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Bitter I, Szekeres G, Cai Q, Feher L, Gimesi-Orszagh J, Kunovszki P, El Khoury AC, Dome P, Rihmer Z. Mortality in patients with major depressive disorder: A nationwide population-based cohort study with 11-year follow-up. Eur Psychiatry 2024; 67:e63. [PMID: 39344202 DOI: 10.1192/j.eurpsy.2024.1771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/01/2024] Open
Abstract
BACKGROUND Major depressive disorder (MDD) is a leading cause of disability and premature mortality. This study compared the overall survival (OS) between patients with MDD and non-MDD controls stratified by gender, age, and comorbidities. METHODS This nationwide population-based cohort study utilized longitudinal patient data (01/01/2010 - 12/31/2020) from the Hungarian National Health Insurance Fund database, which contains healthcare service data for the Hungarian population. Patients with MDD were selected and matched 1:1 to those without MDD using exact matching. The rates of conversion from MDD to bipolar disorder (BD) or schizophrenia were also investigated. RESULTS Overall, 471,773 patients were included in each of the matched MDD and non-MDD groups. Patients with MDD had significantly worse OS than non-MDD controls (hazard ratio [HR] = 1.50; 95% CI: 1.48-1.51; males HR = 1.69, 95% CI: 1.66-1.72; females HR = 1.40, 95% CI: 1.38-1.42). The estimated life expectancy of patients with MDD was 7.8 and 6.0 years less than that of controls aged 20 and 45 years, respectively. Adjusted analyses based on the presence of baseline comorbidities also showed that patients with MDD had worse survival than non-MDD controls (adjusted HR = 1.29, 95% CI: 1.28-1.31). After 11 years of follow-up, the cumulative conversions from MDD to BD and schizophrenia were 6.8 and 3.4%, respectively. Converted patients had significantly worse OS than non-converted patients. CONCLUSIONS Compared with the non-MDD controls, a higher mortality rate in patients with MDD, especially in those with comorbidities and/or who have converted to BD or schizophrenia, suggests that early detection and personalized treatment of MDD may reduce the mortality in patients diagnosed with MDD.
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Affiliation(s)
- Istvan Bitter
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Gyorgy Szekeres
- Department of Psychiatry and Psychotherapy, Saint Rókus Hospital, Semmelweis University, Budapest, Hungary
| | - Qian Cai
- Janssen Global Services, LLC, Titusville, NJ, USA
| | | | | | | | | | - Peter Dome
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
- Nyiro Gyula National Institute for Psychiatry and Addictology, Budapest, Hungary
| | - Zoltan Rihmer
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
- Nyiro Gyula National Institute for Psychiatry and Addictology, Budapest, Hungary
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Ribeiro-Fernández M, Díez-Suárez A, Chang KD, Soutullo CA. Predictors of Transition from Child and Adolescent Bipolar Not Otherwise Specified to Bipolar I Disorder, a Longitudinal 3.9-Year Study. J Clin Med 2024; 13:5656. [PMID: 39407716 PMCID: PMC11477010 DOI: 10.3390/jcm13195656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2024] [Revised: 08/22/2024] [Accepted: 09/18/2024] [Indexed: 10/20/2024] Open
Abstract
Background: Children and adolescents with subthreshold manic symptoms not meeting full DSM criteria for bipolar I or II disorder (BP-I or BP-II) are classified as unspecified bipolar disorder (formerly bipolar not otherwise specified: BP-NOS). Factors associated with transition from BP-II or NOS to BP-I may predict the progression of the disorder. Our objective is to analyze factors associated with transition to BP-I in a Spanish sample of youth with BP-NOS or BP-II. Methods: We included all youth diagnosed with BP before 18 years of age presenting to our clinic (October 1999-December 2014). We assessed clinical factors that may predict transition to BP I with a logistic regression and a multivariable model for data analysis. Results: A total of 72 patients with BP, mean (SD) age 14.5 (10.5-16.0) years, were followed for a median period of 3.9 years. In total, 95.8% of patients retained the BP diagnosis, but they changed type. Baseline BP-I % was 37.5%, and 62.5% at endpoint. BP-NOS decreased from baseline 54.2% to 25% at endpoint. The % of BP-II was 8.3% in both time points, but they were not the same individual patients, as some transitioned from BP-II to BP-I and some BP-NOS changed to BP-II. BP-NOS was stable in 46.1% of patients, but 38.5% transitioned to BP-I over time. Psychotic symptoms during prior depressive episodes (MDD) increased the risk of transition to BP-I by 11-fold. Each individual symptom of mania increased the risk of transition to BP-I by 1.41. Conclusions: BP-NOS was stable in 46.1% of patients, but 38.5% transitioned to BP-I over time. Psychotic symptoms during prior MDD episodes increased the risk of transition from BP-NOS to BP-I.
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Affiliation(s)
- María Ribeiro-Fernández
- Department of Psychiatry, Navarra Medical Complex, Navarra Health System (Spanish National Health System), 31008 Pamplona, Navarra, Spain;
- IdiSNA: Navarra Institute for Health Research, 31008 Pamplona, Navarra, Spain;
| | - Azucena Díez-Suárez
- IdiSNA: Navarra Institute for Health Research, 31008 Pamplona, Navarra, Spain;
- Child and Adolescent Psychiatry Unit, Department of Psychiatry & Medical Psychology, University of Navarra Clinic, 31008 Pamplona, Navarra, Spain
| | - Kiki D. Chang
- Private Practice, Palo Alto, CA 94306, USA;
- Louis A. Faillace Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX 77054, USA
| | - Cesar A. Soutullo
- Louis A. Faillace Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX 77054, USA
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Wang Y, Huang C, Li P, Niu B, Fan T, Wang H, Zhou Y, Chai Y. Machine learning-based discrimination of unipolar depression and bipolar disorder with streamlined shortlist in adolescents of different ages. Comput Biol Med 2024; 182:109107. [PMID: 39288554 DOI: 10.1016/j.compbiomed.2024.109107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 08/30/2024] [Accepted: 09/02/2024] [Indexed: 09/19/2024]
Abstract
BACKGROUND Variations in symptoms and indistinguishable depression episodes of unipolar depression (UD) and bipolar disorder (BD) make the discrimination difficult and time-consuming. For adolescents with high disease prevalence, an efficient diagnostic tool is important for the discrimination and treatment of BU and UD. METHODS This multi-center cross-sectional study involved 1587 UD and 246 BD adolescents aged 12-18. A combination of standard questionnaires and demographic information was collected for the construction of a full-item list. The unequal patient number was balanced with three data balancing algorithms, and 4 machine learning algorithms were compared for the discrimination ability of UD and BD in three age groups: all ages, 12-15 and 16-18. Random forest (RF) with the highest accuracy were used to rank the importance of features/items and construct the 25-item shortlist. A separate dataset was used for the final performance evaluation with the shortlist, and the discrimination ability for UD and BD was investigated. RESULTS RF performed the best for UD and BD discrimination in all 3 age groups (AUC 0.88-0.90). The most important features that differentiate UD from BD belong to Parental Bonding Instrument (PBI) and Loneliness Scale of the University of California at Los Angeles (UCLA). With RF and the 25-item shortlist, the diagnostic accuracy can still reach around 80 %, achieving 95 % of the accuracy levels obtained with all features. CONCLUSIONS Through machine learning algorithms, the most influencing factors for UD and BD classification were recombined and applied for rapid diagnosis. This highly feasible method holds the potential for convenient and accurate diagnosis of young patients in research and clinical practice.
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Affiliation(s)
- Yang Wang
- College of Management, Shenzhen University, Shenzhen, China
| | - Cheng Huang
- Greater Bay Area International Institute for Innovations, Shenzhen University, Shenzhen, China
| | - Pingping Li
- Greater Bay Area International Institute for Innovations, Shenzhen University, Shenzhen, China
| | - Ben Niu
- College of Management, Shenzhen University, Shenzhen, China
| | - Tingxuan Fan
- Greater Bay Area International Institute for Innovations, Shenzhen University, Shenzhen, China
| | - Hairong Wang
- Greater Bay Area International Institute for Innovations, Shenzhen University, Shenzhen, China
| | | | - Yujuan Chai
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518060, China.
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Tondo L, Miola A, Pinna M, Contu M, Baldessarini RJ. Antidepressant-associated diagnostic change from major depressive to bipolar disorder. Acta Psychiatr Scand 2024; 150:126-137. [PMID: 38922810 DOI: 10.1111/acps.13721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 05/27/2024] [Accepted: 06/11/2024] [Indexed: 06/28/2024]
Abstract
BACKGROUND Anticipating diagnostic change from major depressive (MDD) to bipolar disorder (BD) can support better prognosis and treatment, especially of depression but is challenging and reported research results are inconsistent. We therefore assessed clinical characteristics associated with diagnostic change from MDD to BD with antidepressant treatments. METHODS We compared characteristics of 3212 initially MDD patients who became (hypo)manic during antidepressant treatment to those with stable MDD diagnoses as well as with cases of stable, spontaneous BD, using standard bivariate and multivariate statistics. RESULTS Among MDD patients, 6.69% [CI: 5.85-7.61] changed to BD, mostly type II (BD2, 76.7%). BD-converters had higher rates of familial mood disorders (74.1% vs. 57.1%) or BD (33.7% vs. 21.0%) and 2.8-years younger onset than stable MDD patients. They also had more prior depressive recurrences/year, years-of-illness, mood-stabilizer treatment, divorces, fewer children, more suicide attempts and drug-abuse, and higher intake cyclothymia, YMRS and MDQ scores. Predictors independently associated with diagnostic conversion were: more familial BD, depressions/year, unemployment, cyclothymic temperament, suicidal ideation or acts, and fewer children. BD-converters vs. spontaneous BD cases had significantly more suicide attempts, BD2 diagnoses, and affected relatives. Converting to vs. spontaneous BD1 was associated with more ADHD, more suicidal ideation or behavior, MDI course, and younger onset; converting to vs. spontaneous BD2 had more episodes/year, unemployment, ADHD, substance abuse, suicidal ideation or attempts, and more relatives with BD. CONCLUSIONS Few (6.69%) initially MDD subjects converted to BD, most (76.7%) to BD2. Independent predictive associations with diagnostic change included: familial BD, more depressions/year, unemployment, cyclothymic temperament, suicidal behavior and fewer children. Notably, several characteristics were stronger among those changing to BD during antidepressant treatment vs. others with spontaneous BD.
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Affiliation(s)
- Leonardo Tondo
- International Consortium for Mood and Psychotic Disorders Research, Mailman Research Center, McLean Hospital, Belmont, Massachusetts, USA
- Lucio Bini Mood Disorder Center, Cagliari, Italy
- Lucio Bini Mood Disorder Center, Rome, Italy
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA
| | - Alessandro Miola
- International Consortium for Mood and Psychotic Disorders Research, Mailman Research Center, McLean Hospital, Belmont, Massachusetts, USA
- Department of Neuroscience, University of Padova, Padua, Italy
| | - Marco Pinna
- Lucio Bini Mood Disorder Center, Cagliari, Italy
- Section on Psychiatry, Department of Medical Science and Public Health, University of Cagliari, Cagliari, Italy
| | - Martina Contu
- Lucio Bini Mood Disorder Center, Cagliari, Italy
- Section on Psychiatry, Department of Medical Science and Public Health, University of Cagliari, Cagliari, Italy
| | - Ross J Baldessarini
- International Consortium for Mood and Psychotic Disorders Research, Mailman Research Center, McLean Hospital, Belmont, Massachusetts, USA
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA
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Sun H, Yan R, Hua L, Xia Y, Huang Y, Wang X, Yao Z, Lu Q. Based on white matter microstructure to early identify bipolar disorder from patients with depressive episode. J Affect Disord 2024; 350:428-434. [PMID: 38244786 DOI: 10.1016/j.jad.2024.01.147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 01/10/2024] [Accepted: 01/14/2024] [Indexed: 01/22/2024]
Abstract
OBJECTIVE Because of similar clinical manifestations, bipolar disorder (BD) patients are often misdiagnosed as major depressive disorder (MDD). This study aimed to compare the difference between depressed patients later converting to BD and unipolar depression (UD) according to diffusion tensor imaging (DTI). METHOD Patients with MDD (562 participants) in depressive episode states and healthy controls (HCs) (145 participants) were recruited over 10 years. Demographic and magnetic resonance imaging (MRI) data were collected at the time of recruitment. All patients with MDD were followed up for 5 years and classified into the transfer to BD (tBD) group (83 participants) and UD group (160 participants) according to the follow-up results. DTI and functional magnetic resonance imaging at baseline were compared. RESULTS Common abnormalities were found in both tBD and UD groups, including left superior cerebellar peduncle (SCP.L), right anterior limb of the internal capsule (ALIC.R), right superior fronto-occipital fasciculus (SFOF.R), and right inferior fronto-occipital fasciculus (IFOF.R). The tBD showed more extensive abnormalities than the UD in the body of corpus callosum, fornix, left superior corona radiata, left posterior corona radiata, left superior longitudinal fasciculus, and left superior fronto-occipital fasciculus. CONCLUSION The study demonstrated the common and distinct abnormalities of tBD and UD when compared to HC. The tBD group showed more extensive disruptions of white matter integrity, which could be a potential biomarker for the early identification of BD.
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Affiliation(s)
- Hao Sun
- Nanjing Brain Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, Nanjing, China; Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 249 Guangzhou Road, Nanjing 210029, China
| | - Rui Yan
- Nanjing Brain Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, Nanjing, China; Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 249 Guangzhou Road, Nanjing 210029, China
| | - Lingling Hua
- Nanjing Brain Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, Nanjing, China; Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 249 Guangzhou Road, Nanjing 210029, China
| | - Yi Xia
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 249 Guangzhou Road, Nanjing 210029, China
| | - Yinghong Huang
- Nanjing Brain Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, Nanjing, China; Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 249 Guangzhou Road, Nanjing 210029, China
| | - Xiaoqin Wang
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 249 Guangzhou Road, Nanjing 210029, China
| | - Zhijian Yao
- Nanjing Brain Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, Nanjing, China; Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 249 Guangzhou Road, Nanjing 210029, China; School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, China.
| | - Qing Lu
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing 210096, China.
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Rhee SJ, Ohlsson H, Sundquist J, Sundquist K, Kendler KS. Predictors of diagnostic conversion from major depression to bipolar disorder: a Swedish national longitudinal study. Psychol Med 2023; 53:7805-7816. [PMID: 37427550 PMCID: PMC10755232 DOI: 10.1017/s0033291723001848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 05/31/2023] [Accepted: 06/13/2023] [Indexed: 07/11/2023]
Abstract
BACKGROUND It is clinically important to predict the conversion of major depression (MD) to bipolar disorder (BD). Therefore, we sought to identify related conversion rates and risk factors. METHODS This cohort study included the Swedish population born from 1941 onward. Data were collected from Swedish population-based registers. Potential risk factors, including family genetic risk scores (FGRS), which were calculated based on the phenotypes of relatives in the extended family and not molecular data, and demographic/clinical characteristics from these registers were retrieved. Those with first MD registrations from 2006 were followed up until 2018. The conversion rate to BD and related risk factors were analyzed using Cox proportional hazards models. Additional analyses were performed for late converters and with stratification by sex. RESULTS The cumulative incidence of conversion was 5.84% [95% confidence interval (95% CI) 5.72-5.96] for 13 years. In the multivariable analysis, the strongest risk factors for conversion were high FGRS of BD [hazard ratio (HR) = 2.73, 95% CI 2.43-3.08], inpatient treatment settings (HR = 2.64, 95% CI 2.44-2.84), and psychotic depression (HR = 2.58, 95% CI 2.14-3.11). For late converters, the first registration of MD during the teenage years was a stronger risk factor when compared with the baseline model. When the interactions between risk factors and sex were significant, stratification by sex revealed that they were more predictive in females. CONCLUSIONS Family history of BD, inpatient treatment, and psychotic symptoms were the strongest predictors of conversion from MD to BD.
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Affiliation(s)
- Sang Jin Rhee
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea
| | - Henrik Ohlsson
- Center for Primary Health Care Research, Lund University, Malmö, Sweden
| | - Jan Sundquist
- Center for Primary Health Care Research, Lund University, Malmö, Sweden
- Department of Family Medicine and Community Health and Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kristina Sundquist
- Center for Primary Health Care Research, Lund University, Malmö, Sweden
- Department of Family Medicine and Community Health and Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kenneth S. Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
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7
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Liu W, Jiang X, Xin Y, Deng Z, Xie Y, Zhou Y, Wu Y, Sun Q, Kong L, Wu F, Tang Y. Sex effects on differentiating patients with major depressive disorder from bipolar disorder in depressive state: A fMRI study with follow-up. J Affect Disord 2023; 340:396-404. [PMID: 37572701 DOI: 10.1016/j.jad.2023.08.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 08/04/2023] [Accepted: 08/07/2023] [Indexed: 08/14/2023]
Abstract
BACKGROUND Bipolar disorder (BD) is difficult to discriminate from major depressive disorder (MDD) before the appearance of mania or hypomania. This study was designed to identify whether patients with MDD and those who converted to BD are distinguishable using dynamic amplitude low-frequency fluctuations (dALFF) and describe the sex effects on the identification of the two disorders. METHODS We compared the dALFF values of 35 BD patients who converted from MDD during the 2-year follow-up, 99 MDD patients, and 130 healthy controls (HCs) using two-way ANOVA. Pearson's correlation was used to compare dALFF in dysfunctional brain regions and clinical characteristics. RESULTS A main effect of diagnosis was discovered in the frontal and occipital gyrus. For the main effect of sex, both the left middle occipital gyrus and the medial part of the superior frontal gyrus had higher dALFF values in males compared to females. An interaction of sex and diagnosis effect was observed in the right precentral gyrus. Male MDD patients exhibited a higher dALFF value than male BD patients. Additionally, we discovered a higher dALFF value in females than in males in BD patients. WCST scores were positively associated with dALFF values in the frontal and occipital gyrus in MDD patients. Meanwhile, dALFF values in the occipital gyrus positively correlated with WCST in female MDD patients only. LIMITATION Most of the participants were on medication and the sample size was small. CONCLUSIONS Our study is the first to find the non-neglectable role of sex effects in differentiating BD and MDD at an early stage.
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Affiliation(s)
- Wen Liu
- Brain Function Research Section, The First Hospital of China Medical University, Shenyang 110001, Liaoning, PR China; Department of Psychiatry, The First Hospital of China Medical University Shenyang 110001, Liaoning, PR China
| | - Xiaowei Jiang
- Brain Function Research Section, The First Hospital of China Medical University, Shenyang 110001, Liaoning, PR China; Department of Radiology, The First Hospital of China Medical University, Shenyang 110001, Liaoning, PR China
| | - Yide Xin
- Brain Function Research Section, The First Hospital of China Medical University, Shenyang 110001, Liaoning, PR China
| | - Zijing Deng
- Brain Function Research Section, The First Hospital of China Medical University, Shenyang 110001, Liaoning, PR China; Department of Psychiatry, The First Hospital of China Medical University Shenyang 110001, Liaoning, PR China
| | - Yu Xie
- Brain Function Research Section, The First Hospital of China Medical University, Shenyang 110001, Liaoning, PR China
| | - Yifang Zhou
- Brain Function Research Section, The First Hospital of China Medical University, Shenyang 110001, Liaoning, PR China; Department of Psychiatry, The First Hospital of China Medical University Shenyang 110001, Liaoning, PR China
| | - Yifan Wu
- Brain Function Research Section, The First Hospital of China Medical University, Shenyang 110001, Liaoning, PR China; Department of Psychiatry, The First Hospital of China Medical University Shenyang 110001, Liaoning, PR China
| | - Qikun Sun
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang 110001, Liaoning, PR China
| | - Lingtao Kong
- Brain Function Research Section, The First Hospital of China Medical University, Shenyang 110001, Liaoning, PR China; Department of Psychiatry, The First Hospital of China Medical University Shenyang 110001, Liaoning, PR China
| | - Feng Wu
- Brain Function Research Section, The First Hospital of China Medical University, Shenyang 110001, Liaoning, PR China; Department of Psychiatry, The First Hospital of China Medical University Shenyang 110001, Liaoning, PR China
| | - Yanqing Tang
- Department of Psychiatry, The First Hospital of China Medical University Shenyang 110001, Liaoning, PR China; Department of Gerontology, The First Hospital of China Medical University, Shenyang 110001, Liaoning, PR China.
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Saif Alghawli A, Taloba AI. An Enhanced Ant Colony Optimization Mechanism for the Classification of Depressive Disorders. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1332664. [PMID: 35800708 PMCID: PMC9256370 DOI: 10.1155/2022/1332664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 06/13/2022] [Indexed: 11/28/2022]
Abstract
Bipolar disorder is marked by mood swings that alternate between mania and depression. The stages of bipolar disorder (BD), as one of the most common mental conditions, are often misdiagnosed as major depressive disorder (MDD), resulting in ineffective treatment and a poor prognosis. As a result, distinguishing MDD from BD at an earlier phase of the disease may aid in more efficient and targeted treatments. In this research, an enhanced ACO (IACO) technique biologically inspired by and following the required ant colony optimization (ACO) was utilized to minimize the number of features by deleting unrelated or redundant feature data. To distinguish MDD and BD individuals, the selected features were loaded into a support vector machine (SVM), a sophisticated mathematical technique for classification process, regression, functional estimates, and modeling operations. In respect of classifications efficiency and frequency of features extracted, the performance of the IACO method was linked to that of regular ACO, particle swarm optimization (PSO), and genetic algorithm (GA) techniques. The validation was performed using a nested cross-validation (CV) approach to produce nearly reliable estimates of classification error.
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Affiliation(s)
- Abed Saif Alghawli
- Computer Science Department, Prince Sattam Bin Abdulaziz University, Aflaj, Saudi Arabia
| | - Ahmed I. Taloba
- Department of Computer Science, College of Science and Arts in Qurayyat, Jouf University, Sakaka, Saudi Arabia
- Information System Department, Faculty of Computers and Information, Assiut University, Assiut, Egypt
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9
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Jo YT, Joo SW, Kim H, Ahn S, Choi YJ, Choi W, Park SY, Lee J. Diagnostic conversion from unipolar to bipolar affective disorder-A population-based study. J Affect Disord 2022; 301:448-453. [PMID: 35065087 DOI: 10.1016/j.jad.2022.01.082] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 12/09/2021] [Accepted: 01/19/2022] [Indexed: 10/19/2022]
Abstract
OBJECTIVES It is essential to clinically distinguish bipolar affective disorder from unipolar affective disorders. However, patients previously diagnosed with unipolar affective disorder are sometimes later diagnosed with bipolar affective disorder, known as diagnostic conversion. Here we investigated diagnostic conversion using data from a nationwide population-based register. METHODS We obtained claims data from 2007 to 2020 in Korea's Health Insurance Review Agency database and identified a cohort of patients who were diagnosed with unipolar depression in 2009 without prior psychiatric diseases within the previous 2 years. We studied the rate of diagnostic conversion and risk factors, especially antidepressants. RESULTS About 6.5% of patients underwent diagnostic conversion during the observation period. Younger age at disease onset and usage of antidepressants increased the relative risk for diagnostic conversion. Patients using serotonin-norepinephrine reuptake inhibitors (SNRI) showed more than twice the risk compared to no usage of antidepressant. LIMITATION First, this study was based on the population-based register data. Thus, we defined the patient cohort diagnosed with unipolar depression with strict inclusion criteria. Second, the exposure time differed between different antidepressants. Third, we estimated the relative risk for diagnostic conversion compared to no use of antidepressants. Moreover, we could not rule out the potential influence of antidepressant polypharmacy. CONCLUSION We confirmed diagnostic conversion in some patients and identified younger age or usage of antidepressants, especially SNRI, as risk factors. Because unipolar and bipolar affective disorders show different disease courses or prognoses and have different treatment strategies, clinicians should be mindful of diagnostic conversion.
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Affiliation(s)
- Young Tak Jo
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Sung Woo Joo
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Harin Kim
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Soojin Ahn
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Young Jae Choi
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Woohyeok Choi
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - So Yeon Park
- Department of Psychiatry, Yongin Mental Hospital, Yongin-si, Gyeonggi-do, Korea
| | - Jungsun Lee
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
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Xu Z, Chen L, Hu Y, Shen T, Chen Z, Tan T, Gao C, Chen S, Chen W, Chen B, Yuan Y, Zhang Z. A Predictive Model of Risk Factors for Conversion From Major Depressive Disorder to Bipolar Disorder Based on Clinical Characteristics and Circadian Rhythm Gene Polymorphisms. Front Psychiatry 2022; 13:843400. [PMID: 35898634 PMCID: PMC9309512 DOI: 10.3389/fpsyt.2022.843400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Accepted: 06/08/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Bipolar disorder (BD) is easy to be misdiagnosed as major depressive disorder (MDD), which may contribute to a delay in treatment and affect prognosis. Circadian rhythm dysfunction is significantly associated with conversion from MDD to BD. So far, there has been no study that has revealed a relationship between circadian rhythm gene polymorphism and MDD-to-BD conversion. Furthermore, the prediction of MDD-to-BD conversion has not been made by integrating multidimensional data. The study combined clinical and genetic factors to establish a predictive model through machine learning (ML) for MDD-to-BD conversion. METHOD By following up for 5 years, 70 patients with MDD and 68 patients with BD were included in this study at last. Single nucleotide polymorphisms (SNPs) of the circadian rhythm genes were selected for detection. The R software was used to operate feature screening and establish a predictive model. The predictive model was established by logistic regression, which was performed by four evaluation methods. RESULTS It was found that age of onset was a risk factor for MDD-to-BD conversion. The younger the age of onset, the higher the risk of BD. Furthermore, suicide attempts and the number of hospitalizations were associated with MDD-to-BD conversion. Eleven circadian rhythm gene polymorphisms were associated with MDD-to-BD conversion by feature screening. These factors were used to establish two models, and 4 evaluation methods proved that the model with clinical characteristics and SNPs had the better predictive ability. CONCLUSION The risk factors for MDD-to-BD conversion have been found, and a predictive model has been established, with a specific guiding significance for clinical diagnosis.
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Affiliation(s)
- Zhi Xu
- Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, China
| | - Lei Chen
- Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, China
| | - Yunyun Hu
- Key Laboratory of Developmental Genes and Human Disease, Ministry of Education, Institute of Life Sciences, Southeast University, Nanjing, China
| | - Tian Shen
- Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, China
| | - Zimu Chen
- Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, China
| | - Tingting Tan
- Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, China
| | - Chenjie Gao
- Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, China
| | - Suzhen Chen
- Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, China
| | - Wenji Chen
- Department of General Practice, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, China
| | - Bingwei Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, China
| | - Yonggui Yuan
- Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, China.,Key Laboratory of Developmental Genes and Human Disease, Ministry of Education, Institute of Life Sciences, Southeast University, Nanjing, China
| | - Zhijun Zhang
- Department of Neurology, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, China
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Predictors of conversion of major depressive disorder to bipolar disorder. Psychiatry Res 2021; 300:113939. [PMID: 33895442 DOI: 10.1016/j.psychres.2021.113939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 04/10/2021] [Indexed: 11/20/2022]
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