The dark matter of nutrition: Consider garlic, a key ingredient of the Mediterranean diet: the USDA quantifies 67 nutritional components in raw garlic, indicating that this bulbous plant is particularly rich in manganese, vitamin B6 and selenium4. However, a clove of garlic contains more than 2,306 distinct chemical components... As of August 2019, FooDB records the presence of 26,625 distinct biochemicals in food, a number that is expected to increase in the near future. This exceptional chemical diversity could be viewed as the ‘dark matter’ of nutrition, as most of these chemicals remain largely invisible to both epidemiological studies, as well as to the public at large.
To appreciate the transformative potential of a deeper quantitative understanding of the nutritional dark matter, we must realize that our genetic predispositions to specific phenotypes and pathophenotypes can conceivably be modified by these food-based molecules.
Fascinating article! The individual foodome. Found this example of role of interactions particularly interesting:
“Animal products contain L-carnitine, choline and choline-contributing compounds21. These molecules are metabolized by gut bacteria into trimethylamine (TMA), which is converted in the liver to trimethylamine-N-oxide17 (TMAO), a compound linked to coronary events16. Garlic, extra-virgin olive oil and red wine, staple ingredients of the Mediterranean diet, reduce the production of TMAO through allicin and 3,3-dimethylbutan-1-ol (DMB), compounds that block TMA production by gut bacteria.“
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I too found this fascinating. Particularly:
Consider, for example, the polyphenol (–)-epigallocatechin 3-O-gallate (EGCG), an abundant biochemical compound in green tea, with potential therapeutic effects in T2DM. Network-based metrics reveal a proximity between 52 human proteins targets of EGCG32 and 83 proteins associated with T2DM30,33. This offers multiple mechanistic pathways by which to account for the relationship between green tea consumption and its many reported effects on health and disease risk34,35,36, and its glucose-lowering effects observed using in vitro and in vivo models37,3
mwhr
This is only the second time I can think of that I've come across this general concept. Many years ago I ready about the over 3,000 "things" in potatoes (a number that's probably changed since then).
My favorite line:
Indeed, the closer two ingredients are on the phylogenetic tree 46, the more similar is their expected metabolic pathway structure and biochemical composition. Machine learning is ideally suited to combine the known chemical composition of chosen food ingredients over different taxonomical branches with the list of orthologous enzymes in sequenced organisms; the missing chemical information can then be elucidated by learning the appropriate distance metric 47,48 between organisms and clustering correlated groups of pathways and biochemicals 49. Such efforts, taking full advantage of existing knowledge, could offer experimentally verifiable predictions about the missing chemicals and their concentration.
Egad, what's wrong with these researchers. Can't they just spare us the fancy technology and do that on the back of a napkin?
ApoE 3/4 > Thanks in advance for any responses made to my posts.
