Biological Age Prediction From Wearable Device Movement Data Identifies Nutritional and Pharmacological Interventions for Healthy Aging

Retinal microvascular complexity as a putative biomarker of biological age: a pilot study

Physiological changes associated with aging increase the risk for the development of age-related diseases. This increase is non-specific to the type of age-related disease, although each disease develops through a unique pathophysiologic mechanism. People who age at a faster rate develop age-related diseases earlier in their life. They have an older "biological age" compared to their "chronological age". Early detection of individuals with accelerated aging would allow timely intervention to postpone the onset of age-related diseases. This would increase their life expectancy and their length of good quality life. The goal of this study was to investigate whether retinal microvascular complexity could be used as a biomarker of biological age. Retinal images of 68 participants ages ranging from 19 to 82 years were collected in an observational cross-sectional study. Twenty of the old participants had age-related diseases such as hypertension, type 2 diabetes, and/or Alzheimer's dementia. The rest of the participants were healthy. Retinal images were captured by a hand-held, non-mydriatic fundus camera and quantification of the microvascular complexity was performed by using Sholl's, box-counting fractal, and lacunarity analysis. In the healthy subjects, increasing chronological age was associated with lower retinal microvascular complexity measured by Sholl's analysis. Decreased box-counting fractal dimension was present in old patients, and this decrease was 2.1 times faster in participants who had age-related diseases (p = 0.047). Retinal microvascular complexity could be a promising new biomarker of biological age. The data from this study is the first of this kind collected in Montenegro. It is freely available for use.

A metabolomic signature of decelerated physiological aging in human plasma

The degenerative processes that occur during aging increase the risk of disease and impaired health. Meanwhile, interventions that target aging to promote healthy longevity are gaining interest, both academically and in the public. While nutritional and physical interventions exist, efficacy is often difficult to determine. It is therefore imperative that an aging score measuring the biological aging process is available to the wider public. However, simple, interpret, and accessible biological aging scores are lacking. Here, we developed PhysiAge, a physiological aging score based on five accessible parameters that have influence on or reflect the aging process: (1) average daily step count, (2) blood glucose, (3) systolic blood pressure, (4) sex, and (5) age. Here, we found that compared to calendar age alone, PhysiAge better predicts mortality, as well as established muscle aging markers such as decrease in NAD+ levels, increase in oxidative stress, and decline in physical functioning. In order to demonstrate the usefulness of PhysiAge in identifying relevant factors associated with decelerated aging, we calculated PhysiAges for a cohort of aged individuals and obtained mass spectrometry-based blood plasma metabolomic profiles for each individual. Here, we identified a metabolic signature of decelerated aging, which included components of the TCA cycle, including malate, citrate, and isocitrate. Higher abundance of these metabolites was associated with decelerated aging, in line with supplementation studies in model organisms. PhysiAge represents an accessible way for people to track and intervene in their aging trajectories, and identifies a metabolic signature of decelerated aging in human blood plasma, which can be further studied for its causal involvement in human aging.

A set of common buccal CpGs that predict epigenetic age and associate with lifespan-regulating genes

Epigenetic aging clocks are computational models that use DNA methylation sites to predict age. Since cheek swabs are non-invasive and painless, collecting DNA from buccal tissue is highly desirable. Here, we review 11 existing clocks that have been applied to buccal tissue. Two of these were exclusively trained on adults and, while moderately accurate, have not been used to capture health-relevant differences in epigenetic age. Using 130 common CpGs utilized by two or more existing buccal clocks, we generate a proof-of-concept predictor in an adult methylomic dataset. In addition to accurately estimating age (r = 0.95 and mean absolute error = 3.88 years), this clock predicted that Down syndrome subjects were significantly older relative to controls. A literature and database review of CpG-associated genes identified numerous genes (e.g., CLOCK, ELOVL2, and VGF) and molecules (e.g., alpha-linolenic acid, glycine, and spermidine) reported to influence lifespan and/or age-related disease in model organisms.

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130 CpGs have been used by two or more aging clocks applied to human buccal tissue

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Common CpG genes are linked to the adaptive immune system and telomere maintenance

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Common CpGs can be used to build a novel, proof-of-concept epigenetic aging clock

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Several compounds associated with common CpG genes regulate lifespan in animals

Human age reversal: Fact or fiction?

Although chronological age correlates with various age-related diseases and conditions, it does not adequately reflect an individual's functional capacity, well-being, or mortality risk. In contrast, biological age provides information about overall health and indicates how rapidly or slowly a person is aging. Estimates of biological age are thought to be provided by aging clocks, which are computational models (e.g., elastic net) that use a set of inputs (e.g., DNA methylation sites) to make a prediction. In the past decade, aging clock studies have shown that several age-related diseases, social variables, and mental health conditions associate with an increase in predicted biological age relative to chronological age. This phenomenon of age acceleration is linked to a higher risk of premature mortality. More recent research has demonstrated that predicted biological age is sensitive to specific interventions. Human trials have reported that caloric restriction, a plant-based diet, lifestyle changes involving exercise, a drug regime including metformin, and vitamin D3 supplementation are all capable of slowing down or reversing an aging clock. Non-interventional studies have connected high-quality sleep, physical activity, a healthy diet, and other factors to age deceleration. Specific molecules have been associated with the reduction or reversal of predicted biological age, such as the antihypertensive drug doxazosin or the metabolite alpha-ketoglutarate. Although rigorous clinical trials are needed to validate these initial findings, existing data suggest that aging clocks are malleable in humans. Additional research is warranted to better understand these computational models and the clinical significance of lowering or reversing their outputs.

Considerations Regarding Public Use of Longevity Interventions

Public attention and interest for longevity interventions are growing. These can include dietary interventions such as intermittent fasting, physical interventions such as various exercise regimens, or through supplementation of nutraceuticals or administration of pharmaceutics. However, it is unlikely that most interventions identified in model organisms will translate to humans, or that every intervention will benefit each person equally. In the worst case, even detrimental health effects may occur. Therefore, identifying longevity interventions using human data and tracking the aging process in people is of paramount importance as we look towards longevity interventions for the public. In this work, we illustrate how to identify candidate longevity interventions using population data in humans, an approach we have recently employed. We consider metformin as a case-study for potential confounders that influence effectiveness of a longevity intervention, such as lifestyle, sex, genetics, age of administration and the microbiome. Indeed, metformin, like most other longevity interventions, may end up only benefitting a subgroup of individuals. Fortunately, technologies have emerged for tracking the rate of 'biological' aging in individuals, which greatly aids in assessing effectiveness. Recently, we have demonstrated that even wearable devices, accessible to everyone, can be used for this purpose. We therefore propose how to use such approaches to test interventions in the general population. In summary, we advocate that 1) not all interventions will be beneficial for each individual and therefore 2) it is imperative that individuals track their own aging rates to assess healthy aging interventions.