Multibiomarker Disease Activity Score: An Objective Tool for Monitoring Rheumatoid Arthritis?

A Systematic Review and Meta-Analysis

Fanni A. Meznerics; Lajos V. Kemény; Emese Gunther; Eszter Bakó Fanni Dembrovszky; Bence Szabó; Anna Ascsillán; Elmar Lutz; Dezsö Csupor; Péter Hegyi; András Bánvölgyi; György Nagy

Disclosures

Rheumatology. 2023;62(6):2048-2059.

Abstract and Introduction

Abstract

Objectives: The multibiomarker disease activity (MBDA) score is an objective tool for monitoring disease activity in RA. Here we report a systematic review and meta-analysis of the clinical value of the MBDA score in RA.

Methods: We performed a systematic literature search in five medical databases—MEDLINE (via PubMed), Cochrane Library (CENTRAL), Embase, Scopus and Web of Science—from inception to 13 October 2021. Original articles reporting on the performance of the MBDA score's correlation with conventional disease activity measures or the predictive and discriminative values of the MBDA score for radiographic progression, therapy response, remission and relapse were included.

Results: Our systematic search provided a total of 1190 records. After selection and citation searches, we identified 32 eligible studies. We recorded moderate correlations between MBDA score and conventional disease activity measures at baseline [correlation (COR) 0.45 (CI 0.28, 0.59), I ² = 71.0% for the 28-joint DAS with CRP (DAS28-CRP) and COR 0.55 (CI 0.19, 0.78), I ² = 0.0% for DAS28 with ESR] and at follow-up [COR 0.44 (CI 0.28, 0.57, I ² = 70.0% for DAS28-CRP) and found that the odds of radiographic progression were significantly higher for patients with a high baseline MBDA score (>44) than for patients with a low baseline MBDA score (<30) [OR 1.03 (CI 1.02–1.05), I² = 10.0%].

Conclusion: The MBDA score might be used as an objective disease activity marker. In addition, it is also a reliable prognostic marker of radiographic progression.

Introduction

RA is a systemic autoimmune disease affecting ≈0.5–1% of the population.^[1] According to the EULAR recommendations, the aim of the therapy in RA is to achieve remission, or at least low disease activity.^[2] Early treatment with DMARDs and a treat-to-target treatment strategy are recommended by current guidelines and are considered to be the optimal way to prevent long-term functional decline by minimizing cartilage and bone damage.^[3–5]

Given that the treat-to-target therapeutic approach requires close monitoring of disease activity, the need for reliable, objective disease activity measures (DAMs) is undeniable. The currently available, widely used options for monitoring disease activity and progression are either subjective or non-specific: the 28-joint DAS (DAS28), Simplified Disease Activity Index (SDAI), and Clinical Disease Activity Index (CDAI) all include subjective assessments of disease activity by the patient and/or the provider.^[6–8] Although non-specific inflammatory markers such as CRP and ESR are used to calculate the DAS28 and SDAI, the incorporation of a scoring system based on the combination of inflammatory markers and additional biomarkers could further objectify the measurement of disease activity. Structural damage, a major factor defining the course of the disease, can be assessed by radiography and quantified with the Sharp–van der Heijde (SvdH) scoring system.^[9] There are several known risk factors for radiographic progression, including high disease activity monitored by non-specific inflammatory markers such as CRP, RF and ACPA seropositivity.^[10] However, RF and ACPA are not suitable for monitoring disease activity.^[11]

The multibiomarker disease activity (MBDA) score system is an algorithm based on the serum level of 12 biomarkers [IL-6, TNF receptor type 1 (TNFR1), vascular cell adhesion molecule 1 (VCAM-1), epidermal growth factor (EGF), vascular EGF A (VEGF-A), YKL-40, matrix metalloproteinase-1 (MMP-1), MMP-3, CRP, serum amyloid A (SAA), leptin and resistin], resulting in a scale from 0 to 100.^[12] The MBDA score presents an objective disease monitoring system and thus may contribute to personalized therapeutic plans conforming to modern medical views. In addition to monitoring disease activity, the MBDA score may also predict radiographic progression.^[13–16]

Several studies have evaluated the utility of the MBDA score and a meta-analysis has been conducted on the correlation of the MBDA score with conventional DAMs; however, the predictive and discriminative values of the MBDA score has yet to be analysed in a comprehensive manner.^[17] Here we report a systematic review and meta-analysis of the clinical value and utility of the MBDA score for monitoring RA by determining the correlation of the MBDA score with conventional DAMs and the predictive and the discriminative values of the MBDA score for radiographic progression, therapy response, remission and relapse.

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Table 1. Characteristics of included studies
First author, year of publication	Type of publication	Original study type	Original study name	Clinical trial registration number of RCT	Study duration	Time points of study	Country	Treatment	Outcome
Studies included in the meta-analysis
Baker, 2021 [23]	Journal article	POS	Pennsylvania and Philadelphia VA Medical Centre	–	N/A	Baseline^a	USA (Pennsylvania)	MTX, bDMARD, GC	Spearman's correlation with conventional DAMs
Bakker, 2012 [13]	Journal article	RCT	CAMERA	N/A	2 years	Baseline^a, month 1,3,6^a, year 2^b	Netherlands	MTX, CsA, intra-articular GC, NSAID	Pearson's correlation with conventional DAMs^c predicting radiographic progression and remission^c
Bechman, 2018 [24]	Journal article	POS	REMIRA	–	1 year	Months 3, 6, 9 and 12^a	UK	csDMARD, bDMARD, GC	Spearman's correlation with conventional DAMs, relapse^c
Bijlsma, 2013 [25]	Conference abstract	RCT	CAMERA-II	https://isrctn.com (ISRCTN 70365169)	1 year	Baseline^a, months 1–12^a	Netherlands	Group A: MTX + PBO; group B: MTX + GC	Spearman's correlation with conventional DAMs
Bouman, 2017 [27]	Journal article	RCT	DRESS	https://trialregister.nl (NTR3216)	18 months	Baseline^a, months 3, 6, 9, 12, 15 and 18	Netherlands	MTX, csDMARD, ADA, ETN, NSAID, GC	Spearman's correlation with conventional DAMs, predicting radiographic progression^c and relapse^c
Brahe, 2016 [28]	Conference abstract	RCT	OPERA	https://clinicaltrials.gov (NCT00660647)	1 year	Baseline^a, months 3^a, 6 and 12^a	Denmark	Group A: MTX + PBO; group B: MTX + ADA	Spearman's correlation with conventional DAMs
Brahe, 2019 [14]	Journal article	RCT	OPERA	https://clinicaltrials.gov (NCT00660647)	1 year	Baseline^a, months 1, 2, 3^a, 6^a, 9 and 12	Denmark	Group A: MTX + PBO; group B: MTX + ADA	Spearman's correlation with conventional DAMs, predicting radiographic progression and remission^c
Genovese, 2017 [29]	Conference abstract	RCT	DARWIN 1, DARWIN 2	https://clinicaltrials.gov (NCT01888874; NCT01894516)	24 weeks	Baseline^a, week 12^a	USA	Group A: MTX + PBO; group B: MTX + 100 mg filogitinib; group C: MTX + 200 mg filogitinib	Spearman's correlation with conventional DAMs
Hambardzumyan, 2013 [31]	Conference abstract	RCT	SWEFOT	https://clinicaltrials.gov (NCT00764725)	1 year	Baseline^a, year 1^a	Sweden	MTX, other DMARD, IFX	Spearman's correlation with conventional DAMs
Hambardzumyan, 2015 [15]	Conference abstract	RCT	SWEFOT	https://clinicaltrials.gov (NCT00764725)	1 year	Month 3, year 1^b	Sweden	MTX, HCQ, SSZ, IFX	Predicting radiographic progression
Hirata, 2013 [49]	Journal article	RCT	BEST	N/A	1 year	Baseline^a, year 1^a	Netherlands, Japan	DMARD, IFX	Spearman's correlation with conventional DAMs, remission^c
Hirata, 2015 [50]	Journal article	ROS	UOEH	–	1 year	Baseline^a, weeks 24 and 52^a	Japan	ADA, ETN, IFX, MTX	Spearman's correlation with conventional DAMs, therapy response^c
Hirata, 2016 [34]	Journal article	ROS	UOEH	–	7 years	Baseline^a, week 52^a	Japan	MTX, ADA, ETN, IFX	Spearman's correlation with conventional DAMs, predicting radiographic progression^c
Jurgens, 2020 [35]	Journal article	RCT	CAMERA-II	https://www.isrctn.com (ISRCTN 70365169)	1 year	Baseline^a, months 1, 2, 3^a, 4–12	Netherlands	MTX, GC, CsA, ADA, PBO	Spearman's correlation with conventional DAMs
Krabbe, 2017 [36]	Journal article	POS	HURRAH	–	52 weeks	Baseline^a, weeks 26 and 52^a	Denmark	MTX, ADA	Spearman's correlation with conventional DAMs, predicting radiographic progression^c
Lee, 2016 [37]	Journal article	POS	BRASS	–	2 years	Baseline^a	USA (Massachusetts)	csDMARD, bDMARD	Spearman's correlation with conventional DAMs
Li, 2013 [38]	Conference abstract	POS	EIRA	–	3 months	Baseline^a, month 3^a	Sweden	MTX	Spearman's correlation with conventional DAMs, therapy response^c
Ma, 2014 [41]	Conference abstract	POS	REMIRA	–	1 year	Baseline^a, year 1^a	UK	N/A	Spearman's correlation with conventional DAMs
Maijer, 2013 [43]	Conference abstract	POS	Academic Medical Centre Amsterdam	–	2 years	Baseline^a	Netherlands	N/A	Spearman's correlation with conventional DAMs
Reiss, 2016 [46]	Journal article	RCT	ACT-RAY	N/A	24 weeks	Baseline^a, weeks 4, 12 and 24^a	USA (California)	TCZ, MTX, GC	Spearman's correlation with conventional DAMs
Roodenrijs, 2018 [47]	Journal article	POS	LUMC, UMC, HORUS	–	1 year	Baseline^a, month 6^a	Netherlands, UK	RTX, GC	Spearman's correlation with conventional DAMs, therapy response^c
Studies included only in the systematic review
Boeters, 2019 [26]	Journal article	POS	LUMC	–	N/A	Annually	Netherlands	csDMARDS, bDMARDS	Relapse
Hambardzumyan, 2019 [30]	Journal article	RCT	SWEFOT	https://clinicaltrials.gov (NCT00764725)	3 months	months 0, 3	Sweden	MTX, HCQ, SSZ, IFX	Therapy response
He, 2020 [32]	Conference abstract	Database analysis	N/A	–	N/A	Baseline^a	USA	DMARD	Pearson's correlation with conventional DAMs
Hirata, 2012 [33]	Conference abstract	RCT	BEST	N/A	1 year	Baseline, year 1	Netherlands	N/A	remission
Li, 2016 (48)	Journal article	POS	LUMC	–	N/A	Annually	Netherlands	csDMARD, TNFi	predicting radiographic progression
Luedders, 2020 [40]	Journal article	POS	N/A	–	16 weeks	Baseline^a, weeks 8 and 16^a	USA (Nebraska)	MTX, FA, GC, NSAID	Pearson's correlation with conventional DAMs, remission
Ma, 2020 [42]	Journal article	POS	REMIRA	–	1 year	Baseline, months 3 and 6	UK, Singapore	csDMARDs, TNFi, GC	Remission
Markusse, 2014 [16]	Journal article	RCT	BEST	N/A	1 year	Baseline, year 1	Netherlands	csDMARD, IFX, GC	predicting and discriminating radiographic progression
Ghiti Moghadam, 2018 [48]	Journal article	RCT	POET	https://trialregister.nl (NTR3112)	1 year	Baseline, months 3, 6, 9 and 12	Netherlands	csDMARD	Relapse
Razmjou, 2020 [44]	Journal article	POS	N/A	–	12 weeks	Baseline^a, weeks 2, 6 and 12^a	USA (California)	csDMARD, tofacitinib	Pearson's correlation with conventional DAMs
Rech, 2016 [45]	Journal article	RCT	RETRO	https://www.clinicaltrialsregister.eu (2009-015740-42)	1 year	Baseline, months 3, 6, 9 and 12	Germany	csDMARDS, bDMARDS	Relapse

^aTime point used for calculating correlation.
^bTime point used for calculating radiological progression.
^cNot included in the meta-analysis.
ADA: adalimumab; CsA: ciclosporin A; bDMARD: biological DMARD; csDMARD: conventional synthetic DMARD; ETN: etanercept; FA: folic acid; GC: glucocorticoid; IFX: infliximab; N/A: no data available; PBO: placebo; POS: prospective observational study; RCT: randomized clinical trial; ROS: retrospective observational study; RTX: rituximab; TCZ: tocilizumab; TNFi: TNF-α inhibitor.

Table 2. Characteristics of studies evaluating the predictive value of MBDA score and DAS28-CRP for radiographic progression
First author, year of publication	Time of evaluating RP	Definition of RP	Low MBDA score	High MBDA score	Low DAS28-CRP	High DAS28-CRP
Studies included in the meta-analysis
Bakker, 2012 [13]	2 years	>0 units increase of SvdH score	<30	>44	≤2.7	>2.7
Brahe, 2019 [14]	1 year	>2 units increase of SvdH score	<30	>44	≤5.1	>5.1
Hambardzumyan, 2015 [15]	1 year	>5 units increase of SvdH score	<30	>44	≤2.7	>4.1
Studies included in the systematic review
Bouman, 2017 [27]	1.5 years	>0.5 unit increase in SvdH score	<30	>44	<2.7	>4.1
Hirata, 2016 [34]	1 year	>3 unit increase in SvdH score	<30	>44	≤3.2	>5.1
Krabbe, 2017 [36]	0.5, 1 year	N/A	<30	>44	≤3.2	>5.1
Li, 2016 [39]	1 year	>3 unit increase in SvdH score	<30	>44	≤2.67	>4.09
Markusse, 2014 [16]	1 year	>0.5 unit increase in SvdH score	<30	>44	≤2.4	>3.7

N/A: no data available; RP: radiographic progression.

Authors and Disclosures

Fanni A. Meznerics^1,2,†, Lajos V. Kemény^1,3,†, Emese Gunther^2,4, Eszter Bakó^2,5, Fanni Dembrovszky^2,6, Bence Szabó², Anna Ascsillán¹, Elmar Lutz¹, Dezsö Csupor^2,6,7, Péter Hegyi^2,6,8, András Bánvölgyi^1,‡ and György Nagy^9–11,*^,‡

¹Department of Dermatology, Venereology and Dermatooncology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
²Centre for Translational Medicine, Semmelweis University, Budapest, Hungary
³HCEMM-SU Translational Dermatology Research Group, Department of Physiology, Semmelweis University, Budapest, Hungary
⁴Department of Rheumatology, Petz Aladá r County Teaching Hospital, Györ, Hungary
⁵Department of Pharmacognosy, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary
⁶Institute for Translational Medicine, Medical School, Szentá gothai Research Centre, University of Pécs, Pécs, Hungary
⁷Institute of Clinical Pharmacy, Faculty of Pharmacy, University of Szeged, Szeged, Hungary
⁸Institute of Pancreatic Diseases, Semmelweis University, Budapest, Hungary
⁹Department of Rheumatology and Clinical Immunology, Department of Internal Medicine and Oncology, Semmelweis University, Budapest, Hungary
¹⁰Heart and Vascular Centre, Semmelweis University, Budapest, Hungary
¹¹Department of Genetics, Cell- and Immunobiology, Semmelweis University, Budapest, Hungary

*Correspondence to
György Nagy, Department of Rheumatology and Clinical Immunology, Department of Internal Medicine and Oncology, Semmelweis University, 7 Árpád fejedelem útja, Budapest H-1023, Hungary. E-mail: nagy.gyorgy2@med.semmelweis-univ.hu

^†Fanni A. Meznerics and Lajos V. Kemény are both the first authors.

^‡András Bánvölgyi and György Nagy shares the last authorship.

Authors' contributions
L.V.K. and F.A.M. were responsible for conceptualization, project administration, data curation, visualization and writing the original draft. E.G. was responsible for conceptualization, project administration, data curation and visualization. E.B. was responsible for conceptualization and data curation. F.D. was responsible for conceptualization, data curation and methodology. B.S. was responsible for methodology, formal analysis, validation and visualization. A.A. and E.L. were responsible for data curation and visualization. D.C. and P.H. were responsible for conceptualization, methodology and supervision. A.B. and G.N. were responsible for conceptualization, methodology, supervision and writing the original draft. All authors provided critical conceptual input and approved the final version of the article.

Disclosure statement
The authors have declared no conflicts of interest.