Age trajectories of physiological indices in relation to healthy life course

Methods for joint modelling of longitudinal omics data and time-to-event outcomes: Applications to lysophosphatidylcholines in connection to aging and mortality in the Long Life Family Study

Studying relationships between longitudinal changes in omics variables and risks of events requires specific methodologies for joint analyses of longitudinal and time-to-event outcomes. We applied two such approaches (joint models [JM], stochastic process models [SPM]) to longitudinal metabolomics data from the Long Life Family Study focusing on understudied associations of longitudinal changes in lysophosphatidylcholines (LPC) with mortality and aging-related outcomes (23 LPC species, 5,790 measurements of each in 4,011 participants, 1,431 of whom died during follow-up). JM analyses found that higher levels of the majority of LPC species were associated with lower mortality risks, with the largest effect size observed for LPC 15:0/0:0 (hazard ratio: 0.715, 95% CI (0.649, 0.788)). SPM applications to LPC 15:0/0:0 revealed how the association found in JM reflects underlying aging-related processes: decline in robustness to deviations from optimal LPC levels, better ability of males' organisms to return to equilibrium LPC levels (which are higher in females), and increasing gaps between the optimum and equilibrium levels leading to increased mortality risks with age. Our results support LPC as a biomarker of aging and related decline in robustness/resilience, and call for further exploration of factors underlying age-dynamics of LPC in relation to mortality and diseases.

Usefulness of the cardiopulmonary exercise test up to the anaerobic threshold for pati-ents aged ≥ 80 years with cardiovascular disease on cardiac rehabilitation

OBJECTIVE

A cardiopulmonary exercise test provides information regarding appropriate exercise intensity, but there have been few reports on its use in patients over 80 years of age.

DESIGN

Retrospective observational study.

PATIENTS

A total of 511 cardiovascular disease patients who performed a cardiopulmonary exercise test from February 2011 to January 2020 were investigated.

METHODS

Patients were stratified according to age: < 70 years, 70-79 years, and ≥ 80 years, and the results of the cardiopulmonary exercise test up to anaerobic threshold were compared.

RESULTS

Patients in the < 70 age bracket showed higher oxygen consumption, carbon dioxide output, and ventilatory volume and lower ventilation equivalents per oxygen consumption and carbon dioxide output in all time periods. However, there were no significant differences in these parameters or the work rate (70-79 years of age: 41.4 ± 11.7 watts, vs ≥ 80 years: 42.2 ± 10.9 watts, p = 0.95) or oxygen consumption per body weight at anaerobic threshold (12.2 ± 0.2 ml/min/kg, vs 12.1 ± 0.4 ml/min/kg, p = 0.97) between the 70-79 year age bracket and the ≥  80 year age bracket.

CONCLUSION

Even for cardiovascular disease patients age ≥ 80 years, a cardiopulmonary exercise test up to anaerobic threshold can supply useful information for guiding cardiac rehabilitation.

Inflammatory Biomarkers and Gait Impairment in Older Adults: A Systematic Review

Peripheral inflammation and gait speed alterations are common in several neurological disorders and in the aging process, but the association between the two is not well established. The aim of this systematic literary review is to determine whether proinflammatory markers are a positive predictor for gait impairments and their complications, such as falls in older adults, and may represent a risk factor for slow gait speed and its complications. The systematic review was performed in line with the Preferred Report Items for Systematic Review and Meta-Analyses (PRISMA). A protocol for literature searches was structured a priori and designed according to the International Perspective Register of Systemic Review (PROSPERO: CRD42023451108). Peer-reviewed original articles were identified by searching seven electronic databases: Excerpta Medica Database (EMBASE), SciVerse (ScienceDirect), Scopus, PubMed, Medline, Web of Science, and the Cochrane Library. The search strategy was formulated based on a combination of controlled descriptors and/or keywords related to the topic and a manual search was conducted of the reference lists from the initially selected studies to identify other eligible studies. The studies were thoroughly screened using the following inclusion criteria: older adults, spatiotemporal gait characteristics, and proinflammatory markers. A meta-analysis was not performed due to the heterogeneity of the studies, and the results were narratively synthesized. Due to the clinical and methodological heterogeneity, the studies were combined in a narrative synthesis, grouped by the type of biomarkers evaluated. A standardized data extraction form was used to collect the following methodological outcome variables from each of the included studies: author, year, population, age, sample size, spatiotemporal gait parameters such as gait velocity, and proinflammatory markers such as TNF-α, high sensitivity C-reactive (CRP) proteins, and IL-6. We included 21 out of 51 studies in our review, which examined the association between inflammatory biomarkers and gait impairment. This review highlights the role of TNF-α, CRP, and IL-6 in gait impairment. Biomarkers play an important role in the decision-making process, and IL-6 can be an effective biomarker in establishing the diagnosis of slow gait speed. Further longitudinal research is needed to establish the use of molecular biomarkers in monitoring gait impairment.

The Gompertz Law emerges naturally from the inter-dependencies between sub-components in complex organisms

Understanding and facilitating healthy aging has become a major goal in medical research and it is becoming increasingly acknowledged that there is a need for understanding the aging phenotype as a whole rather than focusing on individual factors. Here, we provide a universal explanation for the emergence of Gompertzian mortality patterns using a systems approach to describe aging in complex organisms that consist of many inter-dependent subsystems. Our model relates to the Sufficient-Component Cause Model, widely used within the field of epidemiology, and we show that including inter-dependencies between subsystems and modeling the temporal evolution of subsystem failure results in Gompertizan mortality on the population level. Our model also provides temporal trajectories of mortality-risk for the individual. These results may give insight into understanding how biological age evolves stochastically within the individual, and how this in turn leads to a natural heterogeneity of biological age in a population.

Understanding Alzheimer's disease in the context of aging: Findings from applications of stochastic process models to the Health and Retirement Study

There is growing literature on applications of biodemographic models, including stochastic process models (SPM), to studying regularities of age dynamics of biological variables in relation to aging and disease development. Alzheimer's disease (AD) is especially good candidate for SPM applications because age is a major risk factor for this heterogeneous complex trait. However, such applications are largely lacking. This paper starts filling this gap and applies SPM to data on onset of AD and longitudinal trajectories of body mass index (BMI) constructed from the Health and Retirement Study surveys and Medicare-linked data. We found that APOE e4 carriers are less robust to deviations of trajectories of BMI from the optimal levels compared to non-carriers. We also observed age-related decline in adaptive response (resilience) related to deviations of BMI from optimal levels as well as APOE- and age-dependence in other components related to variability of BMI around the mean allostatic values and accumulation of allostatic load. SPM applications thus allow revealing novel connections between age, genetic factors and longitudinal trajectories of risk factors in the context of AD and aging creating new opportunities for understanding AD development, forecasting trends in AD incidence and prevalence in populations, and studying disparities in those.

Life Course Predictors of Young Men Surviving to Age 90 in a Cohort Study: The Manitoba Follow-up Study

The purpose of this study was to identify factors at various time points in life that are associated with surviving to age 90. Data from men enrolled in a cohort study since 1948 were considered in 12-year intervals. Logistic regression models were constructed with the outcome of surviving to age 90. Factors were: childhood illness, blood pressure (BP), body mass index (BMI), chronic diseases, and electrocardiogram (ECG) findings. After 1996, the Short Form-36 was added. A total of 3,976 men were born in 1928 or earlier, and hence by the end of our study window in 2018, each had the opportunity of surviving to age 90. Of these, 721 did live to beyond his 90th birthday.The factors in 1948 which predicted surviving were: lower diastolic BP, lower BMI, and not smoking. In 1960, these factors were: lower BP, lower BMI, not smoking, and no major ECG changes. In 1972, these factors were lower BP, not smoking, and fewer disease states. In 1984, these factors were lower systolic BP, not smoking, ECG changes, and fewer disease states. In 1996, the factors were fewer disease states and higher physical and mental health functioning. In 2008, only higher physical functioning predicted survival to the age of 90. In young adulthood, risk factors are important predictors of surviving to age 90; in mid-life, chronic illnesses emerge, and in later life, functional status becomes predominant.

Muddy (silty-sand) beaches in semi-arid regions attenuate the contaminants flowing into the sea as a submarine groundwater discharge

Urbanized coastal areas are well-recognized hotspots for the contaminant-enriched groundwater discharge, influencing sensitive coastal ecosystems. The present study investigates how muddy beaches in the semi-arid region alter the contaminant flux flowing into the sea using submarine groundwater discharge (SGD) estimation and hydrogeochemical analysis of coastal waters (groundwater, porewater, and seawater). Fresh SGD carries contaminants such as nutrients and trace metals in the coastal ecosystem, causing increased vulnerability towards eutrophication, harmful algal blooms, and human health. We found that SGD reaching the coast carries immense nutrient flux (155.6 mmol NO₃^- · day^-1; 35 mmol P · day^-1 and 12.4 mmol DSi · day^-1) and trace metal load ranging from 0.1 to 14.9 mmol · day^-1. The nutrient fluxes were higher in the upper saline plume compared to the lower plume. The muddy beach attenuates the nutrients in varying percentages of 9.7 to 22% of NO₃^-, 1.9 to 25.5% of P due to denitrification and phosphorus absorption, and also caused 19.6% reduction of SO₄^2-. The reduction in SO₄^2- leads to the formation of sulfide (HS^-) that promotes the metal precipitation, resulting in the removal of Pb and Cu. This attenuation of nutrients leads to a change in the nutrient ratio (N/P = 7-11) approaching the Redfield ratio, implying the vulnerability of algal bloom at the Dehri beach. Overall, the muddy beach can serve as a natural biogeochemical reactor as it attenuates the nutrient and serves as a source for certain trace metals (Fe, Mn, Zn, and Ni), altering the composition of SGD. Probably this is the first study that emphasizes the attenuation of trace metals in the muddy beaches of a semi-arid region.

Diurnal dynamic range as index of dysregulation of system dynamics. A cortisol examplar using data from the Study of Midlife in the United States

We discuss the importance of including measures of dysregulated system dynamics in the operationalization of allostatic load. The concept of allostatic load, as originally proposed by McEwen and Stellar, included dysregulation not only in the resting state of physiological systems, but also in system dynamics. We describe previous work on cortisol diurnal dynamic range (peak to nadir spread) as an index of the health of the hypothalamic-pituitary-adrenal axis, with compression of dynamic range being a marker of dysregulation. In particular, we review the evidence for a) diurnal dynamic range compression in people from disadvantaged backgrounds, b) cross-sectional association of cortisol diurnal dynamic range compression with dysregulation in other systems' resting states, and c) cross-sectional association of cortisol diurnal dynamic range compression with lower scores on cognitive testing. Then, we present new data from the Study of Midlife in the United States (MIDUS) on longitudinal associations of cortisol dynamic range compression with subsequent cognitive decline and all-cause mortality. Briefly, each standard deviation decrement in cortisol diurnal dynamic range is associated with adjusted mortality hazard ratio of 1.35 (95% confidence interval: 1.19, 1.54). Among those who scored at median or lower in executive functioning at baseline and survive, each standard deviation decrement in cortisol dynamic range is associated with 1% greater decline in executive functioning over a decade (95% confidence interval: 0.4%, 2.0%). We conclude that including measures of system dynamics like diurnal dynamic range in the next generation of allostatic load measurement will likely advance understanding of the cumulative physiological burden of chronic stress and life experiences, and improve the prediction of future health consequences.

Physiological Dysregulation, Frailty, and Impacts on Adverse Health and Functional Outcomes

Background: Multi-system physiological dysregulation (PD) may represent a biological endo-phenotype of clinical frailty. We investigated the co-occurrence of PD with physical frailty and its contributions to the known impact of frailty on adverse health outcomes.

Methods: Data of 2,725 participants from the Singapore Longitudinal Aging Studies (SLAS-2), included baseline measures of physical frailty and PD derived from Mahalanobis distance (Dm) value of 23 blood biomarkers. We analyzed their concurrent association and their impacts on 9-year mortality, MMSE cognition, GDS depression, number of medications, disability, and hospitalization at baseline and follow up (mean 4.5 years).

Results: Global PD (Log₁₀Dm, mean = 1.24, SD = 0.24) was significantly but weakly associated with pre-frailty-and-frailty. Controlling for age, sex and education, pre-frailty-and-frailty (HR = 2.12, 95% CI = 1.51–3.00) and PD (HR = 3.88, 95% CI = 2.15–6.98) predicted mortality. Together in the same model, mortality HR associated with pre-frailty-and-frailty (HR = 1.83, 95% CI = 1.22–2.73) and PD (HR = 3.06, 95% CI = 1.60–5.85) were reduced after additionally adding global PD to the prediction model. The predictive accuracy for mortality were both approximately the same (PD: AUC = 0.62, frailty: AUC = 0.64), but AUC was significantly increased to 0.68 when combined (p < 0.001). Taken into account in the same model, frailty remained significantly associated with all health and functional outcomes, and PD was significantly associated with only MMSE, disability and medications used. In secondary analyses, there were mixed associations of system-specific PDs with frailty and different adverse outcomes.

Conclusions: Co-existing PD and physical frailty independently predict mortality and functional and health outcomes, with increased predictive accuracy when combined. PD appears to be a valid representation of a biological endo-phenotype of frailty, and the potential utility of such subclinical measures of frailty could be further studied.

Effects of Sex and Physical Activity Level on Serum Biomarker-Based Physiological Dysregulation: The Impact to Predict Frailty and Mortality in the Quebec NuAge Cohort

BACKGROUND

Age-related changes in biological processes such as physiological dysregulation (the progressive loss of homeostatic capacity) vary considerably among older adults and may influence health profiles in late life. These differences could be related, at least in part, to the impact of intrinsic and extrinsic factors such as sex and physical activity level (PAL).

OBJECTIVES

The objectives of this study were (1) to assess the magnitude and rate of changes in physiologi-cal dysregulation in men and women according to PAL and (2) to determine whether/how sex and PAL mediate the apparent influence of physiological dysregulation on health outcomes (frailty and mortality).

METHODS

We used data on 1,754 community-dwelling older adults (age = 74.4 ± 4.2 years; women = 52.4%) of the Quebec NuAge cohort study. Physiological dysregulation was calculated based on Mahalanobis distance of 31 biomarkers regrouped into 5 systems: oxygen transport, liver/kidney function, leukopoiesis, micronutrients, and lipids.

RESULTS

As expected, mean physiological dysregulation significantly increased with age while PAL decreased. For the same age and PAL, men showed higher levels of physiological dysregulation globally in 3 systems: oxygen transport, liver/kidney function, and leukopoiesis. Men also showed faster global physiological dysregulation in the liver/kidney and leukopoiesis systems. Overall, high PAL was associated with lower level and slower rate of change of physiological dysregulation. Finally, while mortality and frailty risk significantly increased with physiological dysregulation, there was no evidence for differences in these effects between sexes and PAL.

CONCLUSION

Our results showed that both sex and PAL have a significant effect on physiological dysregulation levels and rates of change. Also, although a higher PAL was associated with lower level and slower rate of change of physiological dysregulation, there was no evidence that PAL attenuates the effect of physiological dysregulation on subsequent declines in health at the end of life. Substantial work remains to understand how modifiable behaviors impact the relationship between physiological dysregulation, frailty, and mortality in men and women.

Exploring the Utility of Hair Endocannabinoids for Monitoring Homeostasis in Bonobos

AbstractQuantifying physiological challenges has gained increasing importance in evolutionary biology, behavioral physiology, and conservation. One matrix that is particularly useful for obtaining long-term records of physiological changes in mammals is hair. Potential markers are components of the endocannabinoid (EC) system, which regulates homeostasis of the brain as well as the endocrine and immune systems. Here, we present results from the first study to measure ECs (anandamide [AEA], 2-archidonyl glycerol [2-AG]) and EC-like compounds (N-palmitoylethanolamine [PEA], N-oleoylethanolamine [OEA], N-stearoylethanolamine [SEA]) in the hair of a nonhuman primate. We found that AEA, SEA, PEA, and OEA can be reliably measured in hair samples. When comparing the measurements of hair from different body parts, we found that variations of some analytes suggest that hair location is likely to affect results. For changes in health status, measurements of ECs and EC-like compounds reflected differences at both intra- and interindividual levels. We concluded that the EC system potentially provides novel tools to assess well-being, health status, and metabolic stress-not only in the hair of humans but also in that of domestic and wild animals. Measuring changes in ECs and EC-like compounds may improve the long-term monitoring of health status in captive and wild primates and may serve as a useful measure in animal welfare programs.

The potential for complex computational models of aging

The gradual accumulation of damage and dysregulation during the aging of living organisms can be quantified. Even so, the aging process is complex and has multiple interacting physiological scales - from the molecular to cellular to whole tissues. In the face of this complexity, we can significantly advance our understanding of aging with the use of computational models that simulate realistic individual trajectories of health as well as mortality. To do so, they must be systems-level models that incorporate interactions between measurable aspects of age-associated changes. To incorporate individual variability in the aging process, models must be stochastic. To be useful they should also be predictive, and so must be fit or parameterized by data from large populations of aging individuals. In this perspective, we outline where we have been, where we are, and where we hope to go with such computational models of aging. Our focus is on data-driven systems-level models, and on their great potential in aging research.

Multi-system physiological dysregulation and ageing in a subsistence population

Humans have the longest post-reproductive lifespans and lowest rates of actuarial ageing among primates. Understanding the links between slow actuarial ageing and physiological change is critical for improving the human 'healthspan'. Physiological dysregulation may be a key feature of ageing in industrialized populations with high burdens of chronic 'diseases of civilization', but little is known about age trajectories of physiological condition in subsistence populations with limited access to public health infrastructure. To better characterize human physiological dysregulation, we examined age trajectories of 40 biomarkers spanning the immune (n = 13 biomarkers), cardiometabolic (n = 14), musculoskeletal (n = 6) and other (n = 7) systems among Tsimane forager-horticulturalists of the Bolivian Amazon using mixed cross-sectional and longitudinal data (n = 22 115 observations). We characterized age-related changes using a multi-system statistical index of physiological dysregulation (Mahalanobis distance; Dm) that increases with age in both humans and other primates. Although individual biomarkers showed varied age profiles, we found a robust increase in age-related dysregulation for Tsimane (β = 0.17-0.18) that was marginally faster than that reported for an industrialized Western sample (β = 0.14-0.16), but slower than that of other non-human primates. We found minimal sex differences in the pace or average level of dysregulation for Tsimane. Our findings highlight some conserved patterns of physiological dysregulation in humans, consistent with the notion that somatic ageing exhibits species-typical patterns, despite cross-cultural variation in environmental exposures, lifestyles and mortality. This article is part of the theme issue 'Evolution of the primate ageing process'.

Implementation of a flexible visiting policy in intensive care unit: A randomized clinical trial

BACKGROUND

Visitation is one of the most critical needs of patients in intensive care units (ICUs). Clinicians should enable a more flexible visitation policy to prevent the adverse effects of a restricted approach.

AIM AND OBJECTIVES

This study aimed at investigating the relationship between increasing visitation time and patients' physiological parameters in intensive units. Additionally, nurses' beliefs and attitudes towards visiting is examined in this research.

DESIGN

Parallel randomized clinical trial.

METHODS

This study was conducted in four general medical ICUs in Iran. In total, 60 patients and 57 nurses participated in this study. The patients' physiological parameters were measured at particular times in both groups. Moreover, the nurses responded to the questionnaire on beliefs and attitudes towards visitation in intensive units before and after the intervention.

RESULTS

Among all the physiological parameters, systolic and diastolic blood pressure and heart rate had a significant decrease within the normal range 10 and 30 minutes after visiting in the intervention group (P < .05), and nurses' beliefs and attitudes score were at the middle of the survey scoring range (0-120). However, no significant difference was found between nurses' belief and attitude score and intervention in any groups (P > .05).

CONCLUSION

The findings indicate that increasing visitation times can lead to a positive effect on the stability and balance of patients' physiological parameters.

RELEVANCE TO CLINICAL PRACTICE

A more flexible visiting policy can provide a therapeutic environment where not only patients' physiological disorders are minimised as much as possible but also a higher level of patients' health and satisfaction are reached.

Functional aging in health and heart failure: the COmPLETE Study

BACKGROUND

Cardiovascular (CV) diseases including heart failure are the leading causes of morbidity, with age being the primary risk factor. The combination of age-related organic functional impairment and reduced physical fitness can drastically impact an individual's healthspan. One's lifespan can potentially be prolonged by the preservation or improvement of physical fitness. However, it remains unclear as to which biomarkers are most suitable for distinguishing between healthy aging and the impaired organ function associated with heart failure. Therefore, a comprehensive assessment of the components of physical fitness and CV function will be performed to identify the most important factors contributing to aging in relation to both health and disease.

METHODS

This cross-sectional investigation will consist of two parts: COmPLETE-Health (C-Health) and COmPLETE-Heart (C-Heart). C-Health will examine the aging trajectories of physical fitness components and CV properties in a healthy population sample aged between 20 and 100 years (n = 490). Separately, C-Heart will assess the same markers in patients at different stages of chronic heart failure (n = 80). The primary outcome to determine the difference between C-Health and C-Heart will be cardiorespiratory fitness as measured by cardiopulmonary exercise testing on a bicycle ergometer. Secondary outcomes will include walking speed, balance, isometric strength, peak power, and handgrip strength. Physical activity as a behavioural component will be assessed objectively via accelerometry. Further, CV assessments will include pulse wave velocity; retinal, arterial, and venous diameters; brachial and retinal arterial endothelial function; carotid intima-media thickness; and systolic and diastolic function. The health distances for C-Health and C-Heart will be calculated using the methodology based on statistical (Mahalanobis) distance applied to measurements of quantitative biomarkers.

DISCUSSION

This research seeks to identify physical fitness and CV biomarkers that best resemble underlying CV risk with age. Further, it will examine which physical fitness markers are impaired most in heart failure. The presented integrative approach could define new recommendations for diagnostic guidance in aging. Ultimately, this study is expected to offer a better understanding of which functional characteristics should be specifically targeted in primary and secondary prevention to achieve an optimal healthspan.

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.

Examination of the Dimensions of Biological Age

The concept of biological age has been used more and more frequently in aging research in attempts to measure the progress of the biological aging process as opposed to the simple passage of time. Several approaches to quantify biological age have been utilized, including the use of biomarkers in the form of serum analytes, epigenetic markers, and deficit or frailty indices. Among these methods, the deficit index possesses a theoretical basis grounded in systems biology by incorporating networks, with their emergent properties, to describe the complex aging system. Application of the deficit index in human aging studies points to the increased energetic demands posed by an aging system that is losing integration. Different aspects of mitochondrial function appear to be responsible in males and females. The gut microbiome loses complexity in tandem with the host, as biological age increases, with likely impact on host metabolism and immunity. Specific DNA methylation changes are associated with biological age. They suggest declining connectivity within the aging network, at the cellular level. The deficit/frailty index may account for at least part of the departure at older ages of the observed mortality in the population from the exponential increase modeled by the Gompertz equation.

"Physiological Dysregulation" as a Promising Measure of Robustness and Resilience in Studies of Aging and a New Indicator of Preclinical Disease

Recently suggested novel implementation of the statistical distance measure (DM) for evaluating "physiological dysregulation" (PD) in aging individuals (based on measuring deviations of multiple biomarkers from baseline or normal physiological states) allows reducing high-dimensional biomarker space into a single PD estimate. Here we constructed DM using biomarker profiles from FRAMCOHORT (Framingham Heart Study) and CHS (Cardiovascular Health Study) Research Materials obtained from the NHLBI Biologic Specimen and Data Repository Information Coordinating Center, and estimated effect of PD on total survival, onset of unhealthy life (proxy for "robustness") and survival following the onset of unhealthy life (proxy for "resilience"). We investigated relationships between PD and declines in stress resistance and adaptive capacity not directly observed in data. PD was more strongly associated with the onset of unhealthy life than with survival after disease suggesting that declines in robustness and resilience with age may have overlapping as well as distinct mechanisms. We conclude that multiple deviations of physiological markers from their normal states (reflected in higher PD) may contribute to increased vulnerability to many diseases and precede their clinical manifestation. This supports potential use of PD in health care as a preclinical indicator of transition from healthy to unhealthy state.

Causal effects of cardiovascular risk factors on onset of major age-related diseases: A time-to-event Mendelian randomization study

BACKGROUNDS

Elucidating the causal effects of common intermediate risk factors on the onset of age-related diseases is indispensable for developing prevention and intervention procedures.

METHODS

We conducted two-stage time-to-event Mendelian randomization meta-analyses combining five large-scale longitudinal cohorts to investigate dynamic causal effects of cardiovascular disease risk factors including body mass index (BMI), systolic blood pressure (SBP), and lipids on the age-at-onset of age-related diseases. We constructed weighted polygenic scores based on genetic markers from previously reported genome-wide association studies as instrumental variables to estimate the causal effects. To avoid false positive due to potential pleiotropic effects of the genetic markers, we performed a leave-one-out sensitivity analysis and an MR-Egger sensitivity analysis that we expanded in the survival context.

RESULTS

Our results show that elevated BMI increases the absolute risk of type 2 diabetes (T2D) (p=7.68e-04), heart failure (p=9.03e-03), and cardiovascular diseases (CVD) (p=1.69e-03) and the causal effects start at different ages. A significant association between BMI and the risk of stroke is observed; however, the sensitivity analyses suggest that the association is attributed to the potential pleiotropic effects of rs2867125 and rs1558902. Raised SBP levels are significantly associated with the development of atrial fibrillation (p=6.42e-03). Low-density lipoprotein cholesterol (LDL-C) levels are inversely associated with the age-at-onset of T2D (p=1.05e-02). In addition, LDL-C and triglycerides are inversely associated with the risks of cancer and T2D, respectively. Nevertheless, the sensitivity analyses suggest that these associations are probably due to pleiotropic effects of several single-nucleotide polymorphisms including rs4970834 and rs1260326.

CONCLUSIONS

Our results highlight the involvement of BMI in the development of multiple age-related diseases. Some observed causal associations can attribute to pleiotropic effects of some genetic variations. These findings have important implications in unraveling causal effects of common risk factors on age-related diseases and guiding effective intervention strategies to reduce the incidence of these diseases.

Air Pollution Stress and the Aging Phenotype: The Telomere Connection

Aging is a complex physiological phenomenon. The question why some subjects grow old while remaining free from disease whereas others prematurely die remains largely unanswered. We focus here on the role of air pollution in biological aging. Hallmarks of aging can be grouped into three main categories: genomic instability, telomere attrition, and epigenetic alterations leading to altered mitochondrial function and cellular senescence. At birth, the initial telomere length of a person is largely determined by environmental factors. Telomere length shortens with each cell division and exposure to air pollution as well as low residential greens space exposure is associated with shorter telomere length. Recent studies show that the estimated effects of particulate air pollution exposure on the telomere mitochondrial axis of aging may play an important role in chronic health effects of air pollution. The exposome encompasses all exposures over an entire life. As telomeres can be considered as the cellular memories of exposure to oxidative stress and inflammation, telomere maintenance may be a proxy for assessing the "exposome". If telomeres are causally related to the aging phenotype and environmental air pollution is an important determinant of telomere length, this might provide new avenues for future preventive strategies.

Systems healthcare: a holistic paradigm for tomorrow

Systems healthcare is a holistic approach to health premised on systems biology and medicine. The approach integrates data from molecules, cells, organs, the individual, families, communities, and the natural and man-made environment. Both extrinsic and intrinsic influences constantly challenge the biological networks associated with wellness. Such influences may dysregulate networks and allow pathobiology to evolve, resulting in early clinical presentation that requires astute assessment and timely intervention for successful mitigation. Herein, we describe the components of relevant biological systems and the nature of progression from at-risk to manifest disease. We illustrate the systems approach by examining two relevant clinical examples: Alzheimer's and cardiovascular diseases. The implications of systems healthcare management are examined through the lens of economics, ethics, policy and the law. Finally, we propose the need to develop new educational paradigms to enhance the training of the health professional in an era of systems medicine.

Modeling Human Mortality from All Diseases in the Five Most Populated Countries of the European Union

Age affects mortality from diseases differently than it affects mortality from external causes, such as accidents. Exclusion of the latter leads to the "all-diseases" category. The age trajectories of mortality from all diseases are studied in the five most populated countries of the EU, and the shape of these 156 age trajectories is investigated in detail. The arithmetic mean of ages where mortality reaches a minimal value is 8.47 years with a 95% confidence interval of [8.08, 8.85] years. Two simple deterministic models fit the age trajectories on the two sides of the mortality minimum. The inverse relationship is valid in all cases prior to this mortality minimum and death rates exactly decreased to three thousandths of its original size during the first 3000 days. After the mortality minimum, the standard Gompertz model fits the data in 63 cases, and the Gompertz model extended by a small quadratic element fits the remaining 93 cases. This analysis indicates that the exponential increase begins before the age of 15 years and that it is overshadowed by non-biological causes. Therefore, the existence of a mechanism switching that would explain the exponential increase in mortality after the age of 35 years is unlikely.

PROX1 gene CC genotype as a major determinant of early onset of type 2 diabetes in slavic study participants from Action in Diabetes and Vascular Disease: Preterax and Diamicron MR Controlled Evaluation study

BACKGROUND

The prevalence of diabetic nephropathy varies according to ethnicity. Environmental as well as genetic factors contribute to the heterogeneity in the presentation of diabetic nephropathy. Our objective was to evaluate this heterogeneity within the Caucasian population.

METHODS

The geo-ethnic origin of the 3409 genotyped Caucasian type 2 diabetes (T2D) patients of Action in Diabetes and Vascular Disease: Preterax and Diamicron MR Controlled Evaluation was determined using principal component analysis. Genome-wide association studies analyses of age of onset of T2D were performed for geo-ethnic groups separately and combined.

RESULTS

The first principal component separated the Caucasian study participants into Slavic and Celtic ethnic origins. Age of onset of diabetes was significantly lower in Slavic patients (P = 7.3 × 10), whereas the prevalence of hypertension (P = 4.9 × 10) and albuminuria (5.1 × 10) were significantly higher. Age of onset of T2D and albuminuria appear to have an important genetic component as the values of these traits were also different between Slavic and Celtic individuals living in the same countries. Common and geo-ethnic-specific loci were found to be associated to age of onset of diabetes. Among the latter, the PROX1/PROX1-AS1 genes (rs340841) had the highest impact. Single-nucleotide polymorphism rs340841 CC genotype was associated with a 4.4 year earlier onset of T2D in Slavic patients living or not in countries with predominant Slavic populations.

CONCLUSION

These results reveal the presence of distinct genetic architectures between Caucasian ethnic groups that likely have clinical relevance, among them PROX1 gene is a strong candidate of early onset of diabetes with variations depending on ethnicity.

Circulating miRNAs and miRNA shuttles as biomarkers: Perspective trajectories of healthy and unhealthy aging

Human aging is a lifelong process characterized by a continuous trade-off between pro-and anti-inflammatory responses, where the best-adapted and/or remodeled genetic/epigenetic profile may develop a longevity phenotype. Centenarians and their offspring represent such a phenotype and their comparison to patients with age-related diseases (ARDs) is expected to maximize the chance to unravel the genetic makeup that better associates with healthy aging trajectories. Seemingly, such comparison is expected to allow the discovery of new biomarkers of longevity together with risk factor for the most common ARDs. MicroRNAs (miRNAs) and their shuttles (extracellular vesicles in particular) are currently conceived as those endowed with the strongest ability to provide information about the trajectories of healthy and unhealthy aging. We review the available data on miRNAs in aging and underpin the evidence suggesting that circulating miRNAs (and cognate shuttles), especially those involved in the regulation of inflammation (inflamma-miRs) may constitute biomarkers capable of reliably depicting healthy and unhealthy aging trajectories.

Absence of association between frailty index and survival in elderly Brazilians: the FIBRA Study

In Brazil, the frailty index has not been evaluated previously for its capacity to predict mortality in community-dwelling elderly. The objective of the current study was to evaluate the association between frailty index and mortality in the elderly. This was a prospective study consisting of data from the FIBRA Network-2008-2009 in Campinas, São Paulo State, with information on community-dwelling older adults from the urban area and through the Mortality Information System. Comparisons and statistical associations were performed with the following tests: Mann-Whitney, Kruskal-Wallis, chi-square, and Cox regression with 95% confidence intervals. A total of 689 older adults participated 72.1 ± 5.3 years), of whom 68.8% were women. The prevalence rate for frailty was 38.8%, compared to 51.6% for pre-frailty and 9.6% for fit elders; overall mean frailty index was higher in women. There was no association between frailty index and chronological age. Cox regression showed that the variables age HR: 1.10; 95%CI: 1.05-1.15) and gender HR: 0.57; 95%CI: 0.33-0.99) were significantly associated with mortality. No association was found between frailty index and mortality HR: 3.02; 95%CI: 0.24-37.64). Frailty index was not capable of predicting mortality in community-dwelling elderly Brazilians.

Aging, frailty and complex networks

When people age their mortality rate increases exponentially, following Gompertz's law. Even so, individuals do not die from old age. Instead, they accumulate age-related illnesses and conditions and so become increasingly vulnerable to death from various external and internal stressors. As a measure of such vulnerability, frailty can be quantified using the frailty index (FI). Larger values of the FI are strongly associated with mortality and other adverse health outcomes. This association, and the insensitivity of the FI to the particular health variables that are included in its construction, makes it a powerful, convenient, and increasingly popular integrative health measure. Still, little is known about why the FI works so well. Our group has recently developed a theoretical network model of health deficits to better understand how changes in health are captured by the FI. In our model, health-related variables are represented by the nodes of a complex network. The network has a scale-free shape or "topology": a few nodes have many connections with other nodes, whereas most nodes have few connections. These nodes can be in two states, either damaged or undamaged. Transitions between damaged and non-damaged states are governed by the stochastic environment of individual nodes. Changes in the degree of damage of connected nodes change the local environment and make further damage more likely. Our model shows how age-dependent acceleration of the FI and of mortality emerges, even without specifying an age-damage relationship or any other time-dependent parameter. We have also used our model to assess how informative individual deficits are with respect to mortality. We find that the information is larger for nodes that are well connected than for nodes that are not. The model supports the idea that aging occurs as an emergent phenomenon, and not as a result of age-specific programming. Instead, aging reflects how damage propagates through a complex network of interconnected elements.

stpm: an R package for stochastic process model

Background

The Stochastic Process Model (SPM) represents a general framework for modeling the joint evolution of repeatedly measured variables and time-to-event outcomes observed in longitudinal studies, i.e., SPM relates the stochastic dynamics of variables (e.g., physiological or biological measures) with the probabilities of end points (e.g., death or system failure). SPM is applicable for analyses of longitudinal data in many research areas; however, there are no publicly available software tools that implement this methodology.

Results

We developed an R package stpm for the SPM-methodology. The package estimates several versions of SPM currently available in the literature including discrete- and continuous-time multidimensional models and a one-dimensional model with time-dependent parameters. Also, the package provides tools for simulation and projection of individual trajectories and hazard functions.

Conclusion

In this paper, we present the first software implementation of the SPM-methodology by providing an R package stpm, which was verified through extensive simulation and validation studies. Future work includes further improvements of the model. Clinical and academic researchers will benefit from using the presented model and software. The R package stpm is available as open source software from the following links: https://cran.r-project.org/package=stpm(stable version) or https://github.com/izhbannikov/spm(developer version).

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1538-7) contains supplementary material, which is available to authorized users.

Onset of mortality increase with age and age trajectories of mortality from all diseases in the four Nordic countries

BACKGROUND

The answer to the question "At what age does aging begin?" is tightly related to the question "Where is the onset of mortality increase with age?" Age affects mortality rates from all diseases differently than it affects mortality rates from nonbiological causes. Mortality increase with age in adult populations has been modeled by many authors, and little attention has been given to mortality decrease with age after birth.

MATERIALS AND METHODS

Nonbiological causes are excluded, and the category "all diseases" is studied. It is analyzed in Denmark, Finland, Norway, and Sweden during the period 1994-2011, and all possible models are screened. Age trajectories of mortality are analyzed separately: before the age category where mortality reaches its minimal value and after the age category.

RESULTS

Resulting age trajectories from all diseases showed a strong minimum, which was hidden in total mortality. The inverse proportion between mortality and age fitted in 54 of 58 cases before mortality minimum. The Gompertz model with two parameters fitted as mortality increased with age in 17 of 58 cases after mortality minimum, and the Gompertz model with a small positive quadratic term fitted data in the remaining 41 cases. The mean age where mortality reached minimal value was 8 (95% confidence interval 7.05-8.95) years. The figures depict an age where the human population has a minimal risk of death from biological causes.

CONCLUSION

Inverse proportion and the Gompertz model fitted data on both sides of the mortality minimum, and three parameters determined the shape of the age-mortality trajectory. Life expectancy should be determined by the two standard Gompertz parameters and also by the single parameter in the model c/x. All-disease mortality represents an alternative tool to study the impact of age. All results are based on published data.

A statistical model to describe longitudinal and correlated metabolic risk factors: the Whitehall II prospective study

Background

Novel epidemiology models are required to link correlated variables over time, especially haemoglobin A1c (HbA1c) and body mass index (BMI) for diabetes prevention policy analysis. This article develops an epidemiology model to correlate metabolic risk factor trajectories.

Method

BMI, fasting plasma glucose, 2-h glucose, HbA1c, systolic blood pressure, total cholesterol and high density lipoprotein (HDL) cholesterol were analysed over 16 years from 8150 participants of the Whitehall II prospective cohort study. Latent growth curve modelling was employed to simultaneously estimate trajectories for multiple metabolic risk factors allowing for variation between individuals. A simulation model compared simulated outcomes with the observed data.

Results

The model identified that the change in BMI was associated with changes in glycaemia, total cholesterol and systolic blood pressure. The statistical analysis quantified associations among the longitudinal risk factor trajectories. Growth in latent glycaemia was positively correlated with systolic blood pressure and negatively correlated with HDL cholesterol. The goodness-of-fit analysis indicates reasonable fit to the data.

Conclusions

This is the first statistical model that estimates trajectories of metabolic risk factors simultaneously for diabetes to predict joint correlated risk factor trajectories. This can inform comparisons of the effectiveness and cost-effectiveness of preventive interventions, which aim to modify metabolic risk factors.

Age and Sex Distributions of Age-Related Biomarker Values in Healthy Older Adults from the Long Life Family Study

OBJECTIVES

To determine reference values for laboratory tests in individuals aged 85 and older.

DESIGN

Cross-sectional cohort study.

SETTING

International.

PARTICIPANTS

Long Life Family Study (LLFS) participants (N~5,000, age: range 25-110, median 67, 45% male).

MEASUREMENTS

Serum biomarkers were selected based on association with aging-related diseases and included complete blood count, lipids (triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, total cholesterol), 25-hydroxyvitamin D2 and D3, vitamin D epi-isomer, diabetes mellitus-related biomarkers (adiponectin, insulin, insulin-like growth factor 1, glucose, glycosylated hemoglobin, soluble receptor for advanced glycation endproduct), kidney disease-related biomarkers (albumin, creatinine, cystatin), endocrine biomarkers (dehydroepiandrosterone, sex-hormone binding globulin, testosterone), markers of inflammation (interleukin 6, high-sensitivity C-reactive protein, N-terminal pro b-type natriuretic peptide), ferritin, and transferrin.

RESULTS

Of 38 measured biomarkers, 34 were significantly correlated with age. Summary statistics were generated for all biomarkers according to sex and 5-year age increments from 50 and up after excluding participants with diseases and treatments that were associated with biomarkers. A biomarker data set was also generated that will be useful for other investigators seeking to compare biomarker levels between studies.

CONCLUSION

Levels of several biomarkers change with older age in healthy individuals. The descriptive statistics identified herein will be useful in future studies and, if replicated in additional studies, might also become useful in clinical practice. The availability of the reference data set will facilitate appropriate calibration of biomarkers measured in different laboratories.

How Genes Modulate Patterns of Aging-Related Changes on the Way to 100: Biodemographic Models and Methods in Genetic Analyses of Longitudinal Data

BACKGROUND AND OBJECTIVE

To clarify mechanisms of genetic regulation of human aging and longevity traits, a number of genome-wide association studies (GWAS) of these traits have been performed. However, the results of these analyses did not meet expectations of the researchers. Most detected genetic associations have not reached a genome-wide level of statistical significance, and suffered from the lack of replication in the studies of independent populations. The reasons for slow progress in this research area include low efficiency of statistical methods used in data analyses, genetic heterogeneity of aging and longevity related traits, possibility of pleiotropic (e.g., age dependent) effects of genetic variants on such traits, underestimation of the effects of (i) mortality selection in genetically heterogeneous cohorts, (ii) external factors and differences in genetic backgrounds of individuals in the populations under study, the weakness of conceptual biological framework that does not fully account for above mentioned factors. One more limitation of conducted studies is that they did not fully realize the potential of longitudinal data that allow for evaluating how genetic influences on life span are mediated by physiological variables and other biomarkers during the life course. The objective of this paper is to address these issues.

DATA AND METHODS

We performed GWAS of human life span using different subsets of data from the original Framingham Heart Study cohort corresponding to different quality control (QC) procedures and used one subset of selected genetic variants for further analyses. We used simulation study to show that approach to combining data improves the quality of GWAS. We used FHS longitudinal data to compare average age trajectories of physiological variables in carriers and non-carriers of selected genetic variants. We used stochastic process model of human mortality and aging to investigate genetic influence on hidden biomarkers of aging and on dynamic interaction between aging and longevity. We investigated properties of genes related to selected variants and their roles in signaling and metabolic pathways.

RESULTS

We showed that the use of different QC procedures results in different sets of genetic variants associated with life span. We selected 24 genetic variants negatively associated with life span. We showed that the joint analyses of genetic data at the time of bio-specimen collection and follow up data substantially improved significance of associations of selected 24 SNPs with life span. We also showed that aging related changes in physiological variables and in hidden biomarkers of aging differ for the groups of carriers and non-carriers of selected variants.

CONCLUSIONS

. The results of these analyses demonstrated benefits of using biodemographic models and methods in genetic association studies of these traits. Our findings showed that the absence of a large number of genetic variants with deleterious effects may make substantial contribution to exceptional longevity. These effects are dynamically mediated by a number of physiological variables and hidden biomarkers of aging. The results of these research demonstrated benefits of using integrative statistical models of mortality risks in genetic studies of human aging and longevity.

Dynamics of biomarkers in relation to aging and mortality

Contemporary longitudinal studies collect repeated measurements of biomarkers allowing one to analyze their dynamics in relation to mortality, morbidity, or other health-related outcomes. Rich and diverse data collected in such studies provide opportunities to investigate how various socio-economic, demographic, behavioral and other variables can interact with biological and genetic factors to produce differential rates of aging in individuals. In this paper, we review some recent publications investigating dynamics of biomarkers in relation to mortality, which use single biomarkers as well as cumulative measures combining information from multiple biomarkers. We also discuss the analytical approach, the stochastic process models, which conceptualizes several aging-related mechanisms in the structure of the model and allows evaluating "hidden" characteristics of aging-related changes indirectly from available longitudinal data on biomarkers and follow-up on mortality or onset of diseases taking into account other relevant factors (both genetic and non-genetic). We also discuss an extension of the approach, which considers ranges of "optimal values" of biomarkers rather than a single optimal value as in the original model. We discuss practical applications of the approach to single biomarkers and cumulative measures highlighting that the potential of applications to cumulative measures is still largely underused.

Single parameter of inverse proportion between mortality and age could determine all mortality indicators in the first year of life

Mortality increase with age in adult population has been studied and modeled by many authors, but relatively little attention has been given to mortality decrease with age after birth. Data split in more detailed age categories can newly test mortality decrease with age. Age trajectories of mortality are studied in 20 age categories in the specific age interval 1-365 days. Four basic models mentioned in literature are tested here. The linear model and the linear model with the specific slope -1 in the log-log scale represent the most successful formalism. Mortality indicators describing the first year could be determined by a single parameter of the model with slope -1 in the log-log scale. All conclusions are based on published data which are presented as a supplement.

Geroscience approaches to increase healthspan and slow aging

For decades, researchers in the biology of aging have focused on defining mechanisms that modulate aging by primarily studying a single metric, sometimes described as the "gold standard" lifespan. Increasingly, geroscience research is turning towards defining functional domains of aging such as the cardiovascular system, skeletal integrity, and metabolic health as being a more direct route to understand why tissues decline in function with age. Each model used in aging research has strengths and weaknesses, yet we know surprisingly little about how critical tissues decline in health with increasing age. Here I discuss popular model systems used in geroscience research and their utility as possible tools in preclinical studies in aging.

Assessment of health status by molecular measures in adults ranging from middle-aged to old: Ready for clinical use?

In addition to measures already used in clinical practice, molecular measures have been proposed to assess health status, but these have not yet been introduced into clinical practice. We aimed to test the association of functional capacity measures used in current practice and molecular measures with age and health status. The cohort consisted of 178 middle-aged to old participants of the Leiden Longevity Study (range 42-82years). We tested associations between functional capacity measures (physical tests: grip strength, 4-meter walk, chair stand test; cognitive tests: Stroop test, digit symbol substitution test and 15-picture learning test) with age and with cardiovascular or metabolic disease as a measure of the health status. These associations with age and health status were also tested for molecular measures (C reactive protein (CRP), numbers of senescent p16INK4a positive cells in the epidermis and dermis and putative immunosenescence (presence of CD57+ T cells)). All functional capacity measures were associated with age. CRP and epidermal p16INK4a positivity were also associated with age, but with smaller estimates. Grip strength and the Stroop test were associated with cardiovascular or metabolic disease, as was epidermal p16INK4a positivity. All associations with cardiovascular or metabolic disease attenuated when adjusting for age. In conclusion, in middle-aged to old persons, the molecular measures tested here were more weakly associated with age and health status than functional capacity measures. Whether these molecular measures associate more closely with health status in the elderly or in specific groups of patients needs to be explored further.

Dynamical network model for age-related health deficits and mortality

How long people live depends on their health, and how it changes with age. Individual health can be tracked by the accumulation of age-related health deficits. The fraction of age-related deficits is a simple quantitative measure of human aging. This quantitative frailty index (F) is as good as chronological age in predicting mortality. In this paper, we use a dynamical network model of deficits to explore the effects of interactions between deficits, deficit damage and repair processes, and the connection between the F and mortality. With our model, we qualitatively reproduce Gompertz's law of increasing human mortality with age, the broadening of the F distribution with age, the characteristic nonlinear increase of the F with age, and the increased mortality of high-frailty individuals. No explicit time-dependence in damage or repair rates is needed in our model. Instead, implicit time-dependence arises through deficit interactions-so that the average deficit damage rates increase, and deficit repair rates decrease, with age. We use a simple mortality criterion, where mortality occurs when the most connected node is damaged.

Predicting all-cause mortality from basic physiology in the Framingham Heart Study

Using longitudinal data from a cohort of 1349 participants in the Framingham Heart Study, we show that as early as 28-38 years of age, almost 10% of variation in future lifespan can be predicted from simple clinical parameters. Specifically, we found diastolic and systolic blood pressure, blood glucose, weight, and body mass index (BMI) to be relevant to lifespan. These and similar parameters have been well-characterized as risk factors in the relatively narrow context of cardiovascular disease and mortality in middle to old age. In contrast, we demonstrate here that such measures can be used to predict all-cause mortality from mid-adulthood onward. Further, we find that different clinical measurements are predictive of lifespan in different age regimes. Specifically, blood pressure and BMI are predictive of all-cause mortality from ages 35 to 60, while blood glucose is predictive from ages 57 to 73. Moreover, we find that several of these parameters are best considered as measures of a rate of 'damage accrual', such that total historical exposure, rather than current measurement values, is the most relevant risk factor (as with pack-years of cigarette smoking). In short, we show that simple physiological measurements have broader lifespan-predictive value than indicated by previous work and that incorporating information from multiple time points can significantly increase that predictive capacity. In general, our results apply equally to both men and women, although some differences exist.

Optimal Versus Realized Trajectories of Physiological Dysregulation in Aging and Their Relation to Sex-Specific Mortality Risk

While longitudinal changes in biomarker levels and their impact on health have been characterized for individual markers, little is known about how overall marker profiles may change during aging and affect mortality risk. We implemented the recently developed measure of physiological dysregulation based on the statistical distance of biomarker profiles in the framework of the stochastic process model of aging, using data on blood pressure, heart rate, cholesterol, glucose, hematocrit, body mass index, and mortality in the Framingham original cohort. This allowed us to evaluate how physiological dysregulation is related to different aging-related characteristics such as decline in stress resistance and adaptive capacity (which typically are not observed in the data and thus can be analyzed only indirectly), and, ultimately, to estimate how such dynamic relationships increase mortality risk with age. We found that physiological dysregulation increases with age; that increased dysregulation is associated with increased mortality, and increasingly so with age; and that, in most but not all cases, there is a decreasing ability to return quickly to baseline physiological state with age. We also revealed substantial sex differences in these processes, with women becoming dysregulated more quickly but with men showing a much greater sensitivity to dysregulation in terms of mortality risk.

Homeostatic dysregulation proceeds in parallel in multiple physiological systems

An increasing number of aging researchers believes that multi-system physiological dysregulation may be a key biological mechanism of aging, but evidence of this has been sparse. Here, we used biomarker data on nearly 33, 000 individuals from four large datasets to test for the presence of multi-system dysregulation. We grouped 37 biomarkers into six a priori groupings representing physiological systems (lipids, immune, oxygen transport, liver function, vitamins, and electrolytes), then calculated dysregulation scores for each system in each individual using statistical distance. Correlations among dysregulation levels across systems were generally weak but significant. Comparison of these results to dysregulation in arbitrary 'systems' generated by random grouping of biomarkers showed that a priori knowledge effectively distinguished the true systems in which dysregulation proceeds most independently. In other words, correlations among dysregulation levels were higher using arbitrary systems, indicating that only a priori systems identified distinct dysregulation processes. Additionally, dysregulation of most systems increased with age and significantly predicted multiple health outcomes including mortality, frailty, diabetes, heart disease, and number of chronic diseases. The six systems differed in how well their dysregulation scores predicted health outcomes and age. These findings present the first unequivocal demonstration of integrated multi-system physiological dysregulation during aging, demonstrating that physiological dysregulation proceeds neither as a single global process nor as a completely independent process in different systems, but rather as a set of system-specific processes likely linked through weak feedback effects. These processes--probably many more than the six measured here--are implicated in aging.

Complex systems dynamics in aging: new evidence, continuing questions

There have long been suggestions that aging is tightly linked to the complex dynamics of the physiological systems that maintain homeostasis, and in particular to dysregulation of regulatory networks of molecules. This review synthesizes recent work that is starting to provide evidence for the importance of such complex systems dynamics in aging. There is now clear evidence that physiological dysregulation—the gradual breakdown in the capacity of complex regulatory networks to maintain homeostasis—is an emergent property of these regulatory networks, and that it plays an important role in aging. It can be measured simply using small numbers of biomarkers. Additionally, there are indications of the importance during aging of emergent physiological processes, functional processes that cannot be easily understood through clear metabolic pathways, but can nonetheless be precisely quantified and studied. The overall role of such complex systems dynamics in aging remains an important open question, and to understand it future studies will need to distinguish and integrate related aspects of aging research, including multi-factorial theories of aging, systems biology, bioinformatics, network approaches, robustness, and loss of complexity.

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, D_M, that uses statistical distance to assess the degree to which an individual’s biomarker profile is normal versus aberrant. However, the sensitivity of D_M 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. D_Ms 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 D_Ms 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 D_M physiological dysregulation and as an emergent property of a complex system.

Biodemographic Analyses of Longitudinal Data on Aging, Health, and Longevity: Recent Advances and Future Perspectives

Biodemography became one of the most innovative and fastest growing areas in demography. This progress is fueled by the growing variability and amount of relevant data available for analyses as well as by methodological developments allowing for addressing new research questions using new approaches that can better utilize the potential of these data. In this review paper, we summarize recent methodological advances in biodemography and their diverse practical applications. Three major topics are covered: (1) computational approaches to reconstruction of age patterns of incidence of geriatric diseases and other characteristics such as recovery rates at the population level using Medicare claims data; (2) methodological advances in genetic and genomic biodemography and applications to research on genetic determinants of longevity and health; and (3) biodemographic models for joint analyses of time-to-event data and longitudinal measurements of biomarkers collected in longitudinal studies on aging. We discuss how such data and methodology can be used in a comprehensive prediction model for joint analyses of incomplete datasets that take into account the wide spectrum of factors affecting health and mortality transitions including genetic factors and hidden mechanisms of aging-related changes in physiological variables in their dynamic connection with health and survival.

Joint Analyses of Longitudinal and Time-to-Event Data in Research on Aging: Implications for Predicting Health and Survival

Longitudinal data on aging, health, and longevity provide a wealth of information to investigate different aspects of the processes of aging and development of diseases leading to death. Statistical methods aimed at analyses of time-to-event data jointly with longitudinal measurements became known as the "joint models" (JM). An important point to consider in analyses of such data in the context of studies on aging, health, and longevity is how to incorporate knowledge and theories about mechanisms and regularities of aging-related changes that accumulate in the research field into respective analytic approaches. In the absence of specific observations of longitudinal dynamics of relevant biomarkers manifesting such mechanisms and regularities, traditional approaches have a rather limited utility to estimate respective parameters that can be meaningfully interpreted from the biological point of view. A conceptual analytic framework for these purposes, the stochastic process model of aging (SPM), has been recently developed in the biodemographic literature. It incorporates available knowledge about mechanisms of aging-related changes, which may be hidden in the individual longitudinal trajectories of physiological variables and this allows for analyzing their indirect impact on risks of diseases and death. Despite, essentially, serving similar purposes, JM and SPM developed in parallel in different disciplines with very limited cross-referencing. Although there were several publications separately reviewing these two approaches, there were no publications presenting both these approaches in some detail. Here, we overview both approaches jointly and provide some new modifications of SPM. We discuss the use of stochastic processes to capture biological variation and heterogeneity in longitudinal patterns and important and promising (but still largely underused) applications of JM and SPM to predictions of individual and population mortality and health-related outcomes.

Trajectories of physiological dysregulation predicts mortality and health outcomes in a consistent manner across three populations

Mechanistic and evolutionary perspectives both agree that aging involves multiple integrated biochemical networks in the organism. In particular, the homeostatic physiological dysregulation (PD) hypothesis contends that aging is caused by the progressive breakdown of key regulatory processes. However, nothing is yet known about the specifics of how PD changes with age and affects health. Using a recently validated measure of PD involving the calculation of a multivariate distance (DM) from biomarker data, we show that PD trajectories predict mortality, frailty, and chronic diseases (cancer, cardiovascular diseases, and diabetes). Specifically, relative risks of outcomes associated with individual slopes in (i.e. rate of) dysregulation range 1.20-1.40 per unit slope. We confirm the results by replicating the analysis using two suites of biomarkers selected with markedly different criteria and, for mortality, in three longitudinal cohort-based studies. Overall, the consistence of effect sizes (direction and magnitude) across data sets, biomarker suites and outcomes suggests that the positive relationship between DM and health outcomes is a general phenomenon found across human populations. Therefore, the study of dysregulation trajectories should allow important insights into aging physiology and provide clinically meaningful predictors of outcomes.

Age trajectories of mortality from all diseases in the six most populated countries of the South America during the last decades

Age trajectories of total mortality represent an irreplaceable source of information about aging. In principle, age affects mortality from all diseases differently than it affects mortality from external causes. External causes (accidents) are excluded here from all causes, and the resultant category "all-diseases" is tested as a helpful tool to better understand the relationship between mortality and age. Age trajectories of all-diseases mortality are studied in the six most populated countries of the South America during 1996-2010. The numbers of deaths for specific causes of death are extracted from the database of WHO, where the ICD-10 revision is used. The all-diseases mortality shows a strong minimum, which is hidden in total mortality. Two simple deterministic models fit the age trajectories of all-diseases mortality. The inverse proportion between mortality and age fits the mortality decreases up to minimum value in all six countries. All previous models describing mortality decline after birth are discussed. Theoretical relationships are derived between the parameter in the first model and standard mortality indicators: Infant mortality, Neonatal mortality, and Postneonatal mortality. The Gompertz model extended with a small positive quadratic element fit the age trajectories of all-diseases mortality after the age of 10 years.

Biomarkers of healthy ageing: expectations and validation

The challenge of devising a set of biomarkers capable of measuring the ageing rate in human subjects was articulated long ago. In recent years, progress in the basic biology of ageing suggests the realistic possibility of preventive or restaurative interventions that may extend healthy lifespan in mammals including human subjects. Specifically, frailty is being increasingly recognised as a clinically relevant syndrome that may be therapeutically addressed. This greatly enhances the need for sensitive and specific biomarkers of healthy ageing that are validated in both experimental animals and, importantly, in human subjects over the whole age range. Here, we will discuss the present challenges and requirements for biomarker validation in human subjects. We propose the central requirements for a validated biomarker of healthy ageing as: (i) better predictive power than chronological age for multiple dimensions of ageing; (ii) identification of the age range in which the marker is informative; (iii) establishment of sensitivity/specificity as indicators of its predictive power at the level of the individual; (iv) minimisation of methodological variation between laboratories.

Robustness and aging--a systems-level perspective

The theory of robustness describes a system level property of evolutionary systems, which predicts tradeoffs of great interest for the systems biology of aging, such as accumulation of non-heritable damage, occurrence of fragilities and limitations in performance, optimized allocation of restricted resources and confined redundancies. According to the robustness paradigm cells and organisms evolved into a state of highly optimized tolerance (HOT), which provides robustness to common perturbations, but causes tradeoffs generally characterized as "robust yet fragile". This raises the question whether the ultimate cause of aging is more than a lack of adaptation, but an inherent fragility of complex evolutionary systems. Since robustness connects to evolutionary designs, consideration of this theory provides a deeper connection between evolutionary aspects of aging, mathematical models and experimental data. In this review several mechanisms influential for aging are re-evaluated in support of robustness tradeoffs. This includes asymmetric cell division improving performance and specialization with limited capacities to prevent and repair age-related damage, as well as feedback control mechanisms optimized to respond to acute stressors, but unable to halt nor revert aging. Improvement in robustness by increasing efficiencies through cellular redundancies in larger organisms alleviates some of the damaging effects of cellular specialization, which can be expressed in allometric relationships. The introduction of the robustness paradigm offers unique insights for aging research and provides novel opportunities for systems biology endeavors.

Biochemical markers of aging for longitudinal studies in humans

Much progress has been made in the past decades in unraveling the mechanisms that are responsible for aging. The discovery that particular gene mutations in experimental species such as yeast, flies, and nematodes are associated with longevity has led to many important insights into pathways that regulate aging processes. However, extrapolating laboratory findings in experimental species to knowledge that is valid for the complexity of human physiology remains a major challenge. Apart from the restricted experimental possibilities, studying aging in humans is further complicated by the development of various age-related diseases. The availability of a set of biomarkers that really reflect underlying aging processes would be of much value in disentangling age-associated pathology from specific aging mechanisms. In this review, we survey the literature to identify promising biochemical markers of aging, with a particular focus on using them in longitudinal studies of aging in humans that entail repeated measurements on easily obtainable material, such as blood samples. Our search strategy was a 2-pronged approach, one focused on general mechanisms of aging and one including studies on clinical biomarkers of age-related diseases.

A novel statistical approach shows evidence for multi-system physiological dysregulation during aging

Previous studies have identified many biomarkers that are associated with aging and related outcomes, but the relevance of these markers for underlying processes and their relationship to hypothesized systemic dysregulation is not clear. We address this gap by presenting a novel method for measuring dysregulation via the joint distribution of multiple biomarkers and assessing associations of dysregulation with age and mortality. Using longitudinal data from the Women's Health and Aging Study, we selected a 14-marker subset from 63 blood measures: those that diverged from the baseline population mean with age. For the 14 markers and all combinatorial sub-subsets we calculated a multivariate distance called the Mahalanobis distance (MHBD) for all observations, indicating how "strange" each individual's biomarker profile was relative to the baseline population mean. In most models, MHBD correlated positively with age, MHBD increased within individuals over time, and higher MHBD predicted higher risk of subsequent mortality. Predictive power increased as more variables were incorporated into the calculation of MHBD. Biomarkers from multiple systems were implicated. These results support hypotheses of simultaneous dysregulation in multiple systems and confirm the need for longitudinal, multivariate approaches to understanding biomarkers in aging.