Low levels of serum LDH are associated with depression and suicide attempts

From energy metabolism to mood regulation: The rise of lactate as a therapeutic target

BACKGROUND

Disruption of cerebral energy metabolism is increasingly recognized as a key factor in the pathophysiology of mood disorders. Lactate, beyond its role as a metabolic byproduct, is now understood to be a critical player in brain energy homeostasis and a modulator of neuronal function. Recent advances in understanding lactate shuttling between astrocytes and neurons have opened new avenues for exploring its multifaceted roles in mood regulation. Exercise, known to modulate brain lactate levels, further underscores the potential of lactate as a therapeutic target in mood disorders.

AIM OF REVIEW

This review delves into the alterations in cerebral lactate associated with mood disorders, emphasizing their implications for brain energy dynamics and signaling pathways. Additionally, we discuss the therapeutic potential of lactate in mood disorders, particularly through its capacity to remodel cerebral function. We conclude by assessing the promise of exercise-induced lactate production as a novel strategy for mood disorder treatment.

KEY SCIENTIFIC CONCEPTS OF THE REVIEW

Alterations in brain lactate may contribute to the pathogenesis of mood disorders. In several studies, lactate is not only a substrate for brain energy metabolism, but also a molecule that triggers signaling cascades. Specifically, lactate is involved in the regulation of neurogenesis, neuroplasticity, endothelial cell function, and microglia lysosomal acidification, therefore improving mood disorders. Meanwhile, exercise as a low-risk intervention strategy can improve mood disorders through lactate regulation. Thus, the evidence from this review supports that lactate could be a potential therapeutic target for mood disorder, contributing to a deeper understanding of mood disorder pathogenesis and intervention.

Proteome analysis of the prefrontal cortex and the application of machine learning models for the identification of potential biomarkers related to suicide

Introduction

Suicide is a significant public health problem, with increased rates in low- and middle-income countries such as Mexico; therefore, suicide prevention is important. Suicide is a complex and multifactorial phenomenon in which biological and social factors are involved. Several studies on the biological mechanisms of suicide have analyzed the proteome of the dorsolateral prefrontal cortex (DLPFC) in people who have died by suicide. The aim of this work was to analyze the protein expression profile in the DLPFC of individuals who died by suicide in comparison to age-matched controls in order to gain information on the molecular basis in the brain of these individuals and the selection of potential biomarkers for the identification of individuals at risk of suicide. In addition, this information was analyzed using machine learning (ML) algorithms to propose a model for predicting suicide.

Methods

Brain tissue (Brodmann area 9) was sampled from male cases (n=9) and age-matched controls (n=7). We analyzed the proteomic differences between the groups using two-dimensional polyacrylamide gel electrophoresis and mass spectrometry. Bioinformatics tools were used to clarify the biological relevance of the differentially expressed proteins. In addition, this information was analyzed using machine learning (ML) algorithms to propose a model for predicting suicide.

Results

Twelve differentially expressed proteins were also identified (t 14 ≤ 0.5). Using Western blotting, we validated the decrease in expression of peroxiredoxin 2 and alpha-internexin in the suicide cases. ML models were trained using densitometry data from the 2D gel images of each selected protein and the models could differentiate between both groups (control and suicide cases).

Discussion

Our exploratory pathway analysis highlighted oxidative stress responses and neurodevelopmental pathways as key processes perturbed in the DLPFC of suicides. Regarding ML models, KNeighborsClassifier was the best predicting conditions. Here we show that these proteins of the DLPFC may help to identify brain processes associated with suicide and they could be validated as potential biomarkers of this outcome.

Risk factors for depression in China based on machine learning algorithms: A cross-sectional survey of 264,557 non-manual workers

BACKGROUND

Factors related to depression differ depending on the population studied, and studies focusing on the population of non-manual workers are lacking. Thus, we aimed to identify the risk factors related to depression in non-manual workers in China.

METHOD

A large-scale cross-sectional survey was conducted between January 1, 2015 and December 31, 2020, which included 264,557 non-manual workers from one large physical examination institution in China. The Patient Health Questionnaire (PHQ-2) was used to measure depression. A total of 73 variables covering aspects of sociodemographic characteristics, general examination data, health history, symptoms, eating habits, work situation, general sleep conditions and laboratory findings were included in the collection and analysis. Machine learning algorithms of neural networks and logistic regressions were used to assess the risk of depression and explore its factors.

RESULTS

Age, feeling fatigue, sleep quality, overeating, waist-to-hip ratio (WHR), and high-density lipoprotein cholesterol (HDLC) were found to be factors of depression. Two prediction models for depression among Chinese non-manual workers were developed with good AUC (0.820), accuracy (0.943), sensitivity (0.743-0.773), and specificity (0.700-0.729).

LIMITATIONS

Data were collected at one time point only, meaning that this study cannot explain the causality of the factor on depression.

CONCLUSIONS

Our finding that age, feeling fatigue, sleep quality, overeating, WHR, and HDL-C were risk factors for depression in non-manual workers may provide strong evidence for health care facilities to develop preventive measures or government policies for non-manual workers.

Anabolic-androgenic steroids are linked to depression and anxiety in male bodybuilders: the hidden psychogenic side of anabolic androgenic steroids

BACKGROUND

The prevalence of anabolic-androgenic steroids (AAS) use is on the rise among athletes and bodybuilders worldwide. In addition to the well-documented adverse effects on hepatic, renal, and reproductive functions, there is an increasing recognition of psychiatric complications associated with AAS use. This study aimed to investigate psychiatric morbidity among male bodybuilders who are AAS users.

METHODS

In this cross-sectional study, 25 male bodybuilders using AAS (mean age 31.2 ± 8.9 years) were compared with a control group of 25 healthy male bodybuilders matched in age (31.3 ± 5.5 years). The demographic, hormonal, and biochemical parameters of the participants were recorded. The impact of AAS use on psychiatric morbidity was assessed using the Beck Anxiety Inventory (BAI) and Beck Depression Inventory (BDI) in both groups.

RESULTS

The BDI and BAI scores were significantly higher in male bodybuilders using anabolic-androgenic steroids (p < 0.0001). While the control group showed no instances of anxiety, seven individuals in the AAS user group reported mild anxiety. No participants in the control group exhibited depression, whereas seven AAS users displayed depressive symptoms (4 mild, 3 moderate). Correlations were observed between lactate dehydrogenase (LDH) levels and BAI scores, creatinine levels and both BAI and BDI scores, as well as between estradiol levels and BDI.

CONCLUSION

The study concluded that AAS use among male bodybuilders is associated with elevated levels of depression and anxiety. Our findings suggest a potential correlation between anxiety and depression levels and the levels of creatinine, LDH, and estradiol in AAS users.

Mood, Anxiety, and Cognitive Alterations in Cancer Patients

BACKGROUND AND OBJECTIVE

To analyze the cytokine profile in cerebrospinal fluid (CSF), as well as mood, anxiety, and cognition profiles in patients with CC.

METHODS

One hundred and nine individuals were evaluated, 37 controls, 18 CWC, and 54 CC patients. Assessments included BDI, HADS, Digit Span, FAS-verbal, Animals/WMS-R, Matrix Reasoning and Vocabulary (WASI), and QLQ-C30.

RESULTS

The CC group exhibited 62.96% depression and probable anxiety/depression, with 75.92% showing attention deficits. The CC and CWC groups demonstrated significant cognitive impairment on the WASI-Vocabulary test (CWC: 13.4 ± 2.2; CC: 15.9 ± 1.1) compared to the control group (Ct: 22.8 ± 1.6; p = 0.0002). In the QLQ-C30 scores, the CC group reported a greater perceived loss of quality of life and health deterioration (score of 17.5 ± 2.6) and lower scores on the Functional Scale (49.8 ± 4.5). The CC group had 18.52% illiteracy, 18.52% incomplete higher education, and 22.22% complete elementary education. The CC group also had lower weight (Ct: 67.8 ± 1.4; CWC: 61.7 ± 3.1; CC: 59.6 ± 1.7; p = 0.0023) and BMI (CC: 21.5 [18.3; 24.8]; Ct: 24.9 [23; 25.8]; p = 0.0021) compared to controls. Cytokines detected in the CSF were MCP-1, VEGF, IL-8, IP-10, and MIP-1β. Higher concentrations of MCP-1 were found in cancer patients (CSC: 571.2 ± 105.8; CC: 399.5 ± 65.9; Ct: 1477 ± 0.1; p < 0.0001), along with lower levels of MIP-1β (CC: 4345 [3060; 7353]) and VEGF (CC: 48.3 ± 2.0; CWC: 49.8 ± 3.8; Ct: 64.8 ± 3.2; p < 0.0001).

CONCLUSIONS

The level of mental impairment (mood, anxiety, and cognitive deficits) correlated with cancer-associated and cachexia-associated inflammation, weight loss, low BMI, elevated C-reactive protein (CRP), leukocytosis, lymphopenia, anemia, hypoalbuminemia, and low scores on the QLQ-C30 questionnaire (Global Health Status, Functional Scale, Symptom Scale). The CC group exhibited a higher prevalence of depression/anxiety, a stronger correlation between depression and inflammation, and greater cognitive impairment in attention, reasoning, and language, alongside lower average educational attainment. The low concentration of certain cytokines in the CSF combined with elevated systemic CRP in cancer and cachexia, associated with mental disorders, presents a paradox that requires further investigation. Higher concentrations of the cytokine MCP-1 in cancer patient groups indicated a positive correlation with the preservation of language abilities in these patients.

Lactate: A New Target for Brain Disorders

Lactate in the brain is produced endogenously and exogenously. The primary functional cells that produce lactate in the brain are astrocytes. Astrocytes release lactate to act on neurons, thereby affecting neuronal function, through a process known as the astrocyte-neuron shuttle. Lactate affects microglial function as well and inhibits microglia-mediated neuroinflammation. Lactate also provides energy, acts as a signaling molecule, and promotes neurogenesis. This article summarizes the role of lactate in cells, animals, and humans. Lactate is a protective molecule against stress in healthy organisms and in the early stages of brain disorders. Thus, lactate may be a potential therapeutic target for brain disorders. Further research on the role of lactate in microglia may have great prospects. This article provides a new perspective and research direction for the study of lacate in brain disorders.

Peripheral Upregulation of Parkinson's Disease-Associated Genes Encoding α-Synuclein, β-Glucocerebrosidase, and Ceramide Glucosyltransferase in Major Depression

Due to the high comorbidity of Parkinson's disease (PD) with major depressive disorder (MDD) and the involvement of sphingolipids in both conditions, we investigated the peripheral expression levels of three primarily PD-associated genes: α-synuclein (SNCA), lysosomal enzyme β-glucocerebrosidase (GBA1), and UDP-glucose ceramide glucosyltransferase (UGCG) in a sex-balanced MDD cohort. Normalized gene expression was determined by quantitative PCR in patients suffering from MDD (unmedicated n = 63, medicated n = 66) and controls (remitted MDD n = 39, healthy subjects n = 61). We observed that expression levels of SNCA (p = 0.036), GBA1 (p = 0.014), and UGCG (p = 0.0002) were higher in currently depressed patients compared to controls and remitted patients, and expression of GBA1 and UGCG decreased in medicated patients during three weeks of therapy. Additionally, in subgroups, expression was positively correlated with the severity of depression and anxiety. Furthermore, we identified correlations between the gene expression levels and PD-related laboratory parameters. Our findings suggest that SNCA, GBA1, and UGCG analysis could be instrumental in the search for biomarkers of MDD and in understanding the overlapping pathological mechanisms underlying neuro-psychiatric diseases.

[Comparative analysis of factors influencing suicidal actions performance before and during the COVID-19 pandemic]

OBJECTIVE

To study the influence of COVID-19, as well as various factors on the rate of deaths caused by suicide.

MATERIAL AND METHODS

The statistical analysis of mortality according to the data of «Bureau of FME» in the period from 2019 to 2020 yrs. and the search of scientific publications over the past 10 years in PubMed database have been conducted. Articles describing the factors influencing suicidal deaths in different population groups were selected from the publications.

RESULTS

Statistically significant features that determine the influence of quarantine measures in the period COVID-19 on the mental component of citizens and suicidal dynamics were considered and established.