| Title: | Evaluating the prediction performance of objective physical activity measures for incident Parkinson's disease in the UK Biobank |
| Journal: | Journal of Neurology |
| Published: | 24 Aug 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37612539/ |
| DOI: | https://doi.org/10.1007/s00415-023-11939-0 |
| Citations: | 2 (2 in last 2 years) as of 8 Aug 2024 |
Publication 9710
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Abstract
BackgroundParkinson'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.MethodsThe 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.ResultsSingle-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.ConclusionsObjective 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.</p>
5 Authors
- Angela Zhao
- Erjia Cui
- Andrew Leroux
- Martin A. Lindquist
- Ciprian M. Crainiceanu
1 Application
| Application ID | Title |
|---|---|
| 33278 | Statistical Analysis of UK Biobank Imaging Database |
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