Deep Learning Derived Visceral Abdominal Fat Predicts Brain Atrophy at Midlife in 10,001 Individuals
Bibliographic record
Abstract
Abstract Background Visceral abdominal fat can predict health outcomes with increasing interest in applications on brain health. Method A total of 10,001 healthy participants from four sites were scanned on 1.5T MR machines with a short whole‐body MR imaging protocol. Core whole body sequences included coronal T1, STIR from vertex to feet, whole‐body axial DWI from vertex to proximal‐thighs and axial T2 TSE without fat suppression from skull base to pelvis. Brain sequences were T1 MPRAGE and 2D FLAIR. Deep learning with FastSurfer trained on 134 participants aged 27‐66 and segmented 96 brain regions. A separate deep learning model was trained to segment abdominal visceral fat from the whole‐body coronal T1. Partial correlation analysis of visceral abdominal fat volume and brain volumes were evaluated, controlling for age, sex, and total intracranial volume. Benjamini‐Hochberg False Discovery Rate of 5% controlled for multiple comparisons. Logistic regression models determined risk of brain total gray and white matter atrophy based upon the highest quartile of visceral fat and lowest quartile of these brain volumes. Result This cohort had an average age of 52.97 ± 13.04 years with a range of 11‐97 years with 52.8% men and 47.2% women. Mean visceral abdominal fat was 3417.81 ± 514.88 ml for overweight persons (BMI ³ 25) and 5355.24 ± 505.04 for obese persons (BMI ³ 30). Even accounting for co‐variates and multiple comparisons, deep learning segmented visceral abdominal fat predicted atrophy in multiple brain regions including: total gray matter volume (Partial R = ‐.09, p = 1.17e‐19), total white matter volume (Partial R = ‐.09, p = 2.25e‐21), hippocampus (Partial R = ‐.02, p = .005), frontal cortex (Partial R = ‐.09, p = 1.79e‐19), temporal lobes (Partial R = ‐.12, p = 2.6e‐30), parietal lobes (Partial R = ‐.04, p = .0004), occipital lobe (Partial R = ‐.03, p = .002), orbital frontal cortex (Partial R = ‐.09, p = 3.76e‐21). Visceral fat predicted increased risk for lower total gray matter (age 20‐39: OR = 5.9; age 40‐59, OR = 5.4; 60‐80, OR = 5.1) and white matter atrophy: (age 20‐39: OR = 3.78; age 40‐59, OR = 4.4; 60‐80, OR = 5.1). Conclusion Deep learning determined increased visceral abdominal fat volume predicts brain volume loss and may represent a novel modifiable factor in determining brain health.
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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 | 0.000 |
| 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 | 0.001 |
| 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".