Evaluating High-Resolution Computed Tomography Derived 3-D Joint Space Metrics of the Metacarpophalangeal Joints Between Rheumatoid Arthritis and Age- and Sex-Matched Control Participants

doi:10.21203/rs.3.rs-2996265/v1

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Evaluating High-Resolution Computed Tomography Derived 3-D Joint Space Metrics of the Metacarpophalangeal Joints Between Rheumatoid Arthritis and Age- and Sex-Matched Control Participants

https://doi.org/10.21203/rs.3.rs-2996265/v1

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Rheumatoid arthritis associated joint space narrowing is commonly evaluated through 2D X-ray radiographs. Unfortunately, changes and overlapping anatomy in smaller joints, such as those found within the hands, hinder conventional radiography. High resolution peripheral quantitative computed tomography (HR-pQCT), an un-paralleled in vivo X-ray-based imaging technique, provides 3D quantitative joint space metrics that may overcome limitations of 2D imaging. However, whether these metrics are sufficient for the differentiation between RA-associated joint changes and those influenced by age, sex, and obesity remains unknown. Therefore, we recruited a cohort of RA patients as well as age- and sex-matched healthy control participants and scanned their 2^nd and 3^rd metacarpophalangeal joints using HR-pQCT.

HR-pQCT-derived 3D joint space metrics (volume, width, standard deviation of width, maximum width, minimum width, and asymmetry) were not significantly different between RA and control groups (p > 0.05). This may be explained by the few RA participants with evidence of radiographic damage included in this study. Joint space volume, mean joint space width (JSW), maximum JSW, minimum JSW were larger in males than females (p < 0.05), while maximum JSW decreased with age. However, there were no significant association between joint space metrics and BMI. Thus, as individuals with RA are expected to have more joint space narrowing, further research is necessary to determine whether additional factors (e.g. co-morbidities) or novel 3D JSW metrics can aid in the detection of early signs of joint space.

Health sciences/Rheumatology

Health sciences/Rheumatology/Musculoskeletal system

Arthritic diseases, such as rheumatoid arthritis (RA), are commonly characterized by inflammation-associated deterioration of joint surfaces which can lead to structural joint changes and joint degeneration. This can result in the loss of gross (e.g., walking, sitting, standing, etc.) and fine (e.g., writing, grabbing objects, etc.) motor skills, leading to an overall decrease in quality-of-life (QOL)^1–3. This gradual loss of joint function is in part due to the loss of cartilage, leading to reduced joint space and eventual bone-on-bone contact. Clinically, the diagnosis of RA-associated joint degeneration can be determined by the two-dimensional (2D) radiographic evidence of joint space narrowing⁴. While clinically, joint space changes are assessed and graded as an estimated proportion of lost space via 2D X-ray radiographs, these assessments are not sensitive to smaller joint space changes and overlapping anatomy (i.e. (sub)luxation), such as those found when imaging the metacarpophalangeal joints of the hand. Thus, high resolution 3D imaging techniques may improve the assessment, detection, and quantification of changes in joint space metrics.

High-resolution peripheral quantitative computed tomography (HR-pQCT) is an X-ray based imaging technique that provides unparalleled 3D in vivo images of extremity joints, such as the MCP joints of the hand commonly affected by RA^5–8. The three dimensionality of HR-pQCT, in contrast to 2D radiographs, allows for accurate and precise, quantitative 3D derived joint space metrics including joint space width, maximum, and minimum⁹. These metrics have been applied to HR-pQCT to assess disease course and treatment effects in RA^10,11. However, while HR-pQCT-derived 3D joint space metrics differed between RA and healthy controls in a proof-of-concept study, the small sample size limited evaluation of the individual variability that can impact 3D joint space metrics¹².

Age, sex, and obesity have been shown to negatively impact joint space narrowing or disease progression in hand osteoarthritis (OA). The continual wear and tear of cartilage with increasing age can contribute to a cartilage thinning, resulting in joint space narrowing^13–15. In addition to known sex-based joint size differences ^16–18, the inverse relationship between obesity and immune response is known to affect OA disease progression^19,20. However, while OA and RA can affect the hand, different joints are typically involved (distal interphalangeal and proximal interphalangeal joints vs metacarpophalangeal joints, respectively). Thus, for RA, an immune-mediated musculoskeletal diseases, understanding the effects of these non-disease related factors (i.e. age, sex and obesity) on 3D joint space width (JSW) metrics from HR-pQCT images is necessary to interpret results JSW results.

Therefore, in this study, we aimed to evaluate whether HR-pQCT-derived 3D joint space metrics could differentiate between RA and age- and sex-matched controls. Additionally, we assessed the effects of age, sex, and obesity (assessed in terms of body mass index, BMI) on HR-pQCT-derived 3D joint space metrics. To accomplish these objectives, we compared 3D joint space metrics from HR-pQCT images of 2nd MCP (MCP2) and 3rd MCP (MCP3) joints between RA patients and their age- and sex-matched controls. By computing various 3D joint space metrics (i.e., volume, width, standard deviation of width, maximum width, minimum width, and asymmetry) from these groups, we determined whether variation in age, sex, and BMI plays a significant role in these acquired quantitative joint metrics. Additionally, we explored how these 3D joint space metrics are related to functional outcomes.

Characteristics of the cohort

From the initial 40 RA participants, 5 were excluded due to a joint replacement (N = 1), presence of gout (N = 1), missed study appointment (N = 1), and study withdrawal (N = 2). The demographic and clinical characteristics of the 35 RA patients and their 35 controls are shown in Table 1.

Table 1

Demographics of the healthy control (N = 35) and RA (N = 35) participants. Multiple parameters for the control were marked as N/A, as the data were not collected or relevant for healthy controls. All values are representative of N = 35 for each group of participants, unless noted otherwise, and are presented as mean ± standard deviation. P-values reflect results of a paired, non-parametric, t-test (Wilcoxon) comparing RA and control participants. Note: Serological parameters were not available for all participants.
	Control (N = 35)	RA (N = 35)	p value
Age	57.0 ± 15.0	57.4 ± 14.7	0.90
Female : Male (Female %)	27 : 8 (77%)	27 : 8 (77%)	N/A
Height (m)	1.57 ± 0.39	1.57 ± 0.39	0.97
Weight (kg)	72.4 ± 17.6	85.5 ± 21.8	0.008*
BMI (kg/m²)	26.2 ± 5.7	30.9 ± 6.7	0.003*
Clinical Parameters
Disease Duration (years) (N = 33)	N/A	15.0 ± 10.1	N/A
28 Swollen Joint Count (N = 33)	N/A	1.7 ± 2.8	N/A
28 Tender Joint Count (N = 33)	N/A	3.0 ± 4.1	N/A
HAQ (0–3) (N = 35)	0.03 ± 0.1	0.86 ± 0.65	< 0.001*
Patient Global Score (0–100) (N = 35)	1.8 ± 8.7	25.3 ± 30.5	< 0.001*
AM Stiffness (min) (N = 35)	1.1 ± 3.2	57.0 ± 153.4	0.04*
DAS28CRP (N = 33)	N/A	3.1 ± 1.3	N/A
DAS28ESR (N = 33)	N/A	3.1 ± 1.5	N/A
CDAI (N = 33) Low CDAI (> 2.8 and ≤ 10) Mid CDAI (> 10 and ≤ 22) High CDAI (> 22)	N/A N/A N/A N/A	10.3 ± 8.4 24 (73%) 4 (12%) 5 (15%)	N/A N/A N/A N/A
Medications Corticosteroids DMARDs Biologic DMARD NSAIDs	N/A N/A N/A N/A	2 (6%) 14 (42%) 19 (58%) 3 (9%)	N/A N/A N/A N/A
Jebsen-Taylor Hand Function Test Times (s)
Most Affected Hand	46.3 ± 8.4	65.5 ± 25.5	< 0.001*
Contralateral Hand	67.3 ± 14.0	66.1 ± 17.2	0.75
Disabilities of the Arm, Shoulder, and Hand (DASH)
Score	27.6 ± 5.7	56.0 ± 19.1	< 0.001*
Serological Parameters
RF(+ : -) (N = 30)	N/A	23 : 7	N/A
ACPA(+ : -) (N = 14)	N/A	10 : 4	N/A
ESR (N = 33)	N/A	14.9 ± 14.0	N/A
CRP (N = 33)	N/A	6.2 ± 7.7	N/A

Note: For the RA participants, the swollen and tender joint count, CDAI, and current medications were collected at their last visit to a rheumatologist, 18 ± 56 days relative to the imaging day. As blood work (ESR and CRP, contributing to DAS28 scores) was performed less frequently, these results were acquired 138 ± 236 days relative to the imaging day. Two RA participants were not registered within the Rheum4U database; thus, some of their metrics were not recorded (i.e. disease duration, swollen and tender joint count, blood work, and current medications). While some of the clinical and serological parameters were marked as N/A for the control participants, part of the inclusion criteria of the control participants required no hand injuries and no diagnosed conditions involving the hand. Thus, swollen and tender joint counts, disease duration, disease severity criteria (i.e. DAS28CRP/ESR and CDAI), medications, and serological parameters were not assessed for these participants.

Increased pain and disability were present within the RA group, when compared to their age- and sex-matched controls, resulting in significant differences between groups in patient global score (p < 0.001), morning stiffness (p < 0.001) and DASH scores²¹ (p < 0.001). These self-reported metrics were also reflected in functional tests examining gross and fine motor skills, whereby Jebsen-Taylor hand function test (JTHFT) scores²² were significantly poorer (p < 0.05) in the most affected hand for RA patients than the controls.

Effects of diagnosis and sex on 3D JSW

We observed no significant differences in joint space metrics as a function of diagnosis (i.e., RA or control) and no interactions between sex and diagnosis were noted (Fig. 1). This, in part, was visualized via a JSW image of joints representing the median of the RA and control group (Fig. 2). We found that females had significantly smaller JSV (MCP2, p < 0.001 and MCP3, p < 0.001); JSW (MCP2, p = 0.02 and MCP3, p = 0.02); JSW_MAX (MCP3, p = 0.006); and JSW_MIN (MCP3, p = 0.01) than males (Fig. 1).

Age, BMI, and Jebsen-Taylor Hand Function Test (JTHFT)

There was a significant negative association between age and JSW_MAX in both MCP2 and MCP3 (p = 0.03). This association was affected by diagnosis (significant interaction between age and diagnosis; p = 0.001 and p = 0.04 for MCP2 and MCP3, respectively). For the remaining 3D joint space metrics (i.e. JSV, JSW, JSW_SD, JSW_MAX, JSW_MIN, and JSW_AS) we observed no significant association with age, diagnosis, nor an interaction effect. Additionally, we observed no significant associations with BMI and JTHFT for any of the 3D joint space metrics (Table 2).

Table 2

Results of multiple linear regression analyses of 3D joint space metrics as a function of Age, BMI, and JTHFT for both MCP2 and MCP3 joints. Displayed are the R² values with their associated p values. * A significant correlation was noted if p < 0.05.
	Age				BMI				JTHFT
	MCP2		MCP3		MCP2		MCP3		MCP3		MCP3
	R²	p value	R²	p value	R²	p value	R²	p value	R²	p value	R²	p value
JSV	0.08	0.14	0.04	0.24	0.01	0.82	0.02	0.60	0.03	0.54	0.03	0.90
JSW	0.01	0.65	0.03	0.44	0.05	0.15	0.07	0.05	0.02	0.64	0.06	0.59
JSW_SD	0.03	0.29	0.06	0.08	0.03	0.76	0.04	0.80	0.02	0.58	0.02	0.71
JSW_MAX	0.12	0.03*	0.08	0.03*	0.08	0.15	0.12	0.09	0.06	0.81	0.09	0.84
JSW_MIN	0.06	0.37	0.09	0.53	0.04	0.51	0.08	0.40	0.05	0.58	0.10	0.66
JSW_AS	0.06	0.77	0.07	0.72	0.08	0.89	0.08	0.98	0.06	0.79	0.10	0.94

Ultrasound

Ultrasound scores for effusion / synovial hypertrophy and inflammation (i.e. positive power doppler signal) were significantly higher in the RA participants when compared to their age- and sex-matched controls in MCP2 and MCP3 of both hands (Table 3). The number of erosions present in MCP2 - MCP5 joints of the most affected hand was higher in RA than control participants (Table 3). The results of ICC revealed poor (effusion / synovial hypertrophy) to excellent (power doppler) reliability (Table 4) – with a higher consistency in the MCP2 joint, compared to MCP3. Additionally, no significant correlations were noted upon comparisons of JTHFT against effusion or power doppler scores (Table 5).

Table 3

Results of a two-way random effects single rater absolute agreement intraclass correlation coefficient (ICC) tests between two raters for ultrasound scores for the RA and control participants. The ICC values and their 95% confidence intervals are presented.
Ultrasound	Control	RA	p value
Effusion / Synovial Hypertrophy (0–3)
Most Affected Hand (MCP2)	1 [IQR 1–1]	1 [IQR 1–2.5]	< 0.001*
Contralateral Hand (MCP2)	1 [IQR 1–1]	1 [IQR 1–1.5]	0.005*
Most Affected Hand (MCP3)	1 [IQR 1–1]	1 [IQR 1–1]	0.02*
Contralateral Hand (MCP3)	1 [IQR 1–1]	1 [IQR 1–1]	0.13
Power Doppler (0–3)
Most Affected Hand (MCP2)	1 [IQR 1–1]	1 [IQR 1–1]	0.008*
Contralateral Hand (MCP2)	1 [IQR 1–1]	1 [IQR 1–1]	0.03*
Most Affected Hand (MCP3)	1 [IQR 1–1]	1 [IQR 1–1.5]	0.004*
Contralateral Hand (MCP3)	1 [IQR 1–1]	1 [IQR 1–1]	0.008*
Erosions
MCP2 {# of People with Erosions}	0 [IQR 0–0] {0}	0 [IQR 0–1] {15}
MCP3 {# of People with Erosions}	0 [IQR 0–0] {1}	0 [IQR 0–0] {7}
MCP4 {# of People with Erosions}	0 [IQR 0–0] {0}	0 [IQR 0–0] {0}
MCP5 {# of People with Erosions}	0 [IQR 0–0] {0}	0 [IQR 0–0] {3}

Table 4

Results of a two-way random effects single rater absolute agreement intraclass correlation coefficient (ICC) tests between two raters for ultrasound scores for the RA and control participants. The ICC values and their 95% confidence intervals are presented.
	ICC	ICC 95% Confidence Interval
Effusion / Synovial Hypertrophy
Right MCP2	0.313	-0.086 < ICC < 0.609
Right MCP3	0.520	0.08 < ICC < 0.748
Left MCP2	0.519	0.077 < ICC < 0.748
Left MCP3	0.292	-0.035 < ICC < 0.548
Power Doppler (PD)
Right MCP2	0.929	0.885 < ICC < 0.957
Right MCP3	0.396	0.162 < ICC < 0.589
Left MCP2	0.828	0.730 < ICC < 0.893
Left MCP3	0.788	0.671 < ICC < 0.867

Table 5

Results of rank correlation tests comparing JTHFT times against ultrasound scores for effusion / synovial hypertrophy and power doppler (PD). A significant correlation is noted if the p < 0.05. P values with a reported N/A were due to zero standard deviation amongst measurements.
	p value
	Control	RA
JTHFT vs. Effusion
MCP2 (CH)	0.38	0.52
MCP3 (CH)	N/A	0.40
MCP2 (NCH)	0.75	0.23
MCP3 (NCH)	0.55	0.81
JTHFT vs. PD
MCP2 (CH)	0.39	0.98
MCP3 (CH)	N/A	0.11
MCP2 (NCH)	0.34	0.97
MCP3 (NCH)	0.55	0.83

The results of our study demonstrated that HR-pQCT-derived 3D JSW metrics did not differ between RA patients and an age- and sex-matched control group despite clear differences in hand function and inflammation. As expected, there was a significant association between some metrics, in particular JSW_MAX, with age and sex. Only the association between JSW_MAX and age depended on diagnosis.

The unexpected absence of a difference in JSW metrics between RA and controls was also visualized in the lack of radiographic damage within the RA participants. While in this study standard X-rays for Sharp van der Heijde (SvdH) scoring²³ were not captured, post-hoc analysis of HR-pQCT scout images revealed 38% of RA (N = 11/29) and 26% of control (N = 5/19) participants had a joint space narrowing SvdH score > 0. Additionally, these individuals had lower than group average minimum JSW (RA JSW_MINAvg = 1.01 (MCP2) and 0.92 (MCP3) mm; control JSW_MINAvg = 1.16 (MCP2) and 1.10 (MCP3) mm). These results reflect previous demonstrations of an association between HR-pQCT-derived JSW and radiographic SvdH joint space scores²⁴, suggesting that HR-pQCT measures are associated with established measures of radiographic progression. Nonetheless, X-ray based joint space narrowing measurements did not differ between the groups. Ultrasound did note larger number of erosions, due to ultrasound’s higher resolution and ability to image at oblique angles when compared to traditional radiographs, and increased inflammation within the RA group – suggesting that, on average, the control group had healthier joints. Overall, while we did not account for comorbidities that may influence joint degeneration, our current findings suggest that HR-pQCT-derived 3D JSW metrics are not able to identify subtle alterations in joint space narrowing beyond what can be detected on radiographs.

Larger JSV, JSW, and JSW_MAXin males than females were consistent with previous findings, and the observation that these metrics are related to overall joint size more than joint degeneration.¹¹ The finding that minimum JSW (JSW_MIN) was smaller in females than males in MCP3 requires further investigation as this was not consistent with results in previous studies using only RA patients, and may reflect the relatively small sample of males in the current study.

The absence of significant associations between JSW metrics and age, with the exception of JSW_MAX, suggests that MCP2 and MCP3 are not significantly affected by age-related degeneration. Hand osteoarthritis can cause joint space narrowing, however the distal interphalangeal, proximal interphalangeal and first carpometacarpal joints are more commonly affected than the MCP joints²⁵. Nonetheless, as cortical bone interruptions, indicative of erosive damage, increase with age²⁶ we recommend considering age as an important variable in HR-pQCT studies in RA.

Obesity can cause systemic inflammation, which is known to be associated with an increased incidence of RA^27–30. Despite its association with RA, obesity has a shown a varied effect on joint space degeneration. While some studies have shown a negative correlation between joint space and obesity³¹, other studies have shown a protective effect^32,33. In our study, HR-pQCT acquired 3D joint space metrics were not associated with BMI, thus (a) higher BMI may have had a protective role or (b) our metrics were not sensitive enough to detect the small changes that may be associated with BMI.

Despite the high resolution available in HR-pQCT images, we did not observe significant group-level differences in 3D JSW metrics, which represented quantitative measures such as joint space volume, width, etc., between RA participants and their age- and sex-matched controls. Further research may be required to determine whether this finding reflects the absence of a true difference between groups or an inability to detect true differences with HR-pQCT. However, for the purposes of clinical trials, it may be more important to determine the ability to sensitively detect changes over time, as well as changes in erosive damage.

Participants

Eighty (80) participants were recruited and provided written consent for this study, which was approved by the Conjoint Health Research Ethics Board at the University of Calgary (REB19-0387), and all methods were carried out in accordance with relevant guidelines and regulations. All participants were > 18 years of age, free of cognitive and physical impairments, and were not pregnant nor planning a pregnancy. RA participants were recruited from the Rheum4U database, an ongoing study that is exploring the quality of care in rheumatoid arthritis patients within Calgary, AB, Canada³⁴. By searching the Rheum4U database, N = 40 RA participants were identified and recruited if (1) they met the American College of Rheumatology / European League Against Rheumatism 2010 classification criteria for RA⁴ and (2) had a disease duration ≥ 6 months. RA participants were excluded if they had any history of a prior MCP injury, replacement, or complete joint space loss in both hands. For the age- (± 2 years) and sex-matched controls, N = 40 control participants were recruited via word-of-mouth. Control participants were included if they had no diagnosis of any hand joint problems or inflammatory arthritis, no current pain or swelling within the hands, and had not sought medical attention for any hand injuries within the past 12 months. For each participant physical characteristics (sex, age, height, and weight), participant reported outcomes (questionnaires), and imaging data were collected.

Questionnaire and Hand Function Test

Participants completed a Health Assessment Questionnaire (HAQ)³⁵ and the Disabilities of Arm, Shoulder, and Hand (DASH)²¹. Following the questionnaires, participants were administered the Jebsen-Taylor Hand Function Test (JTHFT)²², which consists of seven tasks meant to evaluate hand functionality when performing day-to-day routine tasks (e.g. writing, lifting light and heavy weights, etc.)

HR-pQCT Acquisition and Processing

High-resolution peripheral quantitative computed tomography (HR-pQCT, XtremeCT II, Scanco Medical, Brüttisellen, Switzerland) scans of the most affected hand were acquired with three 1 cm stacks, with 25% overlap, allowing for the encompassment of the MCP2 and MCP3 joints ³⁶. Each stack was acquired at 68 keV, 1470 𝜇A, 43 ms dwell time, and with an isotropic 60.7 𝜇m voxel size. Images were scored for motion, based on a previously developed rubric³⁷, and were omitted from further analysis if motion scores > 3.

Joint space width analysis was performed using the algorithm previously published by the Study grouP for x-trEme Computed Tomography in Rheumatoid Arthritis ⁹ collaboration using the manufacturer’s software (uct_evaluation, v6.6; IPL v5.42)⁹. From the automatically generated contours of the metacarpal and phalanx bones, multiple quantitative metrics of the joint space were calculated (Table 6)^9,12.

Table 6

Displayed are the joint space parameters, collected for this study, their abbreviation, and associated definition.
Joint Space Parameter	Abbreviation	Definition
Volume	JSV	Volume of the intra-articular space
Width	JSW	Mean joint space width
Standard Deviation of Width	JSW_SD	Standard deviation of joint space width
Maximum Width	JSW_MAX	Maximum absolute width across joint space
Minimum Width	JSW_MIN	Minimum absolute width across joint space
Width Asymmetry (Max / Min)	JSW_AS	Ratio of maximum to minimum joint space width

Ultrasound Acquisition and Processing

Ultrasound scans (Logiq S8, software version R2.2, GE HealthCare, Chicago, IL, USA) were performed by an ultrasound-trained rheumatologist. Both MCP2 and MCP3 joints of the dominant and non-dominant hand were evaluated for effusion / synovial hypertrophy and inflammation using power doppler. Based on these assessments, the rheumatologist would choose the most affected hand to be used for the following HR-pQCT imaging analysis – to note, this chosen hand may not always be the participant’s dominant hand. Additionally, the MCP2 – MCP5 joints from the most affected hand were further evaluated for the presence of erosions. For effusion and synovial hypertrophy and inflammation grades, the interquartile range (IQR) is presented from the 0–3 grading scale performed by two trained assessors (CP > 10 years and AI > 3 years of experience).

Statistical Analysis

Two-way ANOVAs for each MCP joint (MCP2 and MCP3) and for each joint space metric with diagnosis (RA, control) and sex (male, female) as between-subject factors were performed. Multiple linear regression analyses were performed to compare joint space parameters as a function of age, BMI, and JTHFT test times. To compare JTHFT (continuous variable) against ordinal ultrasound scores (i.e. effusion / synovial hypertrophy and power doppler), a Kendall rank correlation was utilized. Non-parametric, paired t-tests (Wilcoxon) were performed to compare clinical and demographic variables between RA and controls. Specifically, we compared matched hands between groups (i.e., dominant and non-dominant hands for JTHFT times).

While criteria for ultrasound grading (effusion / synovial hypertrophy and power doppler) have been developed they are dependent on the subjective assessment of the observed ultrasound signal. Thus, an intraclass correlation coefficient (ICC) between the two raters was performed. Specifically, we utilized a two-way random effects, single rater, absolute agreement exam (RStudio v2022.12.0 + 353). Classification of ICC values were as follows: < 0.05 = poor reliability, 0.5–0.75 = moderate reliability, 0.75–0.9 = good reliability, and > 0.9 = excellent reliability³⁸.

All statistical analysis were performed in Prism (v9.4.1, GraphPad, San Diego, CA, USA) unless otherwise noted. Statistical significance was noted if p < 0.05.

Availability of Data and Materials

Imaging data acquired for this study are not publicly available in the interest of protecting the confidentiality and privacy of the participants. However, they may be made available to other researchers for non-commercial use upon reasonable request.

Quantitative data analyzed during the current study can be may available upon request. All requests can be directed to Dr. Sarah L. Manske ([email protected]).

Acknowledgements

This project was supported by the Arthritis Society Stars Career Development Award (Manske, STAR-18-0189). Justin Tse was supported by a CIHR Post-Doctoral Fellowship. Dr. Barber is funded through the Arthritis Stars Career Development Award (STAR-19-0611) and from the Canadian Institutes of Health Research-Institute of Musculoskeletal Health and Arthritis (CIHR SI2-169745). Dianne Mosher, Deborah Marshall, Claire Barber, Susanne Benseler, Marinka Twilt, and Paul MacMullan established the Rheum4U Program. Dianne Mosher, Deborah Marshall, and Susanne Benseler act as the Research Operations Committee for the Rheum4U Program. Inelda Gjata, Martina Stevenson and Namneet Sandhu provided project support, helped with data collection, recruitment, and chart reviews. We would like to thank participating physicians and patients who contributed data to the cohort, as well as clinic staff, allied health professionals and registered nurses at the Richmond Road Diagnostic and Treatment Center and South Health Campus Hospital. The Rheum4U Program is supported by unrestricted educational grants from the following pharmaceutical companies: AbbVie; Amgen; Bristol-Myers Squibb (BMS); Celgene; Janssen; Novartis; Organon; Pfizer; Roche; Sanofi; Sandoz; Swedish Orphan Biovitrum AB (publ)³⁹; and Union Chimique Belge (UCB).

F. Matcham, I. C. Scott, L. Rayner, M. Hotopf, G. H. Kingsley, S. Norton, D. L. Scott, S. Steer, The impact of rheumatoid arthritis on quality-of-life assessed using the SF-36: A systematic review and meta-analysis. Semin Arthritis Rheum 44, 123-130 (2014).
A. M. Liphardt, E. Manger, S. Liehr, L. Bieniek, A. Kleyer, D. Simon, K. Tascilar, M. Sticherling, J. Rech, G. Schett, A. J. Hueber, Similar Impact of Psoriatic Arthritis and Rheumatoid Arthritis on Objective and Subjective Parameters of Hand Function. ACR Open Rheumatol 2, 734-740 (2020).
J. A. Sparks, Rheumatoid Arthritis. Annals of Internal Medicine 170, ITC1-ITC16 (2019).
D. Aletaha, T. Neogi, A. J. Silman, J. Funovits, D. T. Felson, C. O. Bingham, 3rd, N. S. Birnbaum, G. R. Burmester, V. P. Bykerk, M. D. Cohen, B. Combe, K. H. Costenbader, M. Dougados, P. Emery, G. Ferraccioli, J. M. Hazes, K. Hobbs, T. W. Huizinga, A. Kavanaugh, J. Kay, T. K. Kvien, T. Laing, P. Mease, H. A. Menard, L. W. Moreland, R. L. Naden, T. Pincus, J. S. Smolen, E. Stanislawska-Biernat, D. Symmons, P. P. Tak, K. S. Upchurch, J. Vencovsky, F. Wolfe, G. Hawker, 2010 Rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Arthritis Rheum 62, 2569-2581 (2010).
E. Lespessailles, N. Ibrahim-Nasser, H. Toumi, R. Chapurlat, Contribution of high resolution peripheral quantitative CT to the management of bone and joint diseases. Joint Bone Spine 85, 301-306 (2018).
K. K. Nishiyama, E. Shane, Clinical Imaging of Bone Microarchitecture with HR-pQCT. Current Osteoporosis Reports 11, 147-155 (2013).
C. P. Figueiredo, M. O. Perez, L. P. Sales, A. C. Medeiros, V. F. Caparbo, R. M. R. Pereira, Bone erosion in the 2nd metacarpophalangeal head: association with its bone mineral density by HR-pQCT in rheumatoid arthritis patients. BMC Musculoskelet Disord 22, 109 (2021).
S. Finzel, S. L. Manske, C. C. M. Barnabe, A. J. Burghardt, H. Marotte, A. Scharmga, E. M. Hauge, R. Chapurlat, K. Engelke, X. Li, B. C. J. van Teeffelen, P. G. Conaghan, K. S. Stok, Reliability and Change in Erosion Measurements by High-resolution Peripheral Quantitative Computed Tomography in a Longitudinal Dataset of Rheumatoid Arthritis Patients. J Rheumatol 48, 348-351 (2021).
K. S. Stok, A. J. Burghardt, S. Boutroy, M. P. H. Peters, S. L. Manske, V. Stadelmann, N. Vilayphiou, J. P. v. d. Bergh, P. Geusens, X. Li, H. Marotte, B. v. Rietbergen, S. K. Boyd, C. Barnabe, f. t. S. Collaboration, Consensus approach for 3D joint space width of metacarpophalangeal joints of rheumatoid arthritis patients using high-resolution peripheral quantitative computed tomography. Quantitative Imaging in Medicine and Surgery 10, 314-325 (2020).
S. C. Brunet, J. J. Tse, M. T. Kuczynski, K. Engelke, S. K. Boyd, C. Barnabe, S. L. Manske, Heterogenous bone response to biologic DMARD therapies in rheumatoid arthritis patients and their relationship to functional indices. Scandinavian Journal of Rheumatology, 1-10 (2021).
S. C. Brunet, S. Finzel, K. Engelke, S. K. Boyd, C. Barnabe, S. L. Manske, Bone changes in early inflammatory arthritis assessed with High-Resolution peripheral Quantitative Computed Tomography (HR-pQCT): A 12-month cohort study. Joint Bone Spine 88, 105065 (2021).
A. J. Burghardt, C. H. Lee, D. Kuo, S. Majumdar, J. B. Imboden, T. M. Link, X. Li, Quantitative in vivo HR-pQCT imaging of 3D wrist and metacarpophalangeal joint space width in rheumatoid arthritis. Ann Biomed Eng 41, 2553-2564 (2013).
A. Pfeil, J. Böttcher, B. E. Seidl, J.-P. Heyne, A. Petrovitch, T. Eidner, H.-J. Mentzel, G. Wolf, G. Hein, W. A. Kaiser, Computer-aided joint space analysis of the metacarpal-phalangeal and proximal-interphalangeal finger joint: normative age-related and gender-specific data. Skeletal Radiology 36, 853-864 (2007).
A. Pfeil, A. Hansch, G. Lehmann, T. Eidner, M. L. Schäfer, P. Oelzner, D. M. Renz, G. Wolf, G. Hein, W. A. Kaiser, J. Böttcher, Impact of sex, age, body mass index and handedness on finger joint space width in patients with prolonged rheumatoid arthritis using computer-aided joint space analysis. Rheumatology International 29, 517-524 (2009).
R. F. Loeser, N. Shakoor, Aging or osteoarthritis: which is the problem? Rheumatic Disease Clinics 29, 653-673 (2003).
S. Khosla, B. L. Riggs, E. J. Atkinson, A. L. Oberg, L. J. McDaniel, M. Holets, J. M. Peterson, L. J. Melton, 3rd, Effects of sex and age on bone microstructure at the ultradistal radius: a population-based noninvasive in vivo assessment. J Bone Miner Res 21, 124-131 (2006).
A. Pfeil, J. Böttcher, M. L. Schäfer, B. E. Seidl, M. Schmidt, A. Petrovitch, J. P. Heyne, G. Lehmann, P. Oelzner, G. Hein, G. Wolf, W. A. Kaiser, Normative reference values of joint space width estimated by computer-aided joint space analysis (CAJSA): the distal interphalangeal joint. J Digit Imaging 21 Suppl 1, 104-112 (2008).
W. Y. Kwok, J. Bijsterbosch, S. H. Malm, N. R. Biermasz, K. Huetink, R. G. Nelissen, I. Meulenbelt, T. W. J. Huizinga, R. van ’t Klooster, B. C. Stoel, M. Kloppenburg, Validity of joint space width measurements in hand osteoarthritis. Osteoarthritis and Cartilage 19, 1349-1355 (2011).
J. J. Cao, Effects of obesity on bone metabolism. J Orthop Surg Res 6, (2011).
L. Moroni, N. Farina, L. Dagna, Obesity and its role in the management of rheumatoid and psoriatic arthritis. Clinical rheumatology 39, 1039-1047 (2020).
C. Gummesson, I. Atroshi, C. Ekdahl, The disabilities of the arm, shoulder and hand (DASH) outcome questionnaire: longitudinal construct validity and measuring self-rated health change after surgery. BMC Musculoskelet Disord 4, 11 (2003).
R. H. Jebsen, N. Taylor, R. B. Trieschmann, M. J. Trotter, L. A. Howard, An objective and standardized test of hand function. Arch Phys Med Rehabil 50, 311-319 (1969).
D. van der Heijde, How to read radiographs according to the Sharp/van der Heijde method. J Rheumatol 27, 261-263 (2000).
S. L. Manske, S. C. Brunet, S. Finzel, K. S. Stok, P. G. Conaghan, S. K. Boyd, C. Barnabe, The SPECTRA Collaboration OMERACT Working Group: Construct Validity of Joint Space Outcomes with High-resolution Peripheral Quantitative Computed Tomography. J Rheumatol 46, 1369-1373 (2019).
M. Kloppenburg, W.-Y. Kwok, Hand osteoarthritis—a heterogeneous disorder. Nature Reviews Rheumatology 8, 22-31 (2012).
M. Peters, A. van Tubergen, A. Scharmga, A. Driessen, B. van Rietbergen, D. Loeffen, R. Weijers, P. Geusens, J. van den Bergh, Assessment of Cortical Interruptions in the Finger Joints of Patients With Rheumatoid Arthritis Using HR-pQCT, Radiography, and MRI. J Bone Miner Res 33, 1676-1685 (2018).
J. Hahn, S. Malspeis, M. Y. Choi, E. Stevens, E. W. Karlson, B. Lu, J. Cui, K. Yoshida, L. Kubzansky, J. A. Sparks, K. H. Costenbader, Association of Healthy Lifestyle Behaviors and the Risk of Developing Rheumatoid Arthritis Among Women. Arthritis Care & Research 75, 272-276 (2023).
C. S. Crowson, E. L. Matteson, J. M. Davis III, S. E. Gabriel, Contribution of obesity to the rise in incidence of rheumatoid arthritis. Arthritis Care & Research 65, 71-77 (2013).
C. Gioia, B. Lucchino, M. G. Tarsitano, C. Iannuccelli, M. Di Franco, Dietary Habits and Nutrition in Rheumatoid Arthritis: Can Diet Influence Disease Development and Clinical Manifestations? Nutrients 12, 1456 (2020).
B. Lu, L. T. Hiraki, J. A. Sparks, S. Malspeis, C.-Y. Chen, J. A. Awosogba, E. V. Arkema, K. H. Costenbader, E. W. Karlson, Being overweight or obese and risk of developing rheumatoid arthritis among women: a prospective cohort study. Annals of the Rheumatic Diseases 73, 1914-1922 (2014).
Ö. B. Çimen, N. A. Incel, Y. Yapici, D. Apaydin, C. Erdoğan, Obesity Related Measurements and Joint Space Width in Patients with Knee Osteoarthritis. Upsala Journal of Medical Sciences 109, 159-164 (2004).
A. H. M. van der Helm-van Mil, S. M. van der Kooij, C. F. Allaart, R. E. M. Toes, T. W. J. Huizinga, A high body mass index has a protective effect on the amount of joint destruction in small joints in early rheumatoid arthritis. Annals of the Rheumatic Diseases 67, 769-774 (2008).
J. Kaufmann, V. Kielstein, S. Kilian, G. Stein, G. Hein, Relation between body mass index and radiological progression in patients with rheumatoid arthritis. J Rheumatol 30, 2350-2355 (2003).
C. E. H. Barber, N. Sandhu, J. A. Rankin, P. MacMullan, D. A. Marshall, C. Barnabe, G. S. Hazlewood, A. Emrick, M. Stevenson, K. L. Then, S. Benseler, M. Twilt, D. Mosher, Rheum4U: Development and testing of a web-based tool for improving the quality of care for patients with rheumatoid arthritis. Clin Exp Rheumatol, (2018).
B. Bruce, J. F. Fries, The Health Assessment Questionnaire (HAQ). Clin Exp Rheumatol 23, S14-18 (2005).
S. C. Brunet, M. T. Kuczynski, J. L. Bhatla, S. Lemay, Y. Pauchard, P. Salat, C. Barnabe, S. L. Manske, The utility of multi-stack alignment and 3D longitudinal image registration to assess bone remodeling in rheumatoid arthritis patients from second generation HR-pQCT scans. BMC Med Imaging 20, 36-36 (2020).
Y. Pauchard, A.-M. Liphardt, H. M. Macdonald, D. A. Hanley, S. K. Boyd, Quality control for bone quality parameters affected by subject motion in high-resolution peripheral quantitative computed tomography. Bone 50, 1304-1310 (2012).
T. K. Koo, M. Y. Li, A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. Journal of Chiropractic Medicine 15, 155-163 (2016).
M. Ostrowska, W. Maśliński, M. Prochorec-Sobieszek, M. Nieciecki, I. Sudoł-Szopińska, Cartilage and bone damage in rheumatoid arthritis. Reumatologia 56, 111-120 (2018).

No competing interests reported.

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Evaluating High-Resolution Computed Tomography Derived 3-D Joint Space Metrics of the Metacarpophalangeal Joints Between Rheumatoid Arthritis and Age- and Sex-Matched Control Participants

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Abstract

Figures

Introduction

Results

Characteristics of the cohort

Effects of diagnosis and sex on 3D JSW

Age, BMI, and Jebsen-Taylor Hand Function Test (JTHFT)

Ultrasound

Discussion

Methods

Participants

Questionnaire and Hand Function Test

HR-pQCT Acquisition and Processing

Ultrasound Acquisition and Processing

Statistical Analysis

Declarations

References

Additional Declarations

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