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Predictive Value of Odor Identification for Incident Dementia: The Shanghai Aging Study
Institute of Neurology, Huashan Hospital, Fudan University, Shanghai, China; National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.
Institute of Neurology, Huashan Hospital, Fudan University, Shanghai, China; National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.
Institute of Neurology, Huashan Hospital, Fudan University, Shanghai, China; National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.
Institute of Neurology, Huashan Hospital, Fudan University, Shanghai, China; National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.
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2020 (English)In: Frontiers in Aging Neuroscience, E-ISSN 1663-4365, Vol. 12, article id 266Article in journal (Refereed) Published
Abstract [en]

Objective: This study aimed to evaluate the value of odors in the olfactory identification (OI) test and other known risk factors for predicting incident dementia in the prospective Shanghai Aging Study.

Methods: At baseline, OI was assessed using the Sniffin' Sticks Screening Test 12, which contains 12 different odors. Cognition assessment and consensus diagnosis were conducted at both baseline and follow-up to identify incident dementia. Four different multivariable logistic regression (MLR) models were used for predicting incident dementia. In the no-odor model, only demographics, lifestyle, and medical history variables were included. In the single-odor model, we further added one single odor to the first model. In the full model, all 12 odors were included. In the stepwise model, the variables were selected using a bidirectional stepwise selection method. The predictive abilities of these models were evaluated by the area under the receiver operating characteristic curve (AUC). The permutation importance method was used to evaluate the relative importance of different odors and other known risk factors.

Results: Seventy-five (8%) incident dementia cases were diagnosed during 4.9 years of follow-up among 947 participants. The full and the stepwise MLR model (AUC = 0.916 and 0.914, respectively) have better predictive abilities compared with those of the no- or single-odor models. The five most important variables are Mini-Mental State Examination (MMSE) score, age, peppermint detection, coronary artery disease, and height in the full model, and MMSE, age, peppermint detection, stroke, and education in the stepwise model. The combination of only the top five variables in the stepwise model (AUC = 0.901 and sensitivity = 0.880) has as a good a predictive ability as other models.

Conclusion: The ability to smell peppermint might be one of the useful indicators for predicting dementia. Combining peppermint detection with MMSE, age, education, and history of stroke may have sensitive and robust predictive value for dementia in older adults.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2020. Vol. 12, article id 266
Keywords [en]
olfactory, odor, dementia, prediction, logistic model, permutation importance method
National Category
Geriatrics Neurology
Identifiers
URN: urn:nbn:se:oru:diva-86033DOI: 10.3389/fnagi.2020.00266ISI: 000568550700001PubMedID: 33005146Scopus ID: 2-s2.0-85090500209OAI: oai:DiVA.org:oru-86033DiVA, id: diva2:1471201
Note

Funding Agencies:

Shanghai Municipal Science and Technology Major Project 2018SHZDZX01

ZJ Lab  

National Natural Science Foundation of China (NSFC) 81773513

Scientific Research Plan Project of Shanghai Science and Technology Committee  17411950701 17411950106

Shanghai Sailing Program  20YF1404000

National Project of Chronic Disease  2016YFC1306400

Available from: 2020-09-28 Created: 2020-09-28 Last updated: 2024-07-04Bibliographically approved

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