Edinburgh Napier University

  Context
Insulin Resistance (IR) contributes to the pathogenesis of type 2 diabetes mellitus (T2DM) and is a risk factor for cardiovascular and neurodegenerative diseases. Amino acid and lipid metabolomic biomarkers associate with future T2DM risk in several epidemiological cohorts. Whether these biomarkers can accurately detect 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 evaluated 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 utilised to stratify IR status in pre-intervention fasting samples, from three independent cohorts (META-PREDICT (M-P, n=179), STRRIDE-AT/RT (S-2, n=116) and STRRIDE-PD (S-PD, n=149)). Linear and Bayesian projective prediction strategies were used to evaluate models for fasting insulin and HOMA2-IR and change in fasting insulin with treatment.

Results
Both insulin assays accurately quantified international standard insulin (R2>0.99), yet agreement for fasting insulin was less congruent (R2=0.65). A mean treatment effect on fasting insulin was only detectable using an 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 HOMA2-IR 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, while none of the clinical-metabolomic biomarkers, identified in cross-sectional studies, are suitable for monitoring longitudinally changes in IR status.