P0506 METABOLIC SYNDROME DIAGNOSIS: AGREEMENT AND DISAGREEMENT BETWEEN DIFFERENT DIAGNOSIS CRITERIA

2009 ◽  
Vol 20 ◽  
pp. S168-S169
Author(s):  
Jb Lopez Martin ◽  
Olga Marin Casajus ◽  
Eduardo Oliveros Acebes ◽  
Maria Ferrer Civeira ◽  
Diana Salor Moral ◽  
...  
2010 ◽  
Vol 86 (4) ◽  
pp. 325-330
Author(s):  
Mônica de Lima Raeder Cavali ◽  
Maria Arlete Meil Schimith Escriv&atilde ◽  
Rosana Sarmento Brasileiro ◽  
José Augusto de Aguiar Carrazedo

Diagnostics ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 192
Author(s):  
Mauricio Barrios ◽  
Miguel Jimeno ◽  
Pedro Villalba ◽  
Edgar Navarro

Metabolic Syndrome (MetS) is a cluster of risk factors that increase the likelihood of heart disease and diabetes mellitus. It is crucial to get diagnosed with time to take preventive measures, especially for patients in locations without proper access to laboratories and medical consultations. This work presented a new methodology to diagnose diseases using data mining that documents all the phases thoroughly for further improvement of the resulting models. We used the methodology to create a new model to diagnose the syndrome without using biochemical variables. We compared similar classification models, using their reported variables and previously obtained data from a study in Colombia. We built a new model and compared it to previous models using the holdout, and random subsampling validation methods to get performance evaluation indicators between the models. Our resulting ANN model used three hidden layers and only Hip Circumference, dichotomous Waist Circumference, and dichotomous blood pressure variables. It gave an Area Under Curve (AUC) of 87.75% by the IDF and 85.12% by HMS MetS diagnosis criteria, higher than previous models. Thanks to our new methodology, diagnosis models can be thoroughly documented for appropriate future comparisons, thus benefiting the diagnosis of the studied diseases.


Neurology ◽  
2016 ◽  
Vol 87 (24) ◽  
pp. 2546-2553 ◽  
Author(s):  
Maurice M. Ohayon ◽  
Kanika Bagai ◽  
Laura W. Roberts ◽  
Arthur S. Walters ◽  
Cristina Milesi

2020 ◽  
Author(s):  
Yang Jiao ◽  
Xuan Xie ◽  
Chunhong Zhang ◽  
Jie Ming ◽  
Shaoyong Xu ◽  
...  

Abstract Aims This study aimed to understand the characteristics of metabolic syndrome (MetS) in populations (especially females) over 50 years old in Xi’an, China, to avail adjusting prevention strategies in similar regions. Methods 3001 people were included, based on data from “Xi'an Community-Based Management of Diabetes in the Elderly”. The prevalence rate was calculated and analyzed stratifying by gender, age and geography. Results The overall prevalence rates for males and females were 41.9±8.3% and 41.4±11.3% according to 2019 Chinese Diabetes Society diagnosis criteria, 32.0±9.0% and 49.7±9.8% according to International Diabetes Federation diagnosis criteria. The prevalence increased with age in females, but not in males. The prevalence of MetS, as well as abdominal obesity and hypertension, was higher in rural females than in urban and suburban females. Logistic regression analysis showed the risk factors included age, premature menopause, low family income and education level, sedentary time >9 hours/day, weight-gain, and family history of hypertension. Conclusions In Xi'an, China, under the current social-economic conditions, the prevalence of MetS in females over 50 years of age stands higher level than that in males, and the prevalence in rural females is higher than that in urban and suburban females, which deserves more attention.


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