Validation of olfactory deficit as a biomarker of Alzheimer disease

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Abstract

Background:

We evaluated smell identification as a biomarker for Alzheimer disease (AD) by assessing its utility in differentiating normal aging from an amnestic disorder and determining its predictive value for conversion from amnestic mild cognitive impairment (aMCI) to AD.

Methods:

Cross-sectional study (AD = 262, aMCI = 110, controls = 194) measuring smell identification (University of Pennsylvania Smell Identification Test [UPSIT]) and cognitive status was performed, as well as longitudinal analysis of aMCI participants (n = 96) with at least 1 year follow-up (mean 477.6 ± 223.3 days), to determine conversion by National Institute of Neurological and Communicative Disorders and Stroke–Alzheimer’s Disease and Related Disorders Association criteria.

Results:

Odor identification and disease status were highly correlated after correcting for age, sex, and APOE (p < 0.001). Receiver operating characteristic (ROC)/area under the curve (AUC) was similar for the 40-item UPSIT, the top 10 smells in our study, and the 10-item subset previously proposed. Smeller/nonsmeller based on the 10-item subset with a cutoff of 7 (≤7, nonsmeller; >7, smeller) had a sensitivity and specificity of 88% and 71% for identifying AD and 74% sensitivity and 71% specificity for identifying an amnestic disorder. A total of 36.4% of participants with impaired olfaction and 17.3% with intact olfaction converted to AD (p = 0.03). The ROC/AUC for prediction of conversion to AD was 0.62.

Conclusions:

Olfactory identification deficit is a useful screening tool for AD-related amnestic disorder, with sensitivity and specificity comparable to other established biomarkers, with benefits such as ease of administration and low cost. Olfactory identification deficit can be utilized to stratify risk of conversion from aMCI to AD and enrich clinical trials of disease-modifying therapy.

Classification of evidence:

This study provides Class III evidence that smell identification (10-item UPSIT subset) accurately identifies patients with amnestic disorders.

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