Application of early warning signs to physiological contexts: a comparison of multivariate indices in patients on long-term hemodialysis.

Early warnings signs (EWSs) can anticipate abrupt changes in system state, known as "critical transitions," by detecting dynamic variations, including increases in variance, autocorrelation (AC), and cross-correlation. Numerous EWSs have been proposed; yet no consensus on which perform best exists. Here, we compared 15 multivariate EWSs in time series of 763 hemodialyzed patients, previously shown to present relevant critical transition dynamics. We calculated five EWSs based on AC, six on variance, one on cross-correlation, and three on AC and variance. We assessed their pairwise correlations, trends before death, and mortality predictive power, alone and in combination. Variance-based EWSs showed stronger correlations (r = 0.663 ± 0.222 vs. 0.170 ± 0.205 for AC-based indices) and a steeper increase before death. Two variance-based EWSs yielded HR95 > 9 (HR95 standing for a scale-invariant metric of hazard ratio), but combining them did not improve the area under the receiver-operating curve (AUC) much compared to using them alone (AUC = 0.798 vs. 0.796 and 0.791). Nevertheless, the AUC reached 0.825 when combining 13 indices. While some indicators did not perform overly well alone, their addition to the best performing EWSs increased the predictive power, suggesting that indices combination captures a broader range of dynamic changes occurring within the system. It is unclear whether this added benefit reflects measurement error of a unified phenomenon or heterogeneity in the nature of signals preceding critical transitions. Finally, the modest predictive performance and weak correlations among some indices call into question their validity, at least in this context.

Physiological Dysregulation Proceeds and Predicts Health Outcomes Similarly in Chinese and Western Populations.

BACKGROUND: A decade ago, we proposed an index of physiological dysregulation based on Mahalanobis distance (DM) that measures how far from the norm an individual biomarker profile is. While extensive validation has been performed, focus was mostly on Western populations with little comparison to developing countries, particularly at a physiological system level. The degree to which the approach would work in other sociocultural contexts and the similarity of dysregulation signatures across diverse populations are still open questions. METHODS: Using 2 data sets from China and 3 from Western countries (United States, United Kingdom, and Italy), we calculated DM globally and per physiological system. We assessed pairwise correlations among systems, difference with age, prediction of mortality and age-related diseases, and sensitivity to interchanging data sets with one another as the reference in DM calculation. RESULTS: Overall, results were comparable across all data sets. Different physiological systems showed distinct dysregulation processes. Association with age was moderate and often nonlinear, similarly for all populations. Mahalanobis distance predicted most health outcomes, although differently by physiological system. Using a Chinese population as the reference when calculating DM for Western populations, or vice versa, led to similar associations with health outcomes, with a few exceptions. CONCLUSIONS: While small differences were noticeable, they did not systematically emerge between Chinese and Western populations, but rather diffusively across all data sets. These findings suggest that DM presents similar properties, notwithstanding sociocultural backgrounds, and that it is equally effective in capturing the loss of homeostasis that occurs during aging in diverse industrial human populations.

Evidence for protein leverage on total energy intake, but not body mass index, in a large cohort of older adults.

BACKGROUND: Protein leverage (PL) is the phenomenon of consuming food until absolute intake of protein approaches a 'target value', such that total energy intake (TEI) varies passively with the ratio of protein: non-protein energy (fat + carbohydrate) in the diet. The PL hypothesis (PLH) suggests that the dilution of protein in energy-dense foods, particularly those rich in carbohydrates and fats, combines with protein leverage to contribute to the global obesity epidemic. Evidence for PL has been reported in younger adults, children and adolescents. This study aimed to test for PL and the protein leverage hypothesis (PLH) in a cohort of older adults. METHODS: We conducted a retrospective analysis of dietary intake in a cohort of 1699 community-dwelling older adults aged 67-84 years from the NuAge cohort. We computed TEI and the energy contribution (EC) from each macronutrient. The strength of leverage of macronutrients was assessed through power functions ( TEI = µ * EC L ). Body mass index (BMI) was calculated, and mixture models were fitted to predict TEI and BMI from macronutrients' ECs. RESULTS: In this cohort of older adults, 53% of individuals had obesity and 1.5% had severe cases. The mean TEI was 7673 kJ and macronutrients' ECs were 50.4%, 33.2% and 16.4%, respectively for carbohydrates, fat, and protein. There was a strong negative association (L = -0.37; p < 0.001) between the protein EC and TEI. Each percent of energy intake from protein reduced TEI by 77 kJ on average, ceteris paribus. However, BMI was unassociated with TEI in this cohort. CONCLUSIONS: Findings indicate clear evidence for PL on TEI, but not on BMI, likely because of aging, body composition, sarcopenia, or protein wasting.

Multidimensional associations between nutrient intake and healthy ageing in humans.

BACKGROUND: Little is known about how normal variation in dietary patterns in humans affects the ageing process. To date, most analyses of the problem have used a unidimensional paradigm, being concerned with the effects of a single nutrient on a single outcome. Perhaps then, our ability to understand the problem has been complicated by the fact that both nutrition and the physiology of ageing are highly complex and multidimensional, involving a high number of functional interactions. Here we apply the multidimensional geometric framework for nutrition to data on biological ageing from 1560 older adults followed over four years to assess on a large-scale how nutrient intake associates with the ageing process. RESULTS: Ageing and age-related loss of homeostasis (physiological dysregulation) were quantified via the integration of blood biomarkers. The effects of diet were modelled using the geometric framework for nutrition, applied to macronutrients and 19 micronutrients/nutrient subclasses. We observed four broad patterns: (1) The optimal level of nutrient intake was dependent on the ageing metric used. Elevated protein intake improved/depressed some ageing parameters, whereas elevated carbohydrate levels improved/depressed others; (2) There were non-linearities where intermediate levels of nutrients performed well for many outcomes (i.e. arguing against a simple more/less is better perspective); (3) There is broad tolerance for nutrient intake patterns that don't deviate too much from norms ('homeostatic plateaus'). (4) Optimal levels of one nutrient often depend on levels of another (e.g. vitamin E and vitamin C). Simpler linear/univariate analytical approaches are insufficient to capture such associations. We present an interactive tool to explore the results in the high-dimensional nutritional space. CONCLUSION: Using multidimensional modelling techniques to test the effects of nutrient intake on physiological dysregulation in an aged population, we identified key patterns of specific nutrients associated with minimal biological ageing. Our approach presents a roadmap for future studies to explore the full complexity of the nutrition-ageing landscape.

Synchrony of biomarker variability indicates a critical transition: Application to mortality prediction in hemodialysis.

Critical transition theory suggests that complex systems should experience increased temporal variability just before abrupt state changes. We tested this hypothesis in 763 patients on long-term hemodialysis, using 11 biomarkers collected every two weeks and all-cause mortality as a proxy for critical transitions. We find that variability-measured by coefficients of variation (CVs)-increases before death for all 11 clinical biomarkers, and is strikingly synchronized across all biomarkers: the first axis of a principal component analysis on all CVs explains 49% of the variance. This axis then generates powerful predictions of mortality (HR95 = 9.7, p < 0.0001, where HR95 is a scale-invariant metric of hazard ratio; AUC up to 0.82) and starts to increase markedly ∼3 months prior to death. Our results provide an early warning sign of physiological collapse and, more broadly, a quantification of joint system dynamics that opens questions of how system modularity may break down before critical transitions.

An objective metric of individual health and aging for population surveys.

BACKGROUND: We have previously developed and validated a biomarker-based metric of overall health status using Mahalanobis distance (DM) to measure how far from the norm of a reference population (RP) an individual's biomarker profile is. DM is not particularly sensitive to the choice of biomarkers; however, this makes comparison across studies difficult. Here we aimed to identify and validate a standard, optimized version of DM that would be highly stable across populations, while using fewer and more commonly measured biomarkers. METHODS: Using three datasets (the Baltimore Longitudinal Study of Aging, Invecchiare in Chianti and the National Health and Nutrition Examination Survey), we selected the most stable sets of biomarkers in all three populations, notably when interchanging RPs across populations. We performed regression models, using a fourth dataset (the Women's Health and Aging Study), to compare the new DM sets to other well-known metrics [allostatic load (AL) and self-assessed health (SAH)] in their association with diverse health outcomes: mortality, frailty, cardiovascular disease (CVD), diabetes, and comorbidity number. RESULTS: A nine- (DM9) and a seventeen-biomarker set (DM17) were identified as highly stable regardless of the chosen RP (e.g.: mean correlation among versions generated by interchanging RPs across dataset of r = 0.94 for both DM9 and DM17). In general, DM17 and DM9 were both competitive compared with AL and SAH in predicting aging correlates, with some exceptions for DM9. For example, DM9, DM17, AL, and SAH all predicted mortality to a similar extent (ranges of hazard ratios of 1.15-1.30, 1.21-1.36, 1.17-1.38, and 1.17-1.49, respectively). On the other hand, DM9 predicted CVD less well than DM17 (ranges of odds ratios of 0.97-1.08, 1.07-1.85, respectively). CONCLUSIONS: The metrics we propose here are easy to measure with data that are already available in a wide array of panel, cohort, and clinical studies. The standardized versions here lose a small amount of predictive power compared to more complete versions, but are nonetheless competitive with existing metrics of overall health. DM17 performs slightly better than DM9 and should be preferred in most cases, but DM9 may still be used when a more limited number of biomarkers is available.

Physiological health indexes predict deterioration and mortality in patients with COVID-19: a comparative study.

Old age is a crucial risk factor for severe coronavirus disease 2019 (COVID-19), with serious or fatal outcomes disproportionately affecting older adults compared with the rest of the population. We proposed that the physiological health status and biological age, beyond the chronological age itself, could be the driving trends affecting COVID-19 severity and mortality. A total of 155 participants hospitalized with confirmed COVID-19 aged 26-94 years were recruited for the study. Four different physiological summary indices were calculated: Klemera and Doubal's biological age, PhenoAge, physiological dysregulation (PD; globally and in specific systems), and integrated albunemia. All of these indices significantly predicted the risk of death (p < 0.01) after adjusting for chronological age and sex. In all models, men were 2.4-4.4-times more likely to die than women. The global PD was shown to be a good predictor of deterioration, with the odds of deterioration increasing by 41.7% per 0.5-unit increase in the global PD. As for death, the odds also increased by 68.3% per 0.5-unit increase in the global PD. Our results are partly attributed to common chronic diseases that aggravate COVID-19, but they also suggest that the underlying physiological state could capture vulnerability to severe COVID-19 and serve as a tool for prognosis that would, in turn, help inpatient management.

Prediction of Mortality in Hemodialysis Patients Using Moving Multivariate Distance.

There is an increasingly widespread use of biomarkers in network physiology to evaluate an organism's physiological state. A recent study showed that albumin variability increases before death in chronic hemodialysis patients. We hypothesized that a multivariate statistical approach would better allow us to capture signals of impending physiological collapse/death. We proposed a Moving Multivariate Distance (MMD), based on the Mahalanobis distance, to quantify the variability of the multivariate biomarker profile as a whole from one visit to the next. Biomarker profiles from a visit were used as the reference to calculate MMD at the subsequent visit. We selected 16 biomarkers (of which 11 are measured every 2 weeks) from blood samples of 763 chronic kidney disease patients hemodialyzed at the CHUS hospital in Quebec, who visited the hospital regularly (∼every 2 weeks) to perform routine blood tests. MMD tended to increase markedly preceding death, indicating an increasing intraindividual multivariate variability presaging a critical transition. In survival analysis, the hazard ratio between the 97.5th percentile and the 2.5th percentile of MMD reached as high as 21.1 [95% CI: 14.3, 31.2], showing that higher variability indicates substantially higher mortality risk. Multivariate approaches to early warning signs of critical transitions hold substantial clinical promise to identify early signs of critical transitions, such as risk of death in hemodialysis patients; future work should also explore whether the MMD approach works in other complex systems (i.e., ecosystems, economies), and should compare it to other multivariate approaches to quantify system variability.

Evidence from two cohorts for the frailty syndrome as an emergent state of parallel dysregulation in multiple physiological systems.

Frailty is a clinical syndrome often present in older adults and characterized by a heightened vulnerability to stressors. The biological antecedents and etiology of frailty are unclear despite decades of research: frailty is associated with dysregulation in a wide range of physiological systems, but no specific cause has been identified. Here, we test predictions stemming from the hypothesis that there is no specific cause: that frailty is an emergent property arising from the complex systems dynamics of the broad loss of organismal homeostasis. Specifically, we use dysregulation of six physiological systems using the Mahalanobis distance approach in two cohorts of older adults to test the breadth, diffuseness, and nonlinearity of associations between frailty and system-specific dysregulation. We find clear support for the breadth of associations between frailty and physiological dysregulation: positive associations of all systems with frailty in at least some analyses. We find partial support for diffuseness: the number of systems or total amount of dysregulation is more important than the identity of the systems dysregulated, but results only partially replicate across cohorts. We find partial support for nonlinearity: trends are exponential but not always significantly so, and power is limited for groups with very high levels of dysregulation. Overall, results are consistent with-but not definitive proof of-frailty as an emergent property of complex systems dynamics. Substantial work remains to understand how frailty relates to underlying physiological dynamics across systems.

What if there's no such thing as "aging"?

Are diseases caused by aging? What are the mechanisms of aging? Do all species age? These hotly debated questions revolve around a unitary definition of aging. Because we use the word "aging" so frequently, both colloquially and scientifically, we rarely pause to consider whether this word maps to an underlying biological phenomenon, or whether it is simply a grab-bag of diverse phenomena linked more by our mental associations than by any underlying biology. Here, we consider how the presence of the colloquial word "aging" generates a cognitive bias towards supposing there is a unitary biological phenomenon. We ask what kind of evidence would support or refute that idea, and subsequently show clear evidence at multiple levels that aging is not a unitary phenomenon. In particular, the known aging pathways lead to heterogeneous outputs, not a single coordinated phenomenon. From levels ranging from cellular/molecular to clinical to demographic to evolutionary, we show how the supposition that aging is a unitary phenomenon can mislead and distract us from asking the best questions. For major sub-disciplines of aging biology, we show how going beyond the notion of unitary aging can hone the paradigm and help advance the pace of discovery.

Lack of consensus on an aging biology paradigm? A global survey reveals an agreement to disagree, and the need for an interdisciplinary framework.

At a recent symposium on aging biology, a debate was held as to whether or not we know what biological aging is. Most of the participants were struck not only by the lack of consensus on this core question, but also on many basic tenets of the field. Accordingly, we undertook a systematic survey of our 71 participants on key questions that were raised during the debate and symposium, eliciting 37 responses. The results confirmed the impression from the symposium: there is marked disagreement on the most fundamental questions in the field, and little consensus on anything other than the heterogeneous nature of aging processes. Areas of major disagreement included what participants viewed as the essence of aging, when it begins, whether aging is programmed or not, whether we currently have a good understanding of aging mechanisms, whether aging is or will be quantifiable, whether aging will be treatable, and whether many non-aging species exist. These disagreements lay bare the urgent need for a more unified and cross-disciplinary paradigm in the biology of aging that will clarify both areas of agreement and disagreement, allowing research to proceed more efficiently. We suggest directions to encourage the emergence of such a paradigm.

An Emergent Integrated Aging Process Conserved Across Primates.

Aging is a complex process emerging from integrated physiological networks. Recent work using principal component analysis (PCA) of multisystem biomarkers proposed a novel fundamental physiological process, "integrated albunemia," which was consistent across human populations and more strongly associated with age and mortality risk than individual biomarkers. Here we tested for integrated albunemia and associations with age and mortality across six diverse nonhuman primate species and humans. PCA of 13 physiological biomarkers recovered in all species a primary axis of variation (PC1) resembling integrated albunemia, which increased with age in all but one species but was less predictive of mortality risk. Within species, PC1 scores were often reliably recovered with a minimal biomarker subset and usually stable between sexes. Even among species, correlations in PC1 structure were often strong, but the effect of phylogeny was inconclusive. Thus, integrated albunemia likely reflects an evolutionarily conserved process across primates and appears to be generally associated with aging but not necessarily with negative impacts on survival. Integrated albunemia is unlikely to be the only conserved emergent physiological process; our findings hence have implications both for the evolution of the aging process and of physiological networks more generally.

Conservation of physiological dysregulation signatures of aging across primates.

Two major goals in the current biology of aging are to identify general mechanisms underlying the aging process and to explain species differences in aging. Recent research in humans suggests that one important driver of aging is dysregulation, the progressive loss of homeostasis in complex biological networks. Yet, there is a lack of comparative data for this hypothesis, and we do not know whether dysregulation is widely associated with aging or how well signals of homeostasis are conserved. To address this knowledge gap, we use unusually detailed longitudinal biomarker data from 10 species of nonhuman primates housed in research centers and data from two human populations to test the hypotheses that (a) greater dysregulation is associated with aging across primates and (b) physiological states characterizing homeostasis are conserved across primates to degrees associated with phylogenetic proximity. To evaluate dysregulation, we employed a multivariate distance measure, calculated from sets of biomarkers, that is associated with aging and mortality in human populations. Dysregulation scores positively correlated with age and risk of mortality in most nonhuman primates studied, and signals of homeostatic state were significantly conserved across species, declining with phylogenetic distance. Our study provides the first broad demonstration of physiological dysregulation associated with aging and mortality risk in multiple nonhuman primates. Our results also imply that emergent signals of homeostasis are evolutionarily conserved, although with notable variation among species, and suggest promising directions for future comparative studies on dysregulation and the aging process.

Physiological predictors of reproductive performance in the European Starling (Sturnus vulgaris).

BACKGROUND: It is widely assumed that variation in fitness components has a physiological basis that might underlie selection on trade-offs, but the mechanisms driving decreased survival and future fecundity remain elusive. Here, we assessed whether physiological variables are related to workload ability or immediate fitness consequences and if they mediate future survival or reproductive success. We used data on 13 physiological variables measured in 93 female European starlings (Sturnus vulgaris) at two breeding stages (incubation, chick-rearing), for first-and second-broods over two years (152 observations). RESULTS: There was little co-variation among the physiological variables, either in incubating or chick-rearing birds, but some systematic physiological differences between the two stages. Chick-rearing birds had lower hematocrit and plasma creatine kinase but higher hemoglobin, triglyceride and uric acid levels. Only plasma corticosterone was repeatable between incubation and chick-rearing. We assessed relationships between incubation or chick-rearing physiology and measures of workload, current productivity, future fecundity or survival in a univariate manner, and found very few significant relationships. Thus, we next explored the utility of multivariate analysis (principal components analysis, Mahalanobis distance) to account for potentially complex physiological integration, but still found no clear associations. CONCLUSIONS: This implies either that a) birds maintained physiological variables within a homeostatic range that did not affect their performance, b) there are relatively few links between physiology and performance, or, more likely, c) that the complexity of these relationships exceeds our ability to measure it. Variability in ecological context may complicate the relationship between physiology and behavior. We thus urge caution regarding the over-interpretation of isolated significant findings, based on single traits in single years, in the literature.

The risks of biomarker-based epidemiology: Associations of circulating calcium levels with age, mortality, and frailty vary substantially across populations.

Recent studies have shown contradictory associations between calcium levels and health outcomes. We suspected these conflicting results were the consequence of more general issues with how biomarkers are analyzed in epidemiological studies, particularly in the context of aging. To demonstrate the risks of typical analyses, we used three longitudinal aging cohort studies and their demographic subsets to analyze how calcium levels change with age and predict risk of mortality and frailty. We show that calcium levels either increase or decrease with age depending on the population, and positively or negatively predict frailty depending on the population and analysis; both age and frailty results showed substantial heterogeneity. Mortality analyses revealed few significant associations but were likely underpowered. Variation in population composition (demographics, diseases, diet, etc.) leads to contradictory findings in the literature for calcium and likely for other biomarkers. Epidemiological studies of biomarkers are particularly sensitive to population composition both because biomarkers generally have non-linear and often non-monotonic relationships with other key variables, notably age and health outcomes, and because there is strong interdependence among biomarkers, which are integrated into complex regulatory networks. Consequently, most biomarkers have multiple physiological roles and are implicated in multiple pathologies. We argue that epidemiological studies of aging using biomarkers must account for these factors, and suggest methods to do this.

Men Sustain Higher Dysregulation Levels Than Women Without Becoming Frail.

The aging process differs in important ways between the sexes, with women living longer but at higher risk for frailty (the male-female health-survival paradox). The underlying biological mechanisms remain poorly understood, but may relate to sex differences in physiological dysregulation patterns. Here, using biomarkers from two longitudinal cohort studies (InCHIANTI and BLSA) and one cross-sectional survey (NHANES), we assess sex differences in trajectories of dysregulation globally and for five physiological systems: oxygen transport, electrolytes, hematopoiesis, lipids, and liver/kidney function. We found higher dysregulation levels in men, both globally and in the oxygen transport and hematopoietic systems (p < .001 for all), though differences for other systems were mixed (electrolytes) or absent (lipids and liver/kidney). There was no clear evidence for sex differences in rates of change in dysregulation with age. Although risk of frailty and mortality increase with dysregulation, there was no evidence for differences in these effects between sexes. These findings imply that the greater susceptibility of women to frailty is not simply due to a tolerance for higher dysregulation; rather, it may actually be men that have a greater tolerance for dysregulation, creating a male-female dysregulation-frailty paradox. However, the precise physiological mechanisms underlying the sex differences appear to be diffuse and hard to pin down.

Statistical distance as a measure of physiological dysregulation is largely robust to variation in its biomarker composition.

Physiological dysregulation may underlie aging and many chronic diseases, but is challenging to quantify because of the complexity of the underlying systems. Recently, we described a measure of physiological dysregulation, DM, that uses statistical distance to assess the degree to which an individual's biomarker profile is normal versus aberrant. However, the sensitivity of DM to details of the calculation method has not yet been systematically assessed. In particular, the number and choice of biomarkers and the definition of the reference population (RP, the population used to define a "normal" profile) may be important. Here, we address this question by validating the method on 44 common clinical biomarkers from three longitudinal cohort studies and one cross-sectional survey. DMs calculated on different biomarker subsets show that while the signal of physiological dysregulation increases with the number of biomarkers included, the value of additional markers diminishes as more are added and inclusion of 10-15 is generally sufficient. As long as enough markers are included, individual markers have little effect on the final metric, and even DMs calculated from mutually exclusive groups of markers correlate with each other at r~0.4-0.5. We also used data subsets to generate thousands of combinations of study populations and RPs to address sensitivity to differences in age range, sex, race, data set, sample size, and their interactions. Results were largely consistent (but not identical) regardless of the choice of RP; however, the signal was generally clearer with a younger and healthier RP, and RPs too different from the study population performed poorly. Accordingly, biomarker and RP choice are not particularly important in most cases, but caution should be used across very different populations or for fine-scale analyses. Biologically, the lack of sensitivity to marker choice and better performance of younger, healthier RPs confirm an interpretation of DM physiological dysregulation and as an emergent property of a complex system.

Detection of a novel, integrative aging process suggests complex physiological integration.

Many studies of aging examine biomarkers one at a time, but complex systems theory and network theory suggest that interpretations of individual markers may be context-dependent. Here, we attempted to detect underlying processes governing the levels of many biomarkers simultaneously by applying principal components analysis to 43 common clinical biomarkers measured longitudinally in 3694 humans from three longitudinal cohort studies on two continents (Women's Health and Aging I & II, InCHIANTI, and the Baltimore Longitudinal Study on Aging). The first axis was associated with anemia, inflammation, and low levels of calcium and albumin. The axis structure was precisely reproduced in all three populations and in all demographic sub-populations (by sex, race, etc.); we call the process represented by the axis "integrated albunemia." Integrated albunemia increases and accelerates with age in all populations, and predicts mortality and frailty--but not chronic disease--even after controlling for age. This suggests a role in the aging process, though causality is not yet clear. Integrated albunemia behaves more stably across populations than its component biomarkers, and thus appears to represent a higher-order physiological process emerging from the structure of underlying regulatory networks. If this is correct, detection of this process has substantial implications for physiological organization more generally.

Inflamm-aging does not simply reflect increases in pro-inflammatory markers.

Many biodemographic studies use biomarkers of inflammation to understand or predict chronic disease and aging. Inflamm-aging, i.e. chronic low-grade inflammation during aging, is commonly characterized by pro-inflammatory biomarkers. However, most studies use just one marker at a time, sometimes leading to conflicting results due to complex interactions among the markers. A multidimensional approach allows a more robust interpretation of the various relationships between the markers. We applied principal component analysis (PCA) to 19 inflammatory biomarkers from the InCHIANTI study. We identified a clear, stable structure among the markers, with the first axis explaining inflammatory activation (both pro- and anti-inflammatory markers loaded strongly and positively) and the second axis innate immune response. The first but not the second axis was strongly correlated with age (r=0.56, p<0.0001, r=0.08 p=0.053), and both were strongly predictive of mortality (hazard ratios per PCA unit (95% CI): 1.33 (1.16-1.53) and 0.87 (0.76-0.98) respectively) and multiple chronic diseases, but in opposite directions. Both axes were more predictive than any individual markers for baseline chronic diseases and mortality. These results show that PCA can uncover a novel biological structure in the relationships among inflammatory markers, and that key axes of this structure play important roles in chronic disease.

Cross-population validation of statistical distance as a measure of physiological dysregulation during aging.

Measuring physiological dysregulation during aging could be a key tool both to understand underlying aging mechanisms and to predict clinical outcomes in patients. However, most existing indices are either circular or hard to interpret biologically. Recently, we showed that statistical distance of 14 common blood biomarkers (a measure of how strange an individual's biomarker profile is) was associated with age and mortality in the WHAS II data set, validating its use as a measure of physiological dysregulation. Here, we extend the analyses to other data sets (WHAS I and InCHIANTI) to assess the stability of the measure across populations. We found that the statistical criteria used to determine the original 14 biomarkers produced diverging results across populations; in other words, had we started with a different data set, we would have chosen a different set of markers. Nonetheless, the same 14 markers (or the subset of 12 available for InCHIANTI) produced highly similar predictions of age and mortality. We include analyses of all combinatorial subsets of the markers and show that results do not depend much on biomarker choice or data set, but that more markers produce a stronger signal. We conclude that statistical distance as a measure of physiological dysregulation is stable across populations in Europe and North America.