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

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          Abstract

          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.

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          The concept of allostasis in biology and biomedicine.

          Living organisms have regular patterns and routines that involve obtaining food and carrying out life history stages such as breeding, migrating, molting, and hibernating. The acquisition, utilization, and storage of energy reserves (and other resources) are critical to lifetime reproductive success. There are also responses to predictable changes, e.g., seasonal, and unpredictable challenges, i.e., storms and natural disasters. Social organization in many populations provides advantages through cooperation in providing basic necessities and beneficial social support. But there are disadvantages owing to conflict in social hierarchies and competition for resources. Here we discuss the concept of allostasis, maintaining stability through change, as a fundamental process through which organisms actively adjust to both predictable and unpredictable events. Allostatic load refers to the cumulative cost to the body of allostasis, with allostatic overload being a state in which serious pathophysiology can occur. Using the balance between energy input and expenditure as the basis for applying the concept of allostasis, we propose two types of allostatic overload. Type 1 allostatic overload occurs when energy demand exceeds supply, resulting in activation of the emergency life history stage. This serves to direct the animal away from normal life history stages into a survival mode that decreases allostatic load and regains positive energy balance. The normal life cycle can be resumed when the perturbation passes. Type 2 allostatic overload begins when there is sufficient or even excess energy consumption accompanied by social conflict and other types of social dysfunction. The latter is the case in human society and certain situations affecting animals in captivity. In all cases, secretion of glucocorticosteroids and activity of other mediators of allostasis such as the autonomic nervous system, CNS neurotransmitters, and inflammatory cytokines wax and wane with allostatic load. If allostatic load is chronically high, then pathologies develop. Type 2 allostatic overload does not trigger an escape response, and can only be counteracted through learning and changes in the social structure.
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            Nonlinear multisystem physiological dysregulation associated with frailty in older women: implications for etiology and treatment.

            Frailty in older adults, defined as a constellation of signs and symptoms, is associated with abnormal levels in individual physiological systems. We tested the hypothesis that it is the critical mass of physiological systems abnormal that is associated with frailty, over and above the status of each individual system, and that the relationship is nonlinear. Using data on women aged 70-79 years from the Women's Health and Aging Studies I and II, multiple analytic approaches assessed the cross-sectional association of frailty with eight physiological measures. Abnormality in each system (anemia, inflammation, insulin-like growth factor-1, dehydroepiandrosterone-sulfate, hemoglobin A1c, micronutrients, adiposity, and fine motor speed) was significantly associated with frailty status. However, adjusting for the level of each system measure, the mean number of systems impaired significantly and nonlinearly predicted frailty. Those with three or more systems impaired were most likely to be frail, with odds of frailty increasing with number of systems at abnormal level, from odds ratios (ORs) of 4.8 to 11 to 26 for those with one to two, three to four, and five or more systems abnormal (p < .05 for all). Finally, two subgroups were identified, one with isolated or no systems abnormal and a second (in 30%) with multiple systems abnormal. The latter group was independently associated with being frail (OR = 2.6, p < .05), adjusting for confounders and chronic diseases and then controlling for individual systems. Overall, these findings indicate that the likelihood of frailty increases nonlinearly in relationship to the number of physiological systems abnormal, and the number of abnormal systems is more predictive than the individual abnormal system. These findings support theories that aggregate loss of complexity, with aging, in physiological systems is an important cause of frailty. Implications are that a threshold loss of complexity, as indicated by number of systems abnormal, may undermine homeostatic adaptive capacity, leading to the development of frailty and its associated risk for subsequent adverse outcomes. It further suggests that replacement of any one deficient system may not be sufficient to prevent or ameliorate frailty.
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              Adaptive β-Cell Proliferation Is Severely Restricted With Advanced Age

              OBJECTIVE Regeneration of the insulin-secreting β-cells is a fundamental research goal that could benefit patients with either type 1 or type 2 diabetes. β-Cell proliferation can be acutely stimulated by a variety of stimuli in young rodents. However, it is unknown whether this adaptive β-cell regeneration capacity is retained into old age. RESEARCH DESIGN AND METHODS We assessed adaptive β-cell proliferation capacity in adult mice across a wide range of ages with a variety of stimuli: partial pancreatectomy, low-dose administration of the β-cell toxin streptozotocin, and exendin-4, a glucagon-like peptide 1 (GLP-1) agonist. β-Cell proliferation was measured by administration of 5-bromo-2′-deoxyuridine (BrdU) in the drinking water. RESULTS Basal β-cell proliferation was severely decreased with advanced age. Partial pancreatectomy greatly stimulated β-cell proliferation in young mice but failed to increase β-cell replication in old mice. Streptozotocin stimulated β-cell replication in young mice but had little effect in old mice. Moreover, administration of GLP-1 agonist exendin-4 stimulated β-cell proliferation in young but not in old mice. Surprisingly, adaptive β-cell proliferation capacity was minimal after 12 months of age, which is early middle age for the adult mouse life span. CONCLUSIONS Adaptive β-cell proliferation is severely restricted with advanced age in mice, whether stimulated by partial pancreatectomy, low-dose streptozotocin, or exendin-4. Thus, β-cells in middle-aged mice appear to be largely postmitotic. Young rodents may not faithfully model the regenerative capacity of β-cells in mature adult mice.
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                Author and article information

                Contributors
                URI : http://frontiersin.org/people/u/74060
                URI : http://frontiersin.org/people/u/165113
                URI : http://frontiersin.org/people/u/77214
                URI : http://frontiersin.org/people/u/121842
                URI : http://frontiersin.org/people/u/77211
                URI : http://frontiersin.org/people/u/77194
                URI : http://frontiersin.org/people/u/77198
                URI : http://frontiersin.org/people/u/65884
                URI : http://frontiersin.org/people/u/35875
                URI : http://frontiersin.org/people/u/37393
                Journal
                Front Public Health
                Front Public Health
                Front. Public Health
                Frontiers in Public Health
                Frontiers Media S.A.
                2296-2565
                25 January 2016
                2016
                : 4
                : 3
                Affiliations
                [1] 1Biodemography of Aging Research Unit (BARU), Social Science Research Institute, Duke University , Durham, NC, USA
                [2] 2Groupe de Recherche PRIMUS, Department of Family Medicine, CHUS-Fleurimont, University of Sherbrooke , Sherbrooke, QC, Canada
                [3] 3The Danish Aging Research Center, University of Southern Denmark , Odense, Denmark
                [4] 4Department of Clinical Genetics, Odense University Hospital , Odense, Denmark
                [5] 5Department of Clinical Biochemistry and Pharmacology, Odense University Hospital , Odense, Denmark
                Author notes

                Edited by: Onyebuchi A. Arah, University of California Los Angeles (UCLA), USA

                Reviewed by: Arnold Mitnitski, Dalhousie University, Canada; Catherine M. Crespi, University of California Los Angeles (UCLA), USA

                *Correspondence: Konstantin G. Arbeev, ka29@ 123456duke.edu

                Specialty section: This article was submitted to Epidemiology, a section of the journal Frontiers in Public Health

                Article
                10.3389/fpubh.2016.00003
                4725219
                26835445
                8c69c7f3-06b1-440f-82a8-c31ced3e981d
                Copyright © 2016 Arbeev, Cohen, Arbeeva, Milot, Stallard, Kulminski, Akushevich, Ukraintseva, Christensen and Yashin.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 04 December 2015
                : 11 January 2016
                Page count
                Figures: 2, Tables: 2, Equations: 4, References: 49, Pages: 12, Words: 11604
                Funding
                Funded by: National Institutes of Health 10.13039/100000002
                Award ID: R01AG030612, R21AG045245, R01AG046860, P01AG043352, P30AG034424, 5U01AG023712-10
                Funded by: Canadian Institutes of Health Research 10.13039/501100000024
                Award ID: 110789, 120305, 119485
                Categories
                Public Health
                Original Research

                physiological dysregulation,stochastic process model,mahalanobis distance,longitudinal data,mortality,sex differences

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