Model-based co-clustering for mixed type data

Identifying comorbidity patterns of mental health disorders in community-dwelling older adults: a cluster analysis

As global life expectancy increases, understanding mental health patterns and their associated risk factors in older adults becomes increasingly critical. Using data from the cross-sectional Trinity Ulster Department of Agriculture study (TUDA, 2008-2012; n = 5186 ; mean age 74.0 years) and a subset of participants followed-up longitudinally (TUDA 5+, 2014-2018; n = 953 ), we perform a multi-view co-clustering analysis to identify distinct mental health profiles and their relationships with potential risk factors. The TUDA multi-view dataset consists of five views: (1) mental health, measured with Center for Epidemiologic Studies Depression Scale [CES-D] and Hospital Anxiety and Depression Scale [HADS], (2) cognitive and neuropsychological function, (3) illness diagnoses and medical prescription history, (4) lifestyle and nutritional attainment, and (5) physical well-being. That is, each participant is described by five distinct sets of features. The mental health view serves as the target feature set, while the other four views are analyzed as potential contributors to mental health risks. Under the multi-view co-clustering framework, for each view data, the participants (rows) are partitioned into different row-clusters, and the features (columns) are partitioned into different column-clusters. Each row-cluster is most effectively explained by the features in one or two column-clusters. Notably, the row-clusterings across views are dependent. By analyzing the associations between row clusters in the mental health view and those in each of the other four views, we can identify which risk factors co-occur and contribute to an increased risk of poor mental health. We identify five distinct row-clusters in the mental-health view data, characterized by varying levels of depression and anxiety: Group 1, mild depressive symptoms and no symptoms of anxiety; Group 2, acute depression and anxiety; Group 3, less severe but persistent depression and anxiety symptoms; Group 4, symptoms of anxiety with no depressive symptoms; and Group 5, no symptoms of either depression or anxiety. Cross-view association analysis revealed the following key insights: Participants in Group 3 exhibit lower neuropsychological function, are older, more likely to live alone, come from more deprived regions, and have reduced physical independence. Contrasting Group 3, participants in Group 2 show better neuropsychological function, greater physical independence, and higher socioeconomic status. Participants in Group 5 report fewer medical diagnoses and prescriptions, more affluent backgrounds, less solitary living, and stronger physical independence. A significant portion of this group aligns with cognitive health row-clusters 1 and 3, suggesting a strong link between cognitive and mental health in older age. Participants with only depressive (Group 1) or anxiety symptoms (Group 4) exhibit notable differences. Those with anxiety symptoms are associated with healthier clusters across other views. The co-clustering methodology also categorizes the questions in the CES-D and HADS scales into meaningful clusters, providing valuable insights into the underlying dimensions of mental health assessment. In the CES-D scale, the questions are divided into four clusters: those related to loneliness and energy, those addressing feelings of insecurity, worthlessness, and fear, those concerning concentration and effort, and those focused on sleep disturbances. Similarly, the HADS questions are grouped into clusters that reflect themes such as a strong sense of impending doom, nervousness or unease, and feelings of tension or restlessness. By organizing the questions from both scales into these smaller groups, the methodology highlights distinct symptom patterns and their varying severity among participants. This approach could be leveraged to develop abridged versions of the assessment scales, enabling faster and more efficient triage in clinical practice.

Symptom and Anatomical Phenotypes Provide Insights Into Interactions of Prolapse Symptoms and Anatomy

IMPORTANCE

Women pursue treatment to relieve symptoms, while surgeons repair anatomy, underlining the importance of the relationship between symptoms and anatomy.

OBJECTIVE

We hypothesized different anatomical and symptom phenotypes associated with pelvic organ prolapse (POP). Our objective was to investigate prevalence of phenotypes to explore associations of symptoms with anatomical defects.

METHODS

We defined 420 anatomical phenotypes from combinations of POP Quantification parameters and 128 symptom phenotypes from symptoms described by condition-specific questionnaires (Pelvic Floor Disorders Inventory, Short Form of the Personal Experience Questionnaire). We applied these to an anonymized database of 719 subjects with symptomatic pelvic floor disorders. Bar graphs were used to illustrate the distribution of anatomical and symptom phenotypes, as well as anatomical phenotypes of patients with specific symptoms. We then used biclustering analysis with the multiple latent block model, to identify patterns of clustered groups of subjects and features.

RESULTS

The most common symptom phenotypes have multiple (3-5) symptoms. A third of the theoretical anatomical phenotypes existed in our cohort. Bar graphs for specific symptom composites demonstrated unique distributions of anatomical phenotypes suggesting associations between anatomy and symptoms. Biclustering converged on 2 subject clusters (C1, C2) and 8 feature clusters. Cluster 1 (68%) represented a younger subpopulation with lower stage POP, more stress urinary incontinence and sexual dysfunction (P < 0.001 all). Cluster 2 had more protrusion (P < 0.001) and obstructed voiding (P = 0.001). Features that clustered together, such as stress urinary incontinence and sexual dysfunction, may represent underlying relationships.

CONCLUSIONS

We demonstrated a relationship between locations of anatomical POP and certain symptoms, which may generate new hypotheses and guide clinical decision making.

Co-clustering of Time-Dependent Data via the Shape Invariant Model

Multivariate time-dependent data, where multiple features are observed over time for a set of individuals, are increasingly widespread in many application domains. To model these data, we need to account for relations among both time instants and variables and, at the same time, for subject heterogeneity. We propose a new co-clustering methodology for grouping individuals and variables simultaneously, designed to handle both functional and longitudinal data. Our approach borrows some concepts from the curve registration framework by embedding the shape invariant model in the latent block model, estimated via a suitable modification of the SEM-Gibbs algorithm. The resulting procedure allows for several user-defined specifications of the notion of cluster that can be chosen on substantive grounds and provides parsimonious summaries of complex time-dependent data by partitioning data matrices into homogeneous blocks. Along with the explicit modelling of time evolution, these aspects allow for an easy interpretation of the clusters, from which also low-dimensional settings may benefit.