Abstract
Background
Brain cancer is an important public health concern in terms of its burden and the cost of its treatment. Evidence on determinants of brain cancer incidence and survival are sparse and inconsistent. We aimed to explore the pre-diagnostic factors for brain cancer incidence and survival in northeastern Iran.
Methods
Data for the current study were derived from the Golestan Cohort Study, the largest cohort study in the Middle East on over 50,000 participants aged 40 to 75 years with a median follow-up of 15 years. Minimally adjusted and multiple Cox proportional hazards models were used to investigate the association of demographic and behavioral risk factors with brain cancer incidence and survival.
Results
Out of the 49,783 cancer free participants recruited at baseline, 77 patients were diagnosed with brain cancer and 62 patients were deceased till the end of the follow-up. Annual cancer mortality rate was 0.31 (95% Confidence Interval: 0.24 – 0.39), one-year survival was 38%, and the median survival was 0.72 years. In the multiple model, Turkmen ethnicity (Hazard Ratio = 0.42 (0.23–0.76)), urban residence (HR = 0.46 (0.25–0.84)), overweight or obesity (HR = 0.48 (0.25–0.93)), and history of animal contact (HR = 0.43 (0.19–0.96)) were associated with a better survival. Patients diagnosed with brain cancer had higher prevalence of hypertension, opium use, and smoking compared to cancer free participants.
Conclusions
Our results indicate a complex interplay of demographic and life style risk factors influencing both the incidence and prognosis of brain cancer. Further research is mandated to inform policy makers of potentially effective preventive initiatives to reduce the burden of this cancer.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12885-025-13642-x.
Keywords: Brain neoplasm, Incidence, Survival, Risk factors, Iran
Background
Among all malignancies, brain cancer is a critical public health concern due to its high mortality rates and its costly treatment [1, 2]. In 2021, the Global Burden of Disease study reported that over 258,000 deaths occurred worldwide due to brain cancer. Additionally, more than 975,000 individuals were living with the disease, and there were 357,000 new diagnoses that year [3, 4]. In Iran, the number of deaths increased from 1,627 deaths in 1990 to 3,893 deaths in 2021, which constitutes 0.7% of total deaths in Iran, though the deaths rates per 100,000 show no change since 1990 [3]. The number of patients suffering from brain cancer in Iran was over 11,274 and the number of new patients reached 4,795 in 2021 [4]. These estimates show the substantial increase in burden of brain cancer in Iran during the past three decades.
The primary risk factors potentially associated with brain cancer burden encompass metabolic risk factors, unhealthy life style, and environmental risk factors [5]. Previous studies have been conducted to clarify the impact of prognostic factors on brain cancer survival including cancer histological type and location [6, 7], age [8, 9], race [10], socioeconomic status [11], physical activity [12], occupational exposures [13], residence [14–16], and ambient air pollution [17]. However, there are still inconsistencies regarding possible risk factors for brain tumor incidence and survival specifically in low and middle income countries [18]. These inconsistencies emphasize the need for updated epidemiological studies in these regions. Considering the increasing burden of brain cancer in our country, it is imperative to enhance our knowledge on risk factors of brain cancer incidence and survival using population-based studies at local scale in Iran as a middle income country [19]. Therefore, this study aims to address the gap in knowledge regarding risk factors of brain cancer incidence and survival in Golestan province by analyzing data from the Golestan Cohort Study (GCS). By examining the sociodemographic, lifestyle, and environmental factors associated with brain cancer incidence and survival, we seek to identify and elucidate patterns unique to this population. These insights can inform targeted interventions and healthcare services aimed at reducing the burden of brain cancer and improving outcomes for affected patients in Iran.
Methods
Design and study population
The data in this study is derived from GCS, launched in January 2004, in which 50,045 participants aged between 40 and 75 years were registered in the Golestan province, northeastern Iran and followed for a median of 15 years. Recruitment was accomplished in June 2008. The details of the GCS have been described in previous studies [20, 21]. Out of the entire recruited individuals, 10,032 are urban residents in Gonbad city and 40,013 are rural residents (all 326 villages in Golestan province). Those who were temporary residents, had been diagnosed with cancer at the time of entering the study, or were not willing to participate, were all excluded from this study. Consequently, the incidence analysis included 49,783 participants and the survival analysis included 77 patients diagnosed with brain cancer during GCS follow-up period. The study design, which was carried out in accordance with the Declaration of Helsinki, was approved by the ethical review committee of the Digestive Diseases Research Institute (DDRI) affiliated to Tehran University of Medical Sciences. At the baseline of this study, after a thorough explanation of the study process and objectives, all participants signed a written informed consent. Consent to participate in the study was obtained from illiterate participants after visiting the study center and explaining the details. A personalized identification card was provided to each participant.
Data collections and measurements
Participants were interviewed by a qualified interviewer, who completed two structured questionnaires, and also underwent physical examination by a general physician. The questionnaire is provided in the appendix. Demographic variables recorded in this study were sex, age at diagnosis, ethnicity (Turkmen or Non_Turkmen), education, place of residence (rural or urban), and socioeconomic status (SES), which was calculated using multiple correspondence analysis based on household assets (personal car, motorbike, black and white TV, color TV, refrigerator, freezer, vacuum and washing machine ownership variables, as well as house ownership, house structure, house size, having a bath in the residence, and occupation) [22]. The calculated score for SES was then divided into three tertiles.
Data on physical activity in MET (Metabolic Equivalent of Task), water source, and animal contact were recorded based on self-report at the baseline phase of GCS. Diabetes was defined as a self-reported diagnosis and/or use of antidiabetic medications. Data on recent and past smoking and opium use was also collected based on self-report. Previous studies showed that self-reported opium use was highly valid in this population in comparison with the presence of codeine and morphine in the urine as the gold standard, with a sensitivity of 0.93 and a specificity of 0.89 [23].
Height and weight were measured. According to the Quetelet equation, body mass index (BMI) was calculated by dividing weight (in kg) by height (in squared meters). BMI was categorized as follows: underweight (BMI < 18.5 kg/m2), normal (18.5 kg/m2 ≤ BMI < 25 kg/m2), and overweight and obese (BMI ≥ 25 kg/m2) based on the protocol of National Institutes of Health in 1998 [24]. Blood pressure was measured twice from each arm in sitting position after ten minutes of rest. Hypertension was defined as a systolic blood pressure (SBP) ≥ 140 mmHg, a diastolic blood pressure (DBP) ≥ 90 mmHg, self-reported diagnosis, or using anti-hypertensive medications [25, 26].
Participant follow-up, outcome assessment, and cause of death determination
To track the participants' health status, annual follow-ups were arranged and participants in this cohort study were followed up for a median of 15 years. Data regarding occurrence of any disease, hospital admissions, and the underlying causes of mortality were collected throughout the annual follow-up. Participants were instructed to report any hospitalizations or new major diseases to the study team. All study data were electronically recorded in a centralized database. Cancer cases were identified through monthly reviews of the Atrak Clinic (a specialized gastroenterology clinic located in Gonbad's main hospital) and Golestan Cancer Registry databases. Active follow-up was conducted through annual telephone calls, supplemented by contacting friends, local health workers, or utilizing provincial death registry reports if phone contact was unsuccessful after seven attempts over two weeks. During follow-up calls, a structured questionnaire was completed including the participant's vital status, any new diagnoses, and hospital admissions since the previous contact.
In this study, brain cancer was defined based on ICD-10 (International Classification of Diseases, 10th Edition) codes: C71.0–9. Mortality, incidence of any cancer, and incidence of gastrointestinal cancers were the three primary outcomes in the GCS. Two external internists independently examine all relevant clinical documents and assign a disease ICD-10 code along with a date of occurrence for each outcome. The assigned ICD-10 codes are then compared, and in cases of discrepancies, a third, more experienced internist reviews the information to make the final determination on the code. In the case of reported death, a general practitioner from the follow-up team visits the household of the deceased participant to conduct a validated verbal autopsy, through interviewing the closest relatives [20]. If a definitive diagnosis is impossible, the cause of death is recorded as "unknown".
Statistical analysis
In this study, qualitative variables are presented as the frequency and percentage and quantitative data are reported as mean and standard deviation (SD). Chi-squared test and student's t-test were applied for assessing the percentage differences of qualitative variables and for comparing the mean age in sub-groups, respectively. Individuals with missing baseline demographic information, lifestyle risk factors, anthropometric measurements, and other essential information were excluded from the analysis.
This study calculated cancer incidence rates per 100,000 person-years for all participants and annual cancer mortality rate for those diagnosed with cancer. For incidence analysis, all participants in the GCS were included in the risk set. The target event was the diagnosis of brain cancer. In this part of the analysis, the Cox regression model was used, with the time of study enrollment and the time of brain cancer diagnosis defined as entry time and exit time, respectively. Individuals who had not experienced brain cancer diagnosis by the last follow-up time were censored, as were those lost to follow-up. For survival analysis, only participants diagnosed with cancer during the cohort follow-up were included. Death from cancer was the target event. The entry time was defined as the time of cancer diagnosis and exit time as the time of death. Censored cases included individuals who were alive at the last follow-up time and those lost to follow-up. Overall survival curve was drawn using Kaplan–Meier method to show the cumulative survival probabilities.
Analyses were performed to explore the determinants for the incidence of brain cancer and also for the survival of brain cancer patients. We fitted the minimally adjusted (for age, sex and urbanization) Cox proportional hazards regression models and the variables that were significant in this model were entered in the multiple Cox proportional hazards regression model to adjust for all potential confounding effects. These models were fitted to obtain the hazard ratio of developing brain cancer as well as the hazard ratio of brain cancer death. A proportional hazard test was used to evaluate the proportional hazard assumptions for each model, which proved to be valid. A p-value of 0.05 or less was considered statistically significant. All statistical analyses were performed using STATA version 17.
Results
The number of participants in Golestan Cohort Study was 50,045. After applying the exclusion criteria, the number of participants was reduced to 49,783. Among them, 77 patients were diagnosed with brain cancer, and 62 patients among them died from brain cancer by the end of the follow-up. A total of 582 participants were lost during the follow-up. Overall, the annual cancer mortality rate was 0.31 (95% Confidence Interval (CI): 0.24 – 0.39) with 202.19 person-years at risk.
Table 1 shows the sociodemographic and habitual characteristics of patients with brain cancer by life status (alive/ dead) and annual cancer mortality rate for those diagnosed with cancer. The mean age of all 77 patients was 60.96 ± 1.01 years and the majority of them were female (53.25%), Turkmen (72.73%), illiterate (67.53%), rural residents (74.03%), and overweight or obese (54.55%), and the majority had animal contact (90.91%). Except for ethnicity, there was no significant differences in baseline characteristics between patients who died and those who survived.
Table 1.
Sociodemographic and habitual characteristics of patients for survival analysis and the brain cancer mortality proportion
| Variable |
Total (N = 77) N(%)a |
Dead (n = 62) n(%)a |
Alive (n = 15) n(%)a |
N of mortality/ Person-Year at risk | Annual Cancer mortality rate (95% CI) | |
|---|---|---|---|---|---|---|
| Sex | Female | 41(53.25) | 36(58.06) | 5(33.33) | 36/96.94 | 0.37(0.27–0.51) |
| Male | 36(46.75) | 26(41.94) | 10(66.67) | 26/105.25 | 0.25(0.17–0.36) | |
| Age (year)b | Mean ± SD | 60.96 ± 1.01 | 61.12 ± 1.11 | 60.26 ± 2.46 | ||
| < 50 | 7(9.09) | 6(9.68) | 1(6.67) | 6/22.67 | 0.26(0.12–0.59) | |
| 50–70 | 56(72.73) | 45(72.58) | 11(73.33) | 45/ 151.35 | 0.29(0.22–0.40) | |
| > = 70 | 14(18.18) | 11(17.74) | 3(20) | 11/28.17 | 0.39(0.22–0.70) | |
| Ethnicityc | non-Turkmen | 21(27.27) | 20(32.26) | 1(6.67) | 20/ 33.05 | 0.60(0.39–0.94) |
| Turkmen | 56(72.73) | 42(67.74) | 14(93.33) | 42/169.14 | 0.24(0.18–0.33) | |
| Education | Illiterate | 52(67.53) | 45(72.58) | 7(46.67) | 45/94.16 | 0.48(0.36–0.64) |
| educated | 25(32.47) | 17(27.42) | 8(53.33) | 17/108.04 | 0.16(0.10–0.25) | |
| Residential area | Rural | 57(74.03) | 48(77.42) | 9(60) | 48/106.40 | 0.45(0.34–0.60) |
| Urban | 20(25.97) | 14(22.58) | 6(40) | 14/95.80 | 0.15(0.09–0.25) | |
| SES | Poor | 33(42.86) | 29(46.77) | 4(26.67) | 29/58.01 | 0.50(0.35–0.72) |
| Medium | 24(31.17) | 19(30.67) | 5(33.33) | 19/59.36 | 0.32(0.20–0.50) | |
| Rich | 20(25.97) | 14(22.58) | 6(40) | 14/84.82 | 0.16(0.10–0.28) | |
| BMI | Normal | 31(40.26) | 28(45.16) | 3(20) | 28/39.54 | 0.71(0.49–1.02) |
| Underweight | 4(5.19) | 4(6.45) | 0(0) | 4/3.32 | 1.02(0.45–3.21) | |
| Overweight & Obese | 42(54.55) | 30(48.39) | 12(80) | 30/159.33 | 0.19(0.13–0.27) | |
| Hypertension | No | 45(58.44) | 37(59.68) | 8(53.33) | 37/121.21 | 0.30(0.22–0.42) |
| Yes | 32(42.56) | 25(40.32) | 7(46.67) | 25/80.98 | 0.31(0.21–0.46) | |
| Diabetes mellitus | No | 72(93.51) | 57(91.94) | 15(100) | 57/189.42 | 0.30(0.23–0.39) |
| Yes | 5(6.49) | 5(8.06) | 0(0) | 5/12.77 | 0.39(0.16–0.94) | |
| Physical activity | First tertile (low) | 29(37.66) | 24(38.71) | 5(33.33) | 24/64.19 | 0.37(0.25–0.58) |
| Second tertile (medium) | 27(35.06) | 23(37.1) | 4(26.67) | 23/48.45 | 0.47(0.31–0.71) | |
| Third tertile (high) | 21(27.27) | 15(24.19) | 6(40) | 15/89.55 | 0.17(0.10–0.28) | |
| Opium ever used | No | 60(77.92) | 49(79.03) | 11(73.33) | 49/161.89 | 0.30(0.23–0.40) |
| Yes | 17(22.08) | 13(20.97) | 4(26.67) | 13/40.3 | 0.32(0.19–0.55) | |
| Cigarette smoking | Never | 56(72.73) | 45(72.58) | 11(73.33) | 45/154.557 | 0.29(0.22–0.39) |
| Ever | 21(27.27) | 17(27.42) | 4(26.67) | 17/47.637 | 0.36(0.22–0.57) | |
| Water source | Pipe water | 63(81.82) | 50(80.65) | 13(86.67) | 50/169.671 | 0.29(0.22–0.39) |
| other | 14(18.18) | 12(19.35) | 2(13.33) | 12/32.52 | 0.37(0.21–0.65) | |
| Animal contact | Never | 7(9.09) | 7(11.29) | 0(0) | 7/7.51 | 0.93(0.44–1.95) |
| Ever | 70(90.91) | 55(88.71) | 15(100) | 55/197.68 | 0.28(0.22–0.37) |
SES Socio Economic Status, BMI Body Mass Index, SD Standard Deviation
aColumn percentage
bAge at which cancer was diagnosed
cStatistically significant difference between living and dead groups (P-value < 0.05)
The minimally adjusted brain cancer survival results and the results of the final model with those variables that showed significant association with brain cancer mortality in the minimally adjusted model are demonstrated in Table 2. According to the minimally adjusted model, Turkmen ethnicity (HR = 0.42 (0.23–0.76)), patients who lived in urban areas (HR = 0.46 (0.25–0.84)), patients who were overweight or obese (HR = 0.48 (0.25–0.93)), and patients who had animal contact (HR = 0.43 (0.19–0.96)) had a higher survival rate.
Table 2.
Minimally adjusted and Multiple Cox proportional hazards regression analysis for survival of brain cancer patients
| Variable | Minimally adjusted model (for age, sex and urbanization) | Multiple model | |
|---|---|---|---|
| Hazard ratio (95% CI) | Hazard ratio (95% CI) | ||
| Sex | Female | Reference | Reference |
| Male | 0.81(0.49–1.36) | - | |
| Age | 1.03(0.99–1.06) | 1.03(0.99–1.06) | |
| Ethnicity | non-Turkmen | Reference | Reference |
| Turkmen | 0.42(0.23–0.76)* | 0.38(0.21–0.71)* | |
| Education | Illiterate | Reference | Reference |
| educated | 0.62(0.33–1.19) | - | |
| Residential area | Rural | Reference | Reference |
| Urban | 0.46(0.25–0.84)* | 0.41(0.20–0.83)* | |
| SES | Poor | Reference | Reference |
| Medium | 0.91(0.5–1.63) | - | |
| Rich | 0.61(0.31–1.2) | - | |
| BMI | Normal | Reference | Reference |
| Underweight | 1.29(0.44–3.84) | 1.03(0.33–3.12) | |
| Overweight & Obese | 0.48(0.25–0.93)* | 0.54(0.29–0.99)* | |
| Hypertension | No | Reference | Reference |
| Yes | 0.74(0.43–1.27) | - | |
| Diabetes mellitus | No | Reference | Reference |
| Yes | 1.45(0.54,3.87) | - | |
| Physical activity | First tertile (low) | Reference | Reference |
| Second tertile (medium) | 1.05(0.53–2.11) | - | |
| Third tertile (high) | 0.61(0.28–1.31) | - | |
| Opium ever used | No | Reference | Reference |
| Yes | 0.86(0.42,1.76) | - | |
| Cigarette smoking | Never | Reference | Reference |
| Ever | 1.37(0.69–2.71) | - | |
| Water source | Pipe water | Reference | Reference |
| other | 1.16(0.6–2.25) | - | |
| Animal contact | Never | Reference | Reference |
| Ever | 0.43(0.19–0.96)* | 0.28(0.12–0.66)* |
SES Socio Economic Status, BMI Body Mass Index
*Significant
In the multiple model as well, Turkmen ethnicity (HR = 0.38 (0.21–0.71)), patients who lived in urban areas (HR = 0.41 (0.20,0.83)), patients who were overweight or obese (HR = 0.54 (0.29–0.99)), and those who had animal contact (HR = 0.28 (0.12–0.66)) were more likely to survive during the follow up.
The overall survival of one year of brain cancer patients was 38%, with a median survival time of 0.72 years (95%CI: (0.48–0.99)) and this index was approximately equal in females (0.74 years with 95%CI: (0.48–1.00)) and males (0.71 years with 95%CI: (0.22–2.00)) (Figs. 1 and 2). The highest median survival time was observed among patients aged 45 to 55 years (1 year) and the lowest for patients aged over 75 years old (0.2 year).
Fig. 1.
Overall survival curve of brain cancer cases both sexes combined
Fig. 2.
Overall survival curve of brain cancer cases by sex
The distribution of risk factors in participants with and without brain cancer and cancer incidence rates per 100,000 person-years for all participants are demonstrated in Table 3. The incidence of brain cancer in this study was 10.95 per 100,000 person-years (95% CI: (8.76–13.70)) and overall follow-up time was 702,608 years. The mean age of participants was 63.96 ± 0.19 years and 57.58% of them were women. Patients with brain cancer had higher prevalence of hypertension (42.56% vs 40.09%), used opium (22.08% vs 16.95%) and smoked (27.27% vs 17.25%). However, except for smoking, the rest of the risk factors had no significant difference between the two sub-groups with and without brain cancer.
Table 3.
Sociodemographic and habitual characteristics of participants with or without brain cancer incidence and the brain cancer incidence rate
| Variable |
Total (N = 49,783) N(%)a |
With brain cancer(n = 77) n(%)a |
Without brain cancer (n = 49,706) n(%)a |
N of Incidence/ Person-Year at risk | Incidence rate per 100,000 Person-Year (95% CI) | |
|---|---|---|---|---|---|---|
| Sex | Female | 28,667(57.58) | 41(53.25) | 28,626(57.59) | 41/413736 | 9.91(7.29–13.46) |
| Male | 21,116(42.42) | 36(46.75) | 21,080(42.41) | 36/288871.88 | 12.46(8.96–17.28) | |
| Age (year)b | Mean ± SD | 63.96 ± 0.19 | 60.96 ± 1.01 | 64.05 ± 0.19 | ||
| < 50 | 24,517(49.25) | 24,484(49.26) | 33(42.86) | 33/367558.71 | 8.98(6.38–12.62) | |
| 50–70 | 22,793(45.78) | 22,752(45.77) | 41(53.25) | 41/309440.75 | 13.25(9.75–17.99) | |
| > = 70 | 2473(4.97) | 2470(4.97) | 3(3.9) | 3/25594.1 | 11.72(3.78–36.34) | |
| Ethnicity*** | non-Turkmen | 12,710(25.53) | 21(27.27) | 12,689(25.53) | 21/ 173,732.36 | 12.09(7.88–18.54) |
| Turkmen | 37,073(74.47) | 56(72.73) | 37,017(74.47) | 56/528875.52 | 10.58(8.15–13.75) | |
| Education | Illiterate | 34,938(70.18) | 52(67.53) | 34,886(70.18) | 52/485754.76 | 10.70(8.16–14.05) |
| educated | 14,845(29.82) | 25(32.47) | 14,820(29.82) | 25/216853.12 | 11.53(7.78–17.06) | |
| Residential area | Rural | 39,825(80) | 57(74.03) | 39,768(80.01) | 57/556926.66 | 10.23(7.89–13.27) |
| Urban | 9958(20) | 20(25.97) | 9938(19.99) | 20/145681.22 | 13.72(8.86–21.28) | |
| SES | Poor | 17,818(35.79) | 33(42.86) | 17,785(35.78) | 33/245487.01 | 13.44(9.56–18.91) |
| Medium | 15,398(30.93) | 24(31.17) | 15,374(30.93) | 24/217228.16 | 11.04(7.40–16.48) | |
| Rich | 16,567(33.26) | 20(25.97) | 16,547(33.29) | 20/239892.71 | 8.34(5.38–12.92) | |
| BMI | Normal | 17,840(35.84) | 31(40.26) | 17,809(35.83) | 31/247797.28 | 12.51(8.8–17.79) |
| Underweight | 2391(4.8) | 4(5.19) | 2387(4.8) | 4/30711.37 | 13.02(4.89–34.70) | |
| Overweight & Obese | 29,544(59.36) | 42(54.55) | 29,502(59.36) | 42/424021.46 | 9.90(7.32–13.40) | |
| Hypertension | No | 29,824(59.91) | 45(58.44) | 29,779(59.91) | 45/433892.69 | 10.37(7.74–13.89) |
| Yes | 19,959(40.09) | 32(42.56) | 19,927(40.09) | 32/268715.19 | 11.91(8.42–16.84) | |
| Diabetes mellitus | No | 46,267(92.94) | 72(93.51) | 46,195(92.94) | 72/658723.23 | 10.93(8.67–13.77) |
| Yes | 3516(7.06) | 5(6.49) | 3511(7.06) | 5/43884.65 | 11.39(4.74–27.37) | |
| Physical activity | First tertile (low) | 18,569(37.96) | 29(37.66) | 18,540(37.96) | 29/247502.74 | 11.71(8.14–16.86) |
| Second tertile (medium) | 17,074(34.91) | 27(35.06) | 17,047(34.91) | 27/246246.14 | 10.96(7.52–15.99) | |
| Third tertile (high) | 13,272(27.13) | 21(27.27) | 13,251(27.13) | 21/195339.01 | 10.75(7.01–16.49) | |
| Opium ever used | No | 41,342(83.04) | 60(77.92) | 41,282(83.05) | 60/594446.35 | 10.09(783–12.99) |
| Yes | 8441(16.09) | 17(22.08) | 8424(16.95) | 17/108161.53 | 15.71(9.77–25.28) | |
| Cigarette smoking*** | Never | 41,188(82.74) | 56(72.73) | 41,132(82.75) | 56/588123.07 | 9.52(7.32–12.37) |
| Ever | 8595(17.26) | 21(27.27) | 8574(17.25) | 21/ 114,484.81 | 18.34(11.95–28.13) | |
| Water source | Pipe water | 41,363(83.09) | 63(81.82) | 41,300(83.09) | 63/583951.37 | 10.79(8.42–13.81) |
| other | 8419(16.91) | 14(18.18) | 8405(16.91) | 14/118641.51 | 11.8(6.99–19.92) | |
| Animal contact | Never | 3348(6.73) | 7(9.09) | 3341(6.72) | 7/48556.1 | 14.42(6.87–30.24) |
| Ever | 46,435(93.27) | 70(90.91) | 46,365(93.28) | 70/48556.1 | 10.96(8.46–13.53) |
SES Socio Economic Status, BMI Body Mass Index, SD Standard Deviation
aColumn percentage
bAge at the time of study entry
***Statistically significant difference between living and dead groups (P-value < 0.05)
Table 4 shows the results of the minimally adjusted Cox model and the multiple model for brain cancer incidence. In the minimally adjusted model, the hazard ratio of brain cancer increased with age (HR = 1.03 (1.00–1.05)) and the hazard of brain cancer in smokers was 2.03 (HR = 2.03 (1.09–3.77)) times higher than non-smokers. Patients with higher socioeconomic status had a lower risk of developing brain cancer (HR = 0.52 (0.29–0.95)). According to Table 4, multiple Cox proportional hazards regression analysis revealed that being older (HR = 1.02 (1.01–1.05)) and smoking (HR = 1.91(1.16–3.16)) were associated with a higher risk of brain cancer incidence.
Table 4.
Minimally adjusted and Multiple Cox proportional hazards regression analysis for incidence of brain cancer
| Variable | Minimally adjusted model (for age, sex and urbanization) | Multiple model | |
|---|---|---|---|
| Hazard ratio (95% CI) | Hazard ratio (95% CI) | ||
| sex | Female | Reference | Reference |
| Male | 1.23(0.79–1.93) | - | |
| Age | 1.03(1–1.05)* | 1.02(1.01–1.05)* | |
| Ethnicity | Non-Turkmen | Reference | Reference |
| Turkmen | 0.99(0.59–1.69) | - | |
| Education | Illiterate | Reference | Reference |
| educated | 1.08(0.6–1.94) | - | |
| Residential area | Rural | Reference | Reference |
| Urban | 1.38(0.83–2.29) | - | |
| SES | Poor | Reference | Reference |
| Medium | 0.78(0.46–1.33) | 0.83(0.49–1.41) | |
| Rich | 0.52(0.29–0.95)* | 0.64(0.36–1.12) | |
| BMI | Normal | Reference | Reference |
| Underweight | 0.74(0.43–1.27) | - | |
| Overweight & Obese | 0.93(0.52–1.65) | - | |
| Hypertension | No | Reference | Reference |
| Yes | 1.02(0.64–1.64) | - | |
| Diabetes mellitus | No | Reference | Reference |
| Yes | 0.96(0.38–2.38) | - | |
| Physical activity | First tertile (low) | Reference | Reference |
| Second tertile (medium) | 1.18(0.64–2.19) | - | |
| Third tertile (high) | 1.19(0.64–2.17) | - | |
| Opium ever used | No | Reference | Reference |
| Yes | 1.5(0.85–2.65) | - | |
| Cigarette smoking | Never | Reference | Reference |
| Ever | 2.03(1.09–3.77)* | 1.91(1.16–3.16)* | |
| Water source | Pipe water | Reference | Reference |
| other | 1.19(0.65–2.19) | - | |
| Animal contact | Never | Reference | Reference |
| Ever | 0.69(0.32–1.51) | - |
SES Socio Economic Status, BMI Body Mass Index, CI Confidence Interval
*Significant
Discussion
In the current study, the incidence rate of brain cancer was found to be 10.95 cases per 100,000 person-months, with a median survival time of approximately 0.72 years. Our analyses, encompassing both minimally adjusted models and multiple models, identified that Turkmen ethnicity, urban residency, overweight or obesity, and animal contact were associated with increased survival rates. Individuals with brain cancer showed a higher prevalence of factors such as hypertension, opium use, and smoking compared to those without the disease. Moreover, older age and smoking were identified as associated with an elevated risk of brain cancer incidence. These findings underscore the complex role of demographic and lifestyle variables in both the incidence and outcomes of brain cancer.
In our study, older age was also associated with a higher risk of brain cancer incidence and mortality. It has been known that the prevalence of primary brain cancers is highest among the elderly, with advanced age typically correlating with poorer prognoses. On the other hand, older patients diagnosed with primary brain tumors may face distinct challenges, including the coexistence of different comorbidities and polypharmacy, reduced tolerance to chemotherapy, and higher susceptibility to radiation-induced neurotoxicity [27].
We observed that urban residence was associated with a better survival rate. This disparity between rural and urban areas is not limited to Iran, as it has been observed in other developing or developed countries such as China [28], Brazil [29], United States [15], Canada [30] and South Korea [31]. Delavar et al. studied 37,581 cases of brain cancer in the United States and reported that those patients who lived in rural areas had a greater risk of mortality compared to residents in urban areas [15]. Similarly, Walker et al. found that living in rural areas had a modest effect on brain survival in patients with glioblastoma and oligo astrocytoma [30]. Possible reasons for this disparity between rural and urban residence include limited access to healthcare services, differences in cancer grade at diagnosis, variations in the quality of medical resources, non-adherence to treatment plans, and being lost to follow-up in rural areas. It has also been found that patients who receive treatment at high-volume medical centers have better prognoses [32]. Moreover, survival rates for cancer patients in rural and urban areas who receive the same care are comparable [33].
In our study, patients who were overweight or obese were more likely to survive during the follow-up. There are various controversial findings in similar previous studies in this regard. There is a common belief that obesity is associated with a poor survival [34, 35]. The term “obesity paradox” describes this confusing phenomenon that excessive body adiposity and higher BMI have been reported to be related to better survival [36]. A recently published study by Cha et al. investigated the relationship between BMI after diagnosis of glioblastoma before surgery [37]. They observed that patients with a higher BMI had a longer median overall survival compared to patients with a lower BMI (cut-off point = 23.0 kg/m2). Also, Valente Aguiar et al. found that obesity was an independent prognostic factor for better survival in patients with glioblastoma [38]. This surprising result can have several explanations. In almost all previous studies, BMI is used as a proxy for obesity, but higher BMI may be associated with greater muscle mass [36, 39]. This confusing result could be due to inadequate measurement of excess fat. In addition, the presence of additional nutrients in excess adipose tissue can help patients combat the side effects of chemotherapy and/or radiotherapy, and can ultimately reduce mortality [40]. Furthermore, downregulated fatty acid synthase in obese patients compared to patients with normal weight has been reported as upregulated fatty acid synthase has oncogenic effects [41]. Decreased fatty acid synthase in obese individuals may explain the paradox of obesity in cancer patients. Further investigation on the impact of weight on brain cancer survival is required to resolve reported discrepancies [42].
In this study, having animal contact was linked to better survival. An investigation by Christensen et al. explored how interactions with animals before or after birth and exposure to farm environments could affect the risk of childhood brain tumors. The findings showed that there was a decreased risk of child brain tumors associated with maternal farm exposure during pregnancy or living on a farm during childhood. There was no systematic pattern in exposure to animals, indicating that factors beyond animal contact might be at play in the protective effect of farm living, which highlights the need for more investigation on these factors [43]. Another study conducted by Ménégoz et al. concluded that residing or working on a farm did not pose a risk for brain tumors, and there was no evident correlation between interactions with farm animals or pets and the risk of brain cancers. Similarly, no relationship was observed between brain tumors and regular contact with humans or animals across different industrial or occupational categories, with the exception of a decreased risk of glioma among biological technicians and general farm workers, and an increased risk of meningioma among cooks [44].
The carcinogenic effects of smoking are undoubtable and recommendations for smoking cessation persist. However exploring its potential association with brain cancers remains vital for revealing brain tumor etiology [45]. In our research, smoking was associated with a higher risk of brain cancer incidence. Smoking also showed a statistically significant higher rate in patients with brain cancer compared to individuals without it (27.27% vs 17.25% p: 0.02). However, there are some inconsistent results in this regard. For instance, a study by Holick et al. found no link between smoking variables and glioma risk, which is the main type of brain cancer. The results of this study suggest that cigarette smoking does not significantly increase adult glioma risk [46]. A meta-analysis by Shao et al. also found no significant link between smoking and glioma risk, suggesting limited evidence for a causal relationship [47]. These inconsistencies necessitate further research in this field.
Although it is statistically non-significant, opium consumption was higher in patients with brain cancer in the current study. Previous studies also indicate a potential connection between opium use and brain cancer, supported by in-vitro evidence of opiates and receptors in brain cancer cells, suggesting a probable effect on tumor proliferation. These insights also underscore the complex interplay between opium use and brain tumor development [48]. Despite the known carcinogenic effects of opium consumption on different cancer types, these associations need to be further investigated particularly for brain cancers [49].
In this study, a higher prevalence of hypertension was found in patients with brain cancer compared to participants without this disease (42.56% vs 40.09%), although it was not statistically significant. Consistent with our findings, a study conducted by Houben et al. on 510 glioma patients reported an association between hypertension and glioma. However, uncertainties persist regarding causality and the underlying mechanisms. Tumor-related intracranial pressure and neurocarcinogenic properties of antihypertensive drugs have been proposed as potential factors [50]. Furthermore, the large pooled cohort study by Edlinger et al. also found a correlation between elevated blood pressure and the risk of primary brain tumors. Herein, it was reported that meningioma risk increased two to four times in highest three systolic blood pressure quintiles compared to the lowest. Meningioma risk also rose along with a combined metabolic syndrome score. However, blood pressure was mentioned to be the key factor. In addition, high-grade glioma risk was notably associated with diastolic blood pressure, doubling in the highest three quintiles than the lowest [51].
The current study on brain cancer incidence and survival in Golestan province has several strengths and limitations. On the positive side, it benefits from a longitudinal design, utilizing data from the GCS to track brain cancer incidence and survival over time. With a substantial sample size of over 50,000 participants, the study's statistical power is enhanced, allowing for analyses of various risk factors associated with brain cancer survival. Comprehensive data collection methods enable a thorough investigation into sociodemographic, lifestyle, and environmental factors. However, the study findings may have limited generalizability beyond the Golestan province population. Data collection through questionnaires and interviews introduces the potential for recall bias, particularly for self-reported information. Additionally, the observational nature of the study limits the ability to establish causality between risk factors and brain cancer outcomes. Ultimately, our failure to record the sub-types of brain tumor is another limitation of the current study as risk factors may differ by histologic sub-types. Also, limitations such as potential bias and the observational nature of the study must be considered when interpreting the findings.
Conclusions
In conclusion, our research examined the incidence and survival outcomes of brain cancer in Golestan province in northeastern Iran, revealing several contributing factors including smoking, age, ethnicity, urban living, obesity, contact with animals, and hypertension. The intricate relationship between demographic and lifestyle elements affecting both the incidence and prognosis of brain cancer highlights the necessity for ongoing studies to deepen our understanding of the fundamental mechanisms and to develop targeted strategies for prevention and treatment.
Supplementary Information
Acknowledgements
We extend our deepest appreciation to all participants of the Golestan Cohort Study (GCS) for their commitment and collaboration throughout the duration of our research. Our thanks go to the Golestan Cohort Study Center staff, our colleagues in the local health networks, and the healthcare providers in our study area for their invaluable contributions. We are also grateful for the support provided by the Golestan University of Medical Sciences in Gorgan, Iran. This work was a joint effort supported by collaborations with the Digestive Disease Research Center at Tehran University of Medical Sciences (Principal Investigator: R.M.), the International Agency for Research on Cancer (Principal Investigator: P.B.), and the National Cancer Institute (Principal Investigator: S.M.D.) and the Golestan University of Medical Sciences, Gorgan, Iran.
Abbreviations
- BMI
Body mass index
- CI
Confidence interval
- DDRI
Digestive Disease Research Institute
- GCS
Golestan Cohort Study
- HR
Hazard ratio
- ICD
International classification of disease
- SD
Standard deviation
- SES
Socio-economic status
Authors’ contributions
O.R., P.P., N.R., M.S., G.R., H.P., S.G.S., and R.M. conceived and/or designed the work that led to the submission, acquired data, and/or played an important role in interpreting the results. P.R., P.P., N.R., S.G.S., and R.M. drafted or revised the manuscript. All authors approved the final version and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Funding
No specific fund has been allocated to this study.
Data availability
Data will be shared upon reasonable request from the corresponding author.
Declarations
Ethics approval and consent to participate
The study design, which was carried out in accordance to with the Declaration of Helsinki, was approved by the ethical review committee of the Digestive Diseases Research Institute (DDRI) affiliated to Tehran University of Medical Sciences. Written informed consent was obtained from all participants before participation.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Supplementary Materials
Data Availability Statement
Data will be shared upon reasonable request from the corresponding author.