Asthma is the second leading cause of mortality among chronic respiratory illnesses. This study provided a comprehensive analysis of the burden of asthma. Data on asthma were extracted from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021. We focused on the effects of age, sex, risk factors, and the socio-demographic index (SDI) on the burden of asthma and calculated the average annual percent change (AAPC) via joinpoint regression. Two-sample Mendelian randomization (MR) was adopted to estimate the causal relationships between risk factors and asthma. The Bayesian age-period-cohort (BAPC) model was used to predict incidence patterns of asthma from 2022 to 2050. In 2021, there was an observed prevalence of asthma, with 3,340 cases per 100,000 people. Males who were below 20 years old had a greater prevalence of asthma. The incidence and prevalence correlated positively with the SDI, whereas mortality and disability-adjusted life years (DALYs) correlated negatively. The contribution of high body mass index (BMI) to asthma DALYs increased by 4.3% worldwide between 1990 and 2021. MR studies have confirmed that high BMI and smoking can increase the risk of asthma. The prediction results indicated that the global age-standardised incidence rate will remain high from 2022 to 2050. The global mortality of patients with asthma is a significant concern. The analysis of the burden of asthma can help formulate public health policies, allocate resources, and prevent asthma. This study was supported by the 10.13039/501100001809 National Natural Science Foundation of China ; Program for Young Talents of Basic Research in Universities of Heilongjiang Province; Marshal Initiative Funding; Mathematical Tianyuan Fund of the 10.13039/501100001809 National Natural Science Foundation of China ; XingLian Outstanding Talent Support Program 2024.

The rapid development of new generation sequencing technology has deepened the understanding of genomes and functional products. RNA-sequencing studies in mammals show that approximately 85% of the DNA sequences have RNA products, for which the length greater than 200 nucleotides (nt) is called long non-coding RNAs (lncRNA). LncRNAs now have been shown to play important epigenetic regulatory roles in key molecular processes, such as gene expression, genetic imprinting, histone modification, chromatin dynamics, and other activities by forming specific structures and interacting with all kinds of molecules. This paper mainly discusses the correlation between the structure and function of lncRNAs with the recent progress in epigenetic regulation, which is important to the understanding of the mechanism of lncRNAs in physiological and pathological processes.

J. Neurochem. (2012) 120, 190–198. Alzheimer’s disease (AD) is a kind of complex neurological disorder. The complex genetic architecture of AD makes genetic analysis difficult. Fortunately, a pathway‐based method to study the existing genome‐wide association studies datasets has been applied into AD. However, no shared Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway was reported. In this study, we performed multiple pathway analyses of French AD genome‐wide association studies dataset (discovery dataset, n = 7360, 2032 cases and 5328 controls) and Pfizer dataset (validation dataset, n = 2220, 1034 cases and 1186 controls). First, we performed multiple pathway analyses by Hypergeometric test, improved gene set enrichment analysis (IGSEA) and Z‐statistic test in KEGG. Using Hypergeometric test, we identified 54 and 25 significant pathways (p < 0.05) in discovery dataset and validation dataset, respectively. Using IGSEA method, we identified three significant pathways in both discovery and validation datasets, respectively. Using Z‐statistic test, we identified 19 significant pathways in validation dataset. Among the significant pathways, cell adhesion molecules (CAM) pathway was identified to be the only consistent signal emerging across multiple analyses in KEGG. After permutation and multiple testing corrections, CAM pathway was significant with p = 2.40E−05 (Hypergeometric test) and p = 3.00E−03 (IGSEA) in discovery dataset. In validation dataset, CAM pathway was significant with p = 1.84E−06 (Hypergeometric test), p = 1.00E−02 (IGSEA) and p = 2.81E−03 (Z‐statistic test). We replicated the association by multiple pathway analyses in Gene Ontology using Hypergeometric test (WebGestalt), modified Fisher’s exact test (DAVID) and Binomial test (PANTHER). Our findings provided further evidence on the association between CAM pathway and AD susceptibility, which would be helpful to study the genetic mechanisms of AD and may significantly assist in the development of therapeutic strategies.