A nomogram including serum iron metabolism-related indicator and cerebral microbleeds for predicting vascular cognitive impairment in patients

Abstract

Background

Detection of serum iron metabolism and peripheral blood ferroptosis indicators may to some extent reflect pathological changes in central nervous system iron deposition such as Alzheimer's disease and vascular cognitive impairment (VCI).

Objective

The study sought to establish the first clinical prediction model related to the iron metabolism model, which helps in the early detection and prevention of VCI.

Methods

The study included 255 patients at Hebei Provincial People's Hospital from January 2023 to November 2024. They were divided into two groups based on VCI diagnostic criteria, with 144 cases in the VCI group and 111 cases in the control group. The nomogram of the VCI diagnostic prediction model was built using logistic regression. The accuracy and discriminative ability of the model were confirmed in three areas: differentiation, calibration, and clinical practicability.

Results

A logistic regression model identified four significant independent predictors of VCI: ferritin (odds ratio (OR) = 1.003, 95% CI: 1.001∼1.006), education (OR = 0.929, 95% CI: 0.871∼0.992), cerebral small vessel disease total load scores (OR = 1.319, 95% CI: 1.039∼1.673), and cerebral microbleeds (OR = 2.020, 95% CI: 1.092∼3.736) after adjustment for potential confounding factors (p < 0.05). The predictive nomogram has good discriminatory ability, calibration ability, and clinical applicability.

Conclusions

Serum ferritin was a significant predictor of VCI in middle-aged elderly people. The predictive model developed for the risk of developing VCI has good clinical applicability, calibration, and discrimination for early VCI screening.