Group bias in Large Language Models (LLMs) is a well-documented issue, its impact in high-stakes domains such as personalized nutritional advice remains under explored. This study introduces the USChainMains dataset to systematically evaluate LLMs, prompting them with names associated with specific racial and gender groups and rigorously quantifying the healthfulness of the generated meal recommendations against established dietary standards. The findings demonstrate that LLMs systematically recommend meals with significantly higher levels of adverse nutrients for names associated with Black, Hispanic, or male individuals, thereby reflecting and potentially reinforcing detrimental dietary stereotypes. Furthermore, our analysis of two common mitigation strategies reveals their limitations. While model scaling improves overall recommendation healthfulness, it is insufficient to eliminate the healthfulness gap between demographic groups. Notably, while augmented reasoning was effective in mitigating gender bias, it did not mitigate racial disparities. This work underscores the necessity of developing more nuanced, group-aware debiasing techniques to ensure AI-driven systems advance, rather than hinder, health equity.