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

Collaborative Cohort of Cohorts for COVID-19 Research (C4R) Study: Study Design

The Collaborative Cohort of Cohorts for COVID-19 Research (C4R) is a national prospective study of adults comprising 14 established US prospective cohort studies. Starting as early as 1971, investigators in the C4R cohort studies have collected data on clinical and subclinical diseases and their risk factors, including behavior, cognition, biomarkers, and social determinants of health. C4R links this pre-coronavirus disease 2019 (COVID-19) phenotyping to information on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and acute and postacute COVID-related illness. C4R is largely population-based, has an age range of 18-108 years, and reflects the racial, ethnic, socioeconomic, and geographic diversity of the United States. C4R ascertains SARS-CoV-2 infection and COVID-19 illness using standardized questionnaires, ascertainment of COVID-related hospitalizations and deaths, and a SARS-CoV-2 serosurvey conducted via dried blood spots. Master protocols leverage existing robust retention rates for telephone and in-person examinations and high-quality event surveillance. Extensive prepandemic data minimize referral, survival, and recall bias. Data are harmonized with research-quality phenotyping unmatched by clinical and survey-based studies; these data will be pooled and shared widely to expedite collaboration and scientific findings. This resource will allow evaluation of risk and resilience factors for COVID-19 severity and outcomes, including postacute sequelae, and assessment of the social and behavioral impact of the pandemic on long-term health trajectories

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

Remodeling of substrate consumption in the murine sTAC model of heart failure

BACKGROUND: Energy metabolism and substrate selection are key aspects of correct myocardial mechanical function. Myocardial preference for oxidizable substrates changes in both hypertrophy and in overt failure. Previous work has shown that glucose oxidation is upregulated in overpressure hypertrophy, but its fate in overt failure is less clear. Anaplerotic flux of pyruvate into the tricarboxylic acid cycle (TCA) has been posited as a secondary fate of glycolysis, aside from pyruvate oxidation or lactate production. METHODS AND RESULTS: A model of heart failure that emulates both valvular and hypertensive heart disease, the severe transaortic constriction (sTAC) mouse, was assayed for changes in substrate preference using metabolomic and carbon-13 flux measurements. Quantitative measures of O(2) consumption in the Langendorff perfused mouse heart were paired with (13)C isotopomer analysis to assess TCA cycle turnover. Since the heart accommodates oxidation of all physiological energy sources, the utilization of carbohydrates, fatty acids, and ketones were measured simultaneously using a triple-tracer NMR method. The fractional contribution of glucose to acetyl-CoA production was upregulated in heart failure, while other sources were not significantly different. A model that includes both pyruvate carboxylation and anaplerosis through succinyl-CoA produced superior fits to the data compared to a model using only pyruvate carboxylation. In the sTAC heart, anaplerosis through succinyl-CoA is elevated, while pyruvate carboxylation was not. Metabolomic data showed depleted TCA cycle intermediate pool sizes versus the control, in agreement with previous results. CONCLUSION: In the sTAC heart failure model, the glucose contribution to acetyl-CoA production was significantly higher, with compensatory changes in fatty acid and ketone oxidation not reaching a significant level. Anaplerosis through succinyl-CoA is also upregulated, and is likely used to preserve TCA cycle intermediate pool sizes. The triple tracer method used here is new, and can be used to assess sources of acetyl-CoA production in any oxidative tissue

In vivo determination of human breast fat composition by 1H magnetic resonance spectroscopy at 7 T

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