Cryo-EM Structure of the Human Cannabinoid Receptor CB2-Gi Signaling Complex.

Drugs selectively targeting CB2 hold promise for treating neurodegenerative disorders, inflammation, and pain while avoiding psychotropic side effects mediated by CB1. The mechanisms underlying CB2 activation and signaling are poorly understood but critical for drug design. Here we report the cryo-EM structure of the human CB2-Gi signaling complex bound to the agonist WIN 55,212-2. The 3D structure reveals the binding mode of WIN 55,212-2 and structural determinants for distinguishing CB2 agonists from antagonists, which are supported by a pair of rationally designed agonist and antagonist. Further structural analyses with computational docking results uncover the differences between CB2 and CB1 in receptor activation, ligand recognition, and Gi coupling. These findings are expected to facilitate rational structure-based discovery of drugs targeting the cannabinoid system.

A role for BYN-1/bystin in cellular uptake and clearance of residual bodies in the Caenorhabditis elegans germline.

GLH/Vasa/DDX4 helicases are core germ-granule proteins that promote germline development and fertility. A yeast-two-hybrid screen using Caenorhabditis elegans GLH-1 as bait identified BYN-1, the homolog of human bystin/BYSL. In humans, bystin promotes cell adhesion and invasion in gliomas, and, with its binding partner trophinin, triggers embryonic implantation into the uterine wall. C. elegans embryos do not implant and lack a homolog of trophinin, but both trophinin and GLH-1 contain unique decapeptide phenylalanine-glycine (FG)-repeat domains. In germ cells, we find endogenous BYN-1 in the nucleolus, partitioned away from cytoplasmic germ granules. However, BYN-1 enters the cytoplasm during spermatogenesis to colocalize with GLH-1. Both proteins become deposited in residual bodies (RBs), which are then engulfed and cleared by the somatic gonad. We show that BYN-1 acts upstream of CED-1 to drive RB engulfment, and that removal of the FG-repeat domains from GLH-1 and GLH-2 can partially phenocopy byn-1 defects in RB clearance. These results point to an evolutionarily conserved pathway whereby cellular uptake is triggered by the cytoplasmic mobilization of bystin/BYN-1 to interact with proteins harboring FG-repeats.

Emerging Therapies in Hypothyroidism.

Levothyroxine (LT4) is effective for most patients with hypothyroidism. However, a minority of the patients remain symptomatic despite the normalization of serum thyrotropin levels. Randomized clinical trials including all types of patients with hypothyroidism revealed that combination levothyroxine and liothyronine (LT4+LT3) therapy is safe and is the preferred choice of patients versus LT4 alone. Many patients who do not fully benefit from LT4 experience improved quality of life and cognition after switching to LT4+LT3. For these patients, new slow-release LT3 formulations that provide stable serum T3 levels are being tested. In addition, progress in regenerative technology has led to the development of human thyroid organoids that restore euthyroidism after being transplanted into hypothyroid mice. Finally, there is a new understanding that, under certain conditions, T3 signaling may be compromised in a tissue-specific fashion while systemic thyroid function is preserved. This is seen, for example, in patients with metabolic (dysfunction)-associated fatty liver disease, for whom liver-selective T3-like molecules have been utilized successfully in clinical trials.

The rearrangement of co-cultured cellular model systems via collective cell migration.

Cancer invasion through the surrounding epithelium and extracellular matrix (ECM) is the one of the main characteristics of cancer progression. While significant effort has been made to predict cancer cells response under various drug therapies, much less attention has been paid to understand the physical interactions between cancer cells and their microenvironment, which are essential for cancer invasion. Considering these physical interactions on various co-cultured in vitro model systems by emphasizing the role of viscoelasticity, the tissue surface tension, solid stress, and their inter-relations is a prerequisite for establishing the main factors that influence cancer cell spread and develop an efficient strategy to suppress it. This review focuses on the role of viscoelasticity caused by collective cell migration (CCM) in the context of mono-cultured and co-cultured cancer systems, and on the modeling approaches aimed at reproducing and understanding these biological systems. In this context, we do not only review previously-published biophysics models for collective cell migration, but also propose new extensions of those models to include solid stress accumulated within the spheroid core region and cell residual stress accumulation caused by CCM.

The dynamics along the biointerface between the epithelial and cancer mesenchymal cells: Modeling consideration.

Epithelial cancer is the one of most lethal cancer type worldwide. Targeting the early stage of disease would allow dramatic improvements in the survival of cancer patients. The early stage of the disease is related to cancer cell spreading across surrounding healthy epithelium. Consequently, deeper insight into cell dynamics along the biointerface between epithelial and cancer (mesenchymal) cells is necessary in order to control the disease as soon as possible. Cell dynamics along this epithelial-cancer biointerface is the result of the interplay between various biological and physical mechanisms. Despite extensive research devoted to study cancer cell spreading across the epithelium, we still do not understand the physical mechanisms which influences the dynamics along the biointerface. These physical mechanisms are related to the interplay between physical parameters such as: (1) interfacial tension between cancer and epithelial subpopulations, (2) established interfacial tension gradients, (3) the bending rigidity of the biointerface and its impact on the interfacial tension, (4) surface tension of the subpopulations, (5) viscoelasticity caused by collective cell migration, and (6) cell residual stress accumulation. The main goal of this study is to review some of these physical parameters in the context of the epithelial/cancer biointerface elaborated on the model system such as the biointerface between breast epithelial MCF-10A cells and cancer MDA-MB-231 cells and then to incorporate these parameters into a new biophysical model that could describe the dynamics of the biointerface. We conclude by discussing three biophysical scenarios for cell dynamics along the biointerface, which can occur depending on the magnitude of the generated shear stress: a smooth biointerface, a slightly-perturbed biointerface and an intensively-perturbed biointerface in the context of the Kelvin-Helmholtz instability. These scenarios are related to the probability of cancer invasion.

Cold Versus Hot Snare Endoscopic Resection of Large Nonpedunculated Colorectal Polyps: Randomized Controlled German CHRONICLE Trial.

BACKGROUND & AIMS: Endoscopic mucosal resection (EMR) is standard therapy for nonpedunculated colorectal polyps ≥20 mm. It has been suggested recently that polyp resection without current (cold resection) may be superior to the standard technique using cutting/coagulation current (hot resection) by reducing adverse events (AEs), but evidence from a randomized trial is missing. METHODS: In this randomized controlled multicentric trial involving 19 centers, nonpedunculated colorectal polyps ≥20 mm were randomly assigned to cold or hot EMR. The primary outcome was major AE (eg, perforation or postendoscopic bleeding). Among secondary outcomes, major AE subcategories, postpolypectomy syndrome, and residual adenoma were most relevant. RESULTS: Between 2021 and 2023, there were 396 polyps in 363 patients (48.2% were female) enrolled for the intention-to-treat analysis. Major AEs occurred in 1.0% of the cold group and in 7.9% of the hot group (P = .001; odds ratio [OR], 0.12; 95% CI, 0.03-0.54). Rates for perforation and postendoscopic bleeding were significantly lower in the cold group, with 0% vs 3.9% (P = .007) and 1.0% vs 4.4% (P = .040). Postpolypectomy syndrome occurred with similar frequency (3.1% vs 4.4%; P = .490). After cold resection, residual adenoma was found more frequently, with 23.7% vs 13.8% (P = .020; OR, 1.94; 95% CI, 1.12-3.38). In multivariable analysis, lesion diameter of ≥4 cm was an independent predictor both for major AEs (OR, 3.37) and residual adenoma (OR, 2.47) and high-grade dysplasia/cancer for residual adenoma (OR, 2.92). CONCLUSIONS: Cold resection of large, nonpedunculated colorectal polyps appears to be considerably safer than hot EMR; however, at the cost of a higher residual adenoma rate. Further studies have to confirm to what extent polyp size and histology can determine an individualized approach. German Clinical Trials Registry (Deutsches Register Klinischer Studien), Number DRKS00025170.

Estimation of optimal treatment regimes with electronic medical record data using the residual life value estimator.

Clinicians and patients must make treatment decisions at a series of key decision points throughout disease progression. A dynamic treatment regime is a set of sequential decision rules that return treatment decisions based on accumulating patient information, like that commonly found in electronic medical record (EMR) data. When applied to a patient population, an optimal treatment regime leads to the most favorable outcome on average. Identifying optimal treatment regimes that maximize residual life is especially desirable for patients with life-threatening diseases such as sepsis, a complex medical condition that involves severe infections with organ dysfunction. We introduce the residual life value estimator (ReLiVE), an estimator for the expected value of cumulative restricted residual life under a fixed treatment regime. Building on ReLiVE, we present a method for estimating an optimal treatment regime that maximizes expected cumulative restricted residual life. Our proposed method, ReLiVE-Q, conducts estimation via the backward induction algorithm Q-learning. We illustrate the utility of ReLiVE-Q in simulation studies, and we apply ReLiVE-Q to estimate an optimal treatment regime for septic patients in the intensive care unit using EMR data from the Multiparameter Intelligent Monitoring Intensive Care database. Ultimately, we demonstrate that ReLiVE-Q leverages accumulating patient information to estimate personalized treatment regimes that optimize a clinically meaningful function of residual life.

DeepGpgs: a novel deep learning framework for predicting arginine methylation sites combined with Gaussian prior and gated self-attention mechanism.

Protein arginine methylation is an important posttranslational modification (PTM) associated with protein functional diversity and pathological conditions including cancer. Identification of methylation binding sites facilitates a better understanding of the molecular function of proteins. Recent developments in the field of deep neural networks have led to a proliferation of deep learning-based methylation identification studies because of their fast and accurate prediction. In this paper, we propose DeepGpgs, an advanced deep learning model incorporating Gaussian prior and gated attention mechanism. We introduce a residual network channel to extract the evolutionary information of proteins. Then we combine the adaptive embedding with bidirectional long short-term memory networks to form a context-shared encoder layer. A gated multi-head attention mechanism is followed to obtain the global information about the sequence. A Gaussian prior is injected into the sequence to assist in predicting PTMs. We also propose a weighted joint loss function to alleviate the false negative problem. We empirically show that DeepGpgs improves Matthews correlation coefficient by 6.3% on the arginine methylation independent test set compared with the existing state-of-the-art methylation site prediction methods. Furthermore, DeepGpgs has good robustness in phosphorylation site prediction of SARS-CoV-2, which indicates that DeepGpgs has good transferability and the potential to be extended to other modification sites prediction. The open-source code and data of the DeepGpgs can be obtained from https://github.com/saizhou1/DeepGpgs.

AGAT-PPIS: a novel protein-protein interaction site predictor based on augmented graph attention network with initial residual and identity mapping.

Identifying protein-protein interaction (PPI) site is an important step in understanding biological activity, apprehending pathological mechanism and designing novel drugs. Developing reliable computational methods for predicting PPI site as screening tools contributes to reduce lots of time and expensive costs for conventional experiments, but how to improve the accuracy is still challenging. We propose a PPI site predictor, called Augmented Graph Attention Network Protein-Protein Interacting Site (AGAT-PPIS), based on AGAT with initial residual and identity mapping, in which eight AGAT layers are connected to mine node embedding representation deeply. AGAT is our augmented version of graph attention network, with added edge features. Besides, extra node features and edge features are introduced to provide more structural information and increase the translation and rotation invariance of the model. On the benchmark test set, AGAT-PPIS significantly surpasses the state-of-the-art method by 8% in Accuracy, 17.1% in Precision, 11.8% in F1-score, 15.1% in Matthews Correlation Coefficient (MCC), 8.1% in Area Under the Receiver Operating Characteristic curve (AUROC), 14.5% in Area Under the Precision-Recall curve (AUPRC), respectively.

Graph deep learning enabled spatial domains identification for spatial transcriptomics.

Advancing spatially resolved transcriptomics (ST) technologies help biologists comprehensively understand organ function and tissue microenvironment. Accurate spatial domain identification is the foundation for delineating genome heterogeneity and cellular interaction. Motivated by this perspective, a graph deep learning (GDL) based spatial clustering approach is constructed in this paper. First, the deep graph infomax module embedded with residual gated graph convolutional neural network is leveraged to address the gene expression profiles and spatial positions in ST. Then, the Bayesian Gaussian mixture model is applied to handle the latent embeddings to generate spatial domains. Designed experiments certify that the presented method is superior to other state-of-the-art GDL-enabled techniques on multiple ST datasets. The codes and dataset used in this manuscript are summarized at https://github.com/narutoten520/SCGDL.

Defect in degradation of glycogenin-exposed residual glycogen in lysosomes is the fundamental pathomechanism of Pompe disease.

Pompe disease is a lysosomal storage disorder that preferentially affects muscles, and it is caused by GAA mutation coding acid alpha-glucosidase in lysosome and glycophagy deficiency. While the initial pathology of Pompe disease is glycogen accumulation in lysosomes, the special role of the lysosomal pathway in glycogen degradation is not fully understood. Hence, we investigated the characteristics of accumulated glycogen and the mechanism underlying glycophagy disturbance in Pompe disease. Skeletal muscle specimens were obtained from the affected sites of patients and mouse models with Pompe disease. Histological analysis, immunoblot analysis, immunofluorescence assay, and lysosome isolation were utilized to analyze the characteristics of accumulated glycogen. Cell culture, lentiviral infection, and the CRISPR/Cas9 approach were utilized to investigate the regulation of glycophagy accumulation. We demonstrated residual glycogen, which was distinguishable from mature glycogen by exposed glycogenin and more α-amylase resistance, accumulated in the skeletal muscle of Pompe disease. Lysosome isolation revealed glycogen-free glycogenin in wild type mouse lysosomes and variously sized glycogenin in Gaa-/- mouse lysosomes. Our study identified that a defect in the degradation of glycogenin-exposed residual glycogen in lysosomes was the fundamental pathological mechanism of Pompe disease. Meanwhile, glycogenin-exposed residual glycogen was absent in other glycogen storage diseases caused by cytoplasmic glycogenolysis deficiencies. In vitro, the generation of residual glycogen resulted from cytoplasmic glycogenolysis. Notably, the inhibition of glycogen phosphorylase led to a reduction in glycogenin-exposed residual glycogen and glycophagy accumulations in cellular models of Pompe disease. Therefore, the lysosomal hydrolysis pathway played a crucial role in the degradation of residual glycogen into glycogenin, which took place in tandem with cytoplasmic glycogenolysis. These findings may offer a novel substrate reduction therapeutic strategy for Pompe disease. © 2024 The Pathological Society of Great Britain and Ireland.

DEEP-EP: Identification of epigenetic protein by ensemble residual convolutional neural network for drug discovery.

Epigenetic proteins (EP) play a role in the progression of a wide range of diseases, including autoimmune disorders, neurological disorders, and cancer. Recognizing their different functions has prompted researchers to investigate them as potential therapeutic targets and pharmacological targets. This paper proposes a novel deep learning-based model that accurately predicts EP. This study introduces a novel deep learning-based model that accurately predicts EP. Our approach entails generating two distinct datasets for training and evaluating the model. We then use three distinct strategies to transform protein sequences to numerical representations: Dipeptide Deviation from Expected Mean (DDE), Dipeptide Composition (DPC), and Group Amino Acid (GAAC). Following that, we train and compare the performance of four advanced deep learning models algorithms: Ensemble Residual Convolutional Neural Network (ERCNN), Generative Adversarial Network (GAN), Convolutional Neural Network (CNN), and Gated Recurrent Unit (GRU). The DDE encoding combined with the ERCNN model demonstrates the best performance on both datasets. This study demonstrates deep learning's potential for precisely predicting EP, which can considerably accelerate research and streamline drug discovery efforts. This analytical method has the potential to find new therapeutic targets and advance our understanding of EP activities in disease.

Early-stage use of hearing aids preserves auditory cortical structure in children with sensorineural hearing loss.

Hearing is critical to spoken language, cognitive, and social development. Little is known about how early auditory experiences impact the brain structure of children with bilateral sensorineural hearing loss. This study examined the influence of hearing aid use and residual hearing on the auditory cortex of children with severe to profound congenital sensorineural hearing loss. We evaluated cortical preservation in 103 young pediatric cochlear implant candidates (55 females and 48 males) by comparing their multivoxel pattern similarity of auditory cortical structure with that of 78 age-matched children with typical hearing. The results demonstrated that early-stage hearing aid use preserved the auditory cortex of children with bilateral congenital sensorineural hearing loss. Children with less residual hearing experienced a more pronounced advantage from hearing aid use. However, this beneficial effect gradually diminished after 17 months of hearing aid use. These findings support timely fitting of hearing aids in conjunction with early implantation to take advantage of neural preservation to maximize auditory and spoken language development.

Deciphering Mechanisms of Respiratory Fetal-to-Neonatal Transition in Very Preterm Infants.

Rationale: The respiratory mechanisms of a successful transition of preterm infants after birth are largely unknown. Objectives: To describe intrapulmonary gas flows during different breathing patterns directly after birth. Methods: Analysis of electrical impedance tomography data from a previous randomized trial in preterm infants at 26-32 weeks gestational age. Electrical impedance tomography data for individual breaths were extracted, and lung volumes as well as ventilation distribution were calculated for end of inspiration, end of expiratory braking and/or holding maneuver, and end of expiration. Measurements and Main Results: Overall, 10,348 breaths from 33 infants were analyzed. We identified three distinct breath types within the first 10 minutes after birth: tidal breathing (44% of all breaths; sinusoidal breathing without expiratory disruption), braking (50%; expiratory brake with a short duration), and holding (6%; expiratory brake with a long duration). Only after holding breaths did end-expiratory lung volume increase: Median (interquartile range [IQR]) = 2.0 AU/kg (0.6 to 4.3), 0.0 (-1.0 to 1.1), and 0.0 (-1.1 to 0.4), respectively; P < 0.001]. This was mediated by intrathoracic air redistribution to the left and non-gravity-dependent parts of the lung through pendelluft gas flows during braking and/or holding maneuvers. Conclusions: Respiratory transition in preterm infants is characterized by unique breathing patterns. Holding breaths contribute to early lung aeration after birth in preterm infants. This is facilitated by air redistribution during braking/holding maneuvers through pendelluft flow, which may prevent lung liquid reflux in this highly adaptive situation. This study deciphers mechanisms for a successful fetal-to-neonatal transition and increases our pathophysiological understanding of this unique moment in life. Clinical trial registered with www.clinicaltrials.gov (NCT04315636).

Diurnal biting of malaria mosquitoes in the Central African Republic indicates residual transmission may be "out of control".

Malaria control interventions target nocturnal feeding of the Anopheles vectors indoors to reduce parasite transmission. Mass deployment of insecticidal bed nets and indoor residual spraying with insecticides, however, may induce mosquitoes to blood-feed at places and at times when humans are not protected. These changes can set a ceiling to the efficacy of these control interventions, resulting in residual malaria transmission. Despite its relevance for disease transmission, the daily rhythmicity of Anopheles biting behavior is poorly documented, most investigations focusing on crepuscular hours and nighttime. By performing mosquito collections 48-h around the clock, both indoors and outdoors, and by modeling biting events using circular statistics, we evaluated the full daily rhythmicity of biting in urban Bangui, Central African Republic. While the bulk of biting by Anopheles gambiae, Anopheles coluzzii, Anopheles funestus, and Anopheles pharoensis occurred from sunset to sunrise outdoors, unexpectedly ∼20 to 30% of indoor biting occurred during daytime. As biting events did not fully conform to any family of circular distributions, we fitted mixtures of von Mises distributions and found that observations were consistent with three compartments, corresponding indoors to populations of early-night, late-night, and daytime-biting events. It is not known whether these populations of biting events correspond to spatiotemporal heterogeneities or also to distinct mosquito genotypes/phenotypes belonging consistently to each compartment. Prevalence of Plasmodium falciparum in nighttime- and daytime-biting mosquitoes was the same. As >50% of biting occurs in Bangui when people are unprotected, malaria control interventions outside the domiciliary environment should be envisaged.

Drug repositioning based on residual attention network and free multiscale adversarial training.

BACKGROUND: Conducting traditional wet experiments to guide drug development is an expensive, time-consuming and risky process. Analyzing drug function and repositioning plays a key role in identifying new therapeutic potential of approved drugs and discovering therapeutic approaches for untreated diseases. Exploring drug-disease associations has far-reaching implications for identifying disease pathogenesis and treatment. However, reliable detection of drug-disease relationships via traditional methods is costly and slow. Therefore, investigations into computational methods for predicting drug-disease associations are currently needed. RESULTS: This paper presents a novel drug-disease association prediction method, RAFGAE. First, RAFGAE integrates known associations between diseases and drugs into a bipartite network. Second, RAFGAE designs the Re_GAT framework, which includes multilayer graph attention networks (GATs) and two residual networks. The multilayer GATs are utilized for learning the node embeddings, which is achieved by aggregating information from multihop neighbors. The two residual networks are used to alleviate the deep network oversmoothing problem, and an attention mechanism is introduced to combine the node embeddings from different attention layers. Third, two graph autoencoders (GAEs) with collaborative training are constructed to simulate label propagation to predict potential associations. On this basis, free multiscale adversarial training (FMAT) is introduced. FMAT enhances node feature quality through small gradient adversarial perturbation iterations, improving the prediction performance. Finally, tenfold cross-validations on two benchmark datasets show that RAFGAE outperforms current methods. In addition, case studies have confirmed that RAFGAE can detect novel drug-disease associations. CONCLUSIONS: The comprehensive experimental results validate the utility and accuracy of RAFGAE. We believe that this method may serve as an excellent predictor for identifying unobserved disease-drug associations.

Predicting drug-protein interactions by preserving the graph information of multi source data.

Examining potential drug-target interactions (DTIs) is a pivotal component of drug discovery and repurposing. Recently, there has been a significant rise in the use of computational techniques to predict DTIs. Nevertheless, previous investigations have predominantly concentrated on assessing either the connections between nodes or the consistency of the network's topological structure in isolation. Such one-sided approaches could severely hinder the accuracy of DTI predictions. In this study, we propose a novel method called TTGCN, which combines heterogeneous graph convolutional neural networks (GCN) and graph attention networks (GAT) to address the task of DTI prediction. TTGCN employs a two-tiered feature learning strategy, utilizing GAT and residual GCN (R-GCN) to extract drug and target embeddings from the diverse network, respectively. These drug and target embeddings are then fused through a mean-pooling layer. Finally, we employ an inductive matrix completion technique to forecast DTIs while preserving the network's node connectivity and topological structure. Our approach demonstrates superior performance in terms of area under the curve and area under the precision-recall curve in experimental comparisons, highlighting its significant advantages in predicting DTIs. Furthermore, case studies provide additional evidence of its ability to identify potential DTIs.

Adaptive loss-guided multi-stage residual ASPP for lesion segmentation and disease detection in cucumber under complex backgrounds.

BACKGROUND: In complex agricultural environments, the presence of shadows, leaf debris, and uneven illumination can hinder the performance of leaf segmentation models for cucumber disease detection. This is further exacerbated by the imbalance in pixel ratios between background and lesion areas, which affects the accuracy of lesion extraction. RESULTS: An original image segmentation framework, the LS-ASPP model, which utilizes a two-stage Atrous Spatial Pyramid Pooling (ASPP) approach combined with adaptive loss to address these challenges has been proposed. The Leaf-ASPP stage employs attention modules and residual structures to capture multi-scale semantic information and enhance edge perception, allowing for precise extraction of leaf contours from complex backgrounds. In the Spot-ASPP stage, we adjust the dilation rate of ASPP and introduce a Convolutional Attention Block Module (CABM) to accurately segment lesion areas. CONCLUSIONS: The LS-ASPP model demonstrates improved performance in semantic segmentation accuracy under complex conditions, providing a robust solution for precise cucumber lesion segmentation. By focusing on challenging pixels and adapting to the specific requirements of agricultural image analysis, our framework has the potential to enhance disease detection accuracy and facilitate timely and effective crop management decisions.

Triglyceride-rich lipoproteins cholesterol, 10-years atherosclerotic cardiovascular disease risk, and risk of myocardial infarction and ischemic stroke.

Triglyceride-rich lipoproteins cholesterol (TRLs-C) has been associated with atherosclerotic cardiovascular disease (ASCVD), even among individuals with low-density lipoprotein cholesterol in the targeted range. We assessed the associations of TRLs-C with myocardial infarction (MI) and ischemic stroke (IS) and compared the associations with those for other traditional lipids (i.e., triglycerides and non-high-density lipoprotein cholesterol [non-HDL-C]). Included were 327,899 participants from the UK Biobank who were free of MI or IS and did not receive lipid-lowering treatment at baseline. Ten-year risk for ASCVD was estimated by the Pooled Cohort Equations and was grouped as low (<7.5%), intermediate (7.5% to <20%), and high risk (≥20%). Multivariable Cox regression models were used to examine the associations of TRLs-C, triglycerides, and non-HDL-C with risk of MI and IS, overall and by the 10-years risk categories. During a median of 12.3 years of follow-up, 8,358 incident MI and 4,400 incident IS cases were identified. After multivariable adjustment, higher TRLs-C was associated with a higher risk of MI (p-trend <0.0001) but not IS (p-trend = 0.074), with similar associations for triglycerides and non-HDL-C. There were interactions between TRLs-C and 10-years ASCVD risk on risk of MI (p-interaction <0.0001) and IS (p-interaction = 0.0003). Hazard ratios (95% CIs) of MI comparing the highest with the lowest quartiles of TRLs-C were 2.10 (1.23-1.30) in the low-risk group, 1.52 (1.38-1.69) in the intermediate-risk group, and 1.22 (1.03-1.45) in the high-risk group. The corresponding estimates for IS were 1.24 (1.05-1.45), 0.94 (0.83-1.07), and 0.83 (0.67-1.04), respectively. Similar interactions with the 10-years ASCVD risk were observed for triglycerides and non-HDL-C on risk of MI and for triglycerides on risk of IS. Elevated levels of TRLs-C (or triglycerides or non-HDL-C) are associated with a higher risk of developing MI and IS (except non-HDL-C) predominantly among individuals who are typically classified as being low-risk. These findings may have implications for more detailed risk stratification and early intervention.

Associations between diabetes-related genetic risk scores and residual beta cell function in type 1 diabetes: the GUTDM1 study.

AIMS/HYPOTHESIS: Use of genetic risk scores (GRS) may help to distinguish between type 1 diabetes and type 2 diabetes, but less is known about whether GRS are associated with disease severity or progression after diagnosis. Therefore, we tested whether GRS are associated with residual beta cell function and glycaemic control in individuals with type 1 diabetes. METHODS: Immunochip arrays and TOPMed were used to genotype a cross-sectional cohort (n=479, age 41.7 ± 14.9 years, duration of diabetes 16.0 years [IQR 6.0-29.0], HbA1c 55.6 ± 12.2 mmol/mol). Several GRS, which were originally developed to assess genetic risk of type 1 diabetes (GRS-1, GRS-2) and type 2 diabetes (GRS-T2D), were calculated. GRS-C1 and GRS-C2 were based on SNPs that have previously been shown to be associated with residual beta cell function. Regression models were used to investigate the association between GRS and residual beta cell function, assessed using the urinary C-peptide/creatinine ratio, and the association between GRS and continuous glucose monitor metrics. RESULTS: Higher GRS-1 and higher GRS-2 both showed a significant association with undetectable UCPCR (OR 0.78; 95% CI 0.69, 0.89 and OR 0.84: 95% CI 0.75, 0.93, respectively), which were attenuated after correction for sex and age of onset (GRS-2) and disease duration (GRS-1). Higher GRS-C2 was associated with detectable urinary C-peptide/creatinine ratio (≥0.01 nmol/mmol) after correction for sex and age of onset (OR 6.95; 95% CI 1.19, 40.75). A higher GRS-T2D was associated with less time below range (TBR) (OR for TBR<4% 1.41; 95% CI 1.01 to 1.96) and lower glucose coefficient of variance (ß -1.53; 95% CI -2.76, -0.29). CONCLUSIONS/INTERPRETATION: Diabetes-related GRS are associated with residual beta cell function in individuals with type 1 diabetes. These findings suggest some genetic contribution to preservation of beta cell function.