| 1. |
- Skantze, Viktor, 1992, et al.
(författare)
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Data-driven analysis and prediction of dynamic postprandial metabolic response to multiple dietary challenges using dynamic mode decomposition
- 2023
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Ingår i: Frontiers in Nutrition. - 2296-861X. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: In the field of precision nutrition, predicting metabolic response to diet and identifying groups of differential responders are two highly desirable steps toward developing tailored dietary strategies. However, data analysis tools are currently lacking, especially for complex settings such as crossover studies with repeated measures. Current methods of analysis often rely on matrix or tensor decompositions, which are well suited for identifying differential responders but lacking in predictive power, or on dynamical systems modeling, which may be used for prediction but typically requires detailed mechanistic knowledge of the system under study. To remedy these shortcomings, we explored dynamic mode decomposition (DMD), which is a recent, data-driven method for deriving low-rank linear dynamical systems from high dimensional data. Combining the two recent developments “parametric DMD” (pDMD) and “DMD with control” (DMDc) enabled us to (i) integrate multiple dietary challenges, (ii) predict the dynamic response in all measured metabolites to new diets from only the metabolite baseline and dietary input, and (iii) identify inter-individual metabolic differences, i.e., metabotypes. To our knowledge, this is the first time DMD has been applied to analyze time-resolved metabolomics data. Results: We demonstrate the potential of pDMDc in a crossover study setting. We could predict the metabolite response to unseen dietary exposures on both measured (R2 = 0.40) and simulated data of increasing size ((Formula presented.) = 0.65), as well as recover clusters of dynamic metabolite responses. We conclude that this method has potential for applications in personalized nutrition and could be useful in guiding metabolite response to target levels. Availability and implementation: The measured data analyzed in this study can be provided upon reasonable request. The simulated data along with a MATLAB implementation of pDMDc is available at https://github.com/FraunhoferChalmersCentre/pDMDc.
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| 2. |
- Skantze, Viktor, 1992, et al.
(författare)
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Differential Responders to a Mixed Meal Tolerance Test Associated with Type 2 Diabetes Risk Factors and Gut Microbiota—Data from the MEDGI-Carb Randomized Controlled Trial
- 2023
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Ingår i: Nutrients. - : MDPI. - 2072-6643 .- 2072-6643. ; 15:20
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Tidskriftsartikel (refereegranskat)abstract
- The global prevalence of type 2 diabetes mellitus (T2DM) has surged in recent decades, and the identification of differential glycemic responders can aid tailored treatment for the prevention of prediabetes and T2DM. A mixed meal tolerance test (MMTT) based on regular foods offers the potential to uncover differential responders in dynamical postprandial events. We aimed to fit a simple mathematical model on dynamic postprandial glucose data from repeated MMTTs among participants with elevated T2DM risk to identify response clusters and investigate their association with T2DM risk factors and gut microbiota. Data were used from a 12-week multi-center dietary intervention trial involving high-risk T2DM adults, comparing high- versus low-glycemic index foods within a Mediterranean diet context (MEDGICarb). Model-based analysis of MMTTs from 155 participants (81 females and 74 males) revealed two distinct plasma glucose response clusters that were associated with baseline gut microbiota. Cluster A, inversely associated with HbA1c and waist circumference and directly with insulin sensitivity, exhibited a contrasting profile to cluster B. Findings imply that a standardized breakfast MMTT using regular foods could effectively distinguish non-diabetic individuals at varying risk levels for T2DM using a simple mechanistic model.
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| 3. |
- Skantze, Viktor, 1992, et al.
(författare)
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Identification of metabotypes in complex biological data using tensor decomposition
- 2023
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 233
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Tidskriftsartikel (refereegranskat)abstract
- Differences in the physiological response to treatment, such as dietary intervention, has led to the development of precision approaches in nutrition and medicine to tailor treatment for improved benefits to the individual. One such approach is to identify metabotypes, i.e., groups of individuals with similar metabolic profiles and/or regulation. Metabotyping has previously been performed using e.g., principal component analysis (PCA) on matrix data. However, metabotyping methods suitable for more complex experimental designs such as repeated measures or cross-over studies are needed. We have developed a metabotyping method for tensor data, based on CANDECOMP/PARAFAC (CP) tensor decomposition. Metabotypes are inferred from CP scores using k-means clustering, and robustness is evaluated using bootstrapping of metabolites. As a proof-of-concept, we identified metabotypes from metabolomics data where 79 metabolites were analyzed in 8 time points postprandially in 17 overweight men that underwent a three-arm dietary crossover intervention. Two metabotypes were found, characterized by differences in amino acid metabolite concentration, that were differentially associated with baseline plasma creatinine (p = 0.007) and with the baseline metabolome (p = 0.004). These results suggest that CP decomposition provides a viable approach for metabotype identification directly from complex, high-dimensional data with improved biological interpretation compared to the more simplistic PCA approach. A simulation study together with results from measured data concluded that several preprocessing methods should be taken into consideration for CP-based metabotyping on complex tensor data.
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| 4. |
- Skantze, Viktor, 1992, et al.
(författare)
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Identifying metabotypes from complex biological data using PARAFAC
- 2021
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Ingår i: Current Developments in Nutrition, Volume 5. - : Elsevier BV. - 2475-2991. ; 5:2, s. 882-
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Research have identified large individual variation in physiological response to diet, which has led to more focused investigations in precision nutrition. One approach towards personalized nutrition is to identify groups of differential responders, so called metabotypes (i.e., clusters of individuals with similar metabolic profiles and/or regulation). Metabotyping has previously been addressed using matrix decomposition tools like principal component analysis (PCA) on data organized in matrix form. However, metabotyping using data from more complex experimental designs, involving e.g., repeated measures over time or multiple treatments (tensor data), requires new methods. We developed a workflow for detecting metabotypes from experimental tensor data. The workflow is based on tensor decomposition, specifically PARAFAC which is conceptually similar to PCA but extended to multidimensional data. Metabotypes, based on metabolomics data were identified from PARAFAC scores using k-means clustering and validated by their association to anthropometric and clinical baseline data. Additionally, we evaluated the robustness of the metabotypes using bootstrapping. Furthermore, we applied the workflow to identify metabotypes using data from a crossover acute post-prandial dietary intervention study on 17 overweight males (BMI 25–30 kg/m2, 41–67 y of age) undergoing three dietary interventions (pickled herring, baked herring and baked beef), measuring 80 metabolites (from GC-MS metabolomics) at 8 time points (0–7h). We identified two metabotypes characterized by differences in amino acid levels, predominantly in the beef diet, that were also associated with creatinine (p = 0.007). The metabotype with higher postprandial amino acid levels was also associated with higher fasting creatinine compared to the other metabotype. Conclusions: The results stress the potential of PARAFAC to discover metabotypes from complex study designs. The workflow is not restricted to our data structure and can be applied to any type of tensor data. However, PARAFAC is sensitive to data pre-processing and further studies where differential metabotypes are related to clinical endpoints are highly warranted. Funding Sources: This work has been supported by the Swedish Foundation for Strategic Research and Formas, which is gratefully acknowledged.
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| 5. |
- Skantze, Viktor, 1992, et al.
(författare)
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Identifying metabotypes from tensor data
- 2022
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Metabolic response to diet shows large individual variation, which warrants tailored dietary recommendation i.e., personalized nutrition (PN). A step towards PN is to tailor diet to groups of individuals with similar metabolic phenotype, so called metabotypes (i.e., clusters of individuals with similar metabolism). Metabotyping of high-dimensional data is commonly performed in matrix form using matrix decompositions (e.g., PCA). However, data from e.g., crossover studies can be conveniently organized in multi-dimensional form (i.e., as tensor data) and methods for detecting metabotypes in such data are still lacking. We therefore aimed to develop and evaluate tools to identify potential metabotypes in high-dimensional tensor data. We developed two methods: The first uses CANDECOMP/PARAFAC (CP) decomposition directly on tensor data where clustering was performed on individual’s scores, whereas the second was developed specifically for time-resolved data and uses dynamic mode decomposition (DMD) to model metabolite dynamics, where clustering was performed on individual’s dynamic state trajectories. We applied the methods to identify metabotypes in data from a crossover acute post-prandial dietary intervention study on 17 overweight males (BMI 25–30 kg/m2, 41–67 y of age) undergoing three dietary interventions (pickled herring, baked herring and baked beef, measuring 79 metabolites (from GC-MS metabolomics) at 8 time points (0-7h). Both methods identified two potential metabotype clusters, predominantly in amino acids after the meat diet. The clustering associated to baseline levels of creatinine, strengthening the plausibility of found metabotypes. The CP method is a general approach, not specific to time-resolved data, and provides better fit if the data is multilinear. Conversely, DMD is designed for time-resolved data, for which it often provides a better fit than CP. We concluded that both the CP and the DMD approach are well suited to identify metabotypes in tensor data from a wide variety of complex experimental designs.
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| 6. |
- Wallman, Mikael, 1979, et al.
(författare)
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An Integrative Approach for Improved Assessment of Cardiovascular Safety Data
- 2021
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Ingår i: Journal of Pharmacology and Experimental Therapeutics. - : American Society for Pharmacology & Experimental Therapeutics (ASPET). - 1521-0103 .- 0022-3565. ; 377:2, s. 218-231
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Tidskriftsartikel (refereegranskat)abstract
- Cardiovascular adverse effects in drug development are a major source of compound attrition. Characterization of blood pressure (BP), heart rate (HR), stroke volume (SV), and QT-interval prolongation are therefore necessary in early discovery. It is, however, common practice to analyze these effects independently of each other. High-resolution time courses are collected via telemetric techniques, but only low-resolution data are analyzed and reported. This ignores co-dependencies among responses (HR, BP, SV, and QT-interval) and separation of system (turnover properties) and drug-specific properties (potencies, efficacies). An analysis of drug exposure-time and high-resolution response-time data of HR and mean arterial blood pressure was performed after acute oral dosing of ivabradine, sildenafil, dofetilide and pimobendan in Han-Wistar rats. All data were modelled jointly including different compounds, exposure- and response-time courses using a non-linear mixed effects-approach. Estimated fractional turnover rates (h-1, %RSE within brackets) were 9.45 (15), 30.7 (7.8), 3.8 (13) and 0.115 (1.7) of QT, HR, TPR and SV, respectively. Potencies (nM, %RSE within brackets) were IC50=475 (11), IC50=4.01 (5.4), EC50=50.6 (93) and IC50=47.8 (16), and efficacies (%RSE within brackets) were Imax=0.944 (1.7), Imax=1.00 (1.3), Emax=0.195 (9.9), and Imax=0.745 (4.6) for ivabradine, sildenafil, dofetilide and pimobendan. Hill-parameters were estimated with good precision, and below unity, indicating a shallow concentration-response relationship. An equilibrium concentration-biomarker response relationship was predicted and displayed graphically. This analysis demonstrates the utility of a model-based approach, integrating data from different studies and compounds, for refined pre-clinical safety margin assessment.
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