LongeCityNews Last Updated: 28 November 2025 - 10:14 AM

A large body of evidence indicates that forms of air pollution harm long-term health. This is largely epidemiological data, observing correlations with incidence of mortality and age-related disease in populations exposed to different levels of pollutants. A number of regions of the world exhibit, through happenstance, very similar populations that are exposed to significantly different levels of particulate and chemical pollutants. Consider studies covering the Puget Sound in the US or parts of China. These natural experiments provide an increased confidence that the observed correlations are a matter of air pollution causing harm to health.

The primary mechanism by which air pollution is thought to accelerate the onset and progression of age-related disease is via induction of chronic inflammation. Airway exposure to pollutants stresses cells, changes their behavior, and contributes to the burden of continual, unresolved inflammation that is characteristic of aging. This exposure exists on a spectrum, with smoking and indoor wood smoke at one end and the less severe degrees of industrial pollution in wealthier parts of the world at the other. Since effects are driven by inflammation, we should expect near all age-related conditions to be aggravated over time by exposure to air pollution, scaling by the severity of the exposure.

Air Pollution Exposure and Muscle Mass and Strength Decline in Older Adults: Results From a Swedish Population-Based Study

Emerging evidence suggests that air quality may impact muscle health. However, most studies are limited by cross-sectional designs or short follow-ups. We assessed the association of long-term exposure to ambient air pollutants with changes in muscle mass and strength in older adults. We included 3,249 participants from the SNAC-K longitudinal study (mean age 74.3 years; 35.8% males). Muscle strength (measured through handgrip and chair stand tests), muscle mass (derived from calf circumference) and physical performance (assessed through walking speed at a usual pace) were assessed over a 12-year period. Probable sarcopenia was defined as reduced muscle strength as per the EWGSOP2 criteria. Residential exposure to PM2.5, PM10, and nitrogen oxide (NOx) was estimated for the 5 years preceding baseline. Cox regressions and linear mixed models examined the association of air pollutant exposure with, respectively, probable sarcopenia and longitudinal changes in muscle parameters.

Over 12 years, the cumulative incidence of probable sarcopenia increased with higher exposure (above vs. below the median values) to NOx (36% vs. 28%), PM2.5 (35% vs. 28%) and PM10 (35% vs. 28%). The association between air pollutant levels and the risk of probable sarcopenia was nonlinear, with an increased risk showing a plateau at very high levels. Higher exposures were associated with an increased risk of developing probable sarcopenia, by 25% for NOx and PM2.5 to 33% for PM10. Elevated pollutant exposure was associated with significantly greater annual declines in lower-limb strength (chair stand test: 0.40-0.48 s) and walking speed (0.004 m/s).

Thus long-term exposure to moderate levels of ambient air pollutants may increase the risk of probable sarcopenia and accelerate declines in lower-limb strength and physical performance in older adults.

In a preprint published in bioRxiv, Prof. Vadim Gladyshev and a team of researchers have used an artificial intelligence-based system to discover a wide variety of potential interventions, including a drug that significantly improves biomarkers of frailty in mice.

Repurposing previous data

Previous research efforts have created a massive dataset in the form of the Gene Expression Omnibus (GEO), which contains the results of a great many experiments related to potentially disease-modifying drugs, many of which are tissue-specific [1]. These researchers refer to this dataset as a “massive missed opportunity” in aging research, because the vast majority of the experiments in the GEO were unrelated to aging and their data was never investigated in that context.

However, investigating all of that data by hand is practically impossible. These researchers note that modern LLMs can “autonomously generate hypotheses, execute complex analytical pipelines, synthesize findings across multiple data sources, and identify patterns that human researchers might overlook.” Combining that ability with the latest generation of clocks, including causality-based clocks such as AdaptAge, CausAge, and DamAge [2], may yield insights that would have simply gotten lost in the noise.

To that end, these researchers created ClockBase Agent, which uses over two million human and murine samples, including both RNA sequencing and epigenetic measurements, and 40 separate aging clocks. Unlike previous efforts in this area, which used simpler AI systems to simply link compounds to improvements in aging biomarkers, ClockBase is built to exhibit real agentic behavior: it uses an LLM to generate hypotheses about this data, then verifies these hypotheses with more in-depth examinations of both the raw data and the literature from which the data was derived.

Much of the data agrees with existing databases

Unsurprisingly, the clocks showed their natures rapidly. The researchers found that first-generation clocks, which were simply meant to estimate chronological age, were strongly correlated with each other, while healthspan-based clocks such as GrimAge were indeed correlated with healthspan and had data clusters accordingly.

Of a total of 43,529 interventions, which included genetics, diseases, pharmacology, and environment, the researchers’ AI model identified 5,756 that were statistically likely to have age-modifying effects. One was the knocking out of IFR4, which is essential in immune cell differentiation, and another was the knockout of Mettl3, which methylates RNA.

The expression of Bach2, which keeps T cells quiescent, was also associated with reduced aging, as was the overexpression of miR-155, a result that the AI gave an extraordinarily low p-value (2.69 * 10^-10), reflecting very high confidence, and the researchers found surprising due to miR-155’s pro-inflammatory effects. On the other hand, the disruption of hedgehog signaling, which is required for tissue homeostasis, and the knockout of H3K9 methyltransferases substantially increased aging; the latter result is wholly unsurprising due to H3K9’s effects on methylation. Most of its results agreed with the existing GeneAge database, and the few that did not could mostly be explained by the negative, age-increasing effects of knocking out “anti-longevity” genes such as Mtor.

The AI agreed wth the consensus that rapamycin and metformin reduce biological aging. It also found that ouabain, a little-known but established senolytic, also substantially reduces aging according to these clocks, as does the dyslipidemia drug fenofibrate. The immunomoulator Serpina3n was strongly linked to reduced aging, while the immune activator 3M-052 accelerated it. Many of the drugs the model identified are already approved by the FDA; unfortunately, it found that nearly two-thirds of the drugs it identified accelerate aging rather than slow it down. Only five of its results were found in the existing DrugAge database, which agreed with the direction of all five.

This model also found that environmental causes led to biological effects. A combination of mechanical overload, which may reflect exercise practices such as resistance training, along with senolytic administration was substantially associated with reductions in age. Hypoxia, the ischemia-reperfusion injury associated with heart attacks and their treatment, infection with viruses, and some metabolic disorders also accelerated age. Exposing embryos to high-intensity light sources accelerates their aging as well.

Overall, the researchers found that their agent found a substantial amount of both corroborating information and potentially actionable new information, stating that it “reveals a substantial set of new intervention candidates for aging research.” While the AI did make a handful of mistakes in its generation of hypotheses, such as being tripped up on clock age versus chronological age and some issues relating to control groups and treatment groups in complex experiments, its overall results provide an immense potential starting point for further work.

Verifying the AI’s data

The researchers took a crucial step to determine if their model was accurate: they used ouabain, the senolytic that the AI identified as being age-decelerating, in their own experiment with standard, 20-month-old, Black 6 mice. They followed the same protocol as the ouabain experiment that the AI had used to generate its conclusion.

In this experiment, the treatment group was far healthier than the control group after three months of intermittent ouabain exposure. This included metrics of frailty, cognitive ability, and fur condition. Their hearts functioned better, as did the microglia in some but not all brain regions. In total, the AI model had correctly identified ouabain as a potential age-modifying drug.

Of course, this was a murine result published in a preprint paper, and ouabain and many of the other interventions will have to go through further experiments and clinical trials before they can be confirmed as treatments and applied to human beings. The AI’s occasional flaws in reasoning mean that, despite the tremendous advances in this field over the past couple of years, it still cannot be fully relied upon to yield perfectly accurate information. However, it is clearly an invaluable tool in giving researchers critical clues that they would probably never have found without it.

Literature

[1] Edgar, R., Domrachev, M., & Lash, A. E. (2002). Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic acids research, 30(1), 207-210.

[2] Ying, K., Paulson, S., Reinhard, J., de Lima Camillo, L. P., Träuble, J., Jokiel, S., … & Biomarkers of Aging Consortium. (2024). An open competition for biomarkers of aging. bioRxiv.

Metabolism is exceedingly complex, and incompletely understood. This is true of individual cells, let alone organisms made up of very large numbers of those cells. Most of the work done on interventions intended to slow aging takes the form of attempts to alter metabolism into a more favorable state in which aging progresses at a modestly slower pace, usually via the use of small molecules. This approach is doomed to failure at this stage of technological progress. We do not know enough of metabolism, we cannot control enough of metabolism. Studies show that combining any two marginally aging-slowing small molecules is as likely as not to produce an interaction that results in a marginal acceleration of aging. Similarly, researchers here demonstrate that a sizable fraction of marginally aging-slowing interventions only work at certain ages, and become marginally aging-accelerating at other ages. And at the end of the day, why is so much of the focus placed on interventions that cannot achieve more than a small benefit?

A growing number of compounds are reported to extend lifespan, but it remains unclear whether they reduce mortality across the entire life course or only at specific ages. This uncertainty persists because the commonly used log-rank test cannot detect age-specific effects. Here, we introduce a new analytical method that addresses this limitation by revealing when, how long, and to what extent interventions alter mortality risk.

Applied to survival data from 42 compounds tested in mice by the National Institute on Aging Interventions Testing Program, our method identified 22 that reduced mortality at certain ages, more than detected by the log-rank test, while 15 increased mortality at certain ages. Most compounds were effective only within restricted age ranges; just 8 reduced mortality late in life, when burdens of aging are greatest. Compared to conventional methods, this approach uncovers more beneficial and harmful effects, offers deeper insight into timing and mechanism, and can guide development of future anti-aging therapies.

Link: https://doi.org/10.1038/s41467-025-65158-4

Muscle tissue is metabolically active, particularly following exercise, in ways that improve function in other tissues. As a class, molecules secreted by muscle cells that affect other tissues are called myokines, and are not presently fully mapped and understood. The research community is actively engaged in identifying myokines and myokine interactions that could be targets for novel therapies that mimic some of the benefits of exercise. Here, researchers show that increased levels of the myokine cathepsin B can reduce the loss of function in the brain that occurs in a mouse model of Alzheimer's disease. Interestingly, this same treatment impairs cognitive function in normal mice, indicating that (a) there can be too much of this myokine in circulation, and (b) the relationship between cathepsin B signaling and cognitive function is likely complex.

Increasing evidence indicates skeletal muscle function is associated with cognition. Muscle-secreted protease Cathepsin B (Ctsb) is linked to memory in animals and humans, but has an unclear role in neurodegenerative diseases. To address this question, we utilized an AAV-vector-mediated approach to express Ctsb in skeletal muscle of APP/PS1 Alzheimer's disease (AD) model mice. Mice were treated with Ctsb at 4 months of age, followed by behavioral analyses 6 months thereafter.

Here we show that muscle-targeted Ctsb treatment results in long-term improvements in motor coordination, memory function, and adult hippocampal neurogenesis, while plaque pathology and neuroinflammation remain unchanged. Additionally, in AD mice, Ctsb treatment normalizes hippocampal, muscle, and plasma proteomic profiles to resemble that of wild-type (WT) controls. In AD mice, Ctsb increases the abundance of hippocampal proteins involved in mRNA metabolism and protein synthesis, including those relevant to adult neurogenesis and memory function. Furthermore, Ctsb treatment enhances plasma metabolic and mitochondrial processes.

In muscle, Ctsb treatment elevates protein translation in AD mice, whereas in WT mice mitochondrial proteins decrease. In WT mice, Ctsb treatment causes memory deficits and results in protein profiles across tissues that are comparable to AD control mice. Overall, the biological changes in the treatment groups are consistent with effects on memory function. Thus, skeletal muscle Ctsb application has potential as an AD therapeutic intervention.

Link: https://doi.org/10.1111/acel.70242