Longitudinal study of Alzheimer's disease biomarkers, allostatic load, and cognition among memory clinic patients
Bibliographic record
Abstract
Allostatic load (AL) is defined as the cumulative dysregulation of neuroendocrine, immunological, metabolic, and cardiovascular systems that increases the susceptibility to stress-related health problems. Several dementia and Alzheimer's disease (AD) risk factors have been identified, yet little is known about the role of AL and its associations with AD biomarkers (e.g., beta-amyloid (Aβ) or tau) and cognitive function among memory clinic patients. Hence, this study aims to assess the association between AL and AD biomarkers, cognitive performance, and cognitive decline after 3-years of follow-up. Data from 188 memory clinic patients were derived from the Cortisol and Stress in AD (Co-STAR) study in Sweden. Participants underwent baseline assessments including blood tests for AL measures (including cortisol, thyroid stimulating hormone, cobalamin, homocysteine, leukocytes, glycated hemoglobin, albumin, high-density and low-density lipoprotein cholesterol, triglycerides, and creatinine), cerebrospinal fluid (CSF) sampling for AD biomarkers and neuropsychological tests including five cognitive domains. Linear regressions were conducted, adjusting for age, sex, and education. Higher AL was associated with lower CSF Aβ1-42 levels (β = −0.175, p = 0.025), reflecting higher brain levels of Aβ1-42. Stratified analyses suggested a significant association among women but not men, although the AL-sex interaction was not statistically significant. AL was not significantly associated with T-tau level (β = −0.030, p = 0.682) and P-tau level (β = 0.091, p = 0.980). There were no significant associations between AL and cognition or cognitive decline after 3 years. This study showed that higher AL was associated with increased brain amyloid accumulation. This suggests that AL may play a role in AD/dementia pathophysiology. Potential sex-related differences should be assessed in further larger studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".