A Prospective Study on Continuous Glucose Monitoring in Glycogen Storage Disease Type Ia: Toward Glycemic Targets

Alessandro Rossi, Annieke Venema, Petra Haarsma, Lude Feldbrugge, Rob Burghard, David Rodriguez-Buritica, Giancarlo Parenti, Maaike H Oosterveer, Terry G J Derks, Alessandro Rossi, Annieke Venema, Petra Haarsma, Lude Feldbrugge, Rob Burghard, David Rodriguez-Buritica, Giancarlo Parenti, Maaike H Oosterveer, Terry G J Derks

Abstract

Context: Although previous research has shown the benefit of continuous glucose monitoring (CGM) for hepatic glycogen storage diseases (GSDs), current lack of prospectively collected CGM metrics and glycemic targets for CGM-derived outcomes in the hepatic GSD population limits its use.

Objective: To assess CGM metrics for glycemic variation and glycemic control in adult patients with GSDIa as compared to matched healthy volunteers.

Design: Prospective CGM data were collected during the ENGLUPRO GSDIa trial (NCT04311307) in which a Dexcom G6 device was used. Ten adult patients with GSDIa and 10 age-, sex- and body mass index-matched healthy volunteers were enrolled. Capillary blood glucose was concurrently measured during 2 standardized 2-hour time intervals. Descriptive [eg, glycemic variability (GV), time below range, time in range (TIR), time above range (TAR)] and advanced (ie, first- and second-order derivatives, Fourier analysis) CGM outcomes were calculated. For each descriptive CGM outcome measure, 95% CIs were computed in patients with GSDIa and healthy volunteers, respectively.

Results: CGM overestimation was higher under preprandial and level 1 hypoglycemia (ie, capillary glucose values ≥ 3.0 mmol/L and < 3.9 mmol/L) conditions. GV and TAR were higher while TIR was lower in patients with GSDIa compared to healthy volunteers (P < 0.05). Three patients with GSDIa showed descriptive CGM outcomes outside the calculated 95% CI in GSDIa patients. Advanced CGM analysis revealed a distinct pattern (ie, first- and second-order derivatives and glucose curve amplitude) in each of these 3 patients within the patients group.

Conclusions: This is the first study to prospectively compare CGM outcomes between adult patients with GSDIa and matched healthy volunteers. The generation of a set of CGM metrics will provide guidance in using and interpreting CGM data in GSDIa and will be useful for the definition of glycemic targets for CGM in patients with GSDIa. Future studies should investigate the prognostic value of CGM outcomes and their major determinants in patients with GSDIa.

Keywords: continuous glucose monitoring; diet; glycogen storage disease type Ia; management; monitoring; precision medicine.

© The Author(s) 2022. Published by Oxford University Press on behalf of the Endocrine Society.

Figures

Figure 1.
Figure 1.
Bland-Altman plots. Difference between continuous glucose monitoring (CGM) values and capillary blood glucose (CBG) measured by the Freestyle Freedom Lite (Abbott, Chicago, IL, USA) device, expressed as absolute (A, C, E, G, I, and K, in mmol/L) and relative (B, D, F, H, J, and L, in %) values. Y-axis shows the absolute (A, C, E, G, I, and K) or relative (B, D, F, H, J, and L) difference between CBG and CGM values at each study time point. X-axis shows the average glucose value at each study time point. Bias (black thick line), 95% CI (grey thin lines), and regression line (diagonal line, various colors) are shown. (A and B) Data were collected under preprandial/fasted [n = 200 (ie, 10 time points × 20 participants)] and fed [n = 200 (ie, 10 time points × 20 participants)] conditions. (C and D) Data were collected under preprandial/fasted conditions [n = 200 (ie, 10 times points × 20 participants)]. (E and F). Data were collected under postprandial conditions [n = 200 (ie, 10 times points × 20 participants). (G and H) Data were collected under level 1 hypoglycemia (ie, capillary glucose values ≥ 3.0 mmol/L and P < 0.01). (B) Bias: −14.50 ± 14.70; Slope: −0.363 ± 0.629 (P > 0.05). (C) Bias: −0.96 ± 0.70; Slope: −0.28 ± 0.06 (P < 0.001). (D) Bias: −17.6 ± 13.1; Slope: −2.0 ± 1.1 (P = 0.06). (E) Bias: −0.74 ± 1.01; Slope: −0.17 ± 0.06 (P < 0.01). (F) Bias: −11.4 ± 15.5; Slope: −0.6 ± 0.9 (P = 0.50). (G) Bias: −1.08 ± 0.79; Slope: −1.71 ± 0.22 (P < 0.001). (H) Bias: −24.2 ± 18.1; Slope: −38.1 ± 5.6 (P < 0.001). (I) Bias: −0.16 ± 0.05; Slope: −0.83 ± 0.97 (P < 0.01). (J) Bias: −0.19 ± 0.86; Slope: −13.82 ± 16.82 (P = 0.82). (K) Bias: −0.03 ± 0.06; Slope: −0.87 ± 0.74 (P = 0.60). (L) Bias: −1.56 ± 0.95; Slope: −15.07 ± 12.10 (P = 0.10).
Figure 1.
Figure 1.
Bland-Altman plots. Difference between continuous glucose monitoring (CGM) values and capillary blood glucose (CBG) measured by the Freestyle Freedom Lite (Abbott, Chicago, IL, USA) device, expressed as absolute (A, C, E, G, I, and K, in mmol/L) and relative (B, D, F, H, J, and L, in %) values. Y-axis shows the absolute (A, C, E, G, I, and K) or relative (B, D, F, H, J, and L) difference between CBG and CGM values at each study time point. X-axis shows the average glucose value at each study time point. Bias (black thick line), 95% CI (grey thin lines), and regression line (diagonal line, various colors) are shown. (A and B) Data were collected under preprandial/fasted [n = 200 (ie, 10 time points × 20 participants)] and fed [n = 200 (ie, 10 time points × 20 participants)] conditions. (C and D) Data were collected under preprandial/fasted conditions [n = 200 (ie, 10 times points × 20 participants)]. (E and F). Data were collected under postprandial conditions [n = 200 (ie, 10 times points × 20 participants). (G and H) Data were collected under level 1 hypoglycemia (ie, capillary glucose values ≥ 3.0 mmol/L and P < 0.01). (B) Bias: −14.50 ± 14.70; Slope: −0.363 ± 0.629 (P > 0.05). (C) Bias: −0.96 ± 0.70; Slope: −0.28 ± 0.06 (P < 0.001). (D) Bias: −17.6 ± 13.1; Slope: −2.0 ± 1.1 (P = 0.06). (E) Bias: −0.74 ± 1.01; Slope: −0.17 ± 0.06 (P < 0.01). (F) Bias: −11.4 ± 15.5; Slope: −0.6 ± 0.9 (P = 0.50). (G) Bias: −1.08 ± 0.79; Slope: −1.71 ± 0.22 (P < 0.001). (H) Bias: −24.2 ± 18.1; Slope: −38.1 ± 5.6 (P < 0.001). (I) Bias: −0.16 ± 0.05; Slope: −0.83 ± 0.97 (P < 0.01). (J) Bias: −0.19 ± 0.86; Slope: −13.82 ± 16.82 (P = 0.82). (K) Bias: −0.03 ± 0.06; Slope: −0.87 ± 0.74 (P = 0.60). (L) Bias: −1.56 ± 0.95; Slope: −15.07 ± 12.10 (P = 0.10).
Figure 2.
Figure 2.
Overnight continuous glucose monitoring (CGM) values in all glycogen storage disease type Ia patients (dark grey) and healthy volunteers (light grey). Mean (thick line) and 95%CI (shaded area) are shown. X-axis shows the time period (ie, 1:00-5:00 am). Y-axis shows the CGM values.
Figure 3.
Figure 3.
Overnight continuous glucose monitoring (CGM) course in participant 007 (light grey), compared to the average CGM values of the remaining 9 glycogen storage disease type Ia patients (dark grey). Mean (thick line) and 95%CI (shaded area) are shown. X-axis shows the time period (ie, 1:00-5:00 am). Y-axis shows the CGM values.

References

    1. Weinstein DA, Steuerwald U, De Souza CFM, Derks TGJ. Inborn errors of metabolism with hypoglycemia: glycogen storage diseases and inherited disorders of gluconeogenesis. Pediatr Clin North Am. 2018;65(2):247-265. doi:10.1016/j.pcl.2017.11.005
    1. Dambska M, Labrador EB, Kuo CL, Weinstein DA. Prevention of complications in glycogen storage disease type Ia with optimization of metabolic control. Pediatr Diabetes. 2017;18(5):327-331. doi:10.1111/pedi.12540
    1. Kishnani PS, Austin SL, Abdenur JE, et al. . Diagnosis and management of glycogen storage disease type I: a practice guideline of the American College of Medical Genetics and Genomics. Genet Med. 2014;16(11):e1. doi:10.1038/gim.2014.128
    1. Peeks F, Steunenberg TAH, de Boer F, et al. . Clinical and biochemical heterogeneity between patients with glycogen storage disease type IA: the added value of CUSUM for metabolic control. J Inherit Metab Dis. 2017;40(5):695-702. doi:10.1007/s10545-017-0039-1
    1. Vashist S. Continuous glucose monitoring systems: a review. Diagnostics (Basel). 2013;3(4):385-412. doi:10.3390/diagnostics3040385
    1. Battelino T, Danne T, Bergenstal RM, et al. . Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range. Diabetes Care. 2019;42(8):1593-1603. doi:10.2337/dci19-0028
    1. Nano J, Carinci F, Okunade O, et al. . A standard set of person-centred outcomes for diabetes mellitus: results of an international and unified approach. Diabet Med. 2020;37(12):2009-2018. doi:10.1111/dme.14286
    1. Maran A, Crepaldi C, Avogaro A, et al. . Continuous glucose monitoring in conditions other than diabetes. Diabetes Metab Res Rev. 2004;20:S50-S55. doi:10.1002/dmrr.518
    1. Kasapkara CS, Cinasal Demir G, Hasanoglu A, Tümer L. Continuous glucose monitoring in children with glycogen storage disease type I. Eur J Clin Nutr. 2014;68:101-105. doi:10.1038/ejcn.2013.186
    1. Peeks F, Hoogeveen IJ, Feldbrugge RL, et al. . A retrospective in-depth analysis of continuous glucose monitoring datasets for patients with hepatic glycogen storage disease: Recommended outcome parameters for glucose management. J Inherit Metab Dis. 2021;44(5):1136-1150. doi:10.1002/jimd.12383
    1. Rossi A, Venema A, Haarsma, P, et al.Supplemental data for: A prospective study on continuous glucose monitoring in glycogen storage disease type Ia: toward reference values, Dryad. June 01, 2022. 10.5061/dryad.gmsbcc2r2
    1. Wadwa RP, Laffel LM, Shah VN, Garg SK. Accuracy of a factory-calibrated, real-time continuous glucose monitoring system during 10 days of use in youth and adults with diabetes. Diabetes Technol Ther. 2018;20(6):395-402. doi:10.1089/dia.2018.0150
    1. Miller M, Strange P. Use of Fourier models for analysis and interpretation of continuous monitoring glucose profiles. J Diabetes Sci Technol. 2007;1(5):630-638. doi:10.1177/193229680700100506
    1. Rake JP, Visser G, Labrune P, Leonard JV, Ullrich K, Smit GP; European Study on Glycogen Storage Disease Type I (ESGSD I). Guidelines for management of glycogen storage disease type I—European Study on Glycogen Storage Disease Type I (ESGSD I). Eur J Pediatr. 2002;161(suppl 1):S112-S119. doi:10.1007/s00431-002-0999-4
    1. Woldaregay AZ, Årsand E, Botsis T, Albers D, Mamykina L, Hartvigsen G. Data-driven blood glucose pattern classification and anomalies detection: machine-learning applications in type 1 diabetes. J Med Internet Res. 2019;21(5):e11030. doi:10.2196/11030
    1. Kriventsov S, Lindsey A, Hayeri A. The Diabits app for smartphone-assisted predictive monitoring of glycemia inpatients with diabetes: retrospective observational study. JMIR Diabetes. 2020;5(3):e18660. doi:10.2196/18660
    1. Miller EM. Using continuous glucose monitoring in clinical practice. Clin Diabetes. 2020;38(5):429-438. doi:10.2337/cd20-0043
    1. White F, Jones SA. The use of continuous glucose monitoring in the practical management of glycogen storage disorders. J Inherit Metab Dis. 2011;34:631-642.25. doi:10.1007/s10545-011-9335-3
    1. Herbert M, Pendyal S, Rairikar MR, Halaby C, Benjamin RW, Kishnani PS. Role of continuous glucose monitoring in the management of glycogen storage disorders. J Inherit Metab Dis. 2018;41:917-927. doi:10.1007/s10545-018-0200-5
    1. Shah VN, Du Bose SN, Li Z, et al. . Continuous glucose monitoring profiles in healthy nondiabetic participants: a multicenter prospective study. J Clin Endocrinol Metab. 2019;104(10): 4356-4364. Doi: 10.1210/jc.2018-02763. Erratum in: J Clin Endocrinol Metab. 2021;107(4):e1775. Doi: 10.1210/clinem/dgab837
    1. Wilmot EG, Choudhary P, Leelarathna L, Baxter M. Glycaemic variability: the under-recognized therapeutic target in type 1 diabetes care. Diabetes Obes Metab. 2019;21(12):2599-2608. doi:10.1111/dom.13842
    1. Ceriello A. Glucose variability and diabetic complications: is it time to treat? Diabetes Care. 2020;43(6):1169-1171. doi:10.2337/dci20-0012
    1. Subramanian S, Hirsch IB. Diabetic kidney disease: is there a role for glycemic variability? Curr Diab Rep. 2018;18(3):13. doi:10.1007/s11892-018-0979-3
    1. Rajas F, Labrune P, Mithieux G. Glycogen storage disease type 1 and diabetes: learning by comparing and contrasting the two disorders. Diabetes Metab. 2013;39(5):377-387. doi:10.1016/j.diabet.2013.03.002
    1. Rossi A, Ruoppolo M, Formisano P, et al. . Insulin-resistance in glycogen storage disease type Ia: linking carbohydrates and mitochondria? J Inherit Metab Dis. 2018;41(6):985-995. doi:10.1007/s10545-018-0149-4
    1. Kaiser N, Gautschi M, Bosanska L, et al. . Glycemic control and complications in glycogen storage disease type I: results from the Swiss registry. Mol Gen Metab. 2019;126:355-361. doi:10.1016/j.ymgme.2019.02.008
    1. Omar AS, Salama A, Allam M, et al. . Association of time in blood glucose range with outcomes following cardiac surgery. BMC Anesthesiol. 2015;15(1):14. doi:10.1186/1471-2253-15-14
    1. Mayeda L, Katz R, Ahmad I, et al. . Glucose time in range and peripheral neuropathy in type 2 diabetes mellitus and chronic kidney disease. BMJ Open Diabetes Res Care. 2020;8(1):e000991. doi:10.1136/bmjdrc-2019-000991
    1. Ajjan R, Slattery D, Wright E. Continous glucose monitoring: a brief review for primary care practitioners. Adv Ther. 2019;36:579-596. doi:10.1007/s12325-019-0870-x
    1. Dexcom, Inc. Does the Dexcom G6 Continuous Glucose Monitoring (CGM) System require calibrations? Accessed October 10, 2021.
    1. Metzger M, Leibowitz G, Wainstein J, Glaser B, Raz I. Reproducibility of glucose measurements using the glucose sensor. Diabetes Care. 2002;25(7):1185-1191. doi:10.2337/diacare.25.7.1185
    1. Cao J, Choi M, Guadagnin E, et al. . mRNA therapy restores euglycemia and prevents liver tumors in murine model of glycogen storage disease. Nat Commun. 2021;12(1):3090. doi:10.1038/s41467-021-23318-2
    1. Derks TGJ, Rodriguez-Buritica DF, Ahmad A, et al. . Glycogen storage disease type Ia: current management options, burden and unmet needs. Nutrients. 2021;13(11):3828. doi:10.3390/nu13113828
    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(4):383-397. doi:10.4093/dmj.2019.0121

Source: PubMed

3
Subscribe