Healthy Lifestyle and Blood Pressure Variability in Young Adults

Abstract BACKGROUND The aim of this study was to assess the relationships between healthy lifestyle metrics and blood pressure variability (BPV) in young and healthy adults. METHODS A population-based sample of 1,999 individuals aged 25-41 years was investigated. A lifestyle-score from 0 (most unhealthy) to 7 (most healthy) was calculated by giving one point for each of the following components: never smoking cigarettes, adhering to a healthy diet, performing moderate or intense physical activity, having a body mass index <25 kg/m2, a total cholesterol <200 mg/dl, a glycated hemoglobin <5.7%, or a conventional BP <120/80 mm Hg. Standardized ambulatory 24-hour BP measurements were obtained in all individuals. BPV was defined as the SD of all individual ambulatory BP recordings. We constructed multivariable linear regression models to assess the relationships between the lifestyle-score and BPV. None of the results were adjusted for multiple testing. RESULTS Median age was 37 years and 46.8% were men. With increasing lifestyle-score, systolic and diastolic BPV is decreasing linearly (P for trend <0.0001), even after multivariable adjustment. Per 1-point increase in lifestyle-score, the β-coefficient (95% confidence interval) for systolic and diastolic 24-hour BPV was −0.03 (−0.03; −0.02) and −0.04 (−0.05; −0.03), respectively, both P for trend <0.0001. These relationships were attenuated but remained statistically significant after additional adjustment for mean individual BP. CONCLUSION In this study of young and healthy adults, adopting a healthy lifestyle was associated with a lower BPV. These associations were independent of mean BP levels

Healthy Lifestyle and Blood Pressure Variability in Young Adults.

BACKGROUND The aim of this study was to assess the relationships between healthy lifestyle metrics and blood pressure variability (BPV) in young and healthy adults. METHODS A population-based sample of 1,999 individuals aged 25-41 years was investigated. A lifestyle-score from 0 (most unhealthy) to 7 (most healthy) was calculated by giving one point for each of the following components: never smoking cigarettes, adhering to a healthy diet, performing moderate or intense physical activity, having a body mass index <25 kg/m2, a total cholesterol <200 mg/dl, a glycated hemoglobin <5.7%, or a conventional BP <120/80 mm Hg. Standardized ambulatory 24-hour BP measurements were obtained in all individuals. BPV was defined as the SD of all individual ambulatory BP recordings. We constructed multivariable linear regression models to assess the relationships between the lifestyle-score and BPV. None of the results were adjusted for multiple testing. RESULTS Median age was 37 years and 46.8% were men. With increasing lifestyle-score, systolic and diastolic BPV is decreasing linearly (P for trend <0.0001), even after multivariable adjustment. Per 1-point increase in lifestyle-score, the β-coefficient (95% confidence interval) for systolic and diastolic 24-hour BPV was -0.03 (-0.03; -0.02) and -0.04 (-0.05; -0.03), respectively, both P for trend <0.0001. These relationships were attenuated but remained statistically significant after additional adjustment for mean individual BP. CONCLUSION In this study of young and healthy adults, adopting a healthy lifestyle was associated with a lower BPV. These associations were independent of mean BP levels

Smart detection of atrial fibrillation

Aims Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice, and its paroxysmal nature makes its detection challenging. In this trial, we evaluated a novel App for its accuracy to differentiate between patients in AF and patients in sinus rhythm (SR) using the plethysmographic sensor of an iPhone 4S and the integrated LED only. Methods and results For signal acquisition, we used an iPhone 4S, positioned with the camera lens and LED light on the index fingertip. A 5 min video file was recorded with the pulse wave extracted from the green light spectrum of the signal. RR intervals were automatically identified. For discrimination between AF and SR, we tested three different statistical methods. Normalized root mean square of successive difference of RR intervals (nRMSSD), Shannon entropy (ShE), and SD1/SD2 index extracted from a Poincare plot. Eighty patients were included in the study (40 patients in AF and 40 patients in SR at the time of examination). For discrimination between AF and SR, ShE yielded the highest sensitivity and specificity with 85 and 95%, respectively. Applying a tachogram filter resulted in an improved sensitivity of 87.5%, when combining ShE and nRMSSD, while specificity remained stable at 95%. A combination of SD1/SD2 index and nRMSSD led to further improvement and resulted in a sensitivity and specificity of 95%. Conclusion The algorithm tested reliably discriminated between SR and AF based on pulse wave signals from a smartphone camera only. Implementation of this algorithm into a smartwatch is the next logical step

Heart Rate Variability and Sleep-Related Breathing Disorders in the General Population

Obstructive sleep apnea seems to have an important influence on the autonomic nervous system. In this study, we assessed the relations of sleep apnea-related parameters with 24-hour heart rate variability (HRV) in a large population of young and healthy adults. Participants aged 25 to 41 years with a body mass index <35 kg/m(2) and without known obstructive sleep apnea were included in a prospective population-based cohort study. HRV was assessed using 24-hour electrocardiographic monitoring. The SD of all normal RR intervals (SDNN) was used as the main HRV variable. Apnea-Hypopnea Index (AHI) and oxygen desaturation index (ODI) were obtained from nighttime pulse oximetry with nasal airflow measurements. We defined sleep-related breathing disorders as an AHI ≥5 or an ODI ≥5. Multivariable regression models were constructed to assess the relation of HRV with either AHI or ODI. Median age of the 1,255 participants was 37 years, 47% were men, and 9.6% had an AHI ≥5. Linear inverse associations of SDNN across AHI and ODI groups were found (p for trend = 0.006 and 0.0004, respectively). The β coefficients (95% CI) for the relation between SDNN and elevated AHI were -0.20 (-0.40 to -0.11), p = 0.04 and -0.29 (-0.47 to -0.11), p = 0.002 for elevated ODI. After adjustment for 24-hour heart rate, the same β coefficients (95% CI) were -0.06 (-0.22 to 0.11), p = 0.51 and -0.14 (-0.30 to 0.01), p = 0.07, respectively. In conclusion, even early stages of sleep-related breathing disorders are inversely associated with HRV in young and healthy adults, suggesting that they are tightly linked with autonomic dysfunction. However, HRV and 24-hour heart rate seem to have common information