# Using PPMI to study long term PD progression

**URL:** <https://rcop.michaeljfox.org/t/using-ppmi-to-study-long-term-pd-progression/366>\
**Category:** Analyzing and Reusing Data\
**Tags:** subtyping, ppmi, data-analysis, how-to\
**Created:** [February 26, 2024, 12:28pm UTC](https://rcop.michaeljfox.org/t/using-ppmi-to-study-long-term-pd-progression/366 "2024-02-26T12:28:30Z")\
**Posts on this page:** 1\
**Showing post:** 6

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**Author:** ![jodiefm](https://sea1.discourse-cdn.com/michaeljfox/user_avatar/rcop.michaeljfox.org/jodiefm/32/210_2.png) [@jodiefm](https://rcop.michaeljfox.org/u/jodiefm)\
**Post date:** [March 22, 2024, 4:58pm UTC](https://rcop.michaeljfox.org/t/using-ppmi-to-study-long-term-pd-progression/366/6 "2024-03-22T16:58:04Z")

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Thanks for the links - yes, I was familiar with the studies showing the instability of the TD/PIGD classification. I think what I will try to do is analyse the long-term stability of some of the data driven classification schemes that several research teams have developed in recent years. My hypothesis is that these are more to do with individual response to PD rather than fundamentally different underlying pathogenic mechanisms, and over time these clusters will be unstable. Of course, genetic versions of PD may well be different and several of these such as SNCA do seem to have significantly different trajectories compared to idiopathic cases.

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