Context. Scarce data on dietary habits in Eastern European countries is available and reports investigated individual food items and not dietary patterns in these populations Objective. To identify dietary patterns and to explore their association with obesity in a sample from Romanian population. Design. Cross-sectional. Subjects and Methods. This was an analysis of data collected from 1398 adult participants in ORO study. Data on lifestyle, eating habits and food frequency consumption were collected. Results. By principal component analysis we identified 3 dietary patterns explaining 31.4% of the diet variation: High meat/High fat pattern, Western pattern and Prudent pattern. High meat/High fat pattern was associated with male gender, lower educational level, living in a rural, smoking and a higher probability for the presence of obesity (OR 1.2 [95%CI: 1.1-1.4]). Western pattern was associated with younger age, a higher level of physical activity and smoking. Prudent pattern was associated with older age, female gender, a higher level of physical activity, not smoking status and a lower probability for the presence of obesity (OR 0.8 [95%CI: 0.7-0.9]). Conclusions. This study provides for the first-time information on the association between dietary patterns in adults from an Eastern European country and the presence of obesity.

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On the basis of the easy-to-determine clinical parameters and on high predictive value, the clinical couple of hypertensive waist could be used as a starting point to screen for metabolic syndrome in Romanian population.

Background Social jetlag (SJL) is a small recurrent circadian rhythm disruption and the most frequent form of circadian rhythm misalignment. The main aim of this study was to investigate the effect of SJL on glycemic control, as assessed by HbA1c, in real‐life settings. Methods In all, 115 consecutive patients with type 1 diabetes (T1D) were analyzed cross‐sectionally. Data on bedtime, sleep onset latency, and wake up time on weekdays and weekends during the previous month were collected from all participants and used to calculate SJL, chronotype, and sleep duration. Sleep quality was assessed by the Pittsburgh Sleep Quality Index (PSQI). A PSQI score > 5 was considered as an indicator of poor sleep quality. Results Patients with SJL ≥ 1 hour had significantly higher adjusted values of HbA1c than those with SJL <1 hour (8.7% vs 8.0%; P = 0.029). In unadjusted multivariate regression analysis, SJL ≥ 1 hour and poor sleep quality were significant predictors of HbA1c values, explaining 22.7% and 23.5%, respectively, of the increase in HbA1c. After adjusting for age, sex, diabetes duration, insulin dose (kg/d), insulin regimen and body mass index, only SJL ≥ 1 hour remained associated with HbA1c (β = 0.253; P = 0.026). There was no significant interaction between SJL ≥ 1 hour and poor sleep quality in either the unadjusted or adjusted models (Pinteraction = 0.914). Conclusions In patients with T1D, SJL is associated with poor glycemic control, acting independently of sleep quality, sleep duration, and chronotype to exert a deleterious effect on glycemic control.

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