How a simple blood test can forecast future health risks

A landmark UK Biobank study shows how a single blood test can forecast the aging pace of your organs, revealing who’s at greatest risk and opening the door to targeted prevention strategies.

Study: Plasma proteomics links brain and immune system aging with healthspan and longevity. Image Credit: AntonSAN / ShutterstockStudy: Plasma proteomics links brain and immune system aging with healthspan and longevity. Image Credit: AntonSAN / Shutterstock

In a recent study published in the journal Nature Medicine, a group of researchers determined whether plasma-derived organ age predictions forecast disease and mortality and identified the organs that most influence human longevity.

Background

Every minute, age-related disorders claim more lives worldwide than infectious diseases. Yet the “ticking” that drives our organs toward decline is far from uniform: 24-year-olds can differ by a decade in the physiologic age of their hearts or brains. That gap shapes how soon someone might face heart failure, dementia, or frailty, turning the abstract concept of aging into a profoundly personal concern. Pinpointing which organs age fastest could sharpen preventive care and steer the booming longevity market away from hype toward measurable benefit. Current biological clocks rarely offer organ-specific insight or reliably predict future disease events, so better tools remain urgently needed.

About the study

In the United Kingdom Biobank (UKB) cohort, investigators analyzed plasma from 44,498 adults aged 40-70 years using an Olink proximity-extension assay to quantify 2,916 proteins. Genes from the Genotype-Tissue Expression (GTEx) atlas flagged proteins enriched ≥ 4-fold in one of eleven organs, including brain, heart, lung, liver, kidney, immune tissue, artery, adipose tissue, intestine, pancreas, and skeletal muscle. For each organ, a Least Absolute Shrinkage and Selection Operator (LASSO) model predicted chronological age in a training set and was validated elsewhere; the residual, z-scored difference created an “age gap.” Cox proportional-hazards models related one-standard-deviation age gaps to incident disease over 17 years, adjusting for age and sex.

Multivariable linear models tested associations with lifestyle, socioeconomic status, and medication use, while Magnetic Resonance Imaging (MRI) brain volumes were compared with plasma-based brain age in a nested sample. Extreme agers were defined by age gaps ≥ 1.5 standard deviations; mortality risk was modeled against the number of aged or youthful organs after further adjustment for serum cystatin C, Phenotypic Age, and Apolipoprotein E (APOE) genotype. Quality control removed extreme outliers, imputed missing values, and applied false-discovery-rate correction. Ethical approval was granted by the North West Multi-Centre Research Ethics Committee, and all participants provided written informed consent. All analyses were executed in Python using packages such as scikit-learn and lifelines.

Study results

The sharp divergence among organ clocks, evidenced by a mean pairwise age-gap correlation of just 0.21, confirms that biological aging progresses asynchronously across tissues. The study also noted sex differences, with males showing older kidneys, immune systems, and intestines on average, while females had older adipose tissue, arteries, and hearts. A one-standard-deviation increase in heart age predicted heart failure (HR = 1.83), pancreas and kidney age predicted chronic kidney disease (HRs of 1.80 and 1.66, respectively), and lung age predicted Chronic Obstructive Pulmonary Disease (COPD) (HR = 1.39). Brain age alone forecasted Alzheimer’s disease with an HR of 1.80, which is approximately the risk conferred by a single APOE4 allele. Youthful brains (≤ -1.5 standard deviations) reduced Alzheimer’s incidence by 74%, paralleling protection from two APOE2 alleles.

Predictive accuracy in the hold-out sample was strong, with a mean absolute error ranging from 2.8 years for kidney to 4.6 years for immune tissue. The study found that organ age models provided additional predictive power for mortality beyond established clinical biomarkers. Importantly, age gaps were associated with future disease onset over a 2- to 17-year follow-up period, providing a window for preventive action.

Smoking, heavy alcohol, processed meat, insomnia, and socioeconomic deprivation accelerated multiple organs, whereas vigorous exercise, oily fish, poultry, and tertiary education kept organs younger. Among 137 common products, ibuprofen, glucosamine, multivitamins, vitamin C, and cod-liver oil were associated with younger kidneys, brains, or pancreas, and conjugated equine estrogen (Premarin) was associated with younger immune and vascular profiles after premature menopause. However, the authors note these cross-sectional findings should be interpreted with caution. Notably, some findings were counterintuitive: youthful-appearing arteries were paradoxically associated with an increased risk for several diseases, including type 2 diabetes and COPD.

Mortality risk was compounded by the number of aged organs. Brain age remained the dominant driver (HR = 1.59 per standard deviation). Possessing 2-4 aged organs doubled mortality, while 5-7 quadrupled it; 8 or more raised the hazard 8.3-fold, with 60% of that group dying within fifteen years. Conversely, a youthful brain or immune system reduced risk (HRs of 0.60 and 0.58, respectively), and having both reduced risks still further (HR = 0.44).

Protein signatures pointed to plausible mechanisms. Neurofilament light chain (NEFL), Glial fibrillary acidic protein (GFAP), and Brevican (BCAN) dominated brain aging; Renin (REN) and N-terminal pro-B-type Natriuretic Peptide (NT-proBNP) led kidney and heart aging; and Matrix Metalloproteinase-9 (MMP9) was a key protein in an enriched immune system pathway linked to neuroinflammation. Plasma and MRI brain clocks weakly agreed (r=0.18), implying they capture complementary stages of neurodegeneration. The authors note that because the study participants were predominantly of European ancestry, the findings may need recalibration for use in more diverse populations. A feature importance analysis highlighted inflammatory and extracellular-matrix pathways as dominant themes, suggesting that immunosenescence and stromal remodeling are shared levers for slowing systemic decline worldwide and may direct future studies.

Conclusions

To summarize, this large-scale proteomic study demonstrates that a single blood draw can map the biological age of individual organs, unmask hidden disease risk, and quantify the life-extending value of resilient brain and immune systems. The findings make aging personal: though everyone advances chronologically, choices like quitting smoking to embracing vigorous exercise or tailored hormone therapy decide whether vital organs sprint or stroll toward degeneration. Integrating plasma clocks into routine care could trigger earlier organ-targeted interventions, evaluate emerging anti-aging drugs, and sharpen public-health messaging. Future work should validate algorithms in diverse populations and test whether safely modifying organ age prolongs healthspan globally.

Journal reference:
  • Oh, H.S.H., Le Guen, Y., Rappoport, N., Urey, D.Y., Farinas, A., Rutledge, J., Channappa, D., Wagner, A.D., Mormino, E., Brunet, A., Greicius, M.D. and Wyss-Coray, T. (2025). Plasma proteomics links brain and immune system aging with healthspan and longevity. Nat Med. DOI: 10.1038/s41591-025-03798-1 https://www.nature.com/articles/s41591-025-03798-1
Vijay Kumar Malesu

Written by

Vijay Kumar Malesu

Vijay holds a Ph.D. in Biotechnology and possesses a deep passion for microbiology. His academic journey has allowed him to delve deeper into understanding the intricate world of microorganisms. Through his research and studies, he has gained expertise in various aspects of microbiology, which includes microbial genetics, microbial physiology, and microbial ecology. Vijay has six years of scientific research experience at renowned research institutes such as the Indian Council for Agricultural Research and KIIT University. He has worked on diverse projects in microbiology, biopolymers, and drug delivery. His contributions to these areas have provided him with a comprehensive understanding of the subject matter and the ability to tackle complex research challenges.    

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