Taking Systems Medicine to Heart

Advancing personalized medicine: Integrating statistical algorithms with omics and nano-omics for enhanced diagnostic accuracy and treatment efficacy

Medical laboratory services enable precise measurement of thousands of biomolecules and have become an inseparable part of high-quality healthcare services, exerting a profound influence on global health outcomes. The integration of omics technologies into laboratory medicine has transformed healthcare, enabling personalized treatments and interventions based on individuals' distinct genetic and metabolic profiles. Interpreting laboratory data relies on reliable reference values. Presently, population-derived references are used for individuals, risking misinterpretation due to population heterogeneity, and leading to medical errors. Thus, personalized references are crucial for precise interpretation of individual laboratory results, and the interpretation of omics data should be based on individualized reference values. We reviewed recent advancements in personalized laboratory medicine, focusing on personalized omics, and discussed strategies for implementing personalized statistical approaches in omics technologies to improve global health and concluded that personalized statistical algorithms for interpretation of omics data have great potential to enhance global health. Finally, we demonstrated that the convergence of nanotechnology and omics sciences is transforming personalized laboratory medicine by providing unparalleled diagnostic precision and innovative therapeutic strategies.

Host-microbe tryptophan partitioning in cardiovascular diseases

The functional interdependencies between the molecular components of a biological process demand for a network medicine platform that integrates systems biology and network science, to explore the interactions among biological components in health and disease. Access to large-scale omics datasets (genomics, transcriptomics, proteomics, metabolomics, metagenomics, phenomics, etc.) has significantly advanced our opportunity along this direction. Studies utilizing these techniques have begun to provide us with a deeper understanding of how the interaction between the intestinal microbes and their host affects the cardiovascular system in health and disease. Within the framework of a multiomics network approach, we highlight here how tryptophan metabolism may orchestrate the host-microbes interaction in cardiovascular diseases and the implications for precision medicine and therapeutics, including nutritional interventions.

Next Generation, Modifiable Cardiometabolic Biomarkers: Mitochondrial Adaptation and Metabolic Resilience: A Scientific Statement From the American Heart Association

Cardiometabolic risk is increasing in prevalence across the life span with disproportionate ramifications for youth at socioeconomic disadvantage. Established risk factors and associated disease progression are harder to reverse as they become entrenched over time; if current trends are unchecked, the consequences for individual and societal wellness will become untenable. Interrelated root causes of ectopic adiposity and insulin resistance are understood but identified late in the trajectory of systemic metabolic dysregulation when traditional cardiometabolic risk factors cross current diagnostic thresholds of disease. Thus, children at cardiometabolic risk are often exposed to suboptimal metabolism over years before they present with clinical symptoms, at which point life-long reliance on pharmacotherapy may only mitigate but not reverse the risk. Leading-edge indicators are needed to detect the earliest departure from healthy metabolism, so that targeted, primordial, and primary prevention of cardiometabolic risk is possible. Better understanding of biomarkers that reflect the earliest transitions to dysmetabolism, beginning in utero, ideally biomarkers that are also mechanistic/causal and modifiable, is critically needed. This scientific statement explores emerging biomarkers of cardiometabolic risk across rapidly evolving and interrelated "omic" fields of research (the epigenome, microbiome, metabolome, lipidome, and inflammasome). Connections in each domain to mitochondrial function are identified that may mediate the favorable responses of each of the omic biomarkers featured to a heart-healthy lifestyle, notably to nutritional interventions. Fuller implementation of evidence-based nutrition must address environmental and socioeconomic disparities that can either facilitate or impede response to therapy.

Multiomics Analysis Provides Novel Pathways Related to Progression of Heart Failure

BACKGROUND

Despite major advances in pharmacological treatment for patients with heart failure, residual mortality remains high. This suggests that important pathways are not yet targeted by current heart failure therapies.

OBJECTIVES

We sought integration of genetic, transcriptomic, and proteomic data in a large cohort of patients with heart failure to detect major pathways related to progression of heart failure leading to death.

METHODS

We used machine learning methodology based on stacked generalization framework and gradient boosting algorithms, using 54 clinical phenotypes, 403 circulating plasma proteins, 36,046 transcript expression levels in whole blood, and 6 million genomic markers to model all-cause mortality in 2,516 patients with heart failure from the BIOSTAT-CHF (Systems BIOlogy Study to TAilored Treatment in Chronic Heart Failure) study. Results were validated in an independent cohort of 1,738 patients.

RESULTS

The mean age of the patients was 70 years (Q1-Q3: 61-78 years), 27% were female, median N-terminal pro-B-type natriuretic peptide was 4,275 ng/L (Q1-Q3: 2,360-8,486 ng/L), and 7% had heart failure with preserved ejection fraction. During a median follow-up of 21 months, 657 (26%) of patients died. The 4 major pathways with a significant association to all-cause mortality were: 1) the PI3K/Akt pathway; 2) the MAPK pathway; 3) the Ras signaling pathway; and 4) epidermal growth factor receptor tyrosine kinase inhibitor resistance. Results were validated in an independent cohort of 1,738 patients.

CONCLUSIONS

A systems biology approach integrating genomic, transcriptomic, and proteomic data identified 4 major pathways related to mortality. These pathways are related to decreased activation of the cardioprotective ERBB2 receptor, which can be modified by neuregulin.

Uncovering personalized glucose responses and circadian rhythms from multiple wearable biosensors with Bayesian dynamical modeling

Wearable biosensors and smartphone applications can measure physiological variables over multiple days in free-living conditions. We measure food and drink ingestion, glucose dynamics, physical activity, heart rate (HR), and heart rate variability (HRV) in 25 healthy participants over 14 days. We develop a Bayesian inference framework to learn personal parameters that quantify circadian rhythms and physiological responses to external stressors. Modeling the effects of ingestion events on glucose levels reveals that slower glucose decay kinetics elicit larger postprandial glucose spikes, and we uncover a circadian baseline rhythm for glucose with high amplitudes in some individuals. Physical activity and circadian rhythms explain as much as 40%-65% of the HR variance, whereas the variance explained for HRV is more heterogeneous across individuals. A more complex model incorporating activity, HR, and HRV explains up to 15% of additional glucose variability, highlighting the relevance of integrating multiple biosensors to better predict glucose dynamics.

Enabling health potential: exploring nonlinear and complex results of osteopathic manual medicine through complex systems theory

Osteopathic medicine is a holistic, patient-specific approach. Explaining the impact of osteopathic manipulative medicine (OMM) has been problematic because many of its effects are nonlinear. Complex systems theory (CST) is explored as a mechanism of understanding the interplay of the body's anatomy and physiology, an illness process, and the effects of OMM. Tensegrity is discussed as an example of an emergent property of the body's systems that affects not only biomechanics but also pathophysiology. Previous explanations of osteopathic philosophy are reviewed. The Host + Disease=Illness paradigm is a way to think through the impact of host and disease factors on an illness state, and how targeted interventions may affect the illness. The Osteopathic 5 Models are another way to view the body's complexity. The area of greatest restriction (AGR) screen can be understood to direct OMM in a way that respects complexity and enables asymmetric and nonlocal results to realize health potential. The impact of this framework is in coherently explaining the impact of osteopathic philosophy and OMM and exploring new approaches to research.

Host Response Modulation Therapy in the Diabetes Mellitus—Periodontitis Conjuncture: A Narrative Review

The inflammatory response of the host in periodontitis is the phenomenon that underlies the onset and evolution of periodontal destructive phenomena. A number of systemic factors, such as diabetes mellitus (DM), can negatively affect the patient with periodontitis, just as the periodontal disease can aggravate the status of the DM patient. Host response modulation therapy involves the use of anti-inflammatory and anti-oxidant products aimed at resolving inflammation, stopping destructive processes, and promoting periodontal healing, all important aspects in patients with high tissue loss rates, such as diabetic patients. This paper reviews the data available in the literature on the relationship between DM and periodontitis, the main substances modulating the inflammatory response (nonsteroidal anti-inflammatory drugs, sub-antimicrobial doses of doxycycline, or omega-3 fatty acids and their products, specialized pro-resolving mediators), as well as their application in diabetic patients.

KATP channel dependent heart multiome atlas

Plasmalemmal ATP sensitive potassium (K_ATP) channels are recognized metabolic sensors, yet their cellular reach is less well understood. Here, transgenic Kir6.2 null hearts devoid of the K_ATP channel pore underwent multiomics surveillance and systems interrogation versus wildtype counterparts. Despite maintained organ performance, the knockout proteome deviated beyond a discrete loss of constitutive K_ATP channel subunits. Multidimensional nano-flow liquid chromatography tandem mass spectrometry resolved 111 differentially expressed proteins and their expanded network neighborhood, dominated by metabolic process engagement. Independent multimodal chemometric gas and liquid chromatography mass spectrometry unveiled differential expression of over one quarter of measured metabolites discriminating the Kir6.2 deficient heart metabolome. Supervised class analogy ranking and unsupervised enrichment analysis prioritized nicotinamide adenine dinucleotide (NAD⁺), affirmed by extensive overrepresentation of NAD⁺ associated circuitry. The remodeled metabolome and proteome revealed functional convergence and an integrated signature of disease susceptibility. Deciphered cardiac patterns were traceable in the corresponding plasma metabolome, with tissue concordant plasma changes offering surrogate metabolite markers of myocardial latent vulnerability. Thus, Kir6.2 deficit precipitates multiome reorganization, mapping a comprehensive atlas of the K_ATP channel dependent landscape.

Big Data in Cardiology: State-of-Art and Future Prospects

Cardiological disorders contribute to a significant portion of the global burden of disease. Cardiology can benefit from Big Data, which are generated and released by different sources and channels, like epidemiological surveys, national registries, electronic clinical records, claims-based databases (epidemiological Big Data), wet-lab, and next-generation sequencing (molecular Big Data), smartphones, smartwatches, and other mobile devices, sensors and wearable technologies, imaging techniques (computational Big Data), non-conventional data streams such as social networks, and web queries (digital Big Data), among others. Big Data is increasingly having a more and more relevant role, being highly ubiquitous and pervasive in contemporary society and paving the way for new, unprecedented perspectives in biomedicine, including cardiology. Big Data can be a real paradigm shift that revolutionizes cardiological practice and clinical research. However, some methodological issues should be properly addressed (like recording and association biases) and some ethical issues should be considered (such as privacy). Therefore, further research in the field is warranted.

Personalized Lifestyle Intervention and Functional Evaluation Health Outcomes SurvEy: Presentation of the LIFEHOUSE Study Using N-of-One Tent-Umbrella-Bucket Design

The working definition of health is often the simple absence of diagnosed disease. This common standard is limiting given that changes in functional health status represent early warning signs of impending health declines. Longitudinal assessment of functional health status may foster prevention of disease occurrence and modify disease progression. The LIFEHOUSE (Lifestyle Intervention and Functional Evaluation-Health Outcomes SurvEy) longitudinal research project explores the impact of personalized lifestyle medicine approaches on functional health determinants. Utilizing an adaptive tent-umbrella-bucket design, the LIFEHOUSE study follows the functional health outcomes of adult participants recruited from a self-insured employee population. Participants were each allocated to the tent of an all-inclusive N-of-one case series. After assessing medical history, nutritional physical exam, baseline functional status (utilizing validated tools to measure metabolic, physical, cognitive, emotional and behavioral functional capacity), serum biomarkers, and genomic and microbiome markers, participants were assigned to applicable umbrellas and buckets. Personalized health programs were developed and implemented using systems biology formalism and functional medicine clinical approaches. The comprehensive database (currently 369 analyzable participants) will yield novel interdisciplinary big-health data and facilitate topological analyses focusing on the interactome among each participant's genomics, microbiome, diet, lifestyle and environment.

Systems biology in cardiovascular disease: a multiomics approach

Omics techniques generate large, multidimensional data that are amenable to analysis by new informatics approaches alongside conventional statistical methods. Systems theories, including network analysis and machine learning, are well placed for analysing these data but must be applied with an understanding of the relevant biological and computational theories. Through applying these techniques to omics data, systems biology addresses the problems posed by the complex organization of biological processes. In this Review, we describe the techniques and sources of omics data, outline network theory, and highlight exemplars of novel approaches that combine gene regulatory and co-expression networks, proteomics, metabolomics, lipidomics and phenomics with informatics techniques to provide new insights into cardiovascular disease. The use of systems approaches will become necessary to integrate data from more than one omic technique. Although understanding the interactions between different omics data requires increasingly complex concepts and methods, we argue that hypothesis-driven investigations and independent validation must still accompany these novel systems biology approaches to realize their full potential.

Identification of disease treatment mechanisms through the multiscale interactome

Most diseases disrupt multiple proteins, and drugs treat such diseases by restoring the functions of the disrupted proteins. How drugs restore these functions, however, is often unknown as a drug's therapeutic effects are not limited to the proteins that the drug directly targets. Here, we develop the multiscale interactome, a powerful approach to explain disease treatment. We integrate disease-perturbed proteins, drug targets, and biological functions into a multiscale interactome network. We then develop a random walk-based method that captures how drug effects propagate through a hierarchy of biological functions and physical protein-protein interactions. On three key pharmacological tasks, the multiscale interactome predicts drug-disease treatment, identifies proteins and biological functions related to treatment, and predicts genes that alter a treatment's efficacy and adverse reactions. Our results indicate that physical interactions between proteins alone cannot explain treatment since many drugs treat diseases by affecting the biological functions disrupted by the disease rather than directly targeting disease proteins or their regulators. We provide a general framework for explaining treatment, even when drugs seem unrelated to the diseases they are recommended for.

Platelet Proteomes, Pathways, and Phenotypes as Informants of Vascular Wellness and Disease

Platelets rapidly undergo responsive transitions in form and function to repair vascular endothelium and mediate hemostasis. In contrast, heterogeneous platelet subpopulations with a range of primed or refractory phenotypes gradually arise in chronic inflammatory and other conditions in a manner that may indicate or support disease. Qualitatively distinguishable platelet phenotypes are increasingly associated with a variety of physiological and pathological circumstances; however, the origins and significance of platelet phenotypic variation remain unclear and conceptually vague. As changes in platelet function in disease exhibit many similarities to platelets following the activation of platelet agonist receptors, the intracellular responses of platelets common to hemostasis and inflammation may provide insights to the molecular basis of platelet phenotype. Here, we review concepts around how protein-level relations-from platelet receptors through intracellular signaling events-may help to define platelet phenotypes in inflammation, immune responses, aging, and other conditions. We further discuss how representing systems-wide platelet proteomics data profiles as circuit-like networks of causally related intracellular events, or, pathway maps, may inform molecular definitions of platelet phenotype. In addition to offering insights into platelets as druggable targets, maps of causally arranged intracellular relations underlying platelet function can also advance precision and interceptive medicine efforts by leveraging platelets as accessible, dynamic, endogenous, circulating biomarkers of vascular wellness and disease. Graphic Abstract: A graphic abstract is available for this article.

An integrative model of cardiometabolic traits identifies two types of metabolic syndrome

Human diseases arise in a complex ecosystem composed of disease mechanisms and the whole-body state. However, the precise nature of the whole-body state and its relations with disease remain obscure. Here we map similarities among clinical parameters in normal physiological settings, including a large collection of metabolic, hemodynamic, and immune parameters, and then use the mapping to dissect phenotypic states. We find that the whole-body state is faithfully represented by a quantitative two-dimensional model. One component of the whole-body state represents ‘metabolic syndrome’ (MetS) – a conventional way to determine the cardiometabolic state. The second component is decoupled from the classical MetS, suggesting a novel ‘non-classical MetS’ that is characterized by dozens of parameters, including dysregulated lipoprotein parameters (e.g. low free cholesterol in small high-density lipoproteins) and attenuated cytokine responses of immune cells to ex vivo stimulations. Both components are associated with disease, but differ in their particular associations, thus opening new avenues for improved personalized diagnosis and treatment. These results provide a practical paradigm to describe whole-body states and to dissect complex disease within the ecosystem of the human body.

Epigenetics in atrial fibrillation: A reappraisal

Atrial fibrillation (AF) is the most common cardiac arrhythmia and an important cause of morbidity and mortality globally. Atrial remodeling includes changes in ion channel expression and function, structural alterations, and neural remodeling, which create an arrhythmogenic milieu resulting in AF initiation and maintenance. Current therapeutic strategies for AF involving ablation and antiarrhythmic drugs are associated with relatively high recurrence and proarrhythmic side effects, respectively. Over the last 2 decades, in an effort to overcome these issues, research has sought to identify the genetic basis for AF thereby gaining insight into the regulatory mechanisms governing its pathophysiology. Despite identification of multiple gene loci associated with AF, thus far none has led to a therapy, indicating additional contributors to pathology. Recently, in the context of expanding knowledge of the epigenome (DNA methylation, histone modifications, and noncoding RNAs), its potential involvement in the onset and progression of AF pathophysiology has started to emerge. Probing the role of various epigenetic mechanisms that contribute to AF may improve our knowledge of this complex disease, identify potential therapeutic targets, and facilitate targeted therapies. Here, we provide a comprehensive review of growing epigenetic features involved in AF pathogenesis and summarize the emerging epigenomic targets for therapy that have been explored in preclinical models of AF.

Cardioinformatics: the nexus of bioinformatics and precision cardiology

Cardiovascular disease (CVD) is the leading cause of death worldwide, causing over 17 million deaths per year, which outpaces global cancer mortality rates. Despite these sobering statistics, most bioinformatics and computational biology research and funding to date has been concentrated predominantly on cancer research, with a relatively modest footprint in CVD. In this paper, we review the existing literary landscape and critically assess the unmet need to further develop an emerging field at the multidisciplinary interface of bioinformatics and precision cardiovascular medicine, which we refer to as 'cardioinformatics'.

The Guangzhou Twin Eye Study: 2019 Update

The primary aim of the Guangzhou Twin Eye Study (GTES) is to explore the impact that genes and environmental influences have on common eye diseases. Since 2006, approximately 1300 pairs of twins, aged 7-15 years, were enrolled at baseline. Progressive phenotypes, such as cycloplegic refraction, axial length, height and weight, have been collected annually. Nonprogressive phenotypes such as parental refraction, corneal thickness, fundus photo, intraocular pressure and DNA were collected once at baseline. We are collaborating with fellow international twin researchers and psychologists to further explore links with general medical conditions. In this article, we review the history, major findings and future research directions for the GTES.

The role of inflammation and genetics in periodontal disease

Periodontitis is a complex disease: (a) various causative factors play a role simultaneously and interact with each other; and (b) the disease is episodic in nature, and bursts of disease activity can be recognized, ie, the disease develops and cycles in a nonlinear fashion. We recognize that various causative factors determine the immune blueprint and, consequently, the immune fitness of a subject. Normally, the host lives in a state of homeostasis or symbiosis with the oral microbiome; however, disturbances in homeostatic balance can occur, because of an aberrant host response (inherited and/or acquired during life). This imbalance results from hyper- or hyporesponsiveness and/or lack of sufficient resolution of inflammation, which in turn is responsible for much of the disease destruction seen in periodontitis. The control of this destruction by anti-inflammatory processes and proresolution processes limits the destruction to the tissues surrounding the teeth. The local inflammatory processes can also become systemic, which in turn affect organs such as the heart. Gingival inflammation also elicits changes in the ecology of the subgingival environment providing optimal conditions for the outgrowth of gram-negative, anaerobic species, which become pathobionts and can propagate periodontal inflammation and can further negatively impact immune fitness. The factors that determine immune fitness are often the same factors that determine the response to the resident biofilm, and are clustered as follows: (a) genetic and epigenetic factors; (b) lifestyle factors, such as smoking, diet, and psychosocial conditions; (c) comorbidities, such as diabetes; and (d) local and dental factors, as well as randomly determined factors (stochasticity). Of critical importance are the pathobionts in a dysbiotic biofilm that drive the viscious cycle. Focusing on genetic factors, currently variants in at least 65 genes have been suggested as being associated with periodontitis based on genome-wide association studies and candidate gene case control studies. These studies have found pleiotropy between periodontitis and cardiovascular diseases. Most of these studies point to potential pathways in the pathogenesis of periodontal disease. Also, most contribute to a small portion of the total risk profile of periodontitis, often limited to specific racial and ethnic groups. To date, 4 genetic loci are shared between atherosclerotic cardiovascular diseases and periodontitis, ie, CDKN2B-AS1(ANRIL), a conserved noncoding element within CAMTA1 upstream of VAMP3, PLG, and a haplotype block at the VAMP8 locus. The shared genes suggest that periodontitis is not causally related to atherosclerotic diseases, but rather both conditions are sequelae of similar (the same?) aberrant inflammatory pathways. In addition to variations in genomic sequences, epigenetic modifications of DNA can affect the genetic blueprint of the host responses. This emerging field will yield new valuable information about susceptibility to periodontitis and subsequent persisting inflammatory reactions in periodontitis. Further studies are required to verify and expand our knowledge base before final cause and effect conclusions about the role of inflammation and genetic factors in periodontitis can be made.

Is proteomics of value in cardiovascular risk assessment?

PURPOSE OF REVIEW

To briefly summarize recently published evidence in the field of cardiovascular proteomics, focusing on its ability to improve cardiovascular risk stratification and critically discussing still open and burning issues and future perspectives of proteomics research.

RECENT FINDINGS

Several epidemiological studies have demonstrated an improvement in cardiovascular risk prediction beyond traditional risk factors by adding novel biomarkers, identified by both discovery and targeted proteomics. However, only a moderate improvement in risk discrimination over clinical variables was observed. Moreover, despite different outcomes there was also a strong overlap of identified candidates, with several of them being already well established cardiovascular risk markers such as growth differentiation factor 15, natriuretic peptides, C-reactive protein, interleukins, and metalloproteases.

SUMMARY

Although proteomics plays a crucial role in biomarker discovery, the modest discriminative ability of this technique raises the possibility that there are still hidden mechanisms in protein regulatory networks, which urgently need to be evaluated to improve a cardiovascular risk assessment to a clinically significant extent.

[Inflammation as a universal pathogenetic link between injury, repair and regeneration, in acute coronary syndrome. From experiment to clinic]

Theory of atherogenesis and its complications underwent numerous changes. Today we observe that inflammation is a universal pathogenetic link between various processes such as atherosclerosis, rupture of atherosclerotic plaques and following myocardial infarction, post-infarction cardiac repair and heart failure. This review discusses examples, difficulties, and prospects of implementation of anti-inflammatory therapies in management of acute coronary syndrome and its complications.

Clinical Applications of System Regulation Medicine

Increasing incidence and poor outcome of chronic non-communicable diseases in western population would require a paradigm shift in the treatments. Guidelines-based medical approaches continue to be the standard rule in clinical practice, although only less than 15% of them are based on high-quality research. For each person who benefits from the 10 best-selling drugs in the USA, a number between 4 and 25 has no one beneficial effect.

The reductionist linear medicine method does not offer solutions in the non-manifest preclinical stage of the disease when it would still be possible to reverse the pathological progression and the axiom “a drug, a target, a symptom” are still inconclusive. Needs additional tools to address these challenges.

System Medicine considers the disease as a dysregulation of the biological networks that changes throughout the evolution of the pathological process and with the comorbidities development. The strength of the networks indicates their ability to withstand dysregulations during the perturbation phases, returning to the state of stability. The treatment of dysregulated networks before the symptomatological manifestation emerges offers the possibility of treating and preventing pathologies in the preclinical phase and potentially reversing the pathological process, stopping it or preventing comorbidities. Furthermore, treating shared networks instead of individual phenotypic symptoms can reduce drug use, offering a solution to the problem of ineffective drug use.

Multimorbidity: Implications and directions for health psychology and behavioral medicine

The increasing prevalence of multimorbidity in the United States and the rest of the world poses problems for patients and for health care providers, care systems, and policy. After clarifying the difference between comorbidity and multimorbidity, this article describes the challenges that the prevalence of multimorbidity presents for well-being, prevention, and medical treatment. We submit that health psychology and behavioral medicine have an important role to play in meeting these challenges because of the holistic vision of health afforded by the foundational biopsychosocial model. Furthermore, opportunities abound for health psychology/behavioral medicine to study how biological, social and psychological factors influence multimorbidity. This article describes three major areas in which health psychologists can contribute to understanding and treatment of multimorbidity: (a) etiology; (b) prevention and self-management; and (c) clinical care. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Heart rate fragmentation: using cardiac pacemaker dynamics to probe the pace of biological aging

This perspectives article discusses the use of a novel set of dynamical biomarkers in the assessment of biological versus chronological age. The basis for this development is a recently delineated property of altered sinoatrial pacemaker-neuroautonomic function, termed heart rate fragmentation (HRF). Fragmented rhythms manifest as an increase in the density of changes in heart rate acceleration sign, not mechanistically explicable by physiological cardiac vagal tone modulation. We reported that HRF increased monotonically with cross-sectional age and that HRF measures, but not conventional heart rate variability metrics, were significantly associated with major incident cardiovascular events in the Multi-Ethnic Study of Atherosclerosis (MESA). Furthermore, HRF measures added value to both Framingham and MESA cardiovascular risk indices. Here, we propose that interventions that fundamentally slow or reverse the pace of biological aging, via system-wide effects, should be associated with a decrease in the degree of HRF and possibly with a reemergence of the nonfragmented ("fluent") patterns associated with more youthful heart rate dynamics.