Dietary factors and the risk of atopic dermatitis: a Mendelian randomisation study.

Previous studies have revealed an association between dietary factors and atopic dermatitis (AD). To explore whether there was a causal relationship between diet and AD, we performed Mendelian randomisation (MR) analysis. The dataset of twenty-one dietary factors was obtained from UK Biobank. The dataset for AD was obtained from the publicly available FinnGen consortium. The main research method was the inverse-variance weighting method, which was supplemented by MR‒Egger, weighted median and weighted mode. In addition, sensitivity analysis was performed to ensure the accuracy of the results. The study revealed that beef intake (OR = 0·351; 95 % CI 0·145, 0·847; P = 0·020) and white bread intake (OR = 0·141; 95 % CI 0·030, 0·656; P = 0·012) may be protective factors against AD. There were no causal relationships between AD and any other dietary intake factors. Sensitivity analysis showed that our results were reliable, and no heterogeneity or pleiotropy was found. Therefore, we believe that beef intake may be associated with a reduced risk of AD. Although white bread was significant in the IVW analysis, there was large uncertainty in the results given the wide 95 % CI. Other factors were not associated with AD in this study.

Diabetes, glycemic profile and risk of vitiligo: A Mendelian randomization study.

BACKGROUD: Previous observational studies have shown that vitiligo usually co-manifests with a variety of dysglycemic diseases, such as Type 1 diabetes mellitus (T1DM) and Type 2 diabetes mellitus (T2DM). Mendelian randomization (MR) analysis was performed to further evaluate the causal association between fasting plasma glucose, glycosylated hemoglobin (HbA1c), T1DM, T2DM and vitiligo. MATERIALS AND METHODS: We used aggregated genome-wide association data from the Integrative Epidemiology Unit (IEU) online database of European adults vitiligo; HbA1c data were from IEU. Fasting blood glucose data were obtained from the European Bioinformatics Institute (EBI). T1DM and T2DM data were from FinnGen. We used bidirectional two-sample and multivariate MR analyses to test whether dysglycemic measures (fasting blood glucose, HbA1c), diabetes-related measures (T1DM, T2DM) are causatively associated with vitiligo. Inverse variance weighting (IVW) method was used as the main test method, MR-Egger, Weighted mode and Weighted median were used as supplementary methods. RESULTS: We found no statistically significant evidence to support a causal association between dysglycemic traits and vitiligo, but in the correlation analysis of diabetic traits, our data supported a positive causal association between T1DM and vitiligo (p = 0.018). In the follow-up multivariate MR analysis, our results still supported this conclusion (p = 0.016), and suggested that HbA1c was not a mediator of T1DM affecting the pathogenesis of vitiligo. No reverse causality was found in any of the reverse MR Analyses of dysglycemic traits and diabetic traits. CONCLUSIONS: Our findings support that T1DM is a risk factor for the development of vitiligo, and this conclusion may explain why the co-presentation of T1DM and vitiligo is often seen in observational studies. Clinical use of measures related to T1DM may be a new idea for the prevention or treatment of vitiligo.

Genetic insights into the gut microbiota and risk of facial skin aging: A Mendelian randomization study.

BACKGROUND: A growing number of experimental studies have shown an association between the gut microbiota (GM) and facial skin aging. However, the causal relationship between GM and facial skin aging remains unclear to date. METHODS: We conducted a two-sample Mendelian randomization (MR) analysis to investigate the potential causal relationship between GM and facial skin aging. MR analysis was mainly performed using the inverse-variance weighting (IVW) method, complemented by the weighted median (MW) method, MR-Egger regression, and weighted mode, and sensitivity analysis was used to test the reliability of MR analysis results. RESULTS: Eleven GM taxa associated with facial skin aging were identified by IVW method analysis, Family Victivallaceae (p = 0.010), Genus Eubacterium coprostanoligenes group (p = 0.038), and Genus Parasutterella (p = 0.011) were negatively associated with facial skin aging, while Phylum Verrucomicrobia (p = 0.034), Family Lactobacillaceae (p = 0.017) and its subgroups Genus Lactobacillus (p = 0.038), Genus Parabacteroides (p = 0.040), Genus Eggerthella (p = 0.049), Genus Family XIII UCG001 (p = 0.036), Genus Phascolarctobacterium (p = 0.027), and Genus Ruminococcaceae UCG005 (p = 0.012) were positively associated with facial skin aging. At Class and Order levels, we did not find a causal relationship between GM and facial skin aging. Results of sensitivity analyses did not show evidence of pleiotropy and heterogeneity. CONCLUSION: Our findings confirm the causal relationship between GM and facial skin aging, providing a new perspective on delaying facial aging.

Pan-genome analysis of the Burkholderia gladioli PV. Cocovenenans reveal the extent of variation in the toxigenic gene cluster.

Burkholderia gladioli has been reported as the pathogen responsible for cases of foodborne illness in many countries. The poisonous bongkrekic acid (BA) produced by B. gladioli was linked to a gene cluster absent in non-pathogenic strains. The whole genome sequence of eight bacteria strains, which were screened from the collected 175 raw food and environmental samples, were assembled and analyzed to detect a significant association of 19 protein-coding genes with the pathogenic status. Except for the common BA synthesis-related gene, several other genes, including the toxin-antitoxin genes, were also absent in the non-pathogenic strains. The bacteria strains with the BA gene cluster were found to form a single cluster in the analysis of all B. gladioli genome assemblies for the variants in the gene cluster. Divergence of this cluster was detected in the analysis for both the flanking sequences and those of the whole genome level, which indicates its complex origin. Genome recombination was found to cause a precise sequence deletion in the gene cluster region, which was found to be predominant in the non-pathogenic strains indicating the possible effect of horizontal gene transfer. Our study provided new information and resources for understanding the evolution and divergence of the B. gladioli species.

Lipid traits and lipid-lowering drug target genes and risk of melanoma: a mendelian randomization study.

Our study aimed to investigate the role of lipids in melanoma risk and the effect of lipid-lowering drug targets on melanoma. Using Mendelian Randomization analysis, we examined the genetic agents of nine lipid-lowering drugs and their association with melanoma risk. We found that genetically proxied inhibition of HMGCR, ABCG5/ABCG8, and ANGPTL3 was associated with a reduced risk of melanoma. On the other hand, inhibition of LPL and Apo-B100 was significantly associated with an increased risk of melanoma. Sensitivity analyses did not reveal any statistical evidence of bias from pleiotropy or genetic confounding. We did not find a robust association between lipid traits NPC1L1, PCSK9, APOC3 inhibition, and melanoma risk. These findings were validated using two independent lipid datasets. Our analysis also revealed that HMGCR, ANGPTL3, and ABCG5/ABCG8 inhibitors reduced melanoma risk independent of their effects on lipids. This suggests that these targets may have potential for melanoma prevention or treatment. In conclusion, our study provides evidence for a causal role of lipids in melanoma risk and highlights specific lipid-lowering drug targets that may be effective in reducing the risk of melanoma. These findings contribute to the understanding of the underlying mechanisms of melanoma development and provide potential avenues for further research and therapeutic interventions.

Relationship between circulating white blood cell count and inflammatory skin disease: a bidirectional mendelian randomization study.

Observational studies have shown a strong association between circulating white blood cell counts (WBC) and inflammatory skin diseases such as acne and psoriasis. However, the causal nature of this relationship is unclear. We performed a two-way two-sample Mendelian randomization (MR) analysis to investigate potential causal relationships between leukocytes and inflammatory skin diseases. The circulating white blood cell count, basophil cell count, leukocyte cell count, lymphocyte cell count, eosinophil cell count, and neutrophil cell count data were obtained from the Blood Cell Consortium (BCX). The data for inflammatory skin disorders, including acne, atopic dermatitis (AD), hidradenitis suppurativa (HS), psoriasis, and seborrheic dermatitis (SD), were obtained from the FinnGen Consortium R10. The primary analysis utilized inverse variance weighting (IVW) along with additional methods such as MR-Egger, weighted mode, and weighted median estimator. To assess heterogeneity among instrument variables, Cochran's Q test was employed, while MR-Egger intercept and MR-PRESSO were used to test for horizontal pleiotropy. IVW demonstrated that an elevated monocyte count was significantly associated with a decreased risk of psoriasis (OR = 0.897, 95% CI: 0.841-0.957, P = 0.001, FDR = 0.016). Additionally, an increased eosinophil count was causally associated with a higher risk of AD (OR = 1.188, 95% CI: 1.093-1.293, P = 0.000, FDR = 0.002). No inverse causal relationship between inflammatory skin disease and circulating white blood cell count was found. In conclusion, this study provides evidence that increased monocyte count is associated with a reduced risk of psoriasis and that there is a causal relationship between increased eosinophil counts and an increased risk of AD. These findings help us understand the potential causal role of specific white blood cell counts in the development of inflammatory skin diseases.

Identification of platelet-related subtypes and diagnostic markers in pediatric Crohn's disease based on WGCNA and machine learning.

Background: The incidence of pediatric Crohn's disease (PCD) is increasing worldwide every year. The challenges in early diagnosis and treatment of PCD persist due to its inherent heterogeneity. This study's objective was to discover novel diagnostic markers and molecular subtypes aimed at enhancing the prognosis for patients suffering from PCD. Methods: Candidate genes were obtained from the GSE117993 dataset and the GSE93624 dataset by weighted gene co-expression network analysis (WGCNA) and differential analysis, followed by intersection with platelet-related genes. Based on this, diagnostic markers were screened by five machine learning algorithms. We constructed predictive models and molecular subtypes based on key markers. The models were evaluated using the GSE101794 dataset as the validation set, combined with receiver operating characteristic curves, decision curve analysis, clinical impact curves, and calibration curves. In addition, we performed pathway enrichment analysis and immune infiltration analysis for different molecular subtypes to assess their differences. Results: Through WGCNA and differential analysis, we successfully identified 44 candidate genes. Following this, employing five machine learning algorithms, we ultimately narrowed it down to five pivotal markers: GNA15, PIK3R3, PLEK, SERPINE1, and STAT1. Using these five key markers as a foundation, we developed a nomogram exhibiting exceptional performance. Furthermore, we distinguished two platelet-related subtypes of PCD through consensus clustering analysis. Subsequent analyses involving pathway enrichment and immune infiltration unveiled notable disparities in gene expression patterns, enrichment pathways, and immune infiltration landscapes between these subtypes. Conclusion: In this study, we have successfully identified five promising diagnostic markers and developed a robust nomogram with high predictive efficacy. Furthermore, the recognition of distinct PCD subtypes enhances our comprehension of potential pathogenic mechanisms and paves the way for future prospects in early diagnosis and personalized treatment.

Gradient differences of immunotherapy efficacy in metastatic melanoma related to sunlight exposure pattern: A population-based study.

Background: Immune checkpoint inhibitors (ICIs) have revolutionized metastatic melanoma (MM) treatment in just a few years. Ultraviolet (UV) in sunlight is the most significant environmental cause of melanoma, which is considered to be the main reason for tumor mutation burden (TMB) increase in melanoma. High TMB usually predicts that PD-1 inhibitors are effective. The sunlight exposure pattern of MM might be a clinical feature that matches TMB. The relationship between sunlight exposure patterns and immunotherapy response in MM is unclear. This study aims to investigate the correlation between sunlight exposure patterns and immunotherapy response in MM and establish nomograms that predict 3- and 5-year overall survival (OS) rate. Methods: We searched the Surveillance, Epidemiology, and End Results (SEER) database and enrolled MM cases from 2005-2016. According to the advent of ICIs in 2011, the era was divided into the non-ICIs era (2005-2010) and the ICIs era (2011-2016). Patients were divided into three cohorts according to the primary site sunlight exposure patterns: head and neck in the first cohort, trunk arms and legs in the second cohort, and acral sites in the third cohort. We compared survival differences for each cohort between the two eras, performed stratified analysis, established nomograms for predicting 3- and 5-year OS rate, and performed internal validation. Results: Comparing the survival difference between the ICIs and non-ICIs era, head and neck melanoma showed the greatest improvement in survival, with 3- and 5-year OS rate increasing by 10.2% and 9.1%, respectively (P=0.00011). In trunk arms and legs melanoma, the 3- and 5-year OS rate increased by 4.6% and 3.9%, respectively (P<0.0001). There is no improvement in survival in acral melanoma (AM) between the two eras (P=0.78). The receiver operating characteristic (ROC) curve, area under the ROC curve (AUC) and calibration graphs show good discrimination and accuracy of nomograms. Decision curve analysis (DCA) suggests good clinical utility of nomograms. Conclusions: Based on the classification of sunlight exposure patterns, there is a gradient difference in immunotherapy efficacy for MM. The degree of sunlight exposure is positively correlated with immunotherapy response. The nomograms are sufficiently accurate to predict 3- and 5-year OS rate for MM, allowing for individualized clinical decisions for future clinical work.