Multivariate approach in analyzing medical data with correlated multiple outcomes: An exploration using ACCORD trial data

Psychosocial factors and hippocampal subfields: The Medea-7T study

Specific subfields within the hippocampus have shown vulnerability to chronic stress, highlighting the importance of looking regionally within the hippocampus to understand the role of psychosocial factors in the development of neurodegenerative diseases. A systematic review on psychosocial factors and hippocampal subfield volumes was performed and showed inconsistent results, highlighting the need for future studies to explore this relationship. The current study aimed to explore the association of psychosocial factors with hippocampal (subfield) volumes, using high-field 7T MRI. Data were from the Memory Depression and Aging (Medea)-7T study, which included 333 participants without dementia. Hippocampal subfields were automatically segmented from T2-weighted images using ASHS software. Generalized linear models accounting for correlated outcomes were used to assess the association between subfields (i.e., entorhinal cortex, subiculum, Cornu Ammonis [CA]1, CA2, CA3, dentate gyrus, and tail) and each psychosocial factor (i.e., depressive symptoms, anxiety symptoms, childhood maltreatment, recent stressful life events, and social support), adjusted for age, sex, and intracranial volume. Neither depression nor anxiety was associated with specific hippocampal (subfield) volumes. A trend for lower total hippocampal volume was found in those reporting childhood maltreatment, and a trend for higher total hippocampal volume was found in those who experienced a recent stressful life event. Among subfields, low social support was associated with lower volume in the CA3 (B = -0.43, 95% CI: -0.72; -0.15). This study suggests possible differential effects among hippocampal (subfield) volumes and psychosocial factors.

Network Analysis for Uncovering the Relationship between Host Response and Clinical Factors to Virus Pathogen: Lessons from SARS-CoV-2

Analysing complex datasets while maintaining the interpretability and explainability of outcomes for clinicians and patients is challenging, not only in viral infections. These datasets often include a variety of heterogeneous clinical, demographic, laboratory, and personal data, and it is not a single factor but a combination of multiple factors that contribute to patient characterisation and host response. Therefore, multivariate approaches are needed to analyse these complex patient datasets, which are impossible to analyse with univariate comparisons (e.g., one immune cell subset versus one clinical factor). Using a SARS-CoV-2 infection as an example, we employed a patient similarity network (PSN) approach to assess the relationship between host immune factors and the clinical course of infection and performed visualisation and data interpretation. A PSN analysis of ~85 immunological (cellular and humoral) and ~70 clinical factors in 250 recruited patients with coronavirus disease (COVID-19) who were sampled four to eight weeks after a PCR-confirmed SARS-CoV-2 infection identified a minimal immune signature, as well as clinical and laboratory factors strongly associated with disease severity. Our study demonstrates the benefits of implementing multivariate network approaches to identify relevant factors and visualise their relationships in a SARS-CoV-2 infection, but the model is generally applicable to any complex dataset.