Literature DB >> 19649563

Lower BMI cutoff for assessing the prevalence of metabolic syndrome in Thai population.

Apilak Worachartcheewan1, Chanin Nantasenamat, Chartchalerm Isarankura-Na-Ayudhya, Phannee Pidetcha, Virapong Prachayasittikul.   

Abstract

This article investigates the prevalence of metabolic syndrome (MS) and the benefits of lowered body mass index (BMI) cutoff point for assessing MS prevalence in a large, nationally representative population sample comprising of 15,365 Thai adults from metropolitan Bangkok who received annual checkup. Prevalence of MS was characterized using the International Diabetes Federation criteria and BMI ≥ 25 kg/m(2) as cutoff revealed that 26.63% of male and 14.90% of female subjects had MS and the prevalence was age dependent. Traditional BMI cutoff of ≥ 30 kg/m(2) underestimated MS prevalence in Thai population while BMI ≥ 25 kg/m(2) was found to be a suitable solution. Common combinations of MS components were identified in order to find common occurrences that may be implicated in the development of diabetes and/or cardiovascular diseases.

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Year:  2009        PMID: 19649563     DOI: 10.1007/s00592-009-0137-0

Source DB:  PubMed          Journal:  Acta Diabetol        ISSN: 0940-5429            Impact factor:   4.280


  6 in total

1.  PyBact: an algorithm for bacterial identification.

Authors:  Chanin Nantasenamat; Likit Preeyanon; Chartchalerm Isarankura-Na-Ayudhya; Virapong Prachayasittikul
Journal:  EXCLI J       Date:  2011-11-24       Impact factor: 4.068

2.  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

3.  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

4.  Comparison of salivary and plasma adiponectin and leptin in patients with metabolic syndrome.

Authors:  Supanee Thanakun; Hisashi Watanabe; Sroisiri Thaweboon; Yuichi Izumi
Journal:  Diabetol Metab Syndr       Date:  2014-02-14       Impact factor: 3.320

5.  Defining a BMI Cut-Off Point for the Iranian Population: The Shiraz Heart Study.

Authors:  Mohammad Ali Babai; Peyman Arasteh; Maryam Hadibarhaghtalab; Mohammad Mehdi Naghizadeh; Alireza Salehi; Alireza Askari; Reza Homayounfar
Journal:  PLoS One       Date:  2016-08-10       Impact factor: 3.240

Review 6.  Data mining for the identification of metabolic syndrome status.

Authors:  Apilak Worachartcheewan; Nalini Schaduangrat; Virapong Prachayasittikul; Chanin Nantasenamat
Journal:  EXCLI J       Date:  2018-01-10       Impact factor: 4.068

  6 in total

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