Diabetes mellitus management in the context of cranial tumors

Marco Foreman, Aashay Patel, Sohum Sheth, Akshay Reddy and Brandon Lucke-Wold*

*Correspondence:
Brandon Lucke-Wold,
Brandon.Lucke-Wold@neurosurgery.ufl.edu

Received: 20 April 2023; Accepted: 16 May 2023; Published: 27 May 2023.

The study of the relationship between cancer and diabetes mellitus (DM) has been under investigation for many decades. Particularly in the field of neurology and neurosurgery, increasing emphasis has been put on the examination of comorbid DM in patients with cranial tumors. Namely, as the most common and invasive type of malignant adult brain tumor, glioblastoma (GBS) has been the focus of said research. Several mechanisms have been described in the attempt to elucidate the underlying association between DM and GBS, with the metabolic phenomenon known as the Warburg effect and its consequential downstream effects serving as the resounding culprits in recent literature. Since the effect seen in cancers like GBS exploits an upregulated form of aerobic glycolysis, the role of a sequela of DM, known as hyperglycemia, will be investigated. In particular, in the treatment of GBS, surgical resection and subsequent chemotherapy and/or radiotherapy are used in conjunction with corticosteroid therapy, the latter of which has been linked to hyperglycemia. Unsurprisingly, comorbid DM patients are significantly susceptible to this disposition. Further, this fact is reflected in recent literature that demonstrates the impact of hyperglycemia on cancer advancement and patient outcomes in several preclinical and clinical studies. Thus, this review will aim to underline the significance of diabetes and glycemic control via standard-of-care treatments such as metformin administration, as well as to describe emerging treatments such as the signaling modulation of insulin-like growth factor and the employment of the ketogenic diet.

Keywords: diabetes mellitus (DM), glioblastomas (GBS), hyperglycemia, Warburg effect, glucocorticoids, diabetes management, ketogenic diet, IGF-1 pathway

Introduction

Cancer, specifically that of the brain and central nervous system, accounts for a substantial proportion of morbidity and mortality in the United States and worldwide (1). This fact is due to brain cancer’s complex pathogenesis and mechanisms of action within the body. Among the numerous types of brain tumors, glioblastomas (GBS) are the most common, accounting for 49.1% of all recorded primary, malignant brain tumors (2, 3). Thus, GBS will serve as a model for the subsequent discussion on the interaction between cranial tumors and the management of one of the most significant chronic disease burdens in the United States—diabetes mellitus (DM) (4).

As mentioned earlier, cancer is a formidable disease that manifests itself in almost every organ system and physiological process, with energy metabolism serving as a key area of study for many decades (5). Specifically, a major biochemical hallmark of tumor cells is the disruptive alteration from oxidative phosphorylation to aerobic glycolysis, termed the Warburg effect (6). Many preclinical and clinical studies have shown that hyperglycemia is associated with a worse prognosis in comorbid cancer patients with DM compared to their non-diabetic counterparts.

In this regard, it is imperative to manage plasma glucose levels in order to effectively curb the underlying molecular mechanism for said impact in the context of comorbid patients afflicted with DM and cranial tumors. To that end, an interesting contraindication can be observed when comorbid patients are prescribed corticosteroids for the treatment of their cancer because it has the potential to disrupt glucose control (7). Therefore, the objective of this study is to discuss diabetes management in the context of steroid administration for cranial tumors, i.e., GBS, and to highlight emerging treatment options to ultimately improve patient outcomes.

Diabetes mellitus and Glioblastomas

Together, cancer and diabetes are amongst the leading causes of morbidity and mortality globally (8). A diagnosis of a cranial tumor alone, such as GBS, accounts for 57% of all gliomas, with a 5-year survival rate of only 5.8% (2).

Thus, comorbid with a disease such as DM, which is estimated to afflict approximately 700 million people by 2045 and is already responsible for 15.9% of morbidity in the United States, the study of the association between the two has become increasingly important for the identification of therapeutic targets and effective management (9).

As a disease characterized by uncontrolled and elevated levels of plasma glucose, known as hyperglycemia, this aspect of DM has been suspected to be the main contributor to the poorer prognosis observed in comorbid patients. According to Supabphol and colleagues, several studies have demonstrated that clinical outcomes depend solely on the level of glycemic control in cancer patients, regardless of DM status.

For example, a statistical analysis of a large cohort of 301,948 participants, aged 16–95 years and filtered for known diabetes, was followed up with several years after a health checkup to reveal that patients with elevated plasma glucose suffered significantly more cancer-related deaths (HR: 1.17; 95% CI: 1.03–1.34; p < 0.05) (10). Furthermore, when specifically looking at the impact of hyperglycemia on prognosis in comorbid GBS patients, it was found to confer a statistically significant poorer outcome on overall patient survival (HR, 1.671; p < 0.001) (11).

In sum, the aforementioned statistics suggest that hyperglycemia is implicated in the molecular mechanisms fundamental to the link between DM and cranial tumors such as GBS. Consequently, the proceeding discussion will attempt to highlight the metabolic utility of glucose and its role in malignancy (12).

Cancer and glucose metabolism

The deregulation of cellular energetics in cancer cells dates back nearly a century, when Otto Warburg observed increased glucose uptake and subsequent fermentation of glucose to lactate in mammalian cells even in the presence of aerobic conditions (13). This metabolic rewiring was termed the “Warburg effect” in the early 1970s and was originally thought to be a consequence of mitochondrial dysfunction (14).

Although this hypothesis was proven partially incorrect, the experimental observations helped lead to the discovery that upregulated glycolysis in cancer metabolism is owed to the excess energy demand of the cells, as well as for the biosynthesis of carbohydrates, fats, and proteins (15). Moreover, an important means to that end is the synthesis of glycolytic precursors, which effectively block the negative feedback loop on adenosine triphosphate (ATP) by preventing the accumulation of nicotinamide adenine dinucleotide (NADH)—a powerful modulator of glycolysis (16).

Consequently, this unique feature of cancer cell metabolism facilitates the synthesis of ATP at an accelerated rate and is speculated to explain why they consume more glucose as compared to normal cells. This phenomenon is particularly advantageous for malignant cells because it ultimately confers a selective advantage in promoting proliferation, survival, and long-term maintenance (13).

In the context of brain cancer, such as cranial tumors, the preceding metabolic mechanisms hold true as a defining hallmark of GBS (17). This upregulated glycolytic switch in cancerous brain tissue has fatal consequences, especially because it is responsible for 60% of our daily glucose intake despite accounting for only 2% of total body weight (18).

Specifically, the central nervous system microenvironment allows for increased glucose utilization via the Warburg effect, which has multiple downstream effects that allow for tumorigenesis and enhanced invasiveness via an acid-mediated invasion hypothesis and signal transduction modulation through radical oxygen species and/or chromatin acetylation (1921). As seen in Figure 1, these events are particularly attractive because they identify a direct role for altered glucose metabolism in promoting metastasis, a deadly characteristic of GBS (13).

FIGURE 1
www.bohrpub.com

Figure 1. Summary of observed roles of the Warburg effect. The Warburg effect, which is described as the increased degree of aerobic glycolysis and the preferential synthesis of lactate, confers malignancies such as GBS with the ability to rapidly grow, proliferate, and survive. The functions through which these events occur have been summarized (13).

Thus, it is not a novel insertion to point out the negative impact hyperglycemia can have on individuals afflicted with a cancer such as GBS and can help explain its prognostic significance. In the following discussion, a few standard-of-care approaches to the treatment of GBS will be highlighted, as well as their combined use with a corticosteroid—with the latter being of particular relevance due to its hyperglycemia-inducing properties (11).

Standard approaches to cranial tumor treatment

Surgical resection

Currently, the majority of patients with GBS receive neurosurgical intervention in the form of surgical resections with standard craniotomies under general anesthesia (22, 23). The purpose underlying these procedures is to excise as much tumor tissue as safely possible—in order to reduce its associated pathogenic effects—and to obtain sufficient amounts of tissue to conduct histological analysis (23, 24). Traditionally, surgical resection entailed performing a craniotomy on the affected patient (22, 25). Contingent on the location of the tumor, the patient can either be awake during the procedure or sedated under general anesthesia (22, 25). Awake procedures are generally restricted to cases where the glioma is located at the pre-central gyrus, Wernicke’s area, Broca’s area, and/or the brain stem—all areas that can be mapped intraoperatively with cortical stimulation for language and sensorimotor function (22, 2527).

This mapping allows for better distinction between tumoral tissue and normal, functional brain parenchyma and is thought to achieve tumor removal more precisely as compared to standard craniotomy procedures (22, 2628). Consequently, this higher precision has led to improved post-operative outcomes (Figure 2).

FIGURE 2
www.bohrpub.com

Figure 2. Operative differences between awake and standard surgical resections. (A) Box plot depicting the extent of resection (EOR) in patient groups of either general anesthesia craniotomy or awake craniotomy in a retrospective matched case-control study. Awake procedures achieve greater EOR (median for awake: 100%; median for general anesthesia: 79.73%; p < 0.0001, Mann–Whitney test) with much less variability as compared to general anesthesia procedures. (B) Kaplan–Meier curves depicting post-operative survival for patient groups of either general anesthesia craniotomy or awake craniotomy. Awake procedures depict a trend of a greater proportion of patients surviving during a longer period (median for awake: 17 months; median for general anesthesia: 15 months), yet results are not significant (p = 0.297, χ2 = 1.1) (28).

Otherwise, intraoperative surgical guidance is conducted with pre-operative diffusion tensor imaging (DTI) via magnetic resonance imaging (MRI), which allows for the visualization of subcortical white matter tracts in multiple planes, or functional MRI (fMRI), which identifies functional regions based on increased regions of blood flow seen on the MRI image (Figure 3) (22, 29).

FIGURE 3
www.bohrpub.com

Figure 3. Pre-operative neuronavigation techniques for standard surgical resection. (A) (Left) Functional magnetic resonance imaging (fMRI) displaying highlighted regions of the motor cortex while a patient taps their finger. The blue region corresponds to the right central sulcus and the red region corresponds to the left central sulcus. (Right) fMRI displaying highlighted regions of the speech cortex while the patient speaks in a task. Red region corresponds to Broca’s area. (B) (Left) T1-weighted MRI scan depicting a tumor near the thalamus (white mass). (Right) Diffusion-tensor image (DTI) of the same patient showing white matter tracts. The colors represent directionality of the tracts: Red is left/right, blue is ascending/descending, and green is anterior/posterior (25).

As with any form of resection, this procedure is a delicate balance between maximizing the amount of tumor removed while preserving the patient’s functional status (30). Many of the factors that are considered when determining the excision threshold include the patient’s age, tumor location within the brain, neurological symptoms, and comorbidities such as cerebral edema, cardiovascular disease, and incidents of deep vein thrombosis (3032).

Historically, patients have been evaluated for pre-operative candidature based on age and the Karnofsky Performance Score (KPS) (22, 33), which assesses the physical ability of the patient to independently complete routine tasks and can be used to predict post-operative outcomes. However, KPS fails to capture the presence and effects of the aforementioned comorbidities (3335).

Thus, there is certainly room for improvement in predicting post-operative success by including these additional factors. In fact, recent studies have purported the need to combine KPS evaluation with measures such as the Charlson Comorbidity Index (CCI) to elucidate operative risk and better determine the candidature of patients with GBS (33, 34, 36).

Among possible treatment options for GBS, the amount of tissue excised with surgical resection has the most prognostic impact on patient survival, with multiple sources stating that a minimum of 70% extent of resection (EOR) has a significant impact on improved survival with minimal recurrence (22, 26, 37, 38).

Furthermore, Proescholdt et al. found that 72.5% of studies report beneficial outcomes following surgical resection (39), highlighting its effectiveness in treating an aggressive disease like GBS.

Chemotherapy and radiotherapy

Patients typically receive additional treatment after surgical resection, such as chemotherapy and radiation therapy (22). Bevacizumab and temozolomide (TMZ)-40 are currently the most effective GBS treatments (40). Bevacizumab, an anti-VEGF antibody that is indicated for newly diagnosed and recurrent GBS, has been shown to increase progression-free survival in patients, although it has no effect on the overall survival rate (4042).

The mechanism of action for TMZ involves leveraging its alkylating nature to methylate cellular DNA, causing DNA damage and inducing cell death (22, 43). When given concomitantly with radiotherapy, TMZ was found to extend the median survival time from approximately 12.2 to 14.6 months (43, 44).

Radiotherapy is the standard treatment for patients with unresectable GBS, as well as adjuvant care for post-resection (45). Radiotherapy involves delivering an external radiation beam, usually photons, using a linear accelerator, which induces DNA damage and cell death in tumor tissue (46, 47).

Steroid use with standard Glioblastoma treatment

Glucocorticoid drugs are additionally utilized in the treatment of patients with GBS. Glucocorticoids, such as dexamethasone at 8–16 mg a day, are often pre-scribed perioperatively during radiotherapy to manage vasogenic cerebral edema and associated symptoms that often present with GBS (48, 49). In the context of surgical resection, glucocorticoids are administered to patients to prevent severe consequences of surgical stress, such as adrenal insufficiency or hemodynamic instability (50).

Additionally, glucocorticoids have demonstrated efficacy in reducing pain, nausea, and vomiting associated with GBS tumors and have an increasing effect on patient appetite (51). Typically, higher doses of glucocorticoids are reserved for GBS patients who have larger tumors and more severe neurological deficits (52).

Despite their effectiveness in managing GBS-related symptoms, the possible adverse effects of glucocorticoids cannot be understated. Prolonged use of glucocorticoids has been found to commonly induce blurred vision, myopathy, tremor, behavioral changes, and many more other systemic effects (49, 51, 52). However, many of these effects can resolve upon cessation (52).

One of the most serious side effects of glucocorticoid administration is the exaggerated effect on patient blood glucose levels (53). In fact, the effect of prolonged glucocorticoid administration is strong enough to cause diabetes in patients who have never had hyperglycemia (54). Interestingly, there are many mechanisms of action that enable glucocorticoids to elevate serum sugar levels.

Glucocorticoids, in particular, can disrupt insulin signaling cascades and promote protein catalysis and amino acid release, all of which can prevent insulin action on the skeletal muscle glucose transporter type 4 (GLUT4) and reduce glucose uptake by 30–50% (54, 55). Along with inducing insulin resistance, glucocorticoids are believed to induce apoptosis in pancreatic beta cells and reduce the number of GLUT2 transporters, effectively hindering pancreatic insulin production (54, 56).

Thus, in patients already diagnosed with DM and even in patients with no prior history, it is of the utmost importance to monitor blood sugar levels if glucocorticoids are to be administered. Of note, if a patient with GBS begins to exhibit hyperglycemic symptoms during treatment or already has DM, endocrinology should be consulted.

Diabetes management in cranial tumors

Diabetes mellitus standard of care

Typically, the first-line treatment for type 2 diabetes mellitus (T2DM) recommended by the American Diabetes Association (ADA) is metformin monotherapy with comprehensive lifestyle modifications, inclusive of weight management and physical activity (57). If patients present with comorbidities such as atherosclerotic cardiovascular disease (ASCVD), chronic kidney disease (CKD), and/or heart failure (HF), glucagon-like peptide 1 receptor (GLP1R) agonists or sodium–glucose cotransporter 2 (SGLT2) inhibitors with or without concurrent metformin are indicated as initial therapy.

Unless contraindicated or poorly tolerated, metformin therapy should be continued. If the glycemic need persists, combination therapy may be considered for patients. With patients who evidence catabolism, hyperglycemic symptoms, and/or high hemoglobin A1C (HbA1C) (>10%) or blood glucose (300 mg/dL), insulin may be recommended and intensified based on whether the patient meets treatment goals. Further, medication and medication-taking behavior should be assessed every three to 6 months to ensure treatment efficacy and individualization.

Since the hallmark of type-1 diabetes mellitus (T1DM) is beta cell destruction, recommendations include injections of basal and prandial insulin, which may be administered multiple times daily for basal and postprandial glycemic control. Another approach commonly used is continuous insulin infusion, delivered subcutaneously. Regardless of insulin delivery method, patient education on matching mealtime insulin dose to carbohydrate intake is critical for self-efficacy. For older adults, assessment of geriatric syndromes and the induction of polypharmacy, cognitive impairment, and functional impairment (i.e., KPS) may further aid in the assessment of diabetes self-management (57).

Diabetes mellitus, hyperglycemia, and Glioblastoma treatment considerations

Diabetes management is extremely important in cancer patients. Patients diagnosed with cancer and T2DM, for example, have a 41% higher risk of long-term mortality from any cause than patients diagnosed with cancer without diabetes, according to Barone and colleagues (58). Moreover, studies suggest the prevalence of T2DM in GBS patients to be ∼16% (5961).

However, evidence suggesting a relationship between diabetes status and cranial tumor outcomes is still controversial. Montemurro and colleagues (62) found that the majority of the literature shows no relationship between T2DM and overall survival in GBS patients. However, meta-analyses do point toward decreased overall survival in GBS patients with hyperglycemia, independent of diabetes status (HR, 1.671; p < 0.001) (11).

Specifically, Tieu and colleagues showed that overall survival in GBS patients with blood glucose levels 113 mg/dL treated with radiation and TMZ was 16 months, compared to 13 months for patients with blood glucose levels 113 mg/dL and lower undergoing similar treatment (Figure 4) (63). Similarly, Welch and Grommes found that GBS patients with a median glucose of 173 mg/dL had an 11-month overall survival compared to 9 months for patients with blood glucose ranging from 174 to 247 mg/dL (64).

FIGURE 4
www.bohrpub.com

Figure 4. Blood glucose-dependent survivability following temozolomide and radiotherapy. (A) Kaplan–Meier curve depicting the survivability of patient groups separated by time-weighted blood glucose concentration (from start of radiotherapy to 4 weeks), following concurrent treatment with temozolomide (TMZ) and radiation. (B) Depicts the same as (A), but with a validation group of a similar patient profile. Both plots show an overall increase in survival in the <6.3 mmol/L glucose patient group (median for <6.3 mmol/L: 16 months; median for >6.3 mmol/L: 13 months; p = 0.03), (A); p = 0.005, (B) (63).

Patients in the study by Mayer et al. showed that those experiencing hyperglycemic episodes saw a significant reduction in overall survival of nearly 50%—a degree of negative effect comparable to incomplete treatment per the Stupp protocol (65, 66). Additionally, McGirt et al. showed median survival in persistently hyperglycemic GBS patients undergoing surgical resection to be 5 months, compared to 11 months for the non-hyperglycemic cohort (67).

Interestingly, Derr et al. were able to demonstrate the progressive decline in overall survival when blood glucose levels increased in GBS patients, even after data were adjusted for average daily glucocorticoid dose, age, and KPS at baseline (p = 0.041) (68). However, this study was performed prior to the standard use of TMZ. Of note, most studies did not utilize HbA1C, often regarded as a better measure of glycemic control than blood glucose, in their analysis.

Nevertheless, Barami et al. noted a similar pattern in the negative association between HbA1C and overall survival in GBS patients (69). Lastly, this trend was further corroborated by Lui and colleagues in 2022, who were able to stratify the isocitrate dehydrogenase (IDH)-wildtype GBS based on molecular subclass—namely, RTK I, RTK II, and mesenchymal (70). While tumor methylation status was not associated with variations in overall survival (p = 0.9), greater glucose levels were associated with shorter overall survival in RTK I (p = 0.08) and mesenchymal tumors (p = 0.05).

This trend was not seen in the RTK II tumor sub-type (p = 0.99). Furthermore, they did not find significant epigenetic or metabolomic alterations amongst GBS tumors in diverse glycemic environments. No paper to date has shown improvement in overall survival in hyperglycemic GBS patients. While the negative effect of hyperglycemia on overall survival is not anomalous for solid state tumors, GBS warrants special consideration as corticosteroid treatment—which is known to have hyperglycemia-inducing effects—is the standard of care for GBS patients (71, 72).

Proposed molecular ramifications of hyperglycemia

The inverse relationship among blood glucose and overall survival in GBS patients appears to be complex with multiple mechanisms. Subtypes of gliomas are shown to have distinct mechanisms of genesis, so the effects of hyperglycemia may be different based on glioma sub-type (73). Furthermore, the distinct pathophysiologies of T1DM vs. T2DM may affect GBS biology differently. One possibility points toward glucose having a direct role in GBS spread through the tumor’s ability to take advantage of glucose-dependent metabolism, even with oxygen present (74).

Increased intracerebral glucose, known to be seen at higher levels in patients without prior hyperglycemia, may enable enhanced use of metabolic substrates needed for propagation by the tumor cells (75, 76). Given the high glucose consumption of high-grade cranial tumors, Simoes et al. demonstrated in a mouse model that subjects with gliomas experience a 2.5-fold rise in intracerebral glucose after induction of hyperglycemia (77, 78).

In healthy mice, the glucose bolus minimally affected the glucose content in the brain. Hyperglycemia can activate a number of intracellular pathways involved in tumor progression, including pro-proliferation AKT/mTOR signaling, WNT/-catenin signaling, and increased leptin levels (7981).

Bao et al. recently showed that hyperglycemia upregulated the in vitro expression of G-protein coupled chemoattractant formyl peptide receptor 1 (FPR1) and epidermal growth factor receptor (EGFR) in GBS tumor models, both of which are known to enhance tumor malignancy (79). Several investigations report that FPR1 is associated with a poorer prognosis in GBS, and studies in mice have shown tumor malignancy to decrease when FPR1 RNA is targeted (82, 83).

FPR1 and EGFR also aid in the invasiveness of GBS by directly mediating vascular endothelial growth factor (VEGF) formation (79, 84). Interestingly, endogenous FPR1 agonist Annexin A1 (AnxA1) is released by necrotic GBS cells, which has the effect of further activating the live GBS cells within the tumor microenvironment (85).

Another explanation for the inverse relationship among hyperglycemia and overall survival in GBS may be through increased insulin levels. Because GBS expresses the same insulin receptors found in the periphery, hyperinsulinemias caused by hyperglycemia may promote tumor proliferation independently (8689). Liu et al. discovered increased insulin signaling in conjunction with PI3K-AKT and MAPK upregulation (70, 90).

In addition, hyperinsulinemia has been shown to mimic tumor cell proliferation via the insulin-like growth factor-1 (IGF-1) cascade (90, 91). In fact, the IGF-1 pathway has been shown to promote astrocyte proliferation, and studies suggest overactivation of the IGF-1 pathway is linked to greater GBS invasiveness and poor outcomes (9092). Consequently, silencing of IGF-binding protein-2 has been shown to inhibit invasiveness in human GBS cells (93).

Corticosteroid contraindications

As mentioned earlier, glucocorticoid-induced hyperglycemia remains a concern in the treatment of GBS. Glucocorticoid use has been linked to poorer overall survival outcomes in several studies (2, 94). For example, Welch and Grommes suggested that steroid use could independently predict shorter overall survival. They showed that T2DM patients who remained steroid-dependent lived 8 months less than those who were tapered off steroid therapy (64).

However, this area is still controversial as it is those with greater symptoms, typically, who require corticosteroid therapy. As a result, an examination of the relationship may be muddled because those receiving corticosteroid therapy may have a more aggressive disease state Even then, Chaichana and colleagues demonstrated that the negative prognostic value of corticosteroid use was not dependent on tumor size (95).

Furthermore, Caramanna showed that corticosteroid use was linked to poorer outcomes in memory function, expressive language, and executive function compared to GBS cohorts not using corticosteroids (96). Accordingly, many clinical trials for GBS have corticosteroid use as an exclusion criterion. This may, however, artificially inflate overall survival values in these studies compared to historical literature. Taken together, current literature suggests corticosteroids, if used, should be closely monitored by endocrinology and tapered in use for comorbid GBS patients with diabetes.

Emerging treatments

As mentioned earlier, the standard practices for treating patients diagnosed with cranial tumors are surgical resection, chemotherapy/radiotherapy, and corticosteroid therapy. However, treating high-grade gliomas in patients with DM tends to be complex due to the potential impact of the IGF-1 signaling pathway in hyperinsulinemic patients (97, 98). Prior studies have shown that the invasiveness of GBS can be associated with hyperactivity of the IGF-1 pathway (90, 99).

Furthermore, corticosteroid treatment in DM patients has been shown to be contraindicated due to blood glucose disruption and its association with induced hyperglycemia, which can potentially contribute to worse outcomes in cranial tumor patients (100). Due to the difficulty in treating these patients, advancements have aimed to provide approaches to circumvent this issue. Recent research has shown that a somatostatin analog that regulates the IGF-1 pathway has the potential to reduce glioblastoma growth in various models (101).

Both doxorubicin (DOX) and AN-162 demonstrated inhibition of cell proliferation and prolonging of tumor doubling time in this context, suggesting potential for clinical use in patients suffering from cranial tumors (101). Similarly, the IGF-1R inhibitor picropodophyllin (PPP) has demonstrated similar effects (102). Further, studies have revealed that cell lines are highly sensitive to PPP and that it inhibits progression of the cell cycle—potentially through necrosis—contributing to its potency (103). Further, PPP’s ability to permeate the blood–brain barrier (BBB) and lack of long-term adverse effects in animals suggests the possibility of progression as a future treatment for cranial tumors, specifically in patients with DM.

The lack of flexibility of cranial tumors in using glucose and ketone bodies for energy, compared to normal brain tissue, is where novel approaches are primarily focused (102, 104106). The ability to lower blood glucose while raising ketone bodies allows for specific tumor targeting. Due to the crucial need for strict glucose control, as it has been seen to improve overall survival in these patients, recent studies have emphasized the need for a proper and unique diet built for optimal glycemic control. The ketogenic diet (KD), also known as ketogenic metabolic therapy (KMT), is defined by the inclusion of fat-rich foods with the exclusion of carbohydrate-rich foods. Together, this diet alteration was the initial attempt to alleviate the potential burden of hyperglycemia for malignant brain cancer (107). Due to the lack of sugars in the diet, the intent was to induce ketone body use for energy rather than glucose, which would mitigate the impact of the Warburg effect. Further observation of the tumor site through positron emission tomography with flurodeoxyglucose showed a significant reduction in glucose uptake (107).

Additionally, studies have shown the anticonvulsant and antiepileptic effect of KD, which would be effective in reducing the need for concomitant glucocorticoid therapy (108). Similarly, diet restriction also provides an anti-angiogenic effect due to a reduction in tumor metabolism (104, 109). The combination of a lack of cerebral blood flow and a lack of glucose for energy can further reduce tumor growth and emphasize apoptosis (110). However, there are many other proposed mechanisms involved when discussing the utility of the KD, as can be seen in Figure 5.

FIGURE 5
www.bohrpub.com

Figure 5. Proposed cellular mechanisms of ketogenic diet’s associated antineoplastic effects. The ketogenic diet (KD) is aimed at lowering glycemic levels and inducing ketosis to differentially affect the metabolism of cancer cells. Together, a deficit in glucose-derived ATP synthesis, reduced nucleotide biosynthesis, and absent redox potential drive malignant cells toward an apoptotic state that is then vulnerable to Rx/Ctx treatment. Concurrently, a decline of systemic levels of IGF-1, insulin, and GH diminish tumorigenicity and metastaticity by inhibiting pro-survival stimuli via Akt/mTOR and Ras/MAPK pathway modulation. Due to insufficient enzymatic machinery, cancer cells will inadequately metabolize ketone bodies (KB)—namely, ß-OHB and AcAc—which will then lead to their subsequent accumulation and further increase proapoptotic stimuli via ROS signaling. Conversely, KBs are efficiently metabolized by non-pathologic brain parenchyma and are thought to induce a neuroprotective state that may prevent metastasis and potentially attenuate damage due to Rx/Ctx treatment. Finally, KD has demonstrated immune-boosting effects through alleviating immune suppression and increasing tumor-reactive immune response (111).

Although the recent advancements have shown promise, far more research must be conducted in larger human populations before they can have any definite effect clinically. Novel advancements and emerging treatments are lacking in the context of patients with DM diagnosed with cranial tumors. Given the substantial increase in the prevalence of DM in the United States, it is imperative that further research focus on therapeutics that circumvent the contraindications for cranial tumor therapy in DM patients (112).

Conclusion

In this review, we identified a clear negative association between hyperglycemia, a major consequence of DM, and its impact on GBS progression and patient prognosis. We first explained the metabolic mechanisms behind this relationship, principally the Warburg effect, and proceeded to highlight the importance of glycemic control to curb its effects via standard-of-care options such as metformin administration. Of importance, we also underlined the contraindication consistently observed in preclinical and clinical trials regarding corticosteroid pharmacotherapy’s pronounced effect on patient blood glucose levels.

Moreover, the potential ramifications of such blood glucose elevations were explained to be implicated in the alteration of several intracellular pathways, as explained earlier, as well as increased insulin levels. Although contraindicated in the context of its hyperglycemic-inducing effects, especially in comorbid DM patients, the literature supports corticosteroid use when used in conjunction with good glycemic control and frequent endocrinology consultation.

Additionally, we introduced recent interventions targeted at the IGF-1 pathway via the administration of somatostatin analogs, such as DOX and AN-162, and an IGF-1R inhibitor known as PPP. Individually, these drugs have shown promise in their ability to alleviate GBS growth. Finally, a more novel approach targeted at the Warburg effect itself, via a diet modification to the KD, was explained.

Author contributions

MF, AP, SS, and AR prepared the original draft. BL-W reviewed and edited. All authors contributed to the article and approved the submitted version.

References

1. Miller K, Ostrom Q, Kruchko C. Brain and other central nervous system tumor statistics, 2021. CA Cancer J Clin. (2021) 71:381–406. doi: 10.3322/caac.21693

CrossRef Full Text | Google Scholar

2. Tan A, Ashley D, López G, Malinzak M, Friedman H, Khasraw M. Management of glioblastoma: state of the art and future directions. CA Cancer J Clin. (2020) 70:299–312. doi: 10.3322/caac.21613

CrossRef Full Text | Google Scholar

3. Shah V, Kochar P. Brain cancer: implication to disease, therapeutic strategies and tumor targeted drug delivery approaches. Recent Patents Anticancer Drug Discov. (2018) 13:70–85. doi: 10.2174/1574892812666171129142023

CrossRef Full Text | Google Scholar

4. Engelgau M, Geiss L, Saaddine J. The evolving diabetes burden in the United States. Ann Intern Med. (2004) 140:945–50. doi: 10.7326/0003-4819-140-11-200406010-00035

CrossRef Full Text | Google Scholar

5. Supabphol S, Seubwai W, Wongkham S, Saengboonmee C. High glucose: an emerging association between diabetes mellitus and cancer progression. J Mol Med. (2021) 99:1175–93. doi: 10.1007/s00109-021-02096-w

CrossRef Full Text | Google Scholar

6. Shaw R. Glucose metabolism and cancer. Curr Opin Cell Biol. (2006) 18:598–608. doi: 10.1016/j.ceb.2006.10.005

CrossRef Full Text | Google Scholar

7. Jannot-Lamotte M, Raccah D. [Management of diabetes during corticosteroid therapy]. Presse Medicale Paris Fr. (2000) 29:263–6.

Google Scholar

8. Ramteke P, Deb A, Shepal V, Bhat M. Hyperglycemia associated metabolic and molecular alterations in cancer risk. Progression, treatment, and mortality. Cancers. (2019) 11:1402. doi: 10.3390/cancers11091402

CrossRef Full Text | Google Scholar

9. Kaul K, Tarr J, Ahmad S, Kohner E, Chibber R. Introduction to Diabetes Mellitus. In: Ahmad S editor. Diabetes: an old disease, a new insight. Berlin: Springer (2013). p. 1–11. doi: 10.1007/978-1-4614-5441-0_1 Advances in Experimental Medicine and Biology.

CrossRef Full Text | Google Scholar

10. Simon J, Thomas F, Czernichow S. Hyperglycaemia is associated with cancer-related but not non-cancer-related deaths: evidence from the IPC cohort. Diabetologia. (2018) 61:1089–97. doi: 10.1007/s00125-017-4540-8

CrossRef Full Text | Google Scholar

11. Lu V, Goyal A, Vaughan L, McDonald K. The impact of hyperglycemia on survival in glioblastoma: a systematic review and meta-analysis. Clin Neurol Neurosurg. (2018) 170:165–9. doi: 10.1016/j.clineuro.2018.05.020

CrossRef Full Text | Google Scholar

12. Hanahan D, Weinberg R. Hallmarks of cancer: the next generation. Cell. (2011) 144:646–74. doi: 10.1016/j.cell.2011.02.013

CrossRef Full Text | Google Scholar

13. Liberti M, Locasale J. The warburg effect: how does it benefit cancer cells? Trends Biochem Sci. (2016) 41:211–8. doi: 10.1016/j.tibs.2015.12.001

CrossRef Full Text | Google Scholar

14. Potter M, Newport E, Morten K. The Warburg effect: 80 years on. Biochem Soc Trans. (2016) 44:1499–505. doi: 10.1042/BST20160094

CrossRef Full Text | Google Scholar

15. Gatenby R, Gillies R. Why do cancers have high aerobic glycolysis? Nat Rev Cancer. (2004) 4:891–9. doi: 10.1038/nrc1478

CrossRef Full Text | Google Scholar

16. Pavlova N, Thompson C. The emerging hallmarks of cancer metabolism. Cell Metab. (2016) 23:27–47.

Google Scholar

17. Marie S, Shinjo S. Metabolism and brain Cancer. Clinics. (2011) 66:33–43. doi: 10.1590/S1807-59322011001300005

CrossRef Full Text | Google Scholar

18. Agnihotri S, Zadeh G. Metabolic reprogramming in glioblastoma: the influence of cancer metabolism on epigenetics and unanswered questions. Neuro Oncol. (2016) 18:160–72.

Google Scholar

19. Estrella V, Chen T, Lloyd M. Acidity generated by the tumor microenvironment drives local invasion. Cancer Res. (2013) 73:1524–35. doi: 10.1158/0008-5472.CAN-12-2796

CrossRef Full Text | Google Scholar

20. Gatenby R, Gawlinski E. A reaction-diffusion model of cancer invasion. Cancer Res. (1996) 56:5745–53.

Google Scholar

21. Wellen K, Thompson C. A two-way street: reciprocal regulation of metabolism and signalling. Nat Rev Mol Cell Biol. (2012) 13:270–6. doi: 10.1038/nrm3305

CrossRef Full Text | Google Scholar

22. Young R, Jamshidi A, Davis G, Sherman J. Current trends in the surgical management and treatment of adult glioblastoma. Ann Transl Med. (2015) 3:121. doi: 10.3978/j.issn.2305-5839.2015.05.10

CrossRef Full Text | Google Scholar

23. Wang L, Liang B, Li Y, Liu X, Huang J, Li Y. What is the advance of extent of resection in glioblastoma surgical treatment—a systematic review. Chin Neurosurg J. (2019) 5:2.

Google Scholar

24. Manrique-Guzmán S, Herrada-Pineda T, Revilla-Pacheco F. Surgical management of Glioblastoma. In: De Vleeschouwer S editor. Glioblastoma. Codon Publications* (2017).

Google Scholar

25. Hentschel S, Lang F. Current surgical management of Glioblastoma. Cancer J. (2003) 9:113–25.

Google Scholar

26. Zhang J, Lee K, Voisin M, Hervey-Jumper S, Berger M, Zadeh G. Awake craniotomy for resection of supratentorial glioblastoma: a systematic review and meta-analysis. Neuro-Oncol Adv. (2020) 2:vdaa111. doi: 10.1093/noajnl/vdaa111

CrossRef Full Text | Google Scholar

27. Hervey-Jumper S, Li J, Lau D. Awake craniotomy to maximize glioma resection: methods and technical nuances over a 27-year period. J Neurosurg. (2015) 123:325–39.

Google Scholar

28. Gerritsen J, Viëtor C, Rizopoulos D. Awake craniotomy versus craniotomy under general anesthesia without surgery adjuncts for supratentorial glioblastoma in eloquent areas: a retrospective matched case-control study. Acta Neurochir. (2019) 161:307–15. doi: 10.1007/s00701-018-03788-y

CrossRef Full Text | Google Scholar

29. Dubey A, Kataria R, Sinha V. Role of Diffusion Tensor Imaging in Brain Tumor Surgery. Asian J Neurosurg. (2018) 13:302–6. doi: 10.4103/ajns.AJNS_226_16

CrossRef Full Text | Google Scholar

30. Müller D, Robe P, Eijgelaar R. Comparing Glioblastoma surgery decisions between teams using brain maps of tumor locations, biopsies, and resections. JCO Clin Cancer Inform. (2019):1–12. doi: 10.1200/CCI.18.00089

CrossRef Full Text | Google Scholar

31. Fisher J, Palmisano S, Schwartzbaum J, Svensson T, Lönn S. Comorbid conditions associated with glioblastoma. J Neurooncol. (2014) 116:585–91. doi: 10.1007/s11060-013-1341-x

CrossRef Full Text | Google Scholar

32. Villani V, Tanzilli A, Telera S. Comorbidities in elderly patients with glioblastoma: a field-practice study. Future Oncol. (2019) 15:841–50. doi: 10.2217/fon-2018-0524

CrossRef Full Text | Google Scholar

33. Ening G, Osterheld F, Capper D, Schmieder K, Brenke C. Charlson comorbidity index: an additional prognostic parameter for preoperative glioblastoma patient stratification. J Cancer Res Clin Oncol. (2015) 141:1131–7. doi: 10.1007/s00432-014-1907-9

CrossRef Full Text | Google Scholar

34. Bruno F, Pellerino A, Pronello E. Elderly Gliobastoma patients: the impact of surgery and adjuvant treatments on survival: a single institution experience. Brain Sci. (2022) 12:632. doi: 10.3390/brainsci12050632

CrossRef Full Text | Google Scholar

35. Péus D, Newcomb N, Hofer S. Appraisal of the Karnofsky performance status and proposal of a simple algorithmic system for its evaluation. BMC Med Inform Decis Mak. (2013) 13:72. doi: 10.1186/1472-6947-13-72

CrossRef Full Text | Google Scholar

36. Barz M, Bette S, Janssen I. Age-adjusted Charlson comorbidity index in recurrent glioblastoma: a new prognostic factor? BMC Neurol. (2022) 22:32. doi: 10.1186/s12883-021-02532-x

CrossRef Full Text | Google Scholar

37. Sanai N, Polley M, McDermott M, Parsa A, Berger M. An extent of resection threshold for newly diagnosed glioblastomas. J Neurosurg. (2011) 115:3–8. doi: 10.3171/2011.2.jns10998

CrossRef Full Text | Google Scholar

38. Chaichana K, Halthore A, Parker S. Factors nvolved in maintaining prolonged functional independence following supratentorial glioblastoma resection. Clinical article. J Neurosurg. (2011) 114:604–12. doi: 10.3171/2010.4.JNS091340

CrossRef Full Text | Google Scholar

39. Proescholdt M, Macher C, Woertgen C, Brawanski A. Level of evidence in the literature concerning brain tumor resection. Clin Neurol Neurosurg. (2005) 107:95–8. doi: 10.1016/j.clineuro.2004.02.025

CrossRef Full Text | Google Scholar

40. Rajaratnam V, Islam M, Yang M, Slaby R, Ramirez H, Mirza S. Glioblastoma: pathogenesis and current status of chemotherapy and other novel treatments. Cancers. (2020) 12:937. doi: 10.3390/cancers12040937

CrossRef Full Text | Google Scholar

41. Davis M. Glioblastoma: overview of disease and treatment. Clin J Oncol Nurs. (2016) 20:S2–8. doi: 10.1188/16.CJON.S1.2-8

CrossRef Full Text | Google Scholar

42. Gilbert M, Dignam J, Armstrong T. A randomized trial of bevacizumab for newly diagnosed glioblastoma. N Engl J Med. (2014) 370:699–708. doi: 10.1056/NEJMoa1308573

CrossRef Full Text | Google Scholar

43. Zhang J, Stevens M, Bradshaw T. Temozolomide: mechanisms of action, repair and resistance. Curr Mol Pharmacol. (2012) 5:102–14. doi: 10.2174/1874467211205010102

CrossRef Full Text | Google Scholar

44. Mann J, Ramakrishna R, Magge R, Wernicke A. Advances in radiotherapy for Glioblastoma. Front Neurol. (2018) 8:748. doi: 10.3389/fneur.2017.00748

CrossRef Full Text | Google Scholar

45. Barani I, Larson D. Radiation therapy of Glioblastoma. In: Raizer J, Parsa A editors. Current understanding and treatment of gliomas. Berlin: Springer International Publishing (2015). p. 49–73. doi: 10.1007/978-3-319-12048-5_4 Cancer Treatment and Research.

CrossRef Full Text | Google Scholar

46. Baskar R, Lee K, Yeo R, Yeoh K. Cancer and radiation therapy: current advances and future directions. Int J Med Sci. (2012) 9:193–9. doi: 10.7150/ijms.3635

CrossRef Full Text | Google Scholar

47. Maier P, Hartmann L, Wenz F, Herskind C. Cellular pathways in response to ionizing radiation and their Targetability for Tumor Radiosensitization. Int J Mol Sci. (2016) 17:102. doi: 10.3390/ijms17010102

CrossRef Full Text | Google Scholar

48. Fernandes C, Costa A, Osório L. Current standards of care in Glioblastoma therapy. In: De Vleeschouwer S editor. Glioblastoma. Codon Publications* (2017).

Google Scholar

49. Petrelli F, De Stefani A, Ghidini A. Steroids use and survival in patients with glioblastoma multiforme: a pooled analysis. J Neurol. (2021) 268:440–7.

Google Scholar

50. Seo K. Perioperative glucocorticoid management based on current evidence. Anesth Pain Med. (2021) 16:8–15.

Google Scholar

51. Dietrich J, Rao K, Pastorino S, Kesari S. Corticosteroids in brain cancer patients: benefits and pitfalls. Expert Rev Clin Pharmacol. (2011) 4:233–42. doi: 10.1586/ecp.11.1

CrossRef Full Text | Google Scholar

52. Pitter K, Tamagno I, Alikhanyan K. Corticosteroids compromise survival in glioblastoma. Brain. (2016) 139:1458–71. doi: 10.1093/brain/aww046

CrossRef Full Text | Google Scholar

53. Izzedine H, Launay-Vacher V, Deybach C, Bourry E, Barrou B, Deray G. Drug-induced diabetes mellitus. Expert Opin Drug Saf. (2005) 4:1097–109. doi: 10.1517/14740338.4.6.1097

CrossRef Full Text | Google Scholar

54. Tamez-Pérez H, Quintanilla-Flores D, Rodríguez-Gutiérrez R, González-González J, Tamez-Peña A. Steroid hyperglycemia: Prevalence, early detection and therapeutic recommendations: a narrative review. World J Diabetes. (2015) 6:1073–81. doi: 10.4239/wjd.v6.i8.1073

CrossRef Full Text | Google Scholar

55. Perez A, Jansen-Chaparro S, Saigi I, Bernal-Lopez M, Miñambres I, Gomez-Huelgas R. Glucocorticoid-induced hyperglycemia. J Diabetes. (2014) 6:9–20. doi: 10.1111/1753-0407.12090

CrossRef Full Text | Google Scholar

56. van Raalte D, Ouwens D, Diamant M. Novel insights into glucocorticoid-mediated diabetogenic effects: towards expansion of therapeutic options? Eur J Clin Invest. (2009) 39:81–93. doi: 10.1111/j.1365-2362.2008.02067.x

CrossRef Full Text | Google Scholar

57. American Diabetes Association Professional Practice Commitee. 9. Pharmacologic approaches to glycemic treatment: standards of medical care in diabetes—2022. Diabetes Care. (2021) 45:S125–43. doi: 10.2337/dc22-S009

CrossRef Full Text | Google Scholar

58. Barone B, Yeh H, Snyder C. Long-term all-cause mortality in cancer patients with preexisting diabetes mellitus: a systematic review and meta-analysis. JAMA. (2008) 300:2754–64. doi: 10.1001/jama.2008.824

CrossRef Full Text | Google Scholar

59. Grommes C, Conway D, Alshekhlee A, Barnholtz-Sloan J. Inverse association of PPARγ agonists use and high grade glioma development. J Neurooncol. (2010) 100:233–9.

Google Scholar

60. Schwartzbaum J, Jonsson F, Ahlbom A. Prior hospitalization for epilepsy, diabetes, and stroke and subsequent glioma and meningioma risk. Cancer Epidemiol Biomarkers Prev. (2005) 14:643–50. doi: 10.1158/1055-9965.EPI-04-0119

CrossRef Full Text | Google Scholar

61. Purow B. For glioma, a sweet side to diabetes. Neuro Oncol. (2016) 18:306–7. doi: 10.1093/neuonc/nov328

CrossRef Full Text | Google Scholar

62. Montemurro N, Perrini P, Rapone B. Clinical risk and overall survival in patients with diabetes mellitus, hyperglycemia and Glioblastoma multiforme. A review of the current literature. Int J Environ Res Public Health. (2020) 17:8501.

Google Scholar

63. Tieu M, Lovblom L, McNamara M. Impact of glycemia on survival of glioblastoma patients treated with radiation and temozolomide. J Neurooncol. (2015) 124:119–26.

Google Scholar

64. Welch M, Grommes C. Retrospective analysis of the effects of steroid therapy and antidiabetic medication on survival in diabetic glioblastoma patients. CNS Oncol. (2013) 2:237–46. doi: 10.2217/cns.13.12

CrossRef Full Text | Google Scholar

65. Mayer A, Vaupel P, Struss H, Giese A, Stockinger M, Schmid-Berger H. Strong adverse prognostic impact of hyperglycemic episodes during adjuvant chemoradiotherapy of glioblastoma multiforme. Strahlenther Onkol. (2014) 190:933–8.

Google Scholar

66. Stupp R, Mason W, van den Bent M. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med. (2005) 352:987–96. doi: 10.1056/NEJMoa043330

CrossRef Full Text | Google Scholar

67. McGirt M, Chaichana K, Gathinji M. Persistent outpatient hyperglycemia is independently associated with decreased survival after primary resection of malignant brain astrocytomas. Neurosurgery. (2008) 63:286–91.

Google Scholar

68. Derr R, Ye X, Islas M, Desideri S, Saudek C, Grossman S. Association between hyperglycemia and survival in patients with newly diagnosed Glioblastoma. J Clin Oncol. (2009) 27:1082–6. doi: 10.1200/JCO.2008.19.1098

CrossRef Full Text | Google Scholar

69. Barami K, Lyon L, Conell C. Type 2 diabetes mellitus and Glioblastoma Multiforme–Assessing risk and survival: results of a large retrospective study and systematic review of the literature. World Neurosurg. (2017) 106:300–7. doi: 10.1016/j.wneu.2017.06.164

CrossRef Full Text | Google Scholar

70. Liu E, Vasudevaraja V, Sviderskiy V. Association of hyperglycemia and molecular subclass on survival in IDH-wildtype glioblastoma. Neuro-Oncol Adv. (2022) 4:vdac163.

Google Scholar

71. Villarreal-Garza C, Shaw-Dulin R, Lara-Medina F. Impact of diabetes and hyperglycemia on survival in advanced breast cancer patients. Exp Diabetes Res. (2012) 2012:732027.

Google Scholar

72. Hosokawa T, Kurosaki M, Tsuchiya K. Hyperglycemia is a significant prognostic factor of hepatocellular carcinoma after curative therapy. World J Gastroenterol. (2013) 19:249–57. doi: 10.3748/wjg.v19.i2.249

CrossRef Full Text | Google Scholar

73. Cohen A, Colman H. Glioma biology and molecular markers. Cancer Treat Res. (2015) 163:15–30. doi: 10.1007/978-3-319-12048-5_2

CrossRef Full Text | Google Scholar

74. Yuen C, Asuthkar S, Guda M, Tsung A, Velpula K. Cancer stem cell molecular reprogramming of the Warburg effect in glioblastomas: a new target gleaned from an old concept. CNS Oncol. (2016) 5:101–8. doi: 10.2217/cns-2015-0006

CrossRef Full Text | Google Scholar

75. Hwang J, Jiang L, Hamza M. Blunted rise in brain glucose levels during hyperglycemia in adults with obesity and T2DM. JCI Insight. (2017) 2:e95913.

Google Scholar

76. Gruetter R, Ugurbil K, Seaquist E. Steady-state cerebral glucose concentrations and transport in the human brain. J Neurochem. (1998) 70:397–408. doi: 10.1046/j.1471-4159.1998.70010397.x

CrossRef Full Text | Google Scholar

77. Di Chiro G, DeLaPaz R, Brooks R. Glucose utilization of cerebral gliomas measured by [18F] fluorodeoxyglucose and positron emission tomography. Neurology. (1982) 32:1323–9. doi: 10.1212/wnl.32.12.1323

CrossRef Full Text | Google Scholar

78. Simões R, García-Martín M, Cerdán S, Arús C. Perturbation of mouse glioma MRS pattern by induced acute hyperglycemia. NMR Biomed. (2008) 21:251–64. doi: 10.1002/nbm.1188

CrossRef Full Text | Google Scholar

79. Bao Z, Chen K, Krepel S. High glucose promotes human glioblastoma cell growth by increasing the expression and function of Chemoattractant and growth factor receptors. Transl Oncol. (2019) 12:1155–63. doi: 10.1016/j.tranon.2019.04.016

CrossRef Full Text | Google Scholar

80. Vasconcelos-dos-Santos A, Loponte H, Mantuano N. Hyperglycemia exacerbates colon cancer malignancy through hexosamine biosynthetic pathway. Oncogenesis. (2017) 6:e306–306. doi: 10.1038/oncsis.2017.2

CrossRef Full Text | Google Scholar

81. Yu Y, Bao Z, Wang X. The G-protein-coupled chemoattractant receptor Fpr2 exacerbates high glucose-mediated proinflammatory responses of Müller glial cells. Front Immunol. (2017) 8:1852. doi: 10.3389/fimmu.2017.01852

CrossRef Full Text | Google Scholar

82. Huang J, Hu J, Bian X. Transactivation of the epidermal growth factor receptor by formylpeptide receptor exacerbates the malignant behavior of human Glioblastoma cells. Cancer Res. (2007) 67:5906–13. doi: 10.1158/0008-5472.CAN-07-0691

CrossRef Full Text | Google Scholar

83. Zhou Y, Bian X, Le Y. Formylpeptide receptor FPR and the rapid growth of malignant human Gliomas. JNCI J Natl Cancer Inst. (2005) 97:823–35. doi: 10.1093/jnci/dji142

CrossRef Full Text | Google Scholar

84. Yao X, Ping Y, Chen J. Glioblastoma stem cells produce vascular endothelial growth factor by activation of a G-protein coupled formylpeptide receptor FPR. J Pathol. (2008) 215:369–76. doi: 10.1002/path.2356

CrossRef Full Text | Google Scholar

85. Yang Y, Liu Y, Yao X. Annexin 1 released by necrotic human glioblastoma cells stimulates tumor cell growth through the formyl peptide receptor 1. Am J Pathol. (2011) 179:1504–12. doi: 10.1016/j.ajpath.2011.05.059

CrossRef Full Text | Google Scholar

86. Djiogue S, Nwabo Kamdje A, Vecchio L. Insulin resistance and cancer: the role of insulin and IGFs. Endocr Relat Cancer. (2013) 20:R1–17. doi: 10.1530/ERC-12-0324

CrossRef Full Text | Google Scholar

87. Fiedler J, Brill C, Blum W, Brenner R. IGF-I and IGF-II stimulate directed cell migration of bone-marrow-derived human mesenchymal progenitor cells. Biochem Biophys Res Commun. (2006) 345:1177–83. doi: 10.1016/j.bbrc.2006.05.034

CrossRef Full Text | Google Scholar

88. Grunberger G, Lowe W, McElduff A, Glick R. Insulin receptor of human cerebral gliomas. Structure and function. J Clin Invest. (1986) 77:997–1005. doi: 10.1172/JCI112402

CrossRef Full Text | Google Scholar

89. Plum L, Schubert M, Brüning J. The role of insulin receptor signaling in the brain. Trends Endocrinol Metab TEM. (2005) 16:59–65. doi: 10.1016/j.tem.2005.01.008

CrossRef Full Text | Google Scholar

90. Wang H, Wang H, Shen W. Insulin-like growth factor binding protein 2 enhances glioblastoma invasion by activating invasionenhancing genes. Cancer Res. (2003) 63:4315–21.

Google Scholar

91. Zumkeller W, Westphal M. The IGF/IGFBP system in CNS malignancy. Mol Pathol MP. (2001) 54:227–9. doi: 10.1136/mp.54.4.227

CrossRef Full Text | Google Scholar

92. McDonald L, O’Sullivan M, Parkinson J. IQGAP1 and IGFBP2: valuable biomarkers for determining prognosis in glioma patients. J Neuropathol Exp Neurol. (2007) 66:405–17. doi: 10.1097/nen.0b013e31804567d7

CrossRef Full Text | Google Scholar

93. Fukushima T, Tezuka T, Shimomura T, Nakano S, Kataoka H. Silencing of insulin-like growth factor-binding protein-2 in human glioblastoma cells reduces both invasiveness and expression of progression-associated gene CD24. J Biol Chem. (2007) 282:18634–44. doi: 10.1074/jbc.M609567200

CrossRef Full Text | Google Scholar

94. Hui C, Rudra S, Ma S, Campian J, Huang J. Impact of overall corticosteroid exposure during chemoradiotherapy on lymphopenia and survival of glioblastoma patients. J Neurooncol. (2019) 143:129–36. doi: 10.1007/s11060-019-03146-7

CrossRef Full Text | Google Scholar

95. Chaichana K, McGirt M, Woodworth G. Persistent outpatient hyperglycemia is independently associated with survival, recurrence and malignant degeneration following surgery for hemispheric low grade gliomas. Neurol Res. (2010) 32:442–8. doi: 10.1179/174313209X431101

CrossRef Full Text | Google Scholar

96. Caramanna I, de Kort J, Brandes A. Corticosteroids use and neurocognitive functioning in patients with recurrent glioblastoma: evidence from european organization for research and treatment of cancer (EORTC) trial 26101. Neuro-Oncol Pract. (2022) 9:310–6. doi: 10.1093/nop/npac022

CrossRef Full Text | Google Scholar

97. Lammers R, Gray A, Schlessinger J, Ullrich A. Differential signalling potential of insulin and IGF-1-receptor cytoplasmic domains. EMBO J. (1989) 8:1369–75.

Google Scholar

98. Hirano H, Lopes M, Laws E. Insulin-like growth factor-1 content and pattern of expression correlateswith histopathologic grade in diffusely infiltrating astrocytomas. Neuro Oncol. (1999) 1:109–19. doi: 10.1093/neuonc/1.2.109

CrossRef Full Text | Google Scholar

99. Santosh V, Arivazhagan A, Sreekanthreddy P. Grade-specific expression of insulin-like growth factor–Binding proteins-2, -3, and -5 in Astrocytomas: IGFBP-3 emerges as a strong predictor of survival in patients with newly diagnosed Glioblastoma. Cancer Epidemiol Biomarkers Prev. (2010) 19:1399–408.

Google Scholar

100. Chambless L, Parker S, Hassam-Malani L, McGirt M, Thompson R. Type 2 diabetes mellitus and obesity are independent risk factors for poor outcome in patients with high-grade glioma. J Neu rooncol. (2012) 106:383–9. doi: 10.1007/s11060-011-0676-4

CrossRef Full Text | Google Scholar

101. Pozsgai E, Schally A, Halmos G, Rick F, Bellyei S. The Inhibitory effect of a novel cytotoxic Somatostatin analogue AN-162 on experimental Glioblastoma. Horm Metab Res. (2010) 42:781–6. doi: 10.1055/s-0030-1261955

CrossRef Full Text | Google Scholar

102. Yin S, Girnita A, Stromberg T. Targeting the like growth factor-1 receptor by picropodophyllin as a treatment option for glioblastoma. Neuro Oncol. (2010) 12:19–27.

Google Scholar

103. Stromberg T. IGF-1 receptor tyrosine kinase inhibition by the cyclolignan PPP induces G2/M-phase accumulation and apoptosis in multiple myeloma cells. Blood. (2006) 107:669–78. doi: 10.1182/blood-2005-01-0306

CrossRef Full Text | Google Scholar

104. Seyfried T, Sanderson T, El-Abbadi M, McGowan R, Mukher- Jee P. Role of glucose and ketone bodies in the metabolic control of experimental brain cancer. Br J Cancer. (2003) 89:1375–82. doi: 10.1038/sj.bjc.6601269

CrossRef Full Text | Google Scholar

105. Rhodes C, Wise R, Gibbs J. In vivo disturbance of the oxidative metabolism of glucose in human cerebral gliomas. Ann Neurol. (1983) 14:614–26. doi: 10.1002/ana.410140604

CrossRef Full Text | Google Scholar

106. Menu E, Jernberg-Wiklund H, De Raeve H. Targeting the IGF-1R using picropodophyllin in the therapeutical 5T2MM mouse model of multiple myeloma: beneficial effects on tumor growth, angiogenesis, bone disease and survival. Int J Cancer. (2007) 121:1857–61. doi: 10.1002/ijc.22845

CrossRef Full Text | Google Scholar

107. Nebeling L, Miraldi F, Shurin S, Lerner E. Effects of a ketogenic diet on tumor metabolism and nutritional status in pediatric oncology patients: two case reports. J Am Coll Nutr. (1995) 14:202–8. doi: 10.1080/07315724.1995.10718495

CrossRef Full Text | Google Scholar

108. Stafstrom C, Bough K. The Ketogenic diet for the treatment of epilepsy: a challenge for nutritional neuroscientists. Nutr Neurosci. (2003) 6:67–79. doi: 10.1080/1028415031000084427

CrossRef Full Text | Google Scholar

109. Mukherjee P, El-Abbadi M, Kasperzyk J, Ranes M, Seyfried T. Dietary restriction reduces angiogenesis and growth in an orthotopic mouse brain tumour model. Br J Cancer. (2002) 86:1615–21. doi: 10.1038/sj.bjc.6600298

CrossRef Full Text | Google Scholar

110. Vanitallie T, Nufert T. Ketones: metabolism’s ugly duckling. Nutr Rev. (2003) 61:327–41. doi: 10.1301/nr.2003.oct.327-341

CrossRef Full Text | Google Scholar

111. Winter S, Loebel F, Dietrich J. Role of ketogenic metabolic therapy in malignant glioma: a systematic review. Crit Rev Oncol Hematol. (2017) 112:41–58. doi: 10.1016/j.critrevonc.2017.02.016

CrossRef Full Text | Google Scholar

112. Koye D, Magliano D, Nelson R, Pavkov M. The global epidemiology of diabetes and kidney disease. Adv Chronic Kidney Dis. (2018) 25:121–32. doi: 10.1053/j.ackd.2017.10.011

CrossRef Full Text | Google Scholar