The influence of diabetes on sleep-derived cardiorespiratory features of the finger pulse wave signal - The population-based SCAPIS study.

ABSTRACT

STUDY OBJECTIVES: Advanced signal processing of photoplethysmographic data enables novel analyses which may improve the understanding of the pathogenesis of dysglycemia associated with sleep disorders. We aimed to identify sleep-related pulse wave characteristics in diabetic patients compared to normoglycemic individuals, independent of cardiovascular-related comorbidities. METHODS: This cross-sectional evaluation of the population-based Swedish CArdioPulmonary bioImage Study (SCAPIS) included overnight oximetry-derived pulse wave data from 3997 subjects (45 % males, age 50-64 years). Metabolic status was classified as normoglycemic (n = 3220), pre-diabetic (n = 544), or diabetic (n = 233). Nine validated pulse wave features proposed to influence cardiovascular risk were derived and compared between metabolic status groups. Logistic prediction models and genetic matching were applied to capture diabetes-related pulse wave characteristics during sleep. The model was controlled for anthropometrics, lifestyle, sleep apnea, and in the final adjustment even for cardiometabolic factors like dyslipidaemia, hypertension, and coronary artery calcification. RESULTS: Pulse wave-derived parameters differed between normoglycemic and diabetic individuals in eight dimensions in unadjusted as well as in the partially adjusted model (anthropometric factors and sleep apnea, p ≤ 0.001). All covariates confirmed significant differences between normoglycemic and diabetic subjects (all p ≤ 0.001). Reduced cardio-respiratory coupling (respiratory-related pulse oscillations) (ß = -0.010, p = 0.012), as well as increased vascular stiffness (shortened pulse propagation time (ß = -0.015, p = 0.001), were independently associated with diabetes even when controlled for cardiometabolic factors. These results were confirmed through a matched cohort comparative analysis. CONCLUSIONS: Photoplethysmographic pulse wave analysis during sleep can be utilized to capture multiple features of modified autonomic regulation and cardiovascular consequences in diabetic subjects. Dampened heart rate variability and increased vascular stiffness during sleep showed the strongest associations with diabetes.