Sex-aware causal inference assessment of the immune system in complex neurodegenerative diseases
Bibliographic record
Abstract
Sex differences, in terms of prevalence, symptoms and disease progression, are established in the aetiology of complex neurodegenerative diseases, including amyotrophic lateral sclerosis, Parkinson's disease and Alzheimer's disease, but the underlying biology driving these differences remains poorly understood. There is emerging evidence from genetic and functional analyses affirming the role of the immune system in such diseases, but a thorough assessment of sex differences in the link between the immune system and neurodegenerative diseases remains lacking. Here, we applied a robust causal inference approach, two-sample Mendelian randomization, to evaluate the causal effect of immune-related protein levels on three neurodegenerative diseases with large-scale sex-stratified genome-wide association data available: amyotrophic lateral sclerosis (females = 10 895 cases, 57 062 controls; males = 15 547 cases, 50 145 controls); Parkinson's disease (females = 7947 cases, 90 662 controls; males = 13 020 cases, 89 660 controls); and Alzheimer's disease (females = 18 822 cases, 281 415 controls; males = 17 293 cases, 213 339 controls). As exposures, we focused on 932 immune system-related proteins with significant protein cis-quantitative trait loci (false discovery rate cut-off < 0.01) from a large sex-combined plasma protein dataset (n = 33 477), for which corresponding genes were included in the Immunology Database and Analysis Portal gene list. We tested for a causal relationship between genetically predicted levels of each of these proteins and each neurodegenerative disease in sex-stratified and sex-combined data, followed by colocalization and estimation of sex-differential effects. We additionally performed exploratory analyses using sex-combined CSF protein cis-quantitative trait loci (n = 971) as exposures. We observed evidence for a sex-differential causal relationship between FCGR2A and Parkinson's disease and between CD2AP, MAMDC2, PCDH17 or CSF3 and Alzheimer's disease. We validated significant results using two independent protein cis-quantitative trait loci datasets for those plasma proteins available. After performing sensitivity analyses, we validated the potential causal relationships of OMG on Parkinson's disease and of GRN, SERPINF2 and TREM2 on Alzheimer's disease. Mendelian randomization with CSF protein cis-quantitative trait loci showed a potential causal effect of ADGRE2, GPNMB and COLEC11 on Parkinson's disease and of CD33 on Alzheimer's disease, without evidence of sex-differential effects. Finally, we substantiated our findings of protein-disease pairs using triangulation, specifically reporting independent supporting evidence from the literature and drug-related databases. Overall, our results point to potential causal effects of genetically predicted levels of immune system-related plasma and CSF proteins in Alzheimer's disease and Parkinson's disease, some of which may be considered as potential candidates for drug development.
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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 | 0.026 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".