Literature DB >> 32277270

Reducing postprandial glucose in dietary intervention studies and the magnitude of the effect on diabetes-related risk factors: a systematic review and meta-analysis.

Carolien Ruijgrok1, Ellen E Blaak2, Léonie Egli3, Pierre Dussort4, Sophie Vinoy5, Simone P Rauh1, Joline W Beulens1,6, M Denise Robertson7, Marjan Alssema1,8.   

Abstract

PURPOSE: Reducing postprandial hyperglycemia has beneficial effects on diabetes-related risk factors, but the magnitude of the reduction needed to achieve such an effect is unknown. The purpose of the study was to quantify the relationship of acute glucose and insulin postprandial responses with longer-term effects on diabetes-related risk factors by performing a systematic review and meta-analysis of dietary intervention studies. <br> METHODS: We systematically searched EMBASE and MEDLINE. Dietary intervention studies among any human population aiming to reduce postprandial glycemia, with actual measures of postprandial glucose (PPG) and/or insulin (PPI) as acute exposures (incremental area under the curve, iAUC) as well as markers of glucose metabolism (fasting glucose, HbA1c) and insulin sensitivity (fasting insulin, HOMA-IR) after at least 4 weeks of diet intervention as outcomes were included. Meta-analyses were performed for the effects on acute exposures and on diabetes-related risk factors. The relationship between changes in acute exposures and changes in risk factor outcomes was estimated by meta-regression analyses. <br> RESULTS: Out of the 13,004 screened papers, 13 papers with 14 comparisons were included in the quantitative analysis. The dietary interventions acutely reduced mean PPG [mean difference (MD), - 0.27 mmol/l; 95% CI - 0.41 to - 0.14], but not mean PPI (MD - 7.47 pmol/l; 95% CI - 16.79 to 1.86). There were no significant overall effects on fasting glucose and insulin. HbA1c was reduced by - 0.20% (95% CI - 0.35 to - 0.05). Changes in acute PPG were significantly associated with changes in fasting plasma glucose (FPG) [per 10% change in PPG: β = 0.085 (95% CI 0.003, 0.167), k = 14], but not with fasting insulin [β = 1.20 (95% CI - 0.32, 2.71), k = 12]. Changes in acute PPI were not associated with changes in FPG [per 10% change in PPI: β = - 0.017 (95% CI - 0.056, 0.022), k = 11]. <br> CONCLUSIONS: Only a limited number of postprandial glucose-lowering dietary intervention studies measured acute postprandial exposures to PPG/PPI during the interventions. In this small heterogeneous set of studies, an association was found between the magnitude of the acute postprandial responses and the change in fasting glucose, but no other outcomes. More studies are needed to quantify the relationship between acute postprandial changes and long-term effects on risk factors.

Entities:  

Keywords:  Glucose; Glycemic index; Glycemic load; HbA1c; Insulin

Mesh:

Substances:

Year:  2020        PMID: 32277270      PMCID: PMC7867534          DOI: 10.1007/s00394-020-02240-1

Source DB:  PubMed          Journal:  Eur J Nutr        ISSN: 1436-6207            Impact factor:   5.614


  37 in total

1.  Five-week, low-glycemic index diet decreases total fat mass and improves plasma lipid profile in moderately overweight nondiabetic men.

Authors:  Clara Bouché; Salwa W Rizkalla; Jing Luo; Hubert Vidal; Annie Veronese; Nathalie Pacher; Caroline Fouquet; Vincent Lang; Gérard Slama
Journal:  Diabetes Care       Date:  2002-05       Impact factor: 19.112

2.  Glycaemic index methodology.

Authors:  F Brouns; I Bjorck; K N Frayn; A L Gibbs; V Lang; G Slama; T M S Wolever
Journal:  Nutr Res Rev       Date:  2005-06       Impact factor: 7.800

Review 3.  Lifestyle intervention for prevention of diabetes: determinants of success for future implementation.

Authors:  Cheryl Roumen; Ellen E Blaak; Eva Corpeleijn
Journal:  Nutr Rev       Date:  2009-03       Impact factor: 7.110

4.  Effect of regression from prediabetes to normal glucose regulation on long-term reduction in diabetes risk: results from the Diabetes Prevention Program Outcomes Study.

Authors:  Leigh Perreault; Qing Pan; Kieren J Mather; Karol E Watson; Richard F Hamman; Steven E Kahn
Journal:  Lancet       Date:  2012-06-09       Impact factor: 79.321

Review 5.  Prevention and Treatment of Type 2 Diabetes: A Pathophysiological-Based Approach.

Authors:  Dorit Samocha-Bonet; Sophie Debs; Jerry R Greenfield
Journal:  Trends Endocrinol Metab       Date:  2018-04-14       Impact factor: 12.015

6.  Glycemic index, glycemic load, and dietary fiber intake and incidence of type 2 diabetes in younger and middle-aged women.

Authors:  Matthias B Schulze; Simin Liu; Eric B Rimm; JoAnn E Manson; Walter C Willett; Frank B Hu
Journal:  Am J Clin Nutr       Date:  2004-08       Impact factor: 7.045

7.  Four-week low-glycemic index breakfast with a modest amount of soluble fibers in type 2 diabetic men.

Authors:  Morvarid Kabir; Jean-Michel Oppert; Hubert Vidal; Francoise Bruzzo; Caroline Fiquet; Pierre Wursch; Gerard Slama; Salwa W Rizkalla
Journal:  Metabolism       Date:  2002-07       Impact factor: 8.694

8.  A carbohydrate-reduced high-protein diet acutely decreases postprandial and diurnal glucose excursions in type 2 diabetes patients.

Authors:  Amirsalar Samkani; Mads J Skytte; Daniel Kandel; Stine Kjaer; Arne Astrup; Carolyn F Deacon; Jens J Holst; Sten Madsbad; Jens F Rehfeld; Steen B Haugaard; Thure Krarup
Journal:  Br J Nutr       Date:  2018-04       Impact factor: 3.718

9.  The Cochrane Collaboration's tool for assessing risk of bias in randomised trials.

Authors:  Julian P T Higgins; Douglas G Altman; Peter C Gøtzsche; Peter Jüni; David Moher; Andrew D Oxman; Jelena Savovic; Kenneth F Schulz; Laura Weeks; Jonathan A C Sterne
Journal:  BMJ       Date:  2011-10-18

10.  Fluctuations in HbA1c are associated with a higher incidence of cardiovascular disease in Japanese patients with type 2 diabetes.

Authors:  Ryotaro Bouchi; Tetsuya Babazono; Michino Mugishima; Naoshi Yoshida; Izumi Nyumura; Kiwako Toya; Toshihide Hayashi; Ko Hanai; Nobue Tanaka; Akiko Ishii; Yasuhiko Iwamoto
Journal:  J Diabetes Investig       Date:  2012-03-28       Impact factor: 4.232

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  2 in total

1.  Personalised Nutritional Recommendations Based on Individual Post-Prandial Glycaemic Responses Improve Glycaemic Metrics and PROMs in Patients with Type 2 Diabetes: A Real-World Assessment.

Authors:  Madlen Ungersboeck; Xiaowen Tang; Vanessa Neeff; Dominic Steele; Pascal Grimm; Matthew Fenech
Journal:  Nutrients       Date:  2022-05-19       Impact factor: 6.706

Review 2.  Nutritional strategies to attenuate postprandial glycemic response.

Authors:  Kenneth Pasmans; Ruth C R Meex; Luc J C van Loon; Ellen E Blaak
Journal:  Obes Rev       Date:  2022-06-10       Impact factor: 10.867

  2 in total

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