SPINA-GR

SPINA-GR
SPINA-GR
Reference range	1.41–9.00 mol/s
Purpose	Medical diagnosis, research
Test of	Insulin sensitivity
	https://doi.org/10.5281/zenodo.7479856; https://doi.org/10.5281/zenodo.15249620

SPINA-GR is a calculated biomarker for insulin sensitivity.^[1]^[a] It represents insulin receptor gain.

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.^[2]

How to determine G_R

The index is derived from a mathematical model of insulin-glucose homeostasis that incorporates fundamental physiological motifs^[3].^[4] For diagnostic purposes, it is calculated from fasting insulin and glucose concentrations with:

${\widehat {G}}_{R}={\frac {{G}_{1}P(\infty )({D}_{R}+\left[I\right](\infty ))}{{G}_{E}\left[I\right](\infty )[G](\infty )}}-{\frac {{D}_{R}}{{G}_{E}[I](\infty )}}-{\frac {1}{{G}_{E}}}$ .^[1]

[I](∞): Fasting Insulin plasma concentration (μU/mL)
[G](∞): Fasting blood glucose concentration (mg/dL)
G₁: Parameter for pharmacokinetics (154.93 s/L)
D_R: EC₅₀ of insulin at its receptor (1,6 nmol/L)
G_E: Effector gain (50 s/mol)
P(∞): Constitutive endogenous glucose production (150 μmol/s)

Clinical significance

Validity

Compared to healthy volunteers, SPINA-GR is significantly reduced in persons with prediabetes and diabetes mellitus, and it correlates with the M value in glucose clamp studies, triceps skinfold, subscapular skinfold and (better than HOMA-IR and QUICKI) with the two-hour value in oral glucose tolerance testing (OGTT), glucose rise in OGTT, waist-to-hip ratio, body fat content (measured via DXA) and the HbA1c fraction.^[1]

Clinical utility

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.^[5]

Scientific implications and other uses

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).^[5]

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).^[6]

Pathophysiological implications

In lean subjects it is significantly higher than in a population with obese persons.^[1] 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.^[5]

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.^[7]

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.^[8]

Notes

^ SPINA is an acronym for "structure parameter inference approach".

References

^ ^a ^b ^c ^d Dietrich, JW; Dasgupta, R; Anoop, S; Jebasingh, F; Kurian, ME; Inbakumari, M; Boehm, BO; Thomas, N (21 October 2022). "SPINA Carb: a simple mathematical model supporting fast in-vivo estimation of insulin sensitivity and beta cell function". Scientific Reports. 12 (1): 17659. Bibcode:2022NatSR..1217659D. doi:10.1038/s41598-022-22531-3. PMC 9587026. PMID 36271244.
^ 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. ISBN 978-3-031-82395-4.
^ 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. ISBN 978-981-96-1924-5.
^ 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.
^ ^a ^b ^c Dietrich, Johannes W.; Abood, Assjana; Dasgupta, Riddhi; Anoop, Shajith; Jebasingh, Felix K.; Spurgeon, R.; Thomas, Nihal; Boehm, Bernhard O. (2 January 2024). "A novel simple disposition index ( SPINA-DI ) from fasting insulin and glucose concentration as a robust measure of carbohydrate homeostasis". Journal of Diabetes. 16 (9): e13525. doi:10.1111/1753-0407.13525. PMC 11418405. PMID 38169110. S2CID 266752689.
^ Bansal, Vikas; Winkelmann, Bernhard R.; Dietrich, Johannes W.; Boehm, Bernhard O. (20 February 2024). "Whole-exome sequencing in familial type 2 diabetes identifies an atypical missense variant in the RyR2 gene". Frontiers in Endocrinology. 15. doi:10.3389/fendo.2024.1258982. PMC 10913019. PMID 38444585.
^ Abu Rached, Nessr; Dietrich, Johannes W.; Ocker, Lennart; Stockfleth, Eggert; Haven, Yannik; Myszkowski, Daniel; Bechara, Falk G. (21 March 2025). "Endotyping Insulin–Glucose Homeostasis in Hidradenitis Suppurativa: The Impact of Diabetes Mellitus and Inflammation". Journal of Clinical Medicine. 14 (7): 2145. doi:10.3390/jcm14072145. PMC 11990022. PMID 40217596.
^ Dietrich, Johannes W. (2024). "P4-Endokrinologie – Kybernetische Perspektiven eines neuen Ansatzes" (PDF). Leibniz Online (in German). 54. doi:10.53201/LEIBNIZONLINE54.