Composite Measure of Physiological Dysregulation as a Predictor of Mortality: The Long Life Family Study

Arbeev, Konstantin G.; Bagley, Olivia; Ukraintseva, Svetlana V.; Duan, Hongzhe; Kulminski, Alexander M.; Stallard, Eric; Wu, Deqing; Christensen, Kaare; Thyagarajan, Bharat; Zmuda, Joseph M.; Yashin, Anatoliy I.

doi:10.3389/fpubh.2020.00056

Cited by 7 publications

(5 citation statements)

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“…Composite measures are based on various combinations of biologic clocks or clinical indices and reflect the complex effect of aging on different physiological systems (e.g., renal, cardiovascular, endocrine, immune, pulmonary) [ 5 , 49 , 50 , 51 ]. Such composites are often used in studies that incorporate age-normalized physiology, an approach that defines biologic age as the age at which a person’s biology would be “normal” in a reference population and defines “pace of aging” as the rate of decline in integrity across multiple organ systems [ 52 ].…”

Section: Measuring Biologic Agementioning

confidence: 99%

Research priorities for measuring biologic age: summary and future directions from the Research Centers Collaborative Network Workshop

et al. 2022

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Biologic aging reflects the genetic, molecular, and cellular changes underlying the development of morbidity and mortality with advancing chronological age. As several potential mechanisms have been identified, there is a growing interest in developing robust measures of biologic age that can better reflect the underlying biology of aging and predict age-related outcomes. To support this endeavor, the Research Centers Collaborative Network (RCCN) conducted a workshop in January 2022 to discuss emerging concepts in the field and identify opportunities to move the science forward. This paper presents workshop proceedings and summarizes the identified research needs, priorities, and recommendations for measuring biologic age. The highest priorities identified were the need for more robust measures, longitudinal studies, multidisciplinary collaborations, and translational approaches.

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Section: Measuring Biologic Agementioning

confidence: 99%

Research priorities for measuring biologic age: summary and future directions from the Research Centers Collaborative Network Workshop

et al. 2022

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“…We used ELSA and HRS datasets because both cohorts collected physical, psychological, and biomarker data that allowed us to evaluate the role that a range of different psychological measures may have in explaining the longitudinal association between obesity and physiological dysregulation in older adults. Furthermore, older adults are at risk of worsening health, and composite measures of physiological dysregulation among the age populations of ELSA and HRS have been shown to be an indicator of preclinical disease (Arbeev et al, 2019) and predictive of subsequent mortality risk (Arbeev et al, 2020). Analysing two different cohorts also enabled us to examine the cultural generalisability of the findings (UK vs.…”

Section: Introductionmentioning

confidence: 99%

Psychological pathways explaining the prospective association between obesity and physiological dysregulation

Putra¹,

Daly²,

Sutin³

et al. 2023

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Objective: Obesity is associated with a range of negative psychological conditions that may also affect physiological health. Across two studies, we tested whether a range of psychological measures explain why obesity is prospectively associated with physiological dysregulation, measured via clinical indicators of cardiovascular, immune system, and metabolic function. Methods: We used comparable 4-year follow-up representative longitudinal data of UK and US older adults (≥ 50 years) from the English Longitudinal Study of Ageing (ELSA) (2008/2009–2012/2013) (Study 1; n= 6,250) and the Health and Retirement Study (HRS) (2008/2010–2012/2014) (Study 2; n=9,664). Fourteen and 21 psychological measures (e.g., depressive symptoms, life satisfaction, weight stigma, positive affect) were tested as candidate mediators in Studies 1 and 2, respectively. Results: Obesity predicted physiological dysregulation at follow-up across both studies. In Study 1, only weight stigma (measured between baseline and follow-up) explained 37% of the association between obesity and physiological dysregulation. In Study 2, only changes in weight stigma from baseline to follow-up (not baseline weight stigma) explained 13% of the effect of obesity on future physiological dysregulation. Mediation by weight stigma in both studies was partially attenuated when changes in body mass index (BMI) from baseline to follow-up were controlled for. No other psychological measures explained the association between obesity and physiological dysregulation in either study. Conclusion: The prospective association between obesity and physiological dysregulation was largely not explained by psychological factors. However, experiencing weight stigma is associated with increased weight gain and this process may explain obesity-related declines in physiological health.

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“…Both individual biomarkers and composite measures based on multiple biomarkers have been studied in relation to morbidity and mortality outcomes (see, e.g., recent reviews in Arbeev et al, 2016;Mitnitski and Rockwood, 2019). Recently, the statistical (Mahalanobis) distance (D M ; denoted in the context of this paper as health distance, HD; we use D M in this paragraph in discussion of previous literature and HD in further narrative), constructed based on the joint distribution of multiple biomarkers, was proposed as a composite measure that can represent the level of physiological dysregulation in an aging organism (Cohen et al, 2013;Arbeev et al, 2019Arbeev et al, , 2020a. It can be used as a measure of aging-related declines in robustness and resilience and as a preclinical indicator of an individual's transition from a healthy to an unhealthy state (Arbeev et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…It can be used as a measure of aging-related declines in robustness and resilience and as a preclinical indicator of an individual's transition from a healthy to an unhealthy state (Arbeev et al, 2019). Numerous studies have confirmed the association of D M with mortality, diseases, and aging-related outcomes (Cohen et al, 2013(Cohen et al, , 2015Milot et al, 2014;Arbeev et al, 2019Arbeev et al, , 2020a, and there is emerging evidence on genetic determinants of the rates of physiological dysregulation represented by D M (Arbeev et al, 2020b). However, applications of this measure to broader sets of biomarkers, including physical fitness ones, and studies of their association with impaired health status such as HF are still lacking.…”

Section: Introductionmentioning

confidence: 99%

Composite Measures of Physical Fitness to Discriminate Between Healthy Aging and Heart Failure: The COmPLETE Study

et al. 2020

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BackgroundAging and changing age demographics represent critical problems of our time. Physiological functions decline with age, often ending in a systemic process that contributes to numerous impairments and age-related diseases including heart failure (HF). We aimed to analyze whether differences in composite measures of physiological function [health distance (HD)], specifically physical fitness, between healthy individuals and patients with HF, can be observed.MethodsThe COmPLETE Project is a cross-sectional study of 526 healthy participants aged 20–91 years and 79 patients with stable HF. Fifty-nine biomarkers characterizing fitness (cardiovascular endurance, muscle strength, and neuromuscular coordination) and general health were assessed. We computed HDs as the Mahalanobis distance for vectors of biomarkers (all and domain-specific subsets) that quantified deviations of individuals’ biomarker profiles from “optimums” in the “reference population” (healthy participants aged <40 years). We fitted linear regressions with HD outcomes and disease status (HF/Healthy) and relevant covariates as predictors and logistic regressions for the disease outcome and sex, age, and age2 as covariates in the base model and the same covariates plus combinations of one or two HDs.ResultsNine out of 10 calculated HDs showed evidence for group differences between Healthy and HF (p ≤ 0.002) and most models presented a negative estimate of the interaction term age by group (p < 0.05 for eight HDs). The predictive performance of the base model for HF cases significantly increased by adding HD General health or HD Fitness [areas under the receiver operating characteristic (ROC) curve (AUCs) 0.63, 0.89, and 0.84, respectively]. HD Cardiovascular endurance alone reached an AUC of 0.88. Further, there is evidence that the combination of HDs Cardiovascular endurance and General health shows superior predictive power compared to single HDs.ConclusionHD composed of physical fitness biomarkers differed between healthy individuals and patients with HF, and differences between groups diminished with increasing age. HDs can successfully predict HF cases, and HD Cardiovascular endurance can significantly increase the predictive power beyond classic clinical biomarkers. Applications of HD could strengthen a comprehensive assessment of physical fitness and may present an optimal target for interventions to slow the decline of physical fitness with aging and, therefore, to increase health span.

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Composite Measure of Physiological Dysregulation as a Predictor of Mortality: The Long Life Family Study

Cited by 7 publications

References 35 publications

Research priorities for measuring biologic age: summary and future directions from the Research Centers Collaborative Network Workshop

Research priorities for measuring biologic age: summary and future directions from the Research Centers Collaborative Network Workshop

Psychological pathways explaining the prospective association between obesity and physiological dysregulation

Composite Measures of Physical Fitness to Discriminate Between Healthy Aging and Heart Failure: The COmPLETE Study

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