Can dementia be predicted using olfactory identification test in the elderly? A Bayesian network analysis

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.