Effects of Time-Restricted Eating on Weight Loss and Other Metabolic Parameters in Women and Men With Overweight and Obesity: The TREAT Randomized Clinical Trial | Cardiology | JAMA Internal Medicine | JAMA Network
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Figure 1.  CONSORT Flow Diagram
CONSORT Flow Diagram

CMT indicates consistent meal timing group; TRE, time-restricted eating group. CONSORT flow diagram describing process of participant recruitment, enrollment, randomization, and data analysis. Participants were excluded from participating if they (1) were older than 64 years (n = 5), (2) had a body mass index (calculated as weight in kilograms divided by height in meters squared) less than 27 (n = 348) or greater than 43 (n = 72), (3) did not regularly consume breakfast (n = 566), (4) were unwilling or unable to skip breakfast (n = 761), (5) had a current or past cancer diagnosis (n = 21), (6) were breastfeeding, pregnant, or planned to be pregnant within 6 months (n = 21), (7) had current diagnosis of type 1 or type 2 diabetes mellitus (n = 177), (8) were taking glucose-lowering drugs (n = 133) or weight loss pills (n = 116), (9) had a history of gastric bypass or any weight-loss surgery (n = 66), (10) had a weight fluctuation of more than 15% in past 5 years (n = 467), (11) had a history of anorexia or bulimia (n = 39), (12) frequently traveled across time zones (n = 99) or worked unusual work hours (n = 182), or (13) were unable to fast for prolonged periods (n = 168).

Figure 2.  Adherence and Weight Change in the Total Cohort
Adherence and Weight Change in the Total Cohort

A, Participants were sent daily adherence surveys through the study application (“Did you adhere to your eating plan on the previous day?” Yes/No). Responses from all completed surveys were analyzed. The percent adherence to protocol is shown over time for consistent meal timing group (CMT) (dashed blue line; n = 41) and time-restricted eating (TRE) participants (solid orange line; n = 44). B, Individual daily weight measurements as recorded from the at-home scale are shown for each participant over time throughout the duration of the study. The individual weight measurements are show as maroon dots for the CMT group (n = 57) and blue dots for TRE group (n = 59). The solid lines represent weight over time as determined from the linear mixed model. C, Waterfall plot showing percent weight change for each participant from the total cohort in the CMT group (left) and TRE group (right).

Table 1.  Baseline Characteristics
Baseline Characteristics
Table 2.  Weight Change in the Total Cohort
Weight Change in the Total Cohort
Table 3.  Body Composition and Energy Expenditure Measurements in the In-Person Cohorta
Body Composition and Energy Expenditure Measurements in the In-Person Cohorta
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    7 Comments for this article
    EXPAND ALL
    A Study that was Poorly Done
    Danielle Hammond |
    16 hours of fasting isn't often enough to lose weight, and the participants were allowed zero calorie beverages. As far as I'm concerned no one should have wasted time and money on this study to try to say that fasting doesn't work.

    Try fasting with only black coffee, tea, or water. And for at least 18-20 hours.
    Have the participants do this for 3 months to become full my adapted. Then present the results.
    CONFLICT OF INTEREST: None Reported
    No Objective Measure of Compliance and Differential Attrition
    Robert Kaestner, Ph.D. | University
    The study measured compliance through self-reports. According to these self reports, 92.1% complied with CMT and 83.5% complied with TRE.

    Further, it is unclear what non-compliance in the CMT group means? Did it include partial intermittent fasting--e.g., skipping breakfast? Did it mean ever not complying? Or not complying consistently?

    Similar questions apply about compliance for the TRE group? And which group is more likely to misreport compliance?

    The absence of compliance information is potentially quite important. For example, if we scale the weight change of compliers was -1.13 (0.94/0.835) kg. Doing the same for CMT group
    yields -0.74 kg. The difference is -0.39 kg., which is 50% larger than the simple difference reported in Table 2 of article.

    But the absence of accurate and reliable compliance information is a serious flaw that diminishes the value of the results reported in the article.

    Then there is significant attrition and it is non-random by treatment arm. This problem too diminishes the value of the results.

    Overall, this article provides little credible evidence of the effect of intermittent fasting.
    CONFLICT OF INTEREST: None Reported
    READ MORE
    Disease markers?
    Fips Alfredsson, PhD | University of Bogota
    Fasting is not just about weight loss. The study did not measure parameters that could assess general health status, which, in the long run, might be more important than weight loss.
    Apart from this, I find another comment a bit misplaced -- more extreme forms of fasting for much longer times of course will have likely promoted statistically significant differences in weight loss; yet, the idea behind studies like this is that a rhythm of 16:8 is what most people can do it, while 18:6 or 20:4 is much harder to achieve and live by for a broad
    sector of the population, and therefore not very feasible as an intervention method.
    Regardless, it would have been important to consider more measures than weight loss to assess the efficiency of 16:8.
    CONFLICT OF INTEREST: None Reported
    READ MORE
    Time-restricted Eating Misunderstood in Study
    MH Tang, MSc | The study states that 3 meals were consumed - obviously the participants will not loose weight if total caloric intake remains unchanged.
    The study states that 3 meals were consumed - obviously the participants will not loose weight if total caloric intake remains unchanged.
    The study authors have also taken the time-restricted eating out of proper context i.e. time-restriction is to bw seen in the context of not consuming 2 of the typical 3 meals in a day - usually breakfast and lunch is skipped, or just breakfast.
    CONFLICT OF INTEREST: None Reported
    Why even do such a study without controlling for what a subject eats?
    Rod Erickson, DC, MS | Miramar College
    As long as the subjects eat the SAD (standard American diet) ad libitum but limit it to 8 hrs/day does not suggest anything different from what is already known.
    CONFLICT OF INTEREST: None Reported
    Everyday American Diet is the Culprit
    Sudah Shaheeb, MD | University
    A clever health care lobbyist made a graph of rate of obesity compared to the increasing number of nutritionists, midlevel providers and diabetologists in the USA. There was a good correlation but does it mean that the rise in the number of professionals is the cause of obesity in the USA ? or does it mean that the uncontrollable rate of obesity gave rise to an increasing number of professionals devoting themselves to nutrition, obesity etc.
    I am a medical anthropologist and as someone has mentioned the problem is the quality of food available for an average American in the
    supermarket. That is why they cannot loose weight.
    If you look at culturally congruent Intermittent fasting as among the people of Okinawa and see the high quality of their food, you would understand why they live longer and why they have lower rates of diabetes and also cancers. Cultural Native Americans throughout the continent have practiced intermittent fasting for centuries and North American Indians are overweight and have high rates of diabetes whereas Indians living in Panama (the Cuna of Cuna Yala) have almost negligible rates of those.
    NEJM 25/12/2019 has a very good article on the biochemistry of the effects of Intermittent fasting which cannot be duplicated by forced ketogenic Diet. I plan to write a blog about this at www.medicoanthropologist.blogspot.com
    Intermittent fasting is beneficial metabolically but not sure of its effect on obese people. Okinawans and all the other Blue Zone people do not look at their nourishment as DIET.
    Whenever possible, please eat FOOD and not manufactured substitute.
    CONFLICT OF INTEREST: None Reported
    READ MORE
    What about circadian rhythm?
    Maria Kravchenko, MD |
    The subjects in the TRE group were allowed to eat until 8 pm. There are many studies looking at the relationship of meal intake to circadian rhythm; these all show that eating later in the day is associated with adverse metabolic effects and weight gain. This study would be stronger if it had compared several groups with different time periods involved.
    CONFLICT OF INTEREST: None Reported
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    Original Investigation
    September 28, 2020

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

    Author Affiliations
    • 1Cardiovascular Research Institute, University of California, San Francisco, San Francisco
    • 2Cardiology Division, University of California, San Francisco, San Francisco
    • 3Center for Vulnerable Populations, University of California, San Francisco, San Francisco
    • 4Clovis Oncology Inc, Boulder, Colorado
    • 5University of Hawai’i Cancer Center, Honolulu
    • 6University of California School of Medicine, San Francisco
    • 7Pennington Biomedical Research Center, Baton Rouge, Louisiana
    JAMA Intern Med. Published online September 28, 2020. doi:10.1001/jamainternmed.2020.4153
    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.

    Abstract

    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

    Introduction

    The prevalence of overweight (body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 25 to 30) and obesity (BMI greater than 30) has increased dramatically recently1 and is associated with increased risk for chronic diseases.2 Even modest weight reduction can improve cardiovascular disease risk.3 However, long-term adherence to lifestyle changes is difficult. Therefore, it is important to find novel lifestyle-modification interventions that are (1) effective in reducing weight and (2) accessible and straightforward to enhance adherence.

    Intermittent fasting (IF) has gained attention as a simple weight loss method. Intermittent fasting refers to eating windows separated by defined periods of fasting (>12 hours and up to 48 hours, or more). Most of the reported benefits of IF are either untested or undertested in humans.4 Time-restricted eating (TRE) is a specific IF protocol involving consistent fasting and eating periods within a 24-hour cycle.

    Time restricted feeding (TRF) prevents weight gain in mice challenged with an isocaloric high-fat diet (HFD)5 and reduces weight and metabolic outcomes in already obese mice.6 Weight loss without a decrease in calorie intake suggests that TRF could affect energy expenditure to achieve a negative calorie balance.

    Prior small studies in humans with overweight or obesity demonstrate that TRE can result in reduced calorie intake and is associated with a decrease in body weight and/or fat mass.7-10 We conducted a randomized clinical trial (RCT) designed to determine the effect of TRE on weight and comprehensive metabolic outcomes in overweight and obese patients. We hypothesized that 8-hour TRE prescribed to individuals with overweight and obesity would lead to weight loss and improvements in metabolic markers compared with individuals following a standard 3-meals-per-day diet (consistent meal timing [CMT]).

    Methods
    Experimental Model and Participant Details

    This study was conducted with approvals from the institutional review board at the University of California, San Francisco (UCSF) and the University of Hawai’i Cancer Center (UHCC). The trial protocol is available in Supplement 1. The clinical trial was registered on ClinicalTrials.gov (NCT03393195).

    Participants were recruited between August 2018 and June 2019 and data collection was completed in October 2019. Overall, 141 participants were enrolled in the study and were randomized. We randomized 25 participants for whom we never received any data. Data were collected from 116 participants; and 105 completed the 12-week protocol. The study was conducted on a custom mobile study application (app) on the Eureka Research Platform. Participants received study surveys through the study app. Participants were given a bluetooth weight scale to use daily, which was connected through the study app. Participants were randomized to 1 of 2 interventions. The study intervention only included recommendations to the timing of food intake (no recommendation for calorie and macronutrient intake or physical activity), and participants received daily reminders about their eating windows through the app. Quiz Ref IDThe CMT group was instructed to eat 3 structured meals per day. Snacking between meals was permitted. The 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 (16 hours fast:8 hours eat). Only noncaloric beverages were permitted outside of the eating widow. Participants provided consent through the app, and received a $50 Visa gift card for participating in the study.

    Weight Measurements

    All participants received an iHealth Lite Bluetooth scale (Model HS4S) to use at home. Participant accounts were linked to the Eureka Research platform. Participants were instructed to use the scale daily in the morning before eating or drinking and prior to structured physical activity.

    In-Person Metabolic Testing

    Participants who lived within 60 miles of UCSF were eligible to undergo extensive in-person metabolic testing at the UCSF Clinical Research Center and the UCSF Body Composition Laboratory as detailed by Ng et al.11 Enrollment was capped at 50 participants, and 50 participants opted into the in-person testing. A total of 46 participants completed all 4 in-person visits.

    Statistical Analysis

    The statistical analysis plan is available in Supplement 2. The primary outcome was change in weight since baseline, measured daily via iHealth scales, in the overall cohort of 116 participants. To estimate the intention-to-treat effect of treatment assignment, we used a linear mixed model with fixed effects for treatment assignment, days since baseline, and their interaction, and random effects for participant and day, with unstructured covariance matrix, accommodating any nonlinearity in the trajectories using 3-knot cubic splines. The treatment effect was estimated by the fitted between-group difference at day 90, net of any baseline difference. In sensitivity analyses, we repeated the analysis after Winsorizing outliers, which was defined as points more than 1.5 times the interquartile range below the 25th or above the 75th percentile of the overall distribution. No adjustments were made to P values or confidence intervals for multiple comparisons for the primary outcome.

    Results

    Of the 141 participants who were randomized to 1 of the 2 interventions, 105 (74.5%) completed the entire 12-week intervention (Figure 1). Of the 36 randomized participants who did not complete the study, 25 never recorded weight measurements (TRE n = 10, CMT n = 15), 8 were lost to follow-up (TRE n = 7, CMT n = 1), and 3 discontinued intervention (TRE n = 2, CMT n = 1). Participants had a mean (SD) age of 46.5 (10.5) years and a mean (SD) weight of 99.2 (16.0) kg (Table 1).

    Quiz Ref IDSelf-reported adherence to the diets was 1002 of 1088 (92.1%) in the CMT group (did not miss any meals) and 1128 of 1351 (83.50%) in the TRE group (ate only within the 8-hour window) (Figure 2A).

    Of the 141 participants randomized in the study, we invited persons living within 60 miles of San Francisco (enrollment was first come, first served and was capped at 50) to undergo comprehensive in-person metabolic testing (referred to as in-person cohort). Overall, 46 of 50 participants completed the entire in-person testing protocol (CMT n = 24, TRE n = 22). Baseline characteristics of both cohorts are shown in Table 1.

    Weight

    Quiz Ref IDThere was a significant decrease in weight in the TRE group (−0.94 kg; 95% CI, −1.68 kg to −0.20 kg; P = .01) and a nonsignificant decrease in weight in the CMT group (−0.68 kg; 95% CI, −1.41 kg to 0.05 kg; P = .07). Importantly, there was no significant difference in weight change between groups (−0.26 kg; 95% CI, −1.30 kg to 0.78 kg; P = .63) (Figure 2, B and C) (Table 2). There was a significant decrease in percentage of baseline weight in the TRE group (−1.17%; 95% CI, −1.89% to −0.45%; P = .002) and in the CMT group (−0.75%; 95% CI, −1.47% to −0.04%, P = .04); however, there was no significant difference between groups (−0.41%; 95% CI, −1.43% to 0.60%; P = .43) (Table 2). There were no statistically significant changes in estimated energy intake or energy expenditure between groups (eFigure 1A and 1B in Supplement 3).

    In the in-person cohort (n = 50), there was a significant decrease in weight in the TRE group using the in-person weight measurements (−1.70 kg; 95% CI, −2.56 kg to −0.83 kg; P < .001) but not in the CMT group (−0.57 kg; 95% CI, −1.40 kg to 0.26 kg; P = .18) (Table 3) (eFigure 2 in Supplement 3). There was a nonsignificant difference in weight loss between groups (−1.13 kg; 99.7% CI, −2.33% to 0.07%; P = .07) (Table 3). There was a significant decrease in percentage of baseline weight in the TRE group (−1.81%; 95% CI, −2.85% to 0.78%; P < .001) but not in the CMT group (−0.65%; 95% CI, −1.64% to 0.34%; P = .19) or between groups (−1.16%; 95% CI, −2.59% to 0.27%; P = .11). There was strong agreement between in-person weight measurements and at-home weight measurements as determined by a Bland-Altman analysis (eFigure 3 in Supplement 3).

    Body Composition and Energy Expenditure

    Quiz Ref IDAs measured by dual-energy x-ray absorptiometry (DXA), there was no significant change in whole body fat mass (FM) in the TRE (−0.51 kg; 95% CI, −1.17 kg to 0.15 kg; P = .13) or the CMT groups (−0.03 kg; 95% CI, −0.66 kg to 0.60 kg; P = .93), and there was no significant difference between groups (−0.48 kg; 99.7% CI, −1.75 kg to 0.79 kg; P = .30) (Table 3). There was a significant decrease in lean mass (calculated as fat-free mass minus bone mineral content) in the TRE (−1.10 kg; 95% CI, −1.73 kg to −0.48 kg; P < .001) but not in the CMT group (−0.35 kg; 95% CI, −0.95 kg to 0.25 kg; P = .25). There was no significant difference in lean mass between groups (−0.75 kg; 99.7% CI, −1.96 kg to 0.45 kg; P = .09). Appendicular lean mass (ALM) was decreased significantly in the TRE group (−0.64 kg; 95% CI, −0.89 kg to −0.39 kg; P < .001) but not in the CMT group (−0.17 kg; 95% CI, −0.41 kg to 0.07 kg; P = .16), and there was a significant difference between groups (−0.47 kg; 95% CI, −0.82 kg to −0.12 kg; P = .009). There was a significant decrease in appendicular lean mass index (ALMI) in the TRE group (−0.22 kg/m2; 95% CI, −0.30 kg/m2 to −0.14 kg/m2; P < .001) but not in the CMT group (−0.06 kg/m2; 95% CI, −0.14 kg/m2 to 0.02 kg/m2; P = .14). The difference in ALMI between groups was also significant (−0.16 kg/m2; 95% CI, −0.27 kg/m2 to −0.05 kg/m2; P = .005). Trunk lean mass significantly decreased in the TRE group (−0.47 kg; 95% CI, −0.88 kg to −0.06 kg; P = .02). There was no significant change in trunk lean mass in the CMT group (−0.15 kg; 95% CI, −0.54 kg to 0.24 kg; P = .45) or between groups (−0.32 kg; 95% CI, −0.89 kg to 0.25 kg; P = .27). For a comprehensive list of all body composition variables analyzed, see eTable 2 in Supplement 3.

    Respiratory quotient (RQ) did not change significantly in the TRE group (0.01; 95% CI, −0.02 to 0.03; P = .82); RQ increased in the CMT group (0.03; 95% CI, 0.01 to 0.06; P = .003), but there was no significant difference between groups (−0.03; 95% CI, −0.06 to −0.01, P = .06). There was no significant difference in resting metabolic rate (RMR) in the TRE (−28.1 kcal/d; 95% CI, −91.8 kcal/d to 35.5 kcal/d; P = .39) or the CMT group (−43.15 kcal/d; 95% CI, −104.2 kcal/d to 18.0 kcal/d; P = .17), and there was no significant difference between groups (15.0 kcal/d; 99.7% CI, −108.1 kcal/d to 138.0 kcal/d; P = .74) (Table 3). There was a significant decrease in total energy expenditure (TEE) in both groups (TRE: −177.9 kcal/d; 95% CI, −285.9 kcal/d to −69.9 kcal/d; P = .001; CMT: −127.3 kcal/d; 95% CI, −230.7 kcal/d to −23.9 kcal/d; P = .02). There was no significant difference between groups (−50.6 kcal/d; 99.7% CI, −259.2 kcal/d to 158.1 kcal/d; P = .51).

    Blood Lipids, Glucose, Insulin, and Cardiometabolic Health Markers

    There were no significant within-group or between-group differences in fasting glucose, fasting insulin, HOMA-IR, HbA1C, triglycerides, total cholesterol, LDL, or HDL levels (eTable 1 in Supplement 3). See eTable 3 in Supplement 3 for all other blood markers analyzed in the study.

    There was no significant difference in systolic blood pressure in the TRE group (−1.69 mm Hg; 95% CI, −5.54 mm Hg to 2.15 mm Hg; P = .39), but there was a significant decrease in the CMT group (−3.86 mm Hg; 95% CI, −7.58 mm Hg to 0.14 mm Hg; P = .04) (eTable 1 in Supplement 3). There was no significant between-group difference in systolic blood pressure (2.17 mm Hg; 95% CI, −3.18 mm Hg to 7.52 mm Hg; P = .43). There was a significant change in diastolic blood pressure in the TRE group (−4.08 mm Hg; 95% CI, −8.11 mm Hg to −0.06 mm Hg; P = .047) but not in the CMT group (−3.01 mm Hg; 95% CI, −6.90 mm Hg to 0.89 mm Hg; P = .13) or between groups (−1.08 mm Hg; 95% CI, −6.67 mm Hg to 4.52 mm Hg; P = .71).

    Sleep Quality Activity Tracking and Food Attitudes

    There were no significant changes in any of the self-reported sleep measures in either group or between groups in the total cohort (eTable 5 in Supplement 3). However, Oura ring data revealed significant changes in sleep efficiency, sleep latency, and awake time in the TRE group and between groups (eTable 6 in Supplement 3).

    The Oura ring data also revealed a significant reduction in daily movement in the TRE group (−2102.14 au; 95% CI, −3162.54 au to −1041.73 au; P < .001) and between groups (−1673.44 au; 95% CI, −3211.11 au to −135.7 au; P = .03) but not in the CMT group (−428.70 au; 95% CI, −1542.25 au to 684.85 au; P = .45). There was a significant decrease in step count in the TRE group (−2498.89 steps; 95% CI, −3939.91 to −1057.88; P < .001) and between groups (−2241.41 steps; 95% CI, −4320.51 to −162.31; P = .04) but not in the CMT group (−257.48 steps; 95% CI, −1756.20 to 1241.23; P = .74). The correlation between change in step count and change in TEE was 0.52 in the TRE group and 0.03 in the CMT group, but the 2 correlations did not differ significantly (eFigure 4 in Supplement 3).

    Discussion

    The TRE is attractive as a weight-loss option in that it does not require tedious, and time-consuming methods such as calorie-counting or adherence to complicated diets. Indeed, we found that self-reported adherence to the TRE schedule was high (Figure 2A); However, in contrast to our hypothesis, there was no greater weight loss with TRE compared with the CMT. In addition, we found among our secondary outcomes that there were few differences between the 2 groups. Specifically:, there were no significant differences in fat mass, fasting insulin, glucose, HbA1C, or blood lipids between the TRE and CMT groups.

    Most humans eat throughout their waking hours.12 We prescribed an 8-hour eating window and did not prescribe calorie or macronutrient guidance so as to offer a simple, real-world recommendation to free-living individuals. We chose a 12 pm to 8 pm eating window because we reasoned that people would find it easier culturally to skip breakfast than dinner—a more social meal in most cultures.

    Our results are consistent with a prior study demonstrating that a recommendation to skip breakfast does not affect weight outcomes in patients trying to lose weight13 but, contradict previous reports describing the beneficial effects of TRE on weight loss and other metabolic risk markers.7,10,14,15

    Wilkinson et al10 found that TRE was associated with an approximately 3% weight loss and improvements in cardiovascular risk markers in patients with Metabolic Syndrome. This single-arm study was small (n = 19) and, importantly, did not have a control group.

    Although the prescribed (12-8 pm) eating window is likely more attractive and more amenable to long-term adherence, it might not be optimal for the metabolic advantages of TRE. Sutton et al15 performed a 5-week RCT comparing early TRE (eTRE: 6-hour eating window with dinner before 3:00 pm) to a control diet (12-hour eating window). They found improved glycemic control and improvements in cardiovascular risk markers without changes in body weight in the eTRE group.

    In analysis of secondary outcomes, we found a significant reduction in lean mass in the TRE group. In the in-person cohort, the average weight loss in the TRE group was 1.70 kg. Of this, 1.10 kg (approximately 65% of weight lost) was lean mass; only 0.51 kg of weight loss was fat mass. Loss of lean mass during weight loss typically accounts for 20% to 30% of total weight loss.16-22 The proportion of lean mass loss in this study (approximately 65%) far exceeds the normal range of 20% to 30%.22 In addition, there was a highly significant between-group difference in ALM. Appendicular lean mass is correlated with nutritional and physical status, and reduced ALM can lead to weakness, disability, and impaired quality of life.23-26 This serves as a caution for patient populations at risk for sarcopenia because TRE could exacerbate muscle loss.27 Finally, the extent of lean mass loss during weight loss has been positively correlated with weight regain.28

    The effect of TRE on lean mass is largely unexplored. Prior studies show that TRE prevents gains in lean mass.29 A follow-up study showed that when calorie intake and protein intake were matched to prestudy consumption, no change in lean mass was observed.30 An RCT comparing TRE in overweight and obese patients demonstrated a significant loss of lean mass compared with controls, but no significant change in fat loss between groups.31 Ad libitum feeding during TRE leads to reduced calorie intake and might also reduce protein intake.9 Together, these data highlight the importance of adequate protein consumption while adhering to a TRE diet. Many studies have shown that adequate/excessive protein consumption during weight loss can mitigate losses in lean mass.16,28,32-35 National Health and Nutrition Examination Survey data show that most daily protein intake occurs during meals, and snacking accounts for a small portion of total daily protein intake.36 The loss of ALM during TRE could be mitigated by increasing the number of meals within the eating window or consuming protein supplements.16,28 Timing of protein consumption may also play a role in changes in lean mass.37-39

    Strengths and Limitations

    Strengths of the study include randomization, an easy to follow, real-world prescription-based intervention, and an appropriate control group. Although there was statistically significant weight loss in the TRE group, there was no difference between groups. This indicates that participation in a weight loss study alone (even in the control group) is sufficient to lead to short-term weight loss and highlights the importance of including a control arm in weight loss studies.

    Quiz Ref IDA limitation is we do not have self-reported measures of energy or macronutrient intake. Although we did not measure calorie intake, mathematical modeling of changes in energy intake suggests that calorie intake did not significantly differ between groups. This model has been validated to be more accurate than self-reported energy intake.40,41 We did not measure changes in protein intake. Given the loss of ALM in participants in the TRE arm and previous reports of decreased protein consumption from TRE,9,29 it is possible that protein intake was altered by TRE in this cohort, and this clearly warrants future study. Finally, the DXA analysis of lean mass did not factor in muscle hydration, so it is possible that changes in hydration could confound the lean mass calculations. To help control for this, participants fasted for more than 12 hours and voided their bladder prior to DXA scans. The change in lean mass in the TRE group was much greater than the loss of body water, so it is unlikely that differences in muscle hydration would account for all of the lean mass loss.

    Conclusions

    In this RCT, a prescription of TRE did not result in weight loss when compared with a control prescription of 3 meals per day. Time-restricted eating did not change any relevant metabolic markers. Finally, there was a decrease in ALM in the TRE group compared with CMT. Together, the results of this study (1) do not support the efficacy of TRE for weight loss, (2) highlight the importance of control interventions, and (3) offer caution about the potential effects of TRE on ALM. Future studies should be aimed at understanding the effects of early vs late TRE and protein intake or timing as a means to offset the loss in ALM.

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    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

    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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