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Early parkinson’s disease: levodopa requirements are associated with the striatal DaT-uptake

J Neural Transm (Vienna). 2025 Aug 16. doi: 10.1007/s00702-025-02999-9. Online ahead of print.

ABSTRACT

Precision medicine driven by clinical biomarkers is the state-of-art management approach for Parkinson’s disease (PD). Whether pattern of striatal dopaminergic deficiency (demonstrated by single-photon emission CT (SPECT) scanning with 123I-Ioflupane, DaTSCAN) could be a biomarker predicting levodopa requirement in early PD is not known. Participants with early PD (disease duration (DD) ≤ 5 years, Hoehn and Yahr (H&Y) ≤ 3) who underwent DaTSCAN as a part of clinical-diagnostic work up and were enrolled in the “Non-motor Longitudinal International Study” (UK National Institute for Health Research Clinical Research Network Number 10084) were included in this cross-sectional analysis. Specific DaTSCAN binding ratios were analyzed for each striatum, caudate nucleus and putamen and the z-scores were derived normalizing the images to age and gender-matched healthy controls from the European-Database-of-DaTSCAN-of-healthy-controls (ENC-DAT). Using linear regression analysis, we explored the impact of DaT-uptake z-scores for more severely affected putamen, caudate nucleus and striatum on the LEDD. Statistically significant predictors identified in the univariable analysis were included in the multivariable analysis with DD and H&Y as additional independent variables. 43 PwP (30% female; age: 61.91 ± 11.45years; DD: 2(0-5) years; H&Y: 2(1-3); LEDD: 424.27 ± 342.62 mg) were assessed 19.12 ± 13.11 months following the DaTSCAN. In a multivariable linear regression analysis, when adjusted for DD and H&Y, z-caudate nucleus (B=-134.073, 95% CI -262.715 – -5.431, p = 0.042) and z-striatum (B=-162.137, 95% CI -306.306 – -17.967, p = 0.028), were statistically significant predictors of LEDD, while z-putamen was not (p = 0.086). In early PD, striatal DaT-uptake z-scores may serve as biomarkers that could aid the LEDD estimation and guide treatment decisions towards personalized care.

PMID:40817940 | DOI:10.1007/s00702-025-02999-9

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