Markers of arterial health could serve as accurate non-invasive predictors of human biological and chronological age

Fedintsev, Alexander; Kashtanova, Daria A.; Tkacheva, Olga N.; Strazhesko, Irina D.; Kudryavtseva, Anna V.; Baranova, Ancha; Moskalev, Alexey

doi:10.18632/aging.101227

Cited by 16 publications

(20 citation statements)

References 47 publications

(51 reference statements)

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“…The fact that some incipient features of vascular aging appeared in the Younger Group is not strange because these were middle-aged individuals. Hence, their biological and chronological ages were increasingly divergent ( Fedintsev et al, 2017 ; Voisin et al, 2020 ; Shireby et al, 2020 preprint). Moreover, vascular alterations in ‘healthy’ younger individuals can be evidenced from other independent studies as well: (i) six-weeks-old rats were divided in two groups: one group received high-fat diet, whilst the other group received normal diet.…”

Section: Brain Insulin-resistancementioning

confidence: 99%

Mapping the transcriptomic changes of endothelial compartment in human hippocampus across aging and mild cognitive impairment

2021

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Compromise of the vascular system has important consequences on cognitive abilities and neurodegeneration. The identification of the main molecular signatures present in the blood vessels of human hippocampus could provide the basis to understand and tackle these pathologies. As direct vascular experimentation in hippocampus is problematic, we achieved this information by computationally disaggregating publicly available whole microarrays data of human hippocampal homogenates. Three conditions were analyzed: ‘Young Adults’, ‘Aged’, and ‘aged with Mild Cognitive Impairment’ (MCI). The genes identified were contrasted against two independent data-sets. Here we show that the endothelial cells from the Younger Group appeared in an ‘activated stage’. In turn, in the Aged Group, the endothelial cells showed a significant loss of response to shear stress, changes in cell adhesion molecules, increased inflammation, brain-insulin resistance, lipidic alterations, and changes in the extracellular matrix. Some specific changes in the MCI group were also detected. Noticeably, in this study the features arisen from the Aged Group (high tortuosity, increased bifurcations, and smooth muscle proliferation), pose the need for further experimental verification to discern between the occurrence of arteriogenesis and/or vascular remodeling by capillary arterialization. This article has an associated First Person interview with the first author of the paper.

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Section: Brain Insulin-resistancementioning

confidence: 99%

Mapping the transcriptomic changes of endothelial compartment in human hippocampus across aging and mild cognitive impairment

2021

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“…For instance, triglycerides, glycated hemoglobin (HbA1c), waist circumference, IL-6 increase with age, but other parameters like albumin, IGF and creatinine clearance go in an opposite direction [ 84 , 85 ]. Many efforts have been made to integrate biomarkers in various health/risk indexes like Healthy Aging Index [ 86 , 87 ], Framingham Risk Score [ 88 , 89 ], Frailty index [ 90 , 91 ], Physiologic Index of comorbidities [ 92 ]. Ultimately, age is the closest estimate of a health status of a person.…”

Section: Predicting Patient’s Age To Evaluate the Predictive Value Ofmentioning

confidence: 99%

Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare

Ojomoko

Yanovich²,

Ostrovski³

et al. 2017

Oncotarget

291

184

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The increased availability of data and recent advancements in artificial intelligence present the unprecedented opportunities in healthcare and major challenges for the patients, developers, providers and regulators. The novel deep learning and transfer learning techniques are turning any data about the person into medical data transforming simple facial pictures and videos into powerful sources of data for predictive analytics. Presently, the patients do not have control over the access privileges to their medical records and remain unaware of the true value of the data they have. In this paper, we provide an overview of the next-generation artificial intelligence and blockchain technologies and present innovative solutions that may be used to accelerate the biomedical research and enable patients with new tools to control and profit from their personal data as well with the incentives to undergo constant health monitoring. We introduce new concepts to appraise and evaluate personal records, including the combination-, time- and relationship-value of the data. We also present a roadmap for a blockchain-enabled decentralized personal health data ecosystem to enable novel approaches for drug discovery, biomarker development, and preventative healthcare. A secure and transparent distributed personal data marketplace utilizing blockchain and deep learning technologies may be able to resolve the challenges faced by the regulators and return the control over personal data including medical records back to the individuals.

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“…The copyright holder for this preprint this version posted June 21, 2021. ; https://doi.org/10.1101/2021.06.17.21259120 doi: medRxiv preprint features extracted from carotid artery duplex scan and pulse wave velocimetry (R-Squared [R 2 ]=55-69%; root mean squared error [RMSE]=5.87-6.91 years) 6 . Cardiac features such as electrocardiograms 7 and heart MRI videos 8 have similarly been used to build chronological age predictors.…”

Section: Introductionmentioning

confidence: 99%

“…For example, atherosclerosis develops, so intima media thickness increases 4 and arterial vessels stiffen, which leads to an increase in systolic blood pressure and decrease in diastolic blood pressure 5 . Fedintsev et al predicted chronological age from scalar features extracted from carotid artery duplex scan and pulse wave velocimetry (R-Squared [R 2 ]=55-69%; root mean squared error [RMSE]=5.87-6.91 years) 6 . Cardiac features such as electrocardiograms 7 and heart MRI videos 8 have similarly been used to build chronological age predictors.…”

Section: Introductionmentioning

confidence: 99%

Predicting arterial age using carotid ultrasound images, pulse wave analysis records, cardiovascular biomarkers and deep learning

et al. 2021

Preprint

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Cardiovascular disease --an age-related disease-- is the leading cause of death worldwide. We built an arterial age predictor by training deep learning models to predict age from 233,388 pulse wave analysis records, 8,279 carotid ultrasound images and arterial health biomarkers (e.g blood pressure) collected from 502,000 UKB participants. We predicted age with a R-Squared of 67.1+/-0.6% and a root mean squared error of 4.29+/-0.04 years. Attention maps for carotid ultrasound images suggest that the predictions are driven by vascular features, for the largest part. Accelerated arterial aging is 32.6+/-7.3% GWAS-heritable, and we identified 192 single nucleotide polymorphisms in 109 genes (e.g NPR3, involved in blood volume and pressure) significantly associated with this phenotype. Similarly, we identified biomarkers (e.g electrocardiogram features), clinical phenotypes (e.g chest pain), diseases (e.g hypertension), environmental (e.g smoking) and socioeconomic (e.g income and education) variables associated with accelerated arterial aging. Finally, carotid ultrasound images, pulse wave analysis records and blood pressure biomarkers capture different facets of arterial aging. For example, carotid ultrasound-measured and pulse wave analysis-measured accelerated arterial aging phenotypes are only .164+/-.009 correlated. In conclusion, our predictor suggests potential lifestyle and therapeutic interventions to slow arterial aging, and could be used to assess the efficiency of emerging rejuvenating therapies on the arterial system.

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Markers of arterial health could serve as accurate non-invasive predictors of human biological and chronological age

Cited by 16 publications

References 47 publications

Mapping the transcriptomic changes of endothelial compartment in human hippocampus across aging and mild cognitive impairment

Mapping the transcriptomic changes of endothelial compartment in human hippocampus across aging and mild cognitive impairment

Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare

Predicting arterial age using carotid ultrasound images, pulse wave analysis records, cardiovascular biomarkers and deep learning

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