Two-dimensional Health State Map to define metabolic health using separated static and dynamic homeostasis features: a proof-of-concept study
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".