Exploring metabolic syndrome serum profiling based on gas chromatography mass spectrometry and random forest models

Zhou, Lin; Gonçalves, Carlos M. Vicente; Dai, Ling; Lü, Hongmei; Huang, Jianhua; Ji, Hongchao; Wang, Dongsheng; Yi, Lunzhao; Liang, Yi‐Zeng

doi:10.1016/j.aca.2014.04.008

Cited by 69 publications

(55 citation statements)

References 37 publications

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“…Therefore, abnormal metabolism of plasma directly affects the quality of sperm. Previous studies have shown that plasma has a strong association with many complex diseases including a variety of cancers, [17][18][19] diabetes, 20 metabolic syndrome 21 and sepsis-induced acute lung injury. 22 Therefore, plasma is suitable for the systematic analysis of MI.…”

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confidence: 99%

Metabolic profiling putatively identifies plasma biomarkers of male infertility using UPLC-ESI-IT-TOFMS

Zeng

Zhang

et al. 2018

RSC Adv.

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Male infertility has become a global health problem. Currently, the diagnosis of male infertility depends on the results of semen quality or requires invasive surgical intervention. The process is complex and timeconsuming. Metabolomics is an emerging platform with unique advantages in disease diagnosis and pathological mechanism research. In this study, ultra-performance liquid chromatography-electrospray ionization-ion trap-time of flight mass spectrometry (UPLC-ESI-IT-TOFMS) combined with chemometrics methods was used to discover potential biomarkers of male infertility based on nontargeted plasma metabolomics. Plasma samples from healthy controls (HC, n ¼ 43) and various types of infertile patients, i.e., patients having oligozoospermia (OS, n ¼ 36), asthenospermia (AS, n ¼ 56) and erectile dysfunction (ED, n ¼ 45) were analyzed by UPLC-ESI-IT-TOFMS. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed. The results of OPLS-DA showed that HCs could be discriminated from infertile patients including OS (R 2 ¼ 0.903,Some potential biomarkers were successfully discovered by variable selection methods and variable important in the projection (VIP) in combination with the T-test. Statistical significance was set at p < 0.05; the Benjamini-Hochberg false discovery rate was used to reduce type 1 errors resulting from multiple comparisons. The identified biomarkers were associated with energy consumption, hormone regulation and antioxidant defenses in spermatogenesis. To elucidate the pathophysiology of male infertility, relative metabolic pathways were studied. It was found that male infertility is closely related to disturbed phospholipid metabolism, amino acid metabolism, steroid hormone biosynthesis metabolism, metabolism of fatty acids and products of carnitine acylation, and purine and pyrimidine metabolisms. Plasma metabolomics provides a novel strategy for the diagnosis of male infertility and offers a new insight to study pathogenesis mechanism.

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confidence: 99%

Metabolic profiling putatively identifies plasma biomarkers of male infertility using UPLC-ESI-IT-TOFMS

Zeng

Zhang

et al. 2018

RSC Adv.

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“…Compared with other machine learning algorithm, RF has many specific properties, such as computational efficiency on large dataset, outstanding performance in the prediction accuracy and good estimation of important variables. During the past decades, RF has been widely used in the field of analytical chemistry and chemometrics [31][32][33][34]. For detailed description of RF, see Ref.…”

Section: Construction Of Model and Assessment Of Performancementioning

confidence: 99%

Large-scale identification of potential drug targets based on the topological features of human protein–protein interaction network

Zou

Zhong

Liu

et al. 2015

Analytica Chimica Acta

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“…Over the past two decades, Liang's group has proposed and employed several discriminant algorithms to address the classification problem in metabolomic research such as partial least square‐discriminant analysis, uncorrelated linear discriminant analysis, kernel Fisher discriminant analysis, Monte Carlo tree, random forest, shrunken centroids regularized discriminant analysis, and support vector machines . For example, Liang's group explored the metabolic characteristics of metabolic syndrome with the help of random forests . Biomarker identification plays an essential role in metabolomics.…”

Section: Application In the Analysis Of Complex Systemsmentioning

confidence: 99%

Chemometrics in instrumental analysis of complex systems—in honor and memory of Yi‐Zeng Liang

Zhang

Yun

et al. 2018

Journal of Chemometrics

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A true pioneer in the fields of chemometrics and analytical chemistry was lost with the passing of Yi-Zeng Liang in October 2016. This is an enormous loss to both the local and global community of chemometrics. Yi-Zeng Liang is recognized as one of the major players in China in the field of chemometrics. As a professor of chemometrics and analytical chemistry in Hunan University and Central South University, he made significant advances within instrumental analysis of complex systems. Liang inspired many students and visiting scholars. As Liang's former students, we have attempted to summarize Liang's contributions to the field over the past 30 years. This review covers different chemometric areas, signal preprocessing, multivariate curve resolution, and multivariate calibration and discusses the application of these methods in traditional Chinese medicine, metabolomics, proteomics, and quantitative structure-activity relationship research.

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Exploring metabolic syndrome serum profiling based on gas chromatography mass spectrometry and random forest models

Cited by 69 publications

References 37 publications

Metabolic profiling putatively identifies plasma biomarkers of male infertility using UPLC-ESI-IT-TOFMS

Metabolic profiling putatively identifies plasma biomarkers of male infertility using UPLC-ESI-IT-TOFMS

Large-scale identification of potential drug targets based on the topological features of human protein–protein interaction network

Chemometrics in instrumental analysis of complex systems—in honor and memory of Yi‐Zeng Liang

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