The method of calculation is based on a time-discrete nonlinear feedback model of insulin-glucose homeostasis that is rooted in the MiMe-NoCoDI modeling platform for endocrine systems.
How to determine GR
The index is derived from a mathematical model of insulin-glucose homeostasis that incorporates fundamental physiological motifs. For diagnostic purposes, it is calculated from fasting insulin and glucose concentrations with:
.
[I](∞): Fasting Insulin plasma concentration (mol/L)
[G](∞): Fasting blood glucose concentration (mol/L) G1: Parameter for pharmacokinetics (154.93 s/L) DR: EC50 of insulin at its receptor (1,6 nmol/L) GE: Effector gain (50 s/mol) P(∞): Constitutive endogenous glucose production (150 μmol/s)
Both in the FAST study, an observational case-control sequencing study including 300 persons from Germany, and in a large sample from the NHANES study, SPINA-GR differed more clearly between subjects with and without diabetes than the corresponding HOMA-IR, HOMA-IS and QUICKI indices.
Scientific implications and other uses
SPINA-GR significantly correlates with biomarkers for the allostatic load. The direction is negative, i. e., higher allostatic load is associated with lower insulin sensitivity.
Together with the secretory capacity of pancreatic beta cells (SPINA-GBeta), SPINA-GR provides the foundation for the definition of a fasting based disposition index of insulin-glucose homeostasis (SPINA-DI).
In combination with SPINA-GBeta and whole-exome sequencing, calculating SPINA-GR helped to identify a new form of monogenetic diabetes (MODY) that is characterised by primary insulin resistance and results from a missense variant of the type 2 ryanodine receptor (RyR2) gene (p.N2291D).
Pathophysiological implications
In lean subjects it is significantly higher than in a population with obese persons. In several populations, SPINA-GR correlated with the area under the glucose curve and 2-hour concentrations of glucose, insulin and proinsulin in oral glucose tolerance testing, concentrations of free fatty acids, ghrelin and adiponectin, and the HbA1c fraction.
SPINA-GR declines with increasing adherence to mediterranean diet, which may be explained by increased use of other macronutrients for energy production. In mice, SPINA-GR is reduced after they were fed a high-fat diet for one and three weeks.
In hidradenitis suppurativa, an inflammatory skin disease, SPINA-GR is reduced. If this state is uncompensated by increased beta-cell function the static disposition index (SPINA-DI) is reduced, resulting in the onset of diabetes mellitus.
Predictive aspects
In a longitudinal evaluation of the NHANES study, a large sample of the general US population, over 10 years, reduced SPINA-DI, calculated as the product of SPINA-GBeta times SPINA-GR, significantly predicted all-cause mortality.
↑Santillán, Moisés (2025). "Quantitative Insights into Glucose Regulation: A Review of Mathematical Modeling Efforts". Dynamics of Physiological Control. Lecture Notes on Mathematical Modelling in the Life Sciences. pp.125–148. doi:10.1007/978-3-031-82396-1_7. ISBN978-3-031-82395-4.
↑Hamou-Maamar, Maghnia (2025). "Mathematical Modeling in Diabetes Care and Innovation". Computational Mathematics and Modelling for Diabetes. Industrial and Applied Mathematics. pp.167–190. doi:10.1007/978-981-96-1925-2_4. ISBN978-981-96-1924-5.
↑Dietrich, Johannes W.; Böhm, Bernhard (27 August 2015). "Die MiMe-NoCoDI-Plattform: Ein Ansatz für die Modellierung biologischer Regelkreise". GMDS 2015; 60. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik: Biometrie und Epidemiologie e.V. (GMDS). doi:10.3205/15gmds058.
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