Latent subtypes of manic or irritable episode symptoms in two population-based cohorts

Background Episodic changes in mood characterise disorders such as bipolar disorder, which includes distinct periods of manic excitability or irritability, along with additional symptoms experienced during these periods. Common clinical understanding informs diagnostic criteria and epidemiological studies reflect clinical thresholds. Aims To use a data-driven approach to defining groupings of symptoms experienced during periods of manic or irritable mood, which could inform understanding of mood disorders and guide case classification by identifying subgroups with homogeneous clinical/functional outcomes. Methods We used latent class analysis (LCA) to conduct an exploration of the latent structure in symptom responses in the UK Biobank and PROTECT studies, by investigating how symptoms, experienced during periods of manic or irritable mood, formed latent subgroups. We tested associations of latent subgroups with sociodemographic characteristics, diagnoses of psychiatric disorders and polygenic risk scores (PRS). Results Five latent classes were identified that captured patterns of symptoms experienced during periods of manic or irritable mood (N=42,183) in UK Biobank. We identified one class that experienced disruptive episodes of mostly irritable mood that was largely comprised of cases of depression/anxiety, and a class of individuals with increased confidence/creativity that reported lower disruptiveness and lower functional impairment. The five latent classes were replicated in an independent cohort, the PROTECT study (N=4,445), with similar distinctions between classes. Conclusion Our data-driven approach to grouping individuals identified distinct latent classes. A dimensional classification of mood disorders informed by our findings will be able to better assess or subtype these disorders in future studies.

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Latent subtypes of manic and/or irritable episode symptoms in two population-based cohorts

The British Journal of Psychiatry ◽

10.1192/bjp.2021.184 ◽

2022 ◽

pp. 1-10

Author(s):

Ryan Arathimos ◽

Chiara Fabbri ◽

Evangelos Vassos ◽

Katrina A. S. Davis ◽

Oliver Pain ◽

...

Keyword(s):

Mood Disorders ◽

Functional Outcomes ◽

Latent Class ◽

Classification Systems ◽

Polygenic Risk Score ◽

Latent Classes ◽

Uk Biobank ◽

Polygenic Risk ◽

Diagnostic Threshold ◽

Irritable Mood

Background Mood disorders are characterised by pronounced symptom heterogeneity, which presents a substantial challenge both to clinical practice and research. Identification of subgroups of individuals with homogeneous symptom profiles that cut across current diagnostic categories could provide insights in to the transdiagnostic relevance of individual symptoms, which current categorical diagnostic systems cannot impart. Aims To identify groups of people with homogeneous clinical characteristics, using symptoms of manic and/or irritable mood, and explore differences between groups in diagnoses, functional outcomes and genetic liability. Method We used latent class analysis on eight binary self-reported symptoms of manic and irritable mood in the UK Biobank and PROTECT studies, to investigate how individuals formed latent subgroups. We tested associations between the latent classes and diagnoses of psychiatric disorders, sociodemographic characteristics and polygenic risk scores. Results Five latent classes were derived in UK Biobank (N = 42 183) and were replicated in the independent PROTECT cohort (N = 4445), including ‘minimally affected’, ‘inactive restless’, active restless’, ‘focused creative’ and ‘extensively affected’ individuals. These classes differed in disorder risk, polygenic risk score and functional outcomes. One class that experienced disruptive episodes of mostly irritable mood largely comprised cases of depression/anxiety, and a class of individuals with increased confidence/creativity reported comparatively lower disruptiveness and functional impairment. Conclusions Findings suggest that data-driven investigations of psychopathological symptoms that include sub-diagnostic threshold conditions can complement research of clinical diagnoses. Improved classification systems of psychopathology could investigate a weighted approach to symptoms, toward a more dimensional classification of mood disorders.

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A data-driven approach for studying the role of body mass in multiple diseases: a phenome-wide registry-based case-control study in the UK Biobank

The Lancet Digital Health ◽

10.1016/s2589-7500(19)30028-7 ◽

2019 ◽

Vol 1 (3) ◽

pp. e116-e126 ◽

Cited By ~ 6

Author(s):

Elina Hyppönen ◽

Anwar Mulugeta ◽

Ang Zhou ◽

Vimaleswaran Karani Santhanakrishnan

Keyword(s):

Body Mass ◽

Case Control Study ◽

Case Control ◽

Data Driven ◽

Uk Biobank ◽

Data Driven Approach ◽

The Uk ◽

Control Study

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Genetically determined risk of keratinocyte carcinoma and risk of other cancers

International Journal of Epidemiology ◽

10.1093/ije/dyaa265 ◽

2020 ◽

Author(s):

Jean Claude Dusingize ◽

Catherine M Olsen ◽

Jiyuan An ◽

Nirmala Pandeya ◽

Upekha E Liyanage ◽

...

Keyword(s):

Genetic Variants ◽

Genome Wide Association Study ◽

Meta Analysis ◽

Epidemiological Studies ◽

Risk Scores ◽

Cancer Site ◽

Uk Biobank ◽

Individual Level ◽

Increased Risk ◽

The Uk

Abstract Background Epidemiological studies have consistently documented an increased risk of developing primary non-cutaneous malignancies among people with a history of keratinocyte carcinoma (KC). However, the mechanisms underlying this association remain unclear. We conducted two separate analyses to test whether genetically predicted KC is related to the risk of developing cancers at other sites. Methods In the first approach (one-sample), we calculated the polygenic risk scores (PRS) for KC using individual-level data in the UK Biobank (n = 394 306) and QSkin cohort (n = 16 896). The association between the KC PRS and each cancer site was assessed using logistic regression. In the secondary (two-sample) approach, we used genome-wide association study (GWAS) summary statistics identified from the most recent GWAS meta-analysis of KC and obtained GWAS data for each cancer site from the UK-Biobank participants only. We used inverse-variance-weighted methods to estimate risks across all genetic variants. Results Using the one-sample approach, we found that the risks of cancer at other sites increased monotonically with KC PRS quartiles, with an odds ratio (OR) of 1.16, 95% confidence interval (CI): 1.13–1.19 for those in KC PRS quartile 4 compared with those in quartile 1. In the two-sample approach, the pooled risk of developing other cancers was statistically significantly elevated, with an OR of 1.05, 95% CI: 1.03–1.07 per doubling in the odds of KC. We observed similar trends of increasing cancer risk with increasing KC PRS in the QSkin cohort. Conclusion Two different genetic approaches provide compelling evidence that an instrumental variable for KC constructed from genetic variants predicts the risk of cancers at other sites.

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A data-driven approach for stratifying psychotic and mood disorders subjects using structural magnitude resonance imaging data

Medical Imaging 2020: Computer-Aided Diagnosis ◽

10.1117/12.2549680 ◽

2020 ◽

Cited By ~ 2

Author(s):

Hooman Rokham ◽

Haleh Falakshahi ◽

Vince D. Calhoun

Keyword(s):

Mood Disorders ◽

Data Driven ◽

Imaging Data ◽

Resonance Imaging ◽

Data Driven Approach

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Associations Between Latent Classes of Perceived Neighborhood Destination Accessibility and Walking Behaviors in Older Adults of a Low-Density and a High-Density City

Journal of Aging and Physical Activity ◽

10.1123/japa.2018-0297 ◽

2019 ◽

Vol 27 (4) ◽

pp. 553-564 ◽

Cited By ~ 3

Author(s):

Ernest Boakye-Dankwa ◽

Anthony Barnett ◽

Nancy A. Pachana ◽

Gavin Turrell ◽

Ester Cerin

Keyword(s):

Older Adults ◽

Hong Kong ◽

Latent Class Analysis ◽

Short Distance ◽

Latent Class ◽

Epidemiological Studies ◽

High Density ◽

Latent Classes ◽

Low Density ◽

Two Cities

To examine associations between perceived destination accessibility within different distances from home and self-reported overall amounts of walking for different purposes among older adults (aged ≥ 65 years) in Brisbane, Australia (N = 793) and Hong Kong, China (N = 484). Perceived neighborhood destination accessibility types were derived from latent class analysis using comparable measures of perceived distance to 12 destinations from epidemiological studies in the two cities. Associations of perceived destination accessibility with measures of within-neighborhood walking were also estimated in Hong Kong participants. Better perceived destination accessibility was positively associated with the likelihood of walking in Brisbane participants only. Perceived destination accessibility within a short distance from home (5-min walk) was negatively related to the amount of within-neighborhood walking for transport in Hong Kong residents who walked. Our findings suggest that providing moderate-to-high, but not extreme, levels of destination accessibility may be optimal for the promotion of walking in older community dwellers.

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Exploring the dominant features of social media for depression detection

Journal of Information Science ◽

10.1177/0165551519860469 ◽

2019 ◽

Vol 46 (6) ◽

pp. 739-759

Author(s):

Jamil Hussain ◽

Fahad Ahmed Satti ◽

Muhammad Afzal ◽

Wajahat Ali Khan ◽

Hafiz Syed Muhammad Bilal ◽

...

Keyword(s):

Social Media ◽

Epidemiological Studies ◽

Data Driven ◽

Patient Portal ◽

Data Set ◽

Machine Learning Classification ◽

Patient Behaviour ◽

Depression Detection ◽

Mental Diseases ◽

Data Driven Approach

Recently, social media have been used by researchers to detect depressive symptoms in individuals using linguistic data from users’ posts. In this study, we propose a framework to identify social information as a significant predictor of depression. Using the proposed framework, we develop an application called the Socially Mediated Patient Portal (SMPP), which detects depression-related markers in Facebook users by applying a data-driven approach with machine learning classification techniques. We examined a data set of 4350 users who were evaluated for depression using the Center for Epidemiological Studies Depression (CES-D) scale. From this analysis, we identified a set of features that can distinguish between individuals with and without depression. Finally, we identified the dominant features that adequately assess individuals with and without depression on social media. The model trained on these features will be helpful to physicians in diagnosing mental diseases and psychiatrists in analysing patient behaviour.

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Zur Anwendung der CES-D bei älteren Menschen: Dimensionsstruktur und Meßartefakte

Diagnostica ◽

10.1026//0012-1924.46.1.29 ◽

2000 ◽

Vol 46 (1) ◽

pp. 29-37 ◽

Cited By ~ 7

Author(s):

Herbert Matschinger ◽

Astrid Schork ◽

Steffi G. Riedel-Heller ◽

Matthias C. Angermeyer

Keyword(s):

Mental State ◽

Latent Class ◽

Mini Mental State Examination ◽

Depression Scale ◽

Epidemiological Studies ◽

Partial Credit ◽

Examination Score ◽

State Examination

Zusammenfassung. Beim Einsatz der Center for Epidemiological Studies Depression Scale (CES-D) stellt sich das Problem der Dimensionalität des Instruments, dessen Lösung durch die Konfundierung eines Teilkonstruktes (“Wohlbefinden”) mit Besonderheiten der Itemformulierung Schwierigkeiten bereitet, da Antwortartefakte zu erwarten sind. Dimensionsstruktur und Eignung der CES-D zur Erfassung der Depression bei älteren Menschen wurden an einer Stichprobe von 663 über 75-jährigen Teilnehmern der “Leipziger Langzeitstudie in der Altenbevölkerung” untersucht. Da sich die Annahme der Gültigkeit eines partial-credit-Rasch-Modells sowohl für die Gesamtstichprobe als auch für eine Teilpopulation als zu restriktiv erwies, wurde ein 3- bzw. 4-Klassen-latent-class-Modell für geordnete Kategorien berechnet und die 4-Klassen-Lösung als den Daten angemessen interpretiert: Drei Klassen zeigten sich im Sinne des Konstrukts “Depression” geordnet, eine Klasse enthielt jene Respondenten, deren Antwortmuster auf ein Antwortartefakt hinwiesen. In dieser Befragtenklasse wird der Depressionsgrad offensichtlich überschätzt. Zusammenhänge mit Alter und Mini-Mental-State-Examination-Score werden dargestellt. Nach unseren Ergebnissen muß die CES-D in einer Altenbevölkerung mit Vorsicht eingesetzt werden, der Summenscore sollte nicht verwendet werden.

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