Cardiovagal Function Measured by the Deep Breathing Test: Relationships With Coronary Atherosclerosis

The relationships between the plasma metabolome and orthostatic blood pressure responses

Whereas autonomic dysfunction and the metabolic syndrome are clinically associated, the relationships with the plasma metabolome is unknown. We explored the association between orthostatic blood pressure responses and 818 plasma metabolites in middle-aged subjects from the general population. We included 3803 out of 6251 subjects (mean age, 57 years; 52% women) from the Malmö sub-cohort of The Swedish CardioPulmonary bioImage Study with information on smoking habits, diabetes, antihypertensive drug treatment, anthropometrics, hemodynamic measurements and 818 plasma metabolites (mass-spectrometry). The associations between each metabolite and orthostatic systolic blood pressure responses were determined using multivariable linear regression analysis and p values were corrected using the Bonferroni method. Six amino acids, five vitamins, co-factors and carbohydrates, nine lipids and two xenobiotics were associated with orthostatic blood pressure after adjusting for age, gender and systolic blood pressure. After additional adjustments for BMI, diabetes, smoking and antihypertensive treatment, the association remained significant for six lipids, four amino acids and one xenobiotic. Twenty-two out of 818 plasma metabolites were associated with orthostatic blood pressure responses. Eleven metabolites, including lipids in the dihydrosphingomyelin and sphingosine pathways, were independently associated with orthostatic systolic blood pressure responses after additional adjustment for markers of cardio-metabolic disease.

Associations between physical activity and autonomic function during deep breathing test: the Swedish CArdioPulmonary bioImage Study (SCAPIS)

PURPOSE

The deep breathing test (DBT) is a sensitive test of cardiovagal function. The aim of this study was to explore associations between physical activity and sedentary time, measured by accelerometer, and autonomic function, using DBT.

METHODS

In the Swedish Cardio-Pulmonary bioImage Study, men and women aged 50-64 were randomly invited from the general population. A total of 4325 subjects who underwent DBT and assessment of physical activity and sedentary time by accelerometery were included. ECG files from 1-min DBT were used to calculate measures of respiratory sinus arrhythmia [RSA; expiration-inspiration (E-I) difference and E/I ratio], heart rate variability [HRV; root mean square of successive differences (RMSSD), standard deviation of heart rates and mean circular resultant]. Low RSA and HRV was defined as the lowest 10% in the population.

RESULTS

For accelerometer-assessed physical activity, there were significant associations between high percentage of sedentary time and low E/I (p < 0.01), and low RMSSD (p < 0.01) in an age- and sex-adjusted model, and between percentage of sedentary time and low RMSSD (p = 0.04) in a risk factor-adjusted model. Low RMSSD was less common in those with a high percentage of moderate to vigorous physical activity (p = 0.04, after risk-factor adjustment). These associations became non-significant when further adjusting for heart rate.

CONCLUSION

We report associations between degree of physical activity and indices of autonomic dysfunction in a large population. The relationships were no longer significant after adjustments for heart rate, indicating that the relationship between physical activity and cardiovagal function partly is accounted for by reduced heart rate.

A subspace projection approach to quantify respiratory variations in the f-wave frequency trend

Background: The autonomic nervous system (ANS) is known as a potent modulator of the initiation and perpetuation of atrial fibrillation (AF), hence information about ANS activity during AF may improve treatment strategy. Respiratory induced ANS variation in the f-waves of the ECG may provide such information.

Objective: This paper proposes a novel approach for improved estimation of such respiratory induced variations and investigates the impact of deep breathing on the f-wave frequency in AF patients.

Methods: A harmonic model is fitted to the f-wave signal to estimate a high-resolution f-wave frequency trend, and an orthogonal subspace projection approach is employed to quantify variations in the frequency trend that are linearly related to respiration using an ECG-derived respiration signal. The performance of the proposed approach is evaluated and compared to that of a previously proposed bandpass filtering approach using simulated f-wave signals. Further, the proposed approach is applied to analyze ECG data recorded for 5 min during baseline and 1 min deep breathing from 28 AF patients from the Swedish cardiopulmonary bioimage study (SCAPIS).

Results: The simulation results show that the estimates of respiratory variations obtained using the proposed approach are more accurate than estimates obtained using the previous approach. Results from the analysis of SCAPIS data show no significant differences between baseline and deep breathing in heart rate (75.5 ± 22.9 vs. 74 ± 22.3) bpm, atrial fibrillation rate (6.93 ± 1.18 vs. 6.94 ± 0.66) Hz and respiratory f-wave frequency variations (0.130 ± 0.042 vs. 0.130 ± 0.034) Hz. However, individual variations are large with changes in heart rate and atrial fibrillatory rate in response to deep breathing ranging from −9% to +5% and −8% to +6%, respectively and there is a weak correlation between changes in heart rate and changes in atrial fibrillatory rate (r = 0.38, p &lt; 0.03).

Conclusion: Respiratory induced f-wave frequency variations were observed at baseline and during deep breathing. No significant changes in the magnitude of these variations in response to deep breathing was observed in the present study population.