Early or delayed time-restricted feeding prevents metabolic impact of obesity in mice

in Journal of Endocrinology
Authors:
Prashant RegmiAdelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia
Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, South Australia, Australia

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Rajesh ChaudharyAdelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia
Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, South Australia, Australia

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Amanda J PageAdelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia
Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, South Australia, Australia

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Amy T HutchisonAdelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia
Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, South Australia, Australia

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Andrew D VincentAdelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia

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Bo LiuAdelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia
Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, South Australia, Australia

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Leonie HeilbronnAdelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia
Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, South Australia, Australia

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Correspondence should be addressed to L Heilbronn: leonie.heilbronn@adelaide.edu.au

*(P Regmi and R Chaudhary contributed equally to this work)

Free access

Time-restricted feeding (TRF) initiated early during the dark phase prevents the metabolic consequences of a high-fat diet in rodent models. However, the metabolic consequences of delaying the initiation of TRF, akin to breakfast skipping in humans, is unclear. We assigned 8-week-old male C57BL/6J mice (n = 192) to chow or high-fat diet ad libitum (AL) for 4 weeks, before randomization to continue AL or 10 h of TRF, initiated at lights off (TRFe) or 4-h after lights off (TRFd) for a further 8 weeks. Oral glucose tolerance tests (1 g/kg), metabolic monitoring and body composition by echoMRI were performed, and tissues were collected at six time points. TRF reduced weight and fat mass vs AL, with a greater reduction in TRFe vs TRFd. TRF improved glucose tolerance and protected mice from high-fat diet-induced hepatosteatosis vs AL, with no difference between TRFe and TRFd. TRF increased the amplitude of Bmal1, Cry1, Per2, Nampt, and Nocturnin mRNA levels in liver. A phase delay in Bmal1, Cry1, Per2, Reverbα, Nampt, NAD, Sirt1, and Nocturnin was observed in TRFd. Thus, delaying TRF limited the weight benefit and induced a phase delay in the hepatic clock, but improved metabolic health. Allowing more flexibility in when TRF is initiated may increase the translational potential of this dietary approach in humans.

Abstract

Time-restricted feeding (TRF) initiated early during the dark phase prevents the metabolic consequences of a high-fat diet in rodent models. However, the metabolic consequences of delaying the initiation of TRF, akin to breakfast skipping in humans, is unclear. We assigned 8-week-old male C57BL/6J mice (n = 192) to chow or high-fat diet ad libitum (AL) for 4 weeks, before randomization to continue AL or 10 h of TRF, initiated at lights off (TRFe) or 4-h after lights off (TRFd) for a further 8 weeks. Oral glucose tolerance tests (1 g/kg), metabolic monitoring and body composition by echoMRI were performed, and tissues were collected at six time points. TRF reduced weight and fat mass vs AL, with a greater reduction in TRFe vs TRFd. TRF improved glucose tolerance and protected mice from high-fat diet-induced hepatosteatosis vs AL, with no difference between TRFe and TRFd. TRF increased the amplitude of Bmal1, Cry1, Per2, Nampt, and Nocturnin mRNA levels in liver. A phase delay in Bmal1, Cry1, Per2, Reverbα, Nampt, NAD, Sirt1, and Nocturnin was observed in TRFd. Thus, delaying TRF limited the weight benefit and induced a phase delay in the hepatic clock, but improved metabolic health. Allowing more flexibility in when TRF is initiated may increase the translational potential of this dietary approach in humans.

Introduction

Time-restricted feeding (TRF) is a dietary tool that limits the duration of food intake for 6–12 h during the active phase of the day, without altering either the amount or quality of food provided (Regmi & Heilbronn 2020). In rodents, TRF limited diet-induced weight gain and protected mice from the metabolic consequences of diverse nutritional challenges, including high-fat-diet (HFD) and high-fat-high-sucrose diet (Hatori et al. 2012, Chaix et al. 2014, Duncan et al. 2016, Sundaram & Yan 2016, Woodie et al. 2018). TRF also reduced body weight and fasting glucose, improved glucose tolerance, reduced blood pressure and reduced atherogenic lipids in people with overweight and obesity (Gill & Panda 2015, Sutton et al. 2018, Wilkinson et al. 2020).

Most TRF studies have initiated TRF early (TRFe), at the onset of the dark phase (Hatori et al. 2012, Chaix et al. 2014, Sundaram & Yan 2016, Woodie et al. 2018). This is likely the optimal time to initiate TRF since glucose tolerance and insulin sensitivity are highest during the dark phase (Rudic et al. 2004). Skipping breakfast in humans (Bi et al. 2015, Jakubowicz et al. 2019), or eating late during the dark phase in mice (Bray et al. 2010) are also linked to weight gain and poorer glucose control. However, implementing TRFe in the general population may be challenging both biologically and socially (Regmi & Heilbronn 2020). Delaying the initiation time of TRF (TRFd) may increase the acceptability of this as a dietary tool in the community. However, the metabolic consequences are not yet clear. In the only human trial to date, TRF initiated at 08:00 or 12:00 h for 1 week equally improved glucose tolerance in participants with obesity (Hutchison et al. 2019). However, there is some evidence that TRFd could limit weight benefit (Delahaye et al. 2018, Shimizu et al. 2018) and induced a phase delay in hepatic clocks after 2 weeks in rodents (Shimizu et al. 2018).

TRF acts partially by facilitating the robust oscillation of clock genes in peripheral organs (Hatori et al. 2012, Chaix et al. 2014, Greenwell et al. 2019, Velingkaar et al. 2020). Interestingly, robust physiological rhythms were restored in clock deficient mice when fed under TRF (Vollmers et al. 2009, Chaix et al. 2019). This suggests that TRF impacts other regulatory factors that drive rhythmic transcriptomes, independently of clock. NAD is a cofactor that plays a pivotal role in energy metabolism, sirtuin (SIRT) function, and biological ageing (Poljsak 2018). The majority of cellular NAD was thought to come from the nicotinamide phosphoribosyltransferase (NAMPT) mediated salvage pathway, whose amplitude in liver was reduced by HFD (Eckel-Mahan et al. 2013). However, another novel source of NAD is Nocturnin, a member of the exonuclease–endonuclease family of proteins, initially considered a deadenylase (Stubblefield et al. 2018), but recently shown to be a NADPH phosphatase (Estrella et al. 2019).

This study examined whether delaying the initiation of TRF improves glucose tolerance and mitigates the adverse health consequences of HFD, and the effects on genes involved in circadian regulation and markers of NAD metabolism in mouse liver. We hypothesized that TRFd would be equally beneficial to TRFe in the prevention of metabolic consequences of HFD, despite inducing a delay in the phase of key hepatic circadian genes and markers of NAD metabolism.

Materials and methods

Animals and diets

All experiments were approved by the South Australian health and medical research institute (SAHMRI) and University of Adelaide animal ethics committee and were conducted in accordance with the Australian code of practice for the care and use of animals for scientific purposes. Eight-week-old C57BL/6J male mice (n = 192, SAHMRI bioresources, Adelaide, Australia) were housed 4 mice/cage under 12 h light:12 h darkness cycle with lights on at 07:00  h (Zeitgeber time (ZT) 0) at 18–24°C. Mice were fed either a standard chow (18% calorie from fat, Teklad global 2018SX, Envigo, Madison, USA) or a lard-based HFD (43% calories from fat, SF16-001, Specialty Feeds, WA, Australia) for 4 weeks. Mice on each diet were then randomized to one of three interventions: (i) continue AL, (ii) 10-h TRF initiated at ZT12 (TRFe), and (iii) 10-h TRF initiated at ZT16 (TRFd) for a further 8 weeks (Fig. 1A). Food consumption was recorded on a weekly basis throughout the study. During TRF, food access was controlled by transferring mice between cages with and without food. AL fed mice were also transferred between feeding cages at the same time to standardize handling. All mice had free access to water throughout the study. Feeding efficiency was calculated as the ratio of body weight gain to calories consumed (Yasumoto et al. 2016). After 8 weeks, mice were sedated with isoflurane at 4-h intervals (ZT 0, 4, 8, 12, 16, 20), to collect blood by cardiac puncture, and were killed by cervical dislocation prior to collection of liver, inguinal and gonadal fat pads.

Figure 1
Figure 1

TRF protects mice from weight gain, adiposity and hepatic fat accumulation. (A) Study design, (B) body weight gain throughout the study (n = 31–33/group), (C) body composition by echoMRI (n = 6/group), (D) gonadal fat to body weight ratio (n = 31–33/group), (E) inguinal fat to body weight ratio (n = 31–33/group), (F) average total calorie consumption per mouse (n = 8/group), (G) feeding efficiency (n = 8/group), (H) liver triglyceride (n = 12/group), (I) liver triglyceride after equal 10 h of fasting in TRFe and TRFd (n = 4/group, statistics for this sub-group was done by t-test). Statistics were performed by two-way ANOVA with diet (chow vs HFD) and intervention (AL, TRFe and TRFd) as fixed variables. Bonferroni’s correction was applied post hoc. Filled bars: AL, hatched bars: TRFe, and open bars: TRFd. (P < 0.05 vs AL, •••P < 0.001 vs AL, *P < 0.05, **P < 0.01, ***P < 0.001). A full color version of this figure is available at https://doi.org/10.1530/JOE-20-0404.

Citation: Journal of Endocrinology 248, 1; 10.1530/JOE-20-0404

Body weight and composition

Body weight was recorded weekly at the end of fasting period during cage transfer (AL: ZT11-12, TRFe: ZT12 and TRFd: ZT16). At 20 weeks of age, body composition was examined at ZT4-5 using an EchoMRI™-500 Body Composition Analyzer (n = 6/group).

Oral glucose tolerance test (GTT) and insulin measurement

At 19 weeks of age, mice were fasted for 6 h and an oral GTT (1 g glucose/kg body weight) was performed at ZT4 (light phase, n = 8/group) or ZT16 (dark phase, n = 7–8/group). Blood glucose was measured at 0, 15, 30, 60, 90 and 120 min via tail vein bleeding using a glucometer (Accu-Chek® Performa II, Roche). Plasma samples at 0, 15, 30 and 60 min were stored at −80°C and later insulin was measured using Ultra-Sensitive Mouse Insulin ELISA Kit (Crystal Chem, USA). Glucose and insulin area under the curve (AUC) were calculated by trapezoidal rule (Allison et al. 1995).

Metabolic cage

At 20 weeks of age, a subset of mice (n = 4–6/group) were individually housed in Promethion® metabolic cages (Sable Systems, Las Vegas, USA) for indirect calorimetry. Mice were acclimatized for 22–24 h and metabolic data acquired for 24 h. Food and water consumption, x, y and z beam breaks, VCO2 and VO2 were measured at 5-min intervals. Respiratory quotient (RQ) and energy expenditure (EE) were calculated as described by Weir equation (Weir 1949). EE was adjusted as raw EE/(body weight)3/4 as previously described (Tschop et al. 2011).

Liver triglycerides and enzyme activity measurement

For triglyceride measurement, liver tissue samples (~50 mg) were first homogenized in 5% NP-40 solution (in ddH2O). The supernatant was separated, and triglyceride was measured using a Triglycerides Assay Kit (Abcam) and was adjusted for tissue weight. For enzyme activity, citrate synthase and β-hydroxyacyl CoA dehydrogenase activity were measured in 6–10 mg liver tissue homogenates by kinetic assay as previously described (Srere 1969, Bergmeyer 1974).

Gene expression analysis

Total RNA was extracted from liver using Trizol (Invitrogen) and cDNA was synthesized using the QuantiTect RT kit (Qiagen). Quantitative real-time PCR was performed as described previously (Liu et al. 2019) using the TaqMan primers and master mix listed in Supplementary Table 1 (see section on supplementary materials given at the end of this article). Hypoxanthine phosphoribosyl transferase (hprt) was used as reference gene and relative gene expression was calculated using 2−ΔCT, where ΔCT = (CTtarget gene − CTreference gene).

Histology

Fresh liver tissue was fixed in 4% buffered paraformaldehyde for ~8 h, dehydrated in 30% sucrose, mounted in Tissue-Tek OCT Compound, and frozen at −80°C. Cryosections (10 μm) were air-dried on gelatine-coated slides, stained using oil red O as previously described (Christie et al. 2018), and scanned under brightfield microscopy.

NAD measurement

NAD was extracted from liver tissue (~20 mg) and NAD-NADH cycling assay was performed using ADH cycling mix at 37°C in dark for 15 min as previously described (Bertoldo et al. 2020). Fluorescence was measured (excitation 340 nm and emission 445 nm) (Chance et al. 1979), and the NAD concentration was determined using a standard curve, and corrected for amount of tissue used.

Western blot

Liver tissue lysates (10 μg of protein) were resolved by SDS-PAGE and transferred onto polyvinylidene fluoride membranes. Membranes were probed for NAMPT (E-3, sc-393444, Santa-Cruz) and β-tubulin (Ab-6046, Abcam). Bands were visualized by chemiluminescence, the intensity was measured using ImageJ software, and presented as relative protein levels.

Calculation and statistical analysis

Statistical analysis was performed by two-way ANOVA with diet (chow and HFD) and intervention (AL, TRFe and TRFd) as fixed factors. A differential effect of intervention in each diet was tested via the interaction between diet and intervention, and Bonferroni’s post hoc was applied. For glucose AUC an additional two-way ANOVA analysis was performed with body weight as co-variate in the model. Comparison of glucose AUCs between chow-AL and HFD- TRFe or TRFd, 10-h fasting triglyceride between TRFe and TRFd, and Western blot results were performed using t-tests (SPSS, IBM). This study was powered to detect observed changes in weight gain, feeding efficiency, glucose AUC, liver triglycerides, and circadian genes. The point estimates of metabolic phenotypes by TRFe and TREd were very similar; hence would require sample size of several hundred in every group to achieve statistical power. All data were included in the analysis. All data are presented as mean ± s.e.m. and P<0.05 was considered statistically significant. Circadian data was analysed by Cosinor regression using R package cosinor of the log transformed gene expression, yi:

article image

where A is the mean, B is the amplitude, and C is the phase shift.

Results

TRF mitigates weight gain, but a 4-h phase delay lessens the effect

Body weight gain was lower in both TRF groups vs AL and in TRFe vs TRFd on both diets (all P ≤ 0.04, Fig. 1B). Body composition by MRI was not different between groups in the chow-fed mice (Fig. 1C). In mice that were fed a HFD, percent fat mass was lower and percent lean mass was higher in both TRF groups vs AL (both P < 0.001) and in TRFe vs TRFd (both P = 0.001, Fig. 1C). TRF also reduced gonadal and inguinal fat vs AL in HFD fed mice (both P < 0.001), and in TRFe vs TRFd (P ≤ 0.037, Fig. 1D and E). TRF did not alter cumulative calorie consumption in chow-fed mice (Fig. 1F). However, a trend towards lower calorie consumption was observed in both TRF groups vs AL in HFD fed mice (P = 0.07). TRF mice on both diets consumed fewer calories in the first week of TRF, and this was partially sustained for 8 weeks in HFD fed mice (Supplementary Fig. 1A). Feeding efficiency was reduced in TRFe vs AL in chow-fed mice (P < 0.001, Fig. 1G). In HFD mice, feeding efficiency was reduced in both TRF groups vs AL (both P < 0.001) and in TRFe vs TRFd (P = 0.025). Liver triglyceride, assessed at ZT8, 12 and 20, was reduced in TRF vs AL (both P < 0.001) in HFD fed mice only (Fig. 1H and Supplementary Figs 2A, B, C, 3), with no difference between TRFe and TRFd. Assessing liver triglyceride after identical fasting length in TRF groups (i.e. ZT8 (TRFe) and ZT12 (TRFd)) did not alter these results (Fig. 1I).

TRF improved the 24-h rhythm in nutrient utilization, irrespective of a 4-h delay

Food intake patterns over 24 h were examined in the metabolic chamber, and presented as average hourly Kcal consumption (Fig. 2A, B and C). Chow-fed AL mice appeared to increase food intake at ZT11, approximately 1 h before the initiation of a dark phase, with two peaks observed at ZT14 and ZT22. HFD-AL mice consumed ~45% of their total calories during the light phase (vs ~30% in chow-AL) and did not exhibit a discernible peak in calorie consumption during the dark phase. The TRF groups exhibited two distinct peaks in food consumption, with both peaks delayed in TRFd mice. TRF did not alter average RQ during the light phase in chow or HFD mice. However, carbohydrate oxidation exceeded 1.0 during the dark phase in chow-fed TRFe mice and was significantly higher vs AL and TRFd (both P < 0.001, Fig. 2D, E and F). Activity was higher during the dark phase in both TRF groups vs AL (P ≤ 0.05) in both diet groups, with no difference between TRFe and TRFd (Fig. 2G, H and I). Active phase EE and total 24-h EE was not significantly different between groups (all P ≥ 0.059, Fig. 2J, K and L). TRF did not alter β-hydroxyacyl CoA dehydrogenase or citrate synthase activity, key enzymes of β-oxidation and TCA cycle respectively, in liver (Supplementary Fig. 2D, E, F, G, H and I).

Figure 2
Figure 2

TRF drives rhythm in nutrient utilization. (A and B) Hourly calorie consumption in chow and HFD (n = 4−6/group), (C) total day and night percentage calorie consumption (n = 4−6/group), (D and E) 24-h hourly RQ (CO2 exhaled/ O2 inhaled) in chow and HFD (n = 4−6/group), (F) average day and night RQ (n = 4−6/group), (G and H) total hourly activity in chow and HFD (n = 4−6/group), (I) total day and night activity (n = 4−6/group), (J and K) 24-h hourly energy expenditure in chow and HFD (n = 4−6/group), (L) total day and night energy expenditure (n = 4−6/group). Statistics were performed by two-way ANOVA with diet (chow and HFD) and intervention (AL, TRFe and TRFd) as fixed variables. Bonferroni’s correction was applied post hoc. Gray area represents dark phase and food availability is indicated by dotted (TRFe) and hatched (TRFd) boxes. Filled bars: AL hatched bars: TRFe, and open bars: TRFd. *P < 0.05, **P < 0.01, ***P < 0.001, $P < 0.05 overall intervention effect in both diets.

Citation: Journal of Endocrinology 248, 1; 10.1530/JOE-20-0404

TRF improved glycaemic profile

Glucose tolerance, as assessed by glucose AUC, was increased in both TRF groups vs AL in both diets, when measured during the light and dark phase (all P ≤ 0.007). This significance was maintained after adjusting for body weight, in the dark phase, but not the light phase. There were similar point estimates and no significant difference in glucose AUC between TRFe and TRFd groups (Fig. 3A, B, E and F). The glucose AUC in TRF groups that were fed HFD was also not different from chow-fed AL mice, suggesting TRF completely protected mice from HFD induced glucose intolerance. Insulin AUC was also lower in both TRF vs AL in mice fed HFD (all P < 0.045, Fig. 3C, D, G and H). Fasting glucose and insulin at ZT4 were also lower in both TRF vs AL mice that were fed HFD (all P ≤ 0.003), but fasting glucose was higher at ZT16 in TRFd vs AL and TRFe in mice fed chow or HFD.

Figure 3
Figure 3

TRF improves glycaemic profile. (A) Blood glucose after 1 g/kg body weight of oral glucose load at ZT4 (n = 8/group), (B) glucose area under the curve at ZT4 (n = 8/group), (C) blood insulin after 1 g/kg of body weight of oral glucose load at ZT4 (n = 7/group), (D) insulin area under the curve at ZT4 (n = 7/group), (E) blood glucose after 1 g/kg body weight of oral glucose load at ZT16 (n = 7−8/group), (F) glucose area under the curve at ZT16 (n = 7−8/group), (G) blood insulin after 1 g/kg of body weight of oral glucose load at ZT16 (n = 7/group), (H) insulin area under the curve at ZT16 (n = 7/group). Statistics were performed by two-way ANOVA with diet (chow and HFD) and intervention (AL, TRFe and TRFd) as fixed variables. Bonferroni’s correction was applied post hoc. Filled bars: AL, hatched bars: TRFe, and open bars: TRFd. *P < 0.05, **P < 0.01, ***P < 0.001; $$P < 0.01, $$$P < 0.001: overall intervention effect in both diets.

Citation: Journal of Endocrinology 248, 1; 10.1530/JOE-20-0404

Both forms of TRF increased amplitude of genes involved in circadian rhythm in liver, but with a phase delay in TRFd

In ad libitum fed mice, HFD did not alter the amplitude (all P ≥ 0.11), mean (all P ≥ 0.38) or phase (all P ≥ 0.06) of any of the circadian regulators vs chow (Fig. 4A, B, C, D, E and F), except for a phase advance in Reverbα (P = 0.01). In chow-fed mice, TRF increased the amplitude of Bmal1, Cry1 and Per2 vs AL (all P ≤ 0.04). In HFD mice, the amplitude of Per2 was increased in both TRF groups vs AL and the amplitude of Reverba was increased in TRFe vs AL (P ≤ 0.04). There was no difference in mean or amplitude of any genes between TRFe and TRFd in either diet. The phase of Bmal1, Cry1, Per2 and Reverbα was delayed and Rora was advanced in TRFd vs AL and TRFe (all P < 0.03) in both diet groups. Additionally, the phase of Per2 was delayed and Rora advanced in TRFe vs AL on both diets.

Figure 4
Figure 4

TRF facilitates robust oscillation of genes involved in circadian rhythm, despite inducing phase delay in TRFd. (A, B, C, D, E and F) Cosinor plots of clock gene expression based on relative mRNA expression at six time points of the day (ZT0, 4, 8, 12, 16 and 20; n = 5−6/time point/group).

Citation: Journal of Endocrinology 248, 1; 10.1530/JOE-20-0404

The circadian rhythms in hepatic levels of markers of NAD metabolism were restored by TRF in mice that were fed a HFD

In ad libitum fed mice, HFD reduced the mean mRNA and protein level of NAMPT (Fig. 5A, B, C, D and Supplementary Fig. 4A, B), and delayed the phase of NAD and Sirt1 (all P < 0.05). TRF increased the amplitude of Nampt in both diets (all P ≤ 0.006), but did not alter NAMPT protein levels. The amplitude of Nocturnin was also increased by TRFe in mice that were fed a HFD (P = 0.03), and by TRFd in mice that were fed a chow diet (P = 0.04). The mean of NAD and Sirt1 was increased by TRF in chow-fed mice, but this was significant only in TRFd vs AL in HFD mice (all P ≤ 0.02). TRFe restored the HFD induced phase shift in NAD and Sirt1 (P < 0.03), whereas the phase of NAD and Sirt1 was delayed in TRFd vs AL in chow-fed mice. The phase of Nampt and Nocturnin was also delayed in TRFd vs AL and TRFe in both diets.

Figure 5
Figure 5

TRF facilitates robust oscillation and restores HFD induced phase shift in markers of NAD metabolism in liver. (A, B, C and D) cosinor plots of Nampt, NAD, Sirt1 and Nocturnin based on relative mRNA expression or tissue levels at six time points of the day (ZT0, 4, 8, 12, 16 and 20; n = 5−6/time point/group).

Citation: Journal of Endocrinology 248, 1; 10.1530/JOE-20-0404

Discussion

TRF is a dietary approach that protects mice from the metabolic consequences of obesity and ageing (Hatori et al. 2012, Chaix et al. 2014, Duncan et al. 2016). To date, most protocols have initiated TRF at the onset of the dark phase in rodents. Humans are geared both biologically (Espelund et al. 2005) and socially (Dunbar 2017) to eat more food later in the day. If there is no allowance for food consumption in the early evening, many individuals could struggle with long term adherence to TRF. The present study examined the effects of delaying the initiation of TRF by 4 h, akin to breakfast skipping, on metabolic parameters in mice that were fed chow or HFD. We showed that TRFd was less effective to reduce body weight and fat mass as compared to TRFe, and induced a phase delay in the hepatic expression of clock genes and markers of NAD metabolism. However, TRFd was effective to increase the amplitude of Per2, Nampt, and Nocturnin, and the mean levels of Cry1, NAD and Sirt1, and protected the mice against the metabolic consequences of HFD.

The present study showed that both forms of TRF improved glucose tolerance in mice that were fed chow or HFD, and rescued hepatic steatosis in mice that were fed HFD. The magnitude of improvements in glucose tolerance in TRFe and TRFd were 17–23% in chow-fed mice and 20–26% in HFD fed mice. Improvements in glucose metabolism were previously reported in TRF mice that were fed a HFD, either the first 6 h or last 6 h of the dark phase (Delahaye et al. 2018). Unlike that study, we allowed 10 h of food access, and standardized the fasting length prior to the assessment of glucose tolerance, which is a known factor in glucose responsiveness (Rudic et al. 2004, Andrikopoulos et al. 2008). We have also previously shown that 1 week of TRF, initiated from 08:00 to 17:00 h or from 12:00 to 21:00 h, was equally effective at improving glucose tolerance in response to a mixed nutrient meal test, after standardised fasting lengths, in men with obesity (Hutchison et al. 2019).

Two studies to date have shown that TRF improves metabolic health, without changing body weight, in mice and humans (Sutton et al. 2018, Woodie et al. 2018). In the present study, lower body weight and fat mass were observed in TRF vs AL mice on both diets, although the improvement in glucose tolerance during the dark phase held after adjusting for body weight. In mice that were fed a HFD, there was a marked reduction in food intake at the start of the TRF protocol, which was partially sustained for 8 weeks. The effects of TRF on food intake is controversial. In mice that were fed HFD, previous studies have reported no differences in food intake (Hatori et al. 2012, Chaix et al. 2014, 2019), initial reductions in food intake (Velingkaar et al. 2020), or reduced cumulative food intake (Sundaram & Yan 2016, Delahaye et al. 2018, Serra et al. 2019). In the present study, food intake was not different between groups in mice that were fed a chow diet, but the TRF mice were more active throughout the dark phase, potentially accounting for the weight difference. An increase in locomotor activity is commonly observed in mice that are fed under restricted feeding schedules (Duncan et al. 2016, Sundaram & Yan 2016, Woodie et al. 2018), and has been coined ‘food anticipatory activity’ (Mistlberger 1994). Furthermore, the increased activity could have partially contributed to the improved metabolic phenotype (Sato et al. 2019) that we observed in TRF mice fed a chow diet in this study. As this study and previous TRF studies have observed reduced body weight (Hatori et al. 2012, Chaix et al. 2014) and arguably reduced food intake (Sundaram & Yan 2016, Delahaye et al. 2018) and increased activity (Duncan et al. 2016, Sundaram & Yan 2016), future studies should include pair-fed groups to unequivocally determine whether the TRF or the reduction in body weight/food intake that occur as a result of the TRF drive the metabolic phenotype observed in these animals. This undertaking would need to be carefully controlled as pair-fed animals tend to consume their allocated food more quickly than ad libitum fed animals (Ellacott et al. 2010), undergoing a form of TRF. This could be overcome by allocating food as discrete meals over 24-h, in a pattern that mimics their ad libitum feeding behaviour (Greenwell et al. 2019).

Some previous studies have observed that TRF increases energy expenditure, independently of activity and body weight (Hatori et al. 2012, Chaix et al. 2019), which could indicate adipose tissue browning, as we have shown occurs in response to intermittent fasting (Liu et al. 2019). However, those studies have calculated energy expenditure per kilogram of body weight (Hatori et al. 2012, Chaix et al. 2019). Adjusting for total body weight leads to artificial reductions in energy expenditure as adipose tissue is less metabolically active and represents a larger proportion of body weight in obese mice (Tschop et al. 2011). There was no evidence of unexplained differences in energy expenditure as a result of TRF in the present study. However, delaying TRF was less effective to reduce body weight and fat mass vs TRFe despite equivalent food intake. This difference in feeding efficiency between TRF subgroups could either be the result of a lower than the detectable difference in energy expenditure, or increased nutrient absorption, but the latter was not assessed.

Both forms of TRF increased the amplitude of key genes that are involved in circadian regulation in liver of mice that were fed a chow diet, but this was significant only for Per2 in mice that were fed an HFD. This contrasts previous studies that have reported increased Bmal1, Cry1, Per2 and Reverbα in TRF mice fed HFD (Hatori et al. 2012, Chaix et al. 2019). However, those studies relied on a visual inspection of the data (Hatori et al. 2012, Chaix et al. 2019, Greenwell et al. 2019), whereas this study applied a more rigorous statistical analysis. The discrepancy between studies could also be due to the lower percentage of dietary fat utilised in this study (43%) vs past studies (60%). As there was not a universal increase in the amplitude and/or mean of genes controlling clocks, particularly in mice fed a HFD, this suggests there is an alternative driving force underpinning improvements in glucose metabolism. This is supported by a recent study that showed that TRF restored glucose metabolism in clock deficient mice (Chaix et al. 2019). In the present study, we observed that both forms of TRF increased the amplitude of Nampt and Nocturnin and increased the mean levels of NAD and Sirt1 on both diets. To our knowledge, this has not previously been examined. A rise in cellular NAD and gain of SIRT1 function delays ageing and improves the metabolic phenotype in animal models (Ramsey et al. 2008, Mitchell et al. 2014, Stromsdorfer et al. 2016, Poljsak 2018) and thus could underpin the anti-ageing benefits of TRF. Increased NAD availability also drives β-oxidation, including β-hydroxyacyl CoA dehydrogenase activity (Canto et al. 2015), enabling increased metabolic flexibility during TRF, which is the capacity of an organism to adapt fuel oxidation according to fuel availability (Galgani et al. 2008).

Delaying the initiation of food intake induced a clear phase delay in multiple genes that are under circadian regulation. In particular, TRFd induced a linear phase delay in Bmal1, Cry1, Per2, Reverbα, Nampt, NAD, Sirt1 and Nocturnin. The phase delay in NAD and Sirt1 could drive the delay in Per2, given the known function of SIRT1 in regulation of Per2 transcription by binding with CLOCK:BMAL1 (Ramsey et al. 2009). Interestingly, we observed the phase of NAD coincided with that of Nampt and Nocturnin. Whilst Nampt is a known source of NAD, the latter finding supports the recent notion the NADPH phosphatase activity of nocturnin provides an alternative source of NAD (Estrella et al. 2019). Future studies should examine whether metabolic improvements in response to TRF are abrogated in Nampt, nocturnin and Sirt1 deficient animal models. This study extends previous findings (Shimizu et al. 2018) which analysed the effects after just 2 weeks, when animals are still adapting to the new diet schedule (Kentish et al. 2018), and did not examine nutrient signalling pathways.

Finally, we observed that fasting glucose at ZT16 was higher in TRFd mice as compared to TRFe and AL in both diets. This could be the result of a delay in the ‘dawn phenomenon’, whereby the early morning rise in cortisol/corticosterone increases hepatic glucose production and blood glucose occurred in TRFd, the equivalent effect takes place in the early dark phase in mice (Bolli et al. 1984, Ando et al. 2016). Daytime restricted feeding also shifts the rise in blood glucose from the pre-dark phase to the pre-light phase (Ando et al. 2016). Together, this study highlights the clear entraining effect of food intake on metabolism. However, the short delay imposed by TRFd did not adversely impact the TRF induced improvements in glucose metabolism and metabolic phenotype. This study provides strong support for allowance to delay the initiation of TRF, so long as there is a stable daily timing of food intake.

This study shows that delaying the initiation of feeding by 4 h does not adversely impact the known beneficial effects of TRF, and produced comparable increases in glucose tolerance. Uniquely, we demonstrate the metabolic benefits of TRFd occurred alongside a phase delay in hepatic clocks and metabolic markers, but with an increase in the amplitude and/or mean of genes involved in nutrient signalling and circadian regulation. There are many physiological and metabolic differences between small animal model organisms and humans, but if this finding translates to humans, the delayed form of TRF is likely to be more acceptable, long-term, in the general population.

Supplementary materials

This is linked to the online version of the paper at https://doi.org/10.1530/JOE-20-0404.

Declaration of interest

The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.

Funding

This research did not receive any specific grant from any funding agency in the public, commercial or not-for-profit sector.

Author contribution statement

P R and L K H designed the study. P R, R C, L K H, B L, A J P conducted study. P R and R C performed the experiments. P R, R C and A V analysed data. P R, R C, A J P, A T H, A V, B L and L K H contributed to data interpretation and preparation of the manuscript. L K H had full access to the data and had primary responsibility for the final publication.

Acknowledgement

P R and R C were supported by Adelaide scholarship international from The University of Adelaide.

References

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    Figure 1

    TRF protects mice from weight gain, adiposity and hepatic fat accumulation. (A) Study design, (B) body weight gain throughout the study (n = 31–33/group), (C) body composition by echoMRI (n = 6/group), (D) gonadal fat to body weight ratio (n = 31–33/group), (E) inguinal fat to body weight ratio (n = 31–33/group), (F) average total calorie consumption per mouse (n = 8/group), (G) feeding efficiency (n = 8/group), (H) liver triglyceride (n = 12/group), (I) liver triglyceride after equal 10 h of fasting in TRFe and TRFd (n = 4/group, statistics for this sub-group was done by t-test). Statistics were performed by two-way ANOVA with diet (chow vs HFD) and intervention (AL, TRFe and TRFd) as fixed variables. Bonferroni’s correction was applied post hoc. Filled bars: AL, hatched bars: TRFe, and open bars: TRFd. (P < 0.05 vs AL, •••P < 0.001 vs AL, *P < 0.05, **P < 0.01, ***P < 0.001). A full color version of this figure is available at https://doi.org/10.1530/JOE-20-0404.

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    Figure 2

    TRF drives rhythm in nutrient utilization. (A and B) Hourly calorie consumption in chow and HFD (n = 4−6/group), (C) total day and night percentage calorie consumption (n = 4−6/group), (D and E) 24-h hourly RQ (CO2 exhaled/ O2 inhaled) in chow and HFD (n = 4−6/group), (F) average day and night RQ (n = 4−6/group), (G and H) total hourly activity in chow and HFD (n = 4−6/group), (I) total day and night activity (n = 4−6/group), (J and K) 24-h hourly energy expenditure in chow and HFD (n = 4−6/group), (L) total day and night energy expenditure (n = 4−6/group). Statistics were performed by two-way ANOVA with diet (chow and HFD) and intervention (AL, TRFe and TRFd) as fixed variables. Bonferroni’s correction was applied post hoc. Gray area represents dark phase and food availability is indicated by dotted (TRFe) and hatched (TRFd) boxes. Filled bars: AL hatched bars: TRFe, and open bars: TRFd. *P < 0.05, **P < 0.01, ***P < 0.001, $P < 0.05 overall intervention effect in both diets.

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    Figure 3

    TRF improves glycaemic profile. (A) Blood glucose after 1 g/kg body weight of oral glucose load at ZT4 (n = 8/group), (B) glucose area under the curve at ZT4 (n = 8/group), (C) blood insulin after 1 g/kg of body weight of oral glucose load at ZT4 (n = 7/group), (D) insulin area under the curve at ZT4 (n = 7/group), (E) blood glucose after 1 g/kg body weight of oral glucose load at ZT16 (n = 7−8/group), (F) glucose area under the curve at ZT16 (n = 7−8/group), (G) blood insulin after 1 g/kg of body weight of oral glucose load at ZT16 (n = 7/group), (H) insulin area under the curve at ZT16 (n = 7/group). Statistics were performed by two-way ANOVA with diet (chow and HFD) and intervention (AL, TRFe and TRFd) as fixed variables. Bonferroni’s correction was applied post hoc. Filled bars: AL, hatched bars: TRFe, and open bars: TRFd. *P < 0.05, **P < 0.01, ***P < 0.001; $$P < 0.01, $$$P < 0.001: overall intervention effect in both diets.

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    Figure 4

    TRF facilitates robust oscillation of genes involved in circadian rhythm, despite inducing phase delay in TRFd. (A, B, C, D, E and F) Cosinor plots of clock gene expression based on relative mRNA expression at six time points of the day (ZT0, 4, 8, 12, 16 and 20; n = 5−6/time point/group).

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    Figure 5

    TRF facilitates robust oscillation and restores HFD induced phase shift in markers of NAD metabolism in liver. (A, B, C and D) cosinor plots of Nampt, NAD, Sirt1 and Nocturnin based on relative mRNA expression or tissue levels at six time points of the day (ZT0, 4, 8, 12, 16 and 20; n = 5−6/time point/group).

  • Allison DB, Paultre F, Maggio C, Mezzitis N & Pi-Sunyer FX 1995 The use of areas under curves in diabetes research. Diabetes Care 18 245250. (https://doi.org/10.2337/diacare.18.2.245)

    • Search Google Scholar
    • Export Citation
  • Ando H, Ushijima K, Shimba S & Fujimura A 2016 Daily fasting blood glucose rhythm in male mice: a role of the circadian clock in the liver. Endocrinology 157 463469. (https://doi.org/10.1210/en.2015-1376)

    • Search Google Scholar
    • Export Citation
  • Andrikopoulos S, Blair AR, Deluca N, Fam BC & Proietto J 2008 Evaluating the glucose tolerance test in mice. American Journal of Physiology: Endocrinology and Metabolism 295 E1323E 1332. (https://doi.org/10.1152/ajpendo.90617.2008)

    • Search Google Scholar
    • Export Citation
  • Bergmeyer HU 1974 Methods of Enzymatic Analysis, p. 1. New York: Academic Press.

  • Bertoldo MJ, Listijono DR, Ho WJ, Riepsamen AH, Goss DM, Richani D, Jin XL, Mahbub S, Campbell JM & Habibalahi A et al. 2020 NAD(+) repletion rescues female fertility during reproductive aging. Cell Reports 30 1670 .e71681.e7. (https://doi.org/10.1016/j.celrep.2020.01.058)

    • Search Google Scholar
    • Export Citation
  • Bi H, Gan Y, Yang C, Chen Y, Tong X & Lu Z 2015 Breakfast skipping and the risk of type 2 diabetes: a meta-analysis of observational studies. Public Health Nutrition 18 30133019. (https://doi.org/10.1017/S1368980015000257)

    • Search Google Scholar
    • Export Citation
  • Bolli GB, De Feo P, De Cosmo S, Perriello G, Ventura MM, Calcinaro F, Lolli C, Campbell P, Brunetti P & Gerich JE 1984 Demonstration of a dawn phenomenon in normal human volunteers. Diabetes 33 11501153. (https://doi.org/10.2337/diab.33.12.1150)

    • Search Google Scholar
    • Export Citation
  • Bray MS, Tsai JY, Villegas-Montoya C, Boland BB, Blasier Z, Egbejimi O, Kueht M & Young ME 2010 Time-of-day-dependent dietary fat consumption influences multiple cardiometabolic syndrome parameters in mice. International Journal of Obesity 34 15891598. (https://doi.org/10.1038/ijo.2010.63)

    • Search Google Scholar
    • Export Citation
  • Canto C, Menzies KJ & Auwerx J 2015 NAD(+) metabolism and the control of energy homeostasis: a balancing act between mitochondria and the nucleus. Cell Metabolism 22 3153. (https://doi.org/10.1016/j.cmet.2015.05.023)

    • Search Google Scholar
    • Export Citation
  • Chaix A, Zarrinpar A, Miu P & Panda S 2014 Time-restricted feeding is a preventative and therapeutic intervention against diverse nutritional challenges. Cell Metabolism 20 9911005. (https://doi.org/10.1016/j.cmet.2014.11.001)

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