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- Ding, Ding, et al.
(författare)
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Can dementia be predicted using olfactory identification test in the elderly? A Bayesian network analysis
- 2020
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Ingår i: Brain and Behavior. - : John Wiley & Sons. - 2162-3279 .- 2162-3279. ; 10:11
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Previous studies suggest that olfactory dysfunction is associated with cognitive decline or dementia.OBJECTIVE: To find a potential association between the olfactory identification (OI) and dementia onset, and build a prediction model for dementia screening in the older population.METHODS: Nine hundred and forty-seven participants from the Shanghai Aging Study were analyzed. The participants were dementia-free and completed OI test using the Sniffin' Sticks Screening Test-12 at baseline. After an average of 4.9-year follow-up, 75 (8%) of the participants were diagnosed with incident dementia. Discrete Bayesian network (DBN) and multivariable logistic regression (MLR) models were used to explore the dependencies of the incident dementia on the baseline demographics, lifestyles, and OI test results.RESULTS: In DBN analysis, odors of orange, cinnamon, peppermint, and pineapple, combined with age and Mini-mental State Examination (MMSE), achieved a high predictive ability for incident dementia, with an area under the receiver operating characteristic curve (AUC) larger than 0.8. The odor cinnamon showed the highest AUC of 0.838 (95% CI: 0.731-0.946) and a high accuracy of 0.867. The DBN incorporating age, MMSE, and one odor test had an accuracy (0.760-0.872 vs. 0.835) comparable to that of the MLR model and revealed the dependency between the variables.CONCLUSION: The DBN using OI test may have predictive ability comparable to MLR analysis and suggest potential causal relationship for further investigation. Identification of odor cinnamon might be a useful indicator for dementia screening and deserve further investigation.
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| 2. |
- Ding, Ding, et al.
(författare)
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Predictive Value of Odor Identification for Incident Dementia : The Shanghai Aging Study
- 2020
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Ingår i: Frontiers in Aging Neuroscience. - : Frontiers Media S.A.. - 1663-4365 .- 1663-4365. ; 12
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Tidskriftsartikel (refereegranskat)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.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.
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