Use of metabotyping for the delivery of personalised nutrition

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(no more than 200 words)1 Scope: Classification of subjects into dietary patterns generally relies on self-reporting 2 dietary data which are prone to error. The aim of the present study was to develop a model 3 for objective classification of people into dietary patterns based on metabolomic data. 4Methods and results: Dietary and urinary metabolomic data from the National Adult 5 Nutrition Survey (NANS) was used in the analysis (n=567). Two-step cluster analysis was 6 applied to the urinary data to identify clusters. The subsequent model was used in an 7 independent cohort to classify people into dietary patterns. Two distinct dietary patterns were 8 identified. Cluster 1 was characterized by significantly higher intakes of breakfast cereals, 9 low fat and skimmed milks, potatoes, fruit and fish, fish dishes (P<0.05) representing a 10 "healthy" cluster. Cluster 2 had significantly higher intakes of chips/processed potatoes, meat 11 products, savory snacks and high-energy beverages (P<0.05) representing an "unhealthy 12 cluster". Classification was supported by significant differences in nutrient status (P<0.05). 13Validation in an independent group revealed that 94% of subjects were correctly classified.

“…Participants in the present study also provided a fasting blood sample. Collection and processing of blood samples has previously been described .…”

Section: Methodsmentioning

confidence: 99%

Section: Dietary Data Collection Biofluid Collection and Anthropometrymentioning

confidence: 99%

Metabolomic‐based identification of clusters that reflect dietary patterns

Gibbons

Carr

McNulty

et al. 2017

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“…Collection of anthropometric measurements, including BMI, waist circumference, body fat, and blood pressure, by trained observers in this cohort has been described elsewhere . Biochemistry parameters were assessed in serum samples using a clinical bioanalyzer (RX Daytona; Randox Laboratories) with details described elsewhere . Diagnosis of MetS was defined according to the National Cholesterol Education Programme's Adult Treatment Panel III criteria for MetS 2001 .…”

Section: Methodsmentioning

confidence: 99%

Plasma fatty acid patterns reflect dietary habits and metabolic health: A cross‐sectional study

Brennan

McNulty

et al. 2016

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“…[ 1,7,16–19 ] In previous work in an Irish cross‐sectional study in adults ( n = 875), named Irish National Adult Nutrition Survey (NANS), O'Donovan et al. [ 20 ] identified three metabolically distinct subgroups of individuals using k ‐means cluster analysis based on the four clinical standard serum parameters triacylglycerol (TAG), total cholesterol (TC), HDL cholesterol, and glucose. This indicates that a small number of parameters seems to be sufficient to identify significant metabotypes.…”

Section: Introductionmentioning

confidence: 99%

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

Riedl

Hillesheim

Wawro

et al. 2020

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Scope: Previous work identified three metabolically homogeneous subgroups of individuals ("metabotypes") using k-means cluster analysis based on fasting serum levels of triacylglycerol, total cholesterol, HDL cholesterol, and glucose. The aim is to reproduce these findings and describe metabotype groups by dietary habits and by incident disease occurrence. Methods and results: 1744 participants from the KORA F4 study and 2221 participants from the KORA FF4 study are assigned to the three metabotype clusters previously identified by minimizing the Euclidean distances. In both KORA studies, the assignment of participants results in three metabolically distinct clusters, with cluster 3 representing the group of participants with the most unfavorable metabolic characteristics. Individuals of cluster 3 are further characterized by the highest incident disease occurrence during follow-up; they also reveal the most unfavorable diet with significantly lowest intakes of vegetables, dairy products, and fibers, and highest intakes of total, red, and processed meat. Conclusion: The three metabotypes originally identified in an Irish population are successfully reproduced. In addition to this validation approach, the observed differences in disease incidence across metabotypes represent an important new finding that strongly supports the metabotyping approach as a tool for risk stratification.