Comparison of energy estimates in chronic kidney disease using doubly-labelled water

Development and validation of age-specific predictive equations for total energy expenditure and physical activity levels for older adults

BACKGROUND

Predicting energy requirements for older adults is compromised by the underpinning data being extrapolated from younger adults.

OBJECTIVES

To generate and validate new total energy expenditure (TEE) predictive equations specifically for older adults using readily available measures (age, weight, height) and to generate and test new physical activity level (PAL) values derived from 1) reference method of indirect calorimetry and 2) predictive equations in adults aged ≥65 y.

METHODS

TEE derived from "gold standard" methods from n = 1657 (n = 1019 females, age range 65-90 y), was used to generate PAL values. PAL ranged 1.28-2.05 for males and 1.26-2.06 for females. Physical activity (PA) coefficients were also estimated and categorized (inactive to very active) from population means. Nonlinear regression was used to develop prediction equations for estimating TEE. Double cross-validation in a randomized, sex-stratified, age-matched 50:50 split, and leave one out cross-validation were performed. Comparisons were made with existing equations.

RESULTS

Equations predicting TEE using the Institute of Medicine method are as follows: For males, TEE = -5680.17 - 17.50 × age (years) + PA coefficient × (6.96 × weight [kilograms] + 44.21 × height [centimeters]) + 1.13 × resting metabolic rate (RMR) (kilojoule/day). For females, TEE = -5290.72 - 8.38 × age (years) + PA coefficient × (9.77 × weight [kilograms] + 41.51 × height [centimeters]) + 1.05 × RMR (kilojoule/day), where PA coefficient values range from 1 (inactive) to 1.51 (highly active) in males and 1 to 1.44 in females respectively. Predictive performance for TEE from anthropometric variables and population mean PA was moderate with limits of agreement approximately ±30%. This improved to ±20% if PA was adjusted for activity category (inactive, low active, active, and very active). Where RMR was included as a predictor variable, the performance improved further to ±10% with a median absolute prediction error of approximately 4%.

CONCLUSIONS

These new TEE prediction equations require only simple anthropometric data and are accurate and reproducible at a group level while performing better than existing equations. Substantial individual variability in PAL in older adults is the major source of variation when applied at an individual level.

Development and validation of new predictive equations for the resting metabolic rate of older adults aged ≥65 y

BACKGROUND

The aging process alters the resting metabolic rate (RMR), but it still accounts for 50%-70% of the total energy needs. The rising proportion of older adults, especially those over 80 y of age, underpins the need for a simple, rapid method to estimate the energy needs of older adults.

OBJECTIVES

This research aimed to generate and validate new RMR equations specifically for older adults and to report their performance and accuracy.

METHODS

Data were sourced to form an international dataset of adults aged ≥65 y (n = 1686, 38.5% male) where RMR was measured using the reference method of indirect calorimetry. Multiple regression was used to predict RMR from age (y), sex, weight (kg), and height (cm). Double cross-validation in a randomized, sex-stratified, age-matched 50:50 split and leave one out cross-validation were performed. The newly generated prediction equations were compared with the existing commonly used equations.

RESULTS

The new prediction equation for males and females aged ≥65 y had an overall improved performance, albeit marginally, when compared with the existing equations. It is described as follows: RMR (kJ/d) = 31.524 × W (kg) + 25.851 × H (cm) - 24.432 × Age (y) + 486.268 × Sex (M = 1, F = 0) + 530.557. Equations stratified by age (65-79.9 y and >80 y) and sex are also provided. The newly created equation estimates RMR within a population mean prediction bias of ∼50 kJ/d (∼1%) for those aged ≥65 y. Accuracy was reduced in adults aged ≥80 y (∼100 kJ/d, ∼2%) but was still within the clinically acceptable range for both males and females. Limits of agreement indicated a poorer performance at an individual level with 1.96-SD limits of approximately ±25%.

CONCLUSIONS

The new equations, using simple measures of weight, height, and age, improved the accuracy in the prediction of RMR in populations in clinical practice. However, no equation performs optimally at the individual level.

Comparison of total and activity energy expenditure estimates from physical activity questionnaires and doubly labelled water: a systematic review and meta-analysis

Physical activity questionnaires (PAQ) could be suitable tools in free-living people for measures of physical activity, total and activity energy expenditure (TEE and AEE). This meta-analysis was performed to determine valid PAQ for estimating TEE and AEE using doubly labelled water (DLW). We identified data from relevant studies by searching Google Scholar, PubMed and Scopus databases. This revealed thirty-eight studies that had validated PAQ with DLW and reported the mean differences between PAQ and DLW measures of TEE (TEEDLW - TEEPAQ) and AEE (AEEDLW - AEEPAQ). We assessed seventy-eight PAQ consisting of fifty-nine PAQ that assessed TEE and thirty-five PAQ that examined AEE. There was no significant difference between TEEPAQ and TEEDLW with a weighted mean difference of -243·3 and a range of -841·4 to 354·6 kJ/d, and a significant weighted mean difference of AEEDLW - AEE PAQ 414·6 and a range of 78·7-750·5. To determine whether any PAQ was a valid tool for estimating TEE and AEE, we carried out a subgroup analysis by type of PAQ. Only Active-Q, administered in two seasons, and 3-d PA diaries were correlated with TEE by DLW at the population level; however, these two PAQ did not demonstrate an acceptable limit of agreement at individual level. For AEE, no PAQ was correlated with DLW either at the population or at the individual levels. Active-Q and 3-d PA diaries were identified as the only valid PAQ for TEE estimation. Further well-designed studies are needed to verify this result and identify additional valid PAQ.

New predictive equations to estimate resting energy expenditure of non-dialysis dependent chronic kidney disease patients

BACKGROUND

Determination of resting energy expenditure (REE) is an important step for the nutritional and medical care of patients with chronic kidney disease (CKD). Methods such as indirect calorimetry or traditional predictive equations are costly or inaccurate to estimate REE of CKD patients. We aimed to develop and validate predictive equations to estimate the REE of non-dialysis dependent-CKD patients.

METHODS

A database comprising REE measured by indirect calorimetry (mREE) of 170 non-dialysis dependent-CKD patients was used to develop (n = 119) and validate (n = 51) a new REE-predictive equation. Fat free mass (FFM) was assessed by anthropometry and by bioelectrical impedance (BIA).

RESULTS

The multiple regression analysis generated three equations: (1) REE (kcal/day) = 854 + 7.4*Weight + 179*Sex - 3.3*Age + 2.1 *eGFR + 26 (if DM) (R² = 0.424); (2) REE (kcal/day) = 678.3 + 14.07*FFM.ant + 54.8*Sex - 2*Age + 2.5*eGFR + 140.7* (if DM) (R² = 0.449); (3) REE (kcal/day) = 668 + 17.1*FFM.BIA - 2.7*Age - 92.7*Sex + 1.3*eGFR - 152.3 (if DM) (R² = 0.45). The estimated REE (eREE) was not different from the mREE (P = 0.181), a high ICC was found and the mean difference between mREE and eREE was not different from zero for the three equations in the validation group. eREE accuracy between 90 and 110% was observed in 55.3%, 62.5% and 61% of the patients for Eqs. (1), (2) and (3), respectively.

CONCLUSION

The equations showed acceptable accuracy for REE prediction making them a valuable tool to support practitioners to provide more reliable energy recommendations for this group of patients.

Energy expenditure estimates in chronic kidney disease using a novel physical activity questionnaire

BACKGROUND

Physical activity (PA) levels are low in patients with advanced chronic kidney disease (CKD), and associate with increased morbidity and mortality. Reliable tools to assess PA in CKD are scarce. We aimed to develop and validate a novel PA questionnaire for use in CKD (CKD-PAQ).

METHODS

In phase 1, a prototype questionnaire was developed based on the validated Recent Physical Activity Questionnaire (RPAQ). Structured feedback on item relevance and clarity was obtained from 40 CKD patients. In phase 2, the questionnaire was refined in 3 iterations in a total of 226 CKD patients against 7-day accelerometer and RPAQ measurements. In phase 3, the definitive CKD-PAQ was compared with RPAQ in 523 CKD patients.

RESULTS

In the final iteration of phase 2, CKD-PAQ data were compared to accelerometer-derived and RPAQ data in 60 patients. Mean daily Metabolic Equivalent of Task (MET) and Total Energy Expenditure (TEE) levels were similar by all methods. Intraclass correlation coefficients showed fair agreement (MET) and good (TEE) between accelerometry and both CKD-PAQ and RPAQ. Agreement between questionnaires was excellent. For mean daily MET bias was 0.035 (SD 0.312) for CKD-PAQ and 0.018 (SD 0.326) for RPAQ. For TEE bias was 91 (SD 518) for CKD-PAQ and 44 (SD 548) kcal for RPAQ. Limits of agreement were wide for both parameters, with less dispersion of CKD-PAQ values. In phase 3, agreement between questionnaires was good (MET) and excellent (TEE). Bias of CKD-PAQ-derived mean daily MET from RPAQ-derived values was 0.031 (SD 0.193) with 95% limits of agreement -0.346 to 0.409. Corresponding values for TEE were 48 (SD 325) and -588 to 685 kcal/day. CKD-PAQ appeared to improve discrimination between low activity groups.

CONCLUSIONS

CKD-PAQ performs comparably to RPAQ though is shorter, easier to complete, may better capture low level activity and improve discrimination between low-activity groups.

Day and night changes in energy expenditure of patients on automated peritoneal dialysis

RATIONALE

Automated peritoneal dialysis (APD) treatment for end-stage kidney disease affords patients a degree of autonomy in everyday life. Clinical investigations of their energy expenditure (EE) are usually based on resting EE, which could mask day and night variations in EE. The aim of this study, therefore, was to compare the components of EE in APD patients and healthy control (C) subjects.

MATERIAL AND METHOD

Patients treated with APD for more than 3 months were compared with C volunteers matched for age and lean body mass (LBM). Biochemical analyses were performed and body composition was determined by DEXA to adjust EE to LBM. Total EE, its different components and respiratory quotients (RQ) were measured by a gas exchange method in calorimetric chambers. Spontaneous total and activity-related EE (AEE) were also measured in free-living conditions over 4 days by a calibrated accelerometer and a heart rate monitor.

RESULTS

APD (n = 7) and C (n = 7) patients did not differ in age and body composition. REE did not differ between the two groups. However, prandial increase in EE adjusted for dietary energy intake was higher in APD patients (+57.5 ± 12.71 kcal/h) than in C subjects (+33.8 ± 10.5 kcal/h, p = 0.003) and nocturnal decrease in EE tended to be lower in APD patients undergoing dialysis sessions (- 4.53 ± 8.37 kcal/h) than in subjects (- 11.8 ± 7.69 kcal/h, p = 0.059). Resting RQ (0.91 ± 0.09 vs 0.81 ± 0.04, p = 0.032) and nocturnal RQ (0.91 ± 0.09 vs 0.81 ± 0.04, p = 0.032) were significantly higher in APD patients, indicating a preferential use of glucose substrate potentially absorbed across the peritoneum. AEE was lower in APD patients (595.9 ± 383.2 kcal/d) than in C subjects (1205.2 ± 370.5 kcal/d, p = 0.011). In contrast, energy intakes were not significantly different (1986 ± 465 vs 2083 ± 377 kcal/d, p = 0.677).

CONCLUSION

Although the two groups had identical resting EE, APD patients had a higher prandial increase in EE, a lower activity-related EE and higher resting and nocturnal RQ than healthy subjects.

Indexing dialysis dose for gender, body size and physical activity: Impact on survival

Current practice basing dialysis dose on urea distribution volume (V) has been questioned. We explored the impact on survival of scaling dialysis dose (Kt) to parameters reflective of metabolic activity. In a multicentre prospective cohort study of 1500 patients on thrice-weekly haemodialysis, body surface area (BSA) and resting energy expenditure (REE) were estimated using validated equations and physical activity by the Recent Physical Activity Questionnaire. Total energy expenditure (TEE) was estimated from REE and physical activity data. Kt was calculated from delivered (single-pool Kt/V)*Watson V. Kt/BSA, Kt/REE and Kt/TEE were then calculated at baseline and 6 monthly during follow-up for 2 years. In adjusted Cox models Kt/TEE, Kt/BSA, Kt/REE, in that order, had lower hazard ratios for death than single-pool Kt/V. On the basis of adjusted survival differences, putative minimum target doses were estimated for Kt/BSA as 27119 ml/m² and Kt/TEE as 25.79 ml/kcal. We identified spKt/V values equivalent to these estimated targets, ranging from 1.4 to 1.8 in patient groups based on gender, body size and physical activity. For sedentary patients, the minimum target dose was 1.4 for large males, 1.5 for small males and 1.7 for women. For active patients the target was 1.8 irrespective of gender and body-weight. Patients achieving these individualised minimum targets had greater adjusted two-year survival compared to those achieving conventional minimum targets. Metabolic activity related parameters, such as Kt/TEE and Kt/BSA, may have a clinically important role in scaling haemodialysis dose. Using such parameters or their spKt/V equivalents to adjust minimum target doses based on gender, body size and habitual physical activity may have a positive impact on survival.

Total energy expenditure in adults aged 65 years and over measured using doubly-labelled water: international data availability and opportunities for data sharing

BACKGROUND

Increasing population lifespan necessitates a greater understanding of nutritional needs in older adults (65 year and over). A synthesis of total energy expenditure in the older population has not been undertaken and is needed to inform nutritional requirements. We aimed to establish the extent of the international evidence for total energy expenditure (TEE) using doubly-labelled water (DLW) in older adults (65 years and over), report challenges in obtaining primary data, and make recommendations for future data sharing.

METHODS

Four databases were searched to identify eligible studies; original research of any study design where participant level TEE was measured using DLW in participants aged ≥65 years. Once studies were identified for inclusion, authors were contacted where data were not publicly available.

RESULTS

Screening was undertaken of 1223 records; the review of 317 full text papers excluded 170 records. Corresponding or first authors of 147 eligible studies were contacted electronically. Participant level data were publicly available or provided by authors for 45 publications (890 participants aged ≥65 years, with 248 aged ≥80 years). Sixty-seven percent of the DLW data in this population were unavailable due to authors unable to be contacted or declining to participate, or data being irretrievable.

CONCLUSIONS

The lack of data access limits the value of the original research and its contribution to nutrition science. Openly accessible DLW data available through publications or a new international data repository would facilitate greater integration of current research with previous findings and ensure evidence is available to support the needs of the ageing population.

TRIAL REGISTRATION

The protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO), registration number CRD42016047549 .

Comparison of estimates of resting energy expenditure equations in haemodialysis patients

PURPOSE

Waste products of metabolism accumulate in patients with chronic kidney disease, and require clearance by haemodialysis (HD). We wished to determine whether there was an association between resting energy expenditure (REE) and total energy expenditure (TEE) in HD patients and body composition.

SUBJECTS/METHODS

We determined REE by recently validated equations (CKD equation) and compared REE with that estimated by standard equations for REE, and TEE calculated from patient reported physical activity, in HD patients with corresponding body composition measured by dual energy X-ray absorptiometry (DEXA) scanning.

RESULTS

We studied 107 patients, 69 male (64.5%), mean age 62.7 ± 15.1 years. The CKD equation REE was 72.5 ± 13.3 watts (W) and TEE 83.2 ± 9.7 W. There was a strong association between REE with body surface area (BSA) (r2 = 0.80), total soft lean and fat lean tissue mass (r2 = 0.69), body mass index (BMI) (r2 = 0.34), all p<0.001. REE estimated using the modified Harris Benedict, Mifflin St. Jeor, Katch McArdle, Bernstein and Robertson equations underestimated REE compared to the CKD equation. TEE was more strongly associated with BSA (r2 = 0.51), appendicular muscle mass (r2 = 0.42), than BMI (r2 = 0.15) all p<0.001.TEE was greater for those employed (104.9 ± 10.7 vs. 83.1 ± 12.3 W, p<0.001), and with no co-morbidity (88.7 ± 14.8 vs. 82.7 ± 12.3 W, p<0.05).

CONCLUSIONS

Standard equations underestimate REE in HD patients compared to the CKD equation. TEE was greater in those with more skeletal muscle mass, in those who were employed and in those with the least co-morbidity. More metabolically active patients may well require greater dialytic clearances.