SPINA-GR
| SPINA-GR | |
|---|---|
| Reference range | 1.41–9.00 mol/s |
| Calculator | https://doi.org/10.5281/zenodo.7479856 |
| Purpose | Medical diagnosis, research |
| Test of | Insulin sensitivity |
SPINA-GR is a calculated biomarker for insulin sensitivity.[1][a] It represents insulin receptor gain.
How to determine GR
[edit]The index is derived from a mathematical model of insulin-glucose homeostasis.[2] For diagnostic purposes, it is calculated from fasting insulin and glucose concentrations with:
.[1]
)}{{G}_{E}\left[I\right](\infty )[G](\infty )}}-{\frac {{D}_{R}}{{G}_{E}[I](\infty )}}-{\frac {1}{{G}_{E}}}}](https://wikimedia.org/api/rest_v1/media/math/render/svg/fb6f6b0ebd5243ed47663596f0541e2875c81540)
[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
[edit]Validity
[edit]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
[edit]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.[3]
Scientific implications and other uses
[edit]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).[3]
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).[4]
Pathophysiological implications
[edit]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.[3]
Predictive aspects
[edit]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.[5]
See also
[edit]Notes
[edit]- ^ SPINA is an acronym for "structure parameter inference approach".
References
[edit]- ^ 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.
- ^ 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.
- ^ Dietrich, Johannes W. (2024). "P4-Endokrinologie – Kybernetische Perspektiven eines neuen Ansatzes" (PDF). Leibniz Online. 54. doi:10.53201/LEIBNIZONLINE54.