Clinical and laboratory characteristics of the patients
The characteristics of the healthy controls are summarised in Table 1. The clinical and laboratory profiles of the RA patients are summarised in Table 2. A total of 125 RA patients with a mean age of 55.7 ± 12.4 years were included, of which 94 (75.2%) were female. Compared to male patients, female patients were significantly younger (54.4 ± 12.7 vs 59.9 ± 10.1, p = 0.033). The average height and weight also showed significant differences between genders (both with p < 0.001), although the two groups had a compatible average BMI, which was overall of 22.6 ± 3.7. The mean DAS28(ESR) of the whole cohort was 5.4 ± 3.3 and the mean value of serum total adiponectin was 25.0 ± 19.1 µg/mL. Serum total adiponectin in an age- and sex-matched healthy population sample was significantly lower (13.6 ± 5.5 µg/mL; p = 0.015 compared with the RA group).
Table 1
Clinical and laboratory characteristics of the enrolled healthy controls
Characteristic
|
Male
|
Female
|
Total
|
P value
|
N (%)
|
9 (26.4)
|
25 (73.5)
|
34 (100)
|
|
Age (years)
|
59.1 ± 10.5
|
53.5 ± 9.3
|
55.1 ± 9.9
|
0.016
|
Height (cm)
|
170.7 ± 8.1
|
156.5 ± 5.3
|
159.8 ± 4.3
|
< 0.001
|
Weight (kg)
|
67.3 ± 7.9
|
55.9 ± 8.6
|
58.9 ± 12.1
|
< 0.001
|
BMI (kg/m2)
|
24.4 ± 1.3
|
22.9 ± 4.0
|
23.7 ± 2.9
|
0.099
|
Adiponectin (µg/mL)
|
12.5 ± 4.0
|
15.2 ± 6.2
|
13.6 ± 5.5
|
0.058
|
Table 2
Clinical and laboratory characteristics of the enrolled patients
Characteristic
|
N (%)
|
Male
|
Female
|
Total
|
P value
|
Gender (N, %)
|
125 (100)
|
31 (24.8)
|
94 (75.2)
|
|
|
Age (years)
|
124 (99.2)
|
60.9 ± 11.4
|
54.0 ± 12.3
|
55.7 ± 12.4
|
0.007
|
Height (cm)
|
122 (97.6)
|
167.2 ± 8.2
|
155.5 ± 5.0
|
158.4 ± 7.8
|
< 0.001
|
Weight (kg)
|
122 (97.6)
|
64.3 ± 11.0
|
54.4 ± 10.3
|
56.8 ± 11.3
|
< 0.001
|
BMI (kg/m2)
|
122 (97.6)
|
23.0 ± 3.2
|
22.4 ± 3.8
|
22.6 ± 3.7
|
0.504
|
Disease duration (month)
|
118 (94.4)
|
105.2 ± 116.4
|
109.0 ± 107.3
|
108.0 ± 109.1
|
0.871
|
SJC
|
123 (98.4)
|
4.5 ± 6.0
|
3.8 ± 6.3
|
4.0 ± 6.2
|
0.637
|
TJC
|
123 (98.4)
|
6.8 ± 8.7
|
6.9 ± 9.0
|
6.9 ± 8.9
|
0.844
|
CRP (mg/dL)
|
91 (72.8)
|
33.9 ± 38.3
|
40.1 ± 57.3
|
38.5 ± 52.8
|
0.627
|
ESR (mm/H)
|
124 (99.2)
|
52.8± 30.9
|
56.2 ± 33.0
|
55.4 ± 32.4
|
0.618
|
DAS28(ESR)
|
123 (98.4)
|
5.7 ± 3.4
|
5.3 ± 3.3
|
5.4 ± 3.3
|
0.594
|
RF (IU/mL)
|
122 (97.6)
|
444.2 ± 685.0
|
321.3 ± 469.3
|
352.5 ± 532.0
|
0.269
|
Anti-CCP (Ru/mL)
|
119 (95.2)
|
242.5 ± 173.1
|
230.8 ± 170.4
|
233.7 ± 170.4
|
0.746
|
Adiponectin (µg/mL)
|
118 (94.4)
|
21.4 ± 20.3
|
26.3 ± 18.6
|
25.0 ± 19.1
|
0.223
|
Sharp score
|
115 (92.0)
|
43.1 ± 63.5
|
44.3 ± 51.7
|
44.0 ± 54.7
|
0.917
|
Abbreviations: BMI: body mass index; CCP: cyclic citrullinated peptides; CRP: C-reactive protein; DAS28: disease activity score of 28 joints; ESR: erythrocyte sedimentation rate; RF: rheumatoid factor; SJC: swollen joint count; TJC: tender joint count. Continuous variables are expressed as means ± standard deviations and categorical data using number (percentage). |
Identification of factors correlated with DAS28(ESR) and Sharp score by univariable analysis
The relationships between different clinical parameters and disease activity measured by DAS28(ESR) were assessed by univariable linear regression analysis. Analyses using the univariable model against DAS28(ESR) indicated significant positive correlations with age and CRP (p = 0.0026 and p = 0.003, respectively; Table 3). In contrast, serum adiponectin was negatively correlated with DAS28(ESR) (p = 0.015). This model did not detect any further significant association between DAS28(ESR) and other variables (p > 0.05) (Table 3).
Table 3
Univariable regression results against DAS28(ESR) or Sharp score
Variables
|
DAS28(ESR)
|
Sharp score
|
β/Odds ratio (95% CI)
|
P
|
β/Odds ratio (95% CI)
|
P
|
GENDER
|
|
|
|
|
Male
|
0
|
|
0
|
|
Female
|
−0.27 (−1.62, 1.09)
|
0.698
|
1.22 (−21.65, 24.09)
|
0.917
|
Age
|
0.07 (0.03, 0.12)
|
0.003
|
0.62 (−0.17, 1.41)
|
0.128
|
Disease duration
|
0.00 (−0.00, 0.01)
|
0.177
|
0.30 (0.22, 0.38)
|
< 0.001
|
Height
|
−0.03 (−0.10, 0.05)
|
0.497
|
−0.26 (−1.54, 1.01)
|
0.688
|
Weight
|
−0.03 (−0.08, 0.03)
|
0.331
|
−0.68 (−1.58, 0.22)
|
0.141
|
BMI
|
−0.06 (−0.23, 0.10)
|
0.444
|
−2.23 (−5.03, 0.56)
|
0.121
|
RF
|
−0.00 (−0.00, 0.00)
|
0.886
|
0.01 (−0.01, 0.03)
|
0.311
|
CCP
|
−0.00 (−0.01, 0.00)
|
0.204
|
−0.01 (−0.07, 0.03)
|
0.853
|
CRP
|
0.02 (0.01, 0.03)
|
0.003
|
0.26 (0.04, 0.049)
|
0.023
|
ESR
|
N/A
|
N/A
|
0.65 (0.36, 0.94)
|
< 0.001
|
SJC
|
N/A
|
N/A
|
2.05 (0.51, 3.59)
|
0.010
|
TJC
|
N/A
|
N/A
|
2.07 (1.02, 3.13)
|
< 0.001
|
Adiponectin
|
−0.04 (−0.07, −0.01)
|
0.015
|
−0.39 (−0.99, 0.20)
|
0.198
|
Abbreviations: BMI: body mass index; CCP: cyclic citrullinated peptides; CRP: C-reactive protein; RF: rheumatoid factor; SJC: swollen joint count; TJC: tender joint count. P < 0.05 is considered statistically significant. |
Analyses using the univariable model against the Sharp score revealed positive correlations with disease duration, CRP, ESR, SJC and TJC (p < 0.001, p = 0.023, p < 0.001, p = 0.010 and p < 0.001, respectively), whereas no correlation was observed with serum adiponectin level (Table 3).
Analyses by multivariable linear regression shows an independent relationship between adiponectin level and DAS28(ESR)
We further explored the relationship between adiponectin level and the outcomes using multivariable linear regression analyses adjusted for confounders. Age, BMI and CRP were finally selected as confounders for DAS28(ESR). Age, BMI, disease duration, CRP, ESR, SJC and TJC were finally selected as confounders for Sharp score.
In the crude models, there was no adjustment for confounders, while in model I, two confounders were adjusted when using DAS28(ESR) and three confounders were adjusted when using the Sharp score. Model II was adjusted for all confounders. DAS28(ESR) and adiponectin were significantly and negatively correlated in all three models (Table 4). When stratified by gender, the same trend existed in female but not male patients. In the models against the Sharp score, after adjusting for confounders, there was a negative association between adiponectin level and Sharp score. In female patients, the same trend also existed (Table 4). The scatter plots of the linear regression obtained after adjusting for all confounders are shown in Figure 1.
Table 4
Multivariable linear relationship stratified by gender, β (95% CI) of adiponectin (µg/mL)
Models
|
Male
|
P
|
Female
|
P
|
Total
|
P
|
Against DAS28(ESR), Per SD increase in adiponectin
|
|
|
Crude model
|
0.00 (−0.06, 0.06)
|
0.944
|
−0.06 (−0.10, −0.02)
|
0.003
|
−0.04 (−0.08, −0.01)
|
0.016
|
Model Ia
|
−0.01 (−0.07, 0.06)
|
0.830
|
−0.07 (−0.11, −0.03)
|
0.001
|
−0.05 (−0.08, −0.01)
|
0.007
|
Model IIb
|
−0.04 (−0.14, 0.05)
|
0.383
|
−0.10 (−0.17, −0.04)
|
0.004
|
−0.07 (−0.13, −0.02)
|
0.008
|
Against Sharp score, Per SD increase in adiponectin
|
|
|
|
Crude model
|
0.13 (−1.24, 1.50)
|
0.858
|
−0.61 (−1.27, 0.05)
|
0.073
|
−0.42 (−1.02, 0.19)
|
0.180
|
Model Ic
|
−0.13 (−1.11, 0.86)
|
0.806
|
−0.95 (−1.54, −0.35)
|
0.003
|
−0.72 (−1.23, −0.20)
|
0.008
|
Model IId
|
−1.00 (−2.94, 0.94)
|
0.328
|
−1.00 (−2.09, −0.08)
|
0.025
|
−0.87 (−1.80, −0.06)
|
0.032
|
Crude model: Univariable model |
a: adjusted for age and BMI. |
b: adjusted for age, BMI and CRP. |
c: adjusted for age, BMI and disease duration. |
d: adjusted for age, BMI, disease duration, CRP, ESR, SJC and TJC. |
P < 0.05 is considered statistically significant. |
Exploration of modifier and interaction effects on adiponectin/DAS28(ESR) or adiponectin/Sharp score associations
We explored potential modifier or interaction effects from age, BMI and CRP, and did not find any potential factor interfering with adiponectin/DAS28(ESR) association. The same analysis was performed for adiponectin/Sharp score association, with the addition of the variables ‘disease duration’, ‘ESR’, ‘SJC’ and ‘TJC’. Similarly, no potential modifier or interaction factor was found.
Sensitivity analysis
The amount of missing data for the different variables ranged from 0 to 27% (Table 2). Eighty-four out of the 125 (67%) patients had complete data for all variables for the main analyses. The distributions of the original and imputed variables are depicted in Supplementary Table S1 (Additional file 1). Regression analyses using multiple imputed datasets gave similar results to those undertaken on the original datasets, as displayed in Supplementary Table S2 (Additional file 1).