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Recommendations Summary

COPD: Monitor and Evaluate Energy Intake and Body Weight for Energy Needs 2019

Click here to see the explanation of recommendation ratings (Strong, Fair, Weak, Consensus, Insufficient Evidence) and labels (Imperative or Conditional). To see more detail on the evidence from which the following recommendations were drawn, use the hyperlinks in the Supporting Evidence Section below.


  • Recommendation(s)

    COPD: Monitor and Evaluate Energy Intake and Body Weight for Energy Needs

    For adults with COPD, the RDN should routinely monitor and evaluate body weight (BW) status and energy intake and adjust the estimated calorie prescription to achieve or maintain an optimal weight. Evidence suggests an association between BW status and both mortality and lung function in adults with COPD. Strong evidence indicates that the lowest BMI groups had higher mortality rates when compared to higher BMI groups. Furthermore, a BMI classification of approximately 25.0kg/m2 to 29.99kg/m2 appeared to lower risk of mortality when compared to both higher and lower BMI classifications. In primarily unadjusted results, fair evidence suggests that BMI was positively associated with FEV1 percentage predicted and FEV1/forced vital capacity (FVC). An increasing BMI was also shown to reduce longitudinal declines in these measures over time. Fair evidence was also found showing improvement in dyspnea scores with higher energy intakes.

    Rating: Fair
    Imperative

    • Risks/Harms of Implementing This Recommendation

      There are no potential risks or harms associated with the application of this recommendation.

    • Conditions of Application

      • Over time, the monitoring of BW against energy intake is probably the most meaningful expression of energy requirement in COPD. If adverse changes in body weight or composition are occurring, equal attention should be paid to the possibility that the patient is not consuming the target intake or that the target intake is not correct.
      • An optimal body weight goal should be individualized for each patient.

    • Potential Costs Associated with Application

      Costs may include expenses related to medical nutrition therapy (MNT) visits from an RDN.

    • Recommendation Narrative

      Energy Intake

      A total of nine studies were included in the evidence analysis supporting the recommendation:

      • Four randomized controlled trials: One positive-quality (Sugawara et al, 2012),  three neutral-quality (Førli and Boe, 2005; Planas et al, 2005; Weekes et al, 2009)
      • Five cross-sectional studies: Three neutral-quality (Lee et al, 2013; Renvall et al, 2009; Yazdanpanah et al, 2010) and two negative-quality (Benton et al 2010; Selvi et al, 2014). 

      There was improvement in dyspnea scores with higher energy intakes, with less robust evidence supporting a beneficial relationship with functional status, healthcare utilization or duration of illness.

      • Respiratory symptoms (three studies): All three studies found significant associations with dyspnea scores (Lee et al, 2013; Sugawara et al, 2012; Weekes et al, 2009)
      • Functional status (one study): The study found a significant association with activities of daily living scores (Weekes et al, 2009)
      • Healthcare utilization (one study): The study found a significant association with number of infections requiring antibiotics (Forli and Boe, 2009).
      • Duration of illness (one study): The study found a significant association with duration of disease in years (Selvi et al, 2014).

      The findings for the impact of energy intake on exacerbations, quality of life (QoL), weight status and body composition were mixed, with the majority supportive of an association.

      • Exacerbations (two studies): One study found a significant association with number of emergency room (ER) visits due to acute exacerbations (AEs) (Lee et al, 2013). One study did not find a association with number of ER visits due to AEs with energy intakes of either 1.7 x or 1.3 x Harris-Benedict Equation (HBE) (Planas et al, 2005). 
      • QoL (three studies): All three studies found significant associations of Chronic Respiratory Disease Questionnaire (CRQ) scores with energy intakes of 1.3 x HBE (Planas et al, 2005), CRQ scores (Sugawara et al, 2012) and St. George Respiratory Quotient (SGRQ) scores (Weekes et al, 2009). One study did not find significant associations for CRQ scores with energy intakes of 1.7 x HBE (Planas et al, 2005).
      • Weight status (seven studies)
        • Six studies found significant associations
          • Weight gain with energy intakes of 1.7 x HBE (Planas et al, 2005)
          • Percentage IBW (Sugawara et al, 2012)
          • Weight gain (Forli and Boe, 2005; Sugawara et al, 2012; Weekes et al, 2009)
          • Body mass index (BMI) (Lee et al, 2013; Renvall et al, 2009).
        • Two studies did not find associations
          • BMI (Benton et al, 2010)
          • BW with energy intakes of 1.3 x HBE (Planas et al, 2005).
      • Body composition (three studies): All three studies found significant associations with fat mass (FM), triceps skinfold (TSF) and fat-free mass index (FFMI) with energy intakes of 1.7 x HBE (Planas et al, 2005), FM, fat mass index (Sugawara et al, 2012) and mid-arm circumference and sum of four skinfold thickness measurements (Weekes et al, 2009). All three studies also did not find associations with mid-arm muscle circumference (Weekes et al, 2009), FFMI and arm circumference (Sugawara et al, 2012) or FM, TSF and FFMI with energy intakes of 1.3 x HBE (Planas et al, 2005).

      The evidence for a relationship between energy intake and lung function, systemic inflammation or exercise capacity was inconsistent.

      • Lung function (five studies): Two studies found significant associations in forced vital capacity (FVC) and sniff pressure (Weekes et al, 2009) and Pmax inspiratory (PImax) (Sugawara et al, 2012). Three studies did not find associations in FEV in one second (FEV1) (Lee et al, 2013; Yazdanpanah et al, 2010), FEV1 with energy intakes of either 1.7 x or 1.3 x HBE (Planas et al, 2005), FVC,  FEV1/FVC and vital capacity (Yazdanpanah et al, 2010), PImax and Pmax expiratory (PEmax) (Weekes et al, 2009) or PEmax (Sugawara et al, 2012). 
      • Systemic inflammation (one study): The study found a significant association with IL-6, but not CRP (Sugawara et al, 2012)
      • Exercise capacity (five studies): Three studies found significant associations with upper and lower body strength (Benton et al, 2010) and six-minute walking distance (Benton et al, 2010; Lee et al, 2013; Sugawara et al, 2012). Two studies did not find associations: Handgrip strength (HGS) with energy intakes of either 1.7 x or 1.3 x HBE (Planas et al, 2005) and HGS (Weekes et al, 2009).

      * For Sugawara et al, 2012, the workgroup considered the between-group analysis only in the conclusion statement. 

      Energy Needs

      A total of five studies were included in the evidence analysis supporting the recommendation: 

      • Three cross-sectional studies: One positive-quality (Slinde et al, 2011), one neutral-quality (Farooqi et al, 2015),  one negative-quality (Ramos et al, 2016) 
      • Two diagnostic, validity or reliability studies: Both neutral-quality (Nordenson et al, 2010; Slinde et al, 2008).

      Predictive equations for estimating RMR: A total of 11 equations were tested for validity in predicting RMR in adults with COPD. Two of these [Moore & Angelillo (MAE), Nordenson] were equations developed specifically for COPD patients, while the other equations were developed for healthy adults [Harris-Benedict (HBE), Mifflin St. Jeor (MSJE), Westerterp, de Oliveira, Owen and four variations of Food and Agriculture Organization of the United Nations/World Health Organization/United Nations University (FAO/WHO/UNU)], which were WHO (including height), WHO (omitting height), Nordic Nutrition Recommendation (NNRE) and the Schofield equation. In two of the studies (Farooqi et al, 2015; Slinde et al, 2011) these equations were evaluated as a starting point for estimating total energy expenditure (TEE).

      • Accuracy: Four of the 11 equations for predicting RMR were tested for accuracy, but only in one study (Slinde et al, 2008). Slinde found that the Westerterp equation yielded an accuracy rate of 68%, followed by the WHO (including height) equation (63%) and HBE (61%). The MAE had the lowest accuracy rate (51%). 
      • Limit of agreement (LOA): LOA was reported for nine equations. LOA as a percentage of the mean between measured and predicted RMR were -23% to +18% for the Westerterp equation (Slinde et al, 2008), about -28% to +10% for the MAE (in this case the negative value is overestimation) (Slinde et al, 2008), less than 25% for WHO (including height) equation and HBE, -45% to +40% for the de Oliveira equation (Ramos et al, 2016), -53% to +33% for the Owen equation (Ramos et al, 2016), -65% to +13% for the MSJE (Ramos et al, 2016), ±19% for the Nordenson equation (Nordenson et al, 2010) and for WHO (omitting height) equation, +18% (Slinde et al, 2011), to as wide as -66% to +24% (Ramos et al, 2016). 
      • Bias: Evidence suggests that the only unbiased estimator of RMR in adults with COPD was the de Oliveira equation. Four other equations were probably1 unbiased. These included the HBE and Westerterp equation, which might overestimate RMR and the Nordenson and WHO (including height) equations, which might underestimate RMR. Two equations (Owen and MSJE) were biased toward underestimation of RMR. The remaining four equations were probably1 biased toward overestimation of RMR [MAE, NNRE, Schofield and WHO (omitting height) equations]. Evidence for the WHO (omitting height) equation suggests it might also underestimate RMR in adults with COPD.

      1Bias was not reported directly in these studies but is inferred from mean predicted RMR compared to mean measured RMR.

      • Thus, the WHO (including height) equation and HBE seem to be equivalent to one another by the parameters of accuracy rate and LOA. If body composition measurements are known, then Westerterp is a better choice for calculation of RMR, because it yields a higher accuracy rate.

      Predictive equations for estimating TEE: Two studies (Farooqi et al, 2015; Slinde et al, 2011) tested three methods for calculating TEE in adults with COPD. In the first method, a pedometer was used to estimate physical activity to compare against doubly labeled water (DLW) (Farooqi et al, 2015). In this method, a multiplier to RMR was assigned based on the number of steps taken and the multiplier was applied to six RMR equations [WHO (omitting height), Schofield, HBE, MAE, NNRE, Nordenson)]. In the other study, motion and position sensors were used as the criterion method to measure TEE. For prediction purposes, two methods were used. The first was a simple ratio of 30kcal per kg body weight (BW). The second was to compute RMR using the WHO (omitting height) equation and then multiplying by 1.7 to calculate TEE (Slinde et al, 2011).

      • Accuracy: Only one of the two studies reported accuracy rate (Farooqi et al, 2015). Accuracy rate for WHO (omitting height) equation x PAL was of 67%, compared to 56% for Schofield equation x PAL, 50% for HBE x PAL, MAE x PAL, and NNRE x PAL, and 21% for the Nordenson equation x PAL
      • LOA: The only estimation methods for which LOA was computed were 30kcal per kg and WHO (omitting height) x 1.7) (Slinde et al, 2011). LOA for both of these methods was 956kcal per day (-48%; +48% of the mean between predicted and measured TEE). 
      • Bias: The WHO (omitting height) x PAL and the MAE x PAL were probably2 unbiased, while the Schofield x PAL, HBE x PAL, NNRE x PAL and Nordenson x PAL equations were probably2 biased (toward underestimation) (Farooqi et al, 2016). An estimate of 30kcal per kg BW yielded a mean difference from measured TEE of 71kcal per day and so was probably unbiased2, whereas another predictive method of WHO (omitting height) x 1.7 probably2 was biased toward overestimation, based on a mean difference from measured TEE of 319kcal per day (Slinde et al, 2011).  

      2Bias was not reported directly in these studies but is inferred from mean predicted TEE compared to mean measured TEE.

      Body Weight

      A total of 22 studies were included in the evidence analysis supporting the recommendation:

      • 11 prospective cohort studies: Three positive-quality (Hallin et al, 2007; Schols et al, 2005; Tsimogianni et al, 2009) and eight neutral-quality (Abston et al, 2017; Baccioglu et al, 2014; Galesanu et al, 2014; Koul et al, 2017; Piquet et al, 2013; Pothirat et al, 2007; Qiu et al, 2009; Rutten et al, 2013)
      • 10 neutral-quality retrospective cohort studies (Jiang et al, 2017; Lainscak et al, 2011; Lim et al, 2017; Marti et al, 2006; O'Donnell et al, 2011; Ranieri et al, 2008; Slinde et al, 2005; Uh et al, 2011; Yamauchi et al, 2014; Zapatero et al, 2013)
      • One negative-quality case-control study (Dimov et al, 2013).

      Body weight and lung function outcomes

      All studies using FEV1 percentage predicted and FEV1/FVC as an outcome found a positive association with BMI or BMI category. In addition,  one longitudinal study showing increasing BMI reduced declines in these measures over time. While these studies appear to indicate that increasing BMI is associated with improvements in FEV1 percentage predicted and FEV1/FVC, most results did not include adjustment for relevant confounders and should be interpreted with caution. Results for other lung function measures (FEV1, FVC, FVC percentage predicted) were either mixed and not significant.

      • FEV1 stratified into quintiles and BMI (one study): One study (Abston et al, 2017) found BMI was not associated with FEV1 in the overall group or in any of the FEV1 quintiles. In each of the FEV1 quintiles, FVC percentage predicted showed inverse associations with BMI, while FEV1/FVC showed a positive association with BMI. 
      • BMI stratified into quartiles and FEV1 (one study): One study (Lainscak et al, 2011) found a positive association between FEV1 (ml per second) and BMI divided into quartiles in subjects hospitalized for an acute exacerbation of COPD (AECOPD), with FEV1 increasing as BMI category increased
      • BMI and lung function (non-adjusted) (six studies): Six studies (Dimov et al, 2013; Galesanu et al, 2014; Hallin et al, 2007; Lim et al, 2017; O'Donnell et al, 2011; Qiu et al, 2009) found BMI was positively associated with FEV1 percentage predicted in analyses that were unadjusted for confounding variables. BMI was positively correlated with FEV1/FVC in two studies (O’Donnell et al; Qiu et al). In addition, Qiu found a higher BMI was also associated with a lower decline in FEV1/FVC and FEV1 percentage predicted over time. Two studies (Galesanu et al; O’Donnell et al) found that BMI was not associated with FVC and FVC percentage predicted. O’Donnell found BMI was not associated with FVC percentage predicted. Galesanu found NS association between FEV1 and BMI, while Lim found FEV1 to be highest in subjects classified as OB by either the WHO or Asian-Pacific methods of BMI. FEV1 was not reported in four studies (Dimov et al; Hallin et al; O’Donnell et al; Qiu et al).

      Body weight and mortality outcomes

      The majority of studies comparing BMI (kg/m2) between survivors and non-survivors with COPD found survivors had a significantly higher BMI. Among studies comparing mortality rates between BMI categories, a lower risk of mortality was found as BMI classification increased. Likewise, those in the lowest BMI groups had higher mortality rates when compared to those in higher BMI groups. In studies evaluating BMI as a predictor of mortality, those in the lowest BMI categories consistently showed higher mortality risk when compared to those in higher BMI classifications. While increasing body weight appeared to be protective when BMI was evaluated as a continuous variable, studies evaluating BMI as a categorical variable found that a BMI of approximately 25.0 to 29.99 had a lower risk of mortality when compared to both higher and lower BMI classifications. 

      • BMI (mean ±SD) between survivors and non-survivors (five studies): Five studies evaluated the difference in BMI between survivors and non-survivors with COPD. Four studies (Hallin et al, 2007; Marti et al, 2006; Ranieri et al, 2008; Tsimogianni et al, 2009) found significantly higher BMI in survivors vs. non-survivors. One study (Galesanu et al, 2014) found no differences in BMI between the groups.
      • Differences in mortality rates between BMI categories (six studies): Two studies (Abston et al, 2017, Lainscak et al, 2011) classified the BMIs of participants into quintiles or quartiles (respectively) and found a lower risk of mortality as BMI categorization increased. In three studies that were unadjusted for confounding variables (Koul et al, 2017; Pothirat, et al, 2007; Uh et al 2011), two studies found higher mortality in the lowest BMI groups (Koul, Pothirat). Koul found higher mortality two years after AECOPD in the lowest BMI group, compared to the 23-24.9 Group. Pothirat found higher mortality in the lowest BMI group compared to the next higher group (both with severe COPD). Uhl found NS differences in cumulative survival rate between all BMI groups. One study (Zapatero et al, 2013) found that the OB Group (using ICD-9 codes) had a 51% reduction in risk of mortality, compared to the NW Group after adjusting for possible confounders in patients hospitalized for AECOPD.
      • BMI and weight status as a predictor of mortality (11 studies)
        • Five studies evaluated BMI (percentage reference weight) as a continuous variable as a predictor of mortality in subjects with COPD. Three studies (Galesanau et al, 2014; Ranieri et al, 2008; Schols et al, 2005) found BMI was a predictor of mortality in univariate analyses. Two studies (Jiang et al, 2017; Ranieri et al, 2008) reported BMI was a predictor of mortality in adjusted multivariate analysis. A fourth study (Galesanu et al, 2014) found NS association between BMI and mortality in adjusted multivariate analysis. The fifth study (Slinde et al, 2005) that used percentage reference weight did not find an association with mortality.
        • Seven studies evaluated BMI as a categorical variable as a predictor of mortality and adjusting for confounding variables (Hallin et al, 2007; Jiang et al, 2017; Marti et al, 2006; Piquet et al, 2013; Rutten et al, 2013, Tsimogianni et al, 2009; Yamauchi et al, 2014). Mortality risk was higher for subjects in the lowest BMI group (ranging from under 18.5 to 25) when compared to higher BMI groups. Yamauchi found higher all-cause in-hospital mortality in the lowest BMI group and lower mortality in the higher BMI categories, compared to the 18.5-22.9 Group in an Asian population. Hallin found higher risk for two-year mortality after AECOPD in all BMI categories compared to the 25-30 Group. Marti found higher risk for both all-cause and respiratory mortality in the lower BMI groups compared to the 25-29.9 Group and higher risk for all-cause mortality in the 20-24.9 Group compared to the 25-29.9 Groups. Piquet found no difference in risk for mortality between the lowest and 20-25 Group, but a lower risk for mortality 48 months after AECOPD in the higher BMI categories. Rutten found the highest two- and three-year survival in subjects in BMI categories 25 to 29.99 compared to lower groups as well as those above 30. Jiang found a higher risk of mortality as BMI group classification decreased. Tsimogianni found that a BMI group of less than 25 had a higher risk of three-year mortality. 

    • Recommendation Strength Rationale

      One conclusion statement supporting the recommendation is Grade I, Good/Strong and two conclusion statements are Grade II, Fair.

    • Minority Opinions

      None.