NIH-funded study will use machine learning to predict individual responses to diet

A brand new federally funded study will use machine learning to predict how an individual responds to a given diet, permitting physicians to supply sufferers personalised diet prescriptions to enhance well being and deal with persistent ailments.

Food lies on the epicenter of well being and illness. But medical diet remains to be restricted to a one-size-fits-all-approach that far too usually fails a big section of the inhabitants.”

Eric Ravussin, Ph.D., Associate Executive Director for Clinical Science, Pennington Biomedical Research Center

“What we want is precision, the power to prescribe diets that account for the elements distinctive to every particular person, corresponding to their genetics, metabolism, physiology, conduct, even the microorganisms of their physique,” mentioned Leanne Redman, Ph.D., Associate Executive Director for Scientific Education at Pennington Biomedical.

Nutrition for Precision Health, powered by the All of Us Research Program will develop a first-of-its-kind algorithm to predict individual responses to meals and dietary routines. The study will recruit 10,000 individuals nationwide from the 1 million U.S. residents who’ve volunteered their well being knowledge for the National Institutes of Health’s All of Us Research Program.

As certainly one of six medical websites within the nation, Pennington Biomedical, in partnership with LSU Health New Orleans and the National Institute of Diabetes and Digestive and Kidney Disease-Phoenix, plans to enroll greater than 2,000 members in three study modules. Drs. Ravussin and Redman are the first investigators on the five-year, $8.6 million grant.

Scientists on the six medical websites will comply with 10,000 members whereas they eat their regular diets. The study will additionally collect knowledge on 1,500 members who will comply with certainly one of three prescription diets whereas residing at residence. A last group of 500 members will comply with the identical diets throughout stays at medical websites.

Researchers will measure blood sugar ranges and biomarkers of cardiometabolic well being, corresponding to insulin resistance, blood strain and blood lipids. Wearables will be used to observe members’ bodily exercise and sleep. Researchers will additionally acquire samples of blood, urine, saliva, hair and stool to assess the impression of individuals’s diets.

“Nutrition for Precision Health brings us a step nearer to precision medication. The study will generate a large dataset, a wealth of biospecimens and the algorithms that will lead to personalised dietary prescriptions that may promote well being, stop coronary heart assaults or strokes, and importantly, deal with well being disparities,” mentioned Pennington Biomedical Executive Director John Kirwan, Ph.D.

NIH awarded $170 million over 5 years, pending the supply of funds, to fund the Nutrition for Precision Health program. The program consists of six medical facilities, a metabolomics and medical assays heart, a microbiome and metagenomics heart, a multimodal knowledge modeling and bioinformatics heart, a analysis coordinating heart, and extra assist to current All of Us infrastructure.

NPH is managed by the NIH Common Fund. It is the primary impartial study to invite numerous members from the All of Us Research Program.

This five-year mission is supported by the NIH Common Fund and administered by the National Institute of Diabetes and Digestive and Kidney Disease of the National Institutes of Health underneath Award Number UG1HD107696. The content material is solely the accountability of the authors and doesn’t essentially signify the official views of the National Institutes of Health.

All of Us and Nutrition for Precision Health, powered by the All of Us Research Program are service marks of the U.S. Department of Health and Human Services.
Source:Pennington Biomedical Research Center

https://www.news-medical.net/news/20220120/NIH-funded-study-will-use-machine-learning-to-predict-individual-responses-to-diet.aspx

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