The immune system is central to health, but its condition varies widely between people of the same age. Genetics, environmental exposures, lifestyle and past infections can influence this variation, including differences in the immune system’s “biological age” — the rate at which it ages relative to a person’s chronological age. These differences have been associated with neurodegeneration, cancer and mortality risk, yet clinicians still lack a routine way to measure overall immune health.
A weakened immune system can increase susceptibility to infections and cancer, while an immune system that is misdirected can attack the body’s own tissues. Changes associated with ageing can also contribute to chronic diseases. Air pollution, smoking, diet and previous infections are among the factors that help shape an individual’s immune system.1,2 Variations in immune-system biological age have been linked to neurodegeneration, cancer and mortality risk.3
Immune cells play a surprising role in exercise endurance
Measuring immune health is difficult because the immune system is a dynamic network that changes over time. It includes specialized organs that support the development and function of immune cells, as well as cells that circulate through the body or reside in tissues.4 Cytokines and chemokines help these cells and tissues communicate.
Laboratory tests provide only a partial view. A white blood cell count can show whether the number of circulating immune cells is unusually high or low, while blood biomarkers such as C-reactive protein can indicate inflammation. Neither measurement is specific to a particular health condition, however. They do not show whether the immune system as a whole is resilient or compromised, or whether it can respond effectively to infections and other challenges.
Advances in immunology, molecular profiling, medical imaging and artificial intelligence are creating new opportunities to assess immune status. Researchers can now begin developing “immunomes” that comprehensively describe immune health across cells, tissues, organs and molecular interactions.
Research initiatives such as the Human Immunome Project are working towards this goal. One of us, JST, is acting as a co-leader, and the project has begun providing the necessary data. What remains unclear is whether, and how, this information can improve patient care. Researchers still need to determine which measurements best capture clinically important aspects of immune health, how stable those measurements are over time, and whether assessing them can improve clinical decisions and ultimately disease outcomes.
Measuring every aspect of the immune system will not be feasible for everyone. Instead, researchers should identify a small number of features that can be assessed easily in clinical practice to detect weakened immune functions or immune decline that is occurring more rapidly than expected. Such assessments could support disease prevention, treatment selection and monitoring of interventions.
For example, these features could help physicians identify accelerated immunosenescence and determine which individuals require closer disease monitoring. In cancer care, measures of immune health could help identify patients most likely to benefit from treatments that depend on the immune system,5,6 as well as those at high risk of side effects.
These measurements could also help explain why people of similar ages and apparently similar health can respond very differently to the same infection. This was evident during the COVID-19 pandemic. Antibody responses to the same vaccine also vary widely between individuals.2,7 If this variation can be measured, clinicians might use it to tailor prevention, treatment and monitoring to each person’s immune system.
Researchers, health systems and funders must find the smallest and most scalable set of measurements that can capture meaningful differences in immune function and guide clinical decisions. The following roadmap describes how the immunome could move from the laboratory into clinical practice.
Build long-term datasets
Scientists first need to generate long-term datasets from large study populations and biobanks. Understanding immune health fully will require repeated measurements of several immune-system components, alongside broader indicators of health and disease in the same people.
Analysing genes and gene expression in individual cells from large research populations and biobanks can show which immune-cell groups are present in different populations. It may also reveal which genes and signalling pathways are active in particular cell populations, as well as which combinations of markers best distinguish healthy, compromised and dysfunctional immune states.2,3,8
Analyses of blood proteins can indicate whether a person is inflamed or whether the immune system is activated. Studying receptors on lymphocytes — immune cells that include T cells and B cells — can reveal the diversity of cells available to recognize threats. These receptors determine which pathogen-derived antigens a cell can recognize; generally, greater receptor diversity allows the immune system to detect a wider range of threats.
Moderna’s cancer vaccine brings hope – now we must accelerate personalized treatments
Researchers also need to track how the immune system changes in response to challenges. Comparing people before and after vaccination provides one standardized way to do this. A person’s immune status before vaccination is already known to predict the strength and characteristics of the subsequent immune response.2,9 The remaining questions are whether a person’s response to routine vaccination can reveal their overall immune capacity10 and whether immune health can be altered before interventions such as vaccination or cancer immunotherapy to improve the response.2,5
Because genetics and environmental factors affect immune status, datasets used to investigate the immunome must reflect the diversity of human immunity.1 For example, the effectiveness of malaria vaccines can vary significantly between populations on different continents.7,11 Yet many existing immune-profiling datasets are concentrated in populations from high-income countries. When studying diverse populations, researchers around the world need to use consistent protocols so that results from different regions can be compared.
Interpreting these complex datasets is another major challenge. Artificial-intelligence tools can identify patterns across thousands of immunoassays, but those patterns might reflect differences between populations, laboratories or technologies rather than underlying immune health. Models therefore need to be tested over time and across independent populations to establish which signals are robust and clinically meaningful.
Scientists working on the Human Immunome Project are developing and applying AI approaches to immunome data collected from diverse populations. An important goal is to create reference maps and foundational models that researchers can use for tasks such as identifying small, manageable sets of measurements suitable for clinical practice.
Identify what works in clinical practice
Once researchers have large-scale data, they need to simplify it. Success should not be measured by the number of immune variables that can be collected, but by whether clinical decisions can be improved using a small, manageable set of variables. Clinical tests should ideally be inexpensive and impose little additional burden on patients. Before AI tools can be used routinely, they must be tested in different hospitals and populations, and researchers must establish whether their results genuinely improve treatment decisions.
One possible clinical tool is an AI model that extracts information about immune health from routine blood tests and clinical details such as age, gender, weight and medical history. In a study published last year,6 such an approach allowed the researchers to predict health outcomes after immunotherapy in patients with cancer.
Source: www.nature.com