Differences in Dietary Intake, Eating Occasion Timings and Eating Windows between Chronotypes in Adults Living with Type 2 Diabetes Mellitus

The field of chronobiology has gained rapid interest over the past two decades particularly in relation to its potential influence on human health and disease [1]. The circadian clock is a complex system which controls the timing of physiological processes in the human body [2]. It is governed by the Suprachiasmatic Nucleus (SCN) which regulates peripheral clocks in organs and molecular clocks in cells. Disruptions in endogenous circadian rhythms resulting from environmental and behavioural factors can lead to desynchronisation between the SCN and peripheral clocks, also referred to as circadian misalignment [3]. This subsequently impacts metabolic and physiological processes, including nutrient absorption and energy expenditure [3,4]. Each organism develops its own circadian rhythm (~24 h) based on the stimuli in their environment (primarily light but also temperature and feeding) [2]. In humans, light stimulates wakefulness and feeding whereas darkness stimulates rest and fasting [3]. Due to this, when food intake is delayed and occurs during the resting phase, it can contribute to circadian misalignment [5].

An individual’s intrinsic circadian rhythm determines their preferred timing of sleep and activity which can be classified using chronotypes [6]. There are three main chronotypes—morning, intermediate (also referred to as “neither”) and evening [6]. Chrono-nutrition is a growing field which investigates the complex relationship between nutrition, circadian rhythms, metabolism and health [4]. Individuals with an evening chronotype tend to have delayed meal timing and greater energy intake later in the day [7]. They have also been found to engage in more unhealthy eating habits, have a higher body mass index (BMI) and higher risk of developing obesity and type 2 diabetes mellitus (T2DM) than other chronotypes [1,7,8]. Whilst it is well recognised that dietary factors have an imperative role in the prevention and management of T2DM [9,10,11], evidence on the impact of chronotype and dietary temporal distribution in people living with T2DM is lacking.

Within T2DM populations, evening chronotypes have been found to have poorer glycaemic control than other chronotypes [12,13]. The mechanisms for these observations are unclear due to limited studies but could be due to the delayed meal timing observed in evening chronotypes. Glucose tolerance is controlled by circadian rhythms and usually peaks during the daytime (when humans typically feed) and reduces during night time (when humans typically fast) [14]. Previous studies in healthy populations have observed that eating during the night, compared to the day, is associated with poorer glucose tolerance, reduced insulin sensitivity and misalignment between central and peripheral circadian rhythms [15,16].

Another aspect of dietary temporal distribution is the eating window (EW) which is defined as the duration of time between the first and last eating occasion (EO) in a 24-h period [17]. Time-restricted feeding (TRF) restricts the daily EW and extends the daily fasting window [18]. A shorter EW has been associated with improved glycaemic control and weight loss outcomes [15,16]. Two recent systematic reviews and meta-analysis found TRF to be superior in promoting weight loss and reduction in fasting blood glucose compared to non time-restricted interventions [19,20].

Early studies in people living with obesity have explored the inter-relationship between chronotypes and EW duration and shown chronotype-adjusted energy-restricted diets to be more effective in promoting weight loss than energy-restricted diets alone [21]. However, this has yet to be fully explored for people living with T2DM. There could be a benefit to prescribing a chronotype-adjusted dietary intervention to overweight/obese individuals with T2DM to elicit better weight loss outcomes which may positively impact glycaemic control. However, there is sparse evidence for the interaction between chronotype, dietary patterns and their temporal distribution in adults with T2DM [22,23,24]. Therefore, the aim of this secondary data analysis is to characterise the dietary intake, EW and timing of EOs by chronotype in a cohort of people living with T2DM.

Participants included in this analysis had data collected as part of the ongoing CODEC (“Chronotype of Patients with type 2 Diabetes and Effect on Glycaemic Control”) observational study (Clinical Trial Registry Number: NCT02973412) between 2016–2021. Ethical approval for this study was granted from the West Midlands—Black Country Research Ethics Committee (16/WM/0457). Full details of the study design and the cohort for the CODEC study have been described elsewhere [25]. Briefly, participants had established T2DM for more than 6 months, an HbA1c ≤ 86 mmol/mol (10%) and were aged between 18–75 years. Those living with Type 1 diabetes, a known sleep disorder (except obstructive sleep apnoea) or BMI over 45 kg/m² were excluded. Eligible participants were recruited from both primary and secondary care settings from four sites across the Midlands, UK (Leicester, Nottingham, Derby, Lincoln). Written informed consent was received from all study participants. Participants working night shifts were excluded from this analysis (n = 1).

At the time of analysis, 808 participants were enrolled in the CODEC study, of which 411 completed the diet diary and were included in the sub-analysis. Of these, all participants had anthropometric, demographic and diet data. Table 1 outlines the characteristics of all included participants, stratified by chronotype. Supplementary Table S1 outlines the participant characteristics of the wider CODEC cohort. Those included were broadly representative of the wider CODEC cohort. However, our sub-group cohort had fewer white Europeans (92.2% vs. 95.5%), older participants (65.2 vs. 62.9 years), lower HbA1c (6.9% vs. 7.2%) and BMI (30.6 kg/m² vs. 31.4 kg/m²) than the wider CODEC population.

Our analysis of this cohort of adults living with T2DM found differences in the timings of EOs and caffeine intake by chronotype, with evening chronotypes consuming the most caffeine and eating later in the day. Importantly, these were relative to sleep timings which suggests that EWs shift in line with sleep patterns. This did not result in differences in energy and macronutrient intake or EW duration across morning, intermediate and evening chronotypes.

Our findings regarding dietary intake extend previous observations from healthy populations into a cohort living with T2DM. Regarding total daily energy intake, our findings mirror those that have found no difference across chronotypes [32,33,34]. However, our results are in contrast with findings from Mota et al., who found that evening chronotypes had significantly higher total daily energy intake compared to morning types [35]. Regarding macronutrient intake, most previous studies report no difference in macronutrient intake across chronotypes [32,36,37,38,39,40]. A small number of studies report conflicting results with some reporting higher carbohydrate intake in morning chronotypes [38,39,41] and others reporting higher carbohydrate intake in evening types [35,40]. Sato-Mito et al. and Mota et al. found that morning types had a higher protein intake compared to evening types [35,41]. Sato-Mito et al. and Maukonen et al. found that evening types had higher total fat intake compared to morning types [33,41]. The differences in the above-mentioned study findings compared to the current study could be attributed to different study populations (undergraduate students [36,41], adults (18–30 years [35], 18–50 years [32,38], 25–74 years [33])) living with or without obesity and using different dietary assessment methodologies (24 h recall vs. estimated diet diaries). Moreover, the studies were based in different countries with very different cultures and associated diets, customs and traditions (e.g., Brazil, Finland, Japan and the United States) which can affect dietary intake and habits.

Caffeine intakes in our cohort, although not excessive in comparison to recommended safe daily intake [42], were found to be higher in evening chronotypes compared to other chronotypes, which is in agreement with previous studies [43,44]. Only Bodur et al. quantified the amount of caffeine consumed in mg, and the average intake of caffeine of their study population was almost double that of our cohort’s [44]. Bodur et al. found that evening chronotypes also had poorer sleep quality and suggested that these individuals may be consuming more caffeine to compensate for the lack of sleep caused by waking up early to tend to social obligations [44]. However, in our cohort, a lower percentage of evening chronotype individuals were employed compared to those with morning chronotype preference which may have impacted our findings. Nonetheless, most of the published studies examining caffeine intake by chronotype recruited undergraduate students and used different methods to quantify the amount of caffeine consumed by participants which limits the generalisability of the mentioned findings. We also observed that evening chronotypes had their last intake of caffeine later than other chronotypes, although this was relative to their sleep onset. Penolazzi et al. also found that evening chronotypes have caffeine later in the day but did not find a difference in the amount of caffeine consumed [45]. However, they did not account for sleep timings.

We explored EW duration as TRF has been observed to positively impact glycaemic control and bodyweight control in people with T2DM and in people who have a shorter EW [18,46]. To our knowledge, there is only one previous study which examined EW duration and its relation to chronotype [47]; however, ours is the first study to investigate this in an adult population with T2DM. Gontijo et al. found no association between EW duration and chronotype in pregnant women but found that a longer EW was associated with better diet quality in the first trimester of pregnancy [47]. In our cohort, the cardio-metabolic profile, anthropometric measures and EW duration were similar across chronotypes.

The timing of EOs by chronotype reported here are in agreement with other studies exploring temporal feeding patterns across chronotypes and are consistent across different population groups [32,38,40,41,48]. These findings suggest that chronotype may be a predictor for the timing of EOs. The link between chronotype and EO timing preference could be an important area for future research since delayed food intake has been linked to circadian desynchronisation and metabolic disturbances [5]. To understand the relevance of meal timing and sleep behaviour, we looked at the relative difference between when individuals wake up and have their first EO and when they have their last EO and go to sleep. We found no difference in the relative duration of time between waking and first EO and last EO and sleep onset. This suggests that evening chronotypes are consuming their meals in line with their chronotype preference (therefore not in circadian misalignment). It is unclear whether delayed meal timing which is aligned with chronotype would still contribute to poorer glycaemic tolerance as suggested by previous studies which did not account for chronotype preference [14,15,16]. Therefore, further research is needed to examine the interplay between chronotype and meal timings on health outcomes.

Lending strength to our analysis is our well -phenotyped cohort which had anthropometric, diet and sleep data. However, there are a number of limitations. Due to the cross-sectional design of the CODEC study and derived data, we were not able to establish any causal relationships between the explored variables. In addition, the majority of our cohort consisted of people in retirement which could have influenced our findings. Although we determined chronotype using a validated questionnaire, across three pre-defined categories, it is possible that other statistical approaches (e.g., cluster analysis) may yield different results. We used self-reported diet diaries as our dietary data collection method which introduce limitations including the potential for misreporting of dietary intake and recall bias. To mitigate limitations, we used standardised diet diaries which collected information for both weekdays and weekend days to account for differences in dietary intake throughout the week. The diet diaries also contained detailed instructions and prompts to support accurate reporting, including images of food portion sizes. Despite this, our cohort’s dietary intake, particularly energy intake, is low compared to other studies in populations with T2DM [49], without T2DM (overweight/obese) [32,39] and with healthy BMI [34,36].

In conclusion, in our cohort of adults living with T2DM, we found no significant difference in dietary intake across chronotypes except for caffeine intake which was highest in evening chronotypes. We found that although there was a significant difference in the timing of EOs with evening chronotypes having delayed EOs, this was relative to their sleep timings. There was no difference in EW duration between chronotypes. Further research is needed to examine the association between dietary intake, temporal distribution and markers of cardiometabolic health. Future studies should also explore the impact of delayed meal timings on circadian rhythms and metabolic outcomes for people with T2DM particularly when accounting for chronotype preferences. This could help inform novel methods in glucose-lowering lifestyle interventions.

The authors would like to thank the participants for taking part in this study. The authors acknowledge the contribution of the research staff and other research sites involved in the study. The authors would also like to thank the members of the Leicester Diabetes Centre Patient and Public Forum for their involvement in the study design. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu15183868/s1↗. Supplementary Table S1: Comparison of those include vs. remainder of CODEC cohort; Supplementary Table S2: Participant characteristics for all participants and stratified by chronotype (unadjusted data); Supplementary Table S3: Adjusted means for dietary variables, eating window and eating occasions and sleep variables by chronotype with main effect, including sleep duration as a co-variate.

Conceptualization, E.R., F.A., S.S.K., J.H.; methodology, S.S.K.; formal analysis, S.S.K., J.H.; data curation, F.A., J.H.; writing—original draft preparation, S.S.K.; writing—review and editing, J.H., E.R, F.A., E.M.B., A.V.R., C.L.E., L.M.G., K.K., T.Y., A.P.H., M.J.D.; project administration, J.H.; funding acquisition, M.J.D., A.P.H. All authors have read and agreed to the published version of the manuscript.

The study was conducted in accordance with the Declaration of Helsinki, and approved by the West Midlands—Black Country Research Ethics Committee (16/WM/0457, date of approval—16/11/2016).

Informed consent was obtained from all subjects involved in the study.

The data that support the findings of this study are available from the senior author [JH], upon reasonable request.

The authors declare no conflict of interest.

This research was supported by the NIHR Leicester Biomedical Research Centre (which is in partnership between University Hospitals of Leicester NHS trust, Loughborough University and the University of Leicester) and the National Institute for Health Research Applied Research Collaboration—East Midlands (NIHR ARC-EM).