Weight gain as a predictor of frontal and temporal lobe volume loss in bipolar disorder: A prospective<scp>MRI</scp>study
Bibliographic record
Abstract
OBJECTIVES: A sizable fraction of people with bipolar I disorder (BDI) experience a deteriorating clinical course with increasingly frequent mood episodes and chronic disability. This is believed to result from neurobiological illness progression, or neuroprogression. Excessive weight gain predicts neuroprogression across multiple brain illnesses, but no prospective studies have investigated this in BDI. The objective of this study was to determine whether BDI patients who experienced clinically significant weight gain (CSWG; gaining ≥7% of baseline weight) over 12 months had greater 12-month brain volume loss in frontal and temporal regions important to BDI. METHODS: In 55 early-stage BDI patients we measured (i) rates of CSWG, (ii) the number of days with mood symptoms, using NIMH LifeCharts, and (iii) baseline and 12-month brain volumes, using 3T MRI. We quantified brain volumes using the longitudinal processing stream in FreeSurfer v6.0. We used general linear models for repeated measures to investigate whether CSWG predicted volume loss, adjusting for potentially confounding clinical and treatment variables. RESULTS: After correction for multiple comparisons, CSWG in patients predicted greater volume loss in the left orbitofrontal cortex (effect size [ES; Cohen's d] = -1.01, P = 0.002), left cingulate gyrus (ES = -1.31, P < 0.001), and left middle temporal gyrus (ES = -0.96, P = 0.004). Middle temporal volume loss predicted more days with depression (β = -0.406, P = 0.010). CONCLUSIONS: These are the first prospective data on weight gain and neuroprogression in BDI. CSWG predicted neuroprogression, and neuroprogression predicted a worse clinical illness course. Trials of weight loss interventions are needed to confirm the causal direction of the weight gain-neuroprogression relationship, and to determine whether weight loss is a disease-modifying treatment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".