Dried Blood Spots to Assess Cardiovascular‐Kidney‐Metabolic Health
Bibliographic record
Abstract
Dried blood spot sampling offers a scalable strategy to close diagnostic gaps and improve global surveillance for cardiovascular-kidney-metabolic syndrome. However, assay performance and the extent of validity vary widely between biomarkers used in cardiovascular-kidney-metabolic health assessment under different settings and have not been well described. To fill this gap, we conducted a systematic search of the literature and a narrative synthesis through April 2024 and included reports with laboratory or field validation measuring biomarkers that can be used in cardiovascular-kidney-metabolic health assessment. We categorized assays into categories based on laboratory validation: excellent performance (r>0.95 with gold standard methods and coefficients of variation <5%), very good performance (r>0.90 and coefficients of variation <10%), reasonable performance (r>0.80 and coefficients of variation <15%), and poor performance (r<0.80 or coefficients of variation >15%). The extent of validation was determined by the total number of field validation studies with strong agreement. Hemoglobin A1c has strong laboratory and field validation and should be considered for expansion into clinical testing in low-resource settings. Traditional lipid biomarkers showed poor performance in field validation studies, but apoB (apolipoprotein B), creatinine, cystatin C, and NT-proBNP (N-terminal prohormone of brain natriuretic peptide) showed promising initial laboratory validation results and deserve greater attention in field validation studies. High-sensitivity C-reactive protein has strong laboratory and field validation but has limited clinical utility. Dried blood spot assays have been developed for biomarkers that offer mechanistic insights including inflammatory and vascular injury markers, fatty acids, malondialdehyde, asymmetric dimethylarginine, trimethylamine N-oxide, carnitines, and omics.
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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 | 0.017 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".