| Literature DB >> 26299443 |
Lukas Mangnus1, Hanna W van Steenbergen2, Elisabet Lindqvist3, Elisabeth Brouwer4, Monique Reijnierse5, Tom W J Huizinga6, Peter K Gregersen7, Ewa Berglin8, Solbritt Rantapää-Dahlqvist9, Désirée van der Heijde10, Annette H M van der Helm-van Mil11.
Abstract
INTRODUCTION: The western population is ageing. It is unknown whether age at diagnosis affects the severity of Rheumatoid Arthritis (RA), we therefore performed the present study.Entities:
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Year: 2015 PMID: 26299443 PMCID: PMC4547419 DOI: 10.1186/s13075-015-0740-0
Source DB: PubMed Journal: Arthritis Res Ther ISSN: 1478-6354 Impact factor: 5.156
Fig. 1Schematic overview of the causal paths that were studied using mediation models as described by Baron and Kenny. The figure illustrates two causal paths that lead to an outcome. A direct path from independent to dependent variable (b) and an indirect path from an independent to a dependent variable through a mediator variable (a,c). To test mediation three tests have to be performed according to Baron and Kenny [36]. 1) The mediator variables were regressed on the independent variable (a), the independent variable should significantly affect the mediator variables. 2) regression analysis of the dependent variable on the independent variable was done (b); in this analysis the independent variable must significantly affect the dependent variable. 3) The dependent variable was regressed on the independent and mediator variable (b and c). When mediation occurs the mediator variable significantly affects the dependent variable and the effect of the independent variable on the dependent variable is closer to zero. In this study we tested whether different mediators could influence the effect of age on radiographic joint damage. The tested mediators were symptom duration at diagnosis, swollen joint count (SJC), tender joint count (TJC), C-reactive protein (CRP), anti-citrullinated protein antibody (ACPA), rheumatoid factor (RF), and inflammation detected on magnetic resonance imaging (MRI). SHS Sharp-van der Heijde score
Characteristics of patients with rheumatoid arthritis included in the longitudinal cohorts studied. Age, symptom duration, TJC, SJC, CRP, ACPA and RF were assessed at baseline
| EAC Part 1 | EAC Part 2 (MRI) | Wichita | Umeå | Groningen | Lund | |
|---|---|---|---|---|---|---|
| Total number of patients | 698 | 56 | 293 | 459 | 278 | 147 |
| Total number of radiographs | 3.643 | 105 | 1.062 | 868 | 865 | 781 |
| Mean number of radiographs per patient (SD) | 5.2 (2.1) | 1.9 (0.3) | 3.6 (2.0) | 1.9 (0.3) | 3.1 (1.4) | 5.3 (0.8) |
| Year of diagnosis | 1993–2006 | 2010–2012 | 1963–1999 | 1995–2010 | 1945–2001 | 1985–1989 |
| Radiographic follow up in years | 7 | 1 | 15 | 2 | 14 | 5 |
| Method of scoring | SHS | SHS | SHS | Larsen | SHS | Larsen |
| Age, years | ||||||
| Mean (SD) | 56.6 (15.6) | 55.9 (14.2) | 48.8 (14.2) | 53.9 (14.5) | 49.3 (12.6) | 50.7 (11.5) |
| Median (IQR) | 58 (46–68) | 59 (46–65) | 49 (39–60) | 56 (45–64) | 50 (40–59) | 51 (43–59) |
| Range | 17.1–92.4 | 21.5–77.8 | 16.0–83.0 | 17.0–83.0 | 18.3–76.3 | 18.0–78.0 |
| Female sex (%) | 474 (67.8) | 31 (55.4) | 226 (77.1) | 321 (69.2) | 196 (70.5) | 98 (66.7) |
| Symptom duration in weeks (IQR) | 19 (11–37) | 18 (11–32) | NA | NA | NA | 43 (29–62) |
| TJC (IQR) | 8 (5.0–12.0) | 7 (4.0–10.5) | NA | NA | NA | NA |
| SJC (IQR) | 8 (4.0–14.0) | 5 (3.5–10.0) | NA | NA | NA | NA |
| ESR (IQR) | 33 (19–54) | 25 (10–41) | NA | NA | NA | NA |
| CRP (IQR) | 17 (8.0–40.0) | 11 (3.0–20.5) | NA | NA | NA | NA |
| ACPA positivity (%) | 365 (53.7) | 33 (63.5) | 238 (82.1) | 339 (73.1) | 162 (79.4) | 114 (80.3) |
| RF positivity (%) | 405 (58.2) | 36 (64.3) | NA | NA | 259 (93.8) | 115 (80.3) |
Age, symptom duration, TJC, SJC, ESR, CRP, ACPA and RF were assessed at baseline
EAC Early Arthritis Clinic, MRI magnetic resonance imaging, TJC tender joint count, SJC swollen joint count, CRP C-reactive protein, ESR erythrocyte sedimentation rate, ACPA anti-citrullinated peptide antibodies, RF rheumatoid factor, SHS Sharp-van der Heijde
Fig. 2Association between age at diagnosis and severity of joint damage in five longitudinal cohorts summarized in a meta-analysis (a) and depicted for patients with rheumatoid arthritis included in the Leiden Early Arthritis Clinic (EAC) for different age categories (b). a Age was entered as a continuous variable in the multivariate normal regression analysis, because the plots of the raw data suggested no interaction of age with time. The meta-analysis (inverse weighted meta-analysis with a random-effect model) summarizes the effects of the age of the different cohorts. An effect size of 1.034 represents a 1.034-fold increase in joint damage per year increase in age. Because these effect sizes were unit-free they could be compared in meta-analysis. b Although age was analyzed as a continuous variable, the predicted Sharp-van der Heijde (SHS) scores per age-category were plotted to illustrate the data. The SHS scores predicted by the multivariate normal regression analysis are presented
Fig. 3Correlation between age and Sharp-van der Heijde erosion (a) and joint space narrowing scores (JSN) (b) at baseline
Fig. 4Sharp-van der Heijde erosion score (a) and joint space narrowing score (b) over time for patients with rheumatoid arthritis from the Early Arthritis Clinic, categorized by age at diagnosis
Mediation analysis in 698 patients with rheumatoid arthritis from the Leiden Early Arthritis Clinic, with radiographic severity of joint damage over 7 years as the outcome variable
| Step 1: effect of age on possible mediators | |||
| Effect ( | 95 % CI |
| |
| SJC | 1.00 | 1.00–1.01 | 0.11 |
| TJC | 1.00 | 1.00–1.01 | 0.55 |
| Symptom duration | 0.992 | 0.988–0.996 | <0.001 |
| CRP | 1.016 | 1.011–1021 | <0.001 |
| RF | 0.99 | 0.98–1.00 | 0.09 |
| ACPA | 0.98 | 0.97–0.99 | <0.001 |
| Step 2: effect of age on radiographic joint damage | |||
| Effect ( | 95 % CI |
| |
| Ageing | 1.034 | 1.029–1.040 | <0.001 |
| Step 3: effect of age and possible mediator on radiographic joint damage | |||
| Effect ( | 95 % CI |
| |
| SJC | 1.00 | 0.99–1.00 | 0.23 |
| Ageing | 1.035 | 1.029–1.040 | <0.001 |
| TJC | 1.00 | 0.98–1.01 | 0.76 |
| Ageing | 1.037 | 1.030–1.044 | <0.001 |
| Symptom duration | 1.003 | 1.002–1.005 | <0.001 |
| Ageing | 1.035 | 1.029–1.040 | <0.001 |
| CRP | 1.003 | 1.001–1.005 | 0.003 |
| Ageing | 1.033 | 1.027–1.038 | <0.001 |
| ACPA | 1.37 | 1.16–1.60 | <0.001 |
| Ageing | 1.035 | 1.030–1.040 | <0.001 |
| RF | 1.30 | 1.10–1.52 | 0.002 |
| Ageing | 1.034 | 1.029–1.039 | <0.001 |
aThe effect size (β) of swollen joint count (SJC), tender joint count (TJC), symptom duration and C-reactive protein (CRP) reflect the increase per year increase of age. For example, the β for CRP is 1.016 this means that for every year increase in age there is 1.016-fold increase in CRP. A β of 0.992 indicates an increase 0.992- fold, hence actually a decrease. The effect size of anti-citrullinated protein antibody (ACPA) and rheumatoid factor (RF) reflect the odds ratio. Step 1, 2 and 3 of the mediation analyses are explained in Fig. 1. In step 1 a linear or logistic regression was used, in step 2 and 3 a multivariate normal regression analysis was used [27]. Also here the effects are per unit. For example, the β for age on joint damage is 1.034/year this means that for every year increase in age there is an increase of 3.4 % this is equal to an increase of 95.2 % every 20 years (1.034^20). All features (SJC, TJC, ACPA, RF, symptom duration, and age) were assessed at baseline
Mediation analysis in 56 patients with rheumatoid arthritis from the Leiden Early Arthritis Clinic, with radiographic severity of joint damage as the outcome
| Step 1: effect of age on possible mediators | |||
| Effect ( | 95 % CI |
| |
| MRI inflammation | 1.018 | 1.002–1.034 | 0.027 |
| Synovitis | 1.011 | 1.00–1.024 | 0.092 |
| BME | 1.021 | 1.00–1.043 | 0.052 |
| Step 2: effect of age on radiographic joint damage | |||
| Effect ( | 95 % CI |
| |
| Ageing | 1.032 | 1.010–1.055 | 0.004 |
| Step 3: effect of age and possible mediator on radiographic joint damage | |||
| Effect ( | 95 % CI |
| |
| MRI inflammation | 1.026 | 1.004–1.047 | 0.018 |
| Ageing | 1.025 | 1.004–1.047 | 0.021 |
| Synovitis | 1.069 | 0.97–1.17 | 0.15 |
| Ageing | 1.029 | 1.007–1.051 | 0.011 |
| BME | 1.039 | 1.011–1.067 | 0.007 |
| Ageing | 1.026 | 1.005–1.047 | 0.014 |
Step 1, 2 and 3 are explained in Fig. 1. In step 1 a linear regression is used, in step 2 and 3 a multivariate normal regression analysis is used [27]. The effects are per unit increase, for example per point increase in rheumatoid arthritis magnetic resonance imaging score (RAMRIS) and per year increase in age; for further explanation see legend of Table 2. MRI magnetic resonance imaging, BME bone marrow edema