Journal Article

Predictive value of biliverdin reductase‐A and homeostasis model assessment of insulin resistance on mild cognitive impairment in patients with type 2 diabetes

Wang, Nan, Lv, Haorui, Zhou, Xiaoyan, Ying, Changjiang, Huang, Jing, Wei, Xiaole, Shen, Li, Cheng, Aifang

2025

Overview

Aims/Introduction To investigate the predictive value of the biliverdin reductase‐A (BVR‐A) and the homeostasis model assessment for insulin resistance (HOMA‐IR) on mild cognitive impairment (MCI) in patients with type 2 diabetes mellitus, and to establish a nomogram model. Materials and Methods This study included 140 patients with type 2 diabetes mellitus. Based on Montreal Cognitive Assessment (MoCA) scores, participants were categorized into the normal cognitive function (T2DM‐NCF) group (65 cases) and the mild cognitive impairment (T2DM‐MCI) group (75 cases). Multivariate logistic regression analysis was performed to identify the factors associated with MCI in patients with type 2 diabetes mellitus. A nomogram prediction model was developed using R software for the selected factors, and its predictability and accuracy were verified. Results Compared with the T2DM‐NCF group, subjects with MCI were older, had a longer duration of diabetes, higher HOMA‐IR, lower BVR‐A, lower cognitive scores, and lower education levels (all P < 0.05). Multivariate logistic regression analysis showed that duration of diabetes (OR = 1.407, 95% CI: 1.163–1.701), HOMA‐IR (OR = 1.741, 95% CI: 1.197–2.53), and BVR‐A (OR = 0.528, 95% CI: 0.392–0.712) were significantly associated with the development of MCI in patients with type 2 diabetes mellitus. The C‐index of the nomogram was 0.863 (95% CI: 0.752–0.937). Conclusions Our findings suggest that BVR‐A and HOMA‐IR are significantly associated with the development of MCI in patients with type 2 diabetes mellitus. The nomogram incorporating BVR‐A and HOMA‐IR aids in predicting the risk of developing MCI in these patients. This study aimed to explore the predictive value of biliverdin reductase‐A (BVR‐A) and homeostasis model assessment for insulin resistance (HOMA‐IR) on mild cognitive impairment (MCI) in patients with type 2 diabetes mellitus, and to establish a predictive model. A total of 140 type 2 diabetes mellitus patients were included in the study and divided into the normal cognitive function group (T2DM‐NCF) and the mild cognitive impairment group (T2DM‐MCI) based on the Montreal Cognitive Assessment (MoCA) scores. Through multivariate logistic regression analysis, it was found that the duration of diabetes, HOMA‐IR, and BVR‐A are independent risk factors affecting the development of MCI in type 2 diabetes mellitus patients. The study also developed a predictive model that includes these risk factors and verified the model's predictive accuracy and consistency index through R software. The results of the study indicate that BVR‐A and HOMA‐IR may serve as predictive indicators for cognitive impairment in type 2 diabetes mellitus patients, and a predictive model that combines these two indicators is helpful in predicting the risk of MCI in patients with type 2 diabetes mellitus .

• Publisher: John Wiley & Sons, Inc,John Wiley and Sons Inc,Wiley
• Subject: Aged / Biliverdin / Biliverdin reductase / Biliverdin reductase‐A / Biomarkers - blood / Body mass index / Brain research / Cholesterol / Clinical Trial / Cognition & reasoning / Cognitive ability / Cognitive Dysfunction - diagnosis / Cognitive Dysfunction - etiology / Cognitive impairment in diabetes / Diabetes / Diabetes mellitus (non-insulin dependent) / Diabetes Mellitus, Type 2 - complications / Education / Female / Glucose / High density lipoprotein / Homeostasis / Hospitals / Humans / Insulin Resistance / Kinases / Male / Metabolism / Middle Aged / Mild cognitive impairment / Nomograms / Oxidative stress / Oxidoreductases Acting on CH-CH Group Donors - blood / Oxidoreductases Acting on CH-CH Group Donors - metabolism / Phosphorylation / Prediction models / Predictive Value of Tests / Prognosis / Proteins / Regression analysis / Software / Statistical analysis