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Given recent technological advances in proteomics, it is now possible to quantify plasma proteomes in large cohorts of patients to screen for biomarkers and to guide the early diagnosis and treatment of depression. Here we used CatBoost machine learning to model and discover biomarkers of depression in UK Biobank data sets (depression n = 4,479, healthy control n = 19,821). CatBoost was employed for model construction, with Shapley Additive Explanations (SHAP) being utilized to interpret the resulting model. Model performance was corroborated through 5-fold cross-validation, and its diagnostic efficacy was evaluated based on the area under the receiver operating characteristic (AUC) curve. A total of 45 depression-related proteins were screened based on the top 20 important features output by the CatBoost model in six data sets. Of the nine diagnostic models for depression, the performance of the traditional risk factor model was improved after the addition of proteomic data, with the best model having an average AUC of 0.764 in the test sets. KEGG pathway analysis of 45 screened proteins showed that the most significant pathway involved was the cytokine-cytokine receptor interaction. It is feasible to explore diagnostic biomarkers of depression using data-driven machine learning methods and large-scale data sets, although the results require validation.
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BACKGROUND: Anhedonia is a core symptom of depression that is closely related to prognosis and treatment outcomes. However, accurate and efficient treatments for anhedonia are lacking, mandating a deeper understanding of the underlying mechanisms. METHODS: A total of 303 patients diagnosed with depression and anhedonia were assessed by the Snaith-Hamilton Pleasure Scale (SHAPS) and magnetic resonance imaging (MRI). The patients were categorized into a low-anhedonia group and a high-anhedonia group using the K-means algorithm. A data-driven approach was used to explore the differences in brain structure and function with different degrees of anhedonia based on MATLAB. A random forest model was used exploratorily to test the predictive ability of differences in brain structure and function on anhedonia in depression. RESULTS: Structural and functional differences were apparent in several brain regions of patients with depression and high-level anhedonia, including in the temporal lobe, paracingulate gyrus, superior frontal gyrus, inferior occipital gyrus, right insular gyrus, and superior parietal lobule. And changes in these brain regions were significantly correlated with scores of SHAPS. CONCLUSIONS: These brain regions may be useful as biomarkers that provide a more objective assessment of anhedonia in depression, laying the foundation for precision medicine in this treatment-resistant, relatively poor prognosis group.
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Transmembrane channel-like (TMC) proteins are expressed throughout the animal kingdom and are thought to encode components of ion channels. Mammals express eight TMCs (mTMC1-8), two of which (mTMC1 and mTMC2) are subunits of mechanotransduction channels. C. elegans expresses two TMCs (TMC-1 and TMC-2), which mediate mechanosensation, egg laying, and alkaline sensing. The mechanisms by which nematode TMCs contribute to such diverse physiological processes and their functional relationship to mammalian mTMCs is unclear. Here, we show that association with accessory proteins tunes nematode TMC-1 to divergent sensory functions. In addition, distinct TMC-1 domains enable touch and alkaline sensing. Strikingly, these domains are segregated in mammals between mTMC1 and mTMC3. Consistent with these findings, mammalian mTMC1 can mediate mechanosensation in nematodes, while mTMC3 can mediate alkaline sensation. We conclude that sequence diversification and association with accessory proteins has led to the emergence of TMC protein complexes with diverse properties and physiological functions.
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Phasic (fast) and tonic (sustained) inhibition of γ-aminobutyric acid (GABA) are fundamental for regulating day-to-day activities, neuronal excitability, and plasticity. However, the mechanisms and physiological functions of glial GABA transductions remain poorly understood. Here, we report that the AMsh glia in Caenorhabditis elegans exhibit both phasic and tonic GABAergic signaling, which distinctively regulate olfactory adaptation and neuronal aging. Through genetic screening, we find that GABA permeates through bestrophin-9/-13/-14 anion channels from AMsh glia, which primarily activate the metabolic GABAB receptor GBB-1 in the neighboring ASH sensory neurons. This tonic action of glial GABA regulates the age-associated changes of ASH neurons and olfactory responses via a conserved signaling pathway, inducing neuroprotection. In addition, the calcium-evoked, vesicular glial GABA release acts upon the ionotropic GABAA receptor LGC-38 in ASH neurons to regulate olfactory adaptation. These findings underscore the fundamental significance of glial GABA in maintaining healthy aging and neuronal stability.
Subject(s)
Adaptation, Physiological , Caenorhabditis elegans , Neuroglia , gamma-Aminobutyric Acid , Animals , gamma-Aminobutyric Acid/metabolism , Neuroglia/metabolism , Neuroglia/physiology , Adaptation, Physiological/physiology , Smell/physiology , Caenorhabditis elegans Proteins/metabolism , Caenorhabditis elegans Proteins/genetics , Signal Transduction/physiology , Cellular Senescence/physiology , Olfactory Receptor Neurons/physiology , Olfactory Receptor Neurons/metabolism , Aging/physiology , Aging/metabolism , Receptors, GABA-A/metabolismABSTRACT
Arterial spin labeling (ASL) can be used to detect differences in perfusion for multiple brain regions thought to be important in major depressive disorder (MDD). However, the potential of cerebral blood flow (CBF) to predict MDD and its correlations between the blood lipid levels and immune markers, which are closely related to MDD and brain function change, remain unclear. The 451 individuals - 298 with MDD and 133 healthy controls who underwent MRI at a single time point with arterial spin labelling and a high resolution T1-weighted structural scan. A proportion of MDD also provided blood samples for analysis of lipid and immune markers. We performed CBF case-control comparisons, random forest model construction, and exploratory correlation analyses. Moreover, we investigated the relationship between gray matter volume (GMV), blood lipids, and the immune system within the same sample to assess the differences in CBF and GMV. We found that the left inferior parietal but supramarginal and angular gyrus were significantly different between the MDD patients and HCs (voxel-wise P < 0.001, cluster-wise FWE correction). And bilateral inferior temporal (ITG), right middle temporal gyrus and left precentral gyrus CBF predict MDD (the area under the receiver operating characteristic curve of the random forest model is 0.717) and that CBF is a more sensitive predictor of MDD than GMV. The left ITG showed a positive correlation trend with immunoglobulin G (r = 0.260) and CD4 counts (r = 0.283). The right ITG showed a correlation trend with Total Cholesterol (r = -0.249) and tumour necrosis factor-alpha (r = -0.295). Immunity and lipids were closely related to CBF change, with the immunity relationship potentially playing a greater role. The interactions between CBF, plasma lipids and immune index could therefore represent an MDD pathophysiological mechanism. The current findings provide evidence for targeted regulation of CBF or immune properties in MDD.
Subject(s)
Depressive Disorder, Major , Gray Matter , Humans , Gray Matter/diagnostic imaging , Gray Matter/pathology , Depressive Disorder, Major/diagnostic imaging , Depressive Disorder, Major/pathology , Depression , Brain/pathology , Magnetic Resonance Imaging , Cerebrovascular Circulation/physiology , Spin Labels , Biomarkers , LipidsABSTRACT
Many central nervous system diseases are closely related to nerve damage caused by dysregulation of the endogenous neurotransmitter glutamate. Exosomes derived from bone marrow mesenchymal stem cells (BMSC-Exos) play an important role in improving injury and regeneration functions. However, its mechanism remains unknown. Therefore, the aim of this study is to investigate whether and how BMSC-Exos improve neurotoxicity caused by glutamate and to fill the gap in the literature. In this study, glutamate-treated HT22 cells were first exposed to mouse-derived BMSC-Exos at different concentrations to observe their effects on HT22 apoptosis. Next, we treated glutamate-treated HT22 cells with mouse-derived BMSC-Exos. We then inhibited the PI3K/Akt/mTOR signaling pathways using the PI3K/Akt inhibitor and the mTOR inhibitor, respectively, and observed the protective effect of mouse-derived BMSC-Exos on HT22 cells treated with glutamate. Our results show that BMSC-Exos reduced apoptosis triggered by glutamate stimulation, increased cell vitality, and decreased the levels of proapoptotic proteins while increasing the levels of anti-apoptotic proteins. The protective effect of BMSC-Exos was weakened when PI3K/Akt inhibitor and mTOR inhibitor were added. To sum up, we draw the following conclusions: BMSC-Exos can reduce neuronal apoptosis and apoptosis-related protein expression after glutamate stimulation by regulating the PI3K/Akt/mTOR signaling pathway.
Subject(s)
Exosomes , MicroRNAs , Neuroprotective Agents , Mice , Animals , Proto-Oncogene Proteins c-akt/metabolism , Phosphatidylinositol 3-Kinases/metabolism , Glutamic Acid/toxicity , Glutamic Acid/metabolism , Neuroprotective Agents/pharmacology , Neuroprotective Agents/metabolism , Exosomes/metabolism , TOR Serine-Threonine Kinases/metabolism , MicroRNAs/metabolismABSTRACT
Aging-induced cognitive impairment is associated with a loss of metabolic homeostasis and plasticity. An emerging idea is that targeting key metabolites is sufficient to impact the function of other organisms. Therefore, more metabolism-targeted therapeutic intervention is needed to improve cognitive impairment. We first conducted untargeted metabolomic analyses and 16S rRNA to identify the aging-associated metabolic adaption and intestinal microbiome change. Untargeted metabolomic analyses of plasma revealed L-arginine metabolic homeostasis was altered during the aging process. Impaired L-arginine metabolic homeostasis was associated with low abundance of intestinal Akkermansia muciniphila (AKK) colonization in mice. Long-term supplementation of AKK outer membranes protein-Amuc_1100, rescued the L-arginine level and restored cognitive impairment in aging mice. Mechanically, Amuc_1100 acted directly as a source of L-arginine and enriched the L-arginine-producing bacteria. In aged brain, Amuc_1100 promoted the superoxide dismutase to alleviated oxidation stress, and increased nitric oxide, derivatives of L-arginine, to improve synaptic plasticity. Meanwhile, L-arginine repaired lipopolysaccharide-induced intestinal barrier damage and promoted growth of colon organoid. Our findings indicated that aging-related cognitive impairment was closely associated with the disorders of L-arginine metabolism. AKK-derived Amuc_1100, as a potential postbiotic, targeting the L-arginine metabolism, might provide a promising therapeutic strategy to maintain the intestinal homeostasis and cognitive function in aging.
Subject(s)
Cognitive Dysfunction , Verrucomicrobia , Mice , Animals , RNA, Ribosomal, 16S , Homeostasis , ArginineABSTRACT
BACKGROUND: Many metabolomics studies of depression have been performed, but these have been limited by their scale. A comprehensive in silico analysis of global metabolite levels in large populations could provide robust insights into the pathological mechanisms underlying depression and candidate clinical biomarkers. METHODS: Depression-associated metabolomics was studied in 2 datasets from the UK Biobank database: participants with lifetime depression (N = 123,459) and participants with current depression (N = 94,921). The Whitehall II cohort (N = 4744) was used for external validation. CatBoost machine learning was used for modeling, and Shapley additive explanations were used to interpret the model. Fivefold cross-validation was used to validate model performance, training the model on 3 of the 5 sets with the remaining 2 sets for validation and testing, respectively. Diagnostic performance was assessed using the area under the receiver operating characteristic curve. RESULTS: In the lifetime depression and current depression datasets and sex-specific analyses, 24 significantly associated metabolic biomarkers were identified, 12 of which overlapped in the 2 datasets. The addition of metabolic features slightly improved the performance of a diagnostic model using traditional (nonmetabolomics) risk factors alone (lifetime depression: area under the curve 0.655 vs. 0.658 with metabolomics; current depression: area under the curve 0.711 vs. 0.716 with metabolomics). CONCLUSIONS: The machine learning model identified 24 metabolic biomarkers associated with depression. If validated, metabolic biomarkers may have future clinical applications as supplementary information to guide early and population-based depression detection.
Subject(s)
Biomarkers , Machine Learning , Metabolomics , Humans , Female , Male , Middle Aged , Biomarkers/metabolism , Aged , Big Data , Depression/metabolism , Depression/diagnosis , Adult , Depressive Disorder/metabolism , Depressive Disorder/diagnosisABSTRACT
Anxious depression is a common subtype of major depressive disorder (MDD) associated with adverse outcomes and severely impaired social function. It is important to clarify the underlying neurobiology of anxious depression to refine the diagnosis and stratify patients for therapy. Here we explored associations between anxiety and brain structure/function in MDD patients. A total of 260 MDD patients and 127 healthy controls underwent three-dimensional T1-weighted structural scanning and resting-state functional magnetic resonance imaging. Demographic data were collected from all participants. Differences in gray matter volume (GMV), (fractional) amplitude of low-frequency fluctuation ((f)ALFF), regional homogeneity (ReHo), and seed point-based functional connectivity were compared between anxious MDD patients, non-anxious MDD patients, and healthy controls. A random forest model was used to predict anxiety in MDD patients using neuroimaging features. Anxious MDD patients showed significant differences in GMV in the left middle temporal gyrus and ReHo in the right superior parietal gyrus and the left precuneus than HCs. Compared with non-anxious MDD patients, patients with anxious MDD showed significantly different GMV in the left inferior temporal gyrus, left superior temporal gyrus, left superior frontal gyrus (orbital part), and left dorsolateral superior frontal gyrus; fALFF in the left middle temporal gyrus; ReHo in the inferior temporal gyrus and the superior frontal gyrus (orbital part); and functional connectivity between the left superior temporal gyrus(temporal pole) and left medial superior frontal gyrus. A diagnostic predictive random forest model built using imaging features and validated by 10-fold cross-validation distinguished anxious from non-anxious MDD with an AUC of 0.802. Patients with anxious depression exhibit dysregulation of brain regions associated with emotion regulation, cognition, and decision-making, and our diagnostic model paves the way for more accurate, objective clinical diagnosis of anxious depression.
Subject(s)
Depressive Disorder, Major , Humans , Depression , Magnetic Resonance Imaging/methods , Brain , Neuroimaging , Machine LearningABSTRACT
BACKGROUND: Insomnia symptoms in patients with major depressive disorder (MDD) are common and deleterious. Childhood trauma, personality traits, interpersonal distress, and social support contribute to insomnia, but how they interact to affect insomnia remains uncertain. METHODS: A total of 791 patients with MDD completed the Insomnia Severity Index, Eysenck Personality Questionnaire, Interpersonal Relationship Comprehensive Diagnostic Scale, Childhood Trauma Questionnaire, Social Support Rating Scale and Hamilton Depression Scale-17. This study utilized network analyses to identify the central symptoms of insomnia and their associations with psychosocial factors. RESULTS: Worrying about sleep was identified as the central symptom in the insomnia network, insomnia and associated personality network, insomnia and associated interpersonal disturbance network, insomnia and associated childhood trauma network, insomnia and associated social support network, and the integrated network of insomnia symptoms and associated psychosocial factors. In the networks of insomnia symptoms and individual psychosocial factors, most psychosocial factors (other than childhood trauma) were directly or indirectly related to insomnia symptoms; however, neuroticism was the only factor directly associated with insomnia symptoms before and after controlling for covariates. In the final integrated network of insomnia symptoms and psychosocial factors, neuroticism was a bridge node and mediated the relationships of social support and interpersonal disturbances with insomnia symptoms, which is clearly presented in the shortest pathways. CONCLUSIONS: Worrying about sleep and neuroticism were prominent in the integrated network of insomnia symptoms and associated psychosocial factors, and the edge between them connected psychosocial factors and insomnia symptoms in MDD patients.
Subject(s)
Depressive Disorder, Major , Sleep Initiation and Maintenance Disorders , Humans , Depression/complications , Depression/psychology , Sleep Initiation and Maintenance Disorders/complications , Depressive Disorder, Major/complications , Depressive Disorder, Major/psychology , PersonalityABSTRACT
Different dopaminergic (DA) neuronal subgroups exhibit distinct vulnerability to stress, while the underlying mechanisms are elusive. Here we report that the transient receptor potential melastatin 2 (TRPM2) channel is preferentially expressed in vulnerable DA neuronal subgroups, which correlates positively with aging in Parkinson's Disease (PD) patients. Overexpression of human TRPM2 in the DA neurons of C. elegans resulted in selective death of ADE but not CEP neurons in aged worms. Mechanistically, TRPM2 activation mediates FZO-1/CED-9-dependent mitochondrial hyperfusion and mitochondrial permeability transition (MPT), leading to ADE death. In mice, TRPM2 knockout reduced vulnerable substantia nigra pars compacta (SNc) DA neuronal death induced by stress. Moreover, the TRPM2-mediated vulnerable DA neuronal death pathway is conserved from C. elegans to toxin-treated mice model and PD patient iPSC-derived DA neurons. The vulnerable SNc DA neuronal loss is the major symptom and cause of PD, and therefore the TRPM2-mediated pathway serves as a promising therapeutic target against PD.
Subject(s)
Caenorhabditis elegans Proteins , Parkinson Disease , TRPM Cation Channels , Humans , Mice , Animals , Aged , Calcium/metabolism , Reactive Oxygen Species/metabolism , TRPM Cation Channels/metabolism , Caenorhabditis elegans/metabolism , Dopaminergic Neurons/metabolism , Parkinson Disease/metabolism , GTP Phosphohydrolases/metabolism , Caenorhabditis elegans Proteins/metabolismABSTRACT
Background: There is a high correlation between the risk of major depressive disorder (MDD) and adverse childhood experiences (ACEs) such as adverse parenting (AP). While there appears to be an association between ACEs and changes in brain structure and function, there have yet to be multimodal neuroimaging studies of associations between parenting style and brain developmental changes in MDD patients. To explore the effect of AP on brain structure and function. Methods: In this cross-sectional study, 125 MDD outpatients were included in the study and divided into the AP group and the optimal parenting (OP) group. Participants completed self-rating scales to assess depressive severity, symptoms, and their parents' styles. They also completed magnetic resonance imaging within one week of filling out the instruments. The differences between groups of gender, educational level, and medications were analyzed using the chi-squared test and those of age, duration of illness, and scores on scales using the independent samples t-test. Differences in gray matter volume (GMV) and resting-state functional connectivity (RS-FC) were assessed between groups. Results: AP was associated with a significant increase in GMV in the right superior parietal lobule (SPL) and FC between the right SPL and the bilateral medial superior frontal cortex in MDD patients. Limitations: The cross-cultural characteristics of AP will result in the lack of generalizability of the findings. Conclusions: The results support the hypothesis that AP during childhood may imprint the brain and affect depressive symptoms in adulthood. Parents should pay attention to the parenting style and avoid a style that lacks warmth.
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Background: Evidence from functional magnetic resonance imaging (fMRI) studies of schizophrenia suggests that interindividual variation in the stationary striatal functional circuit may be correlated with antipsychotic treatment response. However, little is known about the role of the dynamic striatum-related network in predicting patients' clinical improvement. The spontaneous coactivation pattern (CAP) technique has recently been found to be important for elucidating the non-stationary nature of functional brain networks. Methods: Forty-two drug-naive first-episode schizophrenia patients underwent fMRI and T1W imaging before and after 8 weeks of risperidone monotherapy. The striatum was divided into 3 subregions, including the putamen, pallidum, and caudate. Spontaneous CAPs and CAP states were utilized to measure the dynamic characteristics of brain networks. We used DPARSF and Dynamic Brain Connectome software to analyze each subregion-related CAP and CAP state for each group and then compared the between-group differences in the neural network biomarkers. We used Pearson's correlation analysis to determine the associations between the neuroimaging measurements with between-group differences and the improvement in patients' psychopathological symptoms. Results: In the putamen-related CAPs, patients showed significantly increased intensity in the bilateral thalamus, bilateral supplementary motor areas, bilateral medial, and paracingulate gyrus, left paracentral lobule, left medial superior frontal gyrus, and left anterior cingulate gyrus compared with healthy controls. After treatment, thalamic signals in the putamen-related CAP 1 showed a significant increase, while the signals of the medial and paracingulate gyrus in the putamen-related CAP 3 revealed a significant decrease. The increase in thalamic signal intensity in the putamen-related CAP 1 was significantly and positively correlated with the percentage reduction in PANSS_P. Conclusion: This study is the first to combine striatal CAPs and fMRI to explore treatment response-related biomarkers in the early phase of schizophrenia. Our findings suggest that dynamic changes in CAP states in the putamen-thalamus circuit may be potential biomarkers for predicting patients' variation in the short-term treatment response of positive symptoms.
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Background: Antipsychotic treatment-related alterations of cortical thickness (CT) and clinical symptoms have been previously corroborated, but less is known about whether the changes are driven by gene expression and epigenetic modifications. Methods: Utilizing a prospective design, we recruited 42 treatment-naive first-episode schizophrenia patients (FESP) and 38 healthy controls. Patients were scanned by TI weighted imaging before and after 8-week risperidone monotherapy. CT estimation was automatically performed with the FreeSurfer software package. Participants' peripheral blood genomic DNA methylation (DNAm) status, quantified by using Infinium® Human Methylation 450K BeadChip, was examined in parallel with T1 scanning. In total, CT measures from 118 subjects and genomic DNAm status from 114 subjects were finally collected. Partial least squares (PLS) regression was used to detect the spatial associations between longitudinal CT variations after treatment and cortical transcriptomic data acquired from the Allen Human Brain Atlas. Canonical correlation analysis (CCA) was then performed to identify multivariate associations between DNAm of PLS1 genes and patients' clinical improvement. Results: We detected the significant PLS1 component (2,098 genes) related to longitudinal alterations of CT, and the PLS1 genes were significantly enriched in neurobiological processes, and dopaminergic- and cancer-related pathways. Combining Laplacian score and CCA analysis, we further linked DNAm of 33 representative genes from the 2,098 PLS1 genes with patients' reduction rate of clinical symptoms. Conclusions: This study firstly revealed that changes of CT and clinical behaviors after treatment may be transcriptionally and epigenetically underlied. We define a "three-step" roadmap which represents a vital step toward the exploration of treatment- and treatment response-related biomarkers on the basis of multiple omics rather than a single omics type as a strategy for advancing precise care.
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Depression incurs a huge personal and societal burden, impairing cognitive and social functioning and affecting millions of people worldwide. A better understanding of the biological basis of depression could facilitate the development of new and improved therapies. Rodent models have limitations and do not fully recapitulate human disease, hampering clinical translation. Primate models of depression help to bridge this translational gap and facilitate research into the pathophysiology of depression. Here we optimized a protocol for administering unpredictable chronic mild stress (UCMS) to non-human primates and evaluated the influence of UCMS on cognition using the classical Wisconsin General Test Apparatus (WGTA) method. We used resting-state functional MRI to explore changes in amplitude of low-frequency fluctuations and regional homogeneity in rhesus monkeys. Our work highlights that the UCMS paradigm effectively induces behavioral and neurophysiological (functional MRI) changes in monkeys but without significantly impacting cognition. The UCMS protocol requires further optimization in non-human primates to authentically simulate changes in cognition associated with depression.
Subject(s)
Brain , Depression , Animals , Humans , Depression/drug therapy , Macaca mulatta , Brain/diagnostic imaging , Cognition , Neuroimaging , Stress, Psychological/complications , Disease Models, AnimalABSTRACT
Numerous taste receptors and related molecules have been identified in vertebrates and invertebrates. Otopetrin1 has recently been identified as mammalian sour taste receptor which is essential for acid sensation. However, whether other Otopetrin proteins are involved in PH-sensing remains unknown. In C. elegans, there are eight otopetrin homologous genes but their expression patterns and functions have not been reported so far. Through heterologous expression in HEK293T cells, we found that ceOTOP1a can be activated by acid in NMDG+ solution without conventional cations, which generated inward currents and can be blocked by zinc ions. Moreover, we found that Otopetrin channels are widely expressed in numerous tissues, especially in sensory neurons in the nematode. These results suggest that the biophysical characteristics of the Otopetrin channels in nematodes are generally conserved. However, a series of single gene mutations of otopetrins, which were constructed by CRISPR-Cas9 method, did not affect either calcium responses in ASH polymodal sensory neurons to acid stimulation or acid avoidance behaviors, suggesting that Otopetrin channels might have diverse functions among species. This study reveals that nematode Otopetrins are evolutionarily conserved acid-sensitive proton channels, and provides a framework for further revealing the function and mechanisms of Otopetrin channels in both invertebrates and vertebrates.
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BACKGROUND: Anhedonia is an important aspect of adolescent-onset major depressive disorder (MDD) and is associated with increased risk of suicidal behaviors and poor treatment outcomes. However, the neural circuitry underlying this deficit has not been well defined. This study aims to identify the relationships between anhedonia and changes in static and dynamic functional connectivity (FC) in adolescent-onset MDD patients compared with healthy control subjects (HCs) and adult-onset MDD patients. METHODS: A total of 157 participants completed the Snaith-Hamilton Pleasure Scale (SHAPS) to assess hedonic capacity. Resting-state functional imaging scans were analysed using graph theoretical analysis, network-based statistics (NBS) and sliding window correlation analysis to explore the potential patterns of neural network brain disruptions in adolescent-onset MDD. Pearson correlations and support vector machines regression (SVR) were used to explore correlations and predict network measures with SHAPS scores. RESULTS: Compared with those with adult-onset MDD, adolescent-onset MDD patients showed decreased FC in 7 nodes and 6 connections, with the right angular gyrus (AG), left AG and left paracentral lobule having the largest number of connected edges (P = 0.0396, NBS-corrected). Their average FC and SHAPS scores were positively correlated (r = 0.309, P = 0.035). Regarding dynamic FC, compared with HCs, adolescent-onset MDD patients showed a tendency towards a decreased frequency in moderate-intensity brain FC states (P = 0.014), which was significantly and positively correlated with SHAPS scores (r = 0.425, P = 0.003). SVR also revealed AG-centred FC and dynamic FC could predict SHAPS scores (MSE = 27.233, P = 0.001). CONCLUSIONS: These findings provide distinct evidence on the physiological mechanisms of adolescent-onset MDD and anhedonia.
Subject(s)
Anhedonia , Depressive Disorder, Major , Adult , Humans , Adolescent , Anhedonia/physiology , Depressive Disorder, Major/diagnostic imaging , Depression , Brain , Parietal Lobe/diagnostic imaging , Magnetic Resonance Imaging/methodsABSTRACT
BACKGROUND: Vitamin D deficiency is highly prevalent worldwide and is associated with various diseases, including depression. Previous studies on vitamin D and depression have different conclusions. OBJECTIVES: Our study aimed to examine the association between vitamin D levels in seasonal variation and depression. METHODS: A total of 324 patients with first-episode depression aged 18-50 years were recruited for our study. Vitamin D levels were recorded, and PHQ-9 scale evaluation was performed in different seasons. Seasonal variations in vitamin D levels and depressive symptoms were examined. RESULTS: The cohort comprised 77 males and 247 females. 98.1% of patients had insufficient or deficient vitamin D levels. The median vitamin D level was 12 ng/mL; 14.5 ng/mL in summer and 13 ng/mL in autumn, which was significantly higher than 9 ng/mL in spring, and the correlation between vitamin D level and PHQ-9 score was more significant in spring but not in summer and autumn. LIMITATIONS: Our study used cross-sectional data and could not examine the causal relationship of the vitamin D level and depressive symptoms. There are also some possible influencing factors, such as the dietary habits, outdoor sports, and the use of sunscreen were not investigated. CONCLUSION: Observational data showed that the vitamin D level of depression is lower than the normal (30 ng/mL), and it is closely related to depressive symptoms in spring. The seasonal variations in vitamin D levels might play a critical role in Chinese patients with first-episode depression.
Subject(s)
Vitamin D Deficiency , Vitamin D , Male , Female , Humans , Seasons , Cross-Sectional Studies , Depression/epidemiology , East Asian People , Vitamin D Deficiency/epidemiologyABSTRACT
BACKGROUND: Antipsychotic treatment has been conceived to alter brain connectivity, but it is unclear how the changes of network phenotypes relate to the underlying transcriptomics. Given DNA methylation (DNAm) may alter transcriptional levels, we further integrated an imaging-transcriptomic-epigenetic analysis to explore multi-omics treatment response biomarkers. METHODS: Forty-two treatment-naive first-episode schizophrenia patients were scanned by TI weighted (T1W) imaging and DTI before and after 8-week risperidone monotherapy, and their peripheral blood genomic DNAm values were examined in parallel with MRI scanning. Morphometric similarity network (MSN) quantified with DTI and T1W data were used as a marker of treatment-related alterations in interareal cortical connectivity. We utilized partial least squares (PLS) to examine spatial associations between treatment-related MSN variations and cortical transcriptomic data obtained from the Allen Human Brain Atlas. RESULTS: Longitudinal MSN alterations were related to treatment response on cognitive function and general psychopathology symptoms, while DNAm values of 59 PLS1 genes were on negative and positive symptoms. Virtual-histology transcriptomic analysis linked the MSN alterations with the neurobiological, cellular and metabolic pathways or processes, and assigned MSN-related genes to multiple cell types, specifying neurons and glial cells as contributing most to the transcriptomic associations of longitudinal changes in MSN. CONCLUSIONS: We firstly reveal how brain-wide transcriptional levels and cell classes capture molecularly validated cortical connectivity alterations after antipsychotic treatment. Our findings represent a vital step towards the exploration of treatment response biomarkers on the basis of multiple omics rather than a single omics type as a strategy for advancing precise care.
Subject(s)
Antipsychotic Agents , Schizophrenia , Humans , Risperidone/pharmacology , Risperidone/therapeutic use , Schizophrenia/diagnostic imaging , Schizophrenia/drug therapy , Schizophrenia/genetics , Antipsychotic Agents/pharmacology , Antipsychotic Agents/therapeutic use , Magnetic Resonance Imaging , Biomarkers , Epigenesis, GeneticABSTRACT
BACKGROUND: Anxious depression is a common subtype of major depressive disorder (MDD) associated with adverse outcomes and severely impaired social function. The aim of this study was to explore the relationships between child maltreatment, family functioning, social support, interpersonal problems, dysfunctional attitudes, and anxious depression. METHODS: Data were collected from 809 MDD patients. The Hamilton Anxiety Scale (HAMA), Hamilton Depression Scale-17 (HAMD-17), Family Assessment Device (FAD), Childhood Trauma Questionnaire (CTQ), Social Support Rating Scale (SSRS), Interpersonal Relationship Integrative Diagnostic Scale (IRIDS), and Dysfunctional Attitudes Scale (DAS) were administered and recorded. Anxious depression was defined as an anxiety/somatization factor score ≥ 7 on the HAMD-17. Chi-squared tests, Mann-Whitney U tests, distance correlations, and structural equation models were used for data analysis. RESULTS: Two-fifths of MDD patients had comorbid anxiety, and there were significant differences in child maltreatment, family functioning, social support, interpersonal problems, and dysfunctional attitudes between groups. Of these factors, interpersonal relationships were most related to anxiety in MDD patients, and dysfunctional attitudes mediated the relationship between interpersonal relationships and anxiety in MDD patients. LIMITATIONS: This study used cross-sectional data with no further follow-up to assess patient outcomes. This study did not include information about pharmacological treatments. A larger sample size is needed to validate the results. CONCLUSIONS: Psychosocial factors were significantly associated with anxious depression. Interpersonal relationships and dysfunctional attitudes have a direct effect on anxious depression, and interpersonal relationships also mediate the effects of anxious depression via dysfunctional attitudes.