Protective Evidence for Alcohol Consumption in Parkinson’s Disease Risk and Associated Genes of DPP6, SLC39A8, MAD1L1, and RBFOX1

Protective Evidence for Alcohol Consumption in Parkinson’s Disease Risk and Associated Genes of DPP6, SLC39A8, MAD1L1, and RBFOX1 | 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 Article Protective Evidence for Alcohol Consumption in Parkinson’s Disease Risk and Associated Genes of DPP6, SLC39A8, MAD1L1, and RBFOX1 Wei Lu, Yong-Qiang Kong, Hai Tang, Xiu-Li Cheng, Wan-Ruo Zhang, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6754726/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 Background Parkinson’s disease (PD), a leading neurodegenerative disorder, is increasing in prevalence globally due to population ageing. Although alcohol consumption is widespread worldwide, its role in PD pathogenesis remains contentious, with studies reporting both protective and harmful associations. Methods Here, we integrate familial, population-based, and genomic data to investigate the relationship between alcohol intake and PD risk. To elucidate causal mechanisms, we performed Mendelian randomization analyses leveraging published genome-wide association study (GWAS) datasets. Results In a three-generation PD pedigree, only the affected individual—who reported abstinence—carried the fewest PD-associated risk variants, while seven unaffected, alcohol-consuming relatives harbored a greater burden of risk variants, suggesting a potential protective effect of alcohol. Population surveys from two Chinese cities revealed significantly lower alcohol consumption among PD patients compared to controls ( P < 0.01). Cross-national data from 27 countries showed an inverse association between low-to-moderate alcohol intake (< 10 L/capita/year) and PD incidence, whereas heavy consumption (≥ 10 L/capita/year) increased risk. Moderate but frequent alcohol consumption was associated with a reduced genetic risk for PD, potentially mediated by improved sleep quality and mitigating inflammation. Notably, we identified the T-cell surface glycoprotein CD6 isoform as a novel cytokine linking alcohol intake to decreased PD risk, with alcohol consumption reducing circulating CD6 levels. GWAS data further implicated four genes— DPP6 , SLC39A8 , MAD1L1 , and RBFOX1 —in mediating the protective association. Conclusions Together, our findings provide convergent, multi-layered evidence that modified people’s drinking habits may confer protection against PD and highlight potential biological pathways for targeted prevention strategies. Clinical trial number: not applicable. Biological sciences/Computational biology and bioinformatics Biological sciences/Genetics Health sciences/Diseases Health sciences/Risk factors Parkinson’s disease alcohol consumption Mendelian randomization Global Burden of Disease Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Parkinson’s disease (PD) is an age-related neurodegenerative disorder characterized by resting tremors, motor impairment and non-motor symptoms such as sleep disturbances [ 1 ]. Its prevalence has increased significantly in recent years, reaching approximately 4‰ in general population and 10‰ among individuals over 60 years of age [ 2 ]. Both genetic and environmental factors are considered to contribute to the onset of the disease. Nearly a hundred genetic markers have been identified to be associated with the risk of PD [ 3 ], and testified in multi-ancestry cohorts [ 4 ]. While environmental factors of lifestyle exposures, such as alcohol consumption, smoking, and sleep patterns, have been investigated for their potential to delay or prevent the onset of symptoms, the findings remain inconclusive and inconsistent across studies [ 5 , 6 ]. Alcohol consumption is a widespread habit globally, particularly among men. However, the role of alcohol consumption in the development of PD remains highly debated [ 7 , 8 ]. Recent prospective studies have reported inconsistent findings regarding the association between alcohol consumption and PD risk, with results ranging from increased risk to decreased risk or even no significant association [ 9 – 11 ]. In addition, recent Mendelian randomization (MR) studies suggest that alcohol consumption may reduce the risk of PD by approximately 20% [ 12 ]. Alcohol dehydrogenase 1B ( ADH1B ), a key gene involved in alcohol metabolism, has been implicated in PD risk [ 13 ]. Research has indicated that a mutant allele of ADH1B (rs1229984-T), which adversely affects alcohol metabolism, is associated with an increased risk of PD [ 14 ]. Furthermore, some studies suggest that low-to-moderate alcohol intake may stimulate the nervous system [ 15 ], improve the blood circulation [ 16 , 17 ] and enhance sleep quality [ 9 ], potentially delaying or alleviating PD-related tremors and sleep disturbances [ 7 , 18 ]. Therefore, further study is needed to clarify the role of alcohol consumption in the onset of PD. China is experiencing rapid population aging and a growing burden of PD [ 19 ]. Approximately half of adults in China, particularly men, regularly consume Baijiu, a traditional distilled spirit. In this study, we examined the association between alcohol consumption and PD risk using retrospective data from a family with a history of PD, along with epidemiological survey data on alcohol intake rates and PD incidence in two Chinese cities. To validate causal effects of alcohol consumption on PD, we conducted MR analyses leveraging genetic variants (single nucleotide polymorphisms, SNPs) of PD, lifestyle factors (including alcohol consumption, tea and coffee drinking, and sleep quality), and inflammatory cytokines. Our family retrospective and survey data show that among offsprings from families with a history of PD, those who abstained from alcohol consumption exhibited typical resting tremors, whereas those who consumed alcohol regularly did not. Our MR analysis results suggest that alcohol consumption may reduce PD risk, with sleep quality and an inflammatory cytokine (T-cell surface glycoprotein CD6 isoform level) as potential mediating factors. The findings of this study support the notion that low-to-moderate but frequent alcohol consumption may delay or reduce the PD onset by improving sleep quality and decreasing inflammation. Our findings shed new light on the future prevention of PD onset. Methods Family history of PD We investigated a three-generation family with a history of PD. All participants provided written informed consent and completed a demographic survey that included information on gender, age (or age at death), alcohol consumption, smoking, resting tremors, and cognitive impairment. The study was approved by the Ethics Committee of Jiangsu Normal University (Approval No.: H2022-002). All procedures were conducted in accordance with the Declaration of Helsinki. We reviewed the NHGRI–EBI Catalog of human genome-wide association studies (GWAS) ( https://www.ebi.ac.uk/gwas/ ) and identified 35 SNPs associated with PD from 27 previous studies (Supplementary Table S1 ). Genotypes for these SNPs were extracted from 15× whole-genome sequencing data of the family members, and the number of risk alleles was countered for each individual. Clinical data of PD patients We reviewed inpatient records of 100 PD patients at Tianjin Huanhu Hospital (Tianjin, China) between January 2022 and August 2024 (Supplementary Table S2 ), and 144 patients at the Affiliated Hospital of Xuzhou Medical University (Xuzhou, Jiangsu, China) between May 2023 and August 2024 (Supplementary Table S3 ). Demographic data (gender, age, smoking, and alcohol consumption) and medical history (hypertension, diabetes, coronary artery disease, and cerebral infarction) were analyzed. The study was approved by the Review Board of the Affiliated Hospital of Xuzhou Medical University (Approval No.: XYFY2G23-KL266-01). All procedures were conducted in accordance with the Declaration of Helsinki. We examined the gender ratio and adult alcohol consumption rates in Tianjin and Xuzhou, China. According to the seventh national census in 2020, male comprised 51.53% of the population in Tianjin ( https://stats.tj.gov.cn/tjsj_52032/tjgb/202105/t20210521_5457266.html ), and 50.41% in Xuzhou ( https://tjgb.hongheiku.com/11727.html ). A 2015 report by the Tianjin Municipal Health Commission showed that 54.91% of adults consumed alcohol ( https://wsjk.tj.gov.cn/XWZX6600/MTBD3030/202008/t20200828_3581944.html ). In Xuzhou, with a population exceeding 9 million, approximately 5.13 million adults consumed alcohol, corresponding to a drinking proportion of 56.89% ( https://baijiahao.baidu.com/s?id=1772221666501006448&wfr=spider&for=pc ). Two-sample MR design MR uses genetic variants associated with an exposure (e.g., a potential risk factor) to evaluate its causal effect on an outcome [ 20 ]. Supplementary Figure S1 illustrates the MR framework used to assess the causal relationship between risk factors and PD. In this study, SNPs were used as instrumental variables (IVs) to investigate this causal link. For valid causal inference, MR relies on three key assumptions [ 21 ]. The relevance assumption requires that IVs are strongly associated with the exposure or risk factor. The independence assumption requires that the IVs are not associated with confounders, thereby avoiding bias in the exposure-outcome relationship. The exclusion restriction assumption stipulates that the genetic variants influence the outcome solely through the exposure, without acting through alternative pathways [ 22 ]. Data sources for exposure and outcome We obtained all GWAS summary data for both exposures and outcomes from the MRC Integrative Epidemiology Unit’s OpenGWAS database [ 23 ] (IEU OpenGWAS, https://gwas.mrcieu.ac.uk/ ), the NHGRI–EBI GWAS Catalog [ 24 ] ( https://www.ebi.ac.uk/gwas/ ), and previously published genetic studies. GWAS summary statistic data were downloaded from the MRC IEU OpenGWAS and NHGRI-EBI GWAS Catalog for this study: Alcohol consumption (IEU ID: ukb-b-1283), based on UK Biobank data from 112,117 European individuals with 12,935,395 SNPs [ 25 ]. Alcohol drinks per week (IEU ID: ukb-b-73), based on UK Biobank data from 335,394 European individuals with 11,887,865 SNPs [ 26 ]. Alcohol intake frequency (IEU ID: ukb-b-5779), based on UK Biobank data from 462,346 European individuals with 9,851,867 SNPs. Coffee intake (IEU ID: ukb-b-5237), based on UK Biobank data from 428,860 European individuals with 9,851,867 SNPs. Tea intake (IEU ID: ukb-b-6066), based on UK Biobank data from 447,485 European individuals with 9,851,867 SNPs [ 23 ]. Circulating inflammatory proteins (GCST90274758 to GCST90274848) were obtained from the EBI GWAS Catalog, based on genome-wide pQTL mapping of 91 plasma proteins using the Olink Target Inflammation panel comprising 14,824 participants of European ancestry with 12,958,024 SNPs [ 27 ]. PD risk (IEU ID: ieu-b-7) was derived from a GWAS meta-analysis conducted by the International Parkinson’s Disease Genomics Consortium and 23andMe, including 33,674 PD cases and 449,056 controls with 17,891,936 SNPs [ 28 ]. Sleep behaviors traits (IEU IDs: ukb-b-3957, 462,341 European individuals, 9,851,867 SNPs; ukb-b-4424, 460,099 European individuals, 9,851,867 SNPs; ukb-b-sleep, 361,194 European individuals, 11,120,383 SNPs) were obtained from the MRC IEU OpenGWAS database. GWAS summary data of significant SNPs ( P < 5 × 10 − 8 ) for eight sleep-related phenotypes were obtained from the published report [ 6 ]. Further details on data sources were provided in Supplementary Table S4 . Selection of IVs We applied a rigorous selection process to ensure valid causal inference in the MR analysis. First, genetic variants strongly associated with each exposure were identified using GWAS summary statistic data, applying genome-wide significance thresholds of P < 5 × 10⁻⁸. For inflammatory cytokines, a relaxed threshold of P < 5 × 10⁻⁶ was used due to the limited number of available SNPs. Next, we performed linkage disequilibrium clumping (Window size = 10,000 kb; r² = 0.001) to remove highly correlated SNPs and ensure independence among IVs. Finally, we calculated the F-statistic for each IV to assess its strength in relation to the exposure [ 29 ], using the formula: $$\:F=\frac{N-K-1}{K}\text{}\times\:\frac{{R}^{2}}{1-{R}^{2}}$$ where N is the sample size, K is the number of IVs, and R 2 is the proportion of variance in the exposure explained by the IV. An F-statistic greater than 10 was used as the threshold to identify strong IVs [ 29 ]. During the harmonization process, non-concordant or palindromic SNPs with intermediate allele frequencies were excluded to ensure consistency in genetic effect estimates. MR analysis The primary MR analysis was performed using the inverse variance-weighted (IVW) model, which combines variant-specific Wald ratios, weighted by the inverse of their variance, to provide a precise overall estimate of the causal effect. To assess the robustness of the findings, we additionally applied several methods, including MR Egger, Weighted Median, Simple Mode, and Weighted Mode. MR Egger accounts for potential horizontal pleiotropy, while the Weighted Median yields a consistent estimate even if up to 50% of the instruments were invalid. The Simple Mode estimates the causal effect based on the most frequently occurring ratio, and the Weighted Mode enhances the accuracy by weighting the ratios. To assess the robustness of the MR estimates, we conducted sensitivity and heterogeneity analyses. Cochran's Q test examined heterogeneity across IVs, with a significant result ( P < 0.05) indicating potential variations or violations of MR assumptions. The MR Egger intercept test identified directional pleiotropy, with a non-zero intercept ( P < 0.05) suggesting bias from pleiotropic effects. We also applied the MR–PRESSO (Mendelian Randomization Pleiotropy Residual Sum and Outlier) test [ 30 ] to detect and correct outlier SNPs linked to horizontal pleiotropy, with a global test P < 0.05 indicating significant pleiotropy. Finally, a Leave-One-Out sensitivity analysis was performed by iteratively removing each SNP to test its influence on the causal estimate. All analyses were performed using the TwoSampleMR package [ 31 ] (version 0.6.6) in R (version 4.4.0). Global Burden of Disease database Data was extracted from the Global Burden of Disease (GBD) database ( https://vizhub.healthdata.org/gbd-results/ ), alcohol use Summary Exposure Value and PD incidence across global regions covering 200 countries and regions [ 32 ]. Global Health Observatory database The total alcohol per capita (15 + years) consumption (in litres of pure alcohol) data from 2016 to 2018 was extracted from the Global Health Observatory database ( https://www.who.int/data/gho/data/indicators/indicator-details/GHO/alcohol-total-per-capita-(15-years)-consumption-(in-litres-of-pure-alcohol) ). Results Family-based survey suggests an inverse association between alcohol intake and PD onset We collected questionnaire surveys from three generations of a family with a history of PD, which includes two affected individuals, WC2 and WN1 (Fig. 1 ). Recent studies have identified 35 SNP alleles associated with PD risk, indicating that a higher number of these risk alleles correlate with an increased likelihood of developing the disease. Genotyping data were obtained from eight individuals (WN1–WN8) of the third generation, with the number of risk alleles ranges from 23 to 29 (Table 1 ). Patient WN1, aged 71, had 23 risk alleles and exhibited typical head tremors. Another patient, WC2, exhibited head and hand tremors, along with cognitive impairment, and died at the age of 83. Notably, both PD patients in this family did not drink alcohol, while others with frequent drinking habits (30–180 g/week) were symptom-free. According to family data, drinking alcohol did not lead to the onset of PD, while members who did not drink alcohol but also developed PD. All members of the third generation carried alleles related to PD, and this patient WN1 carried the least risky alleles but instead developed PD. It seems that alcohol consumption may reduce or delay the onset of PD symptoms, leading us to pay more attention to the negative correlation between alcohol consumption and PD risk. Table 1 Count of PD risk alleles Family members SNP Risk allele WN1 (PD) WN2 WN3 WN4 WN5 WN6 WN7 WN8 rs823128 A A G AA A G AA AA AA AA AA rs823118 T C T C T C T C T CC CC CC CC rs10797576 T CC CC CC CC CC CC CC CC rs6430538 T TT TT TT TT TT TT TT TT rs353116 C CC C T C T CC C T CC C T CC rs10513789 T GG GG GG GG GG GG GG GG rs12637471 G AA AA AA AA AA AA AA AA rs34311866 C TT TT TT TT TT TT TT TT rs4698412 A A G A G A G GG A G A G GG A G rs4538475 ༟ AG AA AA AA AG AA AA AA rs6812193 C C T C T C T C T TT C T CC CC rs356219 G A G GG GG GG A G GG A G A G rs356220 T C T TT TT TT C T TT C T C T rs2736990 C AG GG GG GG AG GG AG AG rs6532197 G AA A G A G A G A G GG A G A G rs10464059 A AA A G A G A G A G AA AA AA rs9261484 T CC C T CC CC CC CC CC CC rs9267659 A A G GG A G A G GG GG GG GG rs2280104 T C T TT C T C T C T CC CC CC rs329648 T CC CC CC CC CC CC CC CC rs1994090 ༟ TT TT TT TT TT TT TT TT rs76904798 T CC CC CC CC CC CC CC CC rs34637584 A GG GG GG GG GG GG GG GG rs34778348 A GG GG GG GG GG GG GG GG rs11060180 A AA AA AA AA AA AA AA AA rs10847864 T GG G T GG G T TT G T TT G T rs11610045 A GG A G GG A G A G A G GG AA rs12431733 T C T C T C T C T C T C T TT TT rs11158026 C/T CT CT TT CT CC CT CT CT rs4784227 T C T CC C T CC CC CC C T CC rs11868035 G AA AA AA AA AA A G AA AA rs393152 A AA AA AA AA AA AA AA AA rs12456492 G AA AA AA AA A G A G AA A G rs2295545 T C T C T C T CC C T C T CC CC rs1223271 G AA AA AA AA A G A G A G A G Count of risk allele 23 27 24 25 24 29 24 27 Bold and italic indicates risk alleles. PD, Parkinson's disease. Reduced Alcohol Consumption in PD Patients: evidence from Clinical and Census Data Because our family-based analysis suggests an inverse association between alcohol intake and PD risk, we hypothesize that individuals with PD consume less alcohol than those without the disease. To test this hypothesis, we analyzed two independent clinical datasets from hospitals in two Chinese cities: one comprising 100 PD patients (Tianjin residents; aged 36–84 years; 46% male) from Tianjin Huanhu Hospital, and another consisting of 144 PD patients (Xuzhou residents; aged 43–87 years; 50% male) from the Affiliated Hospital of Xuzhou Medical University. According to the 2020 Seventh National Census data (released by the Tianjin and Xuzhou Municipal Bureaus of Statistics), the permanent populations of Tianjin and Xuzhou were approximately 14 million (51.53% male) and 9 million (50.41% male), respectively. The alcohol consumption rates among the general populations in these cities were 54.91% in Tianjin and 56.89% in Xuzhou. In contrast, the rates among PD patients were significantly lower: 9% in Tianjin and 18.87% in Xuzhou (Fig. 2 A). These findings support our hypothesis that individuals with PD consume less alcohol than the general population. Global survey finds lower PD Risk in low-to-moderate drinkers To investigate the correlation between alcohol consumption and the risk of PD, we analyzed years lived with disability (YLDs, per 100,000 people) attributable to high alcohol use and PD in 2021, based on data from the GBD database. Twenty-seven countries across four continents were included, selected for their advanced ageing profiles (Table 2 ). According to the World Report on Ageing and Health by World Health Organization, these countries had ≥ 20% of their populations aged 60 years or older in 2015, with projections indicating this proportion will exceed 25–30% by 2050 [ 33 ]. These demographic patterns provide a relevant context for evaluating alcohol-related and PD-related YLDs (Fig. 2 B). Among the countries analyzed, PD-related YLDs ranged from 16.8 to 68.4 (median = 42.8, mean = 40.0) per 100,000 people, while alcohol-related YLDs varied from 181 to 594 (median = 423.0, mean = 407.5) per 100,000. A negative correlation was observed between PD-related and alcohol-related YLDs (Pearson’s r = − 0.378, P = 0.052; Fig. 2 C). Germany had the highest PD-related YLDs of 68.4 (95% CI: 49.3–89.4), while its alcohol-related YLDs was 457.0 (95% CI: 319.0–623.1). Among the 27 countries, alcohol-related YLDs in Russia was the highest at 594.0 (95% CI: 428.2–801.9), while its PD-related YLDs was only 21.4 (15.0–28.9). China, adjacent to Russia, had alcohol-related YLDs of 210.5 (144.3–289.9), with PD-related YLDs of 51.2 (35.6–68.7). Table 2 YLDs of high alcohol use and PD, alcohol consumption and PD incidence in 27 representative countries* YLDs per 100,000 people (2021) Alcohol, total per capita (15+) per year consumption PD incidence per 100,000 people Country High alcohol use 95% CI PD 95% CI (in litres of pure alcohol, 2016–2018) 95% CI (2018) 95% CI Asia-Oceania Russia 594.0 428.2–801.9 21.4 15.0–28.9 11.2 9.3–13 16.9 14.4–19.4 New Zealand 532.3 370.7–736.8 16.8 11.8–23.5 10.6 8.6–11.5 16.5 14.0–18.9 Korea 456.6 318.1–649.7 23.5 16.6–32.4 9.7 8–11.5 17.7 16.2–19.7 Australia 369.1 251.7–507.4 23.8 16.1–32.8 10.5 8.8–11.8 20.9 18.8–23.1 China 210.5 144.3–289.9 51.2 35.6–68.7 7.1 5.7–8.3 31.3 26.7–36.4 Japan 181.0 114.8–260.6 22.7 15.7–30.7 8.0 6.7–9.2 24.1 20.6–27.7 North America Greenland 528.8 354.2–737.2 17.8 12.5–24.2 – – – – United States of America 378.2 269.5–516.9 28.4 20.6–37.6 9.9 8.2–10.9 24.4 22.3–26.7 Canada 332.4 228.0–452.8 56.8 41.5–73.7 8.9 8–10.4 39.1 37.3–41.1 Europe Poland 568.3 401.1–766.9 28.7 20.4–38.6 11.7 10.7–14.7 23.1 20.7–25.6 Portugal 478.4 334.0–674.0 40.9 28.6–55.0 12.0 10.9–14.9 31.6 28.3–35.5 Austria 478.2 335.1–663.0 46.0 30.8–62.7 12.0 10.6–14 35.5 30.1–40.2 Denmark 468.7 330.1–660.0 42.8 28.2–58.3 10.3 9.4–12.5 32.6 28.7–37.6 Estonia 461.3 312.8–637.1 32.1 22.9–43.2 9.2 – 22.9 20.5–25.4 Germany 457.0 319.0–623.1 68.4 49.3–89.4 12.9 9.7–13.2 52.1 50.2–54.2 Bulgaria 455.3 316.8–637.1 31.5 22.1–42.8 12.7 11–14.6 30.4 27.4–33.1 United Kingdom 440.6 307.4–605.1 41.0 29.2–55.0 11.5 8.9–12 32.1 27.8–36.5 Belgium 438.6 302.5–607.3 44.2 31.2–58.6 11.1 8.1–11.3 32.9 29.3–37.4 Finland 423.0 290.5–589.6 54.2 37.8–74.0 10.8 8.4–11.7 37.9 33.4–43.3 Switzerland 419.9 295.1–581.3 46.5 31.9–63.3 11.5 8–11.3 34.1 29.3–41.3 France 410.5 285.5–567.7 50.0 35.2–67.7 12.3 11.7–15.6 36.2 30.8–41.0 Sweden 362.2 258.4–497.3 40.2 28.3–54.9 8.9 7.6–10.4 31.2 26.3–36.1 Iceland 333.0 227.1–464.0 42.9 30.0–58.4 9.1 7.8–10.8 31.5 27.3–36.2 Netherlands 324.1 224.0–445.1 52.6 37.1–69.1 9.6 7.2–9.9 39.0 33.5–44.7 Italy 314.8 220.9–428.3 43.4 30.8–58.1 7.8 6.9–9.4 35.6 29.7–41.2 Spain 308.2 217.0–428.3 60.1 42.1–80.0 12.7 11.4–15 43.4 38.2–48.9 Greece 276.7 189.7–376.9 52.2 36.9–73.1 10.2 9–12.6 37.7 34.0–42.9 *YLDs and PD incidence data from Global Burden of Disease database; The total alcohol per capita (15 + years) consumption (in litres of pure alcohol) data from the Global Health Observatory database. YLDs, years lived with disability. To further elucidate this relationship, we examined the association between PD incidence in 2018 (GBD) and average alcohol consumption from 2016 to 2018 (Global Health Observatory) across the same countries. This analysis revealed a biphasic trend: PD incidence decreased with increasing alcohol intake up to ~ 10 L per capita per year, then increased with further consumption (Fig. 2 D). These findings suggest that low-to-moderate alcohol intake may delay or reduce PD onset, whereas higher levels may have the opposite effect. MR: inverse correlation between alcohol intake frequency and PD Next, we performed both univariate and multivariate MR analyses to investigate the potential causal association between alcohol consumption and PD onset, utilizing previously published GWAS data. Specifically, we examined the relationship between PD onset (PD cohort: 482,730 individuals; 17,891,936 SNPs) and two key alcohol exposure factors–weekly alcohol consumption and weekly alcohol intake frequency. We first performed a two-sample MR analysis of weekly alcohol consumption in two UK Biobank cohorts: Cohort 1 (112,117 individuals; 12,935,395 SNPs) and Cohort 2 (335,394 individuals; 11,887,865 SNPs). Surprisingly, the results were contradictory: Cohort 1 showed an increased risk of PD with alcohol intake, while Cohort 2 suggested a protective effect. In Cohort 1, four SNPs were significantly associated with the effects of alcohol intake on PD risk. The IVW method yielded an odds ratio (OR) of 4.18 (95% CI: 1.09–16.00, P = 0.037), while the weighted median approach gave an OR of 9.15 (95% CI: 2.65–31.57, P = 0.00046), suggesting an elevated PD risk associated with alcohol consumption (Fig. 3 A). In Cohort 2, 32 SNPs were identified as linked to alcohol intake on PD risk. The IVW estimate indicated a non-significant OR of 1.13 (95% CI: 0.55–2.33, P = 0.733), whereas the weighted median method yielded an OR of 0.65 (95% CI: 0.41–1.03, P = 0.067), suggesting a potential protective effect, albeit not statistically significant (Fig. 3 B). To investigate this discrepancy, we compared the total weekly alcohol consumption between the two cohorts. Individuals in Cohort 2, which exhibited a protective effect, consumed significantly less alcohol on average (bottles/cans of beer + glasses of wine × 5/4 + glasses of liquor × 1.5/1.25 + other: 7.84 ± 8.31 drinks/week × 10 g/drinks = 78.4 ± 83.1 g/week [ 26 ]) than those in Cohort 1 (121.04 ± 132.48 g/week [ 25 ]). These findings suggest that heavy alcohol consumption may increase PD risk, whereas moderate alcohol intake could reduce it. To further explore this hypothesis, we analyzed an additional UK Biobank dataset (Cohort 3, containing 462,346 individuals and 9,851,867 SNPs), which included data on weekly drinking frequency (1–7 times/week). We found an inverse association between drinking frequency and PD risk, with more frequent drinking sessions correlating with a lower PD risk for the same total alcohol intake. This suggests that spreading alcohol consumption across more sessions—without increasing the total intake—may be beneficial for reducing PD risk. In Cohort 3, 91 SNPs were associated with both drinking frequency and PD risk. The IVW analysis suggested a reduced PD risk (OR = 0.75, 95% CI: 0.58–0.98, P = 0.035), whereas the weighted median estimate was non-significant (OR = 1.07, 95% CI: 0.80–1.42, P = 0.667) (Fig. 3 C). Multivariate MR analysis ascertained the above result (OR = 0.70, 95% CI: 0.54–0.90, P = 0.0066) and linked to 83 SNPs (Fig. 3 D). These findings imply that higher drinking frequency may be associated with reduced PD risk, although further validation of these SNPs is warranted. Collectively, these analyses suggest a dual role of alcohol consumption in PD risk: heavy drinking may increase risk, whereas moderate intake or more frequent drinking (at equivalent total alcohol levels) may offer a protective effect against PD development. Alcohol intake reduces PD risk through improved sleep quality Previous studies have suggested an inverse correlation between sleep quality and the risk of PD, with frequent insomnia identified as a potential risk factor for PD onset [ 6 ]. To further investigate this association, we conducted an MR analysis using sleep-related phenotypes from cohorts in the MRC IEU OpenGWAS database and the previous study [ 6 ], which included 11 distinct sleep-related traits. We identified two phenotypes–frequent insomnia and short sleep duration–as potential risk factors for PD, with ORs of 1.20 (95% CI: 1.00–1.44; P = 0.053; 34 SNPs) and 1.30 (95% CI: 0.97–1.74; P = 0.075; 21 SNPs), respectively (Fig. 4 A). Although these associations were only marginally significant, these findings are broadly consistent with previous studies suggesting that better sleep quality may reduce PD risk [ 9 ]. Given that a low dose of alcohol consumption has been reported to improve sleep quality [ 34 ], our results raise the possibility that alcohol intake could serve as a protective factor, potentially lowering PD risk through enhanced sleep quality. Frequent alcohol intake reduces PD risk through cytokine modulation Neuroinflammation has emerged as a pivotal mechanism underlying the pathogenesis of PD [ 35 , 36 ]. Recent studies suggest that low-to-moderate alcohol consumption may exert anti-inflammatory effects [ 37 ], potentially reducing the risk of PD by modulating anti-inflammatory cytokine level. To evaluate this hypothesis, we conducted MR analyses to investigate the causal relationships of inflammatory cytokine levels with alcohol intake frequency and PD risk with inflammatory cytokine levels using a cohort from EBI GWAS Catalog (14,824 individuals, 91 inflammatory cytokines, and 12,958,024 SNPs). We identified eight anti-inflammatory cytokines associated with PD risk. Of these, three were positively associated with PD risk, while the remaining five exhibited inverse associations (Fig. 4 B). Additionally, we found nine cytokines were linked to alcohol intake frequency, one positively and eight inversely (Fig. 4 C). Notably, one cytokine, the T-cell surface glycoprotein CD6 isoform, was associated with both PD risk and alcohol intake frequency. Higher alcohol intake frequency was associated with reduced circulating levels of this cytokine (OR = 0.88; 95% CI: 0.79–0.99; P = 0.030), while higher levels of this cytokine in turn were linked to an increased risk of PD (OR = 1.08; 95% CI: 1.01–1.15; P = 0.031). These findings suggest that frequent alcohol intake may reduce PD risk by modulating inflammatory cytokine level. We identified 92 SNPs significantly associated with both alcohol intake frequency and circulating levels of the T-cell surface glycoprotein CD6 isoform and 15 SNPs significantly associated with both CD6 isoform levels and PD risk. Shared Genetic Variants Linking Alcohol Intake Frequency, Inflammation, Sleep, and PD We further examined the overlap of significant SNPs across the four MR analyses and identified nine SNPs within four genes that were consistently significant in all results (Table 3 ). Among these, eight SNPs were intronic, and one (rs13107325) was a non-synonymous exonic variant. Notably, DPP6 gene (three SNPs: rs6969458, rs2622167, rs9691968) were consistently associated with causal relationships between (1) alcohol intake frequency and PD, (2) alcohol intake frequency and T-cell surface glycoprotein CD6 isoform levels, and (3) CD6 isoform levels and PD. The remaining six SNPs—located in SLC39A8 (rs13107325, rs13135092), MAD1L1 (rs73050128, rs11763750), and RBFOX1 (rs34631026, rs17139246) —were implicated in the causal pathways linking alcohol intake frequency to PD, alcohol intake frequency to CD6 isoform levels, and sleep disorders to PD (Fig. 4 D). These findings suggest that the four identified genes and their associated allelic variants may play important roles in mediating the causal relationships among alcohol intake, sleep quality, circulating inflammatory cytokine levels, and PD risk. Table 3 Variants in four genes intersected in multiple MR processes Official gene symbol Gene name Functional Summary SNPs rs# Chr. Position Func.refGene MR_exposure on outcome effect_allele. exposure other_allele. exposure DPP6 Dipeptidyl peptidase like 6 Amyotrophic lateral sclerosis rs6969458 chr7 153489725 intronic Alcoholic drinks per week on Parkinson's disease (PD) A G rs2622167 chr7 153486704 intronic Alcohol intake frequency on PD; Alcohol intake frequency on T-cell surface glycoprotein CD6 isoform levels A G rs9691968 chr7 153635380 intronic T-cell surface glycoprotein CD6 isoform levels on PD A C SLC39A8 Solute carrier family 39 member 8 Cellular import of zinc at the onset of inflammation rs13107325 chr4 103188709 exonic; nonsynonymous Alcoholic drinks per week on PD; Short sleep duration on PD T C rs13135092 chr4 103198082 intronic Alcohol intake frequency on PD; Alcohol intake frequency on T-cell surface glycoprotein CD6 isoform levels G A MAD1L1 Mitotic arrest deficient 1 like 1 Prevents the onset of anaphase rs73050128 chr7 1961882 intronic Alcohol intake frequency on PD; Alcohol intake frequency on T-cell surface glycoprotein CD6 isoform levels A C rs11763750 chr7 2080114 intronic Short sleep duration on PD G A RBFOX1 RNA binding fox-1 homolog 1 Spinocerebellar ataxia type 2 rs34631026 chr16 6172126 intronic Alcohol intake frequency on PD; Alcohol intake frequency on T-cell surface glycoprotein CD6 isoform levels T C rs17139246 chr16 6106260 intronic Frequent insomnia on PD C T MR, Mendelian randomization. Discussion Over the years, numerous studies have demonstrated that the risk of PD is influenced not only by genetic background but also by various lifestyle and behavioral factors, including alcohol consumption, smoking, and coffee intake [ 5 , 38 , 39 ]. Recent findings suggest that smoking may exert a protective effect against PD, reducing risk by approximately 40% [ 10 ]. Similarly, coffee intake has been associated with a roughly 30% lower risk of PD [ 40 , 41 ]. Emerging research has also highlighted a potential link between alcohol consumption and PD risk. GWAS have identified genes involved in alcohol metabolism—such as ADH1B and ALDH2 —as being associated with PD susceptibility [ 39 , 42 ]. ADH1B gene encodes an alcohol dehydrogenase with high ethanol-oxidizing activity and plays a primary role in ethanol catabolism. ALDH2 encodes an aldehyde dehydrogenase, another key enzyme in the ethanol oxidative pathway. SNPs in both ADH1B and ALDH2 have been linked to PD risk through GWAS analyses. Despite these findings, the relationship between alcohol consumption and PD remains controversial. Some studies suggest that alcohol intake increases the risk of PD [ 9 ], whereas others report a protective effect, indicating a risk reduction of approximately 20% [ 18 , 43 ]. Given that alcohol consumption is a globally prevalent behavior—with nearly 50% of the global population projected to consume alcohol by 2030 [ 44 ]—further research is essential to clarify its role in PD development and to inform effective prevention strategies. This study integrated data from a three-generation Chinese family with a history of PD, epidemiological surveys from two Chinese cities, and global datasets from 27 countries to investigate the association between alcohol consumption and PD risk. In the third generation of the PD-affected family, all eight individuals carried PD-associated risk alleles. Notably, the only individual diagnosed with PD was a non-drinker and carried the fewest risk alleles, whereas the seven unaffected individuals consumed alcohol and harbored a greater number of risk alleles. These observations suggest a potential protective effect of alcohol consumption against PD, independent of genetic predisposition. To further evaluate this association, we analyzed lifestyle data from PD patients in two Chinese cities and compared alcohol consumption rates with those in matched general population cohorts from the same locations. The proportion of alcohol consumers was significantly lower among PD patients than in the general population, consistent with findings from the familial analysis and previous reports [ 8 , 11 ], supporting a possible inverse relationship between alcohol intake and PD risk. At the global level, we examined PD incidence in relation to per capita alcohol consumption across 27 countries. The analysis revealed that low-to-moderate alcohol consumption ( 10 L/capita/year) correlated with increased risk. Token together, these findings consistently indicate that moderate alcohol consumption may confer a protective effect against PD. Therefore, we suggest that people with drinking habits how to drink and reduce the harm. The annual drinking volume of 10L/capita is converted into daily drinking volume, < 230 mL/capita of wine (~ 12% alc/vol), < 550 mL/capita of beer (~ 5% alc/vol), or < 70 mL/capita of Chinese Baijiu (~ 40% alc/vol). To validate the findings from survey data, we conducted MR analyses using large-scale GWAS datasets related to alcohol consumption (weekly intake and drinking frequency) and PD-associated SNPs. The MR results indicated that moderate weekly alcohol intake, as well as higher drinking frequency coupled with lower quantity per occasion, were causally associated with a reduced risk of PD. These genetic findings are consistent with the observational analyses and further support a potential protective role of moderate alcohol consumption against PD. Previous studies have proposed that light alcohol intake may improve sleep quality [ 34 ], and good sleep quality is a potentially neuroprotective lifestyle against PD [ 9 ]. To investigate this potential mechanism, we performed MR analyses incorporating GWAS data on sleep traits and PD risk. The results showed that frequent insomnia and short sleep duration were causally linked to increased PD risk, suggesting that the neuroprotective effects of alcohol may be partially mediated through improvements in sleep regulation. Emerging evidence also implicates neuroinflammation in PD pathogenesis [ 45 ]. To explore this pathway, we examined the relationships among alcohol consumption, inflammatory biomarkers, and PD risk using MR. Notably, we identified the T-cell surface glycoprotein CD6 isoform as a putative mediator: increased drinking frequency was associated with lower circulating levels of this pro-inflammatory factor, which were linked to a decreased risk of PD. These findings suggest that alcohol may influence CD6 + T-cell activity, thereby mitigating neuroinflammation through enhanced immune homeostasis [ 46 ] and reduced oxidative stress [ 47 ]. Collectively, this multi-layered MR analysis supports the hypothesis that moderate alcohol consumption may confer neuroprotection against PD, potentially via sleep enhancement and CD6-mediated immunomodulatory mechanisms. In this study, we identified four genes— DPP6 , SLC39A8 , MAD1L1 , and RBFOX1 —that exhibit pleiotropic associations with both alcohol consumption and PD risk. These genes have been previously implicated in diverse neurological and cellular processes: DPP6 in amyotrophic lateral sclerosis, RBFOX1 in spinocerebellar ataxia type 2, SLC39A8 in inflammatory signaling, and MAD1L1 in mitotic regulation [ 48 ]. DPP6 modulates neuronal excitability via Kv4.2 potassium channels [ 49 ]. Loss of DPP6 function leads to dendritic instability, elevated oxidative stress, and impaired iron homeostasis—pathological features common to tauopathies, dementia, and ferroptosis [ 50 ]. SLC39A8, a transporter of zinc and other transition metals, regulates cellular influx of Zn²⁺, Fe²⁺, and Mn²⁺ [ 51 , 52 ]. Its dysregulation contributes to the accumulation of reactive oxygen species (ROS) and lipid peroxidation, mechanisms implicated in multiple neurodegenerative diseases including PD, Alzheimer’s disease, Huntington’s disease, and amyotrophic lateral sclerosis [ 53 , 54 ]. RBFOX1 encodes an RNA splicing factor that modulates the alternative splicing of focal adhesion genes in both muscular and neuronal tissues [ 55 ]. Aberrant RBFOX1 activity has been linked to prefrontal cortical dysfunction in schizophrenia [ 56 ], indicating a broader role in neuropsychiatric and neurodegenerative disorders. MAD1L1, a key regulator of the mitotic spindle assembly checkpoint, maintains chromosomal stability during cell division. Mutations in MAD1L1 may disrupt cell cycle regulation in vulnerable neuronal populations, potentially contributing to early-onset PD [ 57 ]. While these findings suggest a shared genetic basis linking alcohol consumption and PD, the mechanistic roles of these genes require further functional validation in experimental models. Alcohol, as a dopamine agonist, may provide neuroprotection by reducing oxidative stress and neuroinflammation, helping to safeguard dopaminergic neurons [ 58 – 60 ]. During the aging process, the gradual decline of redox homeostasis leads to accumulated ROS levels in elderly populations. This redox imbalance results in reduced Nrf2 transcriptional activity, thereby compromising the expression of downstream antioxidant enzymes regulated through the antioxidant response element (ARE) pathway [ 61 ]. In neural systems, this progressive deterioration of antioxidative capacity disrupts proteostasis, facilitating aberrant degradation of critical neuronal proteins and subsequent accumulation of misfolded aggregates. Such pathological mechanisms are intrinsically associated with the development of neurodegenerative pathologies, including Alzheimer's disease and PD [ 62 , 63 ]. Notably, acetaldehyde, the primary oxidative metabolite of ethanol, demonstrates potent Nrf2-activating properties through covalent modification of Keap1 cysteine residues [ 64 , 65 ]. This pharmacological action effectively counteracts age-related ROS accumulation, restoring redox equilibrium and reinforcing endogenous antioxidant defenses (Fig. 4 E). Consequently, targeted modulation of the Nrf2-Keap1-ARE signaling axis by acetaldehyde derivatives may represent a promising therapeutic strategy for mitigating neurodegenerative progression and preserving neuronal integrity in ageing population. Conclusion This study suggests that moderate, frequent alcohol consumption may be associated with reduced PD risk. Our data supports alcohol’s potential neuroprotective effect in delaying PD onset, possibly by improving sleep, inhibiting inflammation, and regulating ROS accumulation. These findings offer new insights for PD prevention. Further research is needed to confirm these results and explore the underlying mechanisms. Declarations The authors declare no conflicts of interest. CRediT author statement Wei Lu: Conceptualization, Data curation. Yong-Qiang Kong: Methodology, Software. Hai Tang: Data curation. Xiu-Li Cheng: Data curation. Wan-Ruo Zhang: Investigation. Xiao-Yun Pan: Investigation. Hai-Yu Guo: Visualization. Xuan-Zhu Chen: Writing- Editing. Wei Zhang: Data curation. Jie Zu: Data curation. Dan-Dan Yang: Data curation. Hui-Ling Zou: Investigation. Gui-Yun Cui: Supervision, Funding acquisition. Li-Guo Dong: Writing- Reviewing. Yi-Liang Wei: Conceptualization, Writing- Draft, Reviewing and Editing. Funding This work was funded by the Construction Project of High-Level Hospital of Jiangsu Province (Grant No. GSPJS202418 and No. GSPJS202426) and Medical Science and Technology Innovation Project of Xuzhou Municipal Health Commission (Grant No. XWKYHT20240107). Acknowledgments The authors thank Shao-Yuan Wu (Jiangsu Normal University) for his professional writing assistance. Data availability All data were supplied in Tables and Supplementary Tables. 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Supplementary Files Supplementaryfile.docx SupplementaryFigureS1.tif SupplementaryTableS1.infoof35PDSNPs.xlsx SupplementaryTableS2.TianjinclinicaldataPD.xlsx SupplementaryTableS3.XuzhouclinicaldataPD.xlsx SupplementaryTableS4.MRinfo.xlsx SupplementaryTableS5.MRSNPs.xlsx 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-6754726","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":462206950,"identity":"48ce173c-82eb-492d-8a30-e75d7b1f6f88","order_by":0,"name":"Wei Lu","email":"","orcid":"","institution":"The Affiliated Suqian Hospital of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Lu","suffix":""},{"id":462206951,"identity":"6684b6d1-48b5-4439-8094-2a8c618b1de4","order_by":1,"name":"Yong-Qiang Kong","email":"","orcid":"","institution":"Jiangsu Normal 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University","correspondingAuthor":false,"prefix":"","firstName":"Li-Guo","middleName":"","lastName":"Dong","suffix":""},{"id":462206964,"identity":"4dd77b4f-38bf-4d34-ab6c-80f5c534dad8","order_by":14,"name":"Yi-Liang Wei","email":"data:image/png;base64,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","orcid":"","institution":"Jiangsu Normal University","correspondingAuthor":true,"prefix":"","firstName":"Yi-Liang","middleName":"","lastName":"Wei","suffix":""}],"badges":[],"createdAt":"2025-05-27 02:53:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6754726/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6754726/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83610050,"identity":"8c742de1-b7a5-4abf-8d0c-2dda754b8bf4","added_by":"auto","created_at":"2025-05-29 12:05:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":85694,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFamily tree of PD, risk allele counts and drinking habits\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA three-generation family tree with two PD patients (blue): WC2 in the second generation exhibited resting tremors and cognitive impairment, while WN1 in the third generation had head tremors. Notably, both PD patients in this family did not drink alcohol, while others with frequent drinking habits were symptom-free. SNP data revealed for the third generation, WN1–WN8 carried 23–29 PD-related risk alleles, respectively. PD, Parkinson's disease.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6754726/v1/fae8994de9e2b074c265a805.png"},{"id":83610055,"identity":"a0c9c457-4ef4-4e01-b8cc-f25e5eed6868","added_by":"auto","created_at":"2025-05-29 12:05:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":391943,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePD and alcohol use\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Low alcohol consumption in PD patients. In Tianjin Huanhu Hospital, 9.00% of PD patients (age: 36–84 years, 44% female) consumed alcohol, compared to 54.91% of the general population (\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01). In the Affiliated Hospital of Xuzhou Medical University, 18.87% of PD patients (age: 43–87 years, 50% female) consumed alcohol, compared to 56.89% of the general population (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). (B) YLDs per 100,000 people of high alcohol use and PD around the world in 2021 year. (C) Negative correlation trends (Pearson’s correlation: –0.378, \u003cem\u003eP\u003c/em\u003e = 0.052) of YLDs between high alcohol use and PD in 27 representative countries. (D) Correlation between the total alcohol per capita per year consumption (2016–2018, data from Global Health Observatory database) and PD incidence (2018, data from Global Burden of Disease database) in 27 representative countries. YLDs, years lived with disability.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6754726/v1/7d3861b0a0833e7a7c31c242.png"},{"id":83610061,"identity":"425a064f-86c0-41cd-b1a0-0579de2b3532","added_by":"auto","created_at":"2025-05-29 12:05:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":381520,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePreventive effects of alcohol intake frequency on PD using two-sample MR analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate MR analyses for PD using exposures of (A) alcohol consumption, (B) alcoholic drinks per week, and (C) alcohol intake frequency. (D) Multivariate MR analysis for PD with exposures of coffee, alcohol, and tea intake. MR, Mendelian randomization.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6754726/v1/82ced9a14c5ca4e01488a238.png"},{"id":83610056,"identity":"e46ecb4b-3485-4987-b3c9-2cd257c67202","added_by":"auto","created_at":"2025-05-29 12:05:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":403801,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMR analyses of alcohol, sleep, inflammatory, and PD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Frequent insomnia and short sleep duration correlated with PD. (B) Nine inflammatory cytokines correlated with PD (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). (C) Alcohol intake frequency correlated with nine inflammatory cytokines (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). (D) Nine SNPs in four genes intersected in multiple MR processes. (E) Potential mechanism of alcohol intake for mitigating neurodegenerative progression. Acetaldehyde may counteract age-related ROS accumulation, restoring redox equilibrium and reinforcing endogenous antioxidant defenses in neurons. ROS, reactive oxygen species.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6754726/v1/8e2860d0ebb982338cedefa1.png"},{"id":83612017,"identity":"bf56ced2-9a10-43cc-96f0-a2104c49c7e8","added_by":"auto","created_at":"2025-05-29 12:29:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3152442,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6754726/v1/bd8669c5-887a-4a07-b195-8c60d0ed4459.pdf"},{"id":83610051,"identity":"9c46fec0-9573-4fcf-9a02-71eb00de1926","added_by":"auto","created_at":"2025-05-29 12:05:02","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":16818,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-6754726/v1/24d8579a370ce6e599c29964.docx"},{"id":83610762,"identity":"bfce5086-0d96-43f1-905d-0e2d9db87acd","added_by":"auto","created_at":"2025-05-29 12:13:02","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2995296,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-6754726/v1/bba2417e02da5a5e173086e6.tif"},{"id":83610759,"identity":"259c849a-02ce-405b-b018-f3a84c47c0eb","added_by":"auto","created_at":"2025-05-29 12:13:02","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":20510,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.infoof35PDSNPs.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6754726/v1/5ac7f517331d7d6268dd2913.xlsx"},{"id":83610760,"identity":"51f3b1e0-8f96-451f-aecc-3b95217ebd3b","added_by":"auto","created_at":"2025-05-29 12:13:02","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":19666,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS2.TianjinclinicaldataPD.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6754726/v1/dd0c1ea0f81f2eec7ca1b583.xlsx"},{"id":83610761,"identity":"8d897567-0dea-4192-9cad-e58a3d72d111","added_by":"auto","created_at":"2025-05-29 12:13:02","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":15861,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS3.XuzhouclinicaldataPD.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6754726/v1/b0c3140c6d552ef5fb5466f9.xlsx"},{"id":83610059,"identity":"ba7fe62f-2f0f-4e3d-a1c3-e72fde74be96","added_by":"auto","created_at":"2025-05-29 12:05:02","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":25011,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS4.MRinfo.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6754726/v1/01ffb05773fefa0b675729d6.xlsx"},{"id":83610064,"identity":"2f7e1596-5411-4e53-b9cc-9e0a0b0622b5","added_by":"auto","created_at":"2025-05-29 12:05:02","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1191666,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS5.MRSNPs.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6754726/v1/63d4086834aed9aabb4a5d09.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Protective Evidence for Alcohol Consumption in Parkinson’s Disease Risk and Associated Genes of DPP6, SLC39A8, MAD1L1, and RBFOX1","fulltext":[{"header":"Background","content":"\u003cp\u003eParkinson\u0026rsquo;s disease (PD) is an age-related neurodegenerative disorder characterized by resting tremors, motor impairment and non-motor symptoms such as sleep disturbances [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Its prevalence has increased significantly in recent years, reaching approximately 4\u0026permil; in general population and 10\u0026permil; among individuals over 60 years of age [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Both genetic and environmental factors are considered to contribute to the onset of the disease. Nearly a hundred genetic markers have been identified to be associated with the risk of PD [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and testified in multi-ancestry cohorts [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. While environmental factors of lifestyle exposures, such as alcohol consumption, smoking, and sleep patterns, have been investigated for their potential to delay or prevent the onset of symptoms, the findings remain inconclusive and inconsistent across studies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlcohol consumption is a widespread habit globally, particularly among men. However, the role of alcohol consumption in the development of PD remains highly debated [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Recent prospective studies have reported inconsistent findings regarding the association between alcohol consumption and PD risk, with results ranging from increased risk to decreased risk or even no significant association [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In addition, recent Mendelian randomization (MR) studies suggest that alcohol consumption may reduce the risk of PD by approximately 20% [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Alcohol dehydrogenase 1B (\u003cem\u003eADH1B\u003c/em\u003e), a key gene involved in alcohol metabolism, has been implicated in PD risk [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Research has indicated that a mutant allele of \u003cem\u003eADH1B\u003c/em\u003e (rs1229984-T), which adversely affects alcohol metabolism, is associated with an increased risk of PD [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Furthermore, some studies suggest that low-to-moderate alcohol intake may stimulate the nervous system [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], improve the blood circulation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and enhance sleep quality [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], potentially delaying or alleviating PD-related tremors and sleep disturbances [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Therefore, further study is needed to clarify the role of alcohol consumption in the onset of PD.\u003c/p\u003e \u003cp\u003eChina is experiencing rapid population aging and a growing burden of PD [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Approximately half of adults in China, particularly men, regularly consume Baijiu, a traditional distilled spirit. In this study, we examined the association between alcohol consumption and PD risk using retrospective data from a family with a history of PD, along with epidemiological survey data on alcohol intake rates and PD incidence in two Chinese cities. To validate causal effects of alcohol consumption on PD, we conducted MR analyses leveraging genetic variants (single nucleotide polymorphisms, SNPs) of PD, lifestyle factors (including alcohol consumption, tea and coffee drinking, and sleep quality), and inflammatory cytokines. Our family retrospective and survey data show that among offsprings from families with a history of PD, those who abstained from alcohol consumption exhibited typical resting tremors, whereas those who consumed alcohol regularly did not. Our MR analysis results suggest that alcohol consumption may reduce PD risk, with sleep quality and an inflammatory cytokine (T-cell surface glycoprotein CD6 isoform level) as potential mediating factors. The findings of this study support the notion that low-to-moderate but frequent alcohol consumption may delay or reduce the PD onset by improving sleep quality and decreasing inflammation. Our findings shed new light on the future prevention of PD onset.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eFamily history of PD\u003c/h2\u003e \u003cp\u003eWe investigated a three-generation family with a history of PD. All participants provided written informed consent and completed a demographic survey that included information on gender, age (or age at death), alcohol consumption, smoking, resting tremors, and cognitive impairment. The study was approved by the Ethics Committee of Jiangsu Normal University (Approval No.: H2022-002). All procedures were conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003eWe reviewed the NHGRI\u0026ndash;EBI Catalog of human genome-wide association studies (GWAS) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/gwas/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/gwas/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and identified 35 SNPs associated with PD from 27 previous studies (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Genotypes for these SNPs were extracted from 15\u0026times; whole-genome sequencing data of the family members, and the number of risk alleles was countered for each individual.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClinical data of PD patients\u003c/h3\u003e\n\u003cp\u003eWe reviewed inpatient records of 100 PD patients at Tianjin Huanhu Hospital (Tianjin, China) between January 2022 and August 2024 (Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), and 144 patients at the Affiliated Hospital of Xuzhou Medical University (Xuzhou, Jiangsu, China) between May 2023 and August 2024 (Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Demographic data (gender, age, smoking, and alcohol consumption) and medical history (hypertension, diabetes, coronary artery disease, and cerebral infarction) were analyzed. The study was approved by the Review Board of the Affiliated Hospital of Xuzhou Medical University (Approval No.: XYFY2G23-KL266-01). All procedures were conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003eWe examined the gender ratio and adult alcohol consumption rates in Tianjin and Xuzhou, China. According to the seventh national census in 2020, male comprised 51.53% of the population in Tianjin (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://stats.tj.gov.cn/tjsj_52032/tjgb/202105/t20210521_5457266.html\u003c/span\u003e\u003cspan address=\"https://stats.tj.gov.cn/tjsj_52032/tjgb/202105/t20210521_5457266.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and 50.41% in Xuzhou (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tjgb.hongheiku.com/11727.html\u003c/span\u003e\u003cspan address=\"https://tjgb.hongheiku.com/11727.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA 2015 report by the Tianjin Municipal Health Commission showed that 54.91% of adults consumed alcohol (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wsjk.tj.gov.cn/XWZX6600/MTBD3030/202008/t20200828_3581944.html\u003c/span\u003e\u003cspan address=\"https://wsjk.tj.gov.cn/XWZX6600/MTBD3030/202008/t20200828_3581944.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In Xuzhou, with a population exceeding 9\u0026nbsp;million, approximately 5.13\u0026nbsp;million adults consumed alcohol, corresponding to a drinking proportion of 56.89% (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://baijiahao.baidu.com/s?id=1772221666501006448\u0026amp;wfr=spider\u0026amp;for=pc\u003c/span\u003e\u003cspan address=\"https://baijiahao.baidu.com/s?id=1772221666501006448\u0026amp;wfr=spider\u0026amp;for=pc\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eTwo-sample MR design\u003c/h3\u003e\n\u003cp\u003eMR uses genetic variants associated with an exposure (e.g., a potential risk factor) to evaluate its causal effect on an outcome [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e illustrates the MR framework used to assess the causal relationship between risk factors and PD. In this study, SNPs were used as instrumental variables (IVs) to investigate this causal link. For valid causal inference, MR relies on three key assumptions [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The relevance assumption requires that IVs are strongly associated with the exposure or risk factor. The independence assumption requires that the IVs are not associated with confounders, thereby avoiding bias in the exposure-outcome relationship. The exclusion restriction assumption stipulates that the genetic variants influence the outcome solely through the exposure, without acting through alternative pathways [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eData sources for exposure and outcome\u003c/h3\u003e\n\u003cp\u003eWe obtained all GWAS summary data for both exposures and outcomes from the MRC Integrative Epidemiology Unit\u0026rsquo;s OpenGWAS database [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] (IEU OpenGWAS, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the NHGRI\u0026ndash;EBI GWAS Catalog [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/gwas/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/gwas/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and previously published genetic studies.\u003c/p\u003e \u003cp\u003eGWAS summary statistic data were downloaded from the MRC IEU OpenGWAS and NHGRI-EBI GWAS Catalog for this study:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAlcohol consumption (IEU ID: ukb-b-1283), based on UK Biobank data from 112,117 European individuals with 12,935,395 SNPs [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAlcohol drinks per week (IEU ID: ukb-b-73), based on UK Biobank data from 335,394 European individuals with 11,887,865 SNPs [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAlcohol intake frequency (IEU ID: ukb-b-5779), based on UK Biobank data from 462,346 European individuals with 9,851,867 SNPs.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCoffee intake (IEU ID: ukb-b-5237), based on UK Biobank data from 428,860 European individuals with 9,851,867 SNPs.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTea intake (IEU ID: ukb-b-6066), based on UK Biobank data from 447,485 European individuals with 9,851,867 SNPs [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCirculating inflammatory proteins (GCST90274758 to GCST90274848) were obtained from the EBI GWAS Catalog, based on genome-wide pQTL mapping of 91 plasma proteins using the Olink Target Inflammation panel comprising 14,824 participants of European ancestry with 12,958,024 SNPs [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePD risk (IEU ID: ieu-b-7) was derived from a GWAS meta-analysis conducted by the International Parkinson\u0026rsquo;s Disease Genomics Consortium and 23andMe, including 33,674 PD cases and 449,056 controls with 17,891,936 SNPs [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSleep behaviors traits (IEU IDs: ukb-b-3957, 462,341 European individuals, 9,851,867 SNPs; ukb-b-4424, 460,099 European individuals, 9,851,867 SNPs; ukb-b-sleep, 361,194 European individuals, 11,120,383 SNPs) were obtained from the MRC IEU OpenGWAS database. GWAS summary data of significant SNPs (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) for eight sleep-related phenotypes were obtained from the published report [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eFurther details on data sources were provided in Supplementary Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eSelection of IVs\u003c/h3\u003e\n\u003cp\u003eWe applied a rigorous selection process to ensure valid causal inference in the MR analysis. First, genetic variants strongly associated with each exposure were identified using GWAS summary statistic data, applying genome-wide significance thresholds of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10⁻⁸. For inflammatory cytokines, a relaxed threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10⁻⁶ was used due to the limited number of available SNPs. Next, we performed linkage disequilibrium clumping (Window size\u0026thinsp;=\u0026thinsp;10,000 kb; \u003cem\u003er\u0026sup2;\u003c/em\u003e = 0.001) to remove highly correlated SNPs and ensure independence among IVs. Finally, we calculated the F-statistic for each IV to assess its strength in relation to the exposure [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], using the formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:F=\\frac{N-K-1}{K}\\text{}\\times\\:\\frac{{R}^{2}}{1-{R}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eN\u003c/em\u003e is the sample size, \u003cem\u003eK\u003c/em\u003e is the number of IVs, and \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e is the proportion of variance in the exposure explained by the IV. An F-statistic greater than 10 was used as the threshold to identify strong IVs [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. During the harmonization process, non-concordant or palindromic SNPs with intermediate allele frequencies were excluded to ensure consistency in genetic effect estimates.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMR analysis\u003c/h2\u003e \u003cp\u003eThe primary MR analysis was performed using the inverse variance-weighted (IVW) model, which combines variant-specific Wald ratios, weighted by the inverse of their variance, to provide a precise overall estimate of the causal effect. To assess the robustness of the findings, we additionally applied several methods, including MR Egger, Weighted Median, Simple Mode, and Weighted Mode. MR Egger accounts for potential horizontal pleiotropy, while the Weighted Median yields a consistent estimate even if up to 50% of the instruments were invalid. The Simple Mode estimates the causal effect based on the most frequently occurring ratio, and the Weighted Mode enhances the accuracy by weighting the ratios.\u003c/p\u003e \u003cp\u003eTo assess the robustness of the MR estimates, we conducted sensitivity and heterogeneity analyses. Cochran's Q test examined heterogeneity across IVs, with a significant result (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) indicating potential variations or violations of MR assumptions. The MR Egger intercept test identified directional pleiotropy, with a non-zero intercept (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) suggesting bias from pleiotropic effects. We also applied the MR\u0026ndash;PRESSO (Mendelian Randomization Pleiotropy Residual Sum and Outlier) test [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] to detect and correct outlier SNPs linked to horizontal pleiotropy, with a global test \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating significant pleiotropy. Finally, a Leave-One-Out sensitivity analysis was performed by iteratively removing each SNP to test its influence on the causal estimate. All analyses were performed using the TwoSampleMR package [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] (version 0.6.6) in R (version 4.4.0).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGlobal Burden of Disease database\u003c/h3\u003e\n\u003cp\u003eData was extracted from the Global Burden of Disease (GBD) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://vizhub.healthdata.org/gbd-results/\u003c/span\u003e\u003cspan address=\"https://vizhub.healthdata.org/gbd-results/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), alcohol use Summary Exposure Value and PD incidence across global regions covering 200 countries and regions [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eGlobal Health Observatory database\u003c/h3\u003e\n\u003cp\u003eThe total alcohol per capita (15\u0026thinsp;+\u0026thinsp;years) consumption (in litres of pure alcohol) data from 2016 to 2018 was extracted from the Global Health Observatory database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/data/gho/data/indicators/indicator-details/GHO/alcohol-total-per-capita-(15-years)-consumption-(in-litres-of-pure-alcohol)\u003c/span\u003e\u003cspan address=\"https://www.who.int/data/gho/data/indicators/indicator-details/GHO/alcohol-total-per-capita-(15-years)-consumption-(in-litres-of-pure-alcohol)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eFamily-based survey suggests an inverse association between alcohol intake and PD onset\u003c/h2\u003e \u003cp\u003eWe collected questionnaire surveys from three generations of a family with a history of PD, which includes two affected individuals, WC2 and WN1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Recent studies have identified 35 SNP alleles associated with PD risk, indicating that a higher number of these risk alleles correlate with an increased likelihood of developing the disease. Genotyping data were obtained from eight individuals (WN1\u0026ndash;WN8) of the third generation, with the number of risk alleles ranges from 23 to 29 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Patient WN1, aged 71, had 23 risk alleles and exhibited typical head tremors. Another patient, WC2, exhibited head and hand tremors, along with cognitive impairment, and died at the age of 83. Notably, both PD patients in this family did not drink alcohol, while others with frequent drinking habits (30\u0026ndash;180 g/week) were symptom-free. According to family data, drinking alcohol did not lead to the onset of PD, while members who did not drink alcohol but also developed PD. All members of the third generation carried alleles related to PD, and this patient WN1 carried the least risky alleles but instead developed PD. It seems that alcohol consumption may reduce or delay the onset of PD symptoms, leading us to pay more attention to the negative correlation between alcohol consumption and PD risk.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCount of PD risk alleles\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eFamily members\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk allele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWN1 (PD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWN4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWN5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWN6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWN7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eWN8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers823128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eAA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eAA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eAA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eAA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eAA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eAA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers823118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10797576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers6430538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eTT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e 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align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers4698412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003eG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers4538475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e༟\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e 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align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers2280104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eTT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers329648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1994090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e༟\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers76904798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e 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\u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eC\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eTT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eTT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers11158026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eC/T\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eCT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eCT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eTT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eCT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eCC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eCT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eCT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eCT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers4784227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eC\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers11868035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers393152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eAA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eAA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eAA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eAA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eAA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eAA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eAA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eAA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers12456492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eA\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eA\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers2295545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eC\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1223271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eA\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eA\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eA\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCount of risk allele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eBold and italic indicates risk alleles. PD, Parkinson's disease.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eReduced Alcohol Consumption in PD Patients: evidence from Clinical and Census Data\u003c/h2\u003e \u003cp\u003eBecause our family-based analysis suggests an inverse association between alcohol intake and PD risk, we hypothesize that individuals with PD consume less alcohol than those without the disease. To test this hypothesis, we analyzed two independent clinical datasets from hospitals in two Chinese cities: one comprising 100 PD patients (Tianjin residents; aged 36\u0026ndash;84 years; 46% male) from Tianjin Huanhu Hospital, and another consisting of 144 PD patients (Xuzhou residents; aged 43\u0026ndash;87 years; 50% male) from the Affiliated Hospital of Xuzhou Medical University.\u003c/p\u003e \u003cp\u003eAccording to the 2020 Seventh National Census data (released by the Tianjin and Xuzhou Municipal Bureaus of Statistics), the permanent populations of Tianjin and Xuzhou were approximately 14\u0026nbsp;million (51.53% male) and 9\u0026nbsp;million (50.41% male), respectively. The alcohol consumption rates among the general populations in these cities were 54.91% in Tianjin and 56.89% in Xuzhou. In contrast, the rates among PD patients were significantly lower: 9% in Tianjin and 18.87% in Xuzhou (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). These findings support our hypothesis that individuals with PD consume less alcohol than the general population.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGlobal survey finds lower PD Risk in low-to-moderate drinkers\u003c/h2\u003e \u003cp\u003eTo investigate the correlation between alcohol consumption and the risk of PD, we analyzed years lived with disability (YLDs, per 100,000 people) attributable to high alcohol use and PD in 2021, based on data from the GBD database. Twenty-seven countries across four continents were included, selected for their advanced ageing profiles (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). According to the World Report on Ageing and Health by World Health Organization, these countries had\u0026thinsp;\u0026ge;\u0026thinsp;20% of their populations aged 60 years or older in 2015, with projections indicating this proportion will exceed 25\u0026ndash;30% by 2050 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. These demographic patterns provide a relevant context for evaluating alcohol-related and PD-related YLDs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Among the countries analyzed, PD-related YLDs ranged from 16.8 to 68.4 (median\u0026thinsp;=\u0026thinsp;42.8, mean\u0026thinsp;=\u0026thinsp;40.0) per 100,000 people, while alcohol-related YLDs varied from 181 to 594 (median\u0026thinsp;=\u0026thinsp;423.0, mean\u0026thinsp;=\u0026thinsp;407.5) per 100,000. A negative correlation was observed between PD-related and alcohol-related YLDs (Pearson\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.378, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.052; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Germany had the highest PD-related YLDs of 68.4 (95% CI: 49.3\u0026ndash;89.4), while its alcohol-related YLDs was 457.0 (95% CI: 319.0\u0026ndash;623.1). Among the 27 countries, alcohol-related YLDs in Russia was the highest at 594.0 (95% CI: 428.2\u0026ndash;801.9), while its PD-related YLDs was only 21.4 (15.0\u0026ndash;28.9). China, adjacent to Russia, had alcohol-related YLDs of 210.5 (144.3\u0026ndash;289.9), with PD-related YLDs of 51.2 (35.6\u0026ndash;68.7).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eYLDs of high alcohol use and PD, alcohol consumption and PD incidence in 27 representative countries*\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYLDs per 100,000 people (2021)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eAlcohol, total per capita (15+) per year consumption\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003ePD incidence per 100,000 people\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh alcohol use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(in litres of pure alcohol, 2016\u0026ndash;2018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(2018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAsia-Oceania\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRussia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e594.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e428.2\u0026ndash;801.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.0\u0026ndash;28.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.3\u0026ndash;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e14.4\u0026ndash;19.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNew Zealand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e532.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e370.7\u0026ndash;736.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.8\u0026ndash;23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.6\u0026ndash;11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e14.0\u0026ndash;18.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKorea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e456.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e318.1\u0026ndash;649.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.6\u0026ndash;32.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8\u0026ndash;11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e17.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e16.2\u0026ndash;19.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e369.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e251.7\u0026ndash;507.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.1\u0026ndash;32.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.8\u0026ndash;11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e18.8\u0026ndash;23.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e210.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144.3\u0026ndash;289.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.6\u0026ndash;68.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.7\u0026ndash;8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e31.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e26.7\u0026ndash;36.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e181.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114.8\u0026ndash;260.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.7\u0026ndash;30.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.7\u0026ndash;9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e20.6\u0026ndash;27.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNorth America\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreenland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e528.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e354.2\u0026ndash;737.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.5\u0026ndash;24.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnited States of America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e378.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e269.5\u0026ndash;516.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.6\u0026ndash;37.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.2\u0026ndash;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e22.3\u0026ndash;26.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e332.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e228.0\u0026ndash;452.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.5\u0026ndash;73.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8\u0026ndash;10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e39.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e37.3\u0026ndash;41.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEurope\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" 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\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e20.7\u0026ndash;25.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortugal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e478.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e334.0\u0026ndash;674.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.6\u0026ndash;55.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.9\u0026ndash;14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e31.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e28.3\u0026ndash;35.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAustria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e478.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e335.1\u0026ndash;663.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.8\u0026ndash;62.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd 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\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e20.5\u0026ndash;25.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e457.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e319.0\u0026ndash;623.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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\u003cp\u003eFinland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e423.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e290.5\u0026ndash;589.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.8\u0026ndash;74.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.4\u0026ndash;11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e37.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e33.4\u0026ndash;43.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwitzerland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e419.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e295.1\u0026ndash;581.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.9\u0026ndash;63.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8\u0026ndash;11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e34.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e29.3\u0026ndash;41.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e410.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e285.5\u0026ndash;567.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.2\u0026ndash;67.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.7\u0026ndash;15.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e36.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e30.8\u0026ndash;41.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSweden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e362.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e258.4\u0026ndash;497.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.3\u0026ndash;54.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.6\u0026ndash;10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e31.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e26.3\u0026ndash;36.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIceland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e333.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e227.1\u0026ndash;464.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.0\u0026ndash;58.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.8\u0026ndash;10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e31.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e27.3\u0026ndash;36.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetherlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e324.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e224.0\u0026ndash;445.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.1\u0026ndash;69.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.2\u0026ndash;9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e39.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e33.5\u0026ndash;44.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e314.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e220.9\u0026ndash;428.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.8\u0026ndash;58.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.9\u0026ndash;9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e35.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e29.7\u0026ndash;41.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e308.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e217.0\u0026ndash;428.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.1\u0026ndash;80.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.4\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e43.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e38.2\u0026ndash;48.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreece\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e276.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e189.7\u0026ndash;376.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.9\u0026ndash;73.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9\u0026ndash;12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e37.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e34.0\u0026ndash;42.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e*YLDs and PD incidence data from Global Burden of Disease database; The total alcohol per capita (15\u0026thinsp;+\u0026thinsp;years) consumption (in litres of pure alcohol) data from the Global Health Observatory database. YLDs, years lived with disability.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo further elucidate this relationship, we examined the association between PD incidence in 2018 (GBD) and average alcohol consumption from 2016 to 2018 (Global Health Observatory) across the same countries. This analysis revealed a biphasic trend: PD incidence decreased with increasing alcohol intake up to ~\u0026thinsp;10 L per capita per year, then increased with further consumption (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). These findings suggest that low-to-moderate alcohol intake may delay or reduce PD onset, whereas higher levels may have the opposite effect.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMR: inverse correlation between alcohol intake frequency and PD\u003c/h2\u003e \u003cp\u003eNext, we performed both univariate and multivariate MR analyses to investigate the potential causal association between alcohol consumption and PD onset, utilizing previously published GWAS data. Specifically, we examined the relationship between PD onset (PD cohort: 482,730 individuals; 17,891,936 SNPs) and two key alcohol exposure factors\u0026ndash;weekly alcohol consumption and weekly alcohol intake frequency.\u003c/p\u003e \u003cp\u003eWe first performed a two-sample MR analysis of weekly alcohol consumption in two UK Biobank cohorts: Cohort 1 (112,117 individuals; 12,935,395 SNPs) and Cohort 2 (335,394 individuals; 11,887,865 SNPs). Surprisingly, the results were contradictory: Cohort 1 showed an increased risk of PD with alcohol intake, while Cohort 2 suggested a protective effect. In Cohort 1, four SNPs were significantly associated with the effects of alcohol intake on PD risk. The IVW method yielded an odds ratio (OR) of 4.18 (95% CI: 1.09\u0026ndash;16.00, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037), while the weighted median approach gave an OR of 9.15 (95% CI: 2.65\u0026ndash;31.57, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00046), suggesting an elevated PD risk associated with alcohol consumption (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In Cohort 2, 32 SNPs were identified as linked to alcohol intake on PD risk. The IVW estimate indicated a non-significant OR of 1.13 (95% CI: 0.55\u0026ndash;2.33, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.733), whereas the weighted median method yielded an OR of 0.65 (95% CI: 0.41\u0026ndash;1.03, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.067), suggesting a potential protective effect, albeit not statistically significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). To investigate this discrepancy, we compared the total weekly alcohol consumption between the two cohorts. Individuals in Cohort 2, which exhibited a protective effect, consumed significantly less alcohol on average (bottles/cans of beer\u0026thinsp;+\u0026thinsp;glasses of wine \u0026times; 5/4\u0026thinsp;+\u0026thinsp;glasses of liquor \u0026times; 1.5/1.25\u0026thinsp;+\u0026thinsp;other: 7.84\u0026thinsp;\u0026plusmn;\u0026thinsp;8.31 drinks/week \u0026times; 10 g/drinks\u0026thinsp;=\u0026thinsp;78.4\u0026thinsp;\u0026plusmn;\u0026thinsp;83.1 g/week [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]) than those in Cohort 1 (121.04\u0026thinsp;\u0026plusmn;\u0026thinsp;132.48 g/week [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]). These findings suggest that heavy alcohol consumption may increase PD risk, whereas moderate alcohol intake could reduce it.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further explore this hypothesis, we analyzed an additional UK Biobank dataset (Cohort 3, containing 462,346 individuals and 9,851,867 SNPs), which included data on weekly drinking frequency (1\u0026ndash;7 times/week). We found an inverse association between drinking frequency and PD risk, with more frequent drinking sessions correlating with a lower PD risk for the same total alcohol intake. This suggests that spreading alcohol consumption across more sessions\u0026mdash;without increasing the total intake\u0026mdash;may be beneficial for reducing PD risk. In Cohort 3, 91 SNPs were associated with both drinking frequency and PD risk. The IVW analysis suggested a reduced PD risk (OR\u0026thinsp;=\u0026thinsp;0.75, 95% CI: 0.58\u0026ndash;0.98, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035), whereas the weighted median estimate was non-significant (OR\u0026thinsp;=\u0026thinsp;1.07, 95% CI: 0.80\u0026ndash;1.42, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.667) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Multivariate MR analysis ascertained the above result (OR\u0026thinsp;=\u0026thinsp;0.70, 95% CI: 0.54\u0026ndash;0.90, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0066) and linked to 83 SNPs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). These findings imply that higher drinking frequency may be associated with reduced PD risk, although further validation of these SNPs is warranted.\u003c/p\u003e \u003cp\u003eCollectively, these analyses suggest a dual role of alcohol consumption in PD risk: heavy drinking may increase risk, whereas moderate intake or more frequent drinking (at equivalent total alcohol levels) may offer a protective effect against PD development.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eAlcohol intake reduces PD risk through improved sleep quality\u003c/h2\u003e \u003cp\u003ePrevious studies have suggested an inverse correlation between sleep quality and the risk of PD, with frequent insomnia identified as a potential risk factor for PD onset [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. To further investigate this association, we conducted an MR analysis using sleep-related phenotypes from cohorts in the MRC IEU OpenGWAS database and the previous study [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], which included 11 distinct sleep-related traits. We identified two phenotypes\u0026ndash;frequent insomnia and short sleep duration\u0026ndash;as potential risk factors for PD, with ORs of 1.20 (95% CI: 1.00\u0026ndash;1.44; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.053; 34 SNPs) and 1.30 (95% CI: 0.97\u0026ndash;1.74; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.075; 21 SNPs), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Although these associations were only marginally significant, these findings are broadly consistent with previous studies suggesting that better sleep quality may reduce PD risk [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Given that a low dose of alcohol consumption has been reported to improve sleep quality [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], our results raise the possibility that alcohol intake could serve as a protective factor, potentially lowering PD risk through enhanced sleep quality.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFrequent alcohol intake reduces PD risk through cytokine modulation\u003c/h2\u003e \u003cp\u003eNeuroinflammation has emerged as a pivotal mechanism underlying the pathogenesis of PD [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Recent studies suggest that low-to-moderate alcohol consumption may exert anti-inflammatory effects [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], potentially reducing the risk of PD by modulating anti-inflammatory cytokine level. To evaluate this hypothesis, we conducted MR analyses to investigate the causal relationships of inflammatory cytokine levels with alcohol intake frequency and PD risk with inflammatory cytokine levels using a cohort from EBI GWAS Catalog (14,824 individuals, 91 inflammatory cytokines, and 12,958,024 SNPs).\u003c/p\u003e \u003cp\u003eWe identified eight anti-inflammatory cytokines associated with PD risk. Of these, three were positively associated with PD risk, while the remaining five exhibited inverse associations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Additionally, we found nine cytokines were linked to alcohol intake frequency, one positively and eight inversely (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Notably, one cytokine, the T-cell surface glycoprotein CD6 isoform, was associated with both PD risk and alcohol intake frequency. Higher alcohol intake frequency was associated with reduced circulating levels of this cytokine (OR\u0026thinsp;=\u0026thinsp;0.88; 95% CI: 0.79\u0026ndash;0.99; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030), while higher levels of this cytokine in turn were linked to an increased risk of PD (OR\u0026thinsp;=\u0026thinsp;1.08; 95% CI: 1.01\u0026ndash;1.15; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031). These findings suggest that frequent alcohol intake may reduce PD risk by modulating inflammatory cytokine level.\u003c/p\u003e \u003cp\u003eWe identified 92 SNPs significantly associated with both alcohol intake frequency and circulating levels of the T-cell surface glycoprotein CD6 isoform and 15 SNPs significantly associated with both CD6 isoform levels and PD risk.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eShared Genetic Variants Linking Alcohol Intake Frequency, Inflammation, Sleep, and PD\u003c/h2\u003e \u003cp\u003eWe further examined the overlap of significant SNPs across the four MR analyses and identified nine SNPs within four genes that were consistently significant in all results (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among these, eight SNPs were intronic, and one (rs13107325) was a non-synonymous exonic variant. Notably, \u003cem\u003eDPP6\u003c/em\u003e gene (three SNPs: rs6969458, rs2622167, rs9691968) were consistently associated with causal relationships between (1) alcohol intake frequency and PD, (2) alcohol intake frequency and T-cell surface glycoprotein CD6 isoform levels, and (3) CD6 isoform levels and PD. The remaining six SNPs\u0026mdash;located in \u003cem\u003eSLC39A8\u003c/em\u003e (rs13107325, rs13135092), \u003cem\u003eMAD1L1\u003c/em\u003e (rs73050128, rs11763750), and \u003cem\u003eRBFOX1\u003c/em\u003e (rs34631026, rs17139246) \u0026mdash;were implicated in the causal pathways linking alcohol intake frequency to PD, alcohol intake frequency to CD6 isoform levels, and sleep disorders to PD (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). These findings suggest that the four identified genes and their associated allelic variants may play important roles in mediating the causal relationships among alcohol intake, sleep quality, circulating inflammatory cytokine levels, and PD risk.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariants in four genes intersected in multiple MR processes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOfficial gene symbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFunctional Summary\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSNPs rs#\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChr.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePosition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFunc.refGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMR_exposure on outcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eeffect_allele. exposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eother_allele. exposure\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDPP6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDipeptidyl peptidase like 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmyotrophic lateral sclerosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers6969458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e153489725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAlcoholic drinks per week on Parkinson's disease (PD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers2622167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e153486704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAlcohol intake frequency on PD; Alcohol intake frequency on T-cell surface glycoprotein CD6 isoform levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers9691968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e153635380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eT-cell surface glycoprotein CD6 isoform levels on PD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLC39A8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSolute carrier family 39 member 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCellular import of zinc at the onset of inflammation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers13107325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e103188709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eexonic; nonsynonymous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAlcoholic drinks per week on PD; Short sleep duration on PD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers13135092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e103198082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAlcohol intake frequency on PD; Alcohol intake frequency on T-cell surface glycoprotein CD6 isoform levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAD1L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMitotic arrest deficient 1 like 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrevents the onset of anaphase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers73050128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1961882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAlcohol intake frequency on PD; Alcohol intake frequency on T-cell surface glycoprotein CD6 isoform levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers11763750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2080114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eShort sleep duration on PD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBFOX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRNA binding fox-1 homolog 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpinocerebellar ataxia type 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers34631026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6172126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAlcohol intake frequency on PD; Alcohol intake frequency on T-cell surface glycoprotein CD6 isoform levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers17139246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6106260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFrequent insomnia on PD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eMR, Mendelian randomization.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOver the years, numerous studies have demonstrated that the risk of PD is influenced not only by genetic background but also by various lifestyle and behavioral factors, including alcohol consumption, smoking, and coffee intake [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Recent findings suggest that smoking may exert a protective effect against PD, reducing risk by approximately 40% [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Similarly, coffee intake has been associated with a roughly 30% lower risk of PD [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Emerging research has also highlighted a potential link between alcohol consumption and PD risk. GWAS have identified genes involved in alcohol metabolism\u0026mdash;such as \u003cem\u003eADH1B\u003c/em\u003e and \u003cem\u003eALDH2\u003c/em\u003e\u0026mdash;as being associated with PD susceptibility [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. \u003cem\u003eADH1B\u003c/em\u003e gene encodes an alcohol dehydrogenase with high ethanol-oxidizing activity and plays a primary role in ethanol catabolism. \u003cem\u003eALDH2\u003c/em\u003e encodes an aldehyde dehydrogenase, another key enzyme in the ethanol oxidative pathway. SNPs in both \u003cem\u003eADH1B\u003c/em\u003e and \u003cem\u003eALDH2\u003c/em\u003e have been linked to PD risk through GWAS analyses. Despite these findings, the relationship between alcohol consumption and PD remains controversial. Some studies suggest that alcohol intake increases the risk of PD [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], whereas others report a protective effect, indicating a risk reduction of approximately 20% [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Given that alcohol consumption is a globally prevalent behavior\u0026mdash;with nearly 50% of the global population projected to consume alcohol by 2030 [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u0026mdash;further research is essential to clarify its role in PD development and to inform effective prevention strategies.\u003c/p\u003e \u003cp\u003eThis study integrated data from a three-generation Chinese family with a history of PD, epidemiological surveys from two Chinese cities, and global datasets from 27 countries to investigate the association between alcohol consumption and PD risk. In the third generation of the PD-affected family, all eight individuals carried PD-associated risk alleles. Notably, the only individual diagnosed with PD was a non-drinker and carried the fewest risk alleles, whereas the seven unaffected individuals consumed alcohol and harbored a greater number of risk alleles. These observations suggest a potential protective effect of alcohol consumption against PD, independent of genetic predisposition. To further evaluate this association, we analyzed lifestyle data from PD patients in two Chinese cities and compared alcohol consumption rates with those in matched general population cohorts from the same locations. The proportion of alcohol consumers was significantly lower among PD patients than in the general population, consistent with findings from the familial analysis and previous reports [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], supporting a possible inverse relationship between alcohol intake and PD risk. At the global level, we examined PD incidence in relation to per capita alcohol consumption across 27 countries. The analysis revealed that low-to-moderate alcohol consumption (\u0026lt;\u0026thinsp;10 L/capita/year) was associated with reduced PD incidence, whereas high levels of consumption (\u0026gt;\u0026thinsp;10 L/capita/year) correlated with increased risk. Token together, these findings consistently indicate that moderate alcohol consumption may confer a protective effect against PD. Therefore, we suggest that people with drinking habits how to drink and reduce the harm. The annual drinking volume of 10L/capita is converted into daily drinking volume, \u0026lt; 230 mL/capita of wine (~\u0026thinsp;12% alc/vol), \u0026lt; 550 mL/capita of beer (~\u0026thinsp;5% alc/vol), or \u0026lt;\u0026thinsp;70 mL/capita of Chinese Baijiu (~\u0026thinsp;40% alc/vol).\u003c/p\u003e \u003cp\u003eTo validate the findings from survey data, we conducted MR analyses using large-scale GWAS datasets related to alcohol consumption (weekly intake and drinking frequency) and PD-associated SNPs. The MR results indicated that moderate weekly alcohol intake, as well as higher drinking frequency coupled with lower quantity per occasion, were causally associated with a reduced risk of PD. These genetic findings are consistent with the observational analyses and further support a potential protective role of moderate alcohol consumption against PD. Previous studies have proposed that light alcohol intake may improve sleep quality [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and good sleep quality is a potentially neuroprotective lifestyle against PD [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. To investigate this potential mechanism, we performed MR analyses incorporating GWAS data on sleep traits and PD risk. The results showed that frequent insomnia and short sleep duration were causally linked to increased PD risk, suggesting that the neuroprotective effects of alcohol may be partially mediated through improvements in sleep regulation. Emerging evidence also implicates neuroinflammation in PD pathogenesis [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. To explore this pathway, we examined the relationships among alcohol consumption, inflammatory biomarkers, and PD risk using MR. Notably, we identified the T-cell surface glycoprotein CD6 isoform as a putative mediator: increased drinking frequency was associated with lower circulating levels of this pro-inflammatory factor, which were linked to a decreased risk of PD. These findings suggest that alcohol may influence CD6\u0026thinsp;+\u0026thinsp;T-cell activity, thereby mitigating neuroinflammation through enhanced immune homeostasis [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and reduced oxidative stress [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Collectively, this multi-layered MR analysis supports the hypothesis that moderate alcohol consumption may confer neuroprotection against PD, potentially via sleep enhancement and CD6-mediated immunomodulatory mechanisms.\u003c/p\u003e \u003cp\u003eIn this study, we identified four genes\u0026mdash;\u003cem\u003eDPP6\u003c/em\u003e, \u003cem\u003eSLC39A8\u003c/em\u003e, \u003cem\u003eMAD1L1\u003c/em\u003e, and \u003cem\u003eRBFOX1\u003c/em\u003e\u0026mdash;that exhibit pleiotropic associations with both alcohol consumption and PD risk. These genes have been previously implicated in diverse neurological and cellular processes: DPP6 in amyotrophic lateral sclerosis, RBFOX1 in spinocerebellar ataxia type 2, SLC39A8 in inflammatory signaling, and MAD1L1 in mitotic regulation [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. DPP6 modulates neuronal excitability via Kv4.2 potassium channels [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Loss of DPP6 function leads to dendritic instability, elevated oxidative stress, and impaired iron homeostasis\u0026mdash;pathological features common to tauopathies, dementia, and ferroptosis [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. SLC39A8, a transporter of zinc and other transition metals, regulates cellular influx of Zn\u0026sup2;⁺, Fe\u0026sup2;⁺, and Mn\u0026sup2;⁺ [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Its dysregulation contributes to the accumulation of reactive oxygen species (ROS) and lipid peroxidation, mechanisms implicated in multiple neurodegenerative diseases including PD, Alzheimer\u0026rsquo;s disease, Huntington\u0026rsquo;s disease, and amyotrophic lateral sclerosis [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. RBFOX1 encodes an RNA splicing factor that modulates the alternative splicing of focal adhesion genes in both muscular and neuronal tissues [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Aberrant RBFOX1 activity has been linked to prefrontal cortical dysfunction in schizophrenia [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], indicating a broader role in neuropsychiatric and neurodegenerative disorders. MAD1L1, a key regulator of the mitotic spindle assembly checkpoint, maintains chromosomal stability during cell division. Mutations in MAD1L1 may disrupt cell cycle regulation in vulnerable neuronal populations, potentially contributing to early-onset PD [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. While these findings suggest a shared genetic basis linking alcohol consumption and PD, the mechanistic roles of these genes require further functional validation in experimental models.\u003c/p\u003e \u003cp\u003eAlcohol, as a dopamine agonist, may provide neuroprotection by reducing oxidative stress and neuroinflammation, helping to safeguard dopaminergic neurons [\u003cspan additionalcitationids=\"CR59\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. During the aging process, the gradual decline of redox homeostasis leads to accumulated ROS levels in elderly populations. This redox imbalance results in reduced Nrf2 transcriptional activity, thereby compromising the expression of downstream antioxidant enzymes regulated through the antioxidant response element (ARE) pathway [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. In neural systems, this progressive deterioration of antioxidative capacity disrupts proteostasis, facilitating aberrant degradation of critical neuronal proteins and subsequent accumulation of misfolded aggregates. Such pathological mechanisms are intrinsically associated with the development of neurodegenerative pathologies, including Alzheimer's disease and PD [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Notably, acetaldehyde, the primary oxidative metabolite of ethanol, demonstrates potent Nrf2-activating properties through covalent modification of Keap1 cysteine residues [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. This pharmacological action effectively counteracts age-related ROS accumulation, restoring redox equilibrium and reinforcing endogenous antioxidant defenses (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Consequently, targeted modulation of the Nrf2-Keap1-ARE signaling axis by acetaldehyde derivatives may represent a promising therapeutic strategy for mitigating neurodegenerative progression and preserving neuronal integrity in ageing population.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study suggests that moderate, frequent alcohol consumption may be associated with reduced PD risk. Our data supports alcohol\u0026rsquo;s potential neuroprotective effect in delaying PD onset, possibly by improving sleep, inhibiting inflammation, and regulating ROS accumulation. These findings offer new insights for PD prevention. Further research is needed to confirm these results and explore the underlying mechanisms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT author statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWei Lu: Conceptualization, Data curation. Yong-Qiang Kong: Methodology, Software. Hai Tang: Data curation. Xiu-Li Cheng: Data curation. Wan-Ruo Zhang: Investigation. Xiao-Yun Pan: Investigation. Hai-Yu Guo: Visualization. Xuan-Zhu Chen: Writing- Editing. Wei Zhang: Data curation. Jie Zu: Data curation. Dan-Dan Yang: Data curation. Hui-Ling Zou: Investigation. Gui-Yun Cui: Supervision, Funding acquisition. Li-Guo Dong: Writing- Reviewing. Yi-Liang Wei: Conceptualization, Writing- Draft, Reviewing and Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the Construction Project of High-Level Hospital of Jiangsu Province (Grant No. GSPJS202418 and No. GSPJS202426) and Medical Science and Technology Innovation Project of Xuzhou Municipal Health Commission (Grant No. XWKYHT20240107).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Shao-Yuan Wu (Jiangsu Normal University) for his professional writing assistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data were supplied in Tables and Supplementary Tables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAI technology declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors used ChatGPT-4o for language editing during manuscript preparation. The content was reviewed and revised as necessary, with the authors taking full responsibility for the final publication.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLeite Silva, A. B. R. et al. Premotor, nonmotor and motor symptoms of Parkinson's Disease: A new clinical state of the art. \u003cem\u003eAgeing Res. Rev.\u003c/em\u003e \u003cb\u003e84\u003c/b\u003e, 101834 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu, J. et al. Temporal trends in the prevalence of Parkinson's disease from 1980 to 2023: a systematic review and meta-analysis. \u003cem\u003eLancet Healthy Longev.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, e464\u0026ndash;e79 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShadrina, M. I. \u0026amp; Slominsky, P. A. Genetic Architecture of Parkinson's Disease. \u003cem\u003eBiochem. 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Adv.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, eadr4231 (2025).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Parkinson’s disease, alcohol consumption, Mendelian randomization, Global Burden of Disease","lastPublishedDoi":"10.21203/rs.3.rs-6754726/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6754726/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eParkinson\u0026rsquo;s disease (PD), a leading neurodegenerative disorder, is increasing in prevalence globally due to population ageing. Although alcohol consumption is widespread worldwide, its role in PD pathogenesis remains contentious, with studies reporting both protective and harmful associations.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eHere, we integrate familial, population-based, and genomic data to investigate the relationship between alcohol intake and PD risk. To elucidate causal mechanisms, we performed Mendelian randomization analyses leveraging published genome-wide association study (GWAS) datasets.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn a three-generation PD pedigree, only the affected individual\u0026mdash;who reported abstinence\u0026mdash;carried the fewest PD-associated risk variants, while seven unaffected, alcohol-consuming relatives harbored a greater burden of risk variants, suggesting a potential protective effect of alcohol. Population surveys from two Chinese cities revealed significantly lower alcohol consumption among PD patients compared to controls (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Cross-national data from 27 countries showed an inverse association between low-to-moderate alcohol intake (\u0026lt;\u0026thinsp;10 L/capita/year) and PD incidence, whereas heavy consumption (\u0026ge;\u0026thinsp;10 L/capita/year) increased risk. Moderate but frequent alcohol consumption was associated with a reduced genetic risk for PD, potentially mediated by improved sleep quality and mitigating inflammation. Notably, we identified the T-cell surface glycoprotein CD6 isoform as a novel cytokine linking alcohol intake to decreased PD risk, with alcohol consumption reducing circulating CD6 levels. GWAS data further implicated four genes\u0026mdash;\u003cem\u003eDPP6\u003c/em\u003e, \u003cem\u003eSLC39A8\u003c/em\u003e, \u003cem\u003eMAD1L1\u003c/em\u003e, and \u003cem\u003eRBFOX1\u003c/em\u003e\u0026mdash;in mediating the protective association.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eTogether, our findings provide convergent, multi-layered evidence that modified people\u0026rsquo;s drinking habits may confer protection against PD and highlight potential biological pathways for targeted prevention strategies.\u003c/p\u003e\u003ch2\u003eClinical trial number:\u003c/h2\u003e \u003cp\u003enot applicable.\u003c/p\u003e","manuscriptTitle":"Protective Evidence for Alcohol Consumption in Parkinson’s Disease Risk and Associated Genes of DPP6, SLC39A8, MAD1L1, and RBFOX1","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-29 12:04:57","doi":"10.21203/rs.3.rs-6754726/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":"547a03b2-5bd3-428c-aa69-9459ff94d07f","owner":[],"postedDate":"May 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49076299,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":49076300,"name":"Biological sciences/Genetics"},{"id":49076301,"name":"Health sciences/Diseases"},{"id":49076302,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-05-29T12:04:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-29 12:04:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6754726","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6754726","identity":"rs-6754726","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}