Clinical trial NCT03053518: Validation of Machine Learning Based Personalized Nutrition Algorithm to Reduce Postprandial Glycemic Excursions Among North American Individuals With Newly Diagnosed Type 2 Diabetes
Official title: Validation of Machine Learning Based Personalized Nutrition Algorithm to Reduce Postprandial Glycemic Excursions Among North American Individuals With Newly Diagnosed Type 2 Diabetes
Study type: INTERVENTIONAL
Overall status: TERMINATED
Phase: NA
Conditions: Type2 Diabetes
Keywords: Type 2 Diabetes
Hyperglycemia
Personal Nutrition Project
Lead sponsor: NYU Langone Health
Sponsor class: OTHER
First posted: 2017-02-15
Enrollment: 22
Sex: ALL
Minimum age (years): 21
Maximum age (years): 70
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NON_RANDOMIZED
Intervention model: PARALLEL
Intervention model description: Investigators will recruit approximately 5 cohorts of 4 participants each for the sample size of 20 participants. A new cohort will be randomized approximately every 4 weeks. The maximum number of study participants at any point in the study will be 10.
Primary purpose: TREATMENT

Brief summary: This is an initial validation study of the Personal Nutrition Project (PNP) algorithm in a North American population with recently diagnosed Type 2 Diabetes (T2D). This is a 2-stage, single-group feeding study in 20 individuals, including 10 participants managed with lifestyle alone, and 10 managed with lifestyle plus metformin.

Detailed description: The PNP algorithm, which uses a machine learning algorithm to predict postprandial glycemic, may be efficacious for generating tailored dietary advice to moderate the participant's glycemic response to food.

Eligibility criteria: Inclusion Criteria:

* Age \>21 years to \<70 years
* Diagnosed with T2DM within 2 years with an HbA1c\<7%
* Diabetes management by metformin or lifestyle intervention
* Fasting C-peptide ≥ 0.5 mg/mL (0.17 nmol/L) (to exclude those for whom hyperglycemic exposure is driven by β-cell failure rather than dietary behaviors, as well as those requiring escalation of the medication regime)
* Ownership a smart phone and are willing to use it to monitor multiple factors influencing glycemic response to glycemia (e.g., sleep, physical activity, diet, stress, medication, and hunger)

Exclusion Criteria:

* are unable or unwilling to provide informed consent;
* are unable to participate meaningfully in an intervention that involves self-monitoring using software available in English (e.g., due to uncorrected sight impairment, illiterate, non-English-speaking, dementia);
* are pregnant, are currently trying to become pregnant, or who become pregnant during the study
* are institutionalized (e.g., in a nursing home or personal care facility, or those who are incarcerated and have limited control over self-management)
* have had or are planning to have bariatric surgery during the study
* have a history of heart disease, kidney disease, or retinopathy (to rule-out those with long-standing, undiagnosed T2D)
* those with an active infection requiring antibiotics in the last 3 months or who develop an active infection requiring antibiotics during the study;
* those who use acetaminophen and are unwilling or unable to discontinue its use during the study (acetaminophen affects CGM accuracy)39
* immunosuppressive drugs within three months prior to participation and
* Chronically active inflammatory or neoplastic disease in the three years prior to enrollment.
* Patients with known food allergy.

Primary outcomes:
measure: Observed Incremental Area Under the Curve (iAUCobs)
timeFrame: 2 Hours
Secondary outcomes:
Other outcomes:
Locations:
city: New York
country: United States
facility: New York University School of Medicine
state: New York
Study officials:
affiliation: NYU Langone Health
name: Mary Sevick, MD
role: PRINCIPAL_INVESTIGATOR
References: