Interaction effect of systemic inflammation and modifiable rheumatoid cachexia risk factors on resting energy expenditure in patients with rheumatoid arthritis. Issue 1 (7th January 2022)

• Record Type: Journal Article
• Authors: Hanaoka, Beatriz Y.; Zhao, Jing; Heitman, Kristen; Khan, Fahad; Jarjour, Wael; Volek, Jeff; Brock, Guy; Gower, Barbara A.
• Is Part Of: JCSM clinical reports. Volume 7:Issue 1(2022)
• Page Start: 12
• Page End: 23
• Publication Date: 2022-01-07
• Subjects: Rheumatoid arthritis -- Rheumatoid cachexia -- Resting energy expenditure -- Insulin resistance -- Systemic inflammation -- Dietary protein; Cachexia -- Periodicals; Muscles -- Aging -- Periodicals; Muscles -- Diseases -- Periodicals; Cachexia; Sarcopenia; Muscular Diseases; Muscles -- physiology; Electronic journals; Periodicals; Periodical; 616.74
• DOI: 10.1002/crt2.45
• Languages: English
• ISSNs: 2521-3555

Abstract

Abstract: Background: In rheumatoid cachexia (RC), high resting energy expenditure (REE) is associated with loss of muscle mass driven by proinflammatory cytokines. The objectives of this study were to investigate parameters associated with RC and the interaction between systemic inflammation and modifiable risk factors for RC on REE. Methods: Thirty‐five rheumatoid arthritis (RA) and 19 non‐RA controls comparable in age/sex/race/body mass index (BMI) underwent measures of REE by indirect calorimetry. Homeostasis model assessment for insulin resistance (HOMA‐IR) and serum interleukin‐6 (IL‐6) were used as parameters of IR and systemic inflammation, respectively. Regression models tested association between REE and dependent variables, including pre‐specified interaction tests involving HOMA‐IR and IL‐6 and dietary intake of protein per weight (PPW) and IL‐6. Results: Rheumatoid arthritis subjects were mostly women (94%) and had a median age of 54 years (50.5, 70) and BMI of 30.5 kg/m 2 (26.1, 36.9). Approximately two‐thirds of RA participants were seropositive, with median disease duration [interquartile range (IQR)] and a DAS‐28 C‐reactive protein [IQR] of 7.83 years [4.89, 18.14] and 1.7 mg/day [1.21, 2.78], respectively. RA participants demonstrated significantly higher levels of HOMA‐IR compared with non‐RA controls ( P = 0.006). Fat‐free mass index (FFMI, P = 0.33), REE ( P = 0.68), IL‐6 ( P = 0.13), and estimates of dietary intake including PPW tertiles ( P = 0.83) were not significantly different between RA and non‐RA. In univariate analyses, REE was positively associated with BMI ( P = <0.001), FFMI and FMI ( P = <0.001), and HOMA‐IR ( P = 0.001), but not with PPW ( P = 0.10). After adjustment for age and FFMI, we did not observe significant associations of HOMA‐IR [ β = 4.66, 95% confidence interval (CI) [−33.16, 42.48], P = 0.80], IL‐6 ( β = −9.45, 95% CI [−25.61, 6.72], P = 0.24), or the interaction between HOMA‐IR and IL‐6 ( β = 6.00, 95% CI [−5.47, 17.46], P = 0.29) with REE. In multiple regression models with IL‐6, PPW, and their interaction term, we observed a significant crossover interaction effect between PPW and IL‐6 on REE. The upper tertile of PPW demonstrated a significant negative correlation between REE and IL‐6 ( β = −19.97, 95% CI [−35.41, −4.54], P = 0.01). The lower tertile of PPW demonstrated a significant positive correlation between REE and IL‐6 ( β = 42.24, 95% CI [4.25, 80.23], P = 0.03). This supports an interplay between PPW and IL‐6 on REE. Animal protein intake, which has more of an anabolic effect compared with vegetable protein, was significantly higher among RA subjects in the higher tertile of PPW compared with RA subjects in the lower tertile of PPW ( P = <0.001). Conclusions: While IR can lead to muscle catabolism, IR was not significantly associated with REE in RA individuals. Higher dietary protein intake could attenuate the effect of systemic inflammation on REE in RA patients.