Metabolic Age, an Index Based on Basal Metabolic Rate, Can Predict Individuals That are High Risk of Developing Metabolic Syndrome

• Published in: High blood pressure & cardiovascular prevention Vol. 28; no. 3; pp. 263 - 270
• Main Authors: Vásquez-Alvarez, Sarahi, Bustamante-Villagomez, Sergio K., Vazquez-Marroquin, Gabriela, Porchia, Leonardo M., Pérez-Fuentes, Ricardo, Torres-Rasgado, Enrique, Herrera-Fomperosa, Oscar, Montes-Arana, Ivette, Gonzalez-Mejia, M. Elba
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.05.2021; Springer Nature B.V
• Subjects: Age; Blood pressure; Body fat; Body mass index; Cardiology; Diabetes; Energy; Health risk assessment; Health risks; Lifestyles; Males; Medicine; Medicine & Public Health; Metabolic syndrome; Musculoskeletal system; Obesity; Original Article; Pharmacotherapy; Questionnaires; Triglycerides; Mexico; Disease prevention; Developing countries; Obesity; Latinos; Endocrine and metabolic disease
• ISSN: 1120-9879; 1179-1985
• DOI: 10.1007/s40292-021-00441-1

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.