Allostatic Load Is Linked to Cortical Thickness Changes Depending on Body-Weight Status
Bibliographic record
Abstract
Objective: Overweight (body mass index or BMI ≥ 25 kg/m2) and stress interact with each other in complex ways. Overweight promotes chronic low-inflammation states, while stress is known to mediate caloric intake. Both conditions are linked to several avoidable health problems and to cognitive decline, brain atrophy and dementia. Since it was proposed as a framework for the onset of mental illness, the allostatic load model has received increasing attention. Although changes in health and cognition related to overweight and stress are well documented separately, the association between allostatic load and brain integrity has not been addressed in depth, especially among overweight subjects. Method: Thirty-four healthy overweight-to-obese and 29 lean adults underwent blood testing, neuropsychological examination and magnetic resonance imaging to assess the relationship between cortical thickness and allostatic load, represented as an index of 15 biomarkers (this is, systolic and diastolic arterial tension, glycated hemoglobin, glucose, creatinine, total cholesterol, HDL and LDL cholesterol, triglycerides, c-reactive protein, interleukin-6, insulin, cortisol, fibrinogen and leptin). Results: Allostatic load indexes showed widespread positive and negative significant correlations (p < 0.01) with cortical thickness values depending on body-weight status. Conclusion: The increase of allostatic load is linked to changes in the gray matter composition of regions monitoring behavior, sensory-reward processing and general cognitive function.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".