Table 2. Performance of classification.

The demographic AUC (95% CIs) includes estimates for a logistic model containing parameters of female gender, family history, and age. The UPSIT model includes estimates for a logistic model only containing the total UPSIT parameter. The GRS model includes estimates for a logistic model only containing the GRS parameter. The integrative model includes all parameters from the previous three models into the estimate of classification accuracy between cases and controls. For PPMI, PDBP, PARS, and 23andMe, the AUC estimates were generated comparing PD cases to controls. In PPMI, participants designated as SWEDD were compared to the same controls as the PD cases. In PARS, participants designated at risk were compared to the same controls as the PD cases. The LABS-PD and Penn-Udall studies were case-only, so instead of AUC, the proportion of correctly predicted cases was reported as a measure of classification accuracy (denoted by “*” in the table header) and includes the means and 95% CIs for this metric. At the current time, 23andMe could not provide precision estimates for this table.

Study	PPMI		PDBP	PARS		23andMe	LABS-PD*		Penn-Udall*
Status within study	PD	SWEDD	PD	PD	At risk	PD	PD	SWEDD	PD
Demographic model AUC	0.604	0.609	0.602	0.678	0.479	0.385	0.276	0.385	0.316
UPSIT model AUC	0.901	0.624	0.881	0.994	0.976	0.962	0.925	0.538	0.959
GRS model AUC	0.639	0.569	0.619	0.657	0.533	0.620	0.489	0.231	0.327
Integrative model AUC	0.923	0.707	0.894	0.998	0.962	0.955	0.929	0.692	0.939