Impact of Meal Frequency on Anthropometric Outcomes: A Systematic Review and Network Meta-Analysis of Randomized Controlled Trials

Meal Frequency Does Not Affect Weight Loss in Overweight/Obese Women but Affects the Body Composition: A Randomized Controlled Trial

OBJECTIVES

Studies showing the relationship between meal frequency, weight loss and anthropometric measurements are contradictory. This study aims to determine the effect of meal frequency on weight loss, anthropometric measurements, and body composition.

METHODS

This is a parallel designed randomized control trial that was conducted with 40 female volunteers between the ages of 19-64 years, with a Body Mass Index (BMI) ≥27 who applied to a private clinic. Participants were randomized in two treatment arms (3 meals + 3 snacks/day (n = 20) vs 3 meals/day (n = 20)) and same dietary energy restriction (-500kcal) was applied for 3 months. Food consumption was questioned with 3 day food records, and anthropometric measurements and body composition were measured before the study and repeated each week till the end of the study by the researcher.

RESULTS

All of the participants completed the study period. Body weight (kg), BMI (kg/m2), total body fat (kg), body fat percentage (%), fat free mass (kg) and waist circumference (cm) decreased, while fat free mass percentage (%) increased significantly in both of the groups at the end of the study (p < 0.05). The rate of difference for body weight, BMI (kg/m2) and waist circumference (cm) were similar among the groups. When difference in body composition analyses was examined, the rate of reduction in total body fat (-18.82 ± 4.97% vs -14.87 ± 7.44%) and body fat percentage (%)(-10.79 ± 4.63% vs -7.68 ± 7.04%) and the rate of increase in fat free mass percentage (%)(7.65 ± 3.16% vs 5.04 ± 3.44%) were significantly higher in 3 meals + 3 snacks group (p < 0.05).

CONCLUSION

When energy restricted and balanced weight loss programs are applied, alteration in body weight, BMI and waist circumference is not affected from meal frequency, but body composition does.

CLINICAL TRIAL NUMBER

NCT05581862 (Date of Trial Registration: 13/10/2022).

No Effect of High Eating Frequency Compared with Low Eating Frequency on Appetite and Inflammation Biomarkers: Results from a Randomized Crossover Clinical Trial

BACKGROUND

Eating frequency (EF) focuses on the total number of eating occasions per day and may influence metabolic health.

OBJECTIVES

We sought to examine the effect of high compared with low EF on appetite regulation and inflammatory biomarkers among healthy adults.

METHODS

Data are from a randomized, crossover trial (the Frequency of Eating and Satiety Hormones study). Participants (n = 50) completed 2 isocaloric 21-d study periods of low EF (3 eating occasions/d) and high EF (6 eating occasions/d) in random order with a 14-d washout period in between. Participants were free-living and consumed their own food, using study-directed, structured meal plans with identical foods and total energy in both study periods. On days 1 and 21 of each EF period, fasting blood was collected during in-person clinic visits to assess plasma concentrations of ghrelin, leptin, adiponectin, and high-sensitivity C-reactive protein (hs-CRP). Linear mixed models with EF, diet sequence, and period as fixed effects and participant as random effect were used to estimate the intervention effect. Interaction effects between EF and body fat percentage were examined.

RESULTS

Among the 50 participants who completed the trial, 39 (78%) were women, 30 (60%) were Non-Hispanic White, and 40 (80%) had a body mass index of <25 kg/m2, and the mean age was 32.1 y. The differences between high and low EF in fasting ghrelin (geometric mean difference: 17.76 ng/mL; P = 0.60), leptin (geometric mean difference: 2.09 ng/mL; P = 0.14), adiponectin (geometric mean difference: 381.7 ng/mL; P = 0.32), and hs-CRP (geometric mean difference: -0.018 mg/dL; P = 0.08) were not statistically significant. No significant interaction was observed between EF and body fat percentage on appetite regulation and inflammatory biomarkers.

CONCLUSIONS

No differences was observed in fasting ghrelin, leptin, adiponectin, and hs-CRP comparing high and low EF. Future studies are needed to understand the physiology of EF and appetite as they relate to metabolic health. This trial was registered at clinicaltrials.gov as NCT02392897.

Association of largest meal timing and eating frequency with body mass index and obesity

BACKGROUND AND AIMS

The circadian pattern of eating behaviors has garnered increasing interest as a strategy for obesity prevention and weight loss. It is believed that the benefits stem from aligning food intake with the body's natural daily rhythms. However, the existing body of evidence is limited in scale and scope and there has been insufficient evaluation of temporal eating behaviors, such as the specific time of day in which the highest calorie consumption occurs, meal frequency, and distribution. This research aims to explore the association between the timing of the largest meal of the day and eating frequency with Body Mass Index (BMI) and obesity.

METHODS

Participants (n = 2050, 18-65y) were part of an exploratory cross-sectional and population-based research, with data collection in a virtual environment. Linear regression analyses and restricted cubic splines evaluated differences in BMI associated with independent eating variables [timing of the largest meal, number of meals/day (as continuous and categorical: ≤3 or >3/day), and each largest meal of the day (breakfast/lunch/dinner)]. Logistic regression models were fitted to assess Odds Ratios (OR) and 95 % Confidence Intervals (CI) of obesity associated with the same independent variables.

RESULTS

Our main findings were that the timing of the largest meal and reporting dinner as the largest meal were associated with higher values of BMI (respectively, 0.07 kg/m2 and 0.85 kg/m2) and increased odds of obesity [respectively OR(95%CI):1.04(1.01,1.08), and OR(95%CI):1.67(1.18,2.38)]. Those who realized more than 3 meals/day presented lower values of BMI (-0.14 kg/m2) and 32 % lower odds of having obesity [OR(95%CI):0.68(0.52,0.89)]. Reporting lunch as the largest meal also protected against obesity [OR(95%CI):0.71(0.54,0.93)]. These associations were statistically significant and independent of sex, age, marital status, education level, diet quality, sleep duration, and weekly frequency of physical exercise.

CONCLUSION

Having the largest meal earlier in the day, concentrating the majority of caloric intake during lunch, and consuming more than three meals a day, may present a promising intervention for preventing and treating obesity/overweight.

Temporal patterns of energy intake identified by the latent class analysis in relation to prevalence of overweight and obesity in Iranian adults

We aimed to identify temporal patterns of energy intake and investigate their association with adiposity. We performed a cross-sectional study of 775 adults in Iran. Information about eating occasions across the day was collected by three 24-h dietary recalls. Latent class analysis (LCA) was used to identify temporal eating patterns based on whether or not an eating occasion occurred within each hour of the day. We applied binary logistic regression to estimate the OR and 95 % CI of overweight and obesity (defined as BMI of 25-29·9 and ≥ 30 kg/m2, respectively) across temporal eating patterns while controlling for potential confounders. LCA grouped participants into three exclusive sub-groups named 'Conventional', 'Earlier breakfast' and 'Later lunch'. The 'Conventional' class was characterised by high probability of eating occasions at conventional meal times. 'Earlier breakfast' class was characterised by high probability of a breakfast eating occasion 1 h before the conventional pattern and a dinner eating occasion 1 h after the conventional pattern, and the 'Later lunch' class was characterised by a high probability of a lunch eating occasion 1 h after the conventional pattern. Participants in the 'Earlier breakfast' pattern had a lower likelihood of obesity (adjusted OR: 0·56, 95 % CI: 0·35, 0·95) as compared with the 'Conventional' pattern. There was no difference in the prevalence of obesity or overweight between participants in the 'Later lunch' and the 'Conventional' patterns. We found an inverse association between earlier eating pattern and the likelihood of obesity, but reverse causation may be a plausible explanation.

How do network meta-analyses address intransitivity when assessing certainty of evidence: a systematic survey

OBJECTIVES

To describe how systematic reviews with network meta-analyses (NMAs) that used the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) NMA approach addressed intransitivity when assessing certainty of evidence.

DESIGN

Systematic survey.

DATA SOURCES

Medline, Embase and Cochrane Database of Systematic Reviews from September 2014 to October 2022.

ELIGIBILITY CRITERIA

Systematic reviews of randomised controlled trials with aggregate data NMAs that used the GRADE NMA approach for assessing certainty of evidence.

DATA EXTRACTION AND SYNTHESIS

We documented how reviewers described methods for addressing intransitivity when assessing certainty of evidence, how often they rated down for intransitivity and their explanations for rating down.

RESULTS

Of the 268 eligible systematic reviews, 44.8% (120/268) mentioned intransitivity when describing methods for assessing the certainty of evidence. Of these, 28.3% (34/120) considered effect modifiers and from this subset, 67.6% (23/34) specified the effect modifiers; however, no systematic review noted how they chose the effect modifiers. 15.0% (18/120) mentioned looking for differences between the direct comparisons that inform the indirect estimate. No review specified a threshold for difference in effect modifiers between the direct comparisons that would lead to rating down for intransitivity. Reviewers noted rating down indirect evidence for intransitivity in 33.1% of systematic reviews, and noted intransitivity for network estimates in 23.0% of reviews. Authors provided an explanation for rating down for intransitivity in 59.6% (31/52) of the cases in which they rated down. Of the 31 in which they provided an explanation, 74.2% (23/31) noted they detected differences in effect modifiers and 67.7% (21/31) specified in what effect modifiers they detected differences.

CONCLUSIONS

A third of systematic reviews with NMAs using the GRADE approach rated down for intransitivity. Limitations in reporting of methods to address intransitivity proved considerable. Whether the problem is that reviewers neglected to address rating down for transitivity at all, or whether they did consider but not report, is not clear. At minimum systematic reviews with NMAs need to improve their reporting practices regarding intransitivity; it may well be that they need to improve their practice in transitivity assessment. How to best address intransitivity may remain unclear for many reviewers thus additional GRADE guidance providing practical instructions for addressing intransitivity may be desirable.

Meal Frequency and Multi-Morbidity in a Cypriot Population: A Cross-Sectional Study

Data regarding the effect of specific dietary behaviors, such as meal frequency, on multi-morbidity are scarce. Therefore, the objective of this study was to examine the effect of meal frequency on multi-morbidity in a Cypriot population. A representative sample of 1255 adults >18 years old was surveyed during 2022-2023. Data regarding sociodemographic characteristics, multi-morbidity, and meal frequency consumption were collected through validated questionnaires. Diseases were listed according to the International Classification of Diseases, 10th Revision [ICD-10]. Statistical analysis was conducted using SPSS Statistics v.19.0. Responders who consumed more than three meals and snacks daily had a higher probability of multi-morbidity [OR: 1.505 [95% CI: 1.505-2.069]] compared with those who consumed three or fewer meals and snacks daily. The relation was not statistically significant after adjusting for age and gender and for socioeconomic characteristics. Furthermore, participants who consumed more than three snacks per day had a 1.776 [AOR: 1.616 [95% CI: 1.054-2.476]] higher risk of having multi-morbidity compared with participants who did not consume any snack or consumed one snack per day. The findings suggest that people with multi-morbidity have a higher risk when consuming three or more snacks per day regardless of age, gender, and socioeconomic characteristics.

Meal-timing patterns and chronic disease prevalence in two representative Austrian studies

PURPOSE

This study aimed at describing meal-timing patterns using cluster analysis and explore their association with sleep and chronic diseases, before and during COVID-19 mitigation measures in Austria.

METHODS

Information was collected in two surveys in 2017 (N = 1004) and 2020 (N = 1010) in representative samples of the Austrian population. Timing of main meals, nighttime fasting interval, last-meal-to-bed time, breakfast skipping and eating midpoint were calculated using self-reported information. Cluster analysis was applied to identify meal-timing clusters. Multivariable-adjusted logistic regression models were used to study the association of meal-timing clusters with prevalence of chronic insomnia, depression, diabetes, hypertension, obesity and self-rated bad health status.

RESULTS

In both surveys, median breakfast, lunch and dinner times on weekdays were 7:30, 12:30 and 18:30. One out of four participants skipped breakfast and the median number of eating occasions was 3 in both samples. We observed correlation between the different meal-timing variables. Cluster analysis resulted in the definition of two clusters in each sample (A17 and B17 in 2017, and A20 and B20 in 2020). Clusters A comprised most respondents, with fasting duration of 12-13 h and median eating midpoint between 13:00 and 13:30. Clusters B comprised participants reporting longer fasting intervals and later mealtimes, and a high proportion of breakfast skippers. Chronic insomnia, depression, obesity and self-rated bad health-status were more prevalent in clusters B.

CONCLUSIONS

Austrians reported long fasting intervals and low eating frequency. Meal-timing habits were similar before and during the COVID-19-pandemic. Besides individual characteristics of meal-timing, behavioural patterns need to be evaluated in chrono-nutrition epidemiological studies.

Effect of lunch with different calorie and nutrient balances on dinner-induced postprandial glucose variability

Aim

This study aimed to examine the effect of lunches with different caloric contents (Study 1) and nutrient balances (Study 2) on dinner-induced postprandial glucose fluctuation.

Methods

Energy trial (Study 1): Thirteen healthy young participants (n = 10 men, n = 3 women) were investigated to determine the effects of different caloric intakes at lunch on glucose level variability. The study was comprised of four trials (no lunch, low lunch, standard lunch, and high-energy lunch). Energy balance trial (Study 2): Fourteen healthy young adults (n = 8 men, n = 6 women) were investigated to determine the effect of different nutrient balances during lunch on glucose level variability. The study consisted of four trials (standard, protein-rich, fat-rich, and carbohydrate-rich). In studies 1 and 2, each trial was spaced at least 24 full hours apart, and breakfast and dinner were tested as meals. The mealtimes for each trial were then aligned. Continuous glucose monitoring was used to assess the blood glucose fluctuations.

Results

Study 1: The no-lunch (95% CI 95.5–149.7) and low-energy lunch (95% CI 90.8–143.1) trials had significantly higher values in the incremental area under the curve (iAUC) of postprandial blood glucose at dinner compared to the standard (95% CI 55.4–90.0) and high-energy lunch (95% CI 29.3–54.6) trials (P = 0.006, P = 0.001 vs. none), (P = 0.004, P = 0.001 vs. low-energy trial). Study 2: A significantly higher postprandial blood glucose iAUC for dinner was found in the fat-rich trial (95% CI 58.5–114.0) than that in the protein-rich (95% CI 25.6–63.9) and standard (95% CI 25.6–112.4) trials, (P = 0.006, P = 0.035 vs. fat-rich trial).

Conclusions

Our findings indicate that skipping lunch and low-calorie or high-lipid intake increased postprandial blood glucose levels after dinner.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12986-022-00704-1.

Meal frequency and incidence of type 2 diabetes: a prospective study

Dietary habits play an important role in the development of obesity and type 2 diabetes. However, evidence on association between diet frequency and type 2 diabetes was limited and inconclusive. We aimed to examine the association between meal frequency and risk of type 2 diabetes. The cohort study used data from China Health and Retirement Longitudinal Study of 8874 community-dwelling people aged over 45 years. Participants were classified as eating two meals per day, three meals per day and four meals per day. Multiple Poisson regression models were used to examine risk of 4-year incident type 2 diabetes among people who ate more or less than three meals per day compared with people who ate three meals per day. We documented 706 type 2 diabetes cases during follow-up. After adjustment for known risk factors for type 2 diabetes, except for BMI, participants who ate four meals per day were at a lower risk of type 2 diabetes than those who ate three meals per day (relative risk(RR) = 0·73 (0·58, 0·92)). After further adjustment for baseline BMI, the association was slightly attenuated but remained statistically significant (RR = 0·76 (0·60, 0·97)). Subgroup analysis showed that the fully adjusted RR of type 2 diabetes for people eating four meals per day were 0·66 (0·48, 0·91) and 0·93 (0·65, 1·34) among those had a BMI < 25 and ≥ 25 kg/m², respectively. Eating four meals per day, compared with eating three meals per day was associated with lower risk of developing type 2 diabetes in a Chinese population, particularly in those with a BMI < 25 kg/m².

Ten things to know about ten cardiovascular disease risk factors - 2022

The American Society for Preventive Cardiology (ASPC) "Ten things to know about ten cardiovascular disease risk factors - 2022" is a summary document regarding cardiovascular disease (CVD) risk factors. This 2022 update provides summary tables of ten things to know about 10 CVD risk factors and builds upon the foundation of prior annual versions of "Ten things to know about ten cardiovascular disease risk factors" published since 2020. This 2022 version provides the perspective of ASPC members and includes updated sentinel references (i.e., applicable guidelines and select reviews) for each CVD risk factor section. The ten CVD risk factors include unhealthful dietary intake, physical inactivity, dyslipidemia, pre-diabetes/diabetes, high blood pressure, obesity, considerations of select populations (older age, race/ethnicity, and sex differences), thrombosis (with smoking as a potential contributor to thrombosis), kidney dysfunction and genetics/familial hypercholesterolemia. Other CVD risk factors may be relevant, beyond the CVD risk factors discussed here. However, it is the intent of the ASPC "Ten things to know about ten cardiovascular disease risk factors - 2022" to provide a tabular overview of things to know about ten of the most common CVD risk factors applicable to preventive cardiology and provide ready access to applicable guidelines and sentinel reviews.

Meal patterns associated with energy intake in people with obesity

It is widely assumed that people with obesity have several common eating patterns, including breakfast skipping, eating during the night and high fast-food consumption. However, differences in individual meal and dietary patterns may be crucial to optimising obesity treatment. Therefore, we investigated the inter-individual variation in eating patterns, hypothesising that individuals with obesity show different dietary and meal patterns, and that these associate with self-reported energy intake (rEI) and/or anthropometric measures. Cross-sectional data from 192 participants (aged 20-55 years) with obesity, including 6 d of weighed food records, were analysed. Meal patterns and dietary patterns were derived using exploratory hierarchical cluster analysis and k-means cluster analysis, respectively. Five clear meal patterns were found based on the time-of-day with the highest mean rEI. The daily rEI was highest among 'midnight-eaters' (10 669 (sd 2301) kJ), and significantly (P < 0·05) higher than 'dinner-eaters' (8619 (sd 2301) kJ), 'lunch-eaters' (8703 (sd 2176) kJ) and 'supper-eaters' (8786 (sd 1925) kJ), but not 'regular-eaters' (9749 (sd 2720) kJ). Despite differences of up to 2050 kJ between meal patterns, there were no significant differences in anthropometric measures or physical activity level (PAL). Four dietary patterns were also found with significant differences in intake of specific food groups, but without significant differences in anthropometry, PAL or rEI. Our data highlight meal timing as a determinant of individual energy intake in people with obesity. The study supports the importance of considering a person's specific meal pattern, with possible implications for more person-focused guidelines and targeted advice.

The Effect of Four Weeks Dietary Intervention with 8-Hour Time-Restricted Eating on Body Composition and Cardiometabolic Risk Factors in Young Adults

Recently, intermittent fasting, also known as time-restricted eating (TRE), has become a popular diet trend. Compared to animal studies, there have been few studies and inconclusive findings investigating the effects of TRE in humans. In this study, we examined the effects of 8 h TRE on body weight and cardiometabolic risk factors in young adults who were mainly active at night. A total of 33 young adults completed the 8 h TRE for 4 weeks. Body composition was measured by bioelectrical impedance analysis at baseline and every 2 weeks, and blood samples were collected at baseline and week 4. Daily dietary records were logged throughout the intervention period. Participants experienced significant changes in body weight (−1.0 ± 1.4 kg), body mass index (−0.4 ± 0.5 kg/m²), and body fat (−0.4 ± 1.9%) after 4 weeks of TRE. When participants were divided into weight loss/gain groups based on their weight change in week 4, fat mass reduction was significantly higher in the weight loss group than in the weight gain group. Regarding cardiometabolic risk factors, levels of fasting insulin and insulin resistance improved in the weight loss group after intervention, but not in the weight gain group. All subjects showed late-shifted sleeping patterns, but no significant differences in sleep duration, sleep quality, or psychological measures between the two groups. When meal frequency and energy proportion were evaluated, the average meal frequency was 2.8 ± 0.5 and energy proportions of breakfast, lunch, dinner, and snacks were 4.5, 39.2, 37.6, and 18.5%, respectively; there were no significant differences between the two groups. However, the saturated fat intake at dinner was lower in the weight loss group (3.1 ± 3.2%, 6.0 ± 2.5% respectively). In conclusion, 8 h TRE can be applied as a lifestyle strategy to manage body weight and cardiometabolic risk factors among young adults with late chronotypes.

Negative energy balance during military training: The role of contextual limitations

During multiday training exercises, soldiers almost systematically face a moderate-to-large energy deficit, affecting their body mass and composition and potentially their physical and cognitive performance. Such energy deficits are explained by their inability to increase their energy intake during these highly demanding periods. With the exception of certain scenarios in which rations are voluntarily undersized to maximize the constraints, the energy content of the rations are often sufficient to maintain a neutral energy balance, suggesting that other limitations are responsible for such voluntary and/or spontaneous underconsumption. In this review, the overall aim was to present an overview of the impact of military training on energy balance, a context that stands out by its summation of specific limitations that interfere with energy intake. We first explore the impact of military training on the various components of energy balance (intake and expenditure) and body mass loss. Then, the role of the dimensioning of the rations (total energy content above or below energy expenditure) on energy deficits are addressed. Finally, the potential limitations inherent to military training (training characteristics, food characteristics, timing and context of eating, and the soldiers' attitude) are discussed to identify potential strategies to spontaneously increase energy intake and thus limit the energy deficit.

Higher eating frequency is associated with lower adiposity and robust circadian rhythms: a cross-sectional study

BACKGROUND

Although overweight and obesity are assumed to arise from an energy imbalance, evidence has shown that the frequency and timing of meals are also potential risk factors for obesity. However, the lack of a consistent approach to define eating patterns relative to internal circadian rhythms limits the extent of these findings.

OBJECTIVES

The objective of this study was to investigate the association of the circadian pattern of energy intake with adiposity and with internal circadian rhythms.

METHODS

A total of 260 Spanish adults (aged 20-30 y; 78.1% women) were included in a 6-d cross-sectional study. Participants documented sleep and dietary intake within the study period. From these data, we evaluated the chronotype, eating patterns (meal timing, eating duration, and eating frequency), and we obtained the daily profile of energy intake. In addition, we evaluated the circadian pattern of wrist temperature (internal circadian rhythm marker). Circadian patterns of energy intake and wrist temperature were analyzed, and their association among them and with anthropometric variables and diet quality was studied.

RESULTS

The greater fragmentation of the circadian pattern of energy intake was associated with lower BMI (in kg/m2; -10.55; 95% CI: -16.96, -4.13; P = 0.001). In addition, a greater eating frequency (≥5 eating occasions/d) was significantly associated with lower BMI (-1.88; 95% CI: -3.27, -0.48) and higher energy intake after 20:00 (4.14% of kcal; 95% CI: 1.67, 7.16). Furthermore, a greater eating frequency was associated with lower fragmentation (P = 0.042) and greater stability of the circadian pattern of wrist temperature (P = 0.016).

CONCLUSIONS

The daily pattern of energy intake is associated with adiposity and robust circadian rhythms. Our results shed light on the relevance of eating frequency as a potential zeitgeber for the circadian system. Although more evidence is needed, eating frequency could be considered for future chrono-nutritional recommendations for the prevention of circadian misalignment and obesity.