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Record W4412589709 · doi:10.1038/s44400-025-00024-0

Smoking predicts brain atrophy in 10,134 healthy individuals and is potentially influenced by body mass index

2025· article· en· W4412589709 on OpenAlexaff
Somayeh Meysami, Saurabh K. Garg†, Sam Hashemi, Nasrin Akbari, Ahmed Gouda, Yosef Gavriel Chodakiewitz, Thanh D. Nguyen, Rajpaul Attariwala, Kellyann Niotis, David A. Merrill, Cyrus A. Raji

Bibliographic record

Venuenpj Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsArtificial Intelligence in Medicine (Canada)AXYS Technologies (Canada)
FundersNational Institute on AgingNational Institutes of Health
KeywordsPrecuneusWhite matterAtrophyBrain sizeTemporal lobeBody mass indexMedicineNeuroimagingNeurodegenerationImaging biomarkerInternal medicinePosterior cingulateDementiaCardiologyPsychologyMagnetic resonance imagingNeuroscienceCortex (anatomy)RadiologyCognitionEpilepsy

Abstract

fetched live from OpenAlex

Abstract Cigarette smoking is a risk factor for Alzheimer’s and vascular dementia, but its impact on brain volume loss, a neurodegeneration biomarker on MRI, is unclear. In total, 10,134 participants from 4 sites were scanned with a whole-body 1.5 T MRI protocol with separate dedicated structural neuroimaging with 3D T1 MPRAGE sequences. Smokers versus non-smokers were compared by gray and white matter volumes normalized to total intracranial volume using a two-tailed t -test. Smokers had lower normalized gray ( t = −7.806e+00, p = 6.508e-15) and white matter volumes ( t = −7.374e + 00, p = 1.791e-13) compared to non-smokers. Adjusting for age, sex, study site, BMI, and multiple comparisons, higher pack years of smoking predicted volume loss in such regions as total gray matter volume, total white matter volume, temporal lobe, parietal lobe, hippocampus, precuneus, and posterior cingulate. The inclusion and exclusion of BMI from the model suggested an influence of this variable.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.651

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)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.337
Teacher spread0.319 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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