EnVision CF Multicenter Study of Glucose Tolerance in Cystic Fibrosis

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.

A. Hypotheses and Specific Aims: Cystic Fibrosis Related Diabetes Mellitus (CFRD) has been identified by the CF community as one of the top ten priorities for CF research1. In CF, clinical decline due to dysglycemia begins early, prior to the diagnosis of CFRD2 and increases pulmonary decline and mortality3. However, current definitions of dysglycemia were derived from non-CF populations, and unfortunately, there is presently no way to determine who, of those with CF and dysglycemia, will experience morbidity, mortality or greatest risk for CFRD. However, the CF Center in Milan found that measurable age and sex dependent variables on oral glucose tolerance testing (OGTT) strongly predict β-cell failure 4- the primary driver of clinical decline. We propose a multi-center trial to determine age and sex dependent reference for fsOGTT-derived estimates of β-cell function and insulin sensitivity in the US CF population, and to investigate the relationship of these measures to CF-relevant clinical outcomes as well as exploring potential mechanisms that may be driving beta cell functional decline. Furthermore, we will collect these data in the new age of cystic fibrosis transmembrane conductance regulator (CFTR) modulator therapies, to define a model that will be most relevant for our current generation of CF patients. Specific Aim 1: To determine age and sex dependent reference values from OGTT and OGTT-derived estimates of β-cell function and insulin sensitivity in a diverse population of 475 non-diabetic children and adults with CF, at 4 centers with EnVision Emerging Leaders as site-PIs. Hypothesis: Much like standard growth charts with means and standard-deviation based percentiles, fsOGTT values (glucose, insulin, c-peptide) and the resultant fsOGTT-derived estimates of β-cell function (beta cell sensitivity to glucose, oral glucose insulin sensitivity) will fall along an age and sex-dependent continuum which will be consistent across CF patients at four different US centers. Specific Aim 2: To determine the relationship between OGTT reference values and estimates of β-cell function and insulin sensitivity to clinically relevant outcomes. Hypothesis: The fsOGTT-derived values of insulin, glucose and C-peptide in combination with estimates of β-cell function (beta cell sensitivity to glucose, OGIS) will correlate with important markers of clinical status (FEV-1, BMI Z score, number of pulmonary exacerbations over the past 12 months) in an age and sex dependent manner. Specific Aim 3 (exploratory): To gather additional measures in a subset of this large cohort to illuminate the relationship between normal ranges of beta cell function (as determined above) to possible mechanisms driving abnormal beta cell function, including: abnormal lean body mass, abnormalities of hormones that regulate beta cell function (specifically incretins (glucagon-like peptide-1 (GLP-1), glucose-dependent insulinotropic polypeptide (GIP)), and islet hormones (glucagon, pancreatic polypeptide (PP)), abnormalities of counter-regulation to abnormal insulin secretion (catecholamine levels), and free-living glucose measures collected by continuous glucose monitoring. Additionally, stored serum from 475 patients with exceptionally well characterized metabolic profiles provides an unprecedented opportunity to help inform future research questions. Hypothesis 3a: Lower lean body mass as assessed by DXA is associated with beta cell glucose sensitivity in an age and sex dependent manner. Hypothesis 3b: Patterns of islet hormone and incretin secretion are age and sex dependent and β cell glucose sensitivity will correlate with PP and GIP, but not with glucagon and GLP-1 Hypothesis 3c: Reduced β cell glucose sensitivity is associated with higher rates of reactive hypoglycemia with inappropriate catecholamine and glucagon response during OGTT. Hypothesis 3d: identifiable CGM measures (ex. Peak glucose, time spent >200 mg/dl, standard deviation) will correlate with model derived outcomes specifically β cell glucose sensitivity in an age and sex-dependent manner B. Background and Significance: Cystic Fibrosis Related Diabetes (CFRD) is an independent cause of increased rates of premature death in people with cystic fibrosis (CF)3. Because of this, the CF community has recognized CFRD as one of their top ten research priorities1. Importantly, abnormalities of blood glucose metabolism (dysglycemia) are present in CF likely from birth5 and cause increasingly rapid lung function decline and loss of healthy body mass years before the diagnosis of CFRD is made2. However, it is unclear at what point individuals might benefit from treatment of dysglycemia, as the treatment itself can be an additional burden. Furthermore, with the increasing use of cystic fibrosis transmembrane conductance regulator (CFTR) modulators, the natural history of CFRD development and progression is likely to change. Thus, it is imperative that we better understand which CF patients with dysglycemia are at the greatest risk of excess mortality due to dysglycemia and progression to CFRD. The primary defect leading to dysglycemia in CF is inadequate insulin secretion secondary to abnormal function of the beta cell6. Importantly, understanding beta cell dysfunction is essential to addressing every form of diabetes mellitus, not just CFRD. However, the gold standard tests for beta cell function (hyperglycemic clamp and tracer studies) are complex and therefore they are not easily utilized in large-scale studies. Due to this, there has been extensive effort on the part of many centers to develop methods to assay beta cell function from more readily accessible data points and, ideally, clinically useful ones. Dr. Andrea Mari, from the Institute of Neuroscience in Italy, has developed a model, which can derive multiple indices of beta cell function from frequently sampled OGTT, with excellent concordance to the gold standard hyperglycemic clamp. This is especially relevant in the CF population where OGTT testing is the recommended modality for diabetes screening. Dr. Mari's model has been well-validated in normal subjects7, subjects with type 1 and type 2 diabetes mellitus and in subjects with pre-diabetes8 and is an accepted alternative for the assessment of beta cell function in large studies where clamp protocols are difficult or impossible9. Importantly, Dr. Alberto Battezzatti, a pediatric endocrinologist at the CF center in Milan, Italy, with a long dedicated career interest in CFRD, has expanded Dr. Mari's modeling to the CF population. Dr. Battezzati has shown that there are strong relationships between the insulin, glucose and C-peptide levels measured during 3 hour OGTT testing and the age and sex of CF subjects4. Our goal, in concert with Dr. Battezzati and colleagues is to develop normal ranges for these measures or, in essence, a "growth chart" for the OGTT in CF patients that can accurately predict beta cell dysfunction and eventual progression to CFRD. Next steps are to simplify the model to so that 2-3 discrete samples will accurately predict progression to CFRD and morbidity in CF subjects. The investigators propose a collaboration with Dr. Battezzati and the Italian CF centers. the investigators will validate the Italian model in a large and diverse US population, and correlate it to specific clinical outcomes relevant to morbidity and mortality in CF, which will allow for development of predictors in the model specific to US subjects and ensure clinical relevance to our patients. Furthermore, the investigaros will collect these results and follow patient outcomes in this new era of CFTR modulator therapy, developing a model that will be most relevant for the current generation of patients with CF. This will establish age and sex-dependent "normal ranges" for glucose, insulin and c-peptide values from OGTT as well as determinants of beta cell function derived from OGTT. Establishment of normal ranges of these measures will allow the determination of which patients with CF are the lost abnormal and eventually determine who, within a given glucose tolerance category in CF will experience clinical compromise and progress to CFRD. The long-term goal will be to develop from these normal ranges, including normal ranges for indices of beta cell function and insulin resistance, the most predictive time points and assays to develop a simplified model that will optimally predict risk of clinical decline in a CF patient of a given age and sex. The ideal model will consist of 2- 3 discrete time points to accurately predict clinical deterioration. All four of the investigators on this grant have established or are developing a long term career focus on Cystic Fibrosis Related Diabetes Mellitus and dysglycemia in Cystic Fibrosis. This project represents a significant opportunity to characterize a large cohort of subjects with well-defined dysglycemia and beta cell dysfunction in collaboration with an equally-large international cohort with matching data sets. It represents an unprecedented opportunity to fully explore the depth and breadth of dysglycemia in CF as well as to investigate the impact dysglycemia has on the health of people with CF. The long term value of this project is also enhanced by the establishment of a biorepository of stored samples that will be obtained from this cohort. This will allow the ability in the future to query these samples to further investigate the natural history and pathophysiology of CFRD, and potentially biomarkers with in a large cohort with clearly and accurately defined beta cell function. This group has unique assets to complete this task as we represent four unique CF centers; all with dedicated CF endocrinologists recognized as Emerging Leaders in CF Endocrinology by the EnVision program, with coordinated mentorship though that program by the leading expert in the field of CFRD, Dr. Antoinette Moran. C. Preliminary Studies: Currently the recommended screening test for CFRD is the oral glucose tolerance test (OGTT)10. Although abnormalities on this test predict long term outcomes in CF, the diagnostic endpoints we currently use are based on data from other adults with type 2 diabetes and are not specific to CF. There is emerging evidence that CF patients who do not meet formal criteria for glucose abnormalities by OGTT are still experiencing clinical decline11,12. Therefore, there remains a knowledge gap regarding how we can best predict clinical decline. Given that the primary pathology in CF is insulin deficiency, it is reasonable to extrapolate that measures of beta cell function would be the best indicators of outcomes in CF patients. Typical simplified methods of assessing beta cell function (such as HOMA-B) utilize primarily fasting measurements, which are inappropriate in CF where fasting abnormalities develop after the onset of frank CFRD and long after clinical decline is established. Dr. Andrea Mari has extensive data showing the effectiveness of his model in evaluating beta cell glucose sensitivity (the ability of the beta cell to respond to respond to changes in glucose concentration)7,13,14. The OGTT itself has poor reproducibility, however beta cell glucose sensitivity as measured from frequently sampled OGTT has significantly better reproducibility, with a coefficient of variation around 15-20%7,15. This model of beta cell glucose sensitivity is an extremely accurate parameter to assess glucose control and has been found to be a significant predictor for the development of type 1 and type 2 diabetes in non-diabetic and pre- diabetic populations. This model allows, as well, measurement of first phase insulin secretion (termed "rate sensitivity" - which is directly comparable to the acute insulin response obtained from the gold standard IVGTT)16. This is significant, as loss of first phase insulin secretion is the initial abnormality in the progression to CFRD. This model is highly reproducible, and has also been studied longitudinally15,17 (see figure 1). Preliminary data -islet and incretin hormones: Dr. Larson Ode has devoted her career to understanding the origins of dysglycemia in CF. Her work has upended previous dogma, finding that up to 39% of CF children 3 months to 5 years of age may have dysglycemia18. However, it is not yet known what underlying mechanisms drive dysglycemia in CF. In a previous small dataset (n=25 CF, n=9 control), 9 of 25 CF subjects had dysglycemia (all controls were normal). Stored blood was assayed for islet and incretin hormones to interrogate the difference between children with and without dysglycemia. CF children with abnormal glucose tolerance had higher GIP levels (p<0.03) and a trend toward lower GLP-1 levels compared to normal CF subjects (p= 0.06). All CF subjects had abnormally low PP levels (p<0.01). Glucagon levels were uninformative (unpublished data- see Figure 1). This implies that incretin and islet hormones may play a role in dysglycemia in CF. However, data from larger and older cohorts are needed to fully understand incretin and islet hormone patterns in people with CF. This proposed project will establish a biobank which will allow the study of age based progression in biomarkers of islet dysfunction throughout a diverse cohort including a large age-range of subjects. The deep understanding of CF typical beta cell function changes with age that will be established by this cohort will allow greatly improved understanding of the progression of dysglycemia in CF. Experience with CGM: Dr. Chan currently has a CFFT Shwachman grant to investigate the role of CGM in CF. She also has previously used CGM in other studies, enrolling over 100 obese youth to undergo OGTTs and wear CGM in order to better understand simpler measures of prediabetes and type 2 diabetes screening in the obese adolescent population19,20. Although her CGM in CF study is currently active, recruitment will be ending soon and will not overlap with the timing in this proposed study. The present proposal, with its target enrollment of 475 individuals with CF, offers an unprecedented opportunity to further our understanding of CGM and its relationship with the proposed model and its novel age- and sex-specific outcomes Experience with hypoglycemia research: Dr. Moheet has extensive experience in diabetes research with special expertise in the gold standard tests for beta cell function (insulin clamp studies). His research utilizes methods relevant to the study of in vivo metabolism in human subjects, particularly use of both hyperglycemic and hypoglycemic insulin clamps methodologies. His research has examined the underlying pathogenesis in development of defective counterregulatory response to hypoglycemia in people with type 1 diabetes. Dr. Moheet's substantial background in hypoglycemia research would be invaluable in the conduct of this study. Fasting and reactive hypoglycemia after meals is common in CF patient without diabetes21. Reactive hypoglycemia can be particular bothersome and difficult to manage in some patients with CF. Hypoglycemia has also been frequently reported during OGTT in CF with reported rates for 3 hour OGTT between 15-59%22,23. A recent systematic review noted that, however, that the pathophysiology is poorly understood and highlighted the need for high quality studies to examine the possible etiologies underlying reactive hypoglycemia in CF24. This proposed study will address this knowledge gap by evaluating counterregulatory hormones including glucagon and catecholamines during hypoglycemia and assessing symptomatic hypoglycemia via a validated and structured questionnaire25. This proposed study will provide an unprecedented opportunity to examine the possible mechanisms underlying reactive hypoglycemia in CF in a large cohort of patients Preliminary data-body composition: Dr. Granados has previously investigated the association between body composition (% fat and LBM by DXA) and measurements of insulin resistance (HOMA-IR), insulin sensitivity (Insulin Sensitivity Index - Matsuda (ISIMatsuda)) and β-cell insulin secretion (Φtotal) in 15 CF patients with pancreatic insufficiency between 12-24 years old (mean BMI 19.5 ± 2.87 kg/m2). Her preliminary data showed that insulin resistance significantly correlated with weight z-scores (HOMA-IR:r=0.48, p=0.003) and ISIMatsuda (r=-0.46, p=0.004). She also found a positive correlation between Φtotal, (measurement of insulin secretion in response to glucose) and fat % in the subjects with CF without diabetes (r=0.69, p=0.004). Moreover, there was a trend in those with lower fat % and higher LBM to have better insulin sensitivity (ISIMatsuda:r=-0.45, p=0.09). D. Experimental Design and Methods: Study Hypothesis Primary hypothesis: Much like standard growth charts with means and standard-deviation based percentiles, fsOGTT values (glucose, insulin, c-peptide) and the resultant fsOGTT-derived estimates of β-cell function (beta cell sensitivity to glucose, oral glucose insulin sensitivity) will fall along an age and sex-dependent continuum which will be consistent across CF patients at four different US centers. Secondary hypothesis: The fsOGTT-derived values of insulin, glucose and C-peptide in combination with estimates of β-cell function (beta cell sensitivity to glucose, OGIS) will correlate with important markers of clinical status (FEV-1, BMI Z score, number of pulmonary exacerbations over the past 12 months) in an age and sex dependent manner. Exploratory hypotheses: Hypothesis 3a: Lower lean body mass as assessed by DXA is associated with beta cell glucose sensitivity in an age and sex dependent manner. Hypothesis 3b: Patterns of islet hormone and incretin secretion are age and sex dependent and β cell glucose sensitivity will correlate with PP and GIP, but not with glucagon and GLP-1 Hypothesis 3c: Reduced β cell glucose sensitivity is associated with higher rates of reactive hypoglycemia with inappropriate catecholamine and glucagon response during OGTT. Hypothesis 3d: Identifiable CGM measures (ex. Peak glucose, time spent >200 mg/dl, standard deviation) will correlate with β cell glucose sensitivity in an age and sex dependent manner. Screening and Enrollment The University of Minnesota CF center has 332 CF patients eligible for this study. The University of Iowa has 144 eligible subjects. The University of Colorado pediatric center has approximately 225 eligible subjects. Washington University in St. Louis has 236 eligible subjects. Experience from recruiting previous OGTT based studies in these centers suggests 40-60% of CF subjects will agree to participate. It is expected that approximately 160 subjects/year will participate, collectively over all the sites, to reach a total of 475 subjects by the end of the 3 years of the study. Experimental design and procedures: Study Assessments: Data to be collected at enrollment 1) Age, sex, date of birth, diagnosis of CF (genotype and sweat test results), any diagnosis of diabetes mellitus, date of last hospitalization for pulmonary exacerbation and initiation of any systemic steroid therapy. We will collect a list of the subject's current and active medications from the parent (if the subject is a minor) or from the subject (if the subject is an adult). 2) Medical Records: consent will be obtrained to request the following information from clinic and hospital medical records for participants, with HIPAA authorization: a. medications, results of pulmonary function testing, specifically most recent FEV1% predicted, FVC, CFTR genotype and seat testing results,. history of steroid use, number of hospitalizations for pulmonary exacerbation in the last year (defined as admission for iv antibiotics or steroid therapy), fecal elastase Data to be collected at Study visits Frequently sampled 3-hour oral glucose tolerance test (OGTT): Subjects are admitted to the clinical research unit Fasting status is confirmed An IV is placed for sequential laboratory draws. Topical lidocaine cream will be provided for the IV start site if requested by the family. The oral glucose solution (Glucola) at the dose of 1.75 g/kg is administered by mouth at time zero. Insulin, glucose, c-peptide, and additional serum to store will be obtained at 0, 15, 30, 60, 90, 120, 150 and 180 minutes. Additional blood will be stored for future testing. Each sample should require no more than 1-2 tbsp of blood. Sample amount will be based on patient weight and will not exceed 3 ml/kg/day total blood withdrawn. Samples will be processed, batched, and shipped to the University of Iowa for analysis. If the IV is lost at any point during the study, we will request permission from the family to obtain samples by finger-stick or heel poke. Anthropometrics at each visit. a. Height on stadiometer for all subjects b. Weight on standing scale for all subjects. c. These measurements to be used to calculate BMI and BMI z-score if applicable. Additional data collection. Medication list will be recorded and verified with the subject/parents at the start of each visit. From the medical record: i. Last hospitalization, number of hospitalizations in the past year ii. Last systemic steroid therapy (if ever) iii. Last Antidiabetic medication use (if ever) iv. Diagnosis of diabetes- yes/no, date v. Fecal elastase values vi. CFTR mutation vii. Results of sweat testing viii. Use of CFTR correctors/modulators (if any)- Starting date A subset of the subjects will complete additional assessments evaluating the exploratory endpoints. The goal is that 1/3 of the total subjects will participate in these protocols. DXA scan 1) A DXA scan will be obtained the date of the study visit or within a 2 week period from the OGTT. a. Hologic Inc. (Bedford, MA, USA) machines will be used to standardize results CGM 1) A blinded CGM Freestyle LibrePro (Abbott Diabetes Care Inc) will be placed on the back of the upper arm. Participants will be asked to wear the CGMS for up to 10 days. The CGMS does not require calibration, but participants will be asked not to wear the monitor in water deeper than three feet, or to keep it submerged longer than 30 minutes. Participants will also be instructed not to change any of their current dietary or activity habits so that free-living glucose data are collected They will also be asked to complete a simple log of their activity and dietary intake. Subjects will be given the option of personally returning the CGM and log-sheet to the CTRC or be provided a self-addressed, stamped envelope to return the device and log-sheet at the completion of the recording period. Using software that accompanies the CGM recorder, reports will be generated for each subject providing a summary of the sensor glucose data. These results will be converted into raw data in an excel spreadsheet and saved on a password-protected, secure network server, via a local computer, in a locked office to which only the investigators will have access. i. These raw data include sensor glucose values obtained every 15 minutes during device wear. g. We will review CGM tracings to analyze additional variables including: i. area under the total CGM curve ii. number of excursions >140 mg/dl and >200 mg/dl iii. maximum sensor glucose after first meal of the day, maximum sensor glucose, sensor glucose mean iv. standard deviation and other measures of glycemic variability v. Measures of hypoglycemia specifically % time spent <60 and <70mg/dl. The Freestyle Libre Pro system has been approved in Europe and has been studied in children as young as 4 years of age for up to 14 days of sensor wear. It does not display blood glucose information in real time, and participants will not be given the capability to view the CGMS data during the study. This CGM does not require any calibration or training and can easily be removed and returned Laboratory assays Clinical research assays a. plasma Glucose is run via YSI analyzer in order to return results quickly to subjects, all other assays are batched. Research assays will be performed by Ms. Yi in the flow core at the University of Iowa, all assays except glucose use the same platform, which allows multiple samples to be run accurately on microliters of blood a. GLP-1 (active &total), GIP, PP, glucagon, c-peptide, insulin i. HMHEMAG-34K | MILLIPLEX luminex xMAP Human Metabolic Hormone Magnetic Bead Panel - Metabolism Multiplex Assay (Millipore Sigma, Burlington, MA, USA) ii. No cross-reactivity is seen within the panel, the assays show high recovery (97-105%), good precision (all assays have intra-assay coefficient of variation<10% and inter-assay coefficient of variation <15%, except GLP-1 total which has an inter-assay coefficient of variation of <20%) iii. Insulin an c-peptide are measured on this platform as well to allow for quality control comparison with clinical samples Sample size estimates/ Power calculation: Primary outcomes: Using 187 subjects from a single Italian center, the Italian group has previously defined the 75.0 quantile for various glucose tolerance and beta cell function parameters, including beta-cell glucose sensitivity. As the goal of the present project is to develop the 5th to 95th quantiles, due to the clinical relevance of cut offs based on the 5th and 95th percentile, sample size for the present project was calculated to estimate the 95.0 quantile of a covariate-dependent quantile curve with a pre-specified precision. The primary outcome variable of the quantile curve is beta-cell sensitivity to glucose and the predictor variable is age. On the basis of the available data 4 we expect a linear relationship between beta-cell glucose sensitivity and age with no heteroskedasticity. Therefore the minimum sample size needed to estimate the 95.0 quantile of beta-cell glucose sensitivity -for-age with a 90% probability for it to lie between the 90.5 and 97.4 quantiles (with respect to symmetric tolerance evaluated on a logit scale) is 475 subjects31. For the other outcomes (glucose, insulin, c-peptide) the minimum sample size is smaller than the 475 subjects needed for primary endpoint of beta cell glucose sensitivity. The University of Minnesota CF center has 332 CF patients eligible for this study. The University of Iowa has 144 eligible subjects. The University of Colorado pediatric CF center has 225 eligible subjects. Washington University in St. Louis has 236 eligible subjects. Historically at the University of Minnesota and the University of Iowa, we have had excellent enrollment rates of CF subjects in CFRD studies, so we expect about 40-60% of eligible subjects to enroll at some point over the 3 year duration of the study. Safety evaluation and quality control A formal DSMP will be in place for this project. Please see attached DSMP. There will be no need for efficacy analysis as this is not an interventional study.

cystic fibrosis, insulin, glucose, children, abnormal glucose tolerance, impaired glucose tolerance, indeterminate glycemia, diabetes

frequently sampled oral glucose tolerance testing will be performed in a one-time cross sectional visit

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

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

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

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

a device is placed on the subject's arm that continuously monitors subcutaneous glucose levels for up to 10 days

low dose x-rays are used to measure the subject's bone density. This is the standard test to diagnose osteoporosis

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

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)

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

evaluate the relationships between age and sex-based quantiles for beta cell glucose sensitivity and BMI Z-score

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

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

evaluate the relationships between age and sex-based quantiles for beta cell glucose sensitivity and FEV-1

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

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

evaluate the relationships between age and sex-based quantiles for beta cell glucose sensitivity and pulmonary exacerbations in the previous 12 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

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

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

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

evaluate the relationships between age and sex-based quantiles for OGIS and the number of pulmonary exacerbations in the previous 12 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

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

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

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

evaluate the relationships between age and sex-based quantiles for insulin and number of pulmonary exacerbations in the previous 12 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

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

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

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

evaluate the relationships between age and sex-based quantiles for c-peptide and number of pulmonary exacerbations in the previous 12 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

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 correlation between fat mass and fat free mass as determined by DXA and beta cell function as measured by beta cell sensitivity to glucose

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.

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

Determine whether the area under the curve (AUC) for GLP-1 is a significant predictor of beta cell glucose sensitivity

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.

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

Determine whether the area under the curve (AUC) for GIP is a significant predictor of beta cell glucose sensitivity

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.

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

Determine whether the area under the curve (AUC) for PP is a significant predictor of beta cell glucose sensitivity

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.

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

Determine whether the area under the curve (AUC) for glucagon is a significant predictor of beta cell glucose sensitivity

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.

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

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.

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

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 OGIS, also controlling for potential confounders such as age, sex, genotype classification and therapy with CFTR potentiator/correctors.

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.

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 norepinephrine levels in subject with symptomatic hypoglycemia versus those without hypoglycemia

Mean catecholamine levels (epinephrine, norepinephrine) at blood glucose nadir will be compared between subjects who do and do not report symptomatic hypoglycemia.

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

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.

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

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

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

Inclusion Criteria: Age >/= 6 years Diagnosis of cystic fibrosis CF patients regularly attending the CF centers 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: Diagnosis of type 1 diabetes, type 2 diabetes, or MODY Organ transplantation new diagnosis of CFRD in the past 6 months 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 pulmonary exacerbation associated with systemic steroid requirement in the last 6 months on CFTR corrector less than 6 months prior to enrollment

Battezzati A, Bedogni G, Zazzeron L, Mari A, Battezzati PM, Alicandro G, Bertoli S, Colombo C. Age- and Sex-Dependent Distribution of OGTT-Related Variables in a Population of Cystic Fibrosis Patients. J Clin Endocrinol Metab. 2015 Aug;100(8):2963-71. doi: 10.1210/jc.2015-1512. Epub 2015 Jun 9.

Mari A, Pacini G, Murphy E, Ludvik B, Nolan JJ. A model-based method for assessing insulin sensitivity from the oral glucose tolerance test. Diabetes Care. 2001 Mar;24(3):539-48. doi: 10.2337/diacare.24.3.539. Erratum In: Diabetes Care. 2014 Jul;37(7):2063.

Mari A, Ferrannini E. Beta-cell function assessment from modelling of oral tests: an effective approach. Diabetes Obes Metab. 2008 Nov;10 Suppl 4:77-87. doi: 10.1111/j.1463-1326.2008.00946.x.

https://www.cff.org/Research/Researcher-Resources/Awards-and-Grants/Research-Awards/Clinical-Research-Award-Policies-and-Guidelines.pdf