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Updated: Sep 25, 2025

Author Spotlight: Assessing the Cardiovascular Profile of Patients with Metabolic Syndrome
Published on: September 27, 2024
Variability of biomarkers used for the classification of metabolic syndrome: A repeated measurements study
Till Ittermann1, Marcus Dörr2, Marcello R P Markus3
1Institute for Community Medicine, University Medicine Greifswald, Germany; German Centre for Cardiovascular Research (DZHK), Partner site Greifswald, Greifswald, Germany.
Background And Aims:
The definition of the metabolic syndrome consists of five components. The underlying measurements are subject to intra-individual variability. This repeated measurements study investigated the impact of intra-individual measurement variability on the stability of the diagnosis of metabolic syndrome over 12 months.
Methods And Results:
Twenty-five employees of the University Medicine Greifswald aged 22-70 years were examined once a month over one year. Examinations included blood sampling and anthropometric and blood pressure measurements. Laboratory measurements included glucose, cholesterol (high-density lipoprotein [HDL], and low-density lipoprotein [LDL]), and triglycerides. The metabolic syndrome was defined according to the International Diabetes Federation modified for non-fasting blood samples. Variations in continuous metabolic markers were assessed using coefficients of variation (CV) and intra-class correlation coefficients (ICC). Overall eight participants (32%) were categorized at least once within 12 months as having a metabolic syndrome; in none of those metabolic syndrome was found consistently over the study follow-ups. The Cohen's Kappa for metabolic syndrome was 0.57. CV was highest for triglycerides (27.5%) followed by glucose (10.1%), LDL- (9.5%), and HDL-cholesterol (8.6%). ICC's were lowest for glucose (0.51), triglycerides (0.65), systolic (0.68), and diastolic blood pressure (0.69).
Conclusion:
We showed that the measurement of biomarkers defining the metabolic syndrome is a time-varying condition with implications for the concept of the metabolic syndrome. To account for this uncertainty in prevalence studies we propose to identify uncertain cases according to the current definition of the metabolic syndrome. For analysing associations we recommend to apply probabilistic sensitivity analyses.
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