables considered to represent the components of the metabolic syndrome. Fasting insulin was used as a marker for insulin resistance. Of the 559 subjects without diabetes at baseline, 129 developed diabetes during the 5-year follow-up. Factors identified at baseline were used as independent variables in univariate and multivariate logistic regression models to determine risk factor clusters predicting the development of diabetes.
RESULTS: Four factors were identified in nondiabetic and diabetic subjects. Fasting insulin levels, BMI, and waist-to-hip ratio were associated with one factor. Systolic and diastolic blood pressures were associated with the second factor. Two-hour postload plasma glucose (2-h PG) and serum insulin and fasting plasma glucose were associated with the third factor. Serum total cholesterol and triglycerides were associated with the fourth factor. The first and the third factors predicted the development of diabetes. In diabetic patients at baseline, the combination of systolic and diastolic blood pressure was the most important factor, and urinary albumin excretion rate clustered with fasting and 2-h PG levels.
CONCLUSIONS: Insulin resistance alone does not underlie all features of the metabolic syndrome. Different physiological processes associated with various components of the metabolic syndrome contain unique information about diabetes risk. Microalbunuria is more likely to be a complication of type 2 diabetes or hypertension than a marker for the metabolic syndrome. Copyright 2004 American Diabetes Association

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Year:  2004        PMID: 15451912     DOI: 10.2337/diacare.27.10.2429

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


  16 in total

1.  A decision tree-based approach for identifying urban-rural differences in metabolic syndrome risk factors in the adult Korean population.

Authors:  T N Kim; J M Kim; J C Won; M S Park; S K Lee; S H Yoon; H-R Kim; K S Ko; B D Rhee
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2.  Urinary sodium and potassium excretion and the risk of type 2 diabetes: a prospective study in Finland.

Authors:  G Hu; P Jousilahti; M Peltonen; J Lindström; J Tuomilehto
Journal:  Diabetologia       Date:  2005-06-22       Impact factor: 10.122

3.  Remodelling of biological parameters during human ageing: evidence for complex regulation in longevity and in type 2 diabetes.

Authors:  Liana Spazzafumo; Fabiola Olivieri; Angela Marie Abbatecola; Gastone Castellani; Daniela Monti; Rosamaria Lisa; Roberta Galeazzi; Cristina Sirolla; Roberto Testa; Rita Ostan; Maria Scurti; Calogero Caruso; Sonya Vasto; Rosanna Vescovini; Giulia Ogliari; Daniela Mari; Fabrizia Lattanzio; Claudio Franceschi
Journal:  Age (Dordr)       Date:  2011-12-16

Review 4.  The metabolic syndrome: time for a critical appraisal. Joint statement from the American Diabetes Association and the European Association for the Study of Diabetes.

Authors:  R Kahn; J Buse; E Ferrannini; M Stern
Journal:  Diabetologia       Date:  2005-09       Impact factor: 10.122

5.  Inverse associations between androgens and renal function: the Young Men Cardiovascular Association (YMCA) study.

Authors:  Maciej Tomaszewski; Fadi J Charchar; Christine Maric; Roman Kuzniewicz; Mateusz Gola; Wladyslaw Grzeszczak; Nilesh J Samani; Ewa Zukowska-Szczechowska
Journal:  Am J Hypertens       Date:  2008-10-30       Impact factor: 2.689

6.  Clustering of cardiac risk factors associated with the metabolic syndrome and associations with psychosocial distress in a young Asian Indian population.

Authors:  Sonia Suchday; Mayer Bellehsen; Jennifer P Friedberg; Maureen Almeida; Erica Kaplan
Journal:  J Behav Med       Date:  2013-06-18

7.  Factor relationships of metabolic syndrome and echocardiographic phenotypes in the HyperGEN study.

Authors:  Pinchia Huang; Aldi T Kraja; Weihong Tang; Steven C Hunt; Kari E North; Cora E Lewis; Richard B Devereux; Giovanni de Simone; Donna K Arnett; Treva Rice; Dabeeru C Rao
Journal:  J Hypertens       Date:  2008-07       Impact factor: 4.844

8.  Heterogeneous behavior of lipids according to HbA1c levels undermines the plausibility of metabolic syndrome in type 1 diabetes: data from a nationwide multicenter survey.

Authors:  Fernando M A Giuffrida; Alexis D Guedes; Eloa R Rocco; Denise B Mory; Patricia Dualib; Odelisa S Matos; Reine M Chaves-Fonseca; Roberta A Cobas; Carlos Antonio Negrato; Marilia B Gomes; Sergio A Dib
Journal:  Cardiovasc Diabetol       Date:  2012-12-27       Impact factor: 9.951

9.  The structure of metabolic syndrome components across follow-up survey from childhood to adolescence.

Authors:  Adeleh Bahar; Firoozeh Hosseini Esfahani; Mohammad Asghari Jafarabadi; Yadollah Mehrabi; Fereidoun Azizi
Journal:  Int J Endocrinol Metab       Date:  2012-12-21

10.  Prevalence of metabolic syndrome in a cohort of Chinese schoolchildren: comparison of two definitions and assessment of adipokines as components by factor analysis.

Authors:  Qiaoxuan Wang; Jinhua Yin; Lu Xu; Hong Cheng; Xiaoyuan Zhao; Hongding Xiang; Hugh Simon Lam; Jie Mi; Ming Li
Journal:  BMC Public Health       Date:  2013-03-21       Impact factor: 3.295

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