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Physiological responses to food intake throughout the day

Published online by Cambridge University Press:  25 March 2014

Jonathan D. Johnston*
Affiliation:
Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey GU2 7XH, UK
*
* Corresponding author: Dr Jonathan D. Johnston, fax +44 1483 686401, email [email protected]
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Abstract

Circadian rhythms act to optimise many aspects of our biology and thereby ensure that physiological processes are occurring at the most appropriate time. The importance of this temporal control is demonstrated by the strong associations between circadian disruption, morbidity and disease pathology. There is now a wealth of evidence linking the circadian timing system to metabolic physiology and nutrition. Relationships between these processes are often reciprocal, such that the circadian system drives temporal changes in metabolic pathways and changes in metabolic/nutritional status alter core molecular components of circadian rhythms. Examples of metabolic rhythms include daily changes in glucose homeostasis, insulin sensitivity and postprandial response. Time of day alters lipid and glucose profiles following individual meals whereas, over a longer time scale, meal timing regulates adiposity and body weight; these changes may occur via the ability of timed feeding to synchronise local circadian rhythms in metabolically active tissues. Much of the work in this research field has utilised animal and cellular model systems. Although these studies are highly informative and persuasive, there is a largely unmet need to translate basic biological data to humans. The results of such translational studies may open up possibilities for using timed dietary manipulations to help restore circadian synchrony and downstream physiology. Given the large number of individuals with disrupted rhythms due to, for example, shift work, jet-lag, sleep disorders and blindness, such dietary manipulations could provide widespread improvements in health and also economic performance.

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
The online version of this article is published within an Open Access environment subject to the conditions of the Creative Commons Attribution licence http://creativecommons.org/licenses/by/3.0/
Copyright
Copyright © The Author 2014

Introduction

Circadian rhythms are cyclical endogenous processes that occur with a periodicity of approximately 24 h. They are found throughout the natural world, from simple unicellular organisms through to human beings( Reference Pittendrigh 1 ). Possession of such rhythms enables organisms to anticipate predictable changes in the environment and thus adapt their physiology accordingly. Temporal control over metabolic processes also allows cells and organisms to separate opposing biochemical pathways, for example, redox reactions and anabolism v. catabolism. Moreover, in model species, it has been demonstrated that possession of circadian rhythms that synchronise to environmental changes confers a selective advantage( Reference Ouyang, Andersson and Kondo 2 ).

A great deal of current research is being undertaken at the interface between the circadian timing system, metabolic physiology and nutritional science. Studying how these major biomedical areas inter-relate will not only increase our understanding of healthy metabolism, but may also guide the development of nutritional interventions for body-weight regulation, the management of obesity-related disease and the treatment of circadian disorders associated with shift work, jet-lag, abnormal sleep phase and blindness.

The mammalian circadian timing system

It was recognised over 40 years ago that a small brain region within the anterior hypothalamus, the suprachiasmatic nuclei (SCN), is important for the expression of circadian rhythms in mammals( Reference Moore and Eichler 3 , Reference Stephan and Zucker 4 ). When the SCN are isolated from surrounding brain tissue in vivo, or maintained as tissue explants in vitro, their neurones maintain robust rhythmicity( Reference Inouye and Kawamura 5 Reference Groos and Hendriks 7 ). Furthermore, if SCN tissue from one animal is transplanted to another animal that has had its SCN lesioned, the resulting behavioural rhythms reflect that of the donor animal, not the host( Reference Ralph, Foster and Davis 8 ). It is therefore clear that the SCN play a key role in the generation of mammalian circadian rhythms.

Mammalian clocks outside the SCN, termed ‘peripheral clocks’, were first identified in tissues such as the retina, which exhibits rhythmic hormone secretion when maintained in culture( Reference Tosini and Menaker 9 ). Following the cloning of genetic components of the mammalian clock came the discovery of rhythmic clock gene expression in peripheral tissues( Reference Sakamoto, Nagase and Fukui 10 , Reference Zylka, Shearman and Weaver 11 ). Subsequent advances came from the use of transgenic animals in which reporter gene expression is driven by clock gene elements. Real-time imaging of tissue explants taken from these animals confirmed that many peripheral tissues possess an endogenous clock( Reference Yamazaki, Numano and Abe 12 , Reference Yoo, Yamazaki and Lowrey 13 ). Perhaps most surprisingly, circadian rhythms have also been identified in cultures of immortalised cells( Reference Balsalobre, Damiola and Schibler 14 Reference Welsh, Yoo and Liu 16 ).

In animal models, rhythmic clock gene expression is known to occur in multiple tissues involved in metabolism and nutritional physiology, including the liver, pancreas, gastrointestinal tract, adipose tissue and skeletal muscle( Reference Albrecht 17 , Reference Cagampang and Bruce 18 ). For ethical and technical reasons, molecular analysis of human tissues is difficult and various strategies have been adopted to study human clock gene expression( Reference Johnston 19 ). However, clock gene rhythms have now successfully been observed in human leucocytes( Reference Boivin, James and Wu 20 , Reference Archer, Viola and Kyriakopoulou 21 ), fibroblasts( Reference Brown, Fleury-Olela and Nagoshi 22 , Reference Hasan, Santhi and Lazar 23 ) and adipose tissue( Reference Gomez-Santos, Gomez-Abellan and Madrid 24 , Reference Otway, Mantele and Bretschneider 25 ), with single time point analysis of clock gene expression in other tissues, including pancreatic islets( Reference Stamenkovic, Olsson and Nagorny 26 ).

The presence of rhythms throughout the body requires appropriate physiological mechanisms to keep tissues correctly synchronised to one another. The SCN receive photic information directly from the retina and are readily synchronised to the external light–dark cycle( Reference Schmidt, Do and Dacey 27 ). In normal circumstances, the clock in the SCN then synchronises rhythms elsewhere in the body through a variety of output pathways( Reference Dibner and Schibler 28 ). A commonly used analogy to describe this organisation refers to the SCN as a conductor of an orchestra, with the peripheral tissues representing individual musicians; each of the ‘musicians’ is capable of generating its own time but requires the central ‘conductor’ to ensure that they all maintain correct time relative to each other and thus optimal overall output.

There are many ways through which the SCN can synchronise peripheral tissues. These include endocrine and neuronal pathways, such as the secretion of glucocorticoids and the tone of the autonomic nervous system( Reference Dibner and Schibler 28 ). In addition, by influencing the timing of sleep–wake rhythms, the SCN also dictate the timing of certain behaviours, for example, feeding, which are thought to be critical to the rhythms in peripheral tissues as explained below.

Metabolic functions of circadian timing and specific roles of peripheral clocks

Since the identification of clocks in peripheral tissues, a critical challenge has been to identify their physiological role. An early indication that peripheral clocks had a strong influence on metabolism came from transcriptomic analyses. Depending on the analytical methods used, these studies estimated that up to 20 % of the transcriptome in peripheral tissues exhibits 24 h variation( Reference Akhtar, Reddy and Maywood 29 Reference Zvonic, Ptitsyn and Conrad 33 ). Identification of the rhythmic transcripts revealed a large cluster of genes encoding proteins involved in metabolic pathways. Later proteomic analysis also suggested that up to 20 % of proteins in the mouse liver oscillate with a circadian rhythm, and many of these proteins are indeed involved in important metabolic functions( Reference Reddy, Karp and Maywood 34 ). Technical advances have since permitted direct analysis of the daily metabolome in different tissues. Similar to the transcriptomic and proteomic data, both mouse( Reference Minami, Kasukawa and Kakazu 35 Reference Fustin, Doi and Yamada 37 ) and human( Reference Dallmann, Viola and Tarokh 38 Reference Kasukawa, Sugimoto and Hida 40 ) studies estimate that up to 20 % of the metabolome is under 24 h regulation.

Genetic evidence for a role of circadian clocks in key metabolic processes is now substantial. As discussed previously( Reference Johnston, Frost and Otway 41 ), the precise nature of metabolic abnormality in transgenic animals depends upon their genetic background. Nonetheless the dysregulation of key metabolic processes, including glucose and lipid homeostasis, following disruption of key genes involved in circadian biology reveals fundamental links between circadian genetics and metabolism( Reference Raspe, Duez and Mansen 42 Reference Cho, Zhao and Hatori 47 ). Consistent with these animal data, a number of groups have now reported correlations between aspects of human metabolism and clock gene polymorphisms( Reference Woon, Kaisaki and Braganca 48 Reference Garaulet and Esteban Tardido 53 ).

One limitation of studies involving individuals with ‘whole body’ genetic changes is that they do not clearly indicate the contribution of individual tissue rhythms to whole-organism physiology. Using the Cre-Lox recombinase system to disrupt the Bmal1 (brain and muscle arnt-like protein-1) gene in a tissue-specific manner, circadian rhythms in the liver, pancreas and white adipose tissue have been selectively ‘knocked out’ allowing the in vivo role of their clocks to be investigated. Mice bearing a liver-specific clock disruption exhibit increased glucose clearance following acute challenge, fasting hypoglycaemia and other features suggesting that the hepatic clock regulates glucose export into the blood( Reference Lamia, Storch and Weitz 54 ). By contrast, disruption of the pancreatic clock results in hyperglycaemia, reduced glucose tolerance and impaired insulin secretion( Reference Marcheva, Ramsey and Buhr 55 , Reference Sadacca, Lamia and deLemos 56 ). Finally, knock-out of Bmal1 in white adipose tissue induces obesity; a contributory mechanism to this phenotype is the temporal modification of PUFA signalling from adipocytes to appetite-regulatory regions of the hypothalamus, leading to increased feeding during the resting phase of the day( Reference Paschos, Ibrahim and Song 57 ). Thus, circadian dysfunction in individual tissues can lead to major changes in whole-body energy metabolism.

Timed feeding as a synchroniser of peripheral clocks

The food-entrainable oscillator

Restriction of food availability to a narrow time window each day results in profound reorganisation of behaviour and physiology( Reference Stephan 58 , Reference Mistlberger 59 ). Such temporal restriction induces a bout of activity in advance of food availability, termed ‘food anticipatory activity’ (FAA). This phenomenon was originally observed in rats, but has since been reported in multiple vertebrate and invertebrate species. More detailed analysis in rodents reveals that this FAA is also accompanied by physiological changes including increased core body temperature and serum glucocorticoid concentration.

Interestingly, FAA exhibits properties that are consistent with it being controlled by endogenous circadian clock(s), rather than being merely a food-driven phenomenon. For example, if the temporal window of food availability is abruptly delayed, the onset of FAA takes multiple 24 h cycles to resynchronise to the new feeding time. Moreover, if an animal is completely food deprived, FAA persists at approximately the same time every 24 h for as long as the food deprivation can be maintained( Reference Stephan 58 , Reference Mistlberger 59 ).

The underlying circadian basis of FAA has led to the postulation that animals contain a food-entrainable oscillator (FEO). Although there are reported differences in FAA in mice lacking genetic components of the circadian clock( Reference Challet, Mendoza and Dardente 60 ), these mice do retain the ability to display FAA( Reference Storch and Weitz 61 ). It is therefore believed that the genetic control of the FEO differs from other circadian processes. In keeping with this idea, food anticipatory responses persist in SCN-lesioned animals( Reference Krieger, Hauser and Krey 62 , Reference Stephan 63 ), indicating that the FEO resides in tissue(s) outside of the SCN, the master circadian clock. Some studies have suggested that the FEO may be closely linked to the dorsomedial hypothalamic nuclei, a brain region known to be involved in the homeostatic regulation of feeding( Reference Mieda, Williams and Richardson 64 , Reference Fuller, Lu and Saper 65 ). Other work has highlighted the potential role of extra-hypothalamic brain regions in FAA( Reference Mendoza, Pevet and Felder-Schmittbuhl 66 ). The anatomical localisation of the FEO remains a controversial topic, however( Reference Mistlberger, Buijs and Challet 67 Reference Landry, Kent and Patton 69 ). Indeed the FEO may lie outside of the brain or require the interplay between multiple tissues.

Regulation of rhythms in peripheral tissues

One potential mechanism underlying food-entrainable rhythmicity is the effect of feeding time on peripheral tissue clocks. In normal physiological conditions, the timing of behavioural rhythms, such as feeding, is driven by the SCN and thus represents a mechanism through which the SCN can synchronise rhythms in the periphery. The powerful nature of timed feeding as a circadian signal becomes apparent when food availability is divorced from SCN rhythms.

A typical protocol restricts food availability to nocturnal rodents, so that they can only eat during the light period of a light–dark cycle. This inverts the phase of clock gene rhythms in peripheral tissues, such as the liver, kidney, heart, pancreas, lung, gastrointestinal tract, and brown and white adipose tissue( Reference Zvonic, Ptitsyn and Conrad 33 , Reference Damiola, Le Minh and Preitner 70 Reference Hoogerwerf, Hellmich and Cornelissen 72 ). Full entrainment of the liver rhythms appears to occur within 2–3 d, whereas the other peripheral tissues may require up to 1 week before they exhibit the maximal phase shift. A later mouse study involved removal of food access for the first 6 h of the dark period of a light–dark cycle, with mild energy restriction. After 4 d in this protocol, mice exhibited delayed rhythms of hepatic and plasma TAG concentration, together with delayed rhythms of lipogenic and clock gene expression in both liver and adipose tissue( Reference Yoshida, Shikata and Seki 73 ). When animals are able to eat ad libitum quantities of food during temporal restriction paradigms, SCN rhythms remain locked to the light–dark cycle( Reference Damiola, Le Minh and Preitner 70 , Reference Stokkan, Yamazaki and Tei 71 ). However, the combination of temporal food restriction and hypoenergetic food availability does induce reorganisation of rodent SCN rhythms( Reference Mendoza, Graff and Dardente 74 , Reference Mendoza, Gourmelen and Dumont 75 ). Indeed, hypoenergetic feeding of nocturnal rodents without restricting food availability to the light period alters the phase of SCN-driven rhythms( Reference Mendoza, Drevet and Pevet 76 ) as well as gene expression in peripheral tissues( Reference Kuroda, Tahara and Saito 77 ). Thus the overall effect of feeding on circadian organisation appears to involve an interaction between both the timing and the quantity of food intake (Fig. 1).

Fig. 1 Regulation of the circadian timing system by light and food. Under normal conditions of ad libitum food, light synchronises the master clock, the suprachiasmatic nuclei (SCN), which then synchronises peripheral clocks via neuronal and endocrine pathways, together with control over behavioural activity and thus feeding time. When feeding time (but not energy availability) is restricted, light remains the dominant synchroniser of the SCN, but peripheral clocks are synchronised to feeding time. Under conditions of temporal and energy food restriction, both the SCN and peripheral clocks are synchronised to the feeding time. (A colour version of this figure can be found online at http://www.journals.cambridge.org/nrr)

An important caveat when extrapolating the studies described above to human society is the relevance to standard human meal patterns. A common theme in human society is a feeding pattern of three meals per d. In contrast, most animal studies to date have utilised prolonged ad libitum feeding opportunities restricted to certain phases of the 24 h day. Some studies, however, have developed a ‘humanised’ meal protocol for rodents. When rats are only given their daily energy intake over ‘lunch’ and ‘dinner’ their gene expression rhythms in liver, heart and white adipose tissue are delayed compared with a group receiving the same total energy intake spread over three meals( Reference Wu, Sun and ZhuGe 78 ). Consistent with this finding, studies in mice using a range of mealtime combinations indicate that the first meal following a long fasting period provides an important synchronising signal to peripheral clocks( Reference Kuroda, Tahara and Saito 77 , Reference Hirao, Nagahama and Tsuboi 79 ). To date there are no comparable molecular data from human studies. One rare investigation of timed feeding on human circadian physiology is an experiment in which subjects were fed a single daily carbohydrate-rich meal for 3 d; morning consumption of this meal advanced core body temperature and heart rate, but not melatonin, rhythms compared with evening meal timing( Reference Krauchi, Cajochen and Werth 80 ). Furthermore, a delay in the timing of three daily meals within a fixed light–dark cycle is known to delay the phase of plasma leptin rhythms( Reference Schoeller, Cella and Sinha 81 ), which may be at least partially due to changes in adipose tissue clocks.

Although redundancy of signalling pathways providing input to the relevant tissues hinders elucidation of mechanistic insight into food entrainment, some progress has been made. At the nutritional level, phase shifting of the liver clock seems greater when the starch component of a mixed diet provides a large postprandial glucose concentration( Reference Itokawa, Hirao and Nagahama 82 ). Interestingly, however, ingestion of 100 % glucose, sucrose or maize starch is insufficient to alter the phase of liver rhythms, indicating that mixed macronutrient content may be necessary for food entrainment in the liver( Reference Hirao, Tahara and Kimura 83 ). At the physiological level, temporal food restriction more rapidly resynchronises peripheral clock gene rhythms in mice that have been adrenalectomised, compared with sham-operated controls( Reference Le Minh, Damiola and Tronche 84 ). This finding suggests that glucocorticoid signalling, which is believed to be an endocrine link between the SCN and peripheral clocks( Reference Balsalobre, Brown and Marcacci 85 ), may inhibit or delay the impact of temporal food availability. At the molecular level, it has been demonstrated that rhythmic phosphorylation of key transcription factors CREB and Akt (cAMP response element-binding protein and protein kinase B) in the mouse liver is driven by temporal feeding patterns( Reference Vollmers, Gill and DiTacchio 86 ). In keeping with this result, the expression of multiple genes targeted by molecular nutrient and stress sensors was similarly dependent on feeding time( Reference Vollmers, Gill and DiTacchio 86 ). Finally, mice deficient for the gene Parp1 (poly [ADP-ribose] polymerase 1) or the neurone-specific γ form of protein kinase C (PKCγ) exhibit impaired synchronisation to timed feeding( Reference Asher, Reinke and Altmeyer 87 , Reference Zhang, Abraham and Lin 88 ). Despite these advances, the mechanisms that mediate the effects of food on the circadian system are poorly understood and will doubtless be the subject of multiple further studies.

Human metabolic physiology and postprandial responses vary across the day

Diurnal changes

Diurnal rhythmicity refers to 24 h changes that occur in individuals kept in a varying environment, for example, a 24 h light–dark cycle. Although such rhythms are often relevant to a real-life scenario, there is the possibility that they are driven by environmental fluctuations rather than endogenous processes per se. As a result, they are not considered to be truly circadian.

Arguably the best-characterised daily metabolic rhythms in humans relate to changes in glucose homeostasis. Diurnal changes in glucose tolerance have been recognised in human subjects for many years( Reference Van Cauter, Polonsky and Scheen 89 ). Sensitivity to elevated glucose concentration is greatest in the early morning and then declines over the course of the day, leading to a phenomenon that has been termed ‘afternoon diabetes’. This daily change is not dependent upon changes in gastrointestinal function, but instead appears to be the result of altered glucose utilisation and insulin sensitivity, with maximal insulin sensitivity occurring in the early morning and decreasing throughout the day( Reference Van Cauter, Polonsky and Scheen 89 ).

In addition to glucose homeostasis, the regulation of plasma lipids is also subject to daily variation. Not only are basal concentrations of TAG elevated at night, but also there are diurnal changes in the postprandial TAG response. Ingestion of a meal at night results in increased plasma TAG that remains elevated for longer than the response to the same meal given during the day( Reference Sopowski, Hampton and Ribeiro 90 ). A study of postprandial responses to breakfast and lunch reported approximately 50 % less change in plasma TAG concentration following lunch than breakfast, despite the plasma TAG fraction showing no differences in concentration of [13C]palmitic acid that was included in each meal( Reference Burdge, Jones and Frye 91 ). The physiological basis for temporal differences in postprandial TAG response may therefore be independent of absorption or mobilisation of meal-derived lipids from the gut( Reference Burdge, Jones and Frye 91 ).

A number of groups have studied temporal variation of adipokines, which are adipose-derived hormones that regulate metabolic physiology in the brain and multiple peripheral tissues( Reference Trujillo and Scherer 92 , Reference Galic, Oakhill and Steinberg 93 ). Diurnal rhythms have been reported for many of these hormones, including leptin, adiponectin, chemerin, lipocalin and visfatin( Reference Sinha, Ohannesian and Heiman 94 Reference Benedict, Shostak and Lange 98 ). Although the secretion of these hormones is likely to be governed by multiple factors such as feeding and sleep, detailed analyses of leptin secretion suggest that there is likely to be an underlying circadian component to adipokine rhythmicity( Reference Shea, Hilton and Orlova 99 , Reference Otway, Frost and Johnston 100 ). Furthermore, given the functional roles of adipokines, their rhythmic secretion may make important contributions towards the daily changes in glucose and lipid homeostasis described above.

Identification of endogenous circadian rhythms

In order to unmask truly endogenous circadian rhythms from temporal changes in the environment, a number of different laboratory protocols have been developed. The most widely used of these are the constant routine and forced desynchrony protocols( Reference Duffy and Dijk 101 , Reference Blatter and Cajochen 102 ). In a constant routine, subjects are kept awake in a supine posture in constant dim light, with identical regular (for example, hourly) snacks. Although this protocol effectively removes environmental rhythms, it does result in the development of sleep debt due to the necessity to keep subjects awake. One solution to this problem is to allow subjects to sleep during the dark phase of a light–dark cycle that is sufficiently different from 24 h to permit entrainment of subjects' circadian rhythms; this is the basis of a forced desynchrony protocol. For example, a commonly used variant of this protocol employs a 28 h light–dark cycle that therefore allows subjects to sleep every 28 h, while their circadian rhythms occur (‘free run’) with a frequency of approximately 24 h.

Various research groups have utilised the above protocols to investigate the contribution of the endogenous circadian system to daily rhythms of glucose and lipid metabolism. One study in which 4-hourly meals were administered over a constant routine revealed elevation of both postprandial glucose and TAG during the biological night, especially after high-fat meal intervention over the week preceding the constant routine( Reference Holmback, Forslund and Forslund 103 ). Consistent with this finding, analysis of postprandial responses during a forced desynchrony of 27-h days revealed effects of both circadian time and length of prior wakefulness on glucose and TAG concentration( Reference Morgan, Arendt and Owens 104 ). By contrast, postprandial insulin responses were regulated by circadian time, but not length of wakefulness. A more recent forced desynchrony protocol kept volunteers on 28-h days, each of which contained four meals: breakfast, lunch, dinner and a snack shortly before bedtime( Reference Scheer, Hilton and Mantzoros 105 ). It was found that the times during which subjects were awake and eating during their biological night resulted in multiple cardiometabolic changes, including decreased plasma leptin concentration and increased concentrations of both plasma glucose and insulin. In fact, the postprandial responses of some of these healthy subjects during the biological night were equivalent to the responses of a pre-diabetic individual( Reference Scheer, Hilton and Mantzoros 105 ). In a separate cross-over study using forced desynchrony, volunteers had daily metabolic profiles assessed after both three 21-h days and also three 27-h days. Although there were some differences in response to the 21-h v. 27-h days, both schedules disrupted glucose–insulin metabolism, increased carbohydrate oxidation and reduced protein oxidation, but had little or no effect on appetite or energy balance( Reference Gonnissen, Rutters and Mazuy 106 ). The human circadian system therefore exerts clear influence over key aspects of metabolic physiology.

Effects of body weight on circadian rhythms

The data discussed above describe the interaction between clocks and metabolism on a relatively short-term time frame. Of importance to health is the longer-term relationship between circadian rhythms, metabolic status and body weight. Indeed, many studies in the literature have reported altered daily rhythms in association with factors including altered body weight, presence of metabolic disease and long-term changes in nutrient intake.

Following the demonstration of daily rhythms of plasma leptin in human subjects, it was reported that the percentage amplitude of these rhythms declined in obese individuals( Reference Sinha, Ohannesian and Heiman 94 , Reference Matkovic, Ilich and Badenhop 107 , Reference Saad, Riad-Gabriel and Khan 108 ). However, not all studies have been able to replicate this finding( Reference Yildiz, Suchard and Wong 109 , Reference Mantele, Otway and Middleton 110 ). The reasons for discrepancies are not clear, but may include varied pre-laboratory controls, sex of subjects, extent of obesity (i.e. BMI of 30–35 v. 40+ kg/m2) and distribution of fat within the obese subjects recruited.

Another hormone that appears to demonstrate correlation between rhythm amplitude, body weight and metabolic health is melatonin. An early report that nocturnal melatonin concentration positively correlates with BMI in insulin-sensitive human subjects( Reference Arendt, Hampton and English 111 ) has been supported by recent data reporting elevated amplitude melatonin rhythms in obese non-diabetic men, although blunted melatonin rhythms are present in weight-matched men with type 2 diabetes( Reference Mantele, Otway and Middleton 110 ). In addition, nocturnal melatonin concentration correlates with aspects of the metabolic syndrome in women( Reference Corbalan-Tutau, Madrid and Nicolas 112 ). The functional relevance of these endocrine data is supported by molecular and genetic evidence for a role of melatonin signalling in metabolic physiology and type 2 diabetes mellitus. Common polymorphisms of the human MT2 melatonin receptor have been associated with impaired glucose homeostasis and type 2 diabetes in multiple populations( Reference Bouatia-Naji, Bonnefond and Cavalcanti-Proenca 113 Reference Ronn, Wen and Yang 116 ). Although the calculated risk of developing type 2 diabetes is small for these polymorphisms, subsequent work identified additional rare MT2 variants that confer a much higher risk of diabetes and also disrupt melatonin signalling in cell culture experiments( Reference Bonnefond, Clement and Fawcett 117 ). Evidence from animal models further supports the existence of a physiological link between melatonin, insulin secretion( Reference Mulder, Nagorny and Lyssenko 118 ) and insulin sensitivity( Reference Contreras-Alcantara, Baba and Tosini 119 , Reference Nogueira, Lellis-Santos and Jesus 120 ). When comparing rodent and human data, it should be recognised that elevated melatonin secretion occurs at night in all species, irrespective of whether they are active at night or during the day; therefore direct translation of data relating melatonin to glucose homeostasis from rodents to humans is difficult. Despite this, there is now evidence to support linking low nocturnal melatonin levels in humans, estimated from morning urinary metabolite concentration, with the risk of developing type 2 diabetes( Reference McMullan, Schernhammer and Rimm 121 ) and also insulin resistance in non-diabetic subjects( Reference McMullan, Curhan and Schernhammer 122 ).

Comparison of molecular rhythms in lean and obese individuals has also been addressed by a number of research groups. Reduced amplitude rhythms have been reported in tissues such as adipose( Reference Ando, Yanagihara and Hayashi 123 , Reference Ando, Kumazaki and Motosugi 124 ), liver( Reference Ando, Kumazaki and Motosugi 124 , Reference Ando, Oshima and Yanagihara 125 ) and brain stem( Reference Kaneko, Yamada and Tsukita 126 ) of obese and diabetic mice. However, interpretation of these data is sometimes hampered by the use of different genetic strains in the lean and obese groups. We have recently compared daily profiles of clock gene expression in subcutaneous adipose biopsies taken from lean and obese human subjects and failed to observe any effects of body weight on these molecular rhythms( Reference Otway, Mantele and Bretschneider 25 ). Although it is possible that differences would have been observed if we had been able to serially sample other (for example, visceral) adipose depots, the data nonetheless indicate that obesity per se does not impair clock gene rhythms in all metabolically active tissues.

A small number of animal studies have compared lean and obese groups generated by manipulation of dietary intake, rather than examining the effects of altered body weight due to genetic differences. In most cases, dietary obesity results in clear changes in aspects of the circadian system, but the available results are not entirely consistent. In one study, 6 weeks of high-fat diet altered behavioural and endocrine rhythms, together with changes in gene expression profiles that included reduced amplitude clock gene rhythms in adipose and liver( Reference Kohsaka, Laposky and Ramsey 127 ). In another experiment, mice were fed for 7 weeks with either a high- or low-fat diet, fasted for 24 h and killed in constant darkness. Comparison of clock gene expression in the liver of these animals revealed a phase delay of approximately 3 h with no consistent reduction in rhythm amplitude in the animals fed on a high-fat diet( Reference Barnea, Madar and Froy 128 ). Analysis of two daytime time points in mice maintained on high- or low-fat diets for 11 months also revealed altered liver and kidney clock gene expression( Reference Hsieh, Yang and Tseng 129 ). In contrast to the above studies, which all used male mice, few significant differences were observed in liver and adipose clock gene rhythms from female mice fed high- or low-fat diets for 8 weeks( Reference Yanagihara, Ando and Hayashi 130 ).

The differences in type and magnitude of response observed in mice chronically fed a high-fat diet probably reflect details of experimental design such as sex of the animal, dietary composition and environmental conditions used during the protocol. Sex is an issue of note in regard to human physiology, where sex differences in circadian rhythms( Reference Duffy, Cain and Chang 131 ), adiposity( Reference Tchernof and Després 132 ) and nocturnal postprandial response (described below) have been reported. Furthermore, the extreme changes in dietary intake utilised in animal experiments may not accurately represent typical human diets. It is therefore clear that more research is required in this area before the translational consequences are fully understood.

Relevance to human lifestyle

As would be expected for an emerging research field, the available literature mostly derives from controlled laboratory experiments. However, the biological principles described have far-reaching implications for many people living in contemporary society.

Dietary regulation of body weight

Evidence is rapidly accumulating to support an important role of meal times in the long-term regulation of body weight. Proof-of-concept studies in animal models have utilised different timed feeding paradigms. Mice housed in a light–dark cycle and fed a high-fat diet gain more weight when the food is available only throughout the light phase, when they would usually be resting, than when it is provided throughout the dark phase( Reference Arble, Bass and Laposky 133 ). This body-weight effect becomes statistically significant within 2 weeks and despite trends towards increased energy intake and reduced activity in the light-fed animals, there were no statistically significant changes in these parameters. In a refinement of the protocol, mice were fed a high- or low-fat diet that was available either ad libitum throughout the day, or during a 4 h period in the middle of the light phase( Reference Sherman, Genzer and Cohen 134 ). Remarkably, temporal food restriction resulted in reduced body weight on both high- and low-fat diets, to the extent that mice on a restricted high-fat diet weighed less than those provided with a low-fat diet ad libitum. This occurred despite no difference in energy consumption (as expressed relative to body weight) between the restricted high-fat-diet group and the two low-fat-diet groups, although mice under restricted feeding did exhibit elevated total daily activity compared with ad libitum controls( Reference Sherman, Genzer and Cohen 134 ). Comparing these two studies, it seems that the duration of restricted food availability has profound effects on body-weight regulation, but the mechanisms underlying this phenomenon are not yet clear.

In humans, there is an increasing interest in the effects of meal timing per se on metabolism and body weight. Work in this area has understandably focused to a large degree on meals taken at the start and end of the day, for instance the study of breakfast consumption and individuals with night eating disorders. Evidence suggests a role for regular breakfast consumption in the maintenance of healthy body weight, although the issue has rarely been approached from a chronobiological perspective and questions remain about the causative mechanisms involved( Reference de la Hunty and Gibson 135 , Reference Casazza, Fontaine and Astrup 136 ). Night eating syndrome has recently been included in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). It is broadly characterised by recurrent episodes of nocturnal eating that cannot be better accounted for by other behavioural and psychiatric disorders, and is discussed in detail elsewhere( Reference Berner and Allison 137 ). The relationship between night eating syndrome and body weight is complex, with some variable findings reported in the literature. Despite this, the overall evidence provides compelling associations between night eating and obesity, with a consistent finding that night eating syndrome is more prevalent in overweight and obese groups( Reference Gallant, Lundgren and Drapeau 138 ). The importance of evening meals is further highlighted in studies of subjects without night eating disorder. For example, energy consumption after 20.00 hours has been associated with BMI independently of age, sleep timing and sleep duration( Reference Baron, Reid and Kern 139 ). This group later reported that protein intake within 4 h of sleep onset is associated with elevated BMI after controlling for age, sex, sleep timing and sleep duration( Reference Baron, Reid and Horn 140 ).

The link between food timing and body weight is also apparent in dietary weight-loss studies. A group of 420 individuals undergoing a 20-week weight-loss programme were categorised according to the time at which they ate lunch, which was their main daily meal. Those in the late group lost less weight and at a slower rate than the early group, with no difference in energy intake, energy expenditure, dietary composition or sleep duration( Reference Garaulet, Gomez-Abellan and Alburquerque-Bejar 141 ). In a separate experiment, obese/overweight women consumed energy-restricted diets that differed in the proportion of energy distributed between breakfast and dinner. The women eating more energy at breakfast than dinner not only lost more weight but also exhibited an improved metabolic profile in insulin sensitivity and TAG concentration( Reference Jakubowicz, Barnea and Wainstein 142 ). Together these data support the hypothesis that the timing of food intake is important for body-weight regulation.

Shift work and jet-lag

Since industrialisation, humans have gained the ability to regulate environmental conditions and subsequently alter temporal patterns of behaviour. Indeed the phrase ‘24/7 society’ is now in common usage to describe the constant presence of industrial and social activity. In order to cope with the demands of this modern aspect of society, varied work schedules are now commonplace, with approximately 20 % of the European workforce engaged in night shifts( Reference Costa 143 ). This clearly indicates that a large section of the population experiences regular misalignment of their behaviour with the solar day. A second common cause of abrupt circadian misalignment is jet-lag, the rapid travel across time zones. Although this experience is a rare and transient phenomenon for most people, it nonetheless affects a substantial number of individuals and in some cases (for example, airline crew) can be a regular event. A related phenomenon that is common in society is a weekly change between widely differing sleep times on work and free days. This has been termed ‘social jet-lag’( Reference Wittmann, Dinich and Merrow 144 ) and is associated with elevated BMI( Reference Roenneberg, Allebrandt and Merrow 145 ).

Multiple health problems are associated with shift work, including increased risk of cardiovascular and metabolic disease( Reference Tucker, Marquie and Folkard 146 ). Understanding the aetiology of shift work-related morbidity is complex due to diverse contributory factors such as sleep disturbance, altered social pressure and patterns of food intake( Reference Esquirol, Bongard and Mabile 147 , Reference Lowden, Moreno and Holmback 148 ). For example, although shift workers often report normal total energy intake, there is commonly an altered temporal distribution of feeding characterised by more irregular eating times, more snacking and fewer substantial meals( Reference Lowden, Moreno and Holmback 148 ). Alongside these lifestyle changes it is likely that disrupted circadian physiology is a major contributor to the pathophysiological consequences of shift work. Indeed it is generally recognised that many shift workers in temperate regions poorly adapt circadian rhythms to their work conditions( Reference Folkard 149 ). As a result, these individuals experience prolonged durations of misalignment between their circadian biology and behavioural patterns.

As described previously, postprandial profiles of glucose and TAG concentration vary over the day. Such studies clearly imply that shift workers eat a substantial proportion of their meals during the time of suboptimal glucose and lipid tolerance. This prediction is strengthened by studies of simulated shift work where subjects are subjected to an abrupt shift of typically 6–10 h in their daily routine. Interestingly, the postprandial response in such protocols is altered by the preceding diet. In comparable experiments, the exaggerated postprandial response following a test meal in shifted subjects was reduced for glucose and insulin but increased for TAG following a low-fat pre-meal( Reference Ribeiro, Hampton and Morgan 150 ) rather than a high-fat pre-meal( Reference Hampton, Morgan and Lawrence 151 ). Furthermore, there are reported sex differences in postprandial response, with a more pronounced elevation of TAG in the first night of a simulated night shift in men than in women( Reference Sopowski, Hampton and Ribeiro 90 ).

In real shift workers, there are also postprandial data describing the relative insulin resistance and lipid intolerance following abrupt shift changes( Reference Lund, Arendt and Hampton 152 ). Given the large number of individuals undertaking shift work in modern society, there is a clear need to improve circadian alignment in these individuals. One possible intervention is the manipulation of light, which is able to reset circadian rhythms and also directly improve alertness( Reference Vandewalle, Maquet and Dijk 153 ) even following short exposure times( Reference Chang, Santhi and St Hilaire 154 ). However, timed food may also be a powerful method for resetting rhythms to a new phase. Of particular interest is the ability of timed food to reset peripheral tissue rhythms.

Animal models support the idea that timed food intake could be a valuable intervention to minimise adverse effects of shift work. Mice subjected to an artificial shift work-like environmental schedule exhibit circadian desynchrony and metabolic disturbance( Reference Barclay, Husse and Bode 155 ). Similar findings have been reported in rats exposed to an artificial shift work protocol, although the body-weight increase and metabolic disturbances experienced were attenuated when food availability was restricted to the normal activity phase( Reference Salgado-Delgado, Angeles-Castellanos and Saderi 156 ). Further development of these animal models will permit detailed molecular analysis to complement human studies of shift work and its adverse effects.

Conclusion

A wealth of data from varied experimental approaches provides us with clear links between circadian, metabolic and nutritional biology. These findings provide a strong foundation upon which to model mechanisms underlying the temporal differences in response to food intake. One limitation of the field is that little translational research has yet been performed in human subjects. Understanding the circadian regulation of human metabolism will have profound implications for nutritional science and explain how time of day is important for postprandial physiology. Furthermore, it will also reveal how timed dietary intake can be used as a means to alleviate some of the deleterious effects of circadian misalignment that are experienced by large numbers of people within modern society.

Acknowledgements

The author thanks Dr S. M. Hampton for critically reading an earlier version of this paper.

The author's work cited in the present review was funded by the UK Biotechnology and Biosciences Research Council (BBSRC), Diabetes UK and Stockgrand Ltd. The funders had no role in the design, analysis or writing of this article.

The author has performed consultancy work for Kellogg Marketing and Sales Company (UK) Limited.

References

1 Pittendrigh, CS (1993) Temporal organization: reflections of a Darwinian clock-watcher. Annu Rev Physiol 55, 1654.CrossRefGoogle ScholarPubMed
2 Ouyang, Y, Andersson, CR, Kondo, T, et al. (1998) Resonating circadian clocks enhance fitness in cyanobacteria. Proc Natl Acad Sci U S A 95, 86608664.CrossRefGoogle ScholarPubMed
3 Moore, RY & Eichler, VB (1972) Loss of a circadian adrenal corticosterone rhythm following suprachiasmatic lesions in the rat. Brain Res 42, 201206.CrossRefGoogle ScholarPubMed
4 Stephan, FK & Zucker, I (1972) Circadian rhythms in drinking behavior and locomotor activity of rats are eliminated by hypothalamic lesions. Proc Natl Acad Sci U S A 69, 15831586.CrossRefGoogle ScholarPubMed
5 Inouye, ST & Kawamura, H (1979) Persistence of circadian rhythmicity in a mammalian hypothalamic “island” containing the suprachiasmatic nucleus. Proc Natl Acad Sci U S A 76, 59625966.CrossRefGoogle Scholar
6 Shibata, S, Oomura, Y, Kita, H, et al. (1982) Circadian rhythmic changes of neuronal activity in the suprachiasmatic nucleus of the rat hypothalamic slice. Brain Res 247, 154158.CrossRefGoogle ScholarPubMed
7 Groos, G & Hendriks, J (1982) Circadian rhythms in electrical discharge of rat suprachiasmatic neurones recorded in vitro . Neurosci Lett 34, 283288.CrossRefGoogle ScholarPubMed
8 Ralph, MR, Foster, RG, Davis, FC, et al. (1990) Transplanted suprachiasmatic nucleus determines circadian period. Science 247, 975978.CrossRefGoogle ScholarPubMed
9 Tosini, G & Menaker, M (1996) Circadian rhythms in cultured mammalian retina. Science 272, 419421.CrossRefGoogle ScholarPubMed
10 Sakamoto, K, Nagase, T, Fukui, H, et al. (1998) Multitissue circadian expression of rat period homolog (rPer2) mRNA is governed by the mammalian circadian clock, the suprachiasmatic nucleus in the brain. J Biol Chem 273, 2703927042.CrossRefGoogle ScholarPubMed
11 Zylka, MJ, Shearman, LP, Weaver, DR, et al. (1998) Three period homologs in mammals: differential light responses in the suprachiasmatic circadian clock and oscillating transcripts outside of brain. Neuron 20, 11031110.CrossRefGoogle ScholarPubMed
12 Yamazaki, S, Numano, R, Abe, M, et al. (2000) Resetting central and peripheral circadian oscillators in transgenic rats. Science 288, 682685.CrossRefGoogle ScholarPubMed
13 Yoo, SH, Yamazaki, S, Lowrey, PL, et al. (2004) PERIOD2:LUCIFERASE real-time reporting of circadian dynamics reveals persistent circadian oscillations in mouse peripheral tissues. Proc Natl Acad Sci U S A 101, 53395346.CrossRefGoogle Scholar
14 Balsalobre, A, Damiola, F & Schibler, U (1998) A serum shock induces circadian gene expression in mammalian tissue culture cells. Cell 93, 929937.CrossRefGoogle ScholarPubMed
15 Nagoshi, E, Saini, C, Bauer, C, et al. (2004) Circadian gene expression in individual fibroblasts: cell-autonomous and self-sustained oscillators pass time to daughter cells. Cell 119, 693705.CrossRefGoogle ScholarPubMed
16 Welsh, DK, Yoo, SH, Liu, AC, et al. (2004) Bioluminescence imaging of individual fibroblasts reveals persistent, independently phased circadian rhythms of clock gene expression. Curr Biol 14, 22892295.CrossRefGoogle ScholarPubMed
17 Albrecht, U (2012) Timing to perfection: the biology of central and peripheral circadian clocks. Neuron 74, 246260.CrossRefGoogle ScholarPubMed
18 Cagampang, FR & Bruce, KD (2012) The role of the circadian clock system in nutrition and metabolism. Br J Nutr 108, 381392.CrossRefGoogle ScholarPubMed
19 Johnston, JD (2012) Adipose circadian rhythms: translating cellular and animal studies to human physiology. Mol Cell Endocrinol 349, 4550.CrossRefGoogle ScholarPubMed
20 Boivin, DB, James, FO, Wu, A, et al. (2003) Circadian clock genes oscillate in human peripheral blood mononuclear cells. Blood 102, 41434145.CrossRefGoogle ScholarPubMed
21 Archer, SN, Viola, AU, Kyriakopoulou, V, et al. (2008) Inter-individual differences in habitual sleep timing and entrained phase of endogenous circadian rhythms of BMAL1, PER2 and PER3 mRNA in human leukocytes. Sleep 31, 608617.CrossRefGoogle ScholarPubMed
22 Brown, SA, Fleury-Olela, F, Nagoshi, E, et al. (2005) The period length of fibroblast circadian gene expression varies widely among human individuals. PLoS Biol 3, e338.CrossRefGoogle ScholarPubMed
23 Hasan, S, Santhi, N, Lazar, AS, et al. (2012) Assessment of circadian rhythms in humans: comparison of real-time fibroblast reporter imaging with plasma melatonin. FASEB J 26, 24142423.CrossRefGoogle ScholarPubMed
24 Gomez-Santos, C, Gomez-Abellan, P, Madrid, JA, et al. (2009) Circadian rhythm of clock genes in human adipose explants. Obesity 17, 14811485.CrossRefGoogle ScholarPubMed
25 Otway, DT, Mantele, S, Bretschneider, S, et al. (2011) Rhythmic diurnal gene expression in human adipose tissue from individuals who are lean, overweight, and type 2 diabetic. Diabetes 60, 15771581.CrossRefGoogle ScholarPubMed
26 Stamenkovic, JA, Olsson, AH, Nagorny, CL, et al. (2012) Regulation of core clock genes in human islets. Metabolism 61, 978985.CrossRefGoogle ScholarPubMed
27 Schmidt, TM, Do, MT, Dacey, D, et al. (2011) Melanopsin-positive intrinsically photosensitive retinal ganglion cells: from form to function. J Neurosci 31, 1609416101.CrossRefGoogle ScholarPubMed
28 Dibner, C & Schibler, U (2010) Albrecht U The mammalian circadian timing system: organization and coordination of central and peripheral clocks. Annu Rev Physiol 72, 517549.CrossRefGoogle ScholarPubMed
29 Akhtar, RA, Reddy, AB, Maywood, ES, et al. (2002) Circadian cycling of the mouse liver transcriptome, as revealed by cDNA microarray, is driven by the suprachiasmatic nucleus. Curr Biol 12, 540550.CrossRefGoogle ScholarPubMed
30 Duffield, GE, Best, JD, Meurers, BH, et al. (2002) Circadian programs of transcriptional activation, signaling, and protein turnover revealed by microarray analysis of mammalian cells. Curr Biol 12, 551557.CrossRefGoogle ScholarPubMed
31 Panda, S, Antoch, MP, Miller, BH, et al. (2002) Coordinated transcription of key pathways in the mouse by the circadian clock. Cell 109, 307320.CrossRefGoogle ScholarPubMed
32 Storch, KF, Lipan, O, Leykin, I, et al. (2002) Extensive and divergent circadian gene expression in liver and heart. Nature 417, 7883.CrossRefGoogle ScholarPubMed
33 Zvonic, S, Ptitsyn, AA, Conrad, SA, et al. (2006) Characterization of peripheral circadian clocks in adipose tissues. Diabetes 55, 962970.CrossRefGoogle ScholarPubMed
34 Reddy, AB, Karp, NA, Maywood, ES, et al. (2006) Circadian orchestration of the hepatic proteome. Curr Biol 16, 11071115.CrossRefGoogle ScholarPubMed
35 Minami, Y, Kasukawa, T, Kakazu, Y, et al. (2009) Measurement of internal body time by blood metabolomics. Proc Natl Acad Sci U S A 106, 98909895.CrossRefGoogle ScholarPubMed
36 Eckel-Mahan, KL, Patel, VR, Mohney, RP, et al. (2012) Coordination of the transcriptome and metabolome by the circadian clock. Proc Natl Acad Sci U S A 109, 55415546.CrossRefGoogle ScholarPubMed
37 Fustin, JM, Doi, M, Yamada, H, et al. (2012) Rhythmic nucleotide synthesis in the liver: temporal segregation of metabolites. Cell Rep 1, 341349.CrossRefGoogle ScholarPubMed
38 Dallmann, R, Viola, AU, Tarokh, L, et al. (2012) The human circadian metabolome. Proc Natl Acad Sci U S A 109, 26252629.CrossRefGoogle ScholarPubMed
39 Ang, JE, Revell, V, Mann, A, et al. (2012) Identification of human plasma metabolites exhibiting time-of-day variation using an untargeted liquid chromatography-mass spectrometry metabolomic approach. Chronobiol Int 29, 868881.CrossRefGoogle ScholarPubMed
40 Kasukawa, T, Sugimoto, M, Hida, A, et al. (2012) Human blood metabolite timetable indicates internal body time. Proc Natl Acad Sci U S A 109, 1503615041.CrossRefGoogle ScholarPubMed
41 Johnston, JD, Frost, G & Otway, DT (2009) Adipose tissue, adipocytes and the circadian timing system. Obes Rev 10, Suppl. 2, 5260.CrossRefGoogle ScholarPubMed
42 Raspe, E, Duez, H, Mansen, A, et al. (2002) Identification of Rev-erbα as a physiological repressor of apoC-III gene transcription. J Lipid Res 43, 21722179.CrossRefGoogle ScholarPubMed
43 Rudic, RD, McNamara, P, Curtis, AM, et al. (2004) BMAL1 and CLOCK, two essential components of the circadian clock, are involved in glucose homeostasis. PLoS Biol 2, e377.CrossRefGoogle ScholarPubMed
44 Turek, FW, Joshu, C, Kohsaka, A, et al. (2005) Obesity and metabolic syndrome in circadian Clock mutant mice. Science 308, 10431045.CrossRefGoogle ScholarPubMed
45 Yang, S, Liu, A, Weidenhammer, A, et al. (2009) The role of mPer2 clock gene in glucocorticoid and feeding rhythms. Endocrinology 150, 21532160.CrossRefGoogle ScholarPubMed
46 Costa, MJ, So, AY, Kaasik, K, et al. (2011) Circadian rhythm gene period 3 is an inhibitor of the adipocyte cell fate. J Biol Chem 286, 90639070.CrossRefGoogle ScholarPubMed
47 Cho, H, Zhao, X, Hatori, M, et al. (2012) Regulation of circadian behaviour and metabolism by REV-ERB-α and REV-ERB-β. Nature 485, 123127.CrossRefGoogle ScholarPubMed
48 Woon, PY, Kaisaki, PJ, Braganca, J, et al. (2007) Aryl hydrocarbon receptor nuclear translocator-like (BMAL1) is associated with susceptibility to hypertension and type 2 diabetes. Proc Natl Acad Sci U S A 104, 1441214417.CrossRefGoogle ScholarPubMed
49 Scott, EM, Carter, AM & Grant, PJ (2008) Association between polymorphisms in the Clock gene, obesity and the metabolic syndrome in man. Int J Obes 32, 658662.CrossRefGoogle ScholarPubMed
50 Sookoian, S, Gemma, C, Gianotti, TF, et al. (2008) Genetic variants of Clock transcription factor are associated with individual susceptibility to obesity. Am J Clin Nutr 87, 16061615.CrossRefGoogle ScholarPubMed
51 Tsuzaki, K, Kotani, K, Sano, Y, et al. (2010) The association of the Clock 3111 T/C SNP with lipids and lipoproteins including small dense low-density lipoprotein: results from the Mima study. BMC Med Genet 11, 150.CrossRefGoogle ScholarPubMed
52 Garaulet, M, Corbalan-Tutau, MD, Madrid, JA, et al. (2010) PERIOD2 variants are associated with abdominal obesity, psycho-behavioral factors, and attrition in the dietary treatment of obesity. JAMA 110, 917921.Google ScholarPubMed
53 Garaulet, M, Esteban Tardido, A, et al. (2012) Lee YC. SIRT1 and CLOCK 3111T>C combined genotype is associated with evening preference and weight loss resistance in a behavioral therapy treatment for obesity. Int J Obes 36, 14361441.CrossRefGoogle Scholar
54 Lamia, KA, Storch, KF & Weitz, CJ (2008) Physiological significance of a peripheral tissue circadian clock. Proc Natl Acad Sci U S A 105, 1517215177.CrossRefGoogle ScholarPubMed
55 Marcheva, B, Ramsey, KM, Buhr, ED, et al. (2010) Disruption of the clock components CLOCK and BMAL1 leads to hypoinsulinaemia and diabetes. Nature 466, 627631.CrossRefGoogle ScholarPubMed
56 Sadacca, LA, Lamia, KA, deLemos, AS, et al. (2011) An intrinsic circadian clock of the pancreas is required for normal insulin release and glucose homeostasis in mice. Diabetologia 54, 120124.CrossRefGoogle ScholarPubMed
57 Paschos, GK, Ibrahim, S, Song, WL, et al. (2012) Obesity in mice with adipocyte-specific deletion of clock component Arntl. Nat Med 18, 17681777.CrossRefGoogle ScholarPubMed
58 Stephan, FK (2002) The “other” circadian system: food as a Zeitgeber. J Biol Rhythms 17, 284292.CrossRefGoogle Scholar
59 Mistlberger, RE (2009) Food-anticipatory circadian rhythms: concepts and methods. Eur J Neurosci 30, 17181729.CrossRefGoogle Scholar
60 Challet, E, Mendoza, J, Dardente, H, et al. (2009) Neurogenetics of food anticipation. Eur J Neurosci 30, 16761687.CrossRefGoogle ScholarPubMed
61 Storch, KF & Weitz, CJ (2009) Daily rhythms of food-anticipatory behavioral activity do not require the known circadian clock. Proc Natl Acad Sci U S A 106, 68086813.CrossRefGoogle Scholar
62 Krieger, DT, Hauser, H & Krey, LC (1977) Suprachiasmatic nuclear lesions do not abolish food-shifted circadian adrenal and temperature rhythmicity. Science 197, 398399.CrossRefGoogle Scholar
63 Stephan, FK (1981) Limits of entrainment to periodic feeding in rats with suprachiasmatic lesions. J Comp Physiol 143, 401410.CrossRefGoogle Scholar
64 Mieda, M, Williams, SC, Richardson, JA, et al. (2006) The dorsomedial hypothalamic nucleus as a putative food-entrainable circadian pacemaker. Proc Natl Acad Sci U S A 103, 1215012155.CrossRefGoogle ScholarPubMed
65 Fuller, PM, Lu, J & Saper, CB (2008) Differential rescue of light- and food-entrainable circadian rhythms. Science 320, 10741077.CrossRefGoogle ScholarPubMed
66 Mendoza, J, Pevet, P, Felder-Schmittbuhl, MP, et al. (2010) The cerebellum harbors a circadian oscillator involved in food anticipation. J Neurosci 30, 18941904.CrossRefGoogle ScholarPubMed
67 Mistlberger, RE, Buijs, RM, Challet, E, et al. (2009) Standards of evidence in chronobiology: critical review of a report that restoration of Bmal1 expression in the dorsomedial hypothalamus is sufficient to restore circadian food anticipatory rhythms in Bmal1-/- mice. J Circ Rhythms 7, 3.CrossRefGoogle ScholarPubMed
68 Moriya, T, Aida, R, Kudo, T, et al. (2009) The dorsomedial hypothalamic nucleus is not necessary for food-anticipatory circadian rhythms of behavior, temperature or clock gene expression in mice. Eur J Neurosci 29, 14471460.CrossRefGoogle ScholarPubMed
69 Landry, GJ, Kent, BA, Patton, DF, et al. (2011) Evidence for time-of-day dependent effect of neurotoxic dorsomedial hypothalamic lesions on food anticipatory circadian rhythms in rats. PloS ONE 6, e24187.CrossRefGoogle ScholarPubMed
70 Damiola, F, Le Minh, N, Preitner, N, et al. (2000) Restricted feeding uncouples circadian oscillators in peripheral tissues from the central pacemaker in the suprachiasmatic nucleus. Genes Dev 14, 29502961.CrossRefGoogle ScholarPubMed
71 Stokkan, KA, Yamazaki, S, Tei, H, et al. (2001) Entrainment of the circadian clock in the liver by feeding. Science 291, 490493.CrossRefGoogle ScholarPubMed
72 Hoogerwerf, WA, Hellmich, HL, Cornelissen, G, et al. (2007) Clock gene expression in the murine gastrointestinal tract: endogenous rhythmicity and effects of a feeding regimen. Gastroenterology 133, 12501260.CrossRefGoogle ScholarPubMed
73 Yoshida, C, Shikata, N, Seki, S, et al. (2012) Early nocturnal meal skipping alters the peripheral clock and increases lipogenesis in mice. Nutr Metab 9, 78.CrossRefGoogle ScholarPubMed
74 Mendoza, J, Graff, C, Dardente, H, et al. (2005) Feeding cues alter clock gene oscillations and photic responses in the suprachiasmatic nuclei of mice exposed to a light/dark cycle. J Neurosci 25, 15141522.CrossRefGoogle ScholarPubMed
75 Mendoza, J, Gourmelen, S, Dumont, S, et al. (2012) Setting the main circadian clock of a diurnal mammal by hypocaloric feeding. J Physiol 590, 31553168.CrossRefGoogle ScholarPubMed
76 Mendoza, J, Drevet, K, Pevet, P, et al. (2008) Daily meal timing is not necessary for resetting the main circadian clock by calorie restriction. J Neuroendocrinol 20, 251260.CrossRefGoogle Scholar
77 Kuroda, H, Tahara, Y, Saito, K, et al. (2012) Meal frequency patterns determine the phase of mouse peripheral circadian clocks. Sci Rep 2, 711.CrossRefGoogle ScholarPubMed
78 Wu, T, Sun, L, ZhuGe, F, et al. (2011) Differential roles of breakfast and supper in rats of a daily three-meal schedule upon circadian regulation and physiology. Chronobiol Int 28, 890903.CrossRefGoogle ScholarPubMed
79 Hirao, A, Nagahama, H, Tsuboi, T, et al. (2010) Combination of starvation interval and food volume determines the phase of liver circadian rhythm in Per2:Luc knock-in mice under two meals per day feeding. Am J Physiol 299, G1045G1053.Google ScholarPubMed
80 Krauchi, K, Cajochen, C, Werth, E, et al. (2002) Alteration of internal circadian phase relationships after morning versus evening carbohydrate-rich meals in humans. J Biol Rhythms 17, 364376.CrossRefGoogle ScholarPubMed
81 Schoeller, DA, Cella, LK, Sinha, MK, et al. (1997) Entrainment of the diurnal rhythm of plasma leptin to meal timing. J Clin Invest 100, 18821887.CrossRefGoogle ScholarPubMed
82 Itokawa, M, Hirao, A, Nagahama, H, et al. (2013) Time-restricted feeding of rapidly digested starches causes stronger entrainment of the liver clock in PER2:LUCIFERASE knock-in mice. Nutr Res 33, 109119.CrossRefGoogle ScholarPubMed
83 Hirao, A, Tahara, Y, Kimura, I, et al. (2009) A balanced diet is necessary for proper entrainment signals of the mouse liver clock. PloS ONE 4, e6909.CrossRefGoogle ScholarPubMed
84 Le Minh, N, Damiola, F, Tronche, F, et al. (2001) Glucocorticoid hormones inhibit food-induced phase-shifting of peripheral circadian oscillators. EMBO J 20, 71287136.CrossRefGoogle ScholarPubMed
85 Balsalobre, A, Brown, SA, Marcacci, L, et al. (2000) Resetting of circadian time in peripheral tissues by glucocorticoid signaling. Science 289, 23442347.CrossRefGoogle ScholarPubMed
86 Vollmers, C, Gill, S, DiTacchio, L, et al. (2009) Time of feeding and the intrinsic circadian clock drive rhythms in hepatic gene expression. Proc Natl Acad Sci U S A 106, 2145321458.CrossRefGoogle ScholarPubMed
87 Asher, G, Reinke, H, Altmeyer, M, et al. (2010) Poly(ADP-ribose) polymerase 1 participates in the phase entrainment of circadian clocks to feeding. Cell 142, 943953.CrossRefGoogle ScholarPubMed
88 Zhang, L, Abraham, D, Lin, ST, et al. (2012) PKCγ participates in food entrainment by regulating BMAL1. Proc Natl Acad Sci U S A 109, 2067920684.CrossRefGoogle ScholarPubMed
89 Van Cauter, E, Polonsky, KS & Scheen, AJ (1997) Roles of circadian rhythmicity and sleep in human glucose regulation. Endocr Rev 18, 716738.Google ScholarPubMed
90 Sopowski, MJ, Hampton, SM, Ribeiro, DC, et al. (2001) Postprandial triacylglycerol responses in simulated night and day shift: gender differences. J Biol Rhythms 16, 272276.CrossRefGoogle ScholarPubMed
91 Burdge, GC, Jones, AE, Frye, SM, et al. (2003) Effect of meal sequence on postprandial lipid, glucose and insulin responses in young men. Eur J Clin Nutr 57, 15361544.CrossRefGoogle ScholarPubMed
92 Trujillo, ME & Scherer, PE (2006) Adipose tissue-derived factors: impact on health and disease. Endocr Rev 27, 762778.CrossRefGoogle ScholarPubMed
93 Galic, S, Oakhill, JS & Steinberg, GR (2010) Adipose tissue as an endocrine organ. Mol Cell Endocrinol 316, 129139.CrossRefGoogle ScholarPubMed
94 Sinha, MK, Ohannesian, JP, Heiman, ML, et al. (1996) Nocturnal rise of leptin in lean, obese, and non-insulin-dependent diabetes mellitus subjects. J Clin Invest 97, 13441347.CrossRefGoogle ScholarPubMed
95 Gavrila, A, Peng, CK, Chan, JL, et al. (2003) Diurnal and ultradian dynamics of serum adiponectin in healthy men: comparison with leptin, circulating soluble leptin receptor, and cortisol patterns. J Clin Endocrinol Metab 88, 28382843.CrossRefGoogle ScholarPubMed
96 Parlee, SD, Ernst, MC, Muruganandan, S, et al. (2010) Serum chemerin levels vary with time of day and are modified by obesity and tumor necrosis factor-α. Endocrinology 151, 25902602.CrossRefGoogle ScholarPubMed
97 Scheer, FA, Chan, JL, Fargnoli, J, et al. (2010) Day/night variations of high-molecular-weight adiponectin and lipocalin-2 in healthy men studied under fed and fasted conditions. Diabetologia 53, 24012405.CrossRefGoogle ScholarPubMed
98 Benedict, C, Shostak, A, Lange, T, et al. (2012) Diurnal rhythm of circulating nicotinamide phosphoribosyltransferase (Nampt/visfatin/PBEF): impact of sleep loss and relation to glucose metabolism. J Clin Endocrinol Metab 97, E218E222.CrossRefGoogle ScholarPubMed
99 Shea, SA, Hilton, MF, Orlova, C, et al. (2005) Independent circadian and sleep/wake regulation of adipokines and glucose in humans. J Clin Endocrinol Metab 90, 25372544.CrossRefGoogle ScholarPubMed
100 Otway, DT, Frost, G & Johnston, JD (2009) Circadian rhythmicity in murine pre-adipocyte and adipocyte cells. Chronobiol Int 26, 13401354.CrossRefGoogle ScholarPubMed
101 Duffy, JF & Dijk, DJ (2002) Getting through to circadian oscillators: why use constant routines? J Biol Rhythms 17, 413.CrossRefGoogle ScholarPubMed
102 Blatter, K & Cajochen, C (2007) Circadian rhythms in cognitive performance: methodological constraints, protocols, theoretical underpinnings. Physiol Behav 90, 196208.CrossRefGoogle ScholarPubMed
103 Holmback, U, Forslund, A, Forslund, J, et al. (2002) Metabolic responses to nocturnal eating in men are affected by sources of dietary energy. J Nutr 132, 18921899.CrossRefGoogle ScholarPubMed
104 Morgan, L, Arendt, J, Owens, D, et al. (1998) Effects of the endogenous clock and sleep time on melatonin, insulin, glucose and lipid metabolism. J Endocrinol 157, 443451.CrossRefGoogle ScholarPubMed
105 Scheer, FA, Hilton, MF, Mantzoros, CS, et al. (2009) Adverse metabolic and cardiovascular consequences of circadian misalignment. Proc Natl Acad Sci U S A 106, 44534458.CrossRefGoogle ScholarPubMed
106 Gonnissen, HK, Rutters, F, Mazuy, C, et al. (2012) Effect of a phase advance and phase delay of the 24-h cycle on energy metabolism, appetite, and related hormones. Am J Clin Nutr 96, 689697.CrossRefGoogle ScholarPubMed
107 Matkovic, V, Ilich, JZ, Badenhop, NE, et al. (1997) Gain in body fat is inversely related to the nocturnal rise in serum leptin level in young females. J Clin Endocrinol Metab 82, 13681372.Google Scholar
108 Saad, MF, Riad-Gabriel, MG, Khan, A, et al. (1998) Diurnal and ultradian rhythmicity of plasma leptin: effects of gender and adiposity. J Clin Endocrinol Metab 83, 453459.Google ScholarPubMed
109 Yildiz, BO, Suchard, MA, Wong, ML, et al. (2004) Alterations in the dynamics of circulating ghrelin, adiponectin, and leptin in human obesity. Proc Natl Acad Sci U S A 101, 1043410439.CrossRefGoogle ScholarPubMed
110 Mantele, S, Otway, DT, Middleton, B, et al. (2012) Daily rhythms of plasma melatonin, but not plasma leptin or leptin mRNA, vary between lean, obese and type 2 diabetic men. PLOS ONE 7, e37123.CrossRefGoogle ScholarPubMed
111 Arendt, J, Hampton, S, English, J, et al. (1982) 24-Hour profiles of melatonin, cortisol, insulin, C-peptide and GIP following a meal and subsequent fasting. Clin Endocrinol 16, 8995.CrossRefGoogle ScholarPubMed
112 Corbalan-Tutau, D, Madrid, JA, Nicolas, F, et al. (2014) Daily profile in two circadian markers “melatonin and cortisol” and associations with metabolic syndrome components. Physiol Behav 123, 231235.CrossRefGoogle ScholarPubMed
113 Bouatia-Naji, N, Bonnefond, A, Cavalcanti-Proenca, C, et al. (2009) A variant near MTNR1B is associated with increased fasting plasma glucose levels and type 2 diabetes risk. Nat Genet 41, 8994.CrossRefGoogle ScholarPubMed
114 Lyssenko, V, Nagorny, CL, Erdos, MR, et al. (2009) Common variant in MTNR1B associated with increased risk of type 2 diabetes and impaired early insulin secretion. Nat Genet 41, 8288.CrossRefGoogle ScholarPubMed
115 Prokopenko, I, Langenberg, C, Florez, JC, et al. (2009) Variants in MTNR1B influence fasting glucose levels. Nat Genet 41, 7781.CrossRefGoogle ScholarPubMed
116 Ronn, T, Wen, J, Yang, Z, et al. (2009) A common variant in MTNR1B, encoding melatonin receptor 1B, is associated with type 2 diabetes and fasting plasma glucose in Han Chinese individuals. Diabetologia 52, 830833.CrossRefGoogle ScholarPubMed
117 Bonnefond, A, Clement, N, Fawcett, K, et al. (2012) Rare MTNR1B variants impairing melatonin receptor 1B function contribute to type 2 diabetes. Nat Genet 44, 297301.CrossRefGoogle ScholarPubMed
118 Mulder, H, Nagorny, CL, Lyssenko, V, et al. (2009) Melatonin receptors in pancreatic islets: good morning to a novel type 2 diabetes gene. Diabetologia 52, 12401249.CrossRefGoogle ScholarPubMed
119 Contreras-Alcantara, S, Baba, K & Tosini, G (2010) Removal of melatonin receptor type 1 induces insulin resistance in the mouse. Obesity 18, 18611863.CrossRefGoogle ScholarPubMed
120 Nogueira, TC, Lellis-Santos, C, Jesus, DS, et al. (2011) Absence of melatonin induces night-time hepatic insulin resistance and increased gluconeogenesis due to stimulation of nocturnal unfolded protein response. Endocrinology 152, 12531263.CrossRefGoogle ScholarPubMed
121 McMullan, CJ, Schernhammer, ES, Rimm, EB, et al. (2013) Melatonin secretion and the incidence of type 2 diabetes. JAMA 309, 13881396.CrossRefGoogle ScholarPubMed
122 McMullan, CJ, Curhan, GC, Schernhammer, ES, et al. (2013) Association of nocturnal melatonin secretion with insulin resistance in nondiabetic young women. Am J Epidemiol 178, 231238.CrossRefGoogle ScholarPubMed
123 Ando, H, Yanagihara, H, Hayashi, Y, et al. (2005) Rhythmic messenger ribonucleic acid expression of clock genes and adipocytokines in mouse visceral adipose tissue. Endocrinology 146, 56315636.CrossRefGoogle ScholarPubMed
124 Ando, H, Kumazaki, M, Motosugi, Y, et al. (2011) Impairment of peripheral circadian clocks precedes metabolic abnormalities in ob/ob mice. Endocrinology 152, 13471354.CrossRefGoogle ScholarPubMed
125 Ando, H, Oshima, Y, Yanagihara, H, et al. (2006) Profile of rhythmic gene expression in the livers of obese diabetic KK-A(y) mice. Biochem Biophys Res Comm 346, 12971302.CrossRefGoogle ScholarPubMed
126 Kaneko, K, Yamada, T, Tsukita, S, et al. (2009) Obesity alters circadian expressions of molecular clock genes in the brainstem. Brain Res 1263, 5868.CrossRefGoogle ScholarPubMed
127 Kohsaka, A, Laposky, AD, Ramsey, KM, et al. (2007) High-fat diet disrupts behavioral and molecular circadian rhythms in mice. Cell Metab 6, 414421.CrossRefGoogle ScholarPubMed
128 Barnea, M, Madar, Z & Froy, O (2009) High-fat diet delays and fasting advances the circadian expression of adiponectin signaling components in mouse liver. Endocrinology 150, 161168.CrossRefGoogle ScholarPubMed
129 Hsieh, MC, Yang, SC, Tseng, HL, et al. (2010) Abnormal expressions of circadian-clock and circadian clock-controlled genes in the livers and kidneys of long-term, high-fat-diet-treated mice. Int J Obes 34, 227239.CrossRefGoogle ScholarPubMed
130 Yanagihara, H, Ando, H, Hayashi, Y, et al. (2006) High-fat feeding exerts minimal effects on rhythmic mRNA expression of clock genes in mouse peripheral tissues. Chronobiol Int 23, 905914.CrossRefGoogle ScholarPubMed
131 Duffy, JF, Cain, SW, Chang, AM, et al. (2011) Sex difference in the near-24-hour intrinsic period of the human circadian timing system. Proc Natl Acad Sci U S A 108, Suppl. 3, 1560215608.CrossRefGoogle ScholarPubMed
132 Tchernof, A & Després, JP (2013) Pathophysiology of human visceral obesity: an update. Physiol Rev 93, 359404.CrossRefGoogle ScholarPubMed
133 Arble, DM, Bass, J, Laposky, AD, et al. (2009) Circadian timing of food intake contributes to weight gain. Obesity 17, 21002102.CrossRefGoogle ScholarPubMed
134 Sherman, H, Genzer, Y, Cohen, R, et al. (2012) Timed high-fat diet resets circadian metabolism and prevents obesity. FASEB J 26, 34933502.CrossRefGoogle ScholarPubMed
135 de la Hunty, A & Gibson, S (2013) Ashwell M Does regular breakfast cereal consumption help children and adolescents stay slimmer? A systematic review and meta-analysis. Obes Facts 6, 7085.CrossRefGoogle ScholarPubMed
136 Casazza, K, Fontaine, KR, Astrup, A, et al. (2013) Myths, presumptions, and facts about obesity. New Eng J Med 368, 446454.CrossRefGoogle ScholarPubMed
137 Berner, LA & Allison, KC (2013) Behavioral management of night eating disorders. Psychol Res Behav Manag 6, 18.Google ScholarPubMed
138 Gallant, AR, Lundgren, J & Drapeau, V (2012) The night-eating syndrome and obesity. Obes Rev 13, 528536.CrossRefGoogle Scholar
139 Baron, KG, Reid, KJ, Kern, AS, et al. (2011) Role of sleep timing in caloric intake and BMI. Obesity 19, 13741381.CrossRefGoogle ScholarPubMed
140 Baron, KG, Reid, KJ, Horn, LV, et al. (2013) Contribution of evening macronutrient intake to total caloric intake and body mass index. Appetite 60, 246251.CrossRefGoogle ScholarPubMed
141 Garaulet, M, Gomez-Abellan, P, Alburquerque-Bejar, JJ, et al. (2013) Timing of food intake predicts weight loss effectiveness. Int J Obes 37, 604611.CrossRefGoogle ScholarPubMed
142 Jakubowicz, D, Barnea, M, Wainstein, J, et al. (2013) High caloric intake at breakfast vs. dinner differentially influences weight loss of overweight and obese women. Obesity 21, 25042512.CrossRefGoogle ScholarPubMed
143 Costa, G (2003) Shift work and occupational medicine: an overview. Occup Med 53, 8388.CrossRefGoogle ScholarPubMed
144 Wittmann, M, Dinich, J, Merrow, M, et al. (2006) Social jetlag: misalignment of biological and social time. Chronobiol Int 23, 497509.CrossRefGoogle ScholarPubMed
145 Roenneberg, T, Allebrandt, KV, Merrow, M, et al. (2012) Social jetlag and obesity. Curr Biol 22, 939943.CrossRefGoogle ScholarPubMed
146 Tucker, P, Marquie, JC, Folkard, S, et al. (2012) Shiftwork and metabolic dysfunction. Chronobiol Int 29, 549555.CrossRefGoogle ScholarPubMed
147 Esquirol, Y, Bongard, V, Mabile, L, et al. (2009) Shift work and metabolic syndrome: respective impacts of job strain, physical activity, and dietary rhythms. Chronobiol Int 26, 544559.CrossRefGoogle ScholarPubMed
148 Lowden, A, Moreno, C, Holmback, U, et al. (2010) Eating and shift work - effects on habits, metabolism and performance. Scan J Work Environ Health 36, 150162.CrossRefGoogle ScholarPubMed
149 Folkard, S (2008) Do permanent night workers show circadian adjustment? A review based on the endogenous melatonin rhythm. Chronobiol Int 25, 215224.CrossRefGoogle ScholarPubMed
150 Ribeiro, DC, Hampton, SM, Morgan, L, et al. (1998) Altered postprandial hormone and metabolic responses in a simulated shift work environment. J Endocrinol 158, 305310.CrossRefGoogle Scholar
151 Hampton, SM, Morgan, LM, Lawrence, N, et al. (1996) Postprandial hormone and metabolic responses in simulated shift work. J Endocrinol 151, 259267.CrossRefGoogle ScholarPubMed
152 Lund, J, Arendt, J, Hampton, SM, et al. (2001) Postprandial hormone and metabolic responses amongst shift workers in Antarctica. J Endocrinol 171, 557564.CrossRefGoogle ScholarPubMed
153 Vandewalle, G, Maquet, P & Dijk, DJ (2009) Light as a modulator of cognitive brain function. Trends Cog Sci 13, 429438.CrossRefGoogle ScholarPubMed
154 Chang, AM, Santhi, N, St Hilaire, M, et al. (2012) Human responses to bright light of different durations. J Physiol 590, 31033112.CrossRefGoogle ScholarPubMed
155 Barclay, JL, Husse, J, Bode, B, et al. (2012) Circadian desynchrony promotes metabolic disruption in a mouse model of shiftwork. PLOS ONE 7, e37150.CrossRefGoogle Scholar
156 Salgado-Delgado, R, Angeles-Castellanos, M, Saderi, N, et al. (2010) Food intake during the normal activity phase prevents obesity and circadian desynchrony in a rat model of night work. Endocrinology 151, 10191029.CrossRefGoogle Scholar
Figure 0

Fig. 1 Regulation of the circadian timing system by light and food. Under normal conditions of ad libitum food, light synchronises the master clock, the suprachiasmatic nuclei (SCN), which then synchronises peripheral clocks via neuronal and endocrine pathways, together with control over behavioural activity and thus feeding time. When feeding time (but not energy availability) is restricted, light remains the dominant synchroniser of the SCN, but peripheral clocks are synchronised to feeding time. Under conditions of temporal and energy food restriction, both the SCN and peripheral clocks are synchronised to the feeding time. (A colour version of this figure can be found online at http://www.journals.cambridge.org/nrr)