Retrospective Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Diabetes. Feb 15, 2022; 13(2): 110-125
Published online Feb 15, 2022. doi: 10.4239/wjd.v13.i2.110
Large-scale functional connectivity predicts cognitive impairment related to type 2 diabetes mellitus
An-Ping Shi, Ying Yu, Bo Hu, Yu-Ting Li, Wen Wang, Guang-Bin Cui
An-Ping Shi, Ying Yu, Bo Hu, Yu-Ting Li, Wen Wang, Guang-Bin Cui, Department of Radiology, Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, The Affiliated Tangdu Hospital of Air Force Medical University (Fourth Military Medical University), Xi'an 710038, Shaanxi Province, China
Author contributions: Shi AP performed the data analysis, wrote the draft, conceived and designed the experiments, and rewrote some paragraphs in the introduction and discussion sections; Yu Y, Hu B, Li YT and Wang W obtained grants, conducted the experiments, and contributed to the writing and revision of the manuscript; Cui GB supervised the project, reviewed and edited the manuscript, and managed the submission process; all authors read, revised, and approved the final version of the manuscript.
Supported by the National Natural Science Foundation of China, No. 81771815.
Institutional review board statement: This research program was reviewed and approved by Ethics Committee of Tangdu Hospital.
Informed consent statement: Written informed consent was obtained from all participants before the experiment began.
Conflict-of-interest statement: We have no financial relationships to disclose.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Guang-Bin Cui, BMed, Doctor, PhD, Academic Research, Chief Doctor, Chief Physician, Professor, Research Dean, Department of Radiology, The Affiliated Tangdu Hospital of Air Force Medical University, No. 569 Xinsi Road, Xi'an 710038, Shaanxi Province, China. cuigbtd@163.com
Received: August 23, 2021
Peer-review started: August 23, 2021
First decision: December 4, 2021
Revised: December 10, 2021
Accepted: January 6, 2022
Article in press: January 6, 2022
Published online: February 15, 2022
Processing time: 169 Days and 19.2 Hours
ARTICLE HIGHLIGHTS
Research background

Whole-brain functional connectivity patterns, or large-scale functional connectivity (LSFC) patterns, are both highly unique and reliable in each individual, and similar to a fingerprint, can identify individual differences in personality traits or cognitive functions. Abnormal LSFC patterns have been found in patients with dementia, as well as in those with mild cognitive impairment (MCI), which predicted their cognitive performance. It has been reported that patients with type 2 diabetes mellitus (T2DM) may develop MCI that could progress to dementia. We assessed the applicability of LSFC-related discriminative features to predict the cognitive level of patients with T2DM using a connectome-based predictive modeling (CPM) and support vector machine (SVM).

Research motivation

Whether machine learning techniques like CPM and SVM could utilize LSFC patterns to predict T2DM-related MCI with a high degree of accuracy remains unclear.

Research objectives

To investigate the utility of LSFC for more accurately and reliably predicting the cognitive impairment related to T2DM.

Research methods

Resting-state functional magnetic resonance images were derived from 42 patients with T2DM and 24 healthy controls. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA). Patients with T2DM were divided into two groups, according to the presence (T2DM-C; n = 16) or absence (T2DM-NC; n = 26) of MCI. Brain regions were marked using the Harvard Oxford (HOA-112), automated anatomical labeling (AAL-116), and 264-region functional (Power-264) atlases. LSFC biomarkers for predicting MoCA scores were identified using a new CPM technique. Subsequently, we used the SVM based on LSFC patterns for among-group differentiation. The area under the receiver operating characteristic curve determined the classification appearance.

Research results

CPM could predict MoCA scores in patients with T2DM, indicating that LSFC patterns represent cognition-level measures in these patients. Positive (anti-correlated) LSFC networks based on the Power-264 atlas showed the best predictive performance (r=0.42, P=0.0038); moreover, we observed new brain regions of interest associated with T2DM-related cognition. The area under the receiver operating characteristic curve values (T2DM-NC group vs. T2DM-C group) were 0.65-0.70, with LSFC matrices based on HOA-112 and Power-264 atlases having the highest value (0.70). Most discriminative and attractive LSFCs were related to the default mode network, limbic system, and basal ganglia.

Research conclusions

LSFC provides neuroimaging-based information that may be useful in detecting MCI early and accurately in patients with T2DM and therefore assist with T2DM management.

Research perspectives

Our study provides promising evidence that LSFC can reveal cognitive impairment in patients with T2DM, although further development is needed for clinical application.