Modeling autonomic regulation of cardiac function and heart rate variability in human endotoxemia

The Physiological and Pharmacological Significance of the Circadian Timing of the HPA Axis: A Mathematical Modeling Approach

As a potent endogenous regulator of homeostasis, the circadian time-keeping system synchronizes internal physiology to periodic changes in the external environment to enhance survival. Adapting endogenous rhythms to the external time is accomplished hierarchically with the central pacemaker located in the suprachiasmatic nucleus (SCN) signaling the hypothalamus-pituitary-adrenal (HPA) axis to release hormones, notably cortisol, which help maintain the body's circadian rhythm. Given the essential role of HPA-releasing hormones in regulating physiological functions, including immune response, cell cycle, and energy metabolism, their daily variation is critical for the proper function of the circadian timing system. In this review, we focus on cortisol and key fundamental properties of the HPA axis and highlight their importance in controlling circadian dynamics. We demonstrate how systems-driven, mathematical modeling of the HPA axis complements experimental findings, enhances our understanding of complex physiological systems, helps predict potential mechanisms of action, and elucidates the consequences of circadian disruption. Finally, we outline the implications of circadian regulation in the context of personalized chronotherapy. Focusing on the chrono-pharmacology of synthetic glucocorticoids, we review the challenges and opportunities associated with moving toward personalized therapies that capitalize on circadian rhythms.

Mathematical modeling of mammalian circadian clocks affecting drug and disease responses

To align with daily environmental changes, most physiological processes in mammals exhibit a time-of-day rhythmicity. This circadian control of physiology is intrinsically driven by a cell-autonomous clock gene network present in almost all cells of the body that drives rhythmic expression of genes that regulate numerous molecular and cellular processes. Accordingly, many aspects of pharmacology and toxicology also oscillate in a time-of-day manner giving rise to diverse effects on pharmacokinetics and pharmacodynamics. Genome-wide studies and mathematical modeling are available tools that have significantly improved our understanding of these nonlinear aspects of physiology and therapeutics. In this manuscript current literature and our prior work on the model-based approaches that have been used to explore circadian genomic systems of mammals are reviewed. Such basic understanding and having an integrative approach may provide new strategies for chronotherapeutic drug treatments and yield new insights for the restoration of the circadian system when altered by diseases.

A physiological model of the inflammatory-thermal-pain-cardiovascular interactions during an endotoxin challenge

KEY POINTS

Inflammation in response to bacterial endotoxin challenge impacts physiological functions, including cardiovascular, thermal and pain dynamics, although the mechanisms are poorly understood. We develop an innovative mathematical model incorporating interaction pathways between inflammation and physiological processes observed in response to an endotoxin challenge. We calibrate the model to individual data from 20 subjects in an experimental study of the human inflammatory and physiological responses to endotoxin, and we validate the model against human data from an independent study. Using the model to simulate patient responses to different treatment modalities reveals that a multimodal treatment combining several therapeutic strategies gives the best recovery outcome.

ABSTRACT

Uncontrolled, excessive production of pro-inflammatory mediators from immune cells and traumatized tissues can cause systemic inflammatory conditions such as sepsis, one of the ten leading causes of death in the USA, and one of the three leading causes of death in the intensive care unit. Understanding how inflammation affects physiological processes, including cardiovascular, thermal and pain dynamics, can improve a patient's chance of recovery after an inflammatory event caused by surgery or a severe infection. Although the effects of the autonomic response on the inflammatory system are well-known, knowledge about the reverse interaction is lacking. The present study develops a mathematical model analyzing the inflammatory system's interactions with thermal, pain and cardiovascular dynamics in response to a bacterial endotoxin challenge. We calibrate the model with individual data from an experimental study of the inflammatory and physiological responses to a one-time administration of endotoxin in 20 healthy young men and validate it against data from an independent endotoxin study. We use simulation to explore how various treatments help patients exposed to a sustained pathological input. The treatments explored include bacterial endotoxin adsorption, antipyretics and vasopressors, as well as combinations of these. Our findings suggest that the most favourable recovery outcome is achieved by a multimodal strategy, combining all three interventions to simultaneously remove endotoxin from the body and alleviate symptoms caused by the immune system as it fights the infection.

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.

Proposed mechanisms of relative bradycardia

Relative bradycardia is the term used to describe the mechanism where there is dissociation between pulse and temperature. This finding is important to recognize since it may provide further insights into the potential underlying causes of disease. There is no known proposed mechanism to explain this phenomenon. We hypothesize that relative bradycardia is the central mechanism reflecting and influenced potentially by the direct pathogenic effect on the sinoatrial node as well as cross-talk between the autonomic nervous system and immune system. Cardiac pacemaker cells may act as a target for inflammatory cytokines leading to alteration in heart rate dynamics or their responsiveness to neurotransmitters during systemic inflammation. These factors account for the important role of how the host response to infectious and non-infectious causes influences the appearance of relative bradycardia. We propose several methods that may be useful to confirm the proposed theoretical framework to further enhance our understanding of this paradoxical phenomenon. This includes measuring, during the episode of relative bradycardia, proinflammatory and anti-inflammatory cytokines, monitoring heart rate variability (HRV), and assessing underlying comorbidities and outcomes in patients with the same disease.

Modeling circadian variability of core-clock and clock-controlled genes in four tissues of the rat

Circadian clocks, present in almost all cells of the body, are entrained to rhythmic changes in the environment (e.g. light/dark cycles). Genes responsible for this timekeeping are named core-clock genes, which through transcriptional feedback interactions mediated by transcription factor binding to Ebox/RRE/Dbox elements can generate oscillatory activity of their expression. By regulating the transcription of other clock-controlled genes (CCGs) circadian information is transmitted to tissue and organ levels. Recent studies have indicated that there is a considerable variability of clock-controlled gene expression between tissues both with respect to the circadian genes that are regulated and to their phase lags. In this work, a mathematical model was adapted to explore the dynamics of core-clock and clock-controlled genes measured in four tissues of the rat namely liver, muscle, adipose, and lung. The model efficiently described the synchronous rhythmicity of core-clock genes and further predicted that their phases are mainly regulated by Per2 and Cry1 transcriptional delays and Rev-Erba and Cry1 degradation rates. Similarly, after mining databases for potential Ebox/RRE/Dbox elements in the promoter region of clock-controlled genes, the phase variabilities of the same genes between different tissues were described. The analysis suggests that inter-tissue circadian variability of the same clock-controlled genes is an inherent component of homeostatic function and may arise due to different transcription factor activities on Ebox/RRE/Dbox elements.

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.

Daily variation of gene expression in diverse rat tissues

Circadian information is maintained in mammalian tissues by a cell-autonomous network of transcriptional feedback loops that have evolved to optimally regulate tissue-specific functions. An analysis of daily gene expression in different tissues, as well as an evaluation of inter-tissue circadian variability, is crucial for a systems-level understanding of this transcriptional circuitry. Affymetrix gene chip measurements of liver, muscle, adipose, and lung tissues were obtained from a rich time series light/dark experiment, involving 54 normal rats sacrificed at 18 time points within the 24-hr cycle. Our analysis revealed a high degree of circadian regulation with a variable distribution of phases among the four tissues. Interestingly, only a small number of common genes maintain circadian activity in all tissues, with many of them consisting of "core-clock" components with synchronous rhythms. Our results suggest that inter-tissue circadian variability is a critical component of homeostatic body function and is mediated by diverse signaling pathways that ultimately lead to highly tissue-specific transcription regulation.

Modeling the Influence of Seasonal Differences in the HPA Axis on Synchronization of the Circadian Clock and Cell Cycle

Synchronization of biological functions to environmental signals enables organisms to anticipate and appropriately respond to daily external fluctuations and is critical to the maintenance of homeostasis. Misalignment of circadian rhythms with environmental cues is associated with adverse health outcomes. Cortisol, the downstream effector of hypothalamic-pituitary-adrenal (HPA) activity, facilitates synchronization of peripheral biological processes to the environment. Cortisol levels exhibit substantial seasonal rhythmicity, with peak levels occurring during the short-photoperiod winter months and reduced levels occurring in the long-photoperiod summer season. Seasonal changes in cortisol secretion could therefore alter its entraining capabilities, resulting in a season-dependent modification in the alignment of biological activities with the environment. We develop a mathematical model to investigate the influence of photoperiod-induced seasonal differences in the circadian rhythmicity of the HPA axis on the synchronization of the peripheral circadian clock and cell cycle in a heterogeneous cell population. Model simulations predict that the high-amplitude cortisol rhythms in winter result in the greatest entrainment of peripheral oscillators. Furthermore, simulations predict a circadian gating of the cell cycle with respect to the expression of peripheral clock genes. Seasonal differences in cortisol rhythmicity are also predicted to influence mitotic synchrony, with a high-amplitude winter rhythm resulting in the greatest synchrony and a shift in timing of the cell cycle phases, relative to summer. Our results highlight the primary interactions among the HPA axis, the peripheral circadian clock, and the cell cycle and thereby provide an improved understanding of the implications of circadian misalignment on the synchronization of peripheral regulatory processes.

On the analysis of complex biological supply chains: From Process Systems Engineering to Quantitative Systems Pharmacology

The use of models in biology has become particularly relevant as it enables investigators to develop a mechanistic framework for understanding the operating principles of living systems as well as in quantitatively predicting their response to both pathological perturbations and pharmacological interventions. This application has resulted in a synergistic convergence of systems biology and pharmacokinetic-pharmacodynamic modeling techniques that has led to the emergence of quantitative systems pharmacology (QSP). In this review, we discuss how the foundational principles of chemical process systems engineering inform the progressive development of more physiologically-based systems biology models.

A data-driven modeling approach to identify disease-specific multi-organ networks driving physiological dysregulation

Multiple physiological systems interact throughout the development of a complex disease. Knowledge of the dynamics and connectivity of interactions across physiological systems could facilitate the prevention or mitigation of organ damage underlying complex diseases, many of which are currently refractory to available therapeutics (e.g., hypertension). We studied the regulatory interactions operating within and across organs throughout disease development by integrating in vivo analysis of gene expression dynamics with a reverse engineering approach to infer data-driven dynamic network models of multi-organ gene regulatory influences. We obtained experimental data on the expression of 22 genes across five organs, over a time span that encompassed the development of autonomic nervous system dysfunction and hypertension. We pursued a unique approach for identification of continuous-time models that jointly described the dynamics and structure of multi-organ networks by estimating a sparse subset of ∼12,000 possible gene regulatory interactions. Our analyses revealed that an autonomic dysfunction-specific multi-organ sequence of gene expression activation patterns was associated with a distinct gene regulatory network. We analyzed the model structures for adaptation motifs, and identified disease-specific network motifs involving genes that exhibited aberrant temporal dynamics. Bioinformatic analyses identified disease-specific single nucleotide variants within or near transcription factor binding sites upstream of key genes implicated in maintaining physiological homeostasis. Our approach illustrates a novel framework for investigating the pathogenesis through model-based analysis of multi-organ system dynamics and network properties. Our results yielded novel candidate molecular targets driving the development of cardiovascular disease, metabolic syndrome, and immune dysfunction.

Complex diseases such as hypertension often involve maladaptive autonomic nervous system control over the cardiovascular, renal, hepatic, immune, and endocrine systems. We studied the pathogenesis of physiological homeostasis by examining the temporal dynamics of gene expression levels from multiple organs in an animal model of autonomic dysfunction characterized by cardiovascular disease, metabolic dysregulation, and immune system aberrations. We employed a data-driven modeling approach to jointly predict continuous gene expression dynamics and gene regulatory interactions across organs in the disease and control phenotypes. We combined our analyses of multi-organ gene regulatory network dynamics and connectivity with bioinformatic analyses of genetic mutations that could regulate gene expression. Our multi-organ modeling approach to investigate the mechanisms of complex disease pathogenesis revealed novel candidates for therapeutic interventions against the development and progression of complex diseases involving autonomic nervous system dysfunction.

Modulation of Autonomic Nervous Activity in the Termination of Paroxysmal Atrial Fibrillation

BACKGROUND

Autonomic nervous activity plays a critical role in the genesis of paroxysmal atrial fibrillation (AF, PAF). However, the role of autonomic nervous activity on AF termination has not been elucidated. Heart rate variability (HRV) is widely used to evaluate autonomic nervous activity in humans. The purpose of this study was to assess whether autonomic nervous activity assessed by HRV contributes to AF termination.

METHODS

Electrocardiograms (ECGs) and HRV were studied in patients with termination of sustained (>30 s) PAF by 24-hour ambulatory Holter monitoring. The 20-minute interval after termination of AF was divided into four segments of 5 minutes each, and a frequency analysis was applied to each 5-minute segment.

RESULTS

In 52 AF episodes, the ultra-low-frequency power, very-low-frequency power, low-frequency power (LF), high-frequency power (HF), and total power significantly decreased with time after episodes of AF termination. The LF/HF (L/H) ratio, normalized LF (LFnu), and normalized HF (HFnu) significantly changed after AF termination. Eighteen (35%) episodes had decreased LFnu and increased HFnu (sympathetic withdrawal and vagal activation), which had slower average AF ventricular responses (92 ± 16 beats/min vs 105 ± 24 beats/min, P < 0.05) than the AF termination episodes (n = 34, 65%) with increased LFnu and decreased HFnu (sympathetic activation and vagal withdrawal). Moreover, older patients (aged >65 years) had a higher incidence (n = 27, 75%) of AF termination with increased LFnu and decreased HFnu than did younger patients (aged ≤65 years, n = 7, 44%, P < 0.05).

CONCLUSION

Autonomic changes critically regulate termination of PAF, which is modulated by aging.

A Model-Based Machine Learning Approach to Probing Autonomic Regulation From Nonstationary Vital-Sign Time Series

Physiological variables, such as heart rate (HR), blood pressure (BP) and respiration (RESP), are tightly regulated and coupled under healthy conditions, and a break-down in the coupling has been associated with aging and disease. We present an approach that incorporates physiological modeling within a switching linear dynamical systems (SLDS) framework to assess the various functional components of the autonomic regulation through transfer function analysis of nonstationary multivariate time series of vital signs. We validate our proposed SLDS-based transfer function analysis technique in automatically capturing 1) changes in baroreflex gain due to postural changes in a tilt-table study including ten subjects, and 2) the effect of aging on the autonomic control using HR/RESP recordings from 40 healthy adults. Next, using HR/BP time series of more than 450 adult ICU patients, we show that our technique can be used to reveal coupling changes associated with severe sepsis (AUC = 0.74, sensitivity = 0.74, specificity = 0.60). Our findings indicate that reduced HR/BP coupling is significantly associated with severe sepsis even after adjusting for clinical interventions (P  0.001). These results demonstrate the utility of our approach in phenotyping complex vital-sign dynamics, and in providing mechanistic hypotheses in terms of break-down of autoregulatory systems under healthy and disease conditions.

Autonomic function and brain volume

OBJECTIVE

The aim of this study is to review the evidence on the role of the autonomic nervous system as a determinant of brain volume. Brain volume measures have gained increasing attention given its biological importance, particularly as a measurement of neurodegeneration.

METHODS

Using an integrative approach, we reviewed publications addressing the anatomical and physiological characteristics of brain autonomic innervation focusing on evidence from diverse clinical populations with respect to brain volume.

RESULTS

Multiple mechanisms contribute to changes in brain volume. Autonomic influence on cerebral blood volume is of significant interest.

CONCLUSION

We suggest a role for the autonomic innervation of brain vessels in fluctuations of cerebral blood volume. Further investigation in several clinical populations including multiple sclerosis is warranted to understand the specific role of parenchyma versus blood vessels changes on final brain volume.

The role of the hypothalamic-pituitary-adrenal axis in modulating seasonal changes in immunity

Seasonal changes in environmental conditions are accompanied by significant adjustment of multiple biological processes. In temperate regions, the day fraction, or photoperiod, is a robust environmental cue that synchronizes seasonal variations in neuroendocrine and metabolic function. In this work, we propose a semimechanistic mathematical model that considers the influence of seasonal photoperiod changes as well as cellular and molecular adaptations to investigate the seasonality of immune function. Our model predicts that the circadian rhythms of cortisol, our proinflammatory mediator, and its receptor exhibit seasonal differences in amplitude and phase, oscillating at higher amplitudes in the winter season with peak times occurring later in the day. Furthermore, the reduced photoperiod of winter coupled with seasonal alterations in physiological activity induces a more exacerbated immune response to acute stress, simulated in our studies as the administration of an acute dose of endotoxin. Our findings are therefore in accordance with experimental data that reflect the predominance of a proinflammatory state during the winter months. These changes in circadian rhythm dynamics may play a significant role in the seasonality of disease incidence and regulate the diurnal and seasonal variation of disease symptom severity.

Physiologic variability at the verge of systemic inflammation: multiscale entropy of heart rate variability is affected by very low doses of endotoxin

INTRODUCTION

Human injury or infection induces systemic inflammation with characteristic neuroendocrine responses. Fluctuations in autonomic function during inflammation are reflected by beat-to-beat variation in heart rate, termed heart rate variability (HRV). In the present study, we determine threshold doses of endotoxin needed to induce observable changes in markers of systemic inflammation, investigate whether metrics of HRV exhibit a differing threshold dose from other inflammatory markers, and investigate the size of data sets required for meaningful use of multiscale entropy (MSE) analysis of HRV.

METHODS

Healthy human volunteers (n = 25) were randomized to receive placebo (normal saline) or endotoxin/lipopolysaccharide (LPS): 0.1, 0.25, 0.5, 1.0, or 2.0 ng/kg administered intravenously. Vital signs were recorded every 30 min for 6 h and then at 9, 12, and 24 h after LPS. Blood samples were drawn at specific time points for cytokine measurements. Heart rate variability analysis was performed using electrocardiogram epochs of 5 min. Multiscale entropy for HRV was calculated for all dose groups to scale factor 40.

RESULTS

The lowest significant threshold dose was noted in core temperature at 0.25 ng/kg. Endogenous tumor necrosis factor α and interleukin 6 were significantly responsive at the next dosage level (0.5 ng/kg) along with elevations in circulating leukocytes and heart rate. Responses were exaggerated at higher doses (1 and 2 ng/kg). Time domain and frequency domain HRV metrics similarly suggested a threshold dose, differing from placebo at 1.0 and 2.0 ng/kg, below which no clear pattern in response was evident. By applying repeated-measures analysis of variance across scale factors, a significant decrease in MSE was seen at 1.0 and 2.0 ng/kg by 2 h after exposure to LPS. Although not statistically significant below 1.0 ng/kg, MSE unexpectedly decreased across all groups in an orderly dose-response pattern not seen in the other outcomes.

CONCLUSIONS

By using repeated-measures analysis of variance across scale factors, MSE can detect autonomic change after LPS challenge in a group of 25 subjects using electrocardiogram epochs of only 5 min and entropy analysis to scale factor of only 40, potentially facilitating MSE's wider use as a research tool or bedside monitor. Traditional markers of inflammation generally exhibit threshold dose behavior. In contrast, MSE's apparent continuous dose-response pattern, although not statistically verifiable in this study, suggests a potential subclinical harbinger of infectious or other insult. The possible derangement of autonomic complexity prior to or independent of the cytokine surge cannot be ruled out. Future investigation should focus on confirmation of overt inflammation following observed decreases in MSE in a clinical setting.

Sepsis in standard care: patients' characteristics, effectiveness of antimicrobial therapy and patient outcome--a cohort study

PURPOSE

Fast diagnosis and initiation of appropriate antibiotic therapy is pivotal for the survival of sepsis patients. However, most studies on suspected sepsis patients are conducted in the intensive care unit or in the emergency room setting, neglecting the standard care setting. This study evaluated sepsis risk factors, microbiological accurateness of the initial empiric antimicrobial therapy and its effect on hospital mortality in standard care patients.

METHODS

In this prospective observational cohort study, patients with clinically suspected sepsis meeting two or more SIRS criteria were screened on standard care wards. After hospital discharge, occurrence of an infection was assessed according to standardized criteria, and empirical antibiotic therapy was evaluated using antibiograms of recognized pathogens by expert review.

RESULTS

Of the 2384 screened patients, 298 fulfilled two or more SIRS criteria. Among these were 28.2 % SIRS patients without infection, 46.3 % non-bacteremic/fungemic sepsis patients and 25.5 % bacteremic/fungemic sepsis patients. Occurrence of a malignant disease and chills were associated with a higher risk of patients having bacteremic/fungemic sepsis, whereas other described risk factors remained insignificant. In total, 91.1 % of suspected sepsis patients received empirical antimicrobial therapy, but 41.1 % of bacteremic sepsis patients received inappropriate therapy. Non-surviving bacteremic sepsis patients received a higher proportion of inappropriate therapy than those who survived (p = 0.022).

CONCLUSIONS

A significant proportion of bacteremic sepsis patients receive inappropriate empiric antimicrobial therapy. Our results indicate that rapid availability of microbiological results is vital, since inappropriate antimicrobial therapy tended to increase the hospital mortality of sepsis patients.

Day-night variation in heart rate variability changes induced by endotoxaemia in healthy volunteers

BACKGROUND

Morbidity and mortality in response to sepsis may be dependent on clock time for the initiation of sepsis. Endotoxaemia, an experimental model for systemic inflammation, induces alterations in sympatico-vagal balance in the autonomic nervous system (ANS). The activity of sympathetic and parasympathetic activity can be estimated by measuring heart rate variability (HRV). Based on the intimate link between ANS and the inflammatory response, we hypothesized, that HRV changes seen during endotoxaemia would be different based on time of the day the endotoxaemia is initiated. We investigated day/night variation in endotoxaemia-induced changes in HRV.

METHODS

A randomized, crossover study with 12 healthy men (age 18-31) was conducted. Endotoxaemia were induced by lipopolysaccharide (LPS) endotoxin 0.3 ng/kg b.w. in two visits (day visit and night visit). At the day visit, endotoxaemia were induced at 12:00 h, and at the night visit it was induced at 24:00 h. Holter recordings were started 1 h before administration of LPS, and continued for 10 h. Time-domain and frequency-domain parameters of HRV were analysed.

RESULTS

A total of nine persons finished the study with valid recordings. Endotoxaemia at both night and day resulted in a significant depression in HRV parameters high-frequency power (HF), low-frequency power (LF), standard deviation of normal-to-normal (NN) intervals, root mean square of successive differences and proportion of NN50 divided by total number of NNs (P<0.001). The ratio LF/HF and mean heart rate significantly increased by endotoxaemia (P<0.001). At night-time endotoxaemia, a more pronounced depression of LF, HF and SDNN (P<0.01) and a more pronounced increase in the ratio of LF/HF and mean heart rate (P<0.01) occurred compared with day-time endotoxaemia.

CONCLUSION

Endotoxaemia induced changes in HRV exhibit a day-night difference. This difference may have clinical consequences in patients with sepsis.

Human metabolic response to systemic inflammation: assessment of the concordance between experimental endotoxemia and clinical cases of sepsis/SIRS

Introduction

Two recent, independent, studies conducted novel metabolomics analyses relevant to human sepsis progression; one was a human model of endotoxin (lipopolysaccharide (LPS)) challenge (experimental endotoxemia) and the other was community acquired pneumonia and sepsis outcome diagnostic study (CAPSOD). The purpose of the present study was to assess the concordance of metabolic responses to LPS and community-acquired sepsis.

Methods

We tested the hypothesis that the patterns of metabolic response elicited by endotoxin would agree with those in clinical sepsis. Alterations in the plasma metabolome of the subjects challenged with LPS were compared with those of sepsis patients who had been stratified into two groups: sepsis patients with confirmed infection and non-infected patients who exhibited systemic inflammatory response syndrome (SIRS) criteria. Common metabolites between endotoxemia and both these groups were individually identified, together with their direction of change and functional classifications.

Results

Response to endotoxemia at the metabolome level elicited characteristics that agree well with those observed in sepsis patients despite the high degree of variability in the response of these patients. Moreover, some distinct features of SIRS have been identified. Upon stratification of sepsis patients based on 28-day survival, the direction of change in 21 of 23 metabolites was the same in endotoxemia and sepsis survival groups.

Conclusions

The observed concordance in plasma metabolomes of LPS-treated subjects and sepsis survivors strengthens the relevance of endotoxemia to clinical research as a physiological model of community-acquired sepsis, and gives valuable insights into the metabolic changes that constitute a homeostatic response. Furthermore, recapitulation of metabolic differences between sepsis non-survivors and survivors in LPS-treated subjects can enable further research on the development and assessment of rational clinical therapies to prevent sepsis mortality. Compared with earlier studies which focused exclusively on comparing transcriptional dynamics, the distinct metabolomic responses to systemic inflammation with or without confirmed infection, suggest that the metabolome is much better at differentiating these pathophysiologies. Finally, the metabolic changes in the recovering patients shift towards the LPS-induced response pattern strengthening the notion that the metabolic, as well as transcriptional responses, characteristic to the endotoxemia model represent necessary and “healthy” responses to infectious stimuli.

Electronic supplementary material

The online version of this article (doi:10.1186/s13054-015-0783-2) contains supplementary material, which is available to authorized users.

A Chemical Engineer's Perspective on Health and Disease

Chemical process systems engineering considers complex supply chains which are coupled networks of dynamically interacting systems. The quest to optimize the supply chain while meeting robustness and flexibility constraints in the face of ever changing environments necessitated the development of theoretical and computational tools for the analysis, synthesis and design of such complex engineered architectures. However, it was realized early on that optimality is a complex characteristic required to achieve proper balance between multiple, often competing, objectives. As we begin to unravel life's intricate complexities, we realize that that living systems share similar structural and dynamic characteristics; hence much can be learned about biological complexity from engineered systems. In this article, we draw analogies between concepts in process systems engineering and conceptual models of health and disease; establish connections between these concepts and physiologic modeling; and describe how these mirror onto the physiological counterparts of engineered systems.

Circadian characteristics of permissive and suppressive effects of cortisol and their role in homeostasis and the acute inflammatory response

In this work we explore a semi-mechanistic model that considers cortisol's permissive and suppressive effects through the regulation of cytokine receptors and cytokines respectively. Our model reveals the proactive role of cortisol during the resting period and its reactive character during the body's activity phase. Administration of an acute LPS dose during the night, when cortisol's permissive effects are higher than suppressive, leads to increased cytokine levels compared to LPS administration at morning when cortisol's suppressive effects are higher. Interestingly, our model presents a hysteretic behavior where the relative predominance of permissive or suppressive effects results not only from cortisol levels but also from the previous states of the model. Therefore, for the same cortisol levels, administration of an inflammatory stimulus at cortisol's ascending phase, that follows a time period where cytokine receptor expression is elevated ultimately sensitizing the body for the impending stimulus, leads to higher cytokine expression compared to administration of the same stimulus at cortisol's descending phase.

Effect of adrenoblockers on slow (LF) waves in rabbit heart rate

Propranolol and atenolol were used to examine the role of the adrenergic system in the genesis of slow HR variations (waves) in rabbits, the animals characterized by pronounced sympathetic influences. The control experiments were performed with physiological solution and long-term records in intact rabbits. The harmonic (spectrum) analysis was performed with fast Fourier transform. The adrenoblockers produced no decrease in the power of slow waves. The study did not found the sympathetic genesis of slow variations in rabbit HR.

Temporal metabolic profiling of plasma during endotoxemia in humans

Endotoxemia induced by the administration of low-dose lipopolysaccharide (LPS) to healthy human volunteers is a well-established experimental protocol and has served as a reproducible platform for investigating the responses to systemic inflammation. Because metabolic composition of a tissue or body fluid is uniquely altered by stimuli and provides information about the dominant regulatory mechanisms at various cellular processes, understanding the global metabolic response to systemic inflammation constitutes a major part in this investigation complementing the studies undertaken so far in both clinical and systems biology fields. This article communicates the first proof-of-principle metabonomic analysis, which comprised global biochemical profiles in human plasma samples from healthy subjects given intravenous endotoxin at 2 ng/kg. Concentrations of a total of 366 plasma biochemicals were determined in archived blood samples collected from 15 endotoxin-treated subjects at five time points within 24 h after treatment and compared with control samples collected from four saline-treated subjects. Principal component analysis within this data set determined the sixth hour as a critical time point separating development and recovery phases of the LPS-induced metabolic changes. Consensus clustering of the differential metabolites identified two distinct subsets of metabolites that displayed common coherent profiles with opposing directionality. The first group of metabolites, which were mostly associated with pathways related to lipid metabolism, was upregulated within the first 6 h and downregulated by the 24th hour following LPS administration. The second group of metabolites, in contrast, was first downregulated until the sixth hour, then upregulated. Metabolites in this group were predominantly amino acids or their derivatives. In summary, nontargeted biochemical profiling and unsupervised multivariate analyses highlighted the prominent roles of lipid and protein metabolism in regulating the response to systemic inflammation while also revealing their dynamics in opposite directions.

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

Analysis of heart rate variability (HRV) is a promising diagnostic technique due to the noninvasive nature of the measurements involved and established correlations with disease severity, particularly in inflammation-linked disorders. However, the complexities underlying the interpretation of HRV complicate understanding the mechanisms that cause variability. Despite this, such interpretations are often found in literature. In this paper we explored mathematical modeling of the relationship between the autonomic nervous system and the heart, incorporating basic mechanisms such as perturbing mean values of oscillating autonomic activities and saturating signal transduction pathways to explore their impacts on HRV. We focused our analysis on human endotoxemia, a well-established, controlled experimental model of systemic inflammation that provokes changes in HRV representative of acute stress. By contrasting modeling results with published experimental data and analyses, we found that even a simple model linking the autonomic nervous system and the heart confound the interpretation of HRV changes in human endotoxemia. Multiple plausible alternative hypotheses, encoded in a model-based framework, equally reconciled experimental results. In total, our work illustrates how conventional assumptions about the relationships between autonomic activity and frequency-domain HRV metrics break down, even in a simple model. This underscores the need for further experimental work towards unraveling the underlying mechanisms of autonomic dysfunction and HRV changes in systemic inflammation. Understanding the extent of information encoded in HRV signals is critical in appropriately analyzing prior and future studies.

In silico modeling: methods and applications to trauma and sepsis

OBJECTIVES

To familiarize clinicians with advances in computational disease modeling applied to trauma and sepsis.

DATA SOURCES

PubMed search and review of relevant medical literature.

SUMMARY

Definitions, key methods, and applications of computational modeling to trauma and sepsis are reviewed.

CONCLUSIONS

Computational modeling of inflammation and organ dysfunction at the cellular, organ, whole-organism, and population levels has suggested a positive feedback cycle of inflammation → damage → inflammation that manifests via organ-specific inflammatory switching networks. This structure may manifest as multicompartment "tipping points" that drive multiple organ dysfunction. This process may be amenable to rational inflammation reprogramming.

A Systems Engineering Perspective on Homeostasis and Disease

Engineered systems are coupled networks of interacting sub-systems, whose dynamics are constrained to requirements of robustness and flexibility. They have evolved by design to optimize function in a changing environment and maintain responses within ranges. Analysis, synthesis, and design of complex supply chains aim to identify and explore the laws governing optimally integrated systems. Optimality expresses balance between conflicting objectives while resiliency results from dynamic interactions among elements. Our increasing understanding of life’s multi-scale architecture suggests that living systems share similar characteristics with much to be learned about biological complexity from engineered systems. If health reflects a dynamically stable integration of molecules, cell, tissues, and organs; disease indicates displacement compensated for and corrected by activation and combination of feedback mechanisms through interconnected networks. In this article, we draw analogies between concepts in systems engineering and conceptual models of health and disease; establish connections between these concepts and physiologic modeling; and describe how these mirror onto the physiological counterparts of engineered systems.

Predicting critical transitions in a model of systemic inflammation

The human body can be viewed as a dynamical system, with physiological states such as health and disease broadly representing steady states. From this perspective, and given inter- and intra-individual heterogeneity, an important task is identifying the propensity to transition from one steady state to another, which in practice can occur abruptly. Detecting impending transitions between steady states is of significant importance in many fields, and thus a variety of methods have been developed for this purpose, but lack of data has limited applications in physiology. Here, we propose a model-based approach towards identifying critical transitions in systemic inflammation based on a minimal amount of assumptions about the availability of data and the structure of the system. We derived a warning signal metric to identify forthcoming abrupt transitions occurring in a mathematical model of systemic inflammation with a gradually increasing bacterial load. Intervention to remove the inflammatory stimulus was successful in restoring homeostasis if undertaken when the warning signal was elevated rather than waiting for the state variables of the system themselves to begin moving to a new steady state. The proposed combination of data and model-based analysis for predicting physiological transitions represents a step forward towards the quantitative study of complex biological systems.

Multiscale computational models of complex biological systems

Integration of data across spatial, temporal, and functional scales is a primary focus of biomedical engineering efforts. The advent of powerful computing platforms, coupled with quantitative data from high-throughput experimental methodologies, has allowed multiscale modeling to expand as a means to more comprehensively investigate biological phenomena in experimentally relevant ways. This review aims to highlight recently published multiscale models of biological systems, using their successes to propose the best practices for future model development. We demonstrate that coupling continuous and discrete systems best captures biological information across spatial scales by selecting modeling techniques that are suited to the task. Further, we suggest how to leverage these multiscale models to gain insight into biological systems using quantitative biomedical engineering methods to analyze data in nonintuitive ways. These topics are discussed with a focus on the future of the field, current challenges encountered, and opportunities yet to be realized.

Modeling physiologic variability in human endotoxemia

The control and management of inflammation is a key aspect of clinical care for critical illnesses such as sepsis. In an ideal reaction to injury, the inflammatory response provokes a strong enough response to heal the injury and then restores homeostasis. When inflammation becomes dysregulated, a persistent inflammatory state can lead to significant deleterious effects and clinical challenges. Thus, gaining a better biological understanding of the mechanisms driving the inflammatory response is of the utmost importance. In this review, we discuss our work with the late Stephen F. Lowry to investigate systemic inflammation through systems biology of human endotoxemia. We present our efforts in modeling the human endotoxemia response with a particular focus on physiologic variability. Through modeling, with a focus ultimately on translational applications, we obtain more fundamental understanding of relevant physiological processes. And by taking advantage of the information embedded in biological rhythms, ranging in time scale from high-frequency autonomic oscillations reflected in heart rate variability to circadian rhythms in inflammatory mediators, we gain insight into the underlying physiology.

Translational applications of evaluating physiologic variability in human endotoxemia

Dysregulation of the inflammatory response is a critical component of many clinically challenging disorders such as sepsis. Inflammation is a biological process designed to lead to healing and recovery, ultimately restoring homeostasis; however, the failure to fully achieve those beneficial results can leave a patient in a dangerous persistent inflammatory state. One of the primary challenges in developing novel therapies in this area is that inflammation is comprised of a complex network of interacting pathways. Here, we discuss our approaches towards addressing this problem through computational systems biology, with a particular focus on how the presence of biological rhythms and the disruption of these rhythms in inflammation may be applied in a translational context. By leveraging the information content embedded in physiologic variability, ranging in scale from oscillations in autonomic activity driving short-term heart rate variability to circadian rhythms in immunomodulatory hormones, there is significant potential to gain insight into the underlying physiology.

Systems biology of circadian-immune interactions

There is increasing evidence that the immune system is regulated by circadian rhythms. A wide range of immune parameters, such as the number of red blood cells and peripheral blood mononuclear cells as well as the level of critical immune mediators, such as cytokines, undergo daily fluctuations. Current experimental data indicate that circadian information reaches immune tissues mainly through diurnal patterns of autonomic and endocrine rhythms. In addition, immune factors such as cytokines can also influence the phase of the circadian clock, providing bidirectional flow of circadian information between the neuroendocrine and immune systems. This network of neuroendocrine-immune interactions consists of complexly integrated molecular feedback and feedforward loops that function in synchrony in order to optimize immune response. Chronic stress can disrupt this intrinsic orchestration, as several endocrine signals of chronically stressed patients present blunted rhythmic characteristics. Reprogramming of biological rhythms has recently gained much attention as a potent method to leverage homeostatic circadian controls to ultimately improve clinical outcomes. Elucidation of the intrinsic properties of such complex systems and optimization of intervention strategies require not only an accurate identification of the signaling pathways that mediate host responses, but also a system-level description and evaluation.

Linking Inflammation, Cardiorespiratory Variability, and Neural Control in Acute Inflammation via Computational Modeling

Acute inflammation leads to organ failure by engaging catastrophic feedback loops in which stressed tissue evokes an inflammatory response and, in turn, inflammation damages tissue. Manifestations of this maladaptive inflammatory response include cardio-respiratory dysfunction that may be reflected in reduced heart rate and ventilatory pattern variabilities. We have developed signal-processing algorithms that quantify non-linear deterministic characteristics of variability in biologic signals. Now, coalescing under the aegis of the NIH Computational Biology Program and the Society for Complexity in Acute Illness, two research teams performed iterative experiments and computational modeling on inflammation and cardio-pulmonary dysfunction in sepsis as well as on neural control of respiration and ventilatory pattern variability. These teams, with additional collaborators, have recently formed a multi-institutional, interdisciplinary consortium, whose goal is to delineate the fundamental interrelationship between the inflammatory response and physiologic variability. Multi-scale mathematical modeling and complementary physiological experiments will provide insight into autonomic neural mechanisms that may modulate the inflammatory response to sepsis and simultaneously reduce heart rate and ventilatory pattern variabilities associated with sepsis. This approach integrates computational models of neural control of breathing and cardio-respiratory coupling with models that combine inflammation, cardiovascular function, and heart rate variability. The resulting integrated model will provide mechanistic explanations for the phenomena of respiratory sinus-arrhythmia and cardio-ventilatory coupling observed under normal conditions, and the loss of these properties during sepsis. This approach holds the potential of modeling cross-scale physiological interactions to improve both basic knowledge and clinical management of acute inflammatory diseases such as sepsis and trauma.

Transcriptional implications of ultradian glucocorticoid secretion in homeostasis and in the acute stress response

Endogenous glucocorticoids are secreted by the hypothalamic-pituitary-adrenal (HPA) axis in response to a wide range of stressors. Glucocorticoids exert significant downstream effects, including the regulation of many inflammatory genes. The HPA axis functions such that glucocorticoids are released in a pulsatile manner, producing ultradian rhythms in plasma glucocorticoid levels. It is becoming increasingly evident that this ultradian pulsatility is important in maintaining proper homeostatic regulation and responsiveness to stress. This is particularly interesting from a clinical perspective given that pathological dysfunctions of the HPA axis produce altered ultradian patterns. Modeling this system facilitates the understanding of how glucocorticoid pulsatility arises, how it can be lost, and the transcriptional implications of ultradian rhythms. To approach these questions, we developed a mathematical model that integrates the cyclic production of glucocorticoids by the HPA axis and their downstream effects by integrating existing models of the HPA axis and glucocorticoid pharmacodynamics. This combined model allowed us to evaluate the implications of pulsatility in homeostasis as well as in response to acute stress. The presence of ultradian rhythms allows the system to maintain a lower response to homeostatic levels of glucocorticoids, but diminished feedback within the HPA axis leads to a loss of glucocorticoid rhythmicity. Furthermore, the loss of HPA pulsatility in homeostasis correlates with a decrease in the peak output in response to an acute stressor. These results are important in understanding how cyclic glucocorticoid secretion helps maintain the responsiveness of the HPA axis.