Rebecca Ortiz La Banca Barber, Lisa K Volkening, Sanjeev N Mehta, Eyal Dassau, Lori M Laffel
{"title":"Effects of Macronutrient Intake and Number of Meals on Glycemic Outcomes Over 1 Year in Youth with Type 1 Diabetes.","authors":"Rebecca Ortiz La Banca Barber, Lisa K Volkening, Sanjeev N Mehta, Eyal Dassau, Lori M Laffel","doi":"10.1089/dia.2023.0464","DOIUrl":null,"url":null,"abstract":"<p><p><b><i>Objective:</i></b> Insulin bolus doses derive from glucose levels and planned carbohydrate intake, although fat and protein impact glycemic excursions. We examined the impact of macronutrients and number of daily meals/snacks on glycemic outcomes in youth with type 1 diabetes. <b><i>Methods:</i></b> Youth (<i>N</i> = 136, ages 8-17) with type 1 diabetes completed 3-day food records, wore 3-day masked continuous glucose monitoring, and had A1c measurements every 3 months for 1 year. Diet data were analyzed using Nutrition Data System for Research. Longitudinal mixed models assessed effects of macronutrient intake and number of meals/snacks on glycemic outcomes. <b><i>Results:</i></b> At baseline, youth (48% male) had mean age of 12.8 ± 2.5 years and diabetes duration of 5.9 ± 3.1 years; 73% used insulin pumps. Baseline A1c was 8.1% ± 1.0%, percent time in range 70-180 mg/dL (%TIR) was 49% ± 17%, % time below range <70 mg/dL (%TBR) was 6% ± 8%, % time above range >180 mg/dL (%TAR) was 44% ± 20%, and glycemic variability as coefficient of variation (CV) was 41% ± 8%; macronutrient intake included 48% ± 5% carbohydrate, 36% ± 5% fat, and 16% ± 2% protein. Most youth (56%) reported 3-4 meals/snacks daily (range 1-9). Over 1 year, greater carbohydrate intake was associated with lower A1c (<i>P</i> = 0.0003), more %TBR (<i>P</i> = 0.0006), less %TAR (<i>P</i> = 0.002), and higher CV (<i>P</i> = 0.03). Greater fat intake was associated with higher A1c (<i>P</i> = 0.006), less %TBR (<i>P</i> = 0.002), and more %TAR (<i>P</i> = 0.005). Greater protein intake was associated with higher A1c (<i>P</i> = 0.01). More daily meals/snacks were associated with lower A1c (<i>P</i> = 0.001), higher %TIR (<i>P</i> = 0.0006), and less %TAR (<i>P</i> = 0.0001). <b><i>Conclusions:</i></b> Both fat and protein impact glycemic outcomes. Future automated insulin delivery systems should consider all macronutrients for timely insulin provision. The present research study derived from secondary analysis of the study registered under NCT00999375.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":null,"pages":null},"PeriodicalIF":5.7000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Diabetes technology & therapeutics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1089/dia.2023.0464","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/2/13 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
引用次数: 0
Abstract
Objective: Insulin bolus doses derive from glucose levels and planned carbohydrate intake, although fat and protein impact glycemic excursions. We examined the impact of macronutrients and number of daily meals/snacks on glycemic outcomes in youth with type 1 diabetes. Methods: Youth (N = 136, ages 8-17) with type 1 diabetes completed 3-day food records, wore 3-day masked continuous glucose monitoring, and had A1c measurements every 3 months for 1 year. Diet data were analyzed using Nutrition Data System for Research. Longitudinal mixed models assessed effects of macronutrient intake and number of meals/snacks on glycemic outcomes. Results: At baseline, youth (48% male) had mean age of 12.8 ± 2.5 years and diabetes duration of 5.9 ± 3.1 years; 73% used insulin pumps. Baseline A1c was 8.1% ± 1.0%, percent time in range 70-180 mg/dL (%TIR) was 49% ± 17%, % time below range <70 mg/dL (%TBR) was 6% ± 8%, % time above range >180 mg/dL (%TAR) was 44% ± 20%, and glycemic variability as coefficient of variation (CV) was 41% ± 8%; macronutrient intake included 48% ± 5% carbohydrate, 36% ± 5% fat, and 16% ± 2% protein. Most youth (56%) reported 3-4 meals/snacks daily (range 1-9). Over 1 year, greater carbohydrate intake was associated with lower A1c (P = 0.0003), more %TBR (P = 0.0006), less %TAR (P = 0.002), and higher CV (P = 0.03). Greater fat intake was associated with higher A1c (P = 0.006), less %TBR (P = 0.002), and more %TAR (P = 0.005). Greater protein intake was associated with higher A1c (P = 0.01). More daily meals/snacks were associated with lower A1c (P = 0.001), higher %TIR (P = 0.0006), and less %TAR (P = 0.0001). Conclusions: Both fat and protein impact glycemic outcomes. Future automated insulin delivery systems should consider all macronutrients for timely insulin provision. The present research study derived from secondary analysis of the study registered under NCT00999375.
期刊介绍:
Diabetes Technology & Therapeutics is the only peer-reviewed journal providing healthcare professionals with information on new devices, drugs, drug delivery systems, and software for managing patients with diabetes. This leading international journal delivers practical information and comprehensive coverage of cutting-edge technologies and therapeutics in the field, and each issue highlights new pharmacological and device developments to optimize patient care.