Characteristics of glucose metabolism indexes and continuous glucose monitoring system (CGMS) in patients with insulinoma

Insulinoma is the most common functional islet cell tumor of which the most prominent characteristic is the severe status of hypoglycemia caused by improper insulin secretion. It is a common cause of organic hypoglycemia of which the symptoms are diverse and there is a lack of uniqueness [1]. If the nervous system gradually gets accustomed to the chronical hypoglycemia, there will appear asymptomatic hypoglycemia [2], which shall bring about high rates of missed diagnosis and misdiagnosis. More than 50% of the patients are diagnosed to have suffered from it for over five years [3].

The continuous glucose monitoring system is capable of providing the information about blood glucose in all day consecutively and has unique advantages in assessing the fluctuation of blood glucose and in discovering hypoglycemia. Combining with other clinical glucose metabolism indexes, it can be used to discuss how useful it can be to the qualitative diagnosis of insulinoma.

Insulinoma, first described by Graham in 1927, originates from islet β cells, also known as islet β cell tumors, and has an occurrence rate of around 4/1,000,000. Unlike other pancreatic neuroendocrine tumors, most insulinomas are benign, the malignant ones only account for 5–10% [4], and are commonly seen in single adenomas. Insulinoma often occurs to people from 40 to 50 years old with similar occurrences between men and women.

The diagnosis of insulinoma consists of the qualitative diagnosis and the topographic diagnosis. At present, the qualitative diagnosis, of which the main means is laboratory examination, prevails in confirming the diagnosis. The laboratory examination, including determining the blood glucose of hypoglycemia, determining simultaneously the serum insulin and the C-peptide, and carrying out starvation test, aims at confirming the existence of hypoglycemia and that it is caused by the improper secretion of endogenous insulin, that is, when the plasma glucose concentration ≤2.80 mmol/L, the insulin concentration >25 mIU/L, the Insulin Release Index (IRI) >0.3. The topographic diagnosis is dependent on the imaging examination and due to the small size of the tumor and the difficulty in diagnosis, it is mainly used to instruct the operations.

Comparing the general information of all subjects, it can be found out that for patients in the insulinoma group, the time from the onset of the disease to the confirmation of diagnosis is significantly longer than that for patients in the hypoglycemia group (P < 0.01). In order to prevent the incidence of hypoglycemia during the long course, the patients with insulinoma frequently increase their diets and thus cause the rise of the BMI. Hence is the statistical significance in the differences against the patients in the hypoglycemia and control groups (P < 0.01).

The study demonstrates that the fasting blood glucose of patients with insulinoma is lower than that of patients with hypoglycemia, and the fasting insulin, the fasting C-peptide, and the fasting IRI of patients with insulinoma are higher than those of patients with hypoglycemia. The differences are of statistical significance. Similar to the findings of the study, Han et al. [5], in comparing the glucose metabolism indexes of patients with hypoglycemia caused by various reasons, also discover that in the OGTT (oral glucose tolerance test), the differences of the fasting blood glucose, the fasting IRI and C-peptide indexes among the insulinoma group, the functional hypoglycemia group, the impaired glucose tolerance or diabetes group are of statistical significance. The crucial step in the current qualitative diagnosis of insulinoma is to monitor the hypoglycemia and simultaneously determine the IRI >0.3 when the plasma glucose <2.8 mmol/L. However, there are studies indicating that the possibility of insulinoma cannot be ruled out even if the IRI <0.3 [6], and in the study four people among the 22 patients with insulinoma are with the IRI <0.3. Therefore, to provide more reference parameters for the qualitative diagnosis of insulinoma, it is better to combine with the determination of the fasting blood glucose, the fasting insulin, the fasting C-peptide and the calculation of respective IRI.

It is relatively easy to make a qualitative diagnosis of hypoglycemia when the symptom is demonstrated as typical Whipple’s triad. Clinically, however, the symptoms of hypoglycemia for patients with insulinoma are more complicated and volatile, which include the non-specific autonomic nerve symptoms like profuse sweating, palpitation and trembling, as well as the central nervous system disorders like insanity, behavioral abnormality, personality change, epilepsy, narcolepsy, lethargy, coma [7‐10]. The chronic patients can even suffer a remarkable reduction of blood glucose without any clinical symptoms [11, 12]. According to the study of Ding Bo et al. [13], more than half of the patients with insulinoma suffer from hypoglycemia at night and among the 22 patients with insulinoma in this study, there are 11 of them suffering the lowest blood sugar at night. Governed by the vagus nerves, the symptoms of hypoglycemia at night are still more unconspicuous. The traditional fingerstick glucose monitor only provides snapshots of plasma glucose, so reducing its reliability for quantification of hypoglycaemic episodes, especially at night. The traditional glucose examination is unable to discover the atypical hypoglycemia and the hypoglycemia at night timely. The continuous glucose monitoring system, however, can indirectly reflect the level of blood glucose by monitoring the glucose concentration of interstitial fluid of the subcutaneous tissues with a glucose sensor. Capable of offering the information about the blood glucose in all day, its major advantages lie in the fact that it is able to find out the hyperglycemia and hypoglycemia that are beyond the reach of traditional glucose examination, such as post-meal hyperglycemia and asymptomatic hypoglycemia at night, and it can define the time, frequency, and extent of hypoglycemia, making up for the deficiency of traditional glucose examination, and providing new clues to preliminary diagnosis [14]. The emergence of real-time continuous glucose monitoring system equipped with alarming functions for determining hyperglycemia and hypoglycemia, in particular, is beneficial to the diagnosis of insulinoma, especially the atypical cases. Studies show that CGMS contributes to increasing the detection rate of hypoglycemia and the asymptomatic ones in particular [15‐17]. Han et al. contend that the lowest blood glucose detected by CGMS is of diagnostic value to insulinoma. In this study, there is also a marked difference of the lowest blood glucose between the insulinoma group and the hypoglycemia group and the lowest blood glucose of patients in the former group is remarkably lower than that in the latter group of which the hypoglycemia is caused by other reasons. Due to the different occurrence mechanism of hypoglycemia, the patients with insulinoma have suffered more serious hypoglycemia out of the unstifling secretion of endogenous insulin. In addition, this study suggests that hypoglycemia is of vital danger to the patients with insulinoma because of the high number in occurrence, the seriousness in extent and the long period of disease, therefore it is imperative to have early diagnosis and early treatment.

Currently, foreign literatures have referred to such indexes as MG, SD, LI, HBGI/LBGI, GRADE, MAGE, M value, CONGA, MAG, and J-Index, which are correlated with and different from one another, and reflect various aspects of the glucose level and glucose fluctuation based on different calculations. MAGE is considered to be the most comprehensive index for assessing GV in the past, but the calculation method of MAGE neglects the glucose rise or fall by less than 1 SD, so it only reflects the glucose fluctuation with relatively big amplitude and thereby is not quite objective; M value, with the ideal glucose as the baseline, makes the logarithmic transformation of the glucose fluctuation deviating from the baseline and then takes the average, it is a calculation which puts more emphasis on the deviation of hypoglycemia; LBGI is similar to M value, analysing the frequency and extent of hypoglycemia through the mathematical processing of blood glucose values; whereas CONGA-n represents the standard deviation of all current monitoring results against the monitoring results n hours ago, and it can evaluate the glucose fluctuation within the day in an accurate way, thus being more objective than MAGE [18]. It is a calculation which puts more emphasis on the deviation of hypoglycemia. This study suggests that although there are differences between patients with insulinoma and those with hypoglycemia caused by other reasons in SD and MAGE, they are not statistical enough, while the marked differences in CONGA, LBGI and M value are of statistical significance, therefore these are three indexes that are essential for making a clinical analysis of the glucose fluctuation characteristics of patients overwhelmed by hypoglycemia. Their more exact and sensitive descriptions of the extent and glucose fluctuation of hypoglycemia make it easier to tell clinically the insulinoma from hypoglycemia caused by other reasons. Based on the Youden and area under the ROC curve value, LBGI seems to be of the highest accuracy.

Because of the heterogeneity, the diagnosis and differential diagnosis of hypoglycemia is very complicated, determined mainly on clinical features, insulin levels, onset time and several tests. Glucose profile is only one part of these tests, and has not been studied broadly. Our study maybe provide a new clue to the hypoglycemia, and might be useful to identify insulinoma. Our study also has some limitations. First, the accuracy of CGMS sensor is more effective in elevated glycemic levels than hypoglycemic state, so it requires more frequent calibration by fingerstick tests. In our study, 4–6 fingersticks per day are needed to calibrate the CGMS for every person. However, there is still a need for more intensive research efforts in the future to develop robust, highly precise and specific CGMS, which will employ advanced algorithms to take into account the time lag between the concentration of glucose in the blood and the interstitial fluid. Second, due to the limited sample number, large-scale, multi-center study needs to be carried out for assessing the CGMS diagnostic value.