Age-Related Changes in the Association Between Traumatic Brain Injury and Dementia in Older Men and Women

Examining the non-linear effects of education on Alzheimer's and non-Alzheimer's disease: Sex-stratified analysis of 17,671 older persons

BACKGROUND

Higher educational attainment is strongly associated with a reduced risk of neurocognitive disorders (NCDs). However, the literature is not yet clear on (1) the minimum years of compulsory education that would sufficiently mitigate the risk of NCDs, and (2) whether educational attainment has differential effects across sexes. To bridge these gaps, this study sought to model potential non-linear relationship between years of education and NCD risk, across men and women.

METHODS

The study recruited 17,671 participants from Alzheimer's Disease Centres across United States, aged≥50 years and had normal cognition at baseline. Participants were followed up almost annually, and underwent standardized assessments to diagnose various aetiologies of NCDs. Cox proportional hazard regression was conducted to examine the relationship between years of education and NCD risk, stratified by sexes. Years of education were modelled using restricted cubic spline.

RESULTS

Lesser years of education were associated with higher NCD risk in both sexes (specifically, when <12 years of education; HR 1.80-3.48), yet with key differences across sexes. In women, a linear relationship was observed whereby increasing years of education reduced NCD risk correspondingly; but in men, the relationship was non-linear whereby adding years of education beyond 12 years did not reduce NCD risk further. Men of lower educational attainment were at risk of both Alzheimer's Disease (AD; HR up to 3.45) and non-AD (HR up to 2.57), while women of lower educational attainment were only at risk of AD (HR up to 2.11).

CONCLUSIONS

Educational attainment alters NCD risk differently across sexes, emphasizing the relevance of sex-specific research to better understand the moderating role of sex on NCD risk. The findings also highlight the need to reconsider public policies related to equitable access to education, as well as traditional assumptions on the minimum years of compulsory education.

Traumatic Brain Injury and Risk of Dementia and Alzheimer's Disease: A Systematic Review and Meta-Analysis

INTRODUCTION

Previous studies have investigated the potential role of traumatic brain injury (TBI) in subsequent development of dementia and Alzheimer's disease (AD) but reported inconsistent results. We aim to determine the association between TBI and subsequent occurrence of dementia and AD.

METHODS

We performed a systematic search in PubMed and Web of Science for studies that quantitatively investigated the association between TBI and risk of dementia and AD and were published on or before September 21, 2021. A random-effect model was used to combine the estimates.

RESULTS

Twenty-five eligible articles were included in this meta-analysis. The results suggested that TBI was associated with an increased risk of dementia (pooled odds ratio [OR] = 1.81, 95% confidence interval [CI] = 1.53 - 2.14). However, no association was observed between TBI and Alzheimer's disease (pooled OR = 1.02, 95% CI = 0.91 - 1.15). In the subgroup analysis, TBI with loss of consciousness was not associated with risk of dementia (pooled OR = 0.96, 95% CI = 0.84 - 1.09). Besides, Asian ethnicity, male gender, and mean age of the participants less than 65 were associated with a higher risk of dementia.

CONCLUSION

Our study suggests an increased risk of dementia among individuals with TBI, highlighting the need for more intensive medical monitoring and health education in individuals with TBI. Biological mechanisms linking TBI and the development of dementia are needed in future studies.

Phybrata Sensors and Machine Learning for Enhanced Neurophysiological Diagnosis and Treatment

Concussion injuries remain a significant public health challenge. A significant unmet clinical need remains for tools that allow related physiological impairments and longer-term health risks to be identified earlier, better quantified, and more easily monitored over time. We address this challenge by combining a head-mounted wearable inertial motion unit (IMU)-based physiological vibration acceleration (“phybrata”) sensor and several candidate machine learning (ML) models. The performance of this solution is assessed for both binary classification of concussion patients and multiclass predictions of specific concussion-related neurophysiological impairments. Results are compared with previously reported approaches to ML-based concussion diagnostics. Using phybrata data from a previously reported concussion study population, four different machine learning models (Support Vector Machine, Random Forest Classifier, Extreme Gradient Boost, and Convolutional Neural Network) are first investigated for binary classification of the test population as healthy vs. concussion (Use Case 1). Results are compared for two different data preprocessing pipelines, Time-Series Averaging (TSA) and Non-Time-Series Feature Extraction (NTS). Next, the three best-performing NTS models are compared in terms of their multiclass prediction performance for specific concussion-related impairments: vestibular, neurological, both (Use Case 2). For Use Case 1, the NTS model approach outperformed the TSA approach, with the two best algorithms achieving an F1 score of 0.94. For Use Case 2, the NTS Random Forest model achieved the best performance in the testing set, with an F1 score of 0.90, and identified a wider range of relevant phybrata signal features that contributed to impairment classification compared with manual feature inspection and statistical data analysis. The overall classification performance achieved in the present work exceeds previously reported approaches to ML-based concussion diagnostics using other data sources and ML models. This study also demonstrates the first combination of a wearable IMU-based sensor and ML model that enables both binary classification of concussion patients and multiclass predictions of specific concussion-related neurophysiological impairments.