Mitonuclear interactions influence Alzheimer’s disease risk
Bibliographic record
Abstract
Abstract We examined the associations between mitochondrial DNA haplogroups (MT-hg) and their interactions with a polygenic risk score based on nuclear-encoded mitochondrial genes (nMT-PRS) with risk of dementia and age of onset of dementia (AOO). Logistic regression was used to determine the effect of MT-hgs and nMT-PRS on dementia at baseline (332 controls / 204 cases). Cox proportional hazards models were used to model dementia AOO (n=1047; 433 incident cases). Additionally, we tested for interactions between MT-hg and nMT-PRS in the logistic and Cox models. MT-hg K and a one SD larger nMT-PRS were associated with elevated odds of dementia. Significant antagonistic interactions between the nMT-PRS and MT-hg K and T were observed. Individual MT-hg were not associated with AOO; however, a significant antagonistic interactions was observed between the nMT-PRS and MT-hg T and a synergistic interaction between the nMT-PRS and MT-hg V. These results suggest that MT-hgs influence dementia risk, and that variants in the nuclear and mitochondrial genome interact to influence the age of onset of dementia. Highlights Mitochondrial dysfunction has been proposed to influence dementia risk MT-hg K and T interacted with a genetic risk score to reduce dementia risk MT-hg T and V interacted with a genetic risk score to influence dementia age of onset
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".