Literature DB >> 18617286

The metabolic syndrome identifies a heterogeneous group of metabolic component combinations in the Asia-Pacific region.

Crystal Man Ying Lee1, Rachel R Huxley, Mark Woodward, Paul Zimmet, Jonathan Shaw, Nam H Cho, Hyung Rae Kim, Satu Viali, Makoto Tominaga, Dorte Vistisen, Knut Borch-Johnsen, Stephen Colagiuri.   

Abstract

AIM: To compare the prevalence of metabolic syndrome (MetS) by combinations of MetS components derived from the National Cholesterol Education Program Adult Treatment Panel III (ATPIII) and International Diabetes Federation (IDF) definitions.
METHODS: Four studies with ethnically distinct populations from the Asia-Pacific region were selected from the DETECT-2 study database. The prevalences of combinations of MetS components using the modified ATPIII (modATPIII) and IDF MetS definitions were compared between sexes and across populations.
RESULTS: A total of 22,952 participants from Australia, Japan, Korea and Samoa were included. The age-adjusted prevalence of modATPIII MetS varied from 9.4 to 35.8% in men and 10.3 to 57.2% in women; results for IDF were generally higher. Prevalences of the 16 possible MetS component combinations from the modATPIII definition that result in a diagnosis of MetS ranged from 0 to 12.7%. Of those with IDF-defined abdominal obesity, the prevalences of the 11 IDF-defined MetS component combinations ranged from 0.2 to 18.3%.
CONCLUSIONS: The large variation in the prevalence of possible MetS component combinations to diagnose MetS may explain the different risk of cardiovascular outcomes associated with MetS in different populations, especially since particular combinations of MetS components are associated with different risk of cardiovascular disease.

Entities:  

Mesh:

Substances:

Year:  2008        PMID: 18617286     DOI: 10.1016/j.diabres.2008.05.011

Source DB:  PubMed          Journal:  Diabetes Res Clin Pract        ISSN: 0168-8227            Impact factor:   5.602


  16 in total

1.  Risk profiles for metabolic syndrome and its transition patterns for the elderly in Beijing, 1992-2009.

Authors:  Li-Xin Tao; Wei Wang; Hui-Ping Zhu; Da Huo; Tao Zhou; Lei Pan; Qi Gao; Yan-Xia Luo; Li-Juan Wu; Xia Li; Zhe Tang; Xiu-Hua Guo
Journal:  Endocrine       Date:  2014-01-23       Impact factor: 3.633

2.  The metabolic syndrome: useful concept or clinical tool? Report of a WHO Expert Consultation.

Authors:  R K Simmons; K G M M Alberti; E A M Gale; S Colagiuri; J Tuomilehto; Q Qiao; A Ramachandran; N Tajima; I Brajkovich Mirchov; A Ben-Nakhi; G Reaven; B Hama Sambo; S Mendis; G Roglic
Journal:  Diabetologia       Date:  2009-12-11       Impact factor: 10.122

3.  Age- and sex-specific prevalence and ten-year risk for cardiovascular disease of all 16 risk factor combinations of the metabolic syndrome - A cross-sectional study.

Authors:  Susanne Moebus; Chakrapani Balijepalli; Christian Lösch; Laura Göres; Bernd von Stritzky; Peter Bramlage; Jürgen Wasem; Karl-Heinz Jöckel
Journal:  Cardiovasc Diabetol       Date:  2010-08-09       Impact factor: 9.951

4.  Urban and rural variation in clustering of metabolic syndrome components in the Thai population: results from the fourth National Health Examination Survey 2009.

Authors:  Wichai Aekplakorn; Pattapong Kessomboon; Rassamee Sangthong; Suwat Chariyalertsak; Panwadee Putwatana; Rungkarn Inthawong; Wannee Nitiyanant; Surasak Taneepanichskul
Journal:  BMC Public Health       Date:  2011-11-10       Impact factor: 3.295

5.  Predicting Metabolic Syndrome Using the Random Forest Method.

Authors:  Apilak Worachartcheewan; Watshara Shoombuatong; Phannee Pidetcha; Wuttichai Nopnithipat; Virapong Prachayasittikul; Chanin Nantasenamat
Journal:  ScientificWorldJournal       Date:  2015-07-28

6.  The Relationship of Metabolic Syndrome with Stress, Coronary Heart Disease and Pulmonary Function--An Occupational Cohort-Based Study.

Authors:  Miroslaw Janczura; Grazyna Bochenek; Roman Nowobilski; Jerzy Dropinski; Katarzyna Kotula-Horowitz; Bartosz Laskowicz; Andrzej Stanisz; Jacek Lelakowski; Teresa Domagala
Journal:  PLoS One       Date:  2015-08-14       Impact factor: 3.240

7.  Prevalence of metabolic syndrome and its association with depression in patients with schizophrenia.

Authors:  Sirijit Suttajit; Sutrak Pilakanta
Journal:  Neuropsychiatr Dis Treat       Date:  2013-07-09       Impact factor: 2.570

8.  Quantitative population-health relationship (QPHR) for assessing metabolic syndrome.

Authors:  Apilak Worachartcheewan; Chanin Nantasenamat; Chartchalerm Isarankura-Na-Ayudhya; Virapong Prachayasittikul
Journal:  EXCLI J       Date:  2013-06-26       Impact factor: 4.068

9.  Sociodemographic disparities in the composition of metabolic syndrome components among adults in South Korea.

Authors:  Hyunjung Lim; Tuan Nguyen; Ryowon Choue; Youfa Wang
Journal:  Diabetes Care       Date:  2012-07-26       Impact factor: 19.112

10.  Prevalence of the metabolic syndrome in a rural population in Ghana.

Authors:  Mawuli Gyakobo; Albert Gb Amoah; De-Anne Martey-Marbell; Rachel C Snow
Journal:  BMC Endocr Disord       Date:  2012-10-30       Impact factor: 2.763

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.