The study of biomarkers in biofluids and tissues expanded our understanding of the biological processes that drive physiological and functional manifestations of aging. However, most of these studies were limited to examining one biological compartment, an approach that fails to recognize that aging pervasively affects the whole body. The simultaneous modeling of hundreds of metabolites and proteins across multiple compartments may provide a more detailed picture of healthy aging and point to differences between chronological and biological aging. Herein, we report proteomic analyses of plasma and urine collected in healthy men and women, age 22-92 years. Using these data, we developed a series of metabolomic and proteomic predictors of chronological age for plasma, urine, and skeletal muscle. We then defined a biological aging score, which measures the departure between an individual's predicted age and the expected predicted age for that individual based on the full cohort. We show that these predictors are significantly and independently related to clinical phenotypes important for aging, such as inflammation, iron deficiency anemia, muscle mass, and renal and hepatic functions. Despite a different set of selected biomarkers in each compartment, the different scores reflect a similar degree of deviation from healthy aging in single individuals, thus allowing identification of subjects with significant accelerated or decelerated biological aging.