Accelerated muscle aging alters resting-state brain connectivity in older adults

In a finding that may reshape how clinicians think about healthy aging, a team of Italian neuroscientists and geriatricians has shown that the biological age of a person’s muscles—measured independently of how many birthdays they have had—leaves a measurable fingerprint on the way the brain talks to itself at rest. The study, published in the journal GeroScience, used high-density electroencephalography (EEG) in 101 healthy older adults and found that people whose muscles are aging faster than their bodies show distinctly stronger, more widespread synchronization of brain rhythms than those whose muscles are aging slowly. The result adds a striking new dimension to the so-called muscle–brain axis, the emerging idea that skeletal muscle and the central nervous system are engaged in a continuous, bidirectional dialogue that shapes both physical and cognitive health in later life.

Aging, the authors emphasize, is not a single number. Chronological age tells us little about how quickly any one tissue or organ system is deteriorating, which is why researchers have increasingly turned to organ-specific “biological clocks.” Epigenetic clocks, which estimate biological age from patterns of DNA methylation, have dominated this field. But muscles—the largest organ in the body and the engine of functional autonomy—follow their own nonlinear trajectory, heavily influenced by lifestyle, nutrition, and physical activity. To capture this, the research team, led by Federico Frasca, Chiara Pappalettera, Alessia Cacciotti, and Fabrizio Vecchio of the Brain Connectivity Laboratory at IRCCS San Raffaele in Rome, together with colleagues at the University of Sassari, employed a previously developed “muscle phenotypic clock.” This regression-based model quantifies muscle age (MA) from the battery of functional assessments recommended by the revised European consensus on sarcopenia, EWGSOP-2, including anthropometric estimates of muscle mass, measurements of muscle strength, and motor performance tests such as gait and mobility evaluations.

The key derived quantity is muscle age acceleration (MAA)—the difference between a person’s estimated muscle age and their chronological age. A negative value signals decelerated muscle aging, meaning muscles functionally younger than expected; a positive value signals accelerated aging, a potential early warning sign on the road to sarcopenia, the age-related loss of muscle mass and function that affects a growing share of the world’s aging population. In the new study, participants—all healthy, neurologically intact older adults screened for cognitive impairment and other exclusion criteria—were stratified into three groups based on their MAA: decelerated, normal, and accelerated muscle aging.

To probe the brain side of the equation, each participant underwent eyes-closed resting-state EEG, a century-old technique whose spontaneous rhythms have proven remarkably informative about network-level brain health. The researchers computed magnitude-squared coherence (MSCoh), a frequency-resolved measure of how strongly oscillatory activity at pairs of cortical regions fluctuates in tandem, effectively an index of functional connectivity between brain areas. They then applied graph-theoretical analysis, computing node strength—the sum of a node’s connectivity weights across the whole network—to identify which cortical regions carried the largest differences between muscle-aging groups. Statistical testing used nonparametric permutation-based procedures of the kind standard in modern EEG network science, guarding against spurious findings across the many electrodes, frequency bands, and pairwise comparisons involved.

The results were striking in both direction and topography. Participants with decelerated muscle aging—those with the biologically youngest muscles—showed significantly lower coherence than both the normal and accelerated groups in the Alpha 1 (roughly 8–10 Hz), Alpha 2 (10–12 Hz), and Beta 1 (13–20 Hz) frequency bands. In other words, the parietal and frontal networks that generate these rhythms were less tightly synchronized in people whose muscles were aging slowly. Alpha oscillations are classically associated with the brain’s attentional gating and the efficient allocation of cognitive resources, while beta rhythms are deeply involved in sensorimotor control and the maintenance of the motor status quo; both bands are known to be altered in dementia and in normal aging, where coherence typically increases, a pattern interpreted by many groups as a sign of reduced neural flexibility or compensatory neural “crosstalk.”

The node strength analysis sharpened the picture further. The decelerated MA group showed lower node strength in the right frontal area across the Alpha 1, Beta 1, and Beta 2 bands, while the normal MA group showed lower values in the right temporal region compared with the accelerated MA group. The consistent involvement of right-hemispheric frontal and temporal hubs is noteworthy. Right frontal regions are central to attentional control and executive function, and their connectivity patterns change in characteristic ways with aging and neurodegeneration. The findings align with established models of age-related network reorganization, such as the HAROLD model of hemispheric asymmetry reduction, in which older adults recruit additional homotopic regions to sustain performance. In this framework, the increased synchronization seen in people with accelerated muscle aging may represent a less efficient, more rigid network configuration—perhaps an early neural correlate of the same processes that stiffen muscles and slow gait.

What mechanism could connect the biological age of muscle to the topology of brain networks? The authors and the broader literature point to several converging pathways. Skeletal muscle is not merely a mechanical engine; it is an endocrine organ that secretes myokines during contraction, molecules such as irisin and interleukin-6 variants that influence neuroplasticity, inflammation, and metabolism. Conversely, the corticospinal drive from the motor cortex shapes the very muscle activity that maintains muscle mass, and recent work has suggested that deteriorating corticospinal control may itself be a determinant of sarcopenia. Chronic low-grade inflammation—termed “inflammaging”—is a shared driver of both muscle wasting and cognitive decline, and clinical studies have repeatedly linked sarcopenia to white matter hyperintensities, cognitive impairment, and dementia risk. The new study is distinctive in that it avoids the confounding presence of diagnosed disease: all participants were healthy, meaning the brain-network differences reflect a graded, subclinical gradient of body-brain aging within the normal older population.

Methodologically, the study is notable for its attempt to quantify an organ’s biological age using purely phenotypic, clinically accessible measures. The muscle phenotypic clock, developed by the Sassari group in earlier cross-sectional work on middle-aged and older adults, was built with regularized regression techniques—the elastic net of Zou and Hastie, implemented in machine-learning toolchains such as scikit-learn—combining anthropometrics, bioelectrical estimates of skeletal muscle mass, grip strength and other strength measures, and standardized motor tests including the timed Up-and-Go and the six-minute walk protocol. Because these measures can be gathered in any geriatric clinic, the approach sidesteps the cost and invasiveness of epigenetic sequencing, raising the prospect that a routine functional assessment could one day yield both a muscle age score and, if corroborated by future work, a noninvasive window into brain network health.

The clinical implications cut in both directions. First, MAA could serve as a low-cost screening variable that flags older adults whose brain networks may already be drifting toward the hyper-synchronous patterns associated with cognitive vulnerability, well before symptoms appear. Second, and perhaps more provocatively, the findings suggest that interventions designed to slow muscle aging—progressive resistance training, adequate protein intake, physical activity programs—might do double duty by favorably reshaping brain connectivity. Previous EEG studies have shown that resting-state connectivity predicts motor skill learning and responds to rehabilitation after stroke, and integrated motor-cognitive training programs are increasingly advocated for frail older adults. If muscle age acceleration truly shapes brain network topography, then redirecting that trajectory becomes not merely a matter of preserving mobility but of protecting the brain itself.

The authors are careful to frame the study as hypothesis-generating. It is cross-sectional, so causality cannot be established: accelerated muscle aging might drive brain changes, brain changes might drive muscle decline, or a third factor such as systemic inflammation might drive both. The cohort, while carefully characterized, is limited in size, and EEG coherence reflects only cortical, predominantly radial, current sources rather than the full three-dimensional complexity of brain network dynamics. Longitudinal studies tracking MAA and EEG networks in the same individuals over years, ideally combined with measures of cognitive trajectory and blood-based biomarkers, will be needed to determine whether the muscle clock can predict future brain decline and whether interventions that decelerate muscle aging produce measurable, beneficial shifts in resting-state connectivity.

Even with those caveats, the study lands at a moment of intense interest in biological age clocks and in the muscle–brain axis. The idea that “muscle age” is not just a poetic phrase but a quantifiable, clinically meaningful variable—one that leaves an electrical signature in the resting brain—is likely to energize both the geroscience and neurorehabilitation communities. For a global population aging at unprecedented speed, the promise of a single clinic visit that yields a muscle age, an early read on brain network health, and a personalized prescription of exercise and nutrition is an alluring one. This study offers the first EEG-based evidence that the trajectory of muscle aging and the architecture of resting brain networks are systematically intertwined in healthy older people, and it points toward an era in which keeping the body’s largest organ young may be among the most effective strategies for keeping the brain young too.