A Dermato-Pathophysiology Coherence Framework for Skin Disease Progression

Thesis Abstract

The study investigates the coherence between dermato-pathophysiological processes across common chronic skin diseases to elucidate a unified progression framework that informs prognosis and therapeutic targeting. The problem addressed is the fragmentation of disease models for skin conditions such as atopic dermatitis, psoriasis, and vitiligo, which hampers cross-disease insights into progression and response to treatment. The aim is to develop a Dermato-Pathophysiology Coherence Framework (DPCF) that integrates molecular, cellular, and clinical dimensions to map coherent trajectories of skin disease progression. Specific objectives are (1) to identify shared and divergent pathophysiological pathways across selected skin diseases; (2) to construct a multilevel coherence model linking genetic, immunological, microbiome, and epidermal barrier factors to clinical severity trajectories; (3) to validate the model against longitudinal patient data to assess predictive coherence for progression and flares; (4) to evaluate the framework’s implications for personalized treatment sequencing and outcome forecasting. A mixed-methods design is employed. The population comprises patients diagnosed with atopic dermatitis (n=400), plaque psoriasis (n=400), and vitiligo (n=200) across tertiary dermatology clinics over a 24-month period. A stratified random sampling approach yields 600 participants for longitudinal analysis. Data collection integrates quantitative and qualitative instruments (i) standardized clinical assessments including Eczema Area and Severity Index (EASI), Psoriasis Area and Severity Index (PASI), and Vitiligo Global Improvement Assessment; (ii) molecular and cellular assays—serum cytokine panels (IL-4, IL-13, IL-17, TNF-?), skin transcriptomics focusing on barrier and inflammatory pathways, and microbiome sequencing of lesional and non-lesional sites; (iii) epigenetic markers (DNA methylation profiling of key inflammatory genes); (iv) patient-reported outcomes via the Dermatology Life Quality Index (DLQI) and daily symptom diaries; (v) structured clinical interviews for psychosocial and environmental factors. Validity and reliability are ensured through validated instruments, pilot testing (n=30 per disease), and triangulation across data sources. Data analysis employs (a) structural equation modeling (SEM) to delineate latent constructs of pathophysiological coherence and their relations to clinical trajectories; (b) multilevel growth curve modeling to capture disease progression over time; (c) hierarchical clustering to identify shared and unique pathway constellations; (d) regression analyses to test predictive power of the coherence constructs; (e) thematic analysis of qualitative interviews to contextualize quantitative findings. The theoretical underpinnings draw on the Unified Pathophysiology Theory in dermatology and the Person-Environment-Disease framework, integrating endotype-driven mechanisms with clinical phenotype progression. Key expected findings include (i) identification of a core set of interlinked pathways (epidermal barrier disruption, Th2/Th17/Th1–driven inflammation, keratinocyte hyperproliferation, and microbiome perturbations) that exhibit coherent trajectories across diseases but with disease-specific weighting; (ii) a validated DPCF that explains a significant proportion of variance in progression metrics (anticipating SEM fit indices CFI > 0.95, RMSEA < 0.05); (iii) robust associations between baseline molecular signatures and subsequent flares, enabling risk stratification; (iv) qualitative insights into patient-perceived drivers of progression and adherence that refine the framework’s applicability to real-world care. The study contributes to knowledge by offering a formalized, cross-disease progression model that operationalizes shared etiopathogenic mechanisms into actionable prognostic and therapeutic guidance, thereby bridging molecular pathology and clinical outcomes. The main conclusion anticipates that a coherence-based integration of dermato-pathophysiological processes yields a transferable framework capable of predicting progression patterns and informing personalized intervention sequences. Recommendations include applying the DPCF in clinical decision support to tailor biologic and conventional therapies, prioritizing longitudinal biomarker monitoring for early flare detection, and expanding the framework to include other inflammatory skin diseases. Limitations anticipated involve heterogeneity in treatment histories and environmental exposures, which will be addressed through sensitivity analyses and incorporation of environmental covariates. Future research should explore cross-population validation and integration with digital phenotyping to enhance real-time coherence assessment.