On heart rate variability and autonomic activity in homeostasis and in systemic inflammation

Diminazene aceturate attenuates systemic inflammation via microbiota gut-5-HT brain-spleen sympathetic axis in male mice

The sympathetic arm of the inflammatory reflex is the efferent pathway through which the central nervous system (CNS) can control peripheral immune responses. Diminazene aceturate (DIZE) is an antiparasitic drug that has been reported to exert protective effects on various experimental models of inflammation. However, the pathways by which DIZE promotes a protective immunomodulatory effects still need to be well established, and no studies demonstrate the capacity of DIZE to modulate a neural reflex to control inflammation. C57BL/6 male mice received intraperitoneal administration of DIZE (2 mg/Kg) followed by lipopolysaccharide (LPS, 5 mg/Kg, i.p.). Endotoxemic animals showed hyperresponsiveness to inflammatory signals, while those treated with DIZE promoted the activation of the inflammatory reflex to attenuate the inflammatory response during endotoxemia. The unilateral cervical vagotomy did not affect the anti-inflammatory effect of DIZE in the spleen and serum. At the same time, splenic denervation attenuated tumor necrosis factor (TNF) synthesis in the spleen and serum. Using broad-spectrum antibiotics for two weeks showed that LPS modulated the microbiota to induce a pro-inflammatory profile in the intestine and reduced the serum concentration of tryptophan and serotonin (5-HT), while DIZE restored serum tryptophan and increased the hypothalamic 5-HT levels. Furthermore, the treatment with 4-Chloro-DL-phenylalanine (pcpa, an inhibitor of 5-HT synthesis) abolished the anti-inflammatory effects of the DIZE in the spleen. Our results indicate that DIZE promotes microbiota modulation to increase central 5-HT levels and activates the efferent sympathetic arm of the inflammatory reflex to control splenic TNF production in endotoxemic mice.

Heart rate n-variability (HRnV) measures for prediction of mortality in sepsis patients presenting at the emergency department

Sepsis is a potentially life-threatening condition that requires prompt recognition and treatment. Recently, heart rate variability (HRV), a measure of the cardiac autonomic regulation derived from short electrocardiogram tracings, has been found to correlate with sepsis mortality. This paper presents using novel heart rate n-variability (HRnV) measures for sepsis mortality risk prediction and comparing against current mortality prediction scores. This study was a retrospective cohort study on patients presenting to the emergency department of a tertiary hospital in Singapore between September 2014 to April 2017. Patients were included if they were above 21 years old and were suspected of having sepsis by their attending physician. The primary outcome was 30-day in-hospital mortality. Stepwise multivariable logistic regression model was built to predict the outcome, and the results based on 10-fold cross-validation were presented using receiver operating curve analysis. The final predictive model comprised 21 variables, including four vital signs, two HRV parameters, and 15 HRnV parameters. The area under the curve of the model was 0.77 (95% confidence interval 0.70–0.84), outperforming several established clinical scores. The HRnV measures may have the potential to allow for a rapid, objective, and accurate means of patient risk stratification for sepsis severity and mortality. Our exploration of the use of wealthy inherent information obtained from novel HRnV measures could also create a new perspective for data scientists to develop innovative approaches for ECG analysis and risk monitoring.

Deletion of Orphan G Protein-Coupled Receptor GPR37L1 in Mice Alters Cardiovascular Homeostasis in a Sex-Specific Manner

GPR37L1 is a family A orphan G protein-coupled receptor (GPCR) with a putative role in blood pressure regulation and cardioprotection. In mice, genetic ablation of Gpr37l1 causes sex-dependent effects; female mice lacking Gpr37l1 (GPR37L1^-/-) have a modest but significant elevation in blood pressure, while male GPR37L1^-/- mice are more susceptible to cardiovascular dysfunction following angiotensin II-induced hypertension. Given that this receptor is highly expressed in the brain, we hypothesize that the cardiovascular phenotype of GPR37L1^-/- mice is due to changes in autonomic regulation of blood pressure and heart rate. To investigate this, radiotelemetry was employed to characterize baseline cardiovascular variables in GPR37L1^-/- mice of both sexes compared to wildtype controls, followed by power spectral analysis to quantify short-term fluctuations in blood pressure and heart rate attributable to alterations in autonomic homeostatic mechanisms. Additionally, pharmacological ganglionic blockade was performed to determine vasomotor tone, and environmental stress tests were used to assess whether cardiovascular reactivity was altered in GPR37L1^-/- mice. We observed that mean arterial pressure was significantly lower in female GPR37L1^-/- mice compared to wildtype counterparts, but was unchanged in male GPR37L1^-/- mice. GPR37L1^-/- genotype had a statistically significant positive chronotropic effect on heart rate across both sexes when analyzed by two-way ANOVA. Power spectral analysis of these data revealed a reduction in power in the heart rate spectrum between 0.5 and 3 Hz in female GPR37L1^-/- mice during the diurnal active period, which indicates that GPR37L1^-/- mice may have impaired cardiac vagal drive. GPR37L1^-/- mice of both sexes also exhibited attenuated depressor responses to ganglionic blockade with pentolinium, indicating that GPR37L1 is involved in maintaining sympathetic vasomotor tone. Interestingly, when these mice were subjected to aversive and appetitive behavioral stressors, the female GPR37L1^-/- mice exhibited an attenuation of cardiovascular reactivity to aversive, but not appetitive, environmental stimuli. Together, these results suggest that loss of GPR37L1 affects autonomic maintenance of blood pressure, giving rise to sex-specific cardiovascular changes in GPR37L1^-/- mice.

Serial Heart Rate Variability Measures for Risk Prediction of Septic Patients in the Emergency Department

In this study, we used serial heart rate variability (HRV) measures over 2 hours to improve the prediction of 30-day in-hospital mortality among septic patients in the emergency department (ED). We presented a generalizable methodology for processing and analysing HRV time series (HRVTS) data which may be noisy and incomplete. Feature sets were created from the HRVTS data of 162 patients with suspected sepsis using aggregation-based, deltabased and regression-based series-to-point transformations, and modelled over 100 random stratified splits. An optimized feature set comprising 12 selected HRVTS features performed better than baseline feature sets which only included patient demographics, vital signs and single time-point HRV measures taken at triage. This improved risk stratification approach could be used in the ED to identify high-risk septic patients for appropriate management and disposition.

Fractal correlations and linear analyses of heart rate variability in healthy young people with different levels of physical activity

Changes in cardiac autonomic regulation, expressed by increased sympathetic activity and decreased heart rate variability, have an important relationship with the onset of lethal cardiac phenomena. Therefore, we aimed to evaluate the cardiac autonomic behaviour in young people according to their level of physical activity. Through the International Physical Activity Questionnaire, 55 healthy young non-smokers with no history of previous diseases and whose parents did not suffer from metabolic syndrome were assessed and divided into groups: sedentary (n=12), insufficiently active (n=16), active (n=14), and very active (n=13). We collected respiratory rate, systolic and diastolic blood pressure at rest, and body mass index. Subjects remained supine at rest, and without mental stress for 15 minutes in a controlled environment. Using a cardiofrequency meter (Polar® RS800CX), data were analysed in the time domain, frequency domain, and detrended fluctuation analysis. For the sedentary group, the mean RR and rMSSD were significantly lower, and the insufficiently active group showed higher means, but significantly only for rMSSD. The insufficiently active group showed in the detrended fluctuation analysis that α2 was significantly lower compared with the sedentary, active, and very active groups. We conclude that young, healthy, sedentary individuals present an increased heart rate and that insufficiently active individuals present a decreased fractal correlation and increased parasympathetic activity.

Symbolic analysis of heart rate fluctuations identifies cardiac autonomic modifications during LPS-induced endotoxemia

The present study aimed to compare linear and symbolic dynamics (SD) indices for detecting the autonomic cardiac changes produced by endotoxemia in freely-moving rats. In this context, we analyzed ECG-derived R-R time series in freely moving Dark Agouti rats, which received lipopolysaccharide (LPS, n = 9), or vehicle (V, n = 7). Five minutes R-R time series were assessed every hour up to +12 h and + 24 h post-LPS injection. We found that SD indices showed significant differences at +7 h between V vs. LPS groups and at +9 h between basal levels of LPS (-3 h) and post-LPS injection (pre-LPS vs. post-LPS). In general, SD seems more appropriate than linear indices to evaluate the autonomic changes of endotoxemic rats. Overall, the symbolic parameters detected decreased R-R variability and complexity, which indicate a modification of the autonomic regulation during LPS-induced endotoxemia. This modification is probably related to a reduced activity of the cholinergic anti-inflammatory pathway at the long term.

Combining Heart Rate Variability with Disease Severity Score Variables for Mortality Risk Stratification in Septic Patients Presenting at the Emergency Department

The emergency department (ED) serves as the first point of hospital contact for many septic patients, where risk-stratification would be invaluable. We devised a combination model incorporating demographic, clinical, and heart rate variability (HRV) parameters, alongside individual variables of the Sequential Organ Failure Assessment (SOFA), Acute Physiology and Chronic Health Evaluation II (APACHE II), and Mortality in Emergency Department Sepsis (MEDS) scores for mortality risk-stratification. ED patients fulfilling systemic inflammatory response syndrome criteria were recruited. National Early Warning Score (NEWS), Modified Early Warning Score (MEWS), quick SOFA (qSOFA), SOFA, APACHE II, and MEDS scores were calculated. For the prediction of 30-day in-hospital mortality, combination model performed with an area under the receiver operating characteristic curve of 0.91 (95% confidence interval (CI): 0.88–0.95), outperforming NEWS (0.70, 95% CI: 0.63–0.77), MEWS (0.61, 95% CI 0.53–0.69), qSOFA (0.70, 95% CI 0.63–0.77), SOFA (0.74, 95% CI: 0.67–0.80), APACHE II (0.76, 95% CI: 0.69–0.82), and MEDS scores (0.86, 95% CI: 0.81–0.90). The combination model had an optimal sensitivity and specificity of 91.4% (95% CI: 81.6–96.5%) and 77.9% (95% CI: 72.6–82.4%), respectively. A combination model incorporating clinical, HRV, and disease severity score variables showed superior predictive ability for the mortality risk-stratification of septic patients presenting at the ED.

Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department

Early identification of high-risk septic patients in the emergency department (ED) may guide appropriate management and disposition, thereby improving outcomes. We compared the performance of machine learning models against conventional risk stratification tools, namely the Quick Sequential Organ Failure Assessment (qSOFA), National Early Warning Score (NEWS), Modified Early Warning Score (MEWS), and our previously described Singapore ED Sepsis (SEDS) model, in the prediction of 30-day in-hospital mortality (IHM) among suspected sepsis patients in the ED.Adult patients who presented to Singapore General Hospital (SGH) ED between September 2014 and April 2016, and who met ≥2 of the 4 Systemic Inflammatory Response Syndrome (SIRS) criteria were included. Patient demographics, vital signs and heart rate variability (HRV) measures obtained at triage were used as predictors. Baseline models were created using qSOFA, NEWS, MEWS, and SEDS scores. Candidate models were trained using k-nearest neighbors, random forest, adaptive boosting, gradient boosting and support vector machine. Models were evaluated on F1 score and area under the precision-recall curve (AUPRC).A total of 214 patients were included, of whom 40 (18.7%) met the outcome. Gradient boosting was the best model with a F1 score of 0.50 and AUPRC of 0.35, and performed better than all the baseline comparators (SEDS, F1 0.40, AUPRC 0.22; qSOFA, F1 0.32, AUPRC 0.21; NEWS, F1 0.38, AUPRC 0.28; MEWS, F1 0.30, AUPRC 0.25).A machine learning model can be used to improve prediction of 30-day IHM among suspected sepsis patients in the ED compared to traditional risk stratification tools.

A novel heart rate variability based risk prediction model for septic patients presenting to the emergency department

A quick, objective, non-invasive means of identifying high-risk septic patients in the emergency department (ED) can improve hospital outcomes through early, appropriate management. Heart rate variability (HRV) analysis has been correlated with mortality in critically ill patients. We aimed to develop a Singapore ED sepsis (SEDS) predictive model to assess the risk of 30-day in-hospital mortality in septic patients presenting to the ED. We used demographics, vital signs, and HRV parameters in model building and compared it with the modified early warning score (MEWS), national early warning score (NEWS), and quick sequential organ failure assessment (qSOFA) score.Adult patients clinically suspected to have sepsis in the ED and who met the systemic inflammatory response syndrome (SIRS) criteria were included. Routine triage electrocardiogram segments were used to obtain HRV variables. The primary endpoint was 30-day in-hospital mortality. Multivariate logistic regression was used to derive the SEDS model. MEWS, NEWS, and qSOFA (initial and worst measurements) scores were computed. Receiver operating characteristic (ROC) analysis was used to evaluate their predictive performances.Of the 214 patients included in this study, 40 (18.7%) met the primary endpoint. The SEDS model comprises of 5 components (age, respiratory rate, systolic blood pressure, mean RR interval, and detrended fluctuation analysis α2) and performed with an area under the ROC curve (AUC) of 0.78 (95% confidence interval [CI]: 0.72-0.86), compared with 0.65 (95% CI: 0.56-0.74), 0.70 (95% CI: 0.61-0.79), 0.70 (95% CI: 0.62-0.79), 0.56 (95% CI: 0.46-0.66) by qSOFA (initial), qSOFA (worst), NEWS, and MEWS, respectively.HRV analysis is a useful component in mortality risk prediction for septic patients presenting to the ED.

Asymmetry in Signal Oscillations Contributes to Efficiency of Periodic Systems

Oscillations are an important feature of cellular signaling that result from complex combinations of positive- and negative-feedback loops. The encoding and decoding mechanisms of oscillations based on amplitude and frequency have been extensively discussed in the literature in the context of intercellular and intracellular signaling. However, the fundamental questions of whether and how oscillatory signals offer any competitive advantages-and, if so, what-have not been fully answered. We investigated established oscillatory mechanisms and designed a study to analyze the oscillatory characteristics of signaling molecules and system output in an effort to answer these questions. Two classic oscillators, Goodwin and PER, were selected as the model systems, and corresponding no-feedback models were created for each oscillator to discover the advantage of oscillating signals. Through simulating the original oscillators and the matching no-feedback models, we show that oscillating systems have the capability to achieve better resource-to-output efficiency, and we identify oscillatory characteristics that lead to improved efficiency.

Heart rate variability is reduced during acute uncomplicated diverticulitis

BACKGROUND

The aim of the present study was to report the trajectory of heart rate variability (HRV) indices during a low-grade acute inflammation and their associations to biomarkers for infection.

METHODS

Twelve patients with uncomplicated acute diverticulitis completed this observational study, which composed of 3 sessions of continuous HRV recording from 9 PM to 8 AM during ongoing diverticulitis and at complete remission (baseline). The blood samples were collected at each study session measuring C-reactive protein (CRP) and leukocytes.

RESULTS

This study showed that the trajectories of the HRV indices were decreased both in time and frequency domains during acute diverticulitis compared to baseline. In particular, the indices reflecting the balance of sympathetic and parasympathetic activities were affected: standard deviation of normal-to-normal beats (P = .003), low-frequency power (P < .001), and total power (P = .001). These HRV changes indicate alterations in the autonomic nervous system during acute inflammation. All reductions of mean HRV indices had significant (P < .001) correlations to increased CRP correlations to increased CRP levels during diverticulitis suggesting inflammatory involvement in the observed HRV alterations.

CONCLUSION

We found substantial HRV depression in relation to acute uncomplicated diverticulitis, and this was associated with the elevated CRP levels.

Computational Identification of Mechanistic Factors That Determine the Timing and Intensity of the Inflammatory Response

Timely resolution of inflammation is critical for the restoration of homeostasis in injured or infected tissue. Chronic inflammation is often characterized by a persistent increase in the concentrations of inflammatory cells and molecular mediators, whose distinct amount and timing characteristics offer an opportunity to identify effective therapeutic regulatory targets. Here, we used our recently developed computational model of local inflammation to identify potential targets for molecular interventions and to investigate the effects of individual and combined inhibition of such targets. This was accomplished via the development and application of computational strategies involving the simulation and analysis of thousands of inflammatory scenarios. We found that modulation of macrophage influx and efflux is an effective potential strategy to regulate the amount of inflammatory cells and molecular mediators in both normal and chronic inflammatory scenarios. We identified three molecular mediators − tumor necrosis factor-α (TNF-α), transforming growth factor-β (TGF-β), and the chemokine CXCL8 − as potential molecular targets whose individual or combined inhibition may robustly regulate both the amount and timing properties of the kinetic trajectories for neutrophils and macrophages in chronic inflammation. Modulation of macrophage flux, as well as of the abundance of TNF-α, TGF-β, and CXCL8, may improve the resolution of chronic inflammation.

A recent approach to quantitatively characterize the timing and intensity of the inflammatory response relies on the use of four quantities termed inflammation indices. The values of the inflammation indices may reflect the differences between normal and pathological inflammation, and may be used to gauge the effects of therapeutic interventions aimed to control inflammation. Yet, the specific inflammatory mechanisms that can be targeted to selectively control these indices remain unknown. Here, we developed and applied a computational strategy to identify potential target mechanisms to regulate such indices. We used our recently developed model of local inflammation to simulate thousands of inflammatory scenarios. We then subjected the corresponding inflammation index values to sensitivity and correlation analysis. We found that the inflammation indices may be significantly influenced by the macrophage influx and efflux rates, as well as by the degradation rates of three specific molecular mediators. These results suggested that the indices can be effectively regulated by individual or combined inhibition of those molecular mediators, which we confirmed by computational experiments. Taken together, our results highlight possible targets of therapeutic intervention that can be used to control both the timing and the intensity of the inflammatory response.