Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Mar 8;17(6):945.
doi: 10.3390/nu17060945.

Relationship Between Metabolic Age Determined by Bioimpedance and Insulin Resistance Risk Scales in Spanish Workers

Affiliations

Relationship Between Metabolic Age Determined by Bioimpedance and Insulin Resistance Risk Scales in Spanish Workers

Ignacio Ramírez-Gallegos et al. Nutrients. .

Abstract

Introduction: Metabolic age (MA) is the difference between an individual's actual age and the age of their body based on physiological and biological factors. It is an indicator that reflects a person's physical and biological state, regardless of chronological age. Insulin resistance (IR) is a health disorder in which tissues exhibit a reduced response to the circulating glucose uptake stimulated by insulin. Objective: The aim of this study is to evaluate the association between MA, determined through bioelectrical impedance analysis, and the risk of IR, assessed using validated scales, in a cohort of Spanish workers. Methodology: A descriptive cross-sectional study was conducted on 8590 Spanish workers to assess the association between MA and a set of sociodemographic variables, health habits, and IR risk scales such as the Triglyceride-Glucose Index (TyG Index), Metabolic Score for Insulin Resistance (METS-IR), and Single Point Insulin Sensitivity Estimator (SPISE). Results: All analyzed variables were associated with MA values, with the strongest associations observed for IR risk scale values (OR 4.88 [95% CI 4.12-5.65] for METS-IR, 4.42 [95% CI 3.70-5.15] for SPISE, and 3.42 [95% CI 2.97-3.87] for the TyG Index) and physical activity. Conclusions: Metabolic age is influenced by sociodemographic variables such as age, sex, and social class; health habits such as smoking, physical activity, and adherence to the Mediterranean diet; and by IR risk scale values.

Keywords: Mediterranean diet; insulin resistance; metabolic age; physical activity; smoking; sociodemographic variables.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
PRISMA flowchart of participants in this study.
Figure 2
Figure 2
ROC curves. TyG: Triglyceride–Glucose Index. METS-IR: Metabolic Score for Insulin Resistance. SPISE-IR: Single Point Insulin Sensitivity Estimator.

Similar articles

Cited by

References

    1. DeFilippis A.P., Young R., McEvoy J.W., Michos E.D., Sandfort V., Kronmal R.A., McClelland R.L., Blaha M.J. Risk score overestimation: The impact of individual cardiovascular risk factors and preventive therapies on the performance of the American Heart Association-American College of Cardiology-Atherosclerotic Cardiovascular Disease risk score in a modern multi-ethnic cohort. Eur. Heart J. 2017;38:598–608. - PMC - PubMed
    1. Kain P. COVID-19 Pandemic and Metabolic Aging. Acta Sci. Neurol. 2022;5:30–33. doi: 10.31080/ASNE.2022.05.0479. - DOI - PMC - PubMed
    1. Majzoub A., Elbardisi H., Madani S., Leisegang K., Mahdi M., Agarwal A., Henkel R., Khalafalla K., ElSaid S., Arafa M. Impact of body composition analysis on male sexual function: A metabolic age study. Front. Endocrinol. 2023;13:1050441. doi: 10.3389/fendo.2022.1050441. - DOI - PMC - PubMed
    1. Vásquez-Alvarez S., Bustamante-Villagomez S.K., Vazquez-Marroquin G., Porchia L.M., Pérez-Fuentes R., Torres-Rasgado E., Herrera-Fomperosa O., Montes-Arana I., Gonzalez-Mejia M.E. Metabolic Age, an Index Based on Basal Metabolic Rate, Can Predict Individuals That are High Risk of Developing Metabolic Syndrome. High Blood Press. Cardiovasc. Prev. 2021;28:263–270. doi: 10.1007/s40292-021-00441-1. - DOI - PubMed
    1. Aging Biomarker Consortium. Bao H., Cao J., Chen M., Chen M., Chen W., Chen X., Chen Y., Chen Y., Chen Y., et al. Biomarkers of aging. Sci. China Life Sci. 2023;66:893–1066. doi: 10.1007/s11427-023-2305-0. - DOI - PMC - PubMed

LinkOut - more resources