Evidence map›Paper›PMID 28243501›Full record

ArticleNeurology. Clinical practice2017

Validation of olfactory deficit as a biomarker of Alzheimer disease.

Matthew R Woodward, Chaitanya V Amrutkar, Harshit C Shah, Ralph H B Benedict, Sanjanaa Rajakrishnan, Rachelle S Doody, Li Yan, Kinga Szigeti

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neurology. Clinical practice, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
67citing papers in PubMed, 6 pooled it
5.5field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

67 citing papers in PubMed, 6 syntheses or guidelines pooled it, 120 citations in OpenAlex.

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  18. Infections withFrontiers in aging neuroscience · 2025
    Review
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7 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 1 institution in 1 country.

Matthew R WoodwardAlzheimer's Disease and Memory Disorders Center, Department of Neurology (MRW, CVA, HCS, RHBB, SR, KS), and Department of Bioinformatics (LY), University at Buffalo, SUNY, NY; and Alzheimer's Disease and Memory Disorders Center (RSD), Department of Neurology, Baylor College of Medicine, Houston, TX.
Chaitanya V AmrutkarAlzheimer's Disease and Memory Disorders Center, Department of Neurology (MRW, CVA, HCS, RHBB, SR, KS), and Department of Bioinformatics (LY), University at Buffalo, SUNY, NY; and Alzheimer's Disease and Memory Disorders Center (RSD), Department of Neurology, Baylor College of Medicine, Houston, TX.
Harshit C ShahAlzheimer's Disease and Memory Disorders Center, Department of Neurology (MRW, CVA, HCS, RHBB, SR, KS), and Department of Bioinformatics (LY), University at Buffalo, SUNY, NY; and Alzheimer's Disease and Memory Disorders Center (RSD), Department of Neurology, Baylor College of Medicine, Houston, TX.
Ralph H B BenedictAlzheimer's Disease and Memory Disorders Center, Department of Neurology (MRW, CVA, HCS, RHBB, SR, KS), and Department of Bioinformatics (LY), University at Buffalo, SUNY, NY; and Alzheimer's Disease and Memory Disorders Center (RSD), Department of Neurology, Baylor College of Medicine, Houston, TX.
Sanjanaa RajakrishnanAlzheimer's Disease and Memory Disorders Center, Department of Neurology (MRW, CVA, HCS, RHBB, SR, KS), and Department of Bioinformatics (LY), University at Buffalo, SUNY, NY; and Alzheimer's Disease and Memory Disorders Center (RSD), Department of Neurology, Baylor College of Medicine, Houston, TX.
Rachelle S DoodyAlzheimer's Disease and Memory Disorders Center, Department of Neurology (MRW, CVA, HCS, RHBB, SR, KS), and Department of Bioinformatics (LY), University at Buffalo, SUNY, NY; and Alzheimer's Disease and Memory Disorders Center (RSD), Department of Neurology, Baylor College of Medicine, Houston, TX.
Li YanAlzheimer's Disease and Memory Disorders Center, Department of Neurology (MRW, CVA, HCS, RHBB, SR, KS), and Department of Bioinformatics (LY), University at Buffalo, SUNY, NY; and Alzheimer's Disease and Memory Disorders Center (RSD), Department of Neurology, Baylor College of Medicine, Houston, TX.
Kinga SzigetiAlzheimer's Disease and Memory Disorders Center, Department of Neurology (MRW, CVA, HCS, RHBB, SR, KS), and Department of Bioinformatics (LY), University at Buffalo, SUNY, NY; and Alzheimer's Disease and Memory Disorders Center (RSD), Department of Neurology, Baylor College of Medicine, Houston, TX.
Baylor College of Medicine · US

Funding

Research Training Program in Geriatric Neuropsychiatry for Medical StudentsR25MH071544 · NIMH · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI JESTE, DILIP V. · 2004 to 2013
$2.5M
NIMH NIH HHS R25 MH071544
6 · The paper itself

Abstract

backgroundWe 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.

methodsCross-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.

resultsOdor identification and disease status were highly correlated after correcting for age, sex, and

conclusionsOlfactory 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.

Identifiers

PMID28243501
PMCPMC5310210
OpenAlexW2508942609

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.