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Unipolar Depression

Unipolar depression, also known as major depressive disorder, is a mental health condition characterized by persistent feelings of sadness, hopelessness, and loss of interest in activities.
This condition affects an individual's mood, thoughts, behavior, and physical well-being.
Unipolar depression is a common mental illness that can significantly impact a person's quality of life.
Effective treatment options, including psychotherapy and medication, are available to help manage the symptoms and improve overall well-being.
It's important to seek professional help for proper diagnosis and treatment of unipoanr depression.

Most cited protocols related to «Unipolar Depression»

1
Genetic Architecture of Major Depression
Cited 325 times
Genotyping procedures can be found in the primary reports for each cohort (summarized in Supplementary Table 3). Individual genotype data for all PGC29 samples, GERA, and iPSYCH were processed using the PGC “ricopili” pipeline (URLs) for standardized quality control, imputation, and analysis19 (link). The cohorts from deCODE, Generation Scotland, UK Biobank, and 23andMeD were processed by the collaborating research teams using comparable procedures. SNPs and insertion-deletion polymorphisms were imputed using the 1000 Genomes Project multi-ancestry reference panel (URLs)86 (link). More detailed information on sample QC is provided in the Supplementary Note.
Linkage disequilibrium (LD) score regression (LDSC)22 (link),24 (link) was used to estimate
hSNP2 from GWA summary statistics. Estimates of
hSNP2 on the liability scale depend on the assumed lifetime prevalence of MDD in the population (K), and we assumed K=0.15 but also evaluated a range of estimates of K to explore sensitivity including 95% confidence intervals (Supplementary Fig. 1). LDSC bivariate genetic correlations attributable to genome-wide SNPs (rg) were estimated across all MDD and major depression cohorts and between the full meta-analyzed cohort and other traits and disorders.
LDSC was also used to partition
hSNP2 by genomic features24 (link),46 (link). We tested for enrichment of
hSNP2 based on genomic annotations partitioning
hSNP2 proportional to bp length represented by each annotation. We used the “baseline model” which consists of 53 functional categories. The categories are fully described elsewhere46 (link), and included conserved regions47 (link), USCC gene models (exons, introns, promoters, UTRs), and functional genomic annotations constructed using data from ENCODE 87 (link) and the Roadmap Epigenomics Consortium88 (link). We complemented these annotations by adding introgressed regions from the Neanderthal genome in European populations89 (link) and open chromatin regions from the brain dorsolateral prefrontal cortex. The open chromatin regions were obtained from an ATAC-seq experiment performed in 288 samples (N=135 controls, N=137 schizophrenia, N=10 bipolar, and N=6 affective disorder)90 . Peaks called with MACS91 (link) (1% FDR) were retained if their coordinates overlapped in at least two samples. The peaks were re-centered and set to a fixed width of 300bp using the diffbind R package92 (link). To prevent upward bias in heritability enrichment estimation, we added two categories created by expanding both the Neanderthal introgressed regions and open chromatin regions by 250bp on each side.
We used LDSC to estimate rg between major depression and a range of other disorders, diseases, and human traits22 (link). The intent of these comparisons was to evaluate the extent of shared common variant genetic architectures in order to suggest hypotheses about the fundamental genetic basis of major depression (given its extensive comorbidity with psychiatric and medical conditions and its association with anthropometric and other risk factors). Subject overlap of itself does not bias rg. These rg are mostly based on studies of independent subjects and the estimates should be unbiased by confounding of genetic and non-genetic effects (except if there is genotype by environment correlation). When GWA studies include overlapping samples, rg remains unbiased but the intercept of the LDSC regression is an estimate of the correlation between association statistics attributable to sample overlap. These calculations were done using the internal PGC GWA library and with LD-Hub (URLs)60 (link).
Wray N.R., Ripke S., Mattheisen M., Trzaskowski M., Byrne E.M., Abdellaoui A., Adams M.J., Agerbo E., Air T.M., Andlauer T.F., Bacanu S.A., Bækvad-Hansen M., Beekman A.T., Bigdeli T.B., Binder E.B., Blackwood D.H., Bryois J., Buttenschøn H.N., Bybjerg-Grauholm J., Cai N., Castelao E., Christensen J.H., Clarke T.K., Coleman J.R., Colodro-Conde L., Couvy-Duchesne B., Craddock N., Crawford G.E., Crowley C.A., Dashti H.S., Davies G., Deary I.J., Degenhardt F., Derks E.M., Direk N., Dolan C.V., Dunn E.C., Eley T.C., Eriksson N., Escott-Price V., Kiadeh F.F., Finucane H.K., Forstner A.J., Frank J., Gaspar H.A., Gill M., Giusti-Rodríguez P., Goes F.S., Gordon S.D., Grove J., Hall L.S., Hannon E., Hansen C.S., Hansen T.F., Herms S., Hickie I.B., Hoffmann P., Homuth G., Horn C., Hottenga J.J., Hougaard D.M., Hu M., Hyde C.L., Ising M., Jansen R., Jin F., Jorgenson E., Knowles J.A., Kohane I.S., Kraft J., Kretzschmar W.W., Krogh J., Kutalik Z., Lane J.M., Li Y., Li Y., Lind P.A., Liu X., Lu L., MacIntyre D.J., MacKinnon D.F., Maier R.M., Maier W., Marchini J., Mbarek H., McGrath P., McGuffin P., Medland S.E., Mehta D., Middeldorp C.M., Mihailov E., Milaneschi Y., Milani L., Mill J., Mondimore F.M., Montgomery G.W., Mostafavi S., Mullins N., Nauck M., Ng B., Nivard M.G., Nyholt D.R., O’Reilly P.F., Oskarsson H., Owen M.J., Painter J.N., Pedersen C.B., Pedersen M.G., Peterson R.E., Pettersson E., Peyrot W.J., Pistis G., Posthuma D., Purcell S.M., Quiroz J.A., Qvist P., Rice J.P., Riley B.P., Rivera M., Mirza S.S., Saxena R., Schoevers R., Schulte E.C., Shen L., Shi J., Shyn S.I., Sigurdsson E., Sinnamon G.C., Smit J.H., Smith D.J., Stefansson H., Steinberg S., Stockmeier C.A., Streit F., Strohmaier J., Tansey K.E., Teismann H., Teumer A., Thompson W., Thomson P.A., Thorgeirsson T.E., Tian C., Traylor M., Treutlein J., Trubetskoy V., Uitterlinden A.G., Umbricht D., Van der Auwera S., van Hemert A.M., Viktorin A., Visscher P.M., Wang Y., Webb B.T., Weinsheimer S.M., Wellmann J., Willemsen G., Witt S.H., Wu Y., Xi H.S., Yang J., Zhang F., Arolt V., Baune B.T., Berger K., Boomsma D.I., Cichon S., Dannlowski U., de Geus E., DePaulo J.R., Domenici E., Domschke K., Esko T., Grabe H.J., Hamilton S.P., Hayward C., Heath A.C., Hinds D.A., Kendler K.S., Kloiber S., Lewis G., Li Q.S., Lucae S., Madden P.A., Magnusson P.K., Martin N.G., McIntosh A.M., Metspalu A., Mors O., Mortensen P.B., Müller-Myhsok B., Nordentoft M., Nöthen M.M., O’Donovan M.C., Paciga S.A., Pedersen N.L., Penninx B.W., Perlis R.H., Porteous D.J., Potash J.B., Preisig M., Rietschel M., Schaefer C., Schulze T.G., Smoller J.W., Stefansson K., Tiemeier H., Uher R., Völzke H., Weissman M.M., Werge T., Winslow A.R., Lewis C.M., Levinson D.F., Breen G., Børglum A.D, & Sullivan P.F. (2018). Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nature genetics, 50(5), 668-681.
Publication 2018
ATAC-Seq Brain Chromatin DNA Library Dorsolateral Prefrontal Cortex Europeans Exons Genetic Diversity Genetic Polymorphism Genome Genome-Wide Association Study Genotype Genotyping Techniques Homo sapiens Hypersensitivity INDEL Mutation Introns Mood Disorders Neanderthals Reproduction Schizophrenia Single Nucleotide Polymorphism Unipolar Depression Untranslated Regions
2
Large-Scale Meta-Analysis of Major Depressive Disorder
Cited 87 times
Wray et al. conducted the largest meta-analysis of MDD to date 9 (link), utilising European-ancestry PGC cohorts
with an emphasis placed on obtaining clinically-derived phenotypes for MDD.
Their meta-analysis included the 23andMe_307k discovery cohort 8 (link) and a previous release of the UK Biobank
data 55 . We therefore obtained the
summary statistics from their meta-analysis of major depression with the
23andMe_307k and the previous UK Biobank cohorts removed (PGC_139k). This
provided a total of 12,149,399 variant calls for 43,204 cases and 95,680
controls (n = 138,884, prevalence = 0.31). We excluded variants with an
imputation accuracy threshold < 0.6 or a minor allele frequency <
0.005, which left a total of 10,365,555 variants.
Howard D.M., Adams M.J., Clarke T.K., Hafferty J.D., Gibson J., Shirali M., Coleman J.R., Hagenaars S.P., Ward J., Wigmore E.M., Alloza C., Shen X., Barbu M.C., Xu E.Y., Whalley H.C., Marioni R.E., Porteous D.J., Davies G., Deary I.J., Hemani G., Berger K., Teismann H., Rawal R., Arolt V., Baune B.T., Dannlowski U., Domschke K., Tian C., Hinds D.A., Trzaskowski M., Byrne E.M., Ripke S., Smith D.J., Sullivan P.F., Wray N.R., Breen G., Lewis C.M, & McIntosh A.M. (2019). Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nature neuroscience, 22(3), 343-352.
Publication 2019
Europeans Phenotype Unipolar Depression
3
Relapse Prevention Cognitive Therapy Trial
Cited 43 times

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Publication 2010
Adult AN 12 Cells Cognitive Therapy Condoms Outpatients Patients Pharmacotherapy Population at Risk Recurrence Relapse Relapse Prevention Therapeutics Treatment Protocols Unipolar Depression
4
Multitrait GWAS Identifies GERD Loci
Cited 33 times
Leveraging the strong genetic correlation between GERD and related traits, we applied a multitrait GWAS model combining GWASs for body mass index (BMI), major depressive disorder (MDD), education attainment and GERD (and BE), to identify more susceptibility loci for GERD and BE (figure 1). The brief description for each input GWAS in the multitrait GWAS analysis and the equivalent effect sample size is shown in online supplemental table ST1. Candidate loci for GERD and BE achieving genome-wide significance (p<5e-8) were sent for replication in the independent 23andMe cohort (4 62 753 cases; 1 127 474 controls). Findings from these GWAS analyses were followed up with transcriptome-wide association studies (TWAS) and tissue enrichment analyses. TWAS analysis allows us to infer if there is a relationship between predicted gene-expression levels and GERD/BE risk. Tissue enrichment analyses allow us to assess whether the relevant GERD/BE-associated genes showed differential enrichment across 44 human tissues including oesophageal-related tissues. We finally applied a simple heuristic to dissect aetiological heterogeneity in GERD by separating GERD risk loci into obesity-driven and depression-driven categories; we then assessed these categories for differences in predicted gene expression in various tissues and for their ability to predict BE/EA susceptibility.
Ong J.S., An J., Han X., Law M.H., Nandakumar P., Schumacher J., Gockel I., Bohmer A., Jankowski J., Palles C., Olsen C.M., Neale R.E., Fitzgerald R., Thrift A.P., Vaughan T.L., Buas M.F., Hinds D.A., Gharahkhani P., Kendall B.J, & MacGregor S. (2021). Multitrait genetic association analysis identifies 50 new risk loci for gastro-oesophageal reflux, seven new loci for Barrett’s oesophagus and provides insights into clinical heterogeneity in reflux diagnosis. Gut, 71(6), 1053-1061.
Publication 2021
DNA Replication Esophagus Gastroesophageal Reflux Disease Gene Expression Genes Genetic Heterogeneity Genome Genome-Wide Association Study Homo sapiens Index, Body Mass Obesity Reproduction Susceptibility, Disease Tissues Transcriptome Unipolar Depression
5
PHQ-9 Depression Symptom Assessment
Cited 30 times
The PHQ-9 is a Likert self-report consisting of nine items designed from the nine criteria evaluated by the DSM IV for major depression (MDD). PHQ-9 has four response options (0 = not at all; 1 = several days; 2 = more than half the days; 3 = nearly every day), with scores ranging from 0 to 27. This instrument reports the indicators of depressive symptomatology during the last two weeks.[12 (link)] In other samples, the PHQ-9 has presented adequate levels of reliability (α = 0.84) [31 ], and adequate levels of specificity (>0.90) but low sensitivity, between 0.39 and 0.73 [32 (link)].
Other variables were added to analyze the characteristics of the population as well as the measurement invariance of the model. These variables were: sex, education level (primary education [up to 6 years], secondary education [7–11 years] and superior [≥ 12 years]), age group [33 ] (young [18 to 34], early adulthood [35 to 54], intermediate adulthood [55 to 74], and older adults [≥ 75 years]), socioeconomic status (defined according to household assets and then split in tertiles [low, medium and high]), marital status (married, never married and previously married), residence area (urban and rural) and natural region (coast, mountains and jungle); the latest not used for measurement invariance analysis.
Villarreal-Zegarra D., Copez-Lonzoy A., Bernabé-Ortiz A., Melendez-Torres G.J, & Bazo-Alvarez J.C. (2019). Valid group comparisons can be made with the Patient Health Questionnaire (PHQ-9): A measurement invariance study across groups by demographic characteristics. PLoS ONE, 14(9), e0221717.
Publication 2019
Aged Age Groups Households Hypersensitivity Unipolar Depression

Most recents protocols related to «Unipolar Depression»

1
Epidemiological Study of Respiratory and Mental Health
Electronic medical records updated on January 1, 2020, were used to obtain date of birth to calculate age at infection, obstructive airway disease and upper respiratory disease diagnoses, lifetime posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) diagnoses, and the most recent available measure of body mass index (BMI). Obstructive airway disease category includes diagnoses of asthma and bronchitis, upper respiratory disease includes chronic rhinitis and chronic sinusitis [18 ]. Respiratory diagnoses were determined based on systemic examination by a physician or nurse practitioner and included repeated pulmonary function tests, physical examination, and medical history. BMI scores were analyzed as continuous variable to maximize statistical power [19 , 20 (link)]. Genetic data were used to obtain sex and ancestry information.
Waszczuk M.A., Morozova O., Lhuillier E., Docherty A.R., Shabalin A.A., Yang X., Carr M.A., Clouston S.A., Kotov R, & Luft B.J. (2023). Polygenic risk scores for asthma and allergic disease associate with COVID-19 severity in 9/11 responders. PLOS ONE, 18(3), e0282271.
Publication 2023
Asthma Bronchitis Diagnosis Diagnostic Techniques, Respiratory System Index, Body Mass Infection Lung Diseases, Obstructive Physical Examination Physicians Post-Traumatic Stress Disorder Practitioner, Nurse Respiration Disorders Respiratory Rate Rhinitis Sinusitis Tests, Pulmonary Function Unipolar Depression
2
Suicidal Ideation and Behavior Assessment
The interview included the Paykel questions on passive and active suicidal ideation and behavior (46 (link)). These questions, originally dubbed “suicidal feelings,” have been applied in numerous population-based studies involving older adults [see (2 (link)) for an overview]. Four items cover ideation of increasing intensity; a fifth involves an actual suicide attempt: (1) “Have you ever felt that life was not worth living?” (2), “Have you ever wished you were dead?,” (3) Have you ever thought of taking your life, even if you would not really do it?” (4) “Have you ever reached the point where you seriously considered taking your life, or perhaps made plans how you would go about doing it?,” and (5) “Have you ever made an attempt to take your life?.” As previously reported, inter-rater reliability ranged 0.96 (life-weariness) to 0.74 (seriously considered taking life) (2 (link)). Participants with affirmative responses were asked to report the most recent occurrence of these feelings, and within the current study individuals who reported “yes” on any Paykel question, with the occurrence “past year,” were considered as “cases.”
Dementia and major depressive disorder (MDD) were diagnosed by geriatric psychiatrists. A computerized algorithm based on The Comprehensive Psychopathological Rating Scale (CPRS) items was employed to establish whether participants met diagnostic criteria for MDD (47 (link)). The algorithm was designed to follow the definition of major depressive episode in the Diagnostic and Statistical Manual for Mental Disorders, Fifth Edition (DSM-5) (48 ) as closely as possible: At least five of the nine depressive symptoms or signs listed in DSM-5 had to be present, at least one of which had to be either depressed mood or loss of interest. Exclusion criteria for depression could not be applied due to the difficulty of making etiological judgments in an epidemiological study. Dementia was diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders 3rd Edition Revised (DSM-III-R) (49 ).
Zettergren A., Jonson M., Mellqvist Fässberg M., Najar J., Rydberg Sterner T., Seidu N.M., Kern S., Blennow K., Zetterberg H., Skoog I, & Waern M. (2023). Passive and active suicidal ideation in a population-based sample of older adults: Associations with polygenic risk scores of relevance for suicidal behavior. Frontiers in Psychiatry, 14, 1101956.
Publication 2023
Aged Dementia Depressive Symptoms Fatigue Feelings Infantile Neuroaxonal Dystrophy Mental Disorders Mood Psychiatrist Suicide Attempt Unipolar Depression
3
Suicidality Risk Factors Survey
Participants’ basic demographics of interest were age and gender. The baseline (2018) survey further inquired participants’ experiences on known risk factors of suicidality: Whether they had ever considered suicide, self-harmed, and attempted suicide in their lifetime; and whether they had been diagnosed with psychiatric disorders such as major depressive disorder (MDD) and schizophrenia. All questions on risk factors of suicidality were binary-response (“yes” and “no”) questions.
Junus A, & Yip P.S. (2023). Preventing comorbidity between distress and suicidality: a network analysis. npj Mental Health Research, 2(1), 2.
Publication 2023
Gender Mental Disorders Schizophrenia Suicide Attempt Unipolar Depression
4
Retrospective Study of Unipolar Depression Treated with ECT
From the Q-ECT, we identified patients who were aged ≥18 years and had been treated for a unipolar, non-psychotic depressive episode (ICD-1020 codes of F32 for depressive episode or F33 for recurrent depression, excluding F32.3 and F33.3 for psychotic depression) with at least one ECT session as part of a first-time, index ECT series in Sweden between 1 January 2011 and 31 December 2017 (Fig. 1). When registration of treatment indication was lacking or unclear in the Q-ECT, diagnostic information was added from the NPR. This resulted in 6615 eligible patients with a diagnosis of depression treated with ECT. Patients with any registered lifetime diagnosis of bipolar disorder, manic or hypomanic episode, psychotic disorder or dementia were excluded, as were patients lacking data on the primary outcome measure (CGI-I score). After these exclusions, a total of 4244 patients were included in the final analyses.

Flow chart of study inclusion. CGI-I, Clinical Global Impressions – Improvement Scale; CGI-S, Clinical Global Impressions – Severity Scale; ECT, electroconvulsive therapy; Q-ECT Swedish National Quality Register for ECT; NPR, Swedish National Patient Register.

Nygren A., Reutfors J., Brandt L., Bodén R., Nordenskjöld A, & Tiger M. (2023). Response to electroconvulsive therapy in treatment-resistant depression: nationwide observational follow-up study. BJPsych Open, 9(2), e35.
Publication 2023
Bipolar Disorder Dementia Diagnosis Hypomanic Episode Mania Mental Disorders Patients Psychotic Disorders Unipolar Depression
5
Measuring Emotional and Behavioral Regulation in Families
The Brief Child and Family Phone Interview (BCFPI) [53 (link)] is a structured interview administered by telephone or in person to parents to assess emotional and behavioral problems exhibited by 3- to 18-year-olds referred to child mental health services. The revised Ontario Child Health Study scales (OCHS-R) [54 (link)] provided the item pool for the BCFPI measures. Each item is scored on a 3-point Likert scale from 0 = “never true” to 2 = “often true”. The BCFPI yields six subscales of emotional and behavioral problems linked to the DSM categories of attention-deficit/hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), conduct disorder (CD), seasonal affective disorder (SAD), generalized anxiety disorder (GAD), and major depressive disorder (MDD). The sum of the ADHD, ODD, and CD subscales yields a scale of Externalizing Problems (EXT), and the total score of SAD, GAD, and MDD subscales yields a scale of Internalizing Problems (INT). The scales show good internal consistency: α = 0.85 for EXT, and α = 0.85 for INT. Higher scores on each of the two scales reflect greater behavioral problems.
The Affect Regulation Checklist (ARC) [55 ] is a 12-item, self-report measure for parents or other caregivers to report on their affect regulation and their child’s affect regulation, as well as a youth self-report version. The ARC comes in different versions to be used depending on the informant and the target; for this study, we used the ARC-R (Relation), which explores the parent–adolescent dyadic affective regulation relationship. Items are rated on a 5-point Likert scale ranging from 1 = “a lot like me” to 5 = “not like me”. The measure yields three scales of Affect Dysregulation (e.g., “I find it very difficult to calm down when I am angry about my child and our relationship”), Affect Suppression (e.g., “I try hard not to think about how I feel about my child and our relationship”), and Adaptive Reflection (e.g., “Thinking about why I feel different emotions for my child helps me learn more about our relationship”). The scales show good internal consistency, ranging from α = 0.72 for Affect Suppression, to α = 0.88 for Affect Dysregulation. Higher scores on each of the scales reflect greater use of the affect regulation strategy.
Benzi I.M., Carone N., Moretti M., Ruglioni L., Tracchegiani J, & Barone L. (2023). eCONNECT Parent Group: An Online Attachment-Based Intervention to Reduce Attachment Insecurity, Behavioral Problems, and Emotional Dysregulation in Adolescence. International Journal of Environmental Research and Public Health, 20(4), 3532.
Publication 2023
Acclimatization Adolescent Anger Anxiety Disorders Child Children's Health Conduct Disorder Disorder, Attention Deficit-Hyperactivity Emotions Feelings Mental Health Services Oppositional Defiant Disorder Parent Problem Behavior Reflex Seasonal Affective Disorder Unipolar Depression Youth

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More about "Unipolar Depression"

Unipolar depression, also known as major depressive disorder (MDD), is a prevalent mental health condition characterized by persistent feelings of sadness, hopelessness, and a loss of interest in activities.
This condition can significantly impact an individual's mood, thoughts, behavior, and overall well-being.
Effective treatment options, including psychotherapy (e.g., cognitive-behavioral therapy) and medication (such as antidepressants like selective serotonin reuptake inhibitors), are available to help manage the symptoms and improve the quality of life for those suffering from unipolar depression.
Researchers and clinicians often utilize various tools and technologies to study and diagnose unipolar depression.
Some of these include the Infinium PsychChip v1.0 array, which is a specialized microarray used to assess genetic markers associated with psychiatric disorders, and the SPSS Statistics software (version 20 or 24), a commonly used data analysis tool in psychological and medical research.
Additionally, neuroimaging techniques, such as the Biograph mMR PET/MRI scanner, can provide valuable insights into the neural mechanisms underlying unipolar depression.
It's important to note that unipolar depression is a common mental illness, with an estimated lifetime prevalence of around 16% in the general population.
While the exact causes of unipolar depression are not fully understood, it is believed to involve a complex interplay of genetic, biological, psychological, and environmental factors.
Seeking professional help for proper diagnosis and treatment is crucial, as untreated unipolar depression can have serious consequences, including impaired social and occupational functioning, physical health problems, and an increased risk of suicide.
By leveraging the latest research tools and technologies, researchers and clinicians can continue to advance our understanding of unipolar depression and develop more effective treatment strategies to improve the lives of those affected by this debilitating condition.
Remember, with the right support and interventions, individuals with unipolar depression can manage their symptoms and regain a sense of hope and well-being.