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Zhang W, Sun Q, Chen B, Basta M, Xu C, Li Y. Insomnia symptoms are associated with metabolic syndrome in patients with severe psychiatric disorders. Sleep Med 2021; 83:168-174. [PMID: 34022493 DOI: 10.1016/j.sleep.2021.03.030] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Revised: 03/17/2021] [Accepted: 03/26/2021] [Indexed: 02/05/2023]
Abstract
OBJECTIVE To examine the relationship between insomnia symptoms and metabolic syndrome in patients with severe psychiatric disorders. METHODS We conducted a cross-sectional study including 272 inpatients (mean age: 34.06 ± 11.52 years, 67.3% males) with severe psychiatric disorders consecutively admitted in Shantou University Mental Health Center Inpatient Department. All patients underwent a psychiatric evaluation. Insomnia symptoms were assessed by the Pittsburgh Sleep Quality Index (PSQI) and defined present if PSQI>7. The diagnosis of metabolic syndrome was defined using the new International Diabetes Federation definition based on clinical and laboratory evaluation. RESULTS Among the 272 patients, 94 (34.6%) presented insomnia symptoms. Overall, patients with insomnia symptoms had significantly higher percentage of metabolic syndrome (23.4% vs. 12.4%, p = 0.019) and hypertriglyceridemia (30.9% vs. 19.1%, p = 0.029), and marginally significantly higher levels of fasting insulin (58.75 ± 37.22 pmol/L vs. 51.72 ± 34.09 pmol/L, p = 0.050), homeostasis model assessment of insulin resistance (1.83 ± 1.31 vs. 1.62 ± 1.25, p = 0.055) and percentage of insulin resistance (55.3% vs. 44.4%, p = 0.086) compared to those without insomnia symptoms. Multiple logistic regressions showed that patients with insomnia symptoms had significantly higher odds for metabolic syndrome [odds ratio (OR) = 2.99, 95% confidence interval (CI) = 1.25-7.14], central obesity (OR = 3.02, 95% CI = 1.18-7.76), hypertriglyceridemia (OR = 2.46, 95% CI = 1.28-4.76) and marginally significantly higher odds for insulin resistance (OR = 1.68, 95% CI = 0.93-3.02) after controlling for potential confounders. CONCLUSIONS Within severely mentally ill patients, insomnia symptoms are associated with metabolic syndrome and insulin resistance. It appears that insomnia symptoms are independent clinical indicators of underlying metabolic syndrome in patients with severe psychiatric disorders.
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Affiliation(s)
- Wenjuan Zhang
- Department of Sleep Medicine, Shantou University Mental Health Center, Shantou, Guangdong, China; Sleep Medicine Center, Shantou University Medical College, Shantou, Guangdong, China
| | - Qimeng Sun
- Department of Sleep Medicine, Shantou University Mental Health Center, Shantou, Guangdong, China; Sleep Medicine Center, Shantou University Medical College, Shantou, Guangdong, China
| | - Baixin Chen
- Department of Sleep Medicine, Shantou University Mental Health Center, Shantou, Guangdong, China; Sleep Medicine Center, Shantou University Medical College, Shantou, Guangdong, China
| | - Maria Basta
- Department of Psychiatry, University Hospital of Heraklion, Heraklion, Crete, Greece
| | - Chongtao Xu
- Department of Sleep Medicine, Shantou University Mental Health Center, Shantou, Guangdong, China; Sleep Medicine Center, Shantou University Medical College, Shantou, Guangdong, China
| | - Yun Li
- Department of Sleep Medicine, Shantou University Mental Health Center, Shantou, Guangdong, China; Sleep Medicine Center, Shantou University Medical College, Shantou, Guangdong, China.
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Kim SR, Kim JY, Kim HY, Nho JH, Kim YH, Min SY. Factors related to malnutrition in community-dwelling patients with schizophrenia. Perspect Psychiatr Care 2019; 55:415-423. [PMID: 30430589 DOI: 10.1111/ppc.12327] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Accepted: 09/03/2018] [Indexed: 12/18/2022] Open
Abstract
PURPOSE This study aims to identify demographic, clinical, and nutritional factors related to malnutrition in community-dwelling patients with schizophrenia. DESIGN AND METHODS We used a descriptive cross-sectional design. Between June and July 2016, 188 community-dwelling patients with schizophrenia were included in this study. FINDINGS Of the 188 participants, 4.3% were classified as malnourished, and 40.4% were at risk of malnutrition. Living status, body weight, and appetite were significant factors related to malnutrition in community-dwelling patients with schizophrenia. PRACTICE IMPLICATION Assessment of nutritional status should be considered along with demographic and nutritional factors in community-dwelling patients with schizophrenia.
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Affiliation(s)
- Sung Reul Kim
- Department of Nursing, Korea University, Seoul, South Korea
| | - Ji Young Kim
- Department of Nursing, Chonbuk National University, Jeonju, South Korea
| | - Hye Young Kim
- Department of Nursing, Chonbuk National University, Jeonju, South Korea
| | - Ju-Hee Nho
- Department of Nursing, Chonbuk National University, Jeonju, South Korea
| | - Young Hwa Kim
- Department of Nursing, Chonbuk National University, Jeonju, South Korea
| | - So-Young Min
- Department of Nursing, Semyung University, Jecheon, South Korea
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Zhu Y, Womer FY, Leng H, Chang M, Yin Z, Wei Y, Zhou Q, Fu S, Deng X, Lv J, Song Y, Ma Y, Sun X, Bao J, Wei S, Jiang X, Tan S, Tang Y, Wang F. The Relationship Between Cognitive Dysfunction and Symptom Dimensions Across Schizophrenia, Bipolar Disorder, and Major Depressive Disorder. Front Psychiatry 2019; 10:253. [PMID: 31105603 PMCID: PMC6498739 DOI: 10.3389/fpsyt.2019.00253] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 04/02/2019] [Indexed: 12/18/2022] Open
Abstract
Background: Cognitive dysfunction is considered a core feature among schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD). Despite abundant literature comparing cognitive dysfunction among these disorders, the relationship between cognitive dysfunction and symptom dimensions remains unclear. The study aims are a) to identify the factor structure of the BPRS-18 and b) to examine the relationship between symptom domains and cognitive function across SZ, BD, and MDD. Methods: A total of 716 participants [262 with SZ, 104 with BD, 101 with MDD, and 249 healthy controls (HC)] were included in the study. One hundred eighty participants (59 with SZ, 23 with BD, 24 with MDD, and 74 HC) completed the MATRICS Consensus Cognitive Battery (MCCB), and 507 participants (85 with SZ, 89 with BD, 90 with MDD, and 243 HC) completed the Wisconsin Card Sorting Test (WCST). All patients completed the Brief Psychiatric Rating Scale (BPRS). Results: We identified five BPRS exploratory factor analysis (EFA) factors ("affective symptoms," "psychosis," "negative/disorganized symptoms," "activation," and "noncooperation") and found cognitive dysfunction in all of the participant groups with psychiatric disorders. Negative/disorganized symptoms were the most strongly associated with cognitive dysfunctions across SZ, BD, and MDD. Conclusions: Our findings suggest that cognitive dysfunction severity relates to the negative/disorganized symptom domain across SZ, BD, and MDD, and negative/disorganized symptoms may be an important target for effective cognitive remediation in SZ, BD, and MDD.
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Affiliation(s)
- Yue Zhu
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China.,Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Fay Y Womer
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, United States
| | - Haixia Leng
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China.,Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Miao Chang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China.,Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China.,Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Zhiyang Yin
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China.,Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Yange Wei
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China.,Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Qian Zhou
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China.,Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China.,Shanghai Mental Health Center, Shanghai, China
| | - Shinan Fu
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China.,Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Xin Deng
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China.,Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Jing Lv
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China.,Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Yanzhuo Song
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China.,Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Yinzhu Ma
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China.,Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Xinyu Sun
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China.,Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Jing Bao
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China.,Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Shengnan Wei
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China.,Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Xiaowei Jiang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China.,Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Shuping Tan
- Center for Psychiatric Research, Beijing Huilongguan Hospital, Beijing, China
| | - Yanqing Tang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China.,Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China.,Department of Gerontology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Fei Wang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China.,Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China.,Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
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Kim W, Jang SY, Lee TH, Lee JE, Park EC. Association between continuity of care and subsequent hospitalization and mortality in patients with mood disorders: Results from the Korea National Health Insurance cohort. PLoS One 2018; 13:e0207740. [PMID: 30452465 PMCID: PMC6242689 DOI: 10.1371/journal.pone.0207740] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Accepted: 11/06/2018] [Indexed: 11/18/2022] Open
Abstract
Concerns have been raised about the loss of treatment continuity in unipolar and bipolar depressive disorder patients as continuity of care (COC) may be associated with patient outcomes. This study aimed to examine the relationship between COC and subsequent hospitalization, all-cause mortality, and suicide mortality in individuals with unipolar and bipolar disorder. Data were from the National Health Insurance (NHI) cohort, 2002 to 2013. Study participants included individuals first diagnosed with unipolar depressive disorder or bipolar affective disorder. The independent variable was COC for the first year of outpatient visits after diagnosis, measured using the usual provider of care (UPC) index. The dependent variables were hospitalization in the year after COC measurement, all-cause mortality, and suicide mortality. Analysis was conducted using logistic regression and Cox proportional hazards survival regression. A total of 48,558 individuals were analyzed for hospitalization and 48,947 for all-cause and suicide mortality. Compared to the low COC group, the medium [odds ratio (OR) 0.30, 95 percent confidence interval (95% CI) 0.19–0.47] and the high COC group (OR 0.14, 95% CI 0.09–0.21) showed statistically significant decreased odds of hospitalization. Additionally, lower likelihoods of suicide death were found in the high (HR 0.35, 95% CI 0.16–0.74) compared to the low COC group. The results infer an association between COC after first diagnosis of unipolar or bipolar depressive disorder and hospitalization and suicide mortality, suggesting the potential importance of treatment continuity in improving patient outcomes.
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Affiliation(s)
- Woorim Kim
- Department of Public Health, Graduate School, Yonsei University, Seoul, Republic of Korea
- Institute of Health Services Research, Yonsei University, Seoul, Republic of Korea
| | - Suk-Yong Jang
- Department of Preventive Medicine, Eulji University College of Medicine, Daejeon, Republic of Korea
| | - Tae-Hoon Lee
- Health Insurance Review and Assessment Service, Wonju, Republic of Korea
| | - Joo Eun Lee
- Ajou University School of Medicine, Suwon, Gyeonggi-do, Republic of Korea
| | - Eun-Cheol Park
- Institute of Health Services Research, Yonsei University, Seoul, Republic of Korea
- Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
- * E-mail:
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Van Dorn RA, Desmarais SL, Grimm KJ, Tueller SJ, Johnson KL, Sellers BG, Swartz MS. The latent structure of psychiatric symptoms across mental disorders as measured with the PANSS and BPRS-18. Psychiatry Res 2016; 245:83-90. [PMID: 27529666 PMCID: PMC5067208 DOI: 10.1016/j.psychres.2016.08.029] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2016] [Revised: 07/13/2016] [Accepted: 08/08/2016] [Indexed: 02/04/2023]
Abstract
Raw data were used from five studies of adults with mental illnesses (N=4,480) in an attempt to identify a psychiatric symptoms factor structure, as measured by the Positive and Negative Syndrome Scale or the Brief Psychiatric Rating Scale, that was generalizable across participant characteristics. First, the fit of four extant models was tested via confirmatory factor analysis (CFA), then exploratory factor analyses (EFA) were conducted with a 50% random sample, followed by a CFA with the remaining 50% to confirm the EFA factor structure. Measurement invariance of the factor structure was also examined across diagnosis, sex, race, age, and hospitalization status. The extant models were not generalizable to these data. However, a 4-factor (Affective, Positive, Negative, Disorganized Cognitive Processing) model was identified that retained all items and showed invariance across participant characteristics. It is possible to obtain a psychiatric symptoms factor structure that is generalizable across patient characteristics, which has clinical and research implications. Specifically, future research examining the impact of various interventions on psychiatric symptoms among adults with mental illnesses should confirm, and assuming good model-data fit, use the 4-factor model identified in this study.
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Affiliation(s)
- Richard A Van Dorn
- Behavioral Health Epidemiology Program, RTI International, United States.
| | - Sarah L Desmarais
- Department of Psychology, North Carolina State University, United States
| | - Kevin J Grimm
- Department of Psychology, Arizona State University, United States
| | - Stephen J Tueller
- Behavioral Health Epidemiology Program, RTI International, United States
| | - Kiersten L Johnson
- Behavioral Health Epidemiology Program, RTI International, United States
| | - Brian G Sellers
- Sociology, Anthropology, & Criminology Department, Eastern Michigan University, United States
| | - Marvin S Swartz
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, United States
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