Dietary determinants of postprandial blood glucose control in adults with type 1 diabetes on a hybrid closed-loop system

Claudia Vetrani, Ilaria Calabrese, Luisa Cavagnuolo, Daniela Pacella, Elsa Napolano, Silvia Di Rienzo, Gabriele Riccardi, Angela A Rivellese, Giovanni Annuzzi, Lutgarda Bozzetto, Claudia Vetrani, Ilaria Calabrese, Luisa Cavagnuolo, Daniela Pacella, Elsa Napolano, Silvia Di Rienzo, Gabriele Riccardi, Angela A Rivellese, Giovanni Annuzzi, Lutgarda Bozzetto

Abstract

Aims/hypothesis: The aim of this work was to assess the relationship between meal nutrients and postprandial blood glucose response (PGR) in individuals with type 1 diabetes on a hybrid closed-loop system (HCLS).

Methods: The dietary composition of 1264 meals (398 breakfasts, 441 lunches and 425 dinners) was assessed by 7-day food records completed by 25 individuals with type 1 diabetes on HCLSs (12 men/13 women, mean ± SD age 40 ± 12 years, mean ± SD HbA1c 51 ± 10 mmol/mol [6.9 ± 0.2%]). For each meal, PGR (continuous glucose monitoring metrics, glucose incremental AUCs) and insulin doses (pre-meal boluses, post-meal microboluses automatically delivered by the pump and adjustment boluses) over 6 h were evaluated.

Results: Breakfast, lunch and dinner significantly differed with respect to energy and nutrient intake and insulin doses. The blood glucose postprandial profile showed an earlier peak after breakfast and a slow increase until 4 h after lunch and dinner (p < 0.001). Mean ± SD postprandial time in range (TIR) was better at breakfast (79.3 ± 22.2%) than at lunch (71.3 ± 23.9%) or dinner (70.0 ± 25.9%) (p < 0.001). Significant negative predictors of TIR at breakfast were total energy intake, per cent intake of total protein and monounsaturated fatty acids, glycaemic load and absolute amounts of cholesterol, carbohydrates and simple sugars consumed (p < 0.05 for all). No significant predictors were detected for TIR at lunch. For TIR at dinner, a significant positive predictor was the per cent intake of plant proteins, while negative predictors were glycaemic load and intake amounts of simple sugars and carbohydrate (p < 0.05 for all).

Conclusions/interpretation: This study shows that nutritional factors other than the amount of carbohydrate significantly influence postprandial blood glucose control. These nutritional determinants vary between breakfast, lunch and dinner, with differing effects on postprandial blood glucose profile and insulin requirements, thus remaining a challenge to HCLSs.

Keywords: CGM; Diet composition; Hybrid closed-loop system; Insulin delivery; Postprandial glucose; Type 1 diabetes.

© 2021. The Author(s).

Figures

Fig. 1
Fig. 1
Blood glucose concentrations (continuous line, mean ± SE) and insulin doses delivered by the pump automatically as post-meal microboluses and as adjustment boluses delivered by the participant according to HCLS suggestion (vertical bars) at breakfast (n = 398) (a), lunch (n = 441) (b) and dinner (n = 425) (c)
Fig. 2
Fig. 2
CGM metrics at breakfast, lunch and dinner. Means ± SD; *p < 0.05 vs breakfast (mixed-effect model with post hoc Bonferroni test for multiple comparisons)

References

    1. American Diabetes Association Lifestyle management: standards of medical Care in Diabetes. Diabetes Care. 2019;42:S46–S60. doi: 10.2337/dc19-S005.
    1. Ceriello A, Colagiuri S. International diabetes federation guideline for management of postmeal glucose: a review of recommendations. Diabet Med. 2008;25:1151–1156. doi: 10.1111/j.1464-5491.2008.02565.x.
    1. Bell KJ, Smart CE, Steil GM, Brand-Miller JC, King B, Wolpert HA. Impact of fat, protein, and glycaemic index on postprandial glucose control in type 1 diabetes: implications for intensive diabetes management in the continuous glucose monitoring era. Diabetes Care. 2015;3:1008–1015. doi: 10.2337/dc15-0100.
    1. Paterson MA, Smart CEM, Lopez PE, et al. Increasing the protein quantity in a meal results in dose-dependent effects on postprandial glucose levels in individuals with type 1 diabetes mellitus. Diabet Med. 2017;34:851–854. doi: 10.1111/dme.13347.
    1. Wolpert HA, Atakov-Castillo A, Smith SA, Steil GM. Dietary fat acutely increases glucose concentrations and insulin requirements in patients with type 1 diabetes: implications for carbohydrate-based bolus dose calculation and intensive diabetes management. Diabetes Care. 2013;36:810–816. doi: 10.2337/dc12-0092.
    1. Evert AB. Factors beyond carbohydrate to consider when determining mealtime insulin doses: protein, fat, timing, and technology. Diabetes Spectr. 2020;33:149–155. doi: 10.2337/ds20-0004.
    1. Campbell MD, Walker M, King D, et al. Carbohydrate counting at meal time followed by a small secondary postprandial bolus injection at 3 hours prevents late hyperglycemia, without hypoglycemia, after a high-carbohydrate, high-fat meal in type 1 diabetes. Diabetes Care. 2016;39:e141–e142. doi: 10.2337/dc16-0709.
    1. Bozzetto L, Alderisio A, Giorgini M, et al. Extra-virgin olive oil reduces glycemic response to a high-glycemic index meal in patients with type 1 diabetes: a randomized controlled trial. Diabetes Care. 2016;39:518–524. doi: 10.2337/dc15-2189.
    1. Markova M, Hornemann S, Sucher S, et al. Rate of appearance of amino acids after a meal regulates insulin and glucagon secretion in patients with type 2 diabetes: a randomized clinical trial. Am J Clin Nutr. 2018;108:279–291. doi: 10.1093/ajcn/nqy100.
    1. Bozzetto L, Alderisio A, Clemente G, et al. Gastrointestinal effects of extra-virgin olive oil associated with lower postprandial glycemia in type 1 diabetes. Clin Nutr. 2019;38:2645–2651. doi: 10.1016/j.clnu.2018.11.015.
    1. Bell KJ, Barclay AW, Petocz P, Colagiuri S, Brand-Miller JC. Efficacy of carbohydrate counting in type 1 diabetes: a systematic review and meta-analysis. Lancet Diabetes Endocrinol. 2014;2:133–140. doi: 10.1016/S2213-8587(13)70144-X.
    1. Weisman A, Bai JW, Cardinez M, Kramer CK, Perkins BA. Effect of artificial pancreas systems on glycaemic control in patients with type 1 diabetes: a systematic review and meta-analysis of outpatient randomised controlled trials. Lancet Diabetes Endocrinol. 2017;5:501–512. doi: 10.1016/S2213-8587(17)30167-5.
    1. Bekiari E, Kitsios K, Thabit H, et al. Artificial pancreas treatment for outpatients with type 1 diabetes: systematic review and meta-analysis. BMJ. 2018;k1310:361. doi: 10.1136/bmj.k1310.
    1. Gingras V, Taleb N, Roy-Fleming A, Legault L, Rabasa-Lhoret R. The challenges of achieving postprandial glucose control using closed-loop systems in patients with type 1 diabetes. Diabetes Obes Metab. 2018;20:245–256. doi: 10.1111/dom.13052.
    1. Lehmann V, Zueger T, Zeder A, et al. Lower daily carbohydrate intake is associated with improved glycemic control in adults with type 1 diabetes using a hybrid closed-loop system. Diabetes Care. 2020;43:3102–3105. doi: 10.1111/dom.13052.
    1. Battelino T, Danne T, Bergenstal RM, et al. Clinical target for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range. Diabetes Care. 2019;42:1593–1603. doi: 10.2337/dci19-0028.
    1. Messer LH, Forlenza GP, Sherr JL, et al. Optimizing hybrid closed-loop therapy in adolescents and emerging adults using the MiniMed 670G system. Diabetes Care. 2018;41:789–796. doi: 10.2337/dc17-1682.
    1. Bergenstal RM, Nimri R, Beck RW, et al. A comparison of two hybrid closed-loop systems in adolescents and young adults with type 1 diabetes (FLAIR): a multicentre, randomised, crossover trial. Lancet. 2021;397:208–219. doi: 10.1016/S0140-6736(20)32514-9.
    1. Bozzetto L, Giorgini M, Alderisio A, et al. Glycaemic load versus carbohydrate counting for insulin bolus calculation in patients with type 1 diabetes on insulin pump. Acta Diabetol. 2015;52:865–871. doi: 10.1007/s00592-015-0716-1.
    1. Smart CE, Evans M, O'Connell SM, et al. Both dietary protein and fat increase postprandial glucose excursions in children with type 1 diabetes, and the effect is additive. Diabetes Care. 2013;36:3897–3902. doi: 10.2337/dc13-1195.
    1. Biró G, Hulshof K, Ovesen L, et al. Selection of methodology to assess food intake. Eur J Clin Nutr. 2002;56:S25–S32. doi: 10.1038/sj.ejcn.1601426.

Source: PubMed

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