Aging & Longevity

Construction and Validation of Plasma Protein-Based Musculoskeletal Biological Age and Genetic and Environmental Risk Profiles.

TL;DR

Two musculoskeletal aging clocks (MSKAge and MSKAgeMort) were constructed from 39 plasma proteins using UK Biobank proteomic data, effectively predicting mortality, musculoskeletal disorders, and age-related diseases while revealing genetic variants, environmental pollutants, and zinc as a candidate therapeutic target.

Key Findings

A musculoskeletal protein pool of 39 proteins was constructed by integrating transcriptomic enrichment, protein functional annotation, and literature expertise.

  • The protein pool was derived from UK Biobank proteomic data of 21,070 participants.
  • Three complementary approaches were used to identify musculoskeletal-relevant proteins: transcriptomic enrichment, protein functional annotation, and literature expertise.
  • These 39 proteins formed the basis for both aging clocks developed in the study.

MSKAge demonstrated moderate correlations with chronological age, differing by sex.

  • MSKAge showed a correlation of rfemale = 0.62 in females and rmale = 0.56 in males with chronological age.
  • MSKAge was constructed using proteomic data from 21,070 UK Biobank participants.
  • The sex-stratified approach highlights biological differences in musculoskeletal aging between males and females.

MSKAgeMort demonstrated strong correlations with chronological age, particularly in females.

  • MSKAgeMort showed a correlation of rfemale = 0.93 in females and rmale = 0.88 in males with chronological age.
  • MSKAgeMort was the higher-performing of the two clocks in terms of correlation with chronological age.
  • The mortality-optimized clock (MSKAgeMort) outperformed MSKAge in predicting musculoskeletal outcomes.

Both MSKAgeAccel and MSKAgeMortAccel predicted mortality, musculoskeletal diseases, and age-related diseases, with MSKAgeMortAccel showing particularly strong associations.

  • Acceleration metrics quantified deviations of biological age from chronological age, with positive values indicating accelerated musculoskeletal aging.
  • Associations were assessed using Cox proportional hazards models.
  • MSKAgeMortAccel showed significant associations with osteoarthritis, rheumatoid arthritis, gout, and low back pain.
  • MSKAgeMortAccel was described as the stronger predictor of the two acceleration metrics for musculoskeletal disorders.

Environmental pollutants, psychological factors, and unhealthy lifestyles were identified as determinants of accelerated musculoskeletal aging as measured by MSKAgeMortAccel.

  • Environmental determinants were evaluated using linear regression analyses.
  • Psychological factors were among the modifiable environmental influences on MSKAgeMortAccel.
  • Unhealthy lifestyles were also significantly associated with higher MSKAgeMortAccel values.
  • Environmental pollutants represented an external environmental category linked to accelerated musculoskeletal aging.

Genome-wide association analyses identified 13 genetic variants and 71 genes associated with musculoskeletal aging acceleration.

  • The 71 genes were enriched in epigenetic regulation and extracellular environment-receptor interaction pathways.
  • Genetic analyses were conducted as genome-wide association analyses.
  • The identification of 13 variants provides specific loci for further investigation of genetic susceptibility to accelerated musculoskeletal aging.
  • Enrichment in epigenetic regulation suggests potential mechanisms through which genetic factors influence musculoskeletal aging.

Zinc was identified as a candidate drug for musculoskeletal diseases through drug repurposing analysis.

  • Drug repurposing methodology was used to identify potential therapeutic targets for musculoskeletal diseases.
  • Zinc was the specific compound highlighted as a candidate musculoskeletal drug from this analysis.
  • Drug repurposing leveraged the proteomic and genetic data generated in the study.

What This Means

This research suggests that it is possible to measure how quickly a person's musculoskeletal system (bones, muscles, and joints) is aging using a blood test that looks at 39 specific proteins. The researchers built two 'biological clocks' called MSKAge and MSKAgeMort using blood protein data from over 21,000 people in the UK Biobank. These clocks estimate a person's musculoskeletal biological age, and the difference between this biological age and their actual chronological age (called acceleration) can indicate whether their musculoskeletal system is aging faster or slower than expected. The more advanced clock, MSKAgeMort, was strongly correlated with chronological age (correlations of 0.88–0.93) and was able to predict who was more likely to develop conditions like osteoarthritis, rheumatoid arthritis, gout, and low back pain, as well as who had higher overall mortality risk. The study also investigated what drives accelerated musculoskeletal aging. On the environmental side, exposure to pollutants, poor mental health, and unhealthy lifestyle choices were all linked to faster musculoskeletal aging. On the genetic side, the researchers found 13 genetic variants and 71 genes that appear to influence musculoskeletal aging speed, many of which are involved in epigenetic regulation (how genes are switched on and off) and how cells interact with their surrounding environment. Additionally, using a drug repurposing approach, zinc was flagged as a potential treatment candidate for musculoskeletal diseases. This research matters because musculoskeletal conditions are among the most common causes of disability worldwide, and identifying people at risk earlier could allow for preventive action. The biological clocks developed here could theoretically help stratify individuals by their risk of musculoskeletal decline, and the identification of modifiable environmental factors (like lifestyle and pollutant exposure) suggests potential targets for intervention. The finding that zinc may be relevant to musculoskeletal health also opens a relatively accessible avenue for further investigation.

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Citation

Zhong Y, Xia M, Zhao X, Qu Y, Lei B, Zhen S, et al.. (2026). Construction and Validation of Plasma Protein-Based Musculoskeletal Biological Age and Genetic and Environmental Risk Profiles.. Aging cell. https://doi.org/10.1111/acel.70636