Abstract
Objectives
This study assessed whether circulating levels of adiponectin and leptin are associated with higher mortality in patients with RA.
Methods
Participants were adults from the Veterans Affairs RA Registry. Adipokines and inflammatory cytokines were measured as part of a multi-analyte panel on banked serum at enrolment. Dates and causes of death were derived from the Corporate Data Warehouse and the National Death Index. Covariates were derived from medical record, biorepository and registry databases. Multivariable Cox proportional hazard models evaluated associations between biomarkers and all-cause and cause-specific mortality.
Results
A total of 2583 participants were included. Higher adiponectin levels were associated with older age, male sex, white race, lower BMI, autoantibody seropositivity, radiographic damage, longer disease duration, prednisone use and osteoporosis. Higher adiponectin concentrations were also associated with higher levels of inflammatory cytokines but not higher disease activity at enrolment. Leptin was primarily associated with greater BMI and comorbidity. The highest quartile of adiponectin (vs lowest quartile) was associated with higher all-cause mortality [hazard ratio (HR): 1.46 (95% CI: 1.11, 1.93), P = 0.009] and higher cardiovascular mortality [HR: 1.85 (95% CI: 1.24, 2.75), P = 0.003], after accounting for covariates. Higher leptin levels were also associated with greater all-cause and cancer mortality.
Conclusions
Elevations in adipokines are associated with age, BMI, comorbidity and severe disease features in RA and independently predict early death. Associations between adiponectin and inflammatory cytokines support the hypothesis that chronic subclinical inflammation promotes metabolic changes that drive elevations in adipokines and yield adverse health outcomes.
Keywords: cardiovascular disease, RA, disease activity, mortality
Rheumatology key messages.
Elevations in adipokines are associated with disease features, inflammation and comorbidity among rheumatoid arthritis.
Elevated adiponectin and leptin levels are independently associated with early mortality.
Adipocytokines may help to identify those with dysregulated metabolism and identify high-risk disease phenotypes.
Introduction
Patients with RA have higher mortality rates than the general population due in large part to higher rates of cardiovascular and pulmonary-related death [1]. The risk of death is often observed to be higher in patients with RA whose BMI is low or low-normal, which is often attributed to metabolic changes due to more severe disease, comorbidity, and premature ageing and long-term weight loss in these patients [2, 3].
Adipokines are fat- and muscle-secreted protein hormones that regulate metabolism. These biomarkers serve as metabolic indicators and may also have effects on biological processes involved in appetite regulation as well as energy homeostasis. For example, adiponectin has been termed the ‘starvation signal’ since it is observed to be higher in thin people and increased in the context of rapid weight loss, likely in order to transition cells to the use of lipid oxidation for energy production [4–6]. Adiponectin levels have been associated with cachexia and measures of malnutrition and protein-energy wasting in other clinical settings [4, 7, 8]. Leptin, in contrast, is strongly associated with fat mass and acts to regulate appetite and suppress fat accumulation and storage [9]. Adipokine levels are thus closely tied to body composition abnormalities, most notably total and visceral adiposity.
Since BMI is a poor metabolic indicator in patients with chronic diseases due to alterations in body composition, adipokines may serve as biomarkers of metabolic health and body composition that could help to predict adverse outcomes, including early mortality. Adiponectin, in particular, has been associated with higher mortality in some chronic diseases such as end-stage renal disease and diabetes [10–12] and has been linked to loss of muscle mass and muscle quality as well as deficits in physical functioning in patients at risk for sarcopenia [13–15]. One study, for example, demonstrated a 46% higher mortality per 1 standard deviation higher adiponectin among type 1 diabetics [12]. We recently demonstrated that elevations in both adiponectin and leptin were strongly associated with sarcopenia in patients with RA [15]. Thus, initial evidence suggests that dysregulation of adipokines might occur in RA in association with chronic inflammation, weight loss, altered body composition and loss of physical functioning, and may be hypothesized to be associated with early death.
Elevated levels of adiponectin have been associated with sarcopenia and progressive joint damage in RA [15, 16]. Some, but not all, studies have linked adipokines to clinical disease activity [10, 17]. Yet, few studies in RA have evaluated the associations between adipokines and long-term outcomes including early mortality [18]. We hypothesized that adipokines may provide prognostic information for a number of adverse outcomes among patients with RA, perhaps indirectly, by identifying patients that have experienced adverse metabolic changes and have developed unfavourable body composition. In this study, we determined if adipokine concentrations could independently predict early death patients with RA independent of BMI, comorbid conditions, RA disease activity and other important health factors.
Methods
Study setting
The Veterans Affairs Rheumatoid Arthritis (VARA) study is an ongoing national repository and multicentre disease registry that has been active for >17 years (initiated 2003) [19–26]. At the time this study was conducted, 13 VA sites had contributed data. Veterans with RA are identified by the treating rheumatologist at individual sites and all Veterans who fulfil the 1987 ACR classification criteria for RA and are over 18 years of age are eligible for enrolment [27]. Clinical providers at each site record clinical data at enrolment and at routine follow-up visits as part of routine clinical care. Each individual VA site has been approved by its local IRB to participate in the registry and all study patients provide written informed consent. The registry study was approved by the Corporal Michael J. Crescenz VA IRB (no. 01654) and the laboratory assessments and analyses specific to this study were approved as exempt research.
Adipokine measurements
Adipokines (adiponectin, leptin) were measured on samples, all collected at enrolment and stored at −70°C, using a multi-analyte panel from Meso Scale Discovery (Rockville, MA, USA). Storage length for samples was up to 15 years. Assays were performed as per the manufacturer’s protocols and analysed on the MESO QuickPlex SQ 120 imager (Meso Scale Discovery) (Coefficient of Variation [CV] 2–5%). Adipokine values were log-adjusted to fit a normal distribution and standardized so that a 1-unit difference in the value represented a 1 s.d. difference for all individual analytes.
Cause-specific mortality
Date of death was derived from the National Death Index (NDI) or from the Corporate Data Warehouse (CDW) through January 2020. Cause of death data from the NDI was available through December 2017.
RA disease activity, serologies and inflammatory markers
The results of clinical testing of CRP (mg/dl), tender and swollen joint counts (0–28), and patient/physician global scores (0–100 mm on visual analogue scale) were extracted from the registry and from the VA electronic medical record by querying data in the CDW. Our primary disease activity measure was the Disease Activity Score in 28 joints with CRP (DAS28-CRP) and was categorized as remission (<2.6), low activity (2.61–3.2), moderate activity (3.21–5.09) and high activity (≥5.1) [28]. Missing components for the DAS28-CRP were imputed by carrying values forward from the prior visit. DAS28 with the ESR (DAS28-ESR) was used if CRP was missing at that visit. From banked serum, a second-generation commercial anti-CCP antibody and high sensitivity CRP (hsCRP) were also measured as previously reported [29]. Disability was assessed using the multi-dimensional HAQ (MD-HAQ) during routine clinical visits.
Measurement of circulating cytokines and chemokines
Cytokine and chemokine levels were determined by the V-PLEX multiplex panel from Meso Scale Discovery. These analytes were measured from serum obtained at the time of registry enrolment, the only time point for which samples are routinely banked for these study participants. Following sample collection, specimens were processed and stored at −70°C until time of measurement. We focussed on four cytokines hypothesized to be important in the development of cachexia in RA: IL‐1β, IL‐6, IFN-γ and TNF-α [30]. Cytokine values were log-adjusted to fit a normal distribution and standardized.
Other covariables
Demographics and disease-specific characteristics at enrolment were obtained from the registry database. The presence of nodules and radiographic damage was each reported at enrolment. Current smoking was considered time-invariant (presence or absence of the exposure reported at baseline). BMI was extracted from the vital sign packages available in the CDW and the closest BMI value (within 30 days) to the visit date was utilized. Observations with missing BMI data were imputed by carrying forward from the prior observation. BMI categories were defined as underweight, <20 kg/m2; normal weight, ≥20–25 kg/m2; overweight, ≥25–30 kg/m2; obese, ≥30–35 kg/m2; and severely obese, ≥35 kg/m2. Maximum BMI was operationalized as the greatest BMI measurement available in medical record data prior to enrolment and the percentage change from the maximum historical value was calculated during the pre-enrolment time period. Comorbidity burden was assessed at enrolment using the Rheumatic Disease Comorbidity Index (RDCI) [31]. Individual comorbidities were extracted over a 1-year period preceding registry enrolment from CDW and categorized according to Healthcare Cost and Utilization Project—Clinical Classification Software Refined (HCUP-CCSR). A full list of diagnosis codes included in these categories can be found online [32].
RA treatments were extracted from VA pharmacy databases. Each prescription fill of a drug was defined as a dispensing episode [22]. For each episode, the amount of the drug dispensed and the expected duration of the treatment episode were determined. The expected days of supply were determined based on the dosing instructions. A drug course was defined as a period of continuous treatment consisting of one or more dispensing episodes without a gap of ≥90 days between the expected end of the days of supply for that episode and the start of the subsequent dispensing episode. Participants were considered exposed to the therapy if the current visit occurred during a defined medication course. Glucocorticoid use was considered active at enrolment if an overlapping course occurred within 30 days of enrolment. This approach was 85% accurate compared with chart review.
Statistical analysis
Characteristics of the study population were described by quartiles of adiponectin and leptin. Differences across quartiles were assessed with ANOVA, the Kruskal–Wallis test and the χ2 test as appropriate. The primary analyses utilized multivariable Cox proportional hazards models to assess associations between baseline characteristics and the time to death among all participants, clustering on study site. Factors pre-hypothesized to be potential confounders or those identified to be associated with adipokines in univariate analyses were included in these models. Associations with cause-specific mortality were also assessed. The proportional hazards assumption was tested by visualizing the Shoenfeld residuals.
Subgroup analyses included exploration of the association between adiponectin and mortality in pre-hypothesized subgroups defined by age (> or <50 years), sex and those with >10 years disease duration. Finally, we explored associations between adiponectin quartiles and circulating cytokines using linear regression.
Results
Associations between adipokines and patient characteristics
A total of 2583 registry participants were evaluated at baseline. Enrolment characteristics of the study population are described across adiponectin quartiles in Table 1. Higher adiponectin was associated with older age, a lower proportion of black patients, lower BMI, greater percentage weight loss from maximum weight and lower rates of current smoking. Those with higher adiponectin levels had higher rates of ACPA positivity, higher prevalence of radiographic damage, were more likely to be using prednisone and hydroxychloroquine, and had a longer disease duration, although they were also observed to have numerically lower CRP and DAS28. Spine disease (e.g. spondylosis, degenerative disc disease, spinal stenosis) and osteoporosis were more common in higher quartiles of adiponectin levels, while diabetes was less common. The prevalence of other comorbidities and the RDCI were similar across the quartiles with select exceptions. Weight loss >15% from maximum recorded weight was associated with higher adiponectin (per s.d.) levels independent of age, sex, race and current BMI [β: 0.13 (95% CI: 0.025, 0.25), P = 0.02]. Estimated values from these models for adiponectin based on current BMI and weight change from maximum are shown in Fig. 1.
Table 1.
Enrolment characteristics by adiponectin quartile
| Quartile 1 | Quartile 2 | Quartile 3 | Quartile 4 | P | |
|---|---|---|---|---|---|
| n | 646 | 646 | 646 | 646 | |
| Adiponectin, median (IQR), mg/l | 3 (1, 4) | 9 (7, 11) | 17 (15, 20) | 46 (32, 111) | |
| Age, mean (s.d.), years | 70.8 (10.5) | 71.3 (10.7) | 71.8 (10.4) | 73.5 (9.5) | <0.001 |
| Female, n (%) | 75 (12) | 75 (12) | 75 (12) | 75 (12) | 1 |
| Black, n (%) | 119 (18) | 111 (17) | 99 (15) | 76 (12) | 0.007 |
| Current smoker, n (%) | 154 (24) | 157 (24) | 173 (27) | 140 (22) | 0.002 |
| BMI, mean (s.d.), kg/m2) | 29.7 (5.8) | 29.3 (5.8) | 28.6 (5.8) | 27.8 (5.5) | <0.001 |
| % change max BMI, mean (s.d.) | −9.4 (12.3) | −9.6 (14.1) | −10.5 (14.8) | −12.7 (17.0) | <0.001 |
| RA characteristics | |||||
| DAS28, mean (s.d.) | 3.6 (1.5) | 3.7 (1.5) | 3.6 (1.4) | 3.5 (1.4) | 0.07 |
| MD-HAQ, mean (s.d.) | 0.88 (0.62) | 0.91 (0.64) | 0.89 (0.65) | 0.88 (0.63) | 0.78 |
| Erosive disease, n (%) | 203 (47) | 231 (47) | 254 (52) | 290 (59) | <0.001 |
| Nodules, n (%) | 148 (36) | 152 (34) | 154 (33) | 188 (37) | 0.67 |
| RF positive, n (%) | 362 (56) | 365 (57) | 348 (54) | 325 (50) | 0.06 |
| ACPA positive, n (%) | 423 (65) | 446 (69) | 488 (76) | 479 (75) | <0.001 |
| Disease duration, mean (s.d.), years) | 9.6 (10.5) | 10.2 (10.4) | 11.6 (11.4) | 13.5 (12.0) | <0.001 |
| hsCRP, mean (s.d.), mg/dl | 11.5 (18.3) | 12.9 (19.8) | 12.5 (21.8) | 10.7 (19.3) | 0.007 |
| Methotrexate, n (%) | 328 (51) | 338 (52) | 343 (53) | 325 (50) | 0.74 |
| Glucocorticoids, n (%) | 196 (30) | 223 (35) | 247 (38) | 218 (34) | 0.03 |
| TNFi therapy, n (%) | 159 (25) | 153 (24) | 162 (25) | 180 (28) | 0.34 |
| HCQ, n (%) | 166 (26) | 206 (32) | 212 (33) | 257 (40) | <0.001 |
| Enrolled after 2010, n (%) | 358 (55) | 351 (54) | 313 (48) | 254 (39) | <0.001 |
| Comorbidity | |||||
| RDCI, median (IQR) | 3 (2, 5) | 3 (2, 5) | 3 (2, 5) | 4 (2, 5) | 0.41 |
| COPD/asthma, n (%) | 226 (35) | 216 (33) | 223 (35) | 238 (37) | 0.62 |
| Diabetes, n (%) | 253 (39) | 221 (34) | 186 (29) | 196 (30) | <0.001 |
| Osteoarthritis, n (%) | 546 (85) | 528 (82) | 534 (83) | 550 (85) | 0.29 |
| Hypertension, n (%) | 476 (74) | 471 (73) | 451 (70) | 442 (69) | 0.13 |
| Hyperlipidaemia, n (%) | 536 (84) | 536 (83) | 510 (80) | 506 (80) | 0.18 |
| Coronary artery disease, n (%) | 179 (28) | 179 (28) | 178 (28) | 189 (29) | 0.88 |
| Cerebrovascular disease, n (%) | 45 (7) | 40 (6) | 60 (9) | 64 (10) | 0.04 |
| Other vascular disease, n (%) | 87 (13) | 85 (13) | 82 (13) | 92 (14) | 0.87 |
| Heart failure, n (%) | 74 (11) | 77 (12) | 75 (12) | 79 (12) | 0.97 |
| Venous thrombosis, n (%) | 54 (8) | 53 (8) | 62 (10) | 72 (11) | 0.23 |
| Liver disorder, n (%) | 94 (15) | 92 (14) | 77 (12) | 118 (18) | 0.01 |
| Any neoplasm, n (%) | 303 (47) | 322 (50) | 317 (49) | 344 (53) | 0.14 |
| Skin cancer, n (%) | 53 (8) | 45 (7) | 39 (6) | 40 (6) | 0.40 |
| Lung cancer, n (%) | 13 (2) | 12 (2) | 13 (2) | 11 (2) | 0.94 |
| Prostate cancer, n (%) | 38 (7) | 37 (7) | 31 (5) | 49 (6) | 0.72 |
| Spine disease, n (%) | 303 (47) | 277 (43) | 309 (48) | 349 (54) | <0.001 |
| Osteoporosis, n (%) | 117 (18) | 137 (21) | 131 (20) | 170 (26) | 0.003 |
Total n for erosive disease = 1903; n for nodules = 1804. COPD: chronic obstructive pulmonary disease; DAS28: DAS in 28 Joints; hsCRP: high sensitivity CRP; MD-HAQ: Multidimensional Health Assessment Questionnaire; IQR: interquartile range; RDCI: Rheumatic Disease Comorbidity Index; TNFi: TNF inhibitor.
Fig. 1.
Adiponectin levels by weight and percentage change from maximum BMI, adjusting for age, sex and race
Higher leptin levels were associated with higher BMI, less weight loss from maximum BMI, lower rates of smoking, higher RDCI, and higher rates of a number of individual comorbidities including diabetes, hypertension, hyperlipidaemia, osteoarthritis, spine disease, liver disease, malignancy, venous thrombosis, heart failure and COPD/asthma (Table 2). In contrast to adiponectin, higher leptin levels were associated with a lower prevalence of RF seropositivity and higher MD-HAQ scores. Weight change from maximum was not associated with leptin independent of current BMI (data not shown, all P > 0.05).
Table 2.
Enrolment characteristics by leptin quartile
| Quartile 1 | Quartile 2 | Quartile 3 | Quartile 4 | P | |
|---|---|---|---|---|---|
| n | 646 | 646 | 646 | 646 | |
| Age, mean (s.d.), years | 71.5 (10.7) | 71.6 (10.7) | 71.9 (10.5) | 71.7 (9.8) | 0.94 |
| Leptin, median (IQR), ng/ml | 2 (1, 3) | 6 (5, 8) | 14 (11, 18) | 35 (27, 56) | n/a |
| Female, n (%) | 75 (12) | 75 (12) | 75 (12) | 75 (12) | 1 |
| Black, n (%) | 100 (15) | 94 (15) | 81 (13) | 130 (20) | 0.002 |
| Current smoker, n (%) | 229 (35) | 167 (26) | 126 (20) | 102 (16) | <0.001 |
| BMI, mean (s.d.), kg/m2 | 24.6 (4.0) | 27.4 (3.9) | 30.1 (4.6) | 33.2 (6.3) | <0.001 |
| % change max BMI, mean (s.d.) | 13.2 (16.2) | 11.0 (15.3) | 9.7 (13.1) | 8.4 (13.6) | <0.001 |
| RA characteristics | |||||
| DAS28, mean (s.d.) | 3.6 (1.5) | 3.7 (1.4) | 3.5 (1.4) | 3.6 (1.4) | 0.07 |
| MD-HAQ, mean (s.d.) | 0.91 (0.65) | 0.87 (0.62) | 0.84 (0.63) | 0.95 (0.63) | 0.03 |
| Erosive disease, n (%) | 257 (52) | 249 (52) | 247 (51) | 225 (50) | 0.88 |
| Nodules, n (%) | 164 (37) | 171 (38) | 170 (36) | 137 (30) | 0.06 |
| RF Positive, n (%) | 408 (75) | 462 (79) | 487 (80) | 472 (74) | 0.05 |
| ACPA positive, n (%) | 443 (69) | 455 (70) | 471 (73) | 467 (72) | 0.27 |
| Disease duration, median (IQR), years | 7.3 (2.1, 17.0) | 8.1 (2.4, 17.5) | 6.9 (2.1, 15.7) | 8.2 (2.8, 17.2) | 0.17 |
| hsCRP, median (IQR), mg/dl | 4.9 (1.8, 14) | 5.4 (2.0, 12.8) | 5.1 (2.1, 10.7) | 5.8 (2.5, 12.4) | 0.29 |
| Methotrexate, n (%) | 328 (51) | 341 (53) | 325 (50) | 340 (53) | 0.73 |
| Glucocorticoids, n (%) | 214 (33) | 209 (32) | 225 (35) | 236 (37) | 0.38 |
| TNFi therapy, n (%) | 168 (26) | 163 (25) | 174 (27) | 149 (23) | 0.43 |
| HCQ, n (%) | 166 (26) | 204 (32) | 220 (34) | 251 (39) | <0.001 |
| Enrolled >2010, n (%) | 363 (56) | 336 (52) | 307 (48) | 270 (42) | <0.001 |
| Comorbidity | |||||
| RDCI, median (IQR) | 3 (2, 4) | 3 (2, 5) | 4 (2, 5) | 4 (2, 5) | <0.001 |
| COPD/asthma, n (%) | 207 (32) | 206 (32) | 233 (36) | 257 (40) | 0.007 |
| Diabetes, n (%) | 159 (25) | 195 (30) | 225 (35) | 277 (43) | <0.001 |
| Osteoarthritis, n (%) | 526 (81) | 520 (81) | 545 (84) | 567 (88) | 0.001 |
| Hypertension, n (%) | 400 (62) | 432 (67) | 490 (76) | 518 (80) | <0.001 |
| Hyperlipidaemia, n (%) | 449 (73) | 501 (79) | 550 (86) | 568 (88) | <0.001 |
| Coronary artery disease, n (%) | 151 (23) | 170 (26) | 194 (30) | 210 (32) | 0.001 |
| Cerebrovascular disease, n (%) | 45 (7) | 43 (7) | 53 (8) | 68 (11) | 0.04 |
| Other vascular disease, n (%) | 77 (12) | 75 (12) | 90 (14) | 104 (16) | 0.06 |
| Heart failure, n (%) | 51 (8) | 62 (10) | 72 (11) | 120 (19) | <0.001 |
| Venous thrombosis, n (%) | 48 (7) | 41 (6) | 71 (11) | 81 (13) | <0.001 |
| Liver disorder, n (%) | 85 (13) | 80 (12) | 95 (15) | 121 (19) | 0.006 |
| Any neoplasm, n (%) | 306 (47) | 290 (45) | 325 (50) | 365 (57) | <0.001 |
| Skin cancer, n (%) | 72 (11) | 66 (10) | 67 (10) | 70 (11) | 0.95 |
| Lung cancer, n (%) | 15 (2) | 11 (2) | 16 (2) | 8 (1) | 0.34 |
| Prostate cancer, n (%) | 49 (8) | 39 (6) | 37 (6) | 52 (8) | 0.37 |
| Spine disease, n (%) | 277 (43) | 301 (47) | 309 (48) | 331 (54) | <0.001 |
| Osteoporosis, n (%) | 147 (23) | 129 (20) | 128 (20) | 151 (23) | 0.26 |
Total n for erosive disease = 1903; n for nodules = 1804. COPD: chronic obstructive pulmonary disease; DAS28: DAS in 28 joints; hsCRP: high sensitivity CRP; IQR: interquartile range; MD-HAQ: Multidimensional Health Assessment Questionnaire; RDCI: Rheumatic Disease Comorbidity Index; TNFi: TNF inhibitor.
Associations between adipokines and inflammatory cytokines
The highest quartile of adiponectin was associated with higher levels of circulating inflammatory cytokines with simultaneous trends of higher cytokine expression in the lowest quartile of adiponectin, rendering a J-shaped curve (Fig. 2A). Similar trends were not observed for leptin where numerically lower cytokine levels were generally observed in higher quartiles of leptin (Fig. 2B). Similar relationships were observed in models adjusted for age, sex, race, BMI, DAS28 and calendar date (not shown).
Fig. 2.
Line plots showing inflammatory cytokine levels by adiponectin quartile (A) and leptin quartile (B) (unadjusted)
*P < 0.05 compared with third quartile; †P < 0.05 compared with second quartile; ‡P < 0.05 compared with first quartile.
Associations between adipokines and mortality
There were 2546 participants with follow-up data available included in longitudinal analyses. Of these, 960 participants died during 19 178 person-years of follow-up. The most common causes of death were cardiovascular disease (N = 215), followed by malignancy (N = 154) and lung-disease (N = 118). Cause of death was unavailable for 168 deaths that occurred after December 2017. In adjusted models, higher adiponectin levels were independently associated with higher all-cause mortality in a dose-dependent manner (Fig. 3A). The highest quartile was associated with a 46% increase in the risk of death [hazard ratio (HR): 1.46 (95% CI: 1.11, 1.93), P = 0.009]. Higher adiponectin levels were also associated with a substantially higher risk of cardiovascular death; the highest quartile was associated with an 85% greater risk [HR: 1.85 (95% CI: 1.24, 2.75), P = 0.003]. There were no significant associations with cancer or respiratory mortality (Fig. 3A), though the number of events was lower for these analyses.
Fig. 3.
All-cause and cause-specific mortality in adjusted models by adiponectin quartile (A) and leptin quartile (B)
Adjusted for: age, age2, sex, race, BMI, smoking, disease duration category, TNFi use, methotrexate use, prednisone use, hydroxychloroquine use, ACPA status, RF status, DAS28, MD-HAQ category, RDCI, erosive disease, hypertension, diabetes, hyperlipidaemia, heart failure, coronary artery disease, cerebrovascular disease, other vascular disease, venous thrombosis, osteoarthritis, osteoporosis, spine disease, liver disorder, any neoplasm, skin cancer, date of enrolment and date of enrollment2. DAS28: DAS in 28 joints; MD-HAQ: Multidimensional Health Assessment Questionnaire; RDCI: Rheumatic Disease Comorbidity Index.
Higher quartiles of leptin were also associated with a higher risk of all-cause mortality, with the highest quartile demonstrating a 25% increase in risk compared with the first quartile [HR: 1.25 (95% CI: 1.02, 1.52), P = 0.03] (Fig. 3B). In contrast to adiponectin, higher quartiles of leptin were not significantly associated with the risk of cardiovascular-related mortality but were associated with cancer-related causes of death.
When combined in a single model, both adiponectin and leptin levels were independently associated with overall mortality (Table 3). Based on the independent contributions of these adipokines in this combined model, those who were in the highest quartile of both leptin and adiponectin (n = 191) would be expected to have a 73% increase in risk of death compared with those in the lowest quartile of each [HR: 1.73 (95% CI: 1.22, 2.44), P = 0.002]. Subgroup analyses did not identify significant interactions between adipokines and age, sex and disease duration (data not shown). Results were similar when adjusting further for percentage change in BMI from maximum (Supplementary Table S1, available at Rheumatology online) and inflammatory cytokines (Supplementary Table S2, available at Rheumatology online).
Table 3.
Cox proportional hazards model demonstrating associations between adiponectin and leptin with all-cause mortality after adjustment for potential confounders; both are included in the same model
| Survival, median days | Adjusted risk of death |
||
|---|---|---|---|
| (n = 2546; deaths = 960) | |||
| aHR (95% CI) | P | ||
| Adiponectin | |||
| Q1 | 5564 | 1 (reference) | — |
| Q2 | 4853 | 1.15 (1.04, 1.28) | 0.006 |
| Q3 | 4294 | 1.36 (1.12, 1.65) | 0.002 |
| Q4 | 4350 | 1.44 (1.08, 1.93) | 0.01 |
| (P for trend = 0.01) | |||
| Leptin | |||
| Q1 | 4614 | 1 (reference) | |
| Q2 | 4674 | 1.25 (1.15, 1.35) | <0.001 |
| Q3 | 5169 | 1.08 (0.96, 1.21) | 0.18 |
| Q4 | 4684 | 1.20 (0.98, 1.47) | 0.08 |
| (P for trend = 0.18) | |||
Adjusted for: age, sex, race, BMI, smoking, disease duration category, TNFi use, methotrexate use, prednisone use, hydroxychloroquine use, ACPA status, RF status, DAS28, MD-HAQ category, RDCI, erosive disease, hypertension, diabetes, hyperlipidaemia, heart failure, coronary artery disease, cerebrovascular disease, other vascular disease, venous thrombosis, osteoarthritis, osteoporosis, spine disease, liver disorder, any neoplasm, skin cancer, date of enrolment and date of enrollment2. aHR: adjusted hazard ratio; DAS28: DAS in 28 joints; MD-HAQ: Multidimensional Health Assessment Questionnaire; RDCI: Rheumatic Disease Comorbidity Index.
Discussion
This study demonstrated associations between circulating adiponectin levels and ageing, weight loss, elevations in circulating inflammatory cytokines, and more severe RA features such as seropositivity and radiographic damage. In addition, high adiponectin levels were strongly and independently associated with greater mortality, an association primarily driven by high cardiovascular mortality. Leptin was associated with obesity and excess comorbidity and was also independently associated with greater mortality, primarily cancer-related. Overall these observations support adipokines as biomarkers of metabolic health that may provide value in evaluating long-term risks in patients with RA and perhaps other chronic inflammatory diseases. Since patients with RA are at higher risk of early death overall, biomarkers that may identify individuals at greater long-term risk are of interest.
Prior studies in RA have demonstrated associations between adiponectin and radiographic damage progression, suggesting that it may be associated with a more severe disease phenotype [16, 33]. While the current study did not demonstrate significant associations with clinical disease activity (i.e. DAS28 values), it identified strong associations between adiponectin and radiographic damage, seropositivity, longer disease duration, prednisone use and circulating inflammatory cytokines. Overall, these data support the hypothesis that elevations in adiponectin may identify patients with an altered metabolic profile perhaps as the result of a more severe inflammatory phenotype. An alteration in metabolic profile may represent a cause or consequence of RA disease mechanisms, an area that will require further study. Adiponectin is known to be increased in the setting of weight loss and starvation, and is thought to drive metabolic changes that promote the transition of energy usage to lipid oxidation. A transition to lipid oxidation is a potentially important feature of sarcopenia and cachexia. Indeed, prior studies have also suggested associations between adiponectin and low lean mass in patients with RA, suggesting a possible association with rheumatoid cachexia [15, 34].
Our findings importantly extend observations from other chronic disease settings and among older adults where adiponectin elevations have been associated with premature mortality [12, 13, 35]. In this study, higher adiponectin levels (in the highest quartile) were independently associated with an 85% increase in the risk of cardiovascular death. This apparently paradoxical association, recognizing that adiponectin levels are increased with lower BMI, has been observed in other high-risk patient subgroups including those with diabetes [36]. These findings suggest that adiponectin may be a surrogate biomarker in disease groups for pathological metabolic alterations.
Leptin was primarily associated with cancer-related mortality, an observation consistent with prior evidence of its pro-tumorigenic effects [37]. Specifically, leptin has been linked with biological processes that are well recognized to promote tumour progression such as angiogenesis, metastases and survival/resistance to cellular apoptosis. However, this observational study is unable to determine whether leptin directly promotes the development of cancer in this population or is associated with other processes that are, themselves, predictive. Overall, these data support the assessment of adipokines with the goal of improving risk prediction in these high-risk groups and suggest a shared mechanism of association in multiple populations at risk of early death. Future study aimed at evaluating associations between leptin and cancer incidence may help clarify these relationships.
This study also identified associations between adiponectin and circulating inflammatory cytokines including IL‐1β, IL‐6, IFN-γ and TNF-α, which are important in RA and have been implicated in the development of cachexia [38]. This observation was seen despite a lack of association between adiponectin and clinical disease activity. This observation is novel and, when coupled with previous observations from other groups, supports a relationship between systemic inflammation and adiponectin expression. One prior study demonstrated positive relationships between adiponectin and inflammatory cytokines in individuals at high risk of developing RA [39]. Chronic systemic inflammation may play a role in long-term risks of RA independent of clinical disease activity, particularly among older adults. While this observational study is unable to establish the cause–effect relationship, the associations identified here support the hypothesis that inflammation-related metabolic changes occurring in the context of severe RA, ageing or severe comorbidity might influence adiponectin levels, perhaps through affecting metabolic pathways. While adiponectin has largely been considered to be an ‘anti-inflammatory’ molecule, some have also proposed that adiponectin might play a direct pro-inflammatory role in select circumstances [40].
Leptin levels are known to be strongly associated with body composition, in particular total and regional adiposity. It is therefore critical that associations between adipokines and long-term outcomes be considered independent of BMI or other related anthropometric measures. In the context of adjustment for BMI, high leptin levels imply both higher fat mass and lower lean mass and thus identify those with unfavourable body composition [15]. This study was not able to directly assess lean and fat mass, measures that may also provide important prognostic information about an individual’s metabolic health. Thus, whether leptin is associated with adverse outcomes through its association with altered body composition remains uncertain. In contrast to adiponectin, leptin levels were inversely associated with IL-6 and IL-1β and were not associated with other inflammatory cytokines.
Overall the associations between adipokines and long-term risks in RA suggest that biomarkers of metabolic health may aid in risk prediction, at least with regard to mortality. Further study to evaluate specific long-term risks including outcomes such as cardiovascular events and cancer events are of interest. The question of whether and how to include adipokines in risk prediction tools is of interest and will require further study. Leptin, measured as part of a commercial multi-analyte biomarker panel, is included in other, recently validated risk prediction tools for cardiovascular events, though its specific contribution to cardiovascular disease risk in RA remains unknown [41].
The current study assessed adipokines at enrolment only. Presumably, longitudinal data might have captured additional changes in these measures that might have improved risk prediction. Future study evaluating how changes in these measures might further aid in prognosis would be of interest. In the absence of direct measures of body composition such as visceral adiposity, it is possible that the associations of adipokines with the outcomes measured in this study could relate to unmeasured factors, particularly those related to metabolic obesity and sarcopenia. Samples were stored for up to 15 years, though adiponectin and leptin are both considered stable molecules over multiple freeze–thaw cycles. Finally, while our population was nationally representative of the VA, the population has a high proportion of male subjects. Thus, despite a lack of statistical effect modification between adipokines and sex, the observations herein may not be fully generalizable to women with RA since we did not have sufficient statistical power to perform analyses in women alone.
Strengths of the study include the large sample of patients, comprehensive covariable identification and adjustment, long-term follow-up, and sufficient power to conduct important subgroup analyses and evaluate cause-specific mortality.
In summary, circulating adipokines are associated with severe disease features and comorbidity, and are independent predictors of death in patients with RA. Although requiring additional validation, these results suggest that the assessment of adipokines in both research and clinical settings might help to identify patients at long-term risk of adverse health outcomes. Further study is needed to understand the mechanisms underpinning the associations of adipokines with adverse outcomes, perhaps through identifying metabolic derangements related to severe disease, comorbidity or accelerated ageing that characterize RA.
Supplementary Material
Acknowledgements
J.F.B. would like to acknowledge funding through a Veterans Affairs Clinical Science Research & Development Career Merit Award (I01 CX001703). The contents of this work do not represent the views of the Department of the Veterans Affairs or the United States Government.
Funding: This work was supported by Veterans Affairs VA Merit Awards (I01 CX001703 [J.F.B.], I01 BX0046000 [T.R.M.]), grants from NIAAA (R25AA020818 [T.R.M.]), NIGMS (U54GM115458 [T.R.M.]) and NIAMS (P50AR60772 [T.R.M.]) a VA Career Development Award (IK2 CX002203 [B.R.E.]). and by a Rheumatology Research Foundation Scientist Development Award [K.W.].
Disclosure statement: J.F.B. has received consulting fees from Bristol-Myers Squibb, Pfizer, Gilead, CorEvitas and Burns-White, LLC. B.R.E. has received consulting fees from Boehringer-Ingelheim. T.R.M. has received consulting fees from Pfizer, Horizon Therapeutics and Gilead and has received research support from Bristol-Myers Squibb and Horizon Therapeutics. All other authors have nothing to disclose.
Contributor Information
Joshua F Baker, Philadelphia VA Medical Center; Perelman School of Medicine; Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
Bryant R England, Medicine Service, VA Nebraska-Western Iowa Health Care System and Department of Internal Medicine, Division of Rheumatology & Immunology, University of Nebraska Medical Center, Omaha, NE.
Michael D George, Perelman School of Medicine; Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
Katherine Wysham, VA Puget Sound Healthcare System; University of Washington School of Medicine, Seattle, WA.
Tate Johnson, Medicine Service, VA Nebraska-Western Iowa Health Care System and Department of Internal Medicine, Division of Rheumatology & Immunology, University of Nebraska Medical Center, Omaha, NE.
Gary Kunkel, Salt Lake City VA Medical Center and University of Utah, Salt Lake City, UT.
Brian Sauer, Salt Lake City VA Medical Center and University of Utah, Salt Lake City, UT.
Bartlett C Hamilton, Medicine Service, VA Nebraska-Western Iowa Health Care System and Department of Internal Medicine, Division of Rheumatology & Immunology, University of Nebraska Medical Center, Omaha, NE.
Carlos D Hunter, Medicine Service, VA Nebraska-Western Iowa Health Care System and Department of Internal Medicine, Division of Rheumatology & Immunology, University of Nebraska Medical Center, Omaha, NE.
Michael J Duryee, Medicine Service, VA Nebraska-Western Iowa Health Care System and Department of Internal Medicine, Division of Rheumatology & Immunology, University of Nebraska Medical Center, Omaha, NE.
Paul Monach, VA Boston Healthcare System, Boston, MA.
Gail Kerr, Washington DC VA Medical Center, Washington, DC.
Andreas Reimold, VA North Texas Healthcare System, Dallas, TX, USA.
Rui Xiao, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
Geoff M Thiele, Medicine Service, VA Nebraska-Western Iowa Health Care System and Department of Internal Medicine, Division of Rheumatology & Immunology, University of Nebraska Medical Center, Omaha, NE.
Ted R Mikuls, Medicine Service, VA Nebraska-Western Iowa Health Care System and Department of Internal Medicine, Division of Rheumatology & Immunology, University of Nebraska Medical Center, Omaha, NE.
Data availability statement
The data that support the findings of this study might be made available with permissions from the VA Office of Research & Development and with approval of the VARA steering committee. Restrictions apply to the availability of these data. Data may be made available with appropriate third party permissions.
Supplementary data
Supplementary data are available at Rheumatology online.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data that support the findings of this study might be made available with permissions from the VA Office of Research & Development and with approval of the VARA steering committee. Restrictions apply to the availability of these data. Data may be made available with appropriate third party permissions.