Identification of Sex-Specific Plasma Biomarkers Using Metabolomics for Major Depressive Disorder in Children and Adolescents

Biological links between psychological factors and adolescent depression: childhood trauma, rumination, and resilience

BACKGROUND

The psychosocial factors play an important role in the development of depression in adolescents. we used metabolomics techniques to explore the links among childhood trauma, rumination, resilience, and adolescent depression.

METHODS

We selected 57 adolescent depression patients and 53 healthy adolescents. The Childhood Trauma Questionnaire (CTQ), Hamilton Depression Scale (HAMD), Resilience Scale (CD-RISC), and Redundant Thinking Response Scale (RRS) were employed for the purpose of psychological assessment. The patients were regrouped according to their scores using the 27% high-low grouping method. Blood specimens were collected from all adolescents and metabolic data were obtained using LC-MS.

RESULTS

We found no statistically significant difference between the groups in terms of age, gender, and body mass index (BMI). HAMD, CTQ, and RRS scores were significantly higher in the adolescent depression group (MDD) than in the adolescent healthy control group (HC), and CD-RISP scores were significantly lower than in the HC group (P < 0.001). There were significant differences between the low childhood trauma group (LCT) and high childhood trauma group (HCT), the low rumination group (LRR) and high rumination group (HRR), and the low resilience group (LPR) and high resilience group (HPR) (P < 0.001). RRS, CTQ and HAMD scores were positively correlated, RRS and CTQ scores were positively correlated, CD-RIS was negatively correlated with HAMD, RRS and CTQ scores (P < 0.01). More importantly, we found that DHEAS and LPA (22:6) were identified as significant differential metabolites in both the depressed and normal groups, as well as in the high and low childhood trauma groups. N-Acetyl-L-aspartic acid and DHEAS were identified as significant differential metabolites in both the depressed and normal groups, as well as in the high and low childhood rumination groups. Pseudouridine and LPA(22:6) were identified as significant differential metabolites in both the depressed and normal groups, as well as in the high and low childhood resilience groups.

CONCLUSION

Psychological factors (childhood trauma, rumination, resilience) are biologically linked to the development of depression in adolescents. The impact of rumination on adolescent depression may be associated with DHEA. The impact of childhood trauma and resilience on adolescent depression may be associated with LPA (22:6).

Perspectives on Integrating Biological Assessments to Address the Health Effects of Childhood Adversities

ABSTRACT

A majority of adults in the United States (US) report a range of stressful and potentially traumatic childhood experiences (e.g., physical or sexual abuse, witnessing violence, neglect). Such adversities are associated with a range of mental (e.g., anxiety, mood, and behavioral difficulties) and physical (e.g., cardiovascular illnesses, diabetes, asthma) health problems. Increasingly, precision medicine approaches seek to prevent and treat such multifinal downstream health problems by identifying common etiological pathways (e.g., inflammation and immune pathways) and candidate biomarkers to target interventions. In this context, we review the rationale for continued research to identify biomarkers of childhood adversity. Building on the bioecological theory, we emphasize that individual neurobiological profiles develop within multiple ecological levels (individual, family, neighborhood, macrosocial) that confer both risk and protective factors that can attenuate or amplify biological effects of childhood adversity. Given the limited data on adversity-associated biomarkers for children and adolescents, we discuss future recommendations for research, implications for clinical care, and ethical considerations. Preventing childhood adversity and supporting adversity- and trauma-informed systemic intervention approaches remains our primary recommendation. We highlight the continued need to consider both biomarkers of risk and protective factors across ecological levels in future research.

Development of a predictive model for the U-shaped relationship between the triglyceride glycemic index and depression using machine learning (NHANES 2009-2018)

Background

At present, the relationship between depression and the triglyceride glycemic (TyG) index remains a topic of debate. This study sought to elucidate the relationship between depression and the TyG index to create a predictive model that would help doctors diagnose patients.

Methods

We conducted a cross-sectional study utilizing the National Health and Nutrition Examination Survey (NHANES) dataset, which comprises data from 2009 to 2018. The analysis involved 11,222 adults with a Patient Health Questionnaire-9 (PHQ-9) score of 5 or higher, indicating the presence of depression. As part of the analysis, multiple regression models were used to test whether a linear relationship existed between the TyG index and depression. A threshold effects analysis was used to generate smoothed curves and detect nonlinear correlations. Additionally, the Least Absolute Shrinkage and Selection Operator (LASSO) regression were employed to identify the key risk factors associated with depression. The factors identified were then used to construct the risk prediction nomogram. Finally, Receiver Operating Characteristic (ROC) curves were used to evaluate the discriminative performance of the model.

Results

Multivariable linear regression analysis indicated a strong positive correlation between depression and the TyG index (β: 0.38, 95 % CI: 0.16-0.60, p = 0.0008). A U-shaped relationship with an inflection point was observed at a TyG index of 8.16. The nomogram model, constructed using risk factors identified by LASSO, exhibited a significant predictive value (AUC = 0.888).

Conclusions

The results of this investigation point to a U-shaped association between depression risk and the TyG index among Americans. Those with a TyG index of over 8.16 are significantly more likely to develop depression. These results suggest a possible causal relationship and emphasize the importance of monitoring the TyG index in depression risk assessment.

Non-Targeted Metabolomics Investigation of a Sub-Chronic Variable Stress Model Unveils Sex-Dependent Metabolic Differences Induced by Stress

Depression is twice as prevalent in women as in men, however, most preclinical studies of depression have used male rodent models. This study aimed to examine how stress affects metabolic profiles depending on sex using a rodent depression model: sub-chronic variable stress (SCVS). The SCVS model of male and female mice was established in discovery and validation sets. The stress-induced behavioral phenotypic changes were similar in both sexes, however, the metabolic profiles of female plasma and brain became substantially different after stress, whereas those of males did not. Four stress-differential plasma metabolites-β-hydroxybutyric acid (BHB), L-serine, glycerol, and myo-inositol-could yield biomarker panels with excellent performance to discern the stressed individuals only for females. Disturbances in BHB, glucose, 1,5-anhydrosorbitol, lactic acid, and several fatty acids in the plasma of stressed females implied a systemic metabolic shift to β-oxidation in females. The plasma levels of BHB and corticosterone only in stressed females were observed not only in SCVS but also in an acute stress model. These results collectively suggest a sex difference in the metabolic responses by stress, possibly involving the energy metabolism shift to β-oxidation and the HPA axis dysregulation in females.

Systematic identification of the role of gut microbiota in mental disorders: a TwinsUK cohort study

Mental disorders are complex disorders influenced by multiple genetic, environmental, and biological factors. Specific microbiota imbalances seem to affect mental health status. However, the mechanisms by which microbiota disturbances impact the presence of depression, stress, anxiety, and eating disorders remain poorly understood. Currently, there are no robust biomarkers identified. We proposed a novel pyramid-layer design to accurately identify microbial/metabolomic signatures underlying mental disorders in the TwinsUK registry. Monozygotic and dizygotic twins discordant for mental disorders were screened, in a pairwise manner, for differentially abundant bacterial genera and circulating metabolites. In addition, multivariate analyses were performed, accounting for individual-level confounders. Our pyramid-layer study design allowed us to overcome the limitations of cross-sectional study designs with significant confounder effects and resulted in an association of the abundance of genus Parabacteroides with the diagnosis of mental disorders. Future research should explore the potential role of Parabacteroides as a mediator of mental health status. Our results indicate the potential role of the microbiome as a modifier in mental disorders that might contribute to the development of novel methodologies to assess personal risk and intervention strategies.

Recent Progress in Mass Spectrometry-Based Metabolomics in Major Depressive Disorder Research

Major depressive disorder (MDD) is a serious mental illness with a heavy social burden, but its underlying molecular mechanisms remain unclear. Mass spectrometry (MS)-based metabolomics is providing new insights into the heterogeneous pathophysiology, diagnosis, treatment, and prognosis of MDD by revealing multi-parametric biomarker signatures at the metabolite level. In this comprehensive review, recent developments of MS-based metabolomics in MDD research are summarized from the perspective of analytical platforms (liquid chromatography-MS, gas chromatography-MS, supercritical fluid chromatography-MS, etc.), strategies (untargeted, targeted, and pseudotargeted metabolomics), key metabolite changes (monoamine neurotransmitters, amino acids, lipids, etc.), and antidepressant treatments (both western and traditional Chinese medicines). Depression sub-phenotypes, comorbid depression, and multi-omics approaches are also highlighted to stimulate further advances in MS-based metabolomics in the field of MDD research.

Associations between prefrontal PI (16:0/20:4) lipid, TNC mRNA, and APOA1 protein in schizophrenia: A trans-omics analysis in post-mortem brain

Background

Though various mechanisms have been proposed for the pathophysiology of schizophrenia, the full extent of these mechanisms remains unclear, and little is known about the relationships among them. We carried out trans-omics analyses by comparing the results of the previously reported lipidomics, transcriptomics, and proteomics analyses; all of these studies used common post-mortem brain samples.

Methods

We collected the data from three aforementioned omics studies on 6 common post-mortem samples (3 schizophrenia patients and 3 controls), and analyzed them as a whole group sample. Three correlation analyses were performed for each of the two of three omics studies in these samples. In order to discuss the strength of the correlations in a limited sample size, the p-values of each correlation coefficient were confirmed using the Student's t-test. In addition, partial correlation analysis was also performed for some correlations, to verify the strength of the impact of each factor on the correlations.

Results

The following three factors were strongly correlated with each other: the lipid level of phosphatidylinositol (PI) (16:0/20:4), the amount of TNC mRNA, and the quantitative signal intensity of APOA1 protein. PI (16:0/20:4) and TNC showed a positive correlation, while PI (16:0/20:4) and APOA1, and TNC and APOA1 showed negative correlations. All of these correlations reached at p < 0.01. PI (16:0/20:4) and TNC were decreased in the prefrontal cortex of schizophrenia samples, while APOA1 was increased. Partial correlation analyses among them suggested that PI (16:0/20:4) and TNC have no direct correlation, but their relationships are mediated by APOA1.

Conclusion

The current results suggest that these three factors may provide new clues to elucidate the relationships among the candidate mechanisms of schizophrenia, and support the potential of trans-omics analyses as a new analytical method.

Enterococcus faecalis promotes the progression of colorectal cancer via its metabolite: biliverdin

BACKGROUND

Enterococcus faecalis (Efa) has been shown to be a "driver bacteria" in the occurrence and development of colorectal cancer (CRC). This study aims to explore the effect of specific metabolites of Efa on CRC.

METHODS

The pro-tumor effects of Efa were assessed in colonic epithelial cells. The tumor-stimulating molecule produced by Efa was identified using liquid chromatography mass spectrometry (LC-MS). The proliferative effect of metabolites on CRC cells in vitro was assayed as well. The concentration of vascular endothelial growth factor A (VEGFA) and interleukin-8 (IL-8) was determined using enzyme-linked immunosorbent assay (ELISA). Tubular formation assay of human umbilical vein endothelial cells (HUVEC) and cell migration assay were applied to study angiogenesis. Additionally, western blot analysis was used to investigate key regulatory proteins involved in the angiogenesis pathway. Tumor growth was assessed using mouse models with two CRC cells and human colon cancer organoid.

RESULTS

Co-incubation with the conditioned medium of Efa increased the proliferation of cultured CRC cells. Biliverdin (BV) was determined as the key metabolite produced by Efa using LC-MS screening. BV promoted colony formation and cell proliferation and inhibited cell cycle arrest of cultured CRC cells. BV significantly increased the expression level of IL-8 and VEGFA by regulating the PI3K/AKT/mTOR signaling pathway, leading to the acceleration of angiogenesis in CRC. The up-regulation of proliferation and angiogenesis by BV were also confirmed in mice.

CONCLUSION

In conclusion, BV, as the tumor-stimulating metabolite of Efa, generates proliferative and angiogenic effects on CRC, which is mainly mediated by the activation of PI3K/AKT/mTOR.