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Happy to announce that MEDI, our method to quantify dietary intake through metagenomics is out now. 🧬 🧪🧫🦠 www.nature.com/articles/s42... #metagenomics #microbiome #microbiomesky So what is MEDI? A combination of two things:

nature.comMetagenomic estimation of dietary intake from human stool - Nature MetabolismDiener et al. present a method that allows the estimation of dietary intake from human stool by detecting food-derived DNA in faecal metagenomes.

(1) a linked food database of more than 400 food items that includes nutrition and genomic information with multiple fallback strategies (partial assemblies, higher ranks)

(2) a high specificity identification and quantification pipeline that reliably identifies metagenomic reads coming from food items even when those make up less than 0.001% of the total DNA

One new things we added since the preprint is validation with two controlled feeding studies. In both, MEDI was able to detect the dietary intervention . There was also good agreement with some micro- and macronutrients when creating a standardized portion out of the detected food items.

We were also able to infer the onset of solid food consumption in infants and found good agreement of MEDI with food frequency questionnaires. Last but not least, MEDI could identify dietary patterns associated with metabolic syndrome in a retrospective study.

Of course, there are limitations too. We can't detect foods or additives without DNA such as added sugars or fats for instance. Also there is quite some adsorption and breakdown in the intestinal tract. Building the database is also a bit of a drag and we are looking for ways to make this easier.

Right now MEDI works best with high depth sequencing (>30M reads) and only works for human samples (working on including other hosts). However, it does *not* require new assays and can be used for existing metagenomic sequencing data to add dietary information.

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This seems really useful - we have two projects where we would be very excited to try this out if you think it could work for non-human animals; one aims to estimate the degree to which different gulls are feeding on human foods (and how that influences health and disease measured in various ways).

2nd we do a lot of wild primate diet analyses (with metabarcoding) to explore trophic links and understand origins of disease outbreaks (e.g.,mpox) - if MEDI is sensitive, would be keen to try it out on our collection of shotgun metagenomes, many of which we have already done other diet analyses on

Hi Jan. Sounds fascinating. For non-human we recommend to include the host genome as a decoy. The plan for the next DB release is to include more hosts and allow customization. I would say that MEDI is more specific than sensitive because food DNA is a tiny fraction of poop DNA. But depends.

Hi Christian, this paper caused quite a lot of discussion today in our lab because we are trying a similar thing using metagenomics data from meerkat feces (20+ years or data) and I think we are getting stuck at the database step. Maybe this work can help our PhD student to push past the block.