BACKGROUND: Type 2 diabetes mellitus (T2DM) is characterized by insulin resistance, which is often preceded by poor diet quality, physical inactivity, and weight gain. Evidence shows that lifestyle changes, including increasing diet quality and physical activity can prevent development and improve management of T2DM. It is unknown how diet quality affects specific glycemic traits.
OBJECTIVE: The overall goal of the current study was to evaluate the association between diet quality and continuous glucose monitor (CGM)-derived glycemic traits in non-diabetic individuals.
METHODS: Using data from the Framingham Heart Study, we included participants in the Generation (Gen) 3, New Offspring Spouse, and Omni 2 cohorts without diabetes, who attended the fourth examination and who wore a continuous glucose monitor for ≥3 days from September 2022 to April 2023 (n=569). We further excluded data from 122 participants who did not complete ≥2-day diet records or wear a fit bit physical activity monitor for ≥3 days. We used linear regression models to assess the association of diet quality (macronutrient composition, the Healthy Eating Index [HEI], and HEI components) with CGM-derived mean glucose and glycemic variability, measured using the CGM coefficient of variation (CV) and continuous overall net glycemic action (CONGA1), adjusting for age, sex, CGM device lot number, body mass index (BMI), and physical activity (average Fit bit steps/day).
RESULTS: Of our sample of 447 non-diabetics, we observed 194 (43.4%) with prediabetes, defined as venous fasting glucose ≥126mg/dL, hemoglobin A1c ≥6.5%, or taking glucose lowering medication. We reported that participants with prediabetes had a higher BMI (29 vs. 26 kg/m2), higher mean CGM glucose (123 vs. 113 mg/dL) higher CONGA1 (22.23 vs. 16.5%), similar CV (both 0.2%), and ~3 point lower HEI total score compared to participants with normoglycemia. Among both normoglycemic and prediabetics we observed associations with lower percent energy intake (EI) from total fat and saturated fat, higher percent EI from carbohydrate, and higher refined grains with higher glycemic variability, measured using CONGA1. Many other dietary factors (including total energy, sodium, dairy products, vegetables, fiber, and protein intake) were associated with one of the CGM measures (CONGA1, CV, or mean glucose), but only in normoglycemic or pre-DM participants. No other CGM results consistent among participants from both glycemic status groups.
CONCLUSION: Overall, the daily consumption of more refined grains and total carbohydrates and less fat and protein-containing foods was associated with higher glycemic variability in our non-DM participants. We also observed associations with individual dietary components with some, but not all, glucose metrics, warranting further investigation in larger cohorts.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/47983 |
| Date | 30 January 2024 |
| Creators | Vallejo, Valeria |
| Contributors | Spartano, Nicole |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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