Pub Date : 2026-01-19DOI: 10.1186/s12888-026-07772-0
LuLu Zhang, Deyuan Wu, Mingshan Ye, Liting Wang, Xiaohua Sun, Mingjin Luo, Haidong Song
{"title":"Prevalence and correlates of social dysfunction in community-dwelling patients with bipolar disorder: a cross-sectional study.","authors":"LuLu Zhang, Deyuan Wu, Mingshan Ye, Liting Wang, Xiaohua Sun, Mingjin Luo, Haidong Song","doi":"10.1186/s12888-026-07772-0","DOIUrl":"10.1186/s12888-026-07772-0","url":null,"abstract":"","PeriodicalId":9029,"journal":{"name":"BMC Psychiatry","volume":" ","pages":"156"},"PeriodicalIF":3.4,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12895624/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146003150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Perceived barriers to access mental health services for refugees and asylum seekers: a systematic review of qualitative studies.","authors":"Rebecca Thiel, Léon Gerardo Kreis, Silvia Schneider, Hans-Helmut König, Christian Brettschneider","doi":"10.1186/s12888-026-07802-x","DOIUrl":"10.1186/s12888-026-07802-x","url":null,"abstract":"","PeriodicalId":9029,"journal":{"name":"BMC Psychiatry","volume":" ","pages":"86"},"PeriodicalIF":3.4,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12849350/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146003158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-18DOI: 10.1186/s12888-025-07764-6
Anne Lang, Lina Riedl, Daniela Blank, Adele Brucks, David Goretzko, Nicolas Rüsch, Johannes Hamann, Peter Brieger
{"title":"Disclosure of mental illness towards employers during the return to work process after psychiatric hospitalization.","authors":"Anne Lang, Lina Riedl, Daniela Blank, Adele Brucks, David Goretzko, Nicolas Rüsch, Johannes Hamann, Peter Brieger","doi":"10.1186/s12888-025-07764-6","DOIUrl":"10.1186/s12888-025-07764-6","url":null,"abstract":"","PeriodicalId":9029,"journal":{"name":"BMC Psychiatry","volume":" ","pages":"84"},"PeriodicalIF":3.4,"publicationDate":"2026-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12849060/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145997325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-17DOI: 10.1186/s12888-026-07773-z
Ashlee J Vance, Clayton J Shuman, Sarah Bell, Anca Tilea, Stephanie V Hall, Anna Courant, Kara Zivin
{"title":"Association between perinatal mood and anxiety disorders treatment status and infant outcomes among privately insured.","authors":"Ashlee J Vance, Clayton J Shuman, Sarah Bell, Anca Tilea, Stephanie V Hall, Anna Courant, Kara Zivin","doi":"10.1186/s12888-026-07773-z","DOIUrl":"10.1186/s12888-026-07773-z","url":null,"abstract":"","PeriodicalId":9029,"journal":{"name":"BMC Psychiatry","volume":" ","pages":"149"},"PeriodicalIF":3.4,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12895805/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145994196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: Psychological resilience varies among major depressive disorder (MDD) patients, with some exhibiting high resilience. This challenges the notion of resilience as purely protective and suggests biological heterogeneity. Both resilience and MDD have been linked to metabolic alterations, but their independent and interactive effects remain unclear. This study aims to investigate how resilience and MDD jointly affect metabolic profiles, with a focus on identifying key metabolic and pathway alterations in high-resilience MDD patients compared to healthy controls, and exploring their potential for diagnostic biomarkers.
Methods: Targeted serum metabolomics using UPLC-MS/MS was conducted in MDD patients and healthy controls. Resilience was assessed via the Ego Resiliency Scale (ERS). Interaction effect analysis examined the main and interactive influences of resilience and MDD. Key metabolites in high-resilience MDD were identified by OPLS-DA and pathway enrichment. A logistic regression model with cross-validation assessed diagnostic accuracy in training and test sets.
Results: A total of 271 participants were enrolled, and about one-third of MDD patients exhibited high resilience. The MDD×resilience interaction was not significant, whereas MDD showed a significant main effect on metabolite levels. Five key metabolites were identified in high-resilience individuals, with arginine, methionine, and kynurenine downregulated and threonic and erythronic acids upregulated in the high-resilience MDD group. The diagnostic model achieved an area under the curve (AUC) of 0.811 in the test set.
Conclusions: MDD status-rather than psychologically resilience-was the primary driver of serum metabolic variation. In high-resilience individuals, alterations converged on amino acid pathways (driven by lower arginine, methionine, kynurenine) and pentose-glucuronate axis (with higher threonic and erythronic acids), the latter is potentially linked to redox imbalance. These features may provide novel insights into depression-related metabolic dysregulation.
{"title":"Metabolic profiling in major depressive disorder with high psychological resilience: changes in amino acid and carbohydrate metabolism.","authors":"Runnan Yang, Xi Chen, Guifeng Tan, Jingyi Yang, Jiayu Du, Jing Li, Wenjing Li, Huaibing Wang, Hongru Zhu, Minlan Yuan, Wei Zhang","doi":"10.1186/s12888-026-07798-4","DOIUrl":"10.1186/s12888-026-07798-4","url":null,"abstract":"<p><strong>Background: </strong>Psychological resilience varies among major depressive disorder (MDD) patients, with some exhibiting high resilience. This challenges the notion of resilience as purely protective and suggests biological heterogeneity. Both resilience and MDD have been linked to metabolic alterations, but their independent and interactive effects remain unclear. This study aims to investigate how resilience and MDD jointly affect metabolic profiles, with a focus on identifying key metabolic and pathway alterations in high-resilience MDD patients compared to healthy controls, and exploring their potential for diagnostic biomarkers.</p><p><strong>Methods: </strong>Targeted serum metabolomics using UPLC-MS/MS was conducted in MDD patients and healthy controls. Resilience was assessed via the Ego Resiliency Scale (ERS). Interaction effect analysis examined the main and interactive influences of resilience and MDD. Key metabolites in high-resilience MDD were identified by OPLS-DA and pathway enrichment. A logistic regression model with cross-validation assessed diagnostic accuracy in training and test sets.</p><p><strong>Results: </strong>A total of 271 participants were enrolled, and about one-third of MDD patients exhibited high resilience. The MDD×resilience interaction was not significant, whereas MDD showed a significant main effect on metabolite levels. Five key metabolites were identified in high-resilience individuals, with arginine, methionine, and kynurenine downregulated and threonic and erythronic acids upregulated in the high-resilience MDD group. The diagnostic model achieved an area under the curve (AUC) of 0.811 in the test set.</p><p><strong>Conclusions: </strong>MDD status-rather than psychologically resilience-was the primary driver of serum metabolic variation. In high-resilience individuals, alterations converged on amino acid pathways (driven by lower arginine, methionine, kynurenine) and pentose-glucuronate axis (with higher threonic and erythronic acids), the latter is potentially linked to redox imbalance. These features may provide novel insights into depression-related metabolic dysregulation.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9029,"journal":{"name":"BMC Psychiatry","volume":" ","pages":"151"},"PeriodicalIF":3.4,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12895811/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145988214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p><strong>Background: </strong>Inflammation is implicated in the elevated risk of depressive disorder following myocardial infarction (MI), with platelets serving as a key link between thrombosis, inflammation, and depression. Although the systemic immune-inflammation index (SII), platelet count (PLT), and mean platelet volume (MPV) are readily accessible hematological parameters, their associations with post-MI depressive symptoms remain underexplored. This study investigates these relationships in MI survivors, augmented by machine learning (ML) and SHapley Additive exPlanations (SHAP) analysis for enhanced predictive insights.</p><p><strong>Methods: </strong>This cross-sectional study utilized data from 1,352 adults with self-reported MI history in the National Health and Nutrition Examination Survey (NHANES) 2009-2020. Multivariable logistic regression, subgroup analyses, dose-response curves, and sensitivity analyses were conducted to assess independent associations between depressive symptoms (Patient Health Questionnaire-9 score ≥ 5) and log<sub>2</sub>-transformed SII, PLT, and MPV. Additionally, 14 supervised ML algorithms were benchmarked using 5-fold cross-validation to predict depressive symptoms, with SHAP applied to the top-performing model for feature interpretability.</p><p><strong>Results: </strong>In multivariable regression, log<sub>2</sub>SII (OR = 1.22, 95% CI = 1.06-1.41, P = 0.0069) and log<sub>2</sub>PLT (OR = 1.78, 95% CI = 1.30-2.43, P = 0.0003) showed positive associations with depressive symptoms, while log<sub>2</sub>MPV did not (OR = 0.53, 95% CI = 0.24-1.17, P = 0.1150). Subgroup analyses revealed robust associations for log<sub>2</sub>SII except in females and BMI < 25 kg/m² groups, with no significant interactions for log<sub>2</sub>PLT or log<sub>2</sub>MPV across demographics or comorbidities. Dose-response curves indicated positive correlations with log<sub>2</sub>SII and log<sub>2</sub>PLT, and an inverted U-shaped relationship with log<sub>2</sub>MPV (inflection point: 3.04). Sensitivity analysis confirmed that the results of this study were robust either after using the methods of multiple imputation or excluding stroke participants. We also observed a strong association of log<sub>2</sub>SII and log<sub>2</sub>PLT with trouble sleeping, feeling tired and poor appetite, of log<sub>2</sub>MPV with poor appetite. ML benchmarking identified Random Forest as optimal (AUC = 0.779, R² = 0.229, RMSE = 0.424), outperforming other models. SHAP analysis ranked age (15.8% impact) and log<sub>2</sub>PLT as the top predictors, with higher values of these factors being associated with an increased likelihood of depressive symptoms, thereby reinforcing the interactions between inflammation and platelets.</p><p><strong>Conclusions: </strong>In a nationally representative U.S. sample, elevated log<sub>2</sub>SII and log<sub>2</sub>PLT are independently associated with depressive symptoms in MI survivors, with an inverted U-
背景：炎症与心肌梗死（MI）后抑郁障碍的风险升高有关，血小板在血栓形成、炎症和抑郁之间起关键作用。尽管全身免疫炎症指数（SII）、血小板计数（PLT）和平均血小板体积（MPV）是容易获得的血液学参数，但它们与心肌梗死后抑郁症状的关系仍未得到充分探讨。本研究通过机器学习（ML）和SHapley加性解释（SHAP）分析来增强预测洞察力，调查了心肌梗死幸存者的这些关系。方法：本横断面研究利用了2009-2020年国家健康与营养调查（NHANES）中1,352名自报心肌梗死史的成年人的数据。采用多变量logistic回归、亚组分析、剂量-反应曲线和敏感性分析来评估抑郁症状（患者健康问卷-9评分≥5）与log2转化的SII、PLT和MPV之间的独立关联。此外，使用5倍交叉验证对14种监督ML算法进行基准测试，以预测抑郁症状，并将SHAP应用于表现最佳的模型以获得特征可解释性。结果：在多变量回归中，log2SII （OR = 1.22, 95% CI = 1.06-1.41, P = 0.0069）和log2PLT （OR = 1.78, 95% CI = 1.30-2.43, P = 0.0003）与抑郁症状呈正相关，而log2MPV与抑郁症状无正相关（OR = 0.53, 95% CI = 0.24-1.17, P = 0.1150）。亚组分析显示，除女性外，log2SII与BMI 2PLT或log2MPV在人口统计学或合并症中的相关性很强。剂量-反应曲线与log2SII和log2PLT呈正相关，与log2MPV呈倒u型关系（拐点为3.04）。敏感性分析证实，无论采用多重归算方法还是排除卒中参与者，本研究的结果都是稳健的。我们还观察到log2SII和log2PLT与睡眠困难、疲劳和食欲不振密切相关，log2MPV与食欲不振密切相关。ML基准测试将Random Forest识别为最优模型（AUC = 0.779, R²= 0.229,RMSE = 0.424），优于其他模型。SHAP分析将年龄（15.8%的影响）和log2PLT列为最重要的预测因素，这些因素的较高值与抑郁症状的可能性增加有关，从而加强了炎症和血小板之间的相互作用。结论：在具有全国代表性的美国样本中，log2SII和log2PLT升高与心肌梗死幸存者的抑郁症状独立相关，log2MPV呈倒u型曲线。ML和SHAP的整合证实并完善了从传统回归中收集到的预测性见解，强调年龄和血小板动态是关键驱动因素，并支持对肥胖或中年心肌梗死幸存者等易感亚群进行有针对性的筛查。临床试验号：不适用。
{"title":"Association of depressive symptoms with systemic immune-inflammation index and platelet parameters among survivors of myocardial infarction: a cross-sectional NHANES study enhanced by machine learning and SHAP analysis.","authors":"Zheyi Wang, Qiong Xu, Shencun Yu, Jiao Sun, Jian Lv, Qiaoyi Huang, Chen Huang, Yize Sun","doi":"10.1186/s12888-025-07763-7","DOIUrl":"10.1186/s12888-025-07763-7","url":null,"abstract":"<p><strong>Background: </strong>Inflammation is implicated in the elevated risk of depressive disorder following myocardial infarction (MI), with platelets serving as a key link between thrombosis, inflammation, and depression. Although the systemic immune-inflammation index (SII), platelet count (PLT), and mean platelet volume (MPV) are readily accessible hematological parameters, their associations with post-MI depressive symptoms remain underexplored. This study investigates these relationships in MI survivors, augmented by machine learning (ML) and SHapley Additive exPlanations (SHAP) analysis for enhanced predictive insights.</p><p><strong>Methods: </strong>This cross-sectional study utilized data from 1,352 adults with self-reported MI history in the National Health and Nutrition Examination Survey (NHANES) 2009-2020. Multivariable logistic regression, subgroup analyses, dose-response curves, and sensitivity analyses were conducted to assess independent associations between depressive symptoms (Patient Health Questionnaire-9 score ≥ 5) and log<sub>2</sub>-transformed SII, PLT, and MPV. Additionally, 14 supervised ML algorithms were benchmarked using 5-fold cross-validation to predict depressive symptoms, with SHAP applied to the top-performing model for feature interpretability.</p><p><strong>Results: </strong>In multivariable regression, log<sub>2</sub>SII (OR = 1.22, 95% CI = 1.06-1.41, P = 0.0069) and log<sub>2</sub>PLT (OR = 1.78, 95% CI = 1.30-2.43, P = 0.0003) showed positive associations with depressive symptoms, while log<sub>2</sub>MPV did not (OR = 0.53, 95% CI = 0.24-1.17, P = 0.1150). Subgroup analyses revealed robust associations for log<sub>2</sub>SII except in females and BMI < 25 kg/m² groups, with no significant interactions for log<sub>2</sub>PLT or log<sub>2</sub>MPV across demographics or comorbidities. Dose-response curves indicated positive correlations with log<sub>2</sub>SII and log<sub>2</sub>PLT, and an inverted U-shaped relationship with log<sub>2</sub>MPV (inflection point: 3.04). Sensitivity analysis confirmed that the results of this study were robust either after using the methods of multiple imputation or excluding stroke participants. We also observed a strong association of log<sub>2</sub>SII and log<sub>2</sub>PLT with trouble sleeping, feeling tired and poor appetite, of log<sub>2</sub>MPV with poor appetite. ML benchmarking identified Random Forest as optimal (AUC = 0.779, R² = 0.229, RMSE = 0.424), outperforming other models. SHAP analysis ranked age (15.8% impact) and log<sub>2</sub>PLT as the top predictors, with higher values of these factors being associated with an increased likelihood of depressive symptoms, thereby reinforcing the interactions between inflammation and platelets.</p><p><strong>Conclusions: </strong>In a nationally representative U.S. sample, elevated log<sub>2</sub>SII and log<sub>2</sub>PLT are independently associated with depressive symptoms in MI survivors, with an inverted U-","PeriodicalId":9029,"journal":{"name":"BMC Psychiatry","volume":" ","pages":"148"},"PeriodicalIF":3.4,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12896014/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145994246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-17DOI: 10.1186/s12888-025-07765-5
Keneilwe Molebatsi, Manasi Kumar, Tsholofelo Lobakeng, Bonginkosi Chiliza, Lauren C Ng
{"title":"Trauma descriptions and lived experiences: a phenomenological exploratory study among patients with severe mental illness in Botswana.","authors":"Keneilwe Molebatsi, Manasi Kumar, Tsholofelo Lobakeng, Bonginkosi Chiliza, Lauren C Ng","doi":"10.1186/s12888-025-07765-5","DOIUrl":"10.1186/s12888-025-07765-5","url":null,"abstract":"","PeriodicalId":9029,"journal":{"name":"BMC Psychiatry","volume":" ","pages":"147"},"PeriodicalIF":3.4,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12896076/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145994296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-16DOI: 10.1186/s12888-026-07778-8
Xiaowen Lu, Kaiheng Zhu, Haoxue Wang, Zhen Xiang, Shuai Zhao, Rundong Liu, Jun Tang, Ranran Song
{"title":"The effect of reading problems on emotional and behavioral problems in children: the chain mediating role of parenting stress and parental anxiety/depressive symptoms.","authors":"Xiaowen Lu, Kaiheng Zhu, Haoxue Wang, Zhen Xiang, Shuai Zhao, Rundong Liu, Jun Tang, Ranran Song","doi":"10.1186/s12888-026-07778-8","DOIUrl":"10.1186/s12888-026-07778-8","url":null,"abstract":"","PeriodicalId":9029,"journal":{"name":"BMC Psychiatry","volume":" ","pages":"146"},"PeriodicalIF":3.4,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12896328/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145988126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号