Abstract
AbstractBackgroundUse of digital sensors to passively collect long-term offers a step change in our ability to screen for early signs of disease in the general population. Smartwatch data has been shown to identify Parkinson’s disease (PD) several years before the clinical diagnosis, however, has not been evaluated in comparison to biological and pathological markers such as dopaminergic imaging (DaTscan) or cerebrospinal fluid (CSF) alpha-synuclein seed amplification assay (SAA) in an at-risk cohort.MethodsTo address this, we performed a cohort study using longitudinal clinical assessment data from the Parkinson’s Progression Marker Initiative (PPMI) cohort collected between 2010 and 2020 with additional long-term (mean: 485 days) at-home digital monitoring data (collected 2018-2020) from the Verily Study Watch. We derived a digital risk score and evaluated it in an at-risk cohort (N = 109) consisting of people with genetic markers (LRRK2, GBA) or prodromal symptoms (hyposmia, polysomnography-proven Rapid-Eye-Movement behavioral sleep disorder) without a diagnosis of PD for whom all modalities were available (digital, DaTscan, SAA). The digital risk score was compared to the Movement Disorder Society (MDS) research criteria for prodromal PD, alpha-synuclein SAA and DaTscan.FindingsIn the at-risk cohort (N=109, mean age = 64.62±6.86, 37% male), the digital risk correlated with the MDS research criteria for prodromal PD (r = 0.36, p-value = 1.46x10-4) and was increased in individuals with subthreshold Parkinsonism (UPDRS III > 6) (p-value = 4.99x10-6) and hyposmia (p-value = 3.77x10-2). Notably, the digital risk was correlated with DaTscan putamen binding ratio (r = -0.32, p-value = 6.64x10-4) and CSF SAA (r = 0.2, p-value = 3.9x10-2). The digital risk achieved higher sensitivity in identifying people with SAA positivity (0.71 vs 0.43) or DaTscan positivity (0.43 vs 0.14) than the MDS prodromal score but performed on-par or worse than hyposmia (SAA+: 0.71 vs 0.71, DaT+: 0.48 vs 0.57).InterpretationA digital risk score from smartwatch data could be used as a sensitive screening tool for early detection of PD followed by more specific tests.
Publisher
Cold Spring Harbor Laboratory