Increasing body mass index in an elderly cohort: Effects on the quantitative MR parameters of the brain
Bibliographic record
Abstract
Background Body mass index (BMI) is increasing in a large number of elderly persons. This increase in BMI is known to put one at risk for many "diseases of aging," although less is known about how a change in BMI may affect the brains of the elderly. Purpose To investigate the relationship between BMI and quantitative water content, T1, T2*, and the semi‐quantitative magnetization transfer ratio (MTR) of various structures in elderly brains. Study Type Cross‐sectional. Subjects Forty‐two adults (BMI range: 19.1–33.5 kg/m2, age range: 58–80 years). Field Strength 3T MRI (two multi‐echo gradient echoes, actual flip angle imaging, magnetization prepared rapid gradient echo, fluid attenuated inversion recovery). Assessment The 3D two‐point method was used to derive (semi‐)quantitative parameters in global white (WM) and gray matter (GM) and their regions as defined by the Johns Hopkins University and the Montreal Neurological Institute atlases. Statistical Tests Multivariate linear regression with BMI as principal regressor, corrected for the additional regressors age, gender, and glycated hemoglobin. Spearman correlation between quantitative parameters of the regions showing significant changes and the lipid spectra / C‐reactive protein (CRP). Voxel‐based morphometry and analysis of covariance (ANCOVA) to explore changes in the GM volume. Results T1increased significantly (P < 0.05) in the frontal, temporal, and parietal cortices, while the bilateral corona radiata, right superior longitudinal fasciculus, as well as the corpus callosum showed significant changes in the WM regions. T2* increased significantly in the global WM and left corona radiata. Changes in MTR and the free water content did not reach significance. No significant correlation between any quantitative parameter and the lipid spectra or CRP could be identified. Data Conclusion These results suggest that an elevated BMI predominantly affects T1in WM as well as GM structures in the elderly human brain. Level of Evidence:3 Technical Efficacy:Stage 3 J. Magn. Reson. Imaging 2020;51:514–523.
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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 | 0.000 |
| 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".