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Evaluating the prediction performance of objective physical activity measures for incident Parkinson’s disease in the UK Biobank

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Abstract

Background

Parkinson’s disease (PD) is the fastest-growing neurological condition with over 10 million cases worldwide. While age and sex are known predictors of incident PD, there is a need to identify other predictors. This study compares the prediction performance of accelerometry-derived physical activity (PA) measures and traditional risk factors for incident PD in the UK Biobank.

Methods

The study population consisted of 92,352 UK Biobank participants without PD at baseline (43.8% male, median age 63 years with interquartile range 43–69). 245 participants were diagnosed with PD by April 1, 2021 (586,604 person-years of follow-up). The incident PD prediction performances of 10 traditional predictors and 8 objective PA measures were compared using single- and multi-variable Cox models. Prediction performance was assessed using a novel, stable statistic: the repeated cross-validated concordance (rcvC). Sensitivity analyses were conducted where PD cases diagnosed within the first six months, one year, and two years were deleted.

Results

Single-predictor Cox regression models indicated that all PA measures were statistically significant (p-values < 0.0001). The highest-performing individual predictors were total acceleration (TA) (rcvC = 0.813) among PA measures, and age (rcvC = 0.757) among traditional predictors. The two-step forward-selection process produced a model containing age, sex, and TA (rcvC = 0.851). Adding TA to the model increased the rcvC by 9.8% (p-value < 0.0001). Results were largely unchanged in sensitivity analyses.

Conclusions

Objective PA summaries have better single-predictor model performance than known risk factors and increase the prediction performance substantially when added to models with age and sex.

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Acknowledgements

This research is conducted under UK Biobank Resource Application 33278. The authors thank the UK Biobank participants.

Funding

This work was supported by R01 grants NS060910 and AG075883 from the National Institutes of Health.

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Authors and Affiliations

  1. Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA

    Angela Zhao, Erjia Cui, Martin A. Lindquist & Ciprian M. Crainiceanu

  2. Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA

    Andrew Leroux

  3. Division of Biostatistics, University of Minnesota Twin Cities, Minneapolis, USA

    Erjia Cui

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  1. Angela Zhao
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Corresponding author

Correspondence to Angela Zhao.

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Conflicts of interest

Ciprian Crainiceanu is consulting for Bayer and Johnson and Johnson on methods development for wearable and implantable technologies. The details of these contracts are disclosed through the Johns Hopkins University eDisclose system. The research presented here is not related to and was not supported by this consulting work. All other authors declare no conflicts of interest.

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The UK Biobank received ethical approval (REC reference for UK Biobank 11/NW/0382) and participants have provided written informed consent.

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Zhao, A., Cui, E., Leroux, A. et al. Evaluating the prediction performance of objective physical activity measures for incident Parkinson’s disease in the UK Biobank. J Neurol 270, 5913–5923 (2023). https://doi.org/10.1007/s00415-023-11939-0

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