The Impact of Meal Timing on Risk of Weight Gain and Development of Obesity: a Review of the Current Evidence and Opportunities for Dietary Intervention.

PURPOSE OF REVIEW: The aim of this short review is to provide an updated commentary on the current literature examining the impact of meal timing on obesity and weight gain in adults. The potential mechanisms, including novel and emerging factors, behind timing of food intake across the 24-h period in the development of obesity, and dietary strategies manipulating meal timing to ameliorate weight gain are also explored. RECENT FINDINGS: Dietary patterns that feature meal timing outside of the regular daytime hours can contribute to circadian disruption as food is metabolised in opposition to internal daily rhythms and can feedback on the timekeeping mechanisms setting these rhythms. Epidemiological evidence examining the impact of late meal timing patterns is beginning to suggest that eating at night increases the risk of weight gain over time. Mechanisms contributing to this include changes to the efficiency of metabolism across the day, and dysregulation of appetite hormone and gut microbiota by mis-timed meals. When meals are eaten, in relation to the time of day, is increasingly considered of importance when implementing dietary change in order to address the growing burden of obesity, although further research is required in order to determine optimal patterns.

Does rearranging meal times at night improve cardiovascular risk factors? An Australian pilot randomised trial in night shift workers.

BACKGROUND AND AIMS: Shift workers face an increased risk of cardiovascular disease (CVD), type-2 diabetes and obesity. Eating during the night is a likely contributing factor, as it coincides with the time at which postprandial metabolism is least efficient. In this pilot randomised crossover trial, we examine the effects of a short overnight fast on CVD risk markers (primarily postprandial triglyceride and glucose response) of night shift workers. METHODS AND RESULTS: Night shift workers with abdominal obesity underwent 4-week intervention and control periods, separated by ≥ 2 weeks washout. In the intervention period, an overnight fast (0100 h-0600 h) was implemented, by redistributing 24-h energy intake. Usual dietary habits were followed in the control period. Outcomes between intervention and control were compared using mixed effects linear regression models. Nineteen adults completed the trial [13 females, mean (±SD) age 41 ± 10 years, BMI 30.7 ± 5.7 kg/m2]. Postprandial triglyceride and glucose response post intervention were not different to post control. The overnight fast was well-tolerated by participants with an adherence rate of 95%, assessed by weekly 24-h dietary recalls. Exploratory analysis indicates lower mean body weight post intervention compared to post control (mean difference: -0.9 kg, 95% CI: -1.3 to -0.4). CONCLUSIONS: Night shift workers who habitually ate during their night shifts were able to rearrange their meal times to maintain a small overnight fast, which may have promoted small weight changes. This warrants further investigation into the role of meal timing in mitigating the metabolic consequences of night shift work. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (http://anzctr.org.au/) registered on the 30th May 2017 (ACTRN12617000791336).

Conceptualizing weight management for night shift workers: A mixed-methods systematic review.

Shift workers have an increased risk of obesity and metabolic conditions. This mixed-methods systematic literature review on night shift workers aimed to: (1) identify barriers/enablers of weight management; (2) examine effectiveness of weight management interventions; and (3) determine whether interventions addressed enablers/barriers. Six databases were searched, articles screened by title/abstract, followed by full-text review, and quality assessment. Eligible qualitative studies documented experiences of behaviors related to weight change. Eligible quantitative studies were behavior change interventions with weight/body mass index outcomes. A thematic synthesis was undertaken for qualitative studies using the social-ecological model (SEM). Interventions were synthesized narratively including: weight/body composition change; components mapped by behavior change taxonomy; and SEM. A synthesis was undertaken to identify if interventions addressed perceived enablers/barriers. Eight qualitative (n = 169 participants) and 12 quantitative studies (n = 1142 participants) were included. Barriers predominated discussions: intrapersonal (time, fatigue, stress); interpersonal (work routines/cultural norms); organizational (fatigue, lack of: routine, healthy food options, breaks/predictable work); community (lack of healthy food options). The primary outcome for interventions was not weight loss and most did not address many identified enablers/barriers. One intervention reported a clinically significant weight loss result. Weight loss interventions that address barriers/enablers at multiple SEM levels are needed.

A protocol of a pilot randomised trial (Action-RESPOND) to support rural and regional communities with implementing community-based systems thinking obesity prevention initiatives.

BACKGROUND: Over a quarter of children aged 2-17 years living in Australia are overweight or obese, with a higher prevalence reported in regional and remote communities. Systems thinking approaches that seek to support communities to generate and implement locally appropriate solutions targeting intertwined environmental, political, sociocultural, and individual determinants of obesity have the potential to ameliorate this. There have however been reported challenges with implementation of such initiatives, which may be strengthened by incorporating implementation science methods. METHODS: This pilot randomised controlled trial protocol outlines the development and proposed evaluation of a multicomponent implementation strategy (Action-RESPOND). to increase the implementation of community-based systems thinking child obesity prevention initiatives The target of this intervention is ten rural and regional communities (or local government areas as the unit of allocation) within Northeast Victoria who were participants in a whole-of-systems intervention (RESPOND). Action-RESPOND builds on this intervention by assessing the impact of offering additional implementation strategies to five communities relative to usual care. The development of the multicomponent implementation strategy was informed by the Promoting Action on Research Implementation in Health Services (PARIHS) framework and consists of seven implementation strategies primarily delivered via 'facilitation' methods. Implementation strategies aimed to ensure initiatives implemented are i) evidence-based, ii) address community's specific needs and iii) are suitable for local context. Strategies also aimed to increase the community's capacity to implement, through iv) improving the health promotion team's implementation knowledge and skills, fostering v) leadership, vi) physical resources and vii) community culture to drive implementation. The feasibility, acceptability, potential impact, and cost of the strategy will be assessed at baseline and follow up using surveys administered to key representatives within the community and internal records maintained by the research team. DISCUSSION: By leveraging an existing community-based whole-of-systems intervention, Action-RESPOND offers a unique opportunity to collect pilot feasibility and early empirical data on how to apply implementation and systems science approaches to support obesity prevention in rural and regional communities in Victoria.

Effective interventions in preventing gestational diabetes mellitus: A systematic review and meta-analysis.

BACKGROUND: Lifestyle choices, metformin, and dietary supplements may prevent GDM, but the effect of intervention characteristics has not been identified. This review evaluated intervention characteristics to inform the implementation of GDM prevention interventions. METHODS: Ovid, MEDLINE/PubMed, and EMBASE databases were searched. The Template for Intervention Description and Replication (TIDieR) framework was used to examine intervention characteristics (who, what, when, where, and how). Subgroup analysis was performed by intervention characteristics. RESULTS: 116 studies involving 40,940 participants are included. Group-based physical activity interventions (RR 0.66; 95% CI 0.46, 0.95) reduce the incidence of GDM compared with individual or mixed (individual and group) delivery format (subgroup p-value = 0.04). Physical activity interventions delivered at healthcare facilities reduce the risk of GDM (RR 0.59; 95% CI 0.49, 0.72) compared with home-based interventions (subgroup p-value = 0.03). No other intervention characteristics impact the effectiveness of all other interventions. CONCLUSIONS: Dietary, physical activity, diet plus physical activity, metformin, and myoinositol interventions reduce the incidence of GDM compared with control interventions. Group and healthcare facility-based physical activity interventions show better effectiveness in preventing GDM than individual and community-based interventions. Other intervention characteristics (e.g. utilization of e-health) don't impact the effectiveness of lifestyle interventions, and thus, interventions may require consideration of the local context.

The effect of any given intervention to prevent gestational diabetes (high blood sugar levels that arise during pregnancy) may depend on the way it is delivered (how, when, what, etc). This study reviewed published literature to investigate if the effects of interventions (diet, exercise, metformin, probiotics, myoinositol) to prevent gestational diabetes differ according to the way it is being delivered (e.g., online vs in-person, by health professionals or others, etc.). Exercise delivered to group settings, or those delivered at a healthcare facility worked better to prevent gestational diabetes. Although we did not observe any differences with other delivery characteristics (e.g., online vs in-person), it does not mean they are always equally effective, it is important to consider individual situations when prescribing or developing interventions.

Type 2 Diabetes Mellitus and Sarcopenia as Comorbid Chronic Diseases in Older Adults: Established and Emerging Treatments and Therapies.

Type 2 diabetes mellitus (T2DM) and sarcopenia (low skeletal muscle mass and function) share a bidirectional relationship. The prevalence of these diseases increases with age and they share common risk factors. Skeletal muscle fat infiltration, commonly referred to as myosteatosis, may be a major contributor to both T2DM and sarcopenia in older adults via independent effects on insulin resistance and muscle health. Many strategies to manage T2DM result in energy restriction and subsequent weight loss, and this can lead to significant declines in muscle mass in the absence of resistance exercise, which is also a first-line treatment for sarcopenia. In this review, we highlight recent evidence on established treatments and emerging therapies targeting weight loss and muscle mass and function improvements in older adults with, or at risk of, T2DM and/or sarcopenia. This includes dietary, physical activity and exercise interventions, new generation incretin-based agonists and myostatin-based antagonists, and endoscopic bariatric therapies. We also highlight how digital health technologies and health literacy interventions can increase uptake of, and adherence to, established and emerging treatments and therapies in older adults with T2DM and/or sarcopenia.

Participant characteristics in the prevention of gestational diabetes as evidence for precision medicine: a systematic review and meta-analysis.

BACKGROUND: Precision prevention involves using the unique characteristics of a particular group to determine their responses to preventive interventions. This study aimed to systematically evaluate the participant characteristics associated with responses to interventions in gestational diabetes mellitus (GDM) prevention. METHODS: We searched MEDLINE, EMBASE, and Pubmed to identify lifestyle (diet, physical activity, or both), metformin, myoinositol/inositol and probiotics interventions of GDM prevention published up to May 24, 2022. RESULTS: From 10347 studies, 116 studies (n = 40940 women) are included. Physical activity results in greater GDM reduction in participants with a normal body mass index (BMI) at baseline compared to obese BMI (risk ratio, 95% confidence interval: 0.06 [0.03, 0.14] vs 0.68 [0.26, 1.60]). Combined diet and physical activity interventions result in greater GDM reduction in participants without polycystic ovary syndrome (PCOS) than those with PCOS (0.62 [0.47, 0.82] vs 1.12 [0.78-1.61]) and in those without a history of GDM than those with unspecified GDM history (0.62 [0.47, 0.81] vs 0.85 [0.76, 0.95]). Metformin interventions are more effective in participants with PCOS than those with unspecified status (0.38 [0.19, 0.74] vs 0.59 [0.25, 1.43]), or when commenced preconception than during pregnancy (0.21 [0.11, 0.40] vs 1.15 [0.86-1.55]). Parity, history of having a large-for-gestational-age infant or family history of diabetes have no effect on intervention responses. CONCLUSIONS: GDM prevention through metformin or lifestyle differs according to some individual characteristics. Future research should include trials commencing preconception and provide results disaggregated by a priori defined participant characteristics including social and environmental factors, clinical traits, and other novel risk factors to predict GDM prevention through interventions.

An individual's characteristics, such as medical, biochemical, social, and behavioural may affect their response to interventions aimed at preventing gestational diabetes, which occurs during pregnancy. Here, we evaluated the published literature on interventions such as diet, lifestyle, drug treatment and nutritional supplement and looked at which individual participant characteristics were associated with response to these interventions. Certain participant characteristics were associated with greater prevention of gestational diabetes through particular treatments. Some interventions were more effective when started prior to conception. Future studies should consider individual characteristics when assessing the effects of preventative measures.

Shift workers' perceptions and experiences of adhering to a nutrition intervention at night whilst working: a qualitative study.

This study explored the feasibility of implementing a meal timing intervention during night shift work. Data were collected via semi-structured interviews. Interviews were coded inductively by two researchers independently, then three major themes were collaboratively developed. Subthemes from each major theme were mapped to the theoretical domains framework and the Capability Opportunity Motivation model of behaviour change. Seventeen night shift workers (rotating or permanent) aged between 25 and 65 years were interviewed. Participants predominately worked as health professionals. The feasibility of a simple meal timing intervention to avoid eating between 1 and 6 am on night shift is largely affected by three major influences (1) physical and emotional burden of shift work which drives food temptations; (2) the workplace context including the meal break environment, social and cultural context at work, and break scheduling; and (3) motivation of the individual. Facilitators to avoiding eating at night were, keeping busy, having co-worker support, management support, education of health benefits and/or belief that the intervention was health promoting. The barriers to avoiding eating at night were the emotional and physical toll of working at night leading to comfort eating and not having rest areas away from food environments. To support night shift workers with changing timing of meals, interventions at work should target both individual and organisational level behaviour change.

Time of day difference in postprandial glucose and insulin responses: Systematic review and meta-analysis of acute postprandial studies.

Current dietary trends show that humans consume up to 40% of their energy intake during the night. Those who habitually eat during the night are observed to have an increased risk of metabolic conditions such as type-2 diabetes and cardiovascular disease. Increasing evidence suggest that a biological consequence of eating during the night is a larger postprandial glucose response, compared to meals eaten earlier in the day. However, findings from individual acute postprandial studies have been inconsistent, due to variations in protocols. Therefore, this review aimed to systematically summarize findings from acute postprandial studies and investigate whether postprandial glucose and insulin response at night differs to during the day in healthy adults. This would indicate a possible physiological mechanism linking habitual nighttime eating and increased risk of metabolic conditions. Seven electronic databases were searched in February 2018. Included studies met the following criteria: had a day-time test between 0700 - 1600h, a nighttime test between 2000 and 0400h, the test meals were identical and consumed by the same participant at both day and night time points, preceded by a 3-h fast (minimum). Primary outcome measures were postprandial glucose and insulin incremental area under the curve (iAUC) or area under the curve (AUC). Studies that reported numerical data were included in the meta-analyses, conducted using Stata statistical software (version 13.0, StataCorp, College Station, TX, USA). For eligible studies that did not report numerical data, their authors' conclusions on the effect of time of day on the primary outcome measures were summarized qualitatively. Full text of 172 articles were assessed for eligibility. Fifteen studies met the eligibility criteria, ten of which were included in the meta-analyses. Meta-analysis for glucose showed a lower postprandial glucose response in the day compared to during the night, after an identical meal (SMD = -1.66; 95% CI, -1.97 to -1.36; p < .001). This was supported by the findings from included studies ineligible for meta-analysis. Meta-analysis also showed a lower postprandial insulin response in the day compared to during the night (SMD = -0.35; 95% CI, -0.63 to -0.06; p = .016). However, findings from included studies ineligible for meta-analysis were inconsistent. Our results suggest poor glucose tolerance at night compared to the day. This may be a contributing factor to the increased risk of metabolic diseases observed in those who habitually eat during the night, such as shift workers.

Effect of meal timing on postprandial glucose responses to a low glycemic index meal: A crossover trial in healthy volunteers.

BACKGROUND & AIMS: Glucose metabolism is, in part, regulated by the circadian rhythm. Postprandial glucose response is exaggerated and insulin sensitivity is reduced at night compared with the morning. Sustained poor glucose tolerance may be related to the increased risk of type-2 diabetes mellitus and cardiovascular disease experienced by shift workers. Manipulation of meal type may be able to dampen such postprandial excursions. Therefore, the study's aim was to investigate postprandial glucose and insulin responses to a low glycemic index (GI) meal in the morning compared to night in healthy volunteers. METHODS: An oral glucose tolerance test (OGTT), was undertaken to confirm diurnal glucose response. Participants consumed a glucose solution at 0800h (morning) and 2000h (evening). In a separate trial, participants consumed a low GI meal (3.3 MJ, 48% energy (E) from carbohydrate, 40%E from fat and 11%E from protein, 22 g fiber) at 0800h, 2000h and 0000h (midnight). Postprandial glucose and insulin were collected over 3 h. Incremental area under the curve (iAUC) was calculated and significance tested using Wilcoxon-signed rank. A p-value <0.05 was taken as significant. RESULTS: In the OGTT (n = 10), postprandial glucose iAUC was higher in the evening compared to morning (p = 0.007). In the low GI meal trial (n = 9), postprandial glucose iAUC at evening and midnight were higher than the morning (p = 0.008, p = 0.021) but not significantly different between evening and midnight (p = 0.594). Postprandial insulin iAUC was also higher in the evening and at midnight compared to the morning (p = 0.008 for both). CONCLUSIONS: The current study confirms that meal intake at night, even when comprised of low glycemic ingredients, contributes to higher glucose excursions and concomitantly greater insulin levels, compared with an equivalent meal in the morning. This demonstrates that meal timing has an effect on glucose metabolism, which can be observed from as early as 8pm and persists throughout the night. This identifies meal timing as an important modifiable risk factor for metabolic-related disease, which may have implications for high risk populations such as shift workers but also the general population. TRIAL REGISTRATION: Study ID number: ACTRN12616000164493; Website of trial registry: http://www.anzctr.org.au/.

The Impact of Time of Day on Energy Expenditure: Implications for Long-Term Energy Balance.

There is evidence to indicate that the central biological clock (i.e., our endogenous circadian system) plays a role in physiological processes in the body that impact energy regulation and metabolism. Cross-sectional data suggest that energy consumption later in the day and during the night is associated with weight gain. These findings have led to speculation that when, as well as what, we eat may be important for maintaining energy balance. Emerging literature suggests that prioritising energy intake to earlier during the day may help with body weight maintenance. Evidence from tightly controlled acute experimental studies indicates a disparity in the body's ability to utilise (expend) energy equally across the day and night. Energy expenditure both at rest (resting metabolic rate) and after eating (thermic effect of food) is typically more efficient earlier during the day. In this review, we discuss the key evidence for a circadian pattern in energy utilisation and balance, which depends on meal timing. Whilst there is limited evidence that simply prioritising energy intake to earlier in the day is an effective strategy for weight loss, we highlight the potential benefits of considering the role of meal timing for improving metabolic health and energy balance. This review demonstrates that to advance our understanding of the contribution of the endogenous circadian system toward energy balance, targeted studies that utilise appropriate methodologies are required that focus on meal timing and frequency.

Temporal pattern of eating in night shift workers.

Understanding shift workers dietary intake patterns may inform interventions targeted at lowering chronic disease risk. This study examined the temporal distribution of food intake as shift workers rotate between night shifts, day shift and/or days off to identify differences in energy intake, eating frequency, and adherence to dietary guidelines by shift type (night shift vs. day). Night shift (NS) workers completed a four-day food diary that included a minimum of two night shifts and one-day shift (DS)/day off (DO), recording all food, beverages and time of consumption. Comparisons were between shift types, using ANOVA for continuous data and generalized estimating equations for count data, data reported as mean (SE). When comparing NS and DSDO, there were no differences in energy intake (24 h) (8853 (702) vs. 9041 (605) kJ, n = 22) or adherence to dietary guidelines. There was no difference between the number of eating occasions on NS and DSDO (5.6(0.3) vs 5.1(0.6) occasions) but less energy per eating occasion at night (1661(125) vs 1933(159) kJ). When working NS compared with DSDO there was higher total sugar (%E, 19.1(2.0) vs 15.0(2.4)) and lower saturated fat (%E, 13.8(1.2) vs 15.7(1.3)). Further, DSDO were characterized by a pattern of three main meals and a prolonged fasting period. It is important to determine if reducing eating occasions and providing opportunities for fasting improves metabolic health.

Effect of Night Time Eating on Postprandial Triglyceride Metabolism in Healthy Adults: A Systematic Literature Review.

Eating at night time, as is frequent in shift workers, may contribute to increased cardiovascular disease (CVD) risk through a disruption in usual lipid metabolism, resulting in repeated and sustained hyperlipidemia at night. This systematic review aimed to investigate the impact of eating a meal at night compared with the same meal eaten during the day on postprandial lipemia. Six databases were searched: CINAHL Plus, Cochrane Library, EMBASE, Ovid MEDLINE, Informit, and SCOPUS. Eligible studies were original research cross-over design with a minimum fasting period of 5 h before testing preceded by a standardized control meal; measured postprandial triacylglycerol (TAG) for 5 h or greater; had meal time between 0700 h and 1600 h for day time and between 2000 h and 0400 h for night time; and had within-study test meals (food or drink) that were identical in macronutrient composition and energy. Two authors independently completed eligibility and quality assessment using the American Dietetic Association Quality Criteria Checklist for Primary Research. After removing duplicates, 4,423 articles were screened, yielding 5 studies for qualitative synthesis. All studies identified at least one parameter of the postprandial TAG response that was different as a result of meal time (e.g., the total concentration or the time course kinetics). Two studies reported a greater total TAG concentration (area under curve) at night compared with day, and 3 studies found no difference. Four studies reported that the kinetics of the postprandial time course of TAGs was different at night compared with during the day. Inconsistent reporting in the primary studies was a limitation of the review. Night eating may negatively affect postprandial lipemia and this review shows there is a need to rigorously test this using standardized methods and analysis with larger sample sizes. This is critical for informing strategies to lower CVD risk for shift workers.

Does modifying the timing of meal intake improve cardiovascular risk factors? Protocol of an Australian pilot intervention in night shift workers with abdominal obesity.

INTRODUCTION: Shift work is an independent risk factor for cardiovascular disease (CVD). Shift workers who are awake overnight and sleep during the day are misaligned with their body's endogenous circadian rhythm. Eating at night contributes to this increased risk of CVD by forcing the body to actively break down and process nutrients at night. This pilot study aims to determine whether altering meal timing overnight, in a shift working population, will impact favourably on modifiable risk factors for CVD (postprandial bplasma lipids and glucose concentration). METHODS AND ANALYSIS: A randomised cross-over study with two 4-week test periods, separated by a minimum of a 2-week washout will be undertaken. The effectiveness of redistributing energy intake overnight versus ad libitum eating patterns on CVD risk factors will be examined in night shift workers (n=20), using a standard acute test meal challenge protocol. Primary outcomes (postprandial lipids and glucose) will be compared between the two conditions: post-intervention and post-control period using analysis of variance. Potential effect size estimates to inform sample size calculations for a main trial will also be generated. ETHICS AND DISSEMINATION: Ethics approval has been granted by the Monash University Human Research Ethics Committee (2017-8619-10329). Outcomes from this study will determine whether eliminating food intake for a defined period at night (1-6 am) impacts favourably on metabolic risk factors for CVD in night shift workers. Collective results from this novel trial will be disseminated through peer-reviewed journals, and national and international presentations. The results are essential to inform health promotion policies and guidelines for shift workers, especially those who aim to improve their metabolic health. TRIAL REGISTRATION NUMBER: ACTRN12617000791336; Pre-results.