Uit Foodlog dat hier een artikel over schreef: 

Persoonlijk voedingsadvies aan de hand van een potje plas
Alles wat je eet wordt door het lichaam afgebroken tot kleinere tussen- of eindproducten, ook wel metabolieten genoemd. De stoffen die niet worden gebruikt, verlaten het lichaam via bijvoorbeeld de urine. De samenstelling van metabolieten in je urine wordt daarom gezien als een objectieve indicator van de kwaliteit van je voedingspatroon. Hoe meer overgebleven metabolieten in de urine, hoe minder efficiënt het lichaam omgaat met de voeding die het heeft binnengekregen.
.............

In het onderzoek werd voor 46 metabolieten een correlatie aangetoond met verschillende typen voedingsmiddelen. Denk bijvoorbeeld aan metabolieten van alcohol, die aangeven dat iemand wat gedronken heeft. Zo waren er ook metabolieten die het eten van citrusvruchten, fruit, rood vlees of juist kip en zuivel (calcium) weerspiegelden.

Persoonlijke urine-'vingerafdruk'
Met deze bevindingen ontwikkelde de groep een 5-minuten test waarmee een persoonlijke urine-‘vingerafdruk’ kan worden gemaakt. De biologische samenstelling van een individu bepaalt namelijk welke stoffen hij of zij goed kan verwerken. Isabel Garcia-Perez, collega-onderzoeker van Posma, legt uit: “Deze technologie kan inzichten bieden in de manier waarop voeding door individuen op verschillende manieren wordt verwerkt. Die kunnen gezondheidsprofessionals zoals bijvoorbeeld diëtisten helpen bij het geven van dieetadvies op maat voor individuele patiënten.”>>>>>lees artikel verder in Foodlog

Hier het originele abstract van de studie:

Nutriome–metabolome relationships provide insights into dietary intake and metabolism

Abstract

Dietary assessment traditionally relies on self-reported data, which are often inaccurate and may result in erroneous diet–disease risk associations. We illustrate how urinary metabolic phenotyping can be used as an alternative approach to obtain information on dietary patterns. We used two multipass 24 h dietary recalls, obtained on two occasions on average 3 weeks apart, paired with two 24 h urine collections from 1,848 US individuals; 67 nutrients influenced the urinary metabotype (metabolic phenotype) of 46 structurally identified metabolites characterized by 1H NMR spectroscopy. We investigated the stability of each metabolite over time and showed that the urinary metabolic profile is more stable within individuals than reported dietary patterns. The 46 metabolites accurately predicted healthy and unhealthy dietary patterns in a free-living US cohort, and these predictions were replicated in an independent UK cohort. We mapped these metabolites into a host-microbial metabolic network to identify key pathways and functions related to diet. These data can be used in future studies to evaluate how this set of diet-derived, stable, measurable bioanalytical markers is associated with disease risk. This knowledge may give new insights into biological pathways that characterize the shift from a healthy to an unhealthy metabolic phenotype and hence indicate entry points for prevention and intervention strategies.

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