Distinctive structure, composition and biomechanics of collagen fibrils in vaginal wall connective tissues associated with pelvic organ prolapse.

Collagen is the predominant structural protein within connective tissues. Pelvic organ prolapse (POP) is characterized by weakening of the pelvic floor connective tissues and loss of support for pelvic organs. In this study, we examined the multiscale structure, molecular composition and biomechanics of native collagen fibrils in connective tissues of the posterior vaginal fornix collected from healthy women and POP patients, and established the correlation of these properties with clinical POP quantification (POP-Q) scores. The collagen characteristics, including collagen amount, ratio of Collagen I and Collagen III, collagen fibril d-period, alignment and stiffness, were found to change progressively with the increase of the clinical measurement of Point C, a measure of uterine descent and apical prolapse. The results imply that a severe prolapse is associated with stiffer collagen fibrils, reduced collagen d-period, increased fibril alignment and imbalanced collagen synthesis, degradation and deposition. Additionally, prolapse progression appears to be synchronized with deterioration of the collagen matrix, suggesting that a POP-Q score obtained via a non-invasive clinical test can be potentially used to quantitatively assess collagen abnormality of a patient's local tissue. STATEMENT OF SIGNIFICANCE: Abnormal collagen metabolism and deposition are known to associate with connective tissue disorders, such as pelvic organ prolapse. Quantitative correlation of the biochemical and biophysical characteristics of collagen in a prolapse patient's tissue with the clinical diagnostic measurements is unexplored and unestablished. This study fills the knowledge gap between clinical prolapse quantification and the individual's cellular and molecular disorders leading to connective tissue failure, thus, provides the basis for clinicians to employ personalized treatment that can best manage the patient's condition and to alert pre-symptomatic patients for early management to avoid unwanted surgery.