Cardiac 123I-Meta-Iodobenzylguanidine Imaging as a Biomarker for Body-First Parkinson’s Disease: Linking Peripheral α-Synuclein to Clinical Subtyping
Bibliographic record
Abstract
Recent neuropathological and imaging studies support the concept of "brain-first vs. body-first" Parkinson's disease (PD), which is based on the α-synuclein origin site and connectome model. The body-first phenotype is characterized by early involvement of the peripheral autonomic nervous system, particularly the cardiac sympathetic nerves and enteric nerves. 123I-meta-iodobenzylguanidine (123I-MIBG) myocardial scintigraphy is a well-established method for evaluating cardiac sympathetic innervation. This review explores the potential of 123I-MIBG scintigraphy as a biomarker to differentiate the body-first phenotype from the brain-first phenotype. Reduced 123I-MIBG uptake has been observed in idiopathic rapid eye movement (REM) sleep behavior disorder, pure autonomic failure, and incidental Lewy body disease-conditions strongly associated with prodromal or early-stage PD. Postmortem and biopsy evidence indicates α-synuclein accumulation in cardiac nerves and other peripheral sites, which is consistent with bottom-up progression. α-Synuclein seed amplification assays further corroborate the association between the peripheral α-synuclein burden and reduced 123I-MIBG uptake. While 123I-MIBG myocardial scintigraphy is a promising tool, its limitations include cost, limited availability, and potential confounding from underlying cardiac conditions. Nonetheless, early detection of cardiac sympathetic denervation via 123I-MIBG imaging may enhance diagnosis, support subtype classification, and improve the understanding of PD pathogenesis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".