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I want to get healthier. Close Health Alerts from Harvard Medical School Get helpful tips and guidance for everything from fighting inflammation to finding the best diets for weight loss Foods that cause spikes in glucose levels and create inflammatory responses within the body play an enormous role in the way your body breaks down food for use and is directly related to weight loss.

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Despite showing improved dietary behaviors ie, Healthy Eating Index , the investigators found no significant difference in body weight at 6 months when compared with a nonpersonalized diet group.

The application of a precision nutrition intervention for the purpose of weight loss may require different estimation features from the machine learning algorithm compared with those targeting a reduction in PPGR.

Future studies are needed to develop and test a weight loss—specific algorithm that incorporates key characteristics central to body weight regulation as well as features of the energy balance model, including appetitive hormones eg, leptin, glucagonlike peptide 1 , total energy expenditure, and fat mass.

The dietary intake data indicate a greater decrease in energy intake in the personalized group compared with the standardized group, although these findings do not support the changes in body weight observed in the standardized group.

We assumed the 2 groups would differ in dietary composition, especially dietary carbohydrate intake, given the impact of carbohydrate intake on PPGR. Evidence in the study by Ben-Yacov et al 14 among adults with prediabetes using the same machine learning algorithm found that participants randomized to a postprandial targeting diet similar to our personalized group significantly decreased carbohydrate intake and increased both protein and fat intake compared with those randomized to a Mediterranean diet.

It is unclear whether our null findings in dietary composition are owing to methodological limitations or adherence to the interventions. Participants in the personalized group were directed to record planned meals into the PNP app, review meal scores, and adjust dietary intakes accordingly based on predictive feedback to reduce PPGR.

The complexity and burden imposed may have elicited a negative response with lower-than-expected self-monitoring adherence levels; therefore, participants in the personalized group may have experienced limited exposure to a key element of the intervention ie, meal scores.

However, acceptability of the interventions was not different between the 2 groups eTable 5 in Supplement 2. Adherence to self-monitoring has been shown to be associated with successful weight loss.

Our study has several strengths, including randomization, blinded ascertainment of outcomes, testing of an innovative and personalized intervention, repeated major points of data collection, and good adherence Despite these strengths, our study has several limitations.

The COVID pandemic presented a limitation to our study, resulting in discontinuation of secondary outcomes body composition, REE, and AT and removal of the month measurements. Weight gain and poor dietary behaviors as a result of self-quarantine measures during the pandemic have been reported previously 27 , 28 ; hence, such factors may have affected our study outcomes.

To generate meal scores for the personalized intervention participants, stool samples were shipped to and processed in Israel, as well as reprogramming of the PNP app, which delayed appearance of meal scores in the PNP app. Consequently, delayed exposure to personalized counseling may have limited intervention effects.

Our study sample was well educated, had an underrepresentation of men one-third of the sample , were all English speaking, and were recruited from a single health care system, thus limiting generalizability.

To minimize participant burden, dietary intake data were limited to 1 automated, self-administered, hour dietary assessment collected at each measurement point in a subset of participants, which limited our ability to capture day-to-day variability in dietary intake.

We were unable to detect changes in physical activity during the intervention because the use of wearable monitors to objectively capture physical activity levels was limited to baseline data only.

Furthermore, participant retention was low, particularly in the standardized group. Finally, although our post hoc analyses showed no difference in weight loss before or after the COVID pandemic, there may have been socioenvironmental barriers present that were not captured eg, limitations in food supply, challenges with grocery shopping before the availability of COVID vaccines.

Precision nutrition directly addresses metabolic heterogeneity and may serve as a treatment for obesity and other metabolic diseases. Given that our study is fully in line with the mission of the Nutrition for Precision Health initiative and the Strategic Plan for NIH National Institutes of Health Nutrition Research, future interventions should examine ways to increase dietary self-monitoring adherence and intervention exposure and consider the development and testing of a weight loss—specific predictive algorithm.

Published: September 28, Open Access: This is an open access article distributed under the terms of the CC-BY License. JAMA Network Open. Corresponding Author: Collin J. Popp, PhD, MS, RDN, Institute for Excellence in Health Equity, Center for Healthful Behavior Change, Department of Population Health, NYU Langone Health, Madison Ave, Seventh Floor, Cubicle , New York, NY collin.

popp nyulangone. Author Contributions: Drs Popp and Sevick had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Acquisition, analysis, or interpretation of data: Popp, Hu, Kharmats, Curran, Berube, Wang, Pompeii, Illiano, St-Jules, Mottern, Li, Williams, Schoenthaler, Segal, Godneva, Thomas, Bergman, Sevick.

Drafting of the manuscript: Popp, Hu, Kharmats, Curran, Pompeii, Illiano, Li, Schoenthaler, Segal, Sevick. Critical revision of the manuscript for important intellectual content: Popp, Hu, Kharmats, Curran, Berube, Wang, St-Jules, Mottern, Li, Williams, Segal, Godneva, Thomas, Bergman, Schmidt, Sevick.

Administrative, technical, or material support: Popp, Hu, Mottern, Williams, Segal, Godneva, Thomas, Bergman, Schmidt.

Conflict of Interest Disclosures: Dr Popp reported serving as a sports nutrition consultant for Renaissance Periodization, LLC, outside the submitted work. Dr Segal reported serving as a consultant for DayTwo. No other disclosures were reported. Disclaimer: The contents of this article are solely the responsibility of the authors and do not necessarily reflect the official views of the American Heart Association.

Data Sharing Statement: See Supplement 3. Additional Contributions : Antonia Polyn, MS, Shirley Chen, BS, and Katherine Perdomo, BS all from NYU Grossman School of Medicine contributed to the administrative, technical and material support of the trial.

We thank our study participants, without whom this study would not have been completed. full text icon Full Text. Download PDF Comment. Top of Article Key Points Abstract Introduction Methods Results Discussion Conclusions Article Information References. Visual Abstract.

RCT: Effects of a Personalized Diet to Reduce Postprandial Glycemic Response vs a Low-fat Diet on Weight Loss in Adults With Abnormal Glucose Metabolism and Obesity. View Large Download. Figure 2. Body Weight Change Between Personalized and Standardized Arms.

Table 1. Baseline Characteristics. Table 2. Results of Linear Mixed Regression Analyses: Total Change Over Time in Primary and Secondary Outcomes a. Supplement 1. Trial Protocol. Supplement 2. Study Procedures and Measures eTable 1.

Baseline Demographics, Anthropometrics, and Metabolic Variables Between Study Completers and Dropouts eTable 2. Group Comparisons of Weight Loss Within Participants Enrolled Before and After the COVID Pandemic eTable 3.

Baseline Dietary Intake Between the Standardized and Personalized Groups eTable 4. Changes in Dietary Measures From Baseline Month 0 to 3 and 6 Months eTable 5.

End-of-Study Questionnaire eFigure 1. Schematic of Study Design eFigure 2. Weight Loss Variability Between the Standardized Top and Personalized Bottom Groups eReferences.

Supplement 3. Data Sharing Statement. Ludwig DS, Ebbeling CB. doi: Gardner CD, Trepanowski JF, Del Gobbo LC, et al. Effect of low-fat vs low-carbohydrate diet on month weight loss in overweight adults and the association with genotype pattern or insulin secretion: the DIETFITS randomized clinical trial.

Yancy WS Jr, Olsen MK, Guyton JR, Bakst RP, Westman EC. A low-carbohydrate, ketogenic diet versus a low-fat diet to treat obesity and hyperlipidemia: a randomized, controlled trial.

Bazzano LA, Hu T, Reynolds K, et al. Effects of low-carbohydrate and low-fat diets: a randomized trial. Ebbeling CB, Leidig MM, Feldman HA, Lovesky MM, Ludwig DS.

Effects of a low-glycemic load vs low-fat diet in obese young adults: a randomized trial. Samaha FF, Iqbal N, Seshadri P, et al. A low-carbohydrate as compared with a low-fat diet in severe obesity. Iqbal N, Vetter ML, Moore RH, et al. Effects of a low-intensity intervention that prescribed a low-carbohydrate vs a low-fat diet in obese, diabetic participants.

Brinkworth GD, Noakes M, Buckley JD, Keogh JB, Clifton PM. Long-term effects of a very-low-carbohydrate weight loss diet compared with an isocaloric low-fat diet after 12 mo. Berry SE, Valdes AM, Drew DA, et al. Human postprandial responses to food and potential for precision nutrition.

Zeevi D, Korem T, Zmora N, et al. Personalized nutrition by prediction of glycemic responses. Mendes-Soares H, Raveh-Sadka T, Azulay S, et al.

Assessment of a personalized approach to predicting postprandial glycemic responses to food among individuals without diabetes. Tily H, Patridge E, Cai Y, et al.

Gut microbiome activity contributes to prediction of individual variation in glycemic response in adults. Friedman JH. Greedy function approximation: a gradient boosting machine.

Ben-Yacov O, Godneva A, Rein M, et al. Personalized postprandial glucose response—targeting diet versus Mediterranean diet for glycemic control in prediabetes.

Popp CJ, St-Jules DE, Hu L, et al. The rationale and design of the Personal Diet Study, a randomized clinical trial evaluating a personalized approach to weight loss in individuals with pre-diabetes and early-stage type 2 diabetes.

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