Causal Central Network Remodeling in Diabetic Neuropathy: An Integrated MR-fMRI Study

Abstract

Purpose: Diabetic peripheral neuropathy (DPN) is traditionally viewed as a peripheral disorder, yet emerging evidence implicates central nervous system (CNS) network dysfunction in its pathogenesis, though causal mechanisms remain incompletely understood.

Methods: Bidirectional two-sample Mendelian randomization (MR) analysis examined causal relationships between Resting-State Functional Magnetic Resonance Imaging (rs-fMRI) phenotypes (n=34,691) and DPN (n=96,474). For validation, amplitude of low-frequency fluctuation (ALFF) and functional connectivity (FC) analyses were conducted using rs-fMRI scans from DPN patients (n=16), diabetic controls without DPN (NDPN, n=24), and healthy controls (HC, n=20).

Results: Bidirectional MR demonstrated that: (a) reduced default mode-visual network connectivity causally elevates DPN risk (OR=0.61, P=0.04); (b) DPN promotes subcortical-cerebellar hyperconnectivity (OR=1.04, P=0.01). DPN patients exhibited significantly higher age, triglyceride levels, pain scores, and cognitive impairment relative to comparison groups (all P<0.001). Neuroimaging identified increased ALFF in the left superior frontal gyrus (LSFG) (AUC=0.79, P<0.05), which correlated positively with disease duration, accompanied by decoupled FC with the lingual gyrus but enhanced FC with the precuneus.

Conclusion: This study establishes DPN as a CNS-periphery integrated network disorder: genetic drivers disrupt default mode-visual integration, while compensatory subcortical-cerebellar hyperconnectivity stabilizes motor function via adaptive mechanisms. The LSFG emerges as a neuroadaptive hub, where elevated ALFF and connectivity reorganization (↓lingual gyrus/↑precuneus) reflect dynamic rebalancing between impaired basic vision and enhanced visuospatial processing. These findings redefine DPN pathogenesis beyond pure peripheral neurodegeneration, providing a theoretical foundation for early detection and circuit-targeted neuromodulation therapies.

Keywords: Mendelian random analysis; diabetic peripheral neuropathy; fMRI; low-frequency fluctuations; type 2 diabetes mellitus.

Figure 1

Bidirectional Mendelian Randomization Workflow Between rs-fMRI and DPN. This bidirectional two-sample MR analysis evaluated causal relationships between 191 rs-fMRI phenotypes and DPN. SNPs that were independent and significantly associated with exposure were selected as IVs, and SNPs associated with confounders were discarded. After stringent quality control on IVs, MR analysis was conducted to infer the causality between rs-fMRI phenotype and DPN. Finally, sensitivity analyses were used to assess the robustness of MR inference. Forward and reverse are carried out in the same way.

Figure 2

Ring heat map for the MR analysis. (A) Forward MR Analysis (rs-fMRI for exposure, DPN for outcome). (B) Reverse MR Analysis (DPN for exposure, rs-fMRI for outcome). The causal relationships between 191 rs-fMRI and DPN are illustrated. Each ring represents a different analytical method, and the association between each phenotype and DPN is visualized through variations in color intensity and position.

Figure 3

Causalities in the forward MR analysis. (A) Forest plots demonstrate significant causal relationships estimated using the five MR Methods. The OR represents the magnitude of the effect on the risk of DPN per 1 standard deviation change in the mean rs-fMRI phenotype, and the error line represents the 95% confidence interval. P values were derived from IVW analyses. Right shows the brain anatomical regions of the corresponding rs-fMRI phenotype. (B) Heterogeneity and horizontal pleiotropy analysis of MR analysis.

Figure 4

Causalities in the Reverse MR analysis. (A) Forest plots demonstrate significant causal relationships estimated using the five MR Methods. The OR represents the magnitude of the effect on the risk of rs-fMRI per 1 standard deviation change in the mean DPN phenotype, and the error line represents the 95% confidence interval. P values were derived from IVW analyses. Right shows the brain anatomical regions of the corresponding rs-fMRI phenotype. (B) Heterogeneity and horizontal pleiotropy analysis of MR analysis.

Figure 5

Clusters of significant changes in ALFF values among groups (analyzed using analysis of covariance (ANCOVA) and Scheffé post hoc tests, with FDR correction, P <0.05). Red regions (LSFG, left superior frontal gyrus) indicate that the DPN group has higher ALFF values compared to the HC group.

Figure 6

ROC curves for ALFF that distinguish patients with NDPN from patients with DPN and controls. The AUC of “ALFF_LSFG” index was 0.792, which had moderate diagnostic performance.

Figure 7

Brain regions with significantly different FC using LSFG as seed points. LSFG has an increased connection to PCUN.R and a decreased connection to LING.R.