Low Glycemic Index Diets on Glycemic and Lipid Control Across Diverse Populations: A Systematic Review and Meta-Analysis
Xihan Sun, Shuang Meng, Wenting Peng, Huangkun Li, Hongwang Dong, Ge Song, Wentao QiBackground:
Although low-Glycemic-Index (low-GI) diets are proposed to improve glucose balance and lipid metabolism, findings from Randomized Controlled Trials (RCTs) remain inconsistent.
Objective:
To comprehensively evaluate the overall effects of low-GI diets on glycemic and lipid profiles across diverse populations and to identify potential differences in outcomes among subgroups.
Methods:
This systematic review and meta-analysis, conducted following PRISMA guidelines, searched PubMed, Embase, Cochrane Library, and Web of Science through April 2025. Weighted Mean Difference (WMD) with 95% Confidence Interval (CI) was calculated. Subgroup and sensitivity analyses explored heterogeneity and robustness.
Results:
Twenty-five RCTs involving 1,973 participants (12-75 years) were included. Low-GI diets significantly reduced fasting blood glucose (FBG; -0.19 mmol/L, p = 0.04), glycated hemoglobin (HbA1c; -0.22%, p < 0.01), and low-density lipoprotein cholesterol (LDL-C; -0.15 mmol/L, p < 0.01) compared with control diets. Although not statistically significant, low-GI diets tended to increase High-Density Lipoprotein Cholesterol (HDL-C) and reduce Fasting Insulin (FI), Homeostasis Model Assessment of Insulin Resistance (HOMA-IR), Triglycerides (TG), and Total Cholesterol (TC). Subgroup analyses revealed stronger benefits in Type 2 Diabetes Mellitus (T2DM), individuals with baseline FBG ≥ 6.1 mmol/L, trials with intervention duration ≥ 12 weeks, and people in the Asia-Pacific region. Sensitivity analyses provided confirmation of the reliability of these outcomes.
Conclusions:
Low-GI diets improve glycemic control and modestly lower LDL-C, particularly in metabolically high-risk populations and with sustained intervention. These results highlight low-- GI diets as a practical nutritional strategy for metabolic health, while suggesting that baseline glucose status, intervention length, and population characteristics influence their effectiveness.