Cardiorenal metabolic biomarkers link early life stress to risk of non-communicable diseases and adverse mental health outcomes
Bibliographic record
Abstract
Abstract Stress is one of the most critical determinants of lifetime health and increases the risk of chronic non-communicable diseases. To gain insight into underlying environment-gene interactions, we analyzed the cardiorenal metabolome of adult mice exposed to multidimensional early-life transportation stress. Using proton nuclear magnetic resonance ( 1 H NMR) spectroscopy, we show that early life stress permanently programs metabolic pathways in somatic organs linked to cardiorenal and mental health disorders in later life. Heart and kidneys of stressed mice revealed robust metabolic markers linked to abnormal energy metabolism, branched-chain amino acid biosynthesis and degradation, methylhistidine metabolism, arginine and proline metabolism, glycine and serine metabolism, and aminoacyl-tRNA biosynthesis. These markers were strongly associated with anxiety-like behaviours. Dysregulation of energy and protein metabolism suggests an increased risk of metabolic diseases like insulin resistance, cardiorenal syndrome, diabetes, and obesity. These findings provide novel insights into the direct effects of early life stress on cardiorenal metabolism and are consistent with prior observations of increased non-communicable disease risk in stressed populations. Thus, stress-associated metabolic signatures in somatic organs may provide early predictors of health risks in later life and reveal new candidates for peripheral biomarker detection with diagnostic value.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".