{{Short description|Insulin receptor gain, biomarker}} {{Infobox diagnostic | name = SPINA-GR | image = | alt = | caption = | pronounce = | purpose = Medical diagnosis, research | test of = Insulin sensitivity | based on = | synonyms = | reference_range = 1.41–9.00 mol/s | calculator = https://doi.org/10.5281/zenodo.7479856<br />https://doi.org/10.5281/zenodo.15249620 | DiseasesDB = <!--{{DiseasesDB2|numeric_id}}--> | ICD10 = <!--{{ICD10|Group|Major|minor|LinkGroup|LinkMajor}} or {{ICD10PCS|code|char1/char2/char3/char4}}--> | ICD9 = | ICDO = | MedlinePlus = <!--article_number--> | eMedicine = <!--article_number--> | MeshID = | OPS301 = <!--{{OPS301|code}}--> | LOINC = <!--{{LOINC|code}}--> }} '''SPINA-GR''' is a calculated biomarker for insulin sensitivity.<ref name = "Dietrich_et_al_2022">{{cite journal |last1=Dietrich |first1=JW |last2=Dasgupta |first2=R |last3=Anoop |first3=S |last4=Jebasingh |first4=F |last5=Kurian |first5=ME |last6=Inbakumari |first6=M |last7=Boehm |first7=BO |last8=Thomas |first8=N |title=SPINA Carb: a simple mathematical model supporting fast in-vivo estimation of insulin sensitivity and beta cell function. |journal=Scientific Reports |date=21 October 2022 |volume=12 |issue=1 |page=17659 |doi=10.1038/s41598-022-22531-3 |pmid=36271244|pmc=9587026 |bibcode=2022NatSR..1217659D }}</ref>{{efn|''SPINA'' is an acronym for "structure parameter inference approach".}} 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.<ref name="Santillan_2025">{{cite book |last1=Santillán |first1=Moisés |chapter=Quantitative Insights into Glucose Regulation: A Review of Mathematical Modeling Efforts |title=Dynamics of Physiological Control |series=Lecture Notes on Mathematical Modelling in the Life Sciences |date=2025 |pages=125–148 |doi=10.1007/978-3-031-82396-1_7|isbn=978-3-031-82395-4 }}</ref>
==How to determine G<sub>R</sub>== The index is derived from a mathematical model of insulin-glucose homeostasis that incorporates fundamental physiological motifs.<ref>{{cite book |last1=Hamou-Maamar |first1=Maghnia |chapter=Mathematical Modeling in Diabetes Care and Innovation |title=Computational Mathematics and Modelling for Diabetes |series=Industrial and Applied Mathematics |date=2025 |pages=167–190 |doi=10.1007/978-981-96-1925-2_4|isbn=978-981-96-1924-5 }}</ref><ref>{{cite journal |last1=Dietrich |first1=Johannes W. |last2=Böhm |first2=Bernhard |title=Die MiMe-NoCoDI-Plattform: Ein Ansatz für die Modellierung biologischer Regelkreise |journal=GMDS 2015; 60. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik |date=27 August 2015 |pages=Biometrie und Epidemiologie e.V. (GMDS) |doi=10.3205/15gmds058}}</ref> For diagnostic purposes, it is calculated from fasting insulin and glucose concentrations with:
<math>{\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}}</math>.<ref name = "Dietrich_et_al_2022"/>
[''I''](∞): Fasting Insulin plasma concentration (mol/L)<br /> [''G''](∞): Fasting blood glucose concentration (mol/L)<br /> ''G''<sub>1</sub>: Parameter for pharmacokinetics (154.93 s/L)<br /> ''D''<sub>''R''</sub>: EC<sub>50</sub> of insulin at its receptor (1,6 nmol/L)<br /> ''G''<sub>''E''</sub>: Effector gain (50 s/mol)<br /> ''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.<ref name = "Dietrich_et_al_2022"/>
===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.<ref name="Dietrich_JDiab_2024">{{cite journal |last1=Dietrich |first1=Johannes W. |last2=Abood |first2=Assjana |last3=Dasgupta |first3=Riddhi |last4=Anoop |first4=Shajith |last5=Jebasingh |first5=Felix K. |last6=Spurgeon |first6=R. |last7=Thomas |first7=Nihal |last8=Boehm |first8=Bernhard O. |title=A novel simple disposition index ( SPINA-DI ) from fasting insulin and glucose concentration as a robust measure of carbohydrate homeostasis |journal=Journal of Diabetes |date=2 January 2024 |volume=16 |issue=9 |article-number=e13525 |doi=10.1111/1753-0407.13525 |pmid=38169110 |s2cid=266752689 |doi-access=free |pmc=11418405 }}</ref>
== 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).<ref name="Dietrich_JDiab_2024"/>
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).<ref name="Bansal_2024">{{cite journal |last1=Bansal |first1=Vikas |last2=Winkelmann |first2=Bernhard R. |last3=Dietrich |first3=Johannes W. |last4=Boehm |first4=Bernhard O. |title=Whole-exome sequencing in familial type 2 diabetes identifies an atypical missense variant in the RyR2 gene |journal=Frontiers in Endocrinology |date=20 February 2024 |volume=15 |article-number=1258982 |doi=10.3389/fendo.2024.1258982 |pmid=38444585 |doi-access=free |pmc=10913019 }}</ref>
==Pathophysiological implications== In lean subjects it is significantly higher than in a population with obese persons.<ref name = "Dietrich_et_al_2022"/> 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.<ref name="Dietrich_JDiab_2024"/>
SPINA-GR declines with increasing adherence to mediterranean diet<ref>{{cite journal |last1=Herrera-Carrasco |first1=Karin |last2=Puche-Juarez |first2=Maria |last3=Toledano |first3=Juan Manuel |last4=Ocaña-Peinado |first4=Francisco Manuel |last5=Ochoa |first5=Julio J. |last6=Diaz-Castro |first6=Javier |last7=Moreno-Fernandez |first7=Jorge |title=Combined Effects of Mediterranean Diet Adherence and Physical Activity on Metabolic Homeostasis and Beta-Cell Function in Male Adolescents |journal=Nutrients |date=30 April 2026 |volume=18 |issue=9 |pages=1453 |doi=10.3390/nu18091453 |doi-access=free }}</ref>, which may be explained by increased use of other macronutrients for energy production.
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.<ref name="AbuRached_2025">{{cite journal |last1=Abu Rached |first1=Nessr |last2=Dietrich |first2=Johannes W. |last3=Ocker |first3=Lennart |last4=Stockfleth |first4=Eggert |last5=Haven |first5=Yannik |last6=Myszkowski |first6=Daniel |last7=Bechara |first7=Falk G. |title=Endotyping Insulin–Glucose Homeostasis in Hidradenitis Suppurativa: The Impact of Diabetes Mellitus and Inflammation |journal=Journal of Clinical Medicine |date=21 March 2025 |volume=14 |issue=7 |page=2145 |pmid=40217596 |doi=10.3390/jcm14072145|doi-access=free |pmc=11990022 }}</ref>
==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.<ref>{{cite journal |last1=Dietrich |first1=Johannes W. |title=P4-Endokrinologie – Kybernetische Perspektiven eines neuen Ansatzes |journal=Leibniz Online |date=2024 |volume=54 |doi=10.53201/LEIBNIZONLINE54 |url=https://leibnizsozietaet.de/wp-content/uploads/2024/12/03_03_Kybernetik-2024_DietrichLeibniz-Online-Fachbeitrag.pdf |language=de}}</ref>
== See also == * SPINA-GBeta * SPINA-GD * SPINA-GT * Homeostatic model assessment * QUICKI
==Notes== {{notelist | colwidth = | notes = }}
==References== <references />
==External links== * [https://doi.org/10.5281/zenodo.15874589 Software for calculating SPINA-GBeta and other parameters for endotyping glucose homeostasis]. [https://doi.org/10.5281/zenodo.15249620 (Permanent DOI)], [https://spina.sourceforge.net (General information and US mirror)] * [https://doi.org/10.5281/zenodo.10481776 Functions for R and S for calculating SPINA-GBeta, SPINA-GR and SPINA-DI]. [https://doi.org/10.5281/zenodo.7479856 (Permanent DOI)]
{{Diabetes}} {{Authority control}}
Category:Diabetes Category:Endocrinology Category:Human homeostasis Category:Endocrine procedures Category:Static endocrine function tests