Summary
Exposomics combines measurements of environmental and lifestyle exposures with genetic information. A 2026 analysis found that groups of exposure markers explained more variation in disease-related traits than markers assessed individually, while researchers work to connect those patterns to individual risk.
Exposomics—the study of environmental exposures and lifestyle factors across a person’s life—could help explain why people with similar genetic risks develop different diseases. In a 2026 analysis using data from ten cohorts in the US National Health and Nutrition Examination Survey (NHANES), researchers found that combinations of exposure markers explained more variation in disease-related outcomes than markers considered individually.
The findings point to the potential of measuring many influences together, rather than studying one pollutant, habit or nutrient at a time. Turning those patterns into useful personal risk assessments, however, requires researchers to identify which exposures are linked to meaningful changes in biological pathways.
Why combined exposures matter
Genes contribute to disease risk, but they are only part of the picture. The exposome encompasses non-genetic influences such as diet, medications, physical activity, infections and contact with chemicals, alongside environmental conditions including air pollution. Some exposures can affect how genes are regulated; tobacco smoke, for example, can silence tumour-suppressor genes.
A study led by Chirag Patel compared 619 exposure markers—including blood mercury and vitamin B12—with 305 measurable characteristics, such as lung function and blood sugar. Taken individually, the markers accounted for less than 1% of variation in disease outcomes. When researchers combined 20 exposures, the average rose to 3.5%. Patel said that level is comparable to the predictive power of genetic variants that influence disease.
The analysis also reported that a unique combination of 20 exposures explained 43% of participants’ triglyceride levels. The combination included trans fats and polychlorinated biphenyls (PCBs). Triglycerides are a blood fat and a predictor of cardiovascular disease. These results describe patterns across cohorts; they do not by themselves identify which exposure caused a particular outcome in an individual.
Turning exposure traces into measurements
Exposures can leave measurable traces in blood or urine. These include chemicals, their breakdown products, and biological markers such as proteins or changes associated with gene regulation. Some traces persist: the article describes chemical tags on DNA related to smoking that can remain detectable decades after someone stops.
High-throughput mass spectrometry lets researchers analyse large numbers of samples for proteins and metabolites. A single high-resolution scan can produce 10,000 to 100,000 distinct peaks, but identifying the chemical compounds behind those signals is a major bottleneck. Machine-learning tools are being used to help interpret the data and predict possible biological effects from chemical structures, including for chemicals not yet well characterised.
The Exposome-Scan facility in Leiden, launched in 2024, can identify roughly 700 chemicals in its profiles, including pesticides and flame retardants. Roel Vermeulen, who co-leads the facility, estimates that this represents about 10–20% of the few thousand chemicals that cause significant exposures in people globally.
From cohort patterns to personal care
A central challenge is connecting exposure measurements to changes in biology, then determining whether those changes can help predict disease risk or guide prevention and treatment. Patel’s group has identified finding exposures that perturb biological pathways as a next step. Such links could help researchers move from population-level patterns towards identifying people whose disease risk or progression differs.
That work will draw on long-term cohorts, biobanks, surveys and environmental datasets, as well as laboratory measurements. The Nature account describes the analysis as spanning ten NHANES cohorts but does not give a combined participant count. Its results are therefore best understood as a population-level analysis of exposure markers and disease-related traits, rather than evidence that exposome-guided care has improved clinical outcomes.
The promise of exposomics lies in bringing these varied signals together. More complete exposure profiles could eventually complement genomic information in precision prevention and care, provided researchers can establish which measured patterns are biologically important and useful for individual decisions.