Parsing metabolic heterogeneity in mood disorders: A hypothesis‐driven cluster analysis of glucose and insulin abnormalities
Bibliographic record
Abstract
OBJECTIVES: Metabolically based distinctions for disturbances in glucose and insulin may provide meaningful insights both clinically and mechanistically. METHODS: Data were derived from 352 subjects of previously completed clinical studies with a mood disorder (MD) (bipolar disorder: n = 179, major depressive disorder: n = 173) and 218 healthy controls from the Comprehensive Assessment of Long-Term Effects of Reducing Intake of Energy. We conducted a factor analysis to replicate a priori dissociable factors informed by glucose and insulin levels and indices of insulin resistance and beta-cell function: elevated insulin and insulin resistance ("insulin-IR"), and increased fasting glucose and reduced insulin secretion ("glucotoxicity"). Cluster analyses were conducted, separately in men and women, to evaluate the clinical relevance of subtyping individuals with MDs using insulin-IR and glucotoxicity (GT) factor scores. RESULTS: Factors insulin-IR and GT explained 92.64% and 92.09% of the variance in men and women respectively. Three clusters were replicated in men and women separately: metabolically healthy (MH), high GT, and insulin-resistant (IR). After adjusting for age, gender, study cohort, MD diagnosis, and antipsychotics use, body mass index (BMI) and mean arterial pressure were higher in IR- vs GT- or MH-clustered individuals; GT-clustered individuals had more metabolic syndrome components and higher C-reactive protein. Glucotoxic-clustered subjects reported greater impairments in cognitive function and global functioning when compared to MH- or IR-clustered subjects. CONCLUSIONS: Using simple, cost-effective, and accessible measures, we identified stable, gender-convergent, subgroups of individuals that significantly diverged on measures of cognitive dysfunction, self-reported anhedonia, functional disability, BMI, and blood pressure.
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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) | 0.001 | 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) | 0.000 | 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".