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

Biomarkers selection and mathematical modeling in biological age estimation

Biological age (BA) is important for clinical monitoring and preventing aging-related disorders and disabilities. Clinical and/or cellular biomarkers are measured and integrated in years using mathematical models to display an individual's BA. To date, there is not yet a single or set of biomarker(s) and technique(s) that is validated as providing the BA that reflects the best real aging status of individuals. Herein, a comprehensive overview of aging biomarkers is provided and the potential of genetic variations as proxy indicators of the aging state is highlighted. A comprehensive overview of BA estimation methods is also provided as well as a discussion of their performances, advantages, limitations, and potential approaches to overcome these limitations.

A prospective randomized comparative placebo-controlled double-blind study in two groups to assess the effect of the use of biologically active additives with Siberian fir terpenes for the biological age of a person

A prospective randomized comparative placebo-controlled double-blind study was carried out based on Arterial Indices model of biological age. The study involved 60 men and women aged 40-65 years that were randomly divided into two equal groups of 30 people: the main group and the control one. The study participants from the main group received a dietary supplement containing Siberian fir terpenes, limonene, alpha-linolenic acid, and vitamin E-1 capsule 3 times a day for 90 days. Patients in the comparison group received a placebo according to a similar scheme. Anthropometric and biochemical characteristics of patients from both groups have not undergone any significant changes. According to ultrasound examination of the carotid arteries, we observed a statistically significant decrease in the minimum thickness of the intima-media complex (by 45%). The maximum carotid artery stenosis on the right or left and the expansion index in patients of both groups did not change significantly during treatment. According to the results of applanation tonometry, it was revealed that when taking the studied dietary supplement, the pulse wave velocity significantly decreased compared to the initial one (by 10%). Accordingly, the Arterial Indices biological age decreased by 2.5 years compared to the baseline level in patients of the main group and did not change in patients from the comparison group. Supplementation of fir terpenes in middle-aged patients of both sexes reduces the biological age reflecting the condition of the arteries.

A model of healthy aging based on smartphone interactions reveals advanced behavioral age in neurological disease

Smartphones offer unique opportunities to trace the convoluted behavioral patterns accompanying healthy aging. Here we captured smartphone touchscreen interactions from a healthy population (N = 684, ∼309 million interactions) spanning 16 to 86 years of age and trained a decision tree regression model to estimate chronological age based on the interactions. The interactions were clustered according to their next interval dynamics to quantify diverse smartphone behaviors. The regression model well-estimated the chronological age in health (mean absolute error = 6 years, R² = 0.8). We next deployed this model on a population of stroke survivors (N = 41) to find larger prediction errors such that the estimated age was advanced by 6 years. A similar pattern was observed in people with epilepsy (N = 51), with prediction errors advanced by 10 years. The smartphone behavioral model trained in health can be used to study altered aging in neurological diseases.

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A smartphone data-driven model was trained to estimate chronological age

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The model trained in health performed well to estimate the age

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The same model estimated advanced aging in stroke and epilepsy

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Smartphone-based model of healthy behavior may help understand aging in diseases

Effects of age, gender, and hemisphere on cerebrovascular hemodynamics in children and young adults: Developmental scores and machine learning classifiers

A constant blood supply to the brain is required for mental function. Research with Doppler ultrasonography has important clinical value and burgeoning potential with machine learning applications in studies predicting gestational age and vascular aging. Critically, studies on ultrasound metrics in school-age children are sparse and no machine learning study to date has used color duplex ultrasonography to predict age and classify age-group. The purpose of our study is two-fold: first to document cerebrovascular hemodynamics considering age, gender, and hemisphere in three arteries; and second to construct machine learning models that can predict and classify the age and age-group of a participant using ultrasonography metrics. We record peak systolic, end-diastolic, and time-averaged maximum velocities bilaterally in internal carotid, vertebral, and middle cerebral arteries from 821 participants. Results confirm that ultrasonography values decrease with age and reveal that gender and hemispheres show more similarities than differences, which depend on age, artery, and metric. Machine learning algorithms predict age and classifier models distinguish cerebrovascular hemodynamics between children and adults. Blood velocities, rather than blood vessel diameters, are more important for classifier models, and common and distinct variables contribute to age classification models for males and females.

The metabolome as a biomarker of aging in Drosophila melanogaster

Many biomarkers have been shown to be associated not only with chronological age but also with functional measures of biological age. In human populations, it is difficult to show whether variation in biological age is truly predictive of life expectancy, as such research would require longitudinal studies over many years, or even decades. We followed adult cohorts of 20 Drosophila Genetic Reference Panel (DGRP) strains chosen to represent the breadth of lifespan variation, obtain estimates of lifespan, baseline mortality, and rate of aging, and associate these parameters with age‐specific functional traits including fecundity and climbing activity and with age‐specific targeted metabolomic profiles. We show that activity levels and metabolome‐wide profiles are strongly associated with age, that numerous individual metabolites show a strong association with lifespan, and that the metabolome provides a biological clock that predicts not only sample age but also future mortality rates and lifespan. This study with 20 genotypes and 87 metabolites, while relatively small in scope, establishes strong proof of principle for the fly as a powerful experimental model to test hypotheses about biomarkers and aging and provides further evidence for the potential value of metabolomic profiles as biomarkers of aging.

Biological vascular age and its relationship with cardiovascular risk factors

Aim. To study of the relationship between cardiovascular risk factors and biological vascular age.

Material and methods. The biological vascular age was estimated using models based on the arterial wall parameters. Using multiple logistic and linear regression, we studied the relationship between the biological vascular age and cardiovascular risk factors in 143 people without cardiovascular disease (CVD). Persons with a positive difference between the vascular and chronological age were assigned to the “old” vascular group, and persons with no or negative difference between the vascular and chronological age were assigned to the “young” vascular group.

Results. Linear regression in the “young” vascular group showed an inverse relationship between the difference between the vascular and chronological age with the levels of low-density lipoprotein cholesterol (p=0,001; β±SE=-1,67±0,47), triglycerides (p=0,017; β±SE=-1,66±0,68), urea (p=0,025; β±SE=-0,89±0,39) and insulin resistance index (p=0,001; β±SE=-1,22±0,36). In the “old” vascular group, a direct relationship was found between the difference between the vascular and chronological age and central systolic blood pressure (p=0,015; β±SE=0,10±0,04). According to logistic regression, the likelihood of having “old” vessels increased by 1,23 times with an increase in blood glucose levels by 0,5 mmol/l (p=0,044; odds ratio (OR)=1,23; 95% confidence interval (CI): 1,011,51), the presence of hypertension (p=0,034; OR=3,11; 95% CI: 1,09-8,86) and type 2 diabetes (p=0,025; OR=3,61; 95% CI: 1,1711,09), as well as decreased by 2 times with an increase in high-density lipoprotein cholesterol by 0,3 mmol/l (p=0,003; OR=0,5; 95% CI: 0,32-0,79).

Conclusion. The difference between the biological vascular age and chronological age is associated with traditional CVD risk factors.

Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research

Vascular ageing biomarkers have been found to be predictive of cardiovascular risk independently of classical risk factors, yet are not widely used in clinical practice. In this review, we present two basic approaches for using machine learning (ML) to assess vascular age: parameter estimation and risk classification. We then summarize their role in developing new techniques to assess vascular ageing quickly and accurately. We discuss the methods used to validate ML-based markers, the evidence for their clinical utility, and key directions for future research. The review is complemented by case studies of the use of ML in vascular age assessment which can be replicated using freely available data and code.

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

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.

Summary: An integrative picture about the mechanisms operating in the hippocampal vasculature under normal and pathological scenarios is achieved by the computational dissection of microarray data corresponding to whole tissue samples and focusing on gene splice forms.

[Vascular age in patients with arterial hypertension]

Ageing is considered to be the major and non-modifiable risk factor for the development of hypertension and cardiovascular diseases. During ageing, the vascular system undergoes structural and functional alterations, including endothelial dysfunction, thickening of the vascular wall, reduced distensibility and increased arterial stiffness. Vascular rigidity results from fibrosis and remodelling of the extracellular matrix, processes that are associated with ageing and are amplified in hypertension. These events may be induced by vasoactive agents, such as angiotensin II, endothelin-1, and aldosterone, which are increased in the vasculature during aging and hypertension. Complex interaction between the process of ageing and prohypertensive factors results in accelerated vascular remodelling and fibrosis, as well as increased arterial stiffness. Hypertension accelerates and augments age-related vascular remodelling and dysfunction, and ageing may impact on the severity of vascular damage in hypertension, thus strongly suggesting close interactions between biological ageing and blood pressure elevation. Molecular and cellular mechanisms underlying vascular alterations in ageing and hypertension are common and include aberrant signal transduction, oxidative stress and activation of pro-inflammatory and pro-fibrotic transcription factors. Strategies to suppress age-associated vascular changes can ameliorate vascular damage associated with hypertension. This article looks into vascular alterations occurring during ageing and hypertension, focussing particularly on arterial stiffness and vascular remodelling, also emphasizing the importance of diagnostic methods.

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

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.