Proteomic‐based Biological Aging Clock and MRI Markers of Cerebrovascular Disease: Atherosclerosis Risk in Community Study
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".