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. 2024 Apr 18:11:1341290.
doi: 10.3389/fmolb.2024.1341290. eCollection 2024.

Risk factors and metabolomics of mild cognitive impairment in type 2 diabetes mellitus

Tao Luo #  1 , Xiao Jiang #  1 , Ning Xu #  2 , Xinyu Zhao  1 , Xingjie Xie  1 , Xiuwen Xia  1 , XiaoLong Bian  3 , Haixia Liu  1
Affiliations

Risk factors and metabolomics of mild cognitive impairment in type 2 diabetes mellitus

Tao Luo et al. Front Mol Biosci. .

Abstract

Objective: This study aimed to explore the risk factors, metabolic characteristics, and potential biomarkers of mild cognitive impairment in type 2 diabetes mellitus (T2DM-MCI) and to provide potential evidence for the diagnosis, prevention, and treatment of mild cognitive impairment (MCI) in patients with type 2 diabetes mellitus (T2DM). Methods: A total of 103 patients with T2DM were recruited from the Endocrinology Department of The Second Affiliated Hospital of Dalian Medical University for inclusion in the study. The Montreal Cognitive Assessment (MoCA) was utilized to evaluate the cognitive functioning of all patients. Among them, 50 patients were categorized into the T2DM-MCI group (MoCA score < 26 points), while 53 subjects were classified into the T2DM without cognitive impairment (T2DM-NCI) group (MoCA score ≥ 26 points). Serum samples were collected from the subjects, and metabolomics profiling data were generated by Ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS). These groups were analyzed to investigate the differences in expression of small molecule metabolites, metabolic pathways, and potential specific biomarkers. Results: Comparison between the T2DM-MCI group and T2DM-NCI group revealed significant differences in years of education, history of insulin application, insulin resistance index, insulin-like growth factor-binding protein-3 (IGFBP-3), and creatinine levels. Further binary logistic regression analysis of the variables indicated that low educational level and low serum IGFBP-3 were independent risk factor for T2DM-MCI. Metabolomics analysis revealed that differential expression of 10 metabolites between the T2DM-MCI group and T2DM-NCI group (p < 0.05 and FDR<0.05, VIP>1.5). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analysis revealed that fatty acid degradation was the most significant pathway. Receiver operating characteristic (ROC) analysis shows that lysophosphatidylcholine (LPC) 18:0 exhibited greater diagnostic efficiency. Conclusion: This study revealed that a shorter duration of education and lower serum IGFBP-3 levels are independent risk factors for T2DM-MCI. Serum metabolites were found to be altered in both T2DM-MCI and T2DM-NCI groups. T2DM patients with or without MCI can be distinguished by LPC 18:0. Abnormal lipid metabolism plays a significant role in the development of MCI in T2DM patients.

Keywords: LPC; metabolomics; mild cognitive impairment; risk factors; type 2 diabetes mellitus.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
(A) PLS-DA score plot of the T2DM-MCI group vs. the T2DM-NCI group in ESI+ (R2X = 0.343, R2Y = 0.436, Q2 = 0.238). (B) PLS-DA score plot of the T2DM-MCI group vs. the T2DM-NCI group in ESI- (R2X = 0.33, R2Y = 0.421, Q2 = 0.269).
FIGURE 2
FIGURE 2
The metabolites from comparison between the T2DM-MCI and the T2DM-NCI group.
FIGURE 3
FIGURE 3
The heatmap metabolites from comparison between the T2DM-MCI and the T2DM-NCI group.
FIGURE 4
FIGURE 4
The ROC curve of selected metabolomics. (A) LPC 16:0 sn-1, AUC = 0.681. (B) LPC 16:0 sn-2, AUC = 0.720. (C) LPC 18:0 sn-1, AUC = 0.668. (D) PC 38:6, AUC = 0.670. (E) SM 34:1, AUC = 0.682. (F) SM 36:2, AUC = 0.666. (G) FFA 19:0, AUC = 0.813. (H) FFA 24:1, AUC = 0.786. (I) LPC16:0, AUC = 0.834. (J) LPC18:0, AUC = 0.848.
FIGURE 5
FIGURE 5
Enrichment analysis was performed to confirm the significantly changed metabolic pathway based on the Kyoto Encyclopedia of Genes and Genomes database through Metaboanalyst (A,B).
See this image and copyright information in PMC

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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: the “Xingliao Talent Plan” of Liaoning, China (YXMJ-QN-05), Dalian Science and Technology Innovation Fund, Dalian Science and Technology Bureau (2022JJ12SN048), “1 + X” program for Clinical Competency enhancement–Clinical Research Incubation Project, The Second Hospital of Dalian Medical University (2022LCYJZD03).