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Prediction of the risk of cardiovascular mortality

Morningness-eveningness reflects the timing of the sleep-wake pattern and different aspects of sleep duration and/or quality, which are quite variable among individuals. In individuals of the evening type, social rhythms do not synchronize with the circadian clock. Previous studies demonstrated that evening type individuals tend to have unhealthy eating habits, behavioral health problems, and sleep complains more than morning type subjects [12,13]. More recently, we found inadequate glycemic control in evening type patients with T2DM [14] [15]. These findings suggest that evening type individuals potentially have impaired metabolism by abnormal circadian rhythm.

The prevalence of depression is reportedly higher in T2DM patients than non-T2DM [16]. Given that T2DM-related depression is related to poor glycemic control [17] and micro- and macro-vascular complications [18], partly due to increased counter-regulatory hormones [19], the depression status may be an important risk factor for defective glycemic control and T2DM-related complications in patients with T2DM.

In daily life, people have variable lifestyle patterns that can influence their cardio-renal-metabolic parameters. In this regard, it is conceivable that certain specific lifestyle patterns tend to accumulate in individual patients with T2DM. If we can classify the patients according to such accumulated lifestyle patterns, it would be useful to design treatment strategies tailored to the individual patients with T2DM. The aim of the present study was to explore possible classification of patients with T2DM free of history of CVD, according to clustering of lifestyle factors and mental status by factor analysis. We then investigated the relationships between each cluster of lifestyle factors (e.g., circadian rhythm, habitual sleep, mental state, diet, alcohol consumption, cigarettes consumption, physical activity) and cardio-renal—metabolic parameters, including brachial-ankle pulse wave velocity (baPWV), which is considered a useful predictor of CVD [20].