Objective: Repetitive transcranial magnetic stimulation (rTMS) is beneficial for treatment-resistant patients with obsessive-compulsive disorder (OCD). The serotonin transporter gene (SLC6A4) may be associated with OCD. We aimed to determine whether SLC6A4 impacts the beneficial effects of rTMS in patients with OCD treated with selective serotonin reuptake inhibitors (SSRIs). Methods: Fifty-seven untreated patients with OCD were randomly assigned to receive active or sham rTMS in a 4-week double-blind study. The participants received 1-Hz rTMS over the supplementary motor area once per day, for 5 days a week, for 4 weeks. One of the widely employed SSRIs was utilized at the initiation of active or sham rTMS. Yale–Brown obsessive–compulsive scale (Y-BOCS) scores were used for assessing the symptoms. The most-researched polymorphism of SLC6A4, 5-HTTLPR (L/S), was also examined. Results: Y-BOCS scores in the active group at the completion of the treatment were significantly lower than those in the sham group. Interestingly, the improvement in Y-BOCS scores in patients with the LL genotype treated with active rTMS was significantly (p<0.05) greater than in those treated with sham rTMS. Conversely, rTMS did not produce significant improvements in S allele carriers. Conclusions: The findings of this study suggest that rTMS can augment the beneficial effects of SSRIs in OCD patients with the LL genotype of 5-HTTLPR. Therefore, the presence of 5-HTTLPR (L/S) in SLC6A4 may be a predictable biomarker for the beneficial effects of rTMS, although more studies using larger sample sizes are warranted for confirming the results.

Background: The Reelin (RELN) gene encodes the protein reelin, which is a large extracellular matrix glycoprotein that plays a key role in brain development. Additionally, this protein may be involved in memory formation, neurotransmission, and synaptic plasticity, which have been shown to be disrupted in schizophrenia (SCZ). A decreasing trend in the expression of RELN mRNA in the brain and peripheral blood of SCZ patients has been observed. There is a need to determine whether changes in RELN mRNA expression in SCZ patients are the result of long-term antipsychotic treatment rather than the etiological characteristics of schizophrenia. The expression levels of RELN mRNA in the peripheral blood of 48 healthy controls and 30 SCZ patients before and after 12-weeks of treatment were measured using quantitative real-time PCR. Results: The expression levels of RELN mRNA in the SCZ group were significantly lower than that of healthy controls; however, after 12-weeks of antipsychotic treatment, RELN mRNA levels were significantly increased in the SCZ group. Conclusion: The up-regulation of RELN mRNA expression was current in SCZ patients after antipsychotic treatment, suggesting that the changes in RELN mRNA expression were related to the effect of the antipsychotic treatment.

The purpose of this study is to leverage bioinformatics techniques to identify differentially expressed genes in Alzheimer’s disease (AD), explore potential biomarkers for its early diagnosis, and provide new insights for the early diagnosis and treatment of AD. Two Alzheimer’s disease-associated datasets, GSE66351 and GSE153712, were obtained from the Gene Expression Omnibus (GEO) database. Differential methylation analysis was conducted on the raw data utilizing the R programming language. Key genes were discerned by integrating LASSO regression, Pearson correlation analysis, and protein-protein interaction network analysis (PPI). Furthermore, the functional roles of these genes were investigated via Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses. To assess their causal association with AD, a Mendelian randomization analysis was performed. In two AD datasets, we identified a total of 387 overlapping differential methylation sites, which mapped to 297 genes. The GO enrichment analysis indicated that these genes are involved in a range of biological processes, such as signal transduction, cell cycle regulation, as well as the function of neuronal cell bodies and synapses. Furthermore, KEGG pathway analysis uncovered that these genes play crucial roles in the PI3K-Akt and TGF-beta signaling pathways. By utilizing a combination of LASSO, Pearson correlation analysis, and PPI network interaction analysis, we have identified five pivotal genes: EBF1, IGF1, EGR2, PRDM16, and RBL2. Finally, Mendelian randomization analysis demonstrated that the IVW analysis for cg00000029 (RBL2) yielded statistically significant results with an odds ratio (OR) of 1.201, and a 95 % confidence interval (CI) ranging from 1.089 to 1.325, corresponding to a p -value of 0.000249. This study not only confirmed the known genes linked to AD but, more significantly, revealed the potential connection between the RBL2 gene and AD. Furthermore, it verified a causal link between RBL2 and the risk of AD onset. This finding suggests that the RBL2 gene could serve as a promising biomarker for the early diagnosis of AD, thereby offering novel avenues for future research and clinical interventions.

Objective: This study aimed to identify DNA methylation patterns associated with Very Late-Onset Schizophrenia-like Psychosis (VLOSLP) and to develop methylation-based biomarkers that differentiate VLOSLP from Schizophrenia (SCZ) and Alzheimer’s Disease (AD). Methods: We analyzed methylation microarray datasets (n = 1218) from SCZ and AD patients obtained from the GEO database. We then collected blood samples from VLOSLP patients and age-matched healthy controls (n = 80) at the Wuxi Mental Health Center for methylation microarray profiling and bisulfite sequencing validation. Differential methylation analysis and Gene Ontology (GO) enrichment analysis identified candidate loci. We prioritized key methylation sites through integrated analysis of methylation quantitative trait loci (meQTL), linkage disequilibrium (LD) patterns, and blood-brain methylation correlations. Machine learning algorithms generated diagnostic models, with classification performance evaluated using Area Under the Curve (AUC) metrics. Results: Analysis revealed distinct DNA methylation signatures in VLOSLP patients compared to controls. The GNB5 gene exhibited shared epigenetic modifications across SCZ, AD, and VLOSLP, suggesting a common pathogenic mechanism. The diagnostic model discriminating AD from VLOSLP demonstrated high accuracy, achieving an AUC of 1.0 in the training set and 0.958 in the test set (95% CI: 0.875– 1.000). The AD versus SCZ classification model showed similar robustness, with AUCs of 0.995 and 0.955 in training and test sets, respectively (95% CI: 0.926– 0.983). The SCZ versus VLOSLP model achieved perfect discrimination (AUC = 1.0) in both training and test sets, with substantial clinical utility. Additional analyses suggested distinct molecular subtypes within VLOSLP. Conclusion: Specific DNA methylation alterations in VLOSLP are identified as potential diagnostic biomarkers. These findings may contribute to the development of molecular diagnostic tools, though further validation in larger, independent cohorts is warranted.

Background and Purpose: Intracerebral Aβ protein deposition is an important pathological mechanism of Alzheimer’s disease (AD) and is one of the indicators of early diagnosis of AD. However, invasive lumbar puncture and Aβ PET are difficult to perform in primary units, resulting delays in early diagnosis of AD. In recent years, it has been found that plasma Aβ can reflect the pathological state of AD in early stage, but the results are not consistent. The objective of this study was to explore the association between plasma Aβ 42 levels and AD cognitive impairment and its influencing factors in Chinese elderly population, so as to provide guidance for the clinical application of plasma Aβ 42 as a blood biomarker of AD. Methods: This is a cross-sectional study based on the community population. Plasma samples were collected from 604 healthy controls (HC), 508 mild cognitive impairment (MCI) and 202 dementia with Alzheimer’s type (DAT) patients from three cities. We analyzed the correlation between plasma Aβ 42 levels and cognitive function and the influence of confounding factors on the relationship between plasma Aβ 42 levels and AD. The independent influencing factors of plasma Aβ 42 levels were determined by covariance and linear regression analysis. Results: Our results suggest that there is a special linear relationship between plasma Aβ 42 and cognitive impairment of AD in Chinese elderly population, with Aβ 42 levels slightly decreased in early AD and significantly increased in moderate-to-severe AD (P< 0.01). There are many factors influencing the association between plasma Aβ 42 levels and AD cognitive impairment, and sample source, gender and BMI are independent influencing factors of plasma Aβ 42. Conclusion: This indentifies that plasma Aβ 42 may be a peripheral biomarker for AD screening in Chinese elderly population, but it is necessary to establish standardized detection methods and establish different demarcation criteria for various influencing factors.