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Monitoring Clinical Characteristics and Lifestyle Factors to Reduce Confounding in Studies of Peripheral Vascular Function

Peripheral vascular function may be influenced by multiple factors, including age, baseline clinical characteristics, and environmental factors. Thus, monitoring these attributes is likely to be an important consideration when developing protocols for studies that measure vascular function. This study was conducted to examine the association between peripheral vascular function and baseline clinical measures (heart rate, blood pressure, and various blood markers), and lifestyle factors, including, recent nutrient intake and previous night hours of sleep, in healthy women between the ages of 18–70 y. A secondary analysis was conducted with baseline data from 20 non‐smoking Caucasian that had fasted overnight and reported to the laboratory in the morning for fasting blood draws. Fasting glucose and triglyceride concentrations were measured from fingertip blood; height, weight, and blood pressure were also measured. A 24‐hour dietary recall was conducted to estimate nutrient intake the previous day. Subjects were then fed a meal of 2 waffles, with 1.4 oz syrup, 10 g margarine, and 177 mL flavored water, providing a total of 390 calories, 21 g fat, 43 g carbohydrate, and 4 g protein. At 2.5 hours post meal, reactive hyperemia index, a marker for endothelial dependent vasodilation, and augmentation index, a marker for arterial stiffness, were measured by peripheral arterial tonometry. These data were subjected to stepwise regression (SAS 9.4) to examine the association between vascular function (reactive hyperemia index, augmentation index) and a list of predictors, including age, systolic and diastolic pressure, previous night‐hours of sleep, heart rate, fasting blood glucose, fasting blood triglyceride, and dietary factors from previous day food intake including 12 nutrients (total fat, saturated and trans fat, omega‐3 fatty acids, total carbohydrate, added sugars, protein, arginine, fiber, vitamin C, vitamin E, vitamin A, total carotenoids). The significance level was set at 0.05. Normality of the residuals was checked for both dependent variables. Multicollinearity between predictors was examined by correlation analysis. Results showed that previous day protein intake was inversely associated with reactive hyperemia index and accounted for 39.8% of the total variance, where 50.8% of the variance was explained by the model. Both age and protein intake were positively associated with augmentation index. Hours of sleep on the previous night were inversely associated with augmentation index. Age, hours of sleep, and protein intake accounted for 52%, 9.5% and 7.9%, respectively, of the total variance explained by the model for augmentation index. These data demonstrate the importance of monitoring and controlling subjects’ recent dietary intake and to encourage adequate sleep to reduce confounding effects from these sources, when exploring vascular function as an outcome marker to study any intervention, especially in repeated measure crossover studies.