A Theoretical Framework for Integrating Skin Microbiome Dynamics in Psoriasis Management

Thesis Abstract

The complex interplay between the skin microbiome and psoriasis pathogenesis necessitates a comprehensive theoretical framework to enhance disease management strategies and therapeutic interventions. This study aims to develop a robust, evidence-based theoretical framework that integrates skin microbiome dynamics into psoriasis management, thereby contributing to personalized treatment approaches. It specifically seeks to identify the key microbiome components associated with psoriasis severity, elucidate microbiome-host interactions, and formulate a conceptual model that can inform clinical decision-making and future research. The research adopts a mixed-methods design, combining quantitative microbiological and clinical data collection with qualitative insights to construct an integrative theoretical model. The quantitative component involves a cross-sectional study of 200 psoriasis patients recruited from dermatology clinics across a tertiary healthcare facility. Participants are stratified based on disease severity, classified using the Psoriasis Area and Severity Index (PASI). Skin swab samples are collected from lesional and non-lesional sites for microbial profiling via high-throughput 16S rRNA gene sequencing, processed using the QIIME 2 pipeline. Microbiome diversity, composition, and functional potential are analyzed through alpha and beta diversity metrics, differential abundance testing using DESeq2, and predictive functional profiling with PICRUSt2. The clinical and microbiome data are integrated using multiple regression analyses and machine learning algorithms, such as random forests, to identify predictive microbiome biomarkers linked to disease severity. The qualitative component includes semi-structured interviews with 30 dermatologists and microbiologists to gather expert perspectives on microbiome-host interactions and their relevance in psoriasis management. Thematic analysis using NVivo 12 software is employed to identify key themes informing the conceptual development of the framework. Expected findings indicate significant dysbiosis in lesional skin microbiomes compared to non-lesional sites, characterized by reduced diversity and increased abundance of specific pathogenic taxa such as Staphylococcus aureus. The study anticipates revealing microbiome signatures that are predictive of disease severity and response to treatment. Furthermore, the integration of microbiome profiles with clinical parameters permits the formulation of a conceptual model grounded in the Biopsychosocial Theory and the Ecological Model of Human-Microbiome Interactions, illustrating the dynamic feedback between microbiome alterations, immune responses, and environmental factors in psoriasis. The contribution of this research lies in providing a novel, comprehensive theoretical framework that contextualizes skin microbiome dynamics within psoriasis pathology and management. It bridges existing gaps in understanding microbiome-host interactions, transforms microbiome data into actionable clinical insights, and proposes a basis for microbiome-targeted therapies. The framework enhances existing models by incorporating ecological and systemic perspectives, emphasizing the importance of personalized and microbiome-inclusive approaches. The main conclusion underscores the potential of microbiome-informed strategies to optimize psoriasis treatment outcomes. It advocates for the integration of microbiome analysis into routine clinical assessment and calls for longitudinal studies to validate and refine the proposed framework. Recommendations include developing microbiome-based diagnostic tools, investigating probiotic and prebiotic interventions, and fostering interdisciplinary collaborations between dermatology and microbiome research fields to operationalize and expand this innovative framework in clinical practice.