What is physiologic complexity and how does it change with aging and disease

1. IntroductionA deﬁning but elusive feature of physiologic systems istheir daunting complexity. This complexity arises from theinteraction of a myriad of structural units and regulatoryfeedback loops that operate over a wide range of temporaland spatial scales, enabling the organism to adapt to thestresses of everyday life. Quantifying and modeling theremarkable and often bewildering repertoire of behaviorsexhibited by living organisms is one of the major challengesof contemporary science [4,7]. The combination of nonlin-earity and nonstationarity, more the rule than the exceptionin the output of physiologic systems, poses a major chal-lenge to conventional biostatistical assessments and stan-dard reductionist modeling stratagems. To describe andquantify the mechanisms of these “nonhomeostatic” behav-iors, investigators have employed new techniques derivedfrom complexity theory, including fractal analysis and non-linear dynamics. The appropriate application and interpre-tation of such metrics, however, remains incompletely ex-plored. What is clear is that reliance on any single test maygive a misleading representation of physiological complexity.In this issue, Vaillancourt and Newell critique and sug-gest modiﬁcations to a general dynamical model of patho-physiology that we and others have elaborated over the pasttwo decades [5,6,8,10,13,14,16,20,21,27]. The theory ofcomplexity loss in aging and disease, as currently formu-lated, has two central postulates:1. The output of healthy systems, under certain param-eter conditions, reveals a type of complex variabilityassociated with long-range (fractal) correlations,along with distinct classes of nonlinear interactions;2. This type of multi-scale, nonlinear complexity breaksdown with aging and disease, reducing the adaptivecapabilities of the individual.The term nonlinear applies to systems whose compo-nents interact in a non-additive way. Nonlinear couplingmay lead to an extraordinary range of dynamics, includingdifferent classes of abrupt changes, (such as bifurcations),deterministic chaos, nonlinear phase transitions, pacemakerentrainment and resetting, stochastic resonance, wave phe-nomena (including spiral waves, solitons, and scroll waves),emergent phenomena, and certain types of fractal scaling.Understanding the speciﬁc classes of nonlinear interactionsseen in healthy physiology and characterizing their pertur-bations with aging and disease is just beginning [4,16,27].The term fractal applies to complex-like objects, whichmay be generated by stochastic or nonlinear deterministicmechanisms. Fractal objects show self-similarity (scale-in-variance), such that the smaller-scale structure resemblesthe larger-scale form [10]. Examples in anatomy include theHis-Purkinje network and the tracheobronchial tree. Thefractal concept also extends to complex processes that lacka characteristic, or a single, time scale. Fractal processesgenerate ﬂuctuations over multiple time scales, and theirfrequency spectra typically show an inverse power-law (1/f-like) scaling pattern. Of particular interest is a class offractal processes that demonstrates long-range correlations.This type of “memory” effect has been identiﬁed in theﬂuctuations of the healthy heartbeat, as well as in the inter-stride interval ﬂuctuations in the walking patterns of healthyadults [14,15,21,22].A central caveat when applying concepts and techniquesfrom complexity theory to biomedicine is the recognition