A Mechanism-Informed Symptom Pattern Framework for Endometriosis-Associated Supportive Care: A Narrative Synthesis and Conceptual Hypothesis

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This paper proposes a mechanism-informed framework of four symptom patterns in endometriosis to guide supportive care, with convergence support from genetic data but requiring prospective validation.

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Abstract

Background: Endometriosis is a heterogeneous condition in which symptom profiles, pain phenotypes, and responses to non-pharmacological care vary substantially between patients. Existing classification systems describe lesion distribution but do not organise the functional and symptomatic variation relevant to supportive-care planning. Hypothesis: We propose a literature-derived conceptual framework organising endometriosis symptom heterogeneity into four mechanism-informed patterns — pain sensitisation, gut–immune interaction, neuroendocrine sensitivity, and estrogen clearance burden — overlaid by two cross-cutting modifier axes: immune-inflammatory activity and progesterone responsiveness. Methods: Targeted narrative synthesis of peer-reviewed literature on endometriosis pathophysiology. Pattern boundaries were defined by two operational criteria: mechanistic distinctness and meaningful differentiation in the emphasis on non-pharmacological supportive care. The framework's mechanistic vocabulary was cross-referenced against the 2026 multi-ancestry GWAS of endometriosis (Koller et al., ~1.4M women, 105,869 cases). Discussion: The framework is hypothesis-generating and not a diagnostic or treatment-selection tool. It occupies a supportive-care stratification layer complementary to anatomical and molecular classification systems. Convergence with GWAS-identified loci supports the mechanistic architecture but does not confirm cluster boundaries. Prospective validation using structured longitudinal symptom phenotyping aligned with WERF EPHect standards is required. Conclusion: This conceptual framework provides a structured vocabulary for mechanism-informed supportive-care planning in endometriosis and a testable hypothesis for future cohort research.
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Background

Endometriosis is a heterogeneous condition in which symptom profiles, pain phenotypes, and responses to non-pharmacological care vary substantially between patients. Existing classification systems describe lesion distribution but do not organise the functional and symptomatic variation relevant to supportive-care planning. Hypothesis: We propose a literature-derived conceptual framework organising endometriosis symptom heterogeneity into four mechanism-informed patterns — pain sensitisation, gut–immune interaction, neuroendocrine sensitivity, and estrogen clearance burden — overlaid by two cross-cutting modifier axes: immune-inflammatory activity and progesterone responsiveness.

Methods

Targeted narrative synthesis of peer-reviewed literature on endometriosis pathophysiology. Pattern boundaries were defined by two operational criteria: mechanistic distinctness and meaningful differentiation in the emphasis on non-pharmacological supportive care. The framework's mechanistic vocabulary was cross-referenced against the 2026 multi-ancestry GWAS of endometriosis (Koller et al., ~1.4M women, 105,869 cases).

Discussion

The framework is hypothesis-generating and not a diagnostic or treatment-selection tool. It occupies a supportive-care stratification layer complementary to anatomical and molecular classification systems. Convergence with GWAS-identified loci supports the mechanistic architecture but does not confirm cluster boundaries. Prospective validation using structured longitudinal symptom phenotyping aligned with WERF EPHect standards is required.

Conclusion

This conceptual framework provides a structured vocabulary for mechanism-informed supportive-care planning in endometriosis and a testable hypothesis for future cohort research. Files A Mechanism-Informed Symptom Pattern Framework for Endometriosis-Associated Supportive Care.pdf Files (421.2 kB) | Name | Size | Download all | |---|---|---| | md5:35854fd86ea9798e6dc5495ebd277872 | 421.2 kB | Preview Download |

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