Tuesday 26 July 2016

Prodromal Markers in Parkinson's Disease: Limitations in Longitudinal Studies and Lessons Learned

As with everything, unless you know where you have been in the past, it's hard to know which way to go in the future... a coordinated approach is needed...

Front Aging Neurosci. 2016 Jun 22;8:147. doi: 10.3389/fnagi.2016.00147. eCollection 2016.
Heinzel S, Roeben B, Ben-Shlomo Y, Lerche S, Alves G, Barone P, Behnke S, Berendse HW, Bloem BR, Burn D, Dodel R, Grosset DG, Hu M, Kasten M, Krüger R, Moccia M, Mollenhauer B, Oertel W, Suenkel U, Walter U, Wirdefeldt K, Liepelt-Scarfone I, Maetzler W, Berg D



A growing body of evidence supports a prodromal neurodegenerative process preceding the clinical onset of Parkinson's disease (PD). Studies have identified several different prodromal markers that may have the potential to predict the conversion from healthy to clinical PD but use considerably different approaches. We systematically reviewed 35 longitudinal studies reporting prodromal PD features and evaluated the methodological quality across 10 different predefined domains. We found limitations in the following domains: PD diagnosis (57% of studies), prodromal marker assessments (51%), temporal information on prodromal markers or PD diagnosis (34%), generalizability of results (17%), statistical methods (accounting for at least age as confounder; 17%), study design (14%), and sample size (9%). However, no limitations regarding drop-out (or bias investigation), or report of inclusion/exclusion criteria or prodromal marker associations were revealed. Lessons learned from these limitations and additional aspects of current prodromal marker studies in PD are discussed to provide a basis for the evaluation of findings and the improvement of future research in prodromal PD. The observed heterogeneity of studies, limitations and analyses might be addressed in future longitudinal studies using a, yet to be established, modular minimal set of assessments improving comparability of findings and enabling data sharing and combined analyses across studies.

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