Why people age at different rates is a fundamental unsolved problem in biology. We created a model that predicts an individual's age, taking as input physiological traits that change with age in the large UK Biobank dataset, such as blood pressure, blood metabolites, strength, and stimulus-reaction time. The model's Root Mean Square Error of age prediction (RMSE) is less than 5 years. We argue that the difference between calculated 'biological' age and actual age (delta-Age) reflects an individual's relative youthfulness and possibly their rate of aging. Validating this interpretation, people predicted to be physiologically young for their age have a lower subsequent mortality rate and a higher parental age at death, even though no mortality data were used to calculate delta-Age. A Genome-Wide Association Study (GWAS) of delta-Age, and analysis of environmental factors associated with delta-Age identified known as well as new factors that may influence human aging, including genes involved in synapse biology and a tendency to play computer games. We identify 12 readily-measured physiological traits that together assess a person's biological age and may be used clinically to evaluate therapeutics designed to slow aging and extend healthy life.