[Skip to Content]
[Skip to Content Landing]

Effect of Low-Fat vs Low-Carbohydrate Diet on 12-Month Weight Loss in Overweight Adults and the Association With Genotype Pattern or Insulin SecretionThe DIETFITS Randomized Clinical Trial

Educational Objective
To learn whether genotype or insulin-glucose dynamics modify the effectiveness of weight loss diets.
1 Credit CME
Key Points

Question  What is the effect of a healthy low-fat (HLF) diet vs a healthy low-carbohydrate (HLC) diet on weight change at 12 months and are these effects related to genotype pattern or insulin secretion?

Findings  In this randomized clinical trial among 609 overweight adults, weight change over 12 months was not significantly different for participants in the HLF diet group (−5.3 kg) vs the HLC diet group (−6.0 kg), and there was no significant diet-genotype interaction or diet-insulin interaction with 12-month weight loss.

Meaning  There was no significant difference in 12-month weight loss between the HLF and HLC diets, and neither genotype pattern nor baseline insulin secretion was associated with the dietary effects on weight loss.


Importance  Dietary modification remains key to successful weight loss. Yet, no one dietary strategy is consistently superior to others for the general population. Previous research suggests genotype or insulin-glucose dynamics may modify the effects of diets.

Objective  To determine the effect of a healthy low-fat (HLF) diet vs a healthy low-carbohydrate (HLC) diet on weight change and if genotype pattern or insulin secretion are related to the dietary effects on weight loss.

Design, Setting, and Participants  The Diet Intervention Examining The Factors Interacting with Treatment Success (DIETFITS) randomized clinical trial included 609 adults aged 18 to 50 years without diabetes with a body mass index between 28 and 40. The trial enrollment was from January 29, 2013, through April 14, 2015; the date of final follow-up was May 16, 2016. Participants were randomized to the 12-month HLF or HLC diet. The study also tested whether 3 single-nucleotide polymorphism multilocus genotype responsiveness patterns or insulin secretion (INS-30; blood concentration of insulin 30 minutes after a glucose challenge) were associated with weight loss.

Interventions  Health educators delivered the behavior modification intervention to HLF (n = 305) and HLC (n = 304) participants via 22 diet-specific small group sessions administered over 12 months. The sessions focused on ways to achieve the lowest fat or carbohydrate intake that could be maintained long-term and emphasized diet quality.

Main Outcomes and Measures  Primary outcome was 12-month weight change and determination of whether there were significant interactions among diet type and genotype pattern, diet and insulin secretion, and diet and weight loss.

Results  Among 609 participants randomized (mean age, 40 [SD, 7] years; 57% women; mean body mass index, 33 [SD, 3]; 244 [40%] had a low-fat genotype; 180 [30%] had a low-carbohydrate genotype; mean baseline INS-30, 93 μIU/mL), 481 (79%) completed the trial. In the HLF vs HLC diets, respectively, the mean 12-month macronutrient distributions were 48% vs 30% for carbohydrates, 29% vs 45% for fat, and 21% vs 23% for protein. Weight change at 12 months was −5.3 kg for the HLF diet vs −6.0 kg for the HLC diet (mean between-group difference, 0.7 kg [95% CI, −0.2 to 1.6 kg]). There was no significant diet-genotype pattern interaction (P = .20) or diet-insulin secretion (INS-30) interaction (P = .47) with 12-month weight loss. There were 18 adverse events or serious adverse events that were evenly distributed across the 2 diet groups.

Conclusions and Relevance  In this 12-month weight loss diet study, there was no significant difference in weight change between a healthy low-fat diet vs a healthy low-carbohydrate diet, and neither genotype pattern nor baseline insulin secretion was associated with the dietary effects on weight loss. In the context of these 2 common weight loss diet approaches, neither of the 2 hypothesized predisposing factors was helpful in identifying which diet was better for whom.

Trial Registration  clinicaltrials.gov Identifier: NCT01826591

Sign in to take quiz and track your certificates

Buy This Activity

JN Learning™ is the home for CME and MOC from the JAMA Network. Search by specialty or US state and earn AMA PRA Category 1 CME Credit™ from articles, audio, Clinical Challenges and more. Learn more about CME/MOC

Article Information

Corresponding Author: Christopher D. Gardner, PhD, Stanford Prevention Research Center, Department of Medicine, Stanford University Medical School, 1265 Welch Rd, Stanford, CA 94305 (cgardner@stanford.edu).

Accepted for Publication: January 17, 2018.

Correction: This article was corrected on April 3, 2018, to change the units of measure for the lipid level rows in Table 3 from mmol/L to mg/dL and reverse the corresponding SI conversion factor instructions from divide to multiply. This article was corrected on April 24, 2018, to change the Nutrition Science Initiative funding description.

Author Contributions: Dr Gardner had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Gardner, Rigdon, Ioannidis, Desai, King.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Gardner, Trepanowski, Del Gobbo, Hauser, Rigdon, Desai.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Gardner, Del Gobbo, Hauser, Rigdon, Ioannidis, Desai.

Obtained funding: Gardner.

Administrative, technical, or material support: Gardner, Hauser, King.

Supervision: Gardner, Desai, King.

Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported.

Funding/Support: This study was supported by grant 1R01DK091831 from the National Institute of Diabetes and Digestive and Kidney Diseases, funding from the Nutrition Science Initiative, grants 1K12GM088033 and T32HL007034 from the National Heart, Lung, and Blood Institute, and the Stanford Clinical and Translational Science Award.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the other funders.

Additional Contributions: This study would not have been possible without the work of the following individuals who were affiliated with Stanford University at the time of the study and who received compensation for their work: Jennifer Robinson, PhD, and Antonella Dewell, MS, RD (served as study coordinators), Rise Cherin, MS, RD, Susan Kirkpatrick, RD, CDE, Jae Berman, MS, RD, CSSD, Dalia Perelman, MS, RD, CDE, and Mandy Murphy Carroll, MPH, RD (health educators), Sarah Farzinkhou, MPH, Valerie Alaimo, BS, Margaret Shimer Lawton, MPH, and Diane Demis, BS (diet assessment team), Josephine Hau, MPH, RD, Erin Avery, MS, Alexandra Rossi, BS, Katherine Dotter, BS, RD, and Sarah Mummah, PhD (involved in recruitment, screening, blood sample management, innovation, and other tasks), Ariadna Garcia, MS, FeiFei Qin, MPH, and Vidhya Balasubramanian, MS (involved in statistical support), Alana Koehler, BA (administrative support), and Lucia Aronica, PhD, Jennifer Hartle, DrPH, MHS, CIH, Lisa Offringa, PhD, Kenji Nagao, PhD, Marily Oppezzo, PhD, MS, RD, Benjamin Chrisinger, MUEP, PhD, and Michael Stanton, PhD (aided various phases of the study). We also acknowledge the 609 study participants without whom this investigation would not have been possible.

Flegal  KM, Kruszon-Moran  D, Carroll  MD, Fryar  CD, Ogden  CL.  Trends in obesity among adults in the United States, 2005 to 2014.  JAMA. 2016;315(21):2284-2291.PubMedGoogle ScholarCrossref
Zylke  JW, Bauchner  H.  The unrelenting challenge of obesity.  JAMA. 2016;315(21):2277-2278.PubMedGoogle ScholarCrossref
Gardner  CD, Kiazand  A, Alhassan  S,  et al.  Comparison of the Atkins, Zone, Ornish, and LEARN diets for change in weight and related risk factors among overweight premenopausal women: the A TO Z Weight Loss Study: a randomized trial.  JAMA. 2007;297(9):969-977.PubMedGoogle ScholarCrossref
Sacks  FM, Bray  GA, Carey  VJ,  et al.  Comparison of weight-loss diets with different compositions of fat, protein, and carbohydrates.  N Engl J Med. 2009;360(9):859-873.PubMedGoogle ScholarCrossref
Shai  I, Schwarzfuchs  D, Henkin  Y,  et al; Dietary Intervention Randomized Controlled Trial (DIRECT) Group.  Weight loss with a low-carbohydrate, Mediterranean, or low-fat diet.  N Engl J Med. 2008;359(3):229-241.PubMedGoogle ScholarCrossref
Johnston  BC, Kanters  S, Bandayrel  K,  et al.  Comparison of weight loss among named diet programs in overweight and obese adults: a meta-analysis.  JAMA. 2014;312(9):923-933.PubMedGoogle ScholarCrossref
Field  AE, Camargo  CA  Jr, Ogino  S.  The merits of subtyping obesity: one size does not fit all.  JAMA. 2013;310(20):2147-2148.PubMedGoogle ScholarCrossref
Qi  Q, Bray  GA, Smith  SR, Hu  FB, Sacks  FM, Qi  L.  Insulin receptor substrate 1 gene variation modifies insulin resistance response to weight-loss diets in a 2-year randomized trial: the Preventing Overweight Using Novel Dietary Strategies (POUNDS LOST) trial.  Circulation. 2011;124(5):563-571.PubMedGoogle ScholarCrossref
Dopler Nelson  M, Prabakar  P, Kondragunta  V, Kornman  KS, Gardner  CD. Genetic phenotypes predict weight loss success: the right diet does matter. Paper presented at: joint conference of the 50th Cardiovascular Disease Epidemiology and Prevention and Nutrition, Physical Activity, and Metabolism; March 2-3, 2010; San Francisco, CA.
Stanton  MV, Robinson  JL, Kirkpatrick  SM,  et al.  DIETFITS study (Diet Intervention Examining The Factors Interacting With Treatment Success): study design and methods.  Contemp Clin Trials. 2017;53:151-161.PubMedGoogle ScholarCrossref
Cornier  MA, Donahoo  WT, Pereira  R,  et al.  Insulin sensitivity determines the effectiveness of dietary macronutrient composition on weight loss in obese women.  Obes Res. 2005;13(4):703-709.PubMedGoogle ScholarCrossref
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.  JAMA. 2007;297(19):2092-2102.PubMedGoogle ScholarCrossref
Pittas  AG, Das  SK, Hajduk  CL,  et al.  A low-glycemic load diet facilitates greater weight loss in overweight adults with high insulin secretion but not in overweight adults with low insulin secretion in the CALERIE Trial.  Diabetes Care. 2005;28(12):2939-2941.PubMedGoogle ScholarCrossref
McClain  AD, Otten  JJ, Hekler  EB, Gardner  CD.  Adherence to a low-fat vs low-carbohydrate diet differs by insulin resistance status.  Diabetes Obes Metab. 2013;15(1):87-90.PubMedGoogle ScholarCrossref
Physical Activity Guidelines Advisory Committee, US Office of Disease Prevention and Health Promotion. 2008 physical activity guidelines for Americans. https://health.gov/paguidelines/guidelines/. Accessed November 21, 2017.
Bandura  A.  Self-Efficacy: The Exercise of Control. New York, NY: WH Freeman and Co; 1997.
Foreyt  JP, Goodrick  GK.  Impact on behavior therapy on weight loss.  Am J Health Promot. 1994;8(6):466-468.PubMedGoogle ScholarCrossref
King  AC, Frey-Hewitt  B, Dreon  DM, Wood  PD.  Diet vs exercise in weight maintenance: the effects of minimal intervention strategies on long-term outcomes in men.  Arch Intern Med. 1989;149(12):2741-2746.PubMedGoogle ScholarCrossref
Feskanich  D, Sielaff  BH, Chong  K, Buzzard  IM.  Computerized collection and analysis of dietary intake information.  Comput Methods Programs Biomed. 1989;30(1):47-57.PubMedGoogle ScholarCrossref
Sallis  JF, Haskell  WL, Wood  PD,  et al.  Physical activity assessment methodology in the Five-City Project.  Am J Epidemiol. 1985;121(1):91-106.PubMedGoogle ScholarCrossref
Chiu  KC, Martinez  DS, Yoon  C, Chuang  LM.  Relative contribution of insulin sensitivity and beta-cell function to plasma glucose and insulin concentrations during the oral glucose tolerance test.  Metabolism. 2002;51(1):115-120.PubMedGoogle ScholarCrossref
Phillips  DI, Clark  PM, Hales  CN, Osmond  C.  Understanding oral glucose tolerance: comparison of glucose or insulin measurements during the oral glucose tolerance test with specific measurements of insulin resistance and insulin secretion.  Diabet Med. 1994;11(3):286-292.PubMedGoogle ScholarCrossref
Hron  BM, Ebbeling  CB, Feldman  HA, Ludwig  DS.  Relationship of insulin dynamics to body composition and resting energy expenditure following weight loss.  Obesity (Silver Spring). 2015;23(11):2216-2222.PubMedGoogle ScholarCrossref
Chaput  JP, Tremblay  A, Rimm  EB, Bouchard  C, Ludwig  DS.  A novel interaction between dietary composition and insulin secretion: effects on weight gain in the Quebec Family Study.  Am J Clin Nutr. 2008;87(2):303-309.PubMedGoogle ScholarCrossref
Ludwig  DS, Majzoub  JA, Al-Zahrani  A, Dallal  GE, Blanco  I, Roberts  SB.  High glycemic index foods, overeating, and obesity.  Pediatrics. 1999;103(3):E26.PubMedGoogle ScholarCrossref
Goni  L, Cuervo  M, Milagro  FI, Martínez  JA.  Future perspectives of personalized weight loss interventions based on nutrigenetic, epigenetic, and metagenomic data.  J Nutr. 2016;146(4):905S-912S.PubMedGoogle ScholarCrossref
Diggle  P.  Analysis of Longitudinal Data. Oxford, England: Oxford University Press; 2002.
Batterham  MJ, Tapsell  LC, Charlton  KE.  Analyzing weight loss intervention studies with missing data: which methods should be used?  Nutrition. 2013;29(7-8):1024-1029.PubMedGoogle ScholarCrossref
Kuznetsova  A, Brockhoff  PB, Christensen  RHB. lmerTest: tests in linear mixed effects models. R package version 2.0-33. https://cran.r-project.org/. Accessed January 10, 2018.
Bates  D, Mächler  M, Bolker  BM, Walker  SC.  Fitting linear mixed-effects models using lme4.  J Stat Softw. 2015;67(1):1-48.Google ScholarCrossref
Bray  MS, Loos  RJ, McCaffery  JM,  et al; Conference Working Group.  NIH working group report-using genomic information to guide weight management: from universal to precision treatment.  Obesity (Silver Spring). 2016;24(1):14-22.PubMedGoogle ScholarCrossref
Grau  K, Cauchi  S, Holst  C,  et al.  TCF7L2 rs7903146-macronutrient interaction in obese individuals’ responses to a 10-wk randomized hypoenergetic diet.  Am J Clin Nutr. 2010;91(2):472-479.PubMedGoogle ScholarCrossref
Heianza  Y, Ma  W, Huang  T,  et al.  Macronutrient intake-associated FGF21 genotype modifies effects of weight-loss diets on 2-year changes of central adiposity and body composition: the POUNDS Lost trial.  Diabetes Care. 2016;39(11):1909-1914.PubMedGoogle ScholarCrossref
Qi  Q, Bray  GA, Hu  FB, Sacks  FM, Qi  L.  Weight-loss diets modify glucose-dependent insulinotropic polypeptide receptor rs2287019 genotype effects on changes in body weight, fasting glucose, and insulin resistance: the Preventing Overweight Using Novel Dietary Strategies trial.  Am J Clin Nutr. 2012;95(2):506-513.PubMedGoogle ScholarCrossref
Gardner  CD, Offringa  LC, Hartle  JC, Kapphahn  K, Cherin  R.  Weight loss on low-fat vs low-carbohydrate diets by insulin resistance status among overweight adults and adults with obesity: a randomized pilot trial.  Obesity (Silver Spring). 2016;24(1):79-86.PubMedGoogle ScholarCrossref
Pittas  AG, Roberts  SB.  Dietary composition and weight loss: can we individualize dietary prescriptions according to insulin sensitivity or secretion status?  Nutr Rev. 2006;64(10 pt 1):435-448.PubMedGoogle ScholarCrossref
Sun  X, Ioannidis  JP, Agoritsas  T, Alba  AC, Guyatt  G.  How to use a subgroup analysis: Users’ Guide to the Medical Literature.  JAMA. 2014;311(4):405-411.PubMedGoogle ScholarCrossref
Wallach  JD, Sullivan  PG, Trepanowski  JF, Steyerberg  EW, Ioannidis  JPA.  Sex based subgroup differences in randomized controlled trials: empirical evidence from Cochrane meta-analyses.  BMJ. 2016;355:i5826.PubMedGoogle ScholarCrossref
Brookes  ST, Whitely  E, Egger  M, Smith  GD, Mulheran  PA, Peters  TJ.  Subgroup analyses in randomized trials: risks of subgroup-specific analyses; power and sample size for the interaction test.  J Clin Epidemiol. 2004;57(3):229-236.PubMedGoogle ScholarCrossref
If you are not a JN Learning subscriber, you can either:
Subscribe to JN Learning for one year
Buy this activity
If you are not a JN Learning subscriber, you can either:
Subscribe to JN Learning for one year
Buy this activity
With a personal account, you can:
  • Access free activities and track your credits
  • Personalize content alerts
  • Customize your interests
  • Fully personalize your learning experience
Education Center Collection Sign In Modal Right

Name Your Search

Save Search
With a personal account, you can:
  • Track your credits
  • Personalize content alerts
  • Customize your interests
  • Fully personalize your learning experience

Lookup An Activity



My Saved Searches

You currently have no searches saved.

With a personal account, you can:
  • Access free activities and track your credits
  • Personalize content alerts
  • Customize your interests
  • Fully personalize your learning experience
Education Center Collection Sign In Modal Right