Association of chronotype with eating habits and anthropometric measures in a sample of Iranian adults

Association between chronotype, social jetlag, sleep quality, and academic burnout among nursing students: A cross-sectional study

Academic burnout in nursing students is a serious problem worldwide that results in emotional exhaustion, cynicism and low professional efficacy. The aim of the current study was to examine the association between sleep quality and circadian rhythm indicators and academic burnout among Iranian nursing students. This cross-sectional study was conducted on 325 undergraduate nursing students studying at Qazvin University of Medical Sciences between November and February 2023. Data collection was carried out using self-report questionnaires including demographic characteristics, The Pittsburgh Sleep Quality Index (PSQI), reduced-Morningness-Eveningness Questionnaire (r-MEQ), and the Maslach Burnout Inventory-Student Survey (MBI-SS). Social jetlag (SJL) was defined as the absolute value of the difference between the midpoint of sleep on free days (MSF) and the midpoint of sleep on workdays (MSW). Data were analyzed using univariate and multivariate linear regression models. About 31.4% of participants had evening chronotype and almost half of them suffered from poor sleep quality. About 47.7% of the participants had SJL ≥ 2 h. In the multivariate linear regression model, students with evening chronotype compared to morning chronotype experienced more academic burnout in emotional exhaustion, cynicism and professional efficacy. PSQI score was only correlated with emotional exhaustion and cynicism subscales. Increased SJL was a predictor on all three subscales of academic burnout. The findings suggested that evening chronotype, poor sleep quality, and high SJL were associated with increased likelihood of academic burnout in nursing students. Assessment of circadian preference and sleep quality is beneficial for timely identification and prevention of academic burnout in nursing students.

Saturated fats network identified using Gaussian graphical models is associated with metabolic syndrome in a sample of Iranian adults

BACKGROUND

Gaussian graphical models (GGM) are an innovative method for deriving dietary networks which reflect dietary intake patterns and demonstrate how food groups are consuming in relation to each other, independently. The aim of this study was to derive dietary networks and assess their association with metabolic syndrome in a sample of the Iranian population.

METHODS

In this cross-sectional study, 850 apparently healthy adults were selected from referral health care centers. 168 food items food frequency questionnaire was used to assess dietary intakes. Food networks were driven by applying GGM to 40 food groups. Metabolic syndrome was defined based on the guidelines of the National Cholesterol Education Program Adult Treatment Panel III (ATP III).

RESULTS

Three GGM networks were identified: healthy, unhealthy and saturated fats. Results showed that adherence to saturated fats networks with the centrality of butter, was associated with higher odds of having metabolic syndrome after adjusting for potential confounders (OR = 1.81, 95% CI 1.61-2.82; P trend = 0.009) and higher odds of having hyperglycemia (P trend = 0.04). No significant association was observed between healthy and unhealthy dietary networks with metabolic syndrome, hypertension, hypertriglyceridemia and central obesity. Furthermore, metabolic syndrome components were not related to the identified networks.

CONCLUSION

Our findings suggested that greater adherence to the saturated fats network is associated with higher odds of having metabolic syndrome in Iranians. These findings highlight the effect of dietary intake patterns with metabolic syndrome.