ABSTRACT
Background: Although the relationship between health status and dietary intake has been extensively studied in the general population, there is a lack of research that has specifically examined the association between frequency of breakfast consumption and cardiometabolic risk in university teachers. Objective: To determine the association between the frequency of breakfast consumption and cardiometabolic risk in university teachers. Methods: A cross-sectional study was conducted in 176 teachers from a private university located in the eastern region of Lima, Peru (Mage: 37.0 years; SD: 0.8, range: 24-59 years). The study was conducted during the period from December 2019 to February 2020. Information was collected on anthropometric and biochemical parameters and frequency of breakfast consumption. Multinomial logistic regression models were used to explore the association between frequency of breakfast with sociodemographic, anthropometric, and biochemical variables. Results: The highest prevalence of excess body weight (44.4%) was observed in those who consumed breakfast 0 to 2 days/week, but without statistical differences. Those who reported Low-density lipoprotein cholesterol (LDL-C) < 160 mg/dL were 77% less likely to fall into the 3-5 day/week breakfast frequency category than those who reported a regular frequency of breakfast (6 to 7 days/week) (Adjusted OR = 0.23, 95% CI 0.08 to 0.73; p < 0.05). In addition, teachers who reported a breakfast frequency of 3 to 5 days/week were 83% more likely to have a glucose concentration < 110 mg/dL compared to those who consumed breakfast of 6 to 7 days/week (Adjusted OR = 0.17, 95% CI 0.04 to 0.75; p < 0.05). Conclusion: Skipping breakfast for an extended period of time can have detrimental effects on cardiometabolic health. Promoting the benefits of breakfast could be a health message of great public health interest.
ABSTRACT
Background: People with depression are at increased risk for comorbidities; however, the clustering of comorbidity patterns in these patients is still unclear. Objective: The aim of the study was to identify latent comorbidity patterns and explore the comorbidity network structure that included 12 chronic conditions in adults diagnosed with depressive disorder. Methods: A cross-sectional study was conducted based on secondary data from the 2017 behavioral risk factor surveillance system (BRFSS) covering all 50 American states. A sample of 89,209 U.S. participants, 29,079 men and 60,063 women aged 18 years or older, was considered using exploratory graphical analysis (EGA), a statistical graphical model that includes algorithms for grouping and factoring variables in a multivariate system of network relationships. Results: The EGA findings show that the network presents 3 latent comorbidity patterns, i.e., that comorbidities are grouped into 3 factors. The first group was composed of 7 comorbidities (obesity, cancer, high blood pressure, high blood cholesterol, arthritis, kidney disease, and diabetes). The second pattern of latent comorbidity included the diagnosis of asthma and respiratory diseases. The last factor grouped 3 conditions (heart attack, coronary heart disease, and stroke). Hypertension reported higher measures of network centrality. Conclusion: Associations between chronic conditions were reported; furthermore, they were grouped into 3 latent dimensions of comorbidity and reported network factor loadings. The implementation of care and treatment guidelines and protocols for patients with depressive symptomatology and multimorbidity is suggested.
ABSTRACT
Introduction: Enneagram typologies may impact psychological well-being and stressful situations in college students. However, the literature is still limited in the study of dynamic personality models such as the Enneagram in Spanish-speaking university students, and a better understanding is needed. Objective: To analyze network associations and centrality measures of Enneagram personality typologies in Peruvian university students. Methods: A total of 859 Peruvian university students responded to two instruments assessing: The Pangrazzi's Enneagram personality types and healthy personality to psychosocial stress. All instruments showed good psychometric values (validity and consistency). A regularized cross-sectional network structure was estimated with Gaussian graphical model and the graphical LASSO. Results: Enneagram types 4, 5, and 6 presented the highest and positive associations in the network structure. Type 6 emerged as the node with the highest predictability. The healthy personality and type 7 acted as bridges between the communities, with types 6, 7, and 8 being the most central nodes. Conclusion: The findings suggest that Enneagram type 7 with healthy personality to psychosocial stress plays an important role in the development of the causal activation of the network model. The network shows causal associations between psychosocial stress and types 6, 7, 8, and 9.
