The effect of weight loss on brain age in schizophrenia spectrum disorders
Bibliographic record
Abstract
Individuals with schizophrenia spectrum disorders (SSDs) suffer from metabolic conditions including type 2 diabetes (T2D) and obesity. Moreover, they are at a high risk for cardiovascular disease and this could lead to a shortened life expectancy. Obesity is one of common comorbid conditions in SSDs, which has adverse effects on brain health. However, it is still unknown how metabolic disorders affect brain anatomy in SSDs, and the impacts of weight loss from pharmacological interventions are yet to be studied. This study includes a total of 48 patients with SSDs from three different clinical trials focusing on weight loss interventions. We acquired metabolic parameters, brain anatomical MRI, body mass index (BMI), cognition, and psychopathology scores at baseline and endpoint. We used a convolutional neural network-based classifier to calculate each patient's brain-age gap estimate (brainAGE) based on high-quality brain structural T1 images. We examined the relationship between the reduction in BMI and brainAGE between two timepoints. There was a significant reduction in BMI (p < 0.001) between two timepoints. Additionally, the analysis revealed that none of the cognitive, or psychopathology measures demonstrated significant differences between the timepoints (p > 0.05). The results of the multiple regression analysis showed a positive association between the reduction in BMI and brainAGE (F(2,44) = 3.69, p = 0.03). Furthermore, there were no noteworthy associations observed between brainAGE and the aforementioned parameters (p > 0.05). This study revealed a positive correlation between brainAGE and significant weight loss in SSDs with comorbid obesity.
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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 | 0.000 | 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) | 0.001 | 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".