Genetically predicted protein concentrations in association with Alzheimer's disease risk: Insights from nonlinear protein modeling
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) poses a significant public health burden. Despite extensive research, current therapeutic options offer limited efficacy in halting disease progression. Understanding the systemic physiological changes influencing AD development is crucial for developing innovative strategies for effective treatment. Leveraging genetic variants as instrumental variables, Mendelian randomization and proteome-wide association study have identified numerous protein biomarkers associated with AD risk, yet potential nonlinear associations have largely been overlooked. METHOD: In this study, we applied a nonlinear modeling approach, combining two-stage sliced inverse regression (2SIR) with nonlinear transformations via adjusted inverse regression (AIR), to investigate associations between genetically predicted protein concentrations and AD risk by integrating data from the INTERVAL study, which contains both blood proteome and genome data, and the summary statistics of large genome-wide association study of AD. RESULT: We identified 131 proteins associated with AD after stringent Bonferroni correction. Of these, 46 had been previously reported using linear modeling methods, highlighting the complementarity of the currently used nonlinear approach. Notably, many of the identified proteins have established biological relevance to AD, including APOE, ADAM11, LRP1B and TREML2, indicating that these critical AD-associated proteins could only be identified in instrumental variable analysis by allowing for nonlinear associations. CONCLUSION: Our study underscores the importance of accounting for nonlinear relationships in uncovering important gene products associated with AD. Our method could improve the understanding of AD pathogenesis and inform future therapeutic and preventive strategies to reduce AD burden.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".