Homeostasis is one of the key concepts of physiology and the basis to understand chronic-degenerative disease and human ageing, but is difficult to quantify in clinical practice. The variability of time series resulting from continuous and non-invasive physiological monitoring is conjectured to reflect the underlying homeostatic regulatory processes, but it is not clear why the variability of some variables such as heart rate gives a favourable health prognosis whereas the variability of other variables such as blood pressure implies an increased risk factor. The purpose of the present contribution is to quantify homeostasis using time-series analysis and to offer an explanation for the phenomenology of physiological time series. Within the context of network physiology, which focusses on the interactions between various variables at multiple scales of time and space, it may be understood that different physiological variables may play distinct roles in their respective regulatory mechanisms. In the present contribution, we distinguish between regulated variables, such as blood pressure or core temperature, and physiological responses, such as heart rate and skin temperature. We give evidence that in optimal conditions of youth and health the former are characterized by Gaussian statistics, low variability and represent the stability of the internal environment, whereas the latter are characterized by non-Gaussian distributions, large variability and reflect the adaptive capacity of the human body; in the adverse conditions of ageing and/or disease, adaptive capacity is lost and the variability of physiological responses is diminished, and as a consequence the stability of the internal environment is compromised and its variability increases. Time-series analysis allows one to quantify homeostasis in the optimal conditions of youth and health and the degradation of homeostasis or homeostenosis in the adverse conditions of ageing and/or disease, and may offer an alternative approach to diagnosis in clinical practice.

Metabolic disorders, such as obesity, elevated blood pressure, dyslipidemias, insulin resistance, hyperglycemia, and hyperuricemia have all been identified as risk factors for an epidemic of important and widespread chronic-degenerative diseases, such as type 2 diabetes and cardiovascular disease, that constitute some of the world's most important public health challenges. Their increasing prevalence can be associated with an aging population and to lifestyles within an obesogenic environment. Taking educational level as a proxy for lifestyle, and using both logistic and linear regressions, we study the relation between a wide set of metabolic biomarkers, and educational level, body mass index (BMI), age, and sex as correlates, in a population of 1,073 students, academic and non-academic staff at Mexico's largest university (UNAM). Controlling for BMI and sex, we consider educational level and age as complementary measures—degree and duration—of exposure to metabolic insults. Analyzing the role of education across a wide spectrum of educational levels (from primary school to doctoral degree), we show that higher education correlates to significantly better metabolic health when compared to lower levels, and is associated with significantly less risk for waist circumference, systolic blood pressure, glucose, glycosylated hemoglobin, triglycerides, high density lipoprotein and metabolic syndrome (all p < 0.05); but not for diastolic blood pressure, basal insulin, uric acid, low density lipoprotein, and total cholesterol. We classify each biomarker, and corresponding metabolic disorder, by its associated set of statistically significant correlates. Differences among the sets of significant correlates indicate various aetiologies and the need for targeted population-specific interventions. Thus, variables strongly linked to educational level are candidates for lifestyle change interventions. Hence, public policy efforts should be focused on those metabolic biomarkers strongly linked to education, while adopting a different approach for those biomarkers not linked as they may be poor targets for educational campaigns.

Circadian rhythms become less dominant and less regular with chronic-degenerative disease, such that to accurately assess these pathological conditions it is important to quantify not only periodic characteristics but also more irregular aspects of the corresponding time series. Novel data-adaptive techniques, such as singular spectrum analysis (SSA), allow for the decomposition of experimental time series, in a model-free way, into a trend, quasiperiodic components and noise fluctuations. We compared SSA with the traditional techniques of cosinor analysis and intradaily variability using 1-week continuous actigraphy data in young adults with acute insomnia and healthy age-matched controls. The findings suggest a small but significant delay in circadian components in the subjects with acute insomnia, i.e. a larger acrophase, and alterations in the day-to-day variability of acrophase and amplitude. The power of the ultradian components follows a fractal 1/f power law for controls, whereas for those with acute insomnia this power law breaks down because of an increased variability at the 90min time scale, reminiscent of Kleitman’s basic rest-activity (BRAC) cycles. This suggests that for healthy sleepers attention and activity can be sustained at whatever time scale required by circumstances, whereas for those with acute insomnia this capacity may be impaired and these individuals need to rest or switch activities in order to stay focused. Traditional methods of circadian rhythm analysis are unable to detect the more subtle effects of day-to-day variability and ultradian rhythm fragmentation at the specific 90min time scale.