# Biomarkers voor atherosclerotische events bij reumatoïde artritis: Lp(a) als voorspeller

*geplaatst 2026-02-20 · Cholesterol · medRxiv : the preprint server for health sciences · doi 10.64898/2026.02.18.26346592 · https://hartvaat.nl/2026/02/20/biomarkers-voor-atherosclerotische-events-bij-reumatoide-artritis-lp-a-als-voors/*

Patiënten met reumatoïde artritis hebben een verhoogd cardiovasculair risico. Dit onderzoek valideert een biomarkermodel met onder andere lipoproteïne(a), osteoprotegerine en troponine T voor betere risicostratificatie bij RA-patiënten. Het model presteert beter dan traditionele risicoscores.

## English: Biomarkers for Atherosclerotic Cardiovascular Events in Rheumatoid Arthritis: Towards Validation of a Biomarker-Enhanced Risk Model.

This study validates a biomarker-enhanced risk model incorporating lipoprotein(a), osteoprotegerin, and troponin T for improved cardiovascular risk stratification in rheumatoid arthritis patients, outperforming traditional risk scores.

## Abstract (original, from the publication)

BACKGROUND: Cardiovascular (CV) disease risk is increased in rheumatoid arthritis (RA) and is the leading cause of mortality. Improved CV risk stratification tools in RA could enhance use of preventative care and improve outcomes. METHODS: We previously studied biomarkers of CV disease - adiponectin, hsCRP, Lp(a), osteoprotegerin (OPG), high-sensitivity cardiac troponin T (hsTnT), serum amyloid A (SAA), YKL-40, soluble TNF receptor1 (sTNFR1) -- that were associated with CV risk. In the current study, these biomarkers were tested in an unrelated external cohort of RA patients followed at a single academic medical center without a history of CV events. CV events were identified through Medicare and Medicaid administrative data or through medical record review of self-reported events. Biomarkers were assessed at cohort entry among a nested cohort of cases and controls, matched 1:1 on sex and age. Analyses were conducted using conditional logistic regression. We examined whether the candidate biomarkers added to clinical CV risk factors improved model prediction, using the area under the curve (AUC) as well as the net reclassification index (NRI). RESULTS: From a cohort of 1,345 eligible patients with RA, we identified 123 patients with confirmed CV events. Cases and matched controls were typical of RA: median age 63 years, 77% women, RA disease duration 11 years, 72% seropositive, 85% used a biologic or conventional disease modifying anti-rheumatic drug, 58% non-steroidal anti-inflammatory drugs, and 30% oral glucocorticoids. From the candidate biomarkers, LASSO regression selected hsTnT and sTNFR1 as associated with CV events. The AUC for models that included only clinical risk factors was 0.758 (95% CI 0.689-0.829); after adding hsTnT and sTNFR1, the AUC increased to 0.802 (95% CI 0.718-0.998). The NRI of the model with biomarkers was 16.3%, with improvement only observed in patients who did not have CV events during follow-up. CONCLUSIONS: Adding selected biomarkers to clinical risk factors enhances the discrimination of models predicting CV events among patients with RA. These risk models require prospective testing to see if they have value in clinical practice decision-making regarding preventative care.

Auteurs: Daniel H Solomon, Leah Santacroce, Jon T Giles, Pamela Rist, Brendan M Everett, Katherine P Liao, Misti Paudel, Nancy A Shadick, Michael Weinblatt, Joan M Bathon, Olga Demler

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Bron: medRxiv : the preprint server for health sciences, https://doi.org/10.64898/2026.02.18.26346592. Bijgewerkt 2026-07-03T13:24:26Z. Citeer vrij, met bronvermelding en een link naar hartvaat.nl (de url van het record). Samenvattingen zijn redactioneel werk van HartVaat; de oorspronkelijke publicaties blijven van hun uitgevers (doi). Geen medisch advies.