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Assessing Metabolic Ageing via DNA Methylation Surrogate Markers: A Multicohort Study in Britain, Ireland and the USA
ABSTRACTMetabolomics and epigenomics have been used to develop ‘ageing clocks’ that assess biological age and identify ‘accelerated ageing’. While metabolites are subject to short‐term variation, DNA methylation (DNAm) may capture longer‐term metabolic changes. We aimed to develop a hybrid DNAm‐metabolic clock using DNAm as metabolite surrogates (‘DNAm‐metabolites’) for age prediction. Within the UK Airwave cohort (n = 820), we developed DNAm metabolites by regressing 594 metabolites on DNAm and selected 177 DNAm metabolites and 193 metabolites to construct ‘DNAm‐metabolic’ and ‘metabolic’ clocks. We evaluated clocks in their age prediction and association with noncommunicable disease risk factors. We additionally validated the DNAm‐metabolic clock for the prediction of age and health outcomes in The Irish Longitudinal Study of Ageing (TILDA, n = 488) and the Health and Retirement Study (HRS, n = 4018). Around 70% of DNAm metabolites showed significant metabolite correlations (Pearson's r: > 0.30, p < 10−4) in the Airwave test set and overall stronger age associations than metabolites. The DNAm‐metabolic clock was enriched for metabolic traits and was associated (p < 0.05) with male sex, heavy drinking, anxiety, depression and trauma. In TILDA and HRS, the DNAm‐metabolic clock predicted age (r = 0.73 and 0.69), disability and gait speed (p < 0.05). In HRS, it additionally predicted time to death, diabetes, cardiovascular disease, frailty and grip strength. DNAm metabolite surrogates may facilitate metabolic studies using only DNAm data. Clocks built from DNAm metabolites provided a novel approach to assess metabolic ageing, potentially enabling early detection of metabolic‐related diseases for personalised medicine.
Designing a computer-assisted diagnosis system for cardiomegaly detection and radiology report generation
Chest X-ray (CXR) is a diagnostic tool for cardiothoracic assessment. They make up 50% of all diagnostic imaging tests. With hundreds of images examined every day, radiologists can suffer from fatigue. This fatigue may reduce diagnostic accuracy and slow down report generation. We describe a prototype computer-assisted diagnosis (CAD) pipeline employing computer vision (CV) and Natural Language Processing (NLP). It was trained and evaluated on the publicly available MIMIC-CXR dataset. We perform image quality assessment, view labelling, and segmentation-based cardiomegaly severity classification. We use the output of the severity classification for large language model-based report generation. Four board-certified radiologists assessed the output accuracy of our CAD pipeline. Across the dataset composed of 377,100 CXR images and 227,827 free-text radiology reports, our system identified 0.18% of cases with mixed-sex mentions, 0.02% of poor quality images ( F 1 = 0.81), and 0.28% of wrongly labelled views (accuracy 99.4%). We assigned views for 4.18% of images which have unlabelled views. Our binary cardiomegaly classification model has 95.2% accuracy. The inter-radiologist agreement on evaluating the generated report’s semantics and correctness for radiologist-MIMIC is 0.62 (strict agreement) and 0.85 (relaxed agreement) similar to the radiologist-CAD agreement of 0.55 (strict) and 0.93 (relaxed). Our work found and corrected several incorrect or missing metadata annotations for the MIMIC-CXR dataset. The performance of our CAD system suggests performance on par with human radiologists. Future improvements revolve around improved text generation and the development of CV tools for other diseases.
LAVASET: Latent Variable Stochastic Ensemble of Trees. An ensemble method for correlated datasets with spatial, spectral, and temporal dependencies
Abstract Motivation Random forests (RFs) can deal with a large number of variables, achieve reasonable prediction scores, and yield highly interpretable feature importance values. As such, RFs are appropriate models for feature selection and further dimension reduction. However, RFs are often not appropriate for correlated datasets due to their mode of selecting individual features for splitting. Addressing correlation relationships in high-dimensional datasets is imperative for reducing the number of variables that are assigned high importance, hence making the dimension reduction most efficient. Here, we propose the LAtent VAriable Stochastic Ensemble of Trees (LAVASET) method that derives latent variables based on the distance characteristics of each feature and aims to incorporate the correlation factor in the splitting step. Results Without compromising on performance in the majority of examples, LAVASET outperforms RF by accurately determining feature importance across all correlated variables and ensuring proper distribution of importance values. LAVASET yields mostly non-inferior prediction accuracies to traditional RFs when tested in simulated and real 1D datasets, as well as more complex and high-dimensional 3D datatypes. Unlike traditional RFs, LAVASET is unaffected by single ‘important’ noisy features (false positives), as it considers the local neighbourhood. LAVASET, therefore, highlights neighbourhoods of features, reflecting real signals that collectively impact the model’s predictive ability. Availability and implementation LAVASET is freely available as a standalone package from https://github.com/melkasapi/LAVASET.
Spatial fibroblast niches define Crohn's fistulae.
Crohn's disease often presents with fistulae, abnormal tunnels that connect the intestine to the skin or other organs. Despite their profound effect on morbidity, the molecular basis of fistula formation remains unclear, largely owing to the challenge of capturing intact fistula tracts and their inherent heterogeneity1-3. Here we construct a subcellular-resolution spatial atlas of 68 intestinal fistulae spanning diverse anatomical locations. We describe fistula-associated epithelial, immune and stromal cell states, revealing abnormal zonation of growth factors and morphogens linked to establishment of tunnelling anatomy. We identify fistula-associated stromal (FAS) fibroblasts, which are assembled in concentric layers: a proliferative, lumen-adjacent zone beneath neutrophil and macrophage-rich granulation tissue, an active lesion core of FAS cells and a quiescent, pro-fibrotic outer zone. We examine the architecture of the extracellular matrix in the fistula tract and demonstrate that FAS populations associate with distinct collagen structures, exhibiting properties ranging from proliferation, migration and extracellular matrix remodelling to dense collagen deposition and fibrosis. We define niches supporting epithelialization of fistula tunnels and a FAS-like population that is detected at the base of ulcers in non-penetrating Crohn's disease. Our study demonstrates that common molecular pathways and cellular niches underpin fistulae across intestinal locations, revealing the cellular protagonists of fistula establishment and persistence. This resource will inform the development of model systems and interventions to mitigate aberrant fibroblast activity while preserving their regenerative properties in Crohn's disease.