The impact of blood MCP-1 levels on Alzheimer’s disease with genetic variation of UNC5C and NAV3 loci
Bibliographic record
Abstract
Abstract Background Previous study shows that monocyte chemoattractant protein-1 (MCP-1), which is implicated in the peripheral proinflammatory cascade and blood-brain barrier (BBB) disruption, modulates the genetic risks of AD in established AD loci. Methods In this study, we hypothesized that blood MCP-1 impacts the AD risk of genetic variants beyond known AD loci. We thus performed a genome-wide association study (GWAS) using the logistic regression via generalized estimating equations (GEE) and the Cox proportional-hazards models to examine the interactive effects between single nucleotide polymorphisms (SNPs) and blood MCP-1 level on AD in three cohorts: the Framingham Heart Study (FHS), Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Religious Orders Study/Memory and Aging Project (ROSMAP). Results We identified SNPs in two genes, neuron navigator 3 (NAV3, also named Unc-53 Homolog 3, rs696468) (p < 7.55×10− 9) and Unc-5 Netrin Receptor C (UNC5C rs72659964) (p < 1.07×10− 8) that showed an association between increasing levels of blood MCP-1 and AD. Elevating blood MCP-1 concentrations increased AD risk and AD pathology in genotypes of NAV3 (rs696468-CC) and UNC5C (rs72659964-AT + TT), but did not influence the other counterpart genotypes of these variants. Conclusions NAV3 and UNC5C are homologs and may increase AD risk through dysregulating the functions of neurite outgrowth and guidance. Overall, the association of risk alleles of NAV3 and UNC5C with AD is enhanced by peripheral MCP-1 level, suggesting that lowering the level of blood MCP-1 may reduce the risk of developing AD for people with these genotypes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".