Analysis of serum levels of organochlorine pesticides and related factors in Parkinson’s disease
Bibliographic record
Abstract
Abstract Background: There is evidence that genetic and environmental factors contribute to the onset and progression of Parkinson’s disease (PD). Pesticides are a class of environmental toxins that are linked to increased risk of PD. However, few studies have investigated the interaction between specific pesticides and genetic variants related to PD in the Chinese population. Methods: In this cross-sectional study, 19 serum levels of pesticides were measured. In addition, we also analyzed the interaction between specific pesticides and candidate genetic variants for PD. Finally, we investigated the mechanistic basis for the association between pesticides and increased risk of PD. Results: Serum levels of organochlorine pesticides including α-hexachlorocyclohexane (α-HCH), β-HCH, γ-HCH, δ-HCH, propanil, heptachlor, dieldrin, hexachlorobenzene, p,p’-dichlorodiphenyltrichloroethane (p,p’-DDE) and o,p’-dichloro-diphenyl-trichloroethane (o,p’-DDT) were higher in PD patients than in controls. α-HCH and propanil levels were associated with increased PD risk. Serum levels of dieldrin were associated with Hamilton Depression Scale and Montreal Cognitive Assessment scores in PD patients. Interactions between high pesticide levels and polymorphisms in rs11931074 and rs16940758 (α-HCH or β-HCH interacted with TT genotype in rs11931074 and δ-HCH interacted with TT genotype in rs16940758) were associated with the risk of PD. In cell model, α-HCH and propanil increased the level of reactive oxygen species and decreased the mitochondrial membrane potential. Propanil but not α-HCH induced the aggregation of α-synuclein. Conclusions: Elevated serum levels of α-HCH and propanil are associated with increased risk of PD. Serum levels of dieldrin were associated with depression and cognitive function in PD patients. The interaction between genetic variants and pesticides also increased the risk of PD. Effects of genetic variants and pesticides on the risk of PD should be studied in more detail with a larger sample size to further understand the mechanisms involved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".