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Cui L, Wang Y, Cao L, Wu Z, Peng D, Chen J, Yang H, Rong H, Liu T, Fang Y. Age of onset for major depressive disorder and its association with symptomatology. J Affect Disord 2023; 320:682-690. [PMID: 36183820 DOI: 10.1016/j.jad.2022.09.096] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 05/31/2022] [Accepted: 09/20/2022] [Indexed: 02/02/2023]
Abstract
BACKGROUND The age of onset (AOO) is a key factor for heterogeneity in major depressive disorder (MDD). Looking at the effect of AOO on symptomatology may improve clinical outcomes. This study aims to examine whether and how AOO affects symptomatology using a machine learning approach and latent profile analysis (LPA). METHODS The study enrolled 915 participants diagnosed with MDD from eight hospitals across China. Depressive symptoms were assessed using the 17-item Hamilton Depression Rating Scale. The relationship between symptom profiles and AOO was explored using Random Forest. The effect of AOO on symptom clusters and subtypes was investigated using multiple linear regression and LPA. A continuous AOO indicator was used to conduct the analyses. RESULTS Based on the Random Forest, symptom profiles were closely associated with AOO. The regression model showed that the severity of neurovegetative symptoms was positively associated with AOO (β = 0.18, p < 0.001), and the severity of cognitive-behavioral symptoms was negatively associated with AOO (β = -0.12, p < 0.001). LPA demonstrated that the subgroups characterized by suicide and guilt had earlier onset of depression. The subgroup with the lowest global severity of depression had the latest onset. LIMITATIONS AOO was recalled retrospectively. The relative scarcity of participants with childhood and adolescence onset depression. CONCLUSIONS AOO has an important impact on symptomatology. The findings may enhance clinical evaluations for MDD and assist clinicians in promoting earlier detection and individualized care in vulnerable individuals.
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Affiliation(s)
- Lvchun Cui
- Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Yun Wang
- Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Lan Cao
- Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Zhiguo Wu
- Department of Psychiatry and Psychology, Shanghai Deji Hospital affiliated to Qingdao University, Shanghai 200331, China
| | - Daihui Peng
- Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Jun Chen
- Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Haichen Yang
- Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen 518020, China
| | - Han Rong
- Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen 518020, China
| | - Tiebang Liu
- Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen 518020, China
| | - Yiru Fang
- Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai 200031, China; Shanghai Key Laboratory of Psychotic Disorders, Shanghai 201108, China.
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Pan W, Flint J, Shenhav L, Liu T, Liu M, Hu B, Zhu T. Re-examining the robustness of voice features in predicting depression: Compared with baseline of confounders. PLoS One 2019; 14:e0218172. [PMID: 31220113 PMCID: PMC6586278 DOI: 10.1371/journal.pone.0218172] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Accepted: 05/28/2019] [Indexed: 11/19/2022] Open
Abstract
A large proportion of Depression Disorder patients do not receive an effective diagnosis, which makes it necessary to find a more objective assessment to facilitate a more rapid and accurate diagnosis of depression. Speech data is easy to acquire clinically, its association with depression has been studied, although the actual predictive effect of voice features has not been examined. Thus, we do not have a general understanding of the extent to which voice features contribute to the identification of depression. In this study, we investigated the significance of the association between voice features and depression using binary logistic regression, and the actual classification effect of voice features on depression was re-examined through classification modeling. Nearly 1000 Chinese females participated in this study. Several different datasets was included as test set. We found that 4 voice features (PC1, PC6, PC17, PC24, P<0.05, corrected) made significant contribution to depression, and that the contribution effect of the voice features alone reached 35.65% (Nagelkerke's R2). In classification modeling, voice data based model has consistently higher predicting accuracy(F-measure) than the baseline model of demographic data when tested on different datasets, even across different emotion context. F-measure of voice features alone reached 81%, consistent with existing data. These results demonstrate that voice features are effective in predicting depression and indicate that more sophisticated models based on voice features can be built to help in clinical diagnosis.
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Affiliation(s)
- Wei Pan
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jonathan Flint
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, United States of America
| | - Liat Shenhav
- Department of Computer Science, University of California Los Angeles, Los Angeles, United States of America
| | - Tianli Liu
- Institute of Population Research, Peking University, Beijing, China
| | - Mingming Liu
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Bin Hu
- School of Information Science & Engineering, Lanzhou University, Lanzhou, China
| | - Tingshao Zhu
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- * E-mail:
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Pereverseff RS, Beshai S, Dimova M. First episode indices associated with lifetime chronicity of depression among formerly depressed participants: an exploratory study. J Ment Health 2017; 29:677-683. [PMID: 28488909 DOI: 10.1080/09638237.2017.1322181] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Background: Researchers have found that an earlier age of disorder onset and the severity of the first depressive episode are associated with recurrence in depression. Additionally, neuroticism has been shown to be an important factor in the recurrence of the disorder.Aims: This exploratory study aimed to replicate and extend previous research by examining the relationships between features of the first experienced depressive episode (age of onset, severity), the five factors of personality, and lifetime chronicity of depression.Method: Forty-three previously depressed participants completed measures of personality, current depression, hopelessness, and perfectionism. Previous depressive episodes were assessed using a semi-structured interview, and features of these episodes experienced by participants were recorded.Results: Pearson correlations showed that depression chronicity, measured in the number of weeks depressed in a lifetime, was significantly correlated with age of onset and number of symptoms experienced in the first episode. Personality measures were not significantly associated with depression chronicity.Conclusion: Earlier and more severe first episodes of depression may play an important role in the recurrence of the disorder. Future research should focus on replication and determining the causal role of these features.
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Affiliation(s)
| | - Shadi Beshai
- Department of Psychology, University of Regina, Saskatchewan, Canada, and
| | - Marina Dimova
- Department of Psychology, University of Exeter, England, UK
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Docherty AR, Edwards AC, Yang F, Peterson RE, Sawyers C, Adkins DE, Moore AA, Webb BT, Bacanu SA, Flint J, Kendler KS. Age of onset and family history as indicators of polygenic risk for major depression. Depress Anxiety 2017; 34:446-452. [PMID: 28152564 PMCID: PMC5501985 DOI: 10.1002/da.22607] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Revised: 10/31/2016] [Accepted: 12/12/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The extent to which earlier age of onset (AO) is a reflection of increased genetic risk for major depression (MD) is still unknown. Previous biometrical research has provided mixed empirical evidence for the genetic overlap of AO with MD. If AO is demonstrated to be relevant to molecular polygenic risk for MD, incorporation of AO as a phenotype could enhance future genetic studies. METHODS This research estimated the SNP-based heritability of AO in the China, Oxford and VCU Experimental Research on Genetic Epidemiology (CONVERGE) case-control sample (N = 9,854; MD case, n = 4,927). Common single nucleotide polymorphism heritability of MD was also examined across both high and low median-split AO groups, and best linear unbiased predictor (BLUP) scores of polygenic risk, in split-halves, were used to predict AO. Distributions of genetic risk across early and late AO were compared, and presence of self-reported family history (FH) of MD was also examined as a predictor of AO. RESULTS AO was not significantly heritable and polygenic risk derived from the aggregated effects of common genetic variants did not significantly predict AO in any analysis. AO was modestly but significantly lower in cases with a first-degree genetic FH of MD. CONCLUSIONS Findings indicate that AO is associated with greater self-reported genetic risk for MD in cases, yet not associated with common variant polygenic risk for MD. Future studies of early MD may benefit more from the examination of important moderating variables such as early life events.
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Affiliation(s)
- Anna R. Docherty
- Virginia Institute for Psychiatric & Behavioral Genetics, Virginia Commonwealth University School of Medicine,Corresponding author: Anna R. Docherty, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University School of Medicine, 1P-132 Biotech One, 800 East Leigh Street, Richmond, VA 23220, USA. Telephone: +1 804 828 8127, fax: +1 804 828 1471,
| | - Alexis C. Edwards
- Virginia Institute for Psychiatric & Behavioral Genetics, Virginia Commonwealth University School of Medicine
| | - Fuzhong Yang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine
| | - Roseann E. Peterson
- Virginia Institute for Psychiatric & Behavioral Genetics, Virginia Commonwealth University School of Medicine
| | - Chelsea Sawyers
- Virginia Institute for Psychiatric & Behavioral Genetics, Virginia Commonwealth University School of Medicine
| | - Daniel E. Adkins
- Virginia Institute for Psychiatric & Behavioral Genetics, Virginia Commonwealth University School of Medicine
| | - Ashlee A. Moore
- Virginia Institute for Psychiatric & Behavioral Genetics, Virginia Commonwealth University School of Medicine
| | - Bradley T. Webb
- Virginia Institute for Psychiatric & Behavioral Genetics, Virginia Commonwealth University School of Medicine
| | - Silviu A. Bacanu
- Virginia Institute for Psychiatric & Behavioral Genetics, Virginia Commonwealth University School of Medicine
| | - Jonathan Flint
- Center for Neurobehavioral Genetics, UCLA Semel Institute for Neuroscience and Human Behavior,Department of Psychiatry and Biobehavioral Sciences, UCLA David Geffen School of Medicine
| | - Kenneth S. Kendler
- Virginia Institute for Psychiatric & Behavioral Genetics, Virginia Commonwealth University School of Medicine
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Genome-wide Association for Major Depression Through Age at Onset Stratification: Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium. Biol Psychiatry 2017; 81:325-335. [PMID: 27519822 PMCID: PMC5262436 DOI: 10.1016/j.biopsych.2016.05.010] [Citation(s) in RCA: 134] [Impact Index Per Article: 19.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Revised: 03/26/2016] [Accepted: 05/05/2016] [Indexed: 01/06/2023]
Abstract
BACKGROUND Major depressive disorder (MDD) is a disabling mood disorder, and despite a known heritable component, a large meta-analysis of genome-wide association studies revealed no replicable genetic risk variants. Given prior evidence of heterogeneity by age at onset in MDD, we tested whether genome-wide significant risk variants for MDD could be identified in cases subdivided by age at onset. METHODS Discovery case-control genome-wide association studies were performed where cases were stratified using increasing/decreasing age-at-onset cutoffs; significant single nucleotide polymorphisms were tested in nine independent replication samples, giving a total sample of 22,158 cases and 133,749 control subjects for subsetting. Polygenic score analysis was used to examine whether differences in shared genetic risk exists between earlier and adult-onset MDD with commonly comorbid disorders of schizophrenia, bipolar disorder, Alzheimer's disease, and coronary artery disease. RESULTS We identified one replicated genome-wide significant locus associated with adult-onset (>27 years) MDD (rs7647854, odds ratio: 1.16, 95% confidence interval: 1.11-1.21, p = 5.2 × 10-11). Using polygenic score analyses, we show that earlier-onset MDD is genetically more similar to schizophrenia and bipolar disorder than adult-onset MDD. CONCLUSIONS We demonstrate that using additional phenotype data previously collected by genetic studies to tackle phenotypic heterogeneity in MDD can successfully lead to the discovery of genetic risk factor despite reduced sample size. Furthermore, our results suggest that the genetic susceptibility to MDD differs between adult- and earlier-onset MDD, with earlier-onset cases having a greater genetic overlap with schizophrenia and bipolar disorder.
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Cheng HG, Chen S, McBride O, Phillips MR. Prospective relationship of depressive symptoms, drinking, and tobacco smoking among middle-aged and elderly community-dwelling adults: Results from the China Health and Retirement Longitudinal Study (CHARLS). J Affect Disord 2016; 195:136-43. [PMID: 26895091 DOI: 10.1016/j.jad.2016.02.023] [Citation(s) in RCA: 94] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2015] [Revised: 01/24/2016] [Accepted: 02/06/2016] [Indexed: 10/22/2022]
Abstract
BACKGROUND Previous studies in Western countries have consistently documented positive associations of smoking and heavy drinking with depressive symptoms but a prospective analysis of these relationships among middle-aged and elderly community members in China have not previously been reported. METHODS Using data from the China Health and Retirement Longitudinal Study, a two-wave nationally representative survey conducted in 15,628 adults 45 years of age and older, we estimated the prospective association between depressive symptoms and an array of smoking and drinking behaviors. Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression Scale (CES-D) short form. RESULTS Inverse associations were the dominant pattern of association. For the population as a whole, individuals with baseline depressive symptoms were less likely to start drinking (OR=0.7, 95% CI=0.5, 0.9) or smoking (OR=0.6, 95% CI=0.4, 0.8). Similarly, baseline drinkers and smokers were less likely to develop depressive symptoms (ORdrinkers=0.6, 95% CI=0.5, 0.7; ORsmokers=0.7, 95% CJ=0.6, 0.9). No evidence was found for an increased incidence or persistence of depressive symptoms among high-frequency drinkers or heavy smokers or vice versa. Males who had never smoked prior to the onset of depressive symptoms tended to have more rapid onset of tobacco dependence compared to those without such symptoms. Males and females had different association patterns. LIMITATIONS The study is observational in nature and provides limited evidence for causality. DISCUSSION The results are inconsistent with previous findings in Western countries, throwing into question the presumed universality of the association between alcohol drinking or tobacco use and depression among middle-aged and elderly adults.
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Affiliation(s)
- Hui G Cheng
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA.
| | - Shengnan Chen
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Orla McBride
- School of Psychology, University of Ulster, Londonderry, Northern Ireland
| | - Michael R Phillips
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Departments of Psychiatry and Global Health, Emory University, Atlanta, GA, USA
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Kendler KS, Aggen SH, Li Y, Lewis CM, Breen G, Boomsma DI, Bot M, Penninx BWJH, Flint J. The similarity of the structure of DSM-IV criteria for major depression in depressed women from China, the United States and Europe. Psychol Med 2015; 45:1945-1954. [PMID: 25781917 PMCID: PMC4446696 DOI: 10.1017/s0033291714003067] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2014] [Revised: 11/11/2014] [Accepted: 11/29/2014] [Indexed: 01/23/2023]
Abstract
BACKGROUND Do DSM-IV diagnostic criteria for major depression (MD) in Chinese and Western women perform in a similar manner? METHOD The CONVERGE study included interview-based assessments of women of Han Chinese descent with treated recurrent MD. Using Mplus software, we investigated the overall degree of between-sample measurement invariance (MI) for DSM-IV diagnostic criteria for MD in the CONVERGE sample and samples selected from four major Western studies from the USA and Europe matched to the inclusion criteria of CONVERGE. These analyses were performed one pair at a time. We then compared the results from CONVERGE paired with Western samples to those obtained when examining levels of MI between pairs of the Western samples. RESULTS Assuming a single factor model for the nine diagnostic criteria for MD, the level of MI based on global fit indexes observed between the CONVERGE and the four Western samples was very similar to that seen between the Western samples. Comparable results were obtained when using a two-factor structure for MI testing when applied to the 14 diagnostic criteria for MD disaggregated for weight, appetite, sleep, and psychomotor changes. CONCLUSIONS Despite differences in language, ethnicity and culture, DSM criteria for MD perform similarly in Chinese women with recurrent MD and comparable subjects from the USA and Europe. The DSM criteria for MD may assess depressive symptoms that are relatively insensitive to cultural and ethnic differences. These results support efforts to compare findings from depressed patients in China and Western countries.
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Affiliation(s)
- K. S. Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
- Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - S. H. Aggen
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Y. Li
- Wellcome Trust Centre for Human Genetics, Oxford, UK
| | - C. M. Lewis
- MRC SGDP Centre, Institute of Psychiatry, King's College London, London, UK
| | - G. Breen
- MRC SGDP Centre, Institute of Psychiatry, King's College London, London, UK
- National Institute for Health Research Biomedical Research Centre for Mental Health at the Maudsley and Institute of Psychiatry, King's College London, London, UK
| | - D. I. Boomsma
- Department of Biological Psychology and EMGO Institute of Health and Care Research, VU University, Amsterdam, The Netherlands
| | - M. Bot
- Department of Psychiatry and EMGO Institute of Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands
| | - B. W. J. H. Penninx
- Department of Psychiatry and EMGO Institute of Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands
| | - J. Flint
- Wellcome Trust Centre for Human Genetics, Oxford, UK
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