Aims: To determine the diagnostic value of anti-acetylated peptide antibodies (AAPA) in patients with rheumatoid arthritis (RA).
Methods: Three acetylated peptides (ac-lysine, ac-lysine.inv and ac-ornithine) derived from vimentin were employed to measure AAPA by enzyme-linked immunosorbent assay (ELISA) in sera of 120 patients with early RA (eRA), 195 patients with established RA (est RA), 99 healthy controls (HC), and 216 patients with other inflammatory rheumatic diseases. A carbamylated and a citrullinated version of the vimentin peptide were used additionally. Receiver operating characteristics and logistic regression analyses were used to assess the discriminative capacity of AAPA.
Results: AAPA were detected in 60% of eRA and 68.7% of estRA patients, 22.2% of HC, and 7.1- 30.6% of patients with other rheumatic diseases. Importantly, AAPA were also present in 40% of seronegative RA patients, while antibodies to the carbamylated peptide were detected less frequently. Diagnostic sensitivity of individual peptides for eRA was 28.3%, 35.8%, and 34% for ac-lysine, ac-ornithine, and ac-lysine.inv, respectively. Positive likelihood ratios (LR+) for eRA HC were 14.0, 7.1, and 2.1. While the presence of a single AAPA showed varying specificity (range: 84-98%), the presence of two AAPA increased specificity considerably since 26.7% of eRA, as compared with 6% of disease controls, were double positive. Thus, double positivity discriminated eRA from axial spondyloarthritis with a LR+ of 18.3. Remarkably, triple positivity was 100% specific for RA, being observed in 10% of eRA and 21.5% of estRA patients, even in the absence of RF and ACPA.
Conclusion: AAPA are highly prevalent in early RA and occur also independently of RF and ACPA, thereby reducing the gap of seronegativity. Furthermore, multiple AAPA reactivity increased the specificity for RA, suggesting high diagnostic value of AAPA testing.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8445531 | PMC |
http://dx.doi.org/10.1177/1759720X211022533 | DOI Listing |
Rheumatology (Oxford)
January 2025
The National Axial Spondyloarthritis Society, London, UK.
Cell Mol Biol (Noisy-le-grand)
January 2025
Technical Institute of Al-Diwaniyah, Al-Furat Al-Awsat Technical University (ATU), Iraq.
This study aimed to investigate the association between the interleukin-1 beta (IL-1β) gene polymorphism (rs2853550) and the risk of rheumatoid arthritis (RA) in a sample of the Iraqi population. The study included 100 RA patients and 100 healthy controls. Demographic characteristics, including age and gender, were collected and compared between the two groups.
View Article and Find Full Text PDFJ Cell Mol Med
January 2025
Ataturk Vocational School of Health Services, Department of Medical Laboratory Techniques, Afyonkarahisar Health Sciences University, Afyonkarahisar, Turkey.
The development and progression of osteoarthritis (OA) are believed to involve inflammation. This study aimed to investigate the effects of applying therapeutic ultrasound (US) to human osteoarthritic chondrocytes in continuous and pulsed modes on cell proliferation and proinflammatory cytokine levels. Human osteoarthritic chondrocytes (HC-OA 402OA-05a) were proliferated in appropriate media and then seeded into culture plates.
View Article and Find Full Text PDFBMC Geriatr
January 2025
Department of Cardiology, The Second Hospital & Clinical Medical School, Lanzhou University, No. 82 Cuiyingmen, Lanzhou, 730000, China.
Objective: Constructing a predictive model for the occurrence of heart disease in elderly hypertensive individuals, aiming to provide early risk identification.
Methods: A total of 934 participants aged 60 and above from the China Health and Retirement Longitudinal Study with a 7-year follow-up (2011-2018) were included. Machine learning methods (logistic regression, XGBoost, DNN) were employed to build a model predicting heart disease risk in hypertensive patients.
Sci Rep
January 2025
Department of Orthopaedics, Traditional Chinese Medical Hospital of Gansu Province, Qilihe District, Guazhou Street 418, Lanzhou, 730050,, Gansu, China.
Knee osteoarthritis (KOA) represents a progressive degenerative disorder characterized by the gradual erosion of articular cartilage. This study aimed to develop and validate biomarker-based predictive models for KOA diagnosis using machine learning techniques. Clinical data from 2594 samples were obtained and stratified into training and validation datasets in a 7:3 ratio.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!