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

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.This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at DOI: 10.1007/s11357-022-00657-6. Copyright 2021 The Author(s). Posted with permission

A machine learning approach for potential Super‐Agers identification using neuronal functional connectivity networks

Abstract INTRODUCTION Aging is often associated with cognitive decline. Understanding neural factors that distinguish adults in midlife with superior cognitive abilities (Positive‐Agers) may offer insight into how the aging brain achieves resilience. The goals of this study are to (1) introduce an optimal labeling mechanism to distinguish between Positive‐Agers and Cognitive Decliners, and (2) identify Positive‐Agers using neuronal functional connectivity networks data and demographics. METHODS In this study, principal component analysis initially created latent cognitive trajectories groups. A hybrid algorithm of machine learning and optimization was then designed to predict latent groups using neuronal functional connectivity networks derived from resting state functional magnetic resonance imaging. Specifically, the Optimal Labeling with Bayesian Optimization (OLBO) algorithm used an unsupervised approach, iterating a logistic regression function with Bayesian posterior updating. This study encompassed 6369 adults from the UK Biobank cohort. RESULTS OLBO outperformed baseline models, achieving an area under the curve of 88% when distinguishing between Positive‐Agers and cognitive decliners. DISCUSSION OLBO may be a novel algorithm that distinguishes cognitive trajectories with a high degree of accuracy in cognitively unimpaired adults. Highlights Design an algorithm to distinguish between a Positive‐Ager and a Cognitive‐Decliner. Introduce a mathematical definition for cognitive classes based on cognitive tests. Accurate Positive‐Ager identification using rsfMRI and demographic data (AUC = 0.88). Posterior default mode network has the highest impact on Positive‐Aging odds ratio

APOE, TOMM40, and Sex Interactions on Neural Network Connectivity

The Apolipoprotein E ε4 (APOE ε4) haplotype is the strongest genetic risk factor for late-onset Alzheimer‟s disease (AD). The Translocase of Outer Mitochondrial Membrane-40 (TOMM40) gene maintains cellular bioenergetics, which is disrupted in AD. TOMM40 rs2075650 (‘650) G vs. A carriage is consistently related to neural and cognitive outcomes, but it is unclear if and how it interacts with APOE. We examined 21 orthogonal neural networks among 8,222 middle-aged to aged participants in the UK Biobank cohort. ANOVA and multiple linear regression tested main effects and interactions with APOE and TOMM40 ‘650 genotypes, and if age and sex acted as moderators. APOE ε4 was associated with less strength in multiple networks, while ‘650 G vs. A carriage was related to more language comprehension network strength. In APOE ε4 carriers, ‘650 G-carriage led to less network strength with increasing age, while in non G-carriers this was only seen in women but not men. TOMM40 may shift what happens to network activity in aging APOE ε4 carriers depending on sex