Weight Loss is a Preclinical Signal of Cerebral Amyloid Deposition and Could Predict Cognitive Impairment in Elderly Adults
Bibliographic record
Abstract
BACKGROUND: Higher late-life body mass index (BMI) was associated with reduced risk of Alzheimer's disease (AD), which might be explained by a reverse causal relationship. OBJECTIVE: To investigate whether weight loss was a preclinical manifestation of AD pathologies and could be a predictor of cognitive impairment. METHODS: A total of 1,194 participants (mean age = 73.2 [range: 54 to 91] years, female = 44.5%) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were grouped according to AD biomarker profile as indicated by amyloid (A) and tau (TN) status and clinical stage by clinical dementia rating (CDR). BMI across the biomarker-defined clinical stages was compared with Bonferroni correction. Pearson correlation analysis was performed to test the relationship between the amyloid change by PET and the BMI change. Multiple regression models were used to explore the influences of amyloid pathologies on BMI change as well as the effects of weight loss on longitudinal changes of global cognitive function. RESULTS: BMI was significantly decreased in AD preclinical stage (amyloid positive [A+] and CDR = 0) and dementia stage (A+/TN+ and CDR = 0.5 or 1), compared with the healthy controls (A-/TN-and CDR = 0, p < 0.005), while no significant differences were observed between preclinical AD and AD dementia. Amyloid PET change was inversely correlated with BMI change (p = 0.023, β= -14). Individuals in amyloid positive group exhibited faster weight loss (time×group interaction p = 0.019, β= -0.20) compared to the amyloid negative group. Greater weight loss predicted higher risk of developing cognitive disorders. CONCLUSION: Elders who experienced greater weight loss might belong to preclinical stage of AD and could be targeted for primary prevention of the disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".