Cardiovascular disease risk in inflammatory joint disease: Conventional disease risk factors across inflammatory joint diseases and performance of risk age models in rheumatoid arthritis

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© Grunde Wibetoe, 2019

Series of dissertations submitted to the Faculty of Medicine, University of Oslo

ISBN 978-82-8377-381-1

All rights reserved. No part of this publication may be

reproduced or transmitted, in any form or by any means, without permission.

Cover: Hanne Baadsgaard Utigard.

Print production: Reprosentralen, University of Oslo.

iii

A

All the knowledge I possess everyone else can acquire, but my heart is all my own

– Johann Wolfgang von Goethe (1774)

”

Content

Funding ... vi

Acknowledgements... vii

List of abbreviations ... ix

List of papers ... xii

1. Introduction ... 1

2. Background ... 2

2.1 Inflammatory joint diseases ... 2

2.1.1 Rheumatoid arthritis... 3

2.1.2 Axial Spondyloarthritis ... 6

2.1.3 Psoriatic arthritis ... 9

2.2 Cardiovascular disease in inflammatory joint diseases ... 11

2.2.1 Epidemiology ... 11

2.2.2 Pathogenesis ... 13

2.2.3 Constitutional factors ... 14

2.2.4 Conventional risk factors ... 16

2.2.5 Emerging risk factors related to inflammatory joint disease ... 21

2.3 Cardiovascular disease risk assessment in inflammatory joint diseases ... 22

2.3.1 Absolute risk, relative risk, lifetime risk and risk age estimates... 22

2.3.2 Assessment of risk prediction models ... 27

2.3.3 Risk calculators developed for and validated in the general population ... 27

2.3.4. Rheumatoid arthritis-specific risk calculators... 29

2.3.5 Performance of cardiovascular risk calculators in rheumatoid arthritis... 30

2.3.6 Current recommendations for cardiovascular risk assessment ... 30

2.4 Cardiovascular risk management in inflammatory joint diseases... 31

2.4.1 Lifestyle interventions ... 32

2.4.2 Antihypertensive treatment ... 33

2.4.3 Lipid-lowering therapy ... 34

2.4.4 Control of disease activity and the use of anti-rheumatic medication ... 36

3. General aims and research questions ... 37

3.1 General aims ... 37

3.2 Specific research questions (Q) ... 37

4. Patients and Methods ... 38

4.1 NOCAR ... 38

v

4.1.1 Patient inclusion and study design ... 39

4.1.2 Data collection ... 39

4.2 ATACC-RA... 40

4.2.1 Patient inclusion and study design ... 40

4.2.2 Data collection ... 40

4.3 Statistical methods ... 41

4.3.1 Descriptive statistics ... 41

4.3.2 CVD risk calculations ... 41

4.3.4 C-statistics ... 44

4.4 Legal and ethical aspects ... 44

4.4.1 NOCAR ... 44

4.4.2 ATACC-RA ... 44

5. Summary of results ... 45

5.1 Paper I ... 45

5.2 Paper II ... 46

5.3 Paper III ... 47

5.4. Paper IV ... 48

6. Discussion ... 49

6.1 Methodological considerations ... 49

6.1.1. NOCAR ... 49

6.1.2 ATACC-RA ... 51

6.2 Main results ... 52

6.2.1 Prevalence and treatment of hypertension and elevated cholesterol ... 52

6.2. 2 Obesity, smoking and diabetes mellitus ... 53

6.2.3 Cardiovascular risk estimates: Absolute risk, relative risk and risk age ... 53

6.2.4 Agreement between risk age models... 54

6.2.5 Discriminative ability of risk age models in RA and within RA subsets ... 55

7. Conclusions, clinical implications and future perspectives ... 56

7.1 Answers (A) to research questions ... 56

7.2. Clinical implication and future perspectives ... 57

8. References ... 59

9. Appendix ... 75

10. Papers I-IV ... 81

:3).3,

The work in this thesis was funded by the South-Eastern Norway Regional Authority, Grete Harbitz legacy and Olav Raagholt and Gerd Meidel Raagholts foundation. Eli Lilly and Pfizer have supported logistics for ATACC-RA and NOCAR, in collaborative agreement for independent research. Institutional support was provided by Diakonhjemmet Hospital.

vii

(034<1*),*2*398

There are many people who have encouraged, helped and supported me during my PhD studies.

My main supervisor has been senior researcher, clinical cardiologist and leader of the Preventive Cardio Rheuma Clinic at Diakonhjemmet Hospital, Anne Grete Semb. She is an internationally renowned scientist who has pioneered the field of Cardio Rheumatology.

Thank you, Anne Grete for your engagement, encouragement, enthusiasm and for your confidence in me. Not only have you let me join both well-established nationwide and international collaborative projects, but you have also showed me faith during the development of new projects. This thesis could not have been possible without you!

I would also like to thank my two co-supervisors, professor Tore K. Kvien and MD, PhD/Post doc Silvia Rollefstad. Thank you, Tore for both being the architect behind the highly productive research environment Diakonhjemmet has become, but also for your support and valuable scientific input. Thank you, Silvia for valuable discussions, feedback, advice and excellent contribution in revisions of manuscripts.

I would also like to thank Anne Eirheim at the Preventive Cardio Rheuma Clinic for her bright mood every Monday morning. I am also grateful for the final member of the research group, MD, PhD Eirik Ikdahl. It has been both productive and fun to collaborate closely with you, Eirik. We have spent much time travelling Norway by plane and train (but NO(t by)CAR) and working closely together with the data handling, statistics, analyzes, drafting and revision of NOCAR papers. I have learned a lot through your tips and advice.

Thank you for the excellent companionship throughout these years.

Thanks to all members and co-authors for the NOCAR and ATACC-RA projects, and Cindy Crowson in particular. I would also like to thank the former and current statisticians at Diakonhjemmet, Joe, Inge and Øivind, for engaging statistical tutorials, discussions, advice and collaboration.

During these years, I have been blessed with the company of the clinical

rheumatologists, the administration (the three ladies who make all things run smoothly) and

fellow PhD students and senior scientists located at the ´white house´. I am forever grateful to you and in particular the former and current first floor gang: Marte H., Alexander, Vibeke, Brigitte, Gina, Ellen, Ida, Pernille and Øystein. Thank you for your kindness, help and support, for sharing sorrow as well as celebrating achievements. You have made working hours amusing and rewarding. I hope to keep in touch with you in the following years.

I am forever thankful to my parents who gave me the very best start in life as they could possible give. Finally, thanks to my own superhero and wife Camilla. You make every day wonderful and through your support, help and advice no obstacles seem too high to climb!

ix

.894+&''7*;.&9.438

ACC American College of Cardiology

ACPA Anti-Cyclic Citrullinated Protein Antibody ACR American College of Rheumatology AHA American Heart Association ANOVA Analysis of variance

AntiHT Antihypertensive Treatment

apoA1 Apolipoprotein A1

apoB Apolipoprotein B

AS Ankylosing Spondylitis

ASDAS Ankylosing Spondylitis Disease Activity Score ASAS Assessment of Spondyloarthritis International Society

ATACC-RA A Trans-Atlantic Cardiovascular risk Consortium for Rheumatoid Arthritis AUROC Area Under the Receiver Operatic characteristics Curve

AxSpA Axial Spondyloarthritis

BASDAI Bath Ankylosing Spondylitis Disease Activity Index bDMARDs Biologic Disease Modifying Anti Rheumatic Drugs

boDMARDs Biological original Disease Modifying Anti Rheumatic Drugs BMI Body Mass Index

BP Blood Pressure

CABG Coronary Artery Bypass Graft

CANTOS Canakinumab Antiinflammatory Thrombosis Outcome Study CASPAR Classification Criteria for Psoriatic Arthritis

CDAI Clinical Disease Activity Index CHF Congestive Heart Failure

CI Confidence Interval

CIRT Cardiovascular Inflammation Reduction Trial CRP C reactive protein

csDMARDs Conventional Synthetic Disease Modifying Anti Rheumatic Drugs C – index Concordance index

CORRONA Consortium of Rheumatology Researcher of North America C-statistics Concordance statistics

CVA Cerebrovascular Accident

CVD Cardiovascular Disease

CVDRF Cardiovascular Disease Risk Factor

DAPSA Disiease Activity Index for Psoriatic Arthritis DAS Disease Activity Score

DAS28 Disease Activity Score using 28 joint count dBP Diastolic Blood Pressure

DM Diabetes Mellitus DMARDs Disease-Modifying Antirheumatic Drugs ESH European Society of Hypertension

ERS-RA Expanded Cardiovascular Risk Score for RA patients ESC European Society of Cardiology

ESR Erythrocyte Sedimentation Rate EULAR European League Against Rheumatism FHS Framingham Heart Study

FRS Framingham Heart Study Risk Scores GBD Global Burden of Disease

GC Glucocorticoids

GRAPPA Group for Research and Assessment of Psoriasis and Psoriatic Arthritis

GTI GoTreatIt Rheuma

HbA1c% glycated haemoglobin A1c HDL-C High Density Lipoprotein Cholesterol HMG-CoA Hydroxymethylglutaryl-coenzyme A HLA Human Leukocyte Antigen

HR Hazard Ratio

HT Hypertension

IDEAL Incremental Decrease in End Points through Aggressive Lipid Lowering IFN Interferons

IHD Ishemic Heart Disease IJD Inflammatory Joint Disease IL Interleukin

IQR Inter-Quartile Range

JAK Janus Kinase

LDL-C Low Density Lipoprotein Cholesterol LLT Lipid Lowering Therapy

Lpa Lipoprotein MDA Minimal Disease Activity

mHAQ modified Health Assessment Questionnaire MHC Major Histocompability Complex

MI Myocardial Infarction

mNY criteria Modified New York classification criteria for Ankylosing Spondylitis

MRI Magnetic Resonance Imaging

MTX Methotrexate M&W criteria Moll and Wright criteria

NHANES National Health and Nutrition Examination Survey

NOCAR NOrwegian Collaboration on Atherosclerotic disease in patients with Rheumatic joint diseases

xi Non-HDL-C Non-High Density Lipoprotein Cholesterol

nr-axSpA Non-Radiographic Axial Spondyoarthritis NSAIDs Non-Steroidal Anti-Inflammatory Drugs OGTT Oral Glucose Tolerance Test

OR Odds Ratio

PAD Peripheral Artery Disease

PAR Population Attributable Risk PCI Percutaneous Coronary Intervention

PsA Psoriatic Arthritis

RA Rheumatoid Arthritis

RF Rheumatoid Factor

RR Relative Risk

sBP Systolic Blood Pressure

SCORE Systematic Coronary Risk Evaluation

SCORE O.P. Systematic Coronary Risk Evaluation in Older Persons

SD Standard Deviation

SDAI Simplified Disease Activity Index

SE Standard Error

sDMARDs Synthetic Disease Modifying Anti Rheumatic Drugs SpA Spondyloarthritis

SPRINT Systolic Blood Pressure Intervention Trial

TC Total Cholesterol

TG Triglycerides TIA Transient Ischemic Attack TNFα Tumor Necrosis Factor alpha

TNFi Tumor Necrosis Factor alpha Inhibitors TNT Treating to New Targets

tsDMARDs Targeted Synthetic Disease Modifying Anti Rheumatic Drugs T2DM Type 2 Diabetes Mellitus

UK United Kingdom

US United States of America WHO World Health Organization

.894+5&5*78

I. Wibetoe G, Ikdahl E, Rollefstad S, Olsen IC, Bergsmark K, Kvien TK, Salberg A, Soldal DM, Bakland G, Lexberg Å, Fevang BT, Gulseth HC, Haugeberg G and Semb AG. Cardiovascular disease risk profiles in inflammatory joint entities. Arthritis Res Ther (2017) Jul 3;19(1):153

II. Ikdahl E*, Wibetoe G*, Rollefstad S, Salberg A, Bergsmark K, Kvien TK, Olsen IC, Soldal DM, Bakland G, Lexberg Å, Fevang BT, Gulseth HC,

Haugeberg G and Semb AG. *Shared first authorship. Guideline recommended treatment to targets of cardiovascular risk is inadequate in patients with inflammatory joint diseases. Int J Cardiol (2018). [In press]

III. Wibetoe G, Ikdahl E, Rollefstad S, Olsen IC, Bergsmark K, Kvien TK, Salberg A, Soldal DM, Bakland G, Lexberg Å, Fevang BT, Gulseth HC, Haugeberg G and Semb AG. Discrepancies in risk age and relative risk estimations of cardiovascular disease in patients with inflammatory joint diseases. Int J Cardiol (2018) Feb 1;252:201-206

IV. Wibetoe G, Sexton J, Ikdahl E, Rollefstad S, Kitas GD, van Riel P, Gabriel S, Kvien TK, Douglas K, Sandoo A, Arts EA, Wållberg-Jonsson S, Dahlquist SR, Karpouza G, Dessein PH, Tsang L, El-Gabalawy H, Hitchon CA, Pascual- Ramos V, Contreas-Yañes I, Sfikakis P, González-Gay M, Colunga-Pedraz IJ, Galarza-Delgado DA, Azpiri-Lopez JR, Crowson CS and Semb AG. Prediction of cardiovascular events in rheumatoid arthritis using risk age calculations:

Evaluation of concordance across risk age models. [Submitted].

1

3974):(9.43

Inflammatory joint diseases (IJD) are all characterized by painful, swollen and stiff joints despite having different pathogenic origins (e.g. infectious, reactive/post-infectious, crystal- induced and autoimmune). These diseases exert a high burden on the individual level and on society as they cause agonizing symptoms, impaired work-capacity, reduced quality of life, as well as excess morbidity and mortality ^1-6. Peripheral arthritis, axial inflammation and/or enthesitis constitute the hallmark features of IJD ^7-9, and the numerous extra-articular manifestations reflect the systemic nature of these diseases ^{10 11}. In fact, cardiovascular disease (CVD) represents the major comorbidity and cause of the reduced life-expectancy in the three archetypical chronic, autoimmune IJD: rheumatoid arthritis (RA), axial

spondyloarthritis (axSpA) and psoriatic arthritis (PsA) ^12-14. Although an excess risk of almost all forms for CVD has been reported (of atherosclerotic and non-atherosclerotic origin), atherosclerotic CVD is the leading cause of CVD morbidity and mortality, as for the general population ^15-17.

The majority of CVD events in IJD and in the general population are attributable to the five major conventional CVD risk factors (CVDRFs): hypertension (HT), lipid

abnormalities, smoking, obesity and diabetes mellitus (DM) ¹⁸. Although much focus has been aimed towards the role of chronic high-grade inflammation as a driver of the

atherosclerotic process in IJD patients, conventional CVDRFs are pivotal to their high CVD burden ^19-21. However, it is unknown whether the CVDRF profiles differ across IJD entities.

Similarly, inadequate antihypertensive treatment (AntiHT) and lipid lowering therapy (LLT) has been documented among RA ^22-29, whereas data for cardio protective treatment in

axSpA and PsA patients is very limited and in many cases missing altogether ^30-32. Guidelines on CVD prevention for the general population advise use of absolute risk estimates to support clinical decision making for initiation of preventive medication ³³. Furthermore, relative risk age and risk age calculations have been advocated as useful risk evaluation supplements to reveal high relative risk of CVD concealed by low absolute 10- year risk ³³. Two different risk age models have been proposed, but none of them have been evaluated in either IJD populations, or the general population ^33-35.

The papers included in this thesis strive to fill gaps of knowledge related to CVD in patients with RA, axSpA and PsA by i) evaluating the prevalence of the five major

conventional CVD risk factors (CVDRFs), ii) investigation of the attainment of guideline-

recommended treatment goals for AntiHT and LLT, iii) reporting the agreement between the proposed risk age models and iv) comparing their predictive performance.

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This section describes similarities between RA, axSpA and PsA related to etiology, pathogenesis, clinical features and treatment, whereas the three specific IJD entities are considered separately (section 2.1.1-2.1.3).

The exact underlying causes of RA, axSpA and PsA are unknown. However, it has been postulated that central events in the IJD pathogeneses include post-translational modification of peptides, molecular mimicry and self-peptide binding, subsequent loss of self-tolerance, autoantibody production and formation of immune complexes ^7-9. Genetic susceptibility is linked to genes within the human leukocyte antigen (HLA) class I and II regions for the major histocompability complex (MHC), while established environmental risk factors include smoking and obesity ^{7-9 36 37}. High-grade inflammation is perpetuated by pro-inflammatory cytokines ^{38 39}. Insight into the key signaling molecules have partly been affirmed by the efficacy of synthetic (sDMARDs) and biologic (bDMARDs) disease- modifying antirheumatic drugs (DMARDs) that target specific immune pathways ^{37 40}.

Diagnostic criteria for RA, axSpA and PsA are currently missing. Hence, classification criteria used to select homogenous cohorts are often applied in clinical practice to support diagnostics. Applications of these criteria enable swift identification of individuals who are likely to benefit from treatment ^41-44. Acute phase reactants are also valuable biomarkers for on-going inflammation. Overall disease activity is frequently

assessed through the use of composite scores that combine multiple outcome measures, such as surrogate markers of inflammation, pain, structural damage, physical function, and health-related quality of life. These are also applied to define current disease activity state (using pre-defined cut-off values) and to evaluate treatment response. The management of IJD is based on similar principles, which include very early referral to rheumatologist (aiming for early treatment initiation once diagnosis is made to utilize the window of opportunity), and a ‘treat to target’ approach (adjusting therapy until the pre-defined target is obtained) ^45-51. The distinct DMARDs are primarily classified according to production methods and the currently proposed nomenclature distinguishes between conventional

3 synthetic (cs), targeted synthetic (ts), biological original (bo) and biosimilar DMARDs ⁵² (figure 1).

Figure 1. Introduction of antirheumatic drugs since the 1930s

Abbreviations: NSAIDs: Non-Steroidal Anti-Inflammatory Drugs; DMARDs: Disease- Modifying Antirheumatic Drugs; tsDMARDs; targeted synthetic DMARDs; csDMARDs:

conventional synthetic DMARDs. From Burmester GR et al, Nat Rev Rheum. 2017 Jul;13(7):443-448. Repoduced with permission: License number 4300631208365.

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Epidemiology, aetiology and pathogenesis

Worldwide prevalence of RA was estimated to be approximately 0.2% in the Global Burden of Disease (GBD) study ³⁵. This estimate is in the lower end compared to the highly cited systematic literature review by Alamanos et al reported regional occurrence ranging from 0.2 to 1.7% ^{53 54}. However, epidemiologic studies have largely been based on fulfillment of the stringent 1987 American College of Rheumatology (ACR) classification criteria for RA, which may have led to underestimation of the frequency of RA. In contrast, a recent

Swedish study found a 0.8% prevalence of physician–diagnosed RA ^{55 56}. In Northern Europe, annual incidence of RA is approximately 20-40 cases per million and incidence increases with age until the 7^th decade of life ^{53 54 57}. There is a female predominance of RA with a 2:1 female-male ratio ⁵³. Genetic factors account for more than 90% of the risk and susceptibility is strongly linked to the so-called ‘shared epitope’ (HLA class II locus: HLA- DRB1 alleles), a sequence of amino acids in the peptide-binding groove ^{7 58 59}. Several risk alleles have been identified, including genes encoding for protein tyrosine phosphatase non- receptor type 22 and genes involved in peptide citrullination and/or costimulatory receptors

37. RA is a complex and heterogeneous disease, or possibly a syndrome, associated with a vast number of signs and symptoms ^{37 60}. In seropositive patients, autoantibodies (e.g.

rheumatoid factor [RF], anti-cyclic citrullinated protein antibodies [ACPA] and anti- carbamylated protein antibodies) are detectable years prior to clinical manifestations ⁶⁰. Synovial and systemic inflammation is perpetuated by the innate and adaptive immune system and the key pro-inflammatory cytokines include Tumor Necrosis Factor alpha

(TNFα), Interleukin (IL)–6 and type I interferons (IFNs) ³⁹. Early IJD is likely to represent a distinct pathophysiological phase and the term “window of opportunity” points to the

beneficial effect of very early initiation of DMARDs on future disease outcome ^{61 62}. Clinical features and classification criteria

The classic presentation of RA is symmetrical polyarthritis with a predominance of the small joints of the hands and feet ^{41 63}. Extra-articular features may include constitutional symptoms (weight loss, cachexia, malaise, fatigue and fever), as well as various

complications due to systemic involvement of almost all organ systems ¹¹. Autoantibodies ( [RF] and/or [ACPA]) and elevated acute phase reactants (erythrocyte sedimentation rate [ESR] and C-reactive protein [CRP]) are highly prevalent, which is also reflected by the inclusion of elevated acute phase reactants and autoantibodies in the 2010 classification criteria ⁴¹. Table 1 compares the 1987 ACR and the 2010 ACR/European League Against Rheumatism (EULAR) classification criteria. The 1987 ACR classification criteria are not suitable to identify early RA since late manifestations (nodules and erosions) are part of the items applied for classification ³². The 2010 ACR/EULAR classification criteria on the other hand, have been proven to have superior sensitivity for detection of early disease,

supporting swift initiation of anti-inflammatory treatment as definitive RA is classified upon a sum of >6 points 41 56 59 64.

Treatment and disease assessment

Table 2 presents the commonly applied RA-specific composite scores, including the Disease Activity Score (DAS) with 28 joint counts (DAS28) ⁶⁵ (a modified version of the original DAS ⁶⁶), the Simplified Disease Activity Index (SDAI) ⁶⁷ and the Clinical Disease Activity Index (CDAI) ⁶⁸ with proposed thresholds for defining disease activity states ^{69 70}.

Methotrexate (MTX) is the first line drug, whereas short-term glucocorticoid (GC) treatment is instituted when needed ⁴⁵. Failure to achieve remission or low disease activity may necessitate a second conventional synthetic DMARD (csDMARD), treatment with a Janus Kinase (JAK) inhibitor or a bDMARD. Nociceptive pain is managed with analgesics, and/or Non-Steroidal Anti-inflammatory Drugs (NSAIDs) or cyclooxygenase-inhibitors.

5 Table 1. Classification criteria for rheumatoid arthritis

1987 ACR criteria 2010 ACR/EULAR criteria ^Points

Minimum 6 week duration (1-4): Joint involvement 1) Morning stiffness >1 hour

2) Swelling >3 joint areas 3) >1 Swollen hand joints 4) Symmetrical joint swelling

1 medium/large joint 2-10 medium/large joints 1-3 small joints

4-10 small joints

>10 joints (minimum 1 small joint)

0 1 2 3 5 Presence of (5-7):

5) Rheumatoid nodules 6) RF positivity

7) Erosions on radiographs

Serology

Negative RF and ACPA Low positive RF or ACPA High positive RF or ACPA

0 2 3 Acute phase reactants

Normal CRP and ESR Abnormal CRP or ESR

0 1 Duration of symptoms

<6 weeks

>6 weeks

0 1 Abbreviations: ACR: American College of Rheumatology; EULAR: European League Against Rheumatism; RF: Rheumatoid Factor; ACPA: Anti-Cyclic Citrullinated Peptide;

CRP: C reactive protein; ESR: Erythrocyte Sedimentation Rate

Table 2. Composite scores for disease activity measurement in rheumatoid arthritis

Composite score DAS28 SDAI CDAI

Acute phase reactant

C-reactive protein (CRP) Yes (or ESR) Yes No Erythrocyte sedimentation rate

(ESR)

Yes (or CRP) No No

Joint count

Tender joint count (TJC) Yes Yes Yes

Swollen joint count (SJC) Yes Yes Yes

Assessment of global health parameter (disease activity)

Patient reported (PGA) Yes Yes Yes

Physician/Evaluator reported (EGA) No Yes Yes

Formula Version with ESR:

0.56 x √(TJC28) + 0.28 x √(SJC28) + 0.70 x lognat(ESR) + 0.014 x GH

SJC28 + TJC28 + PGA + EGA + CRP

SJC28 + TJC28 + PGA + EGA Version with CRP:

0.56 x √(TJC28) + 0.28 x √(SJC28) + 0.36 x lognat(CRP+1) + 0.014 x PGA + 0.96 Disease activity cutpoints

Remission <2.6 <3.3 <2.8

Low disease activity 2.6-3.2 >3.3 & <11 >2.8 & <10 Moderate disease activity >3.2-5.1 >11 & <26 >10 & <22

High disease activty >5.1 >26 >22 Abbreviations: DAS28: modified Disease Activity Score using 28 joint count; SDAI:

Simplified Disease Activity Index; CDAI: Clinical Disease Activity Index.

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Axial SpA covers both radiographic disease, most commonly known as ankylosing

spondylitis (AS), and non-radiographic disease (nr-axSpA). These entities are characterized by inflammatory back pain and are mainly distinguished by presence of evident sacroilitis on conventional radiographic imaging. An ongoing debate addresses the issue whether AS (radiographic disease) and nr-axSpA represents two pathophysiologically distinct disease entities with overlapping manifestations or the same disease (e.g. that nr-axSpA represent an early disease phase that, in many patients, progress to AS) ^{8 71}. AxSpA patients are not only at risk of worrisome musculoskeletal manifestations, but are also found to have increased mortality compared to the general population ^{36 72 73}.

Epidemiology, aetiology and pathogenesis

While there are over 50 published studies on the epidemiology of AS, there are limited epidemiologic reports on the occurrence of nr-axSpA ⁷⁴. Geographical differences in AS prevalence is largely explained by regional variation in the distribution of HLA B27

positivity ⁷⁴. For instance, the prevalence varies 10-fold between Nordic/ Arctic populations and the sub-Saharan Africa (0.25% versus 0.02%) ^74-76. In Sweden, Exarchou et al reported a prevalence of 0.18% of physician diagnosed AS ⁷⁷. Available data suggests that nr-axSpA may be at least as common as AS ^78-81. Onset of AxSpA is typically during the 3^rd decade of life ⁸. AS is more common among males (male – female ratio of 2:1- 3:1), while the male predominance is less prominent in nr-axSpA ^{10 74}. More than 90% of AS risk is attributed to genetic factors and the previously mentioned HLA-B27 and other MHC alleles may account for approximately half of the genetic risk combined ^{82 83}. It has been proposed that HLA- B27 positivity is linked to the development of AS through mechanisms driven by molecular mimicry, HLA-B27 misfolding and/or homodimer formation of the HLA-B27 heavy chains;

which subsequently lead to activation of autoreactive Natural Killer cells and T-cells ^{83 84}. Clinical features and classification criteria

The primary manifestation of axSpA is driven by chronic inflammation of the axial skeleton which results in pelvic and lower back pain, stiffness and restricted spinal mobility ³⁶. The inflammatory nature of back pain is characterized by prolonged morning stiffness,

nocturnal worsening and relief upon exercise and NSAIDs, but not by rest. Other symptoms include peripheral arthritis, enthesitis, uveitis, dactylitis, psoriasis and inflammatory bowel disease ¹⁰. Magnetic resonance imaging (MRI) can reveal ongoing axial inflammation.

HLA-B27 positivity is highly frequent and elevated CRP usually reflects inflammatory activity. Table 3 presents the Assessment of Spondyloarthritis International Society (ASAS)

7 classification criteria for axSpA and the 1984 modified New York criteria (mNY) for AS.

Patients have ‘definitive AS’ if they fulfil the radiologic criterion and minimum one clinical criteria. ‘Probable AS’ requires either 3 clinical criteria or fulfilment of the radiologic criterion ⁸⁵. Patients are considered to have axSpA if the ASAS entry criteria (onset of back pain before the age of 45 years with back pain duration >3 months) are present in addition to either HLA-B27 with >2, or sacroilitis with > 1, SpA features ⁴².

Treatment and disease assessment

A simplified overview of two frequently applied composite scores for assessment of overall disease activity, the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) ⁸⁶ and the Ankylosing Spondylitis Disease Activity Score (ASDAS) ^{87 88} are presented below (table 4).

Physical therapy and exercise are essential components of axSpA management and NSAIDs are used to release pain and stiffness. Initiation of TNFα inhibitors (TNFi) may be required in case of persisting disease activity despite initial interventions. Finally, if the first TNFi is inadequate, treatments is switched to a second TNFi or, alternatively, changed to IL-17 inhibition ⁴⁶.

Table 3. Classification criteria for axial spondyloarthritis and ankylosing spondylitis Modified New York criteria for AS ASAS criteria for axSpA Clinical criteria

Radiological

criterion SpA features 1. Low back pain and stiffness

>3 months which improves with exercise but not by rest

Sacroilitis grade >2 bilaterally or sacroilitis grade 3-4 unilaterally

Inflammatory spinal pain Arthritis Enthesitis (heel) Uveitis Dactylitis Psoriasis

Crohn’s/Ulcerative colitis Good response to NSAIDs Family history of SpA HLA-B27

Elevated CRP 2. Limitation of lumbar spine

motion in sagittal & frontal planes

3. Limitation of chest

expansion age & sex corrected

Abbreviations: AS: Ankylosing Spondylitis; ASAS: Assessment of Spondyloarthritis International Society; axSpA: axial spondyloarthritis; NSAIDs: Non-Steroidal Anti- Inflammatory Drugs, SpA: Spondyloarthritis; HLA-B27: Human-Leukocyte Antigen B27;

CRP: C reactive protein.

Table 4. Composite scores for disease activity measurement in ankylosing spondylitis

Composite score BASDAI ASDAS

Acute phase reactants

C-reactive protein (CRP) No Yes

Erythrocyte Sedimentation Rate (ESR) No Yes

Patient reported answer to questions (Q) relating past week symptoms, scored 0-10

Q1: Fatigue/tiredness Yes No

Q2: Overall AS-related neck, back or hip pain Yes Yes Q3: Overall pain/swelling in joints besides neck, back or

hips

Yes Yes Q4: Overall discomfort to touch/pressure from tender areas Yes No

Q5: Overall discomfort from time of waking up Yes No

Q6: Duration of morning stiffness Yes Yes

Assessment of global health parameter (disease activity)

Patient reported No Yes

Formula

[(Q1+Q2+Q4)+

((Q5+Q6)/2)]/5

Version with CRP:

0.12xBack Pain+

0.06 x Duration of Morning Stiffness +

0.11xPatient Global+

0.07xPeripheral pain/swelling+

0.58xLn(CRP+1)

Version with ESR:

0.08xBack Pain+

0.07xDuration of Morning Stiffness+

0.11xPatient Global+

0.09xPeripheral Pain/Swelling+

0.29x√(ESR) Disease activity cut-points

Inactive disease: <1.3

Low disease activity: 1.3-2.1 High disease activity: 2.1-3.5 Suboptimal disease control: >4 Very high disease activity: >3.5 Abbreviations: BASDAI: Bath Ankylosing Spondylitis Disease Activity Index; ASDAS:

Ankylosing Spondylitis Disease Activity Score; AS: Ankylosing Spondylitis.

9 847.&9.(&79-7.9.8

PsA is a profoundly heterogeneous disease characterized by inflammatory arthropathy with associated psoriatic skin and nail manifestations. In 1973 Moll and Wright published a milestone paper establishing PsA as a specific disease entity with various phenotypes ⁸⁹. Epidemiology, aetiology and pathogenesis

In a systematic review from 2015, Ogdie and Weiss found that prevalence of PsA ranged from 0.05 to 0.25% in different populations, whereas 6-41% of psoriasis patients also had PsA ⁹⁰. The gross discrepancy in estimated prevalence is explained by various case definitions, ethnic background and age distribution ^{90 91}. In Denmark, a Scandinavian country with high similarity to the Norwegian population, PsA is present in 0.2% ⁹². In contrast to axSpA and RA, PsA is equally common among male and females ⁹. PsA is a polygenetic disorder that is strongly correlated with a series of MHC class I alleles and characterized by inflammatory responses targeting the entheses, joints and skin ^{9 93}. Th17 mediated effects, TNF signaling and activated T-cells reacting to MHC class I molecules are probably pivotal to the pathogenesis ⁹.

Clinical features and classification criteria

Moll and Wright described the five most characteristic manifestations of PsA: peripheral arthritis, axial disease, enthesitis, dactylitis and skin/ nail disease ⁸⁹. Commonly, the disorder evolves with time as it may present with asymmetrical oligoarthritis and progress to

symmetrical polyarthritis ⁹. Axial inflammation is prevalent in about 40% of the patients ^{9 75}. Table 5 presents the ASAS criteria for peripheral SpA ¹⁷ and the Classification Criteria for Psoriatic Arthritis (CASPAR) criteria for PsA ⁴³. Peripheral SpA is defined as presence of >1 ‘group A’ or >2 ‘group B’ SpA features, whereas fullfilment of CASPAR classification criteria for PsA requires presence of inflammatory articular disease (joint, spine or entheseal) and a sum of >3 points. CASPAR classification criteria have been found to be superior to ASAS classification criteria for peripheral PsA as the latter only partly overlap with rheumatologist-diagnosed PsA ⁹⁴.

Table 5. Peripheral spondyloarthritis/ psoriatic arthritis classification criteria (simplified) ASAS criteria

for peripheral SpA

CASPAR criteria for PsA SpA features Psoriasis (0-2 points)

Current

Personal history Family history Psoriatic nail dystrophy Negative rheumatoid factor Dactylitis

Conventional radiography

Juxta-articular new bone formation

Points 2 1 1 1 1 1 1 Group A:

Uveitis Psoriasis

Crohn’s/Ulcerative colitis

Preceding infection HLA-B27

Sacrolilitis (imaging)

Group B:

Arthritis Enthesitis Dactylitis Prior

inflammatory back pain Family SpA

Abbreviations: ASAS: Assessment of Spondyloarthritis International Society; CASPAR:

Classification Criteria for Psoriatic Arthritis Abbreviations: HLA-B27: Human-Leukocyte Antigen B27; SpA: Spondyloarthritis.

Treatment and disease assessment

The numerous PsA manifestations render it challenging to capture disease activity in a single disease activity score. Until recently, PsA disease activity has been assessed using composite scores originally developed for evaluation of RA and axSpA patients. However, two novel PsA-specific composite scores are gaining increasing interest for assessment of overall disease activity and to evaluate treatment target obtainment. Table 6 present an overview of the formula for evaluation of PsA disease activity according to the Disease Activity Index for Psoriatic Arthritis (DAPSA) ⁹⁵, as well as the criteria for attainment of Minimal Disease Activity (MDA) ⁹⁶ with proposed cutpoints ⁹⁷.

Table 6. Composite scores for disease assessment in psoriatic arthritis

Applied components:

MDA DAPSA

C-reactive protein (CRP) No Yes

Tender joint count (TJC) Yes Yes

Swollen joint count (SJC) Yes Yes

Psoriasis Activity and Severity Index (PASI) and Body Surface Area (BSA)

Yes No Patient reported pain on visual analogue scale

(VAS)

Yes Yes Patient reported disease activity on VAS Yes Yes

Health assessment questionnaire (HAQ) Yes No

Entheses (tender enthesal points) Yes No

Formula Not applicable TJC+SJC+CRP+VAS pain + VAS

disease activity Disease activity cut-points

Minimal Disease Activity:

TJC<1, SJC<1, PASI<1 or BSA<3, VAS pain <15, VAS disease activity <20, HAQ<0.5 & <1 tender enthesal points

Remission: 0-4

Not applicable Low disease activity: 5-14 Not applicable Moderate disease activity: 15-28 Not applicable High disease activity: >28 Abbreviations: MDA: Minimal Disease Activity; DAPSA: Disease Activity Index for Psoriatic Arthritis

11 Treatment recommendations from the Group for Research and Assessment of

Psoriasis and Psoriatic Arthritis (GRAPPA) and EULAR advise use of NSAIDs, GC, sDMARDs, bDMARDs and dermatologic therapies: Specific treatment strategy is tailored to disease manifestations (e.g. distinguishing between peripheral arthritis, axial disease, enthesitis, dactylitis, skin and/or nail disease) ^{47 48}.

&7).4;&8(:1&7).8*&8*.3.3+1&22&947>/4.39).8*&8*8

CVD is a group of diseases of the heart and blood vessels which are highly prevalent in the general population. Indeed, through the GBD study it was been estimated that ~423 million cases of CVD events and ~18 million deaths occurred due to CVD in 2015 ¹⁶. Consequently, CVD represent the leading causes of death worldwide and Roth et al calculated that

ischemic heart disease (e.g. myocardial infarction [MI]) and cerebrovascular disease (ischemic, haemorrhagic and other strokes) separately represent ~9 and ~6 million deaths each year, respectively ¹⁶. Albeit that the pathogeneses for distinct types of CVD differ, atherosclerosis is the dominant underlying pathology responsible for the global CVD endemic. Table 7 presents the major CVD entities covered in the epidemiologic study by Roth et al ¹⁶:

Table 7. Most common global causes of cardiovascular disease related deaths Cardiovascular disease entities

Ischemic heart disease (IHD)

Cerebrovascular accident (CVA) - ischemic haemorrhagic and other strokes Atrial fibrillation and flutter

Peripheral arterial disease (PAD) Aortic aneurysm

Cardiomyopathy and myocarditis Hypertensive heart disease Endocarditis

Rheumatic heart disease

Other cardiovascular diseases (non-rheumatic valvular disorders and pulmonary embolism) 5.)*2.414,>

Expert authorities, such as the European Society of Cardiology (ESC) and the EULAR, recognize RA, axSpA and PsA as patient populations with increased risks of CVD ^{21 33}. Indeed, the excess CVD risk found in RA patients appears to match the elevated risk documented among patients with DM ⁹⁸. In 2011 Avina-Zubieta et al pooled data from a total of 24 studies covering a vast number of RA patients (111,758 individuals) and CVD events (22,927 CVD deaths) and revealed that RA patients had a 50% increased risk for

CVD mortality compared to the general population. This estimate was largely confirmed in an updated 2014 meta-analysis on CVD morbidity by the same research group ^{99 100}, and also reproduced in another meta-analysis by Wiseman et al ¹⁰¹. However, interpretation of these studies is substantially hampered by methodological limitations (lack of control of confounding factors and profound heterogeneity between studies). For instance, Avina- Zubieta et al observed that the highest reported CVD risk estimations were found among clinic based and/or non-inception cohorts of lower methodological quality ^{99 100}. Hence, a carefully designed meta-analysis by Schier et al addressed these issues which therefor represent an important source of evidence for RA-specific CVD risk ¹⁹. In this meta- analysis, the only studies eligible for inclusion were case control and cohort studies that included population-based non-RA control groups with age and sex-adjusted event rates ¹⁹. In line with previous estimations, Schier and colleagues reported that RA patients had approximately 70% increased risk of CVD. In fact, additional analyses also demonstrated a 50% residual CVD risk when adjusting for conventional CVDRFs. Thus, the excess risk of CVD appears to be mediated by both conventional CVDRFs and RA characteristics per se and/or treatment of RA. In accordance with the results of this latter meta-analysis, Crowson et al recently reported a highly comparable residual risk of CVD related to RA

characteristics in an international RA cohort adjusted for CVDRFs ²⁰. Other studies have also added important information by reporting that CVD events in RA patients are characterized by a substantial rate of silent (asymptomatic) MIs, sudden CVD deaths and have a worse prognosis following MI ^102-104. Due to inconsistent data, it remains unknown whether RA patients are at increased risk of CVD already at disease onset 102 105-110.

Data on CVD risk in axSpA are mainly limited to those with radiographic disease (i.e. AS). In 2011, Mathieu et al pooled data from eligible studies published until mid-2009 to compare event rates for CVA (7 studies) and MI (8 studies) in AS and non-AS controls

111. Although there was evidence for significantly higher CVA event rates in AS, only a non-significant trend of increased MI (RR 1.88, 95% CI 0.83-4.28) was revealed. However, an updated meta-analysis published in 2014 demonstrated increased odds ratios (OR) for both MI (OR 1.60, 95% CI 1.32-1.93) and CVA (OR 1.50, 95% CI 1.39-1.62) among AS patients ¹¹². Unfortunately, these studies were both flawed by lack of control for

confounding factors such as age, sex, CVDRFs and CVD preventive medication ^{111 112}. Interestingly, Schier et al recently found only a trend towards increased CVD risk (RR of MI: 1.24, 95% CI 0.93, 1.65) in a meta-analysis containing pooled analyses of three age and

13 sex adjusted studies ^{19 113-115}. Furthermore, two studies that were also adjusted for CVDRFs revealed conflicting results ^{113 114}. However, a recent Swedish, nationwide register study found that AS patients had significantly higher age- and sex-adjusted risk for incident CVD, including coronary heart disease (1.54, 95% CI 1.31-1.82) and CVA (1.25, 95% CI 1.06- 1.48) compared to the general population ¹¹⁶. In conclusion, AS is probably associated with increased risk of atherosclerotic disease. However, it is challenging to quantify the risk on group level; especially due to the individual differences in degree and duration of high- grade inflammation, the initiation of anti-inflammatory treatment as well as other pharmacological and lifestyle interventions initiated for preventing future CVD.

Authors of systematic literature reviews have described PsA patients as a patient population at increased risk of CVD, although more accurate analyses of CVD risk have not been performed until recently. For instance, Polachek et al supported these prior claims when they revealed a RR of 1.55 (95% CI 1.22-1.96) for incident CVD in PsA patients, using data from 11 age and sex adjusted studies ^117-119. This latter finding persisted even in analyses restricted to studies that were also adjusted for CVDRFs (OR 1.84, 95% CI 1.12- 3.02) ¹¹⁹ and was largely confirmed in a subsequent meta-analysis by Schier et al ¹⁹. &9-4,*3*8.8

Atherogenesis, the formation of atherosclerotic plaques is a protracted process and although onset of clinical manifestation of atherosclerosis is usually not observed until midway in life, atherogenesis is often initiated in early years of life. Our understanding of

atherosclerosis has changed considerably. We now know that it is a chronic inflammatory disorder, rather than solely a process of passive lipid accumulation, targeting the arterial wall of large and medium-sized arteries ¹²⁰. The disorder affects various vascular beds, including the coronary, cerebral and peripheral arteries, as well as the aorta. Atherosclerotic plaques are histologically composed of a lipid-rich core and a fibrous cap. Whereas lipid abnormalities are possibly a prerequisite for the pathogenesis, high-grade inflammation appears to accelerate the process ¹²¹. Endothelial injury is triggered by hemodynamic forces, noxious substances (hypercholesterolemia, cigarette toxins, hyperglycaemia), as well as inflammation, resulting in endothelial dysfunction ¹²². Healthy endothelial cells oppose the development of atherosclerotic plaques through inhibition of cell proliferation, anti-

aggregant, anti-coagulant and vasodilative properties ^{122 123}. However, these features are distorted in a dysfunctional endothelium that is more adherent and permeable to monocytes, lymphocytes and lipids which are re-localized to the arterial intima ¹²¹. Migrated monocytes

transform to macrophages, which take up modified low-density lipoprotein-cholesterol (LDL-C) and become foam cells. The lipid core grows with the accumulation of such foam cells which also induce a pro-inflammatory state and mediate proliferation of smooth

muscle cells that result in expansion of the fibrous cap ¹²¹. Growth of atherosclerotic plaques leads to signs and symptoms of ischemia as a consequence of obstruction of arterial blood flow. Alternatively, plaque erosion or rupture can lead to atherotrombotic events, and atherosclerosis can also result in arterial wall-weakening and subsequent aneurysm formation. Figure 2 presents the pre-clinical phase of atherosclerosis and the clinical manifestation of the disease.

Figure 2. Pathophysiology of atherosclerotic cardiovascular disease

Abbreviations: SMC: smooth muscle cell; ECM: extra-cellular matrix. From Kumar V et al.

Robbins Basic Pathology. 10^th Ed. New York. Elsevier, 2018. Reproduced with permission:

License number 4336100971834.

4389.9:9.43&1+&(9478

Several non-correctable demographic characteristics are related to the elevated risk of future CVD. For instance, age is the dominant determinant of CVD risk. The current

understanding is that age can be considered as a measure of time under exposure of the deleterious effects of etiologic CVDRFs. The strong correlation between age and CVD events was clearly illustrated in a cohort study of 3.6 million US adults, in which Savji et al demonstrated that CVD risk doubled for each decade of life ¹²⁴ (figure 3).

15 Figure 3. Prevalence of cardiovascular disease according to age

Prevalence of peripheral arterial disease (A), carotid artery stenosis (B) and abdominal aortic aneurysm (C) in different age deciles. From Savji N et al. J Am Coll Cardiol 2013;61:1736-43. Reproduced with permission: License number 4296521103598.

Male sex is also linked to increased CVD risk. The risk of first MI among males corresponds to the risk observed in women who are ten years older ¹²⁵. Family history of premature CVD in first-degree relatives has also been demonstrated to be a strong predictor of future CVD in several large cohorts ^126-128.

43;*39.43&17.80+&(9478 The Framingham Heart Study

The discovery of several potentially modifiable CVDRFs tracks back to the late 1940´s and the initiation of what became the world´s most renowned population-based cohort study, the Framingham Heart Study (FHS) (www.framinghamheartstudy.org). Indeed, FHS was one of the first long-term observational studies ever initiated and in fact, FHS investigators coined and popularize the term ‘coronary risk factor´ ^{129 130}. The study was commenced in a small community outside Boston in the United States of America (US) in 1948, and the original cohort was sampled from two thirds of the adult population. Since then, the initial 5209 subjects were examined every second year throughout their life, during which time several new cohorts have been added, including the offspring and grandchildren of the original participants. In several of the >3500 FHS generated research papers, the

investigators report on the prevalence and predictive value of important CVDRFs for future CVD (figure 4). In summary, lifestyle-related risk factors such as smoking, physical

inactivity, psychosocial problems, unhealthy diet, excess alcohol consumption and related medical conditions, including obesity, lipid abnormalities, DM and high blood pressure (BP) were established as strong, independent predictors of various CVD outcomes ^131-137. More recently, a group of emerging risk factors have been added to these conventional (also called traditional) CVDRFs (see section 2.2.5).

Figure 4. Research milestones from the Framingham Heart Study

From O’Donnell CJ et al. Rev. Esp. Cardiol 2008;61(3):299-310. Reproduced with permission: No lincense number generated.

17 Global estimation of the impact of risk factors

Several large-scale international case-control studies have tried to quantify the deleterious effect of modifiable CVDRFs by estimating their impact on CVD morbidity and mortality.

For instance, INTERSTROKE enrolled 13477 cases of CVA (10388 with ischemic strokes and 3059 with intracerebral haemorrhage) and 13472 controls across 32 countries, while INTERHEART included 15152 cases of MI and 14820 controls from 52 countries ^138-140. These studies have evaluated correlation strength as well as population attributable risk (PAR) for different CVDRFs individually and collectively. Collectively, INTERSTROKE and INTERHEART indicate that modifiable lifestyle factors account for ~90% of the PAR of CVD (MI and CVA). In a separate US study which ran from 2009 to 2010, five

CVDRFs, namely HT, lipid abnormalities, obesity, DM and smoking were found to account for the majority of CVD-related deaths ¹⁸. In fact, all the aforementioned five lifestyle- related factors are found among the top ten risk factor exposures with the highest attributable burden of disease of any cause (figure 5) ¹⁴¹.

Figure 5. Risk factor exposures associated with global disability adjusted life years

From GRF Collaborators Lancet 2017Sep16; 390(10100):doi10.1016/S0140- 6736(17)32366-8. Reproduced with permission: No license number generated

Hypertension

HT accounts for roughly fifth and half of incident MI and CVA, respectively in the general population ^{139 140}. Furthermore, presence of HT among RA patients is associated with a two- fold increase in CVD risk ¹⁴². The prevailing definition for HT is persistent systolic/diastolic BP (sBP/dBP)>140/90mmHg, although the use of cut-off definitions to define

pathologically elevated BP is somewhat arbitrarily given the near-linear correlation between BP and CVD risk. According to a meta-analysis of 135 population-based studies,

compromising approximately 1 million adults from 90 countries, around one in three (31%) individuals has HT ¹⁴³. As illustrated in data from the National Health and Nutrition

Examination Survey (NHANES), the incidence of HT increases with age and while it is rarely occurring below the age of 45 years (0-11%), it is very frequent (75-77%) among the elderly (65 to 74 years) ¹⁴⁴. Several risk factors for increased BP, including physical

inactivity, stress, obesity and use of medications such as NSAIDs and corticosteroids are known to increase BP are prevalent in IJD patients ^{145 146}. Nevertheless, two meta-analyses did not find significant differences in prevalence of HT or BP levels in RA or AS patients compared to non-IJD controls ^{111 147}. Notably, the reported prevalence estimates of HT in IJD patients is highly variable, ranging from 19 to 57% for RA ^{31 148-156} , 22 to 41% for AS

114 151 156-160 and 22 to 45% for PsA 31 150-152 156 157 160-165 patients.

Lipid abnormalities

There are several terms to describe states of abnormal lipid levels or lipid profiles and despite their widespread use, they are often inconsistently defined and may therefor cause confusion. Hyperlipidemia and hypercholesterolemia reflect pathologically elevated total cholesterol (TC) and/or low-density lipoprotein-cholesterol (LDL-C) or non-high-density lipoprotein-cholesterol (non-HDL-C), triglycerides (TG), apolipoprotein (apo) B and lipoprotein (Lpa). Dyslipidaemia on the other hand, is a term that also encompasses low high-density lipoprotein-cholesterol (HDL-C) and/or apoA1, which are also correlated with excess CVD risk. Various lipid and lipoprotein components have been found to be

independent predictors of future CVD. Through analyses of INTERHEART data, a PAR for apoB/apoA1 ratio was calculated to be 54% (calculated for the top four quintiles versus the lowest quintile) in the general population ¹⁶⁶. Lipid abnormalities are also associated with increased CVD risk in IJD patients. For instance, Baghdadi et al found that

hypercholesterolemia, albeit not properly defined, was associated with a 73% increased risk of CVD morbidity in RA patients ¹⁴². Interestingly, lipid levels have been found to be inversely correlated with inflammatory markers (figure 6) ^{167 168}. Furthermore, there is also

19 an observed trend of rising lipid levels among IJD patients with high-grade inflammation following treatment response to anti-inflammatory drugs, which possible represent a return to habitual lipid values. Hence the cholesterol-associated CVD risk should possibly be interpreted according to the current disease state in RA, axSpA and PsA patients. Seeing as high levels of systemic inflammation is a strong risk factor for CVD, the negative

correlation between lipid levels and inflammation may represent an important obstacle to the development of RA-specific CVD risk calculators. Reported prevalence rates of lipid abnormalities in RA, AS and PsA patients are presented in the appendix (supplementary table 1).

Figure 6. Inverse correlation between lipid levels and inflammation

From Choy E et al. Rheumatology (Oxford) 2014 Dec;53(12):2143-54. Reproduced with permission: License Number 4319210470462.

High body mass index

High body mass index (BMI) signifies excess weight and is a surrogate marker for elevated adipose tissue. BMI >25 kg/m² and >30kg/m² are the standard definitions for overweight and obesity, respectively. World Health Organization (WHO) estimated that 650 million of the global adult populations in 2016 were obese (13% of the worldwide population) ¹⁶⁹. Interestingly, there is a profound difference across regions, as prevalence of obesity (defined as BMI>30 kg/m²) vary from 5% in the South East Asian population to 29% on the

American continent ¹⁷⁰. IJD patients may be at increased risk of unwanted weight gain due to physical inactivity and prolonged used of corticosteroids ^{171 172}. Reported prevalence of

IJD patients with BMI>30kg/m² vary substantially across studies, with a range from 10 to 54% in RA 22 148 151-153 155 173-178, 16-46% in AS 151 157 158 177 179 and 29 to 60% in PsA ^{151 152}

157 161-164 177 (supplementary table 1 in appendix). In a large US study, Radner et al

estimated that only 4% of RA and 6% of PsA patients were obese, in sharp contrast to most other reports ¹⁵⁰. However, these figures were based on diagnostic codes and are thus likely to represent a gross underestimation. Obesity and overweight constitute significant

predictors for all-cause and CVD-related morbidity and mortality for the general population.

In fact, excess weight explains about 20% of CVD events in the general population ^{139 140}

180. Yet, in RA, only a modest or non-significant trend of increased CVD has been documented for overweight and/or obesity ^{20 142}. Regardless, attentive management of obesity in IJD is warranted as obesity increase the risk of developing other CVDRFs (e.g.

HT, DM and hypercholesterolemia) ^181-183. Further, obesity is also known to induce a pro- inflammatory state characterized by impaired anti-inflammatory treatment response ^184-186. Interestingly, IJD patients may have excess fat even in the presence of normal BMI ¹⁸⁷. Furthermore, despite that ‘classic cachexia’ with low BMI related to chronic high grade inflammation is also related to excess CVD risk, this condition is very rare ¹⁸⁸.

Diabetes Mellitus

DM represents a group of metabolic diseases characterized by hyperglycaemia. WHO has proposed a clinical definition in which DM can be diagnosed on the basis of fasting plasma glucose >7 mmol/L (126 mg/dL) and/or plasma glucose > 11 mmol/L (200 mg/dL) two hours after a standardized oral glucose tolerance test (OGTT) ¹⁸⁹. DM can also be diagnosed according to glycated haemoglobin A1c (HbA1c%) if measured HbA1c% is 6.5% or higher.

Obesity is the most important modifiable risk factor for developing Type 2 Diabetes Mellitus (T2DM), accounting for ~90% of adult cases of DM ¹⁹⁰, whereas prolonged GC treatment constitute another independent predictor of DM ¹⁹¹. Possibly, half of all cases of DM are undiagnosed as the prevalence of total DM in the adult general population has been estimated to be 9% worldwide (http://www.idf.org/diabetesatlas). Available data suggest that DM (i.e. T2DM) are more prevalent among RA patients compared to the general population and there are also indications of increased rates in AS and PsA patients (supplementary table 1 in appendix). The association between DM and CVD is well established ³³. Epidemiologic studies from RA and non-RA cohorts indicate a two-fold higher CVD risk in diabetics compared to non-diabetic individuals and that DM accounts for 3-10% of CVD events ^{20 140 192}.

21 Smoking

Globally, about a fifth (21%) of the population are current smokers and IJD patients are more frequently past or current smokers compared to the general population ¹⁴⁷. Reported prevalence of current smoking in various studies on RA, axSpA and PsA patients are presented in the appendix (supplementary table 1). Tobacco exposure inflicts a profound burden on overall morbidity and mortality and it especially represents a major determinant of CVD risk in RA patients as well as in the general population ^{139 140}. In the previously mentioned meta-analysis by Baghdadi et al, smoking was shown to yield a 40% increased risk of CVD in RA patients ¹⁴². Moreover, smoking has a dual role as it is not only a

CVDRF, but is also correlated with increased risk of incident IJD ^{193 194}, high inflammatory disease activity ¹⁹⁵, as is inversely correlated to anti-inflammatory treatment response ¹⁹⁶. Other conventional CVDRFs

The impact of dietary factors, physical inactivity, psychosocial deprivation, unhealthy diet and alcohol consumption on CVD risk has been quantified for the general population ^{139 140}. However, observational studies quantifying the association between these CVDRFs and CVD outcomes in IJD are largely missing. It is likely that they have a similar effect on CVD as in the general population. Clinicians should be aware that IJD patients also are more likely to be inactive compared to the general population, and have increased perceived mental stress and report reduced health-related quality of life ^197-200.

2*7,.3,7.80+&(94787*1&9*)94.3+1&22&947>/4.39).8*&8*

The excess CVD risk in IJD patients cannot be sufficiently explained by conventional CVDRFs ¹⁹. In fact, Crowson et al calculated that whereas conventional CVDRFs

accounted for ~50% of PAR for CVD, RA-related variables could explain 30% of the PAR

20. Whereas, markers of inflammation such as CRP ^{201 202} and IL-6 ²⁰³ have been shown to be predictive of future CVD in the general population, IJD patients with high-grade inflammation, severe disease activity and/or with features of extra-articular manifestations have been found to have higher CVD risk compared to those with less aggressive IJD ^{20 204}. Recently, large-scale, randomized placebo-controlled trials such as the Canakinumab Antiinflammatory Thrombosis Outcome Study (CANTOS) and the the Cardiovascular Inflammation Reduction Trial (CIRT) are investigating the possible role of sDMARDs and bDMARDs, respectively, in secondary prevention of CVD ^{205 206} in non-IJD patients.