Anosognosia Is an Independent Predictor of Conversion From Mild Cognitive Impairment to Alzheimer’s Disease and Is Associated With Reduced Brain Metabolism
Bibliographic record
Abstract
OBJECTIVE: Anosognosia, or impaired illness awareness, is a common feature of Alzheimer's disease (AD) and less so of mild cognitive impairment (MCI). Importantly, anosognosia negatively influences clinical outcomes for patients and their caregivers and may predict the conversion from MCI to AD. This study aimed to examine (1) the relationship between brain glucose metabolism as measured by fluorine-18 fluorodeoxyglucose positron emission tomography (FDG-PET) and anosognosia in patients with MCI and AD and (2) the predictive utility of anosognosia in patients with MCI for later conversion to AD, even when controlling for other factors, including gender, education, apolipoprotein E ε4 carrier status, dementia severity, and cognitive dysfunction. METHODS: Data for 1,062 participants from the Alzheimer's Disease Neuroimaging Initiative database (2003 to August 2015) classified as having AD (n = 191) or MCI (n = 499) or as healthy comparison (HC) subjects (n = 372) were analyzed. HC participants had Mini-Mental State Examination (MMSE) scores from 24 to 30 and a Clinical Dementia Rating (CDR) of 0. MCI participants had MMSE scores from 24 to 30, a memory complaint, objective memory loss, a CDR of 0.5, absence of significant levels of impairment in other cognitive domains, and essentially preserved activities of daily living. AD participants had MMSE scores ≤ 26 and a CDR of ≥ 0.5, and met National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer's Disease and Related Disorders Association criteria for probable AD. Anosognosia was measured with the composite discrepancy score of the study partner and participants' scores on the Everyday Cognition scale (ECog). Bivariate correlations and multiple regression analyses were performed to assess the relationship between anosognosia and FDG-PET findings in each group. Lastly, logistic regression and receiver operating characteristic curve analyses were performed in the MCI sample to determine if anosognosia was predictive of conversion from MCI to AD. RESULTS: Hypometabolism was independently associated with anosognosia in AD, particularly in the posterior cingulate cortex and right angular gyrus. Anosognosia was associated with conversion from MCI to AD within 5 years (OR = 2.74 [95% CI, 1.95 to 3.85], χ²₁ = 33.65, P < .001), even after including covariates (OR = 1.64 [95% CI, 1.12 to 2.40], χ²₁ = 6.43, P = .011). ECog-composite scores ≤ -0.75 were 93% sensitive and 15% specific for conversion from MCI to AD. CONCLUSIONS: Anosognosia in AD is related to brain glucose hypometabolism. Further, anosognosia independently predicts conversion from MCI to AD. The absence of anosognosia may be clinically useful to identify those patients that are unlikely to convert from MCI to AD.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".