Effects of physical form of ß-lactoglobulin and calcium ingestion on GLP-1 secretion, gastric emptying and energy intake in humans: a randomised crossover trial.

The aim of this study was to assess whether adding Ca2+ to aggregate or native forms of ß-lactoglobulin alters gut hormone secretion, gastric emptying rates and energy intake in healthy men and women. Fifteen healthy adults (mean ± sd: 9M/6F, age: 24 ± 5 years) completed four trials in a randomised, double-blind, crossover design. Participants consumed test drinks consisting of 30 g of ß-lactoglobulin in a native form with (NATIVE + MINERALS) and without (NATIVE) a Ca2+-rich mineral supplement and in an aggregated form both with (AGGREG + MINERALS) and without the mineral supplement (AGGREG). Arterialised blood was sampled for 120 min postprandially to determine gut hormone concentrations. Gastric emptying was determined using 13C-acetate and 13C-octanoate, and energy intake was assessed with an ad libitum meal at 120 min. A protein × mineral interaction effect was observed for total glucagon-like peptide-1 (GLP-1TOTAL) incremental AUC (iAUC; P < 0·01), whereby MINERALS + AGGREG increased GLP-1TOTAL iAUC to a greater extent than AGGREG (1882 ± 603 v. 1550 ± 456 pmol·l-1·120 min, P < 0·01), but MINERALS + NATIVE did not meaningfully alter the GLP-1 iAUC compared with NATIVE (1669 ± 547 v. 1844 ± 550 pmol·l-1·120 min, P = 0·09). A protein × minerals interaction effect was also observed for gastric emptying half-life (P < 0·01) whereby MINERALS + NATIVE increased gastric emptying half-life compared with NATIVE (83 ± 14 v. 71 ± 8 min, P < 0·01), whereas no meaningful differences were observed between MINERALS + AGGREG v. AGGREG (P = 0·70). These did not result in any meaningful changes in energy intake (protein × minerals interaction, P = 0·06). These data suggest that the potential for Ca2+ to stimulate GLP-1 secretion at moderate protein doses may depend on protein form. This study was registered at clinicaltrials.gov (NCT04659902).

Ketone monoester ingestion increases postexercise serum erythropoietin concentrations in healthy men.

Intravenous ketone body infusion can increase erythropoietin (EPO) concentrations, but responses to ketone monoester ingestion postexercise are currently unknown. The purpose of this study was to assess the effect of ketone monoester ingestion on postexercise erythropoietin (EPO) concentrations. Nine healthy men completed two trials in a randomized, crossover design (1-wk washout). During trials, participants performed 1 h of cycling (initially alternating between 50% and 90% of maximal aerobic capacity for 2 min each interval, and then 50% and 80%, and 50% and 70% when the higher intensity was unsustainable). Participants ingested 0.8 g·kg-1 sucrose with 0.4 g·kg-1 protein immediately after exercise, and at 1, 2, and 3 h postexercise. During the control trial (CONTROL), no further nutrition was provided, whereas on the ketone monoester trial (KETONE), participants also ingested 0.29 g·kg-1 of the ketone monoester (R)-3-hydroxybutyl (R)-3-hydroxybutyrate immediately postexercise and at 1 and 2 h postexercise. Blood was sampled immediately postexercise, every 15 min in the first hour and hourly thereafter for 4 h. Serum EPO concentrations increased to a greater extent in KETONE than in CONTROL (time × condition interaction: P = 0.046). Peak serum EPO concentrations were higher with KETONE (means ± SD: 9.0 ± 2.3 IU·L-1) compared with CONTROL (7.5 ± 1.5 IU·L-1, P < 0.01). Serum ß-hydroxybutyrate concentrations were also higher, and glucose concentrations lower, with KETONE versus CONTROL (both P < 0.01). In conclusion, ketone monoester ingestion increases postexercise erythropoietin concentrations, revealing a new avenue for orally ingestible ketone monoesters to potentially alter hemoglobin mass.NEW & NOTEWORTHY To our knowledge, this study was the first to assess the effects of ketone monoester ingestion on erythropoietin concentrations after exercise. We demonstrated that ingestion of a ketone monoester postexercise increased serum erythropoietin concentrations and reduced serum glucose concentrations in healthy men. These data reveal the possibility for ketone monoesters to alter hemoglobin mass.

Restricting sugar or carbohydrate intake does not impact physical activity level or energy intake over 24 h despite changes in substrate use: a randomised crossover study in healthy men and women.

PURPOSE: To determine the effects of dietary sugar or carbohydrate restriction on physical activity energy expenditure, energy intake, and physiological outcomes across 24 h. METHODS: In a randomized, open-label crossover design, twenty-five healthy men (n = 10) and women (n = 15) consumed three diets over a 24-h period: moderate carbohydrate and sugar content (MODSUG = 50% carbohydrate [20% sugars], 15% protein, 35% fat); low sugar content (LOWSUG = 50% carbohydrate [< 5% sugars], 15% protein, 35% fat); and low carbohydrate content (LOWCHO = 8% carbohydrate [< 5% sugars], 15% protein, 77% fat). Postprandial metabolic responses to a prescribed breakfast (20% EI) were monitored under laboratory conditions before an ad libitum test lunch, with subsequent diet and physical activity monitoring under free-living conditions until blood sample collection the following morning. RESULTS: The MODSUG, LOWSUG and LOWCHO diets resulted in similar mean [95%CI] rates of both physical activity energy expenditure (771 [624, 919] vs. 677 [565, 789] vs. 802 [614, 991] kcal·d-1; p = 0.29] and energy intake (2071 [1794, 2347] vs. 2195 [1918, 2473] vs. 2194 [1890, 2498] kcal·d-1; P = 0.34), respectively. The LOWCHO condition elicited the lowest glycaemic and insulinaemic responses to breakfast (P < 0.01) but the highest 24-h increase in LDL-cholesterol concentrations (P < 0.001), with no differences between the MODSUG and LOWSUG treatments. Leptin concentrations decreased over 24-h of consuming LOWCHO relative to LOWSUG (p < 0.01). CONCLUSION: When energy density is controlled for, restricting either sugar or total dietary carbohydrate does not modulate physical activity level or energy intake over a 24-h period (~ 19-h free-living) despite substantial metabolic changes. CLINICAL TRIALS REGISTRATION ID: NCT03509610, https://clinicaltrials.gov/show/NCT03509610.

Acute caffeine supplementation and live match-play performance in team-sports: A systematic review (2000-2021).

Caffeine is a psycho-active stimulant that can improve physical and cognitive performance. We systematically reviewed the evidence on the effects of acute caffeine ingestion on physiological parameters, physical and technical-skill performance during high-performance team-sport match-play. Following PRISMA guidelines, studies were identified using scientific databases (PubMed, Web-of-Science, Scopus, and SPORTDiscus) in February 2021. Of 281 results, 13 studies met inclusion, totalling 213 participants. Included studies adopted the randomised double-blinded cross-over design, involving caffeine and control conditions. In studies reporting physiological variables, responses to caffeine included higher peak (n=6/ 8 [n/ total studies measuring the variable]) and mean (n=7/ 9) heart rates, increased blood glucose (n=2/ 2) and lactate (n=2/ 2) concentrations. Improvements in physical performance were widely documented with caffeine, including greater distance coverage (n=7/ 7), high-speed distance coverage (n=5/ 7) and impact frequencies (n=6/ 8). From three studies that assessed technical-skills, it appears caffeine may benefit gross-skill performance, but have no effect, or negatively confound finer technical-skill outcomes. There is compelling evidence that ingesting moderate caffeine doses (~3 to 6 mg·kg-1) ~60 minutes before exercise may improve physical performance in team-sports, whereas evidence is presently too scarce to draw confident conclusions regarding sport-specific skill performance.

Plasma glucagon-like peptide-1 responses to ingestion of protein with increasing doses of milk minerals rich in calcium.

A high dose of whey protein hydrolysate fed with milk minerals rich in calcium (Capolac®) results in enhanced glucagon-like peptide-1 (GLP-1) concentrations in lean individuals; however, the effect of different calcium doses ingested alongside protein is unknown. The present study assessed the dose response of calcium fed alongside 25 g whey protein hydrolysate on GLP-1 concentrations in individuals with overweight/obesity. Eighteen adults (mean ± sd: 8M/10F, 34 ± 18 years, 28·2 ± 2·9 kgm-2) completed four trials in a randomised, double-blind, crossover design. Participants consumed test solutions consisting of 25 g whey protein hydrolysate (CON), supplemented with 3179 mg (LOW), 6363 mg (MED) or 9547 mg (HIGH) Capolac® on different occasions, separated by at least 48 h. The calcium content of test solutions equated to 65, 892, 1719 and 2547 mg, respectively. Arterialised-venous blood was sampled over 180 min to determine plasma concentrations of GLP-1TOTAL, GLP-17-36amide, insulin, glucose, NEFA, and serum concentrations of calcium and albumin. Ad libitum energy intake was measured at 180 min. Time-averaged incremental AUC (iAUC) for GLP-1TOTAL (pmol·l-1·min-1) did not differ between CON (23 ± 4), LOW (25 ± 6), MED (24 ± 5) and HIGH (24 ± 6). Energy intake (kcal) did not differ between CON (940 ± 387), LOW (884 ± 345), MED (920 ± 334) and HIGH (973 ± 390). Co-ingestion of whey protein hydrolysate with Capolac® does not potentiate GLP-1 release in comparison with whey protein hydrolysate alone. The study was registered at clinical trials (NCT03819972).

The effects of glucose-fructose co-ingestion on repeated performance during a day of intensified rugby union training in professional academy players.

This study assessed the effects of glucose-fructose co-ingestion during recovery from high-intensity rugby training on subsequent performance. Nine professional, senior academy Rugby Union players performed two trials in a double-blind, randomized, crossover design. Identical rugby training sessions were separated by a 3-hour recovery period, during which participants ingested protein (0.3 g×kg BM×h-1) and carbohydrate-containing (0.8 g×kg BM×h-1) recovery drinks, comprised of glucose polymers (GLUCOSE ONLY) or a glucose-fructose mixture (GLUCOSE+FRUCTOSE). Performance outcomes were determined from global positioning systems combined with accelerometry and heart rate monitoring. Mean speed during sessions 1 (am) and 2 (pm) of GLUCOSE ONLY was (mean±SD) 118±6 and 117±4 m×min-1, respectively. During GLUCOSE+FRUCTOSE, mean speed during session 1 and 2 was 117±4 and 116±5 m×min-1, respectively (time x trial interaction, p = 0.61). Blood lactate concentrations were higher throughout recovery in GLUCOSE+FRUCTOSE (mean ±SD: 1-h 3.2 ±2.0 mmol×L-1; 3-h 2.1 ±1.2 mmol×L-1) compared to GLUCOSE ONLY (1-h 2.0 ±1.0 mmol×L-1; 3-h 1.4 ±1.0 mmol×L-1; trial effect p = 0.05). Gastrointestinal discomfort low in both conditions. These data suggest glucose-fructose mixtures consumed as protein-carbohydrate recovery drinks following rugby training do not enhance subsequent performance compared to glucose-based recovery drinks.

Determinants of Peak Fat Oxidation Rates During Cycling in Healthy Men and Women.

This study explored lifestyle and biological determinants of peak fat oxidation (PFO) during cycle ergometry, using duplicate measures to account for day-to-day variation. Seventy-three healthy adults (age range: 19-63 years; peak oxygen consumption [V˙O2peak]: 42.4 [10.1] ml·kg BM-1·min-1; n = 32 women]) completed trials 7-28 days apart that assessed resting metabolic rate, a resting venous blood sample, and PFO by indirect calorimetry during an incremental cycling test. Habitual physical activity (combined heart rate accelerometer) and dietary intake (weighed record) were assessed before the first trial. Body composition was assessed 2-7 days after the second identical trial by dual-energy X-ray absorptiometry scan. Multiple linear regressions were performed to identify determinants of PFO (mean of two cycle tests). A total variance of 79% in absolute PFO (g·min-1) was explained with positive coefficients for V˙O2peak (strongest predictor), FATmax (i.e the % of V˙O2peak that PFO occurred at), and resting fat oxidation rate (g·min-1), and negative coefficients for body fat mass (kg) and habitual physical activity level. When expressed relative to fat-free mass, 64% of variance in PFO was explained: positive coefficients for FATmax (strongest predictor), V˙O2peak, and resting fat oxidation rate, and negative coefficients for male sex and fat mass. This duplicate design revealed that biological and lifestyle factors explain a large proportion of variance in PFO during incremental cycling. After accounting for day-to-day variation in PFO, V˙O2peak and FATmax were strong and consistent predictors of PFO.

Physiological responses to maximal eating in men.

This study investigated metabolic, endocrine, appetite and mood responses to a maximal eating occasion in fourteen men (mean: age 28 (sd 5) years, body mass 77·2 (sd 6·6) kg and BMI 24·2 (sd 2·2) kg/m2) who completed two trials in a randomised crossover design. On each occasion, participants ate a homogenous mixed-macronutrient meal (pizza). On one occasion, they ate until 'comfortably full' (ad libitum) and on the other, until they 'could not eat another bite' (maximal). Mean energy intake was double in the maximal (13 024 (95 % CI 10 964, 15 084) kJ; 3113 (95 % CI 2620, 3605) kcal) compared with the ad libitum trial (6627 (95 % CI 5708, 7547) kJ; 1584 (95 % CI 1364, 1804) kcal). Serum insulin incremental AUC (iAUC) increased approximately 1·5-fold in the maximal compared with ad libitum trial (mean: ad libitum 43·8 (95 % CI 28·3, 59·3) nmol/l × 240 min and maximal 67·7 (95 % CI 47·0, 88·5) nmol/l × 240 min, P < 0·01), but glucose iAUC did not differ between trials (ad libitum 94·3 (95 % CI 30·3, 158·2) mmol/l × 240 min and maximal 126·5 (95 % CI 76·9, 176·0) mmol/l × 240 min, P = 0·19). TAG iAUC was approximately 1·5-fold greater in the maximal v. ad libitum trial (ad libitum 98·6 (95 % CI 69·9, 127·2) mmol/l × 240 min and maximal 146·4 (95 % CI 88·6, 204·1) mmol/l × 240 min, P < 0·01). Total glucagon-like peptide-1, glucose-dependent insulinotropic peptide and peptide tyrosine-tyrosine iAUC were greater in the maximal compared with ad libitum trial (P < 0·05). Total ghrelin concentrations decreased to a similar extent, but AUC was slightly lower in the maximal v. ad libitum trial (P = 0·02). There were marked differences on appetite and mood between trials, most notably maximal eating caused a prolonged increase in lethargy. Healthy men have the capacity to eat twice the energy content required to achieve comfortable fullness at a single meal. Postprandial glycaemia is well regulated following initial overeating, with elevated postprandial insulinaemia probably contributing.

Glucose control upon waking is unaffected by hourly sleep fragmentation during the night, but is impaired by morning caffeinated coffee.

Morning coffee is a common remedy following disrupted sleep, yet each factor can independently impair glucose tolerance and insulin sensitivity in healthy adults. Remarkably, the combined effects of sleep fragmentation and coffee on glucose control upon waking per se have never been investigated. In a randomised crossover design, twenty-nine adults (mean age: 21 (sd 1) years, BMI: 24·4 (sd 3·3) kg/m2) underwent three oral glucose tolerance tests (OGTT). One following a habitual night of sleep (Control; in bed, lights-off trying to sleep approximately 23.00-07.00 hours), the others following a night of sleep fragmentation (as Control but waking hourly for 5 min), with and without morning coffee approximately 1 h after waking (approximately 300 mg caffeine as black coffee 30 min prior to OGTT). Individualised peak plasma glucose and insulin concentrations were unaffected by sleep quality but were higher following coffee consumption (mean (normalised CI) for Control, Fragmented and Fragmented + Coffee, respectively; glucose: 8·20 (normalised CI 7·93, 8·47) mmol/l v. 8·23 (normalised CI 7·96, 8·50) mmol/l v. 8·96 (normalised CI 8·70, 9·22) mmol/l; insulin: 265 (normalised CI 247, 283) pmol/l; and 235 (normalised CI 218, 253) pmol/l; and 310 (normalised CI 284, 337) pmol/l). Likewise, incremental AUC for plasma glucose was higher in the Fragmented + Coffee trial compared with Fragmented. Whilst sleep fragmentation did not alter glycaemic or insulinaemic responses to morning glucose ingestion, if a strong caffeinated coffee is consumed, then a reduction in glucose tolerance can be expected.

Co-ingestion of whey protein hydrolysate with milk minerals rich in calcium potently stimulates glucagon-like peptide-1 secretion: an RCT in healthy adults.

PURPOSE: To examine whether calcium type and co-ingestion with protein alter gut hormone availability. METHODS: Healthy adults aged 26 ± 7 years (mean ± SD) completed three randomized, double-blind, crossover studies. In all studies, arterialized blood was sampled postprandially over 120 min to determine GLP-1, GIP and PYY responses, alongside appetite ratings, energy expenditure and blood pressure. In study 1 (n = 20), three treatments matched for total calcium content (1058 mg) were compared: calcium citrate (CALCITR); milk minerals rich in calcium (MILK MINERALS); and milk minerals rich in calcium plus co-ingestion of 50 g whey protein hydrolysate (MILK MINERALS + PROTEIN). In study 2 (n = 6), 50 g whey protein hydrolysate (PROTEIN) was compared to MILK MINERALS + PROTEIN. In study 3 (n = 6), MILK MINERALS was compared to the vehicle of ingestion (water plus sucralose; CONTROL). RESULTS: MILK MINERALS + PROTEIN increased GLP-1 incremental area under the curve (iAUC) by ~ ninefold (43.7 ± 11.1 pmol L-1 120 min; p < 0.001) versus both CALCITR and MILK MINERALS, with no difference detected between CALCITR (6.6 ± 3.7 pmol L-1 120 min) and MILK MINERALS (5.3 ± 3.5 pmol L-1 120 min; p > 0.999). MILK MINERALS + PROTEIN produced a GLP-1 iAUC ~ 25% greater than PROTEIN (p = 0.024; mean difference: 9.1 ± 6.9 pmol L-1 120 min), whereas the difference between MILK MINERALS versus CONTROL was small and non-significant (p = 0.098; mean difference: 4.2 ± 5.1 pmol L-1 120 min). CONCLUSIONS: When ingested alone, milk minerals rich in calcium do not increase GLP-1 secretion compared to calcium citrate. Co-ingesting high-dose whey protein hydrolysate with milk minerals rich in calcium increases postprandial GLP-1 concentrations to some of the highest physiological levels ever reported. Registered at ClinicalTrials.gov: NCT03232034, NCT03370484, NCT03370497.

Frequent Carbohydrate Ingestion Reduces Muscle Glycogen Depletion and Postpones Fatigue Relative to a Single Bolus.

The timing of carbohydrate ingestion and how this influences net muscle glycogen utilization and fatigue has only been investigated in prolonged cycling. Past findings may not translate to running because each exercise mode is distinct both in the metabolic response to carbohydrate ingestion and in the practicalities of carbohydrate ingestion. To this end, a randomized, cross-over design was employed to contrast ingestion of the same sucrose dose either at frequent intervals (15 × 5 g every 5 min) or at a late bolus (1 × 75 g after 75 min) during prolonged treadmill running to exhaustion in six well-trained runners (V˙O2max 61 ± 4 ml·kg-1·min-1). The muscle glycogen utilization rate was lower in every participant over the first 75 min of running (Δ 0.51 mmol·kg dm-1·min-1; 95% confidence interval [-0.02, 1.04] mmol·kg dm-1·min-1) and, subsequently, all were able to run for longer when carbohydrate had been ingested frequently from the start of exercise compared with when carbohydrate was ingested as a single bolus toward the end of exercise (105.6 ± 3.0 vs. 96.4 ± 5.0 min, respectively; Δ 9.3 min, 95% confidence interval [2.8, 15.8] min). A moderate positive correlation was apparent between the magnitude of glycogen sparing over the first 75 min and the improvement in running capacity (r = .58), with no significant difference in muscle glycogen concentrations at the point of exhaustion. This study indicates that failure to ingest carbohydrates from the outset of prolonged running increases reliance on limited endogenous muscle glycogen stores-the ergolytic effects of which cannot be rectified by subsequent carbohydrate ingestion late in exercise.

Fructose and metabolic health: governed by hepatic glycogen status?

Fructose is a commonly ingested dietary sugar which has been implicated in playing a particularly harmful role in the development of metabolic disease. Fructose is primarily metabolised by the liver in humans, and increases rates of hepatic de novo lipogenesis. Fructose increases hepatic de novo lipogenesis via numerous mechanisms: by altering transcriptional and allosteric regulation, interfering with cellular energy sensing, and disrupting the balance between lipid synthesis and lipid oxidation. Hepatic de novo lipogenesis is also upregulated by the inability to synthesise glycogen, either when storage is inhibited in knock-down animal models or storage is saturated in glycogen storage disease. Considering that fructose has the capacity to upregulate hepatic glycogen storage, and replenish these stores more readily following glycogen depleting exercise, the idea that hepatic glycogen storage and hepatic de novo lipogenesis are linked is an attractive prospect. We propose that hepatic glycogen stores may be a key factor in determining the metabolic responses to fructose ingestion, and saturation of hepatic glycogen stores could exacerbate the negative metabolic effects of excessive fructose intake. Since physical activity potently modulates glycogen metabolism, this provides a rationale for considering nutrient-physical activity interactions in metabolic health.

Skipping Breakfast Before Exercise Creates a More Negative 24-hour Energy Balance: A Randomized Controlled Trial in Healthy Physically Active Young Men.

BACKGROUND: At rest, omission of breakfast lowers daily energy intake, but also lowers energy expenditure, attenuating any effect on energy balance. The effect of breakfast omission on energy balance when exercise is prescribed is unclear. OBJECTIVES: The aim of this study was to assess the effect on 24-h energy balance of omitting compared with consuming breakfast prior to exercise. METHODS: Twelve healthy physically active young men (age 23 ± 3 y, body mass index 23.6 ± 2.0 kg/m2) completed 3 trials in a randomized order (separated by >1 week): a breakfast of oats and milk (431 kcal; 65 g carbohydrate, 11 g fat, 19 g protein) followed by rest (BR); breakfast before exercise (BE; 60 min cycling at 50 % peak power output); and overnight fasting before exercise (FE). The 24-h energy intake was calculated based on the food consumed for breakfast, followed by an ad libitum lunch, snacks, and dinner. Indirect calorimetry with heart-rate accelerometry was used to measure substrate utilization and 24-h energy expenditure. A [6,6-2H2]glucose infusion was used to investigate tissue-specific carbohydrate utilization. RESULTS: The 24-h energy balance was -400 kcal (normalized 95% CI: -230, -571 kcal) for the FE trial; this was significantly lower than both the BR trial (492 kcal; normalized 95% CI: 332, 652 kcal) and the BE trial (7 kcal; normalized 95% CI: -153, 177 kcal; both P < 0.01 compared with FE). Plasma glucose utilization in FE (mainly representing liver glucose utilization) was positively correlated with energy intake compensation at lunch (r = 0.62, P = 0.03), suggesting liver carbohydrate plays a role in postexercise energy-balance regulation. CONCLUSIONS: Neither exercise energy expenditure nor restricted energy intake via breakfast omission were completely compensated for postexercise. In healthy men, pre-exercise breakfast omission creates a more negative daily energy balance and could therefore be a useful strategy to induce a short-term energy deficit. This trial was registered at clinicaltrials.gov as NCT02258399.

Preexercise breakfast ingestion versus extended overnight fasting increases postprandial glucose flux after exercise in healthy men.

The aim of this study was to characterize postprandial glucose flux after exercise in the fed versus overnight fasted state and to investigate the potential underlying mechanisms. In a randomized order, twelve men underwent breakfast-rest [(BR) 3 h semirecumbent], breakfast-exercise [(BE) 2 h semirecumbent before 60 min of cycling (50% peak power output)], and overnight fasted exercise [(FE) as per BE omitting breakfast] trials. An oral glucose tolerance test (OGTT) was completed after exercise (after rest on BR). Dual stable isotope tracers ([U-13C] glucose ingestion and [6,6-2H2] glucose infusion) and muscle biopsies were combined to assess postprandial plasma glucose kinetics and intramuscular signaling, respectively. Plasma intestinal fatty acid binding (I-FABP) concentrations were determined as a marker of intestinal damage. Breakfast before exercise increased postexercise plasma glucose disposal rates during the OGTT, from 44 g/120 min in FE {35 to 53 g/120 min [mean (normalized 95% confidence interval)] to 73 g/120 min in BE [55 to 90 g/120 min; P = 0.01]}. This higher plasma glucose disposal rate was, however, offset by increased plasma glucose appearance rates (principally OGTT-derived), resulting in a glycemic response that did not differ between BE and FE ( P = 0.11). Plasma I-FABP concentrations during exercise were 264 pg/ml (196 to 332 pg/ml) lower in BE versus FE ( P = 0.01). Breakfast before exercise increases postexercise postprandial plasma glucose disposal, which is offset (primarily) by increased appearance rates of orally ingested glucose. Therefore, metabolic responses to fed-state exercise cannot be readily inferred from studies conducted in a fasted state.

Venous blood provides lower glucagon-like peptide-1 concentrations than arterialized blood in the postprandial but not the fasted state: Consequences of sampling methods.

NEW FINDINGS: What is the central question of this study? Glucagon-like peptide-1 (GLP-1) is an important obesity/diabetes target, with effects dependent on circulating GLP-1 concentrations. Peripheral tissues extract GLP-1; therefore, sampling venous versus arterialized blood might provide different GLP-1 concentrations. This study examined whether arterialization alters GLP-1 concentrations during fasting and feeding. What is the main finding and its importance? This study demonstrates that venous blood provides lower postprandial but not fasting GLP-1 concentrations versus arterialized blood. Therefore, when accurate assessment of postprandial peripheral availability of GLP-1 is required, blood sampling methods should be considered carefully, reported clearly, and arterialization is recommended. ABSTRACT: Glucagon-like peptide-1 (GLP-1) displays concentration-dependent effects on metabolism, appetite and angiogenesis; therefore, accurate determination of circulating GLP-1 concentrations is important. In this study, we compared GLP-1 concentrations in venous versus arterialized blood in both fasted and fed conditions. Venous and arterialized blood samples were obtained simultaneously from 10 young, healthy men before and 30, 60 and 120 min after ingestion of 75 g glucose. Plasma GLP-1 concentrations increased in response to glucose ingestion (time effect, P < 0.01) and to a lesser extent in venous versus arterialized plasma (time × arterialization interaction, P < 0.01). Accordingly, the plasma incremental area under the curve was lower in venous versus arterialized plasma (974 ± 88 versus 1214 ± 115 pmol l (120 min)-1 , respectively, P = 0.049). In the postprandial state, there was a positive relationship between arterialized GLP-1 concentrations and the venous-arterialized difference in GLP-1 concentrations (r2  = 0.51; P < 0.01). Both arterialized and venous peak GLP-1 concentrations showed positive relationships with peak arterialized insulin concentrations (both r2  > 0.6, P < 0.01). Venous sampling results in lower concentrations of GLP-1 in the postprandial but not the fasted state compared with arterialized blood. This absolute difference is biologically meaningful and is magnified when GLP-1 availability is high. Therefore, sampling from arterialized blood may provide a better chance of detecting small differences in postprandial GLP-1 availability with interventions. If absolute GLP-1 concentrations are of interest, the blood sampling method should be considered carefully and reported clearly.

Prior exercise alters the difference between arterialised and venous glycaemia: implications for blood sampling procedures.

Oral glucose tolerance and insulin sensitivity are common measures, but are determined using various blood sampling methods, employed under many different experimental conditions. This study established whether measures of oral glucose tolerance and oral glucose-derived insulin sensitivity (insulin sensitivity indices; ISI) differ when calculated from venous v. arterialised blood. Critically, we also established whether any differences between sampling methods are consistent across distinct metabolic conditions (after rest v. after exercise). A total of ten healthy men completed two trials in a randomised order, each consisting of a 120-min oral glucose tolerance test (OGTT), either at rest or post-exercise. Blood was sampled simultaneously from a heated hand (arterialised) and an antecubital vein of the contralateral arm (venous). Under both conditions, glucose time-averaged AUC was greater from arterialised compared with venous plasma but importantly, this difference was larger after rest relative to after exercise (0·99 (sd 0·46) v. 0·56 (sd 0·24) mmol/l, respectively; P<0·01). OGTT-derived ISIMatsuda and ISICederholm were lower when calculated from arterialised relative to venous plasma and the arterialised-venous difference was greater after rest v. after exercise (ISIMatsuda: 1·97 (sd 0·81) v. 1·35 (sd 0·57) arbitrary units (au), respectively; ISICederholm : 14·76 (sd 7·83) v. 8·70 (sd 3·95) au, respectively; both P<0·01). Venous blood provides lower postprandial glucose concentrations and higher estimates of insulin sensitivity, compared with arterialised blood. Most importantly, these differences between blood sampling methods are not consistent after rest v. post-exercise, preventing standardised venous-to-arterialised corrections from being readily applied.

Discordance between gut-derived appetite hormones and energy intake in humans.

Gut-derived hormones affect appetite and are thought to play an important role in body weight regulation. Dietary macronutrient composition can influence gut-derived appetite hormone concentrations, thereby providing theoretical basis for why some diets might facilitate weight loss better than others. We investigated postprandial gut-derived appetite hormones in 20 inpatient adults after 2 weeks of eating either a low carbohydrate (LC) or a low fat (LF) diet followed by the alternate diet in random order. A LC meal resulted in significantly greater postprandial GLP-1, GIP, and PYY but lower ghrelin compared to an isocaloric LF meal (all p≤0.02). However, differences in gut-derived appetite hormones were incommensurate with subsequent ad libitum energy intake over the rest of the day, which was 551±103 kcal (p<0.0001) greater with the LC as compared to the LF diet. The effects of gut-derived appetite hormones on ad libitum energy intake can be dominated by other diet-related factors, at least in the short-term.

Imprecision nutrition? Duplicate meals result in unreliable individual glycemic responses measured by continuous glucose monitors across four dietary patterns in adults without diabetes.

Background: Continuous glucose monitors (CGMs) are being used to characterize postprandial glycemic responses and thereby provide personalized dietary advice to minimize glycemic excursions. However, the efficacy of such advice depends on reliable CGM responses. Objective: To explore within-subject variability of CGM responses to duplicate meals in an inpatient setting. Methods: CGM data were collected in two controlled feeding studies (NCT03407053 and NCT03878108) in 30 participants without diabetes capturing 1056 meal responses in duplicate ~1 week apart from four dietary patterns. One study used two different CGMs (Abbott Freestyle Libre Pro and Dexcom G4 Platinum) whereas the other study used only Dexcom. We calculated the incremental area under the curve (iAUC) for each 2-h post-meal period and compared within-subject iAUCs using the same CGM for the duplicate meals using linear correlations, intra-class correlation coefficients (ICC), Bland-Altman analyses, and compared individual variability of glycemic responses to duplicate meals versus different meals using standard deviations (SDs). Results: There were weak to moderate positive linear correlations between within- subject iAUCs for duplicate meals (Abbott r=0.47, p<0.0001, Dexcom r=0.43, p<0.0001), with low within-participant reliability indicated by ICC (Abbott 0.31, Dexcom 0.14). Bland-Altman analyses indicated wide limits of agreement (Abbott -31.3 to 31.5 mg/dL, Dexcom -30.8 to 30.4 mg/dL) but no significant bias of mean iAUCs for duplicate meals (Abbott 0.1 mg/dL, Dexcom -0.2 mg/dL). Individual variability of glycemic responses to duplicate meals was similar to that of different meals evaluated each diet week for both Abbott (SDduplicate = 10.7 mg/dL , SDweek 1 =12.4 mg/dL, SDweek 2 =11.6 mg/dL, p=0.38) and Dexcom (SDduplicate = 11.1 mg/dL, SDweek 1 = 11.5 mg/dL, SDweek 2 =11.9 mg/dL, p=0.60). Conclusions: Individual postprandial CGM responses to duplicate meals were unreliable in adults without diabetes. Personalized diet advice based on CGM measurements in adults without diabetes requires more reliable methods involving aggregated repeated measurements.

Diet order affects energy balance in randomized crossover feeding studies that vary in macronutrients but not ultra-processing.

BACKGROUND: Crossover studies can induce order effects, especially when they lack a wash-out period. OBJECTIVE: To explore diet order effects on energy balance and food intake between randomized diet order groups in two inpatient crossover studies originally designed to compare within-subject differences in ad libitum energy intake between either minimally processed low carbohydrate (LC) versus low fat (LF) diets or macronutrient-matched diets composed of mostly minimally processed food (MPF) or ultra-processed food (UPF). METHODS: Diet order group comparisons of changes in body weight, body composition, and differences in energy expenditure, and food intake were assessed over four weeks in 20 adults randomized to either the LC followed immediately by the LF diet (LC→LF) or the opposite order (LF→LC) as well as 20 adults randomized to either the MPF followed by UPF (MPF→UPF) diets or the opposite order (UPF→MPF). RESULTS: Subjects randomized to LC→LF lost 2.9 ± 1.1 kg more body weight (p < 0.001) and 1.5 ± 0.6 kg more body fat (p = 0.03) than the LF→LC group likely because the LC→LF group consumed 922 ± 304 kcal/d less than the LF→LC group (p = 0.0024). Reduced energy intake in LC→LF vs LF→LC was driven by the last two weeks (-1610 ± 306 kcal/d; p<0.00001) perhaps due to carryover effects of gut adaptations over the first two weeks arising from large differences in the mass of food (1295 ± 209 g/d; p<0.00001) and fiber intake (58 ± 5 g/d; p<0.00001). There were no diet order effects on ad libitum energy intake, body weight, or body composition change between UPF→MPF versus MPF→UPF groups. CONCLUSIONS: Diet order influences daily ad libitum energy intake, body weight change, and fat change within the context of a 4-week crossover inpatient diet study varying in macronutrients, but not varying in extent and purpose of processing. Funding sources: Intramural Research Program of the National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health. Clinical Trial Registration: NCT03407053 and NCT03878108.