Characteristics of retinal image associated with premature ovarian insufficiency: a case- control study

• Main Authors: Jiaman Wu (Author), Liya Tan (Author), Yan Ning (Author), Weiqu Yuan (Author), Zuowei Lee (Author), Fei Ma (Author), Erfeng Wang (Author), Yuanyuan Zhuo (Author)
• Format: Book
• Published: BMC, 2023-07-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s13048-023-01231-0; 1757-2215
• Keywords: Retinal characteristics; Premature ovarian insufficiency; Retinal image analysis; Biostatistics method; Early clinical diagnosis; Gynecology and obstetrics; RG1-991
• Source: Journal of Ovarian Research, Vol 16, Iss 1, Pp 1-9 (2023)

Abstract Purpose To establish an early clinical diagnosis model based on the retinal vascular features associated with POI, supplying a non-invasive way for accurately and early predicted the risk of POI. Methods A total of 78 women with spontaneous POI and 48 healthy women were recruited from the Affiliated Shenzhen Maternity & Child Healthcare Hospital in the study. Retinal characteristics were analyzed using an automated retinal image analysis system. Binary logistic regression was used to identify POI cases and develop predictive models. Results Compared to the normal group, the POI group had larger central retinal artery equivalent (CRAE) (P = 0.006), central retinal vein equivalent (CRVE) (P = 0.001), index of venules asymmetry (Vasym) (P = 0.000); larger bifurcation angles of arterioles (Aangle) (P = 0.001), bifurcation coefficient of venule (BCV) (P = 0.001) and more obvious arteriovenous nipping (Nipping) (P = 0.005), but lower arteriole-to-venule ratio (AVR) (P = 0.012). In the POI group, the odds ratio (OR) of Vasym was 6.72e-32 (95% C.I. 4.62e-49-9.79e-15, P = 0.000), the OR of BCV was 5.66e-20 (95% C.I. 1.93e-34-.0000, P = 5.66e-20) and the OR of Nipping was 6.65e-06 (95% C.I. 6.33e-10-.0698, P = 0.012). Moreover, the area under the ROC curve for the binary logistic regression with retinal characteristics was 0.8582, and the fitting degree of regression models was 60.48% (Prob > chi-square = 0.6048). Conclusion This study demonstrated that retinal image analysis can provide useful information for POI identification and certain characteristics may help with early clinical diagnosis of POI.