by Weight loss is complicated, and there is no one-size-fits-all solution. Patients become frustrated when the edict to “eat less and exercise more” doesn’t work. Personalized weight loss plans are needed, but right now doctors can’t predict which of the myriad of diets — intermittent fasting, ketogenic, Mediterranean, plant-based and paleo — will work for individual patients.
A research team from UCLA hopes to change that. The team is analyzing data from 550,000 MyFitnessPal app users over nearly 2 years to discern behavior patterns that lead to long-term successful weight loss. MyFitnessPal is a nutrition and fitness tracking app with billions of data points on what and when people eat, drink and exercise. The data will be used to develop a personalized weight management approach to prevent cardiovascular disease, diabetes and other obesity-related diseases.
“Our weight is really a reflection of our whole life, including our mood, activity level, living environment, stress, metabolism, genetics, sleep quality and diet quality,” said Zhaoping Li, MD, PhD, professor of medicine and head of the Division of Clinical Nutrition at the University of California, Los Angeles. “With all these confounding factors, at any moment we might respond to one diet and not respond to another diet.”
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The researchers will look at data points such as how many times food is logged per week, whether the user logs water intake, macronutrient intake, amount of exercise per week, types of exercise tracked, sleep and mood. The data will then be segmented by age, gender, and other demographic information to better understand patterns in user populations who are likely to achieve their goals.
The large sample size will allow enough power to divide people into many different categories based on the patterns found. Data from this large retrospective review will be used to determine individualized weight loss plans for optimal calorie and macronutrient intake, as well as exercise for each category which will then be tested in a prospective randomized controlled trial.
Data shared with UCLA will be anonymised to preserve user privacy.
NIH Personalized Weight Loss Research
Dr. Li and colleagues are also participating in the National Institutes of Health (NIH)-funded Nutrition for Precision Health study, which was launched in January 2022. The study is designed to test the effects of 3 diets on health outcomes: 1) the traditional American diet diet; 2) Mediterranean diet plus DASH (Dietary Approaches to Stop Hypertension); and 3) ketogenic diet. The goal is to determine which diet is best for overall health, weight management, prevention of cardiovascular disease and diabetes, as well as other health outcomes. UCLA is one of 14 clinical center enrollment sites nationwide.
The Nutrition for Precision Health study aims to examine individual differences observed in response to different diets by studying the interactions between diet, genes, proteins, the microbiome, metabolism, and other individual contextual factors. Additionally, artificial intelligence (AI) is being used to develop algorithms to predict individual responses to foods and dietary patterns.
The results of the MyFitnessApp and NIH studies can be combined to develop the best overall approach to weight management and disease prevention. “Hopefully, in the next 5 to 10 years, we will have a breakthrough and we can say, for example, that a patient should be on a ketogenic diet for 2 meals followed by a Mediterranean diet for the third meal,” said Dr. Li. “That would be the ultimate goal.”
Sources
MyFitnessPal and UCLA team up to discover the science of success: how and why users achieve their health and fitness goals. Press release. PRNewswire. March 14, 2023. Accessed April 17, 2023. https://www.prnewswire.com/news-releases/myfitnesspal-and-ucla-partner-to-uncover-the-science-of-success-how-and-why-gli -users-reaching-their-health-and-fitness-goals-301771480.html
Nutrition for Precision Health, powered by the All of Us research program. National Institute of Health. Accessed April 17, 2023. https://commonfund.nih.gov/nutritionforprecisionhealth
