Metabolic flexibility and liver function following leucine supplementation during caloric restriction

Metabolic flexibility and liver function following leucine supplementation during caloric restriction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Metabolic flexibility and liver function following leucine supplementation during caloric restriction Kaveri Pathak, Mario Soares, Zhao Yun, Emily Calton This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4220135/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background. Metabolic flexibility (MF) is the capacity to switch from fat to carbohydrate utilization when required, and MF is constrained in the metabolic syndrome (MetS). We determined whether l-leucine (Leu) supplementation enhanced resting energy expenditure (REE), respiratory quotient (RQ), MF, insulin sensitivity and liver function during caloric restriction (CR). Methods. Thirty-seven participants at risk of MetS completed a parallel, double-blind RCT comparing Leu vs placebo during CR. REE and RQ were measured before and every 15 min for 2hr following an OGTT. Blood samples were assayed for clinical chemistry, liver function tests (LFT) and fibroblast growth factor 21 (FGF21). Stumvoll’s insulin sensitivity index (ISI), fatty liver index (FLI) and integrated area under response curves were calculated for REE (iREE) and RQ (iRQ). Metabolic flexibility was defined by iRQ following the OGTT. All measurements were made at the start and end of the trial. Results. Adjusted for pre-trial values and other covariates, fasting REE, RQ, ISI, LFTs, FLI or FGF21 were not different. There were no differences in postprandial iREE, 2hr_FGF21 or 2hr_ISI. However, Leu resulted in a significantly greater iRQ following CR. Partial correlations indicated that iRQ was significantly related to 2hr_ISI (r = 0.53;p = 0.002) and negatively to fasting alanine amino transferase (ALT) (r= -0.52;p = 0.001). iREE was significantly but negatively related to other liver function parameters. Conclusion. Leu supplementation improved MF over CR but did not impact REE, ISI and liver function. Overall, there were significant interrelationships between energy metabolism, ISI and liver function. Health sciences/Diseases/Metabolic disorders Health sciences/Health care/Weight management Health sciences/Health care/Nutrition leucine metabolic flexibility liver function fatty liver index weight loss muscle mass metabolic syndrome glucose tolerance FGF21. Figures Figure 1 Introduction Metabolic flexibility (MF) is the capacity to adapt to a source of fuel depending on its availability, or the metabolic demand for energy ( 1 ). This ability to utilize available substrate resides at the tissue, organ and whole-body level ( 2 , 3 ) and is usually seen as a change from fat oxidation in the fasted state, to carbohydrate oxidation in the fed state ( 1 , 4 ). MF is considered a feature of insulin sensitivity and a hallmark of good health. The inability to switch between different fuels is termed metabolic inflexibility, and is encountered in insulin resistant states including obesity, metabolic syndrome (MetS) and type 2 diabetes (T2DM) ( 3 , 5 ). The liver plays a key role in energy production, and hepatic mitochondria maybe central to ensuring normal human metabolism ( 6 ). Indeed, impaired energy regulation may underscore the propensity of MetS in certain ethnic groups ( 7 ), while recovery from MetS, following weight loss, resulted in a significantly lowered energy production ( 8 ). Further support for mitochondrial regulation comes from observations that mitochondrial targeted antioxidants ameliorated features of MetS in animal models ( 5 , 6 ). One- third of our minimum daily requirement for essential amino acids is met by the three branched chain amino acids (BCAA): leucine, isoleucine and valine ( 9 ). Dysfunctional adipose tissue is seen with increasing adipose tissue mass and linked to a greater inflammatory profile and impaired insulin function. Impaired insulin action underscores many features of the metabolic syndrome such as raised blood pressure, hyperglycaemia, hypertriglyceridemia and low HDL-C. A potential role for leucine (leu) in obesity and in MetS may then be ascribed to its effects on insulin secretion ( 10 ) and improved insulin sensitivity ( 11 ). Leu supplementation increased lean tissue mass ( 12 ) and preserved muscle mass during weight loss ( 13 ). Furthermore, an increased REE and greater fatty acid oxidation (low RQ) ( 14 , 15 ) has been reported following supplementation with the amino acid. Mechanistically, Leu effects on energy and lipid metabolism are brought about through the regulation of uncoupling protein-3 (UCP-3) expression in metabolically active tissues such as skeletal muscle, brown adipose tissue (BAT) and white adipose tissue (WAT) ( 14 , 16 ). Overall, there is a good reason to expect y that Leu would modulate MF. The literature provides mixed outcomes on the effect of weight loss in improving MF. There are some in favour ( 17 ) and some showing no effect or a worsening of MF ( 18 ), hence further research was needed. In our previous report, we had uncovered a beneficial effect of leu vs. placebo during caloric restriction, where a greater preservation of lean tissue mass and its compartments appendicular and non-appendicular tissue mass were seen ( 19 ). We noted this effect to be greatest in males but that requires further confirmation ( 19 ). Given the interplay between Leu intake, muscle mass, insulin sensitivity and hepatic function, we hypothesized that Leu supplementation during caloric restriction could improve MF more than that seen with weight loss alone, and that this could be brought on by improved insulin sensitivity and liver function. Material & Methods This present analysis reports on the secondary endpoints of an 8 week RCT in individuals at risk of the metabolic syndrome ( 19 ). The details of that study design, recruitment of participants, conduct of the dietary intervention and pharmacological compounding of Leu and placebo capsules has been previously described in detail ( 19 ). Briefly, we had conducted a double blind, placebo controlled, parallel, RCT over 8 weeks, that compared changes in body composition and glucose tolerance during caloric restriction between two groups i.e with Leu supplementation or with placebo ( 19 ). The trial was conducted in two phases that included 18 participants in the first phase and 19 in the second phase ( 19 ). The study was prospectively registered at Australian New Zealand Clinical Trial Registry (Trial Id: ACTRN12616001528448). Human ethics approval had been obtained from the institution’s ethics committee (HREC 108/2013), and all participants had provided written informed consent. Energy Expenditure & Metabolic Flexibility Participants attended the research centre at Curtin University (Bentley, Western Australia) following an overnight fast of a minimum 10 hours, 8 hours of sleep and abstinence from alcohol and vigorous exercise for at least 36 hours prior to the measurement. They were instructed not to shower in the morning to avoid any increase in metabolic rate. On arrival, they emptied their bladder and changed into a standard dressing gown, after which their weight and waist circumference were measured. They entered an insulated chamber measuring 5 x 3 meters with a volume of 57.75 m 2 maintained at 25 ο C and rested in bed for 30 min in the supine position ( 20 ). Resting energy expenditure (REE) was measured via indirect calorimetry (Deltatrac II Metabolic Monitor; Datex-Ohmeda, Instrumentarium Corp, Helsinki, Finland). REE and respiratory quotient (RQ) were measured twice for 25 min each with a 10-minute rest in between. Fasting bloods were then drawn by a certified phlebotomist for blood chemistry and this was followed by an oral glucose tolerance test (OGTT) (75g glucose in 300 ml water, Carbo test, Australia). Measurements of REE and RQ were continued from minutes 15–30, 45–60, 75–90 and 105–120 following the OGTT in each person. At the conclusion of the OGTT test, a second blood sample was drawn at 2h. Blood samples were collected for fibroblast growth factor 21 (FGF21), glucose, insulin and liver function tests panel. Metabolic syndrome was determined by the presence of 3 or more metabolic risk factors from the following variables waist circumference, fasting blood sugar levels, fasting (HDL) levels, fasting triglycerides levels or Hypertension ( 21 ). Total and regional body composition including fat mass (FM, kg) and fat-free mass (FFM, kg) was measured by (DEXA, Prodigy™, Lunar Corp. Madison, USA). All participants were offered a beverage of choice and light refreshments before they left the premises. The entire protocol was repeated in each individual 8 weeks later, at the end of the weight loss trial. Calculations: REE was calculated from the average of last 25 min to min recordings of oxygen consumption (O 2 ) and carbon dioxide (CO 2 ) production using the Weir Eq. (22). RQ was calculated as the ratio of CO 2 to O 2 . The area under the curve (AUC) for postprandial REE and RQ was determined using the trapezoid rule. MF was the calculated as the integrated AUC of RQ (iAUC) given by the difference between the total AUC from baseline AUC. In our laboratory, the within-subject CVs (including measurement error) were as follows: 2.1% for fasting RQ, 3.2% for REE, 1.6% for total area under the glucose curve (TAUC) for postprandial RQ and 4.3% for postprandial energy expenditure ( 23 ). Change variables were calculated as 2hr value minus fasting value. Stumvol’s insulin sensitivity index (ISI) ( 24 ) and Fatty liver index (FLI) ( 25 ) were calculated from their original equations. Validation Study The Deltatrac II machine used to measure REE broke down in phase 2 during post leucine supplementation measurements. This affected 12 of 19 participants in that phase. In these 12 participants, we used another available metabolic monitor, TrueOne metabolic cart (Parvo Medics, USA) where gas collection was made via nose-clip and mouthpiece mode as only that was available. The TrueOne shares the same make of oxygen and carbon dioxide analysers as the Deltatrac II. Following repairs to the Deltatrac II, we ran a validation study to determine the difference between the machines and the best predictive equation for converting TrueOne values to Deltatrac II values. In this validation trial, we tested the Deltatrac II in canopy mode to the TrueOne in mouthpiece and nose clip mode. The details of the crossover design and results of the conversion equations for O 2 and CO 2 from TrueOne to Deltatrac II( 25 ), are presented here in supplementary file S1. ( 26 ). Statistics Unadjusted repeated measures ANOVA (RM-ANOVA) with treatment as between-subject factor were used to initially explore the change in outcome variables following weight loss. Analysis of covariance (ANCOVA) via General Linear Model (GLM) univariate approach was used to examine the effect of leucine supplementation vs placebo (Treatment) on fasting REE, RQ, and liver function tests and postprandial variables iRQ2, iREE2, GIT (%), FGF21 and Stumvol’s insulin sensitivity index (ISI). All outcomes at the end of the trial were adjusted for their respective pre-trial (baseline) values. Other covariates included were age, gender, phase, caloric deficit (kcal), change in FM, change in FFM, and change in waist circumference. In addition, all 2-way interactions were assessed and, if non-significant, were removed from the model. Normality was assessed and bootstrapping method with 1000 bootstrap samples was used on outcome variables for deriving robust estimates of standard errors, regression coefficients and corresponding 95% confidence intervals. Marginal means were estimated by substituting the mean values of covariates included, and mean value for the corresponding baseline variable. We ran three sets of analyses for REE and MF that included: ( 1 ) the measured data after machine inter-conversion (n = 37), ( 2 ) a sensitivity analysis that excluded those with a machine failure (n = 25), ( 3 ) an intention to treat analysis (ITT), where the last value was carried forward in those who had a machine failure (N = 37). Finally, partial correlations assessed relationships between postprandial energy expenditure and RQ with various indices of liver function, ISI and number of metabolic syndrome components. Statistical significance was accepted when a p-value was less than 0.05. All the analyses were performed by using SPSS version 29 (SPSS Inc., Chicago, IL, USA). Results Repeated measures: There was a significant decline in REE following weight loss (mean ± SD: 4.71 ± 0.96 vs. 4.47 ± 1.00 kJ/min, P = 0.003), no difference between treatments (P = 0.653) and no treatment*time interaction (P = 0.486). Fasting RQ showed no difference over time (0.811 ± 0.033 vs. 0.806 ± 0.049, P = 0.34), between treatments (P = 0.106) nor treatment*time interaction (P = 0.111). iEE following OGTT did not change following weight loss (p = 0.391), was not different between treatments (P = 0.884) or showed treatment*time interaction (P = 0.354). iRQ was not different over time (P = 0.421) but indicated a significant difference between treatment (placebo: 0.062 ± 0.044 vs. Leu: 0.106 ± 0.042, P = 0.004). This was mainly due to a higher iRQ following the leu arm as denoted by a significant treatment*time interaction (P = 0.006). Fasting ISI increased following weight loss (15 ± 1.15 vs.19.2 ± 2.15, p = 0.031) but did not differ between treatments. Fasting FGF21 declined following weight loss (165 ± 30.92 vs. 116 ± 24.3 pg/ml, P = 0.003) but was not different between treatments. TP (70.9 ± 0.64 vs. 69.0 ± 0.70 g/L, p = 0.002), ALT (38.3 ± 3.81 vs. 32.0 ± 2.93 IU/L, p = 0.016) and ALKPHOS (68.2 ± 2.38 vs. 61.8 ± 3.48 IU/L, p = 0.023) were all reduced following weight loss but showed no difference between treatment arms of RCT. No other significant changes were noted. Table 1 near here Table 1 Fasting RQ, REE, insulin sensitivity and liver function tests on completion of the trial. Variable Placebo (n = 19) Leucine (n = 18) ANCOVA # Mean ¶ (std.error) 95% CI & Mean ¶ (std.error) 95% CI & Treatment effect Gender effect Treatment * Gender interaction Phase effect Treatment * Phase interaction RQ (x2hr) * 1.64 (0.02) 1.61, 1.68 1.61 (0.02) 1.57, 1.65 NS NS NS P = 0.046 NS REE (kJ/minx2hr) * 8.51 (0.2) 8.10, 8.92 8.84 (0.20) 8.42, 9.26 NS NS NS P = 0.05 NS FGF21(pg/ml) 99.73 (17.89) 65.17, 147 123.52 (20.05) 74.36, 184.92 NS NS NS NS NS ISI 22.23 (2.45) 15.35, 31.66 20.86 (2.79) 14.52, 30.64 NS NS NS NS NS MetS components** 2.36 (0.18) 1.95, 2.8 2.26 (0.20) 1.78, 2.69 NS NS NS NS P = 0.013 FLI** 54.7 (2.39) 43.45, 65.73 51.06 (2.64) 38.02, 63.71 NS NS NS NS NS ALP (U/L) 65.27 (3.90) 57.28, 73.26 55.38 (4.35) 46.46, 64.30 NS NS NS NS NS Albumin (g/L) 41.39 (0.40) 40.56, 42.21 40.50 (0.47) 39.54, 41.47 NS NS NS NS NS ALT (U/L) 28.89 (3.67) 22.26, 36.41 34.63 (4.53) 25.65, 43.48 NS NS NS NS NS GGT (U/L) 36.61 (8.64) 21.45, 54.74 36.20 (10.42) 19.03, 59.93 NS NS NS NS NS TP (g/L) 69.40 (0.77) 67.81, 70.98 67.88 (0.87) 66.09, 66.68 NS NS NS P = 0.021 NS BR (µmol/L) 10.64 (0.058) 8.73, 12.52 10.58 (0.66) 8.96, 12.43 NS NS NS P = 0.048 NS N = 37. All data represent values at end of trial. *Fasting RQ and REE expressed as AUC. RQ, respiratory quotient; REE, resting energy expenditure; FGF21, fibroblast growth factor; ISI, stumvol’s insulin sensitivity index; MetS, metabolic syndrome; FLI, fatty liver index; ALP, alkaline phosphatase; ALT, alanine amino transaminase; GGT, gamma glutamyl transferase; TP, total protein; BR, bilirubin. # Variables included: adjusting for corresponding pre-trial variables, treatment, age, gender, phase, caloric deficit, change in FM, change in FFM, change in waist. All 2-way interaction were tested initially and removed from model if NS. NS: non-significant statistically (with a p value > 0.05). ** change in waist not used in adjustment procedure as it is part of the variable. & Bootstrapping method with 1000 bootstrap samples was used for deriving p values, standard errors and 95% confidence intervals for variables that normality was not assumed. ¶ Marginal mean were estimated by substituting the mean values of covariates included, and mean value for the corresponding baseline variable. Table 2 near here Table 2 Postprandial RQ and EE following 8 weeks leucine supplementation and caloric restriction. Variable Placebo (n = 19) Leucine (n = 18) ANCOVA # Mean ¶ (std.error) 95% CI & Mean ¶ (std.error) 95% CI & Treatment effect Gender effect Treatment * Gender interaction Phase effect Treatment * Phase interaction iRQ2 a 0.05 (0.01) 0.03, 0.07 0.11 (0.01) 0.08, 0.14 P < 0.001 NS NS NS NS iEE2 b 0.75 (0.18) 0.38, 1.13 0.52 (0.20) 0.11, 0.93 NS NS NS NS NS GIT2 (%) 3.60 (0.87) 1.81, 5.38 2.48 (0.96) 0.50, 4.46 NS NS NS NS NS Change_ISI -18.75 (3.97) -27.97, -12.60 -18.49 (4.74) -29.85, -11.17 NS NS NS NS NS Change_FGF21 96.6 (20.5) 57.6, 139.3 90.5 (20.7) 54.3, 134.2 NS NS NS NS NS a iRQ2, integrated AUC of post glucose respiratory quotient at trial end; b iEE2, integrated AUC of post glucose energy expenditure at trial end; GIT2, glucose induced thermogenesis at trial end; Change_FGF21, 2hr post glucose minus fasting Fibroblast growth factor 21 at trial end Variables included: adjusting for corresponding pre-trial variable, treatment, age, gender, phase, caloric deficit, change in FM, change in FFM and change in waist. Interactions (treatment*gender, treatment*phrase, and treatment*gender & treatment*phrase) were tested initially and interaction term was removed if it was non-significant. & Bootstrapping method with 1000 bootstrap samples was used for deriving p values, standard errors and 95% confidence intervals for variables that normality was not assumed. ¶ Marginal mean were estimated by substituting the mean values of covariates included, and mean value for the corresponding baseline variable. NS: non-significant statistically (with a p value > 0.05). Figure 1 near here ANCOVA: Tables 1 & 2 display the ANCOVA outcomes for all fasting variables and all postprandial variables, respectively. There were no treatment effects in fasting variables accounting for pre-trial baseline values and other covariates (Table 1 ). iRQ, which reflects metabolic flexibility, was significantly higher following Leu (Table 2 , Fig. 1 ). This was a consistent finding whether we examined the restricted dataset through a sensitivity analysis (Table 3 ) or as the ITT analysis (Table 3 ). No other treatment effects were detected for postprandial outcomes. Table 3 Sensitivity analysis for energy expenditure and RQ in response to an OGTT at end of trial. Variable Placebo (n = 19) Leucine (n = 18) ANCOVA # Mean ¶ (std.error) 95% CI & Mean ¶ (std.error) 95% CI & Treatment effect Gender effect Treatment * Gender interaction Phase effect Treatment * Phase interaction Fasting a RQ_S (x2hr) N = 25 1.63 (0.04) 1.59, 1.69 1.63 (0.12) 1.56, 1.68 NS NS NS NS NS a RQ_ITT (x2hr) N = 37 1.64 (0.01) 1.61,1.67 1.63 (0.02) 1.60, 1.66 NS NS NS NS NS b EE_S (kJ/min x2hr) N = 25 8.28 (0.23) 7.79, 8.77 8.68 (0.25) 8.16, 9.20 NS p = 0.033 NS NS NS b EE_ITT (kJ/min x2hr) N = 37 8.51 (0.18) 8.14, 8.88 8.76 (0.18) 8.39, 9.14 NS p = 0.010 NS P = 0.007 NS Postprandial a iRQ_S (x2hr) N = 25 0.03 (0.01) 0.01, 0.06 0.11 (0.01) 0.08, 0.14 p < 0.001 NS NS NS NS a iRQ_ITT (x2hr) N = 37 0.04 (0.01) 0.02, 0.06 0.10(0.01) 0.07, 0.12 p < 0.001 NS NS NS NS b iEE_S (kJ/min x2hr) N = 25 0.72 (0.26) 0.16, 1.27 0.64 (0.31) -0.02, 1.31 NS NS NS NS NS b iEE_ITT (kJ/min x2hr) N = 37 0.62 (0.16) 0.30, 0.94 0.67 (0.17) 0.31, 1.02 NS NS NS NS NS a RQ_S, sensitivity analysis of fasting RQ deleting n = 12; RQ_ITT, intention to treat fasting RQ with last value carried forward. S_iRQ, sensitivity analysis of iAUC post glucose RQ deleting n = 12; ITT_iRQ, intention to treat iAUC post glucose RQ with last value carried forward. b S_REE, sensitivity analysis of resting energy expenditure deleting n = 12; ITT_REE, intention to treat resting energy expenditure with last value carried forward. S_iEE sensitivity analysis of iAUC postprandial energy expenditure deleting n = 12; ITT_iEE, intention to treat post glucose energy expenditure with last value carried forward. # Variables included: corresponding pre-trial variable, treatment, age, gender, phase, caloric deficit, change in FM, change in FFM and change in waist. All 2-way interaction terms (treatment*gender, treatment*phrase, and treatment*gender & treatment*phrase) were tested initially but removed if NS. & Bootstrapping method with 1000 bootstrap samples was used for deriving p values, standard errors and 95% confidence intervals for variables where normality was not assumed. ¶ Marginal mean were estimated by substituting the mean values of covariates included, and mean value for the corresponding baseline variable. NS: non-significant statistically (with a p value > 0.05). Table 3 near here Correlations: Partial correlations were adjusted for gender, treatment and FFM and FM following weight loss. They indicated significant negative relationships between some fasting liver enzymes on trial completion and iEE (Table 4 ). There were significant relationships between iRQ and 2h_ ISI following OGTT (r = 0.534, P = 0.001), fasting ALT (r= -0.523, p = 0.003) and a trend with number of MetS components on trial completion (r= -0.307, P = 0.078). Other fasting variables were not related to iREE or to iRQ at end of trial (data not shown). Table 4 Partial correlations * between metabolic flexibility, postprandial energy expenditure, insulin sensitivity and liver function tests. iRQ iEE FGF21(pg/ml) r = -0.025 r = 0.149 ISI r = 0.534 r = 0.044 FLI r = -0.166 r = -0.107 ALP (U/L) r = -0.026 r = -0.075 ALT (U/L) r = − 0.523 r = − 0.409 GGT (U/L) r = -0.249 r = -0.466 Albumin (g/L) r = -0.147 r = -0.493 BR (µmol/L) r = 0.000 r = -0.145 TP (g/L) r = -0.044 r = -0.262 No. of MetS components r = -0.307 ¶ r = 0.024 *Data controlled for treatment (placebo vs leucine), gender, FM and FFM at end of trial. Values in bold are P < 0.05. ¶ P = 0.078 iRQ integrated respiratory quotient; iEE, integrated energy expenditure; FGF21, fibroblast growth factor 21; ISI, Insulin sensitivity index; FLI, fatty liver index; ALP, alkaline phosphatase; ALT, alanine amino transferase; GGT gamma-glutamyl transference; BR, bilirubin; TP, total protein; MetS, metabolic syndrome Table 4 near here Discussion MF is a key feature of insulin sensitive states and permits daily metabolism to transition from the fasted to the fed state through the utilization of available fuel. This is seen as a change from the predominant use of fat in an overnight fasted period (low RQ) to the use of carbohydrate utilization (high RQ), usually from the first mixed meal of the day. There are several methods to assess MF in humans. The suggested gold standard technique is based on the hyperinsulinemic-euglycemic clamp combined with indirect calorimetry ( 4 , 27 ) and, sometimes, the inclusion of stable isotopic labels to track substrate utilization( 17 ) These are invasive and highly technical protocols that are restricted to specialized laboratories( 17 ). Researchers have instead used meal based tests, standard glucose loads ( 28 ), and sub-maximal exercise protocols ( 29 ) to monitor the change in substrate oxidation through changes in RQ between rest and the post-test period. The manner of calculating this ‘change’ in RQ, the time period over which it is calculated ( 30 ) and the statistical analysis used, can theoretically influence the interpretation of outcomes ( 28 ) In this RCT we employed the OGTT over 2 hr, calculated the integrated AUC of dependent variables and adjusted the final RCT values for their starting value. In addition, a priori and post-intervention covariates were included in all models developed. We tested the hypothesis that leu may improve MF over the influence of weight loss alone, and this could be brought on by improved insulin sensitivity and liver function. Unadjusted REE, but not RQ, declined with weight loss but was not different between treatment arms. Once adjusted for changes in body composition and other covariates, Leu did not significantly impact any of the fasting metabolic variables, including REE, RQ, FLI (a composite index of liver function), nor any of the standard LFTs (Table 1 ). Postprandial data provided similar outcomes, except for iRQ (Table 2 ). In our analysis, iRQ represents metabolic flexibility which was significantly higher following Leu (Table 2 ). This was a consistent finding whether we conducted a sensitivity analysis or an ITT analysis (Table 3 ). We hence conclude that Leu stimulates MF over and above the effects of caloric restriction per se. FGF21 belongs to a family of peptides involved in a plethora of metabolic functions and is produced in the liver. FGF21 may have a role in energy expenditure (EE) and glucose and lipid metabolism ( 31 , 32 ) and may influence the metabolic inflexibility seen in MetS ( 24 ). Following glucose ingestion, FGF21 shows a biphasic excursion in plasma; an initial decline within 30 min followed by an increase of greater than twofold by 2 h. The data clearly confirm the significant rise in FGF21 following glucose in both arms of the RCT, but with no treatment effect (Table 2 ). In a parallel manner, the change in ISI following glucose dipped significantly but was not different between groups (Table 2 ). These findings were contrary to our expectations where improved postprandial ISI and higher FGF21 may explain the greater expected MF following Leu. The literature to date uncovered just one other human study that measured fasting EE following leu supplementation during CR ( 33 ). Those authors reported no difference in REE between leu and placebo ( 33 ). Leu treatment of human and murine skeletal muscle cells have revealed interesting findings ( 34 ). Leu increased mitochondrial biogenesis and increased carbohydrate oxidation in a dose-dependent manner ( 34 ). The latter findings resonate with our observations of greater MF following Leu (Tables 2 & 3 ). However, an increased energy expenditure that is expected with Leu ( 34 ), and an improved insulin sensitivity( 35 ) was not seen in our study. Both REE and post-meal GIT were similar (Tables 1 & 2 ). Geisler ( 36 ) has suggested that a change in substrate oxidation from lipid to carbohydrate does not necessarily involve a change in EE, since the ratio of ATP generated to oxygen consumed are approximately equal for these fuels. There could be some other reasons too for our observations. The theoretical cost of glucose oxidation is small between 2–4% of the energy content of the meal ( 37 ). Our GIT data are consistent with these values (Table 2 ), but using a single nutrient like glucose, could have been too small a stimulus. Perhaps, a mixed meal providing greater kJ could have separated out such expected effects of leu to stimulate post meal EE. In contrast direct measurements at the cellular level show elevated EE ( 34 ) and 24h measurements of oxygen consumption are elevated in animal models following leu ( 14 , 35 ). Notwithstanding the lack of between-group effects on EE and ISI following leu, we did observe very interesting interrelationships in the data. Partial correlations indicated a significant positive link between MF and ISI at the end of the OGTT and a negative association between MF and ALT (Table 4 ). This supports the well held view that MF varies as a direct function of insulin sensitivity and that improved liver function is also important to fuel switching. In addition, iEE was negatively related to albumin, ALT and GGT respectively (Table 4 ). The liver is involved in energy metabolism ( 3 ), and overall such outcomes indicate that even adjusted for changes in body composition following weight loss, there remain close links between MF, ISI and liver function. Conclusions Leu supplementation improved MF following weight loss. This was not accompanied by differences in EE, ISI, FGF21, FLI or LFTs. Overall, the data support the close links between energy metabolism, insulin sensitivity and liver function. Declarations Ethical Approval: The study was approved by Curtin University’s Human Research Ethics Committee (HR 108/2013). The trial was registered with Australian New Zealand Clinical Trials Registry (ACTRN 12616001528448). Informed, written consent was provided by all participants prior to trial commencement. Competing Interests: MJS is the Editor- in Chief of the EJCN. All other authors declare that there are no competing financial or other conflicts of interests in relation to this submission. Funding: The study was partly funded by a seed grant from the School of Population Health, Curtin University, Perth Western Australia. Author Contribution Statement : MJS conceived the idea, collated the data, assembled tables, wrote the first draft and critically revised the paper. KP conducted the trial, made all the measurements, contributed to data collation, wrote the paper and readied it for submission. YZ conducted the statistical analysis, assembled the tables and figure and contributed to paper writing. EKC assisted in data collection, reviewed tables and critically wrote the manuscript. All authors read and agreed on the final content of the submitted manuscript. Acknowledgments: MJS acknowledges infrastructure support from the School of Population Health, Curtin University. The authors thank the participants for their involvement in this research and Anthony James, Jillian Sherriff and Philip Newsholme for their collaboration over the years. Data Availability Statement: The raw data used in this paper is freely available for non-commercial purposes only. The institution’s human ethics committee must endorse the formal request, and researchers may contact Mario Soares [email protected] in the first instance. References Kelley D, Mandarino LJ. Fuel selection in human skeletal muscle in insulin resistance: a reexamination. Diabetes. 2000;49:677–83. Colosimo S, Mitra SK, Chaudhury T, Marchesini, G. Insulin resistance and metabolic flexibility as drivers of liver and cardiac disease in T2DM. Diabetes Research and Clinical Practice. 2023;206. Bosch M, Parton RG, Pol A. Lipid droplets, bioenergetic fluxes, and metabolic flexibility. Semin Cell Dev Biol. 2020;108:33–46. Goodpaster B, Sparks LM. Metabolic Flexibility in Health and Disease. Cell Metabolism. 2017;25:1027–36. Morio B, Panthu B, Bassot A, Rieusset J Role of mitochondria in liver metabolic health and diseases. Cell Calcium. 2021;94. 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Macotela Y, Emanuelli B, Bang AM, Espinoza DO, Boucher J, Beebe K, Gall W, Kahn CR. Dietary leucine - An environmental modifier of insulin resistance acting on multiple levels of metabolism. PLoS One. 2011;6(6):0021187. Ispoglou T, White H, Preston T, McElhone S, McKenn J, Hind K. Double-blind, placebo-controlled pilot trial of L-Leucine-enriched amino-acid mixtures on body composition and physical performance in men and women aged 65–75 years. Eur J Clin Nutr. 2016;70:182–8. Verreijen A, Verlaan S, Engberink MF, Swinkels S, de Vogel-van den Bosch J, Weijs P. A high whey protein–leucine, and vitamin D–enriched supplement preserves muscle mass during intentional weight loss in obese older adults: a double-blind randomized controlled trial. Am J Clin Nutr. 2015;101:279–86. Zhang Y, Guo K, LeBlanc RE, Loh D, Schwartz GJ, Yu YH. Increasing Dietary Leucine Intake Reduces Diet-Induced Obesity and Improves Glucose and Cholesterol Metabolism in Mice via Multimechanisms. Diabetes. 2007;56:1647–54. Liang C, Curry BJ, Brown PL, Zemel MB Leucine modulates mitochondrial biogenesis and SIRT1-AMPK signaling in C2C12 myotubes. 239750. 2014;2014. McAllan L, Cotter PD, Roche HM, Korpela R, Nilaweera KN. Impact of leucine on energy balance. J Physiol Biochem. 2013;69:155–63. Corpeleijn E, Mensink M, Kooi ME, Roekaerts PM, Saris WH, Blaak EE. Impaired skeletal muscle substrate oxidation in glucose-intolerant men improves after weight loss. Obesity. 2008;16(5):1025–32. Kelley D, Goodpaster B, Wing RR, Simoneau JA. Skeletal muscle fatty acid metabolism in association with insulin resistance, obesity, and weight loss. Am J Physiol Endocrinol Metab. 1999;277:E1130–E41. Pathak K, Zhao Y, Calton EK, James AP, Newsholme P, Sherriff J, Soares MJ. The impact of leucine supplementation on body composition and glucose tolerance following energy restriction: an 8-week RCT in adults at risk of the metabolic syndrome. Eur J Clin Nutr. 2024;78(2):155–62. Nsatimba P, Pathak K, Soares MJ. Ethnic differences in resting metabolic rate, respiratory quotient and body temperature: a comparison of Africans and European Australians. Eur J Nutr. 2016;55(5):1831–8. Alberti K, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120(16):1640-5. Weir J. New methods for calculating metabolic rate with special reference to protein metabolism. j Physiol Anthropol. 1949;109:1–9. Pathak K, Woodman RJ, James AP, Soares MJ. Fasting and glucose induced thermogenesis in response to three ambient temperatures: a randomized crossover trial in the metabolic syndrome. Eur J Clin Nutr. 2018;72(10):1421–30. Pathak K, Soares MJ, Zhao Y, James AP, Sherriff JL, Newsholme P. Postprandial changes in glucose oxidation and insulin sensitivity in metabolic syndrome: Influence of fibroblast growth factor 21 and vitamin D status. Nutriition. 2017;37:37–42. Bedogni G, Bellentani S, Miglioli L, Masutti F, Passalacqua M, Castiglione A, & Tiribelli C. The fatty liver index: a simple and accurate predictor of hepatic steatosis in the general population. BMC Gastroenterol. 2006;6(1):33. Pathak K. Energy metabolism in obesity: Role of vitamin D status, cold exposure and leucine supplementation. Perth, WA: Curtin University; 2016. Galgani J, Moro C, Ravussin E. Metabolic flexibility and insulin resistance. Am J Physiol Endocrinol Metab 2008;295:E1009–E17. Yu E, Le Ngoc-A, Stein AD. Measuring Postprandial Metabolic Flexibility to Assess Metabolic Health and Disease. The J Nutr. 2021;151(11):3284–91. Chavez I. Assessment of metabolic flexibility by measuring maximal fat oxidation during submaximal intensity exercise: Can we improve the analytical procedures? Sprts Med Hlth Sci. 2023;5(2):156–8. Alcantara J, Galgani JE. Association of metabolic flexibility indexes after an oral glucose tolerance test with cardiometabolic risk factors. Eur J Clin Nutr. 2024;78:180–6. Straub L, Wolfrum, C. FGF21, energy expenditure and weight lossdhow much brown fat do you need? Mol Metab 2015;4:605–9. Semba R, Sun K, Egan JM, Crasto C, Carlson OD, Ferrucci L. Relationship of serum fibroblast growth factor 21 with abnormal glucose metabolism and insulin resistance: the Baltimore Longitudinal Study of Aging. J Clin Endocrinol Metab. 2012;97:1375–82. Funderburk L, Heileson j, Peterson M, Willoughby DS. Efficacy of L-Leucine Supplementation Coupled with a Calorie-Restricted Diet to Promote Weight Loss in Mid-Life Women. J Am Coll Nutr. 2021;40(8):699–707. Vaughan R, Garcia-Smith R, Gannon NP, Bisoffi M, Trujillo KA, Conn CA.. Leucine treatment enhances oxidative capacity through complete carbohydrate oxidation and increased mitochondrial density in skeletal muscle cells.. Amino Acids. 2013;45(4):901–11. She P, Reid TM, Bronson SK, Vary TC, Hajnal A, Lynch CJ, Hutson SM. Disruption of BCATm in mice leads to increased energy expenditure associated with the activation of a futile protein turnover cycle. Cell Metab. 2007;6(3):181–94. Geisler JG. Targeting energy expenditure via fuel switching and beyond. Diabetologia. 2011;54(2):237–44. Flatt J. Use and storage of carbohydrate and fat Am J Clin Nutr. 1995;61:952S-95. Additional Declarations There is NO conflict of interest to disclose. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4220135","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":293073454,"identity":"e7f5fdb7-6d74-4d41-845b-8dad2e27d3a2","order_by":0,"name":"Kaveri Pathak","email":"data:image/png;base64,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","orcid":"","institution":"Curtin University","correspondingAuthor":true,"prefix":"","firstName":"Kaveri","middleName":"","lastName":"Pathak","suffix":""},{"id":293073455,"identity":"88fb6fd4-576a-4abb-8f87-b436f3228365","order_by":1,"name":"Mario Soares","email":"","orcid":"https://orcid.org/0000-0001-6071-0272","institution":"Curtin University","correspondingAuthor":false,"prefix":"","firstName":"Mario","middleName":"","lastName":"Soares","suffix":""},{"id":293073456,"identity":"d7b2ff47-cb2d-4249-bd7e-46e44ca06ef3","order_by":2,"name":"Zhao Yun","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zhao","middleName":"","lastName":"Yun","suffix":""},{"id":293073457,"identity":"15ba95f6-3b26-45db-8678-69a791c83b01","order_by":3,"name":"Emily Calton","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Emily","middleName":"","lastName":"Calton","suffix":""}],"badges":[],"createdAt":"2024-04-05 01:00:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4220135/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4220135/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55515615,"identity":"a1ed5c29-6679-41ef-a48f-74bff1d05b07","added_by":"auto","created_at":"2024-04-29 13:11:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":17107,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4220135/v1/b50a257374af67f9804806cf.png"},{"id":76847622,"identity":"41e17b09-028b-4a3c-8b7b-98c2dfc9e987","added_by":"auto","created_at":"2025-02-21 11:19:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":771658,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4220135/v1/79430bb7-c390-4c18-9937-466252f94d9a.pdf"},{"id":55514647,"identity":"d08fa7e4-e62c-4d86-8fd5-b80334d76256","added_by":"auto","created_at":"2024-04-29 13:03:39","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":33192,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfile1FINALdocx.docx","url":"https://assets-eu.researchsquare.com/files/rs-4220135/v1/73f22773cf7b0c9cdbf968f0.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Metabolic flexibility and liver function following leucine supplementation during caloric restriction","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMetabolic flexibility (MF) is the capacity to adapt to a source of fuel depending on its availability, or the metabolic demand for energy (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). This ability to utilize available substrate resides at the tissue, organ and whole-body level (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) and is usually seen as a change from fat oxidation in the fasted state, to carbohydrate oxidation in the fed state (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). MF is considered a feature of insulin sensitivity and a hallmark of good health. The inability to switch between different fuels is termed metabolic inflexibility, and is encountered in insulin resistant states including obesity, metabolic syndrome (MetS) and type 2 diabetes (T2DM) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The liver plays a key role in energy production, and hepatic mitochondria maybe central to ensuring normal human metabolism (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Indeed, impaired energy regulation may underscore the propensity of MetS in certain ethnic groups (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), while recovery from MetS, following weight loss, resulted in a significantly lowered energy production (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Further support for mitochondrial regulation comes from observations that mitochondrial targeted antioxidants ameliorated features of MetS in animal models (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne- third of our minimum daily requirement for essential amino acids is met by the three branched chain amino acids (BCAA): leucine, isoleucine and valine (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Dysfunctional adipose tissue is seen with increasing adipose tissue mass and linked to a greater inflammatory profile and impaired insulin function. Impaired insulin action underscores many features of the metabolic syndrome such as raised blood pressure, hyperglycaemia, hypertriglyceridemia and low HDL-C. A potential role for leucine (leu) in obesity and in MetS may then be ascribed to its effects on insulin secretion (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) and improved insulin sensitivity (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Leu supplementation increased lean tissue mass (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) and preserved muscle mass during weight loss (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Furthermore, an increased REE and greater fatty acid oxidation (low RQ) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) has been reported following supplementation with the amino acid. Mechanistically, Leu effects on energy and lipid metabolism are brought about through the regulation of uncoupling protein-3 (UCP-3) expression in metabolically active tissues such as skeletal muscle, brown adipose tissue (BAT) and white adipose tissue (WAT) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Overall, there is a good reason to expect y that Leu would modulate MF.\u003c/p\u003e \u003cp\u003eThe literature provides mixed outcomes on the effect of weight loss in improving MF. There are some in favour (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) and some showing no effect or a worsening of MF (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), hence further research was needed. In our previous report, we had uncovered a beneficial effect of leu vs. placebo during caloric restriction, where a greater preservation of lean tissue mass and its compartments appendicular and non-appendicular tissue mass were seen (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). We noted this effect to be greatest in males but that requires further confirmation (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Given the interplay between Leu intake, muscle mass, insulin sensitivity and hepatic function, we hypothesized that Leu supplementation during caloric restriction could improve MF more than that seen with weight loss alone, and that this could be brought on by improved insulin sensitivity and liver function.\u003c/p\u003e "},{"header":"Material \u0026 Methods","content":"\u003cp\u003eThis present analysis reports on the secondary endpoints of an 8 week RCT in individuals at risk of the metabolic syndrome (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The details of that study design, recruitment of participants, conduct of the dietary intervention and pharmacological compounding of Leu and placebo capsules has been previously described in detail (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Briefly, we had conducted a double blind, placebo controlled, parallel, RCT over 8 weeks, that compared changes in body composition and glucose tolerance during caloric restriction between two groups i.e with Leu supplementation or with placebo (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The trial was conducted in two phases that included 18 participants in the first phase and 19 in the second phase (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The study was prospectively registered at Australian New Zealand Clinical Trial Registry (Trial Id: ACTRN12616001528448). Human ethics approval had been obtained from the institution\u0026rsquo;s ethics committee (HREC 108/2013), and all participants had provided written informed consent.\u003c/p\u003e \u003cp\u003eEnergy Expenditure \u0026amp; Metabolic Flexibility\u003c/p\u003e \u003cp\u003eParticipants attended the research centre at Curtin University (Bentley, Western Australia) following an overnight fast of a minimum 10 hours, 8 hours of sleep and abstinence from alcohol and vigorous exercise for at least 36 hours prior to the measurement. They were instructed not to shower in the morning to avoid any increase in metabolic rate. On arrival, they emptied their bladder and changed into a standard dressing gown, after which their weight and waist circumference were measured. They entered an insulated chamber measuring 5 x 3 meters with a volume of 57.75 m\u003csup\u003e2\u003c/sup\u003e maintained at 25\u003csup\u003eο\u003c/sup\u003eC and rested in bed for 30 min in the supine position (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Resting energy expenditure (REE) was measured via indirect calorimetry (Deltatrac II Metabolic Monitor; Datex-Ohmeda, Instrumentarium Corp, Helsinki, Finland). REE and respiratory quotient (RQ) were measured twice for 25 min each with a 10-minute rest in between. Fasting bloods were then drawn by a certified phlebotomist for blood chemistry and this was followed by an oral glucose tolerance test (OGTT) (75g glucose in 300 ml water, Carbo test, Australia). Measurements of REE and RQ were continued from minutes 15\u0026ndash;30, 45\u0026ndash;60, 75\u0026ndash;90 and 105\u0026ndash;120 following the OGTT in each person. At the conclusion of the OGTT test, a second blood sample was drawn at 2h. Blood samples were collected for fibroblast growth factor 21 (FGF21), glucose, insulin and liver function tests panel. Metabolic syndrome was determined by the presence of 3 or more metabolic risk factors from the following variables waist circumference, fasting blood sugar levels, fasting (HDL) levels, fasting triglycerides levels or Hypertension (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Total and regional body composition including fat mass (FM, kg) and fat-free mass (FFM, kg) was measured by (DEXA, Prodigy\u0026trade;, Lunar Corp. Madison, USA). All participants were offered a beverage of choice and light refreshments before they left the premises. The entire protocol was repeated in each individual 8 weeks later, at the end of the weight loss trial.\u003c/p\u003e \u003cp\u003eCalculations:\u003c/p\u003e \u003cp\u003eREE was calculated from the average of last 25 min to min recordings of oxygen consumption (O\u003csub\u003e2\u003c/sub\u003e) and carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) production using the Weir Eq.\u0026nbsp;(22). RQ was calculated as the ratio of CO\u003csub\u003e2\u003c/sub\u003e to O\u003csub\u003e2\u003c/sub\u003e. The area under the curve (AUC) for postprandial REE and RQ was determined using the trapezoid rule. MF was the calculated as the integrated AUC of RQ (iAUC) given by the difference between the total AUC from baseline AUC. In our laboratory, the within-subject CVs (including measurement error) were as follows: 2.1% for fasting RQ, 3.2% for REE, 1.6% for total area under the glucose curve (TAUC) for postprandial RQ and 4.3% for postprandial energy expenditure (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Change variables were calculated as 2hr value minus fasting value. Stumvol\u0026rsquo;s insulin sensitivity index (ISI) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) and Fatty liver index (FLI) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) were calculated from their original equations.\u003c/p\u003e \u003cp\u003eValidation Study\u003c/p\u003e \u003cp\u003eThe Deltatrac II machine used to measure REE broke down in phase 2 during post leucine supplementation measurements. This affected 12 of 19 participants in that phase. In these 12 participants, we used another available metabolic monitor, TrueOne metabolic cart (Parvo Medics, USA) where gas collection was made via nose-clip and mouthpiece mode as only that was available. The TrueOne shares the same make of oxygen and carbon dioxide analysers as the Deltatrac II. Following repairs to the Deltatrac II, we ran a validation study to determine the difference between the machines and the best predictive equation for converting TrueOne values to Deltatrac II values. In this validation trial, we tested the Deltatrac II in canopy mode to the TrueOne in mouthpiece and nose clip mode. The details of the crossover design and results of the conversion equations for O\u003csub\u003e2\u003c/sub\u003e and CO\u003csub\u003e2\u003c/sub\u003e from TrueOne to Deltatrac II(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), are presented here in supplementary file S1. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStatistics\u003c/p\u003e \u003cp\u003eUnadjusted repeated measures ANOVA (RM-ANOVA) with treatment as between-subject factor were used to initially explore the change in outcome variables following weight loss. Analysis of covariance (ANCOVA) via General Linear Model (GLM) univariate approach was used to examine the effect of leucine supplementation vs placebo (Treatment) on fasting REE, RQ, and liver function tests and postprandial variables iRQ2, iREE2, GIT (%), FGF21 and Stumvol\u0026rsquo;s insulin sensitivity index (ISI). All outcomes at the end of the trial were adjusted for their respective pre-trial (baseline) values. Other covariates included were age, gender, phase, caloric deficit (kcal), change in FM, change in FFM, and change in waist circumference. In addition, all 2-way interactions were assessed and, if non-significant, were removed from the model. Normality was assessed and bootstrapping method with 1000 bootstrap samples was used on outcome variables for deriving robust estimates of standard errors, regression coefficients and corresponding 95% confidence intervals. Marginal means were estimated by substituting the mean values of covariates included, and mean value for the corresponding baseline variable.\u003c/p\u003e \u003cp\u003eWe ran three sets of analyses for REE and MF that included:\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) the measured data after machine inter-conversion (n\u0026thinsp;=\u0026thinsp;37),\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) a sensitivity analysis that excluded those with a machine failure (n\u0026thinsp;=\u0026thinsp;25),\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) an intention to treat analysis (ITT), where the last value was carried forward in those who had a machine failure (N\u0026thinsp;=\u0026thinsp;37).\u003c/p\u003e \u003cp\u003eFinally, partial correlations assessed relationships between postprandial energy expenditure and RQ with various indices of liver function, ISI and number of metabolic syndrome components. Statistical significance was accepted when a p-value was less than 0.05. All the analyses were performed by using SPSS version 29 (SPSS Inc., Chicago, IL, USA).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eRepeated measures:\u003c/p\u003e \u003cp\u003eThere was a significant decline in REE following weight loss (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD: 4.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96 vs. 4.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00 kJ/min, P\u0026thinsp;=\u0026thinsp;0.003), no difference between treatments (P\u0026thinsp;=\u0026thinsp;0.653) and no treatment*time interaction (P\u0026thinsp;=\u0026thinsp;0.486). Fasting RQ showed no difference over time (0.811\u0026thinsp;\u0026plusmn;\u0026thinsp;0.033 vs. 0.806\u0026thinsp;\u0026plusmn;\u0026thinsp;0.049, P\u0026thinsp;=\u0026thinsp;0.34), between treatments (P\u0026thinsp;=\u0026thinsp;0.106) nor treatment*time interaction (P\u0026thinsp;=\u0026thinsp;0.111). iEE following OGTT did not change following weight loss (p\u0026thinsp;=\u0026thinsp;0.391), was not different between treatments (P\u0026thinsp;=\u0026thinsp;0.884) or showed treatment*time interaction (P\u0026thinsp;=\u0026thinsp;0.354). iRQ was not different over time (P\u0026thinsp;=\u0026thinsp;0.421) but indicated a significant difference between treatment (placebo: 0.062\u0026thinsp;\u0026plusmn;\u0026thinsp;0.044 vs. Leu: 0.106\u0026thinsp;\u0026plusmn;\u0026thinsp;0.042, P\u0026thinsp;=\u0026thinsp;0.004). This was mainly due to a higher iRQ following the leu arm as denoted by a significant treatment*time interaction (P\u0026thinsp;=\u0026thinsp;0.006). Fasting ISI increased following weight loss (15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15 vs.19.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.15, p\u0026thinsp;=\u0026thinsp;0.031) but did not differ between treatments. Fasting FGF21 declined following weight loss (165\u0026thinsp;\u0026plusmn;\u0026thinsp;30.92 vs. 116\u0026thinsp;\u0026plusmn;\u0026thinsp;24.3 pg/ml, P\u0026thinsp;=\u0026thinsp;0.003) but was not different between treatments. TP (70.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64 vs. 69.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70 g/L, p\u0026thinsp;=\u0026thinsp;0.002), ALT (38.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.81 vs. 32.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.93 IU/L, p\u0026thinsp;=\u0026thinsp;0.016) and ALKPHOS (68.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.38 vs. 61.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.48 IU/L, p\u0026thinsp;=\u0026thinsp;0.023) were all reduced following weight loss but showed no difference between treatment arms of RCT. No other significant changes were noted.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cem\u003enear here\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFasting RQ, REE, insulin sensitivity and liver function tests on completion of the trial.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePlacebo (n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eLeucine (n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e \u003cp\u003eANCOVA\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003csup\u003e\u0026para;\u003c/sup\u003e (std.error)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003csup\u003e\u0026para;\u003c/sup\u003e (std.error)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTreatment effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGender effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTreatment * Gender interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePhase effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTreatment * Phase interaction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRQ (x2hr)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.64 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.61, 1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.61 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.57, 1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREE (kJ/minx2hr)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.51 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.10, 8.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.84 (0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.42, 9.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGF21(pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99.73 (17.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.17, 147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123.52 (20.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74.36, 184.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.23 (2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.35, 31.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.86 (2.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.52, 30.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetS components**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.36 (0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.95, 2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.26 (0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.78, 2.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLI**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.7 (2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.45, 65.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.06 (2.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.02, 63.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.27 (3.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.28, 73.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.38 (4.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.46, 64.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.39 (0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.56, 42.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.50 (0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.54, 41.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.89 (3.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.26, 36.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.63 (4.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.65, 43.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.61 (8.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.45, 54.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.20 (10.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.03, 59.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.40 (0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.81, 70.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.88 (0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.09, 66.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBR (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.64 (0.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.73, 12.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.58 (0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.96, 12.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eN\u0026thinsp;=\u0026thinsp;37. All data represent values at end of trial. *Fasting RQ and REE expressed as AUC.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eRQ, respiratory quotient; REE, resting energy expenditure; FGF21, fibroblast growth factor; ISI, stumvol\u0026rsquo;s insulin sensitivity index; MetS, metabolic syndrome; FLI, fatty liver index; ALP, alkaline phosphatase; ALT, alanine amino transaminase; GGT, gamma glutamyl transferase; TP, total protein; BR, bilirubin.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e#\u003c/sup\u003eVariables included: adjusting for corresponding pre-trial variables, treatment, age, gender, phase, caloric deficit, change in FM, change in FFM, change in waist. All 2-way interaction were tested initially and removed from model if NS. NS: non-significant statistically (with a p value\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e** change in waist not used in adjustment procedure as it is part of the variable.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e\u0026amp;\u003c/sup\u003eBootstrapping method with 1000 bootstrap samples was used for deriving p values, standard errors and 95% confidence intervals for variables that normality was not assumed.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e\u0026para;\u003c/sup\u003eMarginal mean were estimated by substituting the mean values of covariates included, and mean value for the corresponding baseline variable.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cem\u003enear here\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePostprandial RQ and EE following 8 weeks leucine supplementation and caloric restriction.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePlacebo (n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eLeucine (n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e \u003cp\u003eANCOVA\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003csup\u003e\u0026para;\u003c/sup\u003e (std.error)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(std.error)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTreatment effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGender effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTreatment * Gender interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePhase effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTreatment * Phase interaction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eiRQ2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03, 0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.08, 0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eiEE2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.75 (0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.38, 1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52 (0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.11, 0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIT2 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.60 (0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.81, 5.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.48 (0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.50, 4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChange_ISI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-18.75 (3.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-27.97, -12.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-18.49 (4.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-29.85, -11.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChange_FGF21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96.6 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.6, 139.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.5 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54.3, 134.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003ea\u003c/sup\u003e iRQ2, integrated AUC of post glucose respiratory quotient at trial end; \u003csup\u003eb\u003c/sup\u003e iEE2, integrated AUC of post glucose energy expenditure at trial end; GIT2, glucose induced thermogenesis at trial end; Change_FGF21, 2hr post glucose minus fasting Fibroblast growth factor 21 at trial end\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eVariables included: adjusting for corresponding pre-trial variable, treatment, age, gender, phase, caloric deficit, change in FM, change in FFM and change in waist. Interactions (treatment*gender, treatment*phrase, and treatment*gender \u0026amp; treatment*phrase) were tested initially and interaction term was removed if it was non-significant.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e\u0026amp;\u003c/sup\u003eBootstrapping method with 1000 bootstrap samples was used for deriving p values, standard errors and 95% confidence intervals for variables that normality was not assumed.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e\u0026para;\u003c/sup\u003eMarginal mean were estimated by substituting the mean values of covariates included, and mean value for the corresponding baseline variable.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNS: non-significant statistically (with a p value\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cem\u003enear here\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eANCOVA:\u003c/h2\u003e \u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026amp; \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e display the ANCOVA outcomes for all fasting variables and all postprandial variables, respectively. There were no treatment effects in fasting variables accounting for pre-trial baseline values and other covariates (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). iRQ, which reflects metabolic flexibility, was significantly higher following Leu (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This was a consistent finding whether we examined the restricted dataset through a sensitivity analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) or as the ITT analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). No other treatment effects were detected for postprandial outcomes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSensitivity analysis for energy expenditure and RQ in response to an OGTT at end of trial.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePlacebo (n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eLeucine (n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e \u003cp\u003eANCOVA\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003csup\u003e\u0026para;\u003c/sup\u003e (std.error)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003csup\u003e\u0026para;\u003c/sup\u003e (std.error)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTreatment effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGender effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTreatment * Gender interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePhase effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTreatment * Phase interaction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eFasting\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eRQ_S (x2hr)\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.63 (0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.59, 1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.63 (0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.56, 1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eRQ_ITT (x2hr)\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.64 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.61,1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.63 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.60, 1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003eEE_S (kJ/min x2hr)\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.28 (0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.79, 8.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.68 (0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.16, 9.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003eEE_ITT (kJ/min x2hr)\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.51 (0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.14, 8.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.76 (0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.39, 9.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePostprandial\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eiRQ_S (x2hr)\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01, 0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08, 0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eiRQ_ITT (x2hr)\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02, 0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07, 0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003eiEE_S (kJ/min x2hr)\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72 (0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16, 1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.64 (0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.02, 1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003eiEE_ITT (kJ/min x2hr)\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.62 (0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.30, 0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67 (0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31, 1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003ea\u003c/sup\u003eRQ_S, sensitivity analysis of fasting RQ deleting n\u0026thinsp;=\u0026thinsp;12; RQ_ITT, intention to treat fasting RQ with last value carried forward.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eS_iRQ, sensitivity analysis of iAUC post glucose RQ deleting n\u0026thinsp;=\u0026thinsp;12; ITT_iRQ, intention to treat iAUC post glucose RQ with last value carried forward.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003eb\u003c/sup\u003e S_REE, sensitivity analysis of resting energy expenditure deleting n\u0026thinsp;=\u0026thinsp;12; ITT_REE, intention to treat resting energy expenditure with last value carried forward.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eS_iEE sensitivity analysis of iAUC postprandial energy expenditure deleting n\u0026thinsp;=\u0026thinsp;12; ITT_iEE, intention to treat post glucose energy expenditure with last value carried forward.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e#\u003c/sup\u003eVariables included: corresponding pre-trial variable, treatment, age, gender, phase, caloric deficit, change in FM, change in FFM and change in waist. All 2-way interaction terms (treatment*gender, treatment*phrase, and treatment*gender \u0026amp; treatment*phrase) were tested initially but removed if NS.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e\u0026amp;\u003c/sup\u003eBootstrapping method with 1000 bootstrap samples was used for deriving p values, standard errors and 95% confidence intervals for variables where normality was not assumed.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e\u0026para;\u003c/sup\u003eMarginal mean were estimated by substituting the mean values of covariates included, and mean value for the corresponding baseline variable.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNS: non-significant statistically (with a p value\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cem\u003enear here\u003c/em\u003e\u003c/p\u003e \u003cp\u003eCorrelations:\u003c/p\u003e \u003cp\u003ePartial correlations were adjusted for gender, treatment and FFM and FM following weight loss. They indicated significant negative relationships between some fasting liver enzymes on trial completion and iEE (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). There were significant relationships between iRQ and 2h_ ISI following OGTT (r\u0026thinsp;=\u0026thinsp;0.534, P\u0026thinsp;=\u0026thinsp;0.001), fasting ALT (r= -0.523, p\u0026thinsp;=\u0026thinsp;0.003) and a trend with number of MetS components on trial completion (r= -0.307, P\u0026thinsp;=\u0026thinsp;0.078). Other fasting variables were not related to iREE or to iRQ at end of trial (data not shown).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePartial correlations\u003csup\u003e*\u003c/sup\u003e between metabolic flexibility, postprandial energy expenditure, insulin sensitivity and liver function tests.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eiRQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eiEE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGF21(pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er = -0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003er = 0.534\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er = 0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er = -0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er = -0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er = -0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er = -0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003er \u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.523\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003er \u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.409\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er = -0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003er = -0.466\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er = -0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003er = -0.493\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBR (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er = 0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er = -0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er = -0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er = -0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of MetS components\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er = -0.307\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e*Data controlled for treatment (placebo vs leucine), gender, FM and FFM at end of trial. Values in bold are P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. \u0026para; P\u0026thinsp;=\u0026thinsp;0.078\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eiRQ integrated respiratory quotient; iEE, integrated energy expenditure; FGF21, fibroblast growth factor 21; ISI, Insulin sensitivity index; FLI, fatty liver index; ALP, alkaline phosphatase; ALT, alanine amino transferase; GGT gamma-glutamyl transference; BR, bilirubin; TP, total protein; MetS, metabolic syndrome\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cem\u003enear here\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMF is a key feature of insulin sensitive states and permits daily metabolism to transition from the fasted to the fed state through the utilization of available fuel. This is seen as a change from the predominant use of fat in an overnight fasted period (low RQ) to the use of carbohydrate utilization (high RQ), usually from the first mixed meal of the day. There are several methods to assess MF in humans. The suggested gold standard technique is based on the hyperinsulinemic-euglycemic clamp combined with indirect calorimetry (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) and, sometimes, the inclusion of stable isotopic labels to track substrate utilization(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) These are invasive and highly technical protocols that are restricted to specialized laboratories(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Researchers have instead used meal based tests, standard glucose loads (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), and sub-maximal exercise protocols (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) to monitor the change in substrate oxidation through changes in RQ between rest and the post-test period. The manner of calculating this \u0026lsquo;change\u0026rsquo; in RQ, the time period over which it is calculated (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) and the statistical analysis used, can theoretically influence the interpretation of outcomes (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) In this RCT we employed the OGTT over 2 hr, calculated the integrated AUC of dependent variables and adjusted the final RCT values for their starting value. In addition, \u003cem\u003ea priori\u003c/em\u003e and post-intervention covariates were included in all models developed. We tested the hypothesis that leu may improve MF over the influence of weight loss alone, and this could be brought on by improved insulin sensitivity and liver function.\u003c/p\u003e \u003cp\u003eUnadjusted REE, but not RQ, declined with weight loss but was not different between treatment arms. Once adjusted for changes in body composition and other covariates, Leu did not significantly impact any of the fasting metabolic variables, including REE, RQ, FLI (a composite index of liver function), nor any of the standard LFTs (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Postprandial data provided similar outcomes, except for iRQ (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In our analysis, iRQ represents metabolic flexibility which was significantly higher following Leu (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This was a consistent finding whether we conducted a sensitivity analysis or an ITT analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). We hence conclude that Leu stimulates MF over and above the effects of caloric restriction per se.\u003c/p\u003e \u003cp\u003eFGF21 belongs to a family of peptides involved in a plethora of metabolic functions and is produced in the liver. FGF21 may have a role in energy expenditure (EE) and glucose and lipid metabolism (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) and may influence the metabolic inflexibility seen in MetS (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Following glucose ingestion, FGF21 shows a biphasic excursion in plasma; an initial decline within 30 min followed by an increase of greater than twofold by 2 h. The data clearly confirm the significant rise in FGF21 following glucose in both arms of the RCT, but with no treatment effect (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In a parallel manner, the change in ISI following glucose dipped significantly but was not different between groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These findings were contrary to our expectations where improved postprandial ISI and higher FGF21 may explain the greater expected MF following Leu.\u003c/p\u003e \u003cp\u003eThe literature to date uncovered just one other human study that measured fasting EE following leu supplementation during CR (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Those authors reported no difference in REE between leu and placebo (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Leu treatment of human and murine skeletal muscle cells have revealed interesting findings (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Leu increased mitochondrial biogenesis and increased carbohydrate oxidation in a dose-dependent manner (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). The latter findings resonate with our observations of greater MF following Leu (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u0026amp; \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, an increased energy expenditure that is expected with Leu (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), and an improved insulin sensitivity(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) was not seen in our study. Both REE and post-meal GIT were similar (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026amp; \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Geisler (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) has suggested that a change in substrate oxidation from lipid to carbohydrate does not necessarily involve a change in EE, since the ratio of ATP generated to oxygen consumed are approximately equal for these fuels. There could be some other reasons too for our observations. The theoretical cost of glucose oxidation is small between 2\u0026ndash;4% of the energy content of the meal (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Our GIT data are consistent with these values (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), but using a single nutrient like glucose, could have been too small a stimulus. Perhaps, a mixed meal providing greater kJ could have separated out such expected effects of leu to stimulate post meal EE. In contrast direct measurements at the cellular level show elevated EE (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) and 24h measurements of oxygen consumption are elevated in animal models following leu (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNotwithstanding the lack of between-group effects on EE and ISI following leu, we did observe very interesting interrelationships in the data. Partial correlations indicated a significant positive link between MF and ISI at the end of the OGTT and a negative association between MF and ALT (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This supports the well held view that MF varies as a direct function of insulin sensitivity and that improved liver function is also important to fuel switching. In addition, iEE was negatively related to albumin, ALT and GGT respectively (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The liver is involved in energy metabolism (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), and overall such outcomes indicate that even adjusted for changes in body composition following weight loss, there remain close links between MF, ISI and liver function.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eLeu supplementation improved MF following weight loss. This was not accompanied by differences in EE, ISI, FGF21, FLI or LFTs. Overall, the data support the close links between energy metabolism, insulin sensitivity and liver function.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical Approval:\u003c/h2\u003e \u003cp\u003e The study was approved by Curtin University\u0026rsquo;s Human Research Ethics Committee (HR 108/2013). The trial was registered with Australian New Zealand Clinical Trials Registry (ACTRN 12616001528448). Informed, written consent was provided by all participants prior to trial commencement.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting Interests:\u003c/strong\u003e \u003cp\u003eMJS is the Editor- in Chief of the EJCN. All other authors declare that there are no competing financial or other conflicts of interests in relation to this submission.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThe study was partly funded by a seed grant from the School of Population Health, Curtin University, Perth Western Australia.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e \u003cp\u003e \u003cb\u003eStatement\u003c/b\u003e: MJS conceived the idea, collated the data, assembled tables, wrote the first draft and critically revised the paper. KP conducted the trial, made all the measurements, contributed to data collation, wrote the paper and readied it for submission. YZ conducted the statistical analysis, assembled the tables and figure and contributed to paper writing. EKC assisted in data collection, reviewed tables and critically wrote the manuscript. All authors read and agreed on the final content of the submitted manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e \u003cp\u003eMJS acknowledges infrastructure support from the School of Population Health, Curtin University. The authors thank the participants for their involvement in this research and Anthony James, Jillian Sherriff and Philip Newsholme for their collaboration over the years.\u003c/p\u003e\u003ch2\u003eData Availability Statement:\u003c/h2\u003e \u003cp\u003eThe raw data used in this paper is freely available for non-commercial purposes only. The institution\u0026rsquo;s human ethics committee must endorse the formal request, and researchers may contact Mario Soares [email protected] in the first instance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKelley D, Mandarino LJ. Fuel selection in human skeletal muscle in insulin resistance: a reexamination. Diabetes. 2000;49:677\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColosimo S, Mitra SK, Chaudhury T, Marchesini, G. Insulin resistance and metabolic flexibility as drivers of liver and cardiac disease in T2DM. Diabetes Research and Clinical Practice. 2023;206.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBosch M, Parton RG, Pol A. Lipid droplets, bioenergetic fluxes, and metabolic flexibility. Semin Cell Dev Biol. 2020;108:33\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoodpaster B, Sparks LM. Metabolic Flexibility in Health and Disease. Cell Metabolism. 2017;25:1027\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorio B, Panthu B, Bassot A, Rieusset J Role of mitochondria in liver metabolic health and diseases. Cell Calcium. 2021;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeillet-Coudray GFR, Ebabe Elle J, Rieusset B, Bonafos B, Chabi D Crouzier K, Zarkovic N, Zarkovic J, Ramos E, Badia MP, Murphy J P, Cristol C, Coudray T. The mitochondrial-targeted antioxidant MitoQ ameliorates metabolic syndrome features in obesogenic diet-fed rats better than Apocynin or Allopurinol. Free Radic Res. 2014;48(10):1232\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWahlqvist M, Chang HY, Chen CC, Hsu CC, Chang WC, Wang WS, Hsiung, CA. Is impaired energy regulation the core of the metabolic syndrome in various ethnic groups of the USA and Taiwan? 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PLoS One. 2011;6(6):0021187.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIspoglou T, White H, Preston T, McElhone S, McKenn J, Hind K. Double-blind, placebo-controlled pilot trial of L-Leucine-enriched amino-acid mixtures on body composition and physical performance in men and women aged 65\u0026ndash;75 years. Eur J Clin Nutr. 2016;70:182\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerreijen A, Verlaan S, Engberink MF, Swinkels S, de Vogel-van den Bosch J, Weijs P. A high whey protein\u0026ndash;leucine, and vitamin D\u0026ndash;enriched supplement preserves muscle mass during intentional weight loss in obese older adults: a double-blind randomized controlled trial. Am J Clin Nutr. 2015;101:279\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Guo K, LeBlanc RE, Loh D, Schwartz GJ, Yu YH. Increasing Dietary Leucine Intake Reduces Diet-Induced Obesity and Improves Glucose and Cholesterol Metabolism in Mice via Multimechanisms. Diabetes. 2007;56:1647\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang C, Curry BJ, Brown PL, Zemel MB Leucine modulates mitochondrial biogenesis and SIRT1-AMPK signaling in C2C12 myotubes. 239750. 2014;2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcAllan L, Cotter PD, Roche HM, Korpela R, Nilaweera KN. Impact of leucine on energy balance. J Physiol Biochem. 2013;69:155\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorpeleijn E, Mensink M, Kooi ME, Roekaerts PM, Saris WH, Blaak EE. Impaired skeletal muscle substrate oxidation in glucose-intolerant men improves after weight loss. Obesity. 2008;16(5):1025\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKelley D, Goodpaster B, Wing RR, Simoneau JA. Skeletal muscle fatty acid metabolism in association with insulin resistance, obesity, and weight loss. Am J Physiol Endocrinol Metab. 1999;277:E1130\u0026ndash;E41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePathak K, Zhao Y, Calton EK, James AP, Newsholme P, Sherriff J, Soares MJ. The impact of leucine supplementation on body composition and glucose tolerance following energy restriction: an 8-week RCT in adults at risk of the metabolic syndrome. Eur J Clin Nutr. 2024;78(2):155\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNsatimba P, Pathak K, Soares MJ. Ethnic differences in resting metabolic rate, respiratory quotient and body temperature: a comparison of Africans and European Australians. Eur J Nutr. 2016;55(5):1831\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlberti K, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120(16):1640-5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeir J. New methods for calculating metabolic rate with special reference to protein metabolism. j Physiol Anthropol. 1949;109:1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePathak K, Woodman RJ, James AP, Soares MJ. Fasting and glucose induced thermogenesis in response to three ambient temperatures: a randomized crossover trial in the metabolic syndrome. Eur J Clin Nutr. 2018;72(10):1421\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePathak K, Soares MJ, Zhao Y, James AP, Sherriff JL, Newsholme P. Postprandial changes in glucose oxidation and insulin sensitivity in metabolic syndrome: Influence of fibroblast growth factor 21 and vitamin D status. Nutriition. 2017;37:37\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBedogni G, Bellentani S, Miglioli L, Masutti F, Passalacqua M, Castiglione A, \u0026amp; Tiribelli C. The fatty liver index: a simple and accurate predictor of hepatic steatosis in the general population. BMC Gastroenterol. 2006;6(1):33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePathak K. Energy metabolism in obesity: Role of vitamin D status, cold exposure and leucine supplementation. Perth, WA: Curtin University; 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalgani J, Moro C, Ravussin E. Metabolic flexibility and insulin resistance. Am J Physiol Endocrinol Metab 2008;295:E1009\u0026ndash;E17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu E, Le Ngoc-A, Stein AD. 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Relationship of serum fibroblast growth factor 21 with abnormal glucose metabolism and insulin resistance: the Baltimore Longitudinal Study of Aging. J Clin Endocrinol Metab. 2012;97:1375\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFunderburk L, Heileson j, Peterson M, Willoughby DS. Efficacy of L-Leucine Supplementation Coupled with a Calorie-Restricted Diet to Promote Weight Loss in Mid-Life Women. J Am Coll Nutr. 2021;40(8):699\u0026ndash;707.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVaughan R, Garcia-Smith R, Gannon NP, Bisoffi M, Trujillo KA, Conn CA.. Leucine treatment enhances oxidative capacity through complete carbohydrate oxidation and increased mitochondrial density in skeletal muscle cells.. Amino Acids. 2013;45(4):901\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShe P, Reid TM, Bronson SK, Vary TC, Hajnal A, Lynch CJ, Hutson SM. Disruption of BCATm in mice leads to increased energy expenditure associated with the activation of a futile protein turnover cycle. Cell Metab. 2007;6(3):181\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeisler JG. Targeting energy expenditure via fuel switching and beyond. Diabetologia. 2011;54(2):237\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlatt J. Use and storage of carbohydrate and fat Am J Clin Nutr. 1995;61:952S-95.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"leucine, metabolic flexibility, liver function, fatty liver index, weight loss, muscle mass, metabolic syndrome, glucose tolerance, FGF21.","lastPublishedDoi":"10.21203/rs.3.rs-4220135/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4220135/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground.\u003c/strong\u003e Metabolic flexibility (MF) is the capacity to switch from fat to carbohydrate utilization when required, and MF is constrained in the metabolic syndrome (MetS). We determined whether l-leucine (Leu) supplementation enhanced resting energy expenditure (REE), respiratory quotient (RQ), MF, insulin sensitivity and liver function during caloric restriction (CR).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods.\u003c/strong\u003e Thirty-seven participants at risk of MetS completed a parallel, double-blind RCT comparing Leu vs placebo during CR. REE and RQ were measured before and every 15 min for 2hr following an OGTT. Blood samples were assayed for clinical chemistry, liver function tests (LFT) and fibroblast growth factor 21 (FGF21). Stumvoll’s insulin sensitivity index (ISI), fatty liver index (FLI) and integrated area under response curves were calculated for REE (iREE) and RQ (iRQ). Metabolic flexibility was defined by iRQ following the OGTT. All measurements were made at the start and end of the trial.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults.\u003c/strong\u003e Adjusted for pre-trial values and other covariates, fasting REE, RQ, ISI, LFTs, FLI or FGF21 were not different. There were no differences in postprandial iREE, 2hr_FGF21 or 2hr_ISI. However, Leu resulted in a significantly greater iRQ following CR. Partial correlations indicated that iRQ was significantly related to 2hr_ISI (r = 0.53;p = 0.002) and negatively to fasting alanine amino transferase (ALT) (r= -0.52;p = 0.001). iREE was significantly but negatively related to other liver function parameters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion. \u003c/strong\u003eLeu supplementation improved MF over CR but did not impact REE, ISI and liver function. Overall, there were significant interrelationships between energy metabolism, ISI and liver function.\u003c/p\u003e","manuscriptTitle":"Metabolic flexibility and liver function following leucine supplementation during caloric restriction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-29 13:03:35","doi":"10.21203/rs.3.rs-4220135/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"454380e0-908c-4bd8-8261-e4d0b0b2cad8","owner":[],"postedDate":"April 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30883584,"name":"Health sciences/Diseases/Metabolic disorders"},{"id":30883585,"name":"Health sciences/Health care/Weight management"},{"id":30883586,"name":"Health sciences/Health care/Nutrition"}],"tags":[],"updatedAt":"2025-02-21T11:11:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-29 13:03:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4220135","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4220135","identity":"rs-4220135","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}