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A promise of genomics in precision medicine is to provide individualized genetic risk predictions. Polygenic risk scores (PRS), computed by aggregating effects from many genomic variants, have been developed as a useful tool in complex disease research. However, the application of PRS as a tool for predicting an individual’s disease susceptibility in a clinical setting is challenging because PRS typically provide a relative measure of risk evaluated at the level of a group of people but not at individual level. Here, we introduce a machine-learning technique, Mondrian Cross-Conformal Prediction (MCCP), to estimate the confidence bounds of PRS-to-disease-risk prediction. MCCP can report disease status conditional probability value for each individual and give a prediction at a desired error level. Moreover, with a user-defined prediction error rate, MCCP can estimate the proportion of sample (coverage) with a correct prediction. The application of polygenic risk scores to individual-level disease susceptibility is challenging, as risk is evaluated at a group-level. Here, the authors describe a machine learning method, Mondrian Cross-Conformal Prediction, that reports disease status conditional probability value at the individual level.

Type 2 diabetes is associated with cardiovascular disease, possibly due to impaired vascular fibrous repair. Yet, the mechanisms are elusive. Here, we investigate alterations in the fibrous repair processes in type 2 diabetes atherosclerotic plaque extracellular matrix by combining multi-omics from the human Carotid Plaque Imaging Project cohort and functional studies. Plaques from type 2 diabetes patients have less collagen. Interestingly, lower levels of transforming growth factor-ß distinguish type 2 diabetes plaques and, in these patients, lower levels of fibrous repair markers are associated with cardiovascular events. Transforming growth factor-ß2 originates mostly from contractile vascular smooth muscle cells that interact with synthetic vascular smooth muscle cells in the cap, leading to collagen formation and vascular smooth muscle cell differentiation. This is regulated by free transforming growth factor-ß2 which is affected by hyperglycemia. Our findings underscore the importance of transforming growth factor-ß2-driven fibrous repair in type 2 diabetes as an area for future therapeutic strategies. The mechanisms underlying atherosclerotic complications in type 2 diabetes are still unknown. Here, the authors provide evidence for an impaired vascular fibrous repair response involving transforming growth factor-ß2-regulated collagen synthesis and cell differentiation as central mechanisms.

Chemogenomics data generally refers to the activity data of chemical compounds on an array of protein targets and represents an important source of information for building in silico target prediction models. The increasing volume of chemogenomics data offers exciting opportunities to build models based on Big Data. Preparing a high quality data set is a vital step in realizing this goal and this work aims to compile such a comprehensive chemogenomics dataset. This dataset comprises over 70 million SAR data points from publicly available databases (PubChem and ChEMBL) including structure, target information and activity annotations. Our aspiration is to create a useful chemogenomics resource reflecting industry-scale data not only for building predictive models of in silico polypharmacology and off-target effects but also for the validation of cheminformatics approaches in general.

Rupture of unstable atherosclerotic plaques is a major cause of mortality. Endothelial-to-mesenchymal transition associates with advanced atherosclerotic plaques and contributes to plaque progression. We examined the role of Twist1 , a transcription factor that drives endothelial-to-mesenchymal transition, in plaque progression by inducible deletion from endothelial cells in hypercholesterolemic mice ( Twist1 ECKO Apo -/- ). Single-cell RNA sequencing coupled to endothelial cell-tracking reveals that Twist1 promotes endothelial-to-mesenchymal transition in advanced atherosclerotic plaques. Histological analyses demonstrate that endothelial Twist1 promotes plaque growth and hallmarks of plaque stability (collagen, ACTA2-positive cells) and reduces features of instability (necrosis, macrophage accumulation). Analysis of cultured human aortic endothelial cells shows that TWIST1 contributes to endothelial-to-mesenchymal transition by promoting migration and proliferation through the transcriptional coactivator PELP1. Additionally, TWIST1 promotes endothelial cell proliferation via AEBP1-dependent upregulation of COL4A1. These findings challenge the prevailing view that endothelial-to-mesenchymal transition uniquely destabilizes plaques, by suggesting that TWIST1-driven endothelial-to-mesenchymal transition can promote plaque stability, offering new insights into atherosclerosis pathophysiology and therapeutic potential. Endothelial-to-mesenchymal transition contributes to plaque progression in atherosclerosis. Here, Ayllon et al. show that the protein TWIST1 drives endothelial cell plasticity in atherosclerosis, promoting plaque growth and stability while reducing features linked to rupture, challenging the view that these cell changes always worsen disease.

The receptor for advanced glycation endproducts (RAGE) has pro-inflammatory and pro-atherogenic effects. Low plasma levels of soluble RAGE (sRAGE), a decoy receptor for RAGE ligands, have been associated with increased risk for major adverse coronary events (MACE) in the general population. We performed a genome-wide association study to identify genetic determinants of plasma sRAGE in 4338 individuals from the cardiovascular arm of the Malmö Diet and Cancer study (MDC-CV). Further, we explored the associations between these genetic variants, incident first-time MACE and mortality in 24,640 unrelated individuals of European ancestry from the MDC cohort. The minor alleles of four single nucleotide polymorphisms (SNPs): rs2070600, rs204993, rs116653040, and rs7306778 were independently associated with lower plasma sRAGE. The minor T (vs. C) allele of rs2070600 was associated with increased risk for MACE [HR 1.13 95% CI (1.02–1.25), P  = 0.016]. Neither SNP was associated with mortality. This is the largest study to demonstrate a link between a genetic sRAGE determinant and CV risk. Only rs2070600, which enhances RAGE function by inducing a Gly82Ser polymorphism in the ligand-binding domain, was associated with MACE. The lack of associations with incident MACE for the other sRAGE-lowering SNPs suggests that this functional RAGE modification is central for the observed relationship.

Aims Reduced lung function and adverse health outcomes are often observed. This study characterizes genetic susceptibility for reduced lung function and risk of developing a range of adverse health outcomes. Methods We studied 27,438 middle-aged adults from the Malmö Diet and Cancer study (MDCS), followed up to 28.8 years. Trait-specific Polygenic scores (PGS) for forced expiratory volume in 1 s (FEV 1 ) and forced vital capacity (FVC) were constructed for each participant using MDCS genetic data and summary statistics from the latest GWAS of lung function. Linear regression models and cox proportional hazards regression models were used to assess associations between adverse health outcomes and lung function-PGS. Results FEV 1 -PGS and FVC-PGS were significantly associated with mean sBP at baseline after adjustments (FEV 1 -PGS Q1 (highest PGS = highest lung function): 140.7mmHg vs. Q4: 141.5mmHg, p-value 0.008). A low FVC-PGS was significantly associated with the risk of future diabetic events after adjustments (Q4 vs. Q1 HR: 1.22 (CI 1.12–1.32), p-trend < 0.001) and had added value to risk prediction models for diabetes. Low FEV 1 -PGS was significantly associated with future coronary events (Q4 vs. Q1 HR: 1.13 (CI: 1.04–1.22), p-trend 0.008). No significant association was found between PGS and sudden cardiac death, chronic kidney disease or all-cause mortality. Results remained largely unchanged in a subgroup of subjects when further adjusted for apolipoproteins. Conclusion Genetic susceptibility for reduced lung function is associated with higher sBP, increased risk of diabetes and to a lesser extent, future coronary events, suggesting etiological roles of lung function on these outcomes. Using PGS, high-risk groups could be early detected to implement early lifestyle changes to mitigate the risk.

Background To identify subjects with rupture-prone atherosclerotic plaques before thrombotic events occur is an unmet clinical need. Thus, this proof-of-concept study aims to determine which rupture-prone plaque features can be detected using clinically available photon-counting computed tomography (PCCT). Methods In this retrospective study, advanced atherosclerotic plaques ( ex vivo , paraffin-embedded) from the Carotid Plaque Imaging Project were scanned by PCCT with reconstructed energy levels (45, 70, 120, 190 keV). Density in HU was measured in 97 regions of interest (ROIs) representing rupture-prone plaque features as demonstrated by histopathology (thrombus, lipid core, necrosis, fibrosis, intraplaque haemorrhage, calcium). The relationship between HU and energy was then assessed using a mixed-effects model for each plaque feature. Results Plaques from five men (age 79 ± 8 [mean ± standard deviation]) were included in the study. Comparing differences in coefficients ( b 1diff ) of matched ROIs on plaque images obtained by PCCT and histology confirmed that calcium was distinguishable from all other analysed features. Of greater novelty, additional rupture-prone plaque features proved discernible from each other, particularly when comparing haemorrhage with fibrous cap ( p  = 0.017), lipids ( p  = 0.003) and necrosis ( p  = 0.004) and thrombus compared to fibrosis ( p  = 0.048), fibrous cap ( p  = 0.028), lipids ( p  = 0.015) and necrosis ( p  = 0.017). Conclusions Clinically available PCCT detects not only calcification, but also other rupture-prone features of human carotid plaques ex vivo . Relevance statement Improved atherosclerotic plaque characterisation by photon-counting CT provides the ability to distinguish not only calcium, but also rupture-prone plaque features such as haemorrhage and thrombus. This may potentially improve monitoring and risk stratification of atherosclerotic patients in order to prevent strokes. Key points • CT of atherosclerotic plaques mainly detects calcium. • Many components, such as intra-plaque haemorrhage and lipids, determine increased plaque rupture risk. • Ex vivo carotid plaque photon-counting CT distinguishes haemorrhage and thrombus. • Improved plaque photon-counting CT evaluation may refine risk stratification accuracy to prevent strokes. Graphical Abstract

Circulating inflammatory markers are essential to human health and disease, and they are often dysregulated or malfunctioning in cancers as well as in cardiovascular, metabolic, immunologic and neuropsychiatric disorders. However, the genetic contribution to the physiological variation of levels of circulating inflammatory markers is largely unknown. Here we report the results of a genome-wide genetic study of blood concentration of ten cytokines, including the hitherto unexplored calcium-binding protein (S100B). The study leverages a unique sample of neonatal blood spots from 9,459 Danish subjects from the iPSYCH initiative. We estimate the SNP-heritability of marker levels as ranging from essentially zero for Erythropoietin (EPO) up to 73% for S100B. We identify and replicate 16 associated genomic regions (p < 5 x 10 −9 ), of which four are novel. We show that the associated variants map to enhancer elements, suggesting a possible transcriptional effect of genomic variants on the cytokine levels. The identification of the genetic architecture underlying the basic levels of cytokines is likely to prompt studies investigating the relationship between cytokines and complex disease. Our results also suggest that the genetic architecture of cytokines is stable from neonatal to adult life.

Background 99m Tc-pertechnetate thyroid scintigraphy is a valid complementary avenue for evaluating thyroid disease in the clinic, the image feature of thyroid scintigram is relatively simple but the interpretation still has a moderate consistency among physicians. Thus, we aimed to develop an artificial intelligence (AI) system to automatically classify the four patterns of thyroid scintigram. Methods We collected 3087 thyroid scintigrams from center 1 to construct the training dataset (n = 2468) and internal validating dataset (n = 619), and another 302 cases from center 2 as external validating datasets. Four pre-trained neural networks that included ResNet50, DenseNet169, InceptionV3, and InceptionResNetV2 were implemented to construct AI models. The models were trained separately with transfer learning. We evaluated each model’s performance with metrics as following: accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), recall, precision, and F1-score. Results The overall accuracy of four pre-trained neural networks in classifying four common uptake patterns of thyroid scintigrams all exceeded 90%, and the InceptionV3 stands out from others. It reached the highest performance with an overall accuracy of 92.73% for internal validation and 87.75% for external validation, respectively. As for each category of thyroid scintigrams, the area under the receiver operator characteristic curve (AUC) was 0.986 for ‘diffusely increased,’ 0.997 for ‘diffusely decreased,’ 0.998 for ‘focal increased,’ and 0.945 for ‘heterogeneous uptake’ in internal validation, respectively. Accordingly, the corresponding performances also obtained an ideal result of 0.939, 1.000, 0.974, and 0.915 in external validation, respectively. Conclusions Deep convolutional neural network-based AI model represented considerable performance in the classification of thyroid scintigrams, which may help physicians improve the interpretation of thyroid scintigrams more consistently and efficiently.