Mood disorders in adult asthma phenotypes

Labor, Marina; Labor, Slavica; Jurić, Ivo; Fijačko, Vladimir; Grle, Sanja Popović; Plavec, Davor

doi:10.1080/02770903.2017.1306546

Cited by 16 publications

(15 citation statements)

References 40 publications

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“…Although 86% of the review studies gave some details regarding their methods for choosing the number of clusters (k), they were generally poorly reported. The most popular approach was the dendrogram, but only Labor et al [72] specified their criteria for cutting the dendrogram. In 14 studies, the dendrogram was the only method mentioned.…”

Section: Principal Findingsmentioning

confidence: 99%

Challenges of Clustering Multimodal Clinical Data: Review of Applications in Asthma Subtyping

Horne¹,

Tibble²,

Sheikh³

et al. 2020

JMIR Med Inform

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Background In the current era of personalized medicine, there is increasing interest in understanding the heterogeneity in disease populations. Cluster analysis is a method commonly used to identify subtypes in heterogeneous disease populations. The clinical data used in such applications are typically multimodal, which can make the application of traditional cluster analysis methods challenging. Objective This study aimed to review the research literature on the application of clustering multimodal clinical data to identify asthma subtypes. We assessed common problems and shortcomings in the application of cluster analysis methods in determining asthma subtypes, such that they can be brought to the attention of the research community and avoided in future studies. Methods We searched PubMed and Scopus bibliographic databases with terms related to cluster analysis and asthma to identify studies that applied dissimilarity-based cluster analysis methods. We recorded the analytic methods used in each study at each step of the cluster analysis process. Results Our literature search identified 63 studies that applied cluster analysis to multimodal clinical data to identify asthma subtypes. The features fed into the cluster algorithms were of a mixed type in 47 (75%) studies and continuous in 12 (19%), and the feature type was unclear in the remaining 4 (6%) studies. A total of 23 (37%) studies used hierarchical clustering with Ward linkage, and 22 (35%) studies used k-means clustering. Of these 45 studies, 39 had mixed-type features, but only 5 specified dissimilarity measures that could handle mixed-type features. A further 9 (14%) studies used a preclustering step to create small clusters to feed on a hierarchical method. The original sample sizes in these 9 studies ranged from 84 to 349. The remaining studies used hierarchical clustering with other linkages (n=3), medoid-based methods (n=3), spectral clustering (n=1), and multiple kernel k-means clustering (n=1), and in 1 study, the methods were unclear. Of 63 studies, 54 (86%) explained the methods used to determine the number of clusters, 24 (38%) studies tested the quality of their cluster solution, and 11 (17%) studies tested the stability of their solution. Reporting of the cluster analysis was generally poor in terms of the methods employed and their justification. Conclusions This review highlights common issues in the application of cluster analysis to multimodal clinical data to identify asthma subtypes. Some of these issues were related to the multimodal nature of the data, but many were more general issues in the application of cluster analysis. Although cluster analysis may be a useful tool for investigating disease subtypes, we recommend that future studies carefully consider the implications of clustering multimodal data, the cluster analysis process itself, and the reporting of methods to facilitate replication and interpretation of findings.

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Section: Principal Findingsmentioning

confidence: 99%

Challenges of Clustering Multimodal Clinical Data: Review of Applications in Asthma Subtyping

Horne¹,

Tibble²,

Sheikh³

et al. 2020

JMIR Med Inform

View full text Add to dashboard Cite

show abstract

“…These comorbidities have been shown to be more prevalent among asthmatic subjects and some may be related to a more severe form of asthma or refractoriness to treatment, and may influence its clinical manifestation and treatment response, impair health-related quality of life and increase demand on resources. The associations of specific asthma phenotypes with specific comorbidities and their impact on asthma control and management have been investigated[ 70 , 77 , 78 ], as such comorbidities may be coincidental findings or they may contribute directly to asthma severity[ 79 ] and to the difficult-to-treat phenotype[ 70 ]. However, in most studies the gender approach was not applied.…”

Section: From Evidence For Gender Differences In Health Conditions Tomentioning

confidence: 99%

Gender medicine: Lessons from COVID-19 and other medical conditions for designing health policy

Machluf

Chaiter

Tal

2020

WJCC

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Gender-specific differences in the prevalence, incidence, comorbidities, prognosis, severity, risk factors, drug-related aspects and outcomes of various medical conditions are well documented. We present a literature review on the extent to which research in this field has developed over the years, and reveal gaps in gender-sensitive awareness between the clinical portrayal and the translation into gender-specific treatment regimens, guidelines and into gender-oriented preventive strategies and health policies. Subsequently, through the lens of gender, we describe these domains in detail for four selected medical conditions: Asthma, obesity and overweight, chronic kidney disease and coronavirus disease 2019. As some of the key gender differences become more apparent during adolescence, we focus on this developmental stage. Finally, we propose a model which is based on three influential issues: (1) Investigating gender-specific medical profiles of related health conditions, rather than a single disease; (2) The dynamics of gender disparities across developmental stages; and (3) An integrative approach which takes into account additional risk factors (ethnicity, socio-demographic variables, minorities, lifestyle habits etc .). Increasing the awareness of gender-specific medicine in daily practice and in tailored guidelines, already among adolescents, may reduce inequities, facilitate the prediction of future trends and properly address the characteristics and needs of certain subpopulations within each gender.

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“…Anxiety and depression are common disorders that affect a large part of the population 1 , and studies have indicated that the prevalence of depression is as high as 6–15% in pregnant women 2,3 . Anxiety and depression are closely associated conditions that may affect not only psychological well-being but also the prognosis of other diseases 4,5 , and depression or anxiety is associated with asthma diagnosis 6 . Previous studies have also shown that maternal psychological stress (anxiety and/or depression) is associated with adverse outcomes during pregnancy, as well as adverse events in the child 7–10 .…”

Section: Introductionmentioning

confidence: 99%

Maternal anxiety, depression and asthma and adverse pregnancy outcomes – a population based study

Rejnö

Lundholm

Öberg

et al. 2019

Sci Rep

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To evaluate associations between maternal anxiety or depression and adverse pregnancy outcomes, taking possible familial confounding and interaction with asthma into account, we conducted a cohort study of all singleton births in Sweden 2001–2013. We retrieved information about pregnancy, diagnoses of anxiety/depression, asthma, and prescribed medication from the Swedish Medical Birth, National Patient, and Prescribed Drug Registers. We estimated associations with regression models, performed cousin and sibling comparisons, and calculated interactions. In 950 301 identified pregnancies; 5.9% had anxiety/depression and 4.0% had asthma. Anxiety/depression was associated with adverse pregnancy outcomes (e.g. preeclampsia, adjusted Odds Ratio 1.17 (95% Confidence Interval 1.12, 1.22), instrumental delivery (1.14 (1.10, 1.18)), elective (1.62 (1.57, 1.68)) and emergency (1.32 (1.28, 1.35)) caesarean section (CS)). Their children had lower birth weight (−54 g (−59, −49)) and shorter gestational age (−0.29 weeks (−0.31, −0.28)). Associations were not confounded by familial factors and asthma did not modify the effect of anxiety/depression for outcomes other than elective CS, p < 0.001. In women with anxiety/depression diagnosis, untreated women had higher odds of elective CS compared to women on medication (1.30 (1.17, 1.43)). In conclusion, anxiety/depression should be considered when evaluating pregnant women’s risk of complications such as preeclampsia and non-vaginal deliveries.

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Mood disorders in adult asthma phenotypes

Abstract: Mood disorders were significantly associated with AA, LOA, and infection-associated asthma, together with comorbid hypertension and the level of asthma control.

Cited by 16 publications

References 40 publications

Challenges of Clustering Multimodal Clinical Data: Review of Applications in Asthma Subtyping

Challenges of Clustering Multimodal Clinical Data: Review of Applications in Asthma Subtyping

Gender medicine: Lessons from COVID-19 and other medical conditions for designing health policy

Maternal anxiety, depression and asthma and adverse pregnancy outcomes – a population based study

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