Introduction: Severe obesity can develop in children with PWS when food intake is not controlled. Maintenance of body weight requires an energy balance, of which energy intake and energy expenditure are important components. Previous studies described a decreased resting energy expenditure (REE) in growth hormone (GH)-untreated children with PWS. In short-term studies, no difference in REE was found between GH-treated and untreated children with PWS. However, there are limited data on REE in children with PWS who were GH-treated for a long period. Methods: This study describes measured REE (mREE), energy intake, and body composition during long-term GH treatment in children with PWS. Patients were treated with 1.0 mg GH/m2/day (∼0.035 mg/kg/day). REE was determined by indirect calorimetry; dietary energy intake was calculated using a 3-day dietary record. Body composition by dual-energy X-ray absorptiometry (DXA) scans. Results: We included 52 GH-treated children with PWS with a mean (SD) age of 8.53 (4.35) years and a median (IQR) GH-treatment duration of 7 (4–11) years. mREE increased with age, but was not associated with GH-treatment duration. A higher LBM was associated with higher mREE. Mean energy intake was significantly lower compared to daily energy requirements (DER) for age- and sex-matched healthy children (p < 0.001), ranging from 23 to 36% less intake in children aged 3.5–12 years to 49% less intake in children aged 12–18 years. Fifty percent of children had a normal REE, 17.3% a decreased REE, and 32.7% an elevated REE, according to predicted REE based on measured REE in a large group of healthy children. Conclusion: In children with PWS, mREE increases with age. GH-treatment duration is not associated, whereas LBM is an important determinant of mREE. Children with PWS have a low to very low energy intake compared to DER for age- and sex-matched children, with a declining intake when becoming older.

Prader-Willi syndrome (PWS) is a rare genetic disorder, caused by the lack of expression of imprinted genes in the PWS region on the paternally derived chromosome 15, caused by a paternal deletion, maternal uniparental disomy, an imprinting center defect, or paternal chromosomal translocation [1, 2]. PWS is characterized by hypotonia, developmental delay, behavioral problems, abnormal body composition with a high body fat percentage and low lean body mass (LBM), hyperphagia, hypogonadism, and short stature [2‒4].

Usually, body weight starts to increase from the age of 2 years, without an increase in calorie intake or interest in food. From the age of 4 years, interest in food and appetite starts to increase followed by hyperphagia. Hyperphagia is typically accompanied by food-seeking behavior and lack of satiety [5]. Severe obesity can develop when food intake is not controlled. Especially in children with PWS, it is important to understand the causes of obesity in order to determine the best lifestyle interventions.

Maintenance of body weight requires an energy balance, of which energy intake and energy expenditure are important components [6]. Previous studies described a decreased resting energy expenditure (REE) in children with PWS, caused by abnormal body composition, particularly a decreased LBM [6‒8]. The obsession toward food consumption combined with a reduced metabolic rate puts children at risk for obesity. However, these children were not treated with growth hormone (GH).

Studies have shown that GH treatment is beneficial for children in PWS. GH treatment improves their body composition, linear growth, physical strength, and cognition [9‒14]. Limited studies investigated the effect of GH treatment on energy intake and energy expenditure in children with PWS. We found no difference in REE during 2 years of GH treatment in children with PWS compared to randomized untreated children with PWS, which is in line with the study of Myers et al. [15, 16]. However, there are very limited data on REE in children with PWS who were GH-treated for a long period.

In our previous study, we calculated REE (kcal/day) using Müller’s equation, validated for the estimation of REE in Dutch children and adolescents [17, 18]. Prediction equations are often used to calculate REE and are mostly based on age, sex, and anthropometrics. There is a wide variation in the accuracy of predictive equations for REE [17, 19]. In a systematic review, the Schofield equation, derived from measured REE in a large group of healthy adults and children [20], was shown to be the most accurate REE prediction (0–1.1% bias) in overweight and obese children and adolescents [19]. The Schofield equation is, therefore, widely used in clinical practice to predict REE. The percentage of predicted REE (REE%) is calculated as measured REE divided by predicted REE (pREE) and multiplied by 100, with normal REE defined as REE% between 90 and 110% of predicted, decreased REE defined as REE% ≤90% and elevated REE defined as REE% ≥110% [19]. However, no prediction equation provides precise REE estimates, which can lead to overestimation or underestimation. Indirect calorimetry is the method of choice for measuring REE [19, 21, 22].

The aim of present study was to investigate measured resting energy expenditure (mREE) in a large group of GH-treated children with PWS across the age range <3.5 until 18 years old. We hypothesized that mREE increases with age. Second, we assessed the association of GH-treatment duration with mREE and determined if body composition was associated with mREE. We hypothesized that GH-treatment duration is not associated with mREE, but we expected to find a positive association between LBM and mREE. Furthermore, we investigated the food intake of children with PWS across the age range <3.5 until 18 years old. We hypothesized that energy intake increases with age. In addition, we investigated their food intake in comparison with the WHO daily requirements for age- and sex-matched healthy children [23] and determined if mREE was associated with food intake. We hypothesized that GH-treated children with PWS with a controlled food intake have a lower energy intake than the WHO daily energy requirements. We expected to find no association between mREE and food intake. Lastly, we investigated the difference between measured REE and predicted REE according to the Schofield equation.

Patients

All participants had PWS confirmed by methylation analysis of the PWS region, and participated in the Dutch PWS Cohort Study [9, 13]. All children were studied from the start of their GH treatment. For the present study, the inclusion criterion was at least one reliable REE measurement during GH treatment. Pubertal stage was divided into prepubertal, defined by a testicular volume less <4 mL in boys and Tanner stage M1 in girls, and pubertal, defined by a testicular volume ≥4 mL in boys and Tanner stage ≥M2 in girls [24].

All participants were followed at the Dutch PWS Reference Center in Rotterdam and received multidisciplinary care by the PWS team in collaboration with pediatric endocrinologists and pediatricians in other Dutch hospitals. The study protocol was approved by Medical Ethics Committee of the Erasmus University Medical Center. Written informed consent was obtained from parents and children older than 12 years. Assent was obtained from children younger than 12 years. The study was conducted according to the guidelines of the Declaration of Helsinki II [25].

Design

This was a prospective study, investigating REE in children with PWS. Patients were treated with GH, administered subcutaneously once daily at bedtime with a dose of 1.0 mg/m2/day (∼0.035 mg/kg/day). The dose was adjusted based on calculated body surface area and lowered in case of serum IGF-1 levels at or above 3 SDS.

Anthropometry

Standing height was measured in centimeters using a Harpenden Stadiometer and supine length with a Harpenden Infantometer (Holtain Ltd., Crosswell, UK). Body weight was measured in kilograms on a calibrated electronic digital scale (Servo Balance KA-20-150S; Servo Berkel Prior, Katwijk, The Netherlands). Height, weight, and body mass index (BMI) standard deviation (SD) scores were calculated with Growth Analyser RCT 4.1 (available at www.growthanalyser.org), based on Dutch Reference values [26, 27].

Resting Energy Expenditure

REE was determined by open-circuit indirect calorimetry using a ventilated hood collection system (Quark RMR, COSMED, Italy). Measurements were conducted after an overnight fast, while the subject was awake but inactive and comfortably resting on a hospital bed. The first 5 min was excluded from the results to allow acclimation. REE measurements (mREE) were performed in steady state, defined as an interval of 5 consecutive minutes whereby VO2 and VCO2 changed by <10% [28]. Preferably, a measurement for at least 20 min was performed. The Quark RMR was calibrated according to the manufacturer’s recommendations prior to measurements.

We compared measured REE to the predicted REE according to the Schofield equation. This equation includes age, sex, weight, and height and is derived from measured REE in a large group of healthy adults and children and, therefore, serves as a control group [20]. The percentage of pREE (REE%) was calculated as measured REE divided by pREE and multiplied by 100, with normal REE defined as REE% between 90 and 110% of predicted, decreased REE defined as REE% ≤90% and elevated REE defined as REE% ≥110%.

Energy Intake

Dietary energy intake was calculated using a 3-day dietary record prior to the visit in the PWS Reference Center. Parents were asked to record the type and amount of all food and beverages consumed by the child for 3 consecutive days. Dietary intake was converted into energy, expressed in kcal/day, using computer software based on a nutrient file compiled from the Dutch Food Composition Table [29]. Macronutrient intake was calculated and expressed as the percentage of total energy intake (fat E%, protein E%, carbohydrate E%). The individual energy intake was compared with age-matched daily energy requirements by the WHO for healthy boys and girls [23]. The average daily nutrient intake was compared to age and sex appropriate dietary references [30].

Body Composition

LBM and fat mass (FM) were measured by dual-energy X-ray absorptiometry scans (Lunar Prodigy; GE Healthcare). The dual-energy X-ray absorptiometry machine was calibrated daily and all scans were made with the same machine. The intra-assay coefficients of variation were 0.41–0.88% for fat tissue and 1.57–4.49% for LBM [31]. LBM was calculated as fat-free mass minus bone mineral content. Fat percentage (FM%) was expressed as the percentage of total body mass. FM% SDS and LBM SDS were calculated according to age- and sex-matched Dutch reference values [32, 33].

Statistics

Statistical analyses were performed with SPSS version 27.0 (SPSS Inc., Chicago, IL, USA). Clinical characteristics are presented as median (interquartile range [IQR]) or mean (SD), as appropriate. Correlations between mREE and child characteristics, GH-treatment duration, and body composition were determined with Pearson’s or Spearman’s correlation coefficient. Multiple linear regression analyses were performed to determine associations of GH-treatment duration and body composition with mREE as dependent variable, with a correction of potential confounders, sex, age and height. Because of a skewed distribution, LBM was log transformed. First, we entered sex, age, height and GH-treatment duration in the model, to investigate the influence of GH treatment on the outcomes (model A). Second, log transformed LBM and FM% were added as independent variables (model B). Independent samples t-test was used to compare energy intake between children with PWS and daily energy requirements for age- and sex-matched healthy children. For this sub-analysis, we divided the patients into four age groups, because of the large age range. Multiple linear regression analysis was performed to determine the association of mREE with food intake as dependent variable, with correction of potential confounders, sex, age and height. p < 0.05 was considered statistically significant.

Baseline Characteristics

Table 1 shows the baseline characteristics of 52 children with PWS (22 males, 30 females). Mean (SD) age was 8.53 (4.35) years. Twenty-seven patients (51.9%) had a deletion, 22 (42.3%) a maternal uniparental disomy, 2 (3.8%) an imprinting center defect, and in 1 (1.9%) the subtype was unknown. All patients were treated with GH, which started at a median (IQR) age of 0.76 (0.6–1.57) years. Median (IQR) GH-treatment duration was 7.08 (4.06–11.02) years. Thirty-five patients were prepubertal and 17 were in puberty, of whom 1 patient received sex steroid replacement therapy for puberty induction. Median (IQR) GH dose was 0.67 (0.50–1.00) mg/m2/day. Mean (SD) height SDS and weight SDS were −0.30 (1.40) and 0.74 (1.78), respectively. The mean (SD) fat percentage was 39.9 (9.4) %. The median (IQR) fat percentage SDS was 1.94 (1.34–2.67). Median (IQR) fasting glucose was 4.9 (4.7–5.1) mmol/L and median (IQR) fasting insulin was 60 (42–104) pmol/L. Eight patients had insulin levels above reference range, but in only four of them, the high levels were observed repeatedly. All patients had glucose levels within the normal range.

Table 1.

Baseline characteristics

Total group
Number (females) 52 (30) 
Age, years, mean (SD) 8.53 (4.35) 
Age at the start of GH treatment, years 0.76 (0.60–1.57) 
Duration of GH treatment, years 7.08 (4.06–11.02) 
GH dose, mg/m2/day 0.67 (0.50–1.00) 
GH dose, mg/kg/day 0.024 (0.018–0.036) 
Genetic subtype, N (%) 
 Deletion 27 (51.9) 
 mUPD 22 (42.3) 
 ICD 2 (3.8) 
 Unknown 1 (1.9) 
Anthropometry 
 Height SDS, mean (SD) −0.30 (1.40) 
 Weight SDS, mean (SD) 0.74 (1.78) 
 BMI (SDS), mean (SD) 1.11 (1.62) 
Body composition  
 Fat percentage SDS 1.94 (1.34–2.67) 
 LBM SDS −1.46 (−2.21 to −0.49) 
REE 
 mREE, kcal/day, mean (SD) 1,249 (410) 
 pREE, kcal/day, mean (SD) 1,213 (362) 
 Mean (SD) difference (mREE-pREE), kcal/day 36 (164) 
 REE%, mean (SD) 103 (15) 
Total group
Number (females) 52 (30) 
Age, years, mean (SD) 8.53 (4.35) 
Age at the start of GH treatment, years 0.76 (0.60–1.57) 
Duration of GH treatment, years 7.08 (4.06–11.02) 
GH dose, mg/m2/day 0.67 (0.50–1.00) 
GH dose, mg/kg/day 0.024 (0.018–0.036) 
Genetic subtype, N (%) 
 Deletion 27 (51.9) 
 mUPD 22 (42.3) 
 ICD 2 (3.8) 
 Unknown 1 (1.9) 
Anthropometry 
 Height SDS, mean (SD) −0.30 (1.40) 
 Weight SDS, mean (SD) 0.74 (1.78) 
 BMI (SDS), mean (SD) 1.11 (1.62) 
Body composition  
 Fat percentage SDS 1.94 (1.34–2.67) 
 LBM SDS −1.46 (−2.21 to −0.49) 
REE 
 mREE, kcal/day, mean (SD) 1,249 (410) 
 pREE, kcal/day, mean (SD) 1,213 (362) 
 Mean (SD) difference (mREE-pREE), kcal/day 36 (164) 
 REE%, mean (SD) 103 (15) 

Data are expressed as median (IQR), unless otherwise stated.

mUPD, maternal uniparental disomy; ICD, imprinting center defect; GH, growth hormone; mREE, measured resting energy expenditure; pREE, predicted resting energy expenditure (based on Schofield equations); REE%, ratio mREE/pREE.

Measured REE

Mean (SD) mREE was 1,249 (410) kcal/day (Table 1). The mREE of the study population according to age is presented in Table 2. The mREE increased with age, being 783 kcal/day in infants and 1,565 kcal/day in children between 12 and 18 years (r = 0.75, p < 0.001). mREE showed a trend toward higher scores in males compared to females (p = 0.08). There was a significant correlation between mREE and height (r = 0.84, p < 0.001), but not with height SDS (r = 0.127, p = 0.37). There was a significant correlation between mREE and pubertal stage (r = 0.66, p < 0.001), but the correlation disappeared when corrected for age (p = 0.27). No correlation was found between mREE and GH dose and LBM (p = 0.53 and p = 0.58, respectively).

Table 2.

REE, energy intake, and body composition according to age

nREEEnergy intakeBody compositionMacronutrient distribution (E%)
mREE, kcal/daypREE, kcal/daynkcal/day% DERBMI SDS, median (IQR)FM% SDS, median (IQR)LBM SDS, median (IQR)FatAMDR (E%)ProteinAMDR (E%)CarbohydrateAMDR (E%)
Total 52 1,248.71 (409.93) 1,212.71 (361.71) 46 1,169.37 (295.03)* 67 1.33 (0.11–2.15) 1.94 (1.34–2.67) −1.46 (−2.21 to −0.49) 27.50 (7.09)  19.12 (4.14)  50.17 (6.86) 45–60 
Infants <3.5 years 782.5 (148.62) 715.00 (81.48) 921.4 (279.40) 92 −0.12 (−1.60 to 1.16) 0.69 (−0.99 to 3.00) −1.15 (−1.97 to 1.35) 30.42 (7.71) 30–40 15.78 (5.24) 5–20 50.90 (10.10) 45–60 
Children 3.5–7 years 18 997.89 (187.10) 1,024.72 (143.99) 16 1,081.81 (292.30)* 77 1.08 (0.53–3.20) 1.84 (1.15–2.97) −0.63 (−1.52 to 0.21) 26.81 (9.65) 25–35 19.71 (4.01) 10–30 49.96 (8.31) 45–60 
Children 7–12 years 13 1,445.69 (449.06) 1,355.62 (308.82) 10 1,220.10 (301.36)* 64 1.75 (0.12–2.38) 2.24 (1.64–2.75) −1.62 (−1.93 to −1.09) 27.81 (5.01) 25–35 19.32 (4.90) 10–30 49.55 (5.56) 45–60 
Children 12–18 years 15 1,565.47 (267.11) 1,513.53 (315.95) 15 1,311.30 (230,97)* 51 1.53 (−0.86 to 2.15) 1.94 (1.34–2.36) −2.29 (−3.46 to −1.41) 27.02 (5.27) 25–35 19.50 (3.13 10–30 50.56 (5.40) 45–60 
nREEEnergy intakeBody compositionMacronutrient distribution (E%)
mREE, kcal/daypREE, kcal/daynkcal/day% DERBMI SDS, median (IQR)FM% SDS, median (IQR)LBM SDS, median (IQR)FatAMDR (E%)ProteinAMDR (E%)CarbohydrateAMDR (E%)
Total 52 1,248.71 (409.93) 1,212.71 (361.71) 46 1,169.37 (295.03)* 67 1.33 (0.11–2.15) 1.94 (1.34–2.67) −1.46 (−2.21 to −0.49) 27.50 (7.09)  19.12 (4.14)  50.17 (6.86) 45–60 
Infants <3.5 years 782.5 (148.62) 715.00 (81.48) 921.4 (279.40) 92 −0.12 (−1.60 to 1.16) 0.69 (−0.99 to 3.00) −1.15 (−1.97 to 1.35) 30.42 (7.71) 30–40 15.78 (5.24) 5–20 50.90 (10.10) 45–60 
Children 3.5–7 years 18 997.89 (187.10) 1,024.72 (143.99) 16 1,081.81 (292.30)* 77 1.08 (0.53–3.20) 1.84 (1.15–2.97) −0.63 (−1.52 to 0.21) 26.81 (9.65) 25–35 19.71 (4.01) 10–30 49.96 (8.31) 45–60 
Children 7–12 years 13 1,445.69 (449.06) 1,355.62 (308.82) 10 1,220.10 (301.36)* 64 1.75 (0.12–2.38) 2.24 (1.64–2.75) −1.62 (−1.93 to −1.09) 27.81 (5.01) 25–35 19.32 (4.90) 10–30 49.55 (5.56) 45–60 
Children 12–18 years 15 1,565.47 (267.11) 1,513.53 (315.95) 15 1,311.30 (230,97)* 51 1.53 (−0.86 to 2.15) 1.94 (1.34–2.36) −2.29 (−3.46 to −1.41) 27.02 (5.27) 25–35 19.50 (3.13 10–30 50.56 (5.40) 45–60 

Data are expressed as mean (SD), unless otherwise stated.

mREE, measured resting energy expenditure; pREE, predicted resting energy expenditure (based on Schofield equations).

FM% SDS and LBM SDS were calculated according to age- and sex-matched Dutch references [32, 33]. AMDR, Acceptable Macronutrient Distribution Range [30].

E% = percentage of total energy intake.

*p<0.001 PWS group versus DER of age- and sex-matched healthy children [23].

Associations of GH Treatment Duration and Body Composition with mREE

The multiple linear regression analysis results are shown in Table 3. GH treatment duration, corrected for sex, age, height, and pubertal stage, was not associated with mREE (model A) (adjusted R2 = 0.70). Corrected for sex, age, height, and pubertal stage, LBM was associated with mREE, whereas FM% was not (model B) (adjusted R2 = 0.75).

Table 3.

Multiple linear regression analyses for mREE

VariablesModel AModel B
B±SEβp valueB±SEβp value
Sex −29.74 −0.04 0.70 −58.22 −0.07 0.45 
Age (years) −37.62 −0.40 0.36 −30.93 −0.33 0.34 
Height (cm) 18.32 1.17 0.00 5.84 0.38 0.30 
Pubertal stage 129.80 0.15 0.34 64.84 0.52 0.61 
GH treatment (years) −8.04 −0.08 0.86    
LBM*    1,322.53 0.72 0.03 
FM%    4.63 0.11 0.25 
Adjusted R2 0.70  0.00 0.75   
VariablesModel AModel B
B±SEβp valueB±SEβp value
Sex −29.74 −0.04 0.70 −58.22 −0.07 0.45 
Age (years) −37.62 −0.40 0.36 −30.93 −0.33 0.34 
Height (cm) 18.32 1.17 0.00 5.84 0.38 0.30 
Pubertal stage 129.80 0.15 0.34 64.84 0.52 0.61 
GH treatment (years) −8.04 −0.08 0.86    
LBM*    1,322.53 0.72 0.03 
FM%    4.63 0.11 0.25 
Adjusted R2 0.70  0.00 0.75   

Results of multiple linear regression analyses, corrected for sex, age, height, and pubertal stage.

B ± SE = unstandardized coefficient B and standard error.

β = standardized coefficient beta.

mREE, measured resting energy expenditure; LBM, lean body mass; FM%, fat mass percentage; GH, growth hormone.

*Log transformed.

Energy Intake

Energy intake according to age is presented in Table 2. The energy intake increased with age, being 921 (279) kcal/day in infants and 1,311 (231) kcal/day in children between 12 and 18 years (r = 0.53, p < 0.001). Mean energy intake was significantly lower compared to daily energy requirements according to WHO (DER%) for age- and sex-matched healthy children (p < 0.001). With increasing age, a significant decrease in energy intake as the percentage of daily requirements (DER%) was observed (r = −0.62, p < 0.001), while BMI SDS did not decrease with age (r = −0.05 p = 0.73). Children between 3.5 and 12 years had 23–36% less intake than the daily energy requirements and children between 12 and 18 years had 49% less intake. No significant difference was found in infants aged <3.5 years. Figure 1 shows the mean energy intake, energy intake as DER% and BMI SDS according to age. The macronutrient contribution to energy intake was 50% from carbohydrate, 19% from protein, and 28% from fat, showing a macronutrient intake within the Acceptable Macronutrient Distribution Range at all ages [30].

Fig. 1.

Energy intake and BMI according to age. DER, daily energy requirements; BMI, body mass index. Standard deviation scores were calculated with Growth Analyser RCT 4.1 (available at www.growthanalyser.org), based on Dutch Reference values [26, 27].

Fig. 1.

Energy intake and BMI according to age. DER, daily energy requirements; BMI, body mass index. Standard deviation scores were calculated with Growth Analyser RCT 4.1 (available at www.growthanalyser.org), based on Dutch Reference values [26, 27].

Close modal

Associations of mREE with Energy Intake

There was a significant correlation between mREE and energy intake (r = 0.46, p = 0.00), but no linear correlation between mREE and fat E%, protein E%, and carbohydrate E%. There was a significant correlation between height and energy intake (r = 0.55, p < 0.001), but not with height SDS (r = 0.06, p = 0.71). The results of the multiple linear regression analysis are shown in Table 4 mREE, corrected for sex, age, and height, was not associated with energy intake (adjusted R2 = 0.254).

Table 4.

Multiple linear regression analysis for energy intake

VariablesB±SEβp value
Sex −66.82 −0.11 0.43 
Age (years) 16.96 0.25 0.53 
Height (cm) 5.19 0.46 0.32 
mREE −0.15 −0.19 0.51 
Adjusted R2 0.25  0.00 
VariablesB±SEβp value
Sex −66.82 −0.11 0.43 
Age (years) 16.96 0.25 0.53 
Height (cm) 5.19 0.46 0.32 
mREE −0.15 −0.19 0.51 
Adjusted R2 0.25  0.00 

Results of multiple linear regression analysis, corrected for sex, age, and height.

B ± SE = unstandardized coefficient B and standard error.

β = standardized coefficient beta.

mREE, measured resting energy expenditure; GH, growth hormone.

Measured REE versus Predicted REE

Mean (SD) difference between mREE and pREE in the total group was 36 (164) kcal/day (Table 1). Fifty percent of the patients had a normal REE (mREE between 90 and 110% of pREE), 17.3% a decreased REE (mREE ≤90% of pREE), and 32.7% an elevated REE (mREE ≥110% of pREE). In children with a GH-treatment duration of 7 years or longer, only 7.7% of the children had a decreased REE (n = 26). The mean difference (SD) between mREE and pREE in the children with decreased REE was −214 (93) kcal/day, showing that mREE is on average 214 kcal lower than calculated with the Schofield equation. Thus, the Schofield equation overestimates REE in these children by on average 214 kcal/day compared to mREE. The mean difference (SD) between mREE and pREE in children with elevated REE was 207 (84) kcal/day, showing that mREE is on average 207 kcal higher than calculated with the Schofield equation. In these children, the Schofield equation underestimates REE compared to mREE.

This is the first study investigating measured REE by indirect calorimetry in a large group of GH-treated children with PWS. Our data show that mREE increases with age, but GH-treatment duration is not associated with mREE. LBM was significantly associated with mREE, but FM percentage was not. Our findings demonstrate that children with PWS have a significantly lower energy intake than the daily energy requirements for age- and sex-matched healthy children, ranging from 23 to 36% less intake in children aged 3.5–12 years to 49% less intake in children aged 12–18 years. Fifty percent of the children had a normal REE (mREE between 90 and 110% of pREE), 17.3% a decreased REE (mREE ≤90% of pREE), and 32.7% an elevated REE (mREE ≥110% of pREE), according to the Schofield equation.

Our data show that mREE increases with age, but, corrected for age, sex, height, and pubertal stage, no association was found between GH-treatment duration and mREE. We previously performed a randomized controlled GH trial for 2 years in children with PWS and found a significant increase of pREE in children with PWS during 2 years of GH treatment. However, it was not significantly different compared with the untreated group [15]. Haqq et al. found a significantly higher mean mREE in children with PWS after 6 months of GH treatment compared with placebo [34], but Myers et al. [16] found no difference in mREE in 2 years of GH. This suggests that, corrected for age, sex, height, and pubertal stage, long-term GH treatment does not significantly influence mREE.

Some studies described a significantly reduced mREE in adults and children with PWS in comparison with obese subjects. However, after adjusting for LBM, mREE was not different between the two groups [7, 35, 36]. We found a significant association between LBM and mREE, which is in line with previous studies [6‒8, 34, 35, 37]. Our data show that it would be best to adjust mREE for LBM for correct interpretation.

Currently, dietary restriction is the most utilized and recommended intervention in the prevention and management of obesity in PWS [38]. It is implemented by most parents and caregivers because they know the high risk of obesity development in children with PWS [39]. Energy intake in our study population was significantly lower compared to daily energy requirements for age- and sex-matched healthy children. With increasing age, a significant decrease in energy intake as the percentage of daily energy requirements was observed to 51% of normal in children above the age of 12 years, which is in line with previous studies [15, 40, 41]. In our study, BMI SDS increased in early childhood, but remained stable thereafter in the age group 12–18 years. In the study of Bakker et al., the BMI SDS in prepubertal children was higher than in young infants, despite the reported decrease in energy intake to 50% of normal [15]. The decrease in food intake in children with PWS, when they become older, suggests that dietary restriction is necessary to maintain a healthy weight.

The calorie-restricted diet in PWS could induce nutritional deficiencies. Most of the children in our study had, however, a macronutrient intake within the Acceptable Macronutrient Distribution Range. The macronutrient intake was consistent with previous studies in children with PWS [15, 41‒43], some of which also found that, despite lower energy intake, dietary intake of most essential nutrients was similar and diet quality was higher compared to those without PWS [42, 43]. In the study of Miller et al. [44], a similar energy-restricted diet with a well-balanced macronutrient composition improved both weight and body composition in children with PWS compared to a simple energy-restricted diet. A well-balanced, nutritionally dense, and calorie-restricted diet is, therefore, recommended for overall weight control in children with PWS.

Several studies investigated which predictive equation provides the most accurate and precise estimate of REE in overweight and obese children and adolescents. These studies have shown that there is a wide variation in the accuracy of predictive equations for REE [17‒19, 22, 45‒47]. The Schofield equation predicted REE in our population with a mean difference of 36 kcal/day. The accuracy of this equation in our study was similar to, or better than that of previously reported studies [17, 19, 22, 45‒49]. For group analysis, this equation is quite accurate and provides useful guidance, but in clinical practice, a more precise equation is important, as an adequate estimation of REE for the individual patient is required. In clinical practice, an REE between 90 and 110% of pREE is considered normal. In our study, only 50% of the patients had a normal REE using the Schofield equation. This means that, in 50% of the patients, pREE can lead to a potential overestimation or underestimation of the measured REE, which may have consequences for the patient’s dietary advice. Whenever available, the use of indirect calorimetry to measure REE is the best option to determine REE. When using any prediction equation, translation to treatment advice of children should be performed with caution.

This is the first study describing measured REE by indirect calorimetry and energy intake in a large group of children with PWS, with a median GH-treatment duration of 7 years. We acknowledge that physical activity influences REE [35]. All children, as part of multidisciplinary care, followed a standard age-appropriate program in which daily physical activity played a significant role, under the guidance of the physiotherapist from the PWS reference center. The number of hours and intensity of physical activity were, however, not precisely recorded by a considerable number of parents, so we could not include this in our analyses. In addition, we recognize that the use of dietary records might underestimate real food intake. Completing food diaries requires a lot of effort from the parents and underreporting and changes in dietary intake behavior are known sources of bias when using dietary intake records [50, 51]. However, dietary records appear to be acceptable as dietary assessment tools and no better objective measure of dietary intake is currently available [52]. Underestimation of the dietary intake might occur as a result of children accessing food without knowledge of their parents. Food-seeking behavior with stealing food and lying about food are behavioral features that can develop in children with PWS, but our study patients are nowadays under close supervision of their parents. Our study did not include an untreated control group of PWS patients, but as the beneficial effects of GH treatment nowadays are well known, we considered it unethical to withhold long-term GH treatment in these patients [9‒15].

In conclusion, our study in 52 GH-treated children with PWS shows that mREE increases with age. However, GH-treatment duration is not associated with mREE, suggesting that long-term GH treatment does not significantly influence mREE. LBM was strongly associated with measured REE, whereas FM percentage was not. In clinical practice, it means that it is important to focus on stimulating physical activity to improve or maintain LBM. They have a low to very low energy intake compared to daily energy requirements for age- and sex-matched children, with a declining intake when becoming older. Fifty percent of children had a normal REE, 17.3% a decreased REE, and 32.7% an elevated REE, according to the Schofield equation.

We express our gratitude to all children and parents for their enthusiastic participation in this study. We thank all collaborating pediatric-endocrinologists; pediatricians, P. Affourtit, S. Walet, and E. Koster; dieticians; and other health care providers, for their collaboration.

The study was approved by the Medical Ethics Committee of the Erasmus Medical Centre/Sophia Children’s Hospital, Rotterdam, The Netherlands (PWS Cohort Study: Protocol Code MEC-2001-230, approved on September 18, 2001). Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

The authors declare that they have no competing interest.

The authors did not receive any specific grant or funding for the work described in this article.

Demi Trueba-Timmermans, Lionne Grootjen, Alicia Juriaans, Gerthe Kerkhof, Edmond Rings, and Anita Hokken-Koelega contributed to this study. Demi Trueba-Timmermans has conducted the statistical analyses and drafted the manuscript. Lionne Grootjen, Alicia Juriaans, Gerthe Kerkhof, Edmond Rings, and Anita Hokken-Koelega critically reviewed the manuscript. Demi Trueba-Timmermans, Lionne Grootjen, Alicia Juriaans, Gerthe Kerkhof, Edmond Rings, and Anita Hokken-Koelega read and approved the final manuscript.

Data are not publicly available due to ethical reasons. Further inquiries can be directed to the corresponding author.

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