Can Dopamine Responsiveness Be Predicted in Parkinson’s Disease Without an Acute Administration Test?

Nacim Betrouni (1) , Caroline Moreau (2, 3) , Anne-Sophie Rolland (1, 4) , Nicolas Carrière (5) , Romain Viard (6, 4) , Renaud Lopes (4, 6) , Gregory Kuchcinski (4, 6) , Alexandre Eusebio (7) , Stephane Thobois , Elodie Hainque (8, 9, 10) , Cecile Hubsch (11) , Olivier Rascol (12) , Christine Brefel , Sophie Drapier (13, 14) , Caroline Giordana (15) , Franck Durif (16) , David Maltête (17, 18) , Dominique Guehl (19) , Lucie Hopes (20) , Tiphaine Rouaud (21) , Bechir Jarraya , Isabelle Benatru , Christine Tranchant , Melissa Tir (22, 23) , Marie Chupin , Eric Bardinet , Luc Defebvre , Jean-Christophe Corvol , David Devos

1 LilNCog - Lille Neurosciences & Cognition - U 1172
2 Irset - Institut de recherche en santé, environnement et travail
3 EHESP - École des Hautes Études en Santé Publique [EHESP]
4 CHU Lille
5 CHRU Lille - Centre Hospitalier Régional Universitaire [Lille]
6 PLBS - Plateformes Lilloises en Biologie et Santé - UAR 2014 - US 41
7 CHU Marseille
8 CHU Pitié-Salpêtrière [AP-HP]
9 SU FM - Sorbonne Université - Faculté de Médecine
10 ICM - Institut du Cerveau = Paris Brain Institute
11 Hôpital de la Fondation Ophtalmologique Adolphe de Rothschild [AP-HP]
12 CHU Toulouse - Centre Hospitalier Universitaire de Toulouse
13 CIC - Centre d'Investigation Clinique [Rennes]
14 CHU Pontchaillou [Rennes]
15 Service de Neurologie [CHU Nice]
16 CHU Gabriel Montpied [Clermont-Ferrand]
17 DC2N - Différenciation et communication neuronale et neuroendocrine
18 CHU Rouen
19 CHU Bordeaux [Bordeaux]
20 Service de neurologie [CHRU Nancy]
21 CHU Nantes - Centre hospitalier universitaire de Nantes
22 CHU Amiens-Picardie
23 LNFP - Laboratoire de Neurosciences Fonctionnelles et Pathologies - UR UPJV 4559

Background: Dopamine responsiveness (dopa-sensitivity) is an important parameter in the management of patients with Parkinson’s disease (PD). For quantification of this parameter, patients undergo a challenge test with acute Levodopa administration after drug withdrawal, which may lead to patient discomfort and use of significant resources. Objective: Our objective was to develop a predictive model combining clinical scores and imaging. Methods: 350 patients, recruited by 13 specialist French centers and considered for deep brain stimulation, underwent an acute L-dopa challenge (dopa-sensitivity > 30%), full assessment, and MRI investigations, including T1w and R2* images. Data were randomly divided into a learning base from 10 centers and data from the remaining centers for testing. A machine selection approach was applied to choose the optimal variables and these were then used in regression modeling. Complexity of the modelling was incremental, while the first model considered only clinical variables, the subsequent included imaging features. The performances were evaluated by comparing the estimated values and actual values Results: Whatever the model, the variables age, sex, disease duration, and motor scores were selected as contributors. The first model used them and the coefficients of determination (R2) was 0.60 for the testing set and 0.69 in the learning set (p < 0.001). The models that added imaging features enhanced the performances: with T1w (R2 = 0.65 and 0.76, p < 0.001) and with R2* (R2 = 0.60 and 0.72, p < 0.001). Conclusion: These results suggest that modeling is potentially a simple way to estimate dopa-sensitivity, but requires confirmation in a larger population, including patients with dopa-sensitivity < 30%

Domaines: Sciences cognitives; Neurosciences

Nacim Betrouni, Caroline Moreau, Anne-Sophie Rolland, Nicolas Carrière, Romain Viard, et al.. Can Dopamine Responsiveness Be Predicted in Parkinson’s Disease Without an Acute Administration Test?. Journal of Parkinson's disease, 2022, 12 (7), pp.2179-2190. ⟨10.3233/JPD-223334⟩. ⟨hal-04013841⟩