POS0717 VALIDATION OF ANTI-14-3-3? (ETA) MULTIPLEX LABORATORY DEVELOPED TEST (LDT) FOR THE DIAGNOSIS OF AXIAL SPONDYLOARTHRITIS (axSpA)
Bibliographic record
Abstract
<h2>Abstract</h2><h3>Background:</h3> Despite significant therapeutic advances and the availability of effective treatment, the diagnosis of axSpA is often delayed by 5-10 years [1]. This can contribute to irreversible spinal damage that can be avoided. Current diagnostic practices depend on serial MRI, radiographic scoring, and HLA-B27 typing, each of which are not readily available to most referring physicians. Other than CRP as an inflammatory marker, there are no easily accessible blood tests with robust assays that may assist in profiling patients with early axSpA to reduce diagnostic delay for better outcomes. Extracellular 14-3-3η (eta) antigen and its corresponding autoantibodies (AAbs) represent a promising set of biomarkers, with emerging diagnostic and prognostic performance in axSpA that may assist early appropriate referrals [2]. <h3>Objectives:</h3> The objectives of this study are to 1) examine the technical performance characteristics and reliability of a newly developed 14-3-3η (eta) AAb multiplex assay, 2) demonstrate its clinical performance in axSpA compared to presumed healthy subjects and 3) generate a multi-analyte algorithm to aid in the diagnosis of axSpA. <h3>Methods:</h3> To establish the technical performance and reliability of the 14-3-3η (eta) AAb assay, several critical parameters were assessed including precision, limit of blank (LoB), limit of detection (LoD), sensitivity, specificity, linearity, hook effect, interference, accuracy, and robustness. For the clinical performance, peptides were prioritized based on Receiver Operator Characteristic (ROC) curve analysis and positivity cut-offs were established for each peptide using the Youden index. Standards were developed against each 14-3-3η peptide to enable quantification of the AAb levels in patient serum. A composite score based on peptide positivity was created and used to determine the strength of the likelihood of an axSpA diagnosis (n = 83) compared to presumed healthy subjects (n = 57). <h3>Results:</h3> The assay demonstrated high precision, with intra-assay repeatability %CVs ranging from 4.5% to 11.4% and intra-laboratory precision %CVs ranging from 8.3% to 14%. The LoD was on an average 3.2X the LoB, with the positivity cut-off at 2.3X the LoD, and 6.5X the LoB. The hook effect was evaluated at concentrations significantly higher than physiologically probable, and all targets did not show a meaningful loss of signal at high concentrations. The only slight exception was Peptide 5 at concentrations equal to and above 160 mg/dL; however, this signal was still 1600 times higher than expected patient values posing negligible risk to a false negative result. Accuracy was high, with a percent agreement of 96.0%. All peptides demonstrated positive predictive values (PPVs) ranging from 70.6% to 83.3%, exceeding the acceptance criteria. The assay showed minimal cross-reactivity and interference from normal serum elements (hemoglobin, bilirubin, triglycerides, cholesterol, and albumin), non-target antibodies (HAMA, heterophile, and infliximab), and organic chemicals (ibuprofen, sulfasalazine, and dexamethasone). All assay targets demonstrated linear dose-dependent signals (1.00 ± 0.05). The 14-3-3η AAb assay yielded good performance despite stress testing through protocol deviations, such as delays in secondary antibody incubation and final reading, confirming its robustness. As presented in Table 1, prioritized peptides 1 - 5 yielded significant areas under the curve (AUC) when discriminating axSpA from presumed healthy controls. Using cut-offs derived from the Youden index for each peptide, positivity scores were assigned and utilized to generate a composite score. The strength of the association for an axSpA diagnosis was evaluated using the Fisher's Exact test, with the model yielding a Chi-Square of 31.8, p < 0.0001 with an Odds Ratio of 8.7 (95% CI 3.9 - 19.6). The relative risk for each group (axSpA or healthy) based on composite positivity status is presented in Table 2. The data demonstrates a patient's higher relative risk for axSpA based on being composite positive when compared to healthy subjects. <b>Table 1.</b> ROC curve for prioritized peptides comparing axSpA to presumed healthy controls. <b>Table 2.</b> Relative Risk (RR) for an axSpA diagnosis based on compositive positivity comparing axSpA patient samples to healthy controls. Results expressed as the RR and 95%CI. <h3>Conclusion:</h3> The 14-3-3η (eta) AAb multiplex assay demonstrates strong technical performance with high precision, accuracy, and robustness. The assay effectively distinguished between axSpA patients and healthy controls, with minimal cross-reactivity and interference. The development of a composite score based on peptide positivity significantly enhanced the diagnostic confidence for axSpA. These findings support the reliability and clinical utility of the assay. Future research will focus on examining peptide expression signatures in patients with various autoimmune conditions with rheumatologic involvement compared to healthy individuals. <h3>REFERENCES:</h3> [1] Maksymowych WP. Biomarkers for Axial Spondyloarthritis Diagnosis, Activity, Prognosis, and Therapy Response. Front Immunol. 2019;10:1664-3224. [2] Maksymowych WP, et al. Autoantibodies to 14-3-3η: Biomarkers for Inflammation and Radiographic Progression in Ankylosing Spondylitis. ACR/ARHP Annual Meeting; 2014. Available from: ACR Abstracts. <h3>Acknowledgements:</h3> <b>NIL</b>. <h3>Disclosure of Interests:</h3> Anthony Marotta Augurex Life Sciences Corp, Augurex Life Sciences Corp, Jason Liggett New Day Diagnostics LLC, New Day Diagnostics LLC, Walter P Maksymowych Abbvie, Eli-Lilly, Novartis, Pfizer, UCB, Abbvie, BMS, Celgene, Eli-Lilly, Galapagos, Pfizer, UCB, Abbvie, BMS, Celgene, Eli-Lilly, Galapagos, Pfizer, UCB, Navneet Sidhu Augurex Life Sciences Corp., Shelley Wilder New Day Diagnostics LLC, Stephen Bleakley Augurex Life Sciences Corp., Stephanie Wichuk: None declared, Norma Biln Augurex Life Sciences Corp., Augurex Life Sciences Corp. © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".