Back

Comparison of metabolic syndrome prevalence using three different definitions: A population-based study in Peru

Vera-Ponce, V. J.; Zuzunaga-Montoya, F. E.; Vasquez-Romero, L. E. M. -; Loayza-Castro, J. A.; Valladares-Garrido, M. J.

2023-10-24 endocrinology
10.1101/2023.10.23.23297432 medRxiv
Show abstract

IntroductionMetabolic syndrome (MetS) is important for public health; however, there are different ways to define it. Objectives1) to estimate the prevalence of MetS using three different definitions: the criteria of the International Diabetes Federation (IDF), the World Health Organization (WHO), and the National Cholesterol Education Programs Adult Treatment Panel III (ATPIII), 2) to identify the factors associated with the presence of MetS according to each criterion, and 3) to evaluate the agreement between these three. Materials and MethodsA secondary and cross-sectional analysis of the database from the Life Stages Food and Nutrition Surveillance Survey (VIANEV) was conducted. For the definition of MetS, the aforementioned definitions were used. A multivariable Poisson regression analysis with robust variance and agreement was evaluated through the Kappa index. ResultsAccording to ATPIII, IDF, and WHO, the prevalence of MetS was 42.60%, 46.78%, and 49.49%, respectively. The agreement between IDF with WHO and ATPIII criteria was 0.42 and 0.45, while for ATPIII and WHO it was 0.44. In general, the associated factors were identified as sex, marital status, age, region of residence, level of physical activity, smoking habit, and body mass index (BMI). However, the association of these varied according to the definition used. ConclusionThe prevalence of MetS varies significantly according to the criteria used. This was higher when the WHO definition was used compared to the others. Additionally, the associated factors varied according to the definition used, although a consistency was observed across all definitions with BMI.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.