Discovery Cohort Research Articles

Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality

Tools for predicting COVID-19 outcomes enable personalized healthcare, potentially easing the disease burden. This collaborative study by 15 institutions across Europe aimed to develop a machine learning model for predicting the risk of in-hospital mortality post-SARS-CoV-2 infection. Blood samples and clinical data from 1286 COVID-19 patients collected from 2020 to 2023 across four cohorts in Europe and Canada were analyzed, with 2906 long non-coding RNAs profiled using targeted sequencing. From a discovery cohort combining three European cohorts and 804 patients, age and the long non-coding RNA LEF1-AS1 were identified as predictive features, yielding an AUC of 0.83 (95% CI 0.82–0.84) and a balanced accuracy of 0.78 (95% CI 0.77–0.79) with a feedforward neural network classifier. Validation in an independent Canadian cohort of 482 patients showed consistent performance. Cox regression analysis indicated that higher levels of LEF1-AS1 correlated with reduced mortality risk (age-adjusted hazard ratio 0.54, 95% CI 0.40–0.74). Quantitative PCR validated LEF1-AS1’s adaptability to be measured in hospital settings. Here, we demonstrate a promising predictive model for enhancing COVID-19 patient management.

What’s UPDOG? A novel tool for trans-ancestral polygenic score prediction

Background Polygenic scores provide an indication of an individual’s genetic propensity for a trait within a test population. These scores are calculated using results from genetic analysis conducted in discovery populations. Where the test and discovery populations have similar ancestries, the predictions are better than when the ancestries differ. As many of the genetic analyses are conducted in European populations this hinders the potential for maximising predictions in many of the currently underrepresented populations in research. Methods To address this, UP and Downstream Genetic scoring (UPDOG) was developed to consider the concordance of genetic variation around lead variants between the discovery and test cohorts before calculating polygenic scores. Where there was non-concordance between the discovery cohort and an individual in the test cohort, the lead variant’s effect was down weighted for that individual. Results UPDOG was tested across four ancestries and six phenotypes and benchmarked against five existing tools for polygenic scoring. In approximately two-thirds of cases UPDOG improved trans-ancestral prediction, although the increases were small. Conclusions The development of novel methodologies aimed at maximising the efficacy of polygenic scores for the global population is of high importance and enables progress towards personalised medicine and universal equality in healthcare.

Sustained IFN signaling is associated with delayed development of SARS-CoV-2-specific immunity.

Plasma RNAemia, delayed antibody responses and inflammation predict COVID-19 outcomes, but the mechanisms underlying these immunovirological patterns are poorly understood. We profile 782 longitudinal plasma samples from 318 hospitalized patients withCOVID-19. Integrated analysis using k-means reveals four patient clusters in a discovery cohort: mechanically ventilated critically-ill cases are subdivided into good prognosis and high-fatality clusters (reproduced in a validation cohort), while non-critical survivors segregate into high and low early antibody responders. Only the high-fatality cluster is enriched for transcriptomic signatures associated with COVID-19 severity, and each cluster has distinct RBD-specific antibody elicitation kinetics. Both critical and non-critical clusters with delayed antibody responses exhibit sustained IFN signatures, which negatively correlate with contemporaneous RBD-specific IgG levels and absolute SARS-CoV-2-specific B and CD4+ T cell frequencies. These data suggest that the "Interferon paradox" previously described in murine LCMV models is operative in COVID-19, with excessive IFN signaling delaying development of adaptive virus-specific immunity.

3D CT Radiomic Analysis Improves Detection of Axillary Lymph Node Metastases Compared to Conventional Features in Patients With Locally Advanced Breast Cancer.

Preoperative detection of axillary lymph node metastases (ALNMs) from breast cancer is suboptimal; however, recent work suggests radiomics may improve detection of ALNMs. This study aims to develop a 3D CT radiomics model to improve detection of ALNMs compared to conventional imaging features in patients with locally advanced breast cancer. Retrospective chart review was performed on patients referred to a specialty breast cancer center between 2015 and 2020 with US-guided biopsy-proven ALNMs and pretreatment chest CT. One hundred and twelve patients (224 lymph nodes) met inclusion and exclusion criteria and were assigned to discovery (n = 150 nodes) and testing (n = 74 nodes) cohorts. US-biopsy images were referenced in identifying ALNMs on CT, with contralateral nodes taken as negative controls. Positive and negative nodes were assessed for conventional features of lymphadenopathy as well as for 107 radiomic features extracted following 3D segmentation. Diagnostic performance of individual and combined radiomic features was evaluated. The strongest conventional imaging feature of ALNMs was short axis diameter ≥10mm with a sensitivity of 64%, specificity of 95%, and area under the curve (AUC) of 0.89 (95% CI, 0.84-0.94). Several radiomic features outperformed conventional features, most notably energy, a measure of voxel density magnitude. This feature demonstrated a sensitivity, specificity, and AUC of 91%, 79%, and 0.94 (95% CI, 0.91-0.98) for the discovery cohort. On the testing cohort, energy scored 92%, 81%, and 0.94 (95% CI, 0.89-0.99) for sensitivity, specificity, and AUC, respectively. Combining radiomic features did not improve AUC compared to energy alone (P = .08). 3D radiomic analysis represents a promising approach for noninvasive and accurate detection of ALNMs.

Integrated proteogenomic analysis for inherited bone marrow failure syndrome.

Recent advances in in-depth data-independent acquisition proteomic analysis have enabled comprehensive quantitative analysis of >10,000 proteins. Herein, an integrated proteogenomic analysis for inherited bone marrow failure syndrome (IBMFS) was performed to reveal their biological features and to develop a proteomic-based diagnostic assay in the discovery cohort; dyskeratosis congenita (n = 12), Fanconi anemia (n = 11), Diamond-Blackfan anemia (DBA, n = 9), Shwachman-Diamond syndrome (SDS, n = 6), ADH5/ALDH2 deficiency (n = 4), and other IBMFS (n = 18). Unsupervised proteomic clustering identified eight independent clusters (C1-C8), with the ribosomal pathway specifically downregulated in C1 and C2, enriched for DBA and SDS, respectively. Six patients with SDS had significantly decreased SBDS protein expression, with two of these not diagnosed by DNA sequencing alone. Four patients with ADH5/ALDH2 deficiency showed significantly reduced ADH5 protein expression. To perform a large-scale rapid IBMFS screening, targeted proteomic analysis was performed on 417 samples from patients with IBMFS-related hematological disorders (n = 390) and healthy controls (n = 27). SBDS and ADH5 protein expressions were significantly reduced in SDS and ADH5/ALDH2 deficiency, respectively. The clinical application of this first integrated proteogenomic analysis would be useful for the diagnosis and screening of IBMFS, where appropriate clinical screening tests are lacking.

Identification of novel snoRNA-based biomarkers for clear cell renal cell carcinoma from urine-derived extracellular vesicles

BackgroundClear cell renal cell carcinoma (ccRCC) is the most common subtype of RCC with high rates of metastasis. Targeted therapies such as tyrosine kinase and checkpoint inhibitors have improved treatment success, but therapy-related side effects and tumor recurrence remain a challenge. As a result, ccRCC still have a high mortality rate. Early detection before metastasis has great potential to improve outcomes, but no suitable biomarker specific for ccRCC is available so far. Therefore, molecular biomarkers derived from body fluids have been investigated over the past decade. Among them, RNAs from urine-derived extracellular vesicles (EVs) are very promising.MethodsRNA was extracted from urine-derived EVs from a cohort of 78 subjects (54 ccRCC patients, 24 urolithiasis controls). RNA-seq was performed on the discovery cohort, a subset of the whole cohort (47 ccRCC, 16 urolithiasis). Reads were then mapped to the genome, and expression was quantified based on 100 nt long contiguous genomic regions. Cluster analysis and differential region expression analysis were performed with adjustment for age and gender. The candidate biomarkers were validated by qPCR in the entire cohort. Receiver operating characteristic, area under the curve and odds ratios were used to evaluate the diagnostic potential of the models.ResultsAn initial cluster analysis of RNA-seq expression data showed separation by the subjects’ gender, but not by tumor status. Therefore, the following analyses were done, adjusting for gender and age. The regions differentially expressed between ccRCC and urolithiasis patients mainly overlapped with small nucleolar RNAs (snoRNAs). The differential expression of four snoRNAs (SNORD99, SNORD22, SNORD26, SNORA50C) was validated by quantitative PCR. Confounder-adjusted regression models were then used to classify the validation cohort into ccRCC and tumor-free subjects. Corresponding accuracies ranged from 0.654 to 0.744. Models combining multiple genes and the risk factors obesity and hypertension showed improved diagnostic performance with an accuracy of up to 0.811 for SNORD99 and SNORA50C (p = 0.0091).ConclusionsOur study uncovered four previously unrecognized snoRNA biomarkers from urine-derived EVs, advancing the search for a robust, easy-to-use ccRCC screening method.

HIV immunological non-responders are characterized by extensive immunosenescence and impaired lymphocyte cytokine production capacity

IntroductionImmunological non-responders (INR) are people living with HIV (PLHIV) who fail to fully restore CD4+ T-cell counts despite complete viral suppression with antiretroviral therapy (ART). INR are at higher risk for non-HIV related morbidity and mortality. Previous research suggest persistent qualitative defects.MethodsThe 2000HIV study (clinical trials NTC03994835) enrolled 1895 PLHIV, divided in a discovery and validation cohort. PLHIV with CD4 T-cell count <350 cells/mm3 after ≥2 years of suppressive ART were defined as INR and were compared to immunological responders (IR) with CD4 T-cell count >500 cells/mm3. Logistic and rank based regression were used to analyze clinical data, extensive innate and adaptive immunophenotyping, and ex vivo monocyte and lymphocyte cytokine production after stimulation with various stimuli.ResultsThe discovery cohort consisted of 62 INR and 1224 IR, the validation cohort of 26 INR and 243 IR. INR were older, had more advanced HIV disease before starting ART and had more frequently a history of non-AIDS related malignancy. INR had lower absolute CD4+ T-cell numbers in all subsets. Activated (HLA-DR+, CD38+) and exhausted (PD1+) subpopulations were proportionally increased in CD4 T-cells. Monocyte and granulocyte immunophenotypes were comparable. INR lymphocytes produced less IL-22, IFN-γ, IL-10 and IL-17 to stimuli. In contrast, monocyte cytokine production did not differ. The proportions of CD4+CD38+HLA-DR+ and CD4+PD1+ subpopulations showed an inversed correlation to lymphocyte cytokine production.ConclusionsINR compared to IR have hyperactivated and exhausted CD4+ T-cells in combination with lymphocyte functional impairment, while innate immune responses were comparable. Our data provide a rationale to consider the use of anti-PD1 therapy in INR.

Plasma Metabolic Profiles-Based Prediction of Induction Chemotherapy Efficacy in Nasopharyngeal Carcinoma: Results of a Bidirectional Clinical Trial.

The efficacy of induction chemotherapy (IC) as a primary treatment for advanced nasopharyngeal carcinoma (NPC) remains a topic of debate, with a lack of dependable biomarkers for predicting its efficacy. This study seeks to establish a predictive classifier utilizing plasma metabolomics profiling. A total of 166 NPC patients enrolled in the clinical trial NCT05682703 and undergoing IC were included in the study. Plasma lipoprotein profiles were obtained using 1H-NMR before and after IC treatment. An AI-assisted radiomics method was developed to effectively evaluate the efficacy. Metabolic biomarkers were identified through a machine learning approach based on a discovery cohort and subsequently validated in a validation cohort that mimicked the most unfavorable scenario in real-world. Our research findings indicate that the effectiveness of IC varies among individual patients, with a correlation observed between efficacy and changes in metabolite profiles. Utilizing machine learning techniques, it was determined that the XGB model exhibited notable efficacy, attaining an Area Under the Curve (AUC) value of 0.792 (95% CI, 0.668-0.913). In the validation cohort, the model exhibited strong stability and generalizability with an AUC of 0.786 (95%CI, 0.533-0.922). In this study, we found that dysregulation of plasma lipoprotein may result in resistance to IC in NPC patients. The prediction model constructed based on the plasma metabolites' profile as good predictive capabilities and potential for real-world generalization. This discovery has implications for the development of treatment strategies and may offer insight into potential targets for enhancing the effectiveness of IC.

Serum and urine lipidomic profiles identify biomarkers diagnostic for seropositive and seronegative rheumatoid arthritis.

Seronegative rheumatoid arthritis (RA) is defined as RA without circulating autoantibodies such as rheumatoid factor and anti-citrullinated protein antibodies; thus, early diagnosis of seronegative RA can be challenging. Here, we aimed to identify diagnostic biomarkers for seronegative RA by performing lipidomic analyses of sera and urine samples from patients with RA. We performed untargeted lipidomic analysis of sera and urine samples from 111 RA patients, 45 osteoarthritis (OA) patients, and 25 healthy controls (HC). These samples were divided into a discovery cohort (n = 97) and a validation cohort (n = 84). Serum samples from 20 patients with systemic lupus erythematosus (SLE) were also used for validation. The serum lipidome profile of RA was distinguishable from that of OA and HC. We identified a panel of ten serum lipids and three urine lipids in the discovery cohort that showed the most significant differences. These were deemed potential lipid biomarker candidates for RA. The serum lipid panel was tested using a validation cohort; the results revealed an accuracy of 79%, a sensitivity of 71%, and a specificity of 86%. Both seropositive and seronegative RA patients were differentiated from patients with OA, SLE, and HC. Three urinary lipids showing differential expression between RA from HC were identified with an accuracy of 84%, but they failed to differentiate RA from OA. There were five lipid pathways that differed between seronegative and seropositive RA. Here, we identified a panel of ten serum lipids as potential biomarkers that can differentiate RA from OA and SLE, regardless of seropositivity. In addition, three urinary lipids had diagnostic utility for differentiating RA from HC.

Cholangiocarcinoma combined with biliary obstruction: an exosomal circRNA signature for diagnosis and early recurrence monitoring

Cholangiocarcinoma (CCA) is a highly malignant biliary tract cancer with currently suboptimal diagnostic and prognostic approaches. We present a novel system to monitor CCA using exosomal circular RNA (circRNA) via serum and biliary liquid biopsies. A pilot cohort consisting of patients with CCA-induced biliary obstruction (CCA-BO, n = 5) and benign biliary obstruction (BBO, n = 5) was used to identify CCA-derived exosomal circRNAs through microarray analysis. This was followed by a discovery cohort (n = 20) to further reveal a CCA-specific circRNA complex (hsa-circ-0000367, hsa-circ-0021647, and hsa-circ-0000288) in both bile and serum exosomes. In vitro and in vivo studies revealed the three circRNAs as promoters of CCA invasiveness. Diagnostic and prognostic models were established and verified by two independent cohorts (training cohort, n = 184; validation cohort, n = 105). An interpreter-free diagnostic model disclosed the diagnostic power of biliary exosomal circRNA signature (Bile-DS, AUROC = 0.947, RR = 6.05) and serum exosomal circRNA signature (Serum-DS, AUROC = 0.861, RR = 4.04) compared with conventional CA19-9 (AUROC = 0.759, RR = 2.08). A prognostic model of CCA undergoing curative-intent surgery was established by calculating early recurrence score, verified with bile samples (Bile-ERS, C-index=0.783) and serum samples (Serum-ERS, C-index = 0.782). These models, combined with other prognostic factors revealed by COX-PH model, enabled the establishment of nomograms for recurrence monitoring of CCA. Our study demonstrates that the exosomal triple-circRNA panel identified in both bile and serum samples serves as a novel diagnostic and prognostic tool for the clinical management of CCA.

Abstract PO4-13-09: Paclitaxel affects the efficacy of PD-1 blockade by interfering with lipid metabolism reprogramming in patients with breast cancer

Abstract Background: The question of how chemotherapeutic drugs affect the efficacy of PD-1/PD-L1 antibodies in patients with breast cancer is still pending. In a previously single-cell sequencing study, we have demonstrated that paclitaxel reduces the level of CXCL13+ T cells and other immune cells critical for atezolizumab to work, leading to inferior efficacy of atezolizumab in triple negative breast cancer (TNBC). How paclitaxel affects these important immune cells remains obscure. Recently, plasma metabolomic and proteomics analysis have shown that patients’ systemic metabolic signatures is correlated with the response of PD-1/PD-L1 antibody. As such, understanding the differential metabolic influences of the diverse chemotherapeutic drugs that comprise an immune response to TNBC offers an opportunity to appropriately select chemotherapeutic backbones for ICIs. Methods: We collected pre- and post-treatment plasma samples from breast cancer patients for metabolomic and proteomic analysis, including four patient cohorts: two independent cohorts (discovery cohort and validation cohort) in which patients were treated with ICIs, one cohort in which patients were treated with paclitaxel, and one cohort of patients treated with nab-paclitaxel. Patients were divided into responder group (R, tumor shrinkage) or non-responder group (NR, tumor increase) according to the change in tumor size after treatment relative to baseline. This was followed by integrating metabolomics and proteomics with single-cell transcriptome, along with in vitro experiments, to shed light on the mechanisms of action leading to ICIs resistance in breast cancer patients. Results: In the discovery cohort, the metabolites that mostly differed between the R and NR groups at baseline were lysoglycerophospholipids. These results were further validated in the validation cohort of patients. The dynamic changes of lysoglycerophospholipids were still significantly different between the R and the NR groups after ICIs treatment in the discovery cohort. It’s worth noting that the level of lysophosphatidylcholines (LPCs), the most prominent lysoglycerophospholipids, increased significantly in the R group, while no significant changes among LPCs were found in the NR group after ICIs treatment. In the validation cohort, the median PFS of LPClow patients was 1.8 months versus 6.8 months in the LPChigh group (P = 0.0068). After one cycle of paclitaxel treatment, the metabolic status of patients changed dramatically, especially the levels of lipids. It is notable that most LPCs were significantly downregulated after paclitaxel treatment. However, in patients who received nab-paclitaxel, few significantly changed lipids were detected after one cycle nab-paclitaxel treatment compared with their baseline conditions. Interestingly, the levels of LPCs were upregulated after one cycle nab-paclitaxel treatment, but decreased after paclitaxel treatment. Single-cell omics results showed that in patients' PBMCs, CXCR4+ NK cells were the most closely related to plasma LPC levels, while FGFBP2+ NK cells were in the tumor microenvironment. We isolated and expanded NK cells from PBMCs for in vitro experiments, and found that LPC stimulation can significantly upregulate the expression of CXCR4+ in NK cells, and further increase the chemotaxis and cytotoxicity of NK cells. Conclusions: In breast cancer patients receiving ICIs, the systemic lipid metabolism status, especially lysoglycerophospholipids, is closely related to the efficacy of immunotherapy. The key molecule LPC, the most prominent lysoglycerophospholipids, may promote the efficacy of immunotherapy by activating NK cells in breast cancer patients. Paclitaxel may impair the efficacy of immunotherapy by affecting the systemic lipid metabolism status, especially by reducing plasma LPC levels, so it is not a suitable partner for ICIs. Citation Format: Hongnan Mo, Jiashu Han, Jingtong Zhai, Xiaoying Sun, Xiuwen Guan, Haili Qian, Fei Ma. Paclitaxel affects the efficacy of PD-1 blockade by interfering with lipid metabolism reprogramming in patients with breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-13-09.

Abstract PO1-25-04: PTN-positive luminal progenitors induce angiogenesis and metastasis in inflammatory breast cancer

Abstract Background Inflammatory breast cancer (IBC) is a lethal subtype of breast cancer characterized by rapidly arising diffuse erythema and edema of the overlying skin that was thought to contribute to rapid metastasis. Dissecting the intricate cellular ecosystems within tumors and affected skin and their complex interplay may be pivotal in unraveling the aggressive nature of IBC. Methods Integrative analysis containing single-cell RNA sequencing, bulk RNA sequencing, and whole exome sequencing, was performed on paired tumor and skin samples from 17 treatment-naive IBC patients and 5 non-IBC patients from multiple centers. Meanwhile, paraffin sections and serum specimens from two centers were conducted to validate the omics results. Additionally, public single-cell transcriptomic data of non-IBC tumors and normal mammary glands, RNA microarray data from IBC cases, and the Cancer Genome Atlas (TCGA) database were utilized to validate the characteristics of IBC microenvironment. The regulatory mechanism was verified by in vitro experiments. Results We delineated the landscapes of IBC tumor and affected skin microenvironment at the single-cell level and demonstrated that the luminal progenitors (LP) cells highly expressing pleiotrophin (PTN) were most significantly enriched in IBC tumors compared to non-IBC tumors and normal mammary glands, in two independent cohorts. Moreover, the serum concentrations of PTN were significantly higher in IBC patients compared with non-IBC patients. Notably, PTN secreted by PTN+ LP cells interacted with Neuropilin 1(NRP1) receptor on endothelial tip cells in both tumor and affected skin of IBC. Moreover, in vitro assay demonstrated knocking down the expression of NRP1 on human umbilical vein endothelial cells significantly blocked PTN-induced migration, tube formation and sprouting, which might be independent of VEGFA. In addition, two immature phenotypes of PVL_MMP9 and PVL_CCL19 cells recruited by endothelial tip cells via PDGFB/PDGFRB axis were accumulated in affected skin of IBC, which might be associated with promotion of malignant cell metastasis. Finally, we revealed that malignant cells in affected skin of IBC had a higher ability of epithelial-mesenchymal transition than IBC intratumoral malignant cells and identified enhanced crosstalk between immature PVL cells and malignant cells in the skin affected with IBC , through TNFSF12-TNFRSF12A, TGFB-TGFBR1/TGFBR2, MDK-SDC1/4/NCL/(ITGA6+ITGB1). Conclusion We identified a unique, non-malignant epithelial cell subpopulation PTN+ LP cells, which induce massive angiogenesis via the PTN-NRP1 axis, recruit the immature phenotype PVL cells accumulated in affected skin of IBC thus promoting tumor progression. Our findings offer comprehensive insights into the pathogenesis of IBC and uncover a previously unknown, promising therapeutic target. Patient of discovery cohort details Clinical and pathology details for inflammatory breast cancer(IBC) and non-IBC patients analysed by single-cell RNA sequencing(scRNA-seq), bulk RNA sequencing(bulk RNA-seq) and whole exome sequencing(WES) in this study. Clinicalpathological feature of validation cohort Citation Format: Mengmeng Zhang, Kaiwen Zhou, Ting Liu, Filipa Lynce, Filipa Lynce, Wendy Chen, Qianjun Chen, Xiaoping Li, Zhongyu Yuan, Juan Xu, Zhenhai Cai, Jianping Guo, Nan Shao, Ying Lin. PTN-positive luminal progenitors induce angiogenesis and metastasis in inflammatory breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-25-04.

Coronary Sinus Metabolite 12,13-diHOME Is a Novel Biomarker for Left Atrial Remodeling in Patients With Atrial Fibrillation.

12,13-dihydroxy-9Z-octadecenoic acid (12,13-diHOME) has shown potential in protecting against heart disease, but its relationship with atrial fibrillation (AF) remains unknown. Coronary sinus (CS) and femoral vein blood samplings were synchronously collected from AF and non-AF subjects (paroxysmal supraventricular tachycardia or idiopathic premature ventricular complexes) who underwent catheter ablation. First, untargeted metabolomic profiling was performed in a discovery cohort (including 12 AF and 12 non-AF subjects) to identify the most promising CS or femoral vein metabolite. Then, the selected metabolite was further measured in a validation cohort (including 119 AF and 103 non-AF subjects) to confirm its relationship with left atrium remodeling and 1-year postablation recurrence of AF. Finally, the biological function of the selected metabolite was validated in a rapid-paced cultured HL-1 atrial cardiomyocytes model. Metabolomic analysis identified CS 12,13-diHOME as the most pronounced change metabolite correlated with left atrium remodeling in the discovery cohort. In the validation cohort, CS 12,13-diHOME was significantly lower in patients with AF than non-AF controls (84.32±20.13 versus 96.24±23.56 pg/mL; P<0.01), and associated with worse structural, functional, and electrical remodeling of left atrium. Multivariable regression analyses further demonstrated that decreased CS 12,13-diHOME was an independent predictor of 1-year postablation recurrence of AF (odds ratio, 0.754 [95% CI, 0.648-0.920]; P=0.005). Biological function validations showed that 12,13-diHOME treatment significantly protect the cell viability, improved the expression of MHC (myosin heavy chain) and L-type calcium channel α1c, and attenuated mitochondrial damage in the rapid-paced cultured HL-1 cardiomyocytes model. CS metabolite 12,13-diHOME is decreased in patients with AF and can serve as a novel biomarker for left atrium remodeling.

Development of interpretable machine learning models for prediction of acute kidney injury after noncardiac surgery: a retrospective cohort study.

Early identification of patients at high-risk of postoperative acute kidney injury (AKI) can facilitate the development of preventive approaches. This study aimed to develop prediction models for postoperative AKI in noncardiac surgery using machine learning algorithms. The authors also evaluated the predictive performance of models that included only preoperative variables or only important predictors. Adult patients undergoing noncardiac surgery were retrospectively included in the study (76457 patients in the discovery cohort and 11910 patients in the validation cohort). AKI was determined using the KDIGO criteria. The prediction model was developed using 87 variables (56 preoperative variables and 31 intraoperative variables). A variety of machine learning algorithms were employed to develop the model, including logistic regression, random forest, extreme gradient boosting, and gradient boosting decision trees. The performance of different models was compared using the area under the receiver operating characteristic curve (AUROC). Shapley Additive Explanations (SHAP) analysis was employed for model interpretation. The patients in the discovery cohort had a median age of 52 years (IQR: 42-61 years), and 1179 patients (1.5%) developed AKI after surgery. The gradient boosting decision trees algorithm showed the best predictive performance using all available variables, or only preoperative variables. The AUROCs were 0.849 (95% CI: 0.835-0.863) and 0.828 (95% CI: 0.813-0.843), respectively. The SHAP analysis showed that age, surgical duration, preoperative serum creatinine, and gamma-glutamyltransferase, as well as American Society of Anesthesiologists physical status III were the most important five features. When gradually reducing the features, the AUROCs decreased from 0.852 (including the top 40 features) to 0.839 (including the top 10 features). In the validation cohort, the authors observed a similar pattern regarding the models' predictive performance. The machine learning models the authors developed had satisfactory predictive performance for identifying high-risk postoperative AKI patients. Furthermore, the authors found that model performance was only slightly affected when only preoperative variables or only the most important predictive features were included.

Risks of alopecia areata in long COVID: Binational population-based cohort studies from South Korea and Japan.

Previous studies have proposed alopecia areata (AA) as a potential outcome of COVID-19 infection among autoimmune diseases, yet the findings might be inconclusive and difficult to generalize due to limited sample sizes and evidence levels. Thus, we aimed to investigate in detail the long-term risk of AA following SARS-CoV-2 infection based on large, binational, general population-based cohort studies. Our study investigated the long-term AA risk after SARS-CoV-2 infection by analyzing bi-national, claim-based cohorts in South Korea and Japan: a Korean nationwide cohort (K-COV-N cohort; discovery cohort; total n = 10 027 506) and a Japanese claims-based cohort (JMDC cohort; validation cohort; total n = 12 218 680). AA was identified based on the international classification of diseases 10th revision code (L63) requiring at least three claims within 1 year. After exposure-driven propensity score matching, SARS-CoV-2 infection was associated with an increased risk of incident AA (aHR, 1.66; 95% CI, 1.38-1.99). This increased risk was observed and persisted for up to 6 months. A similar pattern was observed in the validation cohort. As modifiable factors, severe COVID-19 increased the risk of AA, whereas receiving two or more doses of the COVID-19 vaccine before infection decreased the risk of AA. Through a bi-national cohort study in South Korea and Japan, SARS-CoV-2 infection was associated with an elevated risk for incident AA in the aspect of long COVID.

Inter-individual variation in ovarian reserve after gonadotoxic treatment in female childhood cancer survivors – a genome-wide association study: results from PanCareLIFE

ObjectiveWe aimed to discover new variants associated with low ovarian reserve after gonadotoxic treatment among adult female childhood cancer survivors using a genome-wide association study approach. DesignGenome-wide association study. SubjectsA discovery cohort of adult female childhood cancer survivors, from the pan‐European PanCareLIFE cohort (n=743; median age: 25.8 years), excluding those who received bilateral ovarian irradiation, bilateral oophorectomy, central nerve system or total body irradiation, or stem cell transplantation. Replication was attempted in the USA‐based St. Jude Lifetime Cohort (n=391; median age: 31.3 years). ExposureFemale childhood cancer survivors are at risk of therapy‐related gonadal impairment. Alkylating agents are well‐established risk factors, and the inter‐individual variability in gonadotoxicity may be explained by genetic polymorphisms. Data were collected in real-life conditions and cyclophosphamide equivalent dose was used to quantify alkylation agent exposure. InterventionNo intervention was performed. Main Outcome MeasureAnti‐Müllerian hormone (AMH) levels served as a proxy for ovarian function and findings were combined in a meta‐analysis. ResultsThree genome-wide significant (<5.0x10-8) and 16 genome-wide suggestive (<5.0x10-6) loci were associated with log-transformed AMH levels, adjusted for cyclophosphamide equivalent dose of alkylating agents, age at diagnosis, and age at study in the PanCareLIFE cohort. Based on effect allele frequency (EAF) (>0.01 if not genome-wide significant), p-value (<5.0×10−6), and biological relevance, 15 SNPs were selected for replication. None of the SNPs were statistically significantly associated with AMH levels. A meta-analysis indicated that rs78861946 was associated at borderline genome-wide statistical significance (Reference/effect allele: C/T; EAF: 0.04, Beta (SE): −0.484 (0.091), p‐value= 9.39×10−8). ConclusionThis study found no genetic variants associated with a lower ovarian reserve after gonadotoxic treatment, as the findings of this GWAS were not statistically significant replicated in the replication cohort. Suggestive evidence for potential importance of one variant is briefly discussed, but the lack of statistical significance calls for larger cohort sizes. As the population of childhood cancer survivors is increasing, large-scale and systematic research is needed to identify genetic variants that could aid predictive risk models of gonadotoxicity and as well as fertility preservation options for childhood cancer survivors.

X-linked genetic associations in sporadic thoracic aortic dissection.

The male predominance in sporadic thoracic aortic aneurysm and dissection (TAD) suggests that the X chromosome contributes to TAD, but this has not been tested. We investigated whether X-linked variation-common (minor allele frequency [MAF] ≥0.01) and rare (MAF <0.01)-was associated with sporadic TAD in three cohorts of European descent (Discovery: 364 cases, 874 controls; Replication: 516 cases, 440,131 controls, and ARIC [Atherosclerosis Risk in Communities study]: 753 cases, 2247 controls). For analysis of common variants, we applied a sex-stratified logistic regression model followed by a meta-analysis of sex-specific odds ratios. Furthermore, we conducted a meta-analysis of overlapping common variants between the Discovery and Replication cohorts. For analysis of rare variants, we used a sex-stratified optimized sequence kernel association test model. Common variants results showed no statistically significant findings in the Discovery cohort. An intergenic common variant near SPANXN1 was statistically significant in the Replication cohort (p = 1.81 × 10-8). The highest signal from the meta-analysis of the Discovery and Replication cohorts was a ZNF182 intronic common variant (p = 3.5 × 10-6). In rare variants results, RTL9 reached statistical significance (p = 5.15 × 10-5). Although most of our results were statistically insignificant, our analysis is the most comprehensive X-chromosome association analysis of sporadic TAD to date.

Serum anti-CFL1, anti-EZR, and anti-CYPA autoantibody as diagnostic markers in ovarian cancer

The purpose of this study was to identify novel autoantibodies against tumor-associated antigens (TAAs) and explore a diagnostic panel for Ovarian cancer (OC). Enzyme-linked immunosorbent assay was used to detect the expression of five anti-TAA autoantibodies in the discovery (70 OC and 70 normal controls) and validation cohorts (128 OC and 128 normal controls). Machine learning methods were used to construct a diagnostic panel. Serum samples from 81 patients with benign ovarian disease were used to identify the specificity of anti-TAA autoantibodies for OC. In both the discovery and validation cohorts, the expression of anti-CFL1, anti-EZR, anti-CYPA, and anti-PFN1 was higher in patients with OC than that in normal controls. The area under the receiver operating characteristic curve, sensitivity, and specificity of the panel containing anti-CFL1, anti-EZR, and anti-CYPA were 0.762, 55.56%, and 81.31%. The panel identified 53.06%, 53.33%, and 51.11% of CA125 negative, HE4 negative and the Risk of Ovarian Malignancy Algorithm negative OC patients, respectively. The combination of the three anti-TAA autoantibodies can serve as a favorable diagnostic tool for OC and has the potential to be a complementary biomarker for CA125 and HE4 in the diagnosis of ovarian cancer.

Eosinophils and risk of ulcerative colitis in European population: Evidence from Mendelian randomization study.

Observational studies have indicated that peripheral blood eosinophil count is elevated in individuals diagnosed with ulcerative colitis (UC) and correlates with the disease activity of UC. However, this conclusion contradicts with findings from other studies. Therefore, we employed Mendelian randomization (MR) method to assess the genetic link between eosinophil count and UC. This MR study utilized summary data from genome-wide association studies (GWAS) on eosinophil count and UC. The main approach used for conducting MR analysis was the inverse variance weighted (IVW) method. Meta-analysis of the IVW results was performed alongside multiple sensitivity analyses to confirm the robustness of the MR analysis results. The IVW method unveiled a causal relationship between eosinophil count and UC (OR = 1.18, 95% CI: 1.04-1.33, p = .01) in the discovery cohort. This finding was further corroborated by the replication cohorts (OR = 1.16, 95% CI: 1.04-1.29, p = .01; OR = 1.12, 95% CI: 1.01-1.24, p = .03). The meta-analysis indicated that the overall odds ratio (OR) for all studies was 1.15 (common effect model, 95% CI: 1.08-1.23, p < .01). Sensitivity analysis suggested the absence of heterogeneity and horizontal pleiotropy in all MR analyses. Based on bidirectional two-sample MR analysis, there is an indication that elevated eosinophil count may increase the risk of UC. However, potential confounding factors cannot be ruled out, and further research is necessary to explore how eosinophils contribute to the onset and progression of UC.

Data-driven decomposition and staging of flortaucipir uptake in Alzheimer's disease.

Previous approaches pursuing in vivo staging of tau pathology in Alzheimer's disease (AD) have typically relied on neuropathologically defined criteria. In using predefined systems, these studies may miss spatial deposition patterns which are informative of disease progression. We selected discovery (n=418) and replication (n=132) cohorts with flortaucipir imaging. Non-negative matrix factorization (NMF) was applied to learn tau covariance patterns and develop a tau staging system. Flortaucipir components were also validated by comparison with amyloid burden, gray matter loss, and the expression of AD-related genes. We found eight flortaucipir covariance patterns which were reproducible and overlapped with relevant gene expression maps. Tau stages were associated with AD severity as indexed by dementia status and neuropsychological performance. Comparisons of flortaucipir uptake with amyloid and atrophy also supported our model of tau progression. Data-driven decomposition of flortaucipir uptake provides a novel framework for tau staging which complements existing systems. NMF reveals patterns of tau deposition in AD. Data-driven staging of flortaucipir tracks AD severity. Learned flortaucipir patterns overlap with AD-related gene expression.