Biomarkers of Insulin Resistance and Their Performance as Predictors of Treatment Response in Overweight Adults
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Context
Insulin resistance (IR) contributes to the pathogenesis of type 2 diabetes mellitus and is a risk factor for cardiovascular and neurodegenerative diseases. Amino acid and lipid metabolomic biomarkers associate with future type 2 diabetes mellitus risk in several epidemiological cohorts. Whether these biomarkers can accurately monitor changes in IR status following treatment is unclear.
Objective
Herein we evaluated the performance of clinical and metabolomic biomarker models to forecast altered IR, following lifestyle-based interventions.
Design
We contrasted the performance of two distinct insulin assay types (high-sensitivity ELISA and immunoassay) and built IR diagnostic models using cross-sectional clinical and metabolomic data. These models were used to stratify IR status in preintervention fasting samples, from 3 independent cohorts (META-PREDICT (n = 179), STRRIDE-AT/RT (n = 116), and STRRIDE-PD (n = 149)). Linear and Bayesian projective prediction strategies were used to evaluate models for fasting insulin and homeostatic model assessment 2 for insulin resistance and change in fasting insulin with treatment.
Results
Both insulin assays accurately quantified international standard insulin (R2 > 0.99), yet agreement between fasting insulins was less congruent (R2 = 0.65). A mean treatment effect on fasting insulin was only detectable using the ELISA. Clinical-metabolomic models were statistically related to fasting insulin (R2 0.33–0.39) but with modest capacity to classify IR at a clinically relevant homeostatic model assessment 2 for insulin resistance threshold. Furthermore, no model predicted treatment responses in any cohort.
Conclusion
We demonstrate that the choice of insulin assay is critical when quantifying the influence of treatment on fasting insulin, whereas none of the clinical-metabolomic biomarkers, identified in cross-sectional studies, are suitable for monitoring longitudinally changes in IR status.
