Abstract
Introduction
Time-restricted eating (TRE) has cardiometabolic health benefits by optimizing circadian rhythms. However, limited data are available on the effect of early TRE in young adults. The objective of this pre-post single-arm trial was to test the effect of TRE on body composition and cardiometabolic risk factors and to evaluate changes in meal and sleep timing by TRE among young adults with typically late bedtime.
Methods
This 4-week intervention was conducted in healthy young adults aged 18–39 years. Dietary records with time logs were collected before and during the intervention, and nutrient intake and meal timing were evaluated. Snack packages containing 20 g of protein per day were provided weekly. Body composition was measured weekly using bioelectrical impedance analysis. Blood samples were collected before and after the intervention, and cardiometabolic parameters were evaluated.
Results
Of the 36 screened participants, 34 completed the study (completion rate 94.4%). The average age was 23.4 ± 2.9 years with 64.7% female. The mean wake-up time and bedtime were 09:16 ± 01:26 and 01:51 ± 01:39 with average sleep duration of 7.4 ± 1.4 h. Body weight and fat mass, excluding muscle mass, were significantly reduced over 4 weeks compared to baseline only in the early TRE group starting before noon. The early TRE group also showed significantly reduced fasting glucose, fasting insulin, and serum triglyceride (TG) levels after 4 weeks. However, the late TRE group starting after noon showed no significant changes except a reduced TG level. The meal timing was changed by TRE, where the first meal was delayed and the last meal was shifted. Neither sleep duration nor timing was significantly changed by TRE. Energy intakes were not different, but protein intake increased from 19.2% to 22.6% due to snack packages during intervention. However, no significant correlation between nutrient intakes and body composition changes was found. There were no adverse events related to study participation.
Conclusions
An early TRE regimen may be a feasible and effective strategy to manage body composition and cardiometabolic risk factors in young adults without altering the sleep-wake cycle.
Keywords: Body composition, Cardiometabolic risk, Diet, Obesity management, Weight loss
Introduction
Time-restricted eating (TRE) is a type of intermittent fasting that has gained popularity as a simple dietary strategy for weight loss [1, 2]. TRE involves confining eating to a specific number of hours per day, without the need to count calories or monitor food intake, which leads to an extension of the overnight fasting period by 14–20 h [3, 4]. While animal trials have already provided compelling evidence of the effectiveness of TRE [5, 6], there is emerging evidence in humans that intermittent fasting and TRE can yield mild to moderate weight loss and also benefit cardiometabolic health by improving glucose or lipid metabolism, although the findings are variable [2, 7].
The key putative mechanism underlying TRE is optimization of the circadian rhythm. Misalignment between food intake and the circadian rhythm can contribute to disruption of the latter, which results in cardiometabolic disorders [4]. Late-night eating and high-calorie evening meals are associated with cardiometabolic risk factors, while eating more calories in the morning was reported to be associated with higher insulin sensitivity [8, 9]. As TRE shortens the eating period, it influences late-night eating and energy intake in the evening.
Early TRE (eTRE) is regarded as optimal to maximize metabolic benefits. Several studies reported that eTRE (eating window starts as early as 08:00) improved insulin sensitivity, lipid metabolism, and circadian gene expression [10, 11, 12]. However, this schedule is socially challenging for youth and young adults. In modern society, young adults commonly experience circadian changes (i.e., a shift from morning to evening) due to exposure to artificial light at night as well as continuous access to food throughout the day. More screen time is associated with poorer sleep outcomes and delayed sleep-wake behavior is commonly reported among youth and adolescents worldwide [13]. According to a large-scale survey, younger adults often have an evening-dominant energy intake pattern [14].
Several studies have reported that people with an evening chronotype have more eating occasions and poorer eating habits, where these factors are associated with metabolic disorders due to their effects on insulin sensitivity and satiety hormone levels [14, 15, 16]. With regard to body weight, young adolescents and college freshmen with evening chronotypes showed higher body mass index (BMI) than those with morning chronotypes [17, 18].
In weight loss interventions, it is important to improve body composition that specifically target a decrease in fat mass while maintaining lean body mass, due to the loss of lean body mass having negative health consequences that include lowered resting energy expenditure or an increased risk of sarcopenia [19, 20]. TRE has been reported to reduce body weight by primarily decreasing fat mass [21, 22], but some studies have also reported a significant reduction in lean body mass [3, 23]. High-protein diets have been suggested as a strategy to preserve lean body mass during a period of weight loss [19]. A 12-week intervention of intermittent fasting, resistance training, and a prescribed daily protein intake of more than 1.4 g per kg body weight has been shown to significantly reduce body weight and fat mass while also increasing lean body mass [24].
In this context, it is necessary to elucidate the impact of TRE in young adults with evening-dominant lifestyles, along with supplementation of high-protein snacks. As meal timing and sleep are key cues for circadian alignment, this study was aimed to explore the effects of TRE on body composition, cardiovascular risk factors, and changes in sleep and meal timing in healthy young adults with a delayed sleep cycle.
Materials and Methods
Study Design and Participants
We performed a 4-week single-arm intervention study with 8-h TRE. In the pre-intervention period, volunteers were instructed to complete 3-day dietary records. All participants underwent a baseline assessment, which included general questionnaires, body measurements, and blood collection. Participants were asked to select the eating window that best fit their lifestyle and were required to remain in their assigned eating schedule group throughout the intervention. Although calories were not counted, weekly snack packages that included high-protein foods such as high-protein bars (for a daily protein intake of 20 g) were provided to supplement their diets and minimize muscle loss. During the intervention, a daily dietary record was completed (28 days in total; sleep and meal times were also recorded). Body composition was measured weekly, and blood collection was performed again after 4 weeks (shown in Fig. 1).
Fig. 1.
Study design of 4-week intervention with 8-h time-restricted eating.
The participants were recruited between July 2021 and August 2021 through an online network and notices placed around the campus of the Catholic University of Korea. The inclusion criteria were as follows: healthy young adults aged 18–39 years; no metabolic or other diseases, such as depression; no food allergies, including soybeans and nuts; and <10% weight change in the previous month. Forty-one participants were recruited, but 2 with scheduling conflicts, 2 receiving treatment for other diseases, and 1 above the age cutoff were excluded. After screening, 36 participants began a 4-week TRE intervention; 2 dropped out of the study due to schedule conflicts or loss of contact. Therefore, 34 participants finally completed the intervention (shown in online suppl. Fig. 1; see www.karger.com/doi/10.1159/000527838 for all online suppl. material).
All participants received detailed information about the purpose and process of the study. They provided written informed consent and received a stipend for their participation. All procedures were approved by the Institutional Review Board of the Catholic University of Korea (No. 1040395-202106-05), and the study was registered with the Clinical Research Information Service (KCT0006443), which is an online registration system for clinical trials and clinical research conducted in Korea, and on the WHO International Clinical Trial Registry Platform.
Early and Late TRE Group
To ensure compliance, the eating window was chosen by participants at baseline, and the starting time of the eating window varied by each participant (shown in online suppl. Fig. 2). The majority of participants chose to start eating before noon, but 38.2% (13 participants) chose to start eating from noon onward. According to the eating window, participants were divided into two groups: eTRE and late TRE (lTRE). There was no difference in age and sex distribution by groups (Table 1).
Table 1.
General characteristics of the participants at baseline
| Total (n = 34) | eTRE (n = 21) | /TRE (n = 13) | p value1 | |
|---|---|---|---|---|
| Eating window, n (%) (starting time) | ||||
| <10:00 | 4 (11.8) | 4 (19.1) | ||
| 10:00–11:00 | 8 (23.5) | 8 (38.1) | ||
| 11:00–12:00 | 9 (26.5) | 9 (42.9) | ||
| 12:00–13:00 | 9 (26.5) | 9 (69.2) | ||
| ≤13:00 | 4 (11.8) | 4 (30.8) | ||
| Age, years | 23.4±2.9 | 23.7±3.4 | 22.9±1.9 | 0.6321 |
| Female sex, n (%) | 22 (64.7) | 13 (61.9) | 9 (69.2) | 0.6640 |
| BMI, kg/m2 | 23.6±4.3 | 23.5±3.8 | 23.7±5.1 | 0.7363 |
| Sleep duration, h | 7.4±1.4 | 7.4±1.1 | 7.4±1.9 | 1.0000 |
| Wake-up time, hh:mm | 09:16±01:26 | 09:00±01:22 | 09:42±01:30 | 0.2214 |
| Bedtime, hh:mm | 01:51±01:39 | 01:34±01:33 | 02:19±01:46 | 0.3038 |
| Habitual sleep efficiency, n (%) | ||||
| >85% | 22 (64.7) | 16 (76.2) | 6 (46.2) | 0.1902 |
| 75–84% | 8 (23.5) | 3 (14.3) | 5 (38.5) | |
| 65–74% | 3 (8.8) | 2 (9.5) | 1 (7.7) | |
| <65% | 1 (2.9) | 0 (0.0) | 1 (7.7) | |
| Subjective sleep quality, n (%) | ||||
| Very good | 5 (14.7) | 5 (23.8) | 0 (0.0) | 0.1131 |
| Good | 20 (58.8) | 10 (47.6) | 10 (76.9) | |
| Poor | 9 (26.5) | 6 (28.6) | 3 (23.1) | |
| Sleep quality2, n (%) | ||||
| Optimal (1–5) | 10 (29.4) | 8 (38.1) | 2 (15.4) | 0.3686 |
| Borderline (6–7) | 11 (32.4) | 6 (28.6) | 5 (38.5) | |
| Poor (>8) | 13 (38.2) | 7 (33.3) | 6 (46.2) | |
| Alcohol consumption, n (%) | ||||
| Yes | 23 (67.7) | 10 (47.6) | 13 (100.0) | 0.0015 |
| No | 11 (32.4) | 11 (52.4) | 0 (0.0) | |
| Current smoker, n (%) | ||||
| Yes | 7 (20.6) | 4 (19.1) | 3 (23.1) | 0.7777 |
| No | 27 (79.4) | 17 (81.0) | 10 (76.9) | |
| Physical activity, n (%) | ||||
| High | 9 (26.5) | 5 (23.8) | 4 (30.8) | 0.7427 |
| Moderate | 21 (61.8) | 14 (66.7) | 7 (53.9) | |
| Low | 4 (11.8) | 2 (9.5) | 2 (15.4) |
All values are presented as mean ± SD or n (%). BMI, body mass index; eTRE, early TRE (starting before noon); /TRE, late TRE (starting after noon); SD, standard deviation. 1 p values for group comparisons by the χ2 test for categorical variables and the Wilcoxon rank sum test for continuous variables. 2 Sleep quality was assessed by the Pittsburgh Sleep Quality Index.
Body Measurements
Weight, body composition, and waist circumference were measured weekly in a fasting state after 8 h of fasting. Height was assessed only at baseline using an extensometer (DS-102; Dong Sahn Jenix Co., Ltd., Seoul, South Korea). Weight and body composition were measured by a bioelectrical impedance method (InBody 370S; Inbody Co., Seoul, South Korea), while the subjects wore light clothes and no shoes. BMI was calculated as weight/height2 (kg/m2), and muscle mass (kg), body fat mass (kg), and percent body fat (%) were determined. Waist circumference was measured to the nearest 0.1 cm using a flexible anthropometric tape in a horizontal plane around the highest point of the hip bone.
Cardiometabolic Risk Factors
Samples of whole blood were collected into 5-mL tubes at baseline and after the intervention, at the Catholic University of Korea Bucheon St. Mary's Hospital. About 30 min after collection, the blood samples were centrifuged, and the SST tubes were transferred to the Global Clinical Central Lab (GC Labs, Gyeonggi-do, South Korea) for analysis. Laboratory analysis included fasting glucose, insulin, total cholesterol (TC), high-density lipoprotein cholesterol (HDL cholesterol), low-density lipoprotein cholesterol (LDL cholesterol), and triglycerides (TG). Fasting glucose was analyzed by UV spectrophotometry, insulin was analyzed by electrochemiluminescence immunoassay, and serum lipids (TC, HDL cholesterol, LDL cholesterol, and TG) were analyzed by colorimetry (Cobas 8,000; Roche, Mannheim, Germany). Insulin resistance was evaluated using the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR), calculated using the formula: fasting glucose (mg/dL) × fasting insulin (μU/mL)/405 [25]. Blood pressure was measured three times at 1-min intervals using an automatic electronic blood pressure monitor (HEM-7156; Omron Healthcare Co., Ltd., Kyoto, Japan), and the average values were used for the analysis.
Dietary Assessment and Meal Timing
To compare dietary intake before and during the intervention, participants were asked to record their daily diet on 3 nonconsecutive days (2 weekdays and 1 weekend day) in the week prior to the intervention. The daily dietary records were collected via a mobile messaging app and reviewed by staff for accuracy and completeness. Participants were instructed to indicate the name of each food or drink consumed and the quantity and preparation process, if possible. Meals were classified as breakfast, lunch, or dinner, and snacks between meals were also reported. Daily dietary records were accompanied by a time log of all meals.
“Meal timing” was computed based on the starting time of the first and last meals. The time of the first snack of the day was also recorded. Eating occasions were counted, including snacks. “Meal proportion” refers to the percentage of the daily total energy intake of each meal. All dietary data were entered and nutrient intakes were analyzed using the Diet Evaluation System program developed by the Human Nutrition Research Institute of Seoul National University.
Sleep and Circadian Variables
Sleep data were recorded in the daily time log. Sleep duration was calculated using the bedtime and wake-up time. For investigation of chronotype, mid-sleep was computed as the average midpoint between wake-up and bedtime on workdays and free days. Chronotype refers to sleep phase rather than duration [26].
Sleep quality was assessed by a self-rated questionnaire of the Pittsburgh Sleep Quality Index (PSQI) at baseline [27, 28]. Among the seven PSQI factors, the following six were recorded for the following factors (the factor pertaining to medications for depression was omitted because such individuals were excluded during screening): habitual sleep efficiency, sleep latency, sleep duration, sleep disturbance, subjective sleep quality, and daytime dysfunction. In scoring the PSQI, overall sleep quality was calculated by summing all factors [28] and classified as optimal (1–5), borderline (6–7), or poor (≥8) [27, 29].
Other Covariates
Participants were asked to complete a questionnaire on general characteristics, including lifestyle factors. Participants who indicated that they smoked everyday were classified as current smokers, and those who consumed alcohol more than once a month were classified as drinkers. Physical activity was categorized as low, moderate, or high intensity based on the International Physical Activity Questionnaire (IPAQ) [30]. MET-min/week was calculated according to the formula: MET-min/week = MET level × min of activity × events per week (walking, 3.3 METs; moderate activity, 4.0 METs; intense activity, 8.0 METs). High intensity was defined as at least 1,500 MET-min/week of exercise with at least 3 days of intense physical activity per week or at least 3,000 MET-min/week of exercise in any combination of intensity for more than 7 days a week. Moderate intensity was defined as walking on more than 5 days a week or at least 600 MET-min/week of exercise (any intensity). Low intensity was defined as no high- or moderate-intensity exercise.
Statistical Analysis
The basic characteristics are presented as a number (n) and percentage (%); continuous variables are presented as mean ± standard deviation. The χ2 test was used for analysis of categorical variables, and the Wilcoxon rank sum test was used to compare continuous variables between groups. Within individuals, pre-intervention and intervention period data were compared using the paired t test. The Wilcoxon signed rank test was used by groups. Spearman's partial correlation analysis was performed with adjustments for age, sex, alcohol consumption, smoking status, and physical activity. All data were analyzed using SAS 9.4 software (SAS Institute Inc., Cary, NC, USA). In all analyses, p < 0.05 was taken to indicate statistical significance.
Results
The general characteristics of the 34 participants are presented in Table 1. The mean age was 23.4 ± 2.9 years, the proportion of women was 64.7%, and the mean BMI was 23.6 ± 4.3 kg/m2. The average wake-up and bedtimes at baseline were 09:16 ± 01:26 and 01:51 ± 01:39 (hh:mm), respectively. The mean sleep duration was 7.3 ± 1.5 h. Although 73.5% of the participants reported good or very good subjective sleep quality, 38.2% had poor overall sleep quality in the Pittsburgh Sleep Quality Index. Other components of sleep quality were presented in online supplementary Table 1. With regard to lifestyle characteristics, 67.7% of participants reported drinking alcohol, 20.6% were current smokers, and 26.5% had high levels of physical activity.
The eTRE group starting the eating window before 12:00 showed no differences in age, BMI, sleeping time, or all components of the sleep quality index with lTRE groups. However, all subjects in the lTRE group reported drinking alcohol, compared to only 47.6% of subjects in the eTRE group.
Effects of TRE on Body Composition and Cardiometabolic Parameters
Pre- and post-intervention body composition and cardiometabolic parameters are compared in Table 2. With the exception of muscle mass, the post-intervention body composition measures were significantly decreased compared to the pre-intervention values only in the eTRE group.
Table 2.
Body composition and cardiometabolic parameters in the pre- and post-intervention periods: results of the 4-week, 8-h TRE intervention for healthy young adults
| Total (n = 34) |
p value1 |
eTRE (n = 21) |
p value2 | /TRE (n = 13) |
p value2 | ||||
|---|---|---|---|---|---|---|---|---|---|
| pre-intervention | post-intervention | pre-intervention | post-intervention | pre-intervention | post-intervention | ||||
| Body composition | |||||||||
| Body weight, kg | 67.2±18.3 | 66.1±17.9 | 0.0003 | 66.9±15.5 | 65.5±14.8 | <0.0001 | 67.7±22.8 | 67.1 ±22.7 | 0.2109 |
| BMI, kg/m2 | 23.6±4.3 | 23.2±4.1 | 0.0005 | 23.5±3.8 | 23.0±3.5 | 0.0004 | 23.7±5.1 | 23.5±5.1 | 0.3008 |
| Waist circumference, cm | 76.7±13.0 | 75.1±11.9 | 0.0007 | 76.2±10.7 | 74.4±9.4 | 0.0043 | 77.6±16.6 | 76.1±15.5 | 0.0479 |
| Muscle mass, kg | 26.3±8.2 | 26.1±8.1 | 0.0716 | 26.6±7.8 | 26.4±7.7 | 0.0844 | 25.8±9.1 | 25.7±9.1 | 0.5757 |
| Fat mass, kg | 19.5±7.4 | 18.8±7.3 | 0.0001 | 18.7±6.3 | 17.8±6.0 | 0.0002 | 20.9±9.0 | 20.4±9.0 | 0.1421 |
| Body fat percentage, % | 29.0±6.8 | 28.4±7.1 | 0.0062 | 28.1±7.3 | 27.4±7.5 | 0.0132 | 30.3±6.0 | 30.0±6.2 | 0.2095 |
| Cardiometabolic parameters | |||||||||
| Insulin, µU/mL | 10.8±5.2 | 10.6±12.0 | 0.9115 | 10.9±5.5 | 8.6±4.7 | 0.0286 | 10.6±4.7 | 13.9±18.4 | 0.4043 |
| Fasting glucose, mg/dL | 90.0±8.1 | 87.1 ±7.1 | 0.0414 | 89.8±9.4 | 86.1±6.5 | 0.0131 | 90.3±5.7 | 88.7±7.9 | 0.6069 |
| HOMA-IR | 2.4±1.3 | 2.4±3.2 | 0.9197 | 2.5±1.4 | 1.8±1.1 | 0.0103 | 2.4±1.2 | 3.3±5.0 | 0.3757 |
| TC, mg/dL | 188.1±23.1 | 185.0±24.9 | 0.2287 | 184.4±22.9 | 181.1±23.4 | 0.1693 | 193.9±23.0 | 191.2±27.0 | 0.5974 |
| LDL cholesterol, mg/dL | 104.2±20.3 | 109.5±23.3 | 0.0282 | 101.3±18.6 | 107.3±22.0 | 0.0219 | 108.8±22.7 | 113.1±25.6 | 0.5557 |
| TG, mg/dL | 100.6±65.2 | 79.3±46.2 | 0.0223 | 98.4±79.4 | 74.8±44.3 | 0.0243 | 104.2±34.0 | 86.5±50.2 | 0.0203 |
| HDL cholesterol, mg/dL | 63.2±15.9 | 59.3±13.7 | 0.0014 | 63.1±16.9 | 58.9±13.7 | 0.0123 | 63.3±14.6 | 59.9±14.3 | 0.0706 |
| Systolic blood pressure, mm Hg/dL | 105.5±12.0 | 103.6±10.2 | 0.1555 | 104.0±11.1 | 102.4±9.9 | 0.4684 | 107.9±13.4 | 105.4±10.9 | 0.4143 |
| Diastolic blood pressure, mm Hg/dL | 72.9±6.9 | 71.9±5.5 | 0.2810 | 71.2±5.8 | 70.8±5.3 | 0.9733 | 75.7±7.7 | 73.6±5.5 | 0.1272 |
All values are presented as mean ± SD or n (%). eTRE, early TRE (starting before noon); HDL, high-density lipoprotein; HOMA-IR, Homeostasis Model Assessment of Insulin Resistance; /TRE, late TRE (starting after noon); LDL, low-density lipoprotein; SD, standard deviation; TG, triglyceride.1 p values between pre- and post-intervention periods using the paired t test. 2 p values between pre- and post-intervention periods using the Wilcoxon signed rank test.
With regard to cardiometabolic parameters, the post-intervention fasting glucose, serum TG, and HDL cholesterol levels were significantly decreased, while LDL cholesterol was significantly increased compared to the pre-intervention levels. The eTRE group showed significant improvements in glucose and insulin parameters; the lTRE group showed no such improvements. Serum TG levels were significantly decreased in both groups, but only the eTRE group showed increased post-intervention LDL cholesterol levels and decreased HDL cholesterol levels. There were no differences in systolic or diastolic blood pressure pre-versus post-intervention or between eating window groups.
As shown in Figure 2, the eTRE and lTRE groups showed different weekly changes in body composition measures during the 4-week intervention. In the eTRE group, body weight showed gradual and significant decreases of −0.9, −1.0, −1.3, and −1.4 kg in weeks 1–4, respectively, compared to the baseline. Similar to body weight change, fat mass also gradually and significantly decreased by −0.7 and −0.9 kg, respectively, in weeks 3 and 4. On the other hand, muscle mass decreased slightly by −0.4 kg in the first 2 weeks but increased in the last 2 weeks such that it was 0.3 kg lower than at baseline. The lTRE group showed no significant changes in any measures of body composition by week.
Fig. 2.
Changes in body composition by week during the 4-week, 8-h TRE intervention. *p < 0.05 was from the Wilcoxon signed rank test for individual changes between each week and baseline.
Changes in Sleep and Meal Variables
Changes in sleep and meal timing are presented in Table 3. The sleep duration was not significantly different between the pre-intervention and intervention periods (7.4 ± 1.4 and 7.5 ± 0.8 h, respectively). The wake-up time was 09:16 ± 01:26 (hh:mm) at baseline and 09:13 ± 01:06 (hh:mm) during the intervention, which was not significantly different. Bedtime was 01:51 ± 01:39 (hh:mm) at baseline and shifted to a slightly earlier time of 01:40 ± 01:09 (hh:mm) during the intervention; the difference was not significant. According to the eating window groups, there was no change in sleep duration or sleep timing before intervention and during intervention. Thus, the 4-week TRE intervention had no effect on sleep duration or sleep cycle.
Table 3.
The change of sleep and meal timing related variables via 4 weeks intervention with 8-h TRE in healthy young adults
| Total subjects (n = 34) |
p value1 |
eTRE (n = 21) |
p value2 | /TRE (n = 13) |
p value2 | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| pre-intervention | during intervention | pre-intervention | during intervention | pre-intervention | during intervention | ||||||
| Sleep duration, h | 7.4±1.4 | 7.5±0.8 | 0.5733 | 7.4±1.1 | 7.4±0.7 | 0.9333 | 7.4±1.9 | 7.8±0.9 | 0.5417 | ||
| Sleep timing, hh:mm | |||||||||||
| Wake-up | 09:16±01:26 | 09:13±01:06 | 0.7831 | 09:00±01:22 | 08:50±00:59 | 0.3698 | 09:42±01:30 | 09:50±01:02 | 0.9460 | ||
| Bedtime | 01:51±01:39 | 01:40±01:09 | 0.2558 | 01:34±01:33 | 01:26±01:07 | 0.4274 | 02:19±01:46 | 02:04±01:10 | 0.5879 | ||
| Mid-sleep of free days | 05:53±01:41 | 05:41±01:09 | 0.2692 | 05:33±01:49 | 05:22±01:07 | 0.7246 | 06:27±01:19 | 06:11±01:04 | 0.5417 | ||
| Mid-sleep of workdays | 05:24±01:23 | 05:21±01:05 | 0.7301 | 05:09±01:16 | 05:02±01:01 | 0.4477 | 05:47±01:33 | 05:51±01:03 | 0.8394 | ||
| Meal timing, hh:mm | |||||||||||
| First meal | 10:29±02:24 | 11:51±01:21 | 0.0013 | 10:02±02:17 | 11:08±01:09 | 0.0258 | 11:13±02:31 | 13:01±00:42 | 0.0105 | ||
| Last meal | 18:30±01:32 | 17:43±01:11 | 0.0120 | 18:12±01:28 | 17:03±00:57 | 0.0025 | 18:58±01:35 | 18:47±00:35 | 0.4143 | ||
| Eating occasion, n per day | 3.4±0.9 | 3.3±0.6 | 0.5035 | 3.5±1.0 | 3.3±0.6 | 0.7500 | 3.2±0.7 | 3.2±0.5 | 0.8926 | ||
| Meal | 2.2±0.4 | 2.1±0.3 | 0.1333 | 2.3±0.4 | 2.2±0.4 | 0.0154 | 2.0±0.4 | 2.0±0.1 | 0.8350 | ||
| Snack | 1.2±0.7 | 1.2±0.6 | 0.9917 | 1.2±0.8 | 1.1±0.6 | 0.9866 | 1.2±0.6 | 1.3±0.4 | 1.0000 | ||
| Meal proportion, % E | |||||||||||
| Breakfast | 16.3±12.9 | 12.8±12.2 | 0.1828 | 17.6±13.8 | 14.6±14.5 | 0.2774 | 14.3±11.5 | 0.2±0.4 | 0.0029 | ||
| Lunch | 30.8±10.3 | 35.0±7.6 | 0.0447 | 32.8±11.0 | 36.7±13.3 | 0.8538 | 27.5±8.6 | 41.6±5.0 | 0.0017 | ||
| Dinner | 37.3±12.8 | 33.7±7.6 | 0.1013 | 35.6±11.9 | 34.2±10.4 | 0.0258 | 40.2±14.0 | 40.9±4.1 | 0.8394 | ||
| Snack | 15.6±11.4 | 18.5±6.4 | 0.1119 | 14.1±11.8 | 14.6±7.7 | 0.0995 | 18.0±10.6 | 17.3±5.9 | 0.3396 | ||
Values are presented as mean±SD. SD, standard deviation; eTRE, early TRE (starting before noon); /TRE late TRE (starting after noon).1 Individual difference was tested between pre and during intervention using the paired t-test in total subjects.2 Individual difference was tested between pre and during intervention using the Wilcoxon signed rank test in each group.
On the other hand, the meal timings differed significantly between the pre-intervention and intervention periods. The first meal started at 10:29 ± 02:24 (hh:mm) at baseline and was significantly delayed to 11:51 ± 01:21 (hh:mm) during the intervention. The last meal started at 18:30 ± 01:32 (hh:mm) in the pre-intervention period and was significantly earlier at 17:43 ± 01:11 (hh:mm) during the intervention. These changes were more evident in the eTRE than the lTRE group. The eTRE group showed significantly shifted times in the first and last meals, whereas only the first meal timing was significantly delayed in the lTRE group.
In contrast to meal timings, TRE had no significant effect on the average number of eating occasions (3.4 and 3.3 per day in the pre-intervention and intervention periods, respectively). There was also no difference in the number of eating occasions in eating window groups. TRE altered meal proportions. While the proportion of energy intake at breakfast had a tendency to reduce from 16.3 ± 12.9% in the pre-intervention period to 12.8 ± 12.2% during the intervention, the proportion of energy intake from lunch increased significantly from 30.8 ± 10.3% in the pre-intervention period to 35.0 ± 7.6% during the intervention. The proportions of energy from snacks and dinner were not different between the pre-intervention and intervention periods. The proportion of energy was not much differed by meal in the eTRE group, except for a slight reduction from dinner. However, the lTRE group showed the proportion of energy significantly reduced from breakfast and increased from lunch.
Changes in Nutrient Intake
As shown in Table 4, there were no significant differences in energy intake between the pre-intervention and intervention periods. Carbohydrate and fat intakes were not different between the pre-intervention and intervention periods, but the proportion of energy from carbohydrates was 50.0% ± 7.4% in the pre-intervention period and decreased significantly to 44.8% ± 2.9% during the intervention. Protein accounted for 19.2% ± 4.5% of energy at baseline; this increased significantly to 22.6% ± 2.9% during the intervention due to the provision of the snack packages. Dietary sugar and saturated fat intakes were not different between baseline and during the intervention. These changes in nutrient intake between pre- and during intervention were similar in both the early and late TRE groups.
Table 4.
Changes in nutrient intake by 4-week intervention with 8-h TRE in healthy young adults
| Total subjects (n = 34) |
p value1 |
eTRE (n = 21) |
p value2 | /TRE (n = 13) |
p value2 | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| pre-intervention | during intervention | pre-intervention | during intervention | pre-intervention | during intervention | ||||||
| Energy, kcal | 1,655.2±485.5 | 1,645.0±321.8 | 0.8932 | 1,630.1±503.6 | 1,570.1±314.9 | 0.6995 | 1,695.7±471.8 | 1,766.0±306.4 | 0.4973 | ||
| Carbohydrate, g | 198.4±58.9 | 179.7±35.7 | 0.0540 | 195.3±64.9 | 172.0±36.3 | 0.1074 | 203.3±49.7 | 192.2±32.0 | 0.4548 | ||
| Dietary sugar, g | 43.8±24.4 | 40.7±13.3 | 0.3116 | 40.8±25.2 | 38.9±13.1 | 0.8538 | 48.6±23.3 | 43.7±13.5 | 0.5417 | ||
| Protein, g | 75.7±25.3 | 90.0±18.0 | 0.0011 | 75.9±24.8 | 87.8±19.5 | 0.0258 | 75.3±27.1 | 93.7±15.3 | 0.0266 | ||
| Protein, g/kg | 1.1±0.3 | 1.4±0.3 | 0.0002 | 1.1±0.3 | 1.4±0.2 | 0.0103 | 1.2±0.4 | 1.5±0.3 | 0.0215 | ||
| Fat, g | 56.4±25.3 | 58.2±12.6 | 0.6700 | 55.9±24.9 | 56.8±12.6 | 0.8275 | 57.0±27.0 | 60.4±12.7 | 0.4143 | ||
| Saturated fat, g | 19.6±10.0 | 20.2±4.9 | 0.6936 | 18.9±10.1 | 19.5±5.0 | 0.556 | 20.6±10.1 | 21.4±4.7 | 0.4973 | ||
| Energy proportion (%) | |||||||||||
| Energy for EER | 75.2±20.7 | 74.7±12.3 | 0.8845 | 73.4±21.9 | 70.8±12.6 | 0.7500 | 78.0±19.2 | 80.9±9.1 | 0.4973 | ||
| Carbohydrate | 50.0±7.4 | 44.8±2.9 | 0.0003 | 49.4±7.7 | 44.4±3.2 | 0.0053 | 51.1±7.1 | 45.6±2.5 | 0.0327 | ||
| Dietary sugar | 10.8±4.9 | 10.3±3.2 | 0.3999 | 10.1±5.2 | 10.1±3.3 | 0.5789 | 11.9±4.3 | 10.5±3.2 | 0.1909 | ||
| Protein | 19.2±4.5 | 22.6±2.9 | <0.0001 | 19.6±4.4 | 22.7±2.8 | 0.0018 | 18.6±4.7 | 22.4±3.0 | 0.0105 | ||
| Fat | 30.7±6.6 | 32.6±2.9 | 0.1284 | 31.0±5.4 | 32.9±3.2 | 0.1434 | 30.2±8.3 | 32.0±2.4 | 0.2734 | ||
| Saturated fat | 10.5±2.9 | 11.3±1.6 | 0.1314 | 10.3±2.5 | 11.3±1.7 | 0.2123 | 11.0±3.6 | 11.4±1.3 | 0.5879 | ||
Values are presented as mean±SD. EER, estimated energy requirement; eTRE, early TRE (starting before noon); /TRE late TRE (starting after noon).1 Individual difference was tested between pre and during intervention using the paired t test in total subjects.2 Individual difference was tested between pre and during intervention using the Wilcoxon signed rank test in each group.
Correlations of Meal and Sleep Variables with Changes in Body Composition
Table 5 shows the correlations of sleep and meal variables with the changes in body composition over the 4-week intervention period. After adjusting for potential confounders, sleep-related variables including sleep quality using PSQI showed no relations with changes in body weight or muscle mass. However, the fat mass change was significantly positively related with bedtime and mid-sleep. Meal timings were not correlated with changes in any body composition measures. The average intakes of all nutrients, including protein, during the intervention were not correlated with any of the body composition measures.
Table 5.
Correlations of sleep and meal timing variables during the intervention period with changes in body composition over 4 weeks
| Body composition change, week 4-week 0 |
||||
|---|---|---|---|---|
| body weight, kg | fat mass, kg | muscle mass, kg | BMI, kg/m2 | |
| Sleep variables | ||||
| Sleep duration, h | -0.131 | -0.195 | -0.209 | -0.194 |
| Wake-up time, hh:mm | -0.034 | 0.314 | -0.292 | -0.075 |
| Bedtime, hh:mm | 0.072 | 0.379* | -0.111 | 0.096 |
| Mid-sleep of free day | -0.035 | 0.212 | -0.156 | -0.014 |
| Mid-sleep of weekday | 0.012 | 0.371* | -0.246 | 0.012 |
| Sleep quality (PSQI) | 0.112 | 0.354 | -0.054 | 0.199 |
| Meal timing variables | ||||
| First meal, hh:mm | 0.274 | 0.140 | 0.098 | 0.330 |
| Last meal, hh:mm | 0.229 | 0.013 | 0.110 | 0.293 |
| Nutrient intake | ||||
| Energy, kcal | 0.307 | 0.255 | 0.300 | 0.211 |
| Carbohydrate, % | 0.124 | 0.305 | -0.015 | 0.114 |
| Dietary sugar, % | -0.310 | -0.053 | -0.152 | -0.293 |
| Protein, % | -0.256 | -0.198 | -0.228 | -0.338 |
| Protein, g | 0.044 | 0.060 | 0.056 | -0.116 |
| Fat, % | 0.171 | -0.058 | 0.292 | 0.235 |
| Saturated fat, % | 0.166 | 0.008 | 0.277 | 0.275 |
Partial correlations were adjusted for age, sex, alcohol drinking, smoking, and physical activity. Values are Spearman's correlation coefficients. * p < 0.05. PSQI, Pittsburgh Sleep Quality Index.
Discussion
This study was performed to examine the effects of 4 weeks of TRE on body composition and cardiometabolic risk factors in healthy young adults with a delayed sleep-wake cycle. The results showed that 8-h TRE produced significant weight loss through a reduction of fat mass without any reduction of muscle mass, as well as an improved glycemic response and reduced blood TG levels. However, these benefits were evident only in those whose eating window started before noon.
Our participants were mostly college students with considerably delayed bedtimes (01:40 ± 01:09 (hh:mm) during the intervention). This tendency to delay bedtimes and extend rise times has been reported previously in large-scale surveys of adolescents [31] and college students [29]. The late chronotype can be determined based on mid-sleep on free days later than 05:30 (hh:mm) [32]; the average mid-sleep time on free days was 05:23 in the eTRE group and 06:11 in the lTRE group, indicating that most participants had a late chronotype. The late chronotype is associated with increased BMI, poorer dietary behaviors, and a greater desire for high-fat foods [17, 18]. We also found that the fat mass change over 4 weeks had a significant positive relationship with bedtime. Participants with later bedtimes were statistically more likely to have less fat loss compared to participants with earlier bedtimes. As bedtime was measured by a 28-day daily log, it reflects the usual sleep time of the participants.
Interestingly, although sleep is considered a potential mediator of the relationship between TRE and cardiometabolic risk factors, we found that TRE did not change the sleep cycle. This was consistent with a previous study showing that 12 weeks of TRE did not alter sleep quality or duration in subjects with obesity [33]. It indicates that delayed bedtime would be a potential mediator rather than sleep quality or duration in young adults.
The primary findings of this study were that 8-h TRE with an eating window starting before noon was effective for managing body weight, blood glucose, and lipid profiles without altering the sleep cycle, as well as for restricting energy intake. eTRE is considered to lead to better health outcomes, but limited data are available and the findings reported to date have been inconclusive. A study comparing eTRE (from 08:00 to 14:00) with a control schedule (eating from 08:00 to 20:00) showed that the former regimen improved insulin sensitivity, blood pressure, and oxidative stress [10]. Another study comparing eTRE (08:00–17:00) with delayed TRE (12:00–21:00) showed that both regimens improved glucose tolerance [34]. However, both studies were performed in middle-aged adults with overweight or prediabetes, and the eating window started at 08:00, which is not practical for young adults with a delayed sleep-wake cycle.
Two other studies compared the effects of TRE with different timings. The first study showed no significant differences in obesity-related outcomes between early (before 09:47), intermediate (09:47–10:50), and late (after 10:50) eating windows [35], while the second showed greater reductions of body mass and fat mass in the eTRE (06:00–15:00) than the midday TRE (11:00–20:00) and control groups (ad libitum) [22].
Compared to previous studies, the eating window of the eTRE group in this study was not predetermined. Almost 80% of participants had an eating window start time between 10:00 and noon, with a wide range of start times (08:00 to 12:00). The observed benefits seen despite this wide range have been because the self-selected window led to high adherence and refraining from late-night eating (after 20:00) has greater benefit for managing cardiometabolic risk factors than beginning to eat as early as 08:00. Late-night eating and large evening meals are associated with undesirable health outcomes, such as a higher risk of metabolic syndrome [36] and higher diastolic blood pressure [8]. In addition, a large-scale study revealed that hyperglycemia was associated with late-night eating alone but not breakfast skipping alone [37]. Taken together, the present study suggests that, for young adults with a delayed sleep cycle, refraining from late-night eating may be a more effective way to manage cardiometabolic risk factors than having breakfast as early as 08:00. Further studies are needed to determine the optimal eating window to maximize the effects of TRE; the sleep-wake cycle should also be taken into consideration.
Our eTRE group showed significantly reduced levels of fasting glucose and insulin after the 4-week intervention. This was consistent with a recent systematic review indicating that an eTRE regimen had a beneficial effect on fasting glucose and HOMA-IR [38]. Other reviews also reported the modest desirable changes in fasting glucose, fasting insulin, and HOMA-IR with an eTRE regimen [2, 7].
Compared to glucose metabolism, the findings regarding plasma lipids are highly variable among studies. A significant reduction of serum TG was seen in both our eTRE and lTRE groups, along with increased LDL cholesterol and decreased HDL cholesterol. A study of 12-week TRE in adults with metabolic syndrome showed a trend toward significant reductions of TC and LDL cholesterol [39]. Another study of a 5-week eTRE intervention showed no changes in HDL cholesterol or LDL cholesterol levels but increased TG and TC levels [10]. Glucose metabolism is under strong circadian control and plays an important role in pancreatic beta-cell function [40]. Several rate-limiting steps in lipid metabolism were reported to be related to the circadian system in animal studies, but wide interindividual variability was seen in human studies [41]. Despite inconsistency of previous findings in plasma lipids, we found undesirable changes in LDL cholesterol and HDL cholesterol in the eTRE group. This could be explained by macronutrient change during intervention. Both groups experienced a decrease in carbohydrate and an increase in protein, along with no change in energy intake. It has been consistently reported that low-carbohydrate diets showed a greater reduction in TG but increased LDL cholesterol [42]. However, the change rates in LDL cholesterol and HDL cholesterol compared to baseline were small at 5.5% and −5.3%, respectively. This indicates that dietary composition should be considered to optimize health benefits even if TRE regimen involves no calorie counting.
Although the TRE regimen does not require participants to count calories or restrict any particular foods, we encouraged participants to consume high-protein foods. A previous study reported a significant decrease in body weight, as well as a reduction in lean mass, in a TRE group in a 12-week trial [43], although other studies reported no reductions in fat-free mass [44] or lean mass [45]. According to meta-analyses, high dietary protein intake and protein supplementation were beneficial in terms of body composition and maintenance of lean mass [46, 47]. The snack packages provided in the present study supplied 20 g of protein daily, which significantly increased dietary protein intake from 19.2% ± 4.5% in the pre-intervention period to 22.6% ± 2.9% during the intervention. However, we did not find any significant relations between dietary protein intake and changes in body composition measures.
We also found no difference in energy intake between the pre-intervention and intervention periods. Several TRE studies reported unintentional reductions in energy intake in the shortened eating window [1, 3, 39]. However, the number of eating occasions was not different between our eTRE and lTRE groups; two meals and one snack per day were typical in both groups. According to Dong et al., intermittent fasting and caloric restriction share a number of features, although the former is based on synchronization of physiology with the circadian rhythm (which improves metabolic health) [48].
This study had several limitations. First, this was not a randomized controlled trial. We compared outcomes between eTRE and lTRE groups, but the groups were self-selected. To truly determine the effects of eTRE on body weight and cardiometabolic risk factors, future trials should implement a randomized design. Second, this was a single-arm study without a control group, so we cannot exclude the possibility that the observed changes in body composition were due to some other factor(s). However, the primary outcome was changes in cardiometabolic risk factors after the 4-week intervention, so a pre-post study design was adopted. Finally, the small sample size and relatively short-duration of intervention limit the generalizability of the findings, as well as the ability to detect statistical significance. In particular, the smaller sample size of the lTRE group than that of the eTRE group possibly prevented the results from reaching statistical significance. Further large-scale, long-term studies are needed to confirm our findings.
As well as some limitations, this study also had several strengths. First, daily dietary records with sleep logs were obtained throughout the study period, which allowed for comprehensive evaluation of changes in meal- and sleep-related variables via TRE. In addition, adherence was monitored, and only one participant was lost to follow-up after the TRE intervention began. Second, body composition was measured weekly, which allowed to evaluate trends in body composition over the 4-week intervention.
Conclusion
To our knowledge, this is the first study of TRE in young adults with a delayed sleep-wake cycle. The high level of adherence to TRE and low dropout rate suggest that a self-selected 8-h TRE may be a feasible strategy to manage body weight and cardiometabolic risk factors even for those with a late chronotype without altering the sleep-wake cycle.
Statement of Ethics
This study protocol was reviewed and approved by the Institutional Review Board of the Catholic University of Korea (1040395-202106-05) and approved by the Clinical Research Information Service (KCT0006443), which is an online registration system for clinical trials and clinical research conducted in Korea, and on the WHO International Clinical Trial Registry Platform. All participants received detailed information about the purpose and process of the study. They provided written informed consent and received a stipend for their participation.
Conflict of Interest Statement
The authors have no conflicts of interest to declare.
Funding Sources
This research was supported by the National Research Foundation of Korea (NRF), grant funded by the Korean government (NRF-2020R1F1A1049442), and by grants from the Catholic University of Korea (Research Fund 2022).
Author Contributions
YoonJu Song conceptualized the research, interpreted the results, and supervised all procedures with funding acquisition. JinA Kim carried out the study and analyzed data. All the authors were involved in writing the paper and had given final approval of the submitted and published versions.
Data Availability Statement
All data generated or analyzed during this study are included in this article and its online supplementary material. Further inquiries can be directed to the corresponding author.
Supplementary Material
Supplementary data
Supplementary data
Acknowledgments
The authors deeply thank our study participants.
Funding Statement
This research was supported by the National Research Foundation of Korea (NRF), grant funded by the Korean government (NRF-2020R1F1A1049442), and by grants from the Catholic University of Korea (Research Fund 2022).
References
- 1.Gabel K, Hoddy KK, Haggerty N, Song J, Kroeger CM, Trepanowski JF, et al. Effects of 8-hour time restricted feeding on body weight and metabolic disease risk factors in obese adults: a pilot study. Nutr Healthy Aging. 2018 Jun 15;4((4)):345–353. doi: 10.3233/NHA-170036. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Varady KA, Cienfuegos S, Ezpeleta M, Gabel K. Cardiometabolic benefits of intermittent fasting. Annu Rev Nutr. 2021;41((1)):333–361. doi: 10.1146/annurev-nutr-052020-041327. [DOI] [PubMed] [Google Scholar]
- 3.Chow LS, Manoogian ENC, Alvear A, Fleischer JG, Thor H, Dietsche K, et al. Time-restricted eating effects on body composition and metabolic measures in humans who are overweight: a feasibility study. Obesity. 2020;28((5)):860–869. doi: 10.1002/oby.22756. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Gupta CC, Vincent GE, Coates AM, Khalesi S, Irwin C, Dorrian J, et al. A time to rest, a time to dine: sleep, time-restricted eating, and cardiometabolic health. Nutrients. 2022;14((3)):420. doi: 10.3390/nu14030420. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Chaix A, Zarrinpar A, Miu P, Panda S. Time-restricted feeding is a preventative and therapeutic intervention against diverse nutritional challenges. Cell Metab. 2014 Dec 2;20((6)):991–1005. doi: 10.1016/j.cmet.2014.11.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Chaix A, Lin T, Le HD, Chang MW, Panda S. Time-restricted feeding prevents obesity and metabolic syndrome in mice lacking a circadian clock. Cell Metab. 2019 Feb 5;29((2)):303–19.e4. doi: 10.1016/j.cmet.2018.08.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Świątkiewicz I, Woźniak A, Taub PR. Time-restricted eating and metabolic syndrome: current status and future perspectives. Nutrients. 2021;13((1)):221. doi: 10.3390/nu13010221. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Makarem N, Sears DD, St-Onge MP, Zuraikat FM, Gallo LC, Talavera GA, et al. Habitual nightly fasting duration, eating timing, and eating frequency are associated with cardiometabolic risk in women. Nutrients. 2020;12((10)):3043. doi: 10.3390/nu12103043. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Dashti HS, Gómez-Abellán P, Qian J, Esteban A, Morales E, Scheer FAJL, et al. Late eating is associated with cardiometabolic risk traits, obesogenic behaviors, and impaired weight loss. Am J Clin Nutr. 2021 Oct 6;113((1)):154–161. doi: 10.1093/ajcn/nqaa264. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Sutton EF, Beyl R, Early KS, Cefalu WT, Ravussin E, Peterson CM. Early time-restricted feeding improves insulin sensitivity, blood pressure, and oxidative stress even without weight loss in men with prediabetes. Cell Metab. 2018 Jun 5;27((6)):1212–21.e3. doi: 10.1016/j.cmet.2018.04.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Jamshed H, Beyl R, Della Manna D, Yang E, Ravussin E, Peterson C. Early time-restricted feeding improves 24-hour glucose levels and affects markers of the circadian clock, aging, and autophagy in humans. Nutrients. 2019;11((6)):1234. doi: 10.3390/nu11061234. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Jones R, Pabla P, Mallinson J, Nixon A, Taylor T, Bennett A, et al. Two weeks of early time-restricted feeding (eTRF) improves skeletal muscle insulin and anabolic sensitivity in healthy men. Am J Clin Nutr. 2020;112((4)):1015–1028. doi: 10.1093/ajcn/nqaa192. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Hale L, Guan S. Screen time and sleep among school-aged children and adolescents: a systematic literature review. Sleep Med Rev. 2015;21:50–58. doi: 10.1016/j.smrv.2014.07.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Song X, Wang H, Su C, Wang Z, Huang F, Zhang J, et al. Association of time-of-day energy intake patterns with nutrient intakes, diet quality, and insulin resistance. Nutrients. 2021;13((3)):725. doi: 10.3390/nu13030725. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Maukonen M, Kanerva N, Partonen T, Kronholm E, Tapanainen H, Kontto J, et al. Chronotype differences in timing of energy and macronutrient intakes: a population-based study in adults. Obesity. 2017;25((3)):608–615. doi: 10.1002/oby.21747. [DOI] [PubMed] [Google Scholar]
- 16.Zuraikat FM, St-Onge MP, Makarem N, Boege HL, Xi H, Aggarwal B. Evening chronotype is associated with poorer habitual diet in US women, with dietary energy density mediating a relation of chronotype with cardiovascular health. J Nutr. 2021 May 11;151((5)):1150–1158. doi: 10.1093/jn/nxaa442. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Culnan E, Kloss JD, Grandner M. A prospective study of weight gain associated with chronotype among college freshmen. Chronobiol Int. 2013 Jun;30((5)):682–690. doi: 10.3109/07420528.2013.782311. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Arora T, Taheri S. Associations among late chronotype, body mass index and dietary behaviors in young adolescents. Int J Obes. 2015 Jan;39((1)):39–44. doi: 10.1038/ijo.2014.157. [DOI] [PubMed] [Google Scholar]
- 19.Cava E, Yeat NC, Mittendorfer B. Preserving healthy muscle during weight loss. Adv Nutr. 2017;8((3)):511–519. doi: 10.3945/an.116.014506. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Willoughby D, Hewlings S, Kalman D. Body composition changes in weight loss: strategies and supplementation for maintaining lean body mass, a brief review. Nutrients. 2018;10((12)):1876. doi: 10.3390/nu10121876. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Tinsley GM, La Bounty PM. Effects of intermittent fasting on body composition and clinical health markers in humans. Nutr Rev. 2015;73((10)):661–674. doi: 10.1093/nutrit/nuv041. [DOI] [PubMed] [Google Scholar]
- 22.Xie Z, Sun Y, Ye Y, Hu D, Zhang H, He Z, et al. Randomized controlled trial for time-restricted eating in healthy volunteers without obesity. Nat Commun. 2022;13((1)):1003. doi: 10.1038/s41467-022-28662-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Cienfuegos S, Gabel K, Kalam F, Ezpeleta M, Wiseman E, Pavlou V, et al. Effects of 4- and 6-h time-restricted feeding on weight and cardiometabolic health: a randomized controlled trial in adults with obesity. Cell Metab. 2020;32((3)):366–78.e3. doi: 10.1016/j.cmet.2020.06.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Keenan SJ, Cooke MB, Hassan EB, Chen WS, Sullivan J, Wu SX, et al. Intermittent fasting and continuous energy restriction result in similar changes in body composition and muscle strength when combined with a 12 week resistance training program. Eur J Nutr. 2022;61((4)):2183–2199. doi: 10.1007/s00394-022-02804-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Shashaj B, Luciano R, Contoli B, Morino GS, Spreghini MR, Rustico C, et al. Reference ranges of HOMA-IR in normal-weight and obese young Caucasians. Acta Diabetol. 2016 Apr;53((2)):251–260. doi: 10.1007/s00592-015-0782-4. [DOI] [PubMed] [Google Scholar]
- 26.Roenneberg T, Wirz-Justice A, Merrow M. Life between clocks: daily temporal patterns of human chronotypes. J Biol Rhythms. 2003 Feb;18((1)):80–90. doi: 10.1177/0748730402239679. [DOI] [PubMed] [Google Scholar]
- 27.Buysse DJ, Reynolds CF, 3rd, Monk TH, Berman SR, Kupfer DJ. The pittsburgh sleep quality index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989 May;28((2)):193–213. doi: 10.1016/0165-1781(89)90047-4. [DOI] [PubMed] [Google Scholar]
- 28.Sohn SI, Kim DH, Lee MY, Cho YW. The reliability and validity of the Korean version of the pittsburgh sleep quality index. Sleep Breath. 2012 Sep;16((3)):803–812. doi: 10.1007/s11325-011-0579-9. [DOI] [PubMed] [Google Scholar]
- 29.Lund HG, Reider BD, Whiting AB, Prichard JR. Sleep patterns and predictors of disturbed sleep in a large population of college students. J Adolesc Health. 2010 Feb;46((2)):124–132. doi: 10.1016/j.jadohealth.2009.06.016. [DOI] [PubMed] [Google Scholar]
- 30.Kim BS. Introduction of the Korean version of the international physical activity questionnaire. J Korean Acad Fam Med. 2006;27:348–357. [Google Scholar]
- 31.Gradisar M, Gardner G, Dohnt H. Recent worldwide sleep patterns and problems during adolescence: a review and meta-analysis of age, region, and sleep. Sleep Med. 2011;12((2)):110–118. doi: 10.1016/j.sleep.2010.11.008. [DOI] [PubMed] [Google Scholar]
- 32.Fischer D, Roenneberg T, Vetter C. Chronotype-specific sleep in two versus four consecutive shifts. J Biol Rhythms. 2021 Aug;36((4)):395–409. doi: 10.1177/07487304211006073. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Gabel K, Hoddy KK, Burgess HJ, Varady KA. Effect of 8-h time-restricted feeding on sleep quality and duration in adults with obesity. Appl Physiol Nutr Metab. 2019 Aug;44((8)):903–906. doi: 10.1139/apnm-2019-0032. [DOI] [PubMed] [Google Scholar]
- 34.Hutchison AT, Regmi P, Manoogian ENC, Fleischer JG, Wittert GA, Panda S, et al. Time-restricted feeding improves glucose tolerance in men at risk for type 2 diabetes: a randomized crossover trial. Obesity (Silver Spring) 2019 May;27((5)):724–732. doi: 10.1002/oby.22449. [DOI] [PubMed] [Google Scholar]
- 35.Kesztyüs D, Cermak P, Kesztyüs T, Barzel A. Early or delayed onset of food intake in time-restricted eating: associations with markers of obesity in a secondary analysis of two pilot studies. Int J Environ Res Public Health. 2021 Sep 21;18((18)):9935. doi: 10.3390/ijerph18189935. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Ha K, Song Y. Associations of meal timing and frequency with obesity and metabolic syndrome among Korean adults. Nutrients. 2019 Oct 13;11((10)):2437. doi: 10.3390/nu11102437. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Nakajima K, Suwa K. Association of hyperglycemia in a general Japanese population with late-night-dinner eating alone, but not breakfast skipping alone. J Diabetes Metab Disord. 2015;14:16. doi: 10.1186/s40200-015-0147-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Pureza IROM, Macena ML, da Silva Junior AE, Praxedes DRS, Vasconcelos LGL, Bueno NB. Effect of early time-restricted feeding on the metabolic profile of adults with excess weight: a systematic review with meta-analysis. Clin Nutr. 2021 Apr;40((4)):1788–1799. doi: 10.1016/j.clnu.2020.10.031. [DOI] [PubMed] [Google Scholar]
- 39.Wilkinson MJ, Manoogian ENC, Zadourian A, Lo H, Fakhouri S, Shoghi A, et al. Ten-hour time-restricted eating reduces weight, blood pressure, and atherogenic lipids in patients with metabolic syndrome. Cell Metab. 2020 Jan 7;31((1)):92–104.e5. doi: 10.1016/j.cmet.2019.11.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Mason IC, Qian J, Adler GK, Scheer FAJL. Impact of circadian disruption on glucose metabolism: implications for type 2 diabetes. Diabetologia. 2020 Mar;63((3)):462–472. doi: 10.1007/s00125-019-05059-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Poggiogalle E, Jamshed H, Peterson CM. Circadian regulation of glucose, lipid, and energy metabolism in humans. Metabolism. 2018;84:11–27. doi: 10.1016/j.metabol.2017.11.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Chawla S, Tessarolo Silva F, Amaral Medeiros S, Mekary RA, Radenkovic D. The effect of low-fat and low-carbohydrate diets on weight loss and lipid levels: a systematic review and meta-analysis. Nutrients. 2020;12((12)):3774. doi: 10.3390/nu12123774. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Lowe DA, Wu N, Rohdin-Bibby L, Moore AH, Kelly N, Liu YE, et al. 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. JAMA Intern Med. 2020 Nov 1;180((11)):1491–1499. doi: 10.1001/jamainternmed.2020.4153. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Moro T, Tinsley G, Longo G, Grigoletto D, Bianco A, Ferraris C, et al. Time-restricted eating effects on performance, immune function, and body composition in elite cyclists: a randomized controlled trial. J Int Soc Sports Nutr. 2020;17((1)):65. doi: 10.1186/s12970-020-00396-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Tinsley GM, Forsse JS, Butler NK, Paoli A, Bane AA, La Bounty PM, et al. Time-restricted feeding in young men performing resistance training: a randomized controlled trial. Eur J Sport Sci. 2017;17((2)):200–207. doi: 10.1080/17461391.2016.1223173. [DOI] [PubMed] [Google Scholar]
- 46.Kim JE, O'Connor LE, Sands LP, Slebodnik MB, Campbell WW. Effects of dietary protein intake on body composition changes after weight loss in older adults: a systematic review and meta-analysis. Nutr Rev. 2016;74((3)):210–224. doi: 10.1093/nutrit/nuv065. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Tagawa R, Watanabe D, Ito K, Ueda K, Nakayama K, Sanbongi C, et al. Dose-response relationship between protein intake and muscle mass increase: a systematic review and meta-analysis of randomized controlled trials. Nutr Rev. 2020 Nov 4;79((1)):66–75. doi: 10.1093/nutrit/nuaa104. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Dong TA, Sandesara PB, Dhindsa DS, Mehta A, Arneson LC, Dollar AL, et al. Intermittent fasting: a heart healthy dietary pattern? Am J Med. 2020 Aug;133((8)):901–907. doi: 10.1016/j.amjmed.2020.03.030. [DOI] [PMC free article] [PubMed] [Google Scholar]
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Supplementary Materials
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Supplementary data
Data Availability Statement
All data generated or analyzed during this study are included in this article and its online supplementary material. Further inquiries can be directed to the corresponding author.