Authors :
Benjamin T. Solomon; Dagmar Horvath
Volume/Issue :
Volume 11 - 2026, Issue 1 - January
Google Scholar :
https://tinyurl.com/4d5zz5ev
Scribd :
https://tinyurl.com/y36sd45r
DOI :
https://doi.org/10.38124/ijisrt/26jan107
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
A statistical approach to modeling Medical Time Series (MTS) using Blood Glucose Levels (BGL) is provided, for
the purpose of inferring the characteristics of control theory feedback & feedforward mechanisms underlying Type-II
diabetes (T2D). From this perspective, the multiple mechanisms identified may then be related back to the known pathways
of T2D in further studies for their probative value in establishing the dominant process(es) in blood glucose level (BGL)
dysregulation.
This study identified several BGL regulatory characteristics as stable statistical distributions. When non-diabetics’
(ND) BGL is compared to the BGL of a T2D patient with Multiple Sclerosis (MSD), this preliminary study identifies a lack
of ‘Fine Control’ and a lack of ‘Gross Control’ of BGL. Fine Control is the rate of change of BGL within ±2.75 mg/dL/min
and Gross Control is when this rate of change is > |30.0| mg/dL/min.
In ND, the BGL probability distribution is Gamma (α = 43.3663, β = 10.9212), confirming BGL control/regulatory
processes that maintain the normal BGL range. This Gamma establishes a characteristic sensitivity and responsiveness of
ND BLG regulation based on a sample of 201 ND patients which can be used as a reference for other studies. In the sample
T2D patient, the BGL probability distribution is also Gamma (α = 10.921, β = 12.4693) but has lowered mode, a distribution
that is widened and shifted toward high glucose.
Inferred from this change in Gamma is a lowered ‘sensitivity’ to small decreases in BGL demand, compared to the
‘sensitivity’ to small increases in BGL demand, identifying a ‘sensitivity asymmetry’. This small difference in ‘sensitivity’,
and therefore in the body’s BGL regulatory response, is shown by simulation to produce sustained high BGL. Sensitivity in
this context does not imply a dominant pathological or regulatory mechanism in T2D but is a statistical characterization of
T2D. Therefore, the statistical sensitivity does not by itself distinguish between histological defects, such as for example the
fate of pancreatic beta cells, and non-histological factors, for example the impact of SNO-CoA-assisted nitrosylase.
A diurnal property of BGL was identified. BGL is either in an insulin dominance phase or a glucagon dominance phase,
with macro level insulin dominance between 8:30 pm to 6:30 am and macro level glucagon dominance between 6:30 am to
8:30 pm. At the micro level this phase dominance is observed in the periods between measurements.
Keywords :
Type 2 Diabetic, Blood Glucose, Multiple Sclerosis, Stress.
References :
- Diabetes Overview, National Institute of Diabetes and Digestive and Kidney Diseases, accessed March 26th 2024, https://www.niddk.nih.gov/health-information/diabetes/overview
- Kuzuya T, Nakagawa S, Satoh J, Kanazawa Y, Iwamoto Y, Kobayashi M, Nanjo K, Sasaki A, Seino Y, Ito C, Shima K, Nonaka K, Kadowaki T; Committee of the Japan Diabetes Society on the diagnostic criteria of diabetes mellitus. Report of the Committee on the classification and diagnostic criteria of diabetes mellitus. Diabetes Res Clin Pract. 2002 Jan;55(1):65-85. doi: 10.1016/s0168-8227(01)00365-5. PMID: 11755481.
- María M. Adeva-Andany, Raquel Funcasta-Calderón, Carlos Fernández-Fernández, Elvira Castro-Quintela, Natalia Carneiro-Freire, Metabolic effects of glucagon in humans, Journal of Clinical & Translational Endocrinology, Volume 15, 2019, Pages 45-53, ISSN 2214-6237, https://doi.org/10.1016/j.jcte.2018.12.005. https://www.sciencedirect.com/science/article/pii/S2214623718301443
- Box, Jenkins, Reinsel & Ljung, Time Series Analysis: Forecasting & Control, 5th Edition, John Wiley & Sons, 2016 Amazon.com: Time Series Analysis: Forecasting and Control (Wiley Series in Probability and Statistics): 9781118675021: Box, George E. P., Jenkins, Gwilym M., Reinsel, Gregory C., Ljung, Greta M.: Books
- Galicia-Garcia U, Benito-Vicente A, Jebari S, Larrea-Sebal A, Siddiqi H, Uribe KB, Ostolaza H, Martín C. Pathophysiology of Type 2 Diabetes Mellitus. Int J Mol Sci. 2020 Aug 30;21(17):6275. doi: 10.3390/ijms21176275. PMID: 32872570; PMCID: PMC7503727. Pathophysiology of Type 2 Diabetes Mellitus - PubMed (nih.gov)
- The Multiple Sclerosis Stress Equation, Journal of Medical Statistics and Informatics, Vol 11 Article 1, 2023.https://www.hoajonline.com/journals/pdf/2053-7662-11-1.pdf With a post-publication page 17 edit http://www.xodusonemanagement.com/2053-7662-11-1-(2023-08-13)-with-a-correction.pdf
- Hall H, Perelman D, Breschi A, Limcaoco P, Kellogg R, McLaughlin T, et al. (2018) Glucotypes reveal new patterns of glucose dysregulation. PLoS Biol 16(7): e2005143. https://doi.org/10.1371/journal.pbio.2005143
- Solomon BT, Real World Data Modeling: Applications in Statistics, Physics & Medicine, Scholar’s Press, 2021. https://www.morebooks.shop/store/gb/book/real-world-data-modeling/isbn/978-613-8-95346-3
- Shah VN, DuBose SN, Li Z, Beck RW, Peters AL, Weinstock RS, Kruger D, Tansey M, Sparling D, Woerner S, Vendrame F, Bergenstal R, Tamborlane WV, Watson SE, Sherr J. Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study. J Clin Endocrinol Metab. 2019 Oct 1;104(10):4356-4364. doi: 10.1210/jc.2018-02763. Erratum in: J Clin Endocrinol Metab. 2022 Mar 24;107(4):e1775-e1776. PMID: 31127824; PMCID: PMC7296129.
- Weiss M, Steiner DF, Philipson LH. Insulin Biosynthesis, Secretion, Structure, and Structure-Activity Relationships. [Updated 2014 Feb 1]. In: Feingold KR, Anawalt B, Blackman MR, et al., editors. Endotext [Internet]. South Dartmouth (MA): MDText.com, Inc.; 2000-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK279029/
- Aldous N, Moin ASM, Abdelalim EM. Pancreatic β-cell heterogeneity in adult human islets and stem cell-derived islets. Cell Mol Life Sci. 2023 Jun 3;80(6):176. doi: 10.1007/s00018-023-04815-7. PMID: 37270452; PMCID: PMC10239390.
A statistical approach to modeling Medical Time Series (MTS) using Blood Glucose Levels (BGL) is provided, for
the purpose of inferring the characteristics of control theory feedback & feedforward mechanisms underlying Type-II
diabetes (T2D). From this perspective, the multiple mechanisms identified may then be related back to the known pathways
of T2D in further studies for their probative value in establishing the dominant process(es) in blood glucose level (BGL)
dysregulation.
This study identified several BGL regulatory characteristics as stable statistical distributions. When non-diabetics’
(ND) BGL is compared to the BGL of a T2D patient with Multiple Sclerosis (MSD), this preliminary study identifies a lack
of ‘Fine Control’ and a lack of ‘Gross Control’ of BGL. Fine Control is the rate of change of BGL within ±2.75 mg/dL/min
and Gross Control is when this rate of change is > |30.0| mg/dL/min.
In ND, the BGL probability distribution is Gamma (α = 43.3663, β = 10.9212), confirming BGL control/regulatory
processes that maintain the normal BGL range. This Gamma establishes a characteristic sensitivity and responsiveness of
ND BLG regulation based on a sample of 201 ND patients which can be used as a reference for other studies. In the sample
T2D patient, the BGL probability distribution is also Gamma (α = 10.921, β = 12.4693) but has lowered mode, a distribution
that is widened and shifted toward high glucose.
Inferred from this change in Gamma is a lowered ‘sensitivity’ to small decreases in BGL demand, compared to the
‘sensitivity’ to small increases in BGL demand, identifying a ‘sensitivity asymmetry’. This small difference in ‘sensitivity’,
and therefore in the body’s BGL regulatory response, is shown by simulation to produce sustained high BGL. Sensitivity in
this context does not imply a dominant pathological or regulatory mechanism in T2D but is a statistical characterization of
T2D. Therefore, the statistical sensitivity does not by itself distinguish between histological defects, such as for example the
fate of pancreatic beta cells, and non-histological factors, for example the impact of SNO-CoA-assisted nitrosylase.
A diurnal property of BGL was identified. BGL is either in an insulin dominance phase or a glucagon dominance phase,
with macro level insulin dominance between 8:30 pm to 6:30 am and macro level glucagon dominance between 6:30 am to
8:30 pm. At the micro level this phase dominance is observed in the periods between measurements.
Keywords :
Type 2 Diabetic, Blood Glucose, Multiple Sclerosis, Stress.