Development and validation of a logistic regression model for predicting menstrual irregularity using LDL-C and age in reproductive-aged women: An analysis of NHANES Data

Menstrual irregularity is a common reproductive endocrine disorder among women of reproductive age, serving both as an early indicator of various gynecological diseases and a potential marker of underlying metabolic disturbances. Low-density lipoprotein cholesterol (LDL-C), a key indicator of lipid metabolism, lacks robust epidemiological evidence regarding its association with menstrual irregularity. This study utilized data from the 2013 to 2014 cycle of the National Health and Nutrition Examination Survey, which included (n = 623) women aged 15 to 49 years, to train a logistic regression model for assessing the association between LDL-C levels and menstrual irregularity. The model was externally validated using data from the 2009 to 2010 cycle (n = 799). Model performance was evaluated using the area under the receiver operating characteristic curve (area under the curve [AUC]), calibration plots, and confusion matrices. A nomogram was constructed to enhance clinical interpretability. The analysis indicated that LDL-C levels and age were significant predictors of menstrual irregularity. The AUCs of the model were 0.754 in the training set and 0.765 in the validation set, demonstrating good predictive performance and robustness. Residual analysis and collinearity diagnostics further supported the good fit of the model. The nomogram based on the final model enables visualized assessment of individual risk, aiding clinical risk stratification and decision-making. Elevated LDL-C levels are significantly associated with menstrual irregularity in women of reproductive age, suggesting its potential role as a metabolic biomarker of reproductive health. Longitudinal studies are recommended to clarify the causal relationship and to explore the potential of lipid-lowering interventions in menstrual regulation.