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Diagnosis of diabetes is usually achieved by obtaining a single reading of blood-glucose concentration value from the Oral Glucose Tolerance Test (OGTT). However, the result itself is inadequate in providing insight into the glucose-regulatory etiology of diabetes disease disorder, which is important for treatment purposes. The objective of this project was to conduct clinical simulation and parametric identification of OGTT model for diagnosis of diabetic patient, so as to classify diabetic or at-risk patients into different categories, depending on the nature of their blood-glucose tolerance response to oral injestion of a bolus of glucose. In other words, the patient classification depends on how the blood-glucose concentration varies with time; i.e. how much does it peak, how long does it takes to reach its peak value, how fast does it return to the fasting value, etc. during the oral glucose tolerance test.

To represent this blood-glucose concentration [y(t)] regulatory dynamics, the model selected is a second-order differential equation, of blood-glucose concentration response to a bolus of ingested glucose Gδ(t). This model was then applied to the test subjects by making the model solution-expression for y(t) match the monitored clinical data of blood-glucose concentration at different time intervals, through clinical simulation and parametric identification. The solutions obtained from the model to fit the clinical data were different for normal and diabetic test subjects. The clinical data of "normal" subjects could be simulated by means of an under-damped solution of the model, as: y(t) = (G/ω)e^-ωtsin ωt. The data of "diabetic" patients needed to be simulated by means of an over-damped solution of the model, as: y(t) = (G/ω)e^-Atsin hωt, where G represents the magnitude of the impulse input Gδ(t) to the model (in gms of glucose per litre of blood pool volume), ω is the damped oscillatory frequency of the model, w_n is the natural frequency of the system and .

In order to facilitate differential diagnosis, we developed a non-dimensional diabetic index (DI) expressed as: [Ay_maxT_d/GT_max]. This index can be used to facilitate the diagnosis of diabetes as well as for assessing the risk to becoming diabetic.

Subjective cognitive decline (SCD), characterized by self-perceived subtle cognitive impairment ahead of the appearance of explicit and measurable cognitive deficits, is regarded as the preclinical manifestation of the pathological change continuum of Alzheimer’s disease (AD). We were committed to exploring the amyloid and glucose metabolic signatures related to imminent brain metabolic changes in SCD subjects. This study included 39 subjects (mean age = 71.9 years; 14 males and 25 females) diagnosed with SCD disease and 39 gender-matched healthy controls (HCs) (mean age = 75.2; 16 males and 23 females) with brain [18F] fluorodeoxyglucose positron emission tomography (PET) images and [18F] florbetapir PET images. The standardized uptake value ratios (SUVRs) of PET images within the regions of interest (ROIs) were calculated. Inter-group SUVR differences were assessed by two-sample $t$-testing and receiver operating characteristic curve (ROC) analyses. A generalized linear model (GLM) was employed to evaluate the correlations between amyloid and FDG uptake. Compared with HCs, SCD subjects showed significantly increased amyloid SUVR, as well as significantly increased glucose SUVR in the olfactory, amygdala, thalamus, heschl gyrus, superior and middle temporal gyrus and temporal pole (all $P<0.05$). The amyloid SUVR of thalamus was found to have a better ROC result (area under the curve (AUC): 0.77, 95% confidence interval (CI): 0.66–0.86) in the HC group, as was the case with the glucose SUVR of the middle temporal gyrus (AUC: 0.83, 95% CI: 0.73–0.91). There were significant positive correlations between amyloid and glucose SUVRs ($P<0.05$). The amyloid SUVR of the thalamus showed a significantly better main effect (odd ratio $=$ 2.91, 95% CI: 1.44–6.7, $P<0.001$), and the glucose SUVR of the heschl gyrus indicated an enhanced main effect (odd ratio $=$ 5.08, 95% CI: 1.86–18.15, $P<0.001$). SCD subjects demonstrated significant amyloid accumulation and glucose hypermetabolism in specific brain regions, and amyloid pathology overlapped with regions of glucose abnormality. These findings may advance the understanding of imminent pathological changes in the SCD stage and help to provide clinical guidelines for interventional management.