Two-dimensional Health State Map to define metabolic health using separated static and dynamic homeostasis features: a proof-of-concept study.

Abstract

Defining metabolic health is critical for the earlier reversing of metabolic dysfunction and disease, and fasting-based diagnosis may not adequately assess an individual's metabolic adaptivity under stress. We constructed a novel Health State Map (HSM) comprising a Health Phenotype Score (HPS) with fasting features alone and a Homeostatic Resilience Score (HRS) with five time-point features only (t = 30, 60, 90, 180, 240 min) following a standardized mixed macronutrient tolerance test (MMTT). Among 111 Chinese adults, when the same set of fasting and post-MMTT data as for the HSM was used, the mixed-score was highly correlated with the HPS. The HRS was significantly associated with metabolic syndrome prevalence, independently of the HPS (OR [95% CI]: 0.41 [0.18, 0.92]) and the mixed-score (0.34 [0.15, 0.69]). Moreover, the HRS could discriminate metabolic characteristics unseparated by the HPS and the mixed-score. Participants with higher HRSs had better metabolic traits than those with lower HRSs. Large interpersonal variations were also evidenced by evaluating postprandial homeostatic resiliencies for glucose, lipids and amino acids when participants had similar overall HRSs. Additionally, the HRS was positively associated with physical activity level and specific gut microbiome structure. Collectively, our HSM model might offer a novel approach to precisely define an individual's metabolic health and nutritional capacity.