scholarly journals Corrigendum to: “Osteogenesis Imperfecta: The Impact of Genotype and Clinical Phenotype on Adiposity and Resting Energy Expenditure”

2021 ◽  
Keyword(s):  
Energy Expenditure ◽  
Osteogenesis Imperfecta ◽  
Clinical Phenotype ◽  
Resting Energy Expenditure ◽  
The Impact ◽  
Resting Energy

Osteogenesis Imperfecta: The Impact of Genotype and Clinical Phenotype on Adiposity and Resting Energy Expenditure

2021 ◽  
Author(s):  
Kaitlin L Ballenger ◽  
Nicol Tugarinov ◽  
Sara K Talvacchio ◽  
Marianne M Knue ◽  
An N Dang Do ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Osteogenesis Imperfecta ◽  
Type I Collagen ◽  
Resting Energy Expenditure ◽  
Type I ◽  
Healthy Controls ◽  
Type Iv ◽  
Reduced Mobility ◽  
The Impact ◽  
Resting Energy

Abstract Context Mutations in type I collagen or collagen-related proteins cause Osteogenesis Imperfecta (OI). Energy expenditure and body composition in OI could reflect reduced mobility, or intrinsic defects in osteoblast differentiation increasing adipocyte development. Objective Compare adiposity and resting energy expenditure (REE) in OI and healthy controls (HC), for OI genotype- and Type-associated differences. Design/Setting/Participants We studied 90 participants, 30 with OI (13 COL1A1 Gly, 6 COL1A2 Gly, 3 COL1A1 non-Gly, 2 COL1A2 non-Gly, 6 non-COL; 8 Type III, 16 Type IV, 4 Type VI, 1 Type VII, 1 Type XIV) and 60 HC with sociodemographic characteristics/BMI/BMIz similar to the OI group. Participants underwent dual-energy X-ray absorptiometry to determine lean mass and fat mass percentage (FM%) and REE. FM% and REE were compared, adjusting for covariates to examine the relationship of OI genotypes and phenotypic Types. Results FM% did not differ significantly in all patients with OI versus HC (OI: 36.6±1.9%, HC: 32.7±1.2%, p =0.088). FM% was, however, greater than HC for those with non-COL variants (p=0.018). FM% did not differ from HC among OI Types (p’s>0.05). Overall, covariate-adjusted REE did not differ significantly between OI and HC (OI: 1376.5±44.7 kcal/d, healthy controls: 1377.0±96 kcal/d p=0.345). However, those with non-COL variants (p=0.034) and Type VI OI (p=0.04) had significantly lower REE than HC. Conclusions Overall, patients with OI did not significantly differ in either extra-marrow adiposity or REE from BMI-similar HC. However, reduced REE among those with non-COL variants may contribute to greater adiposity.

Resting energy expenditure in OSAS: the impact of a single CPAP application

2015 ◽  
Vol 20 (1) ◽  
pp. 121-128 ◽  
Author(s):  
Katalin Fekete ◽  
Afroditi K. Boutou ◽  
Georgia Pitsiou ◽  
Nikolaos Chavouzis ◽  
Athanasia Pataka ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Resting Energy Expenditure ◽  
The Impact ◽  
Resting Energy

scholarly journals Is There a Contribution of Structural Brain Phenotypes to the Variance in Resting Energy Expenditure before and after Weight Loss in Overweight Females?

Nutrients ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2759
Author(s):  
Corinna Geisler ◽  
Manfred J. Müller
Keyword(s):  
Weight Loss ◽  
Energy Expenditure ◽  
Brain Metabolism ◽  
Resting Energy Expenditure ◽  
Main Determinant ◽  
Before And After ◽  
Age Range ◽  
The Impact ◽  
Structural Brain ◽  
Resting Energy

Brain gray (GM) and white matter (WM) are associated with resting energy expenditure (REE). The impact of weight loss on GM and WM masses, as well as on their associations with REE and the ratio between body and brain metabolism, i.e., encephalic measure (EM)), are unknown. Longitudinal data of 69 female Caucasian subjects (age range 19–69 years) with detailed information on fat mass (FM), fat free mas (FFM), GM, WM and REE. Mean weight loss was 14.5 ± 11.9 kg with changes in FM (−12.9 ± 9.8 kg), FFM (−1.7 ± 4.8 kg) and REE (−159 ± 191 kcal/24 h) (all p < 0.05). With weight loss, there were no changes in GM and WM. Before and after weight loss, FFM was the main determinant of REE (r2 = 0.483 and 0.413; p < 0.05). After weight loss, GM added to the variances in REE (3.6%), REEadjFFM (6.1%) and the REE on FFM residuals (6.6%). In addition, before and after weight loss GM explained 25.0% and 10.0% of the variances in EM (p < 0.05). Weight loss had no effect on volumes of GM and WM. After weight loss, both, GM added to the variances of REE, REE on FFM residuals and EM.

scholarly journals The Impact of Nutritional Therapy on Resting Energy Expenditure in Malnourished Older Hospitalized Patients

2020 ◽  
Author(s):  
Maryam Pourhassan ◽  
Diana Daubert ◽  
Rainer Wirth
Keyword(s):  
Energy Expenditure ◽  
Older Patients ◽  
Short Form ◽  
Resting Energy Expenditure ◽  
Nutritional Therapy ◽  
Hospitalized Patients ◽  
Oral Nutritional Supplements ◽  
Prospective Longitudinal ◽  
The Impact ◽  
Resting Energy

Abstract Background and aims: Little is known about the effect of nutritional therapy on resting energy expenditure (REE) in malnourished older hospitalized patients. We sought to evaluate longitudinal changes in REE during nutritional therapy and to examine the different factors associated with changes in REE among these patients.Methods Twenty-three malnourished older patients (age range 67–93, 65% women) participated in this prospective longitudinal observational study. Malnutrition was defined as Mini Nutritional Assessment Short Form (MNA-SF) < 8. REE was measured by using indirect calorimetry on hospital admission and at discharge. Body composition (i.e. fat free mass (FFM)) was assessed by bioelectrical impedance analysis. The Parker mobility score was performed to evaluate the patient's mobility. Nutritional support (i.e. high protein and/or high calorie oral nutritional supplements) was provided to all malnourished patients during hospitalization.Results All patients were malnourished with a median MNA-SF score of 6. The median time between two REE measurements was 13 days (interquartile range: 11–15). On admission, REE was significantly lower in patients with lower FFM (P = 0.043) and decreased along with the degree of malnutrition (P = 0.008). REE (+ 212.6 kcal, P = 0.010) and REE/FFM (+ 5.6 kcal/kg, P = 0.021) increased significantly during hospitalization. In a multiple regression analysis, age, gender and BMI followed by MNA-SF score and mobility were the major independent risk factors of changes in REE.Conclusion Low REE in malnourished older patients increased to normal after 2 weeks of nutritional treatment.

scholarly journals Resting Energy Expenditure Relationship with Macronutrients and Gestational Weight Gain: A Pilot Study

Nutrients ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 450
Author(s):  
Kiley B. Vander Wyst ◽  
Matthew P. Buman ◽  
Gabriel Q. Shaibi ◽  
Megan E. Petrov ◽  
Elizabeth Reifsnider ◽  
...  
Keyword(s):  
Weight Gain ◽  
Energy Expenditure ◽  
Gestational Weight Gain ◽  
Maternal Diet ◽  
Resting Energy Expenditure ◽  
Gestational Weight ◽  
And Control ◽  
The Impact ◽  
Hispanic White ◽  
Resting Energy

Resting energy expenditure (REE) comprises 60% of total energy expenditure and variations may be associated with gestational weight gain (GWG) or maternal diet. The objective of this study was to examine the impact of metabolic tracking on GWG and the association with maternal macronutrients. Pregnant women aged 29.8 ± 4.9 years (78.6% non-Hispanic, White) with gestational age (GA) < 17 week were randomized to Breezing™ (n = 16) or control (n = 12) groups for 13 weeks. REE by Breezing™ indirect calorimetry, anthropometrics and dietary intake were collected every two weeks. Early (14–21 weeks), late (21–28 weeks), and overall (14–28 weeks) changes in macronutrients and GWG were calculated. The Breezing™ group had a significantly greater rate of GWG [F (1,23) = 6.8, p = 0.02] in the latter half of the second trimester. Late (−155.3 ± 309.2 vs. 207.1 ± 416.5 kcal, p = 0.01) and overall (−143.8 ± 339.2 vs. 191.8 ± 422.2 kcal, p = 0.03) changes in energy consumption were significantly different between Breezing™ and control groups, respectively. Early changes in REE were positively correlated with overall changes in carbohydrates (r = 0.58, p = 0.02). Regular metabolism tracking alone did not have an impact on GWG. Early shifts in REE might impact GWG later in pregnancy. Investigation in a larger population from preconception through postpartum is needed.

scholarly journals Grade of adiposity affects the impact of fat mass on resting energy expenditure in women

2008 ◽  
Vol 101 (4) ◽  
pp. 474-477 ◽  
Author(s):  
Anja Bosy-Westphal ◽  
Manfred J. Müller ◽  
Michael Boschmann ◽  
Susanne Klaus ◽  
Georg Kreymann ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Fat Mass ◽  
Resting Energy Expenditure ◽  
Regression Coefficients ◽  
Body Fat Mass ◽  
Fat Tissue ◽  
Relative Contribution ◽  
The Impact ◽  
Very High ◽  
Resting Energy

Body fat mass (FM) adds to the variance in resting energy expenditure (REE). However, the nature and extent of this relationship remains unclear. Using a database of 1306 women and a linear regression model, we systematically analysed the contribution of FM to the total variance in REE at different grades of adiposity (ranges of body %FM). After adjusting for age, the relative contribution of FM on REE variance increased from low ( ≤ 10 %FM) to normal (>10– ≤ 30 %FM) and moderately elevated (>30– ≤ 40 %FM) grades of adiposity but decreased sharply at high (>40– ≤ 50 %FM) and very high (>50 %FM) grades of adiposity according to the ratio between regression coefficients. These data suggest that the specific metabolic rate of fat tissue is reduced at high adiposity. This should be considered when REE is normalized for FM in obesity.

scholarly journals Evaluation of Resting Energy Expenditure in Subjects with Severe Obesity and Its Evolution After Bariatric Surgery

2021 ◽  
Author(s):  
Enzamaria Fidilio ◽  
Marta Comas ◽  
Miguel Giribés ◽  
Guillermo Cárdenas ◽  
Ramón Vilallonga ◽  
...  
Keyword(s):  
Bariatric Surgery ◽  
Weight Loss ◽  
Energy Expenditure ◽  
Severe Obesity ◽  
Resting Energy Expenditure ◽  
Successful Weight Loss ◽  
The Impact ◽  
The Relationship ◽  
Resting Energy

Abstract Purpose One major determinant of weight loss is resting energy expenditure (REE). However, data regarding REE is scarce in patients with severe obesity (SO)—BMI>50kg/m2. Most studies used equation in order to estimate REE and not indirect calorimetry (IC) (gold standard). Additionally, there is no reliable data on the impact of bariatric surgery (BS) on REE. Objectives (a) To evaluate the REE in patients with SO; (b) to compare REE measured by IC (mREE) to that calculated by Mifflin St-Jeor equation (eREE); (c) to evaluate the impact of BS on REE and the relationship with evolution post-BS. Material and Methods Single-center observational study including consecutive patients with SO between January 2010 and December 2015, candidates for BS. mREE was determined at baseline, and 1 and 12 months post-BS by IC, using a Vmax metabolic monitor. Results Thirty-nine patients were included: mean age 46.5±11.77 years, 64.1%women. Preoperative mREE was 2320.38±750.81 kcal/day. One month post-BS, the mREE significantly decreased (1537.6 ± 117.46 kcal/day, p = 0.023) and remained unchanged at 12 months (1526.00 ± 123.35 kcal/day; p =0.682). Reduction in mREE after the BS was a predictor of reaching successful weight loss (nadir) and weight regain (5 years follow-up) (AUCROC of 0.841 (95%CI [0.655–0.909], p=0.032) and AUCROC of 0.855 (95% CI [0.639–0.901]), p= 0.027, respectively). eREE was not valid to identify these changes. Conclusion In patients with SO, a significant reduction of mREE occurs 1 month post-BS, unchanged at 12 months, representing the major conditioning of successful weight loss and maintenance post-BS. Graphical abstract

Sign in / Sign up

Export Citation Format

Share Document