Relationships between polymorphisms in keratin genes and Alzheimer’s disease phenotypes.
Bibliographic record
Abstract
Abstract Background Keratins are genetically polymorphic and are the largest subset of intermediate filaments. Keratins are also implicated in signaling pathways, inflammation, and disease states. Keratin 9 (KRT9) protein has been identified to have high diagnostic accuracy for Alzheimer’s disease (AD) (PMID:22045497); it was detected in cerebrospinal fluid (CSF) of AD patients but not healthy controls (PMID:24959311). Plasma keratin 9 concentrations also positively correlated with AD‐associated proteins (e.g., apolipoprotein E and tau) in AD patients (PMID:26973255). Post‐translational modification of keratin 83 (KRT83) protein has been associated with serum asymmetric dimethylarginine levels (PMID:34181092), a vascular risk factor implicated in AD. However, our understanding of the role of genetic variation in AD risk is incomplete. Here, we investigate associations between single nucleotide polymorphisms (SNPs) in KRT9, KRT83, and KRT2 (Keratin 2, couples with keratin 9) and AD biomarker phenotypes. Method Individuals with mild cognitive impairment (MCI) or AD, and clinically normal controls were selected among ADNI participants. Participants underwent genome‐wide genotyping. Keratin gene variants were identified from the genome‐wide data using dbSNP variant IDs and genotypes were coded additively. Linkage‐disequilibrium‐based clumping on minor allele frequency (MAF) was applied to prioritize more common SNPs. CSF‐Aβ42, ‐tau, and phosphorylated‐tau (p‐tau) levels were quantified via Roche Elecsys immunoassays. Brain‐Aβ deposition was evaluated via normalized PET‐18F‐AV‐45 cortical summary measures (SUVR). White matter hyperintensities (WMH) were obtained from MRI 3D‐T1 and FLAIR sequences via an automated atlas‐based segmentation pipeline. Linear regression models controlling for age, sex, Mini‐Mental State Exam, APOE‐ε4 status, and head size were used to assess association and interaction effects between SNPs and the AD phenotypes in R. Result Among included participants (n = 907, 73±7.2 years, 44% female), SNPs in KRT2 (e.g. 3’UTR‐variant rs117041267‐G/A; MAF = 1.4%) and KRT83 (e.g. upstream‐variant KRT83‐rs17119838‐C/T; MAF = 15.6%) were associated with higher CSF‐Aβ42 concentrations (F (2,905) = 4.26,p = 0.014; and F (2,905) = 6.61,p = 0.0014, respectively). Upstream‐variant KRT83‐rs182354391‐C/G (MAF = 1%) showed an association with lower CSF‐tau (F (1,904) = 5.44,p = 0.020). Multiple KRT9 and KRT2 SNPs also showed association with WMH, while KRT2 and KRT83 intron variants showed associations with PET‐Aβ. Conclusion These candidate gene analyses suggest involvement of multiple keratin family genes in AD, as indicated by differences in amyloid and tau biomarkers, and white matter disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".