Olfactory Identification Improve the Prediction of Episodic Memory Function in Individuals at Risk of Alzheimer's Disease: Results from the CIMA‐Q Cohort
Bibliographic record
Abstract
BACKGROUND: Olfactory identification is impaired early in the clinical continuum of Alzheimer's disease (AD) and is already observable in mild cognitive impairment (MCI). A recent meta-analysis showed significant associations between olfactory identification and episodic memory scores in older adults without cognitive impairment. Thus, olfactory identification could potentially serve as a marker for episodic memory decline in individuals at risk of AD. This study aimed to (1) evaluate the predictive value of olfactory identification on episodic memory functioning in individuals with MCI and subjective cognitive decline (SCD) and (2) explore the role of olfactory identification in discriminating between the two groups. METHOD: Using the University of Pennsylvania Smell Identification Test (UPSIT), we assessed the olfactory identification function of 93 participants: 48 with SCD (mean age: 75.82, SD: 5.64) and 45 with MCI (mean age: 80.08, SD: 5.86) from the Consortium for the Early Identification of Alzheimer's Disease (CIMA-Q) cohort. Episodic memory was evaluated using immediate and delayed recall scores from the Rey Auditory Verbal Learning Test (RAVLT). LASSO regression models were applied, with 80% of the data used for training and 20% for testing. Linear Discriminant Analysis (LDA) was applied to assess group classification accuracy using the UPSIT score. RESULT: The UPSIT score demonstrates significant associations with both immediate (ß = 0.56, p < .001) and delayed recall scores (ß = 0.19, p < .001). Incorporating the UPSIT score in predictive models - along with age, sex, and education - improved the explained variance for RAVLT immediate recall from 9% to 19% and for delayed recall from 8% to 20%. The MCI group exhibited significantly lower UPSIT scores compared to the SCD group (p = .01); LDA achieved moderate accuracy (69%) for distinguishing groups, with higher specificity to rule out MCI (79%) than sensitivity to detect it (58%). CONCLUSION: Olfactory identification enhanced the prediction of episodic memory in individuals with SCD or MCI; highlighting its potential utility as a screening tool for cognitive decline associated with AD. However, because olfactory impairment is not specific to AD, further research is necessary to elucidate the underlying mechanisms driving this impairment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".