Causal Links of Factors to Neurodegenerative Diseases and Alzheimer's Risk Prediction Model

Causal Links of Factors to Neurodegenerative Diseases and Alzheimer's Risk Prediction Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Causal Links of Factors to Neurodegenerative Diseases and Alzheimer's Risk Prediction Model Yucheng Tian, Dan Qiu, Zhaowei Wang, Renjie Song, Feiyu Chen, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4866553/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective : This study aimed to explore the causal links between various lifestyle, demographic, cognitive factors and neurodegenerative diseases, and to develop an Alzheimer's disease (AD) risk prediction model. Methods : Mendelian randomization analysis was conducted using genetic variants as instrumental variables to investigate the relationships between lifestyle, demographics, cognitive factors, and neurodegenerative diseases. We used the MR-Egger regression, weighted median method, Inverse Variance Weighting (IVW), and weighted model. Based on the Mendelian analysis results, logistic multivariate analysis was used for validation and to design an AD rating prediction model. Results : Mendelian randomization analysis showed that beer intake was positively correlated with AD risk, whereas education level and cognitive ability were negatively correlated with AD risk. There was a positive correlation between education level, economic status, and risk of Parkinson's. There was a positive correlation between physical activity level and the risk of developing amyotrophic lateral sclerosis, and higher education level is significantly associated with a reduced risk of ALS. Logistic regression analysis showed that age and sex were positively correlated with AD, while education level was negatively correlated with AD. The accuracy of the AD risk prediction model was 78%, which was better than that of the logical model 62%. Conclusion : Mendelian analysis results indicated that there is a causal relationship between beer intake, educational level, cognitive ability and AD. There is a causal relationship between education level, economic status, and Parkinson's disease. There was a causal relationship between physical activity level, education level, and amyotrophic lateral sclerosis. No causal relationship was found between these factors and Lewy body dementia or frontotemporal dementia. The rating prediction model outperformed traditional logistic models in terms of accuracy . Mendelian randomization neurodegenerative diseases rating prediction model Figures Figure 1 Figure 2 Introduction In recent years, an increase in life expectancy has highlighted the growing issue of population aging, accompanied by an increase in the prevalence of chronic conditions among middle-aged and elderly individuals. This trend primarily involves diseases such as hypertension, hyperlipidemia, and diabetes. Within the realm of chronic neurological disorders, Alzheimer's disease (AD) and Parkinson's disease (PD)stand out as the most prevalent neurodegenerative conditions. Lewy body dementia is the second most common neurodegenerative dementia, following Alzheimer's disease. 1) As the demographic shift towards an aging population intensifies, the incidence of Alzheimer's disease is witnessing a corresponding increase. 2) Approximately 6.5 million older Americans will be diagnosed with Alzheimer's disease by 2024, and this number is expected to surge to 13.8 million by 2060. 3) A comprehensive analysis of 14 studies on Parkinson's disease incidence, encompassing populations from Europe, Asia, North America, and South America, reveals a distinct gender disparity. The overall incidence rate of Parkinson's disease among women aged 40 years and older was 37.55 per 100,000, while for men in the same age bracket, it significantly increased to 61.21 per 100,000. For women, the incidence rate of Parkinson's demonstrates a gradual rise, commencing at 2.94 per 100,000 individuals within the 40 to 49-year age group. This rate peaks at 104.99 per 100,000 among those aged 70–79 years, before declining to 66.02 per 100,000 in the cohort aged 80 years and above. A similar pattern is discernible among male counterparts, with the incidence rate rising from 3.59 per 100,000 in the 40 to 49-year age group, peaking at 132.72 per 100,000 in those aged 70 to 79 years, and subsequently declining to 110.48 per 100,000 among individuals over 80 years of age. 4) The global incidence of amyotrophic lateral sclerosis (ALS) is estimated at approximately 4.42 per 100,000 individuals. 5) Furthermore, a significant retrospective cohort study conducted by Logroscino et al. 6) known as the European Frontotemporal Dementia Incidence Study, has uncovered the incidence of frontotemporal lobe degeneration (FTLD) in Europe. This study estimates the annual incidence rate at 2.36 cases per 100,000 individuals, suggesting a rarity yet a noticeable increase compared to previous estimates. Neurodegenerative diseases are characterized by a shared pathology of the losss of neuronal function. However, the intricacies of their pathogenesis remain largely elusive, and therapeutic options are limited. Unfortunately, the vast majority of clinical trials aimed at interventions for Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis have not been successful. 7) Therefore, it is of paramount importance to identify modifiable risk factors and explore novel therapeutic avenues that may slow disease progression or delay the onset of these debilitating conditions. Recently, an increasing number of researchers have focused on the potential link between exercise and neurodegenerative disease. This burgeoning field of study examines the interaction between physical and cognitive activities and their influence on the risk of developing Alzheimer's disease among the elderly. Although these studies hold promising prospects, the evidence accumulated thus far remains insufficient to conclusively confirm the protective efficacy of exercise against these diseases. 8) AD is a primary neurodegenerative disease among the elderly. Notably, a comprehensive cohort study involving 404,840 individuals reported a correlation between physical activity and an increased incidence of various forms of dementia, including Alzheimer's disease. However, this association seems to weaken when physical activity levels are evaluated less than a decade before dementia diagnosis. 9) Another prospective cohort study, which included 10,308 participants and was conducted over an average follow-up period of 27 years, found no evidence of exercise providing neuroprotective benefits against Alzheimer's. 10) Similarly, a recent meta-analysis revealed a negative correlation between physical activity and the risk of Parkinson's disease. 11) A recent study utilizing instrumental variables to analyze self-reported physical activity found an inverse relationship between the risk of Amyotrophic Lateral Sclerosis and light exercise, while a positive association was noted with more intense moderate exercise. 12) There is an urgent need for improved methodologies to elucidate the link between modifiable risk factors and neurodegenerative diseases. Mendelian Randomization (MR), which utilizes valid instrumental variables(IVs) for causal inference, is a powerful method. 13) In our study, we utilized a two-sample MR analysis to comprehensively investigate the impact of various factors on neurodegenerative diseases. Materials and Methods Mendelian Randomization Methods Mendelian randomization analysis is similar to randomized controlled trials, utilizing single nucleotide polymorphisms (SNPs) as instrumental variables to identify causal links between exposure and outcomes, thereby reducing confounding biases. Additionally, Mendelian randomization analysis protects against reverse causality, making it highly effective in establishing causation in various clinical contexts. In our study, we used the GWAS database to conduct a two-sample MR analysis to investigate the causal connections between various factors and neurodegenerative diseases. We used multiple MR analysis methods, including Inverse Variance Weighted (IVW), MR-Egger Regression, Weighted Median, and Weighted Models, to ensure comprehensive and robust findings. To adhere to the foundational principles required to utilize genetic instruments in MR analysis, our study was guided by three pivotal hypotheses. The relevance hypothesis, 14) ensuring that genetic variants (SNPs) are associated with the exposure; the independence hypothesis, 15) asserting that the SNPs are not linked with confounders; and the exclusion-restriction hypothesis, 16) stipulating that the SNPs affect the outcome solely through the exposure. To support the independence hypothesis, we selected SNPs with significant genome-wide associations (P < 5×10 − 8 ) as instrumental variables. Additionally, we set a linkage disequilibrium threshold with an R 2 of 0.001 within a 10,000 kilobase pair (kb) radius to reduce genetic confounding. By applying these stringent criteria, we ensured the independence of each selected SNP, effectively mitigating potential biases from genetic polymorphisms. 17) Our analysis exclusively included SNPs with F-statistics exceeding 10, indicating a robust association. 18) Scoring risk prediction model construction method Drawing inspiration from the design principles of the risk scoring tool utilized in the Framingham Heart Study, 19) the risk factors identified in the above multi-factor logistic regression analysis were stratified. Quantitative scores were assigned to each stratum of risk factors. For instance, considering age, the ages were categorized into 10-year intervals, with the 60–69 age group serving as the basic risk reference category. The score assigned to each risk factor category was calculated as the difference between the grouping of each risk factor and reference value of the basic risk. The ratio of the distance (D) to the constant (B) corresponding to one point in the scoring tool is determined by Equations (2)–(4), where B represents the constant denoting the change in each risk factor associated with a 1-point increase in the scoring tool. Taking age as an example, each five-year increase in age is attributed to one point. \(\:{W}_{i}\) represents the reference value for each risk factor group, and \(\:{W}_{ref}\) represents the basic risk reference value. \(\:{\beta\:}_{i}\) denotes the regression coefficient of the risk factors; the corresponding score for each category are presented in Table 1 . The risk prediction probability value corresponding to each score can be expressed using ( 5 ). $$\:\begin{array}{c}Score=\frac{D}{B}\#\left(2\right)\end{array}$$ $$\:\begin{array}{c}D=\left({W}_{i}-{W}_{ref}\right)\times\:{\beta\:}_{i}\#\left(3\right)\end{array}$$ $$\:\begin{array}{c}B=5\times\:{\beta\:}_{age}\#\left(4\right)\end{array}$$ $$\:\begin{array}{c\hat }{P}=\frac{1}{1+{e}^{-\left(\sum\:_{i=0}^{p}{\beta\:}_{i}{X}_{i}\right)}}\#（5）\end{array}$$ Table 1 Scoring values for various risk factor categories Factor Category Reference value β Score Age 0.059 30–39 34.5 -6.0 40–49 44.5 -4.0 50–59 54.5 -2.0 60–69 64.5 0.0 70–79 74.5 2.0 80–89 84.5 4.0 Sex 0.778 Male 1 2.6 Female 0 0.0 Educational level -0.341 Primary school 0 0.0 Junior high school 1 -1.2 Senior high school 2 -2.3 University 3 -3.5 University and above 4 -4.6 Note: The bold numbers are reference risk values Table 1: Scoring values for various risk factor categories: Category: Different categories within each risk factor. Reference value: The reference value used for calculating the score. β: The regression coefficient of the risk factor. Score: The score assigned to each category of the risk factor based on the formula and calculations described in the methodology. In formula (5): $$\:\sum\:_{i=0}^{p}{\beta\:}_{i}{X}_{i}={A+\beta\:}_{1}{W}_{1}+{\beta\:}_{2}{W}_{2}+{\dots\:+\beta\:}_{i}{W}_{i}+B\times\:\sum\:_{i=1}^{n}{C}_{i}$$ where: \(\:{\beta\:}_{1}\) , \(\:{\beta\:}_{2}\) , …, \(\:{\beta\:}_{i}\) are the regression coefficients of each risk factor in the logistic model. \(\:{W}_{1}\) , \(\:{W}_{2}\) , …, \(\:{W}_{i}\) represent reference values for each risk factor. A is the constant. \(\:\sum\:_{i=1}^{n}{C}_{i}\) is the sum of the scores corresponding to each risk factor. Statistical analysis MR analysis was primarily performed using the IVW approach. Cochran’s Q statistic was computed to evaluate the heterogeneity induced by different genetic variants using the fixed-effect IVW method, with a P value < 0.05 indicating the presence of heterogeneity. In addition, MR-Egger, weighted median, and weighted mode analyses were performed to compare with the results of the IVW method, as they may be biased when genetic variants exhibit horizontal pleiotropy. The MR-Egger regression intercept term was used to assess the possible presence of horizontal pleiotropy, where deviation from zero (P value < 0.05) indicates directional pleiotropy. The dataset utilized for establishing the logistic model was sourced from the Open Access Serial Imaging Study (OASIS) project, encompassing 416 subjects ranging from 18 to 96 years of age. The variables within this dataset include age, sex, educational level, economic situation, mental status examination outcomes, and clinical dementia rating. Data processing was conducted using SPSS version 27.0, and a logistic regression equation was employed to analyze the influencing factors. Initially, a single-factor analysis was performed, followed by the inclusion of statistically significant factors (P < 0.05) into a multi-factor analysis to construct a logistic model. Results The causal effects between neurodegenerative diseases and multiple factors In our analysis, we used 17 SNPs for physical activity level, 22 SNPs for cigarettes per day, 60 SNPs for sleep duration, 106 SNPs for time spent watching television, 76 SNPs for time spent using computer, 25 SNPs for spirits intake, 17 SNPs for red wine intake, 18 SNPs for beer intake, 4 SNPs for white wine intake, 16 SNPs for sex, 430 SNPs for educational level, 42 SNPs for economic situation, 130 SNPs for reaction time, and 68 SNPs for cognitive ability. To illustrate the association with Alzheimer's disease specifically, forest plots are depicted in Supplementary Fig. 1(a)and(b). Utilizing the IVW method, our results show that a one standard deviation (SD) increase in weekly beer intake is associated with a 3.15-fold increased risk of Alzheimer's disease (OR = 3.15, 95%CI [1.40–7.10], P = 0.006). Conversely, a higher educational level reduced the risk by 24% (OR = 0.76, 95%CI [0.64–0.90], P = 0.0019). Likewise, improved cognition may reduce the risk of Alzheimer's disease (OR = 0.92, 95%CI [0.85–0.99], P = 0.0313). Physical activity level, cigarettes per day, sleep duration, time spent watching television, time spent using a computer, spirit intake, red wine intake, white wine intake, sex, economic situation, and reaction time were not shown to have a clear causal association with Alzheimer's disease. Supplementary Figs. 2(a)and(b) illustrate that the IVW method shows a significant positive relationship between educational level, economic situation and the risk of developing Parkinson's disease. Specifically, individuals with higher educational levels had a 45% increased risk (OR = 1.45, 95%CI [1.16–1.81], P = 0.0012), and those with better economic situations have more than double the risk (OR = 2.10, 95%CI [1.30–3.73], P = 0.0023])of developing Parkinson's disease. Supplementary Figs. 3(a)and(b) show that the IVW method showed a significant positive relationship between physical activity level and the risk of developing amyotrophic lateral sclerosis (OR = 1.82, 95%CI [1.05–3.14], P = 0.033). Conversely, a higher educational level (OR = 0.76, 95%CI [0.66–0.87], P = 0.0001) was significantly associated with a reduced risk of ALS, demonstrating a negative relationship. Furthermore, the IVW analyses indicated no causal relationships between these factors and either frontotemporal dementia or Lewy body dementia, as evidenced by p-values greater than 0.05. Influencing factors and pleiotropy analysis of Alzheimer's disease Supplementary Fig. 4–5 illustrate scatter plots demonstrating the relationship between factors (beer intake, cognitive ability, and educational level) and Alzheimer's disease. The IVW line in the funnel plots indicated that beer intake, cognitive ability, and educational level were distributed symmetrically, suggesting the absence of a notable bias. The heterogeneity tests presented in Supplementary Table 1 indicate no significant heterogeneity among beer intake, cognitive ability, educational level, and Alzheimer's disease. Additionally, the P-values of the multi-effect tests for these factors and Alzheimer's disease exceeded 0.05. Influencing factors and pleiotropy analysis of Amyotrophic lateral sclerosis Supplementary Fig. 6–7 depict the scatter plots illustrating the relationship between physical activity level, educational level, and amyotrophic lateral sclerosis. The IVW line on the funnel plot indicates a symmetrical distribution of physical activity level and educational level, suggesting the absence of significant bias. Supplementary Table 2 presents the results of the heterogeneity tests, which demonstrated no potential heterogeneity among physical activity level, educational level, and amyotrophic lateral sclerosis. Additionally, the P-values of the pleiotropic test for data related to amyotrophic lateral sclerosis were calculated. Each factor observed by Egger intercept showed P-values exceeding 0.05, indicating no horizontal pleiotropy. Influencing factors and pleiotropy analysis of Parkinson's disease In Supplementary Figs. 8–9, the scatter plots illustrate the relationship between educational level, economic situation, and Parkinson's disease. The IVW line on the funnel plots demonstrates a symmetrical distribution of educational level and economic situation, indicating the absence of notable bias. Supplementary Table 3 presents the results of the heterogeneity tests, revealing no potential heterogeneity among educational level, economic situation, and Parkinson's disease. Furthermore, the P-values of the pleiotropy test for each exposure variable and the correlation data with Parkinson's were calculated. In the Egger intercept analysis, all P-values exceeded 0.05, indicating the absence of horizontal pleiotropy. The Alzheimer's disease risk prediction model was established Based on the Mendelian analysis of the relationship between Alzheimer's disease and various risk factors as described in the previous section, a logistic model was utilized to assess the relationship between the above risk factors and Alzheimer's disease. Logistic regression analysis is a form of generalized linear regression that is used to identify risk factors for diseases and predict the probability of disease occurrence. Various regression methods are tailored to the different types of dependent variables. In this study, the dependent variable was dichotomous, necessitating the use of a binary logistic regression model. If we denote the incidence rate of Alzheimer's disease as P, then the Logistic regression model between P and the independent variables \(\:{X}_{1}\) , \(\:{X}_{2}\) ,… \(\:{X}_{n}\) is formulated as follows: $$\:\begin{array}{c}P=\frac{{e}^{\left({\beta\:}_{0}+{\beta\:}_{1}{X}_{1}+\cdots\:+{\beta\:}_{n}{X}_{n}\right)}}{1+{e}^{\left({\beta\:}_{0}+{\beta\:}_{1}{X}_{1}+\cdots\:+{\beta\:}_{n}{X}_{n}\right)}}\#（1）\end{array}$$ In formula ( 1 ), \(\:{\beta\:}_{1}\) , \(\:{\beta\:}_{2}\) , …, \(\:{\beta\:}_{i}\) represent the regression coefficients of each independent variable in the Logistic model. \(\:{X}_{1}\) , \(\:{X}_{2}\) , …, \(\:{X}_{i}\) represent the values of the respective independent variables. After screening the data in the database, 236 cases were ultimately included in the single-factor analysis. The analysis results are summarized in Table 2 , indicating significant associations between certain factors and AD risk. Age (OR = 1.068, 95%CI[1.039–1.099], P < 0.01), sex (OR = 1.774, 95%CI [1.026–3.066], P = 0.04), economic situation (OR = 1.402, 95%CI [1.09–1.804], P = 0.008), educational level (OR = 0.726, 95%CI [0.589–0.899], P = 0.003), mental state (OR = 0.425, 95%CI [0.331–0.546], P < 0.01).These results suggest that age, sex, economic situation, educational level, and mental state are significantly associate with Alzheimer's disease. Table 2 Single factor Logistic regression analysis of Alzheimer's disease B SE Wald P OR(95%CI) Age 0.066 0.014 21.066 < 0.01 1.068(1.039,1.099) Sex 0.573 0.279 4.211 0.040 1.774(1.026,3.066) Educational level -0.32 0.109 8.623 0.003 0.726(0.587,0.899) Economic situation 0.338 0.128 6.926 0.008 1.402(1.09,1.804) Mini mental state examination -0.856 0.127 45.082 < 0.01 0.425(0.331,0.546) Table 2: Single factor Logistic regression analysis of Alzheimer's disease: B: Regression Coefficient. SE: Standard Error. Wald: Wald Test Statistic. P: P-value. OR(95%CI): Odds Ratio and Its 95% Confidence Interval. The potential influencing factors identified through single-factor analysis were utilized as independent variables, with the presence or absence of Alzheimer's disease serving as the dependent variable for the multi-factor logistic regression analysis. The findings revealed the following associations(Table 3 ). Age (OR = 1.060, 95%CI [1.032–1.090], P < 0.01), indicating that age is a risk factor for Alzheimer's disease. Sex (OR = 2.177, 95%CI [1.188–3.989], P = 0.012), indicating that sex is also a risk factor for Alzheimer's disease. Educational level (OR = 0.711, 95%CI [0.569–0.887], P = 0.003), indicating that educational level acts as a protective factor against Alzheimer's disease. These results highlight age and sex as risk factors, while educational level emerges as a protective factor against Alzheimer's disease. Table 3 Multivariate Logistic regression analysis of Alzheimer's disease B SE Wald P OR 95%CI Age 0.059 0.014 17.765 < 0.01 1.060 (1.032,1.090) Sex 0.778 0.309 6.344 0.012 2.177 (1.188,3.989) Educational level -0.341 0.113 9.103 0.003 0.711 (0.569,0.887) Constant -3.796 1.123 11.424 < 0.01 0.022 / Table 3: Multivariate Logistic regression analysis of Alzheimer's disease: B: Regression Coefficient. SE: Standard Error. Wald: Wald Test Statistic. P: P-value. OR(95%CI): Odds Ratio and Its 95% Confidence Interval. Testing Alzheimer's disease risk prediction models To verify the accuracy of the rating prediction model, 180 subjects were selected as the training set for model training and 45 subjects as the validation set for model evaluation. ROC curves were generated using SPSS software for specificity and sensitivity analyses. Sensitivity, also known as the true positive rate (TPR) and specificity, is also called the false positive rate (FPR). As shown in Fig. 1 , the Area Under the Curve (AUC) of the logistics model was 0.739, and the AUC value of the Alzheimer's disease rating prediction model was 0.718. A confusion matrix is used to evaluate the accuracy of the model, as shown in Fig. 2 . 45 subjects were selected, 20 of whom had Alzheimer's disease and 25 had non-Alzheimer's disease. The rating prediction model predicted 17 patients with Alzheimer's disease and 18 patients without Alzheimer's disease. In contrast, the logistic model predicted five patients with Alzheimer's disease and 23 patients with non-AD. The accuracy of the prediction model was 78%, and the accuracy of the logistic model was 62%. All things considered, the rating prediction model exhibited a stronger predictive ability than the logistic model. Discussion This study comprehensively explored various neurodegenerative diseases and their various influencing factors. These findings indicate that there is no statistically significant causal relationship between the aforementioned factors and frontotemporal dementia or Lewy body dementia. However, the study observed a statistically significant causal association between beer intake, educational level, cognitive ability, and Alzheimer's disease. Specifically, beer intake increases the risk of Alzheimer's disease. The findings from a cohort study conducted in the Korean population revealed that maintaining light and moderate drinking habits can potentially reduce the risk of dementia. 20) However, the current study identified a causal link specifically between beer intake and Alzheimer's disease. It is worth noting that other alcoholic beverages had no causal relationship with Alzheimer's disease. This distinction could potentially be attributed to compounds present in beer such as purines, niacin, and alcohol, along with neuroprotective elements, such as folic acid and various phenolic compounds. 21) Furthermore, cognitive ability and educational level have been identified as protective factors against Alzheimer's disease. Conversely, a statistically significant causal relationship was observed between Parkinson's and both educational level and economic situation. A higher educational level and better economic situation are found to increase the risk of Parkinson's. In the case of amyotrophic lateral sclerosis, a significant causal association was identified with physical activity level. Surprisingly, strenuous physical activity increased the risk of amyotrophic lateral sclerosis. However, higher educational level is associated with a decreased risk of amyotrophic lateral sclerosis(ALS). However, the Mendelian randomization analysis employed in this study had several limitations. First, the relatively stringent genome-wide correlation threshold (P < 5×10 − 8 ) utilized for screening SNPs might result in the inclusion of a limited number of SNPs, potentially affecting the analysis outcomes. Additionally, most GWAS datasets originate from individual samples of European ancestry, leading to a scarcity of datasets from Asian and African populations. Consequently, the generalizability of these results to all populations warrants further investigation. The Logistic regression analysis conducted in this study revealed that a higher educational level is associated with a decreased risk of Alzheimer's disease. For the elderly, engaging in adult education may enhance their language processing and intellectual abilities. Long-term educational pursuits may also confer beneficial effects on the brain, potentially decreasing the risk of Alzheimer's disease. This could be attributed to factors such as cortical surface area and thickness during the prodromal stage. 22) In this study, the results of multivariate logistic regression analysis revealed a statistically significant association between sex and Alzheimer's disease. Studies by Yin et al. 23) and Wang et al. 24) indicated a higher prevalence rate among women. Furthermore, it is imperative to provide adequate care and appropriate intervention methods for elderly individuals with low educational level, poor economic situation, and poor mental health. By doing so, we can potentially mitigate the prevalence of Alzheimer's disease. This study employed an Alzheimer's disease risk rating prediction model developed based on a logistic model. By analyzing lifestyle factors, personal circumstances, and other pertinent data, this model predicts the risk of developing AD in high-risk groups. Such predictive capabilities facilitate early diagnostic opportunities for healthcare professionals, enabling timely intervention and treatment initiation. For patients, early detection allows for prompt therapeutic intervention, thereby potentially delaying disease progression and enhancing the overall quality of life. This model holds significant clinical value in facilitating proactive healthcare management strategies and contributes to scientific research by providing a tool for investigating Alzheimer's disease risk factors and interventions. The Alzheimer's disease risk rating prediction model developed in this study has several limitations. The study sample size was relatively small, primarily comprising of the American population, which may introduce potential biases and limit the generalizability of the model to other populations. To address this issue, future studies should include a more diverse and representative sample size to enhance the model's applicability and robustness. Additionally, the variables incorporated into the model were obtained through logistic multi-factor analysis, potentially resulting in a limited number of variables included in the analysis, consequently reducing the accuracy of the prediction results. To mitigate this limitation, further research should explore additional causal factors associated with Alzheimer's disease and incorporate relevant risk factors into the model to improve its predictive performance. Declarations Data availability The data sets used and analyzed in the current study are available from the IEU OpenGWAS project, GWAS Catalog, and FinnGen. Please visit: https://www.ebi.ac.uk/gwas/, https://gwas.mrcieu.ac.uk/, and https://r8.finngen.fi/. The dataset utilized for establishing the logistic model was sourced from the open access serial imaging study (OASIS) project(https://www.kaggle.com/datasets/jboysen/mri-and-alzheimers/data) Funding statement This research was not funded by any external sources. Ethics statement There were no human or animal subjects in this study and ethical approval was not applicable. Conflicts of Interest The authors declare no conflicts of interest Acknowledgements We gratefully acknowledge the participants and investigators of the FinnGen study and all GWAS for their summary statistical data. Authors' contributions QD and RSJ provide GWAS data required for Mendelian randomization of neurodegenerative diseases. TYC provide a dataset for conducting logistic regression analysis and was a major contributor in writing the manuscript. WZW and ZWJ determine research objectives. ZWJ、CFY 、CX、CZJ、SDQ and NS review and revise the manuscript. References Wyman-Chick K A, O’Keefe L R, Weintraub D, et al. Prodromal dementia with Lewy bodies: evolution of symptoms and predictors of dementia onset. J Geriatr Psychiatry Neurol, 35(4), 527-534,2022. Song H, Li Y P, Wang S H, et al. Research progress of synaptic dysfunction in Alzheimer's disease. Life Sci, 30(1), 20-26,2018. Rajan K B, Weuve J, Barnes L L, et al. Population estimate of people with clinical Alzheimer's disease and mild cognitive impairment in the United States (2020–2060). Alzheimers Dement, 17(12), 1966-1975,2021. Hirsch L, Jette N, Frolkis A, et al. The incidence of Parkinson's disease: a systematic review and meta-analysis[J]. Neuroepidemiology, 46(4), 292-300,2016. Xu L, Liu T, Liu L, et al. Global variation in prevalence and incidence of amyotrophic lateral sclerosis: a systematic review and meta-analysis. J Neurol, 267, 944-953,2020. Logroscino G, Piccininni M, Graff C, et al. Incidence of syndromes associated with frontotemporal lobar degeneration in 9 European countries. JAMA Neurol, 80(3): 279-286,2023. Revi M. Alzheimer’s disease therapeutic approaches. GeNeDis 2018, 105-116,2020. Brasure M, Desai P, Davila H, et al. Physical activity interventions in preventing cognitive decline and Alzheimer-type dementia: a systematic review. Ann Intern Med, 168(1), 30-38,2018. Kivimäki M, Singh-Manoux A, Pentti J, et al. Physical inactivity, cardiometabolic disease, and risk of dementia: an individual-participant meta-analysis. bmj, 365,2019. Sabia S, Dugravot A, Dartigues J F, et al. Physical activity, cognitive decline, and risk of dementia: 28 year follow-up of Whitehall II cohort study. bmj, 357,2017. Fang X, Han D, Cheng Q, et al. Association of levels of physical activity with risk of Parkinson disease: a systematic review and meta-analysis. JAMA Netw Open, 1(5), e182421-e182421,2018. Bandres‐Ciga S, Noyce A J, Hemani G, et al. Shared polygenic risk and causal inferences in amyotrophic lateral sclerosis. Ann Neurol, 85(4), 470-481,2019. Zhuang Z, Gao M, Yang R, et al. Association of physical activity, sedentary behaviours and sleep duration with cardiovascular diseases and lipid profiles: a Mendelian randomization analysis. Lipids Health Dis, 19(1), 1-11,2020. Emdin C A, Khera A V, Kathiresan S. Mendelian randomization. Jama, 318(19): 1925-1926,2017. Sekula P, Fabiola Del Greco M, Pattaro C, et al. Mendelian randomization as an approach to assess causality using observational data. JASN, 27(11), 3253,2016. Davies N M, Holmes M V, Smith G D. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. bmj, 362,2018. Burgess S, Thompson S G. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol, 32, 377-389,2017. Boggs J M, Beck A, Ritzwoller D P, et al. A quasi-experimental analysis of lethal means assessment and risk for subsequent suicide attempts and deaths. J Gen Intern Med, 35,1709-1714,2020. Wilson P W F, D’Agostino R B, Levy D, et al. Prediction of coronary heart disease using risk factor categories. Circulation, 97(18), 1837-1847,1998. Jeon K H, Han K, Jeong S M, et al. Changes in alcohol consumption and risk of dementia in a nationwide cohort in South Korea. JAMA Netw open 6(2), e2254771-e2254771,2023. Sánchez-Muniz, F.J.; Macho-González, A.; Garcimartín, A.; Santos-López, J.A.; Benedí, J.; Bastida, S.; González-Muñoz, M.J. The Nutritional Components of Beer and Its Relationship with Neurodegeneration and Alzheimer’s Disease. Nutrients, 11, 1558,2019. Zhang X X, Tian Y, Wang Z T, et al. The epidemiology of Alzheimer’s disease modifiable risk factors and prevention. J Prev Alz Dis, 8, 313-321,2021. Yin J H, Zeng Y B, Zhou Z, et al. Analysis of frailty status and its influencing factors in the elderly in China. Chin J Epidemiol, 39(9), 1244-1248,2018. Wang Q Y, Zhu Y L. Prevalence and risk factors of senile dementia in Quzhou city. Chin Mod Doctor, 59(8), 33-38,2021. Additional Declarations No competing interests reported. Supplementary Files file.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4866553","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":337707948,"identity":"fdaf678c-6772-46a6-9caa-70de5e081846","order_by":0,"name":"Yucheng Tian","email":"","orcid":"","institution":"Shaoxing University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yucheng","middleName":"","lastName":"Tian","suffix":""},{"id":337707949,"identity":"013d69eb-f62f-4bde-a8d2-1ec06512d84d","order_by":1,"name":"Dan Qiu","email":"","orcid":"","institution":"Shaoxing University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dan","middleName":"","lastName":"Qiu","suffix":""},{"id":337707950,"identity":"acb921e9-3172-4015-b961-873a681f4ac5","order_by":2,"name":"Zhaowei Wang","email":"","orcid":"","institution":"Shaoxing University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhaowei","middleName":"","lastName":"Wang","suffix":""},{"id":337707951,"identity":"b0f762ce-b580-4600-9faf-0e392d399514","order_by":3,"name":"Renjie Song","email":"","orcid":"","institution":"Shaoxing University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Renjie","middleName":"","lastName":"Song","suffix":""},{"id":337707952,"identity":"12353de5-b539-436e-b155-9b22dd98e948","order_by":4,"name":"Feiyu Chen","email":"","orcid":"","institution":"Shaoxing University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Feiyu","middleName":"","lastName":"Chen","suffix":""},{"id":337707953,"identity":"99ec3412-1c46-41e5-bf2f-fdfa8b136e5e","order_by":5,"name":"Xue Cheng","email":"","orcid":"","institution":"Shaoxing University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Cheng","suffix":""},{"id":337707954,"identity":"2fdcfa39-bf84-4032-980d-83c1c3ea66e6","order_by":6,"name":"Dongqing Sun","email":"","orcid":"","institution":"Shaoxing University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dongqing","middleName":"","lastName":"Sun","suffix":""},{"id":337707955,"identity":"a763b15a-c42e-4b2a-97f0-0318bbd3de7f","order_by":7,"name":"Zhijian Cai","email":"","orcid":"","institution":"University of Oxford","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhijian","middleName":"","lastName":"Cai","suffix":""},{"id":337707956,"identity":"89154966-8823-4a5f-8d93-44ade4c1c0ce","order_by":8,"name":"Nutapong Somjit","email":"","orcid":"","institution":"University of Leeds","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nutapong","middleName":"","lastName":"Somjit","suffix":""},{"id":337707957,"identity":"974ba062-886c-4c8c-8638-04d544b18883","order_by":9,"name":"Weijia Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYDACCSjND8TMpGmRbIBq4SFai8EBYrXIz25+9vBrW13i5hvJBz8XMNyRsyekhXHOMXNj2bbDxmY30pKlZzA8MyZoC7NEgpm0ZNsBObMbOQbSPAyHE3sIaWGTSP8G1FLHYzwj//NvoJZ6glp4JHLMJD+2McsZSOSwgWxJIOgwCYmcMmmGc4eNJc48M7PmMThs2HOAgBb5GenbJH+U1SX2tyc/vs1TcVievYGQNUDADHaLQAKQMCBCOQgw/gCR/IQcNApGwSgYBSMWAADDXDlPKwb5DAAAAABJRU5ErkJggg==","orcid":"","institution":"Shaoxing University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Weijia","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-08-06 07:48:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4866553/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4866553/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63854244,"identity":"5c0844f9-be9d-40be-96a8-e7f84206d36a","added_by":"auto","created_at":"2024-09-03 04:57:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":29564,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve chart of rating prediction model and Logistics model: The AUC of the Logistics model is 0.739, the AUC value of rating prediction model is 0.718.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4866553/v1/92bcda1401fd66fed8be8b22.png"},{"id":63854243,"identity":"d3f35848-aba0-44d5-b916-7c365a3deef8","added_by":"auto","created_at":"2024-09-03 04:57:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":29326,"visible":true,"origin":"","legend":"\u003cp\u003eConfusion matrix prediction diagram of rating prediction model and Logistics model: (A) Confusion matrix plot of rating prediction model (B) Confusion matrix plot of Logistics model\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4866553/v1/33df2fa207b73a26da569d83.png"},{"id":98435715,"identity":"4a37b92e-6d07-4d12-9dca-7bdad0427dc6","added_by":"auto","created_at":"2025-12-17 16:54:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":874041,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4866553/v1/b3af75d8-2b4f-4fff-9583-77cbafbe32e5.pdf"},{"id":63854245,"identity":"7bb35007-c91d-406f-864f-825d6f5d64bc","added_by":"auto","created_at":"2024-09-03 04:57:24","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1681084,"visible":true,"origin":"","legend":"","description":"","filename":"file.docx","url":"https://assets-eu.researchsquare.com/files/rs-4866553/v1/3643ff99a792a44c8ef3b773.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal Links of Factors to Neurodegenerative Diseases and Alzheimer's Risk Prediction Model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent years, an increase in life expectancy has highlighted the growing issue of population aging, accompanied by an increase in the prevalence of chronic conditions among middle-aged and elderly individuals. This trend primarily involves diseases such as hypertension, hyperlipidemia, and diabetes. Within the realm of chronic neurological disorders, Alzheimer's disease (AD) and Parkinson's disease (PD)stand out as the most prevalent neurodegenerative conditions. Lewy body dementia is the second most common neurodegenerative dementia, following Alzheimer's disease. \u003csup\u003e1)\u003c/sup\u003e As the demographic shift towards an aging population intensifies, the incidence of Alzheimer's disease is witnessing a corresponding increase. \u003csup\u003e2)\u003c/sup\u003e Approximately 6.5\u0026nbsp;million older Americans will be diagnosed with Alzheimer's disease by 2024, and this number is expected to surge to 13.8\u0026nbsp;million by 2060.\u003csup\u003e3)\u003c/sup\u003e A comprehensive analysis of 14 studies on Parkinson's disease incidence, encompassing populations from Europe, Asia, North America, and South America, reveals a distinct gender disparity. The overall incidence rate of Parkinson's disease among women aged 40 years and older was 37.55 per 100,000, while for men in the same age bracket, it significantly increased to 61.21 per 100,000. For women, the incidence rate of Parkinson's demonstrates a gradual rise, commencing at 2.94 per 100,000 individuals within the 40 to 49-year age group. This rate peaks at 104.99 per 100,000 among those aged 70\u0026ndash;79 years, before declining to 66.02 per 100,000 in the cohort aged 80 years and above. A similar pattern is discernible among male counterparts, with the incidence rate rising from 3.59 per 100,000 in the 40 to 49-year age group, peaking at 132.72 per 100,000 in those aged 70 to 79 years, and subsequently declining to 110.48 per 100,000 among individuals over 80 years of age.\u003csup\u003e4)\u003c/sup\u003e The global incidence of amyotrophic lateral sclerosis (ALS) is estimated at approximately 4.42 per 100,000 individuals.\u003csup\u003e5)\u003c/sup\u003e Furthermore, a significant retrospective cohort study conducted by Logroscino et al. \u003csup\u003e6)\u003c/sup\u003e known as the European Frontotemporal Dementia Incidence Study, has uncovered the incidence of frontotemporal lobe degeneration (FTLD) in Europe. This study estimates the annual incidence rate at 2.36 cases per 100,000 individuals, suggesting a rarity yet a noticeable increase compared to previous estimates. Neurodegenerative diseases are characterized by a shared pathology of the losss of neuronal function. However, the intricacies of their pathogenesis remain largely elusive, and therapeutic options are limited.\u003c/p\u003e \u003cp\u003eUnfortunately, the vast majority of clinical trials aimed at interventions for Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis have not been successful.\u003csup\u003e7)\u003c/sup\u003e Therefore, it is of paramount importance to identify modifiable risk factors and explore novel therapeutic avenues that may slow disease progression or delay the onset of these debilitating conditions. Recently, an increasing number of researchers have focused on the potential link between exercise and neurodegenerative disease. This burgeoning field of study examines the interaction between physical and cognitive activities and their influence on the risk of developing Alzheimer's disease among the elderly. Although these studies hold promising prospects, the evidence accumulated thus far remains insufficient to conclusively confirm the protective efficacy of exercise against these diseases.\u003csup\u003e8)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAD is a primary neurodegenerative disease among the elderly. Notably, a comprehensive cohort study involving 404,840 individuals reported a correlation between physical activity and an increased incidence of various forms of dementia, including Alzheimer's disease. However, this association seems to weaken when physical activity levels are evaluated less than a decade before dementia diagnosis.\u003csup\u003e9)\u003c/sup\u003e Another prospective cohort study, which included 10,308 participants and was conducted over an average follow-up period of 27 years, found no evidence of exercise providing neuroprotective benefits against Alzheimer's.\u003csup\u003e10)\u003c/sup\u003e Similarly, a recent meta-analysis revealed a negative correlation between physical activity and the risk of Parkinson's disease.\u003csup\u003e11)\u003c/sup\u003e A recent study utilizing instrumental variables to analyze self-reported physical activity found an inverse relationship between the risk of Amyotrophic Lateral Sclerosis and light exercise, while a positive association was noted with more intense moderate exercise.\u003csup\u003e12)\u003c/sup\u003e There is an urgent need for improved methodologies to elucidate the link between modifiable risk factors and neurodegenerative diseases. Mendelian Randomization (MR), which utilizes valid instrumental variables(IVs) for causal inference, is a powerful method. \u003csup\u003e13)\u003c/sup\u003e In our study, we utilized a two-sample MR analysis to comprehensively investigate the impact of various factors on neurodegenerative diseases.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003ch2\u003eMendelian Randomization Methods\u003c/h2\u003e\n\u003cp\u003eMendelian randomization analysis is similar to randomized controlled trials, utilizing single nucleotide polymorphisms (SNPs) as instrumental variables to identify causal links between exposure and outcomes, thereby reducing confounding biases. Additionally, Mendelian randomization analysis protects against reverse causality, making it highly effective in establishing causation in various clinical contexts. In our study, we used the GWAS database to conduct a two-sample MR analysis to investigate the causal connections between various factors and neurodegenerative diseases. We used multiple MR analysis methods, including Inverse Variance Weighted (IVW), MR-Egger Regression, Weighted Median, and Weighted Models, to ensure comprehensive and robust findings. To adhere to the foundational principles required to utilize genetic instruments in MR analysis, our study was guided by three pivotal hypotheses. The relevance hypothesis,\u003csup\u003e14)\u003c/sup\u003e ensuring that genetic variants (SNPs) are associated with the exposure; the independence hypothesis, \u003csup\u003e15)\u003c/sup\u003e asserting that the SNPs are not linked with confounders; and the exclusion-restriction hypothesis,\u003csup\u003e16)\u003c/sup\u003e stipulating that the SNPs affect the outcome solely through the exposure. To support the independence hypothesis, we selected SNPs with significant genome-wide associations (P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) as instrumental variables. Additionally, we set a linkage disequilibrium threshold with an R\u003csup\u003e2\u003c/sup\u003e of 0.001 within a 10,000 kilobase pair (kb) radius to reduce genetic confounding. By applying these stringent criteria, we ensured the independence of each selected SNP, effectively mitigating potential biases from genetic polymorphisms. \u003csup\u003e17)\u003c/sup\u003e Our analysis exclusively included SNPs with F-statistics exceeding 10, indicating a robust association.\u003csup\u003e18)\u003c/sup\u003e\u003c/p\u003e\n\u003ch2\u003eScoring risk prediction model construction method\u003c/h2\u003e\n\u003cp\u003eDrawing inspiration from the design principles of the risk scoring tool utilized in the Framingham Heart Study,\u003csup\u003e19)\u003c/sup\u003e the risk factors identified in the above multi-factor logistic regression analysis were stratified. Quantitative scores were assigned to each stratum of risk factors.\u003c/p\u003e\n\u003cp\u003eFor instance, considering age, the ages were categorized into 10-year intervals, with the 60\u0026ndash;69 age group serving as the basic risk reference category. The score assigned to each risk factor category was calculated as the difference between the grouping of each risk factor and reference value of the basic risk. The ratio of the distance (D) to the constant (B) corresponding to one point in the scoring tool is determined by Equations (2)\u0026ndash;(4), where B represents the constant denoting the change in each risk factor associated with a 1-point increase in the scoring tool.\u003c/p\u003e\n\u003cp\u003eTaking age as an example, each five-year increase in age is attributed to one point. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{W}_{i}\\)\u003c/span\u003e\u003c/span\u003e represents the reference value for each risk factor group, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{W}_{ref}\\)\u003c/span\u003e\u003c/span\u003e represents the basic risk reference value. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e denotes the regression coefficient of the risk factors; the corresponding score for each category are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eThe risk prediction probability value corresponding to each score can be expressed using (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:\\begin{array}{c}Score=\\frac{D}{B}\\#\\left(2\\right)\\end{array}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:\\begin{array}{c}D=\\left({W}_{i}-{W}_{ref}\\right)\\times\\:{\\beta\\:}_{i}\\#\\left(3\\right)\\end{array}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e$$\\:\\begin{array}{c}B=5\\times\\:{\\beta\\:}_{age}\\#\\left(4\\right)\\end{array}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e$$\\:\\begin{array}{c\\hat }{P}=\\frac{1}{1+{e}^{-\\left(\\sum\\:_{i=0}^{p}{\\beta\\:}_{i}{X}_{i}\\right)}}\\#（5）\\end{array}$$\u003c/div\u003e\n\u003c/div\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\u003eScoring values for various risk factor categories\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReference value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScore\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\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.059\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\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u0026ndash;79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u0026ndash;89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.778\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\" rowspan=\"2\"\u003e\u0026nbsp;\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\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.6\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\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducational level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.341\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\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJunior high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSenior high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUniversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUniversity and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote: The bold numbers are reference risk values\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 1: Scoring values for various risk factor categories: Category: Different categories within each risk factor. Reference value: The reference value used for calculating the score. \u0026beta;: The regression coefficient of the risk factor. Score: The score assigned to each category of the risk factor based on the formula and calculations described in the methodology.\u003c/p\u003e\n\u003cp\u003eIn formula (5):\u003c/p\u003e\n\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e$$\\:\\sum\\:_{i=0}^{p}{\\beta\\:}_{i}{X}_{i}={A+\\beta\\:}_{1}{W}_{1}+{\\beta\\:}_{2}{W}_{2}+{\\dots\\:+\\beta\\:}_{i}{W}_{i}+B\\times\\:\\sum\\:_{i=1}^{n}{C}_{i}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{2}\\)\u003c/span\u003e\u003c/span\u003e, \u0026hellip;, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e are the regression coefficients of each risk factor in the logistic model. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{W}_{1}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{W}_{2}\\)\u003c/span\u003e\u003c/span\u003e, \u0026hellip;, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{W}_{i}\\)\u003c/span\u003e\u003c/span\u003e represent reference values for each risk factor. A is the constant. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{i=1}^{n}{C}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the sum of the scores corresponding to each risk factor.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eMR analysis was primarily performed using the IVW approach. Cochran\u0026rsquo;s Q statistic was computed to evaluate the heterogeneity induced by different genetic variants using the fixed-effect IVW method, with a P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating the presence of heterogeneity. In addition, MR-Egger, weighted median, and weighted mode analyses were performed to compare with the results of the IVW method, as they may be biased when genetic variants exhibit horizontal pleiotropy. The MR-Egger regression intercept term was used to assess the possible presence of horizontal pleiotropy, where deviation from zero (P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) indicates directional pleiotropy.\u003c/p\u003e\n \u003cp\u003eThe dataset utilized for establishing the logistic model was sourced from the Open Access Serial Imaging Study (OASIS) project, encompassing 416 subjects ranging from 18 to 96 years of age. The variables within this dataset include age, sex, educational level, economic situation, mental status examination outcomes, and clinical dementia rating. Data processing was conducted using SPSS version 27.0, and a logistic regression equation was employed to analyze the influencing factors. Initially, a single-factor analysis was performed, followed by the inclusion of statistically significant factors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) into a multi-factor analysis to construct a logistic model.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003ch2\u003eThe causal effects between neurodegenerative diseases and multiple factors\u003c/h2\u003e\n\u003cp\u003eIn our analysis, we used 17 SNPs for physical activity level, 22 SNPs for cigarettes per day, 60 SNPs for sleep duration, 106 SNPs for time spent watching television, 76 SNPs for time spent using computer, 25 SNPs for spirits intake, 17 SNPs for red wine intake, 18 SNPs for beer intake, 4 SNPs for white wine intake, 16 SNPs for sex, 430 SNPs for educational level, 42 SNPs for economic situation, 130 SNPs for reaction time, and 68 SNPs for cognitive ability. To illustrate the association with Alzheimer\u0026apos;s disease specifically, forest plots are depicted in Supplementary Fig.\u0026nbsp;1(a)and(b). Utilizing the IVW method, our results show that a one standard deviation (SD) increase in weekly beer intake is associated with a 3.15-fold increased risk of Alzheimer\u0026apos;s disease (OR\u0026thinsp;=\u0026thinsp;3.15, 95%CI [1.40\u0026ndash;7.10], P\u0026thinsp;=\u0026thinsp;0.006). Conversely, a higher educational level reduced the risk by 24% (OR\u0026thinsp;=\u0026thinsp;0.76, 95%CI [0.64\u0026ndash;0.90], P\u0026thinsp;=\u0026thinsp;0.0019). Likewise, improved cognition may reduce the risk of Alzheimer\u0026apos;s disease (OR\u0026thinsp;=\u0026thinsp;0.92, 95%CI [0.85\u0026ndash;0.99], P\u0026thinsp;=\u0026thinsp;0.0313). Physical activity level, cigarettes per day, sleep duration, time spent watching television, time spent using a computer, spirit intake, red wine intake, white wine intake, sex, economic situation, and reaction time were not shown to have a clear causal association with Alzheimer\u0026apos;s disease.\u003c/p\u003e\n\u003cp\u003eSupplementary Figs.\u0026nbsp;2(a)and(b) illustrate that the IVW method shows a significant positive relationship between educational level, economic situation and the risk of developing Parkinson\u0026apos;s disease. Specifically, individuals with higher educational levels had a 45% increased risk (OR\u0026thinsp;=\u0026thinsp;1.45, 95%CI [1.16\u0026ndash;1.81], P\u0026thinsp;=\u0026thinsp;0.0012), and those with better economic situations have more than double the risk (OR\u0026thinsp;=\u0026thinsp;2.10, 95%CI [1.30\u0026ndash;3.73], P\u0026thinsp;=\u0026thinsp;0.0023])of developing Parkinson\u0026apos;s disease.\u003c/p\u003e\n\u003cp\u003eSupplementary Figs.\u0026nbsp;3(a)and(b) show that the IVW method showed a significant positive relationship between physical activity level and the risk of developing amyotrophic lateral sclerosis (OR\u0026thinsp;=\u0026thinsp;1.82, 95%CI [1.05\u0026ndash;3.14], P\u0026thinsp;=\u0026thinsp;0.033). Conversely, a higher educational level (OR\u0026thinsp;=\u0026thinsp;0.76, 95%CI [0.66\u0026ndash;0.87], P\u0026thinsp;=\u0026thinsp;0.0001) was significantly associated with a reduced risk of ALS, demonstrating a negative relationship.\u003c/p\u003e\n\u003cp\u003eFurthermore, the IVW analyses indicated no causal relationships between these factors and either frontotemporal dementia or Lewy body dementia, as evidenced by p-values greater than 0.05.\u003c/p\u003e\n\u003ch2\u003eInfluencing factors and pleiotropy analysis of Alzheimer\u0026apos;s disease\u003c/h2\u003e\n\u003cp\u003eSupplementary Fig.\u0026nbsp;4\u0026ndash;5 illustrate scatter plots demonstrating the relationship between factors (beer intake, cognitive ability, and educational level) and Alzheimer\u0026apos;s disease. The IVW line in the funnel plots indicated that beer intake, cognitive ability, and educational level were distributed symmetrically, suggesting the absence of a notable bias.\u003c/p\u003e\n\u003cp\u003eThe heterogeneity tests presented in Supplementary Table\u0026nbsp;1 indicate no significant heterogeneity among beer intake, cognitive ability, educational level, and Alzheimer\u0026apos;s disease. Additionally, the P-values of the multi-effect tests for these factors and Alzheimer\u0026apos;s disease exceeded 0.05.\u003c/p\u003e\n\u003ch2\u003eInfluencing factors and pleiotropy analysis of Amyotrophic lateral sclerosis\u003c/h2\u003e\n\u003cp\u003eSupplementary Fig.\u0026nbsp;6\u0026ndash;7 depict the scatter plots illustrating the relationship between physical activity level, educational level, and amyotrophic lateral sclerosis. The IVW line on the funnel plot indicates a symmetrical distribution of physical activity level and educational level, suggesting the absence of significant bias.\u003c/p\u003e\n\u003cp\u003eSupplementary Table\u0026nbsp;2 presents the results of the heterogeneity tests, which demonstrated no potential heterogeneity among physical activity level, educational level, and amyotrophic lateral sclerosis. Additionally, the P-values of the pleiotropic test for data related to amyotrophic lateral sclerosis were calculated. Each factor observed by Egger intercept showed P-values exceeding 0.05, indicating no horizontal pleiotropy.\u003c/p\u003e\n\u003ch2\u003eInfluencing factors and pleiotropy analysis of Parkinson\u0026apos;s disease\u003c/h2\u003e\n\u003cp\u003eIn Supplementary Figs.\u0026nbsp;8\u0026ndash;9, the scatter plots illustrate the relationship between educational level, economic situation, and Parkinson\u0026apos;s disease. The IVW line on the funnel plots demonstrates a symmetrical distribution of educational level and economic situation, indicating the absence of notable bias.\u003c/p\u003e\n\u003cp\u003eSupplementary Table\u0026nbsp;3 presents the results of the heterogeneity tests, revealing no potential heterogeneity among educational level, economic situation, and Parkinson\u0026apos;s disease. Furthermore, the P-values of the pleiotropy test for each exposure variable and the correlation data with Parkinson\u0026apos;s were calculated. In the Egger intercept analysis, all P-values exceeded 0.05, indicating the absence of horizontal pleiotropy.\u003c/p\u003e\n\u003ch2\u003eThe Alzheimer\u0026apos;s disease risk prediction model was established\u003c/h2\u003e\n\u003cp\u003eBased on the Mendelian analysis of the relationship between Alzheimer\u0026apos;s disease and various risk factors as described in the previous section, a logistic model was utilized to assess the relationship between the above risk factors and Alzheimer\u0026apos;s disease.\u003c/p\u003e\n\u003cp\u003eLogistic regression analysis is a form of generalized linear regression that is used to identify risk factors for diseases and predict the probability of disease occurrence. Various regression methods are tailored to the different types of dependent variables. In this study, the dependent variable was dichotomous, necessitating the use of a binary logistic regression model.\u003c/p\u003e\n\u003cp\u003eIf we denote the incidence rate of Alzheimer\u0026apos;s disease as P, then the Logistic regression model between P and the independent variables \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{1}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{2}\\)\u003c/span\u003e\u003c/span\u003e,\u0026hellip;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{n}\\)\u003c/span\u003e\u003c/span\u003e is formulated as follows:\u003c/p\u003e\n\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e$$\\:\\begin{array}{c}P=\\frac{{e}^{\\left({\\beta\\:}_{0}+{\\beta\\:}_{1}{X}_{1}+\\cdots\\:+{\\beta\\:}_{n}{X}_{n}\\right)}}{1+{e}^{\\left({\\beta\\:}_{0}+{\\beta\\:}_{1}{X}_{1}+\\cdots\\:+{\\beta\\:}_{n}{X}_{n}\\right)}}\\#（1）\\end{array}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eIn formula (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{2}\\)\u003c/span\u003e\u003c/span\u003e, \u0026hellip;, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e represent the regression coefficients of each independent variable in the Logistic model. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{1}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{2}\\)\u003c/span\u003e\u003c/span\u003e, \u0026hellip;, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i}\\)\u003c/span\u003e\u003c/span\u003e represent the values of the respective independent variables.\u003c/p\u003e\n\u003cp\u003eAfter screening the data in the database, 236 cases were ultimately included in the single-factor analysis. The analysis results are summarized in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, indicating significant associations between certain factors and AD risk. Age (OR\u0026thinsp;=\u0026thinsp;1.068, 95%CI[1.039\u0026ndash;1.099], P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), sex (OR\u0026thinsp;=\u0026thinsp;1.774, 95%CI [1.026\u0026ndash;3.066], P\u0026thinsp;=\u0026thinsp;0.04), economic situation (OR\u0026thinsp;=\u0026thinsp;1.402, 95%CI [1.09\u0026ndash;1.804], P\u0026thinsp;=\u0026thinsp;0.008), educational level (OR\u0026thinsp;=\u0026thinsp;0.726, 95%CI [0.589\u0026ndash;0.899], P\u0026thinsp;=\u0026thinsp;0.003), mental state (OR\u0026thinsp;=\u0026thinsp;0.425, 95%CI [0.331\u0026ndash;0.546], P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).These results suggest that age, sex, economic situation, educational level, and mental state are significantly associate with Alzheimer\u0026apos;s disease.\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSingle factor Logistic regression analysis of Alzheimer\u0026apos;s disease\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWald\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR(95%CI)\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=\"char\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.068(1.039,1.099)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.774(1.026,3.066)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducational level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.726(0.587,0.899)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEconomic situation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.402(1.09,1.804)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMini mental state examination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.425(0.331,0.546)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2: Single factor Logistic regression analysis of Alzheimer\u0026apos;s disease: B: Regression Coefficient. SE: Standard Error. Wald: Wald Test Statistic. P: P-value. OR(95%CI): Odds Ratio and Its 95% Confidence Interval.\u003c/p\u003e\n\u003cp\u003eThe potential influencing factors identified through single-factor analysis were utilized as independent variables, with the presence or absence of Alzheimer\u0026apos;s disease serving as the dependent variable for the multi-factor logistic regression analysis. The findings revealed the following associations(Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Age (OR\u0026thinsp;=\u0026thinsp;1.060, 95%CI [1.032\u0026ndash;1.090], P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that age is a risk factor for Alzheimer\u0026apos;s disease. Sex (OR\u0026thinsp;=\u0026thinsp;2.177, 95%CI [1.188\u0026ndash;3.989], P\u0026thinsp;=\u0026thinsp;0.012), indicating that sex is also a risk factor for Alzheimer\u0026apos;s disease. Educational level (OR\u0026thinsp;=\u0026thinsp;0.711, 95%CI [0.569\u0026ndash;0.887], P\u0026thinsp;=\u0026thinsp;0.003), indicating that educational level acts as a protective factor against Alzheimer\u0026apos;s disease.\u003c/p\u003e\n\u003cp\u003eThese results highlight age and sex as risk factors, while educational level emerges as a protective factor against Alzheimer\u0026apos;s disease.\u003c/p\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\u003eMultivariate Logistic regression analysis of Alzheimer\u0026apos;s disease\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWald\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\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=\"char\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.032,1.090)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.188,3.989)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducational level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.569,0.887)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3: Multivariate Logistic regression analysis of Alzheimer\u0026apos;s disease: B: Regression Coefficient. SE: Standard Error. Wald: Wald Test Statistic. P: P-value. OR(95%CI): Odds Ratio and Its 95% Confidence Interval.\u003c/p\u003e\n\u003ch2\u003eTesting Alzheimer\u0026apos;s disease risk prediction models\u003c/h2\u003e\n\u003cp\u003eTo verify the accuracy of the rating prediction model, 180 subjects were selected as the training set for model training and 45 subjects as the validation set for model evaluation. ROC curves were generated using SPSS software for specificity and sensitivity analyses. Sensitivity, also known as the true positive rate (TPR) and specificity, is also called the false positive rate (FPR). As shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, the Area Under the Curve (AUC) of the logistics model was 0.739, and the AUC value of the Alzheimer\u0026apos;s disease rating prediction model was 0.718. A confusion matrix is used to evaluate the accuracy of the model, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. 45 subjects were selected, 20 of whom had Alzheimer\u0026apos;s disease and 25 had non-Alzheimer\u0026apos;s disease. The rating prediction model predicted 17 patients with Alzheimer\u0026apos;s disease and 18 patients without Alzheimer\u0026apos;s disease. In contrast, the logistic model predicted five patients with Alzheimer\u0026apos;s disease and 23 patients with non-AD. The accuracy of the prediction model was 78%, and the accuracy of the logistic model was 62%. All things considered, the rating prediction model exhibited a stronger predictive ability than the logistic model.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study comprehensively explored various neurodegenerative diseases and their various influencing factors. These findings indicate that there is no statistically significant causal relationship between the aforementioned factors and frontotemporal dementia or Lewy body dementia. However, the study observed a statistically significant causal association between beer intake, educational level, cognitive ability, and Alzheimer's disease. Specifically, beer intake increases the risk of Alzheimer's disease. The findings from a cohort study conducted in the Korean population revealed that maintaining light and moderate drinking habits can potentially reduce the risk of dementia.\u003csup\u003e20)\u003c/sup\u003e However, the current study identified a causal link specifically between beer intake and Alzheimer's disease. It is worth noting that other alcoholic beverages had no causal relationship with Alzheimer's disease. This distinction could potentially be attributed to compounds present in beer such as purines, niacin, and alcohol, along with neuroprotective elements, such as folic acid and various phenolic compounds.\u003csup\u003e21)\u003c/sup\u003e Furthermore, cognitive ability and educational level have been identified as protective factors against Alzheimer's disease. Conversely, a statistically significant causal relationship was observed between Parkinson's and both educational level and economic situation. A higher educational level and better economic situation are found to increase the risk of Parkinson's. In the case of amyotrophic lateral sclerosis, a significant causal association was identified with physical activity level. Surprisingly, strenuous physical activity increased the risk of amyotrophic lateral sclerosis. However, higher educational level is associated with a decreased risk of amyotrophic lateral sclerosis(ALS).\u003c/p\u003e \u003cp\u003eHowever, the Mendelian randomization analysis employed in this study had several limitations. First, the relatively stringent genome-wide correlation threshold (P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) utilized for screening SNPs might result in the inclusion of a limited number of SNPs, potentially affecting the analysis outcomes. Additionally, most GWAS datasets originate from individual samples of European ancestry, leading to a scarcity of datasets from Asian and African populations. Consequently, the generalizability of these results to all populations warrants further investigation.\u003c/p\u003e \u003cp\u003eThe Logistic regression analysis conducted in this study revealed that a higher educational level is associated with a decreased risk of Alzheimer's disease. For the elderly, engaging in adult education may enhance their language processing and intellectual abilities. Long-term educational pursuits may also confer beneficial effects on the brain, potentially decreasing the risk of Alzheimer's disease. This could be attributed to factors such as cortical surface area and thickness during the prodromal stage.\u003csup\u003e22)\u003c/sup\u003e In this study, the results of multivariate logistic regression analysis revealed a statistically significant association between sex and Alzheimer's disease. Studies by Yin et al. \u003csup\u003e23)\u003c/sup\u003e and Wang et al. \u003csup\u003e24)\u003c/sup\u003e indicated a higher prevalence rate among women. Furthermore, it is imperative to provide adequate care and appropriate intervention methods for elderly individuals with low educational level, poor economic situation, and poor mental health. By doing so, we can potentially mitigate the prevalence of Alzheimer's disease. This study employed an Alzheimer's disease risk rating prediction model developed based on a logistic model. By analyzing lifestyle factors, personal circumstances, and other pertinent data, this model predicts the risk of developing AD in high-risk groups. Such predictive capabilities facilitate early diagnostic opportunities for healthcare professionals, enabling timely intervention and treatment initiation. For patients, early detection allows for prompt therapeutic intervention, thereby potentially delaying disease progression and enhancing the overall quality of life. This model holds significant clinical value in facilitating proactive healthcare management strategies and contributes to scientific research by providing a tool for investigating Alzheimer's disease risk factors and interventions.\u003c/p\u003e \u003cp\u003eThe Alzheimer's disease risk rating prediction model developed in this study has several limitations. The study sample size was relatively small, primarily comprising of the American population, which may introduce potential biases and limit the generalizability of the model to other populations. To address this issue, future studies should include a more diverse and representative sample size to enhance the model's applicability and robustness. Additionally, the variables incorporated into the model were obtained through logistic multi-factor analysis, potentially resulting in a limited number of variables included in the analysis, consequently reducing the accuracy of the prediction results. To mitigate this limitation, further research should explore additional causal factors associated with Alzheimer's disease and incorporate relevant risk factors into the model to improve its predictive performance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eThe data sets used and analyzed in the current study are available from the IEU OpenGWAS project, GWAS Catalog, and FinnGen. Please visit: https://www.ebi.ac.uk/gwas/, https://gwas.mrcieu.ac.uk/, and https://r8.finngen.fi/. The dataset utilized for establishing the logistic model was sourced from the open access serial imaging study (OASIS) project(https://www.kaggle.com/datasets/jboysen/mri-and-alzheimers/data)\u003c/p\u003e\n\u003ch2\u003eFunding statement\u003c/h2\u003e\n\u003cp\u003eThis research was not funded by any external sources.\u003c/p\u003e\n\u003ch2\u003eEthics statement\u003c/h2\u003e\n\u003cp\u003eThere were no human or animal subjects in this study and ethical approval was not applicable.\u003c/p\u003e\n\u003ch2\u003eConflicts of Interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflicts of interest\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe gratefully acknowledge the participants and investigators of the FinnGen study and all GWAS for their summary statistical data.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eQD and RSJ provide GWAS data required for Mendelian randomization of neurodegenerative diseases. TYC provide a dataset for conducting logistic regression analysis and was a major contributor in writing the manuscript. WZW and ZWJ determine research objectives. ZWJ、CFY 、CX、CZJ、SDQ and NS review and revise the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWyman-Chick K A, O\u0026rsquo;Keefe L R, Weintraub D, et al. Prodromal dementia with Lewy bodies: evolution of symptoms and predictors of dementia onset. J Geriatr Psychiatry Neurol, 35(4), 527-534,2022.\u003c/li\u003e\n \u003cli\u003eSong H, Li Y P, Wang S H, et al. Research progress of synaptic dysfunction in Alzheimer\u0026apos;s disease. Life Sci, 30(1), 20-26,2018.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eRajan K B, Weuve J, Barnes L L, et al. Population estimate of people with clinical Alzheimer\u0026apos;s disease and mild cognitive impairment in the United States (2020\u0026ndash;2060). Alzheimers Dement, 17(12), 1966-1975,2021.\u003c/li\u003e\n \u003cli\u003eHirsch L, Jette N, Frolkis A, et al. The incidence of Parkinson\u0026apos;s disease: a systematic review and meta-analysis[J]. Neuroepidemiology, 46(4), 292-300,2016.\u003c/li\u003e\n \u003cli\u003eXu L, Liu T, Liu L, et al. Global variation in prevalence and incidence of amyotrophic lateral sclerosis: a systematic review and meta-analysis. J Neurol, 267, 944-953,2020.\u003c/li\u003e\n \u003cli\u003eLogroscino G, Piccininni M, Graff C, et al. Incidence of syndromes associated with frontotemporal lobar degeneration in 9 European countries. JAMA Neurol, 80(3): 279-286,2023.\u003c/li\u003e\n \u003cli\u003eRevi M. Alzheimer\u0026rsquo;s disease therapeutic approaches. GeNeDis 2018, 105-116,2020.\u003c/li\u003e\n \u003cli\u003eBrasure M, Desai P, Davila H, et al. Physical activity interventions in preventing cognitive decline and Alzheimer-type dementia: a systematic review. Ann Intern Med, 168(1), 30-38,2018.\u003c/li\u003e\n \u003cli\u003eKivim\u0026auml;ki M, Singh-Manoux A, Pentti J, et al. Physical inactivity, cardiometabolic disease, and risk of dementia: an individual-participant meta-analysis. bmj, 365,2019.\u003c/li\u003e\n \u003cli\u003eSabia S, Dugravot A, Dartigues J F, et al. Physical activity, cognitive decline, and risk of dementia: 28 year follow-up of Whitehall II cohort study. bmj, 357,2017.\u003c/li\u003e\n \u003cli\u003eFang X, Han D, Cheng Q, et al. Association of levels of physical activity with risk of Parkinson disease: a systematic review and meta-analysis. JAMA Netw Open, 1(5), e182421-e182421,2018.\u003c/li\u003e\n \u003cli\u003eBandres‐Ciga S, Noyce A J, Hemani G, et al. Shared polygenic risk and causal inferences in amyotrophic lateral sclerosis. Ann Neurol, 85(4), 470-481,2019.\u003c/li\u003e\n \u003cli\u003eZhuang Z, Gao M, Yang R, et al. Association of physical activity, sedentary behaviours and sleep duration with cardiovascular diseases and lipid profiles: a Mendelian randomization analysis. Lipids Health Dis, 19(1), 1-11,2020.\u003c/li\u003e\n \u003cli\u003eEmdin C A, Khera A V, Kathiresan S. Mendelian randomization. Jama, 318(19): 1925-1926,2017.\u003c/li\u003e\n \u003cli\u003eSekula P, Fabiola Del Greco M, Pattaro C, et al. Mendelian randomization as an approach to assess causality using observational data. JASN, 27(11), 3253,2016.\u003c/li\u003e\n \u003cli\u003eDavies N M, Holmes M V, Smith G D. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. bmj, 362,2018.\u003c/li\u003e\n \u003cli\u003eBurgess S, Thompson S G. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol, 32, 377-389,2017.\u003c/li\u003e\n \u003cli\u003eBoggs J M, Beck A, Ritzwoller D P, et al. A quasi-experimental analysis of lethal means assessment and risk for subsequent suicide attempts and deaths. J Gen Intern Med, 35,1709-1714,2020.\u003c/li\u003e\n \u003cli\u003eWilson P W F, D\u0026rsquo;Agostino R B, Levy D, et al. Prediction of coronary heart disease using risk factor categories. Circulation, 97(18), 1837-1847,1998.\u003c/li\u003e\n \u003cli\u003eJeon K H, Han K, Jeong S M, et al. Changes in alcohol consumption and risk of dementia in a nationwide cohort in South Korea. JAMA Netw open 6(2), e2254771-e2254771,2023.\u003c/li\u003e\n \u003cli\u003eS\u0026aacute;nchez-Muniz, F.J.; Macho-Gonz\u0026aacute;lez, A.; Garcimart\u0026iacute;n, A.; Santos-L\u0026oacute;pez, J.A.; Bened\u0026iacute;, J.; Bastida, S.; Gonz\u0026aacute;lez-Mu\u0026ntilde;oz, M.J. The Nutritional Components of Beer and Its Relationship with Neurodegeneration and Alzheimer\u0026rsquo;s Disease. Nutrients, 11, 1558,2019.\u003c/li\u003e\n \u003cli\u003eZhang X X, Tian Y, Wang Z T, et al. The epidemiology of Alzheimer\u0026rsquo;s disease modifiable risk factors and prevention. J Prev Alz Dis, 8, 313-321,2021.\u003c/li\u003e\n \u003cli\u003eYin J H, Zeng Y B, Zhou Z, et al. Analysis of frailty status and its influencing factors in the elderly in China. Chin J Epidemiol, 39(9), 1244-1248,2018.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWang Q Y, Zhu Y L. Prevalence and risk factors of senile dementia in Quzhou city. Chin Mod Doctor, 59(8), 33-38,2021.\u0026nbsp;\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":"Mendelian randomization, neurodegenerative diseases, rating prediction model","lastPublishedDoi":"10.21203/rs.3.rs-4866553/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4866553/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: This study aimed to explore the causal links between various lifestyle, demographic, cognitive factors and neurodegenerative diseases, and to develop an Alzheimer's disease (AD) risk prediction model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Mendelian randomization analysis was conducted using genetic variants as instrumental variables to investigate the relationships between lifestyle, demographics, cognitive factors, and neurodegenerative diseases. We used the MR-Egger regression, weighted median method, Inverse Variance Weighting (IVW), and weighted model. Based on the Mendelian analysis results, logistic multivariate analysis was used for validation and to design an AD rating prediction model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Mendelian randomization analysis showed that beer intake was positively correlated with AD risk, whereas education level and cognitive ability were negatively correlated with AD risk. There was a positive correlation between education level, economic status, and risk of Parkinson's. There was a positive correlation between physical activity level and the risk of developing amyotrophic lateral sclerosis, and higher education level is significantly associated with a reduced risk of ALS. Logistic regression analysis showed that age and sex were positively correlated with AD, while education level was negatively correlated with AD. The accuracy of the AD risk prediction model was 78%, which was better than that of the logical model 62%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Mendelian analysis results indicated that there is a causal relationship between beer intake, educational level, cognitive ability and AD. There is a causal relationship between education level, economic status, and Parkinson's disease. There was a causal relationship between physical activity level, education level, and amyotrophic lateral sclerosis. No causal relationship was found between these factors and Lewy body dementia or frontotemporal dementia. The rating prediction model outperformed traditional logistic models in terms of accuracy .\u003c/p\u003e","manuscriptTitle":"Causal Links of Factors to Neurodegenerative Diseases and Alzheimer's Risk Prediction Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-03 04:57:20","doi":"10.21203/rs.3.rs-4866553/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":"2d81ec21-a7ec-4695-b025-ea0b362bfcd4","owner":[],"postedDate":"September 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T22:38:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-03 04:57:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4866553","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4866553","identity":"rs-4866553","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}