Establishment of a novel diagnostic model for Sjögren's syndrome by proteomic fingerprinting

Advances in Sjögren's Syndrome Dry Eye Diagnostics: Biomarkers and Biomolecules beyond Clinical Symptoms

Sjögren's syndrome dry eye (SSDE) is a subset of Sjögren's syndrome marked by dry eye symptoms that is distinct from non-Sjögren's syndrome dry eye (NSSDE). As SSDE can lead to severe complications, its early detection is imperative. However, the differentiation between SSDE and NSSDE remains challenging due to overlapping clinical manifestations. This review endeavors to give a concise overview of the classification, pathophysiology, clinical features and presentation, ocular and systemic complications, clinical diagnosis, and management of SSDE. Despite advancements, limitations in current diagnostic methods underscore the need for novel diagnostic modalities. Thus, the current review examines various diagnostic biomarkers utilized for SSDE identification, encompassing serum, salivary, and tear analyses. Recent advancements in proteomic research and exosomal biomarkers offer promising diagnostic potential. Through a comprehensive literature review spanning from 2016 to 2023, we highlight molecular insights and advanced diagnostic modalities that have the potential to enhance our understanding and diagnosis of SSDE.

Sjögren's syndrome: novel insights from proteomics and miRNA expression analysis

Introduction

Sjögren's syndrome (SS) is a systemic autoimmune disease, which affects the exocrine glands leading to glandular dysfunction and, particularly, symptoms of oral and ocular dryness. The aetiology of SS remains unclear, and the disease lacks distinctive clinical features. The current diagnostic work-up is complex, invasive and often time-consuming. Thus, there is an emerging need for identifying disease-specific and, ideally, non-invasive immunological and molecular biomarkers that can simplify the diagnostic process, allow stratification of patients, and assist in monitoring the disease course and outcome of therapeutic intervention in SS.

Methods

This systematic review addresses the use of proteomics and miRNA-expression profile analyses in this regard.

Results and discussion

Out of 272 papers that were identified and 108 reviewed, a total of 42 papers on proteomics and 23 papers on miRNA analyses in saliva, blood and salivary gland tissue were included in this review. Overall, the proteomic and miRNA studies revealed considerable variations with regard to candidate biomarker proteins and miRNAs, most likely due to variation in sample size, processing and analytical methods, but also reflecting the complexity of SS and patient heterogeneity. However, interesting novel knowledge has emerged and further validation is needed to confirm their potential role as biomarkers in SS.

Establishing Classification Tree Models in Rheumatoid Arthritis Using Combination of Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry and Magnetic Beads

Background: There is no simple method for early diagnosis and evaluation of rheumatoid arthritis (RA). This study aimed to determine potential biomarkers and establish diagnostic patterns for RA using proteomic fingerprint technology combined with magnetic beads.

Methods: The serum protein profiles of 97 RA patients and 76 healthy controls (HCs) were analyzed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) with weak cationic exchange (WCX) magnetic beads. Samples were randomly divided into training (83 RA patients and 56 HCs) and test sets (14 RA patients and 20 HCs). Patients were classified according to their Disease Activity Score: in remission, n = 28; with low disease activity, n = 17; with moderate disease activity, n = 21; with high disease activity, n = 31. There are 44 RA patients alone, 22 RA patients with interstitial lung disease (RA-ILD), 18 RA patients with secondary Sjögren's syndrome (RA-sSS), 6 RA patients with osteonecrosis of the femoral head (RA-ONFH), and 7 RA patients with other complications. Eleven patients were treated with etanercept only for half a year, after which their serum protein profiles were detected. The proteomic pattern was identified by Biomarker Patterns Software, and the potential biomarkers for RA diagnosis were further identified and quantified by enzyme-linked immunosorbent assay.

Results: The diagnostic pattern with four potential protein biomarkers, mass-to-charge (m/z) 3,448.85, 4,716.71, 8,214.29, and 10,645.10, could accurately recognize RA patients from HCs (specificity, 91.57%; sensitivity, 92.86%). The test set were correctly classified by this model (sensitivity, 95%; specificity, 100%). The components containing the four biomarkers were preliminarily retrieved through the ExPasy database, including the C-C motif chemokine 24 (CCL24), putative metallothionein (MT1DP), sarcolipin (SLN), and C-X-C motif chemokine 11 (CCXL11). Only the CCL24 level was detected to have a significant decrease in the serum of RA patients as compared with HCs (p < 0.05). No significant difference was found in others, but a decreasing trend consistent with the down-regulation of the four biomarkers detected by MALDI-TOF-MS was observed. The diagnostic models could effectively discriminate between RA alone and RA with complications (RA-ILD: m/z 10,645.10 and 12,595.86; RA-sSS: m/z 6,635.62 and 33,897.72; RA-ONFH: m/z 2,071.689). The classification model, including m/z 1,130.776, 1,501.065, 2,091.198, and 11,381.87, could distinguish between RA patients with disease activity and those in remission. RA with low disease activity could be efficiently discriminated from other disease activity patients by specific protein biomarkers (m/z 2,032.31, 2,506.214, and Z9286.495). Two biomarkers (m/z 2,032.31 and 4,716.71) were applied to build the classification model for RA patients with moderate and high disease activities. Biological markers for etanercept (m/z 2,671.604064, 5,801.840579, 8,130.195641, and 9,286.49499) were observed between the responder (n = 7) and non-responder groups (n = 4) (p < 0.05).

Conclusion: We successfully established a series of diagnostic models involving RA and RA with complications as well as assessed disease activity. Furthermore, we found that CCL24 may be a valuable auxiliary diagnostic indicator for RA. These results provide reference values for clinical practice in the future.

Application of machine learning in the diagnosis of axial spondyloarthritis

PURPOSE OF REVIEW

In this review article, we describe the development and application of machine-learning models in the field of rheumatology to improve the detection and diagnosis rates of underdiagnosed rheumatologic conditions, such as ankylosing spondylitis and axial spondyloarthritis (axSpA).

RECENT FINDINGS

In an attempt to aid in the earlier diagnosis of axSpA, we developed machine-learning models to predict a diagnosis of ankylosing spondylitis and axSpA using administrative claims and electronic medical record data. Machine-learning algorithms based on medical claims data predicted the diagnosis of ankylosing spondylitis better than a model developed based on clinical characteristics of ankylosing spondylitis. With additional clinical data, machine-learning algorithms developed using electronic medical records identified patients with axSpA with 82.6-91.8% accuracy. These two algorithms have helped us understand potential opportunities and challenges associated with each data set and with different analytic approaches. Efforts to refine and validate these machine-learning models are ongoing.

SUMMARY

We discuss the challenges and benefits of machine-learning models in healthcare, along with potential opportunities for its application in the field of rheumatology, particularly in the early diagnosis of axSpA and ankylosing spondylitis.