Abstract
Introduction
The burden of stroke in patients with hypertension is very high, and its prediction is critical.
Objectives
We aimed to use plasma lipidomics profiling to identify lipid biomarkers for predicting incident stroke in patients with hypertension.
Methods
This was a nested case-control study. Baseline plasma samples were collected from 30 hypertensive patients with newly developed stroke, 30 matched patients with hypertension, 30 matched patients at high risk of stroke, and 30 matched healthy controls. Lipidomics analysis was performed by ultrahigh-performance liquid chromatography–tandem mass spectrometry, and differential lipid metabolites were screened using multivariate and univariate statistical methods. Machine learning methods (least absolute shrinkage and selection operator, random forest) were used to identify candidate biomarkers for predicting stroke in patients with hypertension.
Results
Co-expression network analysis revealed that the key molecular alterations of the lipid network in stroke implicate glycerophospholipid metabolism and choline metabolism. Six lipid metabolites were identified as candidate biomarkers by multivariate statistical and machine learning methods, namely phosphatidyl choline(40:3p)(rep), cholesteryl ester(20:5), monoglyceride(29:5), triglyceride(18:0p/18:1/18:1), triglyceride(18:1/18:2/21:0) and coenzyme(q9). The combination of these six lipid biomarkers exhibited good diagnostic and predictive ability, as it could indicate a risk of stroke at an early stage in patients with hypertension (area under the curve = 0.870; 95% confidence interval: 0.783–0.957).
Conclusions
We determined lipidomic signatures associated with future stroke development and identified new lipid biomarkers for predicting stroke in patients with hypertension. The biomarkers have translational potential and thus may serve as blood-based biomarkers for predicting hypertensive stroke.
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Data Availability
The raw lipidomic data generated in this study have been deposited in the Open Archive for Miscellaneous Data (OMIX) database (https://www.cncb.ac.cn/) with accession Number OMIX004817. Other data that support the findings of this study are available from the corresponding author upon request.
Abbreviations
- AUC:
-
Area under the curve
- BMI:
-
Body mass index
- CV:
-
Coefficient of variation
- CHD:
-
Coronary heart disease
- ChE:
-
Cholesteryl ester
- CHSCs:
-
Community health service centers
- Co:
-
Coenzyme
- DG:
-
Diglyceride
- FC:
-
Fold change
- HDL-C:
-
Hig-density lipoprotein cholesterol
- LASSO:
-
Least absolute shrinkage and selection operator
- LDL-C:
-
Low-density lipoprotein cholesterol
- LOESS:
-
Locally estimated scatterplot smoothing
- LPC:
-
Lysophosphatidylcholine
- MG:
-
Monoglycerde
- PC:
-
Phosphatidylcholine
- PE:
-
Phosphatidylethanolamine
- PG:
-
Phosphatidylglycerol
- PhSM:
-
Phytosphingosine
- PIP:
-
Phosphatidylinositol
- PLS-DA:
-
Partial least squares discriminant analysis
- PS:
-
Phosphatidylserine
- QC:
-
Quality control
- ROC:
-
Receiver operating characteristic
- RF:
-
Random forest
- SiE:
-
Sitosteryl ester
- SM:
-
Sphingomyelin
- STROBE:
-
Strengthening the Reporting of Observational studies in Epidemiology
- TC:
-
Total cholesterol
- TG:
-
Triglyceride
- UPLC-MS/MS:
-
Ultra-high performance liquid chromatography-tandem mass spectrometry
- VIP:
-
Variable importance in projection
- WGCNA:
-
Weighted gene co-expression network analysis
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Acknowledgements
We thank all authors for their contributions to the article.
Funding
This study was supported by the grants from the National Natural Science Foundation of China (82173648); the Natural Science Foundation of Guangdong Province (2022A1515011273); the Shenzhen science and technology project (JCYJ20210324125810024); the Shenzhen Nanshan District Science and Technology Bureau (2020075); the NingboKey Support Medical Discipline (Grant No.2022-F22); the Public Welfare Foundation of Ningbo (2021S108); Medical Scientific Research Foundation of Zhejiang Province; China (2022RC253 and 2021RC028); Zhejiang Provincial Public Service and Application Research Foundation; China (LGC22H260005); Key Program of Ningbo Natural Science Foundation; China (2022J271); Ningbo Leading Top Talent Training Project (2022RC-LJ-01); Internal Fund of Ningbo Institute of Life and Health Industry; University of Chinese Academy of Sciences (2020YJY0212); Ningbo Health Technology Project (2022Y30); Ningbo Natural Science Foundation(2022J275); Project of NINGBO Leading Medical& Health Discipline (2022-B12); the Medical and Health Science and Technology Project in Zhejiang province (2023KY1136 and 2024KY1589); the Ningbo Health Branding Subject Fund (PPXK2018-01) and the HwaMei Reasearch Foundation of Ningbo No.2 Hospital (2021HMKY14, 2022HMKY12 and 2023HMZD01).
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Conceptualization, CYW and LYH; Methodology, JJZ, RJZ and HMR; Formal Analysis, RJZ and LYH; Investigation, JJZ, SX, HW and YNJ; Resources, HMR and CYW; Data Curation, JJZ and TZ; Writing – Original Draft Preparation, JJZ and TZ; Writing – Review & Editing, JJZ, LYH and CYW. All authors reviewed and approved the final manuscript.
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All procedures followed in this study were in accordance with the ethical standards of the Ethics Committee of the Shenzhen Nanshan Centre for Chronic Disease Control (LL20190003) 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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Zeng, J., Zhang, R., Zhao, T. et al. Plasma lipidomic profiling reveals six candidate biomarkers for the prediction of incident stroke in patients with hypertension. Metabolomics 20, 13 (2024). https://doi.org/10.1007/s11306-023-02081-z
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DOI: https://doi.org/10.1007/s11306-023-02081-z