| Title: | Machine learning approaches to predict age from accelerometer records of physical activity at biobank scale |
| Journal: | PLOS Digital Health |
| Published: | 24 Jan 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36812610/ |
| DOI: | https://doi.org/10.1371/journal.pdig.0000176 |
| URL: | https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000176&type=printable |
Publication 10993
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
Physical activity improves quality of life and protects against age-related diseases. With age, physical activity tends to decrease, increasing vulnerability to disease in the elderly. In the following, we trained a neural network to predict age from 115,456 one week-long 100Hz wrist accelerometer recordings from the UK Biobank (mean absolute error = 3.7±0.2 years), using a variety of data structures to capture the complexity of real-world activity. We achieved this performance by preprocessing the raw frequency data as 2,271 scalar features, 113 time series, and four images. We defined accelerated aging for a participant as being predicted older than one's actual age and identified both genetic and environmental exposure factors associated with the new phenotype. We performed a genome wide association on the accelerated aging phenotypes to estimate its heritability (h_g2 = 12.3±0.9%) and identified ten single nucleotide polymorphisms in close proximity to genes in a histone and olfactory cluster on chromosome six (e.g HIST1H1C, OR5V1). Similarly, we identified biomarkers (e.g blood pressure), clinical phenotypes (e.g chest pain), diseases (e.g hypertension), environmental (e.g smoking), and socioeconomic (e.g income and education) variables associated with accelerated aging. Physical activity-derived biological age is a complex phenotype associated with both genetic and non-genetic factors.</p>
6 Authors
- Alan Le Goallec
- Sasha Collin
- M'Hamed Jabri
- Samuel Diai
- Théo Vincent
- Chirag J. Patel
1 Application
| Application ID | Title |
|---|---|
| 52887 | Leveraging medical images and deep learning to characterize biological age |
Enabling scientific discoveries that improve human health