SPINA-GBeta

Last updated
SPINA-GBeta
Reference range 0.64–3.73 pmol/s
PurposeMedical diagnosis, research
Test ofPancreatic beta cell function
https://doi.org/10.5281/zenodo.7479856
https://doi.org/10.5281/zenodo.15249620

SPINA-GBeta is a calculated biomarker for pancreatic beta cell function. [1] [a] It represents the maximum amount of insulin that beta cells can produce per time-unit (e.g. in one second).

Contents

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 GBeta

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:

. [1]

[I](∞): Fasting Insulin plasma concentration (μU/mL)
[G](∞): Fasting blood glucose concentration (mg/dL)
Dβ: EC50 for glucose at beta cells (7 mmol/L)
G3: Parameter for pharmacokinetics (58,8 s/L)

Clinical significance

Validity

SPINA-GBeta significantly correlates with the M value in glucose clamp studies and (better than HOMA-Beta) with the two-hour value in oral glucose tolerance testing (OGTT), glucose rise in OGTT, subscapular skinfold, truncal fat content and the HbA1c fraction. [1]

It has the additional advantage that it circumvents the HOMA-blind zone, which renders the calculation of HOMA-Beta impossible if the fasting glucose concentration is 3.5 mmol/L (63 mg/dL) or below. [5] Unlike HOMA-Beta, SPINA-Beta can be sensibly calculated in the whole range of measurements. [1]

Reliability

In repeated measurements, SPINA-GBeta had higher retest reliability than HOMA-Beta, a measurement for beta cell function from the homeostasis model assessment. [1] [6]

Clinical utility

In the FAST study, an observational case-control sequencing study including 300 persons from Germany, SPINA-GBeta differed more clearly between subjects with and without diabetes than the corresponding HOMA-Beta index. [6]

Scientific implications and other uses

Together with the reconstructed insulin receptor gain (SPINA-GR), SPINA-GBeta provides the foundation for the definition of a fasting based disposition index of insulin-glucose homeostasis (SPINA-DI). [6]

In combination with SPINA-GR and whole-exome sequencing, calculating SPINA-GBeta 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). [7]

Pathophysiological implications

In several populations, SPINA-GBeta 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. [6]

In hidradenitis suppurativa, an inflammatory skin disease, SPINA-GBeta is increased to compensate for reduced insulin sensitivity. This dynamical compensation is insufficient, however, in a subset of affected patients, resulting in a reduced static disposition index (SPINA-DI) and the onset of diabetes mellitus. [8]

Since SPINA-GBeta has higher predictive power than HOMA-Beta, its use has been suggested as a calculated early biomarker for the evolution and pathophysiology of gestational diabetes [9] .

Predictive aspects

In a longitudinal evaluation of the NHANES study, a large sample of the general US population, over 10 years, reduced SPINA-GBeta significantly predicted all-cause mortality. [10]

See also

Notes

  1. ↑ SPINA is an acronym for "structure parameter inference approach".

References

  1. 1 2 3 4 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.
  2. ↑ 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.
  3. ↑ 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.
  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.
  5. ↑ Cersosimo, E; Solis-Herrera, C; Trautmann, ME; Malloy, J; Triplitt, CL (January 2014). "Assessment of pancreatic β-cell function: review of methods and clinical applications". Current Diabetes Reviews. 10 (1): 2–42. doi:10.2174/1573399810666140214093600. PMC   3982570 . PMID   24524730.
  6. 1 2 3 4 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.{{cite journal}}: CS1 maint: article number as page number (link)
  7. ↑ 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 1258982. doi: 10.3389/fendo.2024.1258982 . PMC   10913019 . PMID   38444585.
  8. ↑ 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.
  9. ↑ Assani, MZ; Boldeanu, L; Manolea, MM; Boldeanu, MV; Siloși, I; Assani, AD; Văduva, CC; Dijmărescu, AL (7 September 2025). "From Molecular Insights to Clinical Management of Gestational Diabetes Mellitus-A Narrative Review". International Journal of Molecular Sciences. 26 (17): 8719. doi: 10.3390/ijms26178719 . PMC   12429770 . PMID   40943638.
  10. ↑ Dietrich, Johannes W. (2024). "P4-Endokrinologie – Kybernetische Perspektiven eines neuen Ansatzes" (PDF). Leibniz Online (in German). 54. doi:10.53201/LEIBNIZONLINE54.
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