Pub Date : 2024-09-18DOI: 10.3389/fpsyt.2024.1433990
Huifeng Zhang, Ying Xu, Yaying Xu
BackgroundMany studies worldwide have reported the association between mental health and blood pressure, but the results are mixed, and even contradictory. We aim to investigate the relationship between systolic and diastolic blood pressure and depression in the entire US population.MethodsThis study analyzed cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018. All adults completed 3-4 blood pressure measurements after sitting quietly for 5 minutes. Depression was diagnosed based on the Patient Health Questionnaire (PHQ-9), with a score ≥10 defined as depression. Weighted logistic regression and restricted cubic splines (RCS) were used to assess the relationship between blood pressure and depression. Two-piecewise linear regression was used to determine the inflection point. Additionally, subgroup analyses and interaction tests were conducted to identify potential subgroups. Finally, two sensitivity analyses were conducted.ResultsA total of 26,581 American adults were included, with a mean age of 47.2 years, of whom 13,354 (49.54%) were male; 2,261 individuals were defined as depressed, with a weighted prevalence of 7.41%. All participants’ mean systolic blood pressure (SBP) was 121.7 mmHg, and the mean diastolic blood pressure (DBP) was 70.9 mmHg. RCS showed a nonlinear association between SBP and depression, while DBP showed a positive linear association with depression. Two-piecewise linear regression showed that the inflection point of the association between SBP and depression was 129.7 mmHg. Weighted logistic regression showed that after fully adjusting for depression-related risk factors, there was a significant positive correlation between per 10 mmHg increase in DBP and depression (OR: 1.06, 95% CI: 1.00-1.12, P=0.04); however, only on the left side of the inflection point, SBP tended to decrease the odds of depression (P =0.09). Furthermore, interaction analysis showed that the association between DBP and depression was significantly stronger in cancer patients (P for interaction=0.02); on the left side of the inflection point (<129.7 mmHg), current smokers also significantly interacted with SBP (P for interaction=0.018). Finally, two sensitivity analyses also supported our findings.ConclusionIn the adult population of the United States, there is a positive linear association between DBP and depression, while the association between SBP and depression exhibits a significant threshold effect, maintaining SBP at 129.7 mmHg is associated with the lowest prevalence of depression.
{"title":"Association of diastolic and systolic blood pressure with depression: a cross-sectional study from NHANES 2005-2018","authors":"Huifeng Zhang, Ying Xu, Yaying Xu","doi":"10.3389/fpsyt.2024.1433990","DOIUrl":"https://doi.org/10.3389/fpsyt.2024.1433990","url":null,"abstract":"BackgroundMany studies worldwide have reported the association between mental health and blood pressure, but the results are mixed, and even contradictory. We aim to investigate the relationship between systolic and diastolic blood pressure and depression in the entire US population.MethodsThis study analyzed cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018. All adults completed 3-4 blood pressure measurements after sitting quietly for 5 minutes. Depression was diagnosed based on the Patient Health Questionnaire (PHQ-9), with a score ≥10 defined as depression. Weighted logistic regression and restricted cubic splines (RCS) were used to assess the relationship between blood pressure and depression. Two-piecewise linear regression was used to determine the inflection point. Additionally, subgroup analyses and interaction tests were conducted to identify potential subgroups. Finally, two sensitivity analyses were conducted.ResultsA total of 26,581 American adults were included, with a mean age of 47.2 years, of whom 13,354 (49.54%) were male; 2,261 individuals were defined as depressed, with a weighted prevalence of 7.41%. All participants’ mean systolic blood pressure (SBP) was 121.7 mmHg, and the mean diastolic blood pressure (DBP) was 70.9 mmHg. RCS showed a nonlinear association between SBP and depression, while DBP showed a positive linear association with depression. Two-piecewise linear regression showed that the inflection point of the association between SBP and depression was 129.7 mmHg. Weighted logistic regression showed that after fully adjusting for depression-related risk factors, there was a significant positive correlation between per 10 mmHg increase in DBP and depression (OR: 1.06, 95% CI: 1.00-1.12, P=0.04); however, only on the left side of the inflection point, SBP tended to decrease the odds of depression (P =0.09). Furthermore, interaction analysis showed that the association between DBP and depression was significantly stronger in cancer patients (P for interaction=0.02); on the left side of the inflection point (&lt;129.7 mmHg), current smokers also significantly interacted with SBP (P for interaction=0.018). Finally, two sensitivity analyses also supported our findings.ConclusionIn the adult population of the United States, there is a positive linear association between DBP and depression, while the association between SBP and depression exhibits a significant threshold effect, maintaining SBP at 129.7 mmHg is associated with the lowest prevalence of depression.","PeriodicalId":12605,"journal":{"name":"Frontiers in Psychiatry","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142268972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-18DOI: 10.3389/fpsyt.2024.1422020
Lijuan Liang, Yang Wang, Hui Ma, Ran Zhang, Rongxun Liu, Rongxin Zhu, Zhiguo Zheng, Xizhe Zhang, Fei Wang
BackgroundPrevious studies have classified major depression and healthy control groups based on vocal acoustic features, but the classification accuracy needs to be improved. Therefore, this study utilized deep learning methods to construct classification and prediction models for major depression and healthy control groups.Methods120 participants aged 16–25 participated in this study, included 64 MDD group and 56 HC group. We used the Covarep open-source algorithm to extract a total of 1200 high-level statistical functions for each sample. In addition, we used Python for correlation analysis, and neural network to establish the model to distinguish whether participants experienced depression, predict the total depression score, and evaluate the effectiveness of the classification and prediction model.ResultsThe classification modelling of the major depression and the healthy control groups by relevant and significant vocal acoustic features was 0.90, and the Receiver Operating Characteristic (ROC) curves analysis results showed that the classification accuracy was 84.16%, the sensitivity was 95.38%, and the specificity was 70.9%. The depression prediction model of speech characteristics showed that the predicted score was closely related to the total score of 17 items of the Hamilton Depression Scale(HAMD-17) (r=0.687, P<0.01); and the Mean Absolute Error(MAE) between the model’s predicted score and total HAMD-17 score was 4.51.LimitationThis study’s results may have been influenced by anxiety comorbidities.ConclusionThe vocal acoustic features can not only effectively classify the major depression and the healthy control groups, but also accurately predict the severity of depressive symptoms.
{"title":"Enhanced classification and severity prediction of major depressive disorder using acoustic features and machine learning","authors":"Lijuan Liang, Yang Wang, Hui Ma, Ran Zhang, Rongxun Liu, Rongxin Zhu, Zhiguo Zheng, Xizhe Zhang, Fei Wang","doi":"10.3389/fpsyt.2024.1422020","DOIUrl":"https://doi.org/10.3389/fpsyt.2024.1422020","url":null,"abstract":"BackgroundPrevious studies have classified major depression and healthy control groups based on vocal acoustic features, but the classification accuracy needs to be improved. Therefore, this study utilized deep learning methods to construct classification and prediction models for major depression and healthy control groups.Methods120 participants aged 16–25 participated in this study, included 64 MDD group and 56 HC group. We used the Covarep open-source algorithm to extract a total of 1200 high-level statistical functions for each sample. In addition, we used Python for correlation analysis, and neural network to establish the model to distinguish whether participants experienced depression, predict the total depression score, and evaluate the effectiveness of the classification and prediction model.ResultsThe classification modelling of the major depression and the healthy control groups by relevant and significant vocal acoustic features was 0.90, and the Receiver Operating Characteristic (ROC) curves analysis results showed that the classification accuracy was 84.16%, the sensitivity was 95.38%, and the specificity was 70.9%. The depression prediction model of speech characteristics showed that the predicted score was closely related to the total score of 17 items of the Hamilton Depression Scale(HAMD-17) (r=0.687, P&lt;0.01); and the Mean Absolute Error(MAE) between the model’s predicted score and total HAMD-17 score was 4.51.LimitationThis study’s results may have been influenced by anxiety comorbidities.ConclusionThe vocal acoustic features can not only effectively classify the major depression and the healthy control groups, but also accurately predict the severity of depressive symptoms.","PeriodicalId":12605,"journal":{"name":"Frontiers in Psychiatry","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142268977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-18DOI: 10.3389/fpsyt.2024.1356037
Bright Opoku Ahinkorah, Christa Lam-Cassettari, James Rufus John, Valsamma Eapen
IntroductionUnderstanding associations between psychosocial development in early childhood and formal diagnosis of neurodevelopmental disorders (NDDs) in adolescence is critical for early identification and for tailoring interventions and support. This study investigated whether the Strengths and Difficulties Questionnaire (SDQ) scores in early childhood (4-5 years) predict mental health (MH) problems as evidenced by SDQ scores and formal diagnosis of NDDs in adolescence (16-17 years).MethodsThis study analysed data from a sample of 4968 children and adolescents using data from the Longitudinal Study of Australian Children. We used hierarchical regression models to determine the association between SDQ subscales and total scores at ages 4-5 years (primary exposure) and total SDQ scores and NDD diagnoses at ages 16-17 years (outcomes) whilst controlling for sociodemographic risk factors.ResultsEach unit increase in SDQ score at age 4-5 led to a rise in SDQ scores at age 16-17. Autism and ADHD diagnoses, female gender, lower maternal education, and financial hardship were associated with higher SDQ scores at age 16-17. Furthermore, parent reported SDQ at age 4-5 was linked to higher likelihoods of formal diagnoses of ADHD, autism, and ADHD/autism at age 16-17. Additionally, social determinants of health such as female gender, culturally and linguistically diverse (CALD) backgrounds, and financial hardship were associated with increased odds of ADHD, autism, and ADHD/autism diagnoses at age 16-17.ConclusionOur findings highlight the opportunity for early identification of transdiagnostic developmental and MH issues in the preschool period. Findings also emphasise the critical role of social determinants of health in the longitudinal trajectory of MH and NDDs and highlight the need for implementing early supports for improving peer relations and behavioural support strategies. If coupled with wrap around social care, early support strategies can enhance MH and wellbeing in adolescence and beyond.
{"title":"Prospective associations between early childhood mental health concerns and formal diagnosis of neurodevelopmental disorders in adolescence","authors":"Bright Opoku Ahinkorah, Christa Lam-Cassettari, James Rufus John, Valsamma Eapen","doi":"10.3389/fpsyt.2024.1356037","DOIUrl":"https://doi.org/10.3389/fpsyt.2024.1356037","url":null,"abstract":"IntroductionUnderstanding associations between psychosocial development in early childhood and formal diagnosis of neurodevelopmental disorders (NDDs) in adolescence is critical for early identification and for tailoring interventions and support. This study investigated whether the Strengths and Difficulties Questionnaire (SDQ) scores in early childhood (4-5 years) predict mental health (MH) problems as evidenced by SDQ scores and formal diagnosis of NDDs in adolescence (16-17 years).MethodsThis study analysed data from a sample of 4968 children and adolescents using data from the Longitudinal Study of Australian Children. We used hierarchical regression models to determine the association between SDQ subscales and total scores at ages 4-5 years (primary exposure) and total SDQ scores and NDD diagnoses at ages 16-17 years (outcomes) whilst controlling for sociodemographic risk factors.ResultsEach unit increase in SDQ score at age 4-5 led to a rise in SDQ scores at age 16-17. Autism and ADHD diagnoses, female gender, lower maternal education, and financial hardship were associated with higher SDQ scores at age 16-17. Furthermore, parent reported SDQ at age 4-5 was linked to higher likelihoods of formal diagnoses of ADHD, autism, and ADHD/autism at age 16-17. Additionally, social determinants of health such as female gender, culturally and linguistically diverse (CALD) backgrounds, and financial hardship were associated with increased odds of ADHD, autism, and ADHD/autism diagnoses at age 16-17.ConclusionOur findings highlight the opportunity for early identification of transdiagnostic developmental and MH issues in the preschool period. Findings also emphasise the critical role of social determinants of health in the longitudinal trajectory of MH and NDDs and highlight the need for implementing early supports for improving peer relations and behavioural support strategies. If coupled with wrap around social care, early support strategies can enhance MH and wellbeing in adolescence and beyond.","PeriodicalId":12605,"journal":{"name":"Frontiers in Psychiatry","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142253001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-18DOI: 10.3389/fpsyt.2024.1429437
Wei Yang, Kun Lian, Jing Ye, Yuqi Cheng, Xiufeng Xu
BackgroundMitochondrial dysfunction is an important factor in the pathogenesis of schizophrenia. However, the relationship between mitophagy and schizophrenia remains to be elucidated.MethodsSingle-cell RNA sequencing datasets of peripheral blood and brain organoids from SCZ patients and healthy controls were retrieved. Mitophagy-related genes that were differentially expressed between the two groups were screened. The diagnostic model based on key mitophagy genes was constructed using two machine learning methods, and the relationship between mitophagy and immune cells was analyzed. Single-cell RNA sequencing data of brain organoids was used to calculate the mitophagy score (Mitoscore).ResultsWe found 7 key mitophagy genes to construct a diagnostic model. The mitophagy genes were related to the infiltration of neutrophils, activated dendritic cells, resting NK cells, regulatory T cells, resting memory T cells, and CD8 T cells. In addition, we identified 12 cell clusters based on the Mitoscore, and the most abundant neurons were further divided into three subgroups. Results at the single-cell level showed that Mitohigh_Neuron established a novel interaction with endothelial cells via SPP1 signaling pathway, suggesting their distinct roles in SCZ pathogenesis.ConclusionWe identified a mitophagy signature for schizophrenia that provides new insights into disease pathogenesis and new possibilities for its diagnosis and treatment.
{"title":"Analyses of single-cell and bulk RNA sequencing combined with machine learning reveal the expression patterns of disrupted mitophagy in schizophrenia","authors":"Wei Yang, Kun Lian, Jing Ye, Yuqi Cheng, Xiufeng Xu","doi":"10.3389/fpsyt.2024.1429437","DOIUrl":"https://doi.org/10.3389/fpsyt.2024.1429437","url":null,"abstract":"BackgroundMitochondrial dysfunction is an important factor in the pathogenesis of schizophrenia. However, the relationship between mitophagy and schizophrenia remains to be elucidated.MethodsSingle-cell RNA sequencing datasets of peripheral blood and brain organoids from SCZ patients and healthy controls were retrieved. Mitophagy-related genes that were differentially expressed between the two groups were screened. The diagnostic model based on key mitophagy genes was constructed using two machine learning methods, and the relationship between mitophagy and immune cells was analyzed. Single-cell RNA sequencing data of brain organoids was used to calculate the mitophagy score (Mitoscore).ResultsWe found 7 key mitophagy genes to construct a diagnostic model. The mitophagy genes were related to the infiltration of neutrophils, activated dendritic cells, resting NK cells, regulatory T cells, resting memory T cells, and CD8 T cells. In addition, we identified 12 cell clusters based on the Mitoscore, and the most abundant neurons were further divided into three subgroups. Results at the single-cell level showed that Mitohigh_Neuron established a novel interaction with endothelial cells via SPP1 signaling pathway, suggesting their distinct roles in SCZ pathogenesis.ConclusionWe identified a mitophagy signature for schizophrenia that provides new insights into disease pathogenesis and new possibilities for its diagnosis and treatment.","PeriodicalId":12605,"journal":{"name":"Frontiers in Psychiatry","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142268974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ObjectivesThis study aimed to explore the association between bowel movement disorders and depression in adults.MethodA cross-sectional study was conducted using data from the National Health and Nutritional Examination Survey (NHANES), 2005–2010. Depression, constipation, diarrhea, and fecal incontinence were self-reported via questionnaires. Weighted logistic regression and subgroup analyses were performed to explore the association between bowel movement disorders and the risk of depression. Restricted cubic spline (RCS) was also conducted to investigate the association between bowel movements disorder and depression.ResultsA total of 13,820 participants were collected. Compared to the participants with normal bowel movements, the full-adjusted depression model ORs for constipation and diarrhea were 2.28 (95%CI,1.78-2.92), 1.75 (95%CI,1.31-2.31), respectively. Any kind of bowel leakage were associated with depression. The RCS showed the possible nonlinear association between bowel movement frequency/stool shape and depression.ConclusionsConstipation, diarrhea, and bowel leakage are associated with an increased risk of depression.
{"title":"Association between bowel movement disorders and depressive symptoms: a cross-sectional study","authors":"Linyue Wang, Maosheng Tian, Hongyuan Sun, Jihua Gao, Wenyue Qi, Jiancheng Xu, Yongkang An, Wencong Xu","doi":"10.3389/fpsyt.2024.1449948","DOIUrl":"https://doi.org/10.3389/fpsyt.2024.1449948","url":null,"abstract":"ObjectivesThis study aimed to explore the association between bowel movement disorders and depression in adults.MethodA cross-sectional study was conducted using data from the National Health and Nutritional Examination Survey (NHANES), 2005–2010. Depression, constipation, diarrhea, and fecal incontinence were self-reported via questionnaires. Weighted logistic regression and subgroup analyses were performed to explore the association between bowel movement disorders and the risk of depression. Restricted cubic spline (RCS) was also conducted to investigate the association between bowel movements disorder and depression.ResultsA total of 13,820 participants were collected. Compared to the participants with normal bowel movements, the full-adjusted depression model ORs for constipation and diarrhea were 2.28 (95%CI,1.78-2.92), 1.75 (95%CI,1.31-2.31), respectively. Any kind of bowel leakage were associated with depression. The RCS showed the possible nonlinear association between bowel movement frequency/stool shape and depression.ConclusionsConstipation, diarrhea, and bowel leakage are associated with an increased risk of depression.","PeriodicalId":12605,"journal":{"name":"Frontiers in Psychiatry","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142252999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-18DOI: 10.3389/fpsyt.2024.1425552
Pavel Křenek, Eliška Bartečková, Markéta Makarová, Tomáš Pompa, Jana Fialová Kučerová, Jan Kučera, Alena Damborská, Jana Hořínková, Julie Bienertová-Vašků
ObjectivesThis study aimed to explore the relationship between plasma proteome and the clinical features of Major Depressive Disorder (MDD) during treatment of acute episode.MethodsIn this longitudinal observational study, 26 patients hospitalized for moderate to severe MDD were analyzed. The study utilized Liquid Chromatography with Tandem Mass Spectrometry (LC-MS/MS) alongside clinical metrics, including symptomatology derived from the Montgomery-Åsberg Depression Rating Scale (MADRS). Plasma protein analysis was conducted at the onset of acute depression and 6 weeks into treatment. Analytical methods comprised of Linear Models for Microarray Data (LIMMA), Weighted Correlation Network Analysis (WGCNA), Generalized Linear Models, Random Forests, and The Database for Annotation, Visualization and Integrated Discovery (DAVID).ResultsFive distinct plasma protein modules were identified, correlating with specific biological processes, and uniquely associated with symptom presentation, the disorder’s trajectory, and treatment response. A module rich in proteins related to adaptive immunity was correlated with the manifestation of somatic syndrome, treatment response, and inversely associated with achieving remission. A module associated with cell adhesion was linked to affective symptoms and avolition, and played a role in the initial episodes and treatment response. Another module, characterized by proteins involved in blood coagulation and lipid transport, exhibited negative correlations with a variety of MDD symptoms and was predominantly associated with the manifestation of psychotic symptoms.ConclusionThis research points to a complex interplay between the plasma proteome and MDD’s clinical presentation, suggesting that somatic, affective, and psychotic symptoms may represent distinct endophenotypic manifestations of MDD. These insights hold potential for advancing targeted therapeutic strategies and diagnostic tools.LimitationsThe study’s limited sample size and its naturalistic design, encompassing diverse treatment modalities, present methodological constraints. Furthermore, the analysis focused on peripheral blood proteins, with potential implications for interpretability.
{"title":"Correlating plasma protein profiles with symptomatology and treatment response in acute phase and early remission of major depressive disorder","authors":"Pavel Křenek, Eliška Bartečková, Markéta Makarová, Tomáš Pompa, Jana Fialová Kučerová, Jan Kučera, Alena Damborská, Jana Hořínková, Julie Bienertová-Vašků","doi":"10.3389/fpsyt.2024.1425552","DOIUrl":"https://doi.org/10.3389/fpsyt.2024.1425552","url":null,"abstract":"ObjectivesThis study aimed to explore the relationship between plasma proteome and the clinical features of Major Depressive Disorder (MDD) during treatment of acute episode.MethodsIn this longitudinal observational study, 26 patients hospitalized for moderate to severe MDD were analyzed. The study utilized Liquid Chromatography with Tandem Mass Spectrometry (LC-MS/MS) alongside clinical metrics, including symptomatology derived from the Montgomery-Åsberg Depression Rating Scale (MADRS). Plasma protein analysis was conducted at the onset of acute depression and 6 weeks into treatment. Analytical methods comprised of Linear Models for Microarray Data (LIMMA), Weighted Correlation Network Analysis (WGCNA), Generalized Linear Models, Random Forests, and The Database for Annotation, Visualization and Integrated Discovery (DAVID).ResultsFive distinct plasma protein modules were identified, correlating with specific biological processes, and uniquely associated with symptom presentation, the disorder’s trajectory, and treatment response. A module rich in proteins related to adaptive immunity was correlated with the manifestation of somatic syndrome, treatment response, and inversely associated with achieving remission. A module associated with cell adhesion was linked to affective symptoms and avolition, and played a role in the initial episodes and treatment response. Another module, characterized by proteins involved in blood coagulation and lipid transport, exhibited negative correlations with a variety of MDD symptoms and was predominantly associated with the manifestation of psychotic symptoms.ConclusionThis research points to a complex interplay between the plasma proteome and MDD’s clinical presentation, suggesting that somatic, affective, and psychotic symptoms may represent distinct endophenotypic manifestations of MDD. These insights hold potential for advancing targeted therapeutic strategies and diagnostic tools.LimitationsThe study’s limited sample size and its naturalistic design, encompassing diverse treatment modalities, present methodological constraints. Furthermore, the analysis focused on peripheral blood proteins, with potential implications for interpretability.","PeriodicalId":12605,"journal":{"name":"Frontiers in Psychiatry","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142252998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-18DOI: 10.3389/fpsyt.2024.1455247
Nicola Meda, Josephine Zammarrelli, Fabio Sambataro, Diego De Leo
BackgroundPeople in late adulthood die by suicide at the highest rate worldwide. However, there are still no tools to help predict the risk of death from suicide in old age. Here, we leveraged the Survey of Health, Ageing, and Retirement in Europe (SHARE) prospective dataset to train and test a machine learning model to identify predictors for suicide in late life.MethodsOf more than 16,000 deaths recorded, 74 were suicides. We matched 73 individuals who died by suicide with people who died by accident, according to sex (28.8% female in the total sample), age at death (67 ± 16.4 years), suicidal ideation (measured with the EURO-D scale), and the number of chronic illnesses. A random forest algorithm was trained on demographic data, physical health, depression, and cognitive functioning to extract essential variables for predicting death from suicide and then tested on the test set.ResultsThe random forest algorithm had an accuracy of 79% (95% CI 0.60-0.92, p = 0.002), a sensitivity of.80, and a specificity of.78. Among the variables contributing to the model performance, the three most important factors were how long the participant was ill before death, the frequency of contact with the next of kin and the number of offspring still alive.ConclusionsProspective clinical and social information can predict death from suicide with good accuracy in late adulthood. Most of the variables that surfaced as risk factors can be attributed to the construct of social connectedness, which has been shown to play a decisive role in suicide in late life.
{"title":"Late-life suicide: machine learning predictors from a large European longitudinal cohort","authors":"Nicola Meda, Josephine Zammarrelli, Fabio Sambataro, Diego De Leo","doi":"10.3389/fpsyt.2024.1455247","DOIUrl":"https://doi.org/10.3389/fpsyt.2024.1455247","url":null,"abstract":"BackgroundPeople in late adulthood die by suicide at the highest rate worldwide. However, there are still no tools to help predict the risk of death from suicide in old age. Here, we leveraged the Survey of Health, Ageing, and Retirement in Europe (SHARE) prospective dataset to train and test a machine learning model to identify predictors for suicide in late life.MethodsOf more than 16,000 deaths recorded, 74 were suicides. We matched 73 individuals who died by suicide with people who died by accident, according to sex (28.8% female in the total sample), age at death (67 ± 16.4 years), suicidal ideation (measured with the EURO-D scale), and the number of chronic illnesses. A random forest algorithm was trained on demographic data, physical health, depression, and cognitive functioning to extract essential variables for predicting death from suicide and then tested on the test set.ResultsThe random forest algorithm had an accuracy of 79% (95% CI 0.60-0.92, p = 0.002), a sensitivity of.80, and a specificity of.78. Among the variables contributing to the model performance, the three most important factors were how long the participant was ill before death, the frequency of contact with the next of kin and the number of offspring still alive.ConclusionsProspective clinical and social information can predict death from suicide with good accuracy in late adulthood. Most of the variables that surfaced as risk factors can be attributed to the construct of social connectedness, which has been shown to play a decisive role in suicide in late life.","PeriodicalId":12605,"journal":{"name":"Frontiers in Psychiatry","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142252997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-18DOI: 10.3389/fpsyt.2024.1448623
Jiahui Wang, Caifeng Gao, Cuiyuan Fu, Kun Li
Schizophrenia is a chronic, severe, and disabling mental disorder that significantly impacts individuals’ lives. Long-term treatment with antipsychotic drugs, coupled with the complications of the disease itself, increases the risk of dysphagia in patients. These disorders further heighten the likelihood of choking and asphyxia death among this population. This project aims to comprehensively review the pathological mechanisms behind dysphagia in schizophrenia, alongside proposing early screening and evaluation methods. It also suggests treatment recommendations to mitigate the risks and complications associated with dysphagia in these patients.
{"title":"Dysphagia in schizophrenia: pathological mechanisms and treatment recommendations","authors":"Jiahui Wang, Caifeng Gao, Cuiyuan Fu, Kun Li","doi":"10.3389/fpsyt.2024.1448623","DOIUrl":"https://doi.org/10.3389/fpsyt.2024.1448623","url":null,"abstract":"Schizophrenia is a chronic, severe, and disabling mental disorder that significantly impacts individuals’ lives. Long-term treatment with antipsychotic drugs, coupled with the complications of the disease itself, increases the risk of dysphagia in patients. These disorders further heighten the likelihood of choking and asphyxia death among this population. This project aims to comprehensively review the pathological mechanisms behind dysphagia in schizophrenia, alongside proposing early screening and evaluation methods. It also suggests treatment recommendations to mitigate the risks and complications associated with dysphagia in these patients.","PeriodicalId":12605,"journal":{"name":"Frontiers in Psychiatry","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
BackgroundPost-traumatic stress disorder (PTSD) is one of the most common mental health problems that military personnel encounter. It could be lifelong and affect every aspect of military personnel, including their mental and physical health, family and social interactions, and their work. However, in Ethiopia, the magnitude and its associated factors have not been well investigated.ObjectiveThis study aimed to determine the prevalence of PTSD and its associated factors among military personnel, who were admitted at the Northwest Command Level Three Military Hospital, Bahir Dar, Northwest, Ethiopia, 2022.MethodsAn institution-based cross-sectional study was conducted from 21 June to 21 July 2022, at the Northwest Command Level Three Military Hospital. A computer-generated simple random sampling technique was used to select a total of 627 participants. The 17-item Military Version Checklist was utilized to measure PTSD. The Patient Health Questionnaire, Brief Resilience Coping, and Critical War Zone Experience scale were utilized to measure depression, resilience, and combat exposure, respectively. Descriptive, bivariate, and multivariate binary logistic regressions with odds ratios and a 95% confidence interval were used. The level of significance of the association was determined at a p-value < 0.05.ResultsA total of 612 respondents participated, with a response rate of 97.6%. The prevalence of PTSD in this study was 21.9% (95% CI: 18.6, 25.2). In multivariable regression, female sex [adjusted odds ratio (AOR) = 2.3, 95% CI; 1.3, 3.87], combat personnel (AOR = 2.75, 95% CI; 1.44, 6.36), handling dead bodies (AOR = 2.5, 95% CI,1.24, 5.02), having 4–5 deployments (AOR = 2.94, 95% CI, 1.63, 5.32), having ≥6 deployments (AOR = 3.4, 95% CI, 1.95, 6.17), low resilience coping (AOR = 2.02, 95% CI; 1.16, 3.53), poor social support (AOR = 2.46, 95% CI, 1.39, 4.35), very high combat exposures (AOR = 4.8, 95% CI, 2.03, 11.93), and depression (AOR = 2.8, 95% CI, 1.68, 4.67) were significantly associated with PTSD.ConclusionPTSD is markedly prevalent among the Ethiopian military population, with key risk factors identified as being female, poor social support, low resilience coping skills, handling dead bodies, multiple deployments (four or more), high combat experiences, and depression. Healthcare professionals must prioritize the early diagnosis and intervention of PTSD in vulnerable groups of military personnel.
{"title":"Post-traumatic stress disorder among military personnel admitted at the Northwest Command Level Three Military Hospital, Bahir Dar, Ethiopia, 2022: an institution-based cross-sectional study","authors":"Assasahegn Tedla, Sintayehu Asnakew, Getasew Legas, Birhanu Mengist Munie, Minale Tareke, Micheal Beka","doi":"10.3389/fpsyt.2024.1410630","DOIUrl":"https://doi.org/10.3389/fpsyt.2024.1410630","url":null,"abstract":"BackgroundPost-traumatic stress disorder (PTSD) is one of the most common mental health problems that military personnel encounter. It could be lifelong and affect every aspect of military personnel, including their mental and physical health, family and social interactions, and their work. However, in Ethiopia, the magnitude and its associated factors have not been well investigated.ObjectiveThis study aimed to determine the prevalence of PTSD and its associated factors among military personnel, who were admitted at the Northwest Command Level Three Military Hospital, Bahir Dar, Northwest, Ethiopia, 2022.MethodsAn institution-based cross-sectional study was conducted from 21 June to 21 July 2022, at the Northwest Command Level Three Military Hospital. A computer-generated simple random sampling technique was used to select a total of 627 participants. The 17-item Military Version Checklist was utilized to measure PTSD. The Patient Health Questionnaire, Brief Resilience Coping, and Critical War Zone Experience scale were utilized to measure depression, resilience, and combat exposure, respectively. Descriptive, bivariate, and multivariate binary logistic regressions with odds ratios and a 95% confidence interval were used. The level of significance of the association was determined at a <jats:italic>p</jats:italic>-value &lt; 0.05.ResultsA total of 612 respondents participated, with a response rate of 97.6%. The prevalence of PTSD in this study was 21.9% (95% CI: 18.6, 25.2). In multivariable regression, female sex [adjusted odds ratio (AOR) = 2.3, 95% CI; 1.3, 3.87], combat personnel (AOR = 2.75, 95% CI; 1.44, 6.36), handling dead bodies (AOR = 2.5, 95% CI,1.24, 5.02), having 4–5 deployments (AOR = 2.94, 95% CI, 1.63, 5.32), having ≥6 deployments (AOR = 3.4, 95% CI, 1.95, 6.17), low resilience coping (AOR = 2.02, 95% CI; 1.16, 3.53), poor social support (AOR = 2.46, 95% CI, 1.39, 4.35), very high combat exposures (AOR = 4.8, 95% CI, 2.03, 11.93), and depression (AOR = 2.8, 95% CI, 1.68, 4.67) were significantly associated with PTSD.ConclusionPTSD is markedly prevalent among the Ethiopian military population, with key risk factors identified as being female, poor social support, low resilience coping skills, handling dead bodies, multiple deployments (four or more), high combat experiences, and depression. Healthcare professionals must prioritize the early diagnosis and intervention of PTSD in vulnerable groups of military personnel.","PeriodicalId":12605,"journal":{"name":"Frontiers in Psychiatry","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142253002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background and aimThe COVID-19 pandemic has led to a significant adverse effect on the mental health of healthcare professionals. This study aims to assess the effects of the prolonged pandemic on burnout and mood disorders and to evaluate the influence of positive vaccination beliefs on these factors at a medical center during the extended COVID-19 pandemic.MethodsThis retrospective study analyzed the results of an online questionnaire survey including burnout status and mood disorders from 2020 to 2022. The factors related to mood moderate/severe disorders and the impact of the positive vaccine belief were also explored.ResultsThe initial analysis revealed that healthcare professionals continued to experience significant levels of personal and work-related burnout, along with mood disorders. However, the scores and the percentage of moderate to severe burnout gradually decreased. Notably, the percentage of individuals with moderate to severe mood disorders also gradually declined (2020: 13.4%, 2021: 12.3%, 2022: 11.1%). The number of participants who need professional interventions decreased from 56.2% in 2020 to 45.9% in 2021, and 46% in 2022. Multivariate analysis revealed a positive vaccine belief was associated with a lower risk of moderate/severe mood disorders, with odd ratios (OR) and 95% confidence intervals (95% CI) of 0.38 (0.28 – 0.52) and 0.41 (0.30 – 0.52) in the 2021 and 2022 cohorts, respectively. Further investigation revealed that age over 50 was linked to a positive vaccine belief in 2021 and 2022. Within the 2022 cohort, working as nurses was identified as the independent factor associated with a less positive belief, with the OR and 95% CI of 0.49 (0.27 – 0.90).ConclusionThe findings of the present study suggest burnout and mood disorders are still significant during the pandemic. A positive vaccine belief may mitigate pandemic-related mental distress. Further interventions to enhance the belief combined with other supporting measures are important in a long fight against the pandemic.
{"title":"Positive vaccine beliefs linked to reduced mental stress in healthcare professionals during COVID-19: a retrospective study","authors":"Yu-Yin Lin, Shih-Feng Cho, Yi-Ling Hsieh, Yun-Shiuan Chuang, Chia-En Hsu, Yun-Chen Liu, Chia-Chi Sung, Ya-Hsiu Huang, Wen Ku, Meng-Hsuan Hsieh, Ya-Chin Huang, Hung-Pin Tu, Chao-Ling Wang, Chi-Kung Ho","doi":"10.3389/fpsyt.2024.1402194","DOIUrl":"https://doi.org/10.3389/fpsyt.2024.1402194","url":null,"abstract":"Background and aimThe COVID-19 pandemic has led to a significant adverse effect on the mental health of healthcare professionals. This study aims to assess the effects of the prolonged pandemic on burnout and mood disorders and to evaluate the influence of positive vaccination beliefs on these factors at a medical center during the extended COVID-19 pandemic.MethodsThis retrospective study analyzed the results of an online questionnaire survey including burnout status and mood disorders from 2020 to 2022. The factors related to mood moderate/severe disorders and the impact of the positive vaccine belief were also explored.ResultsThe initial analysis revealed that healthcare professionals continued to experience significant levels of personal and work-related burnout, along with mood disorders. However, the scores and the percentage of moderate to severe burnout gradually decreased. Notably, the percentage of individuals with moderate to severe mood disorders also gradually declined (2020: 13.4%, 2021: 12.3%, 2022: 11.1%). The number of participants who need professional interventions decreased from 56.2% in 2020 to 45.9% in 2021, and 46% in 2022. Multivariate analysis revealed a positive vaccine belief was associated with a lower risk of moderate/severe mood disorders, with odd ratios (OR) and 95% confidence intervals (95% CI) of 0.38 (0.28 – 0.52) and 0.41 (0.30 – 0.52) in the 2021 and 2022 cohorts, respectively. Further investigation revealed that age over 50 was linked to a positive vaccine belief in 2021 and 2022. Within the 2022 cohort, working as nurses was identified as the independent factor associated with a less positive belief, with the OR and 95% CI of 0.49 (0.27 – 0.90).ConclusionThe findings of the present study suggest burnout and mood disorders are still significant during the pandemic. A positive vaccine belief may mitigate pandemic-related mental distress. Further interventions to enhance the belief combined with other supporting measures are important in a long fight against the pandemic.","PeriodicalId":12605,"journal":{"name":"Frontiers in Psychiatry","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142268973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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