Literature DB >> 18603599

Identifying metabolic syndrome without blood tests in young adults--the Terneuzen Birth Cohort.

Marlou L A de Kroon1, Carry M Renders, Esther C C Kuipers, Jacobus P van Wouwe, Stef van Buuren, Guus A de Jonge, Remy A Hirasing.   

Abstract

BACKGROUND: Within the context of the obesity epidemic identifying young adults at risk for type 2 diabetes and cardiovascular disease is important. A practical approach is based on the identification of metabolic syndrome (MetS). Our objective was to develop a simple and efficient stepwise strategy to identify MetS in young adults.
METHODS: Subjects were part of a birth cohort (n = 2599) in Terneuzen, The Netherlands, born in 1977-86. In 2004-05: 642 of these young adults participated in a physical examination and blood tests. Tree regression was used to determine the optimal decision strategy to identify MetS.
RESULTS: Overall prevalence of MetS, defined according to the NCEP ATPIII, was 7.5%. The tree regression yielded an optimal stepwise strategy that eliminated the need for blood tests for the diagnosis of MetS in 50-90% of the cases, depending on the accepted level of error. A large group (52% of the total) with BMI <35 had a normal waist circumference (WC) and normal blood pressure (BP). None of them had MetS. Subjects with BMI > or =35 all had MetS. If BMI <30, 38% had an increased WC or increased BP with a risk of MetS of only 6%. So for them the omission of blood tests could also be considered.
CONCLUSION: In most young adults MetS can be identified or excluded without blood tests by a simple and stepwise strategy, based on the measurement of BMI, WC and BP. This makes it possible to develop simple prevention strategies for young adults at risk for type 2 diabetes and cardiovascular disease.

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Year:  2008        PMID: 18603599     DOI: 10.1093/eurpub/ckn056

Source DB:  PubMed          Journal:  Eur J Public Health        ISSN: 1101-1262            Impact factor:   3.367


  9 in total

1.  Metabolic Age, an Index Based on Basal Metabolic Rate, Can Predict Individuals That are High Risk of Developing Metabolic Syndrome.

Authors:  Sarahi Vásquez-Alvarez; Sergio K Bustamante-Villagomez; Gabriela Vazquez-Marroquin; Leonardo M Porchia; Ricardo Pérez-Fuentes; Enrique Torres-Rasgado; Oscar Herrera-Fomperosa; Ivette Montes-Arana; M Elba Gonzalez-Mejia
Journal:  High Blood Press Cardiovasc Prev       Date:  2021-03-05

2.  The Terneuzen birth cohort: BMI changes between 2 and 6 years correlate strongest with adult overweight.

Authors:  Marlou L A De Kroon; Carry M Renders; Jacobus P Van Wouwe; Stef Van Buuren; Remy A Hirasing
Journal:  PLoS One       Date:  2010-02-11       Impact factor: 3.240

3.  The Terneuzen Birth Cohort: BMI change between 2 and 6 years is most predictive of adult cardiometabolic risk.

Authors:  Marlou L A de Kroon; Carry M Renders; Jacobus P van Wouwe; Stef van Buuren; Remy A Hirasing
Journal:  PLoS One       Date:  2010-11-12       Impact factor: 3.240

4.  The Terneuzen Birth Cohort. Longer exclusive breastfeeding duration is associated with leaner body mass and a healthier diet in young adulthood.

Authors:  Marlou L A De Kroon; Carry M Renders; Michelle P J Buskermolen; Jacobus P Van Wouwe; Stef van Buuren; Remy A Hirasing
Journal:  BMC Pediatr       Date:  2011-05-10       Impact factor: 2.125

5.  Physical Activity and Cardiometabolic Risk Factor Clustering in Young Adults with Obesity.

Authors:  Loretta Dipietro; Yuqing Zhang; Meghan Mavredes; Samuel J Simmens; Jessica A Whiteley; Laura L Hayman; Jamie Faro; Steven K Malin; Ginger Winston; Melissa A Napolitano
Journal:  Med Sci Sports Exerc       Date:  2020-05

6.  Prevalence of metabolic syndrome and metabolic syndrome components in young adults: A pooled analysis.

Authors:  Paul B Nolan; Graeme Carrick-Ranson; James W Stinear; Stacey A Reading; Lance C Dalleck
Journal:  Prev Med Rep       Date:  2017-07-19

7.  Repeatedly measured predictors: a comparison of methods for prediction modeling.

Authors:  Marieke Welten; Marlou L A de Kroon; Carry M Renders; Ewout W Steyerberg; Hein Raat; Jos W R Twisk; Martijn W Heymans
Journal:  Diagn Progn Res       Date:  2018-02-13

8.  Novel Data Mining Methodology for Healthcare Applied to a New Model to Diagnose Metabolic Syndrome without a Blood Test.

Authors:  Mauricio Barrios; Miguel Jimeno; Pedro Villalba; Edgar Navarro
Journal:  Diagnostics (Basel)       Date:  2019-11-15

9.  Validation of a non-invasive method for the early detection of metabolic syndrome: a diagnostic accuracy test in a working population.

Authors:  Manuel Romero-Saldaña; Pedro Tauler; Manuel Vaquero-Abellán; Angel-Arturo López-González; Francisco-José Fuentes-Jiménez; Antoni Aguiló; Carlos Álvarez-Fernández; Guillermo Molina-Recio; Miquel Bennasar-Veny
Journal:  BMJ Open       Date:  2018-10-21       Impact factor: 2.692

  9 in total

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