Evaluation of the Metabotype Concept Identified in an Irish Population in the German KORA Cohort Study

Riedl, Anna; Hillesheim, Elaine; Wawro, Nina; Meisinger, Christa; Peters, Annette; Roden, Michael; Kronenberg, Florian; Herder, Christian; Rathmann, Wolfgang; Völzke, Henry; Reincke, Martín; Köenig, Wolfgang; Wallaschofski, Henri; Daniel, Hannelore; Hauner, Hans; Brennan, Lorraine; Linseisen, Jakob

doi:10.1002/mnfr.201900918

Cited by 10 publications

(19 citation statements)

References 40 publications

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“…Strengths of this study include the use of clustering variables that are routine clinical/diagnostic markers, which makes the framework relevant to a clinical or a primary care setting. Furthermore, the metabotypes were previously validated in a German cohort with additional analysis showing that they were able to distinguish disease prevalence and incidence and dietary intake across the metabotypes [17]. These findings suggest that the metabotypes concept is transferable and applicable to other ethnically similar populations for which the strategy here presented to derive dietary advice could be adapted.…”

Section: Table 4 Total Agreement Between the Dietary Advice Assigned mentioning

confidence: 73%

“…Several studies grouped individuals based on parameters of carbohydrate and lipid and metabolism [ 13 , 41 – 44 ] or metabolite classes as plasma lipoproteins [ 45 ] and fatty acids [ 46 ]. Furthermore, the present metabotype model was previously reported to be reproducible in a German cohort in terms of metabolic characteristics, with metabotype 3 being additionally characterised by the highest prevalence and incidence of metabolic disease and the most unhealthy dietary intake [ 17 ]. Overall, a strong body of evidence exists to support the metabotype approach as a meaningful means of classifying individuals into metabolic groups, which can benefit from targeted dietary advice.…”

Section: Discussionmentioning

confidence: 98%

“…The four variables were chosen as they are routinely measured and widely applicable markers of metabolic health. In addition, the model was reported to be reproducible in a German cohort in terms of metabolic characteristics [17].…”

Section: Metabotypingmentioning

confidence: 92%

“…Using the cluster model previously defined [11,17] and reproduced in a separate population [17], the participants were clustered into three metabotypes based on the concentrations of the following parameters: triacylglycerol, total cholesterol, HDL-cholesterol and glucose. Participants in metabotype 1 were characterised by highest HDL-cholesterol (median 1.90 mmol/L, percentiles 25-75 = 1.74-2.02 mmol/L) and the lowest triacylglycerol (0.75 mmol/L, 0.59-1.00 mmol/L), participants in metabotype 2 had the lowest average concentrations of total cholesterol (3.90 mmol/L, 3.50-4.30 mmol/L), and participants in metabotype 3 were characterised by highest average levels of triacylglycerol (1.73 mmol/L, 1.36-2.53 mmol/L) and total cholesterol (5.50 mmol/L, 4.80-6.00 mmol/L) ( Table 1).…”

Section: Use Of a Metabotype Model To Identify Three Metabolic Groupsmentioning

confidence: 99%

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Optimisation of a metabotype approach to deliver targeted dietary advice

et al. 2020

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Background Targeted nutrition is defined as dietary advice tailored at a group level. Groups known as metabotypes can be identified based on individual metabolic profiles. Metabotypes have been associated with differential responses to diet, which support their use to deliver dietary advice. We aimed to optimise a metabotype approach to deliver targeted dietary advice by encompassing more specific recommendations on nutrient and food intakes and dietary behaviours. Methods Participants (n = 207) were classified into three metabotypes based on four biomarkers (triacylglycerol, total cholesterol, HDL-cholesterol and glucose) and using a k-means cluster model. Participants in metabotype-1 had the highest average HDL-cholesterol, in metabotype-2 the lowest triacylglycerol and total cholesterol, and in metabotype-3 the highest triacylglycerol and total cholesterol. For each participant, dietary advice was assigned using decision trees for both metabotype (group level) and personalised (individual level) approaches. Agreement between methods was compared at the message level and the metabotype approach was optimised to incorporate messages exclusively assigned by the personalised approach and current dietary guidelines. The optimised metabotype approach was subsequently compared with individualised advice manually compiled. Results The metabotype approach comprised advice for improving the intake of saturated fat (69% of participants), fibre (66%) and salt (18%), while the personalised approach assigned advice for improving the intake of folate (63%), fibre (63%), saturated fat (61%), calcium (34%), monounsaturated fat (24%) and salt (14%). Following the optimisation of the metabotype approach, the most frequent messages assigned to address intake of key nutrients were to increase the intake of fruit and vegetables, beans and pulses, dark green vegetables, and oily fish, to limit processed meats and high-fat food products and to choose fibre-rich carbohydrates, low-fat dairy and lean meats (60–69%). An average agreement of 82.8% between metabotype and manual approaches was revealed, with excellent agreements in metabotype-1 (94.4%) and metabotype-3 (92.3%). Conclusions The optimised metabotype approach proved capable of delivering targeted dietary advice for healthy adults, being highly comparable with individualised advice. The next step is to ascertain whether the optimised metabotype approach is effective in changing diet quality.

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Section: Table 4 Total Agreement Between the Dietary Advice Assigned mentioning

confidence: 73%

Section: Discussionmentioning

confidence: 98%

Section: Metabotypingmentioning

confidence: 92%

Section: Use Of a Metabotype Model To Identify Three Metabolic Groupsmentioning

confidence: 99%

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Optimisation of a metabotype approach to deliver targeted dietary advice

et al. 2020

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“…K-means metabolic data analysis has been applied in various fields. For example, in an Irish cohort, triglycerides and glucose in fasting serum samples were clustered using k-means to identify individual dietary habits [15]. In another study, k-means clustered metabolic profiles from liquid chromatography-tandem mass spectrometry (LC-MS/MS) data were used from red wine [16].…”

Section: Introductionmentioning

confidence: 99%

The usefulness of sparse k-means in metabolomics data: An example from breast cancer data

Goudo

Sugimoto

Hiwa

et al. 2022

Preprint

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In processing metabolomics data, multidimensional quantitative data from thousands of metabolites are often sparse, that is, only a small fraction of metabolites are relevant to the phenotype of interest. Clustering is therefore used to discover subtypes from omics data. Sparse processing, which selects important metabolites from the total omics data, is an effective clustering technique. This study investigated the effectiveness of sparse k-means for metabolomics data. Specifically, sparse k-means was used to cluster blood lipid metabolite data of breast cancer patients in two studies: (1) before and after menopause, and (2) pre- and postoperative chemotherapy. In both cases, sparse k-means showed comparable discrimination accuracy with fewer metabolites than k-means. Furthermore, when the L1 norm values were varied, no significant changes were observed. The mean silhouette coefficients of sparse k-means and k-means were (1) 0.38 pm 0.14 (S.D.) and 0.17 pm 0.01, (2) 0.38 pm 0.07 and 0.17 pm 0.01, indicating that feature selection using sparse k-means can improve clustering results. In addition, metabolite selection using sparse k-means was consistent regardless of the test data or the constrained value of the L1 norm, indicating robustness.

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Association between Habitual Diet and the Postprandial Glucose Response—An Enable Study

Reik

Brandl

Schauberger

et al. 2022

Molecular Nutrition Food Res

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Scope: It is inconclusive which factors influence inter-individual variations of postprandial glucose response (PPGR). This study investigates whether the habitual diet is associated with PPGR. Methods and results: Data from healthy adults (young adults with 18-25 years, middle-aged adults with 40-65 years, and older adults with 75-85 years) is collected at baseline and during an oral glucose tolerance test (OGTT) collected. Habitual diet is assessed by a food frequency questionnaire and two 24-h food lists. Associations between habitual diet and glucose incremental area under the curve (iAUC min ) are examined by regression models. The intake of cereals and cereal products is negatively associated with glucose iAUC min (p = 0.002) in the total cohort (N = 459, 50% women, 55 ± 21 years, BMI 26 ± 5 kg m −2 ). Up to 9% of the variance in the glycemic response is explained by the respective dietary parameters identified in the models of the specific age groups. Conclusion: There are age-specific diet-related effects on PPGR. The usual intake of cereals and cereal products seems to play a greater role in PPGR in more than one age group. Further research is needed, to establish how diet can be optimized based on age and PPGR.

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Evaluation of the Metabotype Concept Identified in an Irish Population in the German KORA Cohort Study

Cited by 10 publications

References 40 publications

Optimisation of a metabotype approach to deliver targeted dietary advice

Optimisation of a metabotype approach to deliver targeted dietary advice

The usefulness of sparse k-means in metabolomics data: An example from breast cancer data

Association between Habitual Diet and the Postprandial Glucose Response—An Enable Study

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