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1.
Arch. latinoam. nutr ; Arch. latinoam. nutr;74(2): 107-118, jun. 2024. ilus, tab, graf
Article in English | LILACS, LIVECS | ID: biblio-1561535

ABSTRACT

Introduction: In areas with limited access to healthcare systems, Resting Energy Expenditure (REE) estimation is performed using predictive equations to calculate an individual's caloric requirement. One problem is that these equations were validated in populations with different characteristics from those in Latin America, such as race, height, or body mass, leading to potential errors in the prediction of this parameter. Objective: To determine the REE using predictive formulas compared with bioimpedance in Peruvians. Materials and methods: A comparative analytical cross-sectional study with secondary database analysis of the CRONICAS cohort. Results: we worked with a total of 666 subjects. The Mjeor equation was the one with the highest rating of 0.95, a lower mean absolute percentage error (MAPE) of 4.69%, and equivalence was found with the REE values. In the multiple regression, it was observed that the Mjeor equation was the one that least overestimated the REE, increasing 0.77 Kcal/day (95% CI: 0.769-0.814; p<0.001) for each point that increased the REE determined by bioimpedance. The strength of association between Mjeor and bioimpedance was 0.9037. Furthermore, in the regression of the data (weight, height, age) in the Mjeor equation it was observed that the coefficients obtained were the same as those used in the original equation. Conclusions: The Mjeor equation seems to be the most adequate to estimate the REE in the Peruvian population. Future prospective studies should confirm the usefulness of this formula with potential utility in primary health care(AU)


Introducción: En zonas con acceso limitado a sistemas de salud, la estimación del Gasto Energético en Reposo (GER) se realiza utilizando ecuaciones predictivas para calcular el requerimiento calórico de un individuo. Uno de los problemas es que estas ecuaciones fueron validadas en poblaciones con características diferentes a las latinoamericanas, como raza, talla o masa corporal, lo que conlleva a potenciales errores en la predicción de este parámetro. Objetivo: Determinar el GER mediante fórmulas predictivas comparadas con la bioimpedancia en peruanos. Materiales y métodos: Estudio transversal analítico comparativo con análisis secundario de base de datos de la cohorte CRONICAS. Resultados: Se trabajó con un total de 666 sujetos. La ecuación de Mjeor fue la que obtuvo la puntuación más alta de 0,95, un error medio porcentual absoluto (MAPE) inferior de 4,69%, y se encontró equivalencia con los valores del GER. En la regresión múltiple, se observó que la ecuación de Mjeor fue la que menos sobreestimó el GER, aumentando 0,77 Kcal/día (IC 95%: 0,769-0,814; p<0,001) por cada punto que aumentaba el GER determinado por bioimpedancia. La fuerza de asociación entre Mjeor y bioimpedancia fue de 0,9037. Además, en la regresión de los datos (peso, talla, edad) de la ecuación de Mjeor se observó que los coeficientes obtenidos eran los mismos que los utilizados en la ecuación original. Conclusiones: La ecuación de Mjeor parece ser la más adecuada para estimar el GER en la población peruana. Futuros estudios prospectivos deberán confirmar la utilidad de esta fórmula para su potencial utilidad en la atención primaria de salud(AU)


Subject(s)
Humans , Male , Female , Adolescent , Adult , Middle Aged , Cross-Sectional Studies , Electric Impedance , Energy Metabolism , Forecasting , Body Mass Index , Racial Groups , Diet , Obesity
2.
Front Nutr ; 10: 1296937, 2023.
Article in English | MEDLINE | ID: mdl-38075218

ABSTRACT

Introduction: Migraine is a common and disabling primary headache, and its pathophysiology is not fully understood. Previous studies have suggested that pain can increase humans' Resting Energy Expenditure (REE). However, no previous study has investigated whether the REE of individuals with migraine differs from the general population. Therefore, this study aims to assess whether the REE of women with migraine differs from that of women without headaches. We also tested the accuracy of REE predictive formulas in the migraine patients. Methods: This cross-sectional study involves 131 adult women aged between 18 and 65 years, 83 with migraine and 48 without (controls). We collected clinical, demographic, and anthropometric data. Migraine severity was measured using the Migraine Disability Test and Headache Impact Test, version 6. The REE was measured by indirect calorimetry, and it was compared with the predicted REE calculated by formulas. Results: Patients with migraine had higher REE when compared to controls (p < 0.01). There was a positive correlation between REE and the patient-reported number of migraine attacks per month (Rho = 0.226; p = 0.044). Mifflin-St Jeor and Henry and Rees were the predictive formulas that have more accuracy in predicting REE in women with migraine. Discussion: Considering the benefits of nutritional interventions on treating migraines, accurately measuring REE can positively impact migraine patient care. This study enhances our understanding of the relationship between pain and energy expenditure. Our results also provide valuable insights for healthcare professionals in selecting the most effective predictive formula to calculate energy expenditure in patients with migraine.

3.
Rev Med Inst Mex Seguro Soc ; 61(Suppl 2): S246-S253, 2023 Sep 18.
Article in Spanish | MEDLINE | ID: mdl-38016112

ABSTRACT

Background: Nutrition in the Intensive Care Unit (ICU) is a cornerstone; however, energy requirements are a controversial issue that has not yet been resolved. Calorimetry is the gold standard for calculating energy expenditure, but it is expensive and not available in all ICU areas. Formulas have been developed to calculate basal energy expenditure (BAE) and make the process easier. Objective: To validate the predictive formulas of BAE compared to that obtained with ventilatory indirect calorimetry (IC) within the nutritional assessment in ICU patients. Material and methods: Analytical cross-sectional retrolective study. We performed BAE measurement on patients in the ICU of a third level hospital with ventilatory indirect calorimetry and compared the results obtained with those of the Harris Benedict, Muffin-St. Jeor, Institute of Medicine, and Faisy equations. Results: A total of 49 patients were included; a moderate correlation with statistical significance was found between the BAE measurements obtained by indirect calorimetry, with those obtained by four predictive equations that were studied. The Faisy equation obtained the strongest correction with r = 0.461 (p = 0.001). Conclusion: The correlation between the BAE obtained by predictive equations and by IC goes from mild to moderate, due to the heterogeneity of critical patients and their changing nature throughout their disease.


Introducción: la nutrición en la unidad de cuidados intensivos (UCI) es una piedra angular; sin embargo, los requerimientos energéticos son un tema controversial aún no resuelto. La calorimetría es el estándar de oro para calcular el gasto energético, pero es costosa y no está disponible en todas las áreas de las UCI. Se han desarrollado fórmulas para calcular el gasto energético basal (GEB) y hacer el proceso más sencillo. Objetivo: validar las fórmulas predictivas de GEB comparado con el obtenido con calorimetría indirecta (CI) ventilatoria dentro de la valoración nutricia en los pacientes de UCI. Material y métodos: estudio transversal analítico retrolectivo. Realizamos medición de GEB a los pacientes de la UCI de un hospital de tercer nivel con calorimetría indirecta ventilatoria y se compararon los resultados obtenidos con los de las fórmulas de Harris Benedict, Muffin-St. Jeor, Institute of Medicine y Faisy. Resultados: se incluyeron un total de 49 pacientes; se encontró correlación moderada con significación estadística entre las medidas de GEB obtenidas por calorimetría indirecta, con las obtenidas por cuatro fórmulas predictivas que se estudiaron. La fórmula de Faisy obtuvo la corrección más fuerte con una r = 0.461 (p = 0.001). Conclusión: la correlación entre el GEB obtenido por fórmulas predictivas y por CI es de ligera a moderada, debido a la heterogeneidad del paciente crítico y su naturaleza cambiante a lo largo de su enfermedad.


Subject(s)
Critical Illness , Energy Metabolism , Humans , Calorimetry, Indirect/methods , Cross-Sectional Studies , Nutritional Status
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