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Zheng S, Zeng W, Wu Q, Li W, He Z, Li E, Tang C, Xue X, Qin G, Zhang B, Yin H. Predictive Models for Suicide Attempts in Major Depressive Disorder and the Contribution of EPHX2: A Pilot Integrative Machine Learning Study. Depress Anxiety 2024; 2024:5538257. [PMID: 40226652 PMCID: PMC11918959 DOI: 10.1155/2024/5538257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 03/29/2024] [Accepted: 04/20/2024] [Indexed: 04/15/2025] Open
Abstract
Suicide is a major public health problem caused by a complex interaction of various factors. Major depressive disorder (MDD) is the most prevalent psychiatric disorder associated with suicide; therefore, it is essential to prioritize suicide prediction and prevention within this population. Integrated information from different dimensions, including personality, cognitive function, and social and genetic factors, is necessary to improve the performance of predictive models. Besides, recent studies have indicated the critical roles for EPHX2/P2X2 in the pathophysiology of MDD. Our previous studies found an association of EPHX2 and P2X2 with suicide in MDD. This study is aimed at (1) establishing predictive models with integrated information to distinguish MDD from healthy volunteers, (2) estimating the suicide risk of MDD, and (3) determining the contribution of EPHX2/P2X2. This cross-sectional study was conducted on 472 prospectively collected participants. The machine learning (ML) technique using Extreme Gradient Boosting (XGBoost) classifier was employed to evaluate the performance and relative importance of the extracted characteristics in recognising patients with MDD and depressed suicide attempters (DSA). In independent validation set, the model with clinical and cognitive information could recognise MDD with an area under the receiver operating characteristic curve (AUC) of 0.938 (95% confidence interval (CI), 0.898-0.977), and genetic information did not improve classification performance. The model with clinical, cognitive, and genetic information resulted in a significantly higher AUC of 0.801 (95% CI, 0.719-0.884) for identifying DSA than the model with only clinical information, in which the three single nucleotide polymorphisms of EPHX2 showed important roles. This study successfully established step-by-step predictive ML models to estimate the risk of suicide attempts in MDD. We found that EPHX2 can help improve the performance of suicidal predictive models. This trial is registered with NCT05575713.
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Affiliation(s)
- Shuqiong Zheng
- Department of Psychiatry, Sleep Medicine Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
- Institute of Brain Disease, Nanfang Hospital, Southern Medical University, Guangzhou, China
- Key Laboratory of Mental Health of the Ministry of Education, Southern Medical University, Guangzhou, China
| | - Weixiong Zeng
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Qianyun Wu
- Department of Psychiatry, Sleep Medicine Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
- Institute of Brain Disease, Nanfang Hospital, Southern Medical University, Guangzhou, China
- Key Laboratory of Mental Health of the Ministry of Education, Southern Medical University, Guangzhou, China
| | - Weimin Li
- Department of Psychiatry, Sleep Medicine Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
- Institute of Brain Disease, Nanfang Hospital, Southern Medical University, Guangzhou, China
- Key Laboratory of Mental Health of the Ministry of Education, Southern Medical University, Guangzhou, China
| | - Zilong He
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Enze Li
- Department of Psychiatry, Sleep Medicine Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
- Institute of Brain Disease, Nanfang Hospital, Southern Medical University, Guangzhou, China
- Key Laboratory of Mental Health of the Ministry of Education, Southern Medical University, Guangzhou, China
| | - Chong Tang
- Department of Psychiatry, Sleep Medicine Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
- Institute of Brain Disease, Nanfang Hospital, Southern Medical University, Guangzhou, China
- Key Laboratory of Mental Health of the Ministry of Education, Southern Medical University, Guangzhou, China
| | - Xiang Xue
- Department of Psychiatry, Sleep Medicine Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
- Institute of Brain Disease, Nanfang Hospital, Southern Medical University, Guangzhou, China
- Key Laboratory of Mental Health of the Ministry of Education, Southern Medical University, Guangzhou, China
| | - Genggeng Qin
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Bin Zhang
- Department of Psychiatry, Sleep Medicine Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
- Institute of Brain Disease, Nanfang Hospital, Southern Medical University, Guangzhou, China
- Key Laboratory of Mental Health of the Ministry of Education, Southern Medical University, Guangzhou, China
| | - Honglei Yin
- Department of Psychiatry, The Third Affiliated Hospital, Southern Medical University, Guangzhou, China
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Zhou T, Zhao J, Ma Y, He L, Ren Z, Yang K, Tang J, Liu J, Luo J, Zhang H. Association of cognitive impairment with the interaction between chronic kidney disease and depression: findings from NHANES 2011-2014. BMC Psychiatry 2024; 24:312. [PMID: 38658863 PMCID: PMC11044494 DOI: 10.1186/s12888-024-05769-1] [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: 07/30/2023] [Accepted: 04/16/2024] [Indexed: 04/26/2024] Open
Abstract
BACKGROUND Cognitive impairment (CoI), chronic kidney disease (CKD), and depression are prevalent among older adults and are interrelated, imposing a significant disease burden. This study evaluates the association of CKD and depression with CoI and explores their potential interactions. METHOD Data for this study were sourced from the 2011-2014 National Health and Nutritional Examination Survey (NHANES). Multiple binary logistic regression models assessed the relationship between CKD, depression, and CoI while controlling for confounders. The interactions were measured using the relative excess risk of interaction (RERI), the attributable proportion of interaction (AP), and the synergy index (S). RESULTS A total of 2,666 participants (weighted n = 49,251,515) were included in the study, of which 700 (16.00%) had CoI. After adjusting for confounding factors, the risk of CoI was higher in patients with CKD compared to non-CKD participants (odds ratio [OR] = 1.49, 95% confidence interval [CI]:1.12-1.99). The risk of CoI was significantly increased in patients with depression compared to those without (OR = 2.29, 95% CI: 1.73-3.03). Furthermore, there was a significant additive interaction between CKD and depression in terms of the increased risk of CoI (adjusted RERI = 2.01, [95% CI: 0.31-3.71], adjusted AP = 0.50 [95% CI: 0.25-0.75], adjusted S = 2.97 [95% CI: 1.27-6.92]). CONCLUSION CKD and depression synergistically affect CoI, particularly when moderate-to-severe depression co-occurs with CKD. Clinicians should be mindful of the combined impact on patients with CoI. Further research is needed to elucidate the underlying mechanisms and assess the effects specific to different CKD stages.
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Affiliation(s)
- Tong Zhou
- Department of Nephrology, Affiliated Hospital of North Sichuan Medical College, 1 Maoyuan Road, Nanchong city, Sichuan Province, 637000, China
| | - Jiayu Zhao
- Department of physician, Nanchong Psychosomatic Hospital, Nanchong, China
| | - Yimei Ma
- Department of Nephrology, Affiliated Hospital of North Sichuan Medical College, 1 Maoyuan Road, Nanchong city, Sichuan Province, 637000, China
| | - Linqian He
- Department of Nephrology, Affiliated Hospital of North Sichuan Medical College, 1 Maoyuan Road, Nanchong city, Sichuan Province, 637000, China
| | - Zhouting Ren
- Department of Nephrology, Affiliated Hospital of North Sichuan Medical College, 1 Maoyuan Road, Nanchong city, Sichuan Province, 637000, China
| | - Kun Yang
- Department of Nephrology, Affiliated Hospital of North Sichuan Medical College, 1 Maoyuan Road, Nanchong city, Sichuan Province, 637000, China
| | - Jincheng Tang
- Department of Nephrology, Affiliated Hospital of North Sichuan Medical College, 1 Maoyuan Road, Nanchong city, Sichuan Province, 637000, China
| | - Jiali Liu
- Department of Clinical Medicine, North Sichuan Medical University, Nanchong, China
| | - Jiaming Luo
- Mental Health Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
- School of Psychiatry, North Sichuan Medical College, Nanchong, China
| | - Heping Zhang
- Department of Nephrology, Affiliated Hospital of North Sichuan Medical College, 1 Maoyuan Road, Nanchong city, Sichuan Province, 637000, China.
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Khodanovich M, Naumova A, Kamaeva D, Obukhovskaya V, Vasilieva S, Schastnyy E, Kataeva N, Levina A, Kudabaeva M, Pashkevich V, Moshkina M, Tumentceva Y, Svetlik M. Neurocognitive Changes in Patients with Post-COVID Depression. J Clin Med 2024; 13:1442. [PMID: 38592295 PMCID: PMC10933987 DOI: 10.3390/jcm13051442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 02/16/2024] [Accepted: 02/20/2024] [Indexed: 04/10/2024] Open
Abstract
Background: Depression and cognitive impairment are recognized complications of COVID-19. This study aimed to assess cognitive performance in clinically diagnosed post-COVID depression (PCD, n = 25) patients using neuropsychological testing. Methods: The study involved 71 post-COVID patients with matched control groups: recovered COVID-19 individuals without complications (n = 18) and individuals without prior COVID-19 history (n = 19). A post-COVID depression group (PCD, n = 25) was identified based on psychiatric diagnosis, and a comparison group (noPCD, n = 46) included participants with neurological COVID-19 complications, excluding clinical depression. Results: The PCD patients showed gender-dependent significant cognitive impairment in the MoCA, Word Memory Test (WMT), Stroop task (SCWT), and Trail Making Test (TMT) compared to the controls and noPCD patients. Men with PCD showed worse performances on the SCWT, in MoCA attention score, and on the WMT (immediate and delayed word recall), while women with PCD showed a decline in MoCA total score, an increased processing time with less errors on the TMT, and worse immediate recall. No differences between groups in Sniffin's stick test were found. Conclusions: COVID-related direct (post-COVID symptoms) and depression-mediated (depression itself, male sex, and severity of COVID-19) predictors of decline in memory and information processing speed were identified. Our findings may help to personalize the treatment of depression, taking a patient's gender and severity of previous COVID-19 disease into account.
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Affiliation(s)
- Marina Khodanovich
- Laboratory of Neurobiology, Research Institute of Biology and Biophysics, Tomsk State University, 36 Lenina Ave., Tomsk 634050, Russia; (D.K.); (V.O.); (N.K.); (A.L.); (M.K.); (V.P.); (M.M.); (Y.T.); (M.S.)
| | - Anna Naumova
- Department of Radiology, School of Medicine, South Lake Union Campus, University of Washington, 850 Republican Street, Seattle, WA 98109, USA;
| | - Daria Kamaeva
- Laboratory of Neurobiology, Research Institute of Biology and Biophysics, Tomsk State University, 36 Lenina Ave., Tomsk 634050, Russia; (D.K.); (V.O.); (N.K.); (A.L.); (M.K.); (V.P.); (M.M.); (Y.T.); (M.S.)
- Laboratory of Molecular Genetics and Biochemistry, Mental Health Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, 4 Aleutskaya Street, Tomsk 634014, Russia
| | - Victoria Obukhovskaya
- Laboratory of Neurobiology, Research Institute of Biology and Biophysics, Tomsk State University, 36 Lenina Ave., Tomsk 634050, Russia; (D.K.); (V.O.); (N.K.); (A.L.); (M.K.); (V.P.); (M.M.); (Y.T.); (M.S.)
- Department of Fundamental Psychology and Behavioral Medicine, Siberian State Medical University, 2 Moskovskiy Trakt, Tomsk 6340505, Russia
| | - Svetlana Vasilieva
- Department of Affective States, Mental Health Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, 4 Aleutskaya Street, Tomsk 634014, Russia; (S.V.); (E.S.)
| | - Evgeny Schastnyy
- Department of Affective States, Mental Health Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, 4 Aleutskaya Street, Tomsk 634014, Russia; (S.V.); (E.S.)
| | - Nadezhda Kataeva
- Laboratory of Neurobiology, Research Institute of Biology and Biophysics, Tomsk State University, 36 Lenina Ave., Tomsk 634050, Russia; (D.K.); (V.O.); (N.K.); (A.L.); (M.K.); (V.P.); (M.M.); (Y.T.); (M.S.)
- Department of Neurology and Neurosurgery, Siberian State Medical University, 2 Moskovskiy Trakt, Tomsk 6340505, Russia
| | - Anastasia Levina
- Laboratory of Neurobiology, Research Institute of Biology and Biophysics, Tomsk State University, 36 Lenina Ave., Tomsk 634050, Russia; (D.K.); (V.O.); (N.K.); (A.L.); (M.K.); (V.P.); (M.M.); (Y.T.); (M.S.)
- Medica Diagnostic and Treatment Center, 86 Sovetskaya Street, Tomsk 634510, Russia
| | - Marina Kudabaeva
- Laboratory of Neurobiology, Research Institute of Biology and Biophysics, Tomsk State University, 36 Lenina Ave., Tomsk 634050, Russia; (D.K.); (V.O.); (N.K.); (A.L.); (M.K.); (V.P.); (M.M.); (Y.T.); (M.S.)
| | - Valentina Pashkevich
- Laboratory of Neurobiology, Research Institute of Biology and Biophysics, Tomsk State University, 36 Lenina Ave., Tomsk 634050, Russia; (D.K.); (V.O.); (N.K.); (A.L.); (M.K.); (V.P.); (M.M.); (Y.T.); (M.S.)
| | - Marina Moshkina
- Laboratory of Neurobiology, Research Institute of Biology and Biophysics, Tomsk State University, 36 Lenina Ave., Tomsk 634050, Russia; (D.K.); (V.O.); (N.K.); (A.L.); (M.K.); (V.P.); (M.M.); (Y.T.); (M.S.)
| | - Yana Tumentceva
- Laboratory of Neurobiology, Research Institute of Biology and Biophysics, Tomsk State University, 36 Lenina Ave., Tomsk 634050, Russia; (D.K.); (V.O.); (N.K.); (A.L.); (M.K.); (V.P.); (M.M.); (Y.T.); (M.S.)
| | - Mikhail Svetlik
- Laboratory of Neurobiology, Research Institute of Biology and Biophysics, Tomsk State University, 36 Lenina Ave., Tomsk 634050, Russia; (D.K.); (V.O.); (N.K.); (A.L.); (M.K.); (V.P.); (M.M.); (Y.T.); (M.S.)
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