Iron deposition in subcortical nuclei of Parkinson’s disease: A meta-analysis of quantitative iron-sensitive magnetic resonance imaging studies
Bibliographic record
Abstract
BACKGROUND: Iron deposition plays a crucial role in the pathophysiology of Parkinson's disease (PD), yet the distribution pattern of iron deposition in the subcortical nuclei has been inconsistent across previous studies. We aimed to assess the difference patterns of iron deposition detected by quantitative iron-sensitive magnetic resonance imaging (MRI) between patients with PD and patients with atypical parkinsonian syndromes (APSs), and between patients with PD and healthy controls (HCs). METHODS: A systematic literature search was conducted on PubMed, Embase, and Web of Science databases to identify studies investigating the iron content in PD patients using the iron-sensitive MRI techniques (R2 * and quantitative susceptibility mapping [QSM]), up until May 1, 2023. The quality assessment of case-control and cohort studies was performed using the Newcastle-Ottawa Scale, whereas diagnostic studies were assessed using the Quality Assessment of Diagnostic Accuracy Studies-2. Standardized mean differences and summary estimates of sensitivity, specificity, and area under the curve (AUC) were calculated for iron content, using a random effects model. We also conducted the subgroup-analysis based on the MRI sequence and meta-regression. RESULTS: Seventy-seven studies with 3192 PD, 209 multiple system atrophy (MSA), 174 progressive supranuclear palsy (PSP), and 2447 HCs were included. Elevated iron content in substantia nigra (SN) pars reticulata ( P <0.001) and compacta ( P <0.001), SN ( P <0.001), red nucleus (RN, P <0.001), globus pallidus ( P <0.001), putamen (PUT, P = 0.021), and thalamus ( P = 0.029) were found in PD patients compared with HCs. PD patients showed lower iron content in PUT ( P <0.001), RN ( P = 0.003), SN ( P = 0.017), and caudate nucleus ( P = 0.017) than MSA patients, and lower iron content in RN ( P = 0.001), PUT ( P <0.001), globus pallidus ( P = 0.004), SN ( P = 0.015), and caudate nucleus ( P = 0.001) than PSP patients. The highest diagnostic accuracy distinguishing PD from HCs was observed in SN (AUC: 0.85), and that distinguishing PD from MSA was found in PUT (AUC: 0.90). In addition, the best diagnostic performance was achieved in the RN for distinguishing PD from PSP (AUC: 0.86). CONCLUSIONS: Quantitative iron-sensitive MRI could quantitatively detect the iron content of subcortical nuclei in PD and APSs, while it may be insufficient to accurately diagnose PD. Future studies are needed to explore the role of multimodal MRI in the diagnosis of PD. REGISTRISION: PROSPERO (CRD42022344413).
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.029 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".