Neuropsychiatric Disease and Treatment最新文献

Objective: This review systematically examined the current research on machine learning-based depression risk prediction models for domestic and international older adults, aiming to provide references for methodological development and application in this field.

Design: A scoping review approach was used, following the Participants, Concept, and Context (PCC) framework and incorporating the Health Ecology Model (HEM) as the analytical theoretical framework.

Methods: Based on the PCC principles, the study scope was defined, and a systematic search was conducted in Web of Science, PubMed, Cochrane Library, Embase, CINAHL, CNKI, VIP Database, Wanfang Database, and the Chinese Biomedical Literature Database for literature related to depression in older adults, covering publications up to October 12, 2025. Two researchers independently screened the literature, extracted data, and summarized and analyzed the papers' content.

Results: After screening and full-text review, 15 studies on machine learning-based depression risk prediction models for older adults were included. A total of 90 risk prediction models were covered, with area under the curve (AUC) values ranging from 0.73 to 0.943. Most models were only internally validated, with only 20 models undergoing external validation. Predictors of depression in older adults were mainly at the individual intrinsic traits level (e.g. age, gender, number of chronic diseases, cognitive scores, pain) and the behavioral characteristics level (e.g. sleep duration, physical activity, smoking, drinking) of the HEM. At the same time, interpersonal network, community, environmental, and policy-level factors were less involved. These models are applicable in various settings, including community health service centers, outpatient clinics, long-term care institutions, and home health management.

Conclusion: Research on depression risk prediction models for older adults in China is still in its early stages. Existing models demonstrate good predictive performance, with a manageable risk of bias, and can provide more reliable decision support for healthcare professionals.

Purpose: Chronic subjective tinnitus is commonly accompanied by psychological distress; however, its independent relationship with depressive and anxiety symptoms in otolaryngology outpatients has not been fully delineated. This study sought to estimate the prevalence of mood symptoms among adults with chronic tinnitus and to determine whether tinnitus severity contributes independently to psychological burden.

Patients and methods: In this cross-sectional study, 100 adults with subjective tinnitus of at least six months' duration were evaluated at a tertiary otolaryngology clinic. Participants completed the Tinnitus Handicap Inventory (THI), Beck Depression Inventory-II (BDI-II), and Beck Anxiety Inventory (BAI). Prevalence estimates were compared with population-level data. Associations between tinnitus severity and mood symptoms were examined using correlation analyses and multivariable linear regression adjusted for age, sex, and hearing status.

Results: Mean scores were 47.2 ± 18.3 for THI, 16.1 ± 10.4 for BDI-II, and 19.5 ± 11.2 for BAI. Tinnitus severity showed moderate positive associations with depressive (r = 0.50) and anxiety symptoms (r = 0.48), both p < 0.001. After adjustment, THI scores remained independently associated with higher BDI-II (β = 0.42, p < 0.001) and BAI scores (β = 0.39, p < 0.001). Severe tinnitus (THI ≥58) was linked to increased odds of moderate-to-severe depression (OR 3.10, 95% CI 1.52-6.30) and anxiety (OR 2.84, 95% CI 1.40-5.72). Clinically relevant depressive and anxiety symptoms were identified in 28% and 31% of participants, respectively.

Conclusion: Greater tinnitus severity is independently associated with elevated symptom severity of depression and anxiety. These findings underscore the importance of routine mental health screening and multidisciplinary management in ENT practice.

Purpose: The considerable co-prevalence of major depressive disorders (MDD), anxiety symptoms, and chronic pain (CP) indicates a complex link with significant treatment ramifications. This study elucidates the role of pain sensitivity as a potential etiological factor in anxiety and depression.

Methods: This cross-sectional study recruited 124 participants divided into a health control group (HC, n=48), patients with depression without CP group (MDD, n=38) and comorbidity group of patients with MDD and CP (COM, n=38). Clinical assessments included the Hamilton Depression Rating Scale-24 (HAMD-24), Beck Depression Inventory-II (BDI-II), Hamilton Anxiety Rating Scale-14 (HAMA-14), and Beck Anxiety Inventory (BAI) to evaluate depressive and anxiety symptoms. The 36-Item Short Form Health Survey (SF-36) quantified health-related quality of life, while the Pain Sensitivity Questionnaire (PSQ) and Pain Intensity Numerical Rating Scale (PI-NRS) assessed pain sensitivity and pain intensity, respectively. A piecewise regression model identified clinical thresholds.

Results: The COM group showed higher BAI (U=294.5, p<0.001) and PI-NRS scores (U=166.0, p<0.001) compared to the MDD group. Piecewise regression identified PSQ=4.14 as the critical breakpoint for anxiety's influence on pain sensitivity. Beyond this threshold, BAI and Bodily Pain slopes reversed significantly (R ²=0.2434, p<0.05; R ²=0.2085, p<0.05). The high-PSQ group reported significantly more severe bodily pain (Cohen's d = -0.81, p=0.042).

Conclusion: Our findings reveal that when pain sensitivity exceeds the threshold (PSQ≥4.14), anxiety significantly amplifies pain perception, forming a "pain-anxiety-depression" positive feedback loop. This finding provides an empirical foundation for personalized therapy.

Objective: Co-administration of psychotropic medications is common in major depressive disorder (MDD), yet its patterns and predictors remain underexplored in drug-naïve patients. This study aimed to characterize the prevalence and correlates of co-administration strategies at treatment initiation in China.

Methods: In this multicenter, real-world study, we enrolled 1330 drug-naïve adults experiencing a depressive episode. Sociodemographic, clinical, and treatment data were collected at baseline. Univariate and multivariable logistic regression analyses identified factors associated with co-administration.

Results: Co-administration was utilized in 27.14% of patients. Antipsychotic augmentation was the most common strategy (18.87%), surpassing the use of multiple antidepressants (7.89%), while mood stabilizers (lithium 3.76%; valproate 2.78%) were less frequent. Patients receiving co-administration were typically male, older, had lower education/income, and more severe depressive symptoms. The most prescribed adjunctive antidepressants were SNRIs, NaSSAs, bupropion, and trazodone; the most common antipsychotics were quetiapine, olanzapine, aripiprazole, and risperidone. Multivariable analyses identified sympathetic arousal (OR=1.64) and leaden paralysis (OR=1.30) as key independent predictors.

Conclusion: Co-administration, particularly with antipsychotics, is frequently employed at the first treatment encounter for MDD in China. This practice appears to be a symptom-driven approach, targeting specific features like sympathetic arousal and leaden paralysis, which may reflect a pragmatic adaptation to local clinical realities beyond standard guidelines. Due to exploratory nature of the study, these findings are just hypothesis-generating, warranting further validation through more robust, longitudinal studies.

Purpose: Depression is a multifactorial disorder involving neurotransmitter dysregulation, gut microbiota imbalance, and metabolic disturbances. Low-intensity transcranial focused ultrasound stimulation (LIFUS) holds promise for treating depression. However, the effects of different LIFUS parameter settings on depression-like behaviors, and their potential associations with gut microbiota and fecal metabolite changes, remain largely unexplored. This study aims to investigate the parameter-specific effects of LIFUS on depression-like behaviors in a chronic unpredictable mild stress (CUMS) mouse model, and to explore potential associations with changes in gut microbiota and fecal metabolites.

Methods: To establish a depression model, C57BL/6 mice were subjected to CUMS, while a separate cohort was kept as a control (CON) group. The CUMS-exposed mice were then randomly divided into four groups: CUMSpo, LIFUS1, LIFUS2 and SHAM. Depressive-like behaviors were evaluated using the sucrose preference test (SPT) and forced swim test (FST). The levels of neurotransmitters and Fecal concentrations of metabolites were quantified using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Gut microbiota composition was analyzed by metagenomic sequencing, and α-diversity was assessed using the ACE, Chao1, and Shannon indices. Histopathology was assessed via HE staining.

Results: LIFUS at 1.5 kHz PRF, but not 300 Hz, significantly attenuated CUMS-induced depressive-like behaviors, evidenced by increased sucrose preference and reduced immobility time, without affecting locomotor activity. This behavioral effect was accompanied by a significant increase in cortical glutamate. LIFUS2 protocol was associated with a significant increase in tryptamine, alongside a concurrent trend towards restoring the abundance of Clostridia and enhancing gut microbiota α-diversity. HE staining confirmed protocol safety.

Conclusion: The antidepressant-like effects of LIFUS appear to be associated with multi-systemic alterations, including changes in cortical glutamate, modulation of the gut microbiota, and specific changes in tryptophan metabolism.

Objective: To evaluate the safety and effectiveness of lurasidone in Chinese patients with schizophrenia in a 12-week post-marketing study.

Methods: This 12-week, multicenter, prospective, open-label, single-arm study included schizophrenia patients from 36 sites in Mainland China, who initiated lurasidone between September 2019 and December 2023. Adverse events (AEs) and adverse drug reactions (ADRs) were primary safety endpoints. Other safety assessments included extrapyramidal symptoms (EPS) and weight gain. Effectiveness was evaluated using the Brief Psychiatric Rating Scale (BPRS) at baseline and week 12. Patients were also stratified to assess differences across ages.

Results: A total of 3170 patients were included in the Full Analysis Set (FAS) and 3178 in the Safety Set (SS). The mean daily lurasidone dose was 59.9±20.93 mg. ADRs occurred in 7.9% of patients, with incidences of 8.1%, 8.9%, 5.9%, and 2.3% in the <18, 18-45, 45-65, and >65-year age groups, respectively. EPS was the most common ADR (3.2%), typically emerging in weeks 3-4. Metabolic-related AEs occurred in 4.3% and 2.2% of patients with and without metabolic affecting agents. BPRS scores significantly improved at weeks 2/4, 6/8, and 12 compared with baseline (all P < 0.05): total score (-8.9 ± 9.62, -14.0 ± 12.16, -17.5 ± 13.61), anxiety-depression (-1.5 ± 2.37, -2.5 ± 2.88, -3.3 ± 3.31), anergia (-1.5 ± 2.34, -2.4 ± 2.83, -3.1 ± 3.11), thought disturbance (-2.4 ± 2.98, -3.8 ± 3.72, -4.7 ± 4.07), activation (-1.2 ± 1.97, -1.7 ± 2.38, -2.1 ± 2.63), and hostility-suspiciousness (-2.4 ± 2.81, -3.6 ± 3.44, -4.3 ± 3.76). In <18 years group, BPRS total score also significantly decreased from 46.1±15.10 at baseline to 26.3±9.72 at week 12.

Conclusion: This real-world study demonstrated a favorable safety profile and significant clinical effectiveness of lurasidone in both adult and adolescent population with schizophrenia in China in real-world clinical settings, supporting its use across diverse patient populations.

Trial registration: Shanghai Clinical Research Center for Mental Health (SCRC-MH) NCT04432688. URL: www.smhc.org.cn/.

Purpose: To investigate the clinical relevance of plasma plasminogen activator inhibitor-1 (PAI-1) in pediatric epilepsy, focusing on its associations with seizure severities and therapeutic outcomes.

Methods: We conducted a prospective cohort study to compare the plasma PAI-1 levels quantified by ELISA across children with active epilepsy, seizure-free patients, and healthy controls. Furthermore, subgroup analyses were conducted to assess the impact of AED treatment or long-term drug response to the plasma PAI-1 levels.

Results: PAI-1 levels were 2.1-fold higher in the seizure group than in the control group (p < 0.0001), and 1.3-fold higher than in remission patients (p < 0.0001). No significant difference was observed between the anti-epileptic drug-treated and untreated subgroups (p = 0.0689). Baseline PAI-1 levels predicted 12-month pharmaco-responses, with pharmaco-resistant patients showing 12% higher PAI-1 concentrations than responders (p = 0.0234).

Conclusion: Our findings establish plasma PAI-1 as a promising biomarker for identifying children at high risk for pharmaco-resistant epilepsy, thereby addressing a high-burden condition. The persistence of PAI-1 elevation hints at underlying inflammatory or synaptic pathologies that may be novel therapeutic targets beyond conventional AEDs.

Background: Dementia with Lewy bodies (DLB) exhibits a more aggressive progression and poorer prognosis than Alzheimer's disease (AD), yet clinical differentiation remains challenging. Dysregulated lipid metabolism, implicated in α-synuclein aggregation and neuroinflammation, may offer specific biomarkers for distinguishing DLB and AD.

Methods: This cross-sectional study implemented targeted lipidomic profiling to comprehensively characterize plasma lipidomes in a cohort comprising 50 DLB patients and 56 AD patients. Five machine learning algorithms - least absolute shrinkage and selection operator (LASSO) regression, support vector machine (SVM), random forest (RF), recursive feature elimination (RFE), and stepwise regression - were systematically applied for biomarker discovery.

Results: Significant alterations were observed in 7 lipid classes and 65 specific lipid species in DLB compared to AD. DLB plasma exhibited marked elevations in sphingolipids (total Cer, Hex1Cer, SM), lysophospholipids (LPC, LPE), phosphatidic acid (PA), alongside significant reductions in 45 triacylglycerol (TG) species compared to AD. Five machine learning algorithms consistently identified PA(16:0_16:0) and PA(16:0_20:4) as core discriminators between DLB and AD. The LASSO regression model demonstrated superior generalizability in the test set (AUC=0.916), selecting a 11-lipid panel dominated by PA species, alongside PC(18:0_20:4), ChE(22:4), Hex2Cer(d18:1_22:0), and PE species.

Conclusion: This first comprehensive targeted lipidomics study reveals distinct plasma lipid signatures differentiating DLB from AD, characterized by upregulated sphingolipids, lysophospholipids, and PA, and downregulated TG. Machine learning identified a 11-lipid biomarker panel, highlighting profound disturbances in glycerophospholipid metabolism. These findings provide novel molecular insights into DLB pathogenesis and a promising diagnostic tool for diagnosis.

Purpose: Schizophrenia is a burden on patients' health and finances and long-term antipsychotic treatment is required; treatment response differs among patients. This study aims to leverage data from Chinese hospitals to develop a machine learning (ML) model that predicts antipsychotic treatment efficacy in patients with schizophrenia and to conduct a payer-perspective cost-effectiveness analysis to inform clinical practice.

Patients and methods: This single-center, real-world retrospective cohort study included 834 patients with schizophrenia from a Chinese hospital. Eight models were constructed using ML and performance was assessed. The model with highest accuracy was determined based on the area under the receiver operating characteristic curve (AUC). We used the Shapley Additive Explanations (SHAP) values to determine the relative importance of each factor. Cost-effectiveness and incremental cost-effectiveness analyses were performed to assess cost-effectiveness of various treatments. A univariate sensitivity analysis was also conducted to validate the results.

Results: The top 10 strongly correlated variables, identified through the Boruta algorithm, were selected for in-depth analysis to construct the model. GBM demonstrates the highest performance following a comprehensive evaluation. On the independent test set, our model achieved an AUC of 0.879 (95% CI: 0.833-0.924), an accuracy of 0.836, and a recall of 0.823. Based on this model, we developed and made publicly available an online prediction calculator to assist in clinical decision-making. Among all the treatment regimens, risperidone was the most cost-effective.

Conclusion: The GBM model and its online calculator predict the treatment efficacy for hospitalized schizophrenia patients, aiding doctors in tailoring personalised treatment strategies. Risperidone tablets exhibit the highest cost-effectiveness in treatment, guiding the optimization of treatment plans and cost reduction.

The comorbidity of depression and chronic insomnia is very common in clinical practice, and there is a complex and close relationship between these two diseases, yet its underlying mechanisms remain incompletely understood. In recent years, inflammatory cytokines have been widely studied as important etiological factors for depression comorbid with chronic insomnia. However, whether inflammation is a cause or consequence of this comorbidity remains debated.This review synthesizes emerging evidence to position inflammatory cytokines not merely as correlates, but as central drivers in the pathophysiological nexus of depression and chronic insomnia. It systematically expounds the inflammatory characteristics and clinical significance of patients with depression comorbid with chronic insomnia, with the aim of reviewing the evidence of the association between inflammatory cytokines and depression comorbid with chronic insomnia, and discussing the potential pathophysiological mechanisms that may explain this association, thereby providing a robust scientific foundation for the development of novel ant-inflammatory therapeutic strategies and precision medicine approaches for this challenging comorbidity.