Abstract
Smell dysfunction is a common and underdiagnosed medical condition that can have serious consequences. It is also an early biomarker of neurodegenerative diseases, including Alzheimer's disease, where olfactory deficits precede detectable memory loss. Clinical tests that evaluate the sense of smell face two major challenges. First, human sensitivity to individual odorants varies significantly, so test results may be unreliable in people with low sensitivity to a test odorant but an otherwise normal sense of smell. Second, prior familiarity with odor stimuli can bias smell test performance. We have developed nonsemantic tests for olfactory sensitivity (SMELL-S) and olfactory resolution (SMELL-R) that use mixtures of odorants that have unfamiliar smells. The tests can be self-administered by healthy individuals with minimal training and show high test-retest reliability. Because SMELL-S uses odor mixtures rather than a single molecule, odor-specific insensitivity is averaged out, and the test accurately distinguished people with normal and dysfunctional smell. SMELL-R is a discrimination test in which the difference between two stimulus mixtures can be altered stepwise. This is an advance over current discrimination tests, which ask subjects to discriminate monomolecular odorants whose difference in odor cannot be quantified. SMELL-R showed significantly less bias in scores between North American and Taiwanese subjects than conventional semantically based smell tests that need to be adapted to different languages and cultures. Based on these proof-of-principle results in healthy individuals, we predict that SMELL-S and SMELL-R will be broadly effective in diagnosing smell dysfunction.