Improving dementia prognostication in cognitively normal older adults: conventional versus novel approaches to modelling risk associated with neuropsychiatric symptoms
Bibliographic record
Abstract
Abstract Background In persons with mild cognitive impairment (MCI) neuropsychiatric symptoms (NPS) are consistently associated with greater dementia incidence rates. However, in cognitively normal (NC) individuals, findings are inconsistent. Conventional approaches to NPS modeling are often cross‐sectional and silent on natural history of symptoms, introducing noise. Mild behavioral impairment (MBI) refines identification of risk by specifying that NPS must be later‐life emergent and persistent, increasing signal and reducing noise. Here, we explored associations of both MBI and conventionally‐measured NPS with incident dementia in persons with NC and subjective cognitive decline (SCD). We hypothesized greater dementia incidence in MBI, compared to NPS not meeting MBI criteria (NPS‐not‐MBI) and no NPS. Method National Alzheimer’s Coordinating Center (NACC) participant data were analyzed. MBI was operationalized as no history of psychiatric disorders (to satisfy the symptom emergence criterion) and NPS present at more than two‐thirds of pre‐dementia study visits (to satisfy the symptom persistence criterion). Kaplan‐Meier dementia‐free survival curves were generated for all three NPS groups. Cox proportional hazard models compared dementia incidence rates across groups, adjusted for age, sex, education, race, and APOE‐e4 status. Results The NC sample comprised 1,408 MBI (age = 75.2±9.5; 54.3% female), 5621 NPS‐not‐MBI (age = 71.6±8.8; 65.5% female), and 5078 no‐NPS participants (age = 71.2±8.9; 67.6% female). Persons with MBI had lower dementia‐free survival (p<0.0001, Table 1) and a 3.17‐fold greater dementia incidence rate compared to no‐NPS (CI:2.62‐3.84, p<0.001, Figure 1); no significant differences were found for NPS‐not‐MBI (HR = 1.07, CI:0.91‐1.27, p = 0.420). Similarly, in the SCD subsample (n = 3,555), persons with MBI had a 1.99‐fold greater dementia incidence rate versus no‐NPS (CI:1.46‐2.70, p<0.001); NPS‐not‐MBI did not differ (HR = 0.89, CI:0.68‐1.16, p = 0.375). Conclusions In both NC and SCD, persons with MBI had greater incidence of dementia than no‐NPS, while NPS‐not‐MBI did not. Operationalizing NPS‐related risk in accordance with the MBI criteria of later‐life emergent and persistent symptoms improves the prognostic utility of NPS over conventional approaches. This method of MBI operationalization can be applied to legacy datasets and ongoing cohort studies to identify a high‐risk group. This group can be assessed for neurodegenerative disease biomarkers in advance of the objective cognitive decline that is usually used to identify risk.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".