Monogenic diseases are often studied in isolation. Here we utilize multiomics to assess 22 monogenic immune-mediated conditions with healthy controls. We identified both disease-specific and “pan-disease” signatures and found that inter-individual variations tend to dominate over those attributable to disease conditions or medication use. Both unsupervised analysis of individual immune states and machine learning classification distinguishing age- and sex-matched healthy subjects from monogenic patients converged to a metric of immune health (IHM). In independent datasets, the IHM discriminates healthy from polygenic autoimmune and inflammatory disease states, marks healthy aging, tracks disease activities and treatment responses, and predicts age-dependent antibody responses to vaccination. Thus, deviations from health in diverse conditions and manifestations, including aging, have shared immune consequences, whereby the IHM can quantify immune health even among clinically healthy individuals beyond classic inflammatory markers such as CRP and IL-6. Our work provides tools and a framework for studying human immune health.