Physical activity and metabolic syndrome: A population base study (forest and tree model algorithms)

Area of Science:

• Epidemiology
• Public Health
• Biostatistics

Background:

• Sedentary lifestyles have increased metabolic syndrome (MetS) and cardiovascular disease risk.
• Understanding the relationship between physical activity and MetS is crucial for public health interventions.

Purpose of the Study:

• To investigate the association between physical activity levels and the prevalence of metabolic syndrome (MetS).
• To identify key predictors of MetS using advanced statistical modeling in a population-based cohort.

Main Methods:

• Utilized data from 10,663 individuals aged 40-70 years from the Persian Kharameh cohort study (2017).
• Collected data on physical activity, demographics, anthropometrics, blood pressure, and biochemical markers.
• Employed modern statistical methods, including decision-tree algorithms, for analysis.

Main Results:

• Higher physical activity (fourth quarter) was associated with a 36% lower likelihood of developing MetS compared to the lowest activity group (first quarter).
• Gender was the strongest predictor of MetS (56.0%), followed by comorbidities, age, occupation, and physical activity.
• Specific risk profiles for MetS were identified based on gender, physical activity, age, and comorbidities.

Conclusions:

• Physical activity is a significant factor in predicting and preventing metabolic syndrome.
• Modern statistical techniques offer valuable insights into complex health conditions like MetS.
• Promoting physical activity and lifestyle changes is essential for managing and preventing MetS and its associated risks.