This was reported to the IRB, and, as part of the resolution, their data were removed from the dataset. b These participants were unable to attend the W12 visit in person and only completed surveys and questionnaires remotely. Raw differences are presented in Supplementary Table 5.

Results are presented as boxplots for all participants a , d , as well as for fat-responders b , e and carbohydrate responders c , f separately. In the boxplots, the center line denotes the median value 50th percentile , the bounds of the box represent the 25th and 75th percentiles of the dataset, and the whiskers mark the 5th and 95th percentiles.

Changes in resting systolic blood pressure SBP and DBP did not differ between genotype-concordant and genotype-discordant diets SBP adjusted difference: 4.

Similarly, changes in SBP and DBP did not differ between the high-fat and the high-carbohydrate diet among fat-responders SBP difference: 6. Changes in food cravings did not differ between the genotype-concordant and genotype-discordant diets Table 3. Changes in all other food cravings did not differ between diets among carbohydrate-responders Table 3.

Among fat-responders, changes in food cravings did not differ between diets Table 3. Raw differences are presented in Supplementary Table 6. Changes in restraint, disinhibition, and hunger via EI , and food preferences FPQ did not differ between genotype-concordant and genotype-discordant diets Table 4.

Raw differences are presented in Supplementary Table 7 and baseline scores in these instruments are reported in Supplementary Table 4. Diet preference via Diet Personalization Survey, Table 5 and intervention satisfaction Table 6 did not differ between the genotype-concordant and genotype-discordant diets.

Raw differences are presented in Supplementary Table 8. Adherence to the assigned diets is shown in Fig. We encountered difficulties in obtaining the adherence data from participants due, in part, to the pandemic and needing to move to remote intervention delivery. On average, participants on the high-carbohydrate diet reported consuming Participants on the high-fat diet reported consuming on average Boxplots showing adherence data for the high-carbohydrate diet a , c , e and the high-fat diet b , d , f.

There were 4 adverse or serious adverse events in total. Two adverse events occurred among fat-responders on a high-carbohydrate diet unrelated to the study , and there were 2 serious adverse events 1 among fat-responders on a high-carbohydrate diet, 1 among fat-responders on a high-fat diet that required hospitalization unrelated to study.

We found no difference in WL between individuals on the genotype-concordant vs. genotype-discordant diet. Further, insulin levels or HOMA-IR were not associated with WL. Food cravings tended to decrease among carbohydrate-responders on a high-fat diet compared to those on a high-carbohydrate diet.

Finally, fat-responders on a high-carbohydrate diet tended to decrease resting SBP. The lack of significant and clinically meaningful differences in WL ~0. In contrast to the well-conducted Gardner et al. study non-significant difference in WL of 0.

carbohydrate-responsive genotypes based on 3 SNPs that were predictive in a preliminary retrospective analysis 8 , we determined fat- or carbohydrate-responsive genotypes based on an algorithm involving 10 SNPs. Supported by a recent-meta-analysis 8 trials with 91 SNPs and 63 genetic loci 11 , our results suggest that with the current ability to genotype individuals as fat or carbohydrate-responders, there is no evidence that genotype-concordant diets result in greater WL.

We did not limit recruitment to achieve equal numbers of participants in each genotype-diet group, and this distribution reflects the prevalence in our population. Future studies with larger samples should verify if this uneven distribution between carbohydrate-responders and fat-responders is representative of the general population and further investigate the potential effect on WL among carbohydrate-responders.

Future studies could also consider assigning participants to genotype-concordant diets without specific energy intake targets and examine the diet effects not only on WL but also on cardiovascular risk factors.

Previously, a low-carbohydrate diet without energy intake target resulted in greater improvements in body composition, blood lipids, and estimated year coronary heart disease risk compared to a low-fat diet It would be insightful to investigate whether genotype plays a role in cardiovascular risk reduction following a low-carbohydrate vs.

low-fat diet without calorie restriction. Fasting insulin levels and HOMA-IR did not predict WL. However, these studies involved relatively small sample sizes, and findings of the influence of insulin sensitivity 21 and insulin secretion 9 , 14 on WL via a low-fat vs.

a low-carbohydrate diet are inconsistent. WL can reduce food cravings, particularly for foods restricted on specific diets 22 , contributing to the hypothesis that food cravings are a conditioned expression of hunger due to stimuli paired with eating certain foods Consequently, cravings can be reduced by eliminating or restricting the intake of craved foods.

This hypothesis is partially supported by our results as, among carbohydrate-responders, cravings tended to decrease for high-carbohydrate foods on the high-fat diet.

Nonetheless, cravings also decreased modestly for high-fat foods, which is to be expected as the amount of all foods was restricted, and cravings for specific foods correlate with each other Among fat-responders, a high-carbohydrate diet tended to decrease resting SBP.

Nonetheless, these individuals had the highest mean SBP of the 4 genotype-diet groups at baseline. Thus, this effect could be explained, in whole or partially, by regression to the mean.

Also, all 4 genotype-diet groups had relatively well-controlled blood pressure, leaving little room for improvement through dietary changes, making the non-significant improvements potentially more meaningful. This trial has some limitations. First, the genetic algorithm to classify individuals as fat- or carbohydrate-responders was created based on published literature 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , However, these mostly retrospective studies generally had modest sample sizes, and some of the genotype × diet interactions, which may be false positives, have not been independently replicated.

Further, WL is determined by multiple modifiable and non-modifiable e. More comprehensive knowledge of the role of genetics in WL is needed and should be obtained from genome-wide association studies; however, the sample size and experimental design required to generate that essential information are beyond reach at this time.

Additional limitations of the present study include the relatively small sample size, single-center design, and short time frame.

A longer timeframe 6—month follow-up may have increased the amount and differential weight loss between diets. A larger sample size might have also allowed for detecting differences in clinically important secondary outcomes such as changes in body fat and SBP.

Further, we did not provide meals in this study, which may have affected dietary adherence high-fat vs. However, this choice was made by design, as our study was designed as a pragmatic effectiveness trial with real-world conditions rather than an efficacy trial. Additionally, the adherence data albeit limited suggests that diet adherence was overall satisfactory.

Further, when assessing a potential effect modification by insulin resistance status, using an oral glucose tolerance test AUC or INS rather than HOMA-IR to quantify insulin resistance might have been a better option, as HOMA-IR has limited sensitivity due to its reliance on fasting insulin and glucose levels and it does not reflect differences between tissues e.

Additionally, the assessment of percent body fat via BIA is a limitation as BIA does not provide information on body fat distribution.

Therefore, in our study, participants may have responded better to their assigned diets regardless of their genotype matching, obscuring the specific nutrigenomics effects. In conclusion, in this week RCT, there was no difference in WL between individuals with an a priori determined fat- or carbohydrate-responsive genotype on a high-carbohydrate vs.

high-fat diet with specific energy targets and the same level of energy restriction across diets. The Personalized Nutrition Study POINTS, ClinicalTrials. gov identifier: NCT was a week, single-site, parallel-arm WL trial that was approved by the institutional review board IRB FWA of the Pennington Biomedical Research Center PBRC, Baton Rouge, LA.

Participants were enrolled between October 7, and September 8, Participants were identified a priori as carbohydrate-responders and fat-responders based on their combined genotypes at 10 genetic variant loci and randomized to either a high-carbohydrate or high-fat diet, yielding the following groups: 1 fat-responders receiving a high-fat diet, 2 fat-responders receiving a high-carbohydrate diet, 3 carbohydrate-responders receiving a high-fat diet, and 4 carbohydrate-responders receiving a high-carbohydrate diet.

Participants were recruited from the community. Eligible participants were 18—75 years old, had a BMI of Finally, a genetic profile indicating a predisposition to respond favorably to a high-carbohydrate or high-fat WL diet based on specific SNPs see below was required.

in the last 3 months, being pregnant or breastfeeding, conditions, diseases, or medications that affect body weight or metabolism or could affect risk or study completion, and a genotype indicating a predisposition to respond favorably to neither or both of the specified diets.

The study included 1 orientation visit, 2 clinic visits one before and one after the intervention , and weekly intervention sessions. Carbohydrate- and fat-responders were identified a priori based on their combined genotypes at the following genetic variants: 1 FGF21rs 25 , 2 TCF7L2rs 26 , 43 , 3 IRS1rs 28 , 4 APOA5rs 30 , 31 , 44 , 5 PLIN1rs 27 , 32 , 6 APOA2rs 29 , 33 , 7 FTOrs 34 , 35 , 8 PPARGrs 36 , 9 GIPRrs 37 , and 10 GYS2rs The genetic information was accessed via the raw data from the genealogy tests.

Initially, only 6 SNPs were included and pilot tested, and the scoring criteria were then modified as few participants were deemed carbohydrate- or fat-responders. The original and updated scoring criteria, including a specific example for 1 SNP, are provided in the Supplementary Methods, including Supplementary Tables 1 and 2.

To facilitate meal plan adherence when preparing or selecting meals, the meal plans included a list of ingredients and their amounts for all meals of each day breakfast, lunch, dinner, and 1 daily snack and instructions for meal preparation and participants were provided a food scale.

Baseline energy requirements were calculated with Mifflin-St. The PBRC biostatistics department created the randomization sequence using SAS 9.

REDCap used strata for the inaction of genotype and gender. To ensure a relatively equal baseline BMI between the 4 genotype-diet groups, a randomization scheme was devised that adjusted for BMI, gender, and genotype. Gender and genotype were used as strata, while BMI was used in an a-priori-created randomization equation.

Within each stratum, this equation used block sizes of 6 for females and 4 for males at the start of the study and ended with block sizes of 4 and 2, respectively, to ensure relative balance of group assignments.

Block sizes were assigned during the study by the biostatistician with access only to information about the enrolment progress percent enrolled. Interventionists administering intervention sessions were blind to genotype patterns but not diet type.

Participants were only informed of their genotype carbohydrate- or fat-responder once they completed the study. The 12 weekly intervention group sessions were diet-specific and had a different focus each week Supplementary Material.

Participants were provided a body weight scale and encouraged to weigh daily throughout the intervention and to send pictures of their weights to their interventionist before each intervention session. With very few exceptions, the first intervention session was conducted in person.

Due to the COVID pandemic, almost all subsequent sessions were conducted virtually via webinar Microsoft Teams. At W0 and W12, fasting body weight and waist and hip circumference were measured in the PBRC outpatient clinic. Clinic weights were also measured at all intervention visits though not fasting weights.

Fasting serum glucose and insulin were measured at W0, and HOMA-IR was used to quantify insulin resistance. Appetitive traits were measured with the Eating Inventory EI 46 , food cravings were measured with the Food Craving Inventory FCI 24 , and hedonic food preferences were measured with the Food Preference Questionnaire FPQ 47 at W0 and W12 see Supplementary Methods for details on outcome materials.

Data for these questionnaires were collected and managed using REDCap tools. The Diet Personalization Survey Supplementary Methods was completed at W0 and W12, as well as during the intervention session at W6, and the Intervention Satisfaction Survey Supplementary Methods was conducted at W Data for these surveys were collected and managed using REDCap tools.

As stated above, participants were provided with a kitchen scale and could precisely weigh all ingredients specified in the meal plans for the foods consumed at home. Additional foods that were consumed were weighed and added as well.

Adherence to the macronutrient content of the assigned diets was assessed for three 7-day periods throughout the intervention W4, W8, W The distribution of variables was evaluated by visual examination and the Shapiro-Wilk test. The primary outcome was weight change kg at 12 weeks.

All other measures were secondary endpoints. We used linear mixed models to determine if changes in outcome variables differed among diets.

Covariates in the models included baseline value of the outcome, sex, and race. The mixed-effect model accounted for the correlation of the subject over time, and least-square means based on the estimate from the mixed-effect model were used to test for differences in weight change between diets.

To evaluate whether baseline insulin levels and HOMA-IR needed to be included as covariates, their effects on WL were tested using a linear mixed model, adjusted for diet group and other known covariates. Neither baseline insulin levels nor HOMA-IR was significantly associated with WL; hence these variables were not included as covariates.

The significance level was set to 0. Multiple testing adjustment was performed for secondary outcomes using the Holm-Bonferroni method All analyses were conducted using SAS Windows version 9.

The present study planned to obtain data from up to participants in total, and we aimed to complete 32 participants per genotype-diet group participants in total though we did not limit recruitment to achieve equal numbers of participants in each group.

We hypothesized that participants on a genotype-concordant diet would lose more weight than those on a genotype-discordant diet. Based on previous studies 49 , 50 , we assumed a standard deviation for between-group differences in weight change of 2.

To detect a 2. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. All of the data needed to recapitulate the analysis found within this study can be found in the manuscript, figures and supplementary information.

Source data are provided with this paper. Due to privacy reasons, de-identified data from the study cannot be shared publicly but will be available from the corresponding author christoph. hoechsmann tum.

de immediately following the publication of the paper upon reasonable request. The study protocol and statistical analysis plan will also be available. Fryar, C. Prevalence of overweight, obesity, and severe obesity among adults aged 20 and over: United States, — through — Kopelman, P.

Health risks associated with overweight and obesity. PubMed Google Scholar. Tremmel, M. Economic burden of obesity: a systematic literature review. PubMed PubMed Central Google Scholar. Hruby, A. The epidemiology of obesity: a big picture.

Pharmacoeconomics 33 , — Shai, I. et al. Weight loss with a low-carbohydrate, Mediterranean, or low-fat diet. CAS PubMed Google Scholar. Sacks, F. Comparison of weight-loss diets with different compositions of fat, protein, and carbohydrates. 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.

Rein M, Ben-Yacov O, Godneva A, et al. Effects of personalized diets by prediction of glycemic responses on glycemic control and metabolic health in newly diagnosed T2DM: a randomized dietary intervention pilot trial.

Nunes CL, Jesus F, Francisco R, et al. Adaptive thermogenesis after moderate weight loss: magnitude and methodological issues.

Subar AF, Kirkpatrick SI, Mittl B, et al. The automated self-administered hour dietary recall ASA24 : a resource for researchers, clinicians, and educators from the National Cancer Institute. Thomas DM, Gonzalez MC, Pereira AZ, Redman LM, Heymsfield SB.

Time to correctly predict the amount of weight loss with dieting. Ge L, Sadeghirad B, Ball GDC, et al. Comparison of dietary macronutrient patterns of 14 popular named dietary programmes for weight and cardiovascular risk factor reduction in adults: systematic review and network meta-analysis of randomised trials.

m  PubMed Google Scholar Crossref. Macek P, Terek-Derszniak M, Biskup M, et al. A two-year follow-up cohort study-improved clinical control over CVD risk factors through weight loss in middle-aged and older adults.

Ravussin E, Lillioja S, Anderson TE, Christin L, Bogardus C. Determinants of hour energy expenditure in man: methods and results using a respiratory chamber. Kirk D, Catal C, Tekinerdogan B. Precision nutrition: a systematic literature review.

Celis-Morales C, Livingstone KM, Marsaux CFM, et al; Food4Me Study. Effect of personalized nutrition on health-related behaviour change: evidence from the Food4Me European randomized controlled trial. Hall KD, Farooqi IS, Friedman JM, et al.

The energy balance model of obesity: beyond calories in, calories out. Burke LE, Wang J, Sevick MA. Self-monitoring in weight loss: a systematic review of the literature.

Flanagan EW, Beyl RA, Fearnbach SN, Altazan AD, Martin CK, Redman LM. The impact of COVID stay-at-home orders on health behaviors in adults.

Zeigler Z. COVID self-quarantine and weight gain risk factors in adults. Zeisel SH. Precision personalized nutrition: understanding metabolic heterogeneity. National Institutes of Health. January 8, Accessed April 15, See More About Nutrition, Obesity, Exercise Lifestyle Behaviors Diabetes Diet Diabetes and Endocrinology Obesity.

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This Issue. Views 12, Citations View Metrics. X Facebook More LinkedIn. Cite This Citation Popp CJ , Hu L , Kharmats AY, et al. Original Investigation. September 28, Collin J.

Popp, PhD, MS, RDN 1 ; Lu Hu, PhD 1 ; Anna Y. Kharmats, PhD 1 ; et al Margaret Curran, MS 1 ; Lauren Berube, RDN, PhD 1 ; Chan Wang, PhD 2 ; Mary Lou Pompeii, RDN, CDCES 1 ; Paige Illiano, RDN, MS 1 ; David E.

St-Jules, PhD, RDN 3 ; Meredith Mottern, BS 1 ; Huilin Li, PhD 2 ; Natasha Williams, EdD 1 ; Antoinette Schoenthaler, EdD 1 ; Eran Segal, PhD 4 ; Anastasia Godneva, MS 4 ; Diana Thomas, PhD 5 ; Michael Bergman, MD 1,6 ; Ann Marie Schmidt, MD 7 ; Mary Ann Sevick, ScD 1,6.

Author Affiliations Article Information 1 Institute for Excellence in Health Equity, Center for Healthful Behavior Change, Department of Population Health, NYU Langone Health, New York, New York. visual abstract icon Visual Abstract.

Key Points Question What is the effect of a precision nutrition intervention aimed to reduce the postprandial glycemic response to foods on weight loss in adults with abnormal glucose metabolism and obesity compared with a low-fat diet?

Research Design. Study Groups. Outcome Measurements. Percentage of Weight Loss. Body Composition, Resting Energy Expenditure, and Metabolic Adaptation.

Dietary Intake, Physical Activity, and Adherence to Counseling Sessions and Self-monitoring. Statistical Analysis. Primary Outcome. Secondary Outcomes. Adherence to Counseling Sessions and Self-monitoring. Dietary Measures. Strengths and Limitations.

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