Analysis of high-dimensional metabolomics data with complex temporal dynamics using RM-ASCA+

Erdős, Balázs; Westerhuis, Johan A.; Adriaens, Michiel E.; O’Donovan, Shauna D.; Xie, Rong; Singh-Povel, Cécile M; Smilde, Age K.; Arts, Ilja C. W.

doi:10.1371/journal.pcbi.1011221

Cited by 6 publications

(3 citation statements)

References 40 publications

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“…Depending on the exact background of the study design, such methods can be t -tests (14, 16) or analysis of variance (ANOVA) models (4, 12, 14, 17, 18) to study group differences. More advanced methods consider explicitly the temporal aspect of the data and/or unbalanced designs, e.g., by using linear mixed models (19), extensions of the analysis of variance-simultaneous component analysis (ASCA) (20). A shortcut for analyzing the temporal behavior is sometimes used by extracting features from the dynamic profiles such as the area under the curve (AUC) (11, 21, 22).…”

Section: Figmentioning

confidence: 99%

Characterizing human postprandial metabolic response using multiway data analysis

Yan,

Li,

Horner

et al. 2023

Preprint

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Analysis of time-resolved postprandial metabolomics data can enhance our knowledge about human metabolism by providing a better understanding of similarities and differences in postprandial responses of individuals, with the potential to advance precision nutrition and medicine. Traditional data analysis methods focus on clustering methods relying on summaries of data across individuals or use univariate methods analyzing one metabolite at a time. However, they fail to provide a compact summary revealing the underlying patterns, i.e., groups of subjects, clusters of metabolites, and their temporal profiles. In this study, we analyze NMR (Nuclear Magnetic Resonance) spectroscopy measurements of plasma samples collected at multiple time points during a meal challenge test from 299 individuals from the COPSAC2000cohort. We arrange the data as a three-way array:subjectsbymetabolitesbytime, and use the CAN-DECOMP/PARAFAC (CP) tensor factorization model to capture the underlying patterns. We analyze thefasting statedata to reveal static patterns of subject group differences, and thefasting state-corrected postprandial data to reveal dynamic markers of group differences. Our analysis demonstrates that the CP model reveals replicable and biologically meaningful patterns capturing certain metabolite groups and their temporal profiles, and showing differences among males according to their body mass index (BMI). Furthermore, we observe that certain lipoproteins relate to the group difference differently in the fasting vs. dynamic state in males. While similar dynamic patterns are observed in response to the challenge test in males and females, the BMI-related group difference is only observed in males in the dynamic state.

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Section: Figmentioning

confidence: 99%

Characterizing human postprandial metabolic response using multiway data analysis

Yan,

Li,

Horner

et al. 2023

Preprint

Self Cite

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“…In addition, because the iAUC may not fully capture postprandial metabolite dynamics as iAUCs may be similar for postprandial curves with a different shape, we performed multivariate analysis to compare the shapes of the metabolite responses between IR phenotypes via RM-ASCA + [ 24 , 25 ]. Details can be found in Additional file 1 .…”

Section: Methodsmentioning

confidence: 99%

Hepatic insulin resistance and muscle insulin resistance are characterized by distinct postprandial plasma metabolite profiles: a cross-sectional study

Gijbels,

Erdős,

Trouwborst

et al. 2024

Cardiovasc Diabetol

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Background Tissue-specific insulin resistance (IR) predominantly in muscle (muscle IR) or liver (liver IR) has previously been linked to distinct fasting metabolite profiles, but postprandial metabolite profiles have not been investigated in tissue-specific IR yet. Given the importance of postprandial metabolic impairments in the pathophysiology of cardiometabolic diseases, we compared postprandial plasma metabolite profiles in response to a high-fat mixed meal between individuals with predominant muscle IR or liver IR. Methods This cross-sectional study included data from 214 women and men with BMI 25–40 kg/m2, aged 40–75 years, and with predominant muscle IR or liver IR. Tissue-specific IR was assessed using the muscle insulin sensitivity index (MISI) and hepatic insulin resistance index (HIRI), which were calculated from the glucose and insulin responses during a 7-point oral glucose tolerance test. Plasma samples were collected before (T = 0) and after (T = 30, 60, 120, 240 min) consumption of a high-fat mixed meal and 247 metabolite measures, including lipoproteins, cholesterol, triacylglycerol (TAG), ketone bodies, and amino acids, were quantified using nuclear magnetic resonance spectroscopy. Differences in postprandial plasma metabolite iAUCs between muscle and liver IR were tested using ANCOVA with adjustment for age, sex, center, BMI, and waist-to-hip ratio. P-values were adjusted for a false discovery rate (FDR) of 0.05 using the Benjamini–Hochberg method. Results Sixty-eight postprandial metabolite iAUCs were significantly different between liver and muscle IR. Liver IR was characterized by greater plasma iAUCs of large VLDL (p = 0.004), very large VLDL (p = 0.002), and medium-sized LDL particles (p = 0.026), and by greater iAUCs of TAG in small VLDL (p = 0.025), large VLDL (p = 0.003), very large VLDL (p = 0.002), all LDL subclasses (all p < 0.05), and small HDL particles (p = 0.011), compared to muscle IR. In liver IR, the postprandial plasma fatty acid (FA) profile consisted of a higher percentage of saturated FA (p = 0.013), and a lower percentage of polyunsaturated FA (p = 0.008), compared to muscle IR. Conclusion People with muscle IR or liver IR have distinct postprandial plasma metabolite profiles, with more unfavorable postprandial metabolite responses in those with liver IR compared to muscle IR.

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“…Palii, Carmen G., et al [167] used single-cell proteomics to define the temporal hierarchy of human erythropoiesis, the analysis reveals a timely ordered appearance and disappearance of transient cell populations or stages that accumulate at various positions along the erythroid trajectory, with cells undergoing gradual transitions between these stages. Additionally, Linear Mixed Models (LMMs) have been utilized to quantify temporal changes in metabolite concentrations, offering insights into the dynamics of the metabolic system [168,169].…”

Section: Trajectories Modeling In Multi-omicsmentioning

confidence: 99%

Generative AI models in time varying biomedical data: a systematic review (Preprint)

He,

Sarwal,

Qiu

et al. 2024

Preprint

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BACKGROUND Trajectory modeling is a longstanding challenge in the application of computational methods to healthcare. However, traditional statistical and machine learning methods do not achieve satisfactory results as they often fail to capture the complex underlying distributions of multi-modal health data, and long-term dependencies throughout patients’ medical histories. Recent advances in generative AI have provided powerful tools to represent complex distributions and patterns with minimal underlying assumptions. These have had a major impact in fields such as finance and environmental sciences, and recently researchers have turned to these methods for disease modeling. OBJECTIVE While AI methods have proven powerful, their application in clinical practice remains limited due to their black-box like nature. The proliferation of AI algorithms poses a significant challenge for non-developers to track and incorporate these advances into clinical research and application. In this work, we survey peer-reviewed, generative AI model papers with specific applications in time series health data. METHODS Our search includes single- and multi-modal generative AI models that operate over structured and unstructured data, medical imaging or multi-omics data. We introduce current generative AI methods, review their applications in each data modality and discuss their strengths and weaknesses compared to traditional methods. RESULTS We follow the PRISMA guideline and review 155 articles on generative AI applications in time series healthcare data across modalities. Furthermore, we offer a systematic framework for clinicians to easily identify suitable AI methods for their data and task at hand. CONCLUSIONS We review and critique existing applications of generative AI to time series health data with the aim of bridging the gap between computational methods and clinical application. We also identify shortcomings of existing approaches, and highlight recent advances in generative AI that represent promising directions for healthcare modeling.

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Analysis of high-dimensional metabolomics data with complex temporal dynamics using RM-ASCA+

Cited by 6 publications

References 40 publications

Characterizing human postprandial metabolic response using multiway data analysis

Characterizing human postprandial metabolic response using multiway data analysis

Hepatic insulin resistance and muscle insulin resistance are characterized by distinct postprandial plasma metabolite profiles: a cross-sectional study

Generative AI models in time varying biomedical data: a systematic review (Preprint)

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