Validation of Machine Learning Based Personalized Nutrition Algorithm to Reduce Postprandial Glycemic Excursions Among North American Individuals With Newly Diagnosed Type 2 Diabetes

• 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.