Metabolic characteristics of patients with cognitive impairment related to cerebral small vessel disease based on untargeted metabolomics_West China Medical Journal

Authors：

YANG Shiyun , FENG Zijuan ,  ZHANG Shuting

Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R China;

Corresponding author：

ZHANG Shuting, Email: shutingzhang@scu.edu.cn

Keywords：

Cerebral small vessel disease; cognitive impairment; metabolomics; lipids

DOI：

10.7507/1002-0179.202601010

Video：

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Objective To investigates the metabolic changes in serum between patients with normal cognition of cerebral small vessel disease (CSVD) and those with cognitive impairment of CSVD. It aims to identify distinct metabolic pathways of CSVD-related cognitive impairment, which can provide new research directions for the diagnosis and treatment of this disease. Methods Serum samples from CSVD patients diagnosed in the Department of Neurology, West China Hospital of Sichuan University between July 2021 and December 2023 were used in this study. According to the patients’ Montreal Cognitive Assessment scores, they were divided into cognitively unimpaired CSVD group and cognitively impaired CSVD group. Untargeted metabolomic detection was performed using ultra-high performance liquid chromatography-tandem mass spectrometry. Quality control of the metabolomic data was conducted through correlation analysis of quality control samples. This study constructed an orthogonal partial least squares-discriminant analysis model to examine the relationship between metabolites and sample groups. Different metabolites were selected based on the criteria of P<0.05 and variable importance in projection >1, which were then subjected to metabolic pathway enrichment analysis. Results A total of 157 CSVD patients were included, including 51 cognitively unimpaired CSVD patients and 106 cognitively impaired CSVD patients. Untargeted metabolomics analysis, conducted in both positive and negative ion modes, identified a total of 68 significantly different metabolites between the cognitively unimpaired and cognitively impaired CSVD groups. These metabolites primarily consisted of lipids and lipid-like molecules, amino acids and their metabolites, and steroid hormones. Among these, the serum levels of 21 metabolites were increased in patients with CSVD-related cognitive impairment, while the levels of 47 metabolites were decreased. Further enrichment analysis revealed that these differential metabolites were predominantly enriched in 11 metabolic pathways, which included signaling pathways such as sphingolipid metabolism, protein digestion and absorption, and amino acid biosynthesis. Conclusions Compared with cognitively unimpaired CSVD patients, those with cognitive impairment showed increased levels of endogenous sphingolipids, such as phytosphingosine, and decreased levels of essential amino acids, including valine and leucine, in their serum. This suggests that lipid metabolism reprogramming and energy metabolism disturbances may be the main metabolic features in CSVD-related cognitive impairment. These different metabolites not only serve as promising biomarker candidates for CSVD-related cognitive impairment, but also offer new directions for investigating its pathological mechanisms.

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1. Markus HS, de Leeuw FE. Cerebral small vessel disease: recent advances and future directions. Int J Stroke, 2023, 18(1): 4-14.
2. Mok VCT, Cai Y, Markus HS. Vascular cognitive impairment and dementia: mechanisms, treatment, and future directions. Int J Stroke, 2024, 19(8): 838-856.
3. van der Flier WM, Skoog I, Schneider JA, et al. Vascular cognitive impairment. Nat Rev Dis Primers, 2018, 4: 18003.
4. Zhi N, Ren R, Qi J, et al. The China Alzheimer Report 2025. Gen Psychiatr, 2025, 38(4): e102020.
5. Di Minno A, Gelzo M, Stornaiuolo M, et al. The evolving landscape of untargeted metabolomics. Nutr Metab Cardiovasc Dis, 2021, 31(6): 1645-1652.
6. Schrimpe-Rutledge AC, Codreanu SG, Sherrod SD, et al. Untargeted metabolomics strategies-challenges and emerging directions. J Am Soc Mass Spectrom, 2016, 27(12): 1897-1905.
7. Bauermeister A, Mannochio-Russo H, Costa-Lotufo LV, et al. Mass spectrometry-based metabolomics in microbiome investigations. Nat Rev Microbiol, 2022, 20(3): 143-160.
8. Duering M, Biessels GJ, Brodtmann A, et al. Neuroimaging standards for research into small vessel disease-advances since 2013. Lancet Neurol, 2023, 22(7): 602-618.
9. 胡文立, 杨磊, 李譞婷, 等. 中国脑小血管病诊治专家共识 2021. 中国卒中杂志, 2021, 16(7): 716-726.
10. 中国卒中学会血管性认知障碍分会. 中国血管性认知障碍诊治指南 (2024 版). 中华医学杂志, 104 (31): 2881-2894.
11. Sagar Y, Saborni M, Swapnil Mundhe, et al. Metabolomic profiling unravels the role of sphingolipid pathways in spot blotch resistance in wheat. Acta Physiologiae Plantarum, 2025, 47(67): 67.
12. Triba MN, Le Moyec L, Amathieu R, et al. PLS/OPLS models in metabolomics: the impact of permutation of dataset rows on the K-fold cross-validation quality parameters. Mol Biosyst, 2015, 11(1): 13-19.
13. Blaženović I, Kind T, Ji J, et al. Software tools and approaches for compound identification of LC-MS/MS data in metabolomics. Metabolites, 2018, 8(2): 31.
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15. van den Brink H, Doubal FN, Duering M. Advanced MRI in cerebral small vessel disease. Int J Stroke, 2023, 18(1): 28-35.
16. Naamneh Elzenaty R, du Toit T, Flück CE. Basics of androgen synthesis and action. Best Pract Res Clin Endocrinol Metab, 2022, 36(4): 101665.
17. Francis JC, Swain A. Prostate Organogenesis. Cold Spring Harb Perspect Med, 2018, 8(7): a030353.
18. Lopes RA, Neves KB, Carneiro FS, et al. Testosterone and vascular function in aging. Front Physiol, 2012, 3: 89.
19. Chen L, Xu YX, Wang YS, et al. Prostate cancer microenvironment: multidimensional regulation of immune cells, vascular system, stromal cells, and microbiota. Mol Cancer, 2024, 23(1): 229.
20. Malkin CJ, Pugh PJ, Jones RD, et al. The effect of testosterone replacement on endogenous inflammatory cytokines and lipid profiles in hypogonadal men. J Clin Endocrinol Metab, 2004, 89(7): 3313-3318.
21. Cittadini A, Isidori AM, Salzano A. Testosterone therapy and cardiovascular diseases. Cardiovasc Res, 2022, 118(9): 2039-2057.
22. Salvemini D, Little JW, Doyle T, et al. Roles of reactive oxygen and nitrogen species in pain. Free Radic Biol Med, 2011, 51(5): 951-966.
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24. Adebayo D, Obaseki E, Vasudeva K, et al. Sphingolipid and methionine metabolism in aging. J Cell Sci, 2025, 138(21): jcs264026.
25. Qin Q, Yin Y, Xing Y, et al. Lipid metabolism in the development and progression of vascular cognitive impairment: a systematic review. Front Neurol, 2021, 12: 709134.
26. Liu Y, Chan DKY, Thalamuthu A, et al. Plasma lipidomic biomarker analysis reveals distinct lipid changes in vascular dementia. Comput Struct Biotechnol J, 2020, 18: 1613-1624.
27. Tong B, Ba Y, Li Z, et al. Targeting dysregulated lipid metabolism for the treatment of Alzheimer’s disease and Parkinson’s disease: current advancements and future prospects. Neurobiol Dis, 2024, 196: 106505.
28. Gengatharan JM, Handzlik MK, Chih ZY, et al. Altered sphingolipid biosynthetic flux and lipoprotein trafficking contribute to trans-fat-induced atherosclerosis. Cell Metab, 2025, 37(1): 274-290. e279.
29. O’Neill LA, Kishton RJ, Rathmell J. A guide to immunometabolism for immunologists. Nat Rev Immunol, 2016, 16(9): 553-565.
30. Wang N, Lu S, Cao Z, et al. Pyruvate metabolism enzyme DLAT promotes tumorigenesis by suppressing leucine catabolism. Cell Metab, 2025, 37(6): 1381-1399. e1389.
31. Han M, Bushong EA, Segawa M, et al. Spatial mapping of mitochondrial networks and bioenergetics in lung cancer. Nature, 2023, 615(7953): 712-719.
32. Lampropoulou V, Sergushichev A, Bambouskova M, et al. Itaconate links inhibition of succinate dehydrogenase with macrophage metabolic remodeling and regulation of inflammation. Cell Metab, 2016, 24(1): 158-166.
33. Li Q, Weiss K, Niwa F, et al. Leucine inhibits degradation of outer mitochondrial membrane proteins to adapt mitochondrial respiration. Nat Cell Biol, 2025, 27(11): 1889-1901.
34. Sorrentino V, Romani M, Mouchiroud L, et al. Enhancing mitochondrial proteostasis reduces amyloid-β proteotoxicity. Nature, 2017, 552(7684): 187-193.
35. Boos F, Krämer L, Groh C, et al. Mitochondrial protein-induced stress triggers a global adaptive transcriptional programme. Nat Cell Biol, 2019, 21(4): 442-451.

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Latest ArticlesMetabolic characteristics of patients with cognitive impairment related to cerebral small vessel disease based on untargeted metabolomics

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