Biomarkers of aging


The aging process results in multiple traceable footprints, which can be quantified and used to estimate an organism’s age. Examples of such aging biomarkers include epigenetic changes, telomere attrition, and alterations in gene expression and metabolite concentrations.

More than a dozen aging clocks use molecular features to predict an organism’s age, each of them utilizing different data types and training procedures. Here, we offer a detailed comparison of existing mouse and human aging clocks, discuss their technological limitations and the underlying machine learning algorithms.

We also discuss promising future directions of research in biohorology — the science of measuring the passage of time in living systems. Overall, we expect deep learning, deep neural networks and generative approaches to be the next power tools in this timely and actively developing field.

FULL TEXT: Ageing Research Reviews

Human gut microbiome aging clock


The human gut microbiome is a complex ecosystem that both affects and is affected by its host status. Previous metagenomic analyses of gut microflora revealed associations between specific microbes and host age. Nonetheless there was no reliable way to tell a host’s age based on the gut community composition.

Here we developed a method of predicting hosts’ age based on microflora taxonomic profiles using a cross-study dataset and deep learning. Our best model has an architecture of a deep neural network that achieves the mean absolute error of 5.91 years when tested on external data. We further advance a procedure for inferring the role of particular microbes during human aging and defining them as potential aging biomarkers.

The described intestinal clock represents a unique quantitative model of gut microflora aging and provides a starting point for building host aging and gut community succession into a single narrative.

FULL TEXT (PDF): iScience

Aging clocks: AI-based biomarkers of aging


First published in 2016, predictors of chronological and biological age developed using deep learning are rapidly gaining popularity in the aging research community. These deep aging clocks [that have been developed using artificial intelligence (AI)] can be used in a broad range of applications in the pharmaceutical industry, spanning target identification, drug discovery, data economics, and synthetic patient data generation.

Recent advances in machine learning, coupled with increases in computational power and availability of the large publicly available datasets, have led to a renaissance in AI. These advances have generated substantial investment and hype, and many data scientists and companies are exploiting the surge in AI hype for promotional purposes. This has sown confusion in the market and triggered criticism from scientists working in the pharmaceutical industry, where approval in clinical trials is the ultimate measure of success.

Most of the credible advances in the field have been in deep learning and reinforcement learning. Since 2013, deep learning systems have surpassed human performance in multiple applications, including strategy games as well as image and text recognition. In healthcare, deep learning systems outperformed human dermatologists, ophthalmologists, and radiologists in various tasks. Deep learning also demonstrated significant improvement over conventional machine learning methods in biomedical data analysis.

Biomarkers of aging

During this same period of deep learning progress, aging research has also experienced a renaissance, and new breakthroughs are rapidly emerging. Multiple data types can be used to predict age and associate the prediction with mortality, disease, general well-being, or other biological processes including methylation, gene expression, microbiome, and imaging data.

Since the publication of the first multitissue methylation aging clock by Steven Horvath in 2013, multiple methylation aging clocks and applications of these clocks in humans and mice were developed. Even though these clocks were developed using traditional machine learning approaches – notably linear regression with regularization and the use of a limited number of samples – the results suggest that gradual changes during aging can be tracked using various data types with reasonable accuracy.

FULL TEXT: Trends in Pharmacological Sciences

The aging clock: circadian rhythms and later life

Circadian rhythm (SCN firing). The graph at the top illustrates firing in the suprachiasmatic nucleus (SCN) in younger (blue) vs older adults (red). In mammals, the SCN is the master circadian clock.


Observations of many species have revealed a variety of developmental changes in circadian rhythms of overt behaviors and physiology. In reviewing this literature, it is worthwhile to note some aspects of research design that may influence the conclusions that can be drawn. For example, the vast majority of research in this area is cross-sectional; as such, differences in dependent variables between older and younger individuals that are attributed to age could potentially reflect cohort effects or other confounders. This is a legitimate criticism; however, we believe it is mitigated by the number of studies that have identified similar patterns of results using different samples and age cohorts.

Circadian rhythm (waking). Shifts in preference for morningness versus eveningness, or chronotype, are seen in many species.

Shifts in preference for morningness versus eveningness, or chronotype, and in sleep cycles are among the most consistently observed age-associated circadian changes in many species. Broms and colleagues tracked chronotype longitudinally in 567 adult men in Finland over 23 years (mean age of 56 years at study entry), and found a shift in the distribution toward a “mostly morning” type over years of study. Retrospective self-comparison studies in older adult participants (>60 years) also indicate a tendency to become a “morning person” with increasing age. Taken together, this shift in chronotype appears to be a reliable developmental pattern.

The preference for morningness in older adulthood is expressed in other aspects of behavior, such as cognitive skill performance. The circadian profile of cognitive performance interacts with age, such that older adults who are tested on recognition memory tasks in the early morning perform as well as younger adults, but significantly worse when tested later in the afternoon.

FULL TEXT: J Clinical Investigation