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Record W4390195075 · doi:10.1002/alz.076586

Proteomic‐based Biological Aging Clock and MRI Markers of Cerebrovascular Disease: Atherosclerosis Risk in Community Study

2023· article· en· W4390195075 on OpenAlexaff
Sanaz Sedaghat, Shuo Wang, Jialing Liu, Tim M. Hughes, Behnam Sabayan, Weihong Tang, Josef Coresh, James S. Pankow, Keenan A. Walker, Pamela L. Lutsey, Weihua Guan, Anna E. Prizment

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsSubclinical infectionMedicineHyperintensityInternal medicineCardiologyLogistic regressionBrain sizeBiological ageAtherosclerosis Risk in CommunitiesDiseaseGerontologyMagnetic resonance imagingRadiology

Abstract

fetched live from OpenAlex

Abstract Background Biological aging state can be quantified by composite metrics called aging clocks using proteomics. Proteomic aging clocks (PACs) are accurate, easily measured, and are associated with age‐related diseases including Alzheimer’s Disease and Related Dementias. We aim to investigate whether an accelerated biological aging (a discrepancy between chronological age and PAC) is associated with subclinical cerebrovascular structural changes. Method 1494 participants from the Atherosclerosis Risk in Communities (ARIC) Study with proteomics and 3T brain MRI data at ARIC visit 5 in 2011‐13 (mean age 76, 59% female, 25% Black) were included. Nearly 5000 plasma proteins were measured using the SomaScan assay. PAC was developed using elastic net regression model and was internally validated. Age acceleration was calculated as residuals after regressing PAC on chronological age (positive value indicates biological age is higher than the person’s chronological age). Linear and logistic regression models were used to assess the associations of age acceleration with white matter hyperintensity volume (in cm3, log2 transformed, median [IQR]: 11[6‐20]) and the presence of: subcortical (n = 281), lacunar (n = 263), and cortical infarcts (n = 150), and microbleeds (n = 355). Result Accelerated age (median [IQR]:‐0.04[‐1.4,1.3]; correlation with chronological age = 0) was associated with MRI markers of cerebrovascular disease after adjusting for demographic, cardiovascular risk factors, education, and kidney function. Every five years higher age acceleration was associated with larger white matter hyperintensity volume (Difference:0.34[95%CI 0.19,0.49]), higher odds of lacunar (OR:1.61[1.13,2.30]), subcortical (OR:1.63[1.15, 2.32]), and cortical infarcts (OR:1.72[1.11,2.67]). There was no association with presence of microbleeds (OR:1.25[0.91,1.73]) (Figure). Findings were consistent after excluding participants with clinical stroke and there was no difference between APOEe4+ and APOEe4‐ participants. Conclusion Higher accelerated age is cross‐sectionally associated with a greater prevalence of MRI markers of cerebrovascular disease. Understanding this relation has potential to help with risk stratification and personalized prevention and treatment strategies to promote brain health.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.046
GPT teacher head0.319
Teacher spread0.272 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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