Chrononutrition and Cardiometabolic Health: An Overview of Epidemiological Evidence and Key Future Research Directions.

Chrononutrition is a rapidly evolving field of nutritional epidemiology that addresses the complex relationship between temporal eating patterns, circadian rhythms, and metabolic health, but most prior research has focused on the cardiometabolic consequences of time-restricted feeding and intermittent fasting. The purpose of this topical review is to summarize epidemiological evidence from observational and intervention studies regarding the role of chrononutrition metrics related to eating timing and regularity in cardiometabolic health preservation and cardiovascular disease prevention. Observational studies are limited due to the lack of time-stamped diet data in most population-based studies. Findings from cohort studies generally indicate that breakfast skipping or the later timing of the first eating occasion, a later lunch and dinner, and a greater proportion of caloric intake consumed in the evening are associated with adverse cardiometabolic outcomes, including higher risk for coronary heart disease, hypertension, type 2 diabetes, obesity, dyslipidemia, and systemic inflammation. Randomized controlled trials are also limited, as most in the field of chrononutrition focus on the cardiometabolic consequences of time-restricted feeding. Overall, interventions that shift eating timing patterns to earlier in the day and that restrict evening caloric intake tend to have protective effects on cardiometabolic health, but small sample sizes and short follow-up are notable limitations. Innovation in dietary assessment approaches, to develop low-cost validated tools with acceptable participant burden that reliably capture chrononutrition metrics, is needed for advancing observational evidence. Culturally responsive pragmatic intervention studies with sufficiently large and representative samples are needed to understand the impact of fixed and earlier eating timing schedules on cardiometabolic health. Additional research is warranted to understand the modifiable determinants of temporal eating patterns, to investigate the role of chrononutrition in the context of other dimensions of diet (quantity, quality, and food and nutrition security) in achieving cardiometabolic health equity, and to elucidate underlying physiological mechanisms.

Scoping review: Exploring the relationship between chrononutrition and glycemic responses in the adult population.

Chrononutrition, an emerging body of evidence on the relationship between biological rhythms and metabolism, has been established to be associated with glycemic responses. However, the available evidence is inconsistent, due to protocol variations. Therefore, this review aims to summarize the findings on chrononutrition characteristics and their association with glycemic responses among adults. Systematic searches were conducted across six databases (PubMed, EBSCO Host, ProQuest Central, MEDLINE & Ovid, Scopus and Web of Science) to identify all relevant studies published from January 2012. Two reviewers independently screened the abstracts and full-text articles based on the inclusion and exclusion criteria. Details about population characteristics, study methods and key findings were extracted following the PRISMA-ScR guideline. The quality of selected studies was evaluated using the mixed methods appraisal tool. The searchers identified 49 studies eligible for analysis. The results showed that meal timing, particularly night-time eating and snacking were associated with glycemic responses. Regarding meal regularity, skipping breakfast may affect glycemic responses, but no clear conclusion was drawn about its effect on insulin. The association between meal frequency and glycemic responses was inconclusive. Night fasting duration and restricted eating window are potentially associated with glycemic responses. The current review extensively investigates the association between chrononutrition factors and glycemic responses in adults. However, more prospective cohort and interventional studies are needed to better understand this causal-effect relationship.

Effects of Early and Late Time-Restricted Feeding on Parameters of Metabolic Health: An Explorative Literature Assessment.

Chrono-nutrition (meal timing) aligns food consumption with one's circadian rhythm. The first meal (e.g., breakfast) likely promotes synchronization of peripheral circadian clocks, thereby supporting metabolic health. Time-restricted feeding (TRF) has been shown to reduce body weight (BW) and/or improve cardiovascular biomarkers. In this explorative literature assessment, 13 TRF randomized controlled trials (RCTs) were selected from PubMed and Scopus to evaluate the effects of early (eTRF: first meal before 10:30 a.m.) and late TRF (lTRF: first meal after 11:30 a.m.) on parameters of metabolic health. Although distinct variations in study design were evident between reports, TRF consistently decreased energy intake (EI) and BW, and improved insulin resistance as well as systolic blood pressure. eTRF seemed to have a greater beneficial effect than lTRF on insulin resistance (HOMA-IR). Importantly, most studies did not appear to consider chronotype in their evaluation, which may have underestimated TRF effects. TRF intervention may be a promising approach for risk reduction of human metabolic diseases. To conclusively determine benefits of TRF and identify clear differences between eTRF and lTRF, future studies should be longer-term (≥8 weeks) with well-defined (differences in) feeding windows, include participants chronotypically matching the intervention, and compare outcomes to those of control groups without any dietary limitations.

Trajectory of ghrelin and PYY around a test meal in males and females with avoidant/restrictive food intake disorder versus healthy controls.

Disruptions in appetite-regulating hormones may contribute to the development and/or maintenance of avoidant/restrictive food intake disorder (ARFID). No study has previously assessed fasting levels of orexigenic ghrelin or anorexigenic peptide YY (PYY), nor their trajectory in response to food intake among youth with ARFID across the weight spectrum. We measured fasting and postprandial (30, 60, 120 minutes post-meal) levels of ghrelin and PYY among 127 males and females with full and subthreshold ARFID (n = 95) and healthy controls (HC; n = 32). We used latent growth curve analyses to examine differences in the trajectories of ghrelin and PYY between ARFID and HC. Fasting levels of ghrelin did not differ in ARFID compared to HC. Among ARFID, ghrelin levels declined more gradually than among HC in the first hour post meal (p =.005), but continued to decline between 60 and 120 minutes post meal, whereas HC plateaued (p =.005). Fasting and PYY trajectory did not differ by group. Findings did not change after adjusting for BMI percentile (M(SD)ARFID = 37(35); M(SD)HC = 53(26); p =.006) or calories consumed during the test meal (M(SD)ARFID = 294(118); M(SD)HC = 384 (48); p <.001). These data highlight a distinct trajectory of ghrelin following a test meal in youth with ARFID. Future research should examine ghrelin dysfunction as an etiological or maintenance factor of ARFID.

Impact of a daily legume-based meal on blood and anthropometric parameters in a group of omnivorous adults: A pilot study.

This pilot study aimed to assess the impact of substituting a traditional lunch for a vegetarian legume-based meal on blood and anthropometric parameters in a group of omnivorous adults. A one-group comparison, quasi-experimental dietary intervention was designed. A vegetarian legume-based meal was offered for 8 consecutive weeks (weekdays) to non-vegetarian individuals (n = 26), (28 years [P25 = 20.0, P75 = 35.5]; 21.9 kg/m2 [P25 = 21.3, P75 = 24.8]). Sociodemographic data, health status and lifestyle-related information were recorded. Three-day food records were used to collect food intake at baseline and at the end of the intervention. Anthropometric parameters were recorded and fasting blood analyses were performed following standard procedures. Wilcoxon signed-rank test was used for statistical comparisons. A p-value <0.05 was considered statistically significant. Participants showed a median intake of 79.8 g of cooked legumes per meal, meaning 13 (50.0%) subjects met the Portuguese daily legume intake recommendations during the intervention days. There were no statistically significant differences in anthropometric parameters. Transferrin concentration increased after 8 weeks (+12.5 mg/dL; p = 0.001). Total cholesterol concentration reduced after 8 weeks (-6 mg/dL; p = 0.041), as well as low-density lipoprotein (LDL) cholesterol (-7 mg/dL; p = 0.003). Triglycerides (+9 mg/dL; p = 0.046), fasting glucose (+2 mg/dL; p = 0.037) and HbA1c (+0.1 mg/dL; p = 0.010) concentration increased after the 2-month legume-based trial. Results suggest a cholesterol-lowering potential of legume-rich diets. However, unfavourable results regarding the impact on glucose metabolism-related biomarkers and triglyceride levels were observed. The study's limitations in design and sample size emphasise the importance of conducting further research with larger cohorts to attain more conclusive findings.

Exercise-induced increase in muscle insulin sensitivity in men is amplified when assessed using a meal test.

AIMS/HYPOTHESIS: Exercise has a profound effect on insulin sensitivity in skeletal muscle. The euglycaemic-hyperinsulinaemic clamp (EHC) is the gold standard for assessment of insulin sensitivity but it does not reflect the hyperglycaemia that occurs after eating a meal. In previous EHC investigations, it has been shown that the interstitial glucose concentration in muscle is decreased to a larger extent in previously exercised muscle than in rested muscle. This suggests that previously exercised muscle may increase its glucose uptake more than rested muscle if glucose supply is increased by hyperglycaemia. Therefore, we hypothesised that the exercise-induced increase in muscle insulin sensitivity would appear greater after eating a meal than previously observed with the EHC. METHODS: Ten recreationally active men performed dynamic one-legged knee extensor exercise for 1 h. Following this, both femoral veins and one femoral artery were cannulated. Subsequently, 4 h after exercise, a solid meal followed by two liquid meals were ingested over 1 h and glucose uptake in the two legs was measured for 3 h. Muscle biopsies from both legs were obtained before the meal test and 90 min after the meal test was initiated. Data obtained in previous studies using the EHC (n=106 participants from 13 EHC studies) were used for comparison with the meal-test data obtained in this study. RESULTS: Plasma glucose and insulin peaked 45 min after initiation of the meal test. Following the meal test, leg glucose uptake and glucose clearance increased twice as much in the exercised leg than in the rested leg; this difference is twice as big as that observed in previous investigations using EHCs. Glucose uptake in the rested leg plateaued after 15 min, alongside elevated muscle glucose 6-phosphate levels, suggestive of compromised muscle glucose metabolism. In contrast, glucose uptake in the exercised leg plateaued 45 min after initiation of the meal test and there were no signs of compromised glucose metabolism. Phosphorylation of the TBC1 domain family member 4 (TBC1D4; p-TBC1D4Ser704) and glycogen synthase activity were greater in the exercised leg compared with the rested leg. Muscle interstitial glucose concentration increased with ingestion of meals, although it was 16% lower in the exercised leg than in the rested leg. CONCLUSIONS/INTERPRETATION: Hyperglycaemia after meal ingestion results in larger differences in muscle glucose uptake between rested and exercised muscle than previously observed during EHCs. These findings indicate that the ability of exercise to increase insulin-stimulated muscle glucose uptake is even greater when evaluated with a meal test than has previously been shown with EHCs.

Chrononutrition in traditional European medicine-Ideal meal timing for cardiometabolic health promotion.

Meal timing plays a crucial role for cardiometabolic health, given the circadian regulation of cardiometabolic function. However, to the best of our knowledge, no concept of meal timing exists in traditional European medicine (TEM). Therefore, in this narrative review, we aim to define the optimal time slot for energy intake and optimal energy distribution throughout the day in a context of TEM and explore further implications. By reviewing literature published between 2002 and 2022, we found that optimal timing for energy intake may be between 06:00 and 09:00, 12:00 and 14:00, and between 15:00 and 18:00, with high energy breakfast, medium energy lunch and low energy dinner and possibly further adjustments according to one's chronotype and genetics. Also, timing and distribution of energy intake may serve as a novel therapeutic strategy to optimize coction, a concept describing digestion and metabolism in TEM. Please cite this article as: Eberli NS, Colas L, Gimalac A. Chrononutrition in traditional European medicine-Ideal meal timing for cardiometabolic health promotion. J Integr Med. 2024; 22(2);115-125.

Conditioning by a Previous Experience Impairs the Rewarding Value of a Comfort Meal.

BACKGROUND: Meal ingestion induces a postprandial experience that involves homeostatic and hedonic sensations. Our aim was to determine the effect of aversive conditioning on the postprandial reward of a comfort meal. METHODS: A sham-controlled, randomised, parallel, single-blind study was performed on 12 healthy women (6 per group). A comfort meal was tested before and after coupling the meal with an aversive sensation (conditioning intervention), induced by infusion of lipids via a thin naso-duodenal catheter; in the pre- and post-conditioning tests and in the control group, a sham infusion was performed. Participants were instructed that two recipes of a tasty humus would be tested; however, the same meal was administered with a colour additive in the conditioning and post-conditioning tests. Digestive well-being (primary outcome) was measured every 10 min before and 60 min after ingestion using graded scales. RESULTS: In the aversive conditioning group, the comfort meal in the pre-conditioning test induced a pleasant postprandial experience, which was significantly lower in the post-conditioning test; the effect of aversive conditioning (change from pre- to post-conditioning) was significant as compared to sham conditioning in the control group, which showed no differences between study days. CONCLUSION: The hedonic postprandial response to a comfort meal in healthy women is impaired by aversive conditioning. CLINICALTRIALS: gov ID: NCT04938934.

Parents of preschoolers use multiple strategies to feed their children: Findings from an observational video pilot study.

The current study leveraged observational data collection methods to fill gaps in our understanding of parent approach to feeding as well as child responses to various parental approaches. Specifically, the study aimed to: 1) characterize the broad range of food parenting practices used by parents of preschoolers during shared mealtimes at home, including differences by child gender, and 2) describe child responses to specific parent feeding practices. Forty parent-child dyads participated by recording two in-home shared meals. Meals were coded using a behavioral coding scheme that coded the occurrence of 11 distinct food parenting practices (e.g. indirect and direct commands, praise, bribes) and eight child responses (e.g., eat, refuse, cry/whine) to food parenting practices. Results revealed that parents engaged in a broad range of food parenting practices at meals. On average, parents in our sample used 10.51 (SD 7.83; Range 0-30) total food parenting practices per mealtime with a mean use of 3.38 (SD 1.67; Range 0-8) unique food parenting practices per mealtime. Use of indirect and direct commands to eat were most common; direct and indirect commands were used by 97.5% (n = 39) and 87.5% (n = 35) of parents at meals, respectively. No statistically significant differences were observed by child gender. No one specific feeding practice consistently yielded compliance or refusal to eat from the child, instead child responses were often mixed (e.g., compliance followed by refusal and/or refusal followed by compliance). However, use of praise to prompt eating was the practice that most often resulted in child compliance; 80.8% of children complied following parent's use of praise as a prompt to eat. Findings deepen our understanding of the types and frequency of food parenting practices used by parents of preschoolers during meals eaten at home and illuminate child responses to specific food parenting practices.

Acute postprandial gut hormone, leptin, glucose and insulin responses to resistant starch in obese children: a single blind crossover study.

INTRODUCTION: Resistant starch (RS) has beneficial effects on postprandial glucose metabolism in both animals and adults. Hitherto, there have been no studies in children of the acute metabolic and hormonal effects of RS-containing meals. OBJECTIVES: We aimed to compare serial plasma glucose, insulin, gut hormone, leptin profiles and satiety scores in obese children after meals containing variable amounts of RS. METHODS: This was a single blind, non-randomised, crossover study of 20 obese children aged 10-14 years old without comorbidities. Three test meals containing rice (M1), rice cooked with coconut oil (M2), rice cooked in coconut oil with lentils (M3) were given in sequence after a 12-hour fast . Blood samples were analysed for glucose (PG), insulin, leptin, glucagon-like polypeptide (GLP) 1, ghrelin and peptide YY (PYY) at appropriate times between 0 and 180 min. RESULTS: Meal M2 resulted in significantly lower postprandial glucose values compared with meal M1 (maximal incremental glucose, ∆Cmax, p<0.05; area under the curve, ∆AUC0-3, p<0.01) and meal M3 (maximal concentration, Cmax, p<0.01; ∆Cmax, p<0.001, and ∆AUC0-3p<0.01). M2 also produced lower insulin values compared with M1 (p<0.05). Postprandial ghrelin was significantly higher after M1 compared with M3 (p<0.05). PYY, GLP1 and median satiety scores were not significantly different between the three meals. CONCLUSION: This study shows that M2, the meal containing RS alone, induced beneficial effects on acute postprandial glucose, insulin and ghrelin concentrations in obese children without diabetes. Acute postprandial satiety scores were not significantly affected by the three meals. TRIAL REGISTRATION NUMBER: SLCTR/2020/007.

Increased Meal Size but Reduced Meal-Stimulated Plasma Cholecystokinin Concentrations in Women With Obesity.

To better understand the physiological basis of obesity in women, we investigated whether obesity or menstrual cycle phase affects laboratory test-meal size or meal-stimulated plasma cholecystokinin (CCK) concentration. Women with healthy weight (body mass index [BMI] of 18.5-24.9 kg/m2, N = 16) or obesity (BMI 30-39.9 kg/m2, N = 20) were tested once in the late-follicular or peri-ovulatory phase (LF/PO) and once in the mid-luteal phase (ML). Meals of ham sandwiches were offered and blood was sampled. Menstrual cycle phases were verified with participants' reports of menses and measurements of progesterone and luteinizing hormone (LH) concentrations. Women with obesity ate significantly larger meals than women with healthy weight, (mean, 711 [95% CI, 402-1013] kJ, P = 0.001, during the LF/PO and 426 [105-734] kJ, P = 0.027, larger during the ML). Women with healthy weight ate smaller meals during LF/PO than ML (decrease, 510 [192-821 kJ], P = 0.008), but women with obesity did not (decrease, 226 [-87-542] kJ, P = 0.15). CCK concentrations 18 to 30 minutes after meal onset were lower in women with obesity than in women with healthy weight during LF/PO (3.6 [3.1-4.1] vs 6.1 [4.5-7.7] pmol/L; P = 0.004), but not during ML, with a significant interaction effect (1.8 [1.2-2.4] pmol/L, P = 0.048). Women with obesity consumed larger meals than women with healthy weight but displayed reduced meal-stimulated plasma CCK concentrations. These data are consistent with the hypothesis that a defect in CCK secretion compromises satiation in obese women and contributes to the development or maintenance of obesity.

Nutrient timing and metabolic regulation.

Daily (circadian) rhythms coordinate our physiology and behaviour with regular environmental changes. Molecular clocks in peripheral tissues (e.g. liver, skeletal muscle and adipose) give rise to rhythms in macronutrient metabolism, appetite regulation and the components of energy balance such that our bodies can align the periodic delivery of nutrients with ongoing metabolic requirements. The timing of meals both in absolute terms (i.e. relative to clock time) and in relative terms (i.e. relative to other daily events) is therefore relevant to metabolism and health. Experimental manipulation of feeding-fasting cycles can advance understanding of the effect of absolute and relative timing of meals on metabolism and health. Such studies have extended the overnight fast by regular breakfast omission and revealed that morning fasting can alter the metabolic response to subsequent meals later in the day, whilst also eliciting compensatory behavioural responses (i.e. reduced physical activity). Similarly, restricting energy intake via alternate-day fasting also has the potential to elicit a compensatory reduction in physical activity, and so can undermine weight-loss efforts (i.e. to preserve body fat stores). Interrupting the usual overnight fast (and therefore also the usual sleep cycle) by nocturnal feeding has also been examined and further research is needed to understand the importance of this period for either nutritional intervention or nutritional withdrawal. In summary, it is important for dietary guidelines for human health to consider nutrient timing (i.e. when we eat) alongside the conventional focus on nutrient quantity and nutrient quality (i.e. how much we eat and what we eat).

Face Validity of Observed Meal Patterns Reported with 7-Day Diet Diaries in a Large Population-Based Cohort Using Diurnal Variation in Concentration Biomarkers of Dietary Intake.

In a cross-sectional analysis of a population-based cohort (United Kingdom, N = 21,318, 1993-1998), we studied how associations between meal patterns and non-fasting triglyceride and glucose concentrations were influenced by the hour of day at which the blood sample was collected to ascertain face validity of reported meal patterns, as well as the influence of reporting bias (assessed using formula of energy expenditure) on this association. Meal size (i.e., reported energy content), mealtime and meal frequency were reported using pre-structured 7-day diet diaries. In ANCOVA, sex-specific means of biomarker concentrations were calculated by hour of blood sample collection for quartiles of reported energy intake at breakfast, lunch and dinner (meal size). Significant interactions were observed between breakfast size, sampling time and triglyceride concentrations and between lunch size, sampling time and triglyceride, as well as glucose concentrations. Those skipping breakfast had the lowest triglyceride concentrations in the morning and those skipping lunch had the lowest triglyceride and glucose concentrations in the afternoon, especially among acceptable energy reporters. Eating and drinking occasion frequency was weakly associated with glucose concentrations in women and positively associated with triglyceride concentrations in both sexes; stronger associations were observed for larger vs. smaller meals and among acceptable energy reporters. Associations between meal patterns and concentration biomarkers can be observed when accounting for diurnal variation and underreporting. These findings support the use of 7-day diet diaries for studying associations between meal patterns and health.

Interplay of Dinner Timing and MTNR1B Type 2 Diabetes Risk Variant on Glucose Tolerance and Insulin Secretion: A Randomized Crossover Trial.

OBJECTIVE: We tested whether the concurrence of food intake and elevated concentrations of endogenous melatonin, as occurs with late eating, results in impaired glucose control, in particular in carriers of the type 2 diabetes-associated G allele in the melatonin receptor-1B gene (MTNR1B). RESEARCH DESIGN AND METHODS: In a Spanish natural late-eating population, a randomized, crossover study was performed. Each participant (n = 845) underwent two evening 2-h 75-g oral glucose tolerance tests following an 8-h fast: an early condition scheduled 4 h prior to habitual bedtime ("early dinner timing") and a late condition scheduled 1 h prior to habitual bedtime ("late dinner timing"), simulating an early and a late dinner timing, respectively. Differences in postprandial glucose and insulin responses between early and late dinner timing were determined using incremental area under the curve (AUC) calculated by the trapezoidal method. RESULTS: Melatonin serum levels were 3.5-fold higher in the late versus early condition, with late dinner timing resulting in 6.7% lower insulin AUC and 8.3% higher glucose AUC. The effect of late eating impairing glucose tolerance was stronger in the MTNR1B G-allele carriers than in noncarriers. Genotype differences in glucose tolerance were attributed to reductions in ß-cell function (P for interaction, Pint glucose area under the curve = 0.009, Pint corrected insulin response = 0.022, and Pint disposition index = 0.018). CONCLUSIONS: Concurrently high endogenous melatonin and carbohydrate intake, as typical for late eating, impairs glucose tolerance, especially in MTNR1B G-risk allele carriers, attributable to insulin secretion defects.

A scoping review of intermittent fasting, chronobiology, and metabolism.

Chronobiology plays a crucial role in modulating many physiologic systems in which there is nutritional synergism with meal timing. Given that intermittent fasting (IF) has grown as a flexible dietary method consisting of delayed or early eating windows, this scoping review addresses the effects of IF protocols on metabolism as they relate to clinical nutrition and the circadian system. Although nocturnal habits are associated with circadian misalignments and impaired cardiometabolic profile-and nutritional physiology is better orchestrated during the day-most findings are based on animal experiments or human studies with observational designs or acute meal tests. Well-controlled randomized clinical trials employing IF protocols of delayed or early eating windows have sometimes demonstrated clinical benefits, such as improved glycemic and lipid profiles, as well as weight loss. However, IF does not appear to be more effective than traditional diets at the group level, and its effects largely depend on energy restriction. Thus, efforts must be made to identify patient biological rhythms, preferences, routines, and medical conditions before individual dietary prescription in clinical practice.

Effect of acute dietary- versus combined dietary and exercise-induced energy deficits on subsequent energy intake, appetite and food reward in adolescents with obesity.

BACKGROUND: Acute dietary-induced energy deficits have been shown to favor compensatory appetitive responses. The aim of this study was to compare energy intake (EI), appetite sensations and the hedonic responses to equivalent energy deficits induced by dietary restriction alone and combined with exercise in adolescents with obesity. METHODS: In a within-subjects design, seventeen adolescents with obesity (12-16 years, Tanner stage 3-5, 6 males) randomly completed three 14 h conditions: (i) control (CON); (ii) deficit induced by diet only (Def-EI) and; (iii) deficit induced by combined diet and physical exercise (Def-mixed). Breakfast and lunch were calibrated to generate a 500 kcal deficit in Def-EI and 250 kcal deficit in Def-mixed. A 250 kcal deficit was created through a cycling exercise set at 65% VO2peak in Def-mixed. Ad libitum EI, macronutrients and relative EI (REI) were assessed at dinner, subjective appetite sensations taken at regular intervals, and food reward measured before dinner. RESULTS: EI at dinner was significantly lower in Def-EI compared to CON (p = 0.014; Effect size (ES): -0.59 [-1.07; -0.12]), with no difference between Def-mixed and both CON and Def-EI. Total REI was lower in both deficit conditions compared with CON (Def-mixed: p < 0.001; ES: -3.80 [-4.27; -3.32], Def-EI: p < 0.001; ES: -4.90 [-5.37; -4.42] respectively), indicating incomplete compensation for the energy deficits. Absolute protein ingestion at dinner was lower in Def-EI than Def-mixed (p = 0.037; ES: -0.50 [-0.98; -0.03]) and absolute lipid ingestion was lower in Def-EI than in CON (p = 0.033; ES: -0.51 [-0.99; -0.04]). A higher proportion of protein and a lower proportion of carbohydrates was observed in Def-mixed than in Def-EI (p = 0.078; ES: -0.42 [-0.90; 0.04] and p = 0.067; ES: 0.44 [-0.03; 0.92] respectively). Total area under the curve for appetite sensations were similar between conditions. Explicit liking for sweet relative to savoury food was lower in Def-mixed compared to CON (p = 0.027; ES: -0.53 [-1.01; -0.06]) with no difference in food reward between Def-EI and CON. CONCLUSION: Neither of the two acute isoenergetic deficits led to subsequent appetitive compensation, with the dietary deficit even inducing a lower ad libitum EI at the subsequent dinner. Further studies are needed to better understand the appetitive response to dietary and exercise energy balance manipulations in this population.

Predicting the Macronutrient Composition of Mixed Meals From Dietary Biomarkers in Blood.

Diet monitoring is an essential intervention component for a number of diseases, from type 2 diabetes to cardiovascular diseases. However, current methods for diet monitoring are burdensome and often inaccurate. In prior work, we showed that continuous glucose monitors (CGMs) may be used to predict meal macronutrients (e.g., carbohydrates, protein, fat) by analyzing the shape of the post-prandial glucose response. In this study, we examine a number of additional dietary biomarkers in blood by their ability to improve macronutrient prediction, compared to using CGMs alone. For this purpose, we conducted a nutritional study where (n = 10) participants consumed nine different mixed meals with varied but known macronutrient amounts, and we analyzed the concentration of 33 dietary biomarkers (including amino acids, insulin, triglycerides, and glucose) at various times post-prandially. Then, we built machine learning models to predict macronutrient amounts from (1) individual biomarkers and (2) their combinations. We find that the additional blood biomarkers provide complementary information, and more importantly, achieve lower normalized root mean squared error (NRMSE) for the three macronutrients (carbohydrates: 22.9%; protein: 23.4%; fat: 32.3%) than CGMs alone (carbohydrates: 28.9%, t(18) =1.64, p =0.060; protein: 46.4%, t(18) =5.38, p 0.001; fat: 40.0%, t(18) =2.09, p =0.025). Our main conclusion is that augmenting CGMs to measure these additional dietary biomarkers improves macronutrient prediction performance, and may ultimately lead to the development of automated methods to monitor nutritional intake. This work is significant to biomedical research as it provides a potential solution to the long-standing problem of diet monitoring, facilitating new interventions for a number of diseases.

The relationship between meal carbohydrate quantity and the insulin to carbohydrate ratio required to maintain glycaemia is non-linear in young people with type 1 diabetes: A randomized crossover trial.

OBJECTIVE: To determine if the relationship between meal carbohydrate quantity and the insulin to carbohydrate ratio (ICR) required to maintain glycaemia is linear in people with type 1 diabetes. METHODS: We used an open labelled randomized four-arm cross-over study design. Participants (N = 31) aged 12-27 years, HbA1c ≤ 64 mmol/mol (8.0%) received insulin doses based on the individual's ICR and the study breakfast carbohydrate quantity and then consumed four breakfasts containing 20, 50, 100 and 150 g of carbohydrate over four consecutive days in randomized order. The breakfast fat and protein percentages were standardized. Postprandial glycaemia was assessed by 5 h continuous glucose monitoring. The primary outcome was percent time in range (TIR) and secondary outcomes included hypoglycaemia, glucose excursion and incremental area under the curve. Statistical analysis included linear mixed modelling and Wilcoxon signed rank tests. RESULTS: The 20 g carbohydrate breakfast had the largest proportion of TIR (0.74 ± 0.29 p < 0.04). Hypoglycaemia was more frequent in the 50 g (n = 13, 42%) and 100 g (n = 15, 50%) breakfasts compared to the 20 g (n = 6, 20%) and 150 g (n = 7, 26%) breakfasts (p < 0.029). The 150 g breakfast glucose excursion pattern was different from the smaller breakfasts with the lowest glucose excursion 0-2 h and the highest excursion from 3.5 to 5 h. CONCLUSIONS: A non-linear relationship between insulin requirement and breakfast carbohydrate content was observed, suggesting that strengthened ICRs are needed for meals with ≤20 and ≥150 g of carbohydrate. Meals with ≥150 g of carbohydrate may benefit from dual wave bolusing.

Impact of isoenergetic intake of irregular meal patterns on thermogenesis, glucose metabolism, and appetite: a randomized controlled trial.

BACKGROUND: Evidence is emerging that interdaily meal pattern variability potentially affects response such as thermic effect of food (TEF), macronutrient metabolism, and appetite. OBJECTIVES: To investigate the effect of irregular meal pattern on TEF, glucose, insulin, lipid profile, and appetite regulation in women who are overweight or with obesity and confirmed insulin resistance. DESIGN: In a randomized crossover trial, 9 women [mean ± SD BMI (in kg/m2): 33.3 ± 3.1] with confirmed insulin resistance consumed a regular (14 d; 6 meals/d) and an irregular (14 d; 3-9 meals/d) meal pattern separated by a 14-d washout interval. Identical foods were provided during the interventions, and at the start and end of each meal pattern, participants attended the laboratory after an overnight fast. Energy expenditure, glucose, insulin, lipids, adiponectin, leptin, glucagon-like peptide 1 (GLP-1), peptide YY (PYY), and ghrelin were measured at baseline and for 3 h after consumption of a test drink, after which an ad libitum test meal was offered. Subjective appetite ratings were recorded before and after the test drink, after the ad libitum meal, and during the intervention. Continuous interstitial glucose monitoring was undertaken for 7 consecutive days during each intervention. RESULTS: TEF (over 3 h) was significantly lower postirregular intervention compared with postregular (97.7 ± 19.2 kJ*3 h in postregular visit and 76.7 ± 35.2 kJ*3 h in postirregular visit, paired t test, P = 0.048). Differences in HOMA-IR between the 2 interventions (3.3 ± 1.7 and 3.6 ± 1.6 in postregular and postirregular meal pattern, respectively) were not significant. Net incremental AUC for GLP-1 concentrations (over 3 h) for the postregular meal pattern were higher (864.9 ± 456.1 pmol/L*3 h) than the postirregular meal pattern (487.6 ± 271.7 pmol/L*3 h, paired t test, P = 0.005). CONCLUSIONS: Following a 14-d period of an irregular meal pattern, TEF was significantly less than following a regular meal pattern, potentially compromising weight management if sustained long term. This study was registered at www.clinicaltrials.gov as NCT02582606.

Prediction of Personal Glycemic Responses to Food for Individuals With Type 1 Diabetes Through Integration of Clinical and Microbial Data.

OBJECTIVE: Despite technological advances, results from various clinical trials have repeatedly shown that many individuals with type 1 diabetes (T1D) do not achieve their glycemic goals. One of the major challenges in disease management is the administration of an accurate amount of insulin for each meal that will match the expected postprandial glycemic response (PPGR). The objective of this study was to develop a prediction model for PPGR in individuals with T1D. RESEARCH DESIGN AND METHODS: We recruited individuals with T1D who were using continuous glucose monitoring and continuous subcutaneous insulin infusion devices simultaneously to a prospective cohort and profiled them for 2 weeks. Participants were asked to report real-time dietary intake using a designated mobile app. We measured their PPGRs and devised machine learning algorithms for PPGR prediction, which integrate glucose measurements, insulin dosages, dietary habits, blood parameters, anthropometrics, exercise, and gut microbiota. Data of the PPGR of 900 healthy individuals to 41,371 meals were also integrated into the model. The performance of the models was evaluated with 10-fold cross validation. RESULTS: A total of 121 individuals with T1D, 75 adults and 46 children, were included in the study. PPGR to 6,377 meals was measured. Our PPGR prediction model substantially outperforms a baseline model with emulation of standard of care (correlation of R = 0.59 compared with R = 0.40 for predicted and observed PPGR respectively; P < 10-10). The model was robust across different subpopulations. Feature attribution analysis revealed that glucose levels at meal initiation, glucose trend 30 min prior to meal, meal carbohydrate content, and meal's carbohydrate-to-fat ratio were the most influential features for the model. CONCLUSIONS: Our model enables a more accurate prediction of PPGR and therefore may allow a better adjustment of the required insulin dosage for meals. It can be further implemented in closed loop systems and may lead to rationally designed nutritional interventions personally tailored for individuals with T1D on the basis of meals with expected low glycemic response.