Comparative Study of Brain White Matter Hyperintensity Burden in Adults with Type 2 Diabetes
- VernacularTitle:Чихрийн шижин хэв шинж 2-той насанд хүрэгчдийн тархины цагаан бодисын гиперинтенсив голомтын ачааллын харьцуулсан судалгаа
- Author:
Tserensugir A
1
;
2
;
Oyuntugs B
3
;
Gonshigsuren D
4
;
Tuvshinjargal D
1
;
Tovuudorj A
5
;
Mungunbagana G
6
;
Munkhbaatar D
1
;
Delgerdalai Kh
1
Author Information
1. Department of Radiology, School of Medicine, MNUMS
2. Orgil hospital
3. Department of Endocrinology, School of Medicine, MNUMS
4. Department of Radiology, Grandmed Hospital
5. Department of Neurology, School of Medicine, MNUMS
6. Department of Radiology, Brilliant Hospital
- Publication Type:Journal Article
- Keywords:
Fazekas score;
Magnetic resonance imaging;
Automated segmentation;
Artificial intelligence
- From:
Mongolian Journal of Health Sciences
2026;95(5):253-256
- CountryMongolia
- Language:Mongolian
-
Abstract:
Background:Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder associated with cognitive impairment and structural brain alterations. White matter hyperintensities (WMH) detected on magnetic resonance imaging (MRI) serve as critical neuroimaging markers of cerebral small vessel disease. However, research evaluating the WMH burden in the Mongolian population with T2DM using AI-based automated quantitative methods and comparing these findings with a control group remains limited. This research gap provides the rationale and necessity for the current study.
Aim:To evaluate and compare the burden of brain white matter hyperintensities (WMH) in adults with type 2 diabetes mellitus (T2DM) versus a control group.
Material and Methods:This analytical cross-sectional, retrospective comparative study included participants aged 40 and older who underwent brain MRI at the Mongolia-Japan Hospital of MNUMS between 2019 and 2025. The T2DM group consisted of patients with a confirmed diagnosis, while the control group included individuals without diabetes or prediabetes. After excluding cases with major structural abnormalities, poor image quality, failed automated processing, or incomplete clinical data, 257 out of an initial 283 participants were included in the final analysis (T2DM: 71, Control: 186). 3D T1-weighted MPRAGE and T2-FLAIR MRI sequences were processed using Neurophet AQUA software (version 3.1) to assess total, deep, and periventricular Fazekas scores. Age- and sex-adjusted ordinal logistic regression analysis was performed using SPSS 25.0, with p<0.05 considered statistically significant.
Results:A total of 257 participants (T2DM=71, Control=186) were analyzed. There were significant differences between groups in sex distribution (p=0.046) and age categories (p<0.001). The T2DM group exhibited significantly higher total and deep Fazekas scores compared to the control group (total Fazekas: aOR 10.81, 95% CI 5.20-22.49; deep Fazekas: aOR 8.21, 95% CI 4.32-15.60; both p<0.001). Increasing age was associated with higher total (aOR 1.07, 95% CI 1.03-1.11, p=0.001) and deep (aOR 1.04, 95% CI 1.00-1.08, p=0.037) Fazekas scores. Additionally, female sex was associated with higher total (aOR 1.95, 95% CI 1.04–3.63, p=0.036) and deep (aOR 1.95, 95% CI 1.08–3.51, p=0.026) Fazekas score categories.
Conclusion:Assessment of WMH burden using AI-based automated quantitative MRI processing revealed that T2DM is significantly associated with higher total and deep Fazekas scores. This association remained significant after adjusting for age and sex. These findings suggest that AI-based MRI processing is a suitable method for the objective, quantitative, and standardized assessment of brain WMH burden associated with T2DM.
- Full text:2026100121333458081Чихрийн шижин хэв шинж 2-той насанд.pdf
