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

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

A new federally funded study will use machine learning to predict how an individual responds to a given diet, allowing physicians to offer patients personalized nutrition prescriptions to improve health and treat chronic diseases.

Food lies at the epicenter of health and disease. But clinical nutrition is still limited to a one-size-fits-all-approach that far too often fails a large segment of the population."

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

"What we need is precision, the ability to prescribe diets that account for the factors unique to each person, such as their genetics, metabolism, physiology, behavior, even the microorganisms in their body," said 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 food and dietary routines. The study will recruit 10,000 people nationwide from the 1 million U.S. residents who have volunteered their health data for the National Institutes of Health's All of Us Research Program.

As one of six clinical sites in 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 more than 2,000 participants in three study modules. Drs. Ravussin and Redman are the primary investigators on the five-year, $8.6 million grant.

Scientists at the six clinical sites will follow 10,000 participants while they eat their usual diets. The study will also gather data on 1,500 participants who will follow one of three prescription diets while living at home. A final group of 500 participants will follow the same diets during stays at clinical sites.

Researchers will measure blood sugar levels and biomarkers of cardiometabolic health, such as insulin resistance, blood pressure and blood lipids. Wearables will be used to track participants' physical activity and sleep. Researchers will also collect samples of blood, urine, saliva, hair and stool to assess the impact of people's diets.

"Nutrition for Precision Health brings us a step closer to precision medicine. The study will generate a massive dataset, a wealth of biospecimens and the algorithms that will lead to personalized dietary prescriptions that can promote health, prevent heart attacks or strokes, and importantly, address health disparities," said Pennington Biomedical Executive Director John Kirwan, Ph.D.

NIH awarded $170 million over five years, pending the availability of funds, to fund the Nutrition for Precision Health program. The program includes six clinical centers, a metabolomics and clinical assays center, a microbiome and metagenomics center, a multimodal data modeling and bioinformatics center, a research coordinating center, and additional support to existing All of Us infrastructure.

NPH is managed by the NIH Common Fund. It is the first independent study to invite diverse participants from the All of Us Research Program.

This five-year project 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 under Award Number UG1HD107696. The content is solely the responsibility of the authors and does not necessarily represent 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.

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Pennington Biomedical Research Center

Posted in: Medical Research News | Healthcare News

Tags: Bioinformatics, Blood, Blood Pressure, Blood Sugar, Cancer, Cardiometabolic, Cardiovascular Disease, Chronic, Dementia, Diabetes, Diet, Education, Food, Genetics, Hair, Health and Human Services, Health Disparities, Heart, Insulin, Insulin Resistance, Kidney, Kidney Disease, Lipids, Machine Learning, Medicine, Metabolism, Metabolomics, Metagenomics, Microbiome, Nutrition, Obesity, pH, Physical Activity, Physiology, Precision Medicine, Research, Sleep

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