Wearables-derived risk score for unintrusive detection of α-synuclein aggregation or dopaminergic deficit

TL;DR

Smartwatch data can detect early signs of Parkinson's disease before diagnosis, potentially serving as a non-invasive screening tool.

Contribution

A digital risk score derived from smartwatch data is shown to correlate with biological markers of Parkinson's disease pathology.

Findings

The digital risk score achieved an area under precision-recall curve of 0.96 in distinguishing Parkinson's disease from healthy controls.

The digital risk score correlated more strongly with DaTscan putamen binding ratio than traditional MDS criteria.

Combining hyposmia and digital risk score improved sensitivity to 0.71 in detecting synucleinopathy or neurodegeneration.

Abstract

Smartwatch data has been found to identify Parkinson's disease (PD) several years before the clinical diagnosis. However, it has not been assessed against the gold standard but costly and invasive biological and pathological markers for PD. These include dopaminergic imaging (DaTscan) and cerebrospinal fluid alpha-synuclein seed amplification assay (SAA), which are being studied as markers thought to represent the onset of PD pathology.

Here, we combined clinical and biological data from the Parkinson's Progression Marker Initiative (PPMI) cohort with long-term (mean: 485 days) at-home digital monitoring data collected using the Verily Study Watch. We derived a digital risk score based on sleep, vital signs, and physical activity features to distinguish between PD (N = 143) and healthy controls (N = 34), achieving an area under precision-recall curve of 0.96 ± 0.01. We compared it with the Movement Disorder Society (MDS) research criteria for prodromal PD to detect dopaminergic deficit or α-synuclein aggregation in an at-risk cohort consisting of people with genetic markers or prodromal symptoms without a diagnosis of PD (N = 109, mean age = 64.62 ± 6.86, 40 men and 69 women).

The digital risk correlated with the MDS research criteria (r = 0.36, p-value = 1.46 × 10−4) and was increased in individuals with subthreshold Parkinsonism (p-value = 4.99 × 10−6) and hyposmia (p-value = 3.77 × 10−2). The digital risk was correlated to a stronger degree with DaTscan putamen binding ratio (r = −0.32, p-value = 6.64 × 10−4) than the MDS criteria (r = −0.19, p-value = 6.81 × 10−3) but to a weaker degree with SAA (r = 0.2, p-value = 3.9 × 10−2) than the MDS (r = 0.43, p-value = 1.3 × 10−5). The digital risk score achieved higher sensitivity in identifying synucleinopathy or neurodegeneration (0.59) than the MDS score (0.35) but performed on-par with hyposmia (0.59) with a combination of hyposmia and digital risk score achieving the highest sensitivity (0.71). The digital risk score showed lower precision (0.18) than other models.

A digital risk score from smartwatch data should be further explored as a possible first sensitive screening tool for presence of α-synuclein aggregation or dopaminergic deficit followed by subsequent more specific tests to reduce false positives.

This project is funded by Welsh Government through 10.13039/100012068Health and Care Research Wales, 10.13039/501100000265Medical Research Council (MRC), 10.13039/501100000383Higher Education Funding Council for Wales, 10.13039/501100017510UK Dementia Research Institute, Alzheimer's Society and Alzheimer's Research UK, Dementia Platforms UK, 10.13039/501100000266UKRI Engineering and Physical Sciences Research Council (EPSRC), 10.13039/501100013342NIHR Imperial Biomedical Research Centre (BRC), 10.13039/501100001279Great Ormond Street Hospital and the 10.13039/501100000287Royal Academy of Engineering, Edmond J. Safra Foundation, Ser Cymru II programme, and the European Regional Development Fund.

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