Heterogeneous Effects of Fructose on Blood Lipids in Individuals With Type 2 Diabetes

OBJECTIVE Because of blood lipid concerns, diabetes associations discourage fructose at high intakes. To quantify the effect of fructose on blood lipids in diabetes, we conducted a systematic review and meta-analysis of experimental clinical trials investigating the effect of isocaloric fructose exchange for carbohydrate on triglycerides, total cholesterol, LDL cholesterol, and HDL cholesterol in type 1 and 2 diabetes. RESEARCH DESIGN AND METHODS We searched MEDLINE, EMBASE, CINAHL, and the Cochrane Library for relevant trials of > or =7 days. Data were pooled by the generic inverse variance method and expressed as standardized mean differences with 95% CI. Heterogeneity was assessed by chi(2) tests and quantified by I(2). Meta-regression models identified dose threshold and independent predictors of effects. RESULTS Sixteen trials (236 subjects) met the eligibility criteria. Isocaloric fructose exchange for carbohydrate raised triglycerides and lowered total cholesterol under specific conditions without affecting LDL cholesterol or HDL cholesterol. A triglyceride-raising effect without heterogeneity was seen only in type 2 diabetes when the reference carbohydrate was starch (mean difference 0.24 [95% CI 0.05-0.44]), dose was >60 g/day (0.18 [0.00-0.37]), or follow-up was < or =4 weeks (0.18 [0.00-0.35]). Piecewise meta-regression confirmed a dose threshold of 60 g/day (R(2) = 0.13)/10% energy (R(2) = 0.36). A total cholesterol-lowering effect without heterogeneity was seen only in type 2 diabetes under the following conditions: no randomization and poor study quality (-0.19 [-0.34 to -0.05]), dietary fat >30% energy (-0.33 [-0.52 to -0.15]), or crystalline fructose (-0.28 [-0.47 to -0.09]). Multivariate meta-regression analyses were largely in agreement. CONCLUSIONS Pooled analyses demonstrated conditional triglyceride-raising and total cholesterol-lowering effects of isocaloric fructose exchange for carbohydrate in type 2 diabetes. Recommendations and large-scale future trials need to address the heterogeneity in the data.