Diagnosis and body mass index effects on hippocampal volumes and neurochemistry in bipolar disorder
Bibliographic record
Abstract
We previously reported that higher body mass index (BMI) was associated with greater hippocampal glutamate+glutamine in people with bipolar disorder (BD), but not in non-BD healthy comparator subjects (HSs). In the current report, we extend these findings by examining the impact of BD diagnosis and BMI on hippocampal volumes and the concentrations of several additional neurochemicals in 57 early-stage BD patients and 31 HSs. Using 3-T magnetic resonance imaging and magnetic resonance spectroscopy, we measured bilateral hippocampal volumes and the hippocampal concentrations of four neurochemicals relevant to BD: N-acetylaspartate+N-acteylaspartylglutamate (tNAA), creatine+phosphocreatine (Cre), myoinositol (Ins) and glycerophosphocholine+phosphatidylcholine (Cho). We used multivariate factorial analysis of covariance to investigate the impact of diagnosis (patient vs HS) and BMI category (normal weight vs overweight/obese) on these variables. We found a main effect of diagnosis on hippocampal volumes, with patients having smaller hippocampi than HSs. There was no association between BMI and hippocampal volumes. We found diagnosis and BMI effects on hippocampal neurochemistry, with patients having lower Cre, Ins and Cho, and overweight/obese subjects having higher levels of these chemicals. In patient-only models that controlled for clinical and treatment variables, we detected an additional association between higher BMI and lower tNAA that was absent in HSs. To our knowledge, this was the first study to investigate the relative contributions of BD diagnosis and BMI to hippocampal volumes, and only the second to investigate their contributions to hippocampal chemistry. It provides further evidence that diagnosis and elevated BMI both impact limbic brain areas relevant to BD.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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".