Hypoglycemia
Conditions
Keywords
Microbiome, DNA, Hormones, Genetics
Brief summary
The goal of this study is to identify physiologic and molecular mechanisms that underlie hypoglycemia in the absence of diabetes (or medications that can cause hypoglycemia) and to investigate potential genetic and microbiome differences which contribute to hypoglycemia. We will test the hypothesis that hypoglycemia in the absence of diabetes is linked to genetic variation or the microbiome, and identify whether additional medical history or diagnoses are enriched in the population of patients with hypoglycemia.
Detailed description
Although there are several conditions which have been identified that cause, or contribute to hypoglycemia, diagnosis can be challenging, as the physiologic, and molecular mechanisms are incompletely understood. Additionally, treatment options are relatively limited, and often incompletely effective and/or not well tolerated. Investigating the causative factors and mechanisms of hypoglycemia is important therefore in improving our understanding in order to develop new and more effective approaches to treatment. The current study aims to: 1. more fully characterize clinical history and demographics in patients with diverse forms of hypoglycemia by creating and analyzing a patient database; 2. for a subset of patients, characterize metabolic and hormonal responses to a standard meal; 3. analyze DNA variants in individuals with hypoglycemia; 4. analyze differences in the intestinal microbiome in individuals with hypoglycemia.
Interventions
OTHEREntry of demographic and medical history data into a deidentified database
Entry into repository for analysis.
Targeted resequencing of DNA to identify variants associated with hypoglycemia, comparing participants with hypoglycemia (both surgical and non-surgical) and healthy controls.
DIAGNOSTIC_TESTStool sample for microbiome analysis
Participants will be asked to provide a fecal sample, collected at home, which will be analyzed to determine the types of bacteria present in the feces.
DIAGNOSTIC_TESTMixed meal tolerance test
For a subset of participants: After an overnight fast, participants will be given a standard liquid mixed meal; blood samples will be collected at baseline (fasting) and at defined time points after a meal for metabolic and hormonal analyses.
DIAGNOSTIC_TESTContinuous glucose monitoring
A CGM sensor (Dexcom G4 or other professional version available at onset of study) will be placed in blinded (masked) mode, and will be worn for 10 days. Data will be analyzed to determine patterns of glucose during both day and night intervals.
DIAGNOSTIC_TESTactivity monitor
The activity monitor (Fitbit Charge 2) will be worn by participants for 10 days, to assess activity, concurrent with CGM sensor wear.
Sponsors
Joslin Diabetes Center
Study design
Observational model
COHORT
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
18 Years to 70 Years
Healthy volunteers
Yes
Inclusion criteria
1. For hypoglycemia group without a history of bariatric surgery: Males or females diagnosed with hypoglycemia with prior episodes of neuroglycopenia. 2. For hypoglycemia group with history of upper gastrointestinal surgery: Males or females diagnosed with ongoing hypoglycemia with prior episodes of neuroglycopenia. 3. For non-surgical controls only: Males or females with no history of upper gastrointestinal surgery and no history of hypoglycemia or diabetes. 4. Age 18-70 years of age, inclusive, at screening. 5. Willingness to provide informed consent and attend one study visit, with option to attend a second visit with mixed meal test, and follow all study procedures
Exclusion criteria
1. Active treatment with any diabetes medications except for acarbose; 2. Known insulinoma, gastrinoma, or other neuroendocrine tumor; Additional
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Entry of medical history data into a deidentified database. | March 2020 through March 2025 | Medical history data will be entered into RedCap for analysis. |
| Entry of physical exam data into a deidentified database. | March 2020 through March 2025 | Pertinent physical exam data will be entered into RedCap for analysis. |
| Entry of laboratory data into a deidentified database. | March 2020 through March 2025 | Laboratory data will be entered into RedCap for analysis. |
| Entry of demographic data into a deidentified database. | March 2020 through March 2025 | Demographic data will be entered into RedCap for analysis. |
| Analysis of participant demographics and medical history, comparing the 3 study groups. | March 2025 | Demographic and medical history data will be summarized in RedCap and compared between groups using ANOVA or chi-square testing, depending on the variable analyzed. |
| Targeted resequencing of DNA to identify variants associated with hypoglycemia, comparing patients with hypoglycemia (both surgical and non-surgical) and healthy controls. | March 2025 | Sequence variants identified during targeted resequencing will be summarized and prevalence will be compared between groups and with population databases. Depending on results of targeted resequencing, additional expanded genotyping may be performed. |
| Analysis of microbiome, comparing study groups. | March 2025 | Microbiome will be characterized by sequencing to obtain metagenomic data and pathway analysis; all data will be adjusted for multiple comparisons. |
| Analysis of glucose patterns during masked continuous glucose monitoring (CGM), including time in range, time in hypoglycemia, time in hyperglycemia, comparing the study groups. | March 2025 | For a subset of participants who consent to participate in optional Visit 2, CGM data will be analyzed to assess mean, median, peak, and nadir sensor glucose values, glycemic variability (GV), severity and length of hypoglycemia (% time glucose \<70, \<60, \<54 mg/dL), and number and duration of severe hypoglycemia (sensor glucose \<54, duration \>15 minutes) will be quantified. Metrics will be assessed over 24 hours and during daytime (6 AM to midnight) and nighttime (midnight to 6 AM) independently. |
| Analysis of metabolic responses during mixed meal testing. | March 2025 | For a subset of participants who consent to participate in optional Visit 2, magnitude of hypoglycemia will be correlated with metabolite levels during meal testing. Metabolites will be measured at set time points after the start of the mixed meal. Linear mixed effects modeling will be utilized to identify group- and time-dependent differences in metabolic responses. Data will be checked to ensure variables conform to assumptions of the analysis. Sensitivity analysis will determine whether missing data are randomly associated with clinical or experimental phenotypes, and assess the impact of missing data on conclusions. The relationship between clinical and metabolic variables will be analyzed using Pearson correlation, and adjusted for multiple comparisons using Benjamini-Hochberg testing. |
| Analysis of hormonal responses during mixed meal testing. | March 2025 | For a subset of participants who consent to participate in optional Visit 2, magnitude of hypoglycemia will be correlated with hormone levels during meal testing. Counterregulatory hormones will be measured at set time points after the start of the mixed meal. Linear mixed effects modeling will be utilized to identify group- and time-dependent differences in counterregulatory hormone responses. Data will be checked to ensure variables conform to assumptions of the analysis. Sensitivity analysis will determine whether missing data are randomly associated with clinical or experimental phenotypes, and assess the impact of missing data on conclusions. The relationship between clinical and hormonal variables will be analyzed using Pearson correlation, and adjusted for multiple comparisons using Benjamini-Hochberg testing. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Relationship between metabolic responses and magnitude of hypoglycemia as determined by CGM. | March 2025 | This is for a subset of participants (non-surgical hypoglycemia and controls) participating in optional Visit 2. Magnitude of hypoglycemia will be correlated with metabolite levels during meal testing. |
| Relationship between hormonal responses and magnitude of hypoglycemia as determined by CGM. | March 2025 | This is for a subset of participants (non-surgical hypoglycemia and controls) participating in optional Visit 2. Magnitude of hypoglycemia will be correlated with counterregulatory hormone levels during meal testing. |
| Relationship between metabolic responses and microbiome. | March 2025 | This is for a subset of participants (non-surgical hypoglycemia and controls) participating in optional Visit 2. Metagenomic data will be correlated with metabolic responses during meal testing. |
| Relationship between hormonal responses and microbiome. | March 2025 | This is for a subset of participants (non-surgical hypoglycemia and controls) participating in optional Visit 2. Metagenomic data will be correlated with counterregulatory hormone responses during meal testing. |
Countries
United States
Contacts
PRINCIPAL_INVESTIGATORMary Elizabeth Patti, MD
Joslin Diabetes Center
Outcome results
None listed