Sarahi Vásquez-Alvarez1, Sergio K Bustamante-Villagomez1, Gabriela Vazquez-Marroquin2, Leonardo M Porchia3, Ricardo Pérez-Fuentes1,3, Enrique Torres-Rasgado1, Oscar Herrera-Fomperosa1, Ivette Montes-Arana4, M Elba Gonzalez-Mejia5. 1. Facultad de Medicina, Benemérita Universidad Autónoma de Puebla, Calle 13 Sur 2901 Colonia Volcanes, C.P. 72420, Puebla, Puebla, Mexico. 2. Facultad de nutrición, Benemérita Universidad Autónoma de Puebla, Calle 13 Sur 2901 Colonia Volcanes, C.P. 72420, Puebla, Puebla, Mexico. 3. Laboratorio de Investigación en Fisiopatología de Enfermedades Crónicas, Centro de Investigación Biomédica de Oriente, Instituto Mexicano del Seguro Social, Delegación Puebla, Carretera Federal Atlixco-Metepec Km 4.5, C.P. 42730, Atlixco, Puebla, Mexico. 4. Unidad de Medicina Familiar 2 (UMF-2) del IMSS, Delegación Puebla, Calle 9 Oriente 404, Colonia Centro, C.P. 72000, Puebla, Puebla, Mexico. 5. Facultad de Medicina, Benemérita Universidad Autónoma de Puebla, Calle 13 Sur 2901 Colonia Volcanes, C.P. 72420, Puebla, Puebla, Mexico. elba.gonzalezmejia@gmail.com.
Abstract
INTRODUCTION: Every 10 years, an adult's basal metabolic rate (BMR), independent of their BMI, decreases 1-2% due to skeletal muscle loss, thus decreasing an adult's energy requirement and promoting obesity. Increased obesity augments the risk of developing Metabolic Syndrome (MetS); however, an adult's healthy lifestyle, which increases BMR, can mitigate MetS development. To compare different BMRs for certain ages, Metabolic age (Met-age) was developed. AIM: To assess the association between Met-age and MetS and to determine if Met-age is an indicator of high-risk individuals for MetS. METHODS: Four hundred thirty-five attendees at 2 clinics agreed to participate and gave signed informed consent. MetS risk was assessed by the ESF-I questionnaire. Met-age was determined using a TANITA bio-analyzer. Strengthen of association was determined by calculating Spearman's rho and predictability was evaluated by the area-under-a-receiver-operating characteristic curve (AUC). Difference-in-age (DIA) = [chronological age - Met-age]. RESULTS: There was a difference between the low-risk (n = 155) and the high-risk (n = 280) groups' Met-age (37.8±16.7 v. 62.9±17.3) and DIA (1.3±17.4 v. - 10.5±20.8, p < 0.001). There was a positive correlation between the ESF-I questionnaire and Met-age (rho = - 0.624, p < 0.001) and a negative correlation for DIA (rho = - 0.358, p < 0.001). Met-age was strongly predictive (AUC = 0.84, 95% CI 0.80-0.88), suggesting a 45.5 years cutoff (sensitivity = 83.2%, specificity = 72.3%). DIA was a good predictor (AUC = 0.68, 95% CI 0.63-0.74) with a - 11.5 years cutoff (sensitivity = 52.5%, specificity = 82.8%). CONCLUSION: Met-age highly associated with and is an indicator of high-risk individuals for MetS. This would suggest that increases in Met-age are associated with augmented MetS severity, independent of the individual's chronological age.
INTRODUCTION: Every 10 years, an adult's basal metabolic rate (BMR), independent of their BMI, decreases 1-2% due to skeletal muscle loss, thus decreasing an adult's energy requirement and promoting obesity. Increased obesity augments the risk of developing Metabolic Syndrome (MetS); however, an adult's healthy lifestyle, which increases BMR, can mitigate MetS development. To compare different BMRs for certain ages, Metabolic age (Met-age) was developed. AIM: To assess the association between Met-age and MetS and to determine if Met-age is an indicator of high-risk individuals for MetS. METHODS: Four hundred thirty-five attendees at 2 clinics agreed to participate and gave signed informed consent. MetS risk was assessed by the ESF-I questionnaire. Met-age was determined using a TANITA bio-analyzer. Strengthen of association was determined by calculating Spearman's rho and predictability was evaluated by the area-under-a-receiver-operating characteristic curve (AUC). Difference-in-age (DIA) = [chronological age - Met-age]. RESULTS: There was a difference between the low-risk (n = 155) and the high-risk (n = 280) groups' Met-age (37.8±16.7 v. 62.9±17.3) and DIA (1.3±17.4 v. - 10.5±20.8, p < 0.001). There was a positive correlation between the ESF-I questionnaire and Met-age (rho = - 0.624, p < 0.001) and a negative correlation for DIA (rho = - 0.358, p < 0.001). Met-age was strongly predictive (AUC = 0.84, 95% CI 0.80-0.88), suggesting a 45.5 years cutoff (sensitivity = 83.2%, specificity = 72.3%). DIA was a good predictor (AUC = 0.68, 95% CI 0.63-0.74) with a - 11.5 years cutoff (sensitivity = 52.5%, specificity = 82.8%). CONCLUSION: Met-age highly associated with and is an indicator of high-risk individuals for MetS. This would suggest that increases in Met-age are associated with augmented MetS severity, independent of the individual's chronological age.
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