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SPINA-GR
Insulin receptor gain, biomarker

SPINA-GR is a calculated biomarker for insulin sensitivity. 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.

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How to determine GR

The index is derived from a mathematical model of insulin-glucose homeostasis.4 For diagnostic purposes, it is calculated from fasting insulin and glucose concentrations with:

G ^ R = G 1 P ( ∞ ) ( D R + [ I ] ( ∞ ) ) G E [ I ] ( ∞ ) [ G ] ( ∞ ) − D R G E [ I ] ( ∞ ) − 1 G E {\displaystyle {\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}}}} .5

[I](∞): Fasting Insulin plasma concentration (μU/mL) [G](∞): Fasting blood glucose concentration (mg/dL) 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)

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.6

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.7

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).8

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).9

Pathophysiological implications

In lean subjects it is significantly higher than in a population with obese persons.10 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.11

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.12

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.13

See also

Notes

References

  1. 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. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9587026 ↩

  2. SPINA is an acronym for "structure parameter inference approach". ↩

  3. Santillán, Moisés (2025). "Quantitative Insights into Glucose Regulation: A Review of Mathematical Modeling Efforts". Dynamics of Physiological Control: 125–148. doi:10.1007/978-3-031-82396-1_7. /wiki/Doi_(identifier) ↩

  4. 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. /wiki/Doi_(identifier) ↩

  5. 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. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9587026 ↩

  6. 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. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9587026 ↩

  7. 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. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418405 ↩

  8. 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. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418405 ↩

  9. 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. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10913019 ↩

  10. 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. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9587026 ↩

  11. 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. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418405 ↩

  12. 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. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11990022 ↩

  13. Dietrich, Johannes W. (2024). "P4-Endokrinologie – Kybernetische Perspektiven eines neuen Ansatzes" (PDF). Leibniz Online (in German). 54. doi:10.53201/LEIBNIZONLINE54. https://leibnizsozietaet.de/wp-content/uploads/2024/12/03_03_Kybernetik-2024_DietrichLeibniz-Online-Fachbeitrag.pdf ↩