Patterns of metabolic syndrome and associated factors in women from the ELSA-Brasil: a latent class analysis approach.
Nila Mara Smith GalvãoSheila Maria Alvim de MatosMaria da Conceição Chagas de AlmeidaLigia GabrielliSandhi Maria BarretoEstela Maria Leão de AquinoMaria Inês SchmidtLeila Denise Alves Ferreira AmorimPublished in: Cadernos de saude publica (2023)
This study aimed to identify patterns of metabolic syndrome among women and estimate their prevalence and relationship with sociodemographic and biological characteristics. In total, 5,836 women were evaluated using baseline data from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Patterns of metabolic syndrome were defined via latent class analysis, using the following metabolic abnormalities as indicators: abdominal obesity, hyperglycemia, hypertension, hypertriglyceridemia, and reduced HDL cholesterol. The relationship between these patterns and individual characteristics was assessed using latent class analysis with covariates. Three patterns of metabolic syndrome were identified: high metabolic expression, moderate metabolic expression, and low metabolic expression. The first two patterns represented most women (53.8%) in the study. Women with complete primary or secondary education and belonging to lower social classes were more likely to have higher metabolic expression. Black and mixed-race women were more likely to have moderate metabolic expression. Menopausal women aged 50 years and older were more often classified into patterns of greater health risk. This study addressed the heterogeneous nature of metabolic syndrome, identifying three distinct profiles for the syndrome among women. The combination of abdominal obesity, hyperglycemia, and hypertension represents the main metabolic profile found among ELSA-Brasil participants. Sociodemographic and biological factors were important predictors of patterns of metabolic syndrome.
Keyphrases
- metabolic syndrome
- polycystic ovary syndrome
- insulin resistance
- poor prognosis
- pregnancy outcomes
- uric acid
- healthcare
- cervical cancer screening
- blood pressure
- type diabetes
- cardiovascular risk factors
- mental health
- public health
- binding protein
- long non coding rna
- physical activity
- risk factors
- social media
- electronic health record
- deep learning
- risk assessment
- young adults
- human health