The Role of Glycemic Index and Glycemic Load in the Development of Real-Time Postprandial Glycemic Response Prediction Models for Patients With Gestational Diabetes

Evgenii Pustozerov, Aleksandra Tkachuk, Elena Vasukova, Aleksandra Dronova, Ekaterina Shilova, Anna Anopova, Faina Piven, Tatiana Pervunina, Elena Vasilieva, Elena Grineva, Polina Popova, Evgenii Pustozerov, Aleksandra Tkachuk, Elena Vasukova, Aleksandra Dronova, Ekaterina Shilova, Anna Anopova, Faina Piven, Tatiana Pervunina, Elena Vasilieva, Elena Grineva, Polina Popova

Abstract

The incorporation of glycemic index (GI) and glycemic load (GL) is a promising way to improve the accuracy of postprandial glycemic response (PPGR) prediction for personalized treatment of gestational diabetes (GDM). Our aim was to assess the prediction accuracy for PPGR prediction models with and without GI data in women with GDM and healthy pregnant women. The GI values were sourced from University of Sydney's database and assigned to a food database used in the mobile app DiaCompanion. Weekly continuous glucose monitoring (CGM) data for 124 pregnant women (90 GDM and 34 control) were analyzed together with records of 1489 food intakes. Pearson correlation (R) was used to quantify the accuracy of predicted PPGRs from the model relative to those obtained from CGM. The final model for incremental area under glucose curve (iAUC120) prediction chosen by stepwise multiple linear regression had an R of 0.705 when GI/GL was included among input variables and an R of 0.700 when GI/GL was not included. In linear regression with coefficients acquired using regularization methods, which was tested on the data of new patients, R was 0.584 for both models (with and without inclusion of GI/GL). In conclusion, the incorporation of GI and GL only slightly improved the accuracy of PPGR prediction models when used in remote monitoring.

Keywords: blood glucose prediction; gestational diabetes mellitus; glycemic index; postprandial glycemic response.

Conflict of interest statement

The authors declare that there is no conflict of interest.

Figures

Figure 1
Figure 1
Density plot showing paired distribution of glycemic index (GI) and glycemic load (GL)/carbo in all meals included in the study (n = 1489).
Figure 2
Figure 2
Continuous glucose monitoring (CGM) and food diary matching strategy. On the top: black vertical lines: meal starts written in a paper protocol; red line: meal starts as written in the electronic diary; on the bottom: red lines: meal starts chosen for the final sets; upper marks: postprandial glycemic response (PPGR) features; lower marks: meal features. Green dots represent point estimations of blood glucose (BG) levels made with a glucometer used for sensor calibration, and red points correspond to point estimations of BG 1 h after the meal. It can be seen that meals coming as close as 60 min to each other were ignored, as well as records from the electronic diary, which did not have an exact time specified in the protocol.
Figure 3
Figure 3
Correlation coefficients between PPGR characteristics (iAUC120 on the left, BGRise on the right) and carbohydrates/glycemic load. The number next to each point depicts a patient’s individual identifier. In figure (a): cor_gi_iAUC120: correlation between glycemic load and incremental area under glucose curve 2 h after meal start; cor_carbo_iAUC120: correlation between consumed carbohydrates and incremental area under glucose curve. In figure (b): cor_gi_BGRise: correlation between glycemic load and blood glucose rise from meal start to peak value; cor_carbo_BGRise: correlation between consumed carbohydrates and blood glucose rise from meal start to peak value. Orange: GDM group; brown: healthy pregnant participants.
Figure 4
Figure 4
Prediction of iAUC120 on new patients with the regularized regression model (R = 0.584). Orange dots depict PPGRs whose errors are equal or below 1.0 mmol/L·h (92.3%), while brown dots depict those whose errors are above 1.0 mmol/L·h (7.7%).

References

    1. Metzger B.E., Lowe L.P., Dyer A.R., Trimble E.R., Chaovarindr U., Coustan D.R., Hadden D.R., McCance D.R., Hod M., McIntyre H.D., et al. HAPO Study Cooperative Research Group, Hyperglycemia and adverse pregnancy outcomes. N. Engl. J. Med. 2008;358:1991–2002. doi: 10.1056/NEJMoa0707943.
    1. Ben-Haroush A., Yogev Y., Hod M. Epidemiology of gestational diabetes mellitus and its association with Type 2 diabetes. Diabet. Med. 2004;21:103–113. doi: 10.1046/j.1464-5491.2003.00985.x.
    1. Dabelea D. The predisposition to obesity and diabetes in offspring of diabetic mothers. Diabetes Care. 2007;30:S169–S174. doi: 10.2337/dc07-s211.
    1. Hajj N.E., Schneider E., Lehnen H., Haaf T. Epigenetics and life-long consequences of an adverse nutritional and diabetic intrauterine environment. Reproduction. 2014;148:R111–R120. doi: 10.1530/REP-14-0334.
    1. Gallwitz B. Implications of postprandial glucose and weight control in people with type 2 diabetes: Understanding and implementing the International Diabetes Federation guidelines. Diabetes Care. 2009;32:S322–S325. doi: 10.2337/dc09-S331.
    1. Zeevi D., Korem T., Zmora N., Israeli D., Rothschild D., Weinberger A., Ben-Yacov O., Lador D., Avnit-Sagi T., Lotan-Pompan M., et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015;163:1079–1094. doi: 10.1016/j.cell.2015.11.001.
    1. Mendes-Soares H., Raveh-Sadka T., Azulay S., Edens K., Ben-Shlomo Y., Cohen Y., Ofek T., Bachrach D., Stevens J., Colibaseanu D., et al. Assessment of a personalized approach to predicting postprandial glycemic responses to food among individuals without diabetes. JAMA Netw. Open. 2019;2:e188102. doi: 10.1001/jamanetworkopen.2018.8102.
    1. Mendes-Soares H., Raveh-Sadka T., Azulay S., Ben-Shlomo Y., Cohen Y., Ofek T. Model of personalized postprandial glycemic response to food developed for an Israeli cohort predicts responses in Midwestern American individuals. Am. J. Clin. Nutr. 2019:nqz028. doi: 10.1093/ajcn/nqz028.
    1. Plis K., Bunescu R.C., Marling C.R., Shubrook J. A machine learning approach to predicting blood glucose levels for diabetes management; Proceedings of the 28th AAAI Conference on Artificial Intelligence; Quebec City, QC, Canada. 27–31 July 2014; [(accessed on 13 May 2019)]. pp. 35–39. Available online: .
    1. Wang Y., Wu X., Mo X. A novel adaptive-weighted-average framework for blood glucose prediction. Diabetes Technol. Ther. 2013;15:792–801. doi: 10.1089/dia.2013.0104.
    1. Pérez-Gandía C., Facchinetti A., Sparacino G., Cobelli C., Gómez E.J., Rigla M., Rigla M., De Leiva A., Hernando M.E. Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring. Diabetes Technol. Ther. 2010;12:81–88. doi: 10.1089/dia.2009.0076.
    1. Stahl F. Ph.D. Thesis. Department of Automatic Control, Lund Institute of Technology, Lund University; Lund, Sweden: 2012. Diabetes Mellitus Glucose Prediction by Linear and Bayesian Ensemble Modeling; p. 127.
    1. Pustozerov E., Popova P., Tkachuk A., Bolotko Y., Yuldashev Z., Grineva E. Development and evaluation of a mobile personalized blood glucose prediction system for patients with gestational diabetes mellitus. JMIR mHealth uHealth. 2018;6:e6. doi: 10.2196/mhealth.9236.
    1. Dedov I.I., Krasnopol’skiy V.I., Sukhikh G.T. Russian National Consensus Statement on gestational diabetes: Diagnostics, treatment and postnatal care. Diabetes Mellit. 2012;15:4–10. doi: 10.14341/2072-0351-5531.
    1. American Diabetes Association Standards of Medical Care in Diabetes—2019. Diabetes Care. 2019;42:S1–S2. doi: 10.2337/dc19-Sint01.
    1. Bao J., Atkinson F., Petocz P., Willett W.C., Brand-Miller J.C. Prediction of postprandial glycemia and insulinemia in lean, young, healthy adults: Glycemic load compared with carbohydrate content alone. Am. J. Clin. Nutr. 2011;93:984–996. doi: 10.3945/ajcn.110.005033.
    1. Fabricatore A.N., Ebbeling C.B., Wadden T.A., Ludwig D.S. Continuous glucose monitoring to assess the ecologic validity of dietary glycemic index and glycemic load. Am. J. Clin. Nutr. 2011;94:1519–1524. doi: 10.3945/ajcn.111.020354.
    1. Popova P., Vasilyeva L., Tkachuck A., Puzanov M., Golovkin A., Bolotko Y., Pustozerov E., Vasilyeva E., Li O., Zazerskaya I., et al. A Randomised, Controlled Study of Different Glycaemic Targets during Gestational Diabetes Treatment: Effect on the Level of Adipokines in Cord Blood and ANGPTL4 Expression in Human Umbilical Vein Endothelial Cells. Int. J. Endocrinol. 2018:6481658. doi: 10.1155/2018/6481658.
    1. Metzger B.E. International Association of Diabetes and Pregnancy Study Groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care. 2010;33:676–682. doi: 10.2337/dc10-0719.
    1. Pustozerov E., Popova P. Mobile-based decision support system for gestational diabetes mellitus; Proceedings of the 2018 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT); Yekaterinburg, Russia. 7–8 May 2018; pp. 45–48.
    1. Louie J.C.-Y., Flood V., Turner N., Everingham C., Gwynn J. Methodology for adding glycemic index values to 24-h recalls. Nutrition. 2011;27:59–64. doi: 10.1016/j.nut.2009.12.006.
    1. Sydney University Glycemic Index Research Service . [(accessed on 20 May 2019)];2007 Available online: .
    1. Van Rossum A.G. Python Tutorial. Centrum voor Wiskunde en Informatica (CWI); Amsterdam, The Netherlands: 1995. Technical Report CS-R9526.
    1. Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., et al. Scikit-learn: Machine Learning in Python. JMLR. 2011;12:2825–2830.
    1. Ramos-Leví A.M., Pérez-Ferre N., Fernández M.D., Del Valle L., Bordiu E., Bedia A.R., Herraiz M.A., Torrejn M.J., Calle-Pascual A.L. Risk factors for gestational diabetes mellitus in a large population of women living in Spain: Implications for preventative strategies. Int. J. Endocrinol. 2012;2012:312529. doi: 10.1155/2012/312529.
    1. Popova P.V., Klyushina A.A., Vasilyeva L.B., Tkachuk A.S., Bolotko Y.A., Gerasimov A.S., Pustozerov E.A., Kravchuk E.N., Predeus A., Kostareva A.A., et al. Effect of gene-lifestyle interaction on gestational diabetes risk. Oncotarget. 2017;8:112024–112035. doi: 10.18632/oncotarget.22999.
    1. Efron B., Hastie T., Johnstone I., Tibshirani R. Least angle regression. Ann. Stat. 2004;32:407–499. doi: 10.1214/009053604000000067.
    1. Rubinstein R., Zibulevsky M., Elad M. Efficient Implementation of the K-SVD Algorithm Using Batch Orthogonal Matching Pursuit. Technical Report-CS Technion. [(accessed on 15 May 2019)];2008 Available online: .
    1. Hameed M.A. Ph.D. Thesis. Michigan State University; East Lansing, MI, USA: 2012. Comparative Analysis of Orthogonal Matching Pursuit and Least Angle Regression; p. 81. ISBN 1267309776.
    1. Matthan N.R., Ausman L.M., Meng H., Tighiouart H., Lichtenstein A.H. Estimating the reliability of glycemic index values and potential sources of methodological and biological variability. Am. J. Clin. Nutr. 2016;104:1004–1013. doi: 10.3945/ajcn.116.137208.
    1. Wolever T.M. Is glycaemic index (GI) a valid measure of carbohydrate quality? Eur. J. Clin. Nutr. 2013;67:522–531. doi: 10.1038/ejcn.2013.27.
    1. International Standards Organization . Food Products—Determination of the Glycaemic Index (GI) and Recommendation for Food Classification. ISO; Geneva, Switzerland: 2010. ISO 26642.
    1. Wolever T.M., Csima A., Jenkins D.J., Wong G.S., Josse R.G. The glycemic index: Variation between subjects and predictive difference. J. Am. Coll. Nutr. 1989;8:235–247. doi: 10.1080/07315724.1989.10720298.
    1. Venn B.J., Green T.J. Glycemic index and glycemic load: Measurement issues and their effect on diet–disease relationships. Eur. J. Clin. Nutr. 2007;61:S122–S123. doi: 10.1038/sj.ejcn.1602942.
    1. Augustin L.S.A., Kendall C.W.C., Jenkins D.J.A., Willett W.C., Astrup A., Barclay A.W., Björck I., Brand-Miller J.C., Brighenti F., Buyken A.E., 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. 2005;25:795–815. doi: 10.1016/j.numecd.2015.05.005.
    1. Van Bakel M.M., Slimani N., Feskens E.J., Du H., Beulens J.W., van der Schouw Y.T., Brighenti F., Halkjaer J., Cust A.E., Ferrari P., et al. Methodological challenges in the application of the glycemic index in epidemiological studies using data from the European Prospective Investigation into Cancer and Nutrition. J. Nutr. 2009;139:568–575. doi: 10.3945/jn.108.097121.
    1. Wolever T.M., Jenkins D.J., Josse R.G., Wong G.S., Lee R. The glycemic index: Similarity of values derived in insulin-dependent and non-insulin-dependent diabetic patients. J. Am. Coll. Nutr. 1987;6:295–305. doi: 10.1080/07315724.1987.10720191.
    1. Wolever T.M., Chiasson J.-L., Hunt J.A., Palmason C., Ross S.A., Ryn E.A. Similarity of relative glycaemic but not relative insulinaemic responses in normal, IGT and diabetic subjects. Nutr. Res. 1998;18:1667–1676. doi: 10.1016/S0271-5317(98)00149-3.
    1. Jenkins D.J., Kendall C.W., McKeown-Eyssen G., Josse R.G., Silverberg J., Booth G.L., Vidgen E., Josse A.R., Nguyen T.H., Corrigan S., et al. Effect of a low-glycemic index or a high-cereal fiber diet on type 2 diabetes: A randomized trial. JAMA. 2008;300:2742. doi: 10.1001/jama.2008.808.
    1. Brand-Miller J., Hayne S., Petocz P., Colagiuri S. Low-glycemic index diets in the management of diabetes: A meta-analysis of randomized controlled trials. Diabetes Care. 2003;26:2261–2267. doi: 10.2337/diacare.26.8.2261.
    1. Ajala O., English P., Pinkney J. Systematic review and meta- analysis of different dietary approaches to the management of type 2 diabetes. Am. J. Clin. Nutr. 2013;97:505–516. doi: 10.3945/ajcn.112.042457.
    1. Ojo O., Ojo O.O., Wang X.H., Adegboye A.R.A. The Effects of a Low GI Diet on Cardiometabolic and Inflammatory Parameters in Patients with Type 2 and Gestational Diabetes: A Systematic Review and Meta-Analysis of Randomised Controlled Trials. Nutrients. 2019;11:1584. doi: 10.3390/nu11071584.
    1. Larsen T.M., Dalskov S.M., van Baak M., Jebb S.A., Papadaki A., Pfeiffer A.F., Martínez J.A., Handjieva-Darlenska T., Kunesová M., Pihlsgård M., et al. Diets with high or low protein content and glycemic index for weight-loss maintenance. N. Engl. J. Med. 2010;363:2102–2113. doi: 10.1056/NEJMoa1007137.
    1. Kizirian N.V., Goletzke J., Brodie S., Atkinson F.S., Markovic T.P., Ross G.P., Buyken A., Brand-Miller J.P. Lower glycemic load meals reduce diurnal glycemic oscillations in women with risk factors for gestational diabetes. BMJ. Open Diabetes Res. Care. 2017;5:e000351. doi: 10.1136/bmjdrc-2016-000351.
    1. Dunstan D.W., Kingwell B.A., Larsen R., Healy G.N., Cerin E., Hamilton M.T., Shaw J.E., Bertovic D.A., Zimmet P.Z., Salmon J., et al. Breaking up prolonged sitting reduces postprandial glucose and insulin responses. Diabetes Care. 2012;35:976–983. doi: 10.2337/dc11-1931.
    1. Carpenter D., Dhar S., Mitchell L.M., Fu B., Tyson J., Shwan N.A., Yang F., Thomas M.G., Armour J.A. Obesity, starch digestion and amylase: Association between copy number variants at human salivary (AMY1) and pancreatic (AMY2) amylase genes. Hum. Mol. Genet. 2015;24:3472–3480. doi: 10.1093/hmg/ddv098.
    1. Dodd H., Williams S., Brown R., Venn B. Calculating meal glycemic index by using measured and published food values compared with directly measured meal glycemic index. Am. J. Clin. Nutr. 2011;94:992–996. doi: 10.3945/ajcn.111.012138.
    1. Flint A., Moller B.K., Raben A., Pedersen D., Tetens I., Holst J.J., Astrup A. The use of glycaemic index tables to predict glycaemic index of composite breakfast meals. Br. J. Nutr. 2004;91:979–989. doi: 10.1079/BJN20041124.
    1. Hatonen K.A., Virtamo J., Eriksson J.G., Sinkko H.K., Sundvall J.E., Valsta L.M. Protein and fat modify the glycaemic and insulinaemic responses to a mashed potato-based meal. Br. J. Nutr. 2011;106:248–253. doi: 10.1017/S0007114511000080.

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