Emotional hyper‐reactivity and cardiometabolic risk in remitted bipolar patients: a machine learning approach
Bibliographic record
Abstract
Objective Remitted bipolar disorder (BD) patients frequently present with chronic mood instability and emotional hyper‐reactivity, associated with poor psychosocial functioning and low‐grade inflammation. We investigated emotional hyper‐reactivity as a dimension for characterization of remitted BD patients, and clinical and biological factors for identifying those with and without emotional hyper‐reactivity. Method A total of 635 adult remitted BD patients, evaluated in the French Network of Bipolar Expert Centers from 2010–2015, were assessed for emotional reactivity using the Multidimensional Assessment of Thymic States. Machine learning algorithms were used on clinical and biological variables to enhance characterization of patients. Results After adjustment, patients with emotional hyper‐reactivity ( n = 306) had significantly higher levels of systolic and diastolic blood pressure ( P < 1.0 × 10 −8 ), high‐sensitivity C‐reactive protein ( P < 1.0 × 10 −8 ), fasting glucose ( P < 2.23 × 10 −6 ), glycated hemoglobin ( P = 0.0008) and suicide attempts ( P = 1.4 × 10 −8 ). Using models of combined clinical and biological factors for distinguishing BD patients with and without emotional hyper‐reactivity, the strongest predictors were: systolic and diastolic blood pressure, fasting glucose, C‐reactive protein and number of suicide attempts. This predictive model identified patients with emotional hyper‐reactivity with 84.9% accuracy. Conclusion The assessment of emotional hyper‐reactivity in remitted BD patients is clinically relevant, particularly for identifying those at higher risk of cardiometabolic dysfunction, chronic inflammation, and suicide.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".