AUTHOR=Ding Ding , Xiao Zhenxu , Liang Xiaoniu , Wu Wanqing , Zhao Qianhua , Cao Yang 

TITLE=Predictive Value of Odor Identification for Incident Dementia: The Shanghai Aging Study

JOURNAL=Frontiers in Aging Neuroscience

VOLUME=Volume 12 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2020.00266

DOI=10.3389/fnagi.2020.00266

ISSN=1663-4365

ABSTRACT=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.
Method: 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 the 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 MLR model and the stepwise MLR model (AUC = 0.916 and 0.914, respectively) have better predictive abilities compared with those of the no odor model or single odor models. The five most important variables are Mini-Mental State Examination (MMSE) score, age, peppermint, coronary artery disease, and height in the full model, while MMSE, age, peppermint, stroke, and education in the stepwise model. The combination of only the top five variables in the stepwise model(AUC = 0.901, sensitivity = 0.880)has a good predictive ability as other models.
Conclusion: The ability to identify peppermint may be an important indicator of incident dementia. Combining peppermint with MMSE, age, education, and history of stoke may have sensitive and robust predictive value for dementia in older adults.