Horizontal analysis and longitudinal cohort study of chronic renal failure correlates and cerebral small vessel disease relationship using peak width of skeletonized mean diffusivity

Front Neurol. 2024 Sep 6:15:1461258. doi: 10.3389/fneur.2024.1461258. eCollection 2024.

Abstract

Background and purpose: Peak width of skeletonized mean diffusivity (PSMD) is an MRI-based biomarker that may reflect white matter lesions (WML). PSMD is based on skeletonization of MR DTI data and histogram analysis. Both chronic renal failure (CRF) and WML may be affected by multisystemic small-vessel disorder. We aimed to explore the relationship between PSMD and estimated glomerular filtration rate (eGFR).

Methods: Fifty followed-up CRF patients matched for age, sex, hypertension and smoking status were enrolled and classified into a progressive group (n = 16) and stable group (n = 34) based on eGFR levels. Longitudinal and horizontal differences of PSMD were compared between the progressive and stable groups at the initial and follow-up time points. Pearson's correlation was used for correlation of eGFR with PSMD and WML (per Fazekas scale score). ROC was used to measure the sensitivity of PSMD and WML score to changes of eGFR.

Results: At the follow-up time point, PSMD of the progressive group was significantly higher than at the initial time point (p < 0.001), and PSMD of the progressive group was significantly higher than stable group (p < 0.001). PSMD and eGFR were significantly correlated. AUC curves explored that ΔPSMD (PSMD changes at the follow-up and initial time points) and follow-up PSMD was better for the classification of progressive and stable groups.

Conclusion: PSMD has significant correlation with eGFR, PSMD can reveal a close relationship between WML and CRF.

Keywords: MRI; PSMD; cerebrovascular; chronic renal failure; white matter lesion.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was supported by the following grants: National Key R&D Program NO. 2023YFF1204800 and 2023YFF1204804; The National Natural Science Foundation of China Grant Nos. 8225024 and 1871329; the Shanghai Medical Rising Star Talent Fund; Shanghai Science and Technology Innovation Action Plan (20S31907300).