A novel neural network called Q-FSNet identified 25 metabolites with distinct homeostatic ranges where biological age acceleration is minimized, using data from the Canadian Longitudinal Study on Aging. These metabolites include compounds derived from diet and gut microbiome production, suggesting direct pathways for intervention. This represents a significant methodological advance in aging research. Traditional statistical approaches struggle with the smooth, non-linear relationships inherent in biological systems, often producing oversimplified linear models or jagged local approximations. Q-FSNet's ability to learn continuous response curves without pre-specified change points addresses a fundamental limitation in how we analyze complex physiological data. The identification of metabolic sweet spots offers a more nuanced understanding of healthy aging beyond simple biomarker optimization. Rather than assuming 'more is better' or 'less is better' for health markers, this approach reveals that optimal health often exists within specific ranges. The emphasis on diet- and microbiome-derived metabolites is particularly compelling, as these represent modifiable factors through nutrition and lifestyle interventions, making the findings actionable for precision medicine approaches to longevity.