Multifactorial Risk Assessment and Predictive Modeling of Glioma Based on NHANES Data

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However, the specific roles and interactions of these factors remain inadequately studied. Methods This study utilized data from the National Health and Nutrition Examination Survey (NHANES), including 1,843 participants (163 glioma patients). Multivariate linear regression, neural network analysis, and correlation analysis were conducted to systematically evaluate the effects of age, gender, race, metabolic diseases, mental health, and lifestyle on glioma risk. A predictive risk model was also constructed. Results Age (OR = 1.081, P < 0.001), cardiovascular disease (OR = 2.042, P < 0.001), depression (OR = 2.348, P < 0.001), and alcohol consumption (P < 0.001) were identified as major risk factors for glioma, while diabetes (OR = 0.581, P = 0.003) appeared to have a protective effect. Predictive modeling highlighted age as the most critical predictor (importance score = 0.602), with an area under the ROC curve (AUC) of 0.76, indicating moderate predictive capability. Conclusion This study systematically analyzed multiple risk factors for glioma using NHANES data, identifying key risk factors and constructing a predictive model. The findings provide a theoretical basis for early screening, prevention strategies, and personalized interventions for glioma. Future research should integrate longitudinal data and molecular mechanism analyses to validate these findings and explore causal relationships. Glioma Risk Assessment Metabolic Diseases Depression Alcohol Consumption NHANES Data Figures Figure 1 Figure 2 Figure 3 1 Introduction Glioma is one of the most common primary malignant tumors of the central nervous system[1]. Due to its high heterogeneity, aggressive invasiveness, and poor prognosis, glioma has become a focal point in global neuro-oncology research[2]. According to epidemiological data, the annual incidence of glioma in the United States is approximately 6 cases per 100,000 people, with glioblastoma being the most prevalent type of malignant brain tumor, accounting for 50.1% of all malignant tumors[3]. This disease is more common in males, while non-malignant tumors (such as meningiomas) are more frequent in females. In recent years, advancements in surgery, radiotherapy, chemotherapy, and targeted therapies have improved survival rates for some glioma patients. However, for high-grade gliomas, such as glioblastoma (WHO Grade III and IV), the prognosis remains poor, with a median survival time of only about 15 months[4]. Despite improvements in the 2-year and 3-year survival rates for glioblastoma since 2005, reaching 18% and 11% respectively, the 5-year survival rate remains below 5%, indicating a dire prognosis overall[5]. This severe situation underscores the necessity for comprehensive studies on glioma, particularly on its pathogenic factors, to develop effective prevention and control strategies. The etiology of glioma is believed to result from a combination of genetic susceptibility and environmental factors. Multiple studies have revealed that genetic variations, family history, and certain hereditary syndromes (e.g., Li-Fraumeni syndrome, Turcot syndrome) may significantly increase the risk of glioma[6–9]. Additionally, environmental exposure factors, such as high doses of ionizing radiation and occupational exposure, are also considered important triggers for glioma[10]. In recent years, the potential roles of metabolic diseases and unhealthy lifestyles in glioma development have garnered increasing attention. For example, metabolic disorders such as hypertension and diabetes may contribute to glioma initiation and progression through chronic inflammation and vascular dysfunction[11, 12]. Unhealthy lifestyle habits, including excessive alcohol consumption, smoking, unbalanced diets, and psychological issues (e.g., depression and anxiety), may also influence glioma risk via immune system modulation or neuroendocrine mechanisms[13–15]. However, current research on the specific associations and interactions between these factors and glioma remains insufficient, particularly in terms of multi-factorial analyses and population heterogeneity. Given this context, conducting comprehensive analyses based on nationally representative datasets is of significant value. This study leverages data from the National Health and Nutrition Examination Survey (NHANES), which provides a broad population sample and records extensive variables, including demographic characteristics, health status, lifestyle habits, and biomarkers. Using NHANES data, this study aims to comprehensively explore the effects of factors such as age, gender, race, metabolic diseases, psychological health, and lifestyle on glioma incidence. Furthermore, it seeks to develop a risk prediction model to uncover the potential pathogenic mechanisms of glioma, thereby providing a theoretical basis for early screening and precise interventions. This research also offers scientific support for the formulation of public health policies and the implementation of personalized prevention strategies. 2 Materials and Methods 2.1 Clinical Data The data for this study were obtained from the National Health and Nutrition Examination Survey (NHANES). NHANES is a nationally representative cross-sectional survey conducted by the National Center for Health Statistics (NCHS) and the Centers for Disease Control and Prevention (CDC). Its purpose is to collect representative data on the health status, lifestyle habits, and nutritional conditions of the U.S. civilian population. The data collection process was approved by the NCHS Ethics Review Board, and all participants provided written informed consent. The study population included NHANES participants, covering individuals of various ages, genders, races, and ethnic backgrounds in the United States. The survey included face-to-face interviews, physical examinations, and laboratory tests, conducted by trained professionals. NHANES data were rigorously cleaned and validated, and are made available as publicly accessible datasets for research purposes. 2.2 Data Collection This study aimed to explore risk factors for glioma while incorporating a series of potential covariates. These covariates were identified based on literature support and theoretical reasoning as potentially associated with glioma incidence. Data were collected through standardized questionnaires that gathered participants' demographic and health information, including age, gender, race, poverty income ratio (PIR), body mass index (BMI), diabetes, hyperlipidemia, heart disease, pulse rate, smoking, and drinking behaviors. Missing values in the dataset were imputed using interpolation methods in R software. The diagnosis of glioma was based on self-reported responses in the NHANES questionnaire, which included questions such as "Have you ever been diagnosed with cancer or a malignant tumor?" and "Have you experienced severe dizziness, lightheadedness, fainting, or unsteadiness?" Additional questions addressed severe visual impairment, significant attention difficulties, and walking difficulties. This study aimed to comprehensively analyze these data to identify potential risk factors for glioma. It is worth noting that the diagnosis based on self-reported information may be subject to bias. Further studies incorporating more accurate clinical diagnostic data are needed to validate the findings. 2.3 Statistical Analysis All statistical analyses were conducted following CDC guidelines and employed sampling weights to calculate nationally representative estimates for the non-institutionalized U.S. population. Continuous variables were presented as mean ± standard deviation (Mean ± SD), and weighted t-tests were used to compare differences between groups. Categorical variables were presented as frequencies and percentages, and weighted chi-square tests were employed for group comparisons. Non-normally distributed data were described using medians and interquartile ranges (Median [P25, P75]). Multivariate linear regression analysis was used to evaluate the combined effects of various factors on glioma. Bivariate analysis was performed to explore the association of individual risk factors with glioma. A forest plot was utilized to visualize the analysis of risk factors, providing an intuitive display of the impact of different variables on glioma. 3 Results 3.1 Baseline Characteristics of Patients This study analyzed data from 1,843 participants, including 163 glioma patients and 1,680 controls, after excluding individuals with incomplete data. Significant differences were observed between the two groups in several clinical variables. Age was strongly associated with glioma risk (P < 0.001). The prevalence of glioma was 6.60% among individuals aged 60 years and older, compared to 0.10% in the 18–35 age group. Heart disease was also a significant risk factor (P < 0.001), with a prevalence of 10.00% in patients with heart disease versus 1.80% in those without. Heavy drinking was linked to a higher prevalence of glioma (8.10%) compared to non-drinkers (1.40%, P < 0.001). Gender differences were significant (P = 0.019), with slightly higher prevalence in females (4.70%) than males (4.20%). Non-Hispanic Whites had the highest prevalence among racial groups (6.50%, P < 0.001), and individuals with low income had a higher glioma risk (4.10%, P = 0.007). Among those with depression, severe cases had a significantly higher prevalence of glioma (1.70%, P 0.05), which may reflect sample size limitations or other confounding factors requiring further investigation (Table 1 ). Table 1 Baseline Characteristics Variable Glioma P-value No(n = 1680) Yes(n = 163) Age 18–35 years old 196(10.60%) 2(0.10%) 60 years old 727(39.40%) 122(6.60%) Gender Male 954(51.80%) 77(4.20%) 0.019 Female 726(39.40%) 86(4.70%) Race Mexican American 271(14.70%) 16(0.90%) < 0.001 Non-Hispanic White 729(39.60%) 120(6.50%) Non-Hispanic Black 463(25.10%) 22(1.20%) Non-Hispanic Asian 217(11.80%) 5(0.30%) Marital Status Married/Living with Partner 1087(59.00%) 84(4.60%) < 0.001 Never married 202(11.00%) 8(0.40%) Widowed/ Divorced/ Separated 391(21.20%) 71(3.90%) Educational < high school 168(9.10%) 16(0.90%) 0.516 9th-11th grade 207(11.20%) 29(1.60%) high school 393(21.30%) 30(1.60%) college or above 912(49.50%) 88(4.8%) Income_level Low income 573(31.10%) 75(4.10%) 0.007 Middle income 524(28.40%) 42(2.30%) High income 583(31.60%) 46(2.50%) BMI 30.646 ± 6.985 29.579 ± 6.218 0.061 Hyperlipidemia No 1146(62.20%) 112(6.10%) 0.896 Yes 534(29.0%) 51(2.80%) Diabetes No 868(47.10%) 105(5.70%) 0.002 Yes 812(44.10%) 58(3.10%) Heart Disease No 1496(81.20%) 129(7.00%) < 0.001 Yes 184(10.00%) 34(1.80%) Pulse No 1508(81.80%) 154(8.40%) 0.053 Yes 172(9.30%) 9(0.50%) Depression No or mild depression 1431(77.60%) 108(5.90%) < 0.001 No or mild depression 166(9.00%) 24(1.30%) Severe depression 83(4.50%) 31(1.70%) Hemoglobin 14(13.1 ~ 15.1) 13.6(12.7 ~ 14.4) < 0.001 Energy Intake 2089.02 ± 960.760 1057.55 ± 793.215 0.003 Calcium 26(7 ~ 78) 27(8 ~ 78) 0.317 Protein 5.285 ± 9.912 4.120 ± 8.156 0.146 Fiber 0(0 ~ 1.1) 0(0 ~ 1.6) 0.794 magnesium 26.38 ± 35.64 23.50 ± 26.665 0.316 Sodium 221.75 ± 429.656 176.87 ± 271.81 0.191 Potassium 194.65 ± 272.391 161.66 ± 150.925 0.128 Total Fat 0.26(0 ~ 4.76) 0.3(0.01 ~ 4.76) 0.298 Saturated Fat 0.030(0.002 ~ 1.566) 0.051(0.005 ~ 1.566) 0.302 Smoking Never Smoke 910(49.40%) 75(4.10%) 0.705 Former Smoke 472(25.60%) 53(2.90%) Current Smoke 275(14.90%) 25(1.40%) Drinking Never Drank 348(18.90%) 25(1.40%) < 0.001 Occasionally Drank 590(32.00%) 32(1.70%) Regularly Drank 74(4.00%) 6(0.30%) Heavy Drinker 149(8.1%) 3(0.2%) Abbreviations: BMI, body mass index. 3.2 Forest Plot Analysis of Glioma Risk Factors This study analyzed the association between glioma risk and five variables—hyperlipidemia, diabetes, gender, heart disease, and depression—using a forest plot. The results showed significant differences in the impact of these variables on glioma risk(Figure 1 ). Heart disease was identified as a significant risk factor (OR = 2.042, P < 0.001), with individuals with heart disease being 2.042 times more likely to develop glioma than those without. Similarly, depression was strongly associated with glioma risk (OR = 2.348, P < 0.001), indicating that individuals with severe depression have a 2.348-fold higher risk compared to those with mild or no depression. These findings underscore the importance of heart health and mental well-being in glioma prevention and management. Conversely, diabetes showed a negative association with glioma risk (OR = 0.581, P = 0.003). Diabetic individuals had a 0.581-fold lower risk of glioma compared to non-diabetics, suggesting a potential protective role, though the underlying mechanisms require further study. In contrast, hyperlipidemia (OR = 0.986, P = 0.94) and gender (OR = 1.268, P = 0.179) were not significantly associated with glioma risk, indicating no meaningful differences based on these factors. The forest plot clearly illustrated the odds ratios (ORs) and 95% confidence intervals for each variable. Variables with confidence intervals entirely on one side of 1 (e.g., heart disease, depression, and diabetes) were significantly associated with glioma risk, while those crossing 1 (e.g., hyperlipidemia and gender) showed no significant association. In conclusion, this study highlights heart disease and depression as significant risk factors for glioma, while diabetes may have a protective effect. These findings provide valuable insights for glioma risk assessment and intervention strategies. 3.3 Neural Network Model for Glioma Prediction—Assessment of Feature Importance The importance of predictor variables for glioma risk was evaluated using a neural network model with standardized feature importance scores (Fig. 2 ). Results showed significant differences in the contributions of various features to the model's predictive performance. Age emerged as the most critical variable, with an importance score of 0.602, indicating its key role in glioma risk prediction. This aligns with previous findings that age is strongly associated with glioma incidence. Drinking habits were the second most important variable, with an importance score of 0.159. Although lower than age, drinking behavior still played a notable role in the model, suggesting its potential influence on glioma risk. Other variables, including hyperlipidemia (0.053), pulse (0.061), diabetes (0.061), and heart disease (0.063), showed relatively low importance scores. These results indicate that these factors contributed minimally to the model's predictive ability and had limited discriminatory power. Overall, apart from age and drinking habits, the importance of other variables was not significant. This may reflect a more uniform distribution of these variables in the dataset or a weaker association with glioma risk. Additionally, the title of the chart, "The Importance of Standardization," highlights the impact of the standardization process on the calculation of feature weights, which may have influenced the results. 3.4 Multivariate Linear Regression Analysis of Glioma Risk Factors Using a multivariate linear regression model, this study identified significant associations between glioma incidence and several factors, including age, race, marital status, education, diabetes, pulse, depression, and drinking habits (Table 2 ). Table2: Multiple linear regression model for Glioma B S.E t P-value VIF Age 0.081 0.010 7.802 <0.001 1.250 Gender -0.018 0.015 -1.203 0.229 1.492 Race -0.041 0.008 -5.357 <0.001 1.192 Marital Status 0.019 0.008 2.526 0.012 1.095 Educational 0.016 0.007 2.372 0.018 1.256 Income_level -0.007 0.008 -0.829 0.407 1.223 BMI -0.006 0.010 -0.609 0.542 1.121 Hyperlipidemia 0.004 0.014 0.326 0.744 1.067 Diabetes -0.070 0.014 -5.098 <0.001 1.219 Heart Disease 0.021 0.020 1.061 0.289 1.107 Pulse -0.070 0.022 -3.225 <0.001 1.097 Depression 0.092 0.0122 7.617 <0.001 1.133 Fiber 0.004 0.004 1.088 0.277 1.809 Smoking -0.003 0.008 -0.352 0.725 1.119 Drinking -0.039 0.005 -7.371 <0.001 1.088 Age was the most significant risk factor (B = 0.081, P < 0.001), with older adults (aged 60 and above) showing a substantially higher risk of glioma compared to younger individuals. Depression was also significantly associated with glioma (B = 0.092, P < 0.001), with severe depression patients having a higher risk than those with moderate depression. Race and marital status were additional significant factors: non-White individuals had a lower risk than White individuals (B=-0.041, P < 0.001), and married or cohabiting individuals had a higher risk compared to single individuals (B = 0.019, P = 0.012). Higher levels of education were positively correlated with glioma risk (B = 0.016, P = 0.018). Among health-related factors, diabetes (B=-0.070, P < 0.001) and pulse (B=-0.070, P < 0.001) were negatively associated with glioma risk, suggesting potential protective effects. Drinking habits (B=-0.039, P < 0.001) also showed a negative correlation, but further studies are needed to explore the underlying mechanisms. Other variables, including gender, household income, hyperlipidemia, heart disease, fiber intake, and smoking habits, did not show significant associations with glioma risk. The variance inflation factors (VIFs) for all variables ranged from 1 to 1.5, indicating no severe multicollinearity in the model. 3.5 Correlation Analysis of Glioma Correlation analysis of variables associated with glioma showed significant associations between glioma incidence and age and race. Age was positively correlated with glioma incidence (r = 0.184, P < 0.001), indicating that the risk of glioma increases significantly with age. Race showed a significant negative correlation (r=-0.090, P < 0.001), suggesting differences in glioma risk among racial groups. Gender was also significantly positively correlated with glioma incidence (r = 0.055, P = 0.019), indicating that males have a slightly higher risk of glioma compared to females. Depression severity was another important factor, showing a significant positive correlation with glioma incidence (r = 0.153, P < 0.001), meaning that individuals with more severe depression are at a higher risk of developing glioma. For other variables, such as BMI, hyperlipidemia, diabetes, heart disease, protein intake, sodium intake, and smoking habits, the analysis did not find statistically significant correlations with glioma (P > 0.05). However, some variables, despite not reaching statistical significance, may have potential biological implications. For instance, the negative correlation coefficient for protein intake might suggest an increased risk to some extent. These hypotheses require further investigation and validation. In summary, the results indicate that age and depression are important factors associated with glioma risk, while gender and race are also moderately correlated. Future studies should further explore the specific mechanisms by which these factors contribute to glioma development (Table 3 ). Table 3 Glioma correlation analysis of osteoporosis Variable Correlation Coefficient P-value Age 0.184** < 0.001 Gender 0.055* 0.019 Race -0.090** < 0.001 BMI -0.034 0.144 Hyperlipidemia -0.003 0.896 Diabetes -0.073* 0.002 Heart Disease 0.087** < 0.001 Depression 0.153** < 0.001 Protein -0.001 0.967 Sodium -0.006 0.811 Smoking -0.002 0.941 *When the confidence level (double test) is 0.05, the correlation is significant. **When the confidence level (double test) is 0.001, the correlation is significant. 3.6 Risk Prediction Model for Glioma This study explored the relationships between factors such as age, diabetes, heart disease, and drinking habits with glioma incidence, and developed a risk prediction model. The results indicated that these factors have a certain predictive effect on glioma occurrence. Specifically, age was identified as a key factor influencing glioma risk, with the risk significantly increasing as age rises. Diabetic patients were found to have a higher risk of glioma compared to non-diabetic individuals. Heart disease was another significant risk factor, with individuals suffering from heart disease showing a greater tendency to develop glioma. Additionally, drinking habits were identified as a potential risk factor, with frequent alcohol consumption potentially increasing glioma risk. The predictive performance of the constructed model was evaluated using a ROC curve (Fig. 3 ). The area under the ROC curve (AUC) was 0.76, indicating a moderate predictive ability. This suggests that the model can differentiate between high-risk and low-risk individuals to some extent, although there remains room for improvement in its predictive accuracy. In conclusion, the predictive model developed in this study provides strong support for glioma risk assessment and highlights the critical roles of age, diabetes, heart disease, and drinking habits in risk prediction. Future research could further optimize the model by incorporating additional variables to enhance its predictive accuracy. 4 Discussion This study systematically evaluated the impact of factors such as age, gender, race, metabolic diseases, psychological health, and lifestyle on the risk of glioma using NHANES data. The findings revealed several significant associations, providing critical evidence for glioma etiology research and public health interventions. Firstly, the study identified age as one of the most important risk factors for glioma. The risk was particularly pronounced in individuals aged ≥ 60 years (P < 0.001). This result aligns with previous studies, suggesting that aging may influence glioma onset and progression through mechanisms such as increased genomic instability, reduced immune surveillance, and weakened apoptotic processes[16]. Given the high-risk nature of the elderly population, they should be included in high-risk group screening programs to facilitate early detection and intervention, thereby reducing the disease burden. Secondly, the study found that gender and race significantly influence glioma incidence. Males exhibited a slightly higher risk than females (P = 0.019), suggesting that gender differences may play a critical role in glioma pathogenesis, potentially linked to sex hormone levels, immune function differences, and behavioral exposure patterns[17]. Furthermore, there were significant differences in glioma incidence among different racial groups, with non-Hispanic Whites having a higher risk than other races (P < 0.001). This finding reflects the complex roles of genetic background, lifestyle, and socioeconomic factors in glioma development and highlights the need for focused etiological research on specific racial groups[18]. The study also identified a significant positive association between cardiovascular disease and glioma risk (OR = 2.042, P < 0.001). Although direct evidence linking cardiovascular disease to glioma is currently lacking, this study speculates that cardiovascular disease may play a potential role in glioma initiation and progression through mechanisms such as chronic inflammation, endothelial dysfunction, and angiogenesis. This hypothesis provides a direction for future research to further explore the relationship between cardiovascular disease and glioma. We also found a negative correlation between diabetes and glioma risk (OR = 0.581, P = 0.003). This finding might be related to the reduced insulin sensitivity in diabetic patients and its regulatory effect on tumor metabolic signaling pathways (e.g., PI3K/AKT). Previous studies have shown that insulin promotes glioblastoma growth by activating the InsR and IGF1R signaling pathways[19]. However, whether diabetes reduces tumor progression risk by inhibiting insulin sensitivity requires further validation. Future research should integrate epidemiological and molecular analyses to investigate the dual effects of diabetes on glioma. Additionally, depression was significantly associated with an increased risk of glioma (OR = 2.348, P < 0.001). One study suggested that chronic stress and depression could promote glioblastoma growth and invasion through the activation of the DRD2/ERK/β-catenin pathway, while also being closely associated with poorer prognosis[15]. This finding underscores the importance of mental health in glioma prevention and control, emphasizing the need for enhanced psychological health assessment and intervention in high-risk populations. Our findings also indicated that alcohol consumption is a significant risk factor for glioma (P < 0.001). Although the specific mechanisms remain unclear, it is speculated that frequent or heavy drinking may promote tumor development by generating reactive oxygen species, causing DNA damage, and altering the tumor microenvironment[20]. Strengthening alcohol behavior management in public health strategies may have a positive impact on reducing glioma risk. However, the association between smoking and glioma was not significant in this study. This result may reflect the complex mechanisms of smoking in glioma, which require further exploration in larger sample sizes. Finally, through multivariate linear regression and neural network analyses, this study validated age, alcohol consumption, cardiovascular disease, and depression as core risk factors for glioma. The predictive value of these variables in the model provides a theoretical basis for precise screening and multifactorial intervention strategies. Moreover, the study showed that diabetes might have a protective effect, suggesting the need for further exploration of the complex mechanisms of metabolic diseases in glioma development. Despite the high representativeness of the NHANES data, this study has certain limitations. First, the cross-sectional study design limits causal inference. Second, some variables relied on self-reported data, which may introduce information bias. Additionally, the heterogeneity among different races and regions requires further exploration in future studies. Longitudinal research combined with molecular mechanism analyses is needed to elucidate the etiology of glioma and its complex multifactorial interactions, providing scientific evidence for precision medicine and personalized prevention strategies. 5 Conclusion In summary, this study comprehensively analyzed multiple risk factors for glioma and identified age, cardiovascular disease, depression, and alcohol consumption as key risk factors, with diabetes potentially having a protective effect. The findings provide valuable references for early screening and prevention strategies for glioma and point to future directions for etiological research. Abbreviations National Health and Nutrition Examination Survey (NHANES), National Center for Health Statistics (NCHS), Centers for Disease Control and Prevention (CDC), poverty income ratio (PIR), body mass index (BMI) Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Data Availability Statement The datasets generated and analyzed during the current study are available in the [NHANES] repository/database at [https://wwwn.cdc.gov/nchs/nhanes/Default.aspx]. Competing interests The authors declare that they have no competing interests Funding The research was funded by National Natural Science Foundation of China (Grant no. 82400543) Authors' contributions Yangfan Zou make the acquisition, analysis, Zhaokai He interpretate of data; Ruofan Xu and Tong Lang create of new software used in the work; Lingbing Meng have drafted the work or substantively revised it Acknowledgements Not applicable References Weller M, Wick W, Aldape K, Brada M, Berger M, Pfister SM, et al. Glioma. Nat Rev Dis Primers. 2015;1:15017. Louis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol. 2021;23(8):1231-51. Ostrom QT, Price M, Neff C, Cioffi G, Waite KA, Kruchko C, et al. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2015-2019. Neuro Oncol. 2022;24(Suppl 5):v1-1v95. Weller M, van den Bent M, Tonn JC, Stupp R, Preusser M, Cohen-Jonathan-Moyal E, et al. European Association for Neuro-Oncology (EANO) guideline on the diagnosis and treatment of adult astrocytic and oligodendroglial gliomas. Lancet Oncol. 2017;18(6):e315-315e329. Poon M, Sudlow C, Figueroa JD, Brennan PM. Longer-term (≥ 2 years) survival in patients with glioblastoma in population-based studies pre- and post-2005: a systematic review and meta-analysis. Sci Rep. 2020;10(1):11622. Melin BS, Barnholtz-Sloan JS, Wrensch MR, Johansen C, Il'yasova D, Kinnersley B, et al. Genome-wide association study of glioma subtypes identifies specific differences in genetic susceptibility to glioblastoma and non-glioblastoma tumors. Nat Genet. 2017;49(5):789-94. Chaligne R, Gaiti F, Silverbush D, Schiffman JS, Weisman HR, Kluegel L, et al. Epigenetic encoding, heritability and plasticity of glioma transcriptional cell states. Nat Genet. 2021;53(10):1469-79. Kibe Y, Ohka F, Aoki K, Yamaguchi J, Motomura K, Ito E, et al. Pediatric-type high-grade gliomas with PDGFRA amplification in adult patients with Li-Fraumeni syndrome: clinical and molecular characterization of three cases. Acta Neuropathol Commun. 2024;12(1):57. Malbari F, Lindsay H. Genetics of Common Pediatric Brain Tumors. Pediatr Neurol. 2020;104:3-12. Braganza MZ, Kitahara CM, Berrington de González A, Inskip PD, Johnson KJ, Rajaraman P. Ionizing radiation and the risk of brain and central nervous system tumors: a systematic review. Neuro Oncol. 2012;14(11):1316-24. Seliger C, Ricci C, Meier CR, Bodmer M, Jick SS, Bogdahn U, et al. Diabetes, use of antidiabetic drugs, and the risk of glioma. Neuro Oncol. 2016;18(3):340-9. Houben MP, Louwman WJ, Tijssen CC, Teepen JL, Van Duijn CM, Coebergh JW. Hypertension as a risk factor for glioma? Evidence from a population-based study of comorbidity in glioma patients. Ann Oncol. 2004;15(8):1256-60. Hou L, Jiang J, Liu B, Han W, Wu Y, Zou X, et al. Smoking and adult glioma: a population-based case-control study in China. Neuro Oncol. 2016;18(1):105-13. Kuan AS, Green J, Kitahara CM, Berrington De González A, Key T, K Reeves G, et al. Diet and risk of glioma: combined analysis of 3 large prospective studies in the UK and USA. Neuro Oncol. 2019;21(7):944-52. Wang Y, Wang X, Wang K, Qi J, Zhang Y, Wang X, et al. Chronic stress accelerates glioblastoma progression via DRD2/ERK/β-catenin axis and Dopamine/ERK/TH positive feedback loop. J Exp Clin Cancer Res. 2023;42(1):161. Krigers A, Demetz M, Thomé C, Freyschlag CF. Age is associated with unfavorable neuropathological and radiological features and poor outcome in patients with WHO grade 2 and 3 gliomas. Sci Rep. 2021;11(1):17380. Wang GM, Cioffi G, Patil N, Waite KA, Lanese R, Ostrom QT, et al. Importance of the intersection of age and sex to understand variation in incidence and survival for primary malignant gliomas. Neuro Oncol. 2022;24(2):302-10. Ostrom QT, Cote DJ, Ascha M, Kruchko C, Barnholtz-Sloan JS. Adult Glioma Incidence and Survival by Race or Ethnicity in the United States From 2000 to 2014. JAMA Oncol. 2018;4(9):1254-62. Gong Y, Ma Y, Sinyuk M, Loganathan S, Thompson RC, Sarkaria JN, et al. Insulin-mediated signaling promotes proliferation and survival of glioblastoma through Akt activation. Neuro Oncol. 2016;18(1):48-57. Baglietto L, Giles GG, English DR, Karahalios A, Hopper JL, Severi G. Alcohol consumption and risk of glioblastoma; evidence from the Melbourne Collaborative Cohort Study. Int J Cancer. 2011;128(8):1929-34. Additional Declarations No competing interests reported. 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for Disease Control and Prevention (Hangzhou Health Supervision Institution)","correspondingAuthor":false,"prefix":"","firstName":"Zhaokai","middleName":"","lastName":"He","suffix":""},{"id":494545226,"identity":"d9790857-68f4-49fb-bd3b-e57ab193c017","order_by":2,"name":"Ruofan Xu","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ruofan","middleName":"","lastName":"Xu","suffix":""},{"id":494545228,"identity":"063e1878-20f4-4880-a5dc-c15a79a6eb4d","order_by":3,"name":"Tong Lang","email":"","orcid":"","institution":"Weifang Second People’ s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tong","middleName":"","lastName":"Lang","suffix":""},{"id":494545231,"identity":"2e12f4c3-be82-45a5-9630-5255f25d1829","order_by":4,"name":"Lingbing Meng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYLCCBwYMDAbMQMYHGyDBTIyWBKgWxhlpRGsBYgOQYp40IlTrtvcefpFQcMduOzvv4dc2CYcT+9sZGD98zMGtxezMuTSLBINnyTub+dKsc4BaZhxmYJacuQ2Plhs5ZgYJBoeTDQ7zmBnn/jic23CYgY2ZF5+W+2+QtFgkHM6dT1DLDR7jB0AtdkAtxo8ZgFo2ENRyJscMGMiHEyybecwYexLS6zceZmzG75fjZ4w/fPhz2N6cH8j4kWBtLHf+8MEPH/FoAQI2CSCR2ABlAAFjA171QMD8AUjYwxijYBSMglEwCjAAAMBiVX/i88i1AAAAAElFTkSuQmCC","orcid":"","institution":"Chinese Academy of Medical Sciences, Peking Union Medical College, National Clinical Research Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":true,"prefix":"","firstName":"Lingbing","middleName":"","lastName":"Meng","suffix":""}],"badges":[],"createdAt":"2025-06-27 07:08:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6988840/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6988840/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88235365,"identity":"63aadfba-ba27-4b53-93c0-058298242b4e","added_by":"auto","created_at":"2025-08-04 10:09:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":84926,"visible":true,"origin":"","legend":"\u003cp\u003eForest Plot of Risk Factors for Glioma Disease\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6988840/v1/fcba82a8071229f960b11367.png"},{"id":88235762,"identity":"7f464722-58a2-4e5d-a995-a765c2408c32","added_by":"auto","created_at":"2025-08-04 10:17:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":117483,"visible":true,"origin":"","legend":"\u003cp\u003eImportant Features\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6988840/v1/8f1674b7c9fe8e9b6754df1b.png"},{"id":88235369,"identity":"604544b5-1775-462b-a5f0-6be828c5174c","added_by":"auto","created_at":"2025-08-04 10:09:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":173072,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristic (ROC) Curve\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6988840/v1/caba90e352c8e2e4c86fbf09.png"},{"id":93939721,"identity":"1fe79ed0-c49f-4a73-9d29-a418ffe2d504","added_by":"auto","created_at":"2025-10-20 13:24:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1157608,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6988840/v1/cb5a308c-7065-4a49-ba59-84929f0eedcd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multifactorial Risk Assessment and Predictive Modeling of Glioma Based on NHANES Data","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eGlioma is one of the most common primary malignant tumors of the central nervous system[1]. Due to its high heterogeneity, aggressive invasiveness, and poor prognosis, glioma has become a focal point in global neuro-oncology research[2]. According to epidemiological data, the annual incidence of glioma in the United States is approximately 6 cases per 100,000 people, with glioblastoma being the most prevalent type of malignant brain tumor, accounting for 50.1% of all malignant tumors[3]. This disease is more common in males, while non-malignant tumors (such as meningiomas) are more frequent in females. In recent years, advancements in surgery, radiotherapy, chemotherapy, and targeted therapies have improved survival rates for some glioma patients. However, for high-grade gliomas, such as glioblastoma (WHO Grade III and IV), the prognosis remains poor, with a median survival time of only about 15 months[4]. Despite improvements in the 2-year and 3-year survival rates for glioblastoma since 2005, reaching 18% and 11% respectively, the 5-year survival rate remains below 5%, indicating a dire prognosis overall[5]. This severe situation underscores the necessity for comprehensive studies on glioma, particularly on its pathogenic factors, to develop effective prevention and control strategies.\u003c/p\u003e\u003cp\u003eThe etiology of glioma is believed to result from a combination of genetic susceptibility and environmental factors. Multiple studies have revealed that genetic variations, family history, and certain hereditary syndromes (e.g., Li-Fraumeni syndrome, Turcot syndrome) may significantly increase the risk of glioma[6\u0026ndash;9]. Additionally, environmental exposure factors, such as high doses of ionizing radiation and occupational exposure, are also considered important triggers for glioma[10].\u003c/p\u003e\u003cp\u003eIn recent years, the potential roles of metabolic diseases and unhealthy lifestyles in glioma development have garnered increasing attention. For example, metabolic disorders such as hypertension and diabetes may contribute to glioma initiation and progression through chronic inflammation and vascular dysfunction[11, 12]. Unhealthy lifestyle habits, including excessive alcohol consumption, smoking, unbalanced diets, and psychological issues (e.g., depression and anxiety), may also influence glioma risk via immune system modulation or neuroendocrine mechanisms[13\u0026ndash;15]. However, current research on the specific associations and interactions between these factors and glioma remains insufficient, particularly in terms of multi-factorial analyses and population heterogeneity.\u003c/p\u003e\u003cp\u003eGiven this context, conducting comprehensive analyses based on nationally representative datasets is of significant value. This study leverages data from the National Health and Nutrition Examination Survey (NHANES), which provides a broad population sample and records extensive variables, including demographic characteristics, health status, lifestyle habits, and biomarkers. Using NHANES data, this study aims to comprehensively explore the effects of factors such as age, gender, race, metabolic diseases, psychological health, and lifestyle on glioma incidence. Furthermore, it seeks to develop a risk prediction model to uncover the potential pathogenic mechanisms of glioma, thereby providing a theoretical basis for early screening and precise interventions. This research also offers scientific support for the formulation of public health policies and the implementation of personalized prevention strategies.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Clinical Data\u003c/h2\u003e\u003cp\u003eThe data for this study were obtained from the National Health and Nutrition Examination Survey (NHANES). NHANES is a nationally representative cross-sectional survey conducted by the National Center for Health Statistics (NCHS) and the Centers for Disease Control and Prevention (CDC). Its purpose is to collect representative data on the health status, lifestyle habits, and nutritional conditions of the U.S. civilian population. The data collection process was approved by the NCHS Ethics Review Board, and all participants provided written informed consent. The study population included NHANES participants, covering individuals of various ages, genders, races, and ethnic backgrounds in the United States. The survey included face-to-face interviews, physical examinations, and laboratory tests, conducted by trained professionals. NHANES data were rigorously cleaned and validated, and are made available as publicly accessible datasets for research purposes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data Collection\u003c/h2\u003e\u003cp\u003eThis study aimed to explore risk factors for glioma while incorporating a series of potential covariates. These covariates were identified based on literature support and theoretical reasoning as potentially associated with glioma incidence. Data were collected through standardized questionnaires that gathered participants' demographic and health information, including age, gender, race, poverty income ratio (PIR), body mass index (BMI), diabetes, hyperlipidemia, heart disease, pulse rate, smoking, and drinking behaviors. Missing values in the dataset were imputed using interpolation methods in R software.\u003c/p\u003e\u003cp\u003eThe diagnosis of glioma was based on self-reported responses in the NHANES questionnaire, which included questions such as \"Have you ever been diagnosed with cancer or a malignant tumor?\" and \"Have you experienced severe dizziness, lightheadedness, fainting, or unsteadiness?\" Additional questions addressed severe visual impairment, significant attention difficulties, and walking difficulties. This study aimed to comprehensively analyze these data to identify potential risk factors for glioma.\u003c/p\u003e\u003cp\u003eIt is worth noting that the diagnosis based on self-reported information may be subject to bias. Further studies incorporating more accurate clinical diagnostic data are needed to validate the findings.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were conducted following CDC guidelines and employed sampling weights to calculate nationally representative estimates for the non-institutionalized U.S. population. Continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD), and weighted t-tests were used to compare differences between groups. Categorical variables were presented as frequencies and percentages, and weighted chi-square tests were employed for group comparisons. Non-normally distributed data were described using medians and interquartile ranges (Median [P25, P75]).\u003c/p\u003e\u003cp\u003eMultivariate linear regression analysis was used to evaluate the combined effects of various factors on glioma. Bivariate analysis was performed to explore the association of individual risk factors with glioma. A forest plot was utilized to visualize the analysis of risk factors, providing an intuitive display of the impact of different variables on glioma.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Baseline Characteristics of Patients\u003c/h2\u003e\n \u003cp\u003eThis study analyzed data from 1,843 participants, including 163 glioma patients and 1,680 controls, after excluding individuals with incomplete data. Significant differences were observed between the two groups in several clinical variables.\u003c/p\u003e\n \u003cp\u003eAge was strongly associated with glioma risk (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The prevalence of glioma was 6.60% among individuals aged 60 years and older, compared to 0.10% in the 18\u0026ndash;35 age group. Heart disease was also a significant risk factor (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a prevalence of 10.00% in patients with heart disease versus 1.80% in those without. Heavy drinking was linked to a higher prevalence of glioma (8.10%) compared to non-drinkers (1.40%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cp\u003eGender differences were significant (P\u0026thinsp;=\u0026thinsp;0.019), with slightly higher prevalence in females (4.70%) than males (4.20%). Non-Hispanic Whites had the highest prevalence among racial groups (6.50%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and individuals with low income had a higher glioma risk (4.10%, P\u0026thinsp;=\u0026thinsp;0.007). Among those with depression, severe cases had a significantly higher prevalence of glioma (1.70%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cp\u003eNo significant differences were found in BMI, calcium, fiber, or protein intake (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), which may reflect sample size limitations or other confounding factors requiring further investigation (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline Characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGlioma\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo(n\u0026thinsp;=\u0026thinsp;1680)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYes(n\u0026thinsp;=\u0026thinsp;163)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;35 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e196(10.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(0.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u0026ndash;60 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e757(41.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39(2.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;60 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e727(39.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122(6.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e954(51.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77(4.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e726(39.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86(4.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e271(14.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(0.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e729(39.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120(6.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e463(25.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22(1.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Asian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e217(11.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(0.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eMarital Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried/Living with Partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1087(59.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84(4.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e202(11.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(0.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWidowed/ Divorced/ Separated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e391(21.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71(3.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eEducational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168(9.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(0.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9th-11th grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e207(11.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29(1.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e393(21.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30(1.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecollege or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e912(49.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88(4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eIncome_level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e573(31.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75(4.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e524(28.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42(2.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e583(31.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46(2.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.646\u0026thinsp;\u0026plusmn;\u0026thinsp;6.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.579\u0026thinsp;\u0026plusmn;\u0026thinsp;6.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1146(62.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112(6.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e534(29.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51(2.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e868(47.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105(5.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e812(44.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58(3.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHeart Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1496(81.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129(7.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184(10.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34(1.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePulse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1508(81.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e154(8.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e172(9.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(0.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo or mild depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1431(77.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108(5.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo or mild depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166(9.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24(1.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSevere depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83(4.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31(1.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(13.1\u0026thinsp;~\u0026thinsp;15.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.6(12.7\u0026thinsp;~\u0026thinsp;14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eEnergy Intake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2089.02\u0026thinsp;\u0026plusmn;\u0026thinsp;960.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1057.55\u0026thinsp;\u0026plusmn;\u0026thinsp;793.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCalcium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26(7\u0026thinsp;~\u0026thinsp;78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27(8\u0026thinsp;~\u0026thinsp;78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eProtein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.285\u0026thinsp;\u0026plusmn;\u0026thinsp;9.912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.120\u0026thinsp;\u0026plusmn;\u0026thinsp;8.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFiber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0\u0026thinsp;~\u0026thinsp;1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0\u0026thinsp;~\u0026thinsp;1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003emagnesium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.38\u0026thinsp;\u0026plusmn;\u0026thinsp;35.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.50\u0026thinsp;\u0026plusmn;\u0026thinsp;26.665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSodium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e221.75\u0026thinsp;\u0026plusmn;\u0026thinsp;429.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176.87\u0026thinsp;\u0026plusmn;\u0026thinsp;271.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePotassium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e194.65\u0026thinsp;\u0026plusmn;\u0026thinsp;272.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161.66\u0026thinsp;\u0026plusmn;\u0026thinsp;150.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTotal Fat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26(0\u0026thinsp;~\u0026thinsp;4.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3(0.01\u0026thinsp;~\u0026thinsp;4.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSaturated Fat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.030(0.002\u0026thinsp;~\u0026thinsp;1.566)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051(0.005\u0026thinsp;~\u0026thinsp;1.566)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever Smoke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e910(49.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75(4.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer Smoke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e472(25.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53(2.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent Smoke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e275(14.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25(1.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eDrinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever Drank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e348(18.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25(1.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOccasionally Drank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e590(32.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32(1.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegularly Drank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74(4.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(0.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeavy Drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149(8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eAbbreviations: BMI, body mass index.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Forest Plot Analysis of Glioma Risk Factors\u003c/h2\u003e\n \u003cp\u003eThis study analyzed the association between glioma risk and five variables\u0026mdash;hyperlipidemia, diabetes, gender, heart disease, and depression\u0026mdash;using a forest plot. The results showed significant differences in the impact of these variables on glioma risk(Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eHeart disease was identified as a significant risk factor (OR\u0026thinsp;=\u0026thinsp;2.042, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with individuals with heart disease being 2.042 times more likely to develop glioma than those without. Similarly, depression was strongly associated with glioma risk (OR\u0026thinsp;=\u0026thinsp;2.348, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that individuals with severe depression have a 2.348-fold higher risk compared to those with mild or no depression. These findings underscore the importance of heart health and mental well-being in glioma prevention and management.\u003c/p\u003e\n \u003cp\u003eConversely, diabetes showed a negative association with glioma risk (OR\u0026thinsp;=\u0026thinsp;0.581, P\u0026thinsp;=\u0026thinsp;0.003). Diabetic individuals had a 0.581-fold lower risk of glioma compared to non-diabetics, suggesting a potential protective role, though the underlying mechanisms require further study. In contrast, hyperlipidemia (OR\u0026thinsp;=\u0026thinsp;0.986, P\u0026thinsp;=\u0026thinsp;0.94) and gender (OR\u0026thinsp;=\u0026thinsp;1.268, P\u0026thinsp;=\u0026thinsp;0.179) were not significantly associated with glioma risk, indicating no meaningful differences based on these factors.\u003c/p\u003e\n \u003cp\u003eThe forest plot clearly illustrated the odds ratios (ORs) and 95% confidence intervals for each variable. Variables with confidence intervals entirely on one side of 1 (e.g., heart disease, depression, and diabetes) were significantly associated with glioma risk, while those crossing 1 (e.g., hyperlipidemia and gender) showed no significant association.\u003c/p\u003e\n \u003cp\u003eIn conclusion, this study highlights heart disease and depression as significant risk factors for glioma, while diabetes may have a protective effect. These findings provide valuable insights for glioma risk assessment and intervention strategies.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Neural Network Model for Glioma Prediction\u0026mdash;Assessment of Feature Importance\u003c/h2\u003e\n \u003cp\u003eThe importance of predictor variables for glioma risk was evaluated using a neural network model with standardized feature importance scores (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Results showed significant differences in the contributions of various features to the model\u0026apos;s predictive performance.\u003c/p\u003e\n \u003cp\u003eAge emerged as the most critical variable, with an importance score of 0.602, indicating its key role in glioma risk prediction. This aligns with previous findings that age is strongly associated with glioma incidence. Drinking habits were the second most important variable, with an importance score of 0.159. Although lower than age, drinking behavior still played a notable role in the model, suggesting its potential influence on glioma risk.\u003c/p\u003e\n \u003cp\u003eOther variables, including hyperlipidemia (0.053), pulse (0.061), diabetes (0.061), and heart disease (0.063), showed relatively low importance scores. These results indicate that these factors contributed minimally to the model\u0026apos;s predictive ability and had limited discriminatory power.\u003c/p\u003e\n \u003cp\u003eOverall, apart from age and drinking habits, the importance of other variables was not significant. This may reflect a more uniform distribution of these variables in the dataset or a weaker association with glioma risk. Additionally, the title of the chart, \u0026quot;The Importance of Standardization,\u0026quot; highlights the impact of the standardization process on the calculation of feature weights, which may have influenced the results.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Multivariate Linear Regression Analysis of Glioma Risk Factors\u003c/h2\u003e\n \u003cp\u003eUsing a multivariate linear regression model, this study identified significant associations between glioma incidence and several factors, including age, race, marital status, education, diabetes, pulse, depression, and drinking habits (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eTable2: Multiple linear regression model for Glioma\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eS.E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e7.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-1.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.492\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-5.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eMarital Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e2.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eEducational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e2.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.256\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eIncome_level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-5.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.219\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eHeart Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e1.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003ePulse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-3.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.0122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e7.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eFiber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e1.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.809\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eDrinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-7.371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003c/div\u003e\n \u003cp\u003eAge was the most significant risk factor (B\u0026thinsp;=\u0026thinsp;0.081, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with older adults (aged 60 and above) showing a substantially higher risk of glioma compared to younger individuals. Depression was also significantly associated with glioma (B\u0026thinsp;=\u0026thinsp;0.092, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with severe depression patients having a higher risk than those with moderate depression. Race and marital status were additional significant factors: non-White individuals had a lower risk than White individuals (B=-0.041, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and married or cohabiting individuals had a higher risk compared to single individuals (B\u0026thinsp;=\u0026thinsp;0.019, P\u0026thinsp;=\u0026thinsp;0.012). Higher levels of education were positively correlated with glioma risk (B\u0026thinsp;=\u0026thinsp;0.016, P\u0026thinsp;=\u0026thinsp;0.018).\u003c/p\u003e\n \u003cp\u003eAmong health-related factors, diabetes (B=-0.070, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and pulse (B=-0.070, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were negatively associated with glioma risk, suggesting potential protective effects. Drinking habits (B=-0.039, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) also showed a negative correlation, but further studies are needed to explore the underlying mechanisms.\u003c/p\u003e\n \u003cp\u003eOther variables, including gender, household income, hyperlipidemia, heart disease, fiber intake, and smoking habits, did not show significant associations with glioma risk. The variance inflation factors (VIFs) for all variables ranged from 1 to 1.5, indicating no severe multicollinearity in the model.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Correlation Analysis of Glioma\u003c/h2\u003e\n \u003cp\u003eCorrelation analysis of variables associated with glioma showed significant associations between glioma incidence and age and race. Age was positively correlated with glioma incidence (r\u0026thinsp;=\u0026thinsp;0.184, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that the risk of glioma increases significantly with age. Race showed a significant negative correlation (r=-0.090, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting differences in glioma risk among racial groups.\u003c/p\u003e\n \u003cp\u003eGender was also significantly positively correlated with glioma incidence (r\u0026thinsp;=\u0026thinsp;0.055, P\u0026thinsp;=\u0026thinsp;0.019), indicating that males have a slightly higher risk of glioma compared to females. Depression severity was another important factor, showing a significant positive correlation with glioma incidence (r\u0026thinsp;=\u0026thinsp;0.153, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), meaning that individuals with more severe depression are at a higher risk of developing glioma.\u003c/p\u003e\n \u003cp\u003eFor other variables, such as BMI, hyperlipidemia, diabetes, heart disease, protein intake, sodium intake, and smoking habits, the analysis did not find statistically significant correlations with glioma (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, some variables, despite not reaching statistical significance, may have potential biological implications. For instance, the negative correlation coefficient for protein intake might suggest an increased risk to some extent. These hypotheses require further investigation and validation.\u003c/p\u003e\n \u003cp\u003eIn summary, the results indicate that age and depression are important factors associated with glioma risk, while gender and race are also moderately correlated. Future studies should further explore the specific mechanisms by which these factors contribute to glioma development (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGlioma correlation analysis of osteoporosis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.184**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.055*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.090**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.073*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeart Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.087**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.153**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProtein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSodium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e*When the confidence level (double test) is 0.05, the correlation is significant.\u003c/p\u003e\n \u003cp\u003e**When the confidence level (double test) is 0.001, the correlation is significant.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Risk Prediction Model for Glioma\u003c/h2\u003e\n \u003cp\u003eThis study explored the relationships between factors such as age, diabetes, heart disease, and drinking habits with glioma incidence, and developed a risk prediction model. The results indicated that these factors have a certain predictive effect on glioma occurrence. Specifically, age was identified as a key factor influencing glioma risk, with the risk significantly increasing as age rises. Diabetic patients were found to have a higher risk of glioma compared to non-diabetic individuals. Heart disease was another significant risk factor, with individuals suffering from heart disease showing a greater tendency to develop glioma. Additionally, drinking habits were identified as a potential risk factor, with frequent alcohol consumption potentially increasing glioma risk.\u003c/p\u003e\n \u003cp\u003eThe predictive performance of the constructed model was evaluated using a ROC curve (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The area under the ROC curve (AUC) was 0.76, indicating a moderate predictive ability. This suggests that the model can differentiate between high-risk and low-risk individuals to some extent, although there remains room for improvement in its predictive accuracy.\u003c/p\u003e\n \u003cp\u003eIn conclusion, the predictive model developed in this study provides strong support for glioma risk assessment and highlights the critical roles of age, diabetes, heart disease, and drinking habits in risk prediction. Future research could further optimize the model by incorporating additional variables to enhance its predictive accuracy.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study systematically evaluated the impact of factors such as age, gender, race, metabolic diseases, psychological health, and lifestyle on the risk of glioma using NHANES data. The findings revealed several significant associations, providing critical evidence for glioma etiology research and public health interventions.\u003c/p\u003e\u003cp\u003eFirstly, the study identified age as one of the most important risk factors for glioma. The risk was particularly pronounced in individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This result aligns with previous studies, suggesting that aging may influence glioma onset and progression through mechanisms such as increased genomic instability, reduced immune surveillance, and weakened apoptotic processes[16]. Given the high-risk nature of the elderly population, they should be included in high-risk group screening programs to facilitate early detection and intervention, thereby reducing the disease burden.\u003c/p\u003e\u003cp\u003eSecondly, the study found that gender and race significantly influence glioma incidence. Males exhibited a slightly higher risk than females (P\u0026thinsp;=\u0026thinsp;0.019), suggesting that gender differences may play a critical role in glioma pathogenesis, potentially linked to sex hormone levels, immune function differences, and behavioral exposure patterns[17]. Furthermore, there were significant differences in glioma incidence among different racial groups, with non-Hispanic Whites having a higher risk than other races (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This finding reflects the complex roles of genetic background, lifestyle, and socioeconomic factors in glioma development and highlights the need for focused etiological research on specific racial groups[18].\u003c/p\u003e\u003cp\u003eThe study also identified a significant positive association between cardiovascular disease and glioma risk (OR\u0026thinsp;=\u0026thinsp;2.042, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Although direct evidence linking cardiovascular disease to glioma is currently lacking, this study speculates that cardiovascular disease may play a potential role in glioma initiation and progression through mechanisms such as chronic inflammation, endothelial dysfunction, and angiogenesis. This hypothesis provides a direction for future research to further explore the relationship between cardiovascular disease and glioma.\u003c/p\u003e\u003cp\u003eWe also found a negative correlation between diabetes and glioma risk (OR\u0026thinsp;=\u0026thinsp;0.581, P\u0026thinsp;=\u0026thinsp;0.003). This finding might be related to the reduced insulin sensitivity in diabetic patients and its regulatory effect on tumor metabolic signaling pathways (e.g., PI3K/AKT). Previous studies have shown that insulin promotes glioblastoma growth by activating the InsR and IGF1R signaling pathways[19]. However, whether diabetes reduces tumor progression risk by inhibiting insulin sensitivity requires further validation. Future research should integrate epidemiological and molecular analyses to investigate the dual effects of diabetes on glioma.\u003c/p\u003e\u003cp\u003eAdditionally, depression was significantly associated with an increased risk of glioma (OR\u0026thinsp;=\u0026thinsp;2.348, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). One study suggested that chronic stress and depression could promote glioblastoma growth and invasion through the activation of the DRD2/ERK/β-catenin pathway, while also being closely associated with poorer prognosis[15]. This finding underscores the importance of mental health in glioma prevention and control, emphasizing the need for enhanced psychological health assessment and intervention in high-risk populations.\u003c/p\u003e\u003cp\u003eOur findings also indicated that alcohol consumption is a significant risk factor for glioma (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Although the specific mechanisms remain unclear, it is speculated that frequent or heavy drinking may promote tumor development by generating reactive oxygen species, causing DNA damage, and altering the tumor microenvironment[20]. Strengthening alcohol behavior management in public health strategies may have a positive impact on reducing glioma risk. However, the association between smoking and glioma was not significant in this study. This result may reflect the complex mechanisms of smoking in glioma, which require further exploration in larger sample sizes.\u003c/p\u003e\u003cp\u003eFinally, through multivariate linear regression and neural network analyses, this study validated age, alcohol consumption, cardiovascular disease, and depression as core risk factors for glioma. The predictive value of these variables in the model provides a theoretical basis for precise screening and multifactorial intervention strategies. Moreover, the study showed that diabetes might have a protective effect, suggesting the need for further exploration of the complex mechanisms of metabolic diseases in glioma development.\u003c/p\u003e\u003cp\u003eDespite the high representativeness of the NHANES data, this study has certain limitations. First, the cross-sectional study design limits causal inference. Second, some variables relied on self-reported data, which may introduce information bias. Additionally, the heterogeneity among different races and regions requires further exploration in future studies. Longitudinal research combined with molecular mechanism analyses is needed to elucidate the etiology of glioma and its complex multifactorial interactions, providing scientific evidence for precision medicine and personalized prevention strategies.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn summary, this study comprehensively analyzed multiple risk factors for glioma and identified age, cardiovascular disease, depression, and alcohol consumption as key risk factors, with diabetes potentially having a protective effect. The findings provide valuable references for early screening and prevention strategies for glioma and point to future directions for etiological research.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNational Health and Nutrition Examination Survey (NHANES), National Center for Health Statistics (NCHS), Centers for Disease Control and Prevention (CDC), poverty income ratio (PIR), body mass index (BMI)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available in the [NHANES] repository/database at [https://wwwn.cdc.gov/nchs/nhanes/Default.aspx].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was funded by National Natural Science Foundation of China (Grant no. 82400543)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYangfan Zou make the acquisition, analysis, Zhaokai He interpretate of data; Ruofan Xu and Tong Lang create of new software used in the work; Lingbing Meng have drafted the work or substantively revised it\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWeller M, Wick W, Aldape K, Brada M, Berger M, Pfister SM, et al. Glioma. Nat Rev Dis Primers. 2015;1:15017.\u003c/li\u003e\n\u003cli\u003eLouis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol. 2021;23(8):1231-51.\u003c/li\u003e\n\u003cli\u003eOstrom QT, Price M, Neff C, Cioffi G, Waite KA, Kruchko C, et al. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2015-2019. Neuro Oncol. 2022;24(Suppl 5):v1-1v95.\u003c/li\u003e\n\u003cli\u003eWeller M, van den Bent M, Tonn JC, Stupp R, Preusser M, Cohen-Jonathan-Moyal E, et al. European Association for Neuro-Oncology (EANO) guideline on the diagnosis and treatment of adult astrocytic and oligodendroglial gliomas. Lancet Oncol. 2017;18(6):e315-315e329.\u003c/li\u003e\n\u003cli\u003ePoon M, Sudlow C, Figueroa JD, Brennan PM. Longer-term (\u0026ge;\u0026thinsp;2 years) survival in patients with glioblastoma in population-based studies pre- and post-2005: a systematic review and meta-analysis. Sci Rep. 2020;10(1):11622.\u003c/li\u003e\n\u003cli\u003eMelin BS, Barnholtz-Sloan JS, Wrensch MR, Johansen C, Il\u0026apos;yasova D, Kinnersley B, et al. Genome-wide association study of glioma subtypes identifies specific differences in genetic susceptibility to glioblastoma and non-glioblastoma tumors. Nat Genet. 2017;49(5):789-94.\u003c/li\u003e\n\u003cli\u003eChaligne R, Gaiti F, Silverbush D, Schiffman JS, Weisman HR, Kluegel L, et al. Epigenetic encoding, heritability and plasticity of glioma transcriptional cell states. Nat Genet. 2021;53(10):1469-79.\u003c/li\u003e\n\u003cli\u003eKibe Y, Ohka F, Aoki K, Yamaguchi J, Motomura K, Ito E, et al. Pediatric-type high-grade gliomas with PDGFRA amplification in adult patients with Li-Fraumeni syndrome: clinical and molecular characterization of three cases. Acta Neuropathol Commun. 2024;12(1):57.\u003c/li\u003e\n\u003cli\u003eMalbari F, Lindsay H. Genetics of Common Pediatric Brain Tumors. Pediatr Neurol. 2020;104:3-12.\u003c/li\u003e\n\u003cli\u003eBraganza MZ, Kitahara CM, Berrington de Gonz\u0026aacute;lez A, Inskip PD, Johnson KJ, Rajaraman P. Ionizing radiation and the risk of brain and central nervous system tumors: a systematic review. Neuro Oncol. 2012;14(11):1316-24.\u003c/li\u003e\n\u003cli\u003eSeliger C, Ricci C, Meier CR, Bodmer M, Jick SS, Bogdahn U, et al. Diabetes, use of antidiabetic drugs, and the risk of glioma. Neuro Oncol. 2016;18(3):340-9.\u003c/li\u003e\n\u003cli\u003eHouben MP, Louwman WJ, Tijssen CC, Teepen JL, Van Duijn CM, Coebergh JW. Hypertension as a risk factor for glioma? Evidence from a population-based study of comorbidity in glioma patients. Ann Oncol. 2004;15(8):1256-60.\u003c/li\u003e\n\u003cli\u003eHou L, Jiang J, Liu B, Han W, Wu Y, Zou X, et al. Smoking and adult glioma: a population-based case-control study in China. Neuro Oncol. 2016;18(1):105-13.\u003c/li\u003e\n\u003cli\u003eKuan AS, Green J, Kitahara CM, Berrington De Gonz\u0026aacute;lez A, Key T, K Reeves G, et al. Diet and risk of glioma: combined analysis of 3 large prospective studies in the UK and USA. Neuro Oncol. 2019;21(7):944-52.\u003c/li\u003e\n\u003cli\u003eWang Y, Wang X, Wang K, Qi J, Zhang Y, Wang X, et al. Chronic stress accelerates glioblastoma progression via DRD2/ERK/\u0026beta;-catenin axis and Dopamine/ERK/TH positive feedback loop. J Exp Clin Cancer Res. 2023;42(1):161.\u003c/li\u003e\n\u003cli\u003eKrigers A, Demetz M, Thom\u0026eacute; C, Freyschlag CF. Age is associated with unfavorable neuropathological and radiological features and poor outcome in patients with WHO grade 2 and 3 gliomas. Sci Rep. 2021;11(1):17380.\u003c/li\u003e\n\u003cli\u003eWang GM, Cioffi G, Patil N, Waite KA, Lanese R, Ostrom QT, et al. Importance of the intersection of age and sex to understand variation in incidence and survival for primary malignant gliomas. Neuro Oncol. 2022;24(2):302-10.\u003c/li\u003e\n\u003cli\u003eOstrom QT, Cote DJ, Ascha M, Kruchko C, Barnholtz-Sloan JS. Adult Glioma Incidence and Survival by Race or Ethnicity in the United States From 2000 to 2014. JAMA Oncol. 2018;4(9):1254-62.\u003c/li\u003e\n\u003cli\u003eGong Y, Ma Y, Sinyuk M, Loganathan S, Thompson RC, Sarkaria JN, et al. Insulin-mediated signaling promotes proliferation and survival of glioblastoma through Akt activation. Neuro Oncol. 2016;18(1):48-57.\u003c/li\u003e\n\u003cli\u003eBaglietto L, Giles GG, English DR, Karahalios A, Hopper JL, Severi G. Alcohol consumption and risk of glioblastoma; evidence from the Melbourne Collaborative Cohort Study. Int J Cancer. 2011;128(8):1929-34.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Glioma, Risk Assessment, Metabolic Diseases, Depression, Alcohol Consumption, NHANES Data","lastPublishedDoi":"10.21203/rs.3.rs-6988840/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6988840/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eGlioma is one of the most common malignant tumors of the central nervous system, with a complex etiology influenced by genetic factors, metabolic diseases, lifestyle, and mental health. However, the specific roles and interactions of these factors remain inadequately studied.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study utilized data from the National Health and Nutrition Examination Survey (NHANES), including 1,843 participants (163 glioma patients). Multivariate linear regression, neural network analysis, and correlation analysis were conducted to systematically evaluate the effects of age, gender, race, metabolic diseases, mental health, and lifestyle on glioma risk. A predictive risk model was also constructed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAge (OR\u0026thinsp;=\u0026thinsp;1.081, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), cardiovascular disease (OR\u0026thinsp;=\u0026thinsp;2.042, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), depression (OR\u0026thinsp;=\u0026thinsp;2.348, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and alcohol consumption (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were identified as major risk factors for glioma, while diabetes (OR\u0026thinsp;=\u0026thinsp;0.581, P\u0026thinsp;=\u0026thinsp;0.003) appeared to have a protective effect. Predictive modeling highlighted age as the most critical predictor (importance score\u0026thinsp;=\u0026thinsp;0.602), with an area under the ROC curve (AUC) of 0.76, indicating moderate predictive capability.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study systematically analyzed multiple risk factors for glioma using NHANES data, identifying key risk factors and constructing a predictive model. The findings provide a theoretical basis for early screening, prevention strategies, and personalized interventions for glioma. Future research should integrate longitudinal data and molecular mechanism analyses to validate these findings and explore causal relationships.\u003c/p\u003e","manuscriptTitle":"Multifactorial Risk Assessment and Predictive Modeling of Glioma Based on NHANES Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-04 10:09:40","doi":"10.21203/rs.3.rs-6988840/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1df3df8b-0d44-490b-ba99-e1e45774ead8","owner":[],"postedDate":"August 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T13:24:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-04 10:09:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6988840","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6988840","identity":"rs-6988840","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}