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Record W4406209405 · doi:10.1002/alz.092750

Renal Atrophy Relates to Brain Volume Loss on 7,149 MRI Scans

2024· article· en· W4406209405 on OpenAlexaff
Somayeh Meysami, Cyrus A. Raji, Sam Hashemi, Saurabh Garg, Nasrin Akbari, Ahmed Gouda, Yosef Gavriel Chodakiewitz, Thanh D. Nguyen, Kellyann Niotis, David A. Merrill, Rajpaul Attariwala

Bibliographic record

VenueAlzheimer s & Dementia · 2024
Typearticle
Languageen
FieldMedicine
TopicRenal and Vascular Pathologies
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsAtrophyBrain sizeMedicineVolume (thermodynamics)Magnetic resonance imagingRadiologyNuclear medicinePathologyPhysics

Abstract

fetched live from OpenAlex

Abstract Background Renal atrophy may reflect an end organ consequence of chronic vascular disease. Renal volume loss may therefore provide a window into brain aging and Alzheimer disease risk. Method We obtained whole‐body 1.5T MRI scans on 7,149 healthy individuals across four sites. The participants had a mean age of 53.06 ± 12.95 years (age range: 18‐97 years), with a gender distribution of 48% women and 52% men; 38% were non‐white. Deep learning with FastSurfer on 3D T1 volumetric MPRAGE trained on 134 participants aged 27‐66 and segmented 96 brain regions. Whole body sequences included coronal T1 that allowed for separate segmentation and quantification of renal volumes. Partial correlation analysis controlling for sex and total intracranial volume was done on kidney volumes and the following brain regions: tissue types, lobar structures and Alzheimer disease risk regions – hippocampus, precuneus, and posterior cingulate gyrus. Renal volumes were normalized to the aggregate abdominal organ volumes and tissues such as the liver, spleen and abdominal fat. Multiple comparisons were corrected for with the Bonferroni method. Result Lower renal volumes were correlated with lower gray matter volumes (rp = ‐0.229, p = 5.18×10 −85 ) as well as total white matter volume (rp = ‐0.204, p = 2.51×10 −67 ). The cerebral ventricles, demonstrated a moderate positive correlation with lower renal volumes (rp = 0.099, p = 5.28×10 −16 ) reflective of a generalized parenchymal volume loss. Lower renal volumes also predicted lower lobar volumes in: frontal lobe (rp = ‐0.204, p = 3.34×10 −67 ), temporal lobe (rp = ‐0.236, p = 9.76×10 −90 ), parietal lobe (rp = ‐0.215, p = 2.69×10 −74 ), and occipital lobe (rp = ‐0.207, p = 8.29×10 −69 ), all showed significant negative correlations with lower renal volumes. Lower renal volumes also correlated to lower volumes in AD risk regions: the Hippocampus (rp = ‐0.203, p = 2.89×10 −66 ) and posterior cingulate (rp = ‐0.18, p = 3.06×10 −52 ), precuneus (rp = ‐0.171, p = 8.00×10 −47 ). Conclusion This study demonstrates a strong relationship in a large sample between lower renal volumes and reduced volumes in various brain regions, including gray matter, white matter, and specific lobar structures, as well as Alzheimer's disease risk regions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.003

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.

Opus teacher head0.021
GPT teacher head0.283
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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