Metabolic Testing for Type 1 Diabetes

(LTD Trial)

Recruiting at 1 trial location

Overseen By Mittendorfer Lab

Age: 18 - 65

Sex: Any

Trial Phase: Academic

Sponsor: University of Missouri-Columbia

Must be taking: Insulin

Must not be taking: Lipid metabolism drugs

Disqualifiers: Hypothyroidism, Tobacco use, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Approved in 1 Jurisdiction This treatment is already approved in other countries

Compensation: Varies

Number of Visits: 1

30 Participants Needed

Trial Summary

What is the purpose of this trial?

The purpose of this research study is to understand how type 1 diabetes (T1D) increases the risk for cardiovascular diseases (heart attack and stroke). To this end, the investigators will compare apolipoprotein and triglyceride kinetics in people wtih T1D and healthy control participants.

Will I have to stop taking my current medications?

The trial requires that participants with type 1 diabetes only use insulin and no other diabetes medications. Additionally, you cannot use medications or supplements that affect lipid metabolism (how your body processes fats).

What data supports the effectiveness of metabolic testing for Type 1 Diabetes?

Metabolic testing can help in understanding the complex interactions of carbohydrate, lipid, and protein metabolism in diabetes, which is crucial for precise characterization and management of the disease. Dynamic tests like glucose tolerance tests and glucagon tests are important for evaluating metabolic control and beta-cell function in Type 1 diabetes. ^{1 2 3 4 5}

How does this treatment for Type 1 Diabetes differ from other treatments?

This treatment is unique because it involves metabolic testing to better understand the specific metabolic dysregulation in Type 1 Diabetes, which can help tailor more precise and effective management strategies compared to standard treatments that may not consider individual metabolic profiles. ^{3 5 6 7 8}

Research Team

Bettina Mittendorfer

Principal Investigator

University of Missouri-Columbia

Eligibility Criteria

This study is for adults aged 18-45 with Type 1 Diabetes who haven't had severe hypoglycemia recently, are on a stable insulin regimen without other diabetes meds, aren't obese, and have certain levels of blood fats and sugars. Healthy controls must meet similar criteria but also have normal HbA1c levels.

Inclusion Criteria

Your blood sugar level when you haven't eaten should be less than 100 milligrams per deciliter.

Your HbA1c level is less than 6.5% when you haven't eaten for a long time.

Additional inclusion criteria for control subjects:

Exclusion Criteria

You do more than 1.5 hours of planned exercise every week.

I have a condition like hypothyroidism that affects my cholesterol levels.

Conditions that would make it impossible to complete the study protocol

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Baseline Assessment

Participants undergo baseline assessments including apolipoprotein and triglyceride kinetics using intravenous stable isotope labeled infusions

1 day

1 visit (in-person)

Follow-up

Participants are monitored for safety and effectiveness after baseline assessments

4 weeks

Treatment Details

Interventions

• Metabolic testing

Trial Overview The trial aims to understand how Type 1 Diabetes affects the risk of heart disease by comparing fat and protein particles in the blood (apolipoproteins and triglycerides) between those with T1D and healthy individuals through metabolic testing.

Participant Groups

Group I: Control group Experimental Treatment 1 Intervention

Healthy control group

Group II: T1D group Active Control 1 Intervention

T1D group

Who Is Running the Clinical Trial?

University of Missouri-Columbia

Lead Sponsor

Trials

387

Recruited

629,000+

Washington University School of Medicine

Lead Sponsor

Trials

2,027

Recruited

2,353,000+

University of Washington

Collaborator

Trials

1,858

Recruited

2,023,000+

Findings from Research

Metabolomics, using techniques like mass spectrometry, has shown promise in identifying biomarkers for insulin resistance and type 2 diabetes, particularly focusing on amino acids and lipid metabolism.

Branched-chain amino acids may serve as effective screening biomarkers for early diabetes risk, potentially allowing for timely dietary and exercise interventions to prevent the disease.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Towards metabolic biomarkers of insulin resistance and type 2 diabetes: progress from the metabolome. Roberts, LD., Koulman, A., Griffin, JL.[2022]

Effective diabetes management requires tailored assessment criteria based on the severity of the disease, with plasma and urine glucose measurements remaining the standard for evaluating control.

In mild Type II diabetes, achieving normal glucose levels post-meal is possible, but in severe Type I diabetes, maintaining normoglycemia is typically only feasible before meals, highlighting the risk of hypoglycemia with aggressive treatment.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Methods of assessing diabetic control. Molnar, GD., Marien, GJ., Hunter, AN., et al.[2019]

Diabetes is a complex group of diseases with different causes and responses to treatment, making accurate diagnosis crucial, especially for rare forms like monogenic diabetes.

Many patients with monogenic diabetes, such as MODY or neonatal diabetes, miss out on potential benefits from genetic testing due to high costs and complexity; developing efficient and affordable screening tests using biomarkers could improve diagnosis and treatment.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The clinical application of non-genetic biomarkers for differential diagnosis of monogenic diabetes. Owen, KR., Skupien, J., Malecki, MT.[2023]

Using intermittent continuous glucose monitoring (CGM) alongside traditional A1C testing can enhance diabetes management by providing clearer insights into glucose patterns and their relationship to A1C levels.

The integration of the ambulatory glucose profile (AGP) with novel flash continuous glucose monitoring (FCGM) technologies may improve patient care by making glucose data more accessible and interpretable, although more research is needed to establish best practices.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

PROFESSIONAL FLASH CONTINUOUS GLUCOSE MONITORING WITH AMBULATORY GLUCOSE PROFILE REPORTING TO SUPPLEMENT A1C: RATIONALE AND PRACTICAL IMPLEMENTATION. Hirsch, IB., Verderese, CA.[2022]

Fasting hyperglycemia (≥ 7 mmol/l) is a key diagnostic indicator of diabetes mellitus, which affects multiple metabolic processes including carbohydrate, lipid, and protein metabolism.

Dynamic tests like oral and intravenous glucose tolerance tests, along with HbA1c measurements (targeting < 7%), are essential for accurately assessing metabolic control and beta-cell function in diabetic patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Dynamic tests in the clinical management of diabetes. Picardi, A., Pozzilli, P.[2022]

A study involving 79 subjects identified three key metabolic markers—5-oxoproline, glutamate, and homocysteine—that are significantly altered in patients with fulminant type 1 diabetes (FT1DM) compared to healthy controls and other diabetes types.

These metabolic signatures could help in the rapid and accurate diagnosis of FT1DM, which is crucial for timely treatment to prevent severe clinical consequences.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Serum metabolic signatures of fulminant type 1 diabetes. Lu, J., Zhou, J., Bao, Y., et al.[2015]

The urine c-peptide creatinine ratio (UCPCR) is a promising diagnostic tool for distinguishing between type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM), showing high sensitivity (84.4%) and specificity (91.6%) for T1DM and even higher sensitivity (92.8%) for T2DM when using specific cut-off values.

For monogenic diabetes (MODY), UCPCR demonstrated excellent specificity (98.0%) and good sensitivity (85.2%), indicating its potential effectiveness in clinical settings, although further large-scale studies are needed to confirm its routine use.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Diagnostic Test Accuracy of Urine C-peptide Creatinine Ratio for the Correct Identification of the Type of Diabetes: A Systematic Review. Pappachan, JM., Sunil, B., Fernandez, CJ., et al.[2022]

This study analyzed urine samples from patients with different types of diabetes, including 14 with HNF1A-MODY, 17 with GCK-MODY, 14 with type 2 diabetes, and 34 normoglycaemic controls, using advanced metabolic profiling techniques.

While certain metabolites like betaine, valine, glycine, and glucose were found to be elevated in HNF1A-MODY patients, the study concluded that there are no strong urinary biomarkers to reliably distinguish monogenic diabetes subtypes from type 2 diabetes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Metabolic profiling in Maturity-onset diabetes of the young (MODY) and young onset type 2 diabetes fails to detect robust urinary biomarkers. Gloyn, AL., Faber, JH., Malmodin, D., et al.[2022]

References

1. England pubmed.ncbi.nlm.nih.gov

Towards metabolic biomarkers of insulin resistance and type 2 diabetes: progress from the metabolome. [2022]

2. Germany pubmed.ncbi.nlm.nih.gov

Methods of assessing diabetic control. [2019]

3. Ireland pubmed.ncbi.nlm.nih.gov

The clinical application of non-genetic biomarkers for differential diagnosis of monogenic diabetes. [2023]

4. United States pubmed.ncbi.nlm.nih.gov

PROFESSIONAL FLASH CONTINUOUS GLUCOSE MONITORING WITH AMBULATORY GLUCOSE PROFILE REPORTING TO SUPPLEMENT A1C: RATIONALE AND PRACTICAL IMPLEMENTATION. [2022]

5. Italy pubmed.ncbi.nlm.nih.gov

Dynamic tests in the clinical management of diabetes. [2022]

6. United States pubmed.ncbi.nlm.nih.gov

Serum metabolic signatures of fulminant type 1 diabetes. [2015]

7. England pubmed.ncbi.nlm.nih.gov

Diagnostic Test Accuracy of Urine C-peptide Creatinine Ratio for the Correct Identification of the Type of Diabetes: A Systematic Review. [2022]

8. United States pubmed.ncbi.nlm.nih.gov

Metabolic profiling in Maturity-onset diabetes of the young (MODY) and young onset type 2 diabetes fails to detect robust urinary biomarkers. [2022]