Could Lifetime Lead Exposure Play a Role in Limbic-predominant Age-related TDP-43 Encephalopathy (LATE)?
Bibliographic record
Abstract
Limbic-predominant Age-related TDP-43 Encephalopathy (LATE) is a disease in which the clinical presentation mimics that of Alzheimer's disease. TDP-43 proteinopathy associated with LATE has been identified in more than 20% of autopsies of community-dwelling adults over the age of 80. It is believed to contribute significantly toward tau-negative dementia. Heavy metals such as lead has also been linked to TDP-43 proteinopathy. In particular, lead triggers TDP-43 accumulation and disrupts TDP-43 homeostasis. However, the specific relationship between LATE and lead remains unknown. Before leaded gasoline was phased out during the 1970s and 1980s, average blood lead levels were 15 times what they are today. Thus, each successive birth cohort entering old age has had less cumulative lifeime exposure to lead. Lifetime exposure can be tracked in the tibia bone, where the half-life of lead is many decades. We hypothesize that lead plays a role in the development of LATE. There are two ways to explore the validity of this hypothesis. Generational differences in lead exposure should result in a steady decline in the prevalence of LATE among older adults. We propose the use of tibia bone lead levels be examined in conjunction with brain autopsies from different birth cohorts to examine the link between lead exposure and LATE prevalence, holding age constant. Furthermore, individuals with genetic polymorphisms that confer a greater lead absorption phenotype should display a higher degree of TDP-43 accumulation in autopsies. The results of such studies could provide insight into gene by environment interactions relevant to the development of LATE.
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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 | 0.003 |
| 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".