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Abstract Background and aims Preoperative prediction of axillary lymph node (ALN) burden in patients with early-stage breast cancer is pivotal for individualised treatment. This study aimed to develop a MRI radiomics model for evaluating the ALN burden in early-stage breast cancer and to provide biological interpretability to predictions by integrating radiogenomic data. Methods This study retrospectively analyzed 1211 patients with early-stage breast cancer from four centers, supplemented by data from The Cancer Imaging Archive (TCIA) and Duke University (DUKE). MRI radiomic features were extracted from dynamic contrast-enhanced MRI images and an ALN burden-related radscore was constructed by the backpropagation neural network algorithm. Clinical and combined models were developed, integrating ALN-related clinical variables and radscore. The Kaplan–Meier curve and log-rank test were used to assess the prognostic differences between the predicted high- and low-ALN burden groups in both Center I and DUKE cohorts. Gene set enrichment and immune infiltration analyses based on transcriptomic TCIA and TCIA Breast Cancer dataset were used to investigate the biological significance of the ALN-related radscore. Results The MRI radiomics model demonstrated an area under the curve of 0.781–0.809 in three validation cohorts. The predicted high-risk population demonstrated a poorer prognosis (log-rank P

Abstract Background Postpartum depression is a complex mental health condition that often occurs after childbirth and is characterized by persistent sadness, anxiety, and fatigue. Recent research suggests a metabolic component to the disorder. This study aims to investigate the causal relationship between blood metabolites and postpartum depression using mendelian randomization (MR). Methods This study used a bi-directional MR framework to investigate the causal relationship between 1,400 metabolic biomarkers and postpartum depression. We used two specific genome-wide association studies datasets: one with single nucleotide polymorphisms data from mothers diagnosed with postpartum depression and another with blood metabolite data, both of which focused on people of European ancestry. Genetic variants were chosen as instrumental variables from both datasets using strict criteria to improve the robustness of the MR analysis. The combination of these datasets enabled a thorough examination of genetic influences on metabolic profiles associated with postpartum depression. Statistical analyses were conducted using techniques such as inverse variance weighting, weighted median, and model-based estimation, which enabled rigorous causal inference from the observed associations. postpartum depression was defined using endpoint definitions approved by the FinnGen study’s clinical expert groups, which included leading experts in their respective medical fields. Results The MR analysis identified seven metabolites that could be linked to postpartum depression. Out of these, one metabolite was found to be protective, while six were associated with an increased risk of developing the condition. The results were consistent across multiple MR methods, indicating a significant correlation. Conclusions This study emphasizes the potential of metabolomics for understanding postpartum depression. The discovery of specific metabolites associated with the condition sheds new insights on its pathophysiology and opens up possibilities for future research into targeted treatment strategies.

Background Creatine supplementation is ubiquitously consumed by fitness enthusiasts due to its perceived advantages in enhancing athletic performance. Although there is an increasing concern within this demographic regarding its possible impact on renal function, there is still a lack of rigorous scientific investigations into this alleged association.Methods Data were collected through an online survey on the participants’ demographics, creatine usage and concerns related to renal function. The reliability and validity of the survey were assessed using SPSS software. A total of 1129 participants responded to the survey, and chi-square tests were utilized for data analysis. To explore the potential association between creatine levels (as the exposure) and renal function (as the outcome), we utilized open-access genetic databases, and Mendelian randomization (MR) techniques were used to confirm this correlation.Results Chi-square analysis revealed no significant association between creatine usage and renal function among the participants. Our MR analysis further supported this finding, demonstrating no significant association between creatine levels and six indicators assessing renal function (IVW, all with p values exceeding 0.05). Similar p values were consistently observed across other MR methods, confirming the absence of a statistical correlation.Conclusions This MR study offers compelling evidence indicating that creatine levels are not statistically associated with renal function, suggesting the potential to alleviate concerns within the fitness community and emphasizing the significance of evidence-based decision-making when considering nutritional supplementation.