Abstract
Dementia is very common in the late stage of patient with Parkinson’s disease (PD). We aim to explore its underlying pathogenesis and identify candidate biomarkers using untargeted metabolomics analysis. Consecutive PD patients and healthy controls were recruited. Clinical data were assessed and patients were categorized into Parkinson’s disease without dementia (PDND) and Parkinson’s disease dementia (PDD). Fast plasma samples were obtained and untargeted liquid chromatography-mass spectrometry-based metabolomics analysis was performed. Based on the identified differentially-expressed metabolites from the metabolomics analysis, multivariate linear regression analyses and receiver operating characteristic (ROC) curves were further employed. According to the clinical data, the mean ages of PDND and PDD patients were significantly higher than those of healthy controls. The incidence of hypercholesterolemia was decreased in PDD patients. PDD patients also had lower levels of triglyceride, low-density lipoprotein cholesterol, and apolipoprotein B. There were 24 and 57 differentially expressed metabolites in PDD patients when compared with the healthy controls and PDND patients from the metabolomics analysis. Eleven lipid metabolites were simultaneously decreased between these two groups, and can be further subcategorized into fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, and prenol lipids. The plasma levels of the eleven metabolites were positively correlated with MMSE score and can be candidate biomarkers for PDD patients with areas under the curve ranging from 0.724 to 0.806 based on the ROC curves. Plasma lipoproteins are significantly lower in PDD patients. A panel of eleven lipid metabolites were also decreased and can be candidate biomarkers for the diagnosis of PDD patients. Lipid metabolic dysregulation is involved in the pathogenesis of Parkinson’s disease dementia.
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The datasets presented in this study can be found in the supplementary materials.
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Funding
This study was supported by Hubei Provincial Natural Science Foundation of China (2020CFB232), the Fundamental Research Funds for the Central Universities (2042020kf0056), and Chongqing Health and Family Planning commission (No.2017MSXM023).
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M-XD and Y-DW designed this study. M-XD and LH assessed the clinical data and performed the experiments. M-XD wrote and revised the manuscript.
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All procedures performed in studies involving human participants were in accordance with the ethical standards of the ethics committee of the First Affiliated Hospital of Chongqing Medical University and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.
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Supplementary Information
ESM 1
(DOCX 37 kb)
Supplementary Figure 1
The positive and negative ions of current chromatograms of the quality control sample. (A) The positive ion of current chromatogram; (B) The negative ion of current chromatogram. (PNG 384 kb)
Supplementary Figure 2
The PCA plots and response permutation tests of multivariate statistical analyses between these groups. (A) PCA plot derived from liquid chromatography- mass spectrometry-based metabolomics analysis of the three groups (R2X = 0.236). (B) PCA plot between the HC and PDND groups (R2X = 0.234). (C) PCA plot between the PDND and PDD patients (R2X = 0.25). (D) PCA plot between the HC and PDD patients (R2X = 0.238). (E) Response permutation test indicated the multivariate statistical analysis was valid and not overfitting of the three groups (R2 = (0.0, 0.126), Q2 = (0.0, -0.149)). (F) Response permutation test indicated the multivariate statistical analysis was valid and not overfitting between the HC and PDND groups (R2 = (0.0, 0.157), Q2 = (0.0, -0.105)). (G) Response permutation test indicated the multivariate statistical analysis was valid and not overfitting between the PDND and PDD groups (R2 = (0.0, 0.163), Q2 = (0.0, -0.112)). (H) Response permutation test indicated the multivariate statistical analysis was valid and not overfitting between the HC and PDD groups (R2 = (0.0, 0.156), Q2 = (0.0, -0.116)). PCA, principal component analysis; HC, healthy control; PDND, Parkinson’s disease without dementia; PDD, Parkinson’s disease dementia. (PNG 559 kb)
Supplementary Figure 3
The ROC curves of the differentially expressed metabolites performed by random forests. (A) The ROC curve and its range identified by cross validation of HMDB07014. (B) The ROC curve and its range identified by cross validation of HMDB07072. (C) The ROC curve and its range identified by cross validation of LMSP00000002. (D) The ROC curve and its range identified by cross validation of HMDB07074. (E) The ROC curve and its range identified by cross validation of HMDB02356. (F) The ROC curve and its range identified by cross validation of LMFA01060149. (G) The ROC curve and its range identified by cross validation of LMFA06000104. (H) The ROC curve and its range identified by cross validation of LMFA01020334. (I) The ROC curve and its range identified by cross validation of LMGP10030015. (J) The ROC curve and its range identified by cross validation of LMFA02000031. (K) The ROC curve and its range identified by cross validation of LMPR01070051. (L) The combined ROC curves with different numbers of these metabolites. ROC, receiver operating characteristics. (PNG 1416 kb)
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Dong, MX., Wei, YD. & Hu, L. Lipid metabolic dysregulation is involved in Parkinson’s disease dementia. Metab Brain Dis 36, 463–470 (2021). https://doi.org/10.1007/s11011-020-00665-5
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DOI: https://doi.org/10.1007/s11011-020-00665-5