Validating Imaging Biomarkers of Small Vessel Disease in Diabetic Individuals Using Advanced MRI Techniques.

NCT ID: NCT03754608

Last Updated: 2021-04-19

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 63 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2018-11-07
• Study Completion Date: 2020-08-31

Brief Summary

To develop a set of biomarkers for imaging of small vessel disease in diabetic individuals using advanced MRI techniques. With this the investigators want to document progression of disease both radiologically and clinically.

Detailed Description

The investigators will use a paradigm developed at Yale University called Cognitive Predictive Modelling (CPM) which takes connectivity data and reduces it to two smaller network which predict higher and lower performance in a particular task for which these networks have been optimized. Vascular measures will complement this measure.

Conditions

Small Vessel Disease of Diabetes Mellitus

Study Design

• Observational Model Type: CASE_CONTROL
• Study Time Perspective: PROSPECTIVE

Study Groups

Diabetics/No cognitive impairment

These are individuals with diabetes who do not have vascular cognitive impairment as determined by VASCOG criteria.

MRI including cf-MRI, DTI, ASL and CVR.

Intervention Type: DIAGNOSTIC_TEST

A paradigm developed at Yale University called Cognitive Predictive Modelling which takes connectivity data and reduces it to two smaller network which predict higher and lower performance in a particular task for which these networks have been optimized. This will be supplemented by MRI vascular measures.

Diabetics with vascular cognitive impairment

These are individuals with diabetes who have vascular cognitive impairment as determined by VASCOG criteria.

MRI including cf-MRI, DTI, ASL and CVR.

Intervention Type: DIAGNOSTIC_TEST

A paradigm developed at Yale University called Cognitive Predictive Modelling which takes connectivity data and reduces it to two smaller network which predict higher and lower performance in a particular task for which these networks have been optimized. This will be supplemented by MRI vascular measures.

Interventions

MRI including cf-MRI, DTI, ASL and CVR.

A paradigm developed at Yale University called Cognitive Predictive Modelling which takes connectivity data and reduces it to two smaller network which predict higher and lower performance in a particular task for which these networks have been optimized. This will be supplemented by MRI vascular measures.

Intervention Type: DIAGNOSTIC_TEST

Other Intervention Names

Neuropsychological testing (Cantab), basic laboratory blood tests, small number of CSF

Eligibility Criteria

Inclusion Criteria

A - Vascular cognitive impairment:

1. Presence of diabetes

2. The International Society of Vascular Behavioral and Cognitive Disorders (VASCOG) criteria for vascular cognitive impairment

3. Age between 55 and 90 (inclusive)

4. Score on the Montreal Cognitive Assessment (MOCA) 15 to 23

5. Presence of a responsible caregiver who will accompany mild cognitive impaired (MCI) subjects to all procedures.

6. Evidence of vascular disease

7. The patient should have a capacity to consent.

8. English speakers

B- Cognitively normal elderly Subjects:

1. Presence of diabetes

2. Absence of National Institute on Aging (NIA) - Alzheimer's Association core clinical criteria for probable Alzheimer's Disease (AD) and VASCOG criteria for vascular cognitive impairment.

3. Objective memory scores within normal range for age (do not meet MCI Subjects criterion 2)

4. Age between 55 and 90 (inclusive)

5. English speakers

Exclusion Criteria

1. Any significant neurologic disease (other than vascular cognitive disease and stroke), such as Parkinson's disease, brain tumor, seizure disorder, multiple sclerosis, or history of significant head trauma followed by persistent neurologic deficits.

2. Any significant systemic disease including hepatic failure, heart failure, renal failure, Chronic obstructive pulmonary disease (COPD), active infection and autoimmune disease.

3. Any significant systemic illness or unstable medical condition, including: uncontrolled or insulin-dependent diabetes mellitus, uncorrected hypothyroidism or hyperthyroidism, or systemic cancer.

4. Investigational agents are prohibited 4 weeks prior to entry and for the duration of the study. Previous treatment with an investigational small molecule with anti-amyloid properties or passive immunization against amyloid within 1 year of study entry. Previous treatment with an active immunization against amyloid.

5. History of schizophrenia or other major psychiatric disorder (DSM IV criteria).

6. History of alcohol or substance abuse or dependence (DSM IV criteria) within the past 2 years.

7. Pregnancy, as determined by screening pregnancy tests for pre-menopausal females

8. Impairment of visual or auditory acuity sufficient to interfere with study procedures.

9. Education level < 6 years.

10. Presence of pacemakers, aneurysm clips, artificial heart valves, ear implants, metal fragments or foreign objects in the eyes, skin or body. The presence of claustrophobia, precluding MRI.

11. Drink more than 5 alcoholic drinks per week or any heavy drinking days in the last 30 days

12. Women who are pregnant or nursing, or fail to use one of the following methods of birth control unless she or partner is surgically sterile or she is postmenopausal (hormone contraceptives [oral, implant, injection, patch, or ring], contraceptive sponge, double barrier [diaphragm or condom plus spermicide], or intrauterine device (IUD)

• Minimum Eligible Age: 55 Years
• Maximum Eligible Age: 90 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Yale University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Arash Salardini, MD

Role: PRINCIPAL_INVESTIGATOR

Yale University

Locations

Yale University School of Medicine

New Haven, Connecticut, United States

Countries

United States

Other Identifiers

2000023733

Identifier Type: -

Identifier Source: org_study_id