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Anxiety Disorders

Anxiety Disorders are a group of mental health conditions characterized by persistent and excessive worry, fear, and anxiety that can interfere with daily life.
These disorders include Generalized Anxiety Disorder, Panic Disorder, Social Anxiety Disorder, Specific Phobias, and others.
Symptoms may include restlessness, fatigue, difficulty concentrating, muscle tension, and sleep disturbances.
Effective treatments, such as psychotherapy and medication, are available to help manage Anxiety Disorders and improve quality of life.
Reasearch in this field aims to enhance understanding of the underlying causes, develop more effective interventions, and promote improved outcomes for individuals affected by these prevalent and disruptive conditions.

Most cited protocols related to «Anxiety Disorders»

1
ADOS Severity Metrics Calibration
For comparability, the same sample used to standardize the overall ADOS total (see Gotham et al. 2009 (link)) was also employed to calibrate separate severity metrics for the Social Affect (SA) and Restricted, Repetitive Behavior (RRB) domains. Briefly, this included data from 1,415 individuals ranging in age from 2 to 16 years. With repeated assessments for 25 % of the sample, data from 2,195 ADOSes with contemporaneous best estimate clinical diagnoses were available for analysis. Of these assessments, 1,786 cases were given an autism spectrum disorder diagnosis (ASD; 1,187 Autistic Disorder, 599 Other-ASD) and 409 had a Non-ASD diagnosis. Non-ASD diagnoses included language disorders (27 %), nonspecific intellectual disability (20 %), Down syndrome (14 %), oppositional defiant disorder or ADD/ADHD (13 %), mood or anxiety disorders (8 %), Fetal Alcohol Spectrum Disorders (7 %), other genetic or physical disabilities, such as Fragile X or mild cerebral palsy (6 %) and early developmental delays (5 %).
Individuals were consecutive referrals to specialty clinics in Ann Arbor, Michigan and Chicago, Illinois, and participants in research studies conducted through the University of North Carolina—Chapel Hill, University of Chicago, and University of Michigan. All participants provided informed consent and all procedures related to this project were approved by institutional review boards at the University of Chicago or University of Michigan. Sample characteristics are provided in Table 1.
Hus V., Gotham K, & Lord C. (2014). Standardizing ADOS Domain Scores: Separating Severity of Social Affect and Restricted and Repetitive Behaviors. Journal of autism and developmental disorders, 44(10), 2400-2412.
Publication 2012
Adenosine Anxiety Disorders Autistic Disorder Cerebral Palsy Childbirth Diagnosis Disabled Persons Disorder, Attention Deficit-Hyperactivity Down Syndrome Ethics Committees, Research Fetal Alcohol Syndrome Intellectual Disability Language Disorders Mood Oppositional Defiant Disorder Physical Examination
2
Assessing Psychological Well-being in German Adults
Demographic data included age (≥14 years), sex, marital and employment status, religion, migrant background, education and total income of household.
The German version of the PSS-10 (PSS-10; [10 (link)]) was used to measure the degree to which life in the past month has been experienced as unpredictable, uncontrollable and overwhelming (e.g. “In the last month, how often have you felt nervous and "stressed"?) on a 5-point response scale (0 = “never”, 1=”almost never”, 2=”sometimes”, 3=”fairly often”, 4=”very often”). The scale was forward translated from English to German and subsequently back translated by two interdependent bilingual speakers. After reversing the scores on the four positively stated items (Items 4, 5, 7, and 8), a PSS-10 total score was obtained by summing up all 10 items. Higher scores indicated a higher level of perceived stress. As the PSS is not a diagnostic instrument, there are no cut-off scores.
In addition to the PSS-10, socio-demographic questions and additional psychological variables were measured by validated and standardized self-report inventories. These included screening questionnaires for depression and generalized anxiety (PHQ-4), the short form of the General Procrastination Scale (GPS-K), the Copenhagen Burnout Inventory (CBI), and the Life Satisfaction Questionnaire (FLZ-M) during the interview.
The PHQ-4 [28 (link)] consists of two items reliably assessing the core symptoms of depressed mood and loss of interest plus two screening items of the short form of the GAD-7 (Generalized Anxiety Disorder [GAD]-7 Scale) : “Feeling nervous, anxious or on edge” and “not being able to stop or control worrying”. The frequency of occurrence in the past two weeks was rated from 0 =”not at all”, 1 =”several days”, 2 =”over half the days”, and 3 =”nearly every day”. Answers of the first two items were added into a total score (0 to 6); a score ≥ 3 has a good sensitivity (87 %) and specificity (78 %) for major depression. Cronbach alpha in the present study was = .83. A sum score ≥ 3 (range 0–6) of the other two items indicates generalized anxiety with good sensitivity (86 %) and specificity (83 %), performing well as a screening tool for all anxiety disorders [29 (link)]. The internal consistency in the current study was Cronbach alpha = .77.
Procrastination was assessed by the 9-item short form of the General Procrastination Scale (GPS-K; [30 (link)]). Participants rated how characteristic they considered each behaviour (e.g. “I delay the completion of certain things”) on a 4-point scale (1=”very uncharacteristic” to 4 = “very characteristic”). The scale showed good reliability and validity in a representative German community sample [30 (link)]. The internal consistency was Cronbach alpha = .92.
The Copenhagen Personal Burnout Inventory (CBI; [31 (link)]) is part of the Copenhagen Psychosocial Questionnaire assessing physical and mental exhaustion, independently from work. It assessed the frequency of six items („How often do you feel …“): “tired, physically, emotionally exhausted, unable to go on, weak and prone to illness.” The items were rated on a 5-point scale 1 =”never/almost never”, 2 = “rarely”, 3 = “occasionally”, 4 = “often” to 5 = “always” (COPSOQ; [32 (link)]). The scale was reliable (Cronbach alpha in the present study = .91).
The Questionnaire on Life Satisfaction FLZM [33 ] is a multi-dimensional self-report measure of individual life satisfaction covering eight relevant areas of life (friends, leisure time activities/hobbies, general health, income, work/career school, housing/living conditions, family life and partnership/sexuality). Additionally, a sum score of all dimensions was used as an index of global life satisfaction. Respondents rated the present satisfaction with these dimensions on a scale from 1 = “dissatisfied” to 5 = “very satisfied”. As the scale bases conceptually on different domains, the life satisfaction sum-scores indicated only sufficient internal consistency (Cronbach alpha = .70).
Klein E.M., Brähler E., Dreier M., Reinecke L., Müller K.W., Schmutzer G., Wölfling K, & Beutel M.E. (2016). The German version of the Perceived Stress Scale – psychometric characteristics in a representative German community sample. BMC Psychiatry, 16, 159.
Publication 2016
Anxiety Anxiety Disorders Burnout, Psychological Debility Depressive Symptoms Diagnosis Feelings Friend Households Hypersensitivity Major Depressive Disorder Migrants Nervousness Physical Examination Procrastination Satisfaction
3
Genome-wide Overlap Across Psychiatric Disorders
Genome-wide significant loci for BD were assessed for overlap with genome-wide significant loci for other psychiatric disorders, using the largest available GWAS results for major depression61 (link), schizophrenia60 (link), attention deficit/hyperactivity disorder101 , post-traumatic stress disorder102 , lifetime anxiety disorder103 , Tourette’s Syndrome104 , anorexia nervosa105 , alcohol use disorder or problematic alcohol use68 (link), autism spectrum disorder106 , mood disorders91 (link) and the cross-disorder GWAS of the Psychiatric Genomics Consortium66 (link). The boundaries of the genome-wide significant loci were calculated in the original publications. Overlap of loci was calculated using bedtools v2.29.2107 .
Mullins N., Forstner A.J., O’Connell K.S., Coombes B., Coleman J.R., Qiao Z., Als T.D., Bigdeli T.B., Børte S., Bryois J., Charney A.W., Drange O.K., Gandal M.J., Hagenaars S.P., Ikeda M., Kamitaki N., Kim M., Krebs K., Panagiotaropoulou G., Schilder B.M., Sloofman L.G., Steinberg S., Trubetskoy V., Winsvold B.S., Won H.H., Abramova L., Adorjan K., Agerbo E., Al Eissa M., Albani D., Alliey-Rodriguez N., Anjorin A., Antilla V., Antoniou A., Awasthi S., Baek J.H., Bækvad-Hansen M., Bass N., Bauer M., Beins E.C., Bergen S.E., Birner A., Pedersen C.B., Bøen E., Boks M.P., Bosch R., Brum M., Brumpton B.M., Brunkhorst-Kanaan N., Budde M., Bybjerg-Grauholm J., Byerley W., Cairns M., Casas M., Cervantes P., Clarke T.K., Cruceanu C., Cuellar-Barboza A., Cunningham J., Curtis D., Czerski P.M., Dale A.M., Dalkner N., David F.S., Degenhardt F., Djurovic S., Dobbyn A.L., Douzenis A., Elvsåshagen T., Escott-Price V., Ferrier I.N., Fiorentino A., Foroud T.M., Forty L., Frank J., Frei O., Freimer N.B., Frisén L., Gade K., Garnham J., Gelernter J., Pedersen M.G., Gizer I.R., Gordon S.D., Gordon-Smith K., Greenwood T.A., Grove J., Guzman-Parra J., Ha K., Haraldsson M., Hautzinger M., Heilbronner U., Hellgren D., Herms S., Hoffmann P., Holmans P.A., Huckins L., Jamain S., Johnson J.S., Kalman J.L., Kamatani Y., Kennedy J.L., Kittel-Schneider S., Knowles J.A., Kogevinas M., Koromina M., Kranz T.M., Kranzler H.R., Kubo M., Kupka R., Kushner S.A., Lavebratt C., Lawrence J., Leber M., Lee H.J., Lee P.H., Levy S.E., Lewis C., Liao C., Lucae S., Lundberg M., MacIntyre D.J., Magnusson S.H., Maier W., Maihofer A., Malaspina D., Maratou E., Martinsson L., Mattheisen M., McCarroll S.A., McGregor N.W., McGuffin P., McKay J.D., Medeiros H., Medland S.E., Millischer V., Montgomery G.W., Moran J.L., Morris D.W., Mühleisen T.W., O’Brien N., O’Donovan C., Olde Loohuis L.M., Oruc L., Papiol S., Pardiñas A.F., Perry A., Pfennig A., Porichi E., Potash J.B., Quested D., Raj T., Rapaport M.H., DePaulo J.R., Regeer E.J., Rice J.P., Rivas F., Rivera M., Roth J., Roussos P., Ruderfer D.M., Sánchez-Mora C., Schulte E.C., Senner F., Sharp S., Shilling P.D., Sigurdsson E., Sirignano L., Slaney C., Smeland O.B., Smith D.J., Sobell J.L., Hansen C.S., Artigas M.S., Spijker A.T., Stein D.J., Strauss J.S., Świątkowska B., Terao C., Thorgeirsson T.E., Toma C., Tooney P., Tsermpini E.E., Vawter M.P., Vedder H., Walters J.T., Witt S.H., Xi S., Xu W., Yang J.M., Young A.H., Young H., Zandi P.P., Zhou H., Zillich L., Adolfsson R., Agartz I., Alda M., Alfredsson L., Babadjanova G., Backlund L., Baune B.T., Bellivier F., Bengesser S., Berrettini W.H., Blackwood D.H., Boehnke M., Børglum A.D., Breen G., Carr V.J., Catts S., Corvin A., Craddock N., Dannlowski U., Dikeos D., Esko T., Etain B., Ferentinos P., Frye M., Fullerton J.M., Gawlik M., Gershon E.S., Goes F.S., Green M.J., Grigoroiu-Serbanescu M., Hauser J., Henskens F., Hillert J., Hong K.S., Hougaard D.M., Hultman C.M., Hveem K., Iwata N., Jablensky A.V., Jones I., Jones L.A., Kahn R.S., Kelsoe J.R., Kirov G., Landén M., Leboyer M., Lewis C.M., Li Q.S., Lissowska J., Lochner C., Loughland C., Martin N.G., Mathews C.A., Mayoral F., McElroy S.L., McIntosh A.M., McMahon F.J., Melle I., Michie P., Milani L., Mitchell P.B., Morken G., Mors O., Mortensen P.B., Mowry B., Müller-Myhsok B., Myers R.M., Neale B.M., Nievergelt C.M., Nordentoft M., Nöthen M.M., O’Donovan M.C., Oedegaard K.J., Olsson T., Owen M.J., Paciga S.A., Pantelis C., Pato C., Pato M.T., Patrinos G.P., Perlis R.H., Posthuma D., Ramos-Quiroga J.A., Reif A., Reininghaus E.Z., Ribasés M., Rietschel M., Ripke S., Rouleau G.A., Saito T., Schall U., Schalling M., Schofield P.R., Schulze T.G., Scott L.J., Scott R.J., Serretti A., Weickert C.S., Smoller J.W., Stefansson H., Stefansson K., Stordal E., Streit F., Sullivan P.F., Turecki G., Vaaler A.E., Vieta E., Vincent J.B., Waldman I.D., Weickert T.W., Werge T., Wray N.R., Zwart J.A., Biernacka J.M., Nurnberger J.I., Cichon S., Edenberg H.J., Stahl E.A., McQuillin A., Di Florio A., Ophoff R.A, & Andreassen O.A. (2021). Genome-wide association study of over 40,000 bipolar disorder cases provides new insights into the underlying biology. Nature genetics, 53(6), 817-829.
Publication 2021
Alcohol Use Disorder Anorexia Anxiety Disorders Attention Deficit Disorder Ethanol Genome Genome-Wide Association Study Mental Disorders Mood Pervasive Development Disorders
4
Diagnostic Assessment of DSM-5 Disorders

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Publication 2014
Agoraphobia Alcohol Use Disorder Anxiety Disorders Cannabis Central Nervous System Stimulants Club Drugs Cocaine Conduct Disorder Diagnosis Disorder, Depressive Drug Use Disorders Dysthymic Disorder Hallucinogens Heroin Inhalation Drug Administration Manic Episode Mood Disorders Opioids Panic Disorder Pharmaceutical Preparations Phobia, Social Phobia, Specific Post-Traumatic Stress Disorder Sedatives Solvents Tobacco Products Tobacco Use Disorder Tranquilizing Agents
5
Evaluating WHODAS 2.0 Disability Measure
The WHODAS 2.0 was included in the 2007 Australian National Survey of Mental Health and Well-being conducted by trained interviewers from the Australian Bureau of Statistics. A multistage stratified sample of households was contacted, and the specific person to be interviewed was identified. The survey oversampled younger people (16–24 years) and older people (65–85 years) to improve the reliability of estimates for these groups. This sampling process yielded 8,841 fully-responding households [8] (link). Mental disorders (affective, anxiety and substance use disorders) present in the past twelve months were identified from responses to the World Mental Health Composite International Diagnostic Interview (CIDI). Seven classes of chronic physical conditions (any cancer, diabetes, cardiovascular disease, digestive disorders, musculoskeletal conditions, respiratory problems, or hearing or vision impairment) as assessed by the World Mental Health CIDI to be present in the past twelve months were identified.
The factor structure was explored to confirm the importance of the WHODAS 2.0 as a general measure of activity limitation. First, we did an exploratory Principal Components Analysis (PCA) with oblique rotation (direct oblimin) of polychoric correlations on a random 50% of the sample. Kaiser-Mayer-Olkin's (KMO) measure of sampling adequacy was computed to assess the suitability of the data for factor analysis. Values above 0.6 provide sufficient evidence for the factorability of the correlation matrix [9] . Additionally, Bartlett's test of sphericity was calculated to test whether the correlation matrix was an identity matrix (i.e. no relationship between the items). Second, data from the remaining 50% were used to conduct Confirmatory Factor Analyses (CFA) of polychoric correlations to compare the model identified in the exploratory analysis with a theoretically derived model that posited both a general disability factor and factors related to the six domains of information in the questionnaire. The models were fitted using robust diagonally weighted least squares method of estimation recommended for the analysis of ordinal data. Good model fit is evidenced by a combination of the Tucker-Lewis fit index (TFI>0.90), the comparative fit index (CFI>0.90), the standardized root mean-square residual (SRMR≤0.08), and the root means square error of approximation (RMSEA≤0.08). Finally, to compare nested models the Akaike Information Criterion (AIC) was generated. Generally, the model with the smallest AIC value has the better fit. The statistical models were fitted using the Statistical Package for Social Sciences (SPSS) version 17.0 and LISREL version 8.80.
The distributions of the disability scores on the WHODAS 2.0 were examined and differences between means were assessed by t-tests and two-way ANOVAs, which have been found to be suitable for highly skewed data when the sample size is large [10] (link). The data were weighted to the Australian general population and jack-knife replicate weights were used for statistical estimation to take into account the sampling error arising from the complex survey design. All analyses were conducted using the SUDAAN statistical software package version 10.
Andrews G., Kemp A., Sunderland M., Von Korff M, & Ustun T.B. (2009). Normative Data for the 12 Item WHO Disability Assessment Schedule 2.0. PLoS ONE, 4(12), e8343.
Publication 2009
Anxiety Disorders Cardiovascular Diseases Chronic Condition Diabetes Mellitus Diagnosis Digestive System Disorders Disabled Persons DNA Replication Households Interviewers Malignant Neoplasms Mental Disorders Mental Health Musculoskeletal Diseases neuro-oncological ventral antigen 2, human Physical Examination Plant Roots Respiratory Rate Substance Use Disorders

Most recents protocols related to «Anxiety Disorders»

1
Comorbidity of Substance Use Disorders and Psychiatric Diagnoses
The SUD diagnoses and psychiatric diagnoses according to ICD-10 criteria, were made by a medical specialist or clinical psychologist using standardized clinical interviews and tools.
For the purpose of the current study, information on the dependence-level SUD diagnosis and any co-occurring psychiatric diagnosis was obtained from the medical record. The following binary SUD diagnosis (1 = presence, 0 = absence) were included in analyses: Alcohol use disorder (F10); Opioid use disorder (F11); Cannabis use disorder (F12); Sedatives use disorder (F13); Stimulant use disorder (F15). The psychiatric diagnoses were grouped into the following binary variables (1 = presence, 0 = absence): Mood disorders (F30-F39); Anxiety disorders (F40-F49); Personality disorders (F60-F69); ADHD (F90-F90.0), and other psychiatric diagnoses.
Andersson H.W., Mosti M.P, & Nordfjaern T. (2023). Inpatients in substance use treatment with co-occurring psychiatric disorders: a prospective cohort study of characteristics and relapse predictors. BMC Psychiatry, 23, 152.
Publication 2023
Alcohol Use Disorder Anxiety Disorders Cannabis Diagnosis Diagnosis, Psychiatric Disorder, Attention Deficit-Hyperactivity Mood Disorders Opioid Use Disorder Personality Disorders Psychologist Sedatives
2
Comorbid Psychiatric Disorders in Patients
The proportion of patients with COD was not significantly different for male (45.4%) and female (52.0%) patients (p = 0.139). The prevalence rates for the types of co-occurring psychiatric diagnoses are shown in Table 2. Among patients with COD, anxiety (22.9%) and mood disorders (17.3%) were the two most common psychiatric disorders. About one in five had more than one COD (21.4%), with a higher prevalence rate of multiple CODs among females (30.3%) than males (18.0%). Having anxiety disorders were significantly more prevalent among females (30.3%) than males (19.8%).

Prevalence of co-occurring psychiatric disorders

Total(N = 611)Females(N = 175)Males(N = 434)Females versus malesref
N%N%N%ORp-value
Without COD32252.78448.023754.6
With COD28947.39152.019745.41.300.139
Psychiatric diagnoses1
- Anxiety disorders F40-4914022.95330.38619.81.760.005
- Mood disorders F30-3910617.33620.67016.11.350.191
- ADHD F90.0-F90.97912.92112.05813.40.880.650
- Personality disorders F60-697011.52715.4439.91.660.053
- Multiple CODs13121.453*30.37818.01.98< 0.001

1 Other psychiatric diagnoses (n = 17) included Schizophrenia, F20-F29 (n = 8); Behavioral syndromes associated with physiological disorders and physical factors (n = 16); Mental retardation, F70-F79 (n = 6); Pervasive and specific developmental disorders, F80-89 (n = 16); Behavioral disorders, F91-F98 (n = 8)

Andersson H.W., Mosti M.P, & Nordfjaern T. (2023). Inpatients in substance use treatment with co-occurring psychiatric disorders: a prospective cohort study of characteristics and relapse predictors. BMC Psychiatry, 23, 152.
Publication 2023
Anxiety Disorders Behavior Disorders Cods Developmental Disabilities Diagnosis, Psychiatric Disorder, Attention Deficit-Hyperactivity Females Intellectual Disability Males Mental Disorders Mood Mood Disorders Patients Personality Disorders Physical Examination physiology Schizophrenia Syndrome Woman
3
Comprehensive Assessment of Depression
Questionnaires will be completed through a secure, web-based survey system hosted by the research centre so that participants can complete questionnaires electronically, either at home or during their visit to the research centre. Online questionnaires are sent via a national secure mail platform used by citizens in regular correspondence with public institutions and the health care system.
Measures include several salient domains in the clinical characterisation of the patient, among others, assessments of demographics (e.g., ethnicity, education, and marital status); medical and psychiatric history; depressive symptoms and impact of depression behaviour and day-to-day life; treatment preferences and expectations, life experiences; and a broad range of state and trait psychometrics. Some questionnaires will only be given to patients in subcohorts I-II (Table 2).

Questionaries Additional questionnaires for the subcohort I-II only are in bold

Symptom profile and SeverityCognitive styleUpbringing and life historyFunctioning and quality of life
Inventory of Depressive Symptomatology – self-report (IDS-SR) [34 (link)]Mentalisation Questionnaire (MZQ) [35 (link)]Online Stimulant and Family History Assessment Module (OS-FHAM) [11 (link)]Cognitive Complaints in Bipolar Disorder Rating Assessment (COBRA) [36 (link)]
Dimension of Anger Reactions (DAR-5) [37 (link)]Ruminative Response Scale (RRS) [38 (link)]Child abuse and trauma scale (CATS) [39 (link)]Modified Sheehan Disability Score (mSDS)
Generalised Anxiety Disorder 7-item (GAD-7) [40 (link)]Perth Alexithymia Questionnaire (PAQ) [41 (link)]Parental Bonding Instrument (PBI) [42 (link)]WHO 5 wellbeing index (WHO-5)
Cohen's Perceived Stress Scale (PSS) [43 (link)]Mindful Attention Awareness Scale (MAAS) [44 (link)]Stressful Life Events (SLE) [45 (link)]Changes in Sexual Functioning Questionnaire (CSFQ) [46 (link)]
Brief Symptom Inventory (BSI) [47 (link)]Short form of Metacognitions Questionnaire (MCQ-30) [48 (link)]Questions from the Copenhagen Aging and Midlife Biobank (CAMB) [49 (link)]
Symptom checklist (SCL-10) [50 (link)]Coping Self-Efficacy Scale (CSES) [51 (link)]Revised Sociosexual Orientation Inventory (SOI-R) [52 (link)]
Snaith-Hamilton Pleasure Scale (SHAPS) [53 (link)]
Pittsburgh Sleep Quality Index (PSQI) [54 (link)]
Jensen K.H., Dam V.H., Ganz M., Fisher P.M., Ip C.T., Sankar A., Marstrand-Joergensen M.R., Ozenne B., Osler M., Penninx B.W., Pinborg L.H., Frokjaer V.G., Knudsen G.M, & Jørgensen M.B. (2023). Deep phenotyping towards precision psychiatry of first-episode depression — the Brain Drugs-Depression cohort. BMC Psychiatry, 23, 151.
Publication 2023
Abuse, Child Alexithymia Anger Anxiety Disorders Attention Awareness Cognition Disorders Depressive Symptoms Disabled Persons Ethnicity Life Experiences Mentalization Metacognition Mindfulness Parent Patients Pleasure Psychometrics Rumination, Digestive Wounds and Injuries
4
Biomarker Profiling in Depression
Venous blood samples will be collected for serum, plasma, DNA, and RNA extraction. Identifying biomarkers relevant to the course of depression is an area of research that is evolving rapidly. Thus, based on an ongoing critical literature review, the search for and analysis of specific biomarkers may change during the study period. Currently, the blood biomarkers include inflammation parameters (e.g., high sensitivity CRP) [61 (link), 62 (link)] and neurotrophic factors (e.g., BDNF and S100B) [63 , 64 (link)].
DNA from blood samples will be used for microarray-based genotyping of MDD candidate genes, genes of relevance for MDD (e.g., rs41271330, 5-HTTLPR, COMT, and BDNFval66met), drug metabolism (e.g., CYP2D6, CYP2C19, UGT1A1, ABCB1, ABCC1) and to compute polygenic risk scores in all participants after genome-wide genotyping in the future. DNA will also be used for epigenetic analysis, and circular extrachromosomal DNA, a form of decomposed free DNA [65 (link)], will be extracted and characterised. RNA will be extracted for gene transcription profiles using microarray or TAG-based methods (mRNA and microRNA).
DNA from blood samples will be used for microarray-based genotyping of MDD candidate genes, genes of relevance for MDD (e.g., rs41271330, 5-HTTLPR, COMT, and BDNFval66met), drug metabolism (e.g., CYP2D6, CYP2C19, UGT1A1, ABCB1, ABCC1) and to compute polygenic risk scores in all participants after genome-wide genotyping in the future. DNA will also be used for epigenetic analysis, and circular extrachromosomal DNA, a form of decomposed free DNA [65 (link)], will be extracted and characterised. RNA will be extracted for gene transcription profiles using microarray or TAG-based methods (mRNA and microRNA).
Gene analyses will be based on a priori models of genetic variations known to modulate pharmacotherapy and psychotherapy responses. The results will be used to calculate a polygenic risk score for diagnosis and treatment response and meta-analyses with established polygenic risk scores for MDD and those currently developed for anxiety and anxiety disorders, including treatment response [66 (link)].
Jensen K.H., Dam V.H., Ganz M., Fisher P.M., Ip C.T., Sankar A., Marstrand-Joergensen M.R., Ozenne B., Osler M., Penninx B.W., Pinborg L.H., Frokjaer V.G., Knudsen G.M, & Jørgensen M.B. (2023). Deep phenotyping towards precision psychiatry of first-episode depression — the Brain Drugs-Depression cohort. BMC Psychiatry, 23, 151.
Publication 2023
ABCB1 protein, human ABCC1 protein, human Anxiety Anxiety Disorders Biological Markers BLOOD COMT protein, human CYP2C19 protein, human Cytochrome P-450 CYP2D6 Diagnosis DNA, A-Form DNA, Circular Genes Genetic Diversity Genome Hypersensitivity Inflammation Metabolism Microarray Analysis MicroRNAs Nerve Growth Factors Pharmaceutical Preparations Pharmacotherapy Plasma Psychotherapy RNA, Messenger Serum Transcription, Genetic UGT1A1 protein, human Veins
5
Long-Term Longitudinal Psychiatric Outcomes
After the last 18-month follow-up, the study dataset will be sent to Statistics Denmark with a list of all invited participants to allow a non-participant analysis and long-term follow-up. The study data will be linked with data from the Danish Civil Registration System [76 (link)] e.g., the DNPR [57 (link)], the Danish National Prescription Registry [55 (link), 56 ], and other registries indexing, e.g., hospital admittance, diagnosis- and treatment codes, prescription medications, employment status, living situation (e.g., partner information), and income. We will also examine diagnostic stability [77 (link)], e.g., change of primary diagnosis from first episode depression to an anxiety or personality disorder or later recurrent depressive episode or conversion to bipolar affective disorder.
Jensen K.H., Dam V.H., Ganz M., Fisher P.M., Ip C.T., Sankar A., Marstrand-Joergensen M.R., Ozenne B., Osler M., Penninx B.W., Pinborg L.H., Frokjaer V.G., Knudsen G.M, & Jørgensen M.B. (2023). Deep phenotyping towards precision psychiatry of first-episode depression — the Brain Drugs-Depression cohort. BMC Psychiatry, 23, 151.
Publication 2023
Anxiety Disorders Diagnosis Disorder, Dissociative Personality Disorders Prescription Drugs

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Spss version 26 by IBM
Sourced in United States, Japan, United Kingdom, Germany, Austria, Canada, Belgium, Spain
SPSS version 26 is a statistical software package developed by IBM. It is designed to perform advanced statistical analysis, data management, and data visualization tasks. The software provides a wide range of analytical tools and techniques to help users understand and draw insights from their data.
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Stata 13 by StataCorp
Sourced in United States, Denmark, United Kingdom, Austria, Sweden
Stata 13 is a comprehensive, integrated statistical software package developed by StataCorp. It provides a wide range of data management, statistical analysis, and graphical capabilities. Stata 13 is designed to handle complex data structures and offers a variety of statistical methods for researchers and analysts.
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Stata by StataCorp
Sourced in United States, United Kingdom, Denmark, Austria, Belgium, Spain, Australia, Israel
Stata is a general-purpose statistical software package that provides a comprehensive set of tools for data analysis, management, and visualization. It offers a wide range of statistical methods, including regression analysis, time series analysis, and multilevel modeling, among others. Stata is designed to facilitate the analysis of complex data sets and support the entire research process, from data import to report generation.

More about "Anxiety Disorders"

Anxiety Disorders are a group of mental health issues characterized by persistent and excessive worry, fear, and anxiety that can interfere with daily life.
These conditions include Generalized Anxiety Disorder (GAD), Panic Disorder, Social Anxiety Disorder (SAD), and Specific Phobias, among others.
Symptoms may involve restlessness, fatigue, difficulty concentrating, muscle tension, and sleep disturbances.
Effective treatments, such as psychotherapy (e.g., cognitive-behavioral therapy) and medication, are available to help manage Anxiety Disorders and improve quality of life.
Research in this field aims to enhance understanding of the underlying causes, develop more effective interventions, and promote improved outcomes for individuals affected by these prevalent and disruptive conditions.
Statistical software like SAS version 9.4, SPSS version 22.0, SAS v9.4, SPSS version 21, SPSS version 25, SPSS version 26, Stata 13, and Stata are commonly used for data analysis in Anxiety Disorders research.
These tools can be leveraged to identify patterns, test hypotheses, and generate insights that inform the development of new treatment approaches.
By utilizing AI-powered platforms like PubCompare.ai, researchers can optimize their research protocols, enhance reproducibility, and streamline their workflow, ultimately leading to improved research outcomes in the field of Anxiety Disorders.