Reproducibility of the energy metabolism response to an oral glucose tolerance test: influence of a postcalorimetric correction procedure

Juan M A Alcantara, Guillermo Sanchez-Delgado, Lucas Jurado-Fasoli, Jose E Galgani, Idoia Labayen, Jonatan R Ruiz, Juan M A Alcantara, Guillermo Sanchez-Delgado, Lucas Jurado-Fasoli, Jose E Galgani, Idoia Labayen, Jonatan R Ruiz

Abstract

Purpose: Metabolic flexibility (MetF), which is a surrogate of metabolic health, can be assessed by the change in the respiratory exchange ratio (RER) in response to an oral glucose tolerance test (OGTT). We aimed to determine the day-to-day reproducibility of the energy expenditure (EE) and RER response to an OGTT, and whether a simulation-based postcalorimetric correction of metabolic cart readouts improves day-to-day reproducibility.

Methods: The EE was assessed (12 young adults, 6 women, 27 ± 2 years old) using an Omnical metabolic cart (Maastricht Instruments, Maastricht, The Netherlands) after an overnight fast (12 h) and after a 75-g oral glucose dose on 2 separate days (48 h). On both days, we assessed EE in 7 periods (one 30-min baseline and six 15-min postprandial). The ICcE was performed immediately after each recording period, and capillary glucose concentration (using a digital glucometer) was determined.

Results: We observed a high day-to-day reproducibility for the assessed RER (coefficients of variation [CV] < 4%) and EE (CVs < 9%) in the 7 different periods. In contrast, the RER and EE areas under the curve showed a low day-to-day reproducibility (CV = 22% and 56%, respectively). Contrary to our expectations, the postcalorimetric correction procedure did not influence the day-to-day reproducibility of the energy metabolism response, possibly because the Omnical's accuracy was ~ 100%.

Conclusion: Our study demonstrates that the energy metabolism response to an OGTT is poorly reproducible (CVs > 20%) even using a very accurate metabolic cart. Furthermore, the postcalorimetric correction procedure did not influence the day-to-day reproducibility. Trial registration NCT04320433; March 25, 2020.

Keywords: Glucose tolerance; Indirect calorimetry; Metabolic cart; Postprandial metabolism; Reliability.

Conflict of interest statement

The authors report no conflicts of interest.

© 2022. The Author(s).

Figures

Fig. 1
Fig. 1
Study design (replicated on both testing days, 48 h apart). The anthropometry assessments included height and weight. IC: indirect calorimetry (using a metabolic cart) assessments. RMR: resting metabolic rate assessment/period; ICcE: individual calibration control evaluation proposed by Schadewaldt et al. [25]. The bottle icon represents the 75-g oral glucose dose intake. The drop icons represent the capillary blood glucose level assessments. DXA: dual-energy X-ray absorptiometry assessment. Anthropometry and DXA assessments were performed only on day 1. The study protocol timeline is expressed as minutes (min)
Fig. 2
Fig. 2
Respiratory exchange ratio (RER, A), energy expenditure (EE, B), and carbohydrate (CHO) utilization (C) with and without applying the individual calibration control evaluation procedure (ICcE), and capillary blood glucose concentrations (D) obtained on Visit 1 and Visit 2 tests. Fasting RMR values correspond to the resting metabolic rate (RMR) period, i.e., before the glucose intake, while 15, 45, 75, 105, 135, and 165 represent the time in minutes for gas exchange data after the glucose intake. The bottle icon (x-axis) represents the moment in which the glucose (75-g dose) was provided. P values from two-factor (Time × ICcE) repeated-measures analysis of variance (ANOVA, n = 12, A-C) for Day 1 and Day 2 comparisons. P values from two-factor (Time × Visit) ANOVA (n = 12, Panel D). Results are presented as mean and standard deviation
Fig. 3
Fig. 3
Bland–Altman plots for inter-day reproducibility of the area under the curve for the respiratory exchange ratio (AUC RER; A) and for the energy expenditure (AUC EE; B), with and without applying the individual calibration control evaluation procedure (n = 12). Solid line represents the bias (systematic error) between day 1 and day 2. Dashed lines represent the upper and the lower limits of agreement (mean ± 1.96 standard deviation)

References

    1. Lam YY, Ravussin E. Indirect calorimetry: An indispensable tool to understand and predict obesity. Eur J Clin Nutr. 2017;71:318–322. doi: 10.1038/ejcn.2016.220.
    1. Galgani JE, Fernández-Verdejo R. Pathophysiological role of metabolic flexibility on metabolic health. Obes Rev. 2021;22:1–14. doi: 10.1111/obr.13131.
    1. Rynders C, Bergouignan A, Kealey E, Bessesen D. Ability to adjust nocturnal fat oxidation in response to overfeeding predicts 5-year weight gain in adults. Obesity (Silver Spring) 2017;25:873–880. doi: 10.1002/OBY.21807.
    1. Begaye B, Vinales K, Hollstein T, et al. Impaired metabolic flexibility to high-fat overfeeding predicts future weight gain in healthy adults. Diabetes. 2020;69:181–192. doi: 10.2337/DB19-0719.
    1. Flatt J. The difference in the storage capacities for carbohydrate and for fat, and its implications in the regulation of body weight. Ann N Y Acad Sci. 1987;499:104–123. doi: 10.1111/J.1749-6632.1987.TB36202.X.
    1. Flatt J. Dietary fat, carbohydrate balance, and weight maintenance. Ann N Y Acad Sci. 1993;683:122–140. doi: 10.1111/J.1749-6632.1993.TB35699.X.
    1. Astrup A. The relevance of increased fat oxidation for body-weight management: metabolic inflexibility in the predisposition to weight gain. Obes Rev. 2011;12:859–865. doi: 10.1111/J.1467-789X.2011.00894.X.
    1. Rynders C, Pereira R, Bergouignan A, et al. Associations among dietary fat oxidation responses to overfeeding and weight gain in obesity-prone and resistant adults. Obesity (Silver Spring) 2018;26:1758–1766. doi: 10.1002/OBY.22321.
    1. Galgani J, Ravussin E. Energy metabolism, fuel selection and body weight regulation. Int J Obes. 2008;32:109–119. doi: 10.1038/ijo.2008.246.
    1. Baron A, Brechtel G, Wallace P, Edelman S. Rates and tissue sites of non-insulin- and insulin-mediated glucose uptake in humans. Am J Physiol. 1988 doi: 10.1152/AJPENDO.1988.255.6.E769.
    1. Campbell P, Mandarino L, Gerich J. Quantification of the relative impairment in actions of insulin on hepatic glucose production and peripheral glucose uptake in non-insulin-dependent diabetes mellitus. Metabolism. 1988;37:15–21. doi: 10.1016/0026-0495(88)90023-6.
    1. Chen M, Aguirre R, Hannon T. Methods for measuring risk for type 2 diabetes in youth: the oral glucose tolerance test (OGTT) Curr Diab Rep. 2018 doi: 10.1007/S11892-018-1023-3.
    1. American Diabetes Association 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2020. Diabetes Care. 2020;43:S14–S31. doi: 10.2337/DC20-S002.
    1. Ferrannini E. The theoretical bases of indirect calorimetry: A review. Metabolism. 1988;37:287–301. doi: 10.1016/0026-0495(88)90110-2.
    1. Matarese LE. Indirect calorimetry: Technical aspects. J Am Diet Assoc. 1997;97:S154–S160. doi: 10.1016/S0002-8223(97)00754-2.
    1. Galgani JE, Castro-Sepulveda MA. Influence of a Gas Exchange Correction Procedure on Resting Metabolic Rate and Respiratory Quotient in Humans. Obesity. 2017;25:1941–1947. doi: 10.1002/oby.21981.
    1. Chen KY, Smith S, Ravussin E, et al. Room Indirect Calorimetry Operating and Reporting Standards (RICORS 1.0): A Guide to Conducting and Reporting Human Whole-Room Calorimeter Studies. Obesity. 2020;28:1613–1625. doi: 10.1002/oby.22928.
    1. Schoffelen PFM, Plasqui G. Classical experiments in whole-body metabolism: open-circuit respirometry—diluted flow chamber, hood, or facemask systems. Eur J Appl Physiol. 2017 doi: 10.1007/s00421-017-3735-5.
    1. Alcantara JMA, Sanchez-Delgado G, Martinez-Tellez B, et al. Congruent validity and inter-day reliability of two breath by breath metabolic carts to measure resting metabolic rate in young adults. Nutr Metab Cardiovasc Dis. 2018;28:929–936. doi: 10.1016/j.numecd.2018.03.010.
    1. Cooper JA, Watras AC, O’Brien MJ, et al. Assessing validity and reliability of resting metabolic rate in six gas analysis systems. J Am Diet Assoc. 2009;109:128–132. doi: 10.1016/j.jada.2008.10.004.
    1. Kaviani S, Schoeller DA, Ravussin E, et al. Determining the Accuracy and Reliability of Indirect Calorimeters Utilizing the Methanol Combustion Technique. Nutr Clin Pract. 2018;33:206–216. doi: 10.1002/ncp.10070.
    1. Graf S, Karsegard VL, Viatte V, et al. Comparison of three indirect calorimetry devices and three methods of gas collection: A prospective observational study. Clin Nutr. 2013;8:78.
    1. Black C, Grocott MPW, Singer M. Metabolic monitoring in the intensive care unit: A comparison of the Medgraphics Ultima, Deltatrac II, and Douglas bag collection methods. Br J Anaesth. 2015;114:261–268. doi: 10.1093/bja/aeu365.
    1. Sundström M, Tjäder I, Rooyackers O, Wernerman J. Indirect calorimetry in mechanically ventilated patients. A systematic comparison of three instruments. Clin Nutr. 2013;32:118–121. doi: 10.1016/j.clnu.2012.06.004.
    1. Schadewaldt P, Nowotny B, Strassburger K, et al. Indirect calorimetry in humans: a postcalorimetric evaluation procedure for correction of metabolic monitor variability. Am J Clin Nutr. 2013;97:763–773. doi: 10.3945/ajcn.112.035014.
    1. Fernández-Verdejo R, Aguirre C, Galgani JE. Issues in Measuring and Interpreting Energy Balance and Its Contribution to Obesity. Curr Obes Rep. 2019;8:88–97. doi: 10.1007/s13679-019-00339-z.
    1. Fullmer S, Benson-Davies S, Earthman CP, et al. Evidence analysis library review of best practices for performing indirect calorimetry in healthy and non-critically ill individuals. J Acad Nutr Diet. 2015;115:1417–1446.e2. doi: 10.1016/j.jand.2015.04.003.
    1. Murgatroyd PR, Davies HL, Prentice AM. Intra-individual variability and measurement noise in estimates of energy expenditure by whole body indirect calorimetry. Br J Nutr. 1987;58:347–356. doi: 10.1079/bjn19870104.
    1. de Weir JB. New methods for calculating metabolic rate with special reference to protein metabolism. J Physiol. 1949;109:1–9. doi: 10.1113/jphysiol.1949.sp004363.
    1. Frayn KN. Calculation of substrate oxidation rates in vivo from gaseous exchange. J Appl Physiol Respir Environ Exerc Physiol. 1983;55:628–634. doi: 10.1152/jappl.1983.55.2.628.
    1. Wolever TMS. Effect of blood sampling schedule and method of calculating the area under the curve on validity and precision of glycaemic index values. Br J Nutr. 2004;91:295–300. doi: 10.1079/BJN20031054.
    1. Miodownik S, Melendez J, Carlon VA, et al. Quantitative methanol-burning lung model for validating gas-exchange measurements over wide ranges of FIO2. J Appl Physiol. 1998;84:2177–2182. doi: 10.1152/jappl.1998.84.6.2177.
    1. Bland J, Altman D. Statistical Methods for Assessing Agreement Between Two Methods of Clinical Measurement. Lancet. 1986;327:307–310. doi: 10.1016/S0140-6736(86)90837-8.
    1. Schoeller DA. Making Indirect Calorimetry a Gold Standard for Predicting Energy Requirements for Institutionalized Patients. J Am Diet Assoc. 2007;107:390–392. doi: 10.1016/j.jada.2007.01.030.
    1. Galgani JE, Castro-Sepulveda M, Pérez-Luco C, et al. Validity of predictive equations for resting metabolic rate in healthy humans. Clin Sci (Lond) 2018;132:1741–1751. doi: 10.1042/CS20180317.
    1. Alcantara JMA, Galgani JE, Jurado-Fasoli L, et al. Validity of four commercially available metabolic carts for assessing resting metabolic rate and respiratory exchange ratio in non-ventilated humans. Clin Nutr. 2022;41:746–754. doi: 10.1016/J.CLNU.2022.01.031.
    1. Acheson KJ. Indirect calorimetry: a case for improved standard operating procedures. Eur J Clin Nutr. 2014;68:1–1. doi: 10.1038/ejcn.2013.211.
    1. Allerton TD, Carnero EA, Bock C, et al. Reliability of measurements of energy expenditure and substrate oxidation using whole-room indirect calorimetry. Obesity (Silver Spring) 2021;29:1508–1515. doi: 10.1002/OBY.23226.
    1. Piers LS, Soares MJ, Makan T, Shetty PS. Thermic effect of a meal. 1. Methodology and variation in normal young adults. Br J Nutr. 1992;67:165–175. doi: 10.1079/BJN19920020.
    1. Miles CW, Wong NP, Rumpler WVCJ. Effect of circadian variation in energy expenditure, within-subject variation and weight reduction on thermic effect of food. Eur J Clin Nutr. 1993;47:274–284.
    1. Ruddick-Collins LC, King NA, Byrne NM, Wood RE. Methodological considerations for meal-induced thermogenesis: measurement duration and reproducibility. Br J Nutr. 2013;110:1978–1986. doi: 10.1017/s0007114513001451.
    1. Jagannathan R, DuBose C, Mabundo L, et al. The OGTT is highly reproducible in Africans for the diagnosis of diabetes: Implications for treatment and protocol design. Diabetes Res Clin Pract. 2020 doi: 10.1016/J.DIABRES.2020.108523.
    1. Christophi C, Resnick H, Ratner R, et al. Confirming glycemic status in the Diabetes Prevention Program: implications for diagnosing diabetes in high risk adults. J Diabetes Complications. 2013;27:150–157. doi: 10.1016/J.JDIACOMP.2012.09.012.
    1. Libman I, Barinas-Mitchell E, Bartucci A, et al. Reproducibility of the oral glucose tolerance test in overweight children. J Clin Endocrinol Metab. 2008;93:4231–4237. doi: 10.1210/JC.2008-0801.
    1. DeFronzo R, Abdul-Ghani M. Oral disposition index predicts the development of future diabetes above and beyond fasting and 2-h glucose levels: response to Utzschneider, et al. Diabetes Care. 2009 doi: 10.2337/DC09-0537.
    1. Utzschneider K, Prigeon R, Faulenbach M, et al. Oral disposition index predicts the development of future diabetes above and beyond fasting and 2-h glucose levels. Diabetes Care. 2009;32:335–341. doi: 10.2337/DC08-1478.
    1. Dixon M, Koemel N, Sciarrillo C, et al. The reliability of an abbreviated fat tolerance test: A comparison to the oral glucose tolerance. TEST. 2021;43:428–435. doi: 10.1016/J.CLNESP.2021.03.010.
    1. Hudak S, Huber P, Lamprinou A, et al. Reproducibility and discrimination of different indices of insulin sensitivity and insulin secretion. PLoS ONE. 2021;16:1–17. doi: 10.1371/journal.pone.0258476.
    1. Melanson KJ, Saltzman E, Russell R, Roberts SB. Postabsorptive and postprandial energy expenditure and substrate oxidation do not change during the menstrual cycle in young women. J Nutr. 1996;126:2531–2538. doi: 10.1093/jn/126.10.2531.
    1. Ferraro R, Lillioja S, Fontvieille AM, et al. Lower sedentary metabolic rate in women compared with men. J Clin Invest. 1992;90:780–784. doi: 10.1172/JCI115951.
    1. Benton M, Hutchins A, Dawes J. Effect of menstrual cycle on resting metabolism: A systematic review and meta-analysis. PLoS ONE. 2020 doi: 10.1371/JOURNAL.PONE.0236025.
    1. Péronnet F, Haman F. Low capacity to oxidize fat and body weight. Obes Rev. 2019;20:1367–1383. doi: 10.1111/obr.12910.
    1. Miles-Chan JL, Dulloo AG, Schutz Y. Fasting substrate oxidation at rest assessed by indirect calorimetry: is prior dietary macronutrient level and composition a confounder? Int J Obes (Lond) 2015;39:1114–1117. doi: 10.1038/IJO.2015.29.

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