Locus coeruleus abnormalities on neuromelanin-sensitive MRI in Parkinson's disease: a systematic review and meta-analysis.
Jiale Zhao, Naibijiang Abuliezi, Rurui Wei et al.
✦ AI-curated · Sources linked
In 30 seconds
This systematic review and meta-analysis assessed locus coeruleus (LC) integrity on neuromelanin-sensitive MRI in Parkinson’s disease (PD), pooling independent comparisons with healthy controls using random-effects models. Across 14 cohorts (1,078 participants with PD and 543 controls), LC signal was lower in PD (Hedges’ g = - 0.95, 95% confidence interval (CI) - 1.32 to - 0.57; P = 0.00011). LC volume was also lower in five cohorts. However, signal findings varied substantially between studies, and the authors note that group-level differences do not establish individual diagnostic accuracy.
Key findings
- LC signal was lower in PD across 14 cohorts: Hedges' g = - 0.95, 95% confidence interval (CI) - 1.32 to - 0.57; P = 0.00011.
- Heterogeneity for LC signal was substantial (I 2 = 88.4%), and the 95% prediction interval crossed zero (- 2.28 to 0.38).
- LC volume was lower in PD across five cohorts (215 participants with PD and 134 HCs): g = - 0.87, 95% CI - 1.13 to - 0.62; P = 0.00071; I 2 = 0%.
- No additional secondary-outcome cluster met the criteria for construct compatibility, cohort independence and numerical poolability.
Why it matters
The pooled results support a group-level association between PD and lower LC signal and volume on NM-MRI, while the substantial variability in signal estimates cautions against assuming consistent performance across settings. The review also distinguishes group differences from individual diagnostic accuracy, an important consideration when interpreting imaging biomarkers and assessing their potential clinical role.
What to keep in mind
LC-signal heterogeneity was substantial, and prediction intervals crossed zero, including across sensitivity analyses. The evidence does not establish individual diagnostic accuracy; the authors call for harmonized methods and independent, multicentre and longitudinal validation.