4.7 Article

Effect 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 A Randomized Clinical Trial

Journal

JAMA NETWORK OPEN
Volume 5, Issue 9, Pages -

Publisher

AMER MEDICAL ASSOC
DOI: 10.1001/jamanetworkopen.2022.33760

Keywords

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Funding

  1. American Heart Association [17SFRN33490004]
  2. National Center for Advancing Translational Sciences
  3. National Institutes of Health [UL1TR001445, K99MD012811, R00MD012811, U54MD000538-15, P30DK111022, 5T32HL129953-04]
  4. Health Resources and Services Administration [T32HP22238]

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In this study, a comparison was made between a standardized low-fat diet and a personalized diet in terms of percentage of weight loss in adults with abnormal glucose metabolism and obesity. The results showed that a personalized diet did not lead to greater weight loss compared to a low-fat diet at 6 months.
IMPORTANCE Interindividual variability in postprandial glycemic response (PPGR) to the same foods may explain why low glycemic index or load and low-carbohydrate diet interventions have mixed weight loss outcomes. A precision nutrition approach that estimates personalized PPGR to specific foods may be more efficacious for weight loss. OBJECTIVE To compare a standardized low-fat vs a personalized diet regarding percentage of weight loss in adults with abnormal glucose metabolism and obesity. DESIGN, SETTING, AND PARTICIPANTS The Personal Diet Study was a single-center, population-based, 6-month randomized clinical trial with measurements at baseline (0 months) and 3 and 6 months conducted from February 12, 2018, to October 28, 2021. A total of 269 adults aged 18 to 80 years with a body mass index (calculated as weight in kilograms divided by height in meters squared) ranging from 27 to 50 and a hemoglobin A(1c) level ranging from 5.7% to 8.0% were recruited. Individuals were excluded if receiving medications other than metformin or with evidence of kidney disease, assessed as an estimated glomerular filtration rate of less than 60 mL/min/1.73 m(2) using the Chronic Kidney Disease Epidemiology Collaboration equation, to avoid recruiting patients with advanced type 2 diabetes. INTERVENTIONS Participants were randomized to either a low-fat diet (<25% of energy intake; standardized group) or a personalized diet that estimates PPGR to foods using a machine learning algorithm (personalized group). Participants in both groups received a total of 14 behavioral counseling sessions and self-monitored dietary intake. In addition, the participants in the personalized group received color-coded meal scores on estimated PPGR delivered via a mobile app. MAIN OUTCOMES AND MEASURES The primary outcome was the percentage of weight loss from baseline to 6 months. Secondary outcomes included changes in body composition (fat mass, fat-free mass, and percentage of body weight), resting energy expenditure, and adaptive thermogenesis. Data were collected at baseline and 3 and 6 months. Analysis was based on intention to treat using linear mixed modeling. RESULTS Of a total of 204 adults randomized, 199 (102 in the personalized group vs 97 in the standardized group) contributed data (mean [SD] age, 58 [11] years; 133 women [66.8%]; mean [SD] body mass index, 33.9 [4.8]). Weight change at 6 months was -4.31% (95% CI, -5.37% to -3.24%) for the standardized group and -3.26%(95% CI, -4.25% to -2.26%) for the personalized group, which was not significantly different (difference between groups, 1.05%[95% CI, -0.40% to 2.50%]; P = .16). There were no between-group differences in body composition and adaptive thermogenesis; however, the change in resting energy expenditure was significantly greater in the standardized group from 0 to 6 months (difference between groups, 92.3 [95% CI, 0.9-183.8] kcal/d; P = .05). CONCLUSIONS AND RELEVANCE A personalized diet targeting a reduction in PPGR did not result in greater weight loss compared with a low-fat diet at 6 months. Future studies should assess methods of increasing dietary self-monitoring adherence and intervention exposure.

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