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Jafar A, Pasqua MR. Postprandial glucose-management strategies in type 1 diabetes: Current approaches and prospects with precision medicine and artificial intelligence. Diabetes Obes Metab 2024; 26:1555-1566. [PMID: 38263540 DOI: 10.1111/dom.15463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/01/2024] [Accepted: 01/05/2024] [Indexed: 01/25/2024]
Abstract
Postprandial glucose control can be challenging for individuals with type 1 diabetes, and this can be attributed to many factors, including suboptimal therapy parameters (carbohydrate ratios, correction factors, basal doses) because of physiological changes, meal macronutrients and engagement in postprandial physical activity. This narrative review aims to examine the current postprandial glucose-management strategies tested in clinical trials, including adjusting therapy settings, bolusing for meal macronutrients, adjusting pre-exercise and postexercise meal boluses for postprandial physical activity, and other therapeutic options, for individuals on open-loop and closed-loop therapies. Then we discuss their challenges and future avenues. Despite advancements in insulin delivery devices such as closed-loop systems and decision-support systems, many individuals with type 1 diabetes still struggle to manage their glucose levels. The main challenge is the lack of personalized recommendations, causing suboptimal postprandial glucose control. We suggest that postprandial glucose control can be improved by (i) providing personalized recommendations for meal macronutrients and postprandial activity; (ii) including behavioural recommendations; (iii) using other personalized therapeutic approaches (e.g. glucagon-like peptide-1 receptor agonists, sodium-glucose co-transporter inhibitors, amylin analogues, inhaled insulin) in addition to insulin therapy; and (iv) integrating an interpretability report to explain to individuals about changes in treatment therapy and behavioural recommendations. In addition, we suggest a future avenue to implement precision recommendations for individuals with type 1 diabetes utilizing the potential of deep reinforcement learning and foundation models (such as GPT and BERT), employing different modalities of data including diabetes-related and external background factors (i.e. behavioural, environmental, biological and abnormal events).
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Affiliation(s)
- Adnan Jafar
- Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Melissa-Rosina Pasqua
- Division of Endocrinology, Department of Medicine, McGill University, Montreal, Quebec, Canada
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2
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Strydom H, Delport E, Muchiri J, White Z. The Application of the Food Insulin Index in the Prevention and Management of Insulin Resistance and Diabetes: A Scoping Review. Nutrients 2024; 16:584. [PMID: 38474713 DOI: 10.3390/nu16050584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 02/08/2024] [Accepted: 02/16/2024] [Indexed: 03/14/2024] Open
Abstract
The food insulin index (FII) is a novel algorithm used to determine insulin responses of carbohydrates, proteins, and fats. This scoping review aimed to provide an overview of all scientifically relevant information presented on the application of the FII in the prevention and management of insulin resistance and diabetes. The Arksey and O'Malley framework and the PRISMA Extension for Scoping Reviews 22-item checklist were used to ensure that all areas were covered in the scoping review. Our search identified 394 articles, of which 25 articles were included. Three main themes emerged from the included articles: 1. the association of FII with the development of metabolic syndrome, insulin resistance, and diabetes, 2. the comparison of FII with carbohydrate counting (CC) for the prediction of postprandial insulin response, and 3. the effect of metabolic status on the FII. Studies indicated that the FII can predict postprandial insulin response more accurately than CC, and that a high DII and DIL diet is associated with the development of metabolic syndrome, insulin resistance, and diabetes. The FII could be a valuable tool to use in the prevention and management of T1DM, insulin resistance, and T2DM, but more research is needed in this field.
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Affiliation(s)
- Hildegard Strydom
- Department of Human Nutrition, University of Pretoria, Pretoria 0084, South Africa
| | - Elizabeth Delport
- GI Foundation of South Africa, Nelspruit, Mbombela 1201, South Africa
| | - Jane Muchiri
- Department of Human Nutrition, University of Pretoria, Pretoria 0084, South Africa
| | - Zelda White
- Department of Human Nutrition, University of Pretoria, Pretoria 0084, South Africa
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3
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ElSayed NA, Aleppo G, Bannuru RR, Bruemmer D, Collins BS, Ekhlaspour L, Hilliard ME, Johnson EL, Khunti K, Lingvay I, Matfin G, McCoy RG, Perry ML, Pilla SJ, Polsky S, Prahalad P, Pratley RE, Segal AR, Seley JJ, Stanton RC, Gabbay RA. 14. Children and Adolescents: Standards of Care in Diabetes-2024. Diabetes Care 2024; 47:S258-S281. [PMID: 38078582 PMCID: PMC10725814 DOI: 10.2337/dc24-s014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.
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Cai Y, Li M, Zhang L, Zhang J, Su H. The effect of the modified fat-protein unit algorithm compared with that of carbohydrate counting on postprandial glucose in adults with type-1 diabetes when consuming meals with differing macronutrient compositions: a randomized crossover trial. Nutr Metab (Lond) 2023; 20:43. [PMID: 37845717 PMCID: PMC10580506 DOI: 10.1186/s12986-023-00757-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 08/25/2023] [Indexed: 10/18/2023] Open
Abstract
BACKGROUND The optimization of glucose control in type-1 diabetes is challenged by postprandial glycemic variability. This study aimed to compare the postprandial glycemic effects of carbohydrate counting and the modified fat-protein unit (FPU) algorithms following meals with different protein and fat emphases in adults with type-1 diabetes. METHODS Thirty adults with type-1 diabetes aged 18 to 45 years participated in a randomized crossover trial. In a random order, participants consumed four test meals with equivalent energy and different macronutrient emphases on four separate mornings. The modified FPU algorithms and carbohydrate counting were used to determine the insulin dose for the test meals. A continuous glucose monitoring system was used to measured postprandial glycemia. RESULTS Compared with carbohydrate counting, the modified FPU algorithm significantly decreased the late postprandial mean glucose levels (p = 0.026) in high protein-fat meals. The number of hypoglycemia episodes was similar between insulin dosing algorithms for the high protein-fat meals; hypoglycemic events were considerably higher for the modified FPU in the normal protein-fat meal (p = 0.042). CONCLUSIONS The modified FPU algorithm may improve postprandial glycemic control after consuming high protein-fat meals in adults with type-1 diabetes but may result in increased hypoglycemia risk when used with a normal protein-fat meal.
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Affiliation(s)
- Yunying Cai
- The Endocrinology Department, First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, 650032 China
| | - Mengge Li
- Wenjiang District People’s Hospital of Chengdu, Chengdu, 611130 China
| | - Lun Zhang
- The Clinical Nutrition Department, First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, 650032 China
| | - Jie Zhang
- The Endocrinology Department, First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, 650032 China
| | - Heng Su
- The Endocrinology Department, First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, 650032 China
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Rodriguez E, Villamizar R. Artificial Pancreas: A Review of Meal Detection and Carbohydrates Counting Techniques. Rev Diabet Stud 2022; 18:171-180. [PMCID: PMC9832932 DOI: 10.1900/rds.2022.18.171] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 06/04/2022] [Accepted: 07/14/2022] [Indexed: 12/24/2023] Open
Abstract
OBJECTIVE The development of an artificial pancreas is an open research problem that faces the challenge of creating a control algorithm capable of dosing insulin automatically and driving blood glucose to healthy levels. Many of these approaches, including artificial intelligence, are based on techniques that could result in and undesirable outcome because most of them include neither detect meal intake or meal size information. To overcome that issue, some meal count-detection algorithms reported in scientific publications have shown not only a good performance on blood glucose regulation but fewer hypoglicemia and hyperglycemia events too. METHODS We reviewed the most relevant authors and publications and main databases (particularly SCOPUS and Google Scholar), focusing on algorithms of detection and estimation of meal intake from multiple approaches. RESULTS A wide range of approaches and proposals have been found. The majority of them include trials on in silico patients rather than in vivo ones. Most of procedures require as inputs glucose samples from continuous glucose monitoring devices as basal insulin and bolus as well. Most of approaches could be grouped by 2 categories: mathematical model based and not model based. CONCLUSION A combination of methods seems to reach better results.
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Rein M, Ben-Yacov O, Godneva A, Shilo S, Zmora N, Kolobkov D, Cohen-Dolev N, Wolf BC, Kosower N, Lotan-Pompan M, Weinberger A, Halpern Z, Zelber-Sagi S, Elinav E, Segal E. Effects of personalized diets by prediction of glycemic responses on glycemic control and metabolic health in newly diagnosed T2DM: a randomized dietary intervention pilot trial. BMC Med 2022; 20:56. [PMID: 35135549 PMCID: PMC8826661 DOI: 10.1186/s12916-022-02254-y] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 01/12/2022] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Dietary modifications are crucial for managing newly diagnosed type 2 diabetes mellitus (T2DM) and preventing its health complications, but many patients fail to achieve clinical goals with diet alone. We sought to evaluate the clinical effects of a personalized postprandial-targeting (PPT) diet on glycemic control and metabolic health in individuals with newly diagnosed T2DM as compared to the commonly recommended Mediterranean-style (MED) diet. METHODS We enrolled 23 adults with newly diagnosed T2DM (aged 53.5 ± 8.9 years, 48% males) for a randomized crossover trial of two 2-week-long dietary interventions. Participants were blinded to their assignment to one of the two sequence groups: either PPT-MED or MED-PPT diets. The PPT diet relies on a machine learning algorithm that integrates clinical and microbiome features to predict personal postprandial glucose responses (PPGR). We further evaluated the long-term effects of PPT diet on glycemic control and metabolic health by an additional 6-month PPT intervention (n = 16). Participants were connected to continuous glucose monitoring (CGM) throughout the study and self-recorded dietary intake using a smartphone application. RESULTS In the crossover intervention, the PPT diet lead to significant lower levels of CGM-based measures as compared to the MED diet, including average PPGR (mean difference between diets, - 19.8 ± 16.3 mg/dl × h, p < 0.001), mean glucose (mean difference between diets, - 7.8 ± 5.5 mg/dl, p < 0.001), and daily time of glucose levels > 140 mg/dl (mean difference between diets, - 2.42 ± 1.7 h/day, p < 0.001). Blood fructosamine also decreased significantly more during PPT compared to MED intervention (mean change difference between diets, - 16.4 ± 37 μmol/dl, p < 0.0001). At the end of 6 months, the PPT intervention leads to significant improvements in multiple metabolic health parameters, among them HbA1c (mean ± SD, - 0.39 ± 0.48%, p < 0.001), fasting glucose (- 16.4 ± 24.2 mg/dl, p = 0.02) and triglycerides (- 49 ± 46 mg/dl, p < 0.001). Importantly, 61% of the participants exhibited diabetes remission, as measured by HbA1c < 6.5%. Finally, some clinical improvements were significantly associated with gut microbiome changes per person. CONCLUSION In this crossover trial in subjects with newly diagnosed T2DM, a PPT diet improved CGM-based glycemic measures significantly more than a Mediterranean-style MED diet. Additional 6-month PPT intervention further improved glycemic control and metabolic health parameters, supporting the clinical efficacy of this approach. TRIAL REGISTRATION ClinicalTrials.gov number, NCT01892956.
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Affiliation(s)
- Michal Rein
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, 7610001, Rehovot, Israel.,Department of Molecular Cell Biology, Weizmann Institute of Science, 7610001, Rehovot, Israel.,School of Public Health, University of Haifa, 3498838, Haifa, Israel
| | - Orly Ben-Yacov
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, 7610001, Rehovot, Israel.,Department of Molecular Cell Biology, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Anastasia Godneva
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, 7610001, Rehovot, Israel.,Department of Molecular Cell Biology, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Smadar Shilo
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, 7610001, Rehovot, Israel.,Department of Molecular Cell Biology, Weizmann Institute of Science, 7610001, Rehovot, Israel.,Pediatric Diabetes Unit, Ruth Rappaport Children's Hospital, Rambam Healthcare Campus, Haifa, Israel
| | - Niv Zmora
- Immunology Department, Weizmann Institute of Science, 7610001, Rehovot, Israel.,Digestive Center, Tel Aviv Sourasky Medical Center, 6423906, Tel Aviv, Israel.,Internal Medicine Department, Tel Aviv Sourasky Medical Center, 6423906, Tel Aviv, Israel
| | - Dmitry Kolobkov
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, 7610001, Rehovot, Israel.,Department of Molecular Cell Biology, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Noa Cohen-Dolev
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, 7610001, Rehovot, Israel.,Department of Molecular Cell Biology, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Bat-Chen Wolf
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, 7610001, Rehovot, Israel.,Department of Molecular Cell Biology, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Noa Kosower
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, 7610001, Rehovot, Israel.,Department of Molecular Cell Biology, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Maya Lotan-Pompan
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, 7610001, Rehovot, Israel.,Department of Molecular Cell Biology, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Adina Weinberger
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, 7610001, Rehovot, Israel.,Department of Molecular Cell Biology, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Zamir Halpern
- Digestive Center, Tel Aviv Sourasky Medical Center, 6423906, Tel Aviv, Israel.,Internal Medicine Department, Tel Aviv Sourasky Medical Center, 6423906, Tel Aviv, Israel
| | - Shira Zelber-Sagi
- School of Public Health, University of Haifa, 3498838, Haifa, Israel
| | - Eran Elinav
- Immunology Department, Weizmann Institute of Science, 7610001, Rehovot, Israel.
| | - Eran Segal
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, 7610001, Rehovot, Israel. .,Department of Molecular Cell Biology, Weizmann Institute of Science, 7610001, Rehovot, Israel.
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7
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Akbari A, Sohouli MH, Deliu Lozovanu O, Lotfi M, Nabavizadeh R, Saeidi R. Dietary insulin index and load with risk of breast cancer in a case-control study. Int J Clin Pract 2021; 75:e14883. [PMID: 34534393 DOI: 10.1111/ijcp.14883] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 09/10/2021] [Accepted: 09/13/2021] [Indexed: 01/02/2023] Open
Abstract
OBJECTIVE Circulating insulin levels have been positively associated with risk of breast cancer (BrCa); however, it remains unclear whether a diet inducing an elevated insulin response influences Breast risk. METHODS In this study, 250 newly diagnosed breast cancer patients and 250 hospitalised controls were recruited using convenience sampling. The dietary insulin index (DII) was calculated by dividing the dietary insulin load by the total energy intake. RESULTS Compared with those in the lowest tertiles of DII and dietary insulin load (DIL), subjects in the highest tertile were more likely to be overweight, have a family history of breast and other types of cancer and a history of benign breast diseases. After controlling for multiple potential confounders, a significantly increased BrCa odds was observed in the highest tertiles of DII and DIL score compared with the lowest tertiles (odds ratio (OR): 1.46; 95% CI: 0.67-3.19, P = .006) and (OR: 1.87; 95% CI: 0.92-3.80, P = .038), respectively. CONCLUSIONS Our findings suggest that a diet that induces an elevated postprandial insulin response, indicated by higher DII and DIL scores, may increase the odds of BrCa, especially among women.
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Affiliation(s)
- Atieh Akbari
- Cancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Hassan Sohouli
- Cancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Department of Clinical Nutrition and Dietetics, Faculty of Nutrition and Food Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Mojtaba Lotfi
- Department of Pediatric Endocrinology and Metabolism, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Raheleh Nabavizadeh
- Pediatric Gastroenterology, Hepatology and Nutrition Research Center, Research Institute for Children's Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Reza Saeidi
- Department of Pediatric Endocrinology and Metabolism, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Faculty of Medicine, Mofid Children's Hospital, Neonatal Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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8
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Ben-Yacov O, Godneva A, Rein M, Shilo S, Kolobkov D, Koren N, Cohen Dolev N, Travinsky Shmul T, Wolf BC, Kosower N, Sagiv K, Lotan-Pompan M, Zmora N, Weinberger A, Elinav E, Segal E. Personalized Postprandial Glucose Response-Targeting Diet Versus Mediterranean Diet for Glycemic Control in Prediabetes. Diabetes Care 2021; 44:1980-1991. [PMID: 34301736 DOI: 10.2337/dc21-0162] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 06/15/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To compare the clinical effects of a personalized postprandial-targeting (PPT) diet versus a Mediterranean (MED) diet on glycemic control and metabolic health in prediabetes. RESEARCH DESIGN AND METHODS We randomly assigned adults with prediabetes (n = 225) to follow a MED diet or a PPT diet for a 6-month dietary intervention and additional 6-month follow-up. The PPT diet relies on a machine learning algorithm that integrates clinical and microbiome features to predict personal postprandial glucose responses. During the intervention, all participants were connected to continuous glucose monitoring (CGM) and self-reported dietary intake using a smartphone application. RESULTS Among 225 participants randomized (58.7% women, mean ± SD age 50 ± 7 years, BMI 31.3 ± 5.8 kg/m2, HbA1c, 5.9 ± 0.2% [41 ± 2.4 mmol/mol], fasting plasma glucose 114 ± 12 mg/dL [6.33 ± 0.67 mmol/L]), 200 (89%) completed the 6-month intervention. A total of 177 participants also contributed 12-month follow-up data. Both interventions reduced the daily time with glucose levels >140 mg/dL (7.8 mmol/L) and HbA1c levels, but reductions were significantly greater in PPT compared with MED. The mean 6-month change in "time above 140" was -0.3 ± 0.8 h/day and -1.3 ± 1.5 h/day for MED and PPT, respectively (95% CI between-group difference -1.29 to -0.66, P < 0.001). The mean 6-month change in HbA1c was -0.08 ± 0.19% (-0.9 ± 2.1 mmol/mol) and -0.16 ± 0.24% (-1.7 ± 2.6 mmol/mol) for MED and PPT, respectively (95% CI between-group difference -0.14 to -0.02, P = 0.007). The significant between-group differences were maintained at 12-month follow-up. No significant differences were noted between the groups in a CGM-measured oral glucose tolerance test. CONCLUSIONS In this clinical trial in prediabetes, a PPT diet improved glycemic control significantly more than a MED diet as measured by daily time of glucose levels >140 mg/dL (7.8 mmol/L) and HbA1c. These findings may have implications for dietary advice in clinical practice.
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Affiliation(s)
- Orly Ben-Yacov
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.,Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Anastasia Godneva
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.,Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Michal Rein
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.,Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.,School of Public Health, University of Haifa, Haifa, Israel
| | - Smadar Shilo
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.,Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.,Pediatric Diabetes Unit, Ruth Rappaport Children's Hospital, Rambam Healthcare Campus, Haifa, Israel
| | - Dmitry Kolobkov
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.,Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Netta Koren
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.,Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Noa Cohen Dolev
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.,Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Tamara Travinsky Shmul
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.,Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Bat Chen Wolf
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.,Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Noa Kosower
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.,Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Keren Sagiv
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.,Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Maya Lotan-Pompan
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.,Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Niv Zmora
- Immunology Department, Weizmann Institute of Science, Rehovot, Israel.,Digestive Center, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,Internal Medicine Department, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Adina Weinberger
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.,Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Eran Elinav
- Immunology Department, Weizmann Institute of Science, Rehovot, Israel
| | - Eran Segal
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel .,Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
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Impact of Fat Intake on Blood Glucose Control and Cardiovascular Risk Factors in Children and Adolescents with Type 1 Diabetes. Nutrients 2021; 13:nu13082625. [PMID: 34444784 PMCID: PMC8401117 DOI: 10.3390/nu13082625] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 07/08/2021] [Accepted: 07/12/2021] [Indexed: 12/18/2022] Open
Abstract
Nutrition therapy is a cornerstone of type 1 diabetes (T1D) management. Glycemic control is affected by diet composition, which can contribute to the development of diabetes complications. However, the specific role of macronutrients is still debated, particularly fat intake. This review aims at assessing the relationship between fat intake and glycemic control, cardiovascular risk factors, inflammation, and microbiota, in children and adolescents with T1D. High fat meals are followed by delayed and prolonged hyperglycemia and higher glycated hemoglobin A1c levels have been frequently reported in individuals with T1D consuming high amounts of fat. High fat intake has also been associated with increased cardiovascular risk, which is higher in people with diabetes than in healthy subjects. Finally, high fat meals lead to postprandial pro-inflammatory responses through different mechanisms, including gut microbiota modifications. Different fatty acids were proposed to have a specific role in metabolic regulation, however, further investigation is still necessary. In conclusion, available evidence suggests that a high fat intake should be avoided by children and adolescents with T1D, who should be encouraged to adhere to a healthy and balanced diet, as suggested by ISPAD and ADA recommendations. This nutritional choice might be beneficial for reducing cardiovascular risk and inflammation.
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Smith TA, Smart CE, Fuery MEJ, Howley PP, Knight BA, Harris M, King BR. In children and young people with type 1 diabetes using Pump therapy, an additional 40% of the insulin dose for a high-fat, high-protein breakfast improves postprandial glycaemic excursions: A cross-over trial. Diabet Med 2021; 38:e14511. [PMID: 33405297 DOI: 10.1111/dme.14511] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 12/01/2020] [Accepted: 01/03/2021] [Indexed: 11/30/2022]
Abstract
AIM To determine the insulin requirement for a high-fat, high-protein breakfast to optimise postprandial glycaemic excursions in children and young people with type 1 diabetes using insulin pumps. METHODS In all, 27 participants aged 10-23 years, BMI <95th percentile (2-18 years) or BMI <30 kg/m2 (19-25 years) and HbA1c ≤64 mmol/mol (≤8.0%) consumed a high-fat, high-protein breakfast (carbohydrate: 30 g, fat: 40 g and protein: 50 g) for 4 days. In this cross-over trial, insulin was administered, based on the insulin-to-carbohydrate ratio (ICR) of 100% (control), 120%, 140% and 160%, in an order defined by a randomisation sequence and delivered in a combination bolus, 60% ¼ hr pre-meal and 40% over 3 hr. Postprandial sensor glucose was assessed for 6 hr. RESULTS Comparing 100% ICR, 140% ICR and 160% ICR resulted in significantly lower 6-hr areas under the glucose curves: mean (95%CI) (822 mmol/L.min [605,1039] and 567 [350,784] vs 1249 [1042,1457], p ≤ 0.001) and peak glucose excursions (4.0 mmol/L [3.0,4.9] and 2.7 [1.7,3.6] vs 6.0 [5.0,6.9],p < 0.001). Rates of hypoglycaemia for 100%-160% ICR were 7.7%, 7.7%, 12% and 19% respectively (p ≥ 0.139). With increasing insulin dose, a step-wise reduction in mean glucose excursion was observed from 1 to 6 hr (p = 0.008). CONCLUSIONS Incrementally increasing the insulin dose for a high-fat, high-protein breakfast resulted in a predictable, dose-dependent reduction in postprandial glycaemia: 140% ICR improved postprandial glycaemic excursions without a statistically significant increase in hypoglycaemia. These findings support a safe, practical method for insulin adjustment for high-fat, high-protein meals that can be readily implemented in practice to improve postprandial glycaemia.
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Affiliation(s)
- Tenele A Smith
- Faculty of Health and Medicine, University of Newcastle, Callaghan, Australia
- Hunter Medical Research Institute, New Lambton Heights, Australia
| | - Carmel E Smart
- Faculty of Health and Medicine, University of Newcastle, Callaghan, Australia
- Hunter Medical Research Institute, New Lambton Heights, Australia
- Department of Paediatric Endocrinology, John Hunter Children's Hospital, New Lambton Heights,, Australia
| | - Michelle E J Fuery
- Department of Endocrinology, Queensland Children's Hospital, South Brisbane, Australia
| | - Peter P Howley
- Faculty of Science, University of Newcastle, Callaghan, Australia
| | - Brigid A Knight
- Department of Endocrinology, Queensland Children's Hospital, South Brisbane, Australia
| | - Mark Harris
- Department of Endocrinology, Queensland Children's Hospital, South Brisbane, Australia
| | - Bruce R King
- Faculty of Health and Medicine, University of Newcastle, Callaghan, Australia
- Hunter Medical Research Institute, New Lambton Heights, Australia
- Department of Paediatric Endocrinology, John Hunter Children's Hospital, New Lambton Heights,, Australia
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11
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Dietary Aspects to Incorporate in the Creation of a Mobile Image-Based Dietary Assessment Tool to Manage and Improve Diabetes. Nutrients 2021; 13:nu13041179. [PMID: 33918343 PMCID: PMC8066992 DOI: 10.3390/nu13041179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 03/30/2021] [Accepted: 04/01/2021] [Indexed: 11/17/2022] Open
Abstract
Diabetes is the seventh leading cause of death in United States. Dietary intake and behaviors are essential components of diabetes management. Growing evidence suggests dietary components beyond carbohydrates may critically impact glycemic control. Assessment tools on mobile platforms have the ability to capture multiple aspects of dietary behavior in real-time throughout the day to inform and improve diabetes management and insulin dosing. The objective of this narrative review was to summarize evidence related to dietary behaviors and composition to inform a mobile image-based dietary assessment tool for managing glycemic control of both diabetes types (type 1 and type 2 diabetes). This review investigated the following topics amongst those with diabetes: (1) the role of time of eating occasion on indicators of glycemic control; and (2) the role of macronutrient composition of meals on indicators of glycemic control. A search for articles published after 2000 was completed in PubMed with the following sets of keywords “diabetes/diabetes management/diabetes prevention/diabetes risk”, “dietary behavior/eating patterns/temporal/meal timing/meal frequency”, and “macronutrient composition/glycemic index”. Results showed eating behaviors and meal macronutrient composition may affect glycemic control. Specifically, breakfast skipping, late eating and frequent meal consumption might be associated with poor glycemic control while macronutrient composition and order of the meal could also affect glycemic control. These factors should be considered in designing a dietary assessment tool, which may optimize diabetes management to reduce the burden of this disease.
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Erdal B, Caferoglu Z, Hatipoglu N. The comparison of two mealtime insulin dosing algorithms for high and low glycaemic index meals in adolescents with type 1 diabetes. Diabet Med 2021; 38:e14444. [PMID: 33119135 DOI: 10.1111/dme.14444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 10/26/2020] [Indexed: 11/27/2022]
Abstract
AIMS Postprandial glycaemic variability carries on being a clinical challenge in optimizing glucose control in type 1 diabetes. The aim of this study was to compare the postprandial glycaemic effects of carbohydrate counting and food insulin index algorithms following the consumption of protein-rich, high-fat meals with different glycaemic index (GI) in adolescents with type 1 diabetes. METHODS A randomized, single-blind and crossover trial included 15 adolescents aged 14-18 years with type 1 diabetes. Participants consumed two different test meals with similar energy, macronutrients and food insulin index but the approximately twofold difference in GI, in random order on four consecutive mornings at their home. Insulin dose for high- and low-GI test meals was determined by using the carbohydrate counting and food insulin index algorithms. Four-hour postprandial glycaemia was assessed by the continuous glucose monitoring system. RESULTS Compared with carbohydrate counting, the food insulin index algorithm significantly decreased peak glucose excursion (-57%, p = 0.02), incremental area under the curve (-65%, p = 0.02) and coefficient variation of blood glucose (-37%, p = 0.03) in the high-GI meal, though there was no difference between the two algorithms in the low-GI meal. The occurrence of hypoglycaemia did not significantly differ between insulin dosing algorithms for the high-GI (p = 0.58) and low-GI (p = 0.20) meals. CONCLUSIONS The food insulin index algorithm may be beneficial for postprandial glycaemic control after the consumption of high-GI meals in adolescents with type 1 diabetes.
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Affiliation(s)
- Busra Erdal
- Institute of Health Sciences, Department of Nutrition and Dietetics, Erciyes University, Kayseri, Turkey
| | - Zeynep Caferoglu
- Faculty of Health Sciences, Department of Nutrition and Dietetics, Erciyes University, Kayseri, Turkey
| | - Nihal Hatipoglu
- Faculty of Medicine, Department of Paediatric Endocrinology, Erciyes University, Kayseri, Turkey
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13
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The association between food insulin index and odds of non-alcoholic fatty liver disease (NAFLD) in adults: a case-control study. GASTROENTEROLOGY AND HEPATOLOGY FROM BED TO BENCH 2021; 14:221-228. [PMID: 34221261 PMCID: PMC8245830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
AIM This research aimed to study the association of food insulin index and biochemical parameters with the odds of developing NAFLD in adult Iranians. BACKGROUND Hyperinsulinemia may play an important role in the development of non-alcoholic fatty liver disease (NAFLD) because of the relationship between insulin response and body fat accumulation. METHODS A case-control study of 169 NAFLD patients and 200 healthy adults aged 18-55 years was conducted. Dietary data was collected using a validated 168-item quantitative food frequency questionnaire (FFQ). Food insulin index (FII) was calculated by dividing the total insulin load by total energy intake (kcal/day). Total insulin load (ILoverall) was also calculated using a standard formula. RESULTS Mean participant age was 43.9 ± 5.9 years. Patients with NAFLD were significantly associated with higher body mass index, levels of liver enzymes, triglyceride, low density lipoprotein-cholesterol (LDL), total cholesterol, and fasting blood sugar (FBS) compared to the healthy subjects (p < 0.05). The highest tertiles of FII were associated with higher odds of NAFLD (OR=1.4, 95% CI: 0.88-2.48, p for trend <0.001) and obesity (OR=2.33, 95% CI: 0.97-5.75) compared to the lowest tertiles. Potential confounders for the association were controlled. CONCLUSION This study found that adherence to a diet with high FII was associated with greater odds of NAFLD and overweight or obesity. Additional studies are required to better understand this association.
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14
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Yari Z, Behrouz V, Zand H, Pourvali K. New Insight into Diabetes Management: From Glycemic Index to Dietary Insulin Index. Curr Diabetes Rev 2020; 16:293-300. [PMID: 31203801 DOI: 10.2174/1573399815666190614122626] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Revised: 04/05/2019] [Accepted: 05/03/2019] [Indexed: 12/31/2022]
Abstract
BACKGROUND Despite efforts to control hyperglycemia, diabetes management is still challenging. This may be due to focusing on reducing hyperglycemia and neglecting the importance of hyperinsulinemia; while insulin resistance and resultant hyperinsulinemia preceded diabetes onset and may contribute to disease pathogenesis. OBJECTIVE The present narrative review attempts to provide a new insight into the management of diabetes by exploring different aspects of glycemic index and dietary insulin index. RESULTS The current data available on this topic is limited and heterogeneous. Conventional diet therapy for diabetes management is based on reducing postprandial glycemia through carbohydrate counting, choosing foods with low-glycemic index and low-glycemic load. Since these indicators are only reliant on the carbohydrate content of foods and do not consider the effects of protein and fat on the stimulation of insulin secretion, they cannot provide a comprehensive approach to determine the insulin requirements. CONCLUSION Selecting foods based on carbohydrate counting, glycemic index or glycemic load are common guides to control glycemia in diabetic patients, but neglect the insulin response, thus leading to failure in diabetes management. Therefore, paying attention to insulinemic response along with glycemic response seems to be more effective in managing diabetes.
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Affiliation(s)
- Zahra Yari
- Student Research Committee, Department of Clinical Nutrition and Dietetics, Faculty of Nutrition and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Vahideh Behrouz
- Department of Clinical Nutrition and Dietetics, Faculty of Nutrition and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid Zand
- Department of Cellular and Molecular Nutrition, Faculty of Nutrition and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Katayoun Pourvali
- Department of Cellular and Molecular Nutrition, Faculty of Nutrition and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Kaya N, Kurtoğlu S, Gökmen Özel H. Does meal‐time insulin dosing based on fat‐protein counting give positive results in postprandial glycaemic profile after a high protein‐fat meal in adolescents with type 1 diabetes: a randomised controlled trial. J Hum Nutr Diet 2019; 33:396-403. [DOI: 10.1111/jhn.12711] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Affiliation(s)
- N. Kaya
- Department of Nutrition and Dietetics Faculty of Health Science Erciyes University Kayseri Turkey
| | - S. Kurtoğlu
- Department of Paediatric Endocrinology and Neonatology Memorial Private Hospital Kayseri Turkey
| | - H. Gökmen Özel
- Department of Nutrition and Dietetics Faculty of Health Science Hacettepe University Ankara Turkey
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16
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Postprandial glucose response after the consumption of three mixed meals based on the carbohydrate counting method in adults with type 1 diabetes. A randomized crossover trial. Clin Nutr ESPEN 2019; 31:48-55. [PMID: 31060834 DOI: 10.1016/j.clnesp.2019.03.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Revised: 01/16/2019] [Accepted: 03/10/2019] [Indexed: 11/20/2022]
Abstract
BACKGROUND & AIMS People on intensive insulin therapy usually calculate their premeal insulin dose based on the total amount of consumed carbohydrates. However, arguments have been expressed supporting that also the protein and fat content of the meals should be considered when estimating premeal insulin dose. We examined the effectiveness of the carbohydrate counting method after consumption of mixed meals, and we further explored the effects of added extra virgin olive oil in these mixed meals, in adults with type 1 diabetes. METHODS Twenty adults (35.0 ± 8.9 years, BMI 27 ± 5 kg/m2) with diabetes duration 17 ± 11 years, on intensive insulin therapy with multiple injections, consumed 3 mixed meals (pasticcio, chicken with vegetables and baked giant beans), with and without the addition of 11 ml extra virgin olive oil (total of 6 meals), in random order, with the insulin dose determined by using the carbohydrate counting method. Capillary blood glucose was measured at premeal (baseline) and 30, 60, 90, 120, 150 and 180 min after meal consumption. At every visit, participants were assessed for anthropometric parameters and subjective stress. RESULTS Participants had mean HbA1c 7.5 ± 1.2%, mean carbohydrate to insulin ratio 9:1 IU and stable body weight, waist circumference and subjective stress throughout the study. The mean glucose concentration, for all 6 meals, 120 min postprandially was within target (<180 mg/dl) in nearly 80% of the sample. Addition of olive oil produced sustained increased postprandial glucose concentrations only to pasticcio meal, although within target, and no significant differences were noticed for the grilled chicken with vegetables or the baked giant beans (legume) meals. CONCLUSIONS The carbohydrate-counting method was effective for achieving postprandial glucose levels within target threshold up to 3 h postprandially. Moreover, adding small amounts of dietary fat (extra virgin olive oil) to low fat meals does not significantly alter the postprandial response within the first 3 h, whereas caused a sustained increase in postprandial blood glucose concentrations to the high energy density meal (i.e. the pasticcio meal).
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17
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Lopez PE, Evans M, King BR, Jones TW, Bell K, McElduff P, Davis EA, Smart CE. A randomized comparison of three prandial insulin dosing algorithms for children and adolescents with Type 1 diabetes. Diabet Med 2018; 35:1440-1447. [PMID: 29873107 DOI: 10.1111/dme.13703] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/04/2018] [Indexed: 12/26/2022]
Abstract
AIM To compare systematically the impact of two novel insulin-dosing algorithms (the Pankowska Equation and the Food Insulin Index) with carbohydrate counting on postprandial glucose excursions following a high fat and a high protein meal. METHODS A randomized, crossover trial at two Paediatric Diabetes centres was conducted. On each day, participants consumed a high protein or high fat meal with similar carbohydrate amounts. Insulin was delivered according to carbohydrate counting, the Pankowska Equation or the Food Insulin Index. Subjects fasted for 5 h following the test meal and physical activity was standardized. Postprandial glycaemia was measured for 300 min using continuous glucose monitoring. RESULTS 33 children participated in the study. When compared to carbohydrate counting, the Pankowska Equation resulted in lower glycaemic excursion for 90-240 min after the high protein meal (p < 0.05) and lower peak glycaemic excursion (p < 0.05). The risk of hypoglycaemia was significantly lower for carbohydrate counting and the Food Insulin Index compared to the Pankowska Equation (OR 0.76 carbohydrate counting vs. the Pankowska Equation and 0.81 the Food Insulin Index vs. the Pankowska Equation). There was no significant difference in glycaemic excursions when carbohydrate counting was compared to the Food Insulin Index. CONCLUSION The Pankowska Equation resulted in reduced postprandial hyperglycaemia at the expense of an increase in hypoglycaemia. There were no significant differences when carbohydrate counting was compared to the Food Insulin Index. Further research is required to optimize prandial insulin dosing.
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Affiliation(s)
- P E Lopez
- Hunter Medical Research Institute, Newcastle, NSW, Australia
- John Hunter Children's Hospital, Newcastle, NSW, Australia
- University of Newcastle, Newcastle, NSW, Australia
| | - M Evans
- Telethon Kids Institute, University of Western Australia, Perth, WA, Australia
| | - B R King
- Hunter Medical Research Institute, Newcastle, NSW, Australia
- John Hunter Children's Hospital, Newcastle, NSW, Australia
- University of Newcastle, Newcastle, NSW, Australia
| | - T W Jones
- Telethon Kids Institute, University of Western Australia, Perth, WA, Australia
| | - K Bell
- University of Sydney, NSW, Australia
| | - P McElduff
- Hunter Medical Research Institute, Newcastle, NSW, Australia
- University of Newcastle, Newcastle, NSW, Australia
| | - E A Davis
- Telethon Kids Institute, University of Western Australia, Perth, WA, Australia
| | - C E Smart
- Hunter Medical Research Institute, Newcastle, NSW, Australia
- John Hunter Children's Hospital, Newcastle, NSW, Australia
- University of Newcastle, Newcastle, NSW, Australia
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18
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Smart CE, Annan F, Higgins LA, Jelleryd E, Lopez M, Acerini CL. ISPAD Clinical Practice Consensus Guidelines 2018: Nutritional management in children and adolescents with diabetes. Pediatr Diabetes 2018; 19 Suppl 27:136-154. [PMID: 30062718 DOI: 10.1111/pedi.12738] [Citation(s) in RCA: 113] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Accepted: 07/16/2018] [Indexed: 02/06/2023] Open
Affiliation(s)
- Carmel E Smart
- Department of Paediatric Endocrinology, John Hunter Children's Hospital, Newcastle, NSW, Australia.,School of Health Sciences, University of Newcastle, Newcastle, NSW, Australia
| | | | | | | | | | - Carlo L Acerini
- Department of Paediatrics, University of Cambridge, Cambridge, UK
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19
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Vaz EC, Porfírio GJM, Nunes HRDC, Nunes-Nogueira VDS. Effectiveness and safety of carbohydrate counting in the management of adult patients with type 1 diabetes mellitus: a systematic review and meta-analysis. ARCHIVES OF ENDOCRINOLOGY AND METABOLISM 2018; 62:337-345. [PMID: 29791661 PMCID: PMC10118793 DOI: 10.20945/2359-3997000000045] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Accepted: 02/07/2018] [Indexed: 11/23/2022]
Abstract
OBJECTIVE This study aimed to evaluate the effectiveness and safety of carbohydrate counting (CHOC) in the treatment of adult patients with type 1 diabetes mellitus (DM1). MATERIALS AND METHODS We performed a systematic review of randomized studies that compared CHOC with general dietary advice in adult patients with DM1. The primary outcomes were changes in glycated hemoglobin (HbA1c), quality of life, and episodes of severe hypoglycemia. We searched the following electronic databases: Embase, PubMed, Lilacs, and the Cochrane Central Register of Controlled Trials. The quality of evidence was analyzed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE). RESULTS A total of 3,190 articles were identified, and two reviewers independently screened the titles and abstracts. From the 15 potentially eligible studies, five were included, and 10 were excluded because of the lack of randomization or different control/intervention groups. Meta-analysis showed that the final HbA1c was significantly lower in the CHOC group than in the control group (mean difference, random, 95% CI: -0.49 (-0.85, -0.13), p = 0.006). The meta-analysis of severe hypoglycemia and quality of life did not show any significant differences between the groups. According to the GRADE, the quality of evidence for severe hypoglycemia, quality of life, and change in HbA1c was low, very low, and moderate, respectively. CONCLUSION The meta-analysis showed evidence favoring the use of CHOC in the management of DM1. However, this benefit was limited to final HbA1c, which was significantly lower in the CHOC than in the control group.
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Affiliation(s)
- Eliege Carolina Vaz
- Departamento de Clínica Médica, Faculdade de Medicina de Botucatu, Universidade Estadual de São Paulo (Unesp), Botucatu, SP, Brasil
| | - Gustavo José Martiniano Porfírio
- Centro Cochrane do Brasil, Disciplina de Medicina de Urgência e Medicina Baseada em Evidências, Universidade Federal de São Paulo (Unifesp), São Paulo, SP, Brasil
| | - Hélio Rubens de Carvalho Nunes
- Departamento de Saúde Pública, Faculdade de Medicina de Botucatu, Universidade Estadual de São Paulo (Unesp), Botucatu, SP, Brasil
| | - Vania Dos Santos Nunes-Nogueira
- Departamento de Clínica Médica, Faculdade de Medicina de Botucatu, Universidade Estadual de São Paulo (Unesp), Botucatu, SP, Brasil
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Herron A, Sullivan C, Brouillard E, Steenkamp D. Late to the Party: Importance of Dietary Fat and Protein in the Intensive Management of Type 1 Diabetes. A Case Report. J Endocr Soc 2017; 1:1002-1005. [PMID: 29264550 PMCID: PMC5686679 DOI: 10.1210/js.2017-00158] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Accepted: 06/20/2017] [Indexed: 11/19/2022] Open
Abstract
Insulin dosing in type 1 diabetes (T1D) has been focused primarily on carbohydrate intake, but recent evidence highlights the importance of dietary fat and protein in glycemic excursions. Several methods have been developed to incorporate dietary fat and protein into insulin dose calculations, including fat–protein units (FPUs) that estimate insulin requirements based on ingested fat and protein, as well as extended combination insulin boluses. However, insulin dosing based on meal fat and protein content is challenging to incorporate into clinical practice. We present the case of a 40-year-old man with T1D using continuous subcutaneous insulin infusions and continuous glucose monitoring. He followed a diet that restricted carbohydrate intake, with compensatory increases in dietary protein and fat. He had poor glycemic control with frequent postprandial hyperglycemia. He began incorporating FPUs into his insulin dosing calculations and used extended dual wave boluses to administer prandial insulin. Over the next 6 months he experienced a significant improvement in glycemic control. Fat and protein have both been shown to cause delayed postprandial hyperglycemia, leading to poor glycemic control with carbohydrate-focused insulin dosing in our patient on a high-fat, high-protein diet. It is difficult to incorporate dietary fat and protein into insulin dosing in the clinical setting. However, our patient experienced an improvement in glycemic control with the application of FPUs and dual wave boluses in prandial insulin dosing, showing that methods such as these can be used successfully in T1D management.
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Affiliation(s)
- Ann Herron
- Boston Medical Center, Department of Medicine, Boston, Massachusetts 02118
| | - Catherine Sullivan
- Boston Medical Center, Section of Endocrinology, Diabetes, and Nutrition, Boston, Massachusetts 02118
| | - Elizabeth Brouillard
- Boston Medical Center, Section of Endocrinology, Diabetes, and Nutrition, Boston, Massachusetts 02118
| | - Devin Steenkamp
- Boston University School of Medicine, Boston Medical Center, Section of Endocrinology, Diabetes, and Nutrition, Boston, Massachusetts 02118
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Deeb A, Al Hajeri A, Alhmoudi I, Nagelkerke N. Accurate Carbohydrate Counting Is an Important Determinant of Postprandial Glycemia in Children and Adolescents With Type 1 Diabetes on Insulin Pump Therapy. J Diabetes Sci Technol 2017; 11:753-758. [PMID: 27872168 PMCID: PMC5588816 DOI: 10.1177/1932296816679850] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Carbohydrate (CHO) counting is a key nutritional intervention utilized in the management of diabetes to optimize postprandial glycemia. The aim of the study was to examine the impact of accuracy of CHO counting on the postprandial glucose in children and adolescents with type 1 diabetes on insulin pump therapy. METHODS Children/adolescents with type 1 diabetes who were on insulin pump therapy for a minimum of 6 months are enrolled in the study. Patients were instructed to record details of meals consumed, estimated CHO count per meal, and 2-hour postprandial glucose readings over 3-5 days. Meals' CHO contents were recounted by an experienced clinical dietician, and those within 20% of the dietician's counting were considered accurate. RESULTS A total of 30 patients (21 females) were enrolled. Age range (median) was 8-18 (SD 13) years. Data of 247 meals were analyzed. A total of 165 (67%) meals' CHO contents were accurately counted. Of those, 90 meals (55%) had in-target postprandial glucose ( P < .000). There was an inverse relationship between inaccurate CHO estimates and postprandial glucose. Of the 63 underestimated meals, 55 had above-target glucose, while 12 of the 19 overestimated meals were followed by low glucose. There was no association between accuracy and meal size (Spearman's rho = .019). CONCLUSION Accuracy of CHO counting is an important determining factor of postprandial glycemia. However, other factors should be considered when advising on prandial insulin calculation. Underestimation and overestimation of CHO result in postprandial hyperglycemia and hypoglycemia, respectively. Accuracy does not correlate with meal size.
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Affiliation(s)
- Asma Deeb
- Paediatric Endocrinology Department, Mafraq Hospital, Abu Dhabi, United Arab Emirates
- Asma Deeb, MBBS, MD, Paediatric Endocrinology Department, Mafraq Hospital, Abu Dhabi, United Arab Emirates.
| | - Ahlam Al Hajeri
- Paediatric Endocrinology Department, Mafraq Hospital, Abu Dhabi, United Arab Emirates
| | - Iman Alhmoudi
- Paediatric Endocrinology Department, Mafraq Hospital, Abu Dhabi, United Arab Emirates
| | - Nico Nagelkerke
- Institute of Public Health, United Arab Emirates University, Al Ain, United Arab Emirates
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Bell KJ, Toschi E, Steil GM, Wolpert HA. Optimized Mealtime Insulin Dosing for Fat and Protein in Type 1 Diabetes: Application of a Model-Based Approach to Derive Insulin Doses for Open-Loop Diabetes Management. Diabetes Care 2016; 39:1631-4. [PMID: 27388474 DOI: 10.2337/dc15-2855] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2015] [Accepted: 05/24/2016] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To determine insulin dose adjustments required for coverage of high-fat, high-protein (HFHP) meals in type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS Ten adults with T1D received low-fat, low-protein (LFLP) and HFHP meals with identical carbohydrate content, covered with identical insulin doses. On subsequent occasions, subjects repeated the HFHP meal with an adaptive model-predictive insulin bolus until target postprandial glycemic control was achieved. RESULTS With the same insulin dose, the HFHP increased the glucose incremental area under the curve over twofold (13,320 ± 2,960 vs. 27,092 ± 1,709 mg/dL ⋅ min; P = 0.0013). To achieve target glucose control following the HFHP, 65% more insulin was required (range 17%-124%) with a 30%/70% split over 2.4 h. CONCLUSIONS This study demonstrates that insulin dose calculations need to consider meal composition in addition to carbohydrate content and provides the foundation for new insulin-dosing algorithms to cover meals of varying macronutrient composition.
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Affiliation(s)
- Kirstine J Bell
- Charles Perkins Centre and the School of Molecular Bioscience, The University of Sydney, Sydney, New South Wales, Australia Joslin Diabetes Center, Boston, MA
| | - Elena Toschi
- Joslin Diabetes Center, Boston, MA Harvard Medical School, Boston, MA
| | - Garry M Steil
- Harvard Medical School, Boston, MA Boston Children's Hospital, Boston, MA
| | - Howard A Wolpert
- Joslin Diabetes Center, Boston, MA Harvard Medical School, Boston, MA
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Algorithms to Improve the Prediction of Postprandial Insulinaemia in Response to Common Foods. Nutrients 2016; 8:210. [PMID: 27070641 PMCID: PMC4848679 DOI: 10.3390/nu8040210] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2016] [Revised: 03/29/2016] [Accepted: 04/01/2016] [Indexed: 12/21/2022] Open
Abstract
Dietary patterns that induce excessive insulin secretion may contribute to worsening insulin resistance and beta-cell dysfunction. Our aim was to generate mathematical algorithms to improve the prediction of postprandial glycaemia and insulinaemia for foods of known nutrient composition, glycemic index (GI) and glycemic load (GL). We used an expanded database of food insulin index (FII) values generated by testing 1000 kJ portions of 147 common foods relative to a reference food in lean, young, healthy volunteers. Simple and multiple linear regression analyses were applied to validate previously generated equations for predicting insulinaemia, and develop improved predictive models. Large differences in insulinaemic responses within and between food groups were evident. GL, GI and available carbohydrate content were the strongest predictors of the FII, explaining 55%, 51% and 47% of variation respectively. Fat, protein and sugar were significant but relatively weak predictors, accounting for only 31%, 7% and 13% of the variation respectively. Nutritional composition alone explained only 50% of variability. The best algorithm included a measure of glycemic response, sugar and protein content and explained 78% of variation. Knowledge of the GI or glycaemic response to 1000 kJ portions together with nutrient composition therefore provides a good approximation for ranking of foods according to their “insulin demand”.
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Bell KJ, Gray R, Munns D, Petocz P, Steil G, Howard G, Colagiuri S, Brand-Miller JC. Clinical Application of the Food Insulin Index for Mealtime Insulin Dosing in Adults with Type 1 Diabetes: A Randomized Controlled Trial. Diabetes Technol Ther 2016; 18:218-25. [PMID: 26840067 DOI: 10.1089/dia.2015.0254] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
BACKGROUND The Food Insulin Index (FII) is a novel algorithm for ranking foods based on their insulin demand relative to an isoenergetic reference food. We compared the effect of carbohydrate counting (CC) versus the FII algorithm for estimating insulin dosage on glycemic control in type 1 diabetes. MATERIALS AND METHODS In a randomized, controlled trial, adults (n = 26) using insulin pump therapy were assigned to using either traditional CC or the novel Food Insulin Demand (FID) counting for 12 weeks. Subjects participated in group education and individual sessions. At baseline and on completion of the trial, glycated hemoglobin A1c (HbA1c), day-long glycemia (6-day continuous glucose monitoring), fasting lipids, and C-reactive protein were determined. RESULTS Changes in HbA1c from baseline to 12 weeks were small and not significant in both groups (mean ± SEM; FII vs. CC, -0.1 ± 0.1% vs. -0.3 ± 0.2%; P = 0.855). The incremental area under the curve following breakfast declined significantly among the FID counters with no change in the CC group (FID vs. CC, -93 ± 41 mmol/L/min [P = 0.043] vs. 4 ± 50 mmol/L/min [P = 0.938]; between groups, P = 0.143). The mean amplitude of the glycemic excursion (MAGE) was significantly reduced among the FID counters (FID vs. CC, -6.1 ± 1.0 vs. -1.3 ± 1.0 mmol/L; P = 0.003), and only the FID counters experienced a trend (-44% vs. +11%; P = 0.057) to reduced hypoglycemia. CONCLUSIONS In a 12-week pilot study, MAGE and postprandial glycemia following breakfast were significantly improved with FII counting versus CC, despite no significant differences in HbA1c.
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Affiliation(s)
- Kirstine J Bell
- 1 Charles Perkins Centre, The University of Sydney , Sydney, New South Wales, Australia
- 2 School of Molecular Bioscience, The University of Sydney , Sydney, New South Wales, Australia
| | - Robyn Gray
- 3 Sydney Insulin Pump Clinic , Sydney, New South Wales, Australia
| | - Diane Munns
- 3 Sydney Insulin Pump Clinic , Sydney, New South Wales, Australia
| | - Peter Petocz
- 4 Department of Statistics, Macquarie University , Sydney, New South Wales, Australia
| | - Garry Steil
- 5 Harvard Medical School , Boston, Massachusetts
- 6 Children's Hospital , Boston, Massachusetts
| | - Gabrielle Howard
- 3 Sydney Insulin Pump Clinic , Sydney, New South Wales, Australia
| | - Stephen Colagiuri
- 1 Charles Perkins Centre, The University of Sydney , Sydney, New South Wales, Australia
- 2 School of Molecular Bioscience, The University of Sydney , Sydney, New South Wales, Australia
| | - Jennie C Brand-Miller
- 1 Charles Perkins Centre, The University of Sydney , Sydney, New South Wales, Australia
- 2 School of Molecular Bioscience, The University of Sydney , Sydney, New South Wales, Australia
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26
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Affiliation(s)
- Francesca Annan
- Department of Nutrition and Dietetics, Alder Hey Children's NHS Foundation Trust , Liverpool, United Kingdom
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Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, Ben-Yacov O, Lador D, Avnit-Sagi T, Lotan-Pompan M, Suez J, Mahdi JA, Matot E, Malka G, Kosower N, Rein M, Zilberman-Schapira G, Dohnalová L, Pevsner-Fischer M, Bikovsky R, Halpern Z, Elinav E, Segal E. Personalized Nutrition by Prediction of Glycemic Responses. Cell 2016; 163:1079-1094. [PMID: 26590418 DOI: 10.1016/j.cell.2015.11.001] [Citation(s) in RCA: 1490] [Impact Index Per Article: 186.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2015] [Revised: 10/29/2015] [Accepted: 10/30/2015] [Indexed: 02/06/2023]
Abstract
Elevated postprandial blood glucose levels constitute a global epidemic and a major risk factor for prediabetes and type II diabetes, but existing dietary methods for controlling them have limited efficacy. Here, we continuously monitored week-long glucose levels in an 800-person cohort, measured responses to 46,898 meals, and found high variability in the response to identical meals, suggesting that universal dietary recommendations may have limited utility. We devised a machine-learning algorithm that integrates blood parameters, dietary habits, anthropometrics, physical activity, and gut microbiota measured in this cohort and showed that it accurately predicts personalized postprandial glycemic response to real-life meals. We validated these predictions in an independent 100-person cohort. Finally, a blinded randomized controlled dietary intervention based on this algorithm resulted in significantly lower postprandial responses and consistent alterations to gut microbiota configuration. Together, our results suggest that personalized diets may successfully modify elevated postprandial blood glucose and its metabolic consequences. VIDEO ABSTRACT.
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Affiliation(s)
- David Zeevi
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Tal Korem
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Niv Zmora
- Immunology Department, Weizmann Institute of Science, Rehovot 7610001, Israel; Internal Medicine Department, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel; Research Center for Digestive Tract and Liver Diseases, Tel Aviv Sourasky Medical Center, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6423906, Israel
| | - David Israeli
- Day Care Unit and the Laboratory of Imaging and Brain Stimulation, Kfar Shaul Hospital, Jerusalem Center for Mental Health, Jerusalem 9106000, Israel
| | - Daphna Rothschild
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Adina Weinberger
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Orly Ben-Yacov
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Dar Lador
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Tali Avnit-Sagi
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Maya Lotan-Pompan
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Jotham Suez
- Immunology Department, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Jemal Ali Mahdi
- Immunology Department, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Elad Matot
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Gal Malka
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Noa Kosower
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Michal Rein
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | | | - Lenka Dohnalová
- Immunology Department, Weizmann Institute of Science, Rehovot 7610001, Israel
| | | | - Rony Bikovsky
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Zamir Halpern
- Research Center for Digestive Tract and Liver Diseases, Tel Aviv Sourasky Medical Center, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6423906, Israel; Digestive Center, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel
| | - Eran Elinav
- Immunology Department, Weizmann Institute of Science, Rehovot 7610001, Israel.
| | - Eran Segal
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel.
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Trief PM, Cibula D, Rodriguez E, Akel B, Weinstock RS. Incorrect Insulin Administration: A Problem That Warrants Attention. Clin Diabetes 2016; 34:25-33. [PMID: 26807006 PMCID: PMC4714726 DOI: 10.2337/diaclin.34.1.25] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
In Brief Incorrect administration of insulin (e.g., too little, too much, or at wrong times) can result in transient and serious hypo- and hyperglycemia, wide glycemic excursions, and diabetic ketoacidosis. The authors systematically assessed the insulin-related knowledge and injection skills of a sample of adults with diabetes and found that errors in self-administering insulin, including choosing an incorrect insulin dose, were common. Injection site selection and diabetes numeracy were also concerns. Correct timing of injections and confidence in choosing correct doses, but not skills scores, related to better A1C and blood glucose levels.
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Affiliation(s)
- Paula M. Trief
- Department of Psychiatry and Behavioral Sciences, State University of New York (SUNY) Upstate Medical University, Syracuse, NY
| | - Donald Cibula
- Department of Public Health and Preventive Medicine, SUNY Upstate Medical University, Syracuse, NY
| | - Elaine Rodriguez
- Department of Medicine, SUNY Upstate Medical University, Syracuse, NY
| | - Bridget Akel
- Department of Medicine, SUNY Upstate Medical University, Syracuse, NY
| | - Ruth S. Weinstock
- Department of Medicine, SUNY Upstate Medical University, Syracuse, NY
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Bell KJ, Bao J, Petocz P, Colagiuri S, Brand-Miller JC. Validation of the food insulin index in lean, young, healthy individuals, and type 2 diabetes in the context of mixed meals: an acute randomized crossover trial. Am J Clin Nutr 2015; 102:801-6. [PMID: 26354547 DOI: 10.3945/ajcn.115.112904] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2015] [Accepted: 08/04/2015] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND The Food Insulin Index (FII) is a novel classification of single foods based on insulin responses in healthy subjects relative to an isoenergetic reference food. OBJECTIVE Our aim was to compare day-long responses to 2 nutrient-matched diets predicted to have either high or low insulin demand in healthy controls and individuals with type 2 diabetes (T2DM). DESIGN Twenty adults (10 healthy adults and 10 adults with T2DM) were recruited. On separate mornings, subjects consumed either a high- or low-FII diet in random order. Diets consisted of 3 consecutive meals (breakfast, morning tea, and lunch), matched for macronutrients, fiber, and glycemic index (GI), but with 2-fold difference in insulin demand as predicted by the FII of the component foods. Postprandial glycemia and insulinemia were measured in capillary plasma at regular intervals over 8 h. RESULTS As predicted by their GI, there were no differences in glycemic responses between the 2 diets in either group (mean ± SEM; healthy: 6.2 ± 0.2 compared with 6.1 ± 0.1 mmol/L · min, P = 0.429; T2DM: 9.9 ± 1.3 compared with 10.3 ± 1.6 mmol/L · min, P = 0.485). Compared with the high-FII diet, mean postprandial insulin response over 8 h was 53% lower with the low-FII diet in healthy subjects (mean ± SEM; incremental AUCinsulin 31,900 ± 4100 pmol/L · min compared with 68,100 ± 11,400 pmol/L · min, P = 0.003) and 41% lower in subjects with T2DM (mean ± SEM; incremental AUCinsulin 11,000 ± 1800 pmol/L · min compared with 18,700 ± 3100 pmol/L · min, P = 0.018). Incremental AUCinsulin was statistically significantly different between diets when groups were combined (P = 0.001). CONCLUSIONS The FII algorithm may be a useful tool for reducing postprandial hyperinsulinemia in T2DM, thereby potentially improving insulin resistance and β-cell function. This trial was registered at the Australian New Zealand Clinical Trials Registry as ACTRN12611000654954.
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Affiliation(s)
- Kirstine J Bell
- Boden Institute of Obesity, Nutrition, Exercise and Eating Disorders, Charles Perkins Centre, and the School of Molecular Bioscience, University of Sydney, Sydney, Australia, and
| | - Jiansong Bao
- Boden Institute of Obesity, Nutrition, Exercise and Eating Disorders, Charles Perkins Centre, and the School of Molecular Bioscience, University of Sydney, Sydney, Australia, and
| | - Peter Petocz
- Department of Statistics, Macquarie University, Sydney, Australia
| | - Stephen Colagiuri
- Boden Institute of Obesity, Nutrition, Exercise and Eating Disorders, Charles Perkins Centre, and the School of Molecular Bioscience, University of Sydney, Sydney, Australia, and
| | - Jennie C Brand-Miller
- Boden Institute of Obesity, Nutrition, Exercise and Eating Disorders, Charles Perkins Centre, and the School of Molecular Bioscience, University of Sydney, Sydney, Australia, and
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Paterson M, Bell KJ, O’Connell SM, Smart CE, Shafat A, King B. The Role of Dietary Protein and Fat in Glycaemic Control in Type 1 Diabetes: Implications for Intensive Diabetes Management. Curr Diab Rep 2015; 15:61. [PMID: 26202844 PMCID: PMC4512569 DOI: 10.1007/s11892-015-0630-5] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
A primary focus of the management of type 1 diabetes has been on matching prandial insulin therapy with carbohydrate amount consumed. However, even with the introduction of more flexible intensive insulin regimes, people with type 1 diabetes still struggle to achieve optimal glycaemic control. More recently, dietary fat and protein have been recognised as having a significant impact on postprandial blood glucose levels. Fat and protein independently increase the postprandial glucose excursions and together their effect is additive. This article reviews how the fat and protein in a meal impact the postprandial glycaemic response and discusses practical approaches to managing this in clinical practice. These insights have significant implications for patient education, mealtime insulin dose calculations and dosing strategies.
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Affiliation(s)
- Megan Paterson
- />Department of Paediatric Diabetes and Endocrinology, John Hunter Children’s Hospital, Newcastle, NSW Australia
- />Hunter Medical Research Institute, School of Medicine and Public Health, University of Newcastle, Rankin Park, NSW Australia
| | - Kirstine J. Bell
- />Hunter Medical Research Institute, School of Medicine and Public Health, University of Newcastle, Rankin Park, NSW Australia
| | - Susan M. O’Connell
- />Department of Paediatrics and Child Health, Cork University Hospital, Cork, Ireland
| | - Carmel E. Smart
- />Department of Paediatric Diabetes and Endocrinology, John Hunter Children’s Hospital, Newcastle, NSW Australia
- />Hunter Medical Research Institute, School of Medicine and Public Health, University of Newcastle, Rankin Park, NSW Australia
| | - Amir Shafat
- />Physiology, School of Medicine, National University of Ireland, Galway, Galway, Ireland
| | - Bruce King
- />Department of Paediatric Diabetes and Endocrinology, John Hunter Children’s Hospital, Newcastle, NSW Australia
- />Hunter Medical Research Institute, School of Medicine and Public Health, University of Newcastle, Rankin Park, NSW Australia
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31
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Bell KJ, Smart CE, Steil GM, Brand-Miller JC, King B, Wolpert HA. Impact of fat, protein, and glycemic index on postprandial glucose control in type 1 diabetes: implications for intensive diabetes management in the continuous glucose monitoring era. Diabetes Care 2015; 38:1008-15. [PMID: 25998293 DOI: 10.2337/dc15-0100] [Citation(s) in RCA: 210] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
BACKGROUND Continuous glucose monitoring highlights the complexity of postprandial glucose patterns present in type 1 diabetes and points to the limitations of current approaches to mealtime insulin dosing based primarily on carbohydrate counting. METHODS A systematic review of all relevant biomedical databases, including MEDLINE, Embase, CINAHL, and the Cochrane Central Register of Controlled Trials, was conducted to identify research on the effects of dietary fat, protein, and glycemic index (GI) on acute postprandial glucose control in type 1 diabetes and prandial insulin dosing strategies for these dietary factors. RESULTS All studies examining the effect of fat (n = 7), protein (n = 7), and GI (n = 7) indicated that these dietary factors modify postprandial glycemia. Late postprandial hyperglycemia was the predominant effect of dietary fat; however, in some studies, glucose concentrations were reduced in the first 2-3 h, possibly due to delayed gastric emptying. Ten studies examining insulin bolus dose and delivery patterns required for high-fat and/or high-protein meals were identified. Because of methodological differences and limitations in experimental design, study findings were inconsistent regarding optimal bolus delivery pattern; however, the studies indicated that high-fat/protein meals require more insulin than lower-fat/protein meals with identical carbohydrate content. CONCLUSIONS These studies have important implications for clinical practice and patient education and point to the need for research focused on the development of new insulin dosing algorithms based on meal composition rather than on carbohydrate content alone.
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Affiliation(s)
- Kirstine J Bell
- Charles Perkins Centre and the School of Molecular Bioscience, The University of Sydney, Sydney, Australia Joslin Diabetes Center, Boston, MA
| | - Carmel E Smart
- Department of Paediatric Endocrinology and Diabetes, John Hunter Children's Hospital, Newcastle, Australia Hunter Medical Research Institute, School of Medicine and Public Health, University of Newcastle, Rankin Park, Australia
| | - Garry M Steil
- Children's Hospital, Boston, MA Harvard Medical School, Boston, MA
| | - Jennie C Brand-Miller
- Charles Perkins Centre and the School of Molecular Bioscience, The University of Sydney, Sydney, Australia
| | - Bruce King
- Department of Paediatric Endocrinology and Diabetes, John Hunter Children's Hospital, Newcastle, Australia Hunter Medical Research Institute, School of Medicine and Public Health, University of Newcastle, Rankin Park, Australia
| | - Howard A Wolpert
- Joslin Diabetes Center, Boston, MA Harvard Medical School, Boston, MA
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32
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Smart CE, Annan F, Bruno LPC, Higgins LA, Acerini CL. ISPAD Clinical Practice Consensus Guidelines 2014. Nutritional management in children and adolescents with diabetes. Pediatr Diabetes 2014; 15 Suppl 20:135-53. [PMID: 25182313 DOI: 10.1111/pedi.12175] [Citation(s) in RCA: 78] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2014] [Accepted: 06/11/2014] [Indexed: 12/13/2022] Open
Affiliation(s)
- Carmel E Smart
- Department of Endocrinology, John Hunter Children's Hospital, Newcastle, Australia
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Bell KJ, Gray R, Munns D, Petocz P, Howard G, Colagiuri S, Brand-Miller JC. Estimating insulin demand for protein-containing foods using the food insulin index. Eur J Clin Nutr 2014; 68:1055-9. [PMID: 25005674 DOI: 10.1038/ejcn.2014.126] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2014] [Revised: 04/18/2014] [Accepted: 05/20/2014] [Indexed: 11/09/2022]
Abstract
BACKGROUND/OBJECTIVE The Food Insulin Index (FII) is a novel algorithm for ranking foods on the basis of insulin responses in healthy subjects relative to an isoenergetic reference food. Our aim was to compare postprandial glycemic responses in adults with type 1 diabetes who used both carbohydrate counting and the FII algorithm to estimate the insulin dosage for a variety of protein-containing foods. SUBJECTS/METHODS A total of 11 adults on insulin pump therapy consumed six individual foods (steak, battered fish, poached eggs, low-fat yoghurt, baked beans and peanuts) on two occasions in random order, with the insulin dose determined once by the FII algorithm and once with carbohydrate counting. Postprandial glycemia was measured in capillary blood glucose samples at 15-30 min intervals over 3 h. Researchers and participants were blinded to treatment. RESULTS Compared with carbohydrate counting, the FII algorithm significantly reduced the mean blood glucose level (5.7±0.2 vs 6.5±0.2 mmol/l, P=0.003) and the mean change in blood glucose level (-0.7±0.2 vs 0.1±0.2 mmol/l, P=0.001). Peak blood glucose was reached earlier using the FII algorithm than using carbohydrate counting (34±5 vs 56±7 min, P=0.007). The risk of hypoglycemia was similar in both treatments (48% vs 33% for FII vs carbohydrate counting, respectively, P=0.155). CONCLUSIONS In adults with type 1 diabetes, compared with carbohydrate counting, the novel FII algorithm improved postprandial hyperglycemia after consumption of protein-containing foods.
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Affiliation(s)
- K J Bell
- Boden Institute of Obesity, Nutrition, Exercise and Eating Disorders, School of Molecular Bioscience, The University of Sydney, Sydney, NSW, Australia
| | - R Gray
- Sydney Insulin Pump Clinic, Sydney, NSW, Australia
| | - D Munns
- Sydney Insulin Pump Clinic, Sydney, NSW, Australia
| | - P Petocz
- Department of Statistics, Macquarie University, Sydney, NSW, Australia
| | - G Howard
- Sydney Insulin Pump Clinic, Sydney, NSW, Australia
| | - S Colagiuri
- Boden Institute of Obesity, Nutrition, Exercise and Eating Disorders, School of Molecular Bioscience, The University of Sydney, Sydney, NSW, Australia
| | - J C Brand-Miller
- Boden Institute of Obesity, Nutrition, Exercise and Eating Disorders, School of Molecular Bioscience, The University of Sydney, Sydney, NSW, Australia
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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. [PMID: 23193216 PMCID: PMC3609492 DOI: 10.2337/dc12-0092] [Citation(s) in RCA: 122] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Current guidelines for intensive treatment of type 1 diabetes base the mealtime insulin bolus calculation exclusively on carbohydrate counting. There is strong evidence that free fatty acids impair insulin sensitivity. We hypothesized that patients with type 1 diabetes would require more insulin coverage for higher-fat meals than lower-fat meals with identical carbohydrate content. RESEARCH DESIGN AND METHODS We used a crossover design comparing two 18-h periods of closed-loop glucose control after high-fat (HF) dinner compared with low-fat (LF) dinner. Each dinner had identical carbohydrate and protein content, but different fat content (60 vs. 10 g). RESULTS Seven patients with type 1 diabetes (age, 55 ± 12 years; A1C 7.2 ± 0.8%) successfully completed the protocol. HF dinner required more insulin than LF dinner (12.6 ± 1.9 units vs. 9.0 ± 1.3 units; P = 0.01) and, despite the additional insulin, caused more hyperglycemia (area under the curve >120 mg/dL = 16,967 ± 2,778 vs. 8,350 ± 1,907 mg/dL⋅min; P < 0001). Carbohydrate-to-insulin ratio for HF dinner was significantly lower (9 ± 2 vs. 13 ± 3 g/unit; P = 0.01). There were marked interindividual differences in the effect of dietary fat on insulin requirements (percent increase significantly correlated with daily insulin requirement; R(2) = 0.64; P = 0.03). CONCLUSIONS This evidence that dietary fat increases glucose levels and insulin requirements highlights the limitations of the current carbohydrate-based approach to bolus dose calculation. These findings point to the need for alternative insulin dosing algorithms for higher-fat meals and suggest that dietary fat intake is an important nutritional consideration for glycemic control in individuals with type 1 diabetes.
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Abstract
BACKGROUND The clinical significance of blood glucose meter (BGM) error in the presence of increasing carbohydrate errors in diabetes patients who use both the BGM result and the carbohydrate estimation to dose insulin is unknown. METHODS This Monte Carlo simulation modeled diabetes patients who calculate insulin dosages based on BGM results and carbohydrate estimations. It evaluated the likelihood of on-target insulin dosing and clinically significant insulin dose errors based on data from five BGMs with different levels of performance (expressed as bias and imprecision [coefficient of variation (%CV)]) and increasing levels of carbohydrate estimation errors. The study was performed across three separate preprandial glucose (PPG) ranges (90-150, 150-270, and 270-450 mg/dl). RESULTS When carbohydrate estimation is accurate (%CV = 0%), the likelihood for on-target insulin doses ranged 50.1-98.5%. The likelihood depended on BGM performance and PPG range. In the presence of carbohydrate estimation errors (%CV = 5-20%), the likelihood of on-target insulin dosages markedly decreased (range, 27.2-80.1%) for all BGMs, the likelihood of insulin underdosing (range, 0-12.8%) and overdosing (range, 0-32.3%) increased, and the influence of BGM error on insulin dosing accuracy was blunted. Even in the presence of carbohydrate error, the BGM with the best performance (bias 1.35% and %CV = 4.84) had the highest probability for on-target insulin dosages. CONCLUSIONS Both BGM and carbohydrate estimation error contribute to insulin dosing inaccuracies. The BGM with the best performance was associated with the most on-target insulin dosages.
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Affiliation(s)
- Naunihal S Virdi
- Medical Affairs, LifeScan Inc., Milpitas, California 95035, USA.
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Abstract
Hypoglycemia is the most important and common side effect of insulin therapy. It is also the rate limiting factor in safely achieving excellent glycemic control. A three-fold increased risk of severe hypoglycemia occurs in both type 1 and type 2 diabetes with tight glucose control. This dictates a need to individualize therapy and glycemia goals to minimize this risk. Several ways to reduce hypoglycemia risk are recognized and discussed. They include frequent monitoring of blood sugars with home blood glucose tests and sometimes continuous glucose monitoring (CGM) in order to identify hypoglycemia particularly in hypoglycemia unawareness. Considerations include prompt measured hypoglycemia treatment, attempts to reduce glycemic variability, balancing basal and meal insulin therapy, a pattern therapy approach and use of a physiological mimicry with insulin analogues in a flexible manner. Methods to achieve adequate control while focusing on minimizing the risk of hypoglycemia are delineated in this article.
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Affiliation(s)
- Anthony L McCall
- Division of Endocrinology, University of Virginia School of Medicine, 450 Ray C. Hunt Drive, Charlottesville, VA 22903, USA.
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