Characterization of Artifact Influence on the Classification of Glucose Time Series Using Sample Entropy Statistics

[EN] This paper analyses the performance of SampEn and one of its derivatives, Fuzzy Entropy (FuzzyEn), in the context of artifacted blood glucose time series classification. This is a difficult and practically unexplored framework, where the availability of more sensitive and reliable measures could be of great clinical impact. Although the advent of new blood glucose monitoring technologies may reduce the incidence of the problems stated above, incorrect device or sensor manipulation, patient adherence, sensor detachment, time constraints, adoption barriers or affordability can still result in relatively short and artifacted records, as the ones analyzed in this paper or in other similar works. This study is aimed at characterizing the changes induced by such artifacts, enabling the arrangement of countermeasures in advance when possible. Despite the presence of these disturbances, results demonstrate that SampEn and FuzzyEn are sufficiently robust to achieve a significant classification performance, using records obtained from patients with duodenal-jejunal exclusion. The classification results, in terms of area under the ROC of up to 0.9, with several tests yielding AUC values also greater than 0.8, and in terms of a leave-one-out average classification accuracy of 80%, confirm the potential of these measures in this context despite the presence of artifacts, with SampEn having slightly better performance than FuzzyEn.The Czech partners were supported by DROIKEM000023001 and RVOVFN64165. No funding was received to support this research work by the Spanish partners.Cuesta Frau, D.; Novák, D.; Burda, V.; Molina Picó, A.; Vargas-Rojo, B.; Mraz, M.; Kavalkova, P.... (2018). Characterization of Artifact Influence on the Classification of Glucose Time Series Using Sample Entropy Statistics. Entropy. 20(11):1-18. https://doi.org/10.3390/e20110871S118201

Influence of Duodenal-Jejunal Implantation on Glucose Dynamics: A Pilot Study Using Different Nonlinear Methods

[EN] Diabetes is a disease of great and rising prevalence, with the obesity epidemic being a significant contributing risk factor. Duodenal¿jejunal bypass liner (DJBL) is a reversible implant that mimics the effects of more aggressive surgical procedures, such as gastric bypass, to induce weight loss. We hypothesized that DJBL also influences the glucose dynamics in type II diabetes, based on the induced changes already demonstrated in other physiological characteristics and parameters. In order to assess the validity of this assumption, we conducted a quantitative analysis based on several nonlinear algorithms (Lempel¿Ziv Complexity, Sample Entropy, Permutation Entropy, and modified Permutation Entropy), well suited to the characterization of biomedical time series. We applied them to glucose records drawn from two extreme cases available of DJBL implantation: before and after 10 months. The results confirmed the hypothesis and an accuracy of 86.4% was achieved with modified Permutation Entropy. Other metrics also yielded significant classification accuracy results, all above 70%, provided a suitable parameter configuration was chosen. With the Leave¿One¿Out method, the results were very similar, between 72% and 82% classification accuracy. There was also a decrease in entropy of glycaemia records during the time interval studied. These findings provide a solid foundation to assess how glucose metabolism may be influenced by DJBL implantation and opens a new line of research in this field.The Czech clinical partners were supported by DRO IKEM 000023001 and RVO VFN 64165. The Czech technical partners were supported by Research Centre for Informatics grant numbers CZ.02.1.01/0.0/16 - 019/0000765 and SGS16/231/OHK3/3T/13-Support of interactive approaches to biomedical data acquisition and processing. No funding was received to support this research work by the Spanish and British partnersCuesta Frau, D.; Novák, D.; Burda, V.; Abasolo, D.; Adjei, T.; Varela, M.; Vargas, B.... (2019). Influence of Duodenal-Jejunal Implantation on Glucose Dynamics: A Pilot Study Using Different Nonlinear Methods. Complexity. 2019. https://doi.org/10.1155/2019/6070518S2019Kassirer, J. P., & Angell, M. (1998). Losing Weight — An Ill-Fated New Year’s Resolution. New England Journal of Medicine, 338(1), 52-54. doi:10.1056/nejm199801013380109Van Gaal, L., & Dirinck, E. (2016). Pharmacological Approaches in the Treatment and Maintenance of Weight Loss. Diabetes Care, 39(Supplement 2), S260-S267. doi:10.2337/dcs15-3016De Jonge, C., Rensen, S. S., Verdam, F. J., Vincent, R. P., Bloom, S. R., Buurman, W. A., … Greve, J. W. M. (2015). Impact of Duodenal-Jejunal Exclusion on Satiety Hormones. Obesity Surgery, 26(3), 672-678. doi:10.1007/s11695-015-1889-yMuñoz, R., Dominguez, A., Muñoz, F., Muñoz, C., Slako, M., Turiel, D., … Escalona, A. (2013). Baseline glycated hemoglobin levels are associated with duodenal-jejunal bypass liner-induced weight loss in obese patients. Surgical Endoscopy, 28(4), 1056-1062. doi:10.1007/s00464-013-3283-yOgata, H., Tokuyama, K., Nagasaka, S., Ando, A., Kusaka, I., Sato, N., … Yamamoto, Y. (2007). Long-range Correlated Glucose Fluctuations in Diabetes. Methods of Information in Medicine, 46(02), 222-226. doi:10.1055/s-0038-1625411Rodríguez de Castro, C., Vigil, L., Vargas, B., García Delgado, E., García Carretero, R., Ruiz-Galiana, J., & Varela, M. (2016). Glucose time series complexity as a predictor of type 2 diabetes. Diabetes/Metabolism Research and Reviews, 33(2), e2831. doi:10.1002/dmrr.2831DeFronzo, R. A. (2004). Pathogenesis of type 2 diabetes mellitus. Medical Clinics of North America, 88(4), 787-835. doi:10.1016/j.mcna.2004.04.013Zhang, X.-S., Roy, R. J., & Jensen, E. W. (2001). EEG complexity as a measure of depth of anesthesia for patients. IEEE Transactions on Biomedical Engineering, 48(12), 1424-1433. doi:10.1109/10.966601Bandt, C., & Pompe, B. (2002). Permutation Entropy: A Natural Complexity Measure for Time Series. Physical Review Letters, 88(17). doi:10.1103/physrevlett.88.174102Bian, C., Qin, C., Ma, Q. D. Y., & Shen, Q. (2012). Modified permutation-entropy analysis of heartbeat dynamics. Physical Review E, 85(2). doi:10.1103/physreve.85.021906Zhao, L., Wei, S., Zhang, C., Zhang, Y., Jiang, X., Liu, F., & Liu, C. (2015). Determination of Sample Entropy and Fuzzy Measure Entropy Parameters for Distinguishing Congestive Heart Failure from Normal Sinus Rhythm Subjects. Entropy, 17(12), 6270-6288. doi:10.3390/e17096270Weinstein, R. L., Schwartz, S. L., Brazg, R. L., Bugler, J. R., Peyser, T. A., & McGarraugh, G. V. (2007). Accuracy of the 5-Day FreeStyle Navigator Continuous Glucose Monitoring System: Comparison with frequent laboratory reference measurements. Diabetes Care, 30(5), 1125-1130. doi:10.2337/dc06-1602Weber, C., & Schnell, O. (2009). The Assessment of Glycemic Variability and Its Impact on Diabetes-Related Complications: An Overview. Diabetes Technology & Therapeutics, 11(10), 623-633. doi:10.1089/dia.2009.0043Cuesta-Frau, D., Miró-Martínez, P., Oltra-Crespo, S., Jordán-Núñez, J., Vargas, B., González, P., & Varela-Entrecanales, M. (2018). Model Selection for Body Temperature Signal Classification Using Both Amplitude and Ordinality-Based Entropy Measures. Entropy, 20(11), 853. doi:10.3390/e20110853Cuesta–Frau, D., Miró–Martínez, P., Oltra–Crespo, S., Jordán–Núñez, J., Vargas, B., & Vigil, L. (2018). Classification of glucose records from patients at diabetes risk using a combined permutation entropy algorithm. Computer Methods and Programs in Biomedicine, 165, 197-204. doi:10.1016/j.cmpb.2018.08.018Cuesta–Frau, D., Varela–Entrecanales, M., Molina–Picó, A., & Vargas, B. (2018). Patterns with Equal Values in Permutation Entropy: Do They Really Matter for Biosignal Classification? Complexity, 2018, 1-15. doi:10.1155/2018/1324696Mayer, C. C., Bachler, M., Hörtenhuber, M., Stocker, C., Holzinger, A., & Wassertheurer, S. (2014). Selection of entropy-measure parameters for knowledge discovery in heart rate variability data. BMC Bioinformatics, 15(S6). doi:10.1186/1471-2105-15-s6-s2Sheng Lu, Xinnian Chen, Kanters, J. K., Solomon, I. C., & Chon, K. H. (2008). Automatic Selection of the Threshold Value $r$ for Approximate Entropy. IEEE Transactions on Biomedical Engineering, 55(8), 1966-1972. doi:10.1109/tbme.2008.919870Crenier, L., Lytrivi, M., Van Dalem, A., Keymeulen, B., & Corvilain, B. (2016). Glucose Complexity Estimates Insulin Resistance in Either Nondiabetic Individuals or in Type 1 Diabetes. The Journal of Clinical Endocrinology & Metabolism, 101(4), 1490-1497. doi:10.1210/jc.2015-4035Cuesta, D., Varela, M., Miró, P., Galdós, P., Abásolo, D., Hornero, R., & Aboy, M. (2007). Predicting survival in critical patients by use of body temperature regularity measurement based on approximate entropy. Medical & Biological Engineering & Computing, 45(7), 671-678. doi:10.1007/s11517-007-0200-3Chen, W., Zhuang, J., Yu, W., & Wang, Z. (2009). Measuring complexity using FuzzyEn, ApEn, and SampEn. Medical Engineering & Physics, 31(1), 61-68. doi:10.1016/j.medengphy.2008.04.005Xiao-Feng, L., & Yue, W. (2009). Fine-grained permutation entropy as a measure of natural complexity for time series. Chinese Physics B, 18(7), 2690-2695. doi:10.1088/1674-1056/18/7/011Fadlallah, B., Chen, B., Keil, A., & Príncipe, J. (2013). Weighted-permutation entropy: A complexity measure for time series incorporating amplitude information. Physical Review E, 87(2). doi:10.1103/physreve.87.02291

Influence of glucometric 'dynamical' variables on Duodenal-Jejunal Bypass Liner (DJBL) anthropometric and metabolic outcomes

[EN] Background The endoscopically implanted duodenal-jejunal bypass liner (DJBL) is an attractive alternative to bariatric surgery for obese diabetic patients. This article aims to study dynamical aspects of the glycaemic profile that may influence DJBL effects. Methods Thirty patients underwent DJBL implantation and were followed for 10 months. Continuous glucose monitoring (CGM) was performed before implantation and at month 10. Dynamical variables from CGM were measured: coefficient of variation of glycaemia, mean amplitude of glycaemic excursions (MAGE), detrended fluctuation analysis (DFA), % of time with glycaemia under 6.1 mmol/L (TU6.1), area over 7.8 mmol/L (AO7.8) and time in range. We analysed the correlation between changes in both anthropometric (body mass index, BMI and waist circumference) and metabolic (fasting blood glucose, FBG and HbA1c) variables and dynamical CGM-derived metrics and searched for variables in the basal CGM that could predict successful outcomes. Results There was a poor correlation between anthropometric and metabolic outcomes. There was a strong correlation between anthropometric changes and changes in glycaemic tonic control ( increment BMI- increment TU6.1: rho = - 0.67, P < .01) and between metabolic outcomes and glycaemic phasic control ( increment FBG- increment AO7.8: r = .60, P < .01). Basal AO7.8 was a powerful predictor of successful metabolic outcome (0.85 in patients with AO7.8 above the median vs 0.31 in patients with AO7.8 below the median: Chi-squared = 5.67, P = .02). Conclusions In our population, anthropometric outcomes of DJBL correlate with improvement in tonic control of glycaemia, while metabolic outcomes correlate preferentially with improvement in phasic control. Assessment of basal phasic control may help in candidate profiling for DJBL implantation.Research Center for Informatics, Grant/Award Number: CZ.02.1.01/0.0/0.0/16_019/0000765; Biomedical data acquisition, processing and visualization, Grant/Award Number: SGS19/171/OHK3/3T/13; MH CZ - DRO ("IKEM, IN 00023001"); RVO VFN64165Colás, A.; Varela, M.; Mraz, M.; Novak, D.; Cuesta Frau, D.; Vigil, L.; Benes, M.... (2020). Influence of glucometric 'dynamical' variables on Duodenal-Jejunal Bypass Liner (DJBL) anthropometric and metabolic outcomes. Diabetes/Metabolism Research and Reviews. 36(4):1-9. https://doi.org/10.1002/dmrr.3287S19364O’Rahilly, S., & Savill, J. (1997). Science, medicine, and the future Non-insulin dependent diabetes mellitus: the gathering storm. BMJ, 314(7085), 955-955. doi:10.1136/bmj.314.7085.955World Health Organization.Global Health Risks: Mortality and Burden of Disease Attributable to Selected Major Risks.Geneva:World Health Organization.2009; 62 p.Hossain, P., Kawar, B., & El Nahas, M. (2007). Obesity and Diabetes in the Developing World — A Growing Challenge. New England Journal of Medicine, 356(3), 213-215. doi:10.1056/nejmp068177Ogurtsova, K., da Rocha Fernandes, J. D., Huang, Y., Linnenkamp, U., Guariguata, L., Cho, N. H., … Makaroff, L. E. (2017). IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes Research and Clinical Practice, 128, 40-50. doi:10.1016/j.diabres.2017.03.024Beagley, J., Guariguata, L., Weil, C., & Motala, A. A. (2014). Global estimates of undiagnosed diabetes in adults. Diabetes Research and Clinical Practice, 103(2), 150-160. doi:10.1016/j.diabres.2013.11.001Zimmet, P., Alberti, K. G. M. M., & Shaw, J. (2001). Global and societal implications of the diabetes epidemic. Nature, 414(6865), 782-787. doi:10.1038/414782aChatterjee, S., Khunti, K., & Davies, M. J. (2017). Type 2 diabetes. The Lancet, 389(10085), 2239-2251. doi:10.1016/s0140-6736(17)30058-2Haffner, S. M., Lehto, S., Rönnemaa, T., Pyörälä, K., & Laakso, M. (1998). Mortality from Coronary Heart Disease in Subjects with Type 2 Diabetes and in Nondiabetic Subjects with and without Prior Myocardial Infarction. New England Journal of Medicine, 339(4), 229-234. doi:10.1056/nejm199807233390404Rubino, F., & Cummings, D. E. (2012). The coming of age of metabolic surgery. Nature Reviews Endocrinology, 8(12), 702-704. doi:10.1038/nrendo.2012.207Pournaras, D. J., Glicksman, C., Vincent, R. P., Kuganolipava, S., Alaghband-Zadeh, J., Mahon, D., … le Roux, C. W. (2012). The Role of Bile After Roux-en-Y Gastric Bypass in Promoting Weight Loss and Improving Glycaemic Control. Endocrinology, 153(8), 3613-3619. doi:10.1210/en.2011-2145Cummings, D. E. (2009). Endocrine mechanisms mediating remission of diabetes after gastric bypass surgery. International Journal of Obesity, 33(S1), S33-S40. doi:10.1038/ijo.2009.15Ribaric, G., Buchwald, J. N., & McGlennon, T. W. (2013). Diabetes and Weight in Comparative Studies of Bariatric Surgery vs Conventional Medical Therapy: A Systematic Review and Meta-Analysis. Obesity Surgery, 24(3), 437-455. doi:10.1007/s11695-013-1160-3Kwok, C. S., Pradhan, A., Khan, M. A., Anderson, S. G., Keavney, B. D., Myint, P. K., … Loke, Y. K. (2014). Bariatric surgery and its impact on cardiovascular disease and mortality: A systematic review and meta-analysis. International Journal of Cardiology, 173(1), 20-28. doi:10.1016/j.ijcard.2014.02.026Rubino, F., Nathan, D. M., Eckel, R. H., Schauer, P. R., Alberti, K. G. M. M., Zimmet, P. Z., … Cummings, D. E. (2016). Metabolic Surgery in the Treatment Algorithm for Type 2 Diabetes: A Joint Statement by International Diabetes Organizations. Diabetes Care, 39(6), 861-877. doi:10.2337/dc16-0236Afonso, B. B., Rosenthal, R., Li, K. M., Zapatier, J., & Szomstein, S. (2010). Perceived barriers to bariatric surgery among morbidly obese patients. Surgery for Obesity and Related Diseases, 6(1), 16-21. doi:10.1016/j.soard.2009.07.006Patel, S. R., Mason, J., & Hakim, N. (2012). The Duodenal-Jejunal Bypass Sleeve (EndoBarrier Gastrointestinal Liner) for Weight Loss and Treatment of Type II Diabetes. Indian Journal of Surgery, 74(4), 275-277. doi:10.1007/s12262-012-0721-3Kumar, N. (2016). Weight loss endoscopy: Development, applications, and current status. World Journal of Gastroenterology, 22(31), 7069. doi:10.3748/wjg.v22.i31.7069Sullivan, S., Edmundowicz, S. A., & Thompson, C. C. (2017). Endoscopic Bariatric and Metabolic Therapies: New and Emerging Technologies. Gastroenterology, 152(7), 1791-1801. doi:10.1053/j.gastro.2017.01.044Rohde, U., Hedbäck, N., Gluud, L. L., Vilsbøll, T., & Knop, F. K. (2016). Effect of the EndoBarrier Gastrointestinal Liner on obesity and type 2 diabetes: a systematic review and meta-analysis. Diabetes, Obesity and Metabolism, 18(3), 300-305. doi:10.1111/dom.12603Rodriguez-Grunert, L., Galvao Neto, M. P., Alamo, M., Ramos, A. C., Baez, P. B., & Tarnoff, M. (2008). First human experience with endoscopically delivered and retrieved duodenal-jejunal bypass sleeve. Surgery for Obesity and Related Diseases, 4(1), 55-59. doi:10.1016/j.soard.2007.07.012Rodriguez, L., Reyes, E., Fagalde, P., Oltra, M. S., Saba, J., Aylwin, C. G., … Sorli, C. (2009). Pilot Clinical Study of an Endoscopic, Removable Duodenal-Jejunal Bypass Liner for the Treatment of Type 2 Diabetes. Diabetes Technology & Therapeutics, 11(11), 725-732. doi:10.1089/dia.2009.0063Escalona, A., Pimentel, F., Sharp, A., Becerra, P., Slako, M., Turiel, D., … Gersin, K. (2012). Weight Loss and Metabolic Improvement in Morbidly Obese Subjects Implanted for 1 Year With an Endoscopic Duodenal-Jejunal Bypass Liner. Annals of Surgery, 255(6), 1080-1085. doi:10.1097/sla.0b013e31825498c4De Jonge, C., Rensen, S. S., Verdam, F. J., Vincent, R. P., Bloom, S. R., Buurman, W. A., … Greve, J. W. M. (2013). Endoscopic Duodenal–Jejunal Bypass Liner Rapidly Improves Type 2 Diabetes. Obesity Surgery, 23(9), 1354-1360. doi:10.1007/s11695-013-0921-3Cohen, R., le Roux, C. W., Papamargaritis, D., Salles, J. E., Petry, T., Correa, J. L., … Sorli, C. (2013). Role of proximal gut exclusion from food on glucose homeostasis in patients with Type 2 diabetes. Diabetic Medicine, 30(12), 1482-1486. doi:10.1111/dme.12268Haluzík, M., Kratochvílová, H., Haluzíková, D., & Mráz, M. (2018). Gut as an emerging organ for the treatment of diabetes: focus on mechanism of action of bariatric and endoscopic interventions. Journal of Endocrinology, 237(1), R1-R17. doi:10.1530/joe-17-0438El Khoury, L., Chouillard, E., Chahine, E., Saikaly, E., Debs, T., & Kassir, R. (2018). Metabolic Surgery and Diabesity: a Systematic Review. Obesity Surgery, 28(7), 2069-2077. doi:10.1007/s11695-018-3252-6Thaler, J. P., & Cummings, D. E. (2009). Hormonal and Metabolic Mechanisms of Diabetes Remission after Gastrointestinal Surgery. Endocrinology, 150(6), 2518-2525. doi:10.1210/en.2009-0367Kaválková, P., Mráz, M., Trachta, P., Kloučková, J., Cinkajzlová, A., Lacinová, Z., … Haluzík, M. (2016). Endocrine effects of duodenal–jejunal exclusion in obese patients with type 2 diabetes mellitus. Journal of Endocrinology, 231(1), 11-22. doi:10.1530/joe-16-0206Mingrone, G., Panunzi, S., De Gaetano, A., Guidone, C., Iaconelli, A., Leccesi, L., … Rubino, F. (2012). Bariatric Surgery versus Conventional Medical Therapy for Type 2 Diabetes. New England Journal of Medicine, 366(17), 1577-1585. doi:10.1056/nejmoa1200111Mingrone, G., Panunzi, S., De Gaetano, A., Guidone, C., Iaconelli, A., Nanni, G., … Rubino, F. (2015). Bariatric–metabolic surgery versus conventional medical treatment in obese patients with type 2 diabetes: 5 year follow-up of an open-label, single-centre, randomised controlled trial. The Lancet, 386(9997), 964-973. doi:10.1016/s0140-6736(15)00075-6Sjöström, L., Peltonen, M., Jacobson, P., Ahlin, S., Andersson-Assarsson, J., Anveden, Å., … Carlsson, L. M. S. (2014). Association of Bariatric Surgery With Long-term Remission of Type 2 Diabetes and With Microvascular and Macrovascular Complications. JAMA, 311(22), 2297. doi:10.1001/jama.2014.5988Monnier, L., Mas, E., Ginet, C., Michel, F., Villon, L., Cristol, J.-P., & Colette, C. (2006). Activation of Oxidative Stress by Acute Glucose Fluctuations Compared With Sustained Chronic Hyperglycemia in Patients With Type 2 Diabetes. JAMA, 295(14), 1681. doi:10.1001/jama.295.14.1681Ceriello, A., Esposito, K., Piconi, L., Ihnat, M. A., Thorpe, J. E., Testa, R., … Giugliano, D. (2008). Oscillating Glucose Is More Deleterious to Endothelial Function and Oxidative Stress Than Mean Glucose in Normal and Type 2 Diabetic Patients. Diabetes, 57(5), 1349-1354. doi:10.2337/db08-0063Di Flaviani, A., Picconi, F., Di Stefano, P., Giordani, I., Malandrucco, I., Maggio, P., … Frontoni, S. (2011). Impact of Glycemic and Blood Pressure Variability on Surrogate Measures of Cardiovascular Outcomes in Type 2 Diabetic Patients. Diabetes Care, 34(7), 1605-1609. doi:10.2337/dc11-0034Nusca, A., Tuccinardi, D., Albano, M., Cavallaro, C., Ricottini, E., Manfrini, S., … Di Sciascio, G. (2018). Glycemic variability in the development of cardiovascular complications in diabetes. Diabetes/Metabolism Research and Reviews, 34(8), e3047. doi:10.1002/dmrr.3047Dungan, K. M., Binkley, P., Nagaraja, H. N., Schuster, D., & Osei, K. (2011). The effect of glycaemic control and glycaemic variability on mortality in patients hospitalized with congestive heart failure. Diabetes/Metabolism Research and Reviews, 27(1), 85-93. doi:10.1002/dmrr.1155Monnier, L., Colette, C., & Owens, D. R. (2009). Integrating glycaemic variability in the glycaemic disorders of type 2 diabetes: a move towards a unified glucose tetrad concept. Diabetes/Metabolism Research and Reviews, 25(5), 393-402. doi:10.1002/dmrr.962Zaccardi, F., Pitocco, D., & Ghirlanda, G. (2009). Glycemic risk factors of diabetic vascular complications: the role of glycemic variability. Diabetes/Metabolism Research and Reviews, 25(3), 199-207. doi:10.1002/dmrr.938Frontoni, S., Di Bartolo, P., Avogaro, A., Bosi, E., Paolisso, G., & Ceriello, A. (2013). Glucose variability: An emerging target for the treatment of diabetes mellitus. Diabetes Research and Clinical Practice, 102(2), 86-95. doi:10.1016/j.diabres.2013.09.007Service, F. J., Molnar, G. D., Rosevear, J. W., Ackerman, E., Gatewood, L. C., & Taylor, W. F. (1970). Mean Amplitude of Glycemic Excursions, a Measure of Diabetic Instability. Diabetes, 19(9), 644-655. doi:10.2337/diab.19.9.644Freire, A. X., & Murillo, L. C. (2010). How «sweet» complexity is and how «bitter» variability can be; the new aspect of intensive care unit hyperglycemia*. Critical Care Medicine, 38(3), 996-997. doi:10.1097/ccm.0b013e3181ce217eLundelin, K., Vigil, L., Bua, S., Gomez-Mestre, I., Honrubia, T., & Varela, M. (2010). Differences in complexity of glycemic profile in survivors and nonsurvivors in an intensive care unit: A pilot study*. Critical Care Medicine, 38(3), 849-854. doi:10.1097/ccm.0b013e3181ce49cfVarela, M. (2008). The route to diabetes: Loss of complexity in the glycemic profile from health through the metabolic syndrome to type 2 diabetes. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, Volume 1, 3-11. doi:10.2147/dmso.s3812Peng, C. ‐K., Havlin, S., Stanley, H. E., & Goldberger, A. L. (1995). Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos: An Interdisciplinary Journal of Nonlinear Science, 5(1), 82-87. doi:10.1063/1.166141Ogata, H., Tokuyama, K., Nagasaka, S., Tsuchita, T., Kusaka, I., Ishibashi, S., … Yamamoto, Y. (2012). The lack of long-range negative correlations in glucose dynamics is associated with worse glucose control in patients with diabetes mellitus. Metabolism, 61(7), 1041-1050. doi:10.1016/j.metabol.2011.12.007Rodríguez de Castro, C., Vigil, L., Vargas, B., García Delgado, E., García Carretero, R., Ruiz-Galiana, J., & Varela, M. (2016). Glucose time series complexity as a predictor of type 2 diabetes. Diabetes/Metabolism Research and Reviews, 33(2), e2831. doi:10.1002/dmrr.2831Abdul-Ghani, M. A., Williams, K., DeFronzo, R., & Stern, M. (2006). Risk of Progression to Type 2 Diabetes Based on Relationship Between Postload Plasma Glucose and Fasting Plasma Glucose. Diabetes Care, 29(7), 1613-1618. doi:10.2337/dc05-1711Nathan, D. M., Davidson, M. B., DeFronzo, R. A., Heine, R. J., Henry, R. R., Pratley, R., & Zinman, B. (2007). Impaired Fasting Glucose and Impaired Glucose Tolerance: Implications for care. Diabetes Care, 30(3), 753-759. doi:10.2337/dc07-9920Abdul-Ghani, M. A., Tripathy, D., & DeFronzo, R. A. (2006). Contributions of  -Cell Dysfunction and Insulin Resistance to the Pathogenesis of Impaired Glucose Tolerance and Impaired Fasting Glucose. Diabetes Care, 29(5), 1130-1139. doi:10.2337/dc05-2179Colas, A., Vigil, L., Rodríguez de Castro, C., Vargas, B., & Varela, M. (2018). New insights from continuous glucose monitoring into the route to diabetes. Diabetes/Metabolism Research and Reviews, 34(5), e3002. doi:10.1002/dmrr.3002Meyer, C., Pimenta, W., Woerle, H. J., Van Haeften, T., Szoke, E., Mitrakou, A., & Gerich, J. (2006). Different Mechanisms for Impaired Fasting Glucose and Impaired Postprandial Glucose Tolerance in Humans. Diabetes Care, 29(8), 1909-1914. doi:10.2337/dc06-0438Charles, M. A., Fontbonne, A., Thibult, N., Warnet, J.-M., Rosselin, G. E., & Eschwege, E. (1991). Risk Factors for NIDDM in White Population: Paris Prospective Study. Diabetes, 40(7), 796-799. doi:10.2337/diab.40.7.796Staimez, L. R., Weber, M. B., Ranjani, H., Ali, M. K., Echouffo-Tcheugui, J. B., Phillips, L. S., … Narayan, K. M. V. (2013). Evidence of Reduced β-Cell Function in Asian Indians With Mild Dysglycemia. Diabetes Care, 36(9), 2772-2778. doi:10.2337/dc12-2290Danne, T., Nimri, R., Battelino, T., Bergenstal, R. M., Close, K. L., DeVries, J. H., … Phillip, M. (2017). International Consensus on Use of Continuous Glucose Monitoring. Diabetes Care, 40(12), 1631-1640. doi:10.2337/dc17-1600(2018). 7. Diabetes Technology: Standards of Medical Care in Diabetes—2019. Diabetes Care, 42(Supplement 1), S71-S80. doi:10.2337/dc19-s007Lu, J., Ma, X., Zhou, J., Zhang, L., Mo, Y., Ying, L., … Jia, W. (2018). Association of Time in Range, as Assessed by Continuous Glucose Monitoring, With Diabetic Retinopathy in Type 2 Diabetes. Diabetes Care, 41(11), 2370-2376. doi:10.2337/dc18-1131Dixon, J. B., & O’Brien, P. E. (2002). Health Outcomes of Severely Obese Type 2 Diabetic Subjects 1 Year After Laparoscopic Adjustable Gastric Banding. Diabetes Care, 25(2), 358-363. doi:10.2337/diacare.25.2.358Beck, R. W., Bergenstal, R. M., Riddlesworth, T. D., Kollman, C., Li, Z., Brown, A. S., & Close, K. L. (2018). Validation of Time in Range as an Outcome Measure for Diabetes Clinical Trials. Diabetes Care, 42(3), 400-405. doi:10.2337/dc18-1444(2018). Need for Regulatory Change to Incorporate Beyond A1C Glycemic Metrics. Diabetes Care, 41(6), e92-e94. doi:10.2337/dci18-0010Advani, A. (2019). Positioning time in range in diabetes management. Diabetologia, 63(2), 242-252. doi:10.1007/s00125-019-05027-0Kovatchev, B. P. (2017). Metrics for glycaemic control — from HbA1c to continuous glucose monitoring. Nature Reviews Endocrinology, 13(7), 425-436. doi:10.1038/nrendo.2017.3Narayan, K. M. V. (2016). Type 2 Diabetes: Why We Are Winning the Battle but Losing the War? 2015 Kelly West Award Lecture. Diabetes Care, 39(5), 653-663. doi:10.2337/dc16-0205Bonora, E., Targher, G., Alberiche, M., Bonadonna, R. C., Saggiani, F., Zenere, M. B., … Muggeo, M. (2000). Homeostasis model assessment closely mirrors the glucose clamp technique in the assessment of insulin sensitivity: studies in subjects with various degrees of glucose tolerance and insulin sensitivity. Diabetes Care, 23(1), 57-63. doi:10.2337/diacare.23.1.57Schouten, R., Rijs, C. S., Bouvy, N. D., Hameeteman, W., Koek, G. H., Janssen, I. M. C., & Greve, J.-W. M. (2010). A Multicenter, Randomized Efficacy Study of the EndoBarrier Gastrointestinal Liner for Presurgical Weight Loss Prior to Bariatric Surgery. Annals of Surgery, 251(2), 236-243. doi:10.1097/sla.0b013e3181bdfbffWood, G. C., Mirshahi, T., Still, C. D., & Hirsch, A. G. (2016). Association of DiaRem Score With Cure of Type 2 Diabetes Following Bariatric Surgery. JAMA Surgery, 151(8), 779. doi:10.1001/jamasurg.2016.0251Klonoff, D. C. (2005). Continuous Glucose Monitoring: Roadmap for 21st century diabetes therapy. Diabetes Care, 28(5), 1231-1239. doi:10.2337/diacare.28.5.1231Rodbard, D. (2016). Continuous Glucose Monitoring: A Review of Successes, Challenges, and Opportunities. Diabetes Technology & Therapeutics, 18(S2), S2-3-S2-13. doi:10.1089/dia.2015.0417Buchwald, H., Avidor, Y., Braunwald, E., Jensen, M. D., Pories, W., Fahrbach, K., & Schoelles, K. (2004). Bariatric Surgery. JAMA, 292(14), 1724. doi:10.1001/jama.292.14.1724Koehestanie, P., Betzel, B., Aarts, E. O., Janssen, I. M. C., Wahab, P., & Berends, F. J. (2015). Is reimplantation of the duodenal-jejunal bypass liner feasible? Surgery for Obesity and Related Diseases, 11(5), 1099-1104. doi:10.1016/j.soard.2015.01.01