Inflammation in Alzheimer’s disease: do sex and APOE matter?
Bibliographic record
Abstract
ABSTRACT Alzheimer’s disease (AD) disproportionately affects females with steeper cognitive decline and more neuropathology compared to males, which is exacerbated in females carrying the APOEε4 allele. The risk of developing AD is also higher in female APOEε4 carriers in earlier age groups (aged 65-75), and the progression from cognitively normal to mild cognitive impairment (MCI) and to AD may be influenced by sex. Inflammation is observed in AD and is related to aging, stress, and neuroplasticity, and although studies are scarce, sex differences are noted in inflammation. The objective of this study was to investigate underlying physiological inflammatory mechanisms that may help explain why there are sex differences in AD and APOEε4 carriers. We investigated, using the ADNI database, the effect of sex and APOE genotype (non-carriers or carriers of 1 and 2 APOEε4 alleles) and sex and diagnosis (cognitively normal (CN), MCI, AD) on CSF (N= 279) and plasma (N= 527) markers of stress and inflammation. We found CSF IL-16 and IL-8 levels were significantly lower in female non-carriers of APOEε4 alleles compared to males, whereas levels were similar between the sexes among carriers of APOEε4 alleles. Furthermore, females had on average higher levels of plasma CRP and ICAM1 but lower levels of CSF ICAM1, IL-8, IL-16, and IgA than males. Carrying APOEε4 alleles and diagnosis (MCI and AD) decreased plasma CRP in both sexes. Sex differences in inflammatory biomarkers support that the underlying physiological changes during aging differ by sex and tissue origin.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".