| Title: | Bioenergetic and vascular predictors of potential super-ager and cognitive decline trajectories - a UK Biobank Random Forest classification study |
| Journal: | GeroScience |
| Published: | 15 Sep 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36104610/ |
| DOI: | https://doi.org/10.1007/s11357-022-00657-6 |
| URL: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9886787 |
| Citations: | 5 (4 in last 2 years) as of 8 Aug 2024 |
Publication 12231
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Abstract
Abstract</p>
Aging has often been characterized by progressive cognitive decline in memory and especially executive function. Yet some adults, aged 80 years or older, are "super-agers" that exhibit cognitive performance like younger adults. It is unknown if there are adults in mid-life with similar superior cognitive performance ("positive-aging") versus cognitive decline over time and if there are blood biomarkers that can distinguish between these groups. Among 1303 participants in UK Biobank, latent growth curve models classified participants into different cognitive groups based on longitudinal fluid intelligence (FI) scores over 7-9 years. Random Forest (RF) classification was then used to predict cognitive trajectory types using longitudinal predictors including demographic, vascular, bioenergetic, and immune factors. Feature ranking importance and performance metrics of the model were reported. Despite model complexity, we achieved a precision of 77% when determining who would be in the "positive-aging" group (n = 563) vs. cognitive decline group (n = 380). Among the top fifteen features, an equal number were related to either vascular health or cellular bioenergetics but not demographics like age, sex, or socioeconomic status. Sensitivity analyses showed worse model results when combining a cognitive maintainer group (n = 360) with the positive-aging or cognitive decline group. Our results suggest that optimal cognitive aging may not be related to age per se but biological factors that may be amenable to lifestyle or pharmacological changes.</p>
12 Authors
- Parvin Mohammadiarvejeh
- Brandon S. Klinedinst
- Qian Wang
- Tianqi Li
- Brittany Larsen
- Amy Pollpeter
- Shannin N. Moody
- Sara A. Willette
- Jon P. Mochel
- Karin Allenspach
- Guiping Hu
- Auriel A. Willette
1 Application
| Application ID | Title |
|---|---|
| 25057 | SIM-BIO-CIS (pronounced Symbiosis) Signalling, Imaging and Metabolic Biobank Composition at Iowa State |
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