Demographic variation in incidence of adult glioma by subtype, United States, 1992-2007

Study population

We utilized publicly-available data from the SEER population-based cancer registries (SEER Research Data [1973-2007], released April 2010, based on the November 2009 submission) and associated SEER U.S. population data (SEER Program Populations [1969-2007] http://www.seer.cancer.gov/popdata, released November 2009) [17]. We included in our analyses adult glioma cases diagnosed between 1992 and 2007. Cases diagnosed between 1992 and 1999 were from the SEER 13 Registries Database (Alaska Native, Atlanta, Connecticut, Detroit, Hawaii, Iowa, Los Angeles, New Mexico, Rural Georgia, San Francisco-Oakland, San Jose-Monterey, Seattle-Puget Sound, and Utah). Cases diagnosed between 2000 and 2007 were from the SEER 17 Registries Database (SEER 13 plus Greater California, Kentucky, Louisiana, and New Jersey). The SEER 13 registries cover about 14% and the SEER 17 registries cover about 26% of the total U.S. population.

We restricted our analyses to adult glioma because different morphologic types of glioma predominate in adults compared to children [2, 5, 6]. We defined glioma as a malignant brain neoplasm (International Classification of Diseases for Oncology, third edition [ICD-O-3] topography codes C710-C719 and behavior code 3) with an ICD-O-3 morphology code of 9380 (glioma, malignant; glioma, not otherwise specified [NOS]), 9382 (mixed glioma), 9400 (astrocytoma, NOS), 9401 (astrocytoma, anaplastic), 9410 (protoplasmic astrocytoma), 9411 (gemistocytic astrocytoma), 9420 (fibrillary astrocytoma), 9440 (glioblastoma, NOS), 9441 (giant cell glioblastoma), 9442 (gliosarcoma), 9450 (oligodendroglioma, NOS), 9451 (oligodendroglioma, anaplastic), or 9460 (oligodendroblastoma) [18]. We defined GBM as ICD-O-3 morphology codes 9440, 9441, and 9442. We defined non-GBM as all other codes except 9380 (glioma, malignant; glioma, NOS).

For valid classification of adult glioma as GBM versus non-GBM, we made two necessary exclusions. First, we excluded cases with the non-specific code 9380 because such cases could not be classified as GBM versus non-GBM. Second, we excluded cases that were not microscopically confirmed because valid distinction between GBM and non-GBM requires microscopic examination of tumor tissue and cannot be accomplished by imaging alone [19–21].

Data analysis and statistical methods

We downloaded sex-, age-, race-, Hispanic-ethnicity-, and calendar-year-specific numbers of cases and person-years at risk from the SEER website. We classified age into eight five-year age groups ranging from 30-34 to 65-69. We divided calendar-years into four four-year periods (1992-1995, 1996-1999, 2000-2003, and 2004-2007). Our race/ethnic group categories were non-Hispanic White, Hispanic White, Black, Asian/Pacific Islander, and American Indian/Alaskan Native.

For each of our two glioma subtypes, we calculated sex-and age-specific incidence rates (for each race/ethnicity group) by dividing the number of cases by the number of person-years at risk. We calculated sex-specific, age-standardized incidence rates (ASRs) and 95% confidence intervals (CIs) (for each race/ethnicity group) by direct standardization of the age-specific incidence rates to the 2000 U.S. Standard Population (Census P25-1130). We used Poisson regression to calculate adjusted rate ratios (RRs) and 95% CIs. We modeled age as a categorical variable or as the natural logarithm of age (log_e(age)), where age was defined as the midpoint of the five-year age group. We modeled calendar period as a categorical or interval variable, the latter being used to calculate the p-value for period trend. Results for models with and without adjustment for cancer registry did not meaningfully differ; we only present the unadjusted results. To determine a p-value for heterogeneity, we entered the appropriate cross-product term into the Poisson model and conducted a likelihood ratio test for its addition, with the appropriate degrees of freedom. To further explore the relationship between glioma subtype incidence and age, we calculated adjusted RRs for 5-year age groups using Poisson regression and used the calculated RRs to perform weighted linear least squares regression of log₁₀(RR) versus log₁₀(age). We also performed weighted linear least squares regression of log₁₀(age-specific incidence rate) versus log₁₀(age). We performed Poisson regressions using Proc Genmod of SAS version 9.1; statistical tests were two-sided with α = 0.05.

Race/ethnic group

Sex

In Table 2, we also tested the hypothesis that sex variation in incidence differs according to glioma subtype. For each race/ethnic group and glioma subtype, males had a higher incidence rate than females. For each race/ethnic group, the male/female RR was higher for GBM than for non-GBM, except for American Indians/Alaska Natives (for whom the 95% confidence intervals were wide). The overall male/female RR, derived from a Poisson model with sex as the independent variable and age, period, and race/ethnic group as covariates, was 1.61 (95% CI = 1.56, 1.66) for GBM versus 1.38 (95% CI = 1.32, 1.44) for non-GBM. This difference was highly significant (p-value for heterogeneity < 0.0001).

The p-value for heterogeneity for the variation in male/female RR by race/ethnic group was 0.0013 for GBM and 0.0011 for non-GBM, indicating that the male/female RR varied by race/ethnic group for each glioma subtype. However, when we excluded Hispanic Whites, among whom we observed the lowest male/female RR for both GBM and non-GBM, the p-value for heterogeneity was no longer significant (p = 0.76 for GBM and p = 0.85 for non-GBM).

Age

In Poisson models including age (categorical variable with 5-year age groups), sex, and a cross-product term between age and sex, adjusted for period and race/ethnic group, the p-value for the cross-product term was 0.37 for GBM and 0.12 for non-GBM, indicating homogeneous age effects in males and females for GBM and non-GBM, respectively. However, the difference in age effect between GBM and non-GBM was highly significant (p-value for heterogeneity < 0.0001).

To further explore the hypothesis that age variation in incidence differs according to glioma subtype, for each subtype we calculated adjusted RRs for age by 5-year age group using Poisson regression, adjusting for period, race/ethnic group, and sex. We then used these adjusted RRs in weighted least squares regressions of log₁₀(RR) versus log₁₀(age) and found reasonable fits, with the GBM incidence rate increasing proportionally with approximately the 4^th power of age (slope = 4.17; 95% CI: 3.80, 4.54; adjusted-R² = 0.99), and the non-GBM incidence rate increasing proportionally with approximately the square root of age (slope = 0.48; 95% CI = 0.25, 0.72; adjusted-R² = 0.78), showing the marked difference in age effect between GBM and non-GBM. We then calculated unadjusted RRs by 5-year age group and subtype using Poisson regression and used the unadjusted RRs in weighted least squares regression of log₁₀(RR) versus log₁₀(age). We found slopes and adjusted-R²s similar to those found when using adjusted RRs, showing that it would be valid to plot log₁₀(age-specific incidence rate) versus log₁₀(age) without adjustment, which we show in Figure 1.

In Table 4, we also tested the hypothesis that the log_e(age) effect differs according to race/ethnic group for each subtype. For GBM, the slope for log_e(age) for each race/ethnic group was approximately 4. However, there was some evidence for heterogeneity (p-value for heterogeneity = 0.021), with Asians/Pacific Islanders and American Indians/Alaskan Natives having smaller slopes than the other race/ethnic groups. For non-GBM, the slopes by race/ethnic group ranged form 0.14 to 0.95, with a p-value for heterogeneity of 0.0005, with the smallest slopes again observed among Asians/Pacific Islanders and American Indians/Alaskan Natives.

Calendar period

Age

The GBM incidence rate increased in proportion to approximately the 4^th power of age, similar to what was found in an analysis of SEER data among Whites in 1973-1982 [22]. Because secondary GBM cases are known to have a younger age distribution than primary GBM cases [9], the increase in primary GBM incidence with age (after removing secondary GBM) must be somewhat steeper, rising toward the 5^th power, which would make the age-incidence curve for primary GBM similar to that of many carcinomas, which typically increase in incidence approximately in proportion to between the 4^th and 6^th power of age [23]. On the other hand, the non-GBM incidence rate increased in proportion to approximately the square root of age, an age-incidence pattern that is quite unusual [23]. The previous SEER analysis among Whites in 1973-1982 found slopes of 1.7 for astrocytomas and 1.0 for oligodendrogliomas [22].

The pronounced difference in age-incidence curves between GBM (mainly primary GBM) and non-GBM suggests a fundamental difference in the genesis of these glioma subtypes, as has been suggested previously [22]. Mathematical models of carcinogenesis suggest that development of cancer types with steep log(incidence)-log(age) slopes, such as primary GBM, involve a large number of stable genetic or epigenetic changes (slope plus one; thus 5-6 changes for primary GBM) [24, 25] or rapid expansion of a premalignant clone [26], whereas development of cancer types with shallow log(incidence)-log(age) slopes, such as non-GBM, involve a small number of stable genetic or epigenetic changes (as few as one or two) or slow expansion of a premalignant clone.

Indeed, GBM exhibits greater molecular complexity than non-GBM, with the implication that the path to GBM involves a greater number of molecular alterations [7]. Furthermore, the steeper age-incidence curve for GBM may indicate a greater role for age-related processes such as immunosenescence [27] or reduced efficiency of DNA repair mechanisms [28]. Given that among malignancies (brain or otherwise), non-GBM (as well as secondary GBM) are unique in exhibiting a high frequency (as high as 80%) of IDH mutations [15, 16, 29, 30], which appear to be early events in non-GBM development [16], one can speculate that these mutations drive an atypical path to malignancy.

For both GBM and non-GBM, the log(age) slope was similar for males and females. However, within the context of the large difference in age-incidence curves between GBM and non-GBM, we observed subtle, but statistically significant, variation in age-incidence curves among race/ethnic groups. For both subtypes, Asians/Pacific Islanders and American Indians/Alaskan Natives had the smallest slopes. We observed a similar result for Asians/Pacific Islanders in our previous work on brain cancer as a whole [1].

Race/ethnic group

For race/ethnic group variation, we observed an important commonality between GBM and non-GBM. For each subtype, compared to non-Hispanic Whites, the incidence rate among Blacks, Asian/Pacific Islanders, and American Indians/Alaskan Natives was substantially lower (one-fourth to one-half for GBM; about two-fifths for non-GBM). However, secondary to this primary effect, race/ethnic group variation in incidence was less for non-GBM than for GBM, a difference that was highly statistically significant but only moderate in magnitude.

There is evidence for race/ethnic group differences in genetic pathways to glioma [31–33]. Furthermore, genome-wide association studies have identified several genetic susceptibility regions for glioma [34, 35]. Given the genotype variability across race/ethnic groups [36], it is possible that variation in the frequency of susceptibility alleles across race/ethnic groups explains at least some of the race/ethnic group variation in glioma incidence, including the race/ethnic group heterogeneity in the relationship between glioma incidence and age. The commonality between GBM and non-GBM in race/ethnic group variation suggests that at least some of the susceptibility loci that may help explain race/ethnic group variation in glioma incidence would be the same for GBM and non-GBM, although some susceptibility loci appear to show specificity with respect to glioma subtype [37–39].

Sex

As with race/ethnic group variation, we observed an important commonality between GBM and non-GBM for sex. For each subtype, the incidence rate was higher for males than for females; this male excess of glioma is well known [2, 3, 5, 6]. However, we did find the male/female RR to be somewhat higher for GBM (1.6) than for non-GBM (1.4), a result that was highly statistically significant. We previously suggested that the male/female difference in brain cancer incidence is biologically based [1], and that an explanation should be sought in genetic differences between males and females, sex hormones, and/or female reproductive factors [40]. Now we would add that any explanation should take into account the difference in the male/female RR between glioma subtypes.

For GBM and non-GBM, respectively, we observed similar male/female RRs among race/ethnic groups, with the exception of Hispanic Whites, among whom the male/female RR was anomalously low. Further work is required to determine whether the latter result has a biological basis or stems from an unidentified bias. It is noteworthy that the quality of the SEER Hispanic ethnicity variable was found to be moderate to substantial, but not excellent [41].

In our previous work, we found that for adult brain cancer as a whole (most of which are glioma), the male/female RR was higher in the peri- and post-menopausal age range than in the pre-menopausal age range [1]. However, in the current study, we found no evidence for an association between male/female RR and age for GBM and only suggestive evidence (p = 0.09) for a higher RR in the post-menopausal age range for non-GBM. We also found that the result for all glioma appeared to have been confounded by glioma subtype, with non-GBM both having a lower male/female RR and occurring at younger ages compared to GBM, which would explain the result from our previous work.

Calendar period

We observed trends of increasing GBM incidence rate and decreasing non-GBM incidence rate with more recent period, with an overall trend for GBM and non-GBM combined of slightly decreasing rate. Although the latter trend was statistically significant (p = 0.037), we do not conclude that the overall incidence of glioma decreased over the 16 year period of observation because the small 3% decrease between 1992-1995 and 2004-2007 could easily be explained by an unrecognized bias. Furthermore, given the well-known inter-observer variability in glioma diagnosis [10, 42–45], the increasing trend of GBM incidence coupled with the decreasing trend of non-GBM incidence may at least partly be due to a secular trend in diagnostic fashion, as opposed to real changes in incidence of these two subtypes.

Limitations

Due to the known inter-observer variability in the detailed morphologic classification of non-GBM in particular [10], we did not attempt to sub-classify these tumors into the traditional categories of astrocytoma, oligodendroglioma, and mixed oligoastrocytoma or by grade. Furthermore, we were unable to distinguish primary from secondary GBM and were unable to take into account known molecular heterogeneity within the two broad categories of GBM [46] and non-GBM [47]. Another limitation was possible selection bias due to differential diagnosis of microscopically confirmed glioma that had a specific morphology code (not the nonspecific code 9380), according to demographic factors. However, our restriction to ages 30-69 years, an age range with a high proportion of cases both microscopically confirmed and with a specific morphology code, tended to minimize the potential for any such bias appreciably distorting the results. This restriction was at the expense of generalizability to a broader age range.