Eating architecture is a term that describes meal frequency, meal timing and meal size and the daily variation in each of these. The aim of this study was to determine the relationship between components of eating architecture on body fat and markers of glycaemic control in healthy adults at increased risk of type 2 diabetes (T2DM). Participants (n 73, 39 males, age 58·8 (8·1) years, BMI 33·4 (4·4) kg/m2) recorded food intake and wore accelerometers and continuous glucose monitors (CGM) for 7–14 d under free-living conditions. Body fat and glycated Hb (HbA1c) were also measured. The mean and day-to-day variation (calculated as the standard deviation during the monitoring period) of each component of eating architecture were calculated. Multivariable linear regression models were constructed for three separate outcome variables (body fat mass, mean CGM glucose and HbA1c) for each component of eating architecture before and after adjustment for confounders. Higher variability in the time of first meal consumption was associated with increased body fat mass after adjusting for confounders (β = 0·227, 95 % CI: 0·019, 0·434, P = 0·033). Increased variability in the time lag from waking to first meal consumption was also positively associated with increased HbA1c after adjustment (β = 0·285, 95 % CI: 0·040, 0·530, P = 0·023). Low day-to-day variability in first meal consumption was associated with lower body fat and improved glucose control in adults at increased risk of T2DM. Routine consumption of meals may optimise temporal regulation to anticipate and respond appropriately to a glucose challenge.

Circadian rhythms, metabolic processes and dietary intake are inextricably linked. Timing of food intake is a modifiable temporal cue for the circadian system and may be influenced by numerous factors, including individual chronotype – an indicator of an individual’s circadian rhythm in relation to the light–dark cycle. This scoping review examines temporal patterns of eating across chronotypes and assesses tools that have been used to collect data on temporal patterns of eating and chronotype. A systematic search identified thirty-six studies in which aspects of temporal patterns of eating, including meal timings; meal skipping; energy distribution across the day; meal frequency; time interval between meals, or meals and wake/sleep times; midpoint of food/energy intake; meal regularity; and duration of eating window, were presented in relation to chronotype. Findings indicate that, compared with morning chronotypes, evening chronotypes tend to skip meals more frequently, have later mealtimes, and distribute greater energy intake towards later times of the day. More studies should explore the difference in meal regularity and duration of eating window amongst chronotypes. Currently, tools used in collecting data on chronotype and temporal patterns of eating are varied, limiting the direct comparison of findings between studies. Development of a standardised assessment tool will allow future studies to confidently compare findings to inform the development and assessment of guidelines that provide recommendations on temporal patterns of eating for optimal health.

Irregular breakfast consumption and food timing patterns in relation to weight status and inflammation were investigated in a cross-sectional manner among 644 participants in the Cancer Prevention Study-3 Diet Assessment Sub-study. Breakfast consumption, and the individual means and the intra-individual standard deviation (isd) of time at first intake of the day, duration of daily intake window and midpoint of daily intake window were collected via six 24-h recalls and examined in relation to BMI, waist circumference (WC) and inflammation (glycoprotein acetyl (GlycA)). Compared with consuming breakfast on all six recalls, linear regression models showed those who consumed breakfast on 4 or 5 of the days had a 1·29 (95 % CI 0·19, 2·38) and 1·64 (95 % CI 0·12, 3·16) kg/m2 higher BMI; no association was found for consuming breakfast ≤3 d. At 1 h later, the average time of first intake was associated with a 0·44 (95 % CI 0·04, 0·84) kg/m2 higher BMI. A 1-h increase in the isd of first intake was associated with a 1·12 (95 % CI 0·49, 1·75) kg/m2 higher BMI; isd in duration and midpoint of intake window were significant prior to additional adjustment for isd in the first intake. One-hour increases in isd for the first intake time (β: 0·15; 95 % CI 0·04, 0·26) and the midpoint of intake window (β: 0·16; 95 % CI 0·02, 0·31) were associated with higher GlycA. No associations were observed for WC independent of BMI. The results provide evidence that irregularity in breakfast consumption and daily intake timing patterns, particularly early in the day, may be related to weight status and inflammation.

To our knowledge, no study has assessed the relationships between patterns of dietary behaviours, identified by latent class analysis (LCA), and chronic uninvestigated dyspepsia (CUD). The present study was conducted to determine the association between the patterns of dietary behaviours, identified by LCA, and CUD in a large sample of adults. In a cross-sectional study conducted on 4763 Iranian adults, we assessed the patterns of dietary behaviours in four domains, including ‘meal patterns’, ‘eating rate’, ‘intra-meal fluid intake’ and ‘meal-to-sleep interval’, as identified by LCA, using a pre-tested comprehensive questionnaire. Patients with CUD were identified using the Rome III diagnostic criteria. CUD was prevalent in 15·2 % (95 % CI 14·4, 16·2 %; n 723) of patients. Early satiation occurred in 6·3 % (n 302) of patients, bothersome postprandial fullness in 8·0 % (n 384) of patients and epigastric pain in 7·8 % (n 371) of patients. We defined two distinct classes of meal patterns: ‘regular’ and ‘irregular’. For eating rates, three classes were defined: ‘moderate’, ‘moderate-to-slow’ and ‘moderate-to-fast’. Participants were identified as ingesting fluid with meals in two major classes: ‘moderate intra-meal drinking’ and ‘high intra-meal drinking’. In terms of the interval between meals and sleeping, two distinct classes were identified: ‘short meal-to-sleep interval’ and ‘long meal-to-sleep interval’. After controlling for potential confounders, the ‘irregular meal pattern’ was significantly associated with a greater odds of CUD (OR 1·42, 95 % CI 1·12, 1·78) compared with a ‘regular meal pattern’. Individuals with a ‘moderate-to-fast eating rate’ were more likely to have CUD compared with those who had a ‘moderate eating rate’ (OR 1·42, 95 % CI 1·15, 1·75). Patterns of the ‘meal-to-sleep interval’ and ‘intra-meal fluid intake’ were not significantly associated with CUD. In conclusion, the ‘irregular meal pattern’ and the ‘moderate-to-fast eating rate’ were significantly associated with a greater odds of CUD. Further prospective investigations are warranted to confirm this association.