Early life family disadvantages and major depression in adulthood

1999 ◽  
Vol 174 (2) ◽  
pp. 112-120 ◽  
Author(s):  
Hartwin S. Sadowski ◽  
Blanca Ugarte ◽  
Israel Kolvin ◽  
Carole E. Kaplan ◽  
Jacqueline Barnes
Keyword(s):  
Mental Health ◽  
Major Depressive Disorder ◽  
Major Depression ◽  
Depressive Disorder ◽  
Early Life ◽  
Major Depressive ◽  
Adult Depression ◽  
Increased Risk ◽  
Quality Of Parenting

BackgroundThere is evidence that exposure to social and family disadvantages in childhood are a risk factor for adult depression.AimsTo explore the effects of multiple adversity in early childhood on adult depression, and the relative effects of the different adversities.MethodThis study utilises data from the Newcastle Thousand Family Study. Information on childhood disadvantages was collected when the participants were 5 years old, and information on mental health was gathered when they were 33 years old. Mental health data were scrutinised blind to the evidence of early disadvantage, and best-estimate diagnoses of major depressive disorder were made according to DSM–III–R criteria.ResultsMultiple family disadvantages in childhood substantially increase the risk of suffering a major depressive disorder in adulthood. Such disadvantages include family or marital relationship instability, a combination of poor mothering and poor physical care, and a combination of dependence on social welfare and overcrowding. For females major depression was linked in particular to the quality of parenting in early life.ConclusionsSocial and family (especially multiple family) disadvantages during childhood predispose individuals to an increased risk of major depression in adulthood.

scholarly journals A Review of the Role of Interleukin-2 in the Pathophysiology of Major Depressive Disorder in Hospitalized Patients

2021 ◽  
Vol 6 (3) ◽  
pp. 85-92
Author(s):  
Seyed Alireza Seyed Ebrahimi ◽  
Elham Karamian ◽  
Zahra Sadat Goli ◽  
Leila Sadat Mirseifi
Keyword(s):  
Major Depressive Disorder ◽  
Major Depression ◽  
Depressive Disorder ◽  
Inflammatory Cytokines ◽  
Interleukin 2 ◽  
Hospitalized Patients ◽  
The Body ◽  
Major Depressive ◽  
Increased Risk

Background: Hospitalization due to any reason or medical condition is associated with fear, anxiety, and depression. Psychological and physiological factors have a significant impact on hospitalization outcomes. Objectives: Given the functional importance of inflammatory cytokines and studies in previous studies on the relationship between inflammatory cytokines and major depressive disorder, we will focus more on studies on the role of interleukin 2 (IL-2) in the pathophysiology of major depressive disorder in hospitalized patients. Methods: We used PubMed, Scopus, and Elsevier databases to search for articles from 1999 to 2021, emphasizing the studies of the last five years. Results: In general, there was no consistent pattern in the observed relationships between cytokine concentrations or changes and clinical signs of significant depression. IL-2 and IL, two receptors in the body, play an essential role in the treatment and the pathophysiology of depression and major depression. Conclusion: Finally, it can be concluded that hospitalization generally exposes the patient to inflammation. Studies show an increased risk of inflammation following hospitalization of patients, and many studies confirm the association of major depression with inflammatory cytokines and, more concentrated, IL-2.

scholarly journals Obesity, food insecurity, and depression among females

2020 ◽  
Vol 78 (1) ◽  
Author(s):  
Manik Ahuja ◽  
Thiveya Sathiyaseelan ◽  
Rajvi J. Wani ◽  
Praveen Fernandopulle
Keyword(s):  
Mental Health ◽  
Logistic Regression ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Food Insecurity ◽  
Mental Health Problems ◽  
Major Depressive ◽  
Logistic Regression Models ◽  
Increased Risk ◽  
The Impact

Abstract Background Nutritional psychiatry is an emerging field of research and it is currently exploring the impact of nutrition and obesity on brain function and mental illness. Prior studies links between obesity, nutrition and depression among women. However, less is known how food insecurity may moderate that relationship. Methods Data were employed from the Collaborative Psychiatric Epidemiology Surveys (CPES), 2001–2003. Two logistic regression models were Logistic regression was used to determine the association between obesity, gender, food insecurity, and past year Major Depressive Disorder (MDD). We then stratified by gender, and tested the association between obesity and past year MDD, and if food insecurity moderated the association. Results Obesity was associated with an increased risk for past year Major Depressive Disorder (MDD) among females (AOR = 1.35; 95% CI 1.17–1.55) and was not associated among males (AOR = 1.07; 95% CI, 0.86–1.32). Women who reported that reported both obesity and food insecurity reported higher odds of past year MDD episode (AOR = 3.16; 95% CI, 2.36–4.21, than women who did not report food insecurity (AOR = 1.08; 95% CI, 1.02–1.38). Conclusion With rising rates of mental health problems, females should be closely monitored to understand how poor diets, food insecurity, and obesity play a role in mental health outcomes. It is recommended that clinicians and treatment providers consider the patient’s diet and access to nutritious foods when conducting their assessment.

DSM–III Major Depressive Disorder in the Community

1989 ◽  
Vol 155 (1) ◽  
pp. 48-54 ◽  
Author(s):  
William W. Eaton ◽  
Amy Dryman ◽  
Ann Sorenson ◽  
Allan McCutcheon
Keyword(s):  
Mental Health ◽  
Major Depressive Disorder ◽  
Major Depression ◽  
Depressive Disorder ◽  
Catchment Area ◽  
Latent Class ◽  
Major Depressive ◽  
The Third ◽  
Epidemiological Surveys ◽  
Strong Resemblance

The fit of the structure of DSM–III major depressive disorder to data from two large epidemiological surveys is assessed by latent class analysis. The surveys were conducted at the Baltimore and Raleigh–Durham sites of the National Institute of Mental Health (NIMH) Epidemiologic Catchment Area Program. Three classes are required to fit the data, and the third class bears a strong resemblance to major depressive disorder, although it requires slightly more symptoms to be present than DSM–III. The derived structure replicates successfully for Baltimore and Raleigh-Durham, with a prevalence of the major depression category of 0.9% for both sites.

Schizophrenia and major depression: Resilience, coping styles, personality traits, self-esteem and quality of life

2017 ◽  
Vol 41 (S1) ◽  
pp. S192-S193
Author(s):  
S. Di Marco ◽  
A. Feggi ◽  
E. Cammarata ◽  
L. Girardi ◽  
F. Bert ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Mental Health ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Coping Strategies ◽  
Self Esteem ◽  
Major Depressive ◽  
Perceived Quality Of Life ◽  
Schizophrenic Patients

IntroductionResilience is commonly defined as positive adaptation to adverse events or as the ability to maintain or regain mental health after exposure to difficulties. According to the bio-psycho-social model, resilience is influenced by self-esteem, coping strategies and personality traits. In schizophrenic patients, resilience seems to affect real-life functioning, while in mood disorders, resilience influences the longitudinal course of the disorder, reducing the frequency of relapses and improving drugs response.ObjectivesThe aim of this study is to asses levels of resilience and self-esteem, coping strategies, perceived quality of life and temperament characteristics in a sample composed by patients with major depressive disorder and patients affected by schizophrenia.MethodsWe collected a sample composed by 40 patients with major depressive disorder and 40 patients affected by schizophrenia patients recruited at the “Maggiore della Carità” Hospital in Novara, Italy. The assessment protocol included: Resilience Scale for Adults (RSA), Coping Orientation to Problems Experienced Inventory–Brief (BRIEF–COPE), Rosenberg Self-esteem Scale (RSES), Paykel List Of Stressful Events, Temperamental and Character Inventory (TCI) and Short form 36 (SF-36). Comparison of qualitative data was performed by means of the χ2, a t-test was performed for continuous normal-distribution variables otherwise a non-parametric Mann–Whitney test was performed. Statistical significance was set at P ≤ 0.05.ConclusionsIn patients with major depressive disorder resilience were associated with a good self-perception of physical and mental health, higher self-esteem levels and problem-focused/emotion focused coping strategies. In schizophrenic patients, sample there was no positive correlation between resilience and perceived quality of life. Further implications will be discussed.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals The overlap of genetic susceptibility to schizophrenia and cardiometabolic disease can be used to identify metabolically different groups of individuals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rona J. Strawbridge ◽  
Keira J. A. Johnston ◽  
Mark E. S. Bailey ◽  
Damiano Baldassarre ◽  
Breda Cullen ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
European Ancestry ◽  
Cardiometabolic Disease ◽  
Metabolic Profiles ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Major Depressive ◽  
Increased Risk

AbstractUnderstanding why individuals with severe mental illness (Schizophrenia, Bipolar Disorder and Major Depressive Disorder) have increased risk of cardiometabolic disease (including obesity, type 2 diabetes and cardiovascular disease), and identifying those at highest risk of cardiometabolic disease are important priority areas for researchers. For individuals with European ancestry we explored whether genetic variation could identify sub-groups with different metabolic profiles. Loci associated with schizophrenia, bipolar disorder and major depressive disorder from previous genome-wide association studies and loci that were also implicated in cardiometabolic processes and diseases were selected. In the IMPROVE study (a high cardiovascular risk sample) and UK Biobank (general population sample) multidimensional scaling was applied to genetic variants implicated in both psychiatric and cardiometabolic disorders. Visual inspection of the resulting plots used to identify distinct clusters. Differences between these clusters were assessed using chi-squared and Kruskall-Wallis tests. In IMPROVE, genetic loci associated with both schizophrenia and cardiometabolic disease (but not bipolar disorder or major depressive disorder) identified three groups of individuals with distinct metabolic profiles. This grouping was replicated within UK Biobank, with somewhat less distinction between metabolic profiles. This work focused on individuals of European ancestry and is unlikely to apply to more genetically diverse populations. Overall, this study provides proof of concept that common biology underlying mental and physical illness may help to stratify subsets of individuals with different cardiometabolic profiles.

scholarly journals The Symptom Structure of Postdisaster Major Depression: Convergence of Evidence from 11 Disaster Studies Using Consistent Methods

2021 ◽  
Vol 11 (1) ◽  
pp. 8
Author(s):  
Carol S. North ◽  
David Baron
Keyword(s):  
Major Depressive Disorder ◽  
Major Depression ◽  
Depressive Disorder ◽  
Diagnostic Assessment ◽  
Self Report ◽  
Depression Symptoms ◽  
Symptom Scale ◽  
Major Depressive ◽  
Report Symptom ◽  
Symptom Patterns

Agreement has not been achieved across symptom factor studies of major depressive disorder, and no studies have identified characteristic postdisaster depressive symptom structures. This study examined the symptom structure of major depression across two databases of 1181 survivors of 11 disasters studied using consistent research methods and full diagnostic assessment, addressing limitations of prior self-report symptom-scale studies. The sample included 808 directly-exposed survivors of 10 disasters assessed 1–6 months post disaster and 373 employees of 8 organizations affected by the September 11, 2001 terrorist attacks assessed nearly 3 years after the attacks. Consistent symptom patterns identifying postdisaster major depression were not found across the 2 databases, and database factor analyses suggested a cohesive grouping of depression symptoms. In conclusion, this study did not find symptom clusters identifying postdisaster major depression to guide the construction and validation of screeners for this disorder. A full diagnostic assessment for identification of postdisaster major depressive disorder remains necessary.

Impacts on Quality of Life with Escitalopram Monotherapy and Aripiprazole Augmentation in Patients with Major Depressive Disorder: A CAN-BIND Report

2021 ◽  
Author(s):  
Emma Morton ◽  
Venkat Bhat ◽  
Peter Giacobbe ◽  
Wendy Lou ◽  
Erin E. Michalak ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Repeated Measures ◽  
Short Form ◽  
Treatment Phase ◽  
Major Depressive ◽  
Symptomatic Remission ◽  
Substantial Degree

Abstract Introduction Many individuals with major depressive disorder (MDD) do not respond to initial antidepressant monotherapy. Adjunctive aripiprazole is recommended for treatment non-response; however, the impacts on quality of life (QoL) for individuals who receive this second-line treatment strategy have not been described. Methods We evaluated secondary QoL outcomes in patients with MDD (n=179). After 8 weeks of escitalopram, non-responders (<50% decrease in clinician-rated depression) were treated with adjunctive aripiprazole for 8 weeks (n=97); responders continued escitalopram (n=82). A repeated-measures ANOVA evaluated change in Quality of Life Enjoyment and Satisfaction Short Form scores. QoL was described relative to normative benchmarks. Results Escitalopram responders experienced the most QoL improvements in the first treatment phase. For non-responders, QoL improved with a large effect during adjunctive aripiprazole treatment. At the endpoint, 47% of patients achieving symptomatic remission still had impaired QoL. Discussion Individuals who were treated with adjunctive aripiprazole after non-response to escitalopram experienced improved QoL, but a substantial degree of QoL impairment persisted. Since QoL deficits may predict MDD recurrence, attention to ways to support this outcome is required.

scholarly journals A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jakub Tomasik ◽  
Sung Yeon Sarah Han ◽  
Giles Barton-Owen ◽  
Dan-Mircea Mirea ◽  
Nayra A. Martin-Key ◽  
...  
Keyword(s):  
Mental Health ◽  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Diagnostic Algorithm ◽  
Health Questionnaire ◽  
Major Depressive ◽  
Blood Biomarker ◽  
Mental Health Questionnaire ◽  
Biomarker Data

AbstractThe vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18–45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86–0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86–0.91) and 0.90 (0.87–0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57–0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD.

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