Assessing the associations of 1,400 blood metabolites with major depressive disorder: a Mendelian randomization study.

Background and objectives: Major Depressive Disorder (MDD) is one of the most prevalent and debilitating health conditions worldwide. Previous studies have reported a link between metabolic dysregulation and MDD. However, evidence for a causal relationship between blood metabolites and MDD is lacking. Methods: Using a two-sample bidirectional Mendelian randomization analysis (MR), we assessed the causal relationship between 1,400 serum metabolites and Major Depressive Disorder (MDD). The Inverse Variance Weighted method (IVW) was employed to estimate the causal association between exposures and outcomes. Additionally, MR-Egger regression, weighted median, simple mode, and weighted mode methods were used as supplementary approaches for a comprehensive appraisal of the causality between blood metabolites and MDD. Pleiotropy and heterogeneity tests were also conducted. Lastly, the relevant metabolites were subjected to metabolite function analysis, and a reverse MR was implemented to explore the potential influence of MDD on these metabolites. Results: After rigorous screening, we identified 34 known metabolites, 13 unknown metabolites, and 18 metabolite ratios associated with Major Depressive Disorder (MDD). Among all metabolites, 33 were found to have positive associations, and 32 had negative associations. The top five metabolites that increased the risk of MDD were the Arachidonate (20:4n6) to linoleate (18:2n6) ratio, LysoPE(18:0/0:0), N-acetyl-beta-alanine levels, Arachidonate (20:4n6) to oleate to vaccenate (18:1) ratio, Glutaminylglutamine, and Threonine to pyruvate ratio. Conversely, the top five metabolites that decreased the risk of MDD were N6-Acetyl-L-lysine, Oleoyl-linoleoyl-glycerol (18:1 to 18:2) [2] to linoleoyl-arachidonoyl-glycerol (18:2 to 20:4) [2] ratio, Methionine to phosphate ratio, Pregnanediol 3-O-glucuronide, and 6-Oxopiperidine-2-carboxylic acid. Metabolite function enrichment was primarily concentrated in pathways such as Bile Acid Biosynthesis (FDR=0.177), Glutathione Metabolism (FDR=0.177), Threonine, and 2-Oxobutanoate Degradation (FDR=0.177). In addition, enrichment was noted in pathways like Valine, Leucine, and Isoleucine Biosynthesis (p=0.04), as well as Ascorbate and Aldarate Metabolism (p=0.04). Discussion: Within a pool of 1,400 blood metabolites, we identified 34 known metabolites and 13 unknown metabolites, as well as 18 metabolite ratios associated with Major Depressive Disorder (MDD). Additionally, three functionally enriched groups and two metabolic pathways were selected. The integration of genomics and metabolomics has provided significant insights for the screening and prevention of MDD.

Global biomarker trends in Parkinson's disease research: A bibliometric analysis.

As the second most common neurodegenerative disease globally, Parkinson's disease (PD) affects millions of people worldwide. In recent years, the scientific publications related to PD biomarker research have exploded, reflecting the growing interest in unraveling the complex pathophysiology of PD. In this study, we aim to use various bibliometric tools to identify key scientific concepts, detect emerging trends, and analyze the global trends and development of PD biomarker research.The research encompasses various stages of biomarker development, including exploration, identification, and multi-modal research. MOVEMENT DISORDERS emerged as the leading journal in terms of publications and citations. Key authors such as Mollenhauer and Salem were identified, while the University of Pennsylvania and USA stood out in collaboration and research output. NEUROSCIENCES emerged as the most important research direction. Key biomarker categories include α-synuclein-related markers, neurotransmitter-related markers, inflammation and immune system-related markers, oxidative stress and mitochondrial function-related markers, and brain imaging-related markers. Furthermore, future trends in PD biomarker research focus on exosomes and plasma biomarkers, miRNA, cerebrospinal fluid biomarkers, machine learning applications, and animal models of PD. These trends contribute to early diagnosis, disease progression monitoring, and understanding the pathological mechanisms of PD.

Du-moxibustion ameliorates depression-like behavior and neuroinflammation in chronic unpredictable mild stress-induced mice.

BACKGROUND: Neuroinflammation is involved in the advancement of depression. Du-moxibustion can treat depression. Here, we explored whether Du-moxibustion could alleviate neuroglia-associated neuro-inflammatory process in chronic unpredictable mild stress (CUMS) mice. METHODS: C57BL/6J mice were distributed into five groups. Except for the CON group, other four groups underwent CUMS for four consecutive weeks, and Du-moxibustion was given simultaneously after modeling. Behavioral tests were then carried out. Additionally, Western blot was conducted to measure the relative expression levels of high-mobility group box 1 (HMGB1), toll-like receptor 4 (TLR4), myeloid differentiation factor 88 (MyD88), and nuclear factor-kappa B (NF-κB). Immunofluorescence was employed to evaluate the positive cells of ionized calcium binding adapter molecule 1 (Iba-1) and glial fibrillary acidic protein (GFAP). Furthermore, interleukin-1 beta (IL-1ß) and tumor necrosis factor-alpha (TNF-α) were analyzed using an ELISA assay. RESULTS: We found that CUMS induced depression-like behaviors, such as reduced sucrose preference ratio, decreased locomotor and exploratory activity, decreased the time in open arms and prolonged immobility. Furthermore, versus the CON group, the expression of HMGB1, TLR4, MyD88, NF-κB, positive cells of Iba-1, IL-1ß and TNF-α were increased but positive cells of GFAP were decreased in CUMS group. However, the detrimental effects were ameliorated by treatment with CUMS+FLU and CUMS+DM. LIMITATIONS: A shortage of this study is that only CUMS model of depression were used, while other depression model were not included. CONCLUSIONS: Du-moxibustion alleviates depression-like behaviors in CUMS mice mainly by reducing neuroinflammation, which offers novel insights into the potential treatment of depression.

Global biomarker trends in triple-negative breast cancer research: a bibliometric analysis.

BACKGROUND: Triple-negative breast cancer (TNBC) is defined as breast cancer that is negative for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER-2) in cancer tissue. The lack of specific biomarkers makes the diagnosis and prognosis of TNBC challenging. METHOD: A comprehensive literature review and bibliometric analysis was performed using CiteSpace, VOSviewer and Scimago Graphica. RESULTS: TNBC biomarker research has been growing rapidly in recent years, reflecting the enormous academic interest in TNBC biomarker research. A total of 127 journals published relevant studies and 1749 authors were involved in the field, with developed countries such as the United States, France, and the United Kingdom contributing greatly to the field. Collaborative network analysis found that the research in this field has not yet formed good communication and interaction, and the partnership should be strengthened in the future in order to promote the in-depth development of TNBC biomarker research. Comprehensive analysis of keywords and co-cited literature, etc. found that TNBC biomarker research mainly focuses on immune checkpoint markers, microenvironment-related markers, circulating tumour DNA, metabolic markers, genomics markers and so on. These research hotspots will help to better understand the molecular characteristics and biological processes of TNBC, and provide more accurate biomarkers for its diagnosis, treatment and prognosis. CONCLUSIONS: The bibliometric analysis highlighted global trends and key directions in TNBC biomarker research. Future developments in TNBC biomarker research are likely to be in the direction of multi-omics integration, meticulous study of the microenvironment, targeted therapeutic biomarkers, application of liquid biopsy, application of machine learning and artificial intelligence, and individualised therapeutic strategies. Young scholars should learn and collaborate across disciplines, pay attention to new technologies and methods, improve their data analysis skills, and continue to follow up on the latest research trends in order to meet the challenges and opportunities in the field of TNBC biomarkers.