Dataset Information

Phenotypic but not genetically predicted heart rate variability associated with all-cause mortality.

ABSTRACT: Low heart rate variability (HRV) has been widely reported as a predictor for increased mortality. However, the molecular mechanisms are poorly understood. Therefore, this study aimed to identify novel genetic loci associated with HRV and assess the association of phenotypic HRV and genetically predicted HRV with mortality. In a GWAS of 46,075 European ancestry individuals from UK biobank, we identified 17 independent genome-wide significant genetic variants in 16 loci associated with HRV traits. Notably, eight of these loci (RNF220, GNB4, LINCR-002, KLHL3/HNRNPA0, CHRM2, KCNJ5, MED13L, and C160rf72) have not been reported previously. In a prospective phenotypic relationship between HRV and mortality during a median follow-up of seven years, individuals with lower HRV had higher risk of dying from any cause. Genetically predicted HRV, as determined by the genetic risk scores, was not associated with mortality. To the best of our knowledge, the findings provide novel biological insights into the mechanisms underlying HRV. These results also underline the role of the cardiac autonomic nervous system, as indexed by HRV, in predicting mortality.

SUBMITTER: Tegegne BS

PROVIDER: S-EPMC10558565 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Phenotypic but not genetically predicted heart rate variability associated with all-cause mortality.

Tegegne Balewgizie S BS Said M Abdullah MA Ani Alireza A van Roon Arie M AM Shah Sonia S de Geus Eco J C EJC van der Harst Pim P Riese Harriëtte H Nolte Ilja M IM Snieder Harold H

Communications biology 20231006 1

Low heart rate variability (HRV) has been widely reported as a predictor for increased mortality. However, the molecular mechanisms are poorly understood. Therefore, this study aimed to identify novel genetic loci associated with HRV and assess the association of phenotypic HRV and genetically predicted HRV with mortality. In a GWAS of 46,075 European ancestry individuals from UK biobank, we identified 17 independent genome-wide significant genetic variants in 16 loci associated with HRV traits. ...[more]

PMID: 37803156

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Project description:Background: Heart rate variability (HRV), reflecting circadian rhythm of heart rate, is reported to be associated with clinical outcomes in stage 5 chronic kidney disease (CKD5) patients. Whether CKD related factors combined with HRV can improve the predictive ability for their death remains uncertain. Here we evaluated the prognosis value of nomogram model based on HRV and clinical risk factors for all-cause mortality in CKD5 patients. Methods: CKD5 patients were enrolled from multicenter between 2011 and 2019 in China. HRV parameters based on 24-h Holter and clinical risk factors associated with all-cause mortality were analyzed by multivariate Cox regression. The relationships between HRV and all-cause mortality were displayed by restricted cubic spline graphs. The predictive ability of nomogram model based on clinical risk factors and HRV were evaluated for survival rate. Results: CKD5 patients included survival subgroup (n = 155) and all-cause mortality subgroup (n = 45), with the median follow-up time of 48 months. Logarithm of standard deviation of all sinus R-R intervals (lnSDNN) (4.40 ± 0.39 vs. 4.32 ± 0.42; p = 0.007) and logarithm of standard deviation of average NN intervals for each 5 min (lnSDANN) (4.27 ± 0.41 vs. 4.17 ± 0.41; p = 0.008) were significantly higher in survival subgroup than all-cause mortality subgroup. On the basis of multivariate Cox regression analysis, the lnSDNN (HR = 0.35, 95%CI: 0.17-0.73, p = 0.01) and lnSDANN (HR = 0.36, 95% CI: 0.17-0.77, p = 0.01) were associated with all-cause mortality, their relationships were negative linear. Spearman's correlation analysis showed that lnSDNN and lnSDANN were highly correlated, so we chose lnSDNN, sex, age, BMI, diabetic mellitus (DM), β-receptor blocker, blood glucose, phosphorus and ln intact parathyroid hormone (iPTH) levels to build the nomogram model. The area under the curve (AUC) values based on lnSDNN nomogram model for predicting 3-year and 5-year survival rates were 79.44% and 81.27%, respectively. Conclusion: In CKD5 patients decreased SDNN and SDANN measured by HRV were related with their all-cause mortality, meanwhile, SDNN and SDANN were highly correlated. Nomogram model integrated SDNN and clinical risk factors are promising for evaluating their prognosis.

Project description:BackgroundAutonomic dysregulation is one of the recognized pathophysiological mechanisms in sepsis, generating the hypothesis that heart rate variability (HRV) can be used to predict mortality in sepsis.MethodsThis was a systematic review of studies evaluating HRV as a predictor of death in patients with sepsis. The search was performed by independent researchers in PubMed, LILACS and Cochrane, including papers in English, Portuguese or Spanish, indexed until August 20th, 2017 with at least 10 patients. Study quality was assessed by Newcastle-Ottawa Scale. To analyze the results, we divided the articles between those who measured HRV for short-term recordings (≤ 1 hour), and those who did long-term recordings (≥ 24 hours).ResultsNine studies were included with a total of 536 patients. All of them were observational studies. Studies quality varied from 4 to 7 stars in Newcastle-Ottawa Scale. The mortality rate in the studies ranged from 8 to 61%. Seven studies performed HRV analysis in short-term recordings. With the exception of one study that did not explain which group had the lowest results, all other studies showed reduction of several HRV parameters in the non-survivors in relation to the surviving septic patients. SDNN (Standard deviation of the Normal to Normal interval), TP (Total Power), VLF (Very Low Frequency Power), LF (Low Frequency Power), LF/HF (Low Frequency Power / High Frequency Power), nLF (Normalized Low Frequency Power), α1/α2 (short-term and long-term fractal scaling coefficients from DFA) and r-MSSD (Square root of the squared mean of the difference of successive NN-intervals) of the non-survivor group were reduced in relation to the survivors in at least one study. Two studies found that SDNN is associated with mortality in sepsis, even after adjusting for possible confounding factors. Three studies performed HRV analysis using long-term recordings. Only one of these studies found difference between surviving and non-surviving groups, and even so, in only one HRV parameter: LogHF.ConclusionsSeveral HRV parameters are reduced in nonsurviving septic patients in short-term recording. Two studies have found that SDNN is associated with mortality in sepsis, even after adjusting for possible confounding factors.

Project description:BackgroundHeart rate variability (HRV) has prognostic value for predicting mortality in both cardiovascular and sepsis patients. Decreased HRV has been associated with increased mortality and morbidity. However, the prognostic significance of HRV in critically ill patients COVID-19 pneumonia still remains unknown. The current study aimed to (1) evaluate prognostic utility of HRV parameters on outcomes in patients with severe COVID-19 pneumonia and (2) assess the correlation between HRV parameters and inflammatory markers.MethodsConsecutive critically ill patients with COVID-19 pneumonia admitted to a tertiary referral intensive care unit from October 2021 to June 2022 in Bangkok, Thailand were enrolled. HRV parameters over the 24 h following intensive care unit admission were recorded using telemetry and analyzed using the Holter program (Philips Holter 2010 Plus/1810 Series). Receiver-operating characteristic (ROC) curve analysis was used to determine optimum threshold cutoffs of various HRV parameters. Formal comparisons of in-hospital mortality between patients with and without a decrease in HRV were performed using Cox regression after adjusting for potential confounders.ResultsA total of 65 patients were enrolled in the study. Patients were classified into two groups: survivors (n = 44, 68 %) and non-survivors (n = 21, 32 %). The standard deviation of normal-to-normal intervals (SDNN) was significantly lower in non-survivors than in survivors (70.30 vs. 105.95; p = 0.03). The SDNN predicted in-hospital mortality with an area under the ROC curve of 0.67 (95 % CI 0.55-0.79). At a cutoff of 70 ms, the SDNN showed a sensitivity and specificity of 0.48 and 0.86. The low SDNN group (<70 ms) demonstrated higher median ferritin, IL-6, and hs-C-reactive protein levels than did the normal SDNN group, although such differences did not reach statistical significance (1139.0 vs. 508.4; p = 0.137 and 91.2 vs. 64.4; p = 0.352, respectively). After adjusting for potential confounders in the multivariable model, the adjusted hazard ratio for in-hospital mortality in those with SDNN <70 ms was 3.70 (95 % CI 1.34-10.24).ConclusionA decrease in SDNN, a commonly used HRV parameter, was associated with mortality and inflammatory biomarkers in critically ill patients with COVID-19 pneumonia.

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Phenotypic but not genetically predicted heart rate variability associated with all-cause mortality.

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Phenotypic but not genetically predicted heart rate variability associated with all-cause mortality.

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