by Sarah Steinmetz and Amber Brouillette Change in DASH diet score and cardiovascular risk factors in youth with type 1 and type 2 diabetes mellitus: The SEARCH for Diabetes in Youth Study by Sarah Steinmetz and Amber Brouillette

Background/Rationale Observational studies and clinical trials have shown an association with adherence to DASH diet and improvement in CVD risk factors in adults and youth SEARCH (a cross-sectional analysis) showed a DASH-like diet was inversely related to CVD risk factors, including: HTN, total cholesterol, LDL, LDL:HDL, and HbA1C DASH diet: emphasizes: fruits, vegetables, low-fat dairy, whole grains, fish/poultry/nuts, lean red meats, and limited sugar and sweets low-saturated fat, cholesterol, total fat, and sodium No prospective study has assessed the relationship between CVD risk factors and DASH diet adherence in youth with or without diabetes

Objective To investigate whether a change in a DASH diet score was associated with change in CVD risk over multiple time points in youth with both type 1 and type 2 diabetes

Methods Used data per SEARCH protocol data from baseline visit and 2 follow-up visits at 12 and 60 months CV risk measures: DBP, SBP, HDL, LDL, TC, TAGs, HbA1C, waist circumference, and BMI (as z-score) DASH adherence measured by 85 point FFQ each component with maximum score of 10 points: grains, vegetables, fruits, dairy, meat, nuts/seeds/legumes, fats/oils and sweets (80 points total) Each person assessed for: diabetes duration, age, gender, race/ethnicity, income, study site, height, weight, waist circumference, and physical activity

Methods Sample Size: Inclusion criteria: Exclusion criteria: youth with diabetes diagnosis in 2002-2005, >10 years old, had diabetes for at least 6 months at initial visit had completed baseline FFQ Exclusion criteria: missing CVD related measures those fasting for <8 hours at any visit Sample Size: 797 total; 617 with T1DM, 180 with T2DM The final sample consisted of 797 participants. Of the 617 youth with type 1 diabetes, 278 had a complete 12-month follow-up visit (i.e. including diet information) and 231 had a complete 60-month follow-up visit. A total of 65 and 53 participants, respectively, had complete follow-up visits of the 180 youth with type 2 diabetes.

Statistics Stratified by diabetes type To assess the relationship between DASH score and CVD risk factors: longitudinal mixed-models with a random intercept to account for within-subject dependence CVD risk factor measurements modeled as a function of baseline DASH scores and change in DASH scores from baseline displayed relationship between: baseline DASH score and outcome if a change in DASH score was associated with longitudinal outcome Models adjusted for: age, disease duration, race/ethnicity, sex, study site, income, height, BMI z-score, and waist circumference

Results Mean CVD risk factor values and change in DASH diet score: Type 1 diabetes no symmetrical change in DASH score over time (follow up 1: mean change of -0.18, SD=10.1; follow up 2: -0.41, SD=11.2) increase in BP, total cholesterol, TAGs, and HbA1C Type 2 diabetes small overall mean change in DASH score (follow up 1: -0.36, SD=10.7; follow up2: 1.99, SD=11.4) increase in HDL and HbA1C

Results Longitudinal mixed-models separating the effect of diet at baseline from the effect of change in diet and included time-varying covariates per risk factor: Type 1 diabetes Inverse association between change in DASH diet score and HbA1C 10-point increase in DASH=0.20% decrease in HbA1C Type 2 diabetes Inverse association between DASH diet score and systolic BP 10-point increase in DASH=2.02mmHg decrease in SBP Cross-sectional relationship between baseline DASH score and LDL:HDL (B1=-0.14, p=0.0443); baseline DASH score and total cholesterol (B1=-7.78, p=0.0093)

Conclusion Positive change in diet associated with improvements in HbA1C (T1DM) and systolic BP (T2DM) Good quality dietary intake (DASH diet) can be beneficial in youth with type 1 and type 2 diabetes Strengths: longitudinal modeling for assessment of DASH diet changes cross-sectional effect of baseline diet Limitations: small sample size (although modeling approach accounted for loss of follow-up by many time points) no information on changes in macro/micronutrients provided