Blood protein clocks flag higher risks of death and chronic disease

By Pooja Toshniwal PahariaReviewed by Susha Cheriyedath, M.Sc.Jun 30 2026

A major European study suggests blood protein patterns can reveal accelerated biological aging, flagging higher risks of death and chronic disease years before diagnosis.

Study: Associations of proteomic age clocks with lifestyle risk factors, incident chronic diseases and mortality in two European cohorts. Image Credit: ArtemisDiana / Shutterstock

A recent pan-European study published in the journal Nature Aging highlights associations between proteomic analysis-derived biological aging, lifestyle factors, and the incidence of chronic diseases.

Researchers found that accelerated aging was also associated with a higher risk of all-cause mortality. In fact, each additional year of the global age gap was associated with a 13% higher risk of all-cause mortality. 

The predictive performance of the proteomic aging clocks for mortality outcomes was also comparable to that of established risk factors. These findings suggest that biological aging, as measured by an individual’s plasma protein profile, if integrated with routine investigations in the future, could one day help clinicians evaluate the likelihood of morbidity and mortality, improving risk stratification and prognosis determination.

Scientists use various measures to estimate an individual's biological age. Proteomic clocks estimate biological age through protein profiling. Researchers believe that proteomic clocks may help identify people at risk of chronic diseases based on their biological age.

By analyzing protein levels in the body, these clocks may also help uncover molecular pathways linking age-related changes with chronic diseases, supporting future precision medicine approaches for more targeted care.

About the study

In the present study, researchers investigated associations between proteomic clock-based biological age and incident chronic diseases and all-cause mortality. To do so, they used the SomaScan proteomic platform to estimate proteomic age in 17,473 individuals who participated in the European Prospective Investigation into Cancer and Nutrition (EPIC). The study comprised individuals from Spain, Italy, the United Kingdom (UK), Germany, and the Netherlands, aged 35 to 75 years.

Analysis of participant plasma samples using the SomaScan platform enabled the team to calculate systemic and organ-specific aging. They averaged proteomic age estimates and age gaps from five conventional proteomic clocks (Tanaka, Lehallier, Wang, Oh, and Sathyan) to create a global clock. They then evaluated associations between biological age and lifestyle factors, including the healthy lifestyle index (HLI), among participants followed for up to 28 years.

The researchers then estimated hazard ratios (HRs) for a 1-standard-deviation increase in the age gap using Cox regression models to assess the risks of chronic disease development and all-cause mortality.

These models were adjusted for body mass index (BMI), smoking status, alcohol intake, healthy diet score, physical activity, and educational attainment. The team identified incident cases through linkage with disease registries, care and drug registers, hospitalization records, and mortality data, and classified outcomes using the International Classification of Diseases, tenth revision (ICD-10) codes.

They also replicated the findings among the British Whitehall II prospective cohort study participants. They also performed sensitivity analyses by restricting the study to never-smokers and to events occurring after the first 2 or 5 years after enrollment.

Results

Acceleration in biological aging, as measured by the integrated proteomic clock, was associated with lifestyle factors such as physical inactivity, alcohol intake, and smoking. However, BMI and diet quality were not associated with the Global age gap, and lifestyle associations varied across individual clocks.

Accelerated aging, defined as an advanced biological age relative to chronological age, was also associated with an increased risk of chronic diseases, including cardiovascular disease, dementia, and malignancies of the oral cavity, pharynx, larynx, esophagus, liver, lungs, and kidneys.

Stomach, kidney, and lung cancers showed the most robust associations with organ-specific aging. Similar results were obtained for mortality risk prediction using proteomic aging clocks and lifestyle factors. The Global proteomic clock showed a strong association with death from any cause (HR, 1.4). The estimated increase in all-cause mortality risk per year of age gap ranged from 5.0% to 13%. The global clock yielded HR values of 1.3-1.4 for coronary heart disease and stroke, and 1.2 for dementia. Using this integrated clock, the team obtained HR values of 1.6, 1.5, 1.3, and 1.3 for liver, upper aero-digestive tract (UDAT), lung, and kidney cancers, respectively.

Organ-specific age gaps showed strong associations with cancers affecting the corresponding organ. The strongest association was observed between kidney biological age and renal cancer (HR, 1.6). Organ-specific aging in lungs and intestines also increased the risk of lung cancer and stomach cancer, respectively (HR, 1.4 for both). The sensitivity analysis yielded largely similar results, except for attenuations in kidney and lung cancer, indicating the robustness of the primary findings.

The Global Proteomic Aging Clock predicted mortality from any cause as accurately as conventional risk factors. Combining the findings with established risk factors further improved mortality prediction compared with using risk factors alone.

Conclusions

The findings highlight proteomic aging clocks as promising biomarkers for determining the likelihood of developing systemic age-related chronic diseases, including cancer. The Global Proteomic Clock was strongly associated with the incidence of many chronic diseases and all-cause mortality. Organ-specific proteomic clocks most strongly predicted kidney, lung, and stomach malignancies via age gaps in kidney, lung, and intestinal tissues.

The proteomic age also showed mortality-prediction performance similar to that of conventional risk factors, with improved results when used in combination. As this was an observational study, the findings do not establish causality.

If validated in further studies, including more diverse populations, genetic analyses, and primary-care trials, healthcare providers could integrate lifestyle risk factors such as alcohol intake, smoking habits, and physical inactivity, and proteome-based biological aging analysis in risk assessment protocols.

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