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Sleep Disruption and Levels of Lipids in Caucasians

Results are presented as mean±SD or median (interquartile range: 25% to 75%) for continuous variables or number (proportion) of patients for categorical variables. Some parameters were logarithmically transformed to approximate a normal distribution. We used factor analysis with oblique promax rotation to reduce the complexity of lifestyle patterns of patients with T2DM by morningness-eveningness, quality of sleep including sleep duration, depressive status, energy intake, alcohol consumption, cigarettes smoking, and physical activity. Factors with an eigenvalue >1 were retained. Individual metabolites with a factor loading of >|0.3| are reported as composing that factor for simplicity. The factor scores for each lifestyle pattern and for each subject were calculated by summing each lifestyle score weighted by their factor loadings. The estimated factor scores were categorized into quintiles. Trend association across the quintile was evaluated by linear regression analysis for continuous variables and logistic regression analysis for categorical variables. We developed three models to evaluate the trend. The first model was unadjusted, second model was adjusted for age and gender, and third model adjusted for age, gender, and body mass index (BMI). The model for eGFR was only adjusted for BMI. Multivariate regression analysis adjusting for age, gender and BMI was performed to investigate what types of lifestyle patterns identified in this study were related with cardio-renal-metabolic parameters. Statistical tests were two-sided with 5% significant level. All analyses were performed using the SAS software version 9.3 (SAS Institute, Cary, North Carolina)