Effectiveness of Astaxanthin as a Feed Supplement to Improve Growth Performance and Feed Utilization in Aquaculture Animals: A Meta-Analysis

• Published in: Antioxidants
• Main Authors: Bowen Li, Chunxiu Chen, Xiaoqing Zhou, Huiru Liu, Zhixiong Zhou, Xiaoyu Wang, Jian Liang, Yongjun Guo, Shuang Liang
• Format: Article
• Language: English
• Published: MDPI AG 2025-05-01
• Subjects: astaxanthin; aquaculture animals; growth performance; feed utilization; meta-analysis
• Online Access: https://www.mdpi.com/2076-3921/14/5/609

Aquaculture, a vital component of global food supply, faces challenges from environmental stressors that compromise aquatic animal health and productivity. Astaxanthin, a potent carotenoid antioxidant, has shown promise in enhancing growth and stress resilience in aquaculture species, yet its effects remain inconsistent across studies. This meta-analysis systematically evaluates the efficacy of dietary astaxanthin supplementation on growth, feed utilization, antioxidant capacity, and immune function in aquaculture animals. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 64 studies (33 species, 964 comparisons) published prior to 2025 were analyzed using a random-effects model. Results demonstrated that astaxanthin significantly improved final body weight, weight gain rate, specific growth rate, survival rate, and protein efficiency ratio, while reducing feed conversion ratio. Additionally, it enhanced digestive enzyme activities, hepatopancreas antioxidant biomarkers, and immune parameters. The subgroup analysis revealed differences related to species, trophic level, and habitat, and estimated the optimal dose for key indicators. Despite heterogeneity and publication bias, adjusted effect sizes remained significant for most outcomes. These findings underscore astaxanthin’s potential as a multifunctional feed additive to promote sustainable aquaculture, though its efficacy depends on species, dosage, and environmental context, warranting further mechanistic and optimization studies.