Comprehensive analysis of epigenetic clocks reveals associations between disproportionate biological ageing and hippocampal volume

The concept of age acceleration, the difference between biological age and chronological age, is of growing interest, particularly with respect to age-related disorders, such as Alzheimer’s Disease (AD). Whilst studies have reported associations with AD risk and related phenotypes, there remains a lack of consensus on these associations. Here we aimed to comprehensively investigate the relationship between five recognised measures of age acceleration, based on DNA methylation patterns (DNAm age), and cross-sectional and longitudinal cognition and AD-related neuroimaging phenotypes (volumetric MRI and Amyloid-β PET) in the Australian Imaging, Biomarkers and Lifestyle (AIBL) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Significant associations were observed between age acceleration using the Hannum epigenetic clock and cross-sectional hippocampal volume in AIBL and replicated in ADNI. In AIBL, several other findings were observed cross-sectionally, including a significant association between hippocampal volume and the Hannum and Phenoage epigenetic clocks. Further, significant associations were also observed between hippocampal volume and the Zhang and Phenoage epigenetic clocks within Amyloid-β positive individuals. However, these were not validated within the ADNI cohort. No associations between age acceleration and other Alzheimer’s disease-related phenotypes, including measures of cognition or brain Amyloid-β burden, were observed, and there was no association with longitudinal change in any phenotype. This study presents a link between age acceleration, as determined using DNA methylation, and hippocampal volume that was statistically significant across two highly characterised cohorts. The results presented in this study contribute to a growing literature that supports the role of epigenetic modifications in ageing and AD-related phenotypes

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique\u2014Subtype and Stage Inference (SuStaIn)\u2014able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer\u2019s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 7 10 124 ) or temporal stage (p = 3.96 7 10 125 ). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine

Spinal Cord Injury: How Can We Improve the Classification and Quantification of Its Severity and Prognosis?

The preservation of functional neural tissue after spinal cord injury (SCI) is the basis for spontaneous neurological recovery. Some injured patients in the acute phase have more potential for recovery than others. This fact is problematic for the construction of clinical trials because enrollment of subjects with variable recovery potential makes it difficult to detect effects, requires large sample sizes, and risks Type II errors. In addition, the current methods to assess injury and recovery are non-quantitative and not sensitive. It is likely that therapeutic combinations will be necessary to cause substantially improved function after SCI, thus we need highly sensitive techniques to evaluate changes in motor, sensory, autonomic and other functions. We review several emerging neurophysiological techniques with high sensitivity. Quantitative methods to evaluate residual tissue sparing after severe acute SCI have not entered widespread clinical use. This reduces the ability to correlate structural preservation with clinical outcome following SCI resulting in enrollment of subjects with varying patterns of tissue preservation and injury into clinical trials. We propose that the inclusion of additional measures of injury severity, pattern, and individual genetic characteristics may enable stratification in clinical trials to make the testing of therapeutic interventions more effective and efficient. New imaging techniques to assess tract injury and demyelination and methods to quantify tissue injury, inflammatory markers, and neuroglial biochemical changes may improve the evaluation of injury severity, and the correlation with neurological outcome, and measure the effects of treatment more robustly than is currently possible. The ability to test such a multimodality approach will require a high degree of collaboration between clinical and research centers and government research support. When the most informative of these assessments is determined, it may be possible to identify patients with substantial recovery potential, improve selection criteria and conduct more efficient clinical trials