Purpose: Our study aims to delineate the epidemiological distribution of pulmonary carcinoids, including atypical carcinoid (AC) and typical carcinoid (TC), identify independent prognostic factors, develop an integrative nomogram and examine the effects of various surgical modalities on atypical carcinoid‐specific survival (ACSS).Methods: Joinpoint regression model and age‐group distribution diagram were applied to determine the epidemiological trend of the pulmonary carcinoids. Univariate and least absolute shrinkage and selection operator (LASSO)‐based Cox regression models were used to identify independent factors, and a nomogram and web‐based predictor were developed to evaluate prognosis of AC patients individually. We performed Kaplan–Meier survival analyses to compare the scope of various surgical interventions, with and without G‐computation adjustment, utilising restricted mean survival time (RMST) to assess survival disparities.Results: A total of 1132 patients were recruited from the Surveillance, Epidemiology, and End Results database (SEER) and a separate medical centre in China. The mean age of AC patients was 63.4 years and a smoking history was identified in 79.8% of AC patients. Joinpoint analysis shows rising annual rates of new AC and carcinoid cases among lung cancers. Both the proportion of pulmonary TC and AC within the total lung cancer population exhibits an L‐shaped trend across successive age groups. The nomogram predicted 1, 3 and 5 years of AC with excellent accuracy and discrimination. Kaplan–Meier survival analyses, conducted both pre‐ and post‐adjustment, demonstrated that sublobar resection's survival outcomes were not inferior to those of lobectomy in patients with stage I‐II and stage III disease.Conclusion: This study is the first to reveal epidemiological trends in pulmonary carcinoids over the past decade and across various age cohorts. For patients with early‐stage AC, sublobar resection may be a viable surgical recommendation. The established nomogram and web‐based calculator demonstrated decent accuracy and practicality.

This study aims to clarify the genetic associations between Sjögren’s Disease (SD) and cardiovascular disease (CVD) outcomes, and to conduct an in-depth exploration of specific pleiotropic susceptibility genes. We performed two-sample and multivariable Mendelian randomization (MR) analysis to investigate the association between SD and the risk of ischemic heart disease (IHD) and stroke. Linkage disequilibrium score regression (LDSC) and Bayesian co-localization analyses were employed to assess the genetic associations between traits. Cross-phenotype analyses were employed to identify shared variants and genes, followed by a Transcriptome-Wide Association Study (TWAS) and Multi-marker Analysis of Genomic Annotation (MAGMA) based on Multi-Trait Analysis of GWAS (MTAG) results. To validate the pleiotropic genes, we further analyzed tissue-specific differentially expressed genes (DEGs) related to SD using RNA sequencing data. The two-sample and multivariable MR analyses revealed that SD confers a genetic vulnerability to IHD and stroke. LDSC and co-localization analyses indicated a strong genetic linkage between SD and CVDs. Cross-phenotype analyses identified 38 and 37 pleiotropic single nucleotide polymorphisms (SNPs) for SD-Stroke and SD-IHD, respectively, primarily located within the MHC class region on 6p21.32:33 loci. Additionally, TWAS and MAGMA analyses identified pleiotropic genes located outside the MHC regions—seven associated with stroke (UHRF1BP1, SNRPC, BLK, FAM167A, ARHGAP27, C8orf12, and PLEKHM1) and two associated with IHD (UHRF1BP1 and SNRPC). Proxy variants within these genes in SD suggested an increased causal risk for stroke or IHD. Co-localization analysis further reinforced that SD and stroke share significant SNPs within the loci of FAM167A, BLK, C8orf12, SNRPC, and UHRF1BP1. DEG analysis revealed a significant up-regulation of the identified genes in SD-specific tissues. SD appears genetically predisposed to an increased risk of CVDs. Moreover, this research not only identified pleiotropic genes shared between SD and CVDs, but also, for the first time, detected key gene expressions that elevate CVD risk in SD patients—findings that may offer promising therapeutic targets for patient management.

Individuals with chronic diseases are at higher risk of sarcopenia, and precise prediction is essential for its prevention. This study aims to develop a risk scoring model using longitudinal data to predict the probability of sarcopenia in this population over next 3–5 years, thereby enabling early warning and intervention. Using data from a nationwide survey initiated in 2011, we selected patient data records from wave 1 (2011–2012) and follow-up data from wave 3 (2015–2016) as the study cohort. Retrospective data collection included demographic information, health conditions, and biochemical markers. After excluding records with missing values, a total of 2891 adults with chronic conditions were enrolled. Sarcopenia was assessed based on the Asian Working Group for Sarcopenia (AWGS) 2019 guidelines. A generalized linear mixed model (GLMM) with random effects and diverse machine learning models were utilized to explore feature contributions to sarcopenia risk. The Recursive Feature Elimination (RFE) algorithm was employed to optimize the full Multilayer Perceptron (MLP) model and develop an online application tool. Among total population, 580 (20.1%) individuals were diagnosed with sarcopenia in wave 1 (2011–2012), and 638 (22.1%) were diagnosed in wave 3 (2015–2016), while 2165 (74.9%) individuals were not diagnosed with sarcopenia across the study period. MLP model, performed better than other three classic machine learning models, demonstrated a ROC AUC of 0.912, a PR AUC of 0.401, a sensitivity of 0.875, a specificity of 0.844, a Kappa value of 0.376, and an F1 score of 0.44. According to MLP model-based SHapley Additive exPlanations (SHAP) scoring, weight, age, BMI, height, total cholesterol, PEF, and gender were identified as the most important features of chronic disease individuals for sarcopenia. Using the RFE algorithm, we selected six key variables—weight, age, BMI, height, total cholesterol, and gender—achieving an ROC AUC of about 0.9 for the online application tool. We developed an MLP machine learning model that incorporates only six easily accessible variables, enabling the prediction of sarcopenia risk in individuals with chronic diseases. Additionally, we created a practical online application tool to assist in decision-making and streamline clinical assessments.

The intricate shared genetic architecture underlying allergic disorders—including allergic asthma, atopic dermatitis, contact dermatitis, allergic rhinitis, allergic conjunctivitis, allergic urticaria, anaphylaxis, and eosinophilic esophagitis—remains incompletely characterized. Our study employed genomic structural equation modeling (Genomic SEM) to define the common factor representing the shared genetic architecture of allergic disorders. Coupled with diverse post-GWAS analytical methods, we aimed to discover susceptible loci and investigate genetic associations with external traits. Furthermore, we explored enriched genetic pathways, cellular layers, and genomic elements, and investigated putative plasma protein biomarkers. Polygenic risk score (PRS) analyses, leveraging our integrated GWAS data, were conducted to assess chromosomal-level risk associations for allergic disorders. A well-fitted genomic SEM integrated GWAS data, revealing the shared genetic architecture of allergic disorders. We identified a total of 2038 genome-wide significant SNP loci (p < 5e-8), including 31 previously unreported loci. Fine-mapping of variants and gene sets pinpointed 2 causal variants and 31 candidate susceptible genes. Genetic correlation analyses further illuminated the shared genetic architecture underlying multiple traits, notably psychiatric disorders. Preliminary findings identified four putative causal plasma protein biomarkers. Notably, this study presents the first comprehensive genetic characterization of allergic disorders through a GWAS analysis of an unmeasured composite phenotype, providing novel insights into shared etiological pathways across these conditions.

Background: Understanding the genetic links between acute pancreatitis (AP) and its infectious comorbidities is crucial for prognosis and therapy, yet remains underexplored. Methods: We conducted a comprehensive post‐GWAS analysis using large‐scale summary statistics for AP and 16 infectious diseases. To pinpoint pleiotropic genes, we integrated multi‐omics data via transcriptome‐wide and proteome‐wide association studies, and resolved cell‐type‐specific effects using single‐cell analysis. Extensive locus colocalization analyses were performed to validate our findings by estimating the probability of shared causal variants. Results: This computational discovery phase prioritized 29 high‐confidence pleiotropic genes, including established loci ( SPINK1, CRP ) and novel candidates ( ERBB2, ALDH2, FLOT1 ). To functionally validate and contextualize these findings, we performed bulk transcriptomic analysis on peripheral blood from AP patients and employed gsMap, a spatial GWAS mapping algorithm, to integrate our genetic data with transcriptomics from a murine AP model, comparing pathological versus normal tissue. These analyses confirmed that the identified genes are dynamically regulated in a severity‐dependent manner in patients and are activated within specific pathological niches in pancreatic tissue. Conclusion: In conclusion, this study provides a genetic map linking AP and its infectious comorbidities, offering insights into potential prevention strategies and highlighting novel therapeutic targets for further investigation and validation.

The accuracy of indices widely used to evaluate lung metastasis (LM) in patients with kidney cancer (KC) is insufficient. Therefore, we aimed at developing a model to estimate the risk of developing LM in KC based on a large population size and machine learning algorithms. Demographic and clinicopathologic variables of patients with KC diagnosed between 2004 and 2017 were retrospectively analyzed. We performed a univariate logistic regression analysis to identify risk factors for LM in patients with KC. Six machine learning (ML) classifiers were established and tuned using the ten-fold cross-validation method. External validation was performed using clinicopathologic information from 492 patients from the Southwest Hospital, Chongqing, China. Algorithm performance was estimated by analyzing the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, precision, recall, F1 score, clinical decision analysis (DCA), and clinical utility curve (CUC). A total of 52,714 eligible patients diagnosed with KC were enrolled, of whom 2,618 developed LM. Variables of age, sex, race, T stage, N stage, tumor size, histology, and grade were identified as important for the prediction of LM. The extreme gradient boosting (XGB) algorithm performed better than other models in both the internal validation (AUC: 0.913, sensitivity: 0.873, specificity: 0.809, and F1 score: 0.325) and the external validation (AUC: 0.904, sensitivity: 0.750, specificity: 0.878, and F1 score: 0.364). This study established a predictive model for LM in KC patients based on ML algorithms which showed high accuracy and applicative value. A web-based predictor was built using the XGB model to help clinicians make more rational and personalized decisions.