BackgroundNon-alcoholic fatty liver disease (NAFLD) is associated with cardiovascular atherosclerosis independent of classical risk factors. This study investigated the influence of NAFLD on autonomic changes, which is currently unknown.MethodsSubjects without an overt history of cardiovascular disease were enrolled during health checkups. The subjects diagnosed for NAFLD using ultrasonography underwent 5-min heart rate variability (HRV) measurements that was analyzed using the following indices: (1) the time domain with the standard deviation of N-N (SDNN) intervals and root mean square of successive differences between adjacent N-N intervals (rMSSD); (2) the frequency domain with low frequency (LF) and high frequency (HF) components; and (3) symbolic dynamics analysis. Routine blood biochemistry data and serum leptin levels were analyzed. Homeostasis model assessment of insulin resistance (HOMA-IR) was measured.ResultsOf the 497 subjects (mean age, 46.2 years), 176 (35.4%) had NAFLD. The HRV indices (Ln SDNN, Ln rMSSD, Ln LF, and Ln HF) were significantly decreased in the NAFLD group (3.51 vs 3.62 ms, 3.06 vs 3.22 ms, 5.26 vs 5.49 ms2, 4.49 vs 5.21 ms2, respectively, all P<0.05). Ln SDNN was significantly lower in the NAFLD group after adjustment for age, sex, hypertension, dyslipidemia, metabolic syndrome, body mass index, smoking, estimated glomerular filtration rate, HOMA-IR, and leptin (P<0.05). In the symbolic dynamic analysis, 0 V percentage was significantly higher in the NAFLD group (33.8% vs 28.7%, P = 0.001) and significantly correlated with linear HRV indices (Ln SDNN, Ln rMSSD, and Ln HF).ConclusionsNAFLD is associated with decreased Ln SDNN and increased 0 V percentage. The former association was independent of conventional cardiovascular risk factors and serum biomarkers (insulin resistance and leptin). Further risk stratification of autonomic dysfunction with falls or cardiovascular diseases by these HRV parameters is required in patients with NAFLD.

No abstract

Heart failure (HF) is a major cardiovascular disease worldwide, and the early detection and diagnosis remain challenges. Recently, heart rhythm complexity analysis, derived from non-linear heart rate variability (HRV) analysis, has been proposed as a non-invasive method to detect diseases and predict outcomes. In this study, we aimed to investigate the diagnostic value of heart rhythm complexity in HF patients. We prospectively analyzed 55 patients with symptomatic HF with impaired left ventricular ejection fraction and 97 participants without HF symptoms and normal LVEF as controls. Traditional linear HRV parameters and heart rhythm complexity including detrended fluctuation analysis (DFA) and multiscale entropy (MSE) were analyzed. The traditional linear HRV, MSE parameters and DFAα1 were significantly lower in HF patients compared with controls. In regression analysis, DFAα1 and MSE scale 5 remained significant predictors after adjusting for multiple clinical variables. Among all HRV parameters, MSE scale 5 had the greatest power to differentiate the HF patients from the controls in receiver operating characteristic curve analysis (area under the curve: 0.844). In conclusion, heart rhythm complexity appears to be a promising tool for the detection and diagnosis of HF.

Background Cardiovascular disease is the leading cause of morbidity and mortality in patients with end‐stage renal disease. Heart rhythm complexity analysis has been shown to be useful in predicting outcomes in various diseases; however, data on patients with end‐stage renal disease are limited. In this study, we analyzed the association between heart rhythm complexity and long‐term cardiovascular outcomes in patients with end‐stage renal disease receiving peritoneal dialysis. Methods and Results We prospectively enrolled 133 patients receiving peritoneal dialysis and analyzed linear heart rate variability and heart rhythm complexity variables including detrended fluctuation analysis ( DFA ) and multiscale entropy. The primary outcome was cardiovascular mortality, and the secondary outcome was the occurrence of major adverse cardiovascular events. After a median of 6.37 years of follow‐up, 21 patients (22%) died from cardiovascular causes. These patients had a significantly lower low‐frequency band of heart rate variability, low/high‐frequency band ratio, total power band of heart rate variability, heart rate turbulence slope, deceleration capacity, short‐term DFA (DFAα1); and multiscale entropy slopes 1 to 5, scale 5, area 1 to 5, and area 6 to 20 compared with the patients who did not die from cardiovascular causes. Time‐dependent receiver operating characteristic curve analysis showed that DFA α1 had the greatest discriminatory power for cardiovascular mortality (area under the curve: 0.763) and major adverse cardiovascular events (area under the curve: 0.730). The best cutoff value for DFA α1 was 0.98 to predict both cardiovascular mortality and major adverse cardiovascular events. Multivariate Cox regression analysis showed that DFA α1 (hazard ratio: 0.076; 95% CI , 0.016–0.366; P =0.001) and area 1 to 5 (hazard ratio: 0.645; 95% CI , 0.447–0.930; P =0.019) were significantly associated with cardiovascular mortality. Conclusions Heart rhythm complexity appears to be a promising noninvasive tool to predict long‐term cardiovascular outcomes in patients receiving peritoneal dialysis.

Pulmonary hypertension is a fatal disease, however reliable prognostic tools are lacking. Heart rhythm complexity analysis is derived from non-linear heart rate variability (HRV) analysis and has shown excellent performance in predicting clinical outcomes in several cardiovascular diseases. However, heart rhythm complexity has not previously been studied in pulmonary hypertension patients. We prospectively analyzed 57 patients with pulmonary hypertension (31 with pulmonary arterial hypertension and 26 with chronic thromboembolic pulmonary hypertension) and compared them to 57 age- and sex-matched control subjects. Heart rhythm complexity including detrended fluctuation analysis (DFA) and multiscale entropy (MSE) and linear HRV parameters were analyzed. The patients with pulmonary hypertension had significantly lower mean RR, SDRR, pNN 20 , VLF, LF, LF/HF ratio, DFAα1, MSE slope 5, scale 5, area 1–5 and area 6–20 compared to the controls. Receiver operating characteristic curve analysis showed that heart rhythm complexity parameters were better than traditional HRV parameters to predict pulmonary hypertension. Among all parameters, scale 5 had the greatest power to differentiate the pulmonary hypertension patients from controls (AUC: 0.845, P < 0.001). Furthermore, adding heart rhythm complexity parameters significantly improved the discriminatory power of the traditional HRV parameters in both net reclassification improvement and integrated discrimination improvement models. In conclusion, the patients with pulmonary hypertension had worse heart rhythm complexity. MSE parameters, especially scale 5, had excellent single discriminatory power to predict whether or not patients had pulmonary hypertension.