Cross-Population Validity of Biomarkers

Why Validity Can Shift

Biomarkers are often developed in specific cohorts. Differences in genetics, environment, and healthcare can change how well a biomarker performs in other populations, requiring explicit evaluation of calibration and discrimination in each target group. [1] [2]

Training Data Bias

If a biomarker is trained on a narrow demographic, it may be less accurate for people outside that group. This can lead to systematic over- or under-estimation of biological age, particularly across ancestry, socioeconomic, and health-status gradients. [3] [4]

Technical and Lifestyle Factors

Laboratory methods, diet, smoking, and medication can influence biomarker levels. These factors can differ across regions, affecting comparability and the clinical validity of thresholds derived in one setting. Harmonization work shows that assay platforms and specimen types can shift absolute values even when rank order is preserved. [5] [6]

Improving Generalizability

Researchers validate biomarkers across independent datasets and adjust models for population-specific differences. Broader sampling, external validation, and multi-cohort harmonization improve fairness and utility. [2] [5]

Summary

Cross-population validity is essential for trustworthy ageing biomarkers. Without it, measures can be misleading when applied beyond their original context. [2] [7]

References

1. Huang, Y., & Pepe, M. S. (2009). Biomarker evaluation and comparison using the controls as a reference population. Biostatistics, 10(2), 228-244. https://academic.oup.com/biostatistics/article/10/2/228/259645
2. Moqri, M., et al. (2024). Validation of biomarkers of aging. Nature Medicine, 30(6), 1455-1467. https://pmc.ncbi.nlm.nih.gov/articles/PMC12824367/
3. McGlinchey, E., et al. (2024). Biomarkers of neurodegeneration across the Global South. The Lancet Healthy Longevity, 5(9), e611-e627. https://www.thelancet.com/journals/lanhl/article/PIIS2666-7568(24)00132-6/fulltext
4. Herzog, C. M. S., et al. (2024). Challenges and recommendations for the translation of biomarkers of aging. Nature Aging, 4(11), 1231-1241. https://pmc.ncbi.nlm.nih.gov/articles/PMC12824367/
5. Hu, P., Kohler, S., & Goldman, N. (2024). Harmonization of four biomarkers across nine nationally representative studies of people 50 years of age and over. American Journal of Human Biology, 36(4), e24030. https://onlinelibrary.wiley.com/doi/full/10.1002/ajhb.24030
6. Bossuyt, P. M. (2010). Clinical validity: defining biomarker performance. Scandinavian Journal of Clinical and Laboratory Investigation Supplement, 70(242), 46-52. https://pubmed.ncbi.nlm.nih.gov/20515277/
7. Crimmins, E. M. (2011). Biomarkers related to aging in human populations. Current Gerontology and Geriatrics Research, 2011, 471738. https://pmc.ncbi.nlm.nih.gov/articles/PMC5938178/