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EnVision CF Multicenter Study of Glucose Tolerance in Cystic Fibrosis

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT03650712
Recruitment Status : Active, not recruiting
First Posted : August 29, 2018
Last Update Posted : December 29, 2021
Sponsor:
Information provided by (Responsible Party):
Katie Larson Ode, University of Iowa

Brief Summary:
Cystic Fibrosis Related Diabetes has been identified by the CF community as one of the top ten priorities for CF research. In CF clinical decline due to dysglycemia begins early, prior to diagnosis of diabetes and increases mortality from pulmonary disease. There is presently no way to determine who, of those with dysglycemia, will experience clinical compromise. However, the CF Center in Milan has found that measurable age- and sex-dependent variables on oral glucose tolerance testing (OGTT) predict β-cell failure-the primary driver of decline in CF. the investigators propose a multi-center trial to develop nomograms of age and sex dependent reference values for OGTT-derived measures including glucose, insulin, c-peptide, and the resultant OGTT-derived estimates of β-cell function, β cell sensitivity to glucose, and oral glucose insulin sensitivity (OGIS) and to determine correlation of these with clinical status (FEV-1, BMI z score, number of pulmonary exacerbations over the past 12 months). In a subset of the cohort the investigators will perform additional studies to determine possible mechanisms driving abnormal β cell function, including the role of lean body mass (as measured by DXA), impact of incretin (GLP-1, GIP) and islet hormones (glucagon, pancreatic polypeptide) on β cell function and the relationship of reactive hypoglycemia and catecholamine responses to β cell function, as well as the relationship of β cell sensitivity to glucose as determined by our model to abnormalities in blood glucose found in a period of free living after the study (determined by continuous glucose monitoring measures (Peak glucose, time spent >200 mg/dl, standard deviation). the investigators will also develop a biobank of stored samples to allow expansion to the full cohort if warranted and to enable future studies of dysglycemia and diabetes in CF. the investigator's eventual goal is utilization of the nomograms to determine the minimum number of measures to accurately predict risk for clinical decline from dysglycemia in CF.

Condition or disease Intervention/treatment Phase
Cystic Fibrosis-related Diabetes Diagnostic Test: Oral glucose tolerance test Diagnostic Test: Continuous glucose monitoring Diagnostic Test: Dexa scan Not Applicable

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Study Type : Interventional  (Clinical Trial)
Actual Enrollment : 404 participants
Allocation: Non-Randomized
Intervention Model: Single Group Assignment
Masking: Double (Participant, Care Provider)
Primary Purpose: Diagnostic
Official Title: EnVision CF Multicenter Study of Glucose Tolerance in Cystic Fibrosis
Actual Study Start Date : July 1, 2019
Estimated Primary Completion Date : August 31, 2022
Estimated Study Completion Date : January 31, 2023

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Cystic Fibrosis
Drug Information available for: Dextrose

Arm Intervention/treatment
frequently sampled oral glucose tolerance testing
frequently sampled oral glucose tolerance testing will be performed in a one-time cross sectional visit
Diagnostic Test: Oral glucose tolerance test
oral glucose solution is given by mouth, blood is drawn prior to the administration of the oral glucose beverage and then at timed intervals afterward. This is the standard test to diagnosis cystic fibrosis related diabetes mellitus. However, this study will have more time points than standard screening for cystic fibrosis related diabetes mellitus

Frequently sampled oral glucose tolerance testing anc CGM
frequently sampled oral glucose tolerance testing will be performed in a one-time cross sectional visit + continuous glucose monitor will be placed after the visit and mailed back
Diagnostic Test: Oral glucose tolerance test
oral glucose solution is given by mouth, blood is drawn prior to the administration of the oral glucose beverage and then at timed intervals afterward. This is the standard test to diagnosis cystic fibrosis related diabetes mellitus. However, this study will have more time points than standard screening for cystic fibrosis related diabetes mellitus

Diagnostic Test: Continuous glucose monitoring
a device is placed on the subject's arm that continuously monitors subcutaneous glucose levels for up to 10 days
Other Names:
  • CGM
  • CGMS

frequently sampled oral glucose tolerance testing and DXA
frequently sampled oral glucose tolerance testing will be performed in a one-time cross sectional visit + a DXA scan will be done at the same visit
Diagnostic Test: Oral glucose tolerance test
oral glucose solution is given by mouth, blood is drawn prior to the administration of the oral glucose beverage and then at timed intervals afterward. This is the standard test to diagnosis cystic fibrosis related diabetes mellitus. However, this study will have more time points than standard screening for cystic fibrosis related diabetes mellitus

Diagnostic Test: Dexa scan
low dose x-rays are used to measure the subject's bone density. This is the standard test to diagnose osteoporosis
Other Names:
  • DXA
  • DEXA

frequently sampled oral glucose tolerance testing, CGM & DXA
frequently sampled oral glucose tolerance testing will be performed in a one-time cross sectional visit + continuous glucose monitor will be placed after the visit and mailed back + a DXA scan will be done at the same visit
Diagnostic Test: Oral glucose tolerance test
oral glucose solution is given by mouth, blood is drawn prior to the administration of the oral glucose beverage and then at timed intervals afterward. This is the standard test to diagnosis cystic fibrosis related diabetes mellitus. However, this study will have more time points than standard screening for cystic fibrosis related diabetes mellitus

Diagnostic Test: Continuous glucose monitoring
a device is placed on the subject's arm that continuously monitors subcutaneous glucose levels for up to 10 days
Other Names:
  • CGM
  • CGMS

Diagnostic Test: Dexa scan
low dose x-rays are used to measure the subject's bone density. This is the standard test to diagnose osteoporosis
Other Names:
  • DXA
  • DEXA




Primary Outcome Measures :
  1. age- and sex-based nomograms for beta cell glucose sensitivity [ Time Frame: the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    The primary endpoint is relationship of beta cell sensitivity to glucose to age. To assess the primary endpoint, the following method will be used: Quantiles of beta-cell glucose sensitivity will be calculated using quantile regression. The outcome variable of the quantile curve is beta-cell glucose sensitivity (continuous, picomol per minute-1 per meter-2 per millimole-1) and the predictor variable is age (continuous, years). On the basis of the available data, we expect a linear relationship between beta-cell glucose sensitivity and age with no heteroskedasticy

  2. age- and sex-based nomograms for OGIS (oral glucose insulin sensitivity) [ Time Frame: the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    The primary outcome of this study is to establish age- and sex-based nomograms ("growth charts") ranging from the 5th-95th % for OGIS (oral glucose insulin sensitivity)


Secondary Outcome Measures :
  1. evaluate the relationships between age and sex-based quantiles for beta cell glucose sensitivity and BMI Z-score [ Time Frame: data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    The relationship of the nomogram for beta cell glucose sensitivity will be assessed by quantile regression with the outcome variable beta cell glucose sensitivity, and the predictor the outcomes BMI Z score

  2. evaluate the relationships between age and sex-based quantiles for beta cell glucose sensitivity and FEV-1 [ Time Frame: the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    The relationship of the nomogram for beta cell glucose sensitivity will be assessed by quantile regression with the outcome variable beta cell glucose sensitivity, and the predictor the outcomes FEV1 score

  3. evaluate the relationships between age and sex-based quantiles for beta cell glucose sensitivity and pulmonary exacerbations in the previous 12 months [ Time Frame: the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    The relationship of the nomogram for beta cell glucose sensitivity will be assessed by quantile regression with the outcome variable beta cell glucose sensitivity, and the predictor the outcome pulmonary exacerbations in the previous 12 months

  4. evaluate the relationships between age and sex-based quantiles for OGIS and BMI z-score [ Time Frame: the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    The relationship of the nomogram for OGIS will be assessed by quantile regression with the outcome variable OGIS, and the predictor the outcomes BMI Z score

  5. evaluate the relationships between age and sex-based quantiles for OGIS and FEV-1 [ Time Frame: the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    The relationship of the nomogram for OGIS will be assessed by quantile regression with the outcome variable OGIS, and the predictor the outcomes FEV-1

  6. evaluate the relationships between age and sex-based quantiles for OGIS and the number of pulmonary exacerbations in the previous 12 months [ Time Frame: the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    The relationship of the nomogram for OGIS will be assessed by quantile regression with the outcome variable OGIS, and the predictor the outcome the number of pulmonary exacerbations in the previous 12 months

  7. evaluate the relationships between age and sex-based quantiles for insulin and BMI z-score [ Time Frame: the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    The relationship of the nomogram for insulin will be assessed by quantile regression with the outcome variable insulin, and the predictor the outcomes BMI Z score

  8. evaluate the relationships between age and sex-based quantiles for insulin and FEV-1 [ Time Frame: the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    The relationship of the nomogram for insulin will be assessed by quantile regression with the outcome variable insulin, and the predictor the outcomes FEV-1

  9. evaluate the relationships between age and sex-based quantiles for insulin and number of pulmonary exacerbations in the previous 12 months [ Time Frame: the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    The relationship of the nomogram for insulin will be assessed by quantile regression with the outcome variable insulin, and the predictor the number of pulmonary exacerbations in the previous 12 months

  10. evaluate the relationships between age and sex-based quantiles for c-peptide and BMI z-score [ Time Frame: the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    The relationship of the nomogram for beta c-peptide will be assessed by quantile regression with the outcome variable c-peptide, and the predictor the outcomes BMI Z score

  11. evaluate the relationships between age and sex-based quantiles for c-peptide and FEV-1 [ Time Frame: the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    The relationship of the nomogram for beta c-peptide will be assessed by quantile regression with the outcome variable c-peptide, and the predictor the outcomes FEV-1

  12. evaluate the relationships between age and sex-based quantiles for c-peptide and number of pulmonary exacerbations in the previous 12 months [ Time Frame: the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    The relationship of the nomogram for beta c-peptide will be assessed by the same method (quantile regression) with the outcome variable c-peptide, and the predictor the outcomes number of pulmonary exacerbations in the previous 12 months


Other Outcome Measures:
  1. the correlation between fat mass and fat free mass as determined by DXA and beta cell function as measured by beta cell sensitivity to glucose [ Time Frame: the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    We will measure the correlation between fat mass and fat free mass as determined by DXA and beta cell function as measured by beta cell sensitivity to glucose. The model will be adjusted for BMI z-score, weight Z score, sex, age, and genotype classification, as well as therapy with CFTR potentiator/correctors.

  2. Determine whether the area under the curve (AUC) for GLP-1 is a significant predictor of beta cell glucose sensitivity [ Time Frame: the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    linear regressions will be used to model the responses We will then determine, in a multivariate model if GLP-1 is a significant predictor of beta cell glucose sensitivity, also controlling for potential confounders such as age, sex, genotype classification and therapy with CFTR potentiator/correctors.

  3. Determine whether the area under the curve (AUC) for GIP is a significant predictor of beta cell glucose sensitivity [ Time Frame: the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    linear regressions will be used to model the responses We will then determine, in a multivariate model if GIP is a significant predictor of beta cell glucose sensitivity, also controlling for potential confounders such as age, sex, genotype classification and therapy with CFTR potentiator/correctors.

  4. Determine whether the area under the curve (AUC) for PP is a significant predictor of beta cell glucose sensitivity [ Time Frame: the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    linear regressions will be used to model the responses We will then determine, in a multivariate model if PP is a significant predictor of beta cell glucose sensitivity, also controlling for potential confounders such as age, sex, genotype classification and therapy with CFTR potentiator/correctors.

  5. Determine whether the area under the curve (AUC) for glucagon is a significant predictor of beta cell glucose sensitivity [ Time Frame: the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    linear regressions will be used to model the responses We will then determine, in a multivariate model if glucagon is a significant predictor of beta cell glucose sensitivity, also controlling for potential confounders such as age, sex, genotype classification and therapy with CFTR potentiator/correctors.

  6. Determine whether the area under the curve (AUC) for GLP-1 is a significant predictor of OGIS [ Time Frame: the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    linear regressions will be used to model the responses We will then determine, in a multivariate model if GLP-1 is a significant predictor of OGIS, also controlling for potential confounders such as age, sex, genotype classification and therapy with CFTR potentiator/correctors

  7. Determine whether the area under the curve (AUC) for GIP is a significant predictor of OGIS [ Time Frame: the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    linear regressions will be used to model the responses We will then determine, in a multivariate model if GIP is a significant predictor of OGIS, also controlling for potential confounders such as age, sex, genotype classification and therapy with CFTR potentiator/correctors.

  8. Determine whether the area under the curve (AUC) for PP is a significant predictor of OGIS [ Time Frame: the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    linear regressions will be used to model the responses We will then determine, in a multivariate model if PP is a significant predictor of OGIS, also controlling for potential confounders such as age, sex, genotype classification and therapy with CFTR potentiator/correctors

  9. Determine whether the area under the curve (AUC) for glucagon is a significant predictor of OGIS [ Time Frame: data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    linear regressions will be used to model the responses We will then determine, in a multivariate model if glucagon is a significant predictor of OGIS, also controlling for potential confounders such as age, sex, genotype classification and therapy with CFTR potentiator/correctors.

  10. mean epinephrine levels in subject with symptomatic hypoglycemia versus those without hypoglycemia [ Time Frame: the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    Mean catecholamine levels (epinephrine, norepinephrine) at blood glucose nadir will be compared between subjects who do and do not report symptomatic hypoglycemia.

  11. mean norepinephrine levels in subject with symptomatic hypoglycemia versus those without hypoglycemia [ Time Frame: the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    Mean catecholamine levels (epinephrine, norepinephrine) at blood glucose nadir will be compared between subjects who do and do not report symptomatic hypoglycemia.

  12. Correlation between CGM Time spent >140 mg/dl, and b-cell glucose sensitivity [ Time Frame: the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    linear regressions will be used to model the responses We will then determine, in a multivariate model Correlation between CGM Time spent >140 mg/dl, and b-cell glucose sensitivity will be determined, controlling for potential confounders

  13. Correlation between CGM Time spent >200 mg/dl, and b-cell glucose sensitivity [ Time Frame: the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    linear regressions will be used to model the responses We will then determine, in a multivariate model Correlation between CGM Time spent >200 mg/dl, and b-cell glucose sensitivity will be determined, controlling for potential confounders.

  14. Correlation between CGM peak glucose and b-cell glucose sensitivity [ Time Frame: the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    linear regressions will be used to model the responses We will then determine, in a multivariate modelCorrelation between CGM peak glucose and b-cell glucose sensitivity will be determined, controlling for potential confounders

  15. Correlation between CGM standard deviation of sensor glucose values and b-cell glucose sensitivity [ Time Frame: the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months ]
    linear regressions will be used to model the responses We will then determine, in a multivariate modelCorrelation between CGM standard deviation of sensor glucose values and b-cell glucose sensitivity will be determined, controlling for potential confounders



Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.


Layout table for eligibility information
Ages Eligible for Study:   6 Years and older   (Child, Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Criteria

Inclusion Criteria:

  1. Age >/= 6 years
  2. Diagnosis of cystic fibrosis
  3. CF patients regularly attending the CF centers
  4. Clinically stable in previous 3wks:

    • absence of major clinical events including pulmonary exacerbations,
    • no change in their habitual treatment regimen including introduction of antibiotics or steroids in the past 3 weeks

Exclusion Criteria:

  1. Diagnosis of type 1 diabetes, type 2 diabetes, or MODY
  2. Organ transplantation
  3. new diagnosis of CFRD in the past 6 months
  4. antidiabetic treatment in past 6 mos (insulin or oral hypoglycemic agents)

    -patients with previous CFRD diagnosis, but not currently taking insulin/glucose-lowering medications for at least 6 months should be included

  5. pulmonary exacerbation associated with systemic steroid requirement in the last 6 months
  6. on CFTR corrector less than 6 months prior to enrollment

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03650712


Locations
Layout table for location information
United States, Colorado
University of Colorado
Aurora, Colorado, United States, 80045
United States, Iowa
University of Iowa
Iowa City, Iowa, United States, 52242
United States, Minnesota
University of Minnesota
Minneapolis, Minnesota, United States, 55455
United States, Missouri
Washington University St. Louis
Saint Louis, Missouri, United States, 63110
Sponsors and Collaborators
Katie Larson Ode
Investigators
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Principal Investigator: Katie Larson Ode, MD University of Iowa
Additional Information:
Publications:
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Responsible Party: Katie Larson Ode, Clinical Associate Professor Pediatric Endocrinology & Diabetes, University of Iowa
ClinicalTrials.gov Identifier: NCT03650712    
Other Study ID Numbers: 201809715
First Posted: August 29, 2018    Key Record Dates
Last Update Posted: December 29, 2021
Last Verified: December 2021
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: Yes
Product Manufactured in and Exported from the U.S.: Yes
Keywords provided by Katie Larson Ode, University of Iowa:
cystic fibrosis
insulin
glucose
children
abnormal glucose tolerance
impaired glucose tolerance
indeterminate glycemia
diabetes
Additional relevant MeSH terms:
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Cystic Fibrosis
Fibrosis
Pathologic Processes
Pancreatic Diseases
Digestive System Diseases
Lung Diseases
Respiratory Tract Diseases
Genetic Diseases, Inborn
Infant, Newborn, Diseases