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Effects of Time-Restricted Eating on Weight Loss and Other Metabolic Parameters in Women and Men With Overweight and ObesityThe TREAT Randomized Clinical Trial

Educational Objective: To determine the effect of 16:8-hour time-restricted eating on weight loss and metabolic risk markers.
1 Credit CME
Key Points

Question  What is the effect of time-restricted eating on weight loss and metabolic health in patients with overweight and obesity?

Findings  In this prospective randomized clinical trial that included 116 adults with overweight or obesity, time-restricted eating was associated with a modest decrease (1.17%) in weight that was not significantly different from the decrease in the control group (0.75%).

Meaning  Time-restricted eating did not confer weight loss or cardiometabolic benefits in this study.


Importance  The efficacy and safety of time-restricted eating have not been explored in large randomized clinical trials.

Objective  To determine the effect of 16:8-hour time-restricted eating on weight loss and metabolic risk markers.

Interventions  Participants were randomized such that the consistent meal timing (CMT) group was instructed to eat 3 structured meals per day, and the time-restricted eating (TRE) group was instructed to eat ad libitum from 12:00 pm until 8:00 pm and completely abstain from caloric intake from 8:00 pm until 12:00 pm the following day.

Design, Setting, and Participants  This 12-week randomized clinical trial including men and women aged 18 to 64 years with a body mass index (BMI, calculated as weight in kilograms divided by height in meters squared) of 27 to 43 was conducted on a custom mobile study application. Participants received a Bluetooth scale. Participants lived anywhere in the United States, with a subset of 50 participants living near San Francisco, California, who underwent in-person testing.

Main Outcomes and Measures  The primary outcome was weight loss. Secondary outcomes from the in-person cohort included changes in weight, fat mass, lean mass, fasting insulin, fasting glucose, hemoglobin A1c levels, estimated energy intake, total energy expenditure, and resting energy expenditure.

Results  Overall, 116 participants (mean [SD] age, 46.5 [10.5] years; 70 [60.3%] men) were included in the study. There was a significant decrease in weight in the TRE (−0.94 kg; 95% CI, −1.68 to −0.20; P = .01), but no significant change in the CMT group (−0.68 kg; 95% CI, -1.41 to 0.05, P = .07) or between groups (−0.26 kg; 95% CI, −1.30 to 0.78; P = .63). In the in-person cohort (n = 25 TRE, n = 25 CMT), there was a significant within-group decrease in weight in the TRE group (−1.70 kg; 95% CI, −2.56 to −0.83; P < .001). There was also a significant difference in appendicular lean mass index between groups (−0.16 kg/m2; 95% CI, −0.27 to −0.05; P = .005). There were no significant changes in any of the other secondary outcomes within or between groups. There were no differences in estimated energy intake between groups.

Conclusions and Relevance  Time-restricted eating, in the absence of other interventions, is not more effective in weight loss than eating throughout the day.

Trial Registration  ClinicalTrials.gov Identifiers: NCT03393195 and NCT03637855

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CME Disclosure Statement: Unless noted, all individuals in control of content reported no relevant financial relationships. If applicable, all relevant financial relationships have been mitigated.

Article Information

Accepted for Publication: July 6, 2020.

Corresponding Author: Ethan J. Weiss, MD, University of California, San Francisco, 555 Mission Bay Blvd S, Room 352Y, San Francisco, CA 94143 (ethan.weiss@ucsf.edu).

Published Online: September 28, 2020. doi:10.1001/jamainternmed.2020.4153

Correction: This article was corrected on November 2, 2020, to fix an error in panel C of Figure 2. the y-axis labels were incorrect and the data on the right side of the panel were incorrectly aligned. It was corrected again on February 22, 2021, to correct pervasive data errors in the Methods section.

Author Contributions: Dr Weiss 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: Lowe, Moore, Olgin, Weiss.

Acquisition, analysis, or interpretation of data: Lowe, Wu, Rohdin-Bibby, Kelly, Liu, Phillip, Vittinghoff, Heymsfield, Olgin, Shepherd, Weiss.

Drafting of the manuscript: Lowe, Phillip, Olgin, Weiss.

Critical revision of the manuscript for important intellectual content: Lowe, Wu, Rohdin-Bibby, Moore, Kelly, Liu, Vittinghoff, Heymsfield, Olgin, Shepherd, Weiss.

Statistical analysis: Vittinghoff, Olgin, Weiss.

Obtained funding: Lowe, Heymsfield, Olgin, Shepherd, Weiss.

Administrative, technical, or material support: Lowe, Wu, Rohdin-Bibby, Moore, Kelly, Liu, Phillip, Heymsfield, Olgin, Weiss.

Supervision: Lowe, Olgin, Shepherd, Weiss.

Conflict of Interest Disclosures: Dr Lowe reported personal fees from Keyto outside the submitted work. Dr Vittinghoff reported personal fees from UCSF during the conduct of the study. Dr Heymsfield reported personal fees from Medifast Medical Advisory Board and personal fees from Tanita Medical Advisory Board outside the submitted work. Dr Weiss reported grants from NIH, grants from James Peter Read Foundation, nonfinancial support from Mocacare Inc, and nonfinancial support from IHealth labs during the conduct of the study; he also is a cofounder and equity stake holder of Keyto, Inc; and owns stock and was formerly on the board of Virta, Inc. No other disclosures were reported.

Funding/Support: This research was funded by the University of California, San Francisco, Cardiology Division’s Cardiology Innovations Award Program and the National Institute of Diabetes and Digestive and Kidney Diseases (NIH R01 DK109008). Additional support came from the James Peter Read Foundation. Bluetooth connect scales were graciously gifted from iHealth Labs Inc. MOCACuff blood pressure cuffs were gifted from MOCACARE. Health tracking rings were gifted from Oura.

Role of the Funder/Sponsor: The University of California, San Francisco 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.

Additional Contributions: We thank Kevin Hall, PhD, National Institute of Diabetes and Digestive and Kidney Diseases, for critical reading of the manuscript and for providing software for calculation of energy intake. We also thank Juen Guo, National Institute of Diabetes and Digestive and Kidney Diseases, for providing software for calculation of energy intake.

Data Sharing Statement: See Supplement 4.

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