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BackgroundDespite a growing body of evidence that acne impacts mental disorders, the actual causality has not been established for the possible presence of recall bias and confounders in observational studies.MethodsWe performed a two-sample Mendelian randomization (MR) analysis to evaluate the effect of acne on the risk of six common mental disorders, i.e., depression, anxiety, schizophrenia, obsessive–compulsive disorder (OCD), bipolar disorder, and post-traumatic stress disorder (PTSD). We acquired genetic instruments for assessing acne from the largest genome-wide association study (GWAS) of acne (N = 615,396) and collected summary statistics from the largest available GWAS for depression (N = 500,199), anxiety (N = 17,310), schizophrenia (N = 130,644), OCD (N = 9,725), bipolar disorder (N = 413,466), and PTSD (N = 174,659). Next, we performed the two-sample MR analysis using four methods: inverse-variance weighted method, MR-Egger, weighted median, and MR pleiotropy residual sum and outliers. Sensitivity analysis was also performed for heterogeneity and pleiotropy tests.ResultsThere was no evidence of a causal impact of acne on the risk of depression [odds ratio (OR): 1.002, p = 0.874], anxiety (OR: 0.961, p = 0.49), OCD (OR: 0.979, p = 0.741), bipolar disorder (OR: 0.972, p = 0.261), and PTSD (OR: 1.054, p = 0.069). Moreover, a mild protective effect of acne against schizophrenia was observed (OR: 0.944; p = 0.033).ConclusionThe increased prevalence of mental disorders observed in patients with acne in clinical practice was caused by modifiable factors, and was not a direct outcome of acne. Therefore, strategies targeting the elimination of potential factors and minimization of the occurrence of adverse mental events in acne should be implemented.

BackgroundIn recent years, frontal fibrosing alopecia (FFA), a type of scarring alopecia, has attracted increasing attention. Several studies have reported the frequent occurrence of rosacea in FFA; however, the association between FFA and rosacea and the underlying pathogenesis have not been thoroughly clarified. Thus, this study aimed to quantify these relationships and investigate their shared molecular mechanisms.MethodsWe evaluated the association between FFA and rosacea by analyzing clinical data from nine observational studies. We then analyzed the gene expression profiles of FFA and rosacea. First, differential expression analysis and weighted gene co-expression network analysis were used to identify the common differentially expressed genes (DEGs). Later, we conducted a functional enrichment analysis and protein-protein interaction network and used seven algorithms to identify hub genes. Then, we performed a correlation analysis between the hub genes and the gene set variation analysis scores of common pathways in the gene set enrichment analysis (GSEA). The results were validated using different datasets. Finally, transcription factors were predicted and verified, and CIBERSORT and single-sample GSEA were used to estimate the infiltrating immune cells.ResultsPatients with FFA had significantly higher odds for rosacea (pooled odds ratio [OR], 2.46; 95% confidence interval [CI], 1.78–3.40), and the pooled prevalence of rosacea in patients with FFA was 23% (95% CI, 14–23%). Furthermore, we identified 115 co-DEGs and 13 hub genes (CCR5, CCL19, CD2, CD38, CD83, CXCL8, CXCL9, CXCL10, CXCL11, CXCR4, IRF1, IRF8, and PTPRC). Seven pathways showed a high correlation with these hub genes. In addition, one TF, STAT1, was highly expressed in both diseases, and the results of the immune infiltration analysis indicated the importance of M1 macrophages and effector memory CD8+ T cells.ConclusionThis study contributes to the understanding of the relationship between FFA and rosacea, and based on the hub genes, we reveal the potential pathologies shared by the two diseases. This finding provides new insights of underlying molecular mechanisms and it may inspire future research on this comorbidity.

Background: The coronary atherosclerotic burden in patients with ST-segment elevation myocardial infarction (STEMI) has been identified as the main predictor of prognosis. However, the association of lipoprotein(a) [Lp(a)], a well-established proatherogenic factor, with atherosclerotic burden in patients with STEMI is unclear.Methods: In total, 1,359 patients who underwent percutaneous coronary intervention (PCI) for STEMI were included in analyses. Three prespecified models with adjustment for demographic parameters and risk factors were evaluated. Generalized additive models and restricted cubic spline analyses were used to assess the relationships of Lp(a) with Gensini scores and the no-reflow phenomenon. Kaplan–Meier curves were generated to explore the predictive value of Lp(a) for long-term all-cause mortality. Furthermore, mRNA expression levels of LPA in different groups were compared using the GEO database.Results: Patients in the highest tertile according to Lp(a) levels had an increased incidence of heart failure during hospitalization. Furthermore, patients with high levels of Lp(a) (>19.1 mg/dL) had sharply increased risks for a higher Gensini score (Pfor trend = 0.03) and no-reflow (Pfor trend = 0.002) after adjustment for demographic parameters and risk factors. During a median follow-up of 930 days, 132 deaths (9.95%) were registered. Patients with high levels of Lp(a) (>19.1 mg/dL) had the worst long-term prognosis (Pfor trend < 0.0001). In a subgroup analysis, patients with higher Lp(a) still had the highest all-cause mortality. Additionally, the mRNA expression levels of LPA in patients with STEMI with lower cardiac function were higher than those in other groups (P = 0.003). A higher coronary atherosclerotic burden was correlated with higher LPA expression (P = 0.01).Conclusion: This study provides the first evidence that Lp(a) (at both the protein and mRNA levels) is independently associated with coronary atherosclerotic lesions and prognosis in patients with STEMI treated with PCI.Clinical Trial Registration:http://www.chictr.org.cn/index.aspx, identifier: ChiCTR1900028516.

Increasing evidence has indicated that modulation of epigenetic mechanisms, especially methylation and long-non-coding RNA (lncRNA) regulation, plays a pivotal role in the process of atherosclerosis; however, few studies focused on revealing the epigenetic-related subgroups during atherosclerotic progression using unsupervised clustering analysis. Hence, we aimed to identify the epigenetics-related differentially expressed genes associated with atherosclerosis subtypes and characterize their clinical utility in atherosclerosis. Eighty samples with expression data (GSE40231) and 49 samples with methylation data (GSE46394) from a large artery plaque were downloaded from the GEO database, and aberrantly methylated–differentially expressed (AMDE) genes were identified based on the relationship between methylation and expression. Furthermore, we conducted weighted correlation network analysis (WGCNA) and co-expression analysis to identify the core AMDE genes strongly involved in atherosclerosis. K-means clustering was used to characterize two subtypes of atherosclerosis in GSE40231, and then 29 samples were recognized as validation dataset (GSE28829). In a blood sample cohort (GSE90074), chi-square test and logistic analysis were performed to explore the clinical implication of the K-means clusters. Furthermore, significance analysis of microarrays and prediction analysis of microarrays (PAM) were applied to identify the signature AMDE genes. Moreover, the classification performance of signature AMDE gene-based classifier from PAM was validated in another blood sample cohort (GSE34822). A total of 1,569 AMDE mRNAs and eight AMDE long non-coding RNAs (lncRNAs) were identified by differential analysis. Through the WGCNA and co-expression analysis, 32 AMDE mRNAs and seven AMDE lncRNAs were identified as the core genes involved in atherosclerosis development. Functional analysis revealed that AMDE genes were strongly related to inflammation and axon guidance. In the clinical analysis, the atherosclerotic subtypes were associated with the severity of coronary artery disease and risk of adverse events. Eight genes, including PARP15, SERGEF, PDGFD, MRPL45, UBR1, STAU1, WIZ, and LSM4, were selected as the signature AMDE genes that most significantly differentiated between atherosclerotic subtypes. Ultimately, the area under the curve of signature AMDE gene-based classifier for atherosclerotic subtypes was 0.858 and 0.812 in GSE90074 and GSE34822, respectively. This study identified the AMDE genes (lncRNAs and mRNAs) that could be implemented in clinical clustering to recognize high-risk atherosclerotic patients.