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Record W4403525667 · doi:10.1161/jaha.124.035941

Choroid Plexus Volume in Rural Chinese Older Adults: Distribution and Association With Cardiovascular Risk Factors and Cerebral Small Vessel Disease

2024· article· en· W4403525667 on OpenAlexfundno aff
Chunyan Li, Huisi Zhang, Jiafeng Wang, Xiaodong Han, Cuicui Liu, Yuanjing Li, Tao Gong, Tingting Hou, Yongxiang Wang, Lin Cong, Grégoria Kalpouzos, Joanna M. Wardlaw, Lin Song, Yifeng Du, Chengxuan Qiu

Bibliographic record

VenueJournal of the American Heart Association · 2024
Typearticle
Languageen
FieldNeuroscience
TopicCerebrospinal fluid and hydrocephalus
Canadian institutionsnot available
FundersVetenskapsrådetKarolinska InstitutetResearch Councils UKNational Natural Science Foundation of ChinaSwedish Foundation for International Cooperation in Research and Higher EducationShandong First Medical UniversityWeston Brain Institute
KeywordsMedicineHyperintensityDiffusion MRICardiologyInternal medicineDiabetes mellitusPopulationDementiaMagnetic resonance imagingDiseaseRadiologyEndocrinology

Abstract

fetched live from OpenAlex

Background The choroid plexus (CP) is involved in neurodegenerative diseases. However, the association of CP with cardiovascular risk factors and cerebral small vessel disease in older adults remains unclear. Methods and Results This population‐based study included 1263 participants (60 years and older) from the MIND‐China (Multimodal Interventions to Delay Dementia and Disability in Rural China) substudy (2018–2020), of which 111 individuals completed diffusion tensor imaging examination. CP volume was automatically segmented. White matter hyperintensities (WMHs), enlarged perivascular spaces (EPVS), cerebral microbleeds, and lacunes were assessed following the Standards for Reporting Vascular Changes on Neuroimaging 1. Peak width of skeletonized mean diffusivity and free water were derived from diffusion tensor imaging images. We used linear regression models to evaluate the association between CP volume and cardiovascular risk factors, WMH volumes, and diffusion tensor imaging metrics, and logistic regression models to examine the association between CP volume and EPVS, cerebral microbleeds, and lacunes. The CP volume increased with age ( P <0.001). Men (β coefficient=0.47 [95% CI, 0.29–0.64]) and participants with diabetes (β coefficient=0.16 [95% CI, 0.01–0.31]) had larger CP volumes than women and individuals without diabetes, respectively ( P <0.05). Greater CP volume was significantly associated with larger total and periventricular WMH volumes and moderate to severe EPVS in basal ganglia ( P <0.05) but not with deep WMHs, EPVS in centrum semiovale, lacunes, or cerebral microbleeds. In the diffusion tensor imaging subsample, enlarged CP was significantly associated with higher peak width of skeletonized mean diffusivity and free water of periventricular and deep white matter ( P <0.05). Conclusions An enlarged CP is associated with larger global and periventricular WMH volume and higher likelihoods of EPVS in basal ganglia and impaired white matter integrity, suggesting that an enlarged CP may represent a precursor of cerebral small vessel disease.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.218
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2024
Admission routes1
Has abstractyes

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