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Journal of Inherited Metabolic Disease

, Volume 38, Issue 6, pp 1093–1098 | Cite as

Alternative nighttime nutrition regimens in glycogen storage disease type I: a controlled crossover study

  • Michel HochuliEmail author
  • Emanuel Christ
  • Fabian Meienberg
  • Roger Lehmann
  • Jan Krützfeldt
  • Matthias R. Baumgartner
Original Article

Abstract

Background

Traditional approaches for nighttime glycemic control in glycogen storage disease type I (GSDI) include continuous tube feeding, or ingestion of uncooked corn starch (CS) at bedtime. A modified corn starch (MCS) has been shown to prolong euglycemia in some patients. The aim of this study was to evaluate whether stable nighttime glucose control can be achieved with other types of slowly digested carbohydrates in adult GSDI patients.

Methods

In this cross-over study, nocturnal glucose control and fasting times were assessed with three different nocturnal nutrition regimens in five patients, using continuous glucose monitoring (CGMS) in an outpatient everyday life setting. For each patient, continuous glucose profiles were measured after ingestion of (1) CS, (2) MCS or (3) a pasta meal at bedtime, during 5 to 6 consecutive nights for each regimen.

Results

Stable nocturnal glucose control was achieved for all patients with a pasta meal, with a mean duration of glycemia >3.5 mmol/l of 7.6 h (range 5.7-10.8), and >4 mmol/l of 7 h (5.2-9.2), similar to CS and MCS. Fasting glucose before breakfast on workdays (after 7.1 ± 0.8 h) was not significantly different between the three interventions (CS 4.1 ± 0.5 mmol/l, MCS 4.6 ± 0.7 mmol/l, pasta 4.3 ± 0.9 mmol/l). During prolonged fasting on weekends, longer duration of normoglycemia was achieved with CS or MCS than with pasta.

Conclusion

Consumption of cooked pasta is a suitable and more palatable alternative to uncooked corn starch to achieve nighttime glucose control in adult patients with GSDI.

Keywords

Glucose Control Corn Starch Resistant Starch Glycogen Storage Disease Type Nocturnal Hypoglycemia 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported by ‘radiz – Rare Disease Initiative Zurich, Clinical Research Priority Program University of Zurich’. The authors thank patients for participation in this study. We thank Claudia Morach for dietetic advice and instruction of patients, and Ilona Kehret for the help in glucose sensor instruction.

Conflict of interest

Michel Hochuli, Emanuel Christ, Fabian Meienberg, Roger Lehmann, Jan Krützfeldt and Matthias Baumgartner declare that they have no conflict of interest.

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Copyright information

© SSIEM 2015

Authors and Affiliations

  • Michel Hochuli
    • 1
    • 5
    Email author
  • Emanuel Christ
    • 3
  • Fabian Meienberg
    • 4
  • Roger Lehmann
    • 1
  • Jan Krützfeldt
    • 1
  • Matthias R. Baumgartner
    • 2
    • 5
  1. 1.Division of Endocrinology, Diabetes, and Clinical NutritionUniversity Hospital ZurichZurichSwitzerland
  2. 2.Division of Metabolism and Children’s Research CenterUniversity Children’s HospitalZurichSwitzerland
  3. 3.Division of Endocrinology, Diabetology and Clinical NutritionUniversity Hospital of BernBernSwitzerland
  4. 4.Endocrinology, Diabetes, and MetabolismUniversity Hospital BaselBaselSwitzerland
  5. 5.Radiz – Rare Disease Initiative Zurich, Clinical Research Priority Program for Rare DiseasesUniversity of ZurichZurichSwitzerland

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