Genetics of physiological dysregulation: findings from the long life family study using joint models

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

doi:10.18632/aging.102987

Cited by 7 publications

(8 citation statements)

References 75 publications

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“…The degree of frailty, that is, variability in risk, is a widely used, theoretically grounded means of quantifying health in old age. Other candidate integrative measures of health include 'biological age' such as provided by 'physiological dysregulation' 16 , DNA methylation clocks 17,18 , telomere length and other aging biomarkers 19,20 . Frailty is a coarsely grained, systemlevel measure that can be used across levels from molecular and cellular measures to tissues to organs to whole organisms 21 -and can also be applied to animals 22 .…”

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confidence: 99%

The degree of frailty as a translational measure of health in aging

2021

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confidence: 99%

The degree of frailty as a translational measure of health in aging

2021

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“…We found that the rate of increase in PD is a significant predictor of worsening health status manifested by onset of major complex diseases such as cancer, cardiovascular diseases or type 2 diabetes ( Arbeev et al, 2019 ). We also found that genes associated with the increase in PD in our genome-wide association study (GWAS) of the Long Life Family Study participants are significantly enriched for involvement in axon guidance and synaptic function ( Arbeev et al, 2020 ). Notably, pathways and molecules regulating axon guidance and synaptic function have also been broadly connected to AD and related traits (e.g., amyloid, tau, neurodegeneration) in the literature ( Cissé et al, 2011 ; Jun et al, 2014 Wetzel-Smith et al, 2014 ; Jiang et al, 2015 ; Giri et al, 2016 ; Hashimoto et al, 2016 ; Kang et al, 2016 ; Zuko et al, 2016 ; Oguro-Ando et al, 2017 ; Han et al, 2018 ; Li et al, 2018 ; Liu et al, 2018 ; Lee et al, 2019 ; Whelan et al, 2019 ; Yang et al, 2019 ; Bamford et al, 2020 ; Cuestas Torres and Cardenas, 2020 ; Chen et al, 2021 ; Zhang et al, 2021 ).…”

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confidence: 90%

Interactions between genes involved in physiological dysregulation and axon guidance: role in Alzheimer’s disease

Arbeev,

Ukraintseva,

Bagley

et al. 2023

Front. Genet.

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Dysregulation of physiological processes may contribute to Alzheimer’s disease (AD) development. We previously found that an increase in the level of physiological dysregulation (PD) in the aging body is associated with declining resilience and robustness to major diseases. Also, our genome-wide association study found that genes associated with the age-related increase in PD frequently represented pathways implicated in axon guidance and synaptic function, which in turn were linked to AD and related traits (e.g., amyloid, tau, neurodegeneration) in the literature. Here, we tested the hypothesis that genes involved in PD and axon guidance/synapse function may jointly influence onset of AD. We assessed the impact of interactions between SNPs in such genes on AD onset in the Long Life Family Study and sought to replicate the findings in the Health and Retirement Study. We found significant interactions between SNPs in the UNC5C and CNTN6, and PLXNA4 and EPHB2 genes that influenced AD onset in both datasets. Associations with individual SNPs were not statistically significant. Our findings, thus, support a major role of genetic interactions in the heterogeneity of AD and suggest the joint contribution of genes involved in PD and axon guidance/synapse function (essential for the maintenance of complex neural networks) to AD development.

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“…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.…”

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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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Genetics of physiological dysregulation: findings from the long life family study using joint models

Cited by 7 publications

References 75 publications

The degree of frailty as a translational measure of health in aging

The degree of frailty as a translational measure of health in aging

Interactions between genes involved in physiological dysregulation and axon guidance: role in Alzheimer’s disease

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

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