Fight Aging!

As illustrated by the last decade or so of research, any sufficiently complex set of biological data can be mined via machine learning to produce algorithms that report chronological age and incorporate some sensitivity to biological age. Biological data changes over time, and many of those changes are characteristic of age. The processes and dysfunctions of age touch on all mechanisms in the body, given time. The hypothetical perfect measure of biological age would accurately predict mortality risk, and be a comprehensive reflection of the burden of damage and dysfunction resulting from processes of aging. That may be impossible to achieve, but good enough clocks of biological aging will greatly speed progress towards therapies capable of treating aging.

Everyone suffers from the same processes of aging, and those processes tend to interact with one another, so if one pulls ahead, then it will make other accelerate as well. Nonetheless, the progression of aging is a stochastic process, a sequence of essentially random occurrences of damage, and random interactions between damaged components. There will be a distribution of outcomes even in identical bodies. Thus just as we see different people aging at different rates, we would expect that, in one individual, sometimes the state of dysfunction and damage will be worse in one tissue or organ, better in another.

In today's open access paper, researchers demonstrate that this is in fact the case. They do so by using data on circulating proteins that are generated by specific tissues, and which can be obtained from a blood sample. Given that data, machine learning approaches derive aging clock algorithms that are specific to those tissues. The results show that a fraction of people exhibit accelerated aging in one organ. As for all newly created clocks, it is entirely unclear as to which specific underlying processes of aging drive the observed changes and outcomes, but nonetheless one might hope that the existence of aging clocks will help to improve outcomes in research, medicine, and lifestyle choices.

Organ aging signatures in the plasma proteome track health and disease

While many methods to measure molecular aging in humans have been developed, most of them provide just a single measure of aging for the whole body. This is difficult to interpret given the complexity of human aging trajectories. Some recent methods have used clinical chemistry markers which include some markers of organ function. However, many of these markers have low organ specificity, making them difficult to interpret for organ-specific aging. Methods to measure brain aging have used MRI-based brain volume and functional connectivity measurements, which are costly and do not provide molecular insights, or have required tissue samples, which prevents their application in living persons. Building off the wealth of literature and clinical practice that uses certain organ-specific plasma proteins to noninvasively assess aspects of organ health, such as alanine transaminase for liver damage, we hypothesized that comprehensive quantification of organ-specific proteins in plasma could enable minimally invasive assessment and tracking of human aging for any organ.

Animal studies show aging varies between individuals as well as between organs within an individual, but whether this is true in humans and its effect on age-related diseases is unknown. We utilized levels of human blood plasma proteins originating from specific organs to measure organ-specific aging differences in living individuals. Using machine learning models, we analysed aging in 11 major organs and estimated organ age reproducibly in five independent cohorts encompassing 5,676 adults across the human lifespan.

We discovered nearly 20% of the population show strongly accelerated age in one organ and 1.7% are multi-organ agers. Accelerated organ aging confers 20-50% higher mortality risk, and organ-specific diseases relate to faster aging of those organs. We find individuals with accelerated heart aging have a 250% increased heart failure risk and accelerated brain and vascular aging predict Alzheimer's disease (AD) progression independently from and as strongly as plasma pTau-181, the current best blood-based biomarker for AD. Our models link vascular calcification, extracellular matrix alterations, and synaptic protein shedding to early cognitive decline. We introduce a simple and interpretable method to study organ aging using plasma proteomics data, predicting diseases and aging effects.