Can Proteins Reveal How Fast Someone Is Aging?

Not everyone ages at the same rate.

Two individuals may share the same chronological age, yet differ significantly in their physical health, cognitive function, and risk of age-related disease. This discrepancy has driven growing interest in biomarkers of biological age, measurable indicators that can provide a more accurate picture of how an individual is aging.

While genomics and transcriptomics have advanced our understanding of aging biology, proteins offer an alternative viewpoint into the aging process. As the functional molecules responsible for carrying out cellular processes, proteins reflect both an individual's genetic makeup and the cumulative effects of lifestyle, environment, and disease, potentially providing a more dynamic view of biological aging.

Advances in proteomics, the large-scale study of proteins, are enabling researchers to identify aging-associated protein signatures and develop new measures of biological age. Among the leaders in this field is Dr. Luigi Ferrucci, the scientific director of the National Institute on Aging, whose work has helped guide the development and clinical interpretation of aging biomarkers.

“Unlike genomic markers, which largely reflect predisposition, the proteome is dynamic and responsive to physiological state, environmental exposures, and disease processes. This makes proteomic measures particularly well suited to capturing ongoing biological changes, including those associated with aging and disease progression,” said Ferrucci.

Understanding aging, therefore, may require looking beyond genes and focusing on the proteins that drive cellular function and health.

From mass spectrometry to high-throughput protein profiling

Advances in analytical technologies have driven the rapid growth of aging proteomics. Until relatively recently, researchers could only measure a limited number of proteins at a time, restricting efforts to identify comprehensive aging signatures; but today, modern proteomics platforms can quantify thousands of proteins simultaneously from a single sample.

Mass spectrometry remains the foundation of proteomics research. In discovery-based studies, proteins are identified and quantified without prior assumptions, allowing researchers to find previously unknown biomarkers. Improvements in data acquisition strategies, including data-dependent and data-independent approaches, have increased the depth and reproducibility of protein measurements. Quantitative workflows further enable researchers to compare protein abundance across individuals, tissues, and age groups.

Alongside mass spectrometry, affinity-based platforms have transformed large-scale biomarker research. These technologies use highly specific binding reagents to measure large panels of circulating proteins across population-scale cohorts, making them particularly valuable for aging studies involving thousands of participants.

At the same time, advances in computational analysis have become essential. Modern proteomics datasets generate enormous volumes of information. Machine learning approaches are being used to identify complex patterns, distinguish meaningful biological signals from noise, and develop models capable of predicting biological age.

“Advances in high-throughput, multiplexed proteomic platforms now allow the simultaneous measurement of thousands of proteins from small sample volumes, increasing the feasibility of integrating these markers into clinical workflows,” said Ferrucci.

Together, these technological innovations have dramatically expanded the number of proteins that can be measured and linked to aging.

Emerging biomarkers and signatures of biological age

As technologies have improved, researchers have begun to expose a complex network of protein changes associated with aging.

“Proteomic biomarkers are especially promising because they sit at the interface between biological mechanisms and clinical phenotypes,” said Ferrucci.

Rather than revealing a single biomarker that defines biological age, proteomics is painting a picture of aging as a multifaceted, system-wide process, suggesting that biological aging is less like a single process and more like a gradual breakdown in the mechanisms that maintain cellular and physiological health.

Many studies have identified plasma proteins that correlate strongly with age and age-related outcomes. Some of the strongest age-related signals detected involve inflammation and immune regulation. Chronic low-grade inflammation, often referred to as “inflammaging,” has been linked to many age-related conditions and is reflected in changes to numerous circulating proteins. These molecular signatures suggest that the immune system plays a role in defining how individuals age and how susceptible they become to disease.

Proteomics has also highlighted the importance of proteostasis, the cellular systems responsible for maintaining protein quality and function. With age, these quality-control mechanisms gradually decline, contributing to the accumulation of damaged or misfolded proteins. These disruptions can affect multiple tissues and have been implicated in several age-related disorders.

However, these changes do not occur in isolation.

Metabolic and lipid-related proteins have emerged as another important class of aging biomarkers, reflecting changes in energy metabolism and physiological regulation that occur across the lifespan. Meanwhile, analyses of different tissues have revealed that organs do not age uniformly. Instead, each tissue appears to follow its own molecular trajectory, with distinct protein signatures emerging over time.

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In parallel, growing attention is being paid to senoproteins and other age-associated secreted factors released by senescent cells. These proteins may help explain how local cellular aging can influence systemic health and contribute to age-related decline in distant tissues.

The integration of these diverse protein signatures has led to the development of proteomic aging clocks, computational models that estimate biological age using panels of proteins rather than individual biomarkers.

Can proteomics improve how we measure and manage aging?

The promise of proteomics lies in improving how aging is measured and managed in clinical settings.

Unlike many biomarkers that act primarily as indicators of disease risk, proteins are often directly involved in biological processes that drive pathology, “which enhances their interpretability and clinical relevance,” said Ferrucci.

This has generated considerable interest in proteomics-based aging clocks, which could help identify individuals at elevated risk of disease, monitor responses to interventions, and support more personalized approaches to healthcare.

Researchers are also exploring how proteomic biomarkers could be combined with genomic, epigenetic, and metabolomic data to produce a more comprehensive picture of biological aging.

Despite the excitement, significant challenges remain before these tools can be adopted routinely in healthcare.

“We are making substantial progress, but we are not yet at the point of routine clinical implementation. Several proteomic aging clocks show strong associations with mortality, multimorbidity, and functional decline across cohorts, which is encouraging. However, before these tools can be used in routine care, we need clearer evidence that they provide actionable information beyond established clinical measures and that their use leads to improved outcomes,” explained Ferrucci.

“Standardization across platforms, reproducibility in diverse populations, and the development of clinically meaningful thresholds are also essential,” he added.

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Ferrucci believes proteomic aging clocks may first find a role in specialized settings.

“In my view, proteomic aging clocks are likely to enter clinical practice first in targeted contexts—such as risk stratification in high-risk populations or as endpoints in clinical trials—before broader adoption in general health assessments,” Ferrucci said.

“The most urgent next step is to demonstrate clinical utility—specifically, that proteomic biomarkers can meaningfully improve decision-making and patient outcomes beyond what is already achievable with existing tools,” he said. “This will require prospective studies embedded in clinical settings, where proteomic measures are used to guide interventions and their impact on outcomes is evaluated.”

“Ultimately, success will depend not only on analytical performance but on whether these biomarkers can be translated into clear, actionable insights for clinicians and patients,” he said.

The future of aging biomarkers

High-throughput analytical platforms, sophisticated computational tools, and large population studies are revealing how complex networks of proteins change across the lifespan.

This work is shifting the field away from the search for a single marker of aging and towards an appreciation of aging as an interconnected biological process involving inflammation, metabolism, proteostasis, and tissue-specific functional decline.

Proteomics offers a uniquely functional perspective, capturing molecular changes that often lie closer to physiology and disease than genetic measures alone.

However, important challenges remain: “One of the major challenges is ensuring that proteomic biomarkers are valid and reliable across populations with different genetic backgrounds, environmental exposures, and disease burdens. Protein levels can be influenced by a wide range of factors, including diet, inflammation, comorbidities, and socioeconomic context, which may differ substantially across groups,” said Ferrucci.

“Many existing studies have been conducted in relatively homogeneous populations, limiting generalizability. In addition, technical variability across platforms and differences in sample handling can introduce bias,” he said.

“Addressing these issues will require large, well-characterized, diverse cohorts, harmonization of analytical methods, and careful evaluation of how social and biological factors interact to influence proteomic profiles. Without this, there is a real risk of developing biomarkers that perform well in research settings but less well in real-world clinical populations,” Ferrucci concluded.