Intercorrelated variability in blood and hemodynamic biomarkers reveals physiological network in hemodialysis patients
Bibliographic record
Abstract
Increased intra-individual variability of a variety of biomarkers is generally associated with poor health and reflects physiological dysregulation. Correlations among these biomarker variabilities should then represent interactions among heterogeneous biomarker regulatory systems. Herein, in an attempt to elucidate the network structure of physiological systems, we probed the inter-variability correlations of 22 biomarkers. Time series data on 19 blood-based and 3 hemodynamic biomarkers were collected over a one-year period for 334 hemodialysis patients, and their variabilities were evaluated by coefficients of variation. The network diagram exhibited six clusters in the physiological systems, corresponding to the regulatory domains for metabolism, inflammation, circulation, liver, salt, and protein. These domains were captured as latent factors in exploratory and confirmatory factor analyses (CFA). The 6-factor CFA model indicates that dysregulation in each of the domains manifests itself as increased variability in a specific set of biomarkers. Comparison of a diabetic and non-diabetic group within the cohort by multi-group CFA revealed that the diabetic cohort showed reduced capacities in the metabolism and salt domains and higher variabilities of the biomarkers belonging to these domains. The variability-based network analysis visualizes the concept of homeostasis and could be a valuable tool for exploring both healthy and pathological conditions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".