title: New resting energy expenditure prediction equations for patients with rheumatoid arthritis.
abstract: OBJECTIVE
  Resting energy expenditure (REE), one of the main components of total energy expenditure, can be measured via indirect calorimetry and/or predicted from equations. The latter may be misleading in RA, as they do not take into account the metabolic alterations occurring in RA. The objectives of this study are to evaluate the accuracy of widely used REE-predictive equations in RA patients against measured REE and to develop RA-specific equations.
  
  
  METHODS
  We assessed REE (via indirect calorimetry and several predictive equations), fat-free mass (FFM; via bioelectrical impedance) and disease activity (CRP) in RA patients and healthy controls. Data from 60 RA patients (experimental group) were used to assess the accuracy of existing REE equations and to develop new equations. The new equations were validated in an independent cross-validation group of 22 RA patients. These two groups were merged and two final equations were developed.
  
  
  RESULTS
  All equations significantly under-predicted measured REE (from 15% to 18.2%, all at P < 0.001) in the RA experimental group, but not in the control group. After both equations demonstrated a high validity in the cross-validation group, the new final REE prediction equations developed from the total RA sample (n = 82) were: Model 1: REE (kcal/day) = 126.1 x FFM(0.638) x CRP(0.045) (R(2) = 0.70) and Model 2: REE (kcal/day) = 598.8 x weight(0.47) x age(-0.29) x CRP(0.066) (R(2) = 0.62).
  
  
  CONCLUSIONS
  The new equations provide an accurate prediction of REE in RA patients and could be used for clinical monitoring of resting metabolism of these patients without the requirement for specialized personnel.
authors.affiliation: School of Sport, Performing Arts and Leisure, University of Wolverhampton, Gorway rd, Walsall Campus, Wolverhampton, WSI 3BD, UK. G.Metsios@wlv.ac.uk
authors.forename: G S
authors.initials: GS
authors.lastname: Metsios
authors.forename: A
authors.initials: A
authors.lastname: Stavropoulos-Kalinoglou
authors.forename: V F
authors.initials: VF
authors.lastname: Panoulas
authors.forename: Y
authors.initials: Y
authors.lastname: Koutedakis
authors.forename: A M
authors.initials: AM
authors.lastname: Nevill
authors.forename: K M J
authors.initials: KM
authors.lastname: Douglas
authors.forename: M
authors.initials: M
authors.lastname: Kita
authors.forename: G D
authors.initials: GD
authors.lastname: Kitas
mesh_terms: D000368:Aged; D000886:Anthropometry / Q000379:methods; D018501:Antirheumatic Agents / Q000627:therapeutic use; D001172:Arthritis, Rheumatoid / Q000097:blood / Q000188:drug therapy / Q000378:metabolism*; D015415:Biomarkers / Q000097:blood; D001835:Body Weight; D002097:C-Reactive Protein / Q000378:metabolism; D002153:Calorimetry, Indirect; D004734:Energy Metabolism; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015203:Reproducibility of Results; D012720:Severity of Illness Index; D014409:Tumor Necrosis Factor-alpha / Q000037:antagonists & inhibitors
chemical_list: D018501:Antirheumatic Agents; D015415:Biomarkers; D014409:Tumor Necrosis Factor-alpha; D002097:C-Reactive Protein