Validation of an Enzyme‐Linked Immunosorbent Assay for Measuring Leptin, a Key Metabolic Hormone, in Dried Blood Spot Samples
Bibliographic record
Abstract
ABSTRACT Objectives Leptin is an established biomarker of appetite regulation and energy status. Problematically, heavy reliance on invasive venipuncture sampling has limited leptin research with diverse human populations and groups such as children. Key questions remain about leptin's evolution and biological roles across the full range of humans. Here, we present and validate a new minimally invasive approach for measuring leptin in finger‐prick dried blood spots (DBS) using a commercial ELISA kit. Methods The Human Leptin Quantikine QuicKit ELISA (R&D Systems, QK398) was validated using matched serum and DBS samples from 40 adults. Passing–Bablok regression assessed the relationship between leptin DBS and leptin serum . Dilutional linearity, reliability, spike‐and‐recovery, limit of detection, and stability tests evaluated assay performance and potential DBS matrix interference. Results Leptin was reliably measured in all DBS samples (average = 312 pg/mL), with DBS intra‐ and inter‐assay CVs of 3.3% and 2.0%, respectively. Matched leptin DBS and leptin serum measurements showed excellent agreement (Pearson's R = 0.97), with no apparent bias (Bland‐Altman bias = 4.7). Leptin measurement in DBS was stable for at least 72 h at 26.2°C and 37°C and showed no degradation across eight freeze–thaw cycles ( p > 0.05). Conclusions Leptin can be reliably and stably measured in minimally invasive DBS samples, expanding research on energetics and appetite regulation across a wider range of human groups and settings.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".