The emerging roles of next-generation metabolomics in critical care nutrition

Li, Kefeng; Tong, Henry H.Y.; Chen, Yuwei; Sun, Yizhu; Wang, Jing

doi:10.1080/10408398.2022.2113761

Cited by 4 publications

(3 citation statements)

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“…3 By exploring and analyzing the clinical metabolomics data, researchers can better understand disease mechanisms, enhance disease diagnostic efficiency, and optimize the drug discovery process. 4,5 Thoroughly analyzing metabolomics data is, therefore, critical for advancing precision medicine. However, current clinical metabolomics data analysis presents several challenges, including small sample sizes, high dimensionality, and complex intercorrelations.…”

Section: ■ Introductionmentioning

confidence: 99%

“…The metabolome provides the “snapshot” of the current status of an organism and reflects the direct readout of cellular processes in response to both internal and external stimuli . By exploring and analyzing the clinical metabolomics data, researchers can better understand disease mechanisms, enhance disease diagnostic efficiency, and optimize the drug discovery process. , Thoroughly analyzing metabolomics data is, therefore, critical for advancing precision medicine. However, current clinical metabolomics data analysis presents several challenges, including small sample sizes, high dimensionality, and complex intercorrelations. , Additionally, existing data analysis methods are often prone to overfitting when applied to clinical metabolomics data analysis, which may critically impact the model performance …”

Section: Introductionmentioning

confidence: 99%

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MetDIT: Transforming and Analyzing Clinical Metabolomics Data with Convolutional Neural Networks

Sha,

Meng,

Luo

et al. 2024

Anal. Chem.

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Clinical metabolomics is growing as an essential tool for precision medicine. However, classical machine learning algorithms struggle to comprehensively encode and analyze the metabolomics data due to their high dimensionality and complex intercorrelations. This article introduces a new method called MetDIT, designed to analyze intricate metabolomics data effectively using deep convolutional neural networks (CNN). MetDIT comprises two components: TransOmics and NetOmics. Since CNN models have difficulty in processing one-dimensional (1D) sequence data efficiently, we developed TransOmics, a framework that transforms sequence data into two-dimensional (2D) images while maintaining a one-to-one correspondence between the sequences and images. NetOmics, the second component, leverages a CNN architecture to extract more discriminative representations from the transformed samples. To overcome the overfitting due to the small sample size and class imbalance, we introduced a feature augmentation module (FAM) and a loss function to improve the model performance. Furthermore, we systematically optimized the model backbone and image resolution to balance the model parameters and computational costs. To demonstrate the performance of the proposed MetDIT, we conducted extensive experiments using three different clinical metabolomics data sets and achieved better classification performance than classical machine learning methods used in metabolomics, including Random Forest, SVM, XGBoost, and LightGBM. The source code is available at the GitHub repository at https://github.com/Li-OmicsLab/MetDIT, and the WebApp can be found at http://metdit.bioinformatics.vip/.

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Section: ■ Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MetDIT: Transforming and Analyzing Clinical Metabolomics Data with Convolutional Neural Networks

Sha,

Meng,

Luo

et al. 2024

Anal. Chem.

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“…For many years, changes in the concentration of amino acids in plasma and tissues have been the focus of many studies [2]. More recently, this research has been extended into the field of metabolomics with the qualitative and sometimes quantitative measurement of the concentration of amino acids and other substrates [3][4][5]. In critically ill patients, both increases and decreases in plasma amino acid concentrations have been observed [6 & ].…”

Section: Introductionmentioning

confidence: 99%

Amino acid kinetics in the critically ill

Deutz,

Haines,

Wischmeier

et al. 2023

Current Opinion in Clinical Nutrition &Amp; Metabolic Care

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Purpose of review Stable isotope methods have been used for many years to assess whole body protein and amino acid kinetics in critically ill patients. In recent years, new isotope approaches and tracer insights have been developed. The tracer pulse approach has some advantages above the established primed-continuous tracer infusion approach because of the high amount of metabolic information obtained, easy applicability, and low tracer costs. Effects of disease severity and sex on amino acid kinetics in ICU patients will also be addressed. Recent findings Current knowledge was synthesized on specific perturbations in amino acid metabolism in critically ill patients, employing novel methodologies such as the pulse tracer approach and computational modeling. Variations were evaluated in amino acid production and linked to severity of critical illness, as measured by SOFA score, and sex. Production of the branched-chain amino acids (BCAAs), glutamine, tau-methylhistidine and hydroxyproline were elevated in critical illness, likely related to increased transamination of the individual BCAAs or increased breakdown of proteins. Citrulline production was reduced, indicative of impaired gut mucosa function. Sex and disease severity independently influenced amino acid kinetics in ICU patients. Summary Novel tracer and computational approaches have been developed to simultaneously measure postabsorptive kinetics of multiple amino acids that can be used in critical illness. The collective findings lay the groundwork for targeted individualized nutritional strategies in ICU settings aimed at enhancing patient outcomes taking into account disease severity and sex.

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