A Comparison of the Indirect Calorimetry and Different Energy Equations for the Determination of Resting Energy Expenditure of Patients With Renal Transplantation.

OBJECTIVE: We aimed to evaluate the agreement between the resting energy expenditure (REE) obtained by indirect calorimetry and eight prediction equations in adult patients with renal transplantation and a newly developed REE prediction equation for use in patients with renal transplantation in the clinic. METHODS: A total of 51 patients (30 males and 21 females) were involved in the study. The REE was measured by indirect calorimetry and compared with the previous prediction equations. The agreement was assessed by the interclass correlation coefficient and by Bland-Altman plot analysis. RESULTS: No significant difference was found in terms of age and body mass index between the genders. Differences between the predicted and measured REEs were maximum in the Bernstein equation (-478 kcal) and minimum in the Cunningham equation (-69 kcal). It was found that underprediction values varied from 27.5% (chronic kidney disease equation) to 98.0% (Bernstein equation). The highest overprediction value was found in the Schofield equation (17.7%). The Cunningham equation and the new equation had the lowest root mean square error (265 kcal/day). In this study, fat-free mass (FFM) was found to be the most significant variable in multiple regression analysis (r2: 0.55). The new specific equation based on FFM was generated as 424.2 + 24.7∗FFM (kg). Besides that, it was found that the new equation and Cunningham equation were distributed randomly according to Bland-Altman analysis. A supplementary new equation based on available anthropometric measurements was developed as -1996.8 + 19.1∗height (cm) + 7.2∗body weight (kg). CONCLUSION: This study showed that most of the predictive equations significantly underestimated REE. In patients with renal transplantation, if the REE is not measurable by indirect calorimetry, the use of the proposed equations will be more accurate.

Understanding the role of the gut microbiome in gastrointestinal cancer: A review.

Gastrointestinal cancer represents one of the most diagnosed types of cancer. Cancer is a genetic and multifactorial disease, influenced by the host and environmental factors. It has been stated that 20% of cancer is caused by microorganisms such as Helicobacter pylori, hepatitis B and C virus, and human papillomavirus. In addition to these well-known microorganisms associated with cancer, it has been shown differences in the composition of the microbiota between healthy individuals and cancer patients. Some studies have suggested the existence of the selected microorganisms and their metabolites that can promote or inhibit tumorigenesis via some mechanisms. Recent findings have shown that gut microbiome and their metabolites can act as cancer promotors or inhibitors. It has been shown that gastrointestinal cancer can be caused by a dysregulation of the expression of non-coding RNA (ncRNA) through the gut microbiome. This review will summarize the latest reports regarding the relationship among gut microbiome, ncRNAs, and gastrointestinal cancer. The potential applications of diagnosing and cancer treatments will be discussed.