The Role of Iron Homeostasis Imbalance in T2DM-Associated Cognitive Dysfunction: A Prospective Cohort Study Utilizing Quantitative Susceptibility Mapping

Abstract

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder that significantly impacts cognitive health. Although the vascular complications of T2DM have been extensively studied, research on brain iron deposition in T2DM remains scarce, and few studies have directly linked iron accumulation in cognition-related subcortical nuclei to cognitive dysfunction. This study aims to evaluate brain iron deposition using quantitative susceptibility mapping (QSM) and identify key subcortical nuclei associated with T2DM-related cognitive decline. A total of 224 participants were recruited, including 112 T2DM patients and 112 healthy controls. QSM was used to assess iron deposition in subcortical nuclei. Structural equation modeling was employed to construct interaction models between metabolic changes, susceptibility values, and cognitive function. Additionally, polynomial regression analysis was performed to evaluate the association between glycemic variability and the QSM values of subcortical nuclei. Our findings confirmed that T2DM patients exhibited pronounced iron deposition in the caudate and putamen compared to healthy controls. Correlation analyses showed that higher QSM values in the anterior putamen, posterior putamen, and posterior caudate were associated with slower processing speed (SDMT), reduced memory performance (AVLT) and poorer executive function (TMT, SCWT), indicating that greater iron accumulation in these nuclei is associated with poorer cognitive performance. In our SEM, metabolic dysregulation was significantly associated with higher subcortical susceptibility (β = 0.224, p = 0.010). The model further demonstrated that susceptibility values partially mediated the effect of metabolic factors on cognition (indirect effect β = -0.056, p = 0.018) and that the overall impact of metabolic dysregulation on cognition remained significant (β = -0.142, p = 0.037). Polynomial regression found that HbA1c was the strongest predictor of anterior putamen susceptibility, and a similar pattern was observed in the posterior caudate. The study demonstrates that the role of brain iron deposition in T2DM-related cognitive dysfunction. These findings reveal an important underlying mechanism of T2DM-induced cognitive impairment and provide evidence for early intervention strategies to mitigate cognitive decline in T2DM patients.

Keywords: cognitive dysfunction; iron deposition; quantitative susceptibility mapping; subcortical nuclei; type 2 diabetes.

FIGURE 1

Patient selection and study design. Flow chart of the cohort patient selection process and inclusion and exclusion criteria. AVLT, auditory verbal learning test; FBG, fasting blood glucose; HbA1c, glycated hemoglobin; HDL, high‐density lipoprotein cholesterol; LDL, low‐density lipoprotein cholesterol; MEDI+0, morphology‐enabled dipole Inversion with an automatic uniform cerebrospinal fluid zero reference algorithm; MEDI, morphology‐enabled dipole Inversion; SCWT, Stroop Color and Word Test; SDMT, symbol‐digit modalities test; TG, triglycerides; TMT, trail making test.

FIGURE 2

Flowchart of QSM image spatial registration, normalization, and ROI segmentation. By registering QSM images to T1‐weighted images, spatial consistency across different imaging modalities is achieved, enabling precise localization of subcortical regions in the QSM images. Subsequently, using the subcortex atlas derived from the redefinition of functional connectivity gradients and regional boundaries, QSM values for 32 subcortical regions are calculated. 3D‐T1, 3D T1‐weighted imaging; GM, gray matter; MNI, Montreal Neurological Institute; QSM, quantitative susceptibility mapping; WM, white matter.

FIGURE 3

Correlations between subcortical nuclei magnetic susceptibility values and cognitive test scores. p‐values from Spearman correlations were Bonferroni‐corrected; the significance threshold was set at p < 0.05/16 = 0.003. Scatterplots with overlaid Spearman partial correlation lines; marginal density plots show the distribution of each variable. Spearman correlations are presented. The included cognitive tests are SCWT (Stroop Color‐Word Test, evaluating the ability to resolve color‐word interference), AVLT (Auditory Verbal Learning Test, measuring memory performance in auditory verbal tasks), TMT (Trail Making Test, highlighting motivational characteristics), and SDMT (Symbol Digit Modalities Test, assessing cognitive processing speed and symbol‐digit association skills). aPUT, anterior putamen; lh, left hemisphere; pPUT, posterior putamen; pCAU, posterior caudate; rh, right hemisphere; THA‐DA, dorsoanterior thalamus.

FIGURE 4

Non‐linear relationship curve between magnetic susceptibility values of subcortical nuclei and HbA1c. aPUT, anterior putamen; lh, left hemisphere; pCAU, posterior caudate; rh, right hemisphere; THA‐DA, dorsoanterior thalamus.

FIGURE 5

Structural equation model of cognitive, metabolic, and magnetic susceptibility values interactions. The relationships among metabolic changes, magnetic susceptibility values of subcortical nuclei, and cognitive function are depicted in the SEM. The term “HbA1c squared” represents the squared value of HbA1c (HbA1c * HbA1c). Significant paths are marked as follows: *p < 0.05, **p < 0.01, and ***p < 0.001, indicating the strength of the statistical associations.