As the global aging issue grows, dementia, particularly Alzheimer’s disease (AD), has become a major public health challenge for everyone. This study utilizes the Global Burden of Disease (GBD) database to analyze trends in the epidemiology of AD and other dementias from 1990 to 2021 and to predict future burdens to 2040. We examined global, regional, and national data on AD and other dementias, focusing on incidence, prevalence, and Disability-Adjusted Life Years (DALYs). Joinpoint regression analysis was employed to identify significant changes in trends over time. The effects of age, period, and birth cohort on the risk of AD and other dementias were analyzed. Additionally, the impact of aging, population growth, and epidemiological changes on DALYs was assessed across different Socio-demographic Index (SDI) quintiles. The global burden of AD and other dementias has significantly increased, with the highest incidence, prevalence, and DALYs observed in East Asia. A notable increase in prevalence was observed in females over 65 years compared to males. Joinpoint regression analysis revealed substantial changes in trends in 1995, 2005, 2011, and 2019, with a noticeable acceleration post-2011, especially after 2019. Age was a significant risk factor, with a sharp increase in risk after 60 years of age. Epidemiological changes had a minor impact globally but varied by region and gender. Bayesian age-period-cohort modeling predicts sustained growth through 2040, with age-standardized incidence and prevalence rates projected to reach 144.85 and 821.80 per 100,000 respectively, driven predominantly by aging populations in high SDI regions and demographic expansion in low SDI regions. The global burden of AD and other dementias is escalating, with a pronounced increase expected by 2040. This study highlights the need for targeted interventions, particularly in regions with higher burdens and among older populations. The findings underscore the importance of considering SDI, age, and gender when planning public health strategies to address the growing challenge of AD and other dementias.

Purpose. To clarify the relationship between certain genotypes or alleles of the APOE gene and the onset risk of Parkinson’s disease dementia (PDD). Methods. The PubMed, Cochrane, Embase, CBM, CNKI, and Wanfang databases were searched to identify all case-control studies and cohort studies published before October 30, 2017, that investigated the association between the APOE gene and the onset of PDD. Manual information retrieval was also performed. All studies that met the quality requirements were included in a meta-analysis performed using RevMan 5.3 software. Results. The meta-analysis included 17 studies, with a total of 820 patients in the PDD group and 1,922 in the non-PDD group. The influence of the APOE gene on PDD onset was analyzed from three aspects: five genotypes vs. ε3/3, ε2+/ε4+ vs. ε3/3, and ε4+ vs. ε4−. The risk factors for PDD may include the genotypes ε3/4 (OR 1.47, 95% CI 1.14–1.89) and ε4/4 (OR 2.93, 95% CI 1.20–7.14). In patients with PDD, there was no significant difference in the distribution of ε2+ vs. ε3/3 (OR 1.35, 95% CI 0.97–1.87, P=0.07). The risk of PDD was 1.61 times greater in ε4+ compared with ε3/3 (OR 1.61, 95% CI 1.24–2.08, P=0.0003). As the results indicated that ε2+ did not play a role as a risk factor or a protective factor, we divided the population into ε4+ and ε4− for the meta-analysis and found that, among patients with Parkinson’s disease, the dementia risk of those with ε4+ was 1.72 times greater than that of those with ε4− (OR 1.72, 95% CI 1.41–2.10, P<0.00001). Subgroup analysis in accordance with different geographical regions revealed that ε4+ was a risk factor for PDD in people from all regions. Conclusions. Among the APOE genotypes, ε2+ is neither a risk factor nor a protective factor for PDD, while ε4+ is a risk factor for PDD. The present results are applicable to Asian, European, and American patients with Parkinson’s disease. Regarding the single APOE genotypes, ε3/4 and ε4/4 may be risk factors for PDD; however, further studies with large sample sizes are needed to verify this.

Purpose. This study aimed to investigate the underlying molecular mechanisms of Parkinson’s disease (PD) by bioinformatics.Methods. Using the microarray dataset GSE72267 from the Gene Expression Omnibus database, which included 40 blood samples from PD patients and 19 matched controls, differentially expressed genes (DEGs) were identified after data preprocessing, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Protein-protein interaction (PPI) network, microRNA- (miRNA-) target regulatory network, and transcription factor- (TF-) target regulatory networks were constructed.Results. Of 819 DEGs obtained, 359 were upregulated and 460 were downregulated. Two GO terms, “rRNA processing” and “cytoplasm,” and two KEGG pathways, “metabolic pathways” and “TNF signaling pathway,” played roles in PD development. Intercellular adhesion molecule 1 (ICAM1) was the hub node in the PPI network; hsa-miR-7-5p, hsa-miR-433-3p, and hsa-miR-133b participated in PD pathogenesis. Six TFs, including zinc finger and BTB domain-containing 7A, ovo-like transcriptional repressor 1, GATA-binding protein 3, transcription factor dp-1, SMAD family member 1, and quiescin sulfhydryl oxidase 1, were related to PD.Conclusions. “rRNA processing,” “cytoplasm,” “metabolic pathways,” and “TNF signaling pathway” were key pathways involved in PD.ICAM1, hsa-miR-7-5p, hsa-miR-433-3p, hsa-miR-133b, and the abovementioned six TFs might play important roles in PD development.

Glioma is a highly invasive primary brain tumour, making it challenging to accurately predict prognosis for glioma patients. Cuproptosis is a recently discovered cell death attracting significant attention in the tumour field. Whether cuproptosis‐related genes have prognostic predictive value has not been clarified. In this study, uni‐/multi‐variate Cox and Lasso regression analyses were applied to construct a risk model based on cuproptosis‐related lncRNAs using TCGA and CGGA cohorts. A nomogram was constructed to quantify individual risk, including clinical and genic characteristics and risk. GO and KEGG analyses were used to define functional enrichment of DEGs. Tumour mutation burden (TMB) and immune checkpoint analyses were performed to evaluate potential responses to ICI therapy. Ten prognostic lncRNAs were obtained from Cox regression. Based on the median risk score, patients were divided into high‐ and low‐risk groups. Either for grade 2–3 or for grade 4, glioma patients with high‐risk exhibited significant poorer prognoses. The risk was an independent risk factor associated with overall survival. The high‐risk group was functionally associated with immune responses and cancer‐related pathways. The high‐risk group was associated with higher TMB scores. The expression levels of many immune checkpoints in the high‐risk group were significantly higher than those in the low‐risk group. Differentiated immune pathways were primarily enriched in the IFN response, immune checkpoint and T‐cell co‐stimulation pathways. In conclusion, we established a risk model based on cuproptosis‐related lncRNAs showing excellent prognostic prediction ability but also indicating the immuno‐microenvironment status of glioma.

The neurotoxin 6-hydroxydopamine (6-OHDA) has been widely exploited as a tool for modeling Parkinson's disease (PD) in the rat. This study aimed to provide a comprehensive profile of the mRNAs and long noncoding RNAs (lncRNAs) in rats treated with 6-OHDA as a model of PD. Female SPF Wistar rats were randomly divided into two groups: a PD model group and a control group. The PD model was induced by 6-OHDA injection. RNA-seq analysis was performed on 6-OHDA-treated rats and corresponding controls. Novel lncRNAs were identified. Differentially expressed genes (DEGs) and differentially expressed lncRNAs were identified in the PD group compared with controls. Gene Ontology function and pathway enrichment analyses were conducted on the DEGs, followed by construction of a protein–protein interaction (PPI) network. In addition, prediction of lncRNA target genes and function prediction of lncRNAs were performed. Moreover, microRNAs (miRNAs) that interacted with the DEGs and differentially expressed lncRNAs were predicted to construct a miRNA–lncRNA–mRNA regulatory network. A total of 536 DEGs and 512 differentially expressed lncRNAs (44 up-regulated and 10 down-regulated known lncRNAs; 407 up-regulated and 51 down-regulated novel lncRNAs) were identified in the PD rat model compared with controls. The DEGs and target genes of lncRNAs were mainly associated with the innate immune response, 2′-5′-oligoadenylate synthetase activity, GTPase activity, GTP binding and the RIG-I-like receptor signaling pathway. IRF7 and ISG15 were hub proteins in the PPI network. Many mRNAs and lncRNAs interacted with other molecules in a competing endogenous RNA network, such as MAS1, TMPRSS2, NPTX1, XLOC_016191, XLOC_026924 and XLOC_005439. We conclude that IRF7, ISG15, MAS1, TMPRSS2, NPTX1, XLOC_016191, XLOC_026924 and XLOC_005439 may contribute critical roles in the pathogenesis of PD.

Purpose. This study aimed to explore new core genes related to the occurrence of Parkinsons disease (PD) and core genes that can lead to the progression of PD. Methods. The expression profile data of GSE42966, which contained six substantia nigra tissues isolated from normal individuals and nine substantia nigra tissues isolated from patients with PD, were obtained from Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified, followed by functional enrichment analysis and protein-protein interaction (PPI) network construction. We then identified 10 hub genes and analyzed their expression in different Braak stages. Results. A total of 773 DEGs were identified that were significantly enriched in metabolic pathways. Ten hub genes were identified through the PPI network, namely, GNG3, MAPK1, FPR1, ATP5B, GNG2, PRKACA, HRAS, HSPA8, PSAP, and GABBR2. The expression of HRAS was different in patients with PD with Braak stages 3 and 4. Conclusion. These 10 hub genes and the metabolic pathways they are enriched in may be involved in the pathogenesis of PD. HRAS may have potential value in predicting the progression of PD.