Risk of Death from Alzheimer’s Disease Associated with Brain Tumor,Glioma,and Glioblastoma

INTRODUCTION

Alzheimer’s disease (AD) is among the leading causes of death worldwide and is characterized by the accumulation of insoluble forms of amyloid-β (Aβ) plaques in brain extracellular spaces and the aggregation of the microtubule protein tau as neurofibrillary tangles in neurons. It has an age-specific incidence rate that increases with age, and most patients (>95%) have the sporadic form, which usually has a late onset at age > 65 years [1, 2]. The prevalence of AD has been increasing dramatically in recent years, causing great damage to both patients and society [3, 4]. However, its pathogenesis and mechanism remain largely unknown, which to some extent hinders the development of effective therapeutics. Primary brain tumors originate in the brain. Glioma is the most common type of primary brain tumor in adults, accounting for approximately 28% of all primary brain tumors and 80% of all malignant brain tumors [5]. Evidence suggests that gliomas are derived from neuroglial stem or progenitor cells and can be classified into astrocytomas, oligodendrogliomas, mixed oligoastrocytic gliomas, and ependymomas based on histological appearance and into WHO grades 1–4 according to degrees of malignancy [6]. Approximately 45% of glioma patients present with glioblastoma, the most common and most malignant subtype, which is WHO grade 4 and has a 5-year survival rate of only 5%. More than 70% of GBM patients are > 65 years old [7], although the disease can occur in other age groups with distinctly different genetic features from those in the elderly group [5].

Previous epidemiological studies have reported an inverse association between AD and cancer, and several explanations have been proposed, such as the Warburg effect theory, the two-hit hypothesis theory, the unfolded protein response theory, chronic inflammation, age-related metabolic deregulation, and study bias [8 –12]. Compared with other tumors, brain tumors have a pronounced effect on the surrounding brain tissues both physically and biologically [13]. Emerging genomic, transcriptomic, and epigenetic profiling has revealed some distinct features of gliomas and glioblastomas from other brain tumors. Based on these facts, we hypothesized that AD risks associated with brain tumors, gliomas, and glioblastomas are likely different from those associated with other non-neurological tumors. Several previous studies have investigated the association between AD and brain tumors/gliomas/glioblastomas but have led to controversial results [14 –19]. Moreover, no study has compared this association with other tumors. It is clear that more real-world data or clinical studies are needed to draw any conclusion regarding the relationship between AD and brain tumors/gliomas/glioblastomas. This case-control study aimed to compare the risk of AD death in patients with brain tumors, gliomas, and glioblastomas with that in patients with other non-neurological tumors.

METHODS

Study population

The study included subjects > 20 years old who were diagnosed with only one primary tumor, excluding head (outside brain), neck, and peripheral neural tumors, which was confirmed microscopically from 1975–2019, and died from AD or causes other than neoplasms, AD, cerebrovascular diseases, or social events (e.g., suicide, homicide, accidents) (Fig. 1). Only cases that were confirmed microscopically at diagnosis and by autopsy or death certificate at death were selected. We excluded those who died at age≤65 years, those with invalid data on race, tumor-directed surgery or radiation therapy, and those with perineural invasion by combining the site code and histology recodes. Finally, 24,441 subjects were identified as the case group, and 122,205 subjects were selected as the control group through random digit dialing according to a 1:5 match ratio.

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Fig. 1

Flowchart of subjects included and excluded in the study.

RESULTS

Table 1 presents the distribution of demographics, tumor characteristics, and treatments in the case and control groups (Fig. 2). As expected, subjects who died from AD were significantly older than those who died from other causes (87.4±6.8 versus 82.7±8.3 years), and female patients had a significantly higher risk of dying by AD than male patients. There was also a significant difference in the distribution of race between the case and control groups. When dividing tumor behavior into three grades (benign, borderline/in situ, malignant), it was significantly related to patient death from AD. A higher prevalence of benign (0.1% versus 0.03%) and borderline/in situ (7.3% versus 4.6%) tumors was found in patients who died from AD than in those who died from other causes, while a lower prevalence of malignant tumors was found in patients who died from AD (92.7% versus 95.4%). Patients who died from AD had a slightly lower proportion of brain tumor diagnoses, but this difference was not statistically significant (p = 0.227). Moreover, patients who died from AD had a significantly lower proportion of glioma (p = 0.007) or glioblastoma diagnoses (p = 0.001). For treatment, both radiation therapy and tumor-directed surgery were significantly related to death from AD (both p < 0.001). The area under the receiver operating characteristic (ROC) curve (AUC) for the adjusted logistic regression model was 0.682 (95% CI, 0.679–0.686).

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Fig. 2

Distribution of demographics, tumor characteristics, and treatments in the case and control groups.

Table 2 presents the results of logistic regression analyses regarding glioma or glioblastoma. Crude ORs were first calculated, and then adjusted ORs were calculated by constructing regression models including potential confounders. The first adjusted regression model included age at death, sex, race, tumor behavior, glioma, radiation therapy, and tumor-directed surgery; and the second adjusted model included age at death, sex, race, tumor behavior, glioblastoma, radiation therapy, and tumor-directed surgery. Glioma was related to a significantly decreased risk of AD death in the unadjusted model (odds ratio: 0.52, 95% CI: 0.32–0.85), but it was not included in the adjusted model. Patients diagnosed with glioblastoma had a significantly lower risk of AD death (0.19, 0.05–0.77) than those with other tumors. In both adjusted models, subjects who were older (1.07, 1.07–1.08; 1.1, 1.1–1.1) and female (1.35, 1.31–1.40) were related to significantly increased risks of AD death. White (0.41, 0.29–0.59), Asian or Pacific Islander (0.44, 0.31–0.6), and Black (0.28, 0.20–0.41; 0.29, 0.20–0.41)) people had significantly lower risks of AD death than American Indian/Alaska Native people. Subjects with borderline/in situ tumors (0.43, 0.24–0.78) and malignant tumors (0.31, 0.17–0.56) had significantly lower risks of AD death than those with benign tumors. For treatments, patients without radiation therapy (0.75, 0.72–0.79; 0.75, 0.72–0.78) or tumor-directed surgery (0.82, 0.77–0.87) had a significantly lower risk of AD death than those who had radiation therapy or tumor-directed surgery. The area under the receiver operating characteristic (ROC) curve (AUC) for the adjusted logistic regression model was 0.682 (95% CI, 0.679–0.686). Figure 3 illustrates the adjusted estimates of the association between demographic/tumor characteristics and AD death.

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Fig. 3

Adjusted estimates for the association of demographic and tumor characteristics with death from Alzheimer’s disease.

DISCUSSION

This study examined the risk of death from AD associated with primary brain tumors, gliomas, and glioblastomas compared with other primary tumors. The risk of death from AD in patients with brain tumors was not significantly different from that in patients with other tumors; glioma was significantly related to a lower risk of AD death than other tumors in univariable analyses but was not significant in adjusted logistic regression analysis; glioblastoma was significantly more strongly inversely related to AD death than other tumors. Additionally, older age, female sex, American Indian/Alaska Native race, benign tumors, radiation therapy, and tumor-directed surgery were associated with a significantly increased risk of AD death in patients with tumors, which was consistent with previous literature [8 , 20]. Since AD is a chronic disease with long preclinical and prodromal phases, we cannot identify the temporal relations between AD and glioma/glioblastoma, or the relationship between AD-like pathological changes and glioma/glioblastoma in this study but can only suggest an inverse relation between their incidences.

Some previous studies have explored the relationship between AD and brain tumors/gliomas/glioblastomas. Several clinical studies have examined the risk of AD in patients with brain tumors/gliomas/glioblastomas, but none of them have compared the risk in patients with other tumors. Since an inverse association between AD and cancer has been found, the relations between AD and brain tumor/glioma/glioblastoma in these studies may be confounded by the general impact of cancer on AD. For brain tumors, a clinical study found a significantly positive correlation between the incidences of AD and malignant brain tumors by linear regression analysis of incidence data from 19 US states in 2000–2004 [15]. However, the estimate was crude and could be confounded by many factors such as regional disparities in medical diagnosis capacity. Another SEER population-based study found that the risk of death from AD was higher in patients with brain cancer than in patients in the normal group in both the first 10 years and > 10 years following the diagnosis of cancer, but a significance test was not conducted [16]. In terms of glioma, an epidemiological study found a significantly positive relationship between AD and glioma mortality rates using mortality data from US CDC Wonder. It also analyzed TCGA data and identified some common AD and glioma genes, including TREM2, SPI1, CD33, and INPP5D [14]. Mendelian randomization study identified glioma as a risk factor for late-onset AD after screening 1037 risk factors/medical conditions and 31 drugs [21].

For glioblastoma, a pathological case-control study found a slightly lower prevalence of AD pathology (senile plaque and neurofibrillary tangles) in patients with glioblastoma multiforme (GBM) than those without (42% versus 48%), but the difference was not significant, which may be partly due to the relatively small sample size (90 in total) [17]. Another pathological study found an increased expression level of autophagy-related p62 and microglial infiltration but no difference in deposits of neurodegeneration-associated proteins in glioblastoma and surrounding tissue compared with the non-neurological control group [18]. Additionally, several bioinformatic studies have explored the molecular basis of the relationship between AD and glioblastoma by genetic or transcriptomic profiling. The first bioinformatic study was conducted in 2013, which analyzed inversely regulated genes and signaling pathways in AD and GBM by transcriptional analyses [22]. It was discovered that Aβ in AD can suppress gliomagenesis and GBM growth through the ERK-AKT-p21 cell cycle pathway and anti-angiogenesis pathway. A subsequent transcriptomic study reported that most differentially expressed genes (DEGs) in AD and glioblastoma were AD+/glioblastoma+ or AD-/glioblastoma-, which was regarded by the researchers to partially account for the positive relation between AD and glioblastoma [19]. However, genes expressed in the same direction are not necessarily related to a positive relationship between these two diseases because they may have different effects in different disease contexts, which may be affected by other factors such as those genes expressed oppositely, and thus can lead to opposing disease outcomes. The study also identified 301 processes relevant to DEGs in AD and glioblastoma by functional enrichment analyses, among which AD+/glioblastoma+ processes were mainly related to the immune system, and AD-/glioblastoma- processes were mainly related to neuronal synaptic transmission/generation. Likewise, another study identified 122 inverse DEGs, 4 gene modules, and 13 hub genes in AD and GBM through transcriptional analyses [23]. The DEGs were enriched in the AMPK, cell cycle, and cellular senescence pathways, which suggested the important roles of these pathways in AD and GBM. Apart from that, a study found a strongly negative association in expression levels of de-regulated miRNAs in GBM and AD [24]. Recently, a study further examined the DEGs within microglia in AD and GBM at both the gene and network levels [25]. Eleven common dysregulated genes were found, among which FURIN and BACE1 were related to both Aβ plaque formation and cancer biology. The mediating pathways connecting the two diseases were found to be related to the inflammatory response, lipid metabolism disorder, and cell proliferation.

The discovery that glioma/glioblastoma was more strongly inversely associated with AD death than other tumors in this study suggests a unique negative neurological association between glioma/glioblastoma and AD, which may be due to their opposite pathogeneses or reciprocal inhibitions in disease processes. First, glioma and AD may have opposite pathogeneses such as genetic predispositions, although both are aging-related diseases and can share some common molecular changes. Second, they can form opposite microenvironments in the brain and suppress each other in the initial pathology processes. Brain tumors have been demonstrated to form specific immunological tumor microenvironments (TMEs) that vary by tumor characteristics such as histological type and location [26]. In glioma, constituents, including tumor cells, immune cells, and other nonhematopoietic cells (neurons, astrocytes, oligodendrocytes), interact with each other and form an overall immunosuppressive TME that promotes tumorigenesis and tumor progression [13]. GBM is considered a “cold tumor” with an overall immunosuppressive nature as well [26]. In contrast, AD is characterized by a chronic neuroinflammatory condition in the brain, which is contributed by both brain resident and peripheral innate and adaptive immune systems [27]. Therefore, a brain microenvironment of glioma may inhibit AD occurrence, and vice versa, a brain with AD may suppress tumorigenesis and progression of glioma. Some studies have reported the mechanism behind the anti-glioma effect of AD. For example, tau protein was found to play a significant role in glioma and GBM [28]. A study revealed that gliomas expressing high levels of MAPT, which encodes tau protein, had a better clinical prognosis [29]. Another study reported that tau protein inhibited EGFR signaling to impede glioma progression [30]. Since an inverse association between systemic inflammation (such as cytomegalovirus infection, allergy, atopic diseases) and glioma risk has been found in some studies, the chronic inflammatory condition in AD is very likely to suppress glioma but needs further investigation [5 , 31–33].

This study has some strengths. It was based on real-world clinical data, had a large research sample and was a well-designed case-control study. Potential cofounders were selected and analyzed. Stratified analyses regarding brain tumor, glioma, and glioblastoma were conducted step by step. All these factors ensure the credibility of the study. However, this study also has several limitations. It was based on a single database. We did not analyze the detailed socioeconomic factors and tumor characteristics of patients due to the lack of useful information in the SEER database, such as educational degree, income level, tumor location, and tumor size which may also affect AD risk.

In conclusion, this study identified a stronger inverse relationship between glioma and AD death than other tumors, especially between glioblastoma and AD death. This suggests the existence of opposite pathogeneses or reciprocal inhibitions in the processes of these two diseases. To verify this hypothesis, more real-world data, clinical studies, and autopsy pathological studies worldwide are needed in the future. Advanced genetic and transcriptomic profiling analyses are also necessary to elucidate risk factors and protective traits in the field of association between glioma and AD. Moreover, the elucidation of specific roles of prominent pathways such as immune responses in both AD and glioma may promote the exploration of therapeutic links in these two diseases and thus help develop potential therapeutics by manipulating the brain microenvironment toward the other [34].