Allostatic load, emotional hyper‐reactivity, and functioning in individuals with bipolar disorder
Bibliographic record
Abstract
OBJECTIVES: Diagnosis and management of bipolar disorder (BD) are limited by the absence of available biomarkers. Allostatic load (AL) represents the strain that stress, including the effects of acute phases and inter-episode chronic mood instability, exerts on interconnected biological systems. This study aimed to operationalize an AL index and explore whether it could be relevant to better characterize BD patients with and without emotional hyper-reactivity particularly those at higher risk of immune-cardiometabolic dysregulation and functional impairment. METHODS: Levels of biomarkers of chronic inflammation (hsCRP and albumin), cardiovascular (systolic/diastolic blood pressure) and metabolic functions (fasting glucose, glycosylated hemoglobin, total cholesterol, LDL, HDL, and triglycerides) were measured in 1072 adult BD outpatients. Patients were classified in two groups (with/without emotional hyper-reactivity) assessed by the Multidimensional Assessment of Thymic States scale. An Allostatic Load Index for BD (BALLI), comprising six biomarkers, was constructed using data-driven biomarker selection. RESULTS: BALLI showed 81.1% accuracy with good sensitivity (81%) and specificity (81.2%) for characterizing BD patients presenting emotional hyper-reactivity, elevated risk of inflammation (increased hsCRP, hypoalbuminemia) and cardiometabolic disturbances (hypertension, hyperglycemia, and hypertriglyceridemia). Patients classified by the BALLI as presenting emotional hyper-reactivity had significantly lower global and cognitive functioning than those without emotional hyper-reactivity (P < .0001). CONCLUSIONS: A multidimensional approach based on a simple AL score (eg, BALLI) and dimensions of behavior (eg, emotional hyper-reactivity) alongside mood is clinically relevant. AL index could be a useful tool to detect multisystemic physiological dysregulations in BD patients with/without emotional hyper-reactivity particularly those at higher risk of immune-cardiometabolic disturbances and functional impairment.
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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 | 0.002 |
| 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 | 0.000 | 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".