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Zhang H, Xu Y, Xu Y. The value of the platelet/high-density lipoprotein cholesterol ratio in predicting depression and its cardiovascular disease mortality: a population-based observational study. Front Endocrinol (Lausanne) 2024; 15:1402336. [PMID: 39149124 PMCID: PMC11325088 DOI: 10.3389/fendo.2024.1402336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Accepted: 07/15/2024] [Indexed: 08/17/2024] Open
Abstract
Background Cardiovascular disease (CVD) and depression have a bidirectional association, with inflammation and metabolic factors being common important triggers for both conditions. However, as a novel inflammatory and metabolic marker, platelet-to-HDL-C ratio (PHR) has not been established in relation to depression and cardiovascular disease. Materials and methods Participants aged 20 years and older were included in the 2005-2018 NHANES database. PHR was calculated as the ratio of platelet count (1000 cells/μL) to HDL-C (mmol/L). The Patient Health Questionnaire (PHQ-9) was used to diagnose depression, with a cutoff value of 10. Weighted logistic regression analysis and restricted cubic spline (RCS) analysis were employed to examine the association between PHR and depression-related features. Additionally, weighted COX regression and RCS were used to analyze the association of PHR with CVD mortality in patients with depression. Receiver operating characteristic curves were used to assess whether PHR had an advantage over HDL-C in predicting depression. Finally, the mediating role of PHR in the latest cardiovascular health indicator Life's Essential 8 and depression was explored. Results A total of 26,970 eligible participants were included, including 2,308 individuals with depression, representing approximately 160 million U.S. adults when weighted. After full adjustment, we estimated that the odds ratio (OR) of depression associated with a per standard deviation (SD) increase in PHR was 1.06 (95% CI: 1.01-1.12, P=0.03). The restricted cubic spline (RCS) analysis indicated a linear association (Nonlinear P=0.113). When PHR was divided into four groups based on quartiles and included in the model after full adjustment for depression risk factors, participants in quartile 2, quartile 3, and quartile 4 of PHR showed a trend of increasing risk of depression compared to the lowest quartile group (P trend=0.01). In addition, weighted COX regression and RCS revealed that a per SD increase in PHR was associated with a higher risk of CVD mortality among patients with depression (HR: 1.38, 95% CI: 1.05-1.81, P=0.02, Nonlinear P=0.400). Subgroup analyses showed that current alcohol consumption enhanced the association between PHR and depression (P for interaction=0.017). Furthermore, the areas under the ROC curves (AUC) were 0.556 (95% CI, 0.544-0.568; P < 0.001) for PHR and 0.536 (95% CI, 0.524-0.549; P < 0.001) for HDL-C (PDeLong = 0.025). Finally, mediation analysis indicated that PHR was an intermediate mechanism between LE8 and depression (mediation proportion=5.02%, P=0.02). Conclusion In U.S. adults, an increase in PHR linearly increases the risk of depression and CVD mortality among individuals with depression. Additionally, PHR has a better predictive advantage for depression compared to HDL-C. Furthermore, PHR significantly mediates the association between LE8 scores and depression.
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Affiliation(s)
- Huifeng Zhang
- Department of Cardiovascular, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
| | - Ying Xu
- Department of Hematology, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
| | - Yaying Xu
- Department of Endocrinology, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
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Menculini G, Cinesi G, Scopetta F, Cardelli M, Caramanico G, Balducci PM, De Giorgi F, Moretti P, Tortorella A. Major challenges in youth psychopathology: treatment-resistant depression. A narrative review. Front Psychiatry 2024; 15:1417977. [PMID: 39056019 PMCID: PMC11269237 DOI: 10.3389/fpsyt.2024.1417977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 06/20/2024] [Indexed: 07/28/2024] Open
Abstract
Major depressive disorder (MDD) represents a major health issue in adolescents and young adults, leading to high levels of disability and profoundly impacting overall functioning. The clinical presentation of MDD in this vulnerable age group may slightly differ from what can be observed in adult populations, and psychopharmacological strategies do not always lead to optimal response. Resistance to antidepressant treatment has a prevalence estimated around 40% in youths suffering from MDD and is associated with higher comorbidity rates and suicidality. Several factors, encompassing biological, environmental, and clinical features, may contribute to the emergence of treatment-resistant depression (TRD) in adolescents and young adults. Furthermore, TRD may underpin the presence of an unrecognized bipolar diathesis, increasing the overall complexity of the clinical picture and posing major differential diagnosis challenges in the clinical practice. After summarizing current evidence on epidemiological and clinical correlates of TRD in adolescents and young adults, the present review also provides an overview of possible treatment strategies, including novel fast-acting antidepressants. Despite these pharmacological agents are promising in this population, their usage is expected to rely on risk-benefit ratio and to be considered in the context of integrated models of care.
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Affiliation(s)
- Giulia Menculini
- Section of Psychiatry, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Gianmarco Cinesi
- Section of Psychiatry, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Francesca Scopetta
- Section of Psychiatry, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Matteo Cardelli
- Section of Psychiatry, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Guido Caramanico
- Section of Psychiatry, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Pierfrancesco Maria Balducci
- Section of Psychiatry, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
- Community Mental Health Center “CSM Terni”, Department of Psychiatry, Local Health Unit USL Umbria 2, Terni, Italy
| | - Filippo De Giorgi
- Division of Psychiatry, Clinical Psychology and Rehabilitation, General Hospital of Perugia, Perugia, Italy
| | - Patrizia Moretti
- Section of Psychiatry, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Alfonso Tortorella
- Section of Psychiatry, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
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Kawilapat S, Traisathit P, Maneeton N, Prasitwattanaseree S, Kongsuk T, Arunpongpaisal S, Leejongpermpoon J, Sukhawaha S, Maneeton B. Factor Structures in the Depressive Symptoms Domains in the 9Q for Northern Thai Adults and Their Association with Chronic Diseases. Behav Sci (Basel) 2024; 14:577. [PMID: 39062400 PMCID: PMC11274117 DOI: 10.3390/bs14070577] [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: 03/28/2024] [Revised: 06/03/2024] [Accepted: 07/01/2024] [Indexed: 07/28/2024] Open
Abstract
Most of the common models to examine depression are one-factor models; however, previous studies provided several-factor structure models on each depressive symptom using the Patient Health Questionnaire-9 (PHQ-9). The Nine-Questions Depression-Rating Scale (9Q) is an alternative assessment tool that was developed for assessing the severity of depressive symptoms in Thai adults. This study aimed to examine the factor structure of this tool based on the factor structure models for the PHQ-9 provided in previous studies using confirmatory factor analysis (CFA). We also examined the association of chronic diseases and depressive symptoms using the Multiple Indicators Multiple Causes model among 1346 participants aged 19 years old or more without psychiatric disorders. The results show that the two-factor CFA model with six items in the cognitive-affective domain and three items in the somatic domain provided the best fit for depressive symptoms in the study population (RMSEA = 0.077, CFI = 0.953, TLI = 0.936). Dyslipidemia was positively associated with both cognitive-affective symptoms (β = 0.120) and somatic depressive symptoms (β = 0.080). Allergies were associated with a higher level of cognitive-affective depressive symptoms (β = 0.087), while migraine (β = 0.114) and peptic ulcer disease (β = 0.062) were associated with a higher level of somatic symptoms. Increased age was associated with a lower level of somatic symptoms (β = -0.088). Our findings suggested that considering depressive symptoms as two dimensions yields a better fit for depressive symptoms. The co-occurrence of chronic diseases associated with depressive symptoms should be monitored.
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Affiliation(s)
- Suttipong Kawilapat
- Department of Statistics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand; (S.K.); (P.T.); (S.P.)
- Department of Psychiatry, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand;
| | - Patrinee Traisathit
- Department of Statistics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand; (S.K.); (P.T.); (S.P.)
| | - Narong Maneeton
- Department of Psychiatry, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand;
| | - Sukon Prasitwattanaseree
- Department of Statistics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand; (S.K.); (P.T.); (S.P.)
| | - Thoranin Kongsuk
- Prasrimahabhodi Psychiatric Hospital, Ubon Ratchathani 34000, Thailand; (T.K.); (J.L.); (S.S.)
- Somdet Chaopraya Institute of Psychiatry, Bangkok 10600, Thailand
| | - Suwanna Arunpongpaisal
- Department of Psychiatry, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand;
| | - Jintana Leejongpermpoon
- Prasrimahabhodi Psychiatric Hospital, Ubon Ratchathani 34000, Thailand; (T.K.); (J.L.); (S.S.)
| | - Supattra Sukhawaha
- Prasrimahabhodi Psychiatric Hospital, Ubon Ratchathani 34000, Thailand; (T.K.); (J.L.); (S.S.)
| | - Benchalak Maneeton
- Department of Psychiatry, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand;
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Wan W, Yu Y. Association between the triglyceride glucose index and depression: a meta-analysis. Front Psychiatry 2024; 15:1390631. [PMID: 38966187 PMCID: PMC11222386 DOI: 10.3389/fpsyt.2024.1390631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 05/27/2024] [Indexed: 07/06/2024] Open
Abstract
Background Obesity and diabetes have been associated with depressive symptoms. The aim of this systematic review and meta-analysis was to evaluate the association between the triglyceride glucose index (TyG index) a novel indicator of insulin resistance (IR) and depression in the adult population. Methods Relevant observational studies were acquired through comprehensive searches of the Medline, Web of Science, Embase, Wanfang, and China National Knowledge Internet databases. To account for heterogeneity, a random-effects model was employed to combine the findings. Additionally, multiple subgroup analyses were conducted to assess the impact of various study characteristics on the outcome. Results The meta-analysis comprised eight datasets from six cross-sectional studies, encompassing a total of 28,973 adults. The pooled findings suggested that subjects with a high TyG index, compared to those with a low TyG index, were associated with a higher prevalence of depression (odds ratio [OR]: 1.41, 95% confidence interval (CI): 1.28-1.56, p<0.001; I2 = 19%). Sensitivity analyses, by omitting one dataset at a time, showed consistent results (OR: 1.39-1.45, p<0.05). Further subgroup analyses showed consistent results in participants aged <50 years old and in those aged ≥50 years old, in men and in women, in studies with different cutoff values for the TyG index, and in studies with different methods for the diagnosis of depression (for each subgroup difference, p>0.05). Conclusion A high TyG index may be associated with a higher prevalence of depression in the adult population.
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Affiliation(s)
- Weitao Wan
- Department of Psychiatry, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, Hubei, China
| | - Yi Yu
- Department of Psychiatry, Wuchang Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, Hubei, China
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Liu Z, Sun L, Sun F, Zhang Y, Wang J, Zhang Z, Sun G, Sun L, Yang R, Yao G, Liu Y. The abnormalities of lipid metabolism in children and adolescents with major depressive disorder and relationship with suicidal ideation and attempted suicide. Heliyon 2024; 10:e30344. [PMID: 38726112 PMCID: PMC11079100 DOI: 10.1016/j.heliyon.2024.e30344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 04/11/2024] [Accepted: 04/24/2024] [Indexed: 05/12/2024] Open
Abstract
Background Major depressive disorder (MDD) is a widespread health issue in many countries, which has an extremely negative impact on the health of children and adolescents in particular. In the context of depression and metabolic disorders, dyslipidemia and metabolism-related problems become more prominent comorbidities. However, they continue to be the main barrier to the successful recovery of the clinical progress. In this study we investigated the rate of dyslipidemia, additional risk factors among Chinese children and adolescents with MDD, and association of the suicidal behavior with lipid levels. Methods The study took 756 people from the Third People's Hospital of Fuyang between January 2020 and December 2021, aged between 8 and 18, with major depressive disorders diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). We determined the FBG (fasting blood glucose) and lipid parameters in all subjects and also investigated the history of suicidal ideation, the cases of attempted suicide, and the scores of depressive symptoms. Sociodemographic and clinical data were gathered and analyzed using the SPSS-23.0 version. Results The prevalence of hypercholesterolemia, hypertriglyceridemia, high LDL-C, and low HDL-C were 5.42 % (41/756), 10.58 % (80/756), 3.84 % (29/756) and 5.42 % (41/756) respectively. For hypercholesterolemia and hypertriglyceridemia, they were positive associated with suicidal ideation and suicide attempts, and the positive correlation is shown between low HDL-C levels and suicide attempts. Nevertheless, non-ideation and inversely suicidal attempts were not discovered among high-LDL-C subjects. Logistic analysis showed that high levels of FBG (OR = 2.86, 95 % CI: 1.31-6.25, P = 0.008) and worse LDL-C (OR = 357.82, 95 % CI: 66.16-1935.10, P < 0.001) are the independent associated factors for hypercholesterolemia. More hospitalizations (OR = 1.89, 95 % CI: 1.07-3.35, P = 0.028), obesity (OR = 2.55, 95 % CI: 1.25-5.18, P = 0.010), high levels of TC (OR = 2.15, 95 % CI: 1.03-4.48, P = 0.042), and higher doses of antidepressants (OR = 1.02, 95 % CI: 1.00-1.04, P = 0.029) were independently associated factors for hypertriglyceridemia, while high levels of HDL-C (OR = 0.11, 95 % CI: 0.04-0.31, P < 0.001) were protective factors. In addition, high levels of TC (OR = 113.94, 95 % CI: 20.01-648.85) were statistically different (P < 0.001) and suggested that the factor was significantly related to high LDL-C. Meanwhile, older age (OR = 1.25, 95 % CI: 1.02-1.52, P = 0.030) and high levels of TG (OR = 3.00, 95 % CI: 1.98-4.55, P < 0.001) were independent factors contributing to low HDL-C. Conclusion The high prevalence of dyslipidemia in childhood and adolescence among children and adolescents with depressive disorder has become a public health issue. Hypercholesterolemia and hypertriglyceridemia showed a positive correlation with suicidal thoughts and suicidal attempts. Monitoring the incidence of suicidal thoughts and attempts among them would carry some predictor meaning in therapy and for jumping back to health.
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Affiliation(s)
- Zhiwei Liu
- Department of Psychiatry, The Third People's Hospital of Fuyang, Fuyang, 236015, China
- Department of Psychiatry, Fuyang Mental Health Center, Fuyang, 236015, China
| | - Liang Sun
- Department of Psychiatry, The Third People's Hospital of Fuyang, Fuyang, 236015, China
- Department of Psychiatry, Fuyang Mental Health Center, Fuyang, 236015, China
| | - Feng Sun
- Department of Psychiatry, The Third People's Hospital of Fuyang, Fuyang, 236015, China
- Department of Psychiatry, Fuyang Mental Health Center, Fuyang, 236015, China
| | - Yulong Zhang
- Department of Psychiatry, Chaohu Hospital of Anhui Medical University, Hefei, 238000, China
- Department of Psychiatry, School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 238000, China
| | - Juan Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 610036, China
- Department of Psychiatry, The Fourth People's Hospital of Chengdu, Chengdu, 610036, China
| | - Zhaokun Zhang
- Department of Psychiatry, The Third People's Hospital of Fuyang, Fuyang, 236015, China
- Department of Psychiatry, Fuyang Mental Health Center, Fuyang, 236015, China
| | - Guangying Sun
- Department of Psychiatry, The Third People's Hospital of Fuyang, Fuyang, 236015, China
- Department of Psychiatry, Fuyang Mental Health Center, Fuyang, 236015, China
| | - Longlong Sun
- Department of Psychiatry, The Third People's Hospital of Fuyang, Fuyang, 236015, China
- Department of Psychiatry, Fuyang Mental Health Center, Fuyang, 236015, China
| | - Rongchun Yang
- Department of Psychiatry, The Third People's Hospital of Fuyang, Fuyang, 236015, China
- Department of Psychiatry, Fuyang Mental Health Center, Fuyang, 236015, China
| | - Gaofeng Yao
- Department of Psychiatry, The Third People's Hospital of Fuyang, Fuyang, 236015, China
- Department of Psychiatry, Fuyang Mental Health Center, Fuyang, 236015, China
| | - Yun Liu
- Department of Psychiatry, The Third People's Hospital of Fuyang, Fuyang, 236015, China
- Department of Psychiatry, Fuyang Mental Health Center, Fuyang, 236015, China
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Zhou X, Tao XL, Zhang L, Yang QK, Li ZJ, Dai L, Lei Y, Zhu G, Wu ZF, Yang H, Shen KF, Xu CM, Liang P, Zheng X. Association between cardiometabolic index and depression: National Health and Nutrition Examination Survey (NHANES) 2011-2014. J Affect Disord 2024; 351:939-947. [PMID: 38341157 DOI: 10.1016/j.jad.2024.02.024] [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: 09/30/2023] [Revised: 02/03/2024] [Accepted: 02/07/2024] [Indexed: 02/12/2024]
Abstract
BACKGROUND Emerging evidence suggests a common pathophysiological basis for metabolic disorders and mental diseases. Despite the existence of reports suggesting a strong connection between dyslipidemia and depression, a comprehensive and reliable indicator to identify depression is still lacking. Cardiometabolic index (CMI) is an integrated index calculated from three vital metabolic indicators, including triglyceride (TG), high-density lipoprotein cholesterol (HDLC) and waist height ratio (WHtR). OBJECTIVE This study aims to explore the association between CMI and depression. METHODS Cross-sectional data of participants with complete information of CMI, depression, and other covariates were obtained from the National Health and Nutrition Examination Survey (NHANES). Weighted student's t-test and Chi-square test were used to identify the differences between two groups. Weighted multivariate logistic regression model, restricted cubic spline (RCS) regression analysis, subgroup analysis and interaction tests were conducted to explore the association between CMI and depression. Receiver operating curve (ROC) analysis and area under the curve (AUC) were also utilized to evaluate the performance of CMI in identifying depression. RESULTS A positive correlation between CMI and depression was observed in 3794 participants included in the study, which was further confirmed to be non-linear via RCS regression analysis, with two significant inflection points being identified, including 0.9522 and 1.58. In the crude or adjusted models, individuals with a CMI level ≥ 0.9522 exhibited remarkably increased risk for developing depression. CMI got an AUC of 0.748 in identifying depression. Subgroup analyses and interaction tests indicate that the association between CMI and depression remained consistent across different subgroups and was not modified by other covariates except drinking. Those who are current drinkers and with a high CMI are more susceptible to suffer depression. CONCLUSIONS An elevated CMI is linked to increased risk for depression. Addressing dyslipidemia and improving lipid levels may potentially lower the risk for depression.
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Affiliation(s)
- Xiang Zhou
- Department of Neurosurgery, Epilepsy Research Center of PLA, Xinqiao Hospital, Army Medical University, Chongqing 400037, China; Cadet Brigade 4, College of Basic Medicine, Army Medical University, Chongqing 400038, China
| | - Xiao-Liang Tao
- National & Regional United Engineering Lab of Tissue Engineering, Department of Orthopedics, Southwest Hospital, Army Medical University, Chongqing 400038, China
| | - Li Zhang
- Department of Neurosurgery, Epilepsy Research Center of PLA, Xinqiao Hospital, Army Medical University, Chongqing 400037, China; Department of neurosurgery, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics. No.136 of Zhong shan Second Road, Yu zhong District, Chongqing 400014, China
| | - Qian-Kun Yang
- National & Regional United Engineering Lab of Tissue Engineering, Department of Orthopedics, Southwest Hospital, Army Medical University, Chongqing 400038, China
| | - Zi-Jiao Li
- Cadet Brigade 4, College of Basic Medicine, Army Medical University, Chongqing 400038, China
| | - Lu Dai
- Chongqing Institute for Brain and Intelligence, Guang yang Bay Laboratory, Chongqing 400064, China
| | - Ya Lei
- Chongqing Institute for Brain and Intelligence, Guang yang Bay Laboratory, Chongqing 400064, China
| | - Gang Zhu
- Department of Neurosurgery, Epilepsy Research Center of PLA, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Zhi-Feng Wu
- Department of Pediatrics, Second Affiliated Hospital of the Army Medical University, Chongqing 400037, China
| | - Hui Yang
- Department of Neurosurgery, Epilepsy Research Center of PLA, Xinqiao Hospital, Army Medical University, Chongqing 400037, China; Chongqing Institute for Brain and Intelligence, Guang yang Bay Laboratory, Chongqing 400064, China
| | - Kai-Feng Shen
- Department of Neurosurgery, Epilepsy Research Center of PLA, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Chun-Mei Xu
- Department of Neurology, the Second People's Hospital of Liangshan Yi Autonomous Prefecture, China
| | - Ping Liang
- Department of neurosurgery, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics. No.136 of Zhong shan Second Road, Yu zhong District, Chongqing 400014, China
| | - Xin Zheng
- Department of Neurosurgery, Epilepsy Research Center of PLA, Xinqiao Hospital, Army Medical University, Chongqing 400037, China.
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7
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Wang H, Chen X, Lin F, Zheng J, Chen K, Wang X, Lin X, Chen X, Ye Q, Wang Y, Wu X, Cai G. Association between ethylene oxide levels and depressive symptoms: A cross-sectional study based on NHANES 2013-2018 database. J Affect Disord 2024; 348:135-142. [PMID: 38154580 DOI: 10.1016/j.jad.2023.12.050] [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: 07/27/2023] [Revised: 10/29/2023] [Accepted: 12/23/2023] [Indexed: 12/30/2023]
Abstract
BACKGROUND AND AIM Ethylene oxide (EO) is a commonly used compound with known health risks. However, the specific association between EO exposure and the development of depressive symptoms has not been well established. Therefore, this study aimed to examine the potential association between EO exposure, as indicated by hemoglobin adduct of ethylene oxide (HbEO) levels, and the occurrence of depressive symptoms. METHODS We employed logistic regression, restricted cubic spline, and subgroup analysis to investigate the association between EO exposure and the occurrence of depressive symptoms. Additionally, we conducted a mediating effect analysis to explore the potential factors influencing the association between EO exposure and depressive symptoms. RESULTS Elevated HbEO levels were associated with the development of depressive symptoms. After adjusting for potential confounders, the highest quartile of HbEO levels showed an odds ratio (OR) of 3.37 [95 % confidence interval (CI): 1.87-6.10, P = 0.002] compared with the lowest quartile. Additionally, a linear association was observed between HbEO levels and the risk of depressive symptoms. We also revealed that the levels of several inflammatory factors and triglycerides mediated the association between EO exposure and the occurrence of depressive symptoms. CONCLUSIONS Higher levels of EO exposure were related to an increased risk of developing depressive symptoms. The analysis also suggested that the inflammatory response might play a mediating role in the pathway from EO exposure to depressive symptoms.
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Affiliation(s)
- Huaicheng Wang
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou 350001, China; Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou 350001, China; cFujian Key Laboratory of Molecular Neurology, Fujian Medical University, 88 Jiaotong Road, Fuzhou 350001, China; Fujian medical University, Fuzhou, China
| | - Xuanjie Chen
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou 350001, China; Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou 350001, China; cFujian Key Laboratory of Molecular Neurology, Fujian Medical University, 88 Jiaotong Road, Fuzhou 350001, China; Fujian medical University, Fuzhou, China
| | - Fabin Lin
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou 350001, China; Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou 350001, China; cFujian Key Laboratory of Molecular Neurology, Fujian Medical University, 88 Jiaotong Road, Fuzhou 350001, China; Fujian medical University, Fuzhou, China; Department of Neurosurgery, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou 350001, China
| | - Jiayi Zheng
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou 350001, China; Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou 350001, China; cFujian Key Laboratory of Molecular Neurology, Fujian Medical University, 88 Jiaotong Road, Fuzhou 350001, China; Fujian medical University, Fuzhou, China
| | - Ke Chen
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou 350001, China; Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou 350001, China; cFujian Key Laboratory of Molecular Neurology, Fujian Medical University, 88 Jiaotong Road, Fuzhou 350001, China; Fujian medical University, Fuzhou, China
| | - Xuefei Wang
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou 350001, China; Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou 350001, China; cFujian Key Laboratory of Molecular Neurology, Fujian Medical University, 88 Jiaotong Road, Fuzhou 350001, China; Fujian medical University, Fuzhou, China
| | - Xuan Lin
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou 350001, China; Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou 350001, China; cFujian Key Laboratory of Molecular Neurology, Fujian Medical University, 88 Jiaotong Road, Fuzhou 350001, China; Fujian medical University, Fuzhou, China
| | - Xiaochun Chen
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou 350001, China; Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou 350001, China; cFujian Key Laboratory of Molecular Neurology, Fujian Medical University, 88 Jiaotong Road, Fuzhou 350001, China; Fujian medical University, Fuzhou, China
| | - Qinyong Ye
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou 350001, China; Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou 350001, China; cFujian Key Laboratory of Molecular Neurology, Fujian Medical University, 88 Jiaotong Road, Fuzhou 350001, China; Fujian medical University, Fuzhou, China
| | - Yingqing Wang
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou 350001, China; Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou 350001, China; cFujian Key Laboratory of Molecular Neurology, Fujian Medical University, 88 Jiaotong Road, Fuzhou 350001, China; Fujian medical University, Fuzhou, China.
| | - Xilin Wu
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou 350001, China; Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou 350001, China; cFujian Key Laboratory of Molecular Neurology, Fujian Medical University, 88 Jiaotong Road, Fuzhou 350001, China; Fujian medical University, Fuzhou, China.
| | - Guoen Cai
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou 350001, China; Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou 350001, China; cFujian Key Laboratory of Molecular Neurology, Fujian Medical University, 88 Jiaotong Road, Fuzhou 350001, China; Fujian medical University, Fuzhou, China.
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Yang R, Lin Z, Cai Y, Chen N, Zhou Y, Zhang J, Hong G. Assessing the risk of prenatal depressive symptoms in Chinese women: an integrated evaluation of serum metabolome, multivitamin supplement intake, and clinical blood indicators. Front Psychiatry 2024; 14:1234461. [PMID: 38274432 PMCID: PMC10808622 DOI: 10.3389/fpsyt.2023.1234461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 12/11/2023] [Indexed: 01/27/2024] Open
Abstract
Background Prenatal depressive symptoms (PDS) is a serious public health problem. This study aimed to develop an integrated panel and nomogram to assess at-risk populations by examining the association of PDS with the serum metabolome, multivitamin supplement intake, and clinical blood indicators. Methods This study comprised 221 pregnant women, categorized into PDS and non-PDS groups based on the Edinburgh postnatal depression scale. The participants were divided into training and test sets according to their enrollment time. We conducted logistic regression analysis to identify risk factors, and employed liquid chromatography/high resolution mass spectrometry-based serum metabolome analysis to identify metabolic biomarkers. Multiple factor analysis was used to combine risk factors, clinical blood indicators and key metabolites, and then a nomogram was developed to estimate the probability of PDS. Results We identified 36 important differential serum metabolites as PDS biomarkers, mainly involved in amino acid metabolism and lipid metabolism. Multivitamin intake works as a protective factor for PDS. The nomogram model, including multivitamin intake, HDL-C and three key metabolites (histidine, estrone and valylasparagine), exhibited an AUC of 0.855 in the training set and 0.774 in the test set, and the calibration curves showed good agreement, indicating that the model had good stability. Conclusion Our approach integrates multiple models to identify metabolic biomarkers for PDS, ensuring their robustness. Furthermore, the inclusion of dietary factors and clinical blood indicators allows for a comprehensive characterization of each participant. The analysis culminated in an intuitive nomogram based on multimodal data, displaying potential performance in initial PDS risk assessment.
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Affiliation(s)
- Rongrong Yang
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Integration in Vaccine Research, School of Public Health, Xiamen University, Xiamen, China
| | - Zhenguo Lin
- Department of Clinical Medicine, Xiamen Medical College, Xiamen, China
| | - Yanhua Cai
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Integration in Vaccine Research, School of Public Health, Xiamen University, Xiamen, China
| | - Nan Chen
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Integration in Vaccine Research, School of Public Health, Xiamen University, Xiamen, China
| | - Ying Zhou
- Department of Obstetrics and Gynecology, Clinical Medical Research Center for Obstetrics and Gynecology Diseases, The First Affiliated Hospital of Xiamen University, Xiamen, China
| | - Jie Zhang
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Integration in Vaccine Research, School of Public Health, Xiamen University, Xiamen, China
| | - Guolin Hong
- Department of Laboratory Medicine, Xiamen Key Laboratory of Genetic Testing, The First Affiliated Hospital of Xiamen University, School of Public Health, Xiamen University, Xiamen, China
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