Continuous glucose monitoring as a close to real life alternative to meal studies - a pilot study with a functional drink containing amino acids and chromium

Azat Samigullin, Per M Humpert, Elin Östman, Azat Samigullin, Per M Humpert, Elin Östman

Abstract

This pilot study aimed to evaluate a continuous glucose monitoring (CGM) based approach to study the effects of a functional drink containing specific amino acids and chromium picolinate (FD) and a combination of FD with a juice (FDJ) on postprandial glucose in a close to real life setting. The predefined primary endpoint for this study was the 120-min incremental area under the glucose curve (iAUC0-120min ) after meals. It was estimated that using CGM and repeated meals in 6 participants could be sufficient to match the power of the previous study in regards to the quantity of meals. Participants followed a pre-specified meal schedule over 9 days and consumed the drinks three times daily with main meals. Differences between drinks were analyzed by analysis of covariances (ANCOVA) with subject number and activity as random factors and nutrient composition as covariates. In 156 meals available for analysis, a significant 34% reduction of glucose iAUC0-120min was shown for FDJ (p < 0.001). FD did not show a significant effect on its own, but a significant reduction of 17.6% (p = 0.007) was shown in pooled data for FD and FDJ. While the differences between the two functional drinks used were not the primary focus of this study, it was sufficiently powered to detect previously described effects in 60 participants in a cross-over design under laboratory settings. The design presented defines a novel and cost-effective approach using CGM devices and app-based lifestyle tracking for studying nutritional effects on glucose at home in a close to real-life setting.

Keywords: amino acids (AAs); continuous glucose monitoring (CGM); dietary intervention glycemic control; dietary intervention randomized controlled trial; functional food (FF); glycemic index and glycemic load; meal study; postprandial glycemia.

Conflict of interest statement

This study received funding from Double Good AB. The funder had the following involvement with the study: EÖ is co-inventor of the patent family describing the functional drink studied, and works for Aventure AB/Double Good AB, that has a license to use the patent. starScience GmbH AS and PH have received funding for other studies by Aventure AB/Double Good AB. PH holds shares of Double Good AB. AS is employed by starScience GmbH.

Copyright © 2022 Samigullin, Humpert and Östman.

Figures

Figure 1
Figure 1
iAUC0−120 values adjusting for subject and physical activity in the 120 min before or after the beginning of a meal. Covariates appearing in the model are evaluated at the following values: total carbohydrates = 65.3 g, total protein = 21.3 g, total fats = 25.5 g. *p < 0.001.

References

    1. Bergman M, Chetrit A, Roth J, Jagannathan R, Sevick M, Dankner R. One-hour post-load plasma glucose level during the ogtt predicts dysglycemia: observations from the 24year follow-up of the israel study of glucose intolerance, obesity and hypertension. Diabetes Res Clin Pract. (2016) 120:221–8. 10.1016/j.diabres.2016.08.013
    1. Coutinho M, Gerstein HC, Wang Y, Yusuf S. The relationship between glucose and incident cardiovascular events. a metaregression analysis of published data from 20 studies of 95,783 individuals followed for 124 years. Diabetes Care. (1999) 22:233–40. 10.2337/diacare.22.2.233
    1. Einarson TR, Machado M, Henk Hemels ME. Blood glucose and subsequent cardiovascular disease: update of a meta-analysis. Curr Med Res Opin. (2011) 27:2155–63. 10.1185/03007995.2011.626760
    1. International Diabetes Federation . IDF Diabetes Atlas. Brussels: International Diabetes Federation; (2017).
    1. Kodama S, Saito K, Tanaka S, Horikawa C, Fujiwara K, Hirasawa R, et al. . Fasting and post-challenge glucose as quantitative cardiovascular risk factors: a meta-analysis. J Atheroscler Thromb. (2012) 19:385–96. 10.5551/jat.10975
    1. Ning F, Zhang L, Dekker JM, Onat A, Stehouwer CD, Yudkin JS, et al. . Development of coronary heart disease and ischemic stroke in relation to fasting and 2-hour plasma glucose levels in the normal range. Cardiovasc Diabetol. (2012) 11:76. 10.1186/1475-2840-11-76
    1. Rohling M, Martin T, Wonnemann M, Kragl M, Klein HH, Heinemann L, et al. . Determination of postprandial glycemic responses by continuous glucose monitoring in a real-world setting. Nutrients. (2019) 11:2305. 10.3390/nu11102305
    1. Shahim B, De Bacquer D, De Backer G, Gyberg V, Kotseva K, Mellbin L, et al. . The prognostic value of fasting plasma glucose, two-hour postload glucose, and Hba. Diabetes Care. (2017) 40:1233–40. 10.2337/dc17-0245
    1. Chiasson JL, Josse RG, Gomis R, Hanefeld M, Karasik A, Laakso M. Acarbose for prevention of Type 2 diabetes mellitus: the stop-niddm randomised trial. Lancet (London, England). (2002) 359:2072–7. 10.1016/S0140-6736(02)08905-5
    1. Chiasson JL, Josse RG, Gomis R, Hanefeld M, Karasik A, Laakso M. Acarbose treatment and the risk of cardiovascular disease and hypertension in patients with impaired glucose tolerance: the stop-niddm trial. J Am Med Assoc. (2003) 290:486–94. 10.1001/jama.290.4.486
    1. Holman RR, Coleman RL, Chan JCN, Chiasson JL, Feng H, Ge J, et al. . Effects of acarbose on cardiovascular and diabetes outcomes in patients with coronary heart disease and impaired glucose tolerance (Ace): a randomised, double-blind, placebo-controlled trial. Lancet Diabetes Endocrinol. (2017) 5:877–86. 10.1016/S2213-8587(17)30309-1
    1. Augustin LS, Kendall CW, Jenkins DJ, Willett WC, Astrup A, Barclay AW, et al. . Glycemic index, glycemic load and glycemic response: an international scientific consensus summit from the international carbohydrate quality consortium (Icqc). Nutr Metab Cardiovasc Dis. (2015) 25:795–815. 10.1016/j.numecd.2015.05.005
    1. Barclay AW, Petocz P, McMillan-Price J, Flood VM, Prvan T, Mitchell P, et al. . Glycemic index, glycemic load, and chronic disease risk - a meta-analysis of observational studies. Am J Clin Nutr. (2008) 87:627–37. 10.1093/ajcn/87.3.627
    1. Bechthold A, Boeing H, Schwedhelm C, Hoffmann G, Knuppel S, Iqbal K, et al. . Food groups and risk of coronary heart disease, stroke and heart failure: a systematic review and dose-response meta-analysis of prospective studies. Crit Rev Food Sci Nutr. (2019) 59:1071–90. 10.1080/10408398.217.1392288
    1. Salmeron J, Ascherio A, Rimm EB, Colditz GA, Spiegelman D, Jenkins DJ, et al. . Dietary fiber, glycemic load, and risk of niddm in men. Diabetes Care. (1997) 20:545–50. 10.2337/diacare.20.4.545
    1. Salmeron J, Manson JE, Stampfer MJ, Colditz GA, Wing AL, Willett WC. Dietary fiber, glycemic load, and risk of non-insulin-dependent diabetes mellitus in women. J Am Med Assoc. (1997) 277:472–7. 10.1001/jama.277.6.472
    1. Schulze MB, Martinez-Gonzalez MA, Fung TT, Lichtenstein AH, Forouhi NG. Food based dietary patterns and chronic disease prevention. BMJ. (2018) 361:k2396. 10.1136/bmj.k2396
    1. Ceriello A, Taboga C, Tonutti L, Quagliaro L, Piconi L, Bais B, et al. . Evidence for an independent and cumulative effect of postprandial hypertriglyceridemia and hyperglycemia on endothelial dysfunction and oxidative stress generation: effects of short- and long-term simvastatin treatment. Circulation. (2002) 106:1211–8. 10.1161/01.CIR.0000027569.76671.A8
    1. Humpert PM. Oxidative stress and glucose metabolism–is there a need to revisit effects of insulin treatment? Diabetologia. (2010) 53:403–5. 10.1007/s00125-009-1652-9
    1. Node K, Inoue T. Postprandial hyperglycemia as an etiological factor in vascular failure. Cardiovasc Diabetol. (2009) 8:23. 10.1186/1475-2840-8-23
    1. Hanefeld M, Koehler C, Schaper F, Fuecker K, Henkel E, Temelkova-Kurktschiev T. Postprandial plasma glucose is an independent risk factor for increased carotid intima-media thickness in non-diabetic individuals. Atherosclerosis. (1999) 144:229–35.
    1. Le Floch JP, Escuyer P, Baudin E, Baudon D, Perlemuter L. Blood glucose area under the curve. Methodological aspects Diabetes Care. (1990) 13:172–5. 10.2337/diacare.13.2.172
    1. Brouns F, Björck I, Frayn KN, Gibbs AL, Lang V, Slama G, et al. . Glycaemic index methodology. Nutr Res Rev. (2005) 18:145–71. 10.1079/NRR2005100
    1. Ostman E, Samigullin A, Heyman-Linden L, Andersson K, Bjorck I, Oste R, et al. . A novel nutritional supplement containing amino acids and chromium decreases postprandial glucose response in a randomized, double-blind, placebo-controlled study. PLoS One. (2020) 15:e0234237. 10.1371/journal.pone.0234237
    1. Cappon G, Vettoretti M, Sparacino G, Facchinetti A. Continuous glucose monitoring sensors for diabetes management: a review of technologies and applications. Diabetes Metab J. (2019) 43:383–97. 10.4093/dmj.2019.0121
    1. Zisser HC, Bailey TS, Schwartz S, Ratner RE, Wise J. Accuracy of the seven continuous glucose monitoring system: comparison with frequently sampled venous glucose measurements. J Diabetes Sci Technol. (2009) 3:1146–54. 10.1177/193229680900300519
    1. Davis GM, Spanakis EK, Migdal AL, Singh LG, Albury B, Urrutia MA, et al. . Accuracy of dexcom G6 continuous glucose monitoring in non–critically ill hospitalized patients with diabetes. Diabetes Care. (2021) 44:1641–6. 10.2337/dc20-2856
    1. Gómez AM, Umpierrez GE, Muñoz OM, Herrera F, Rubio C, Aschner P, et al. . Continuous glucose monitoring versus capillary point-of-care testing for inpatient glycemic control in type 2 diabetes patients hospitalized in the general ward and treated with a basal bolus insulin regimen. J Diabetes Sci Technol. (2015) 10:325–9. 10.1177/1932296815602905
    1. Samigullin A, Anderson K, Heyman-Lindén L, Öste R, Östman E, Humpert P. A drink containing 5 amino acids and chromium picolinate decreases postprandial glucose: a meta-analysis of controlled trials. Diabetologie und Stoffwechsel. (2018) 13:47. 10.1055/s-0038-1641895
    1. Ostman E, Forslund A, Oste R, Bjorck I. A drink containing amino acids and chromium picolinate improves postprandial glycemia at breakfast in healthy, overweight subjects. Func Foods Health Dis. (2017) 7:88–97. 10.31989/ffhd.v7i2.304
    1. Cheng KC Li Y, Cheng JT. Merit of incremental area under the curve (iauc) in nutrition is varied in pharmacological assay - a review. Clin J Diabetes Care Control. (2018) 1:180008. Available online at:
    1. Hill Jones N. Finding the area under a curve using Jmp and a trapezoidal rule. JMPer Cable. (1997):9–11.
    1. Yeh S-T. Using Trapezoidal Rule for the Area under a Curve Calculation. Buffalo, NY: NESUG; (2022). p. 1–5.
    1. Field A. Discovering Statistics Using IBM SPSS Statistics. London: Sage; (2009).
    1. Committee ADAPP. 6. Glycemic targets: standards of medical care in diabetes-−2022. Diabetes Care. (2021) 45(Supplement. 1):S83–96. 10.2337/dc22-S006
    1. Battelino T, Danne T, Bergenstal RM, Amiel SA, Beck R, Biester T, et al. . Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range. Diabetes Care. (2019) 42:1593–603.
    1. Del Prato S. Loss of early insulin secretion leads to postprandial hyperglycaemia. Diabetologia. (2003) 46:M2–8. 10.1007/s00125-002-0930-6
    1. Meyer C, Pimenta W, Woerle HJ, Van Haeften T, Szoke E, Mitrakou A, et al. . Different mechanisms for impaired fasting glucose and impaired postprandial glucose tolerance in humans. Diabetes Care. (2006) 29:1909–14. 10.2337/dc06-0438
    1. Gunnerud UJ, Heinzle C, Holst JJ, Östman EM, Björck IM. Effects of pre-meal drinks with protein and amino acids on glycemic and metabolic responses at a subsequent composite meal. PLoS One. (2012) 7:e44731. 10.1371/journal.pone.0044731

Source: PubMed

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