Effects of the ketogenic diet on nutritional status, resting energy expenditure, and substrate oxidation in patients with medically refractory epilepsy: A 6-month prospective observational study

2012 ◽  
Vol 31 (2) ◽  
pp. 246-249 ◽  
Author(s):  
Anna Tagliabue ◽  
Simona Bertoli ◽  
Claudia Trentani ◽  
Paola Borrelli ◽  
Pierangelo Veggiotti
2021 ◽  
pp. 1-8
Author(s):  
Huijuan Ruan ◽  
Qingya Tang ◽  
Qi Yang ◽  
Fangwen Hu ◽  
Wei Cai

<b><i>Objective:</i></b> Several predictive equations have been used to estimate patients’ energy expenditure. The study aimed to describe the characteristics of resting energy expenditure (REE) in patients undergoing mechanical ventilation during early postoperative stage after cardiac surgery and evaluate the validity of 9 REE predictive equations. <b><i>Methods:</i></b> This was a prospective observational study. Patients aged 18–80 years old, undergone open-heart surgery, were enrolled between January 2017 and 2018. The measured REE (mREE) was evaluated via indirect calorimetry (IC). The predictive resting energy expenditure (pREE) was suggested by 9 predictive equations, including Harris-Benedict (HB), HB coefficient method, Ireton-Jones, Owen, Mifflin, Liu, 25 × body weight (BW), 30 × BW, and 35 × BW. The association between mREE and pREE was assessed by Pearson’s correlation, paired <i>t</i> test, Bland-Altman method, and the limits of agreement (LOA). <b><i>Results:</i></b> mREE was related to gender, BMI, age, and body temperature. mREE was significantly correlated with pREE, as calculated by 9 equations (all <i>p</i> &#x3c; 0.05). There was no significant difference between pREE and mREE, as calculated by 30 × BW kcal/kg/day (<i>t</i> = 0.782, <i>p</i> = 0.435), while significant differences were noted between mREE and pREE calculated by other equations (all <i>p</i> &#x3c; 0.05). Taking the 30 × BW equation as a suitable candidate, most of the data points were within LOA, and the percentage was 95.6% (129/135). Considering the rationality of clinical use, accurate predictions (%) were calculated, and only 40.74% was acceptable. <b><i>Conclusions:</i></b> The 30 × BW equation is relatively acceptable for estimating REE in 9 predictive equations in the early stage after heart surgery. However, the IC method should be the first choice if it is feasible.


2020 ◽  
Vol 48 (5) ◽  
pp. e380-e390
Author(s):  
Pei Chien Tah ◽  
Zheng-Yii Lee ◽  
Bee Koon Poh ◽  
Hazreen Abdul Majid ◽  
Vineya-Rai Hakumat-Rai ◽  
...  

Nutrition ◽  
2018 ◽  
Vol 51-52 ◽  
pp. 60-65 ◽  
Author(s):  
Micheline Tereza Pires Souza ◽  
Pierre Singer ◽  
Gislaine Aparecida Ozorio ◽  
Vitor Modesto Rosa ◽  
Maria Manuela Ferreira Alves ◽  
...  

2012 ◽  
Vol 6 (S3) ◽  
Author(s):  
Antonio Paoli ◽  
Keith Grimaldi ◽  
Antonino Bianco ◽  
Alessandra Lodi ◽  
Lorenzo Cenci ◽  
...  

2021 ◽  
Vol 15 (Supplement_1) ◽  
pp. S526-S527
Author(s):  
G Kornitzer ◽  
J Breton ◽  
P Poinsot ◽  
D Godin ◽  
K Grzywacz ◽  
...  

Abstract Background Crohn’s Disease (CD) is known to affect nutritional status and linear growth in affected children. Patients with CD often have decreased oral intake, malabsorption, and increased intestinal losses. Basal metabolic rate may be affected by chronic inflammation and states of anorexia or malnutrition in these patients. In this study, our aim was to compare the effect of different induction regimens in children with CD on resting energy expenditure (REE) and nutritional status. Methods We recruited patients under 18 years old with new-onset CD or relapse, diagnosed at our centre over a three-year period from July 2016. Patients included had one of the following induction regimens: corticosteroids, exclusive enteral nutrition (EEN), or anti-TNF therapy (Infliximab). REE was assessed at baseline and 6 to 8 weeks after induction. REE (kcal/d) was measured using an open-circuit indirect calorimeter with computerized metabolic cart (Vmax Encore, Vyaire Medical). Secondary outcomes included anthropometrics and clinical and biochemical response, defined by improved wPCDAI and negative inflammatory markers and fecal calprotectin, respectively. Results 17 patients were enrolled and 8 patients excluded (loss to follow-up (n=3), therapeutic change (n=3), revised diagnosis (n=2)). 9 patients completed REE assessments (44.4% anti-TNF (n=4), 44.4% EEN (n=4), 11.1% corticosteroid (n=1)). 3 out of 4 patients on anti-TNF had clinical and biochemical response, while only 1 of 4 patients responded to EEN. For patients in the EEN group, mean BMI change was +0.9 (SD 0.4), compared to +0.4 (SD 1.1) in the anti-TNF group. There was no difference in REE change between treatment groups. Data was then pooled based on response to treatment. 100% of non-responders had increased per cent of predicted REE (REEPP), while 75% of responders decreased their REEPP. Mean REEPP change in non-responders was +12.5% (1, 22) vs. -4.3% (-10, 6) in responders. Figure I. Relationship between REE and weight at baseline and on follow-up in non-responders. Figure II. Relationship between REE and weight at baseline and on follow-up in responders. Conclusion Our results suggest that induction regimen did not impact REE change on follow-up. In our patients, clinical response to therapy was related to a tendency to decrease REE. Patients who did not achieve remission after induction therapy increased their REE. We suspect that this increase in basal metabolic rate is related to persistent inflammation despite improved nutritional status. Further studies with larger patient populations are needed to infer significance and compare subgroups based on body composition.


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