Validation of Machine Learning Based Personalized Nutrition Algorithm to Reduce Postprandial Glycemic Excursions Among North American Individuals With Newly Diagnosed Type 2 Diabetes
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Type2 Diabetes
- Sponsor
- NYU Langone Health
- Enrollment
- 22
- Locations
- 1
- Primary Endpoint
- Observed Incremental Area Under the Curve (iAUCobs)
- Status
- Terminated
- Last Updated
- 5 years ago
Overview
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.
Investigators
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.
Outcomes
Primary Outcomes
Observed Incremental Area Under the Curve (iAUCobs)
Time Frame: 2 Hours
Observed incremental area under the curve (iAUCobs) at 2 hours following each meal and snack will be evaluated via CGM using the Abbott Freestyle Libre Pro, which captures interstitial glucose every 5 minutes. A sensor is inserted into the participant's upper arm. Participants will be blinded to glycemia tracings.