The noninvasive characterization of the brain metabolic phenotype in healthy minipigs can be used to detect reference values underlying homeostasis and to carefully assess early temporospatial changes over time due to injury or genetic alterations.
The relationship between cerebral glucose metabolism and glucose transporter expression can be investigated by analyzing dynamic PET images after the administration of the [ 18F]FDG tracer. This analysis includes fitting the experimental data with specific kinetic models.
For what we believe is the first time, we assessed the most appropriate kinetic model to characterize the glucose metabolism of minipig brains using [ 18F]FDG dynamic PET images. In fact, the results establish the kinetic microparameters of the brain regions of healthy minipigs. Our analysis defines reliable reference values of the parameters in normal Göttingen minipig brains which will be helpful in detecting early changes in cerebral viability during the onset of neurological diseases.
Although [ 18F]FDG PET scans are considered the gold standard for evaluating glucose use and cell viability in brain regions of humans , rodents , piglets , dogs , and non-human primates , the use of dynamic [18 F]FDG to explore changes in cerebral glucose metabolism in young Göttingen minipigs remains a desirable goal since they are a breed of small swine increasingly used to study cognitive and behavioral disorders ([20,21,22]).
To date, the cerebral glucose metabolism has been studied in piglets , Danish Yorkshire land pigs , and domestic pigs, . In all these studies, however, only the net influx rate macro-parameter was evaluated, and the corresponding cerebral metabolic rate of glucose (CMRgl) was shown.
Considering the value of the lumped constant equal to 0.44 as in , our CMRgl results are slightly lower than those obtained in : 21.2 (± 7.9) mmol/min per 100 g of tissue in , compared to 13.10 (± 5.49) from 2TCi model and 17.63 (± 8.42) from 2TC model.
In , the net influx rate was evaluated by both multilinear regression and graphical analysis from [ 18F]FDG dynamic brain PET images. The cerebral net influx rate macro-parameter Ki values were of the same order of magnitude as our results, although our minipigs showed slightly higher values i.e. about 0.017 ml/g/min in compared to 0.069 ml/g/min as mean value in the global region from 2TCi model. From the 2TC model, fitting the Ki value was higher both than the 2TCi model and the values presented in . Another study showing results on the [ 18F]FDG uptake is described in , in domestic pigs, which investigates infectious lesion regions and focuses mainly on the evaluation of the net uptake rate Ki. However, in  osteomyelitic and soft tissue lesions, not including the brain, were considered.
It should also be highlighted that we used a different breed of small pigs from that used in [17, 23, 24].
As for the choice of the model that best fits the experimental data, according to the AICc coefficient values shown in Table 2, the difference in coefficients between the 2TCi and 2TC models was not significant, thus we considered both models in the analysis. Compared to the 2TCi model, the 2TC model requires the estimation of the additional parameter k4 which represents the reverse hydrolysis reaction that converts FDG-6P into FDG in tissues.
Tables 3 and 4 show the quantitative estimates of the parameters and enable us to more accurately evaluate the ability of the method to characterize the cerebral metabolism of the deeply sedated Göttingen minipig.
The analysis showed that the parameter values were not significantly different between the regions, with similar values when considering the brain as a whole (penultimate row of Tables 3 and 4). In terms of the data obtained from both models, the only region with parameter values that differed from the global one is the cerebellum which has above all the K1, and consequently, the K1/k2 and Ki, lower than for the other regions. This is also confirmed by the shape of the TAC curve obtained from the cerebellum ROIs (see TACc curve in Fig. 2) which has a different shape to the other curves, especially in the initial ascent phase of the TAC, which most influences the value of K1.
Our data are in accordance with a previous study showing that PET radioligand activity cleared over time much faster in the cerebellum compared to other brain regions in propofol-sedated Gottingen minipigs . Although propofol leads to a 35% decrease in the cerebral metabolic rate of oxygen and a 39% decrease in cerebral blood flow , the administration of ketamine and xylazine, which are known to result in a lower uptake of FDG, led to a lower cerebellar uptake compared to other brain regions .
For this reason, to set normal values for the pig brain as a whole (i.e. as a single large region of interest), it is preferable to consider only the regions with a higher metabolism, therefore excluding the cerebellum, as shown in Tables 3 and 4. In fact, the Retention Index (RI) values, which show the best relation with biological and clinical parameters, were also very similar between different regions of interest, except for the cerebellum which showed a 51% lower RI than the mean value of the other regions (seeTable 5).
The comparison of the results obtained from the estimation using the 2TCi model and the 2TC model (Tables 3 and 4, respectively) shows that the average parameter values are almost all very similar, with the exception of K3 and consequently Ki. The estimated value of K3 (and therefore Ki) from the 2TCi model is lower than that estimated from the 2TC model. This may be due to the fact that in the 2TC model, the k4 is not null; this means that part of the FDG-6P is transformed into FDG, but always keeping the same amount of FDG-6P in the tissue. However, the low values obtained of k4 (see Table 4) suggest that the cerebral metabolism is predominantly irreversible, although a small fraction of the tracer converted to FDG seems present. The high values of standard deviation from the k4 estimation suggest that to obtain a more precise evaluation of this parameter, more experimental data need to be evaluated, possibly with a higher time sampling, and with less noise.
A qualitative view of the resulting parameter values from the two models (Figs. 3 and 4) shows whether there are large variations in the estimated values.
In Figs. 3 and 4, the plus symbols (+) represent outliers, i.e. 1.5 * IQR (interquartile range) away from the top or bottom of the box. The number of outliers is not very high; most concern the parameter K1, for the left occipital, left temporal, left parietal, right and left frontal regions, and are shown in the results of both the 2TCi and 2TC models and regard the results of minipigs 5 and 6. Also, for k3 there are some outliers, mainly in the 2TCi model which are related to minipigs 5 and 6. These outliers are again present in the Ki values from the 2TC model in all regions except for the right occipital and cerebellum. Again, these outliers concern minipig 5 which is evidently the most anomalous in the estimation of micro and macro-parameters. However, we included this pig in the analysis, to take into account the possible variability in the measured data.
The variability in the estimated parameters (see standard deviation values in Tables 3, 4 and 5 and dimensions of boxplots in Figs. 3 and 4), in addition to the experimental variability, is due to various causes related to both data acquisition and analysis. For example, the spillover effect in the PET images, due to the limited spatial resolution, leads to an error in the evaluation of the correct emission in the region of interest and therefore in the estimation of the parameters, which is most noticeable for small ROIs. Furthermore, the noise in the images, and therefore in the TAC curves, makes the correct estimate of the parameters more complex, leading to a variability in the results, which is even more evident for those parameters that are mostly influenced by the last part of the curve, such as k3, k4 and, consequently, Ki.
On the other hand, the variability in the RI index due to noise is much lower (see sd values in Table 5), since integrals of the TAC curves are made for its estimate (Eq. (4)).
Unfortunately, it is not possible to perform a PET scan in awake fasted Gottingen minipigs and the use of anesthetic drugs is the limitation of our in vivo study which should be considered in designing protocol including dynamic [ 18F]FDG PET to analyze regional brain activity. Similarly, anesthetics impede myocardial FDG uptake in mice , and pigs , however the magnitude of effects depends on the type of anesthetics used. Therefore, the selection of anesthetic agents should be seriously taken into account when minipigs undergo PET imaging with [18 F]FDG. Besides these methodological aspects, the present study shows normality values of the brain metabolic dynamic parameters in minipigs using kinetic models applied to a PET scanner for a hospital setting.