Depression Symptoms in Perimenopausal Women with Somatic Pain: Nomogram Construction Based on a Logistic Regression Model

Feng Gao¹nian Shi Zhang^1*nan Ze Sun^2*jian Wei Wang¹

• ¹Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Liaoning Province, China
• ²School of Business, Nantong University, Nantong, Jiangsu Province, China

Objective: This study investigated the factors influencing depressive symptoms in women with somatic pain during the perimenopausal period in China and established and validated a nomogram prediction model. Methods: The predictive model is based on data from the China Health and Retirement Longitudinal Study (CHARLS), which focused on individuals aged 45-59 years with somatic pain during the perimenopausal period. The study utilized participants from the CHARLS 2018 wave, 30 factors including individual characteristics, health behaviors, living environment, family economic status, and social participation, were analyzed in this study. To ensure the model's reliability, the study cohort was randomly split into a training set (80%) and a validation set (20%). The χ 2 tests and a Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis were used to identify the most effective predictors of the model. The logistic regression model was employed to investigate the factors associated with depressive symptoms in perimenopausal women with somatic pain. A nomogram was constructed to develop a prediction model, and calibration curves were used to assess the accuracy of the nomogram model. The model's performance was evaluated using the area under the curve (AUC) and decision curve analysis (DCA). Results: In total, 2,265 perimenopausal women were included in the final analysis, of whom 1,402 (61.90%) experienced somatic pain. Multifactorial logistic regression identified marital status, pain distress, self-perceived general health, activities of daily living (ADL), sleep deprivation, life satisfaction, and air quality satisfaction, as predictive risk factors for perimenopausal women with somatic pain. The predictive model achieved an AUC of 0.7010 (95%CI = 0.677-0.725) in the training set and 0.7015 (95%CI =0.653-0.749) in the validation set. The nomogram showed excellent predictive ability according to receiver operating characteristic (ROC) and DCA, and the model may help in the early detection of high-risk depression symptoms in perimenopausal women with somatic pain, thereby enabling the timely initiation of appropriate treatment interventions.The nomogram constructed in this study can be used to identify the factors related to depression in women with perimenopausal somatic pain.