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Bioenergetic and vascular predictors of potential super-ager and cognitive decline trajectories—a UK Biobank Random Forest classification study

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Abstract

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.

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Acknowledgements

This research was conducted using the UK Biobank Resource under Application Number 25057.

Funding

The study was funded by the Iowa State University, NIH R00 AG047282, and AARGD-17–529552. No funding provider had any role in the conception, collection, execution, or publication of this work.

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Author notes

  1. Parvin Mohammadiarvejeh and Brandon S. Klinedinst contributed equally to this work.

Authors and Affiliations

  1. Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA, USA

    Parvin Mohammadiarvejeh & Guiping Hu

  2. Department of Medicine, University of Washington, Seattle, WA, USA

    Brandon S. Klinedinst

  3. Department of Food Science and Human Nutrition, Iowa State University, 1109 HSNB, 2302 Osborn Drive, Ames, IA, 50011-1078, USA

    Qian Wang & Auriel A. Willette

  4. Genetics and Genomics Graduate Program, Iowa State University, Ames, IA, USA

    Tianqi Li & Auriel A. Willette

  5. Neuroscience Graduate Program, Iowa State University, Ames, IA, USA

    Brittany Larsen

  6. Bioinformatics and Computational Biology Graduate Program, Iowa State University, Ames, IA, USA

    Amy Pollpeter & Auriel A. Willette

  7. Department of Human Development and Family Studies, Iowa State University, Ames, IA, USA

    Shannin N. Moody

  8. IAC Tracker Inc, Ames, IA, USA

    Sara A. Willette & Auriel A. Willette

  9. Department of Biomedical Sciences, Iowa State University, Ames, IA, USA

    Jon P. Mochel

  10. Department of Veterinary Clinical Sciences, Iowa State University, Ames, IA, USA

    Karin Allenspach

  11. Department of Neurology, University of Iowa, Iowa City, IA, USA

    Auriel A. Willette

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  1. Parvin Mohammadiarvejeh
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Mohammadiarvejeh, P., Klinedinst, B.S., Wang, Q. et al. Bioenergetic and vascular predictors of potential super-ager and cognitive decline trajectories—a UK Biobank Random Forest classification study. GeroScience 45, 491–505 (2023). https://doi.org/10.1007/s11357-022-00657-6

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