Overweight and Obesity, Body Fat, Waist Circumference, and Anemia in Peruvian University Students: A Cross-Sectional Study.
Ruth B Quiliche CastañedaJosué E Turpo ChaparroJesús Hanco TorresYaquelin E Calizaya-MillaPercy G Ruiz MamaniPublished in: Journal of nutrition and metabolism (2021)
The university represents a critical space for students in terms of prevalence of malnutrition. The objective of this study was to determine the body mass index (BMI), body fat percentage (% BF), waist circumference (WC), and anemia in university students. A cross-sectional study was carried out in 2,285 university students from Lima, Peru. The sample was selected by nonprobability convenience sampling. Anthropometric data and hemoglobin levels were measured. The Chi-square test was used. The analysis of the associated factors was done using binary logistic regression. A significance level of 5% was considered. There were no significant differences between men and women in BMI ( p > 0.05). The men presented significantly high and very high levels of % BF ( p < 0.001). The proportion of women who presented anemia and high and very high WC was significantly higher compared to men ( p < 0.001). Being older than 27 years (OR B = 2.07; 95% CI = 1.19-3.6), being male (OR B = 2.68; 95% CI = 2.02-3.55), studying at the engineering faculty (OR B = 1.39; 95% CI = 1.09-1.79), having excess body fat (OR B = 8.17; 95% CI = 6.13-10.87), and having an elevated WC (OR B = 35.51; 95% CI = 25.06-50.33) significantly predicted overweight/obesity. The findings of this study suggest that college students, especially males and those who are not enrolled in health sciences colleges, should be a priority in healthy lifestyle interventions, particularly nutritional education programs, to reduce the prevalence of overweight and obesity.
Keyphrases
- body mass index
- physical activity
- weight gain
- chronic kidney disease
- risk factors
- weight loss
- healthcare
- metabolic syndrome
- public health
- iron deficiency
- cardiovascular disease
- middle aged
- mental health
- insulin resistance
- type diabetes
- polycystic ovary syndrome
- pregnant women
- machine learning
- climate change
- electronic health record
- risk assessment
- medical education
- big data
- data analysis
- human health
- pregnancy outcomes
- deep learning
- community dwelling