Longitudinal associations of absolute versus relative moderate-to-vigorous physical activity with brain microstructural decline in aging

Circulating Neurofilament Light Chain Levels Increase with Age and Are Associated with Worse Physical Function and Body Composition in Men but Not in Women

This study aimed to assess the relationship between age-related changes in Neurofilament Light Chain (NFL), a marker of neuronal function, and various factors including muscle function, body composition, and metabolomic markers. The study included 40 participants, aged 20 to 85 years. NFL levels were measured, and muscle function, body composition, and metabolomic markers were assessed. NFL levels increased significantly with age, particularly in men. Negative correlations were found between NFL levels and measures of muscle function, such as grip strength, walking speed, and chair test performance, indicating a decline in muscle performance with increasing NFL. These associations were more pronounced in men. NFL levels also negatively correlated with muscle quality in men, as measured by 50 kHz phase angle. In terms of body composition, NFL was positively correlated with markers of fat mass and negatively correlated with markers of muscle mass, predominantly in men. Metabolomic analysis revealed significant associations between NFL levels and specific metabolites, with gender-dependent relationships observed. This study provides insights into the relationship between circulating serum NFL, muscle function, and aging. Our findings hint at circulating NFL as a potential early marker of age-associated neurodegenerative processes, especially in men.

Lower Myelin Content Is Associated With Lower Gait Speed in Cognitively Unimpaired Adults

Mounting evidence indicates that abnormal gait speed predicts the progression of neurodegenerative diseases, including Alzheimer's disease. Understanding the relationship between white matter integrity, especially myelination, and motor function is crucial to the diagnosis and treatment of neurodegenerative diseases. We recruited 118 cognitively unimpaired adults across an extended age range of 22-94 years to examine associations between rapid or usual gait speeds and cerebral myelin content. Using our advanced multicomponent magnetic resonance relaxometry method, we measured myelin water fraction (MWF), a direct measure of myelin content, as well as longitudinal and transverse relaxation rates (R1 and R2), sensitive but nonspecific magnetic resonance imaging measures of myelin content. After adjusting for covariates and excluding 22 data sets due to cognitive impairments or artifacts, our results indicate that participants with higher rapid gait speed exhibited higher MWF, R1, and R2 values, that is, higher myelin content. These associations were statistically significant within several white matter brain regions, particularly the frontal and parietal lobes, splenium, anterior corona radiata, and superior fronto-occipital and longitudinal fasciculus. In contrast, we did not find any significant associations between usual gait speed and MWF, R1, or R2, which suggests that rapid gait speed may be a more sensitive marker of demyelination than usual gait speed. These findings advance our understanding on the implication of myelination in gait impairment among cognitively unimpaired adults, providing further evidence of the interconnection between white matter integrity and motor function.

Investigating Links between Moderate-to-Vigorous Physical Activity and Self-Rated Health Status in Adolescents: The Mediating Roles of Emotional Intelligence and Psychosocial Stress

Adolescence represents a crucial phase, characterized by rapid physical and mental development and numerous challenges. Physical activity plays a vital role in the mental well-being of adolescents; however, due to the prevailing educational philosophy prioritizing academic performance, adolescent participation in physical activities has yet to reach its full potential. Thus, this study aims to investigate the effects of moderate-to-vigorous physical activity on adolescents' emotional intelligence, psychosocial stress, and self-rated health status. To achieve this objective, a cluster sampling method was employed to collect data from 600 adolescents in 10 schools across five municipal districts of Changsha, China. A total of 426 valid questionnaires were returned and analyzed. Utilizing AMOS v.23, a structural equation model was constructed to validate the hypotheses. The findings reveal that moderate-to-vigorous physical activity significantly impacts adolescents' emotional intelligence and self-rated health status. Conversely, it exerts a significant negative influence on their psychosocial stress. Moreover, emotional intelligence and psychosocial stress mediate the relationship between moderate-to-vigorous physical activity and self-rated health status. In light of these results, education departments, schools, and families must embrace a paradigm shift in educational philosophies and provide robust support for adolescents to engage in moderate-to-vigorous physical activities.

Dual cognitive and mobility impairments and future dementia - Setting a research agenda

Dual cognitive and mobility impairments are associated with an increased risk of dementia. Recent studies examining temporal trajectories of mobility and cognitive function in aging found that dual decline is associated with higher dementia risk than memory decline or gait decline only. Although initial data show that individuals with dual decline or impairment have excessive cardiovascular and metabolic risk factors, the causes of dual decline or what underlies dual decline with a high risk of dementia remain largely unknown. In December 2021, the National Institute on Aging Intramural and Extramural Programs jointly organized a workshop on Biology Underlying Moving and Thinking to explore the hypothesis that older persons with dual decline may develop dementia through a specific pathophysiological pathway. The working group discussed assessment methods for dual decline and possible mechanisms connecting dual decline with dementia risk and pinpointed the most critical questions to be addressed from a translational perspective.

Machine learning for predicting neurodegenerative diseases in the general older population: a cohort study

BACKGROUND

In the older general population, neurodegenerative diseases (NDs) are associated with increased disability, decreased physical and cognitive function. Detecting risk factors can help implement prevention measures. Using deep neural networks (DNNs), a machine-learning algorithm could be an alternative to Cox regression in tabular datasets with many predictive features. We aimed to compare the performance of different types of DNNs with regularized Cox proportional hazards models to predict NDs in the older general population.

METHODS

We performed a longitudinal analysis with participants of the English Longitudinal Study of Ageing. We included men and women with no NDs at baseline, aged 60 years and older, assessed every 2 years from 2004 to 2005 (wave2) to 2016-2017 (wave 8). The features were a set of 91 epidemiological and clinical baseline variables. The outcome was new events of Parkinson's, Alzheimer or dementia. After applying multiple imputations, we trained three DNN algorithms: Feedforward, TabTransformer, and Dense Convolutional (Densenet). In addition, we trained two algorithms based on Cox models: Elastic Net regularization (CoxEn) and selected features (CoxSf).

RESULTS

5433 participants were included in wave 2. During follow-up, 12.7% participants developed NDs. Although the five models predicted NDs events, the discriminative ability was superior using TabTransformer (Uno's C-statistic (coefficient (95% confidence intervals)) 0.757 (0.702, 0.805). TabTransformer showed superior time-dependent balanced accuracy (0.834 (0.779, 0.889)) and specificity (0.855 (0.0.773, 0.909)) than the other models. With the CoxSf (hazard ratio (95% confidence intervals)), age (10.0 (6.9, 14.7)), poor hearing (1.3 (1.1, 1.5)) and weight loss 1.3 (1.1, 1.6)) were associated with a higher DNN risk. In contrast, executive function (0.3 (0.2, 0.6)), memory (0, 0, 0.1)), increased gait speed (0.2, (0.1, 0.4)), vigorous physical activity (0.7, 0.6, 0.9)) and higher BMI (0.4 (0.2, 0.8)) were associated with a lower DNN risk.

CONCLUSION

TabTransformer is promising for prediction of NDs with heterogeneous tabular datasets with numerous features. Moreover, it can handle censored data. However, Cox models perform well and are easier to interpret than DNNs. Therefore, they are still a good choice for NDs.