The Systemic Immune-Inflammation Index is Linked to Cataracts: Insights from NHANES 2005-2008

The Systemic Immune-Inflammation Index is Linked to Cataracts: Insights from NHANES 2005-2008 | 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 The Systemic Immune-Inflammation Index is Linked to Cataracts: Insights from NHANES 2005-2008 Xiang Li, Guo-lei Du, Shi-Nan Wu, Si-Qi Zhang, Zhi-Jie Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4593241/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Objective To investigate the association between the Systemic Immune-Inflammation Index (SII) and cataract occurrence using the National Health and Nutrition Examination Survey (NHANES) 2005–2008 data. Methods In this population-based cross-sectional study, data from 9,665 NHANES participants were analyzed. The study included adults with complete data on SII, cataract status, and relevant covariates. Logistic regression models adjusted for demographic, clinical, and laboratory variables were used to evaluate the association between SII levels and the risk of cataracts. Results The results reveal a notable link between higher SII levels and a heightened risk of cataracts. Individuals in the top SII quartile exhibited a higher incidence of cataracts compared to those in the bottom quartile, a trend that remained consistent after adjustments in various models. Quantile regression analyses further supported the connection between increased SII levels and the likelihood of cataracts. Conclusion Our analysis establishes a linear association between raised SII levels and an elevated risk of cataracts, underscoring systemic inflammation as a crucial element in cataract formation. These findings propose that SII could be an effective biomarker for cataract risk assessment and underline the significance of managing systemic inflammation to prevent cataracts. Health sciences/Diseases/Eye diseases Health sciences/Health care/Public health Systemic Immune-Inflammation Index cataracts NHANES systemic inflammation risk assessment Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The lens, positioned behind the iris yet ahead of the vitreous body and retina, is a key player in directing light onto the retina. Originating from ectodermal tissue, it contains epithelial cells that produce lens fibers, leading to the lens's thickening over time. Cataracts, marked by diminished lens clarity, impair vision, reduce contrast sensitivity, affect color perception, and cause glare(Asbell et al., 2005 , Leffler et al., 2020, Tewari et al., 2019 ). The World Health Organization reports that cataracts account for approximately 46% of the nearly 180 million global cases of visual impairments(Demmin and Silverstein, 2020 ). Despite being treatable, cataracts are a leading cause of vision loss worldwide, posing significant public health challenges(Cedrone et al., 1999 , Hashemi et al., 2009 ). Factors such as aging, smoking, diabetes, and exposure to ultraviolet light are linked to the development of age-related cataracts(Liu et al., 2017 ). While cataract surgery can markedly improve vision, its affordability and the limited availability of surgeons in some regions remain obstacles(Yan et al., 2019 ). The SII, a marker for systemic inflammatory response, is linked to the prognosis in elderly patients with tumors of the digestive system(Tian et al., 2022 , Hu et al., 2014 ). This index is calculated using a specific formula: platelets × neutrophils ÷ lymphocytes(Hu et al., 2014 ). SII serves as a mortality predictor in tumor patients, with higher SII values indicating a greater mortality risk(Tian et al., 2022 , He et al., 2022 , Li et al., 2021 ). Such correlations might stem from the disequilibrium between pro-tumor and anti-tumor agents within the body during tumor states, characterized by increases in neutrophils and platelets, a decrease in lymphocytes, and consequently, a rise in SII levels. The utility of SII has been broadening, demonstrating its potential in predicting disease severity and assessing treatment efficacy in various studies(Wu et al., 2022 , Xie et al., 2022 , Wang et al., 2021 , Tang et al., 2022 , Qin et al., 2022 ). Importantly, recent research in ophthalmology has underscored a notable link between SII and primary open-angle glaucoma(Tang et al., 2020 ). NHANES, a cross-sectional study, aims to gather detailed information on the health and nutritional conditions of U.S. households. Utilizing a complex, stratified, multistage probability cluster sampling approach, it aims to accurately represent the U.S. population(Hartwell et al., 2019 ). Despite its extensive use in various research domains, investigations into the relationship between the SII and cataracts using the NHANES database remain unexplored. Therefore, leveraging NHANES data, our team undertook a population-based cross-sectional analysis to explore the possible link between SII and adult cataract incidence. Materials and Methods Data source and subject selection The NHANES systematically tracks the health and nutritional trends within the U.S. populace, utilizing an intricate, multistage probabilistic sampling technique. For further insights into NHANES's detailed methodology, refer to their website at http://www.cdc.gov/nchs/nhanes/index.htm . Ethical clearance for the study was granted by the National Center for Health Statistics’ethics committee, with all subjects providing written consent(Mahemuti et al., 2023 ). Our study excluded individuals lacking SII data, cataract information, and other necessary covariate details, resulting in a study population of 9665. The selection process for the sample is depicted in Fig. 1 . [Figure 1 ] Cataract identification criteria The National Health and Nutrition Examination Survey queried adults aged 20 and above on their eye surgery history for cataracts prior to their eye examination(Park et al., 2016 ). Individuals confirming such surgeries were identified as cataract cases in this study. Those who did not respond or provided ambiguous answers were omitted. Considering the rising occurrence and the expanded criteria for cataract surgery within the U.S.(Lundström et al., 2015 ), self-reported surgeries are presumed to signify clinically significant cataracts. This approach for cataract identification aligns with methodologies used in previous studies(Zhang et al., 2012 , Wang et al., 2016). The definition of systemic immune-inflammation index Complete Blood Count (CBC) metrics utilize the Beckman Coulter methodology, combining counting and sizing techniques, an automated system for diluting and mixing samples, and a single beam photometer to measure hemoglobin levels. White Blood Cell (WBC) differentials apply VCS technology. The Beckman Coulter DxH 800 instrument, positioned within the NHANES mobile examination center (MEC), performs CBC analyses on participant blood samples, offering a comprehensive cell distribution profile. In alignment with prior research, SII is determined by multiplying the platelet and neutrophil counts, then dividing by the lymphocyte count(Hu et al., 2014 , Qin et al., 2022 ). Covariates assessment Demographic variables, including age, race/ethnicity, gender, education level, marital status, and Body Mass Index (BMI), were selected as covariates in our study. This demographic information was collected via computer-assisted personal interviews(Xing et al., 2023 ). Given the known correlations between socio-economic status, living conditions, and health, these demographic factors were used to infer the social and living circumstances of the participants. Additionally, diabetes mellitus, a recognized risk factor for cataracts(Thompson and Lakhani, 2015 ), was incorporated as a covariate, with diabetes status determined through participants' self-reports(Zhang et al., 2023). Statistical methods Data analysis was conducted using R2 and EmpowerStats software from X&Y Solutions, Inc., based in Boston, MA, accessible at http://www.empowerstats.com . This analysis took into account the NHANES' complex sampling framework by integrating sampling weights, strata, and primary sampling units. Continuous variables were reported as means ± standard errors, and categorical variables as percentages ± SE. Chi-square or T-tests were applied to explore demographic differences. The SII data exhibited a right-skewed distribution, leading to the application of a natural logarithm transformation for statistical analysis. Logistic regression models were employed to evaluate the association between SII levels and cataract risk, with Model 1 being unadjusted; Model 2 adjusted for age, race, gender, education, and marital status; and Model 3 additionally adjusted for BMI and diabetes mellitus. These analyses identified significant relationships between SII levels and the likelihood of cataracts. Quantile regression analyses further investigated these associations. Forest plots visually depicted the logistic regression outcomes, while smooth curve fitting explored the potential linear connections between SII levels and cataract occurrence, considering p-values < 0.05 as indicating statistical significance. Results Description of baseline information of the study sample Our study leveraged the NHANES dataset, initially encompassing 20,497 participants. Following the removal of cases with incomplete SII or eye data, 9,665 individuals were retained, and 10,832 were excluded. This screening process is depicted in Fig. 1 . Of the participants analyzed, 48.39% were male, with an average age of 49.44 ± 18.24 years. The mean SII value stood at 603.24 ± 368.46. Cataracts were identified in 9.29% of the cohort. Participants were categorized based on their hyperlipidemia status, as detailed in Table 1 . The occurrence of cataracts showed significant correlations with several factors, including age, gender, race, education level, marital status, BMI, diabetes presence, and SII values, all with p-values < 0.05. Individuals diagnosed with cataracts typically were older, female, of non-Hispanic white ethnicity, had achieved a high school education level, were married or cohabiting, exhibited higher BMI, had diabetes, and presented elevated SII levels compared to their counterparts without cataracts. Table 1 Characteristics of participants stratified by cataract from NHANES 2005–2008. Variables Non-Cataract Cataract p-Value N 8767 898 Age (years) 46.88 ± 16.92 74.44 ± 9.79 < 0.001 BMI (kg/m3) 28.95 ± 6.73 28.22 ± 5.98 0.002 SII(mm³) 596.92 ± 359.75 665.00 ± 440.15 < 0.001 Gender,n(%) 0.022 Male 4275 (48.76%) 402 (44.77%) Female 4492 (51.24%) 496 (55.23%) Race < 0.001 Mexican 1758 (20.05%) 69 (7.68%) Other Hispanic 671 (7.65%) 53 (5.90%) Non-Hispanic white 4105 (46.82%) 637 (70.94%) Non-Hispanic black 1869 (21.32%) 112 (12.47%) Other race 364 (4.15%) 27 (3.01%) Education < 0.001 Less than 9th grade 1040 (11.86%) 185 (20.60%) 9–11Th grade (includes 12th grade with no diploma) 1435 (16.37%) 146 (16.26%) High school grad/ged or equivalent 2106 (24.02%) 238 (26.50%) Some college or AA degre 2418 (27.58%) 197 (21.94%) College graduate or above 1768 (20.17%) 132 (14.70%) Marital Status < 0.001 Married or living with partner 5529 (63.07%) 458 (51.00%) Unmarried or other 3238 (36.93%) 440 (49.00%) DIABETES MELLITUS < 0.001 Yes 853 (9.73%) 232 (25.84%) No 7914 (90.27%) 666 (74.16%) Mean ± SD for continuous variables: the p-value was calculated by a weighted linear regression model. % for categorical variables: the p-value was calculated by a weighted chi-square test. BMI, body mass index. [Table 1 ] Association between SII and Cataract Observing modest effect sizes, the study applied a logarithmic transformation to the SII, converting it into lnSII. Table 2 summarizes and Fig. 2 illustrates the outcomes of the multivariable regression analysis, which elucidate the lnSII's relationship with cataracts. A consistent negative link between SII and cataracts manifested across all models (model 1: 1.29 [1.14, 1.47]; model 2: 1.20 [1.04, 1.39]; model 3: 1.20 [1.04, 1.39]).Following this, smooth curve fitting illustrated the linear association between SII levels and the incidence of cataracts, as shown in Fig. 3 , with adjustments for age, gender, race, education, marital status, BMI, and diabetes. Table 2 Association between SII and cataract. Exposure Model 1 a OR (95%CI) p-Value Model 2 b OR (95%CI) p-Value Model 3 c OR (95%Cl) p-Value LNSII 1.29 (1.14, 1.47) < 0.0001 1.20 (1.04, 1.39) 0.0112 1.20 (1.04, 1.39) 0.0118 a Model 1:a no adjusted. b Model 2:adjusted for age, race, gender, educational level and marital status. c Model 3:further adjusted for BMI and diabetes mellitus. [Table 2 ] [Figure 2 ] [Figure 3 ] Relationship of different quartiles of SII with the presence of cataract Table 3 and Fig. 4 analyze the relationship between different SII quartiles and cataract prevalence. In the highest quartile (Q4), all models demonstrated a negative correlation between SII levels and cataract likelihood. The odds ratios (ORs) indicated that increased SII values correlate with a higher cataract risk: Model 1 had an OR of 1.49 (95% CI = 1.23–1.81, p < 0.05), Model 2 an OR of 1.33 (95% CI = 1.05–1.69, p < 0.05), and Model 3 an OR of 1.34 (95% CI = 1.06–1.70, p < 0.05). Table 3 Association between lnSII and cataract in different quartiles Exposure Model 1 a OR (95%CI) p-Value Model 2 b OR (95%CI) p-Value Model 3 c OR (95%Cl) p-Value lnSII quartiles Q1 1 1 1 Q2 1.00 (0.82, 1.24) 0.9635 0.97 (0.76, 1.25) 0.8373 0.98 (0.77, 1.26) 0.8752 Q3 1.16 (0.95, 1.42) 0.1411 1.17 (0.92, 1.49) 0.2086 1.18 (0.92, 1.51) 0.1854 Q4 1.49 (1.23, 1.81) < 0.0001 1.33 (1.05, 1.69) 0.0172 1.34 (1.06, 1.70) 0.0157 a Model 1: no adjusted. b Model 2:adjusted for age, race, gender, educational level and marital status. c Model 3:further adjusted for BMI and diabetes mellitus. [Table 3 ] [Figure 4 ] Discussion In this research, we employed the NHANES database to examine the potential link between the SII and cataract development. Our findings from this nationally representative cross-sectional analysis reveal a linear association between SII levels and cataract risk, highlighting that elevated SII values correlate with a higher likelihood of cataract formation. According to our understanding, this research pioneers the examination of the link between SII and cataracts. Systemic inflammation is measurable through various biochemical or hematological markers commonly assessed in routine blood analyses, or via ratios derived from these markers(Sylman et al., 2018 ). The SII, a novel and consistent marker of inflammation, is computed using the formula: platelet count × neutrophil count / lymphocyte count(Hu et al., 2014 , Tong et al., 2017 ). A heightened SII level signifies an inflammatory environment characterized by an increase in neutrophils and a decrease in lymphocytes, which may contribute to the initiation and progression of diverse diseases. The elevation in SII not only mirrors an inflammatory state but could also suggest a disruption in immune regulation. Cells such as neutrophils and lymphocytes are pivotal in controlling inflammation and immune reactions. Therefore, a high SII might reflect a condition of persistent immune activation, commonly seen in the etiology of various chronic diseases, including age-associated ailments like cataracts(Ferrucci and Fabbri, 2018 ). The connection between inflammatory states and the formation of cataracts may also involve the action of inflammatory mediators and cytokines. For instance, cytokines released during inflammation, such as tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6), can directly affect the intraocular environment, promoting oxidative damage and apoptosis in lens epithelial cells, thereby accelerating cataract formation(Loukovaara et al., 2017 ). The inflammatory changes in the lens microenvironment serve as a key intermediary linking SII to cataract development. When SII is elevated, there is an increase in inflammatory factors in the blood, which can enter the eye through ocular circulation, altering the lens microenvironment. Inflammatory factors like TNF-α and IL-6 can not only directly damage lens cells but also activate inflammatory pathways within the lens, inducing further cellular responses, including the production and release of cytokines, thereby exacerbating oxidative stress and cellular damage in the lens(Rajashekhar et al., 2014 ). Changes in SII may also reflect the infiltration status of immune cells, especially neutrophils and lymphocytes, in the eye. The activation and infiltration of immune cells can cause direct physical damage to the lens or lead to lens cell injury and death indirectly through the release of inflammatory mediators and enzymes, thus accelerating cataract development. Oxidative stress is closely related to mitochondrial function. In conditions of inflammation and high SII, increased oxidative stress can lead to mitochondrial dysfunction, playing a crucial role in cataract formation. Mitochondrial dysfunction can result in reduced ATP production and metabolic imbalance within cells, affecting the normal function and survival of lens cells. Moreover, mitochondrial dysfunction can increase the production of reactive oxygen species (ROS) within cells, intensifying oxidative stress(Nunnari and Suomalainen, 2012 ). Furthermore, prolonged inflammatory responses and oxidative stress may lead to the depletion of the body's antioxidant defense mechanisms. For example, the activity of antioxidant enzymes such as super oxide dismutase (SOD) and glutathione peroxidase (GP) may decrease, along with reduced levels of antioxidants, diminishing the eye's ability to resist ROS and further exacerbating oxidative damage to the lens. Given the correlation between SII and cataracts, future research could explore therapeutic strategies targeting inflammation and oxidative stress. This may include developing new drugs or nutritional supplements aimed at mitigating inflammatory responses, enhancing antioxidant defenses, or directly scavenging ROS. Further studies should assess the potential value of SII in clinically evaluating cataract risk and monitoring disease progression. With a deeper understanding of the specific connection between SII and cataract development, SII could become an important marker for predicting cataract risk and guiding intervention measures. Our research benefits from using a large, nationally representative sample and adjusting for key demographic, examinational, and laboratory factors, enhancing the credibility and applicability of our findings. Nonetheless, it has limitations, primarily due to its cross-sectional nature, which precludes establishing causality. Future prospective studies with substantial sample sizes are essential to clarify causal links. Conclusion Utilizing the NHANES database, this study unveils for the first time a linear relationship between the SII and the occurrence of cataracts. Our findings indicate that higher levels of SII correlate with an increased risk of developing cataracts. This discovery provides a new biomarker reference for the risk assessment and early prevention of cataracts, highlighting the significant role of systemic inflammation in the development of cataracts. Abbreviation SII: Systemic Immune-Inflammation Index; NHANES: National Health and Nutrition Examination Survey; CBC: Complete Blood Count; WBC: White Blood Cell; MEC: mobile examination center; BMI: Body Mass Index; SE: standard errors; OR: odds ratio; TNF-α: tumor necrosis factor-alpha; IL-6: interleukin-6; ROS: reactive oxygen species; SOD: super oxide dismutase; GP:glutathione peroxidase; Declarations Supportive foundations The study described was supported by grants from a Key Project and a Lab Project at Chongqing Three Gorges Medical College, China (SYS20210021), and a project supported by the Chongqing Education Commission Science and Technology Research Program (KJQN202302715). Ethical considerations and informed consent Considering that the NHANES database is publicly accessible, and patient records are anonymous and de-identified, it does not involve informed consent or ethical approval. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contribution X L conceived the research idea. X L, GL Du, SN W, SZ and ZZ conducted data cleaning and literature review. XL, GLD, SN W, and SQ Z, contributed to drafting and critically revising the work for intellectual content. X L, GL D, SN W, and SQ Z, conducted the analysis and created the figures and tables. JF T provided a critical review of the manuscript. All authors have read and approved the manuscript. Acknowledgments The authors would like to thank all reviewers for their valuable comments. Data Availability Publicly available datasets were analyzed in this study. This data can be found at: https://www.cdc.gov/nchs/nhanes/index.htm. References ASBELL, P. A., DUALAN, I., MINDEL, J., BROCKS, D., AHMAD, M. & EPSTEIN, S. 2005. Age-related cataract. 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Cite Share Download PDF Status: Published Journal Publication published 02 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 07 Oct, 2024 Reviews received at journal 04 Oct, 2024 Reviewers agreed at journal 10 Sep, 2024 Reviews received at journal 28 Jun, 2024 Reviewers agreed at journal 24 Jun, 2024 Reviewers invited by journal 24 Jun, 2024 Editor assigned by journal 24 Jun, 2024 Editor invited by journal 22 Jun, 2024 Submission checks completed at journal 21 Jun, 2024 First submitted to journal 17 Jun, 2024 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. 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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-4593241","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":320510103,"identity":"03a57cd6-0c3a-4e66-8289-cb198321fbf8","order_by":0,"name":"Xiang Li","email":"","orcid":"","institution":"Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Li","suffix":""},{"id":320510105,"identity":"e2e96047-7ce5-4d1d-9f5d-85d17788fa4f","order_by":1,"name":"Guo-lei Du","email":"","orcid":"","institution":"Weihai Institute for Bionics-Jilin University，Weihai","correspondingAuthor":false,"prefix":"","firstName":"Guo-lei","middleName":"","lastName":"Du","suffix":""},{"id":320510106,"identity":"a12d410f-a4d5-4a94-b67e-b5ab501d4db3","order_by":2,"name":"Shi-Nan Wu","email":"","orcid":"","institution":"Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Shi-Nan","middleName":"","lastName":"Wu","suffix":""},{"id":320510109,"identity":"e59415a8-9e67-4864-b72b-84ca420fc195","order_by":3,"name":"Si-Qi Zhang","email":"","orcid":"","institution":"Xiang'an Hospital of Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Si-Qi","middleName":"","lastName":"Zhang","suffix":""},{"id":320510113,"identity":"583c964c-1b23-4103-9338-ae48c05eebc9","order_by":4,"name":"Zhi-Jie Zhang","email":"","orcid":"","institution":"Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhi-Jie","middleName":"","lastName":"Zhang","suffix":""},{"id":320510115,"identity":"8fa25166-9168-47e9-975e-1dfb8c7b9dba","order_by":5,"name":"Jia-feng Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYBAC+/kPGx9IVLAx8xOtxYAh+bCBxRk+dskG4rWkpUlUtsjxGxwgVos5wxkziZsNZtLGx5M3MPyo2EZYi2Vjj7HlzB1pxmZnnhUw9py5TYQ1h3kMb0ueOZZsdiPHgJmxjRgtx3gMpP+2/a/fPINYLQZn2JIkJNvYmA0kiNUiOYP5sIHEGTZmCaBfDhLlF34JRmhUtidvfPCjghi/IEAC8VGD0EKqjlEwCkbBKBghAABVGTztaBiH0gAAAABJRU5ErkJggg==","orcid":"","institution":"Chongqing Three Gorges Medical College","correspondingAuthor":true,"prefix":"","firstName":"Jia-feng","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2024-06-17 09:40:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4593241/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4593241/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-84204-7","type":"published","date":"2025-01-02T15:57:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60600572,"identity":"3b172802-0890-4081-ad0f-e65d1e4ae4b0","added_by":"auto","created_at":"2024-07-18 16:02:11","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":192164,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreening process of the included studies\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4593241/v1/b4501d6090b4cec7ba84b35a.jpg"},{"id":60600574,"identity":"e58238a7-a697-491c-8268-21f38a8ff843","added_by":"auto","created_at":"2024-07-18 16:02:11","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":189573,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of logistic regression results\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4593241/v1/31abed9ac386fe5bbbd84460.jpg"},{"id":60600571,"identity":"582a6795-0457-492f-bd7f-6a7827ae9d1c","added_by":"auto","created_at":"2024-07-18 16:02:11","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":29227,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSmoothed Curve Fitting Plot\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4593241/v1/c4705da5fb3c931c9f1003b1.jpg"},{"id":60600573,"identity":"745b0c2c-709e-4b43-9b56-95627362836a","added_by":"auto","created_at":"2024-07-18 16:02:11","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":454550,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of logistic regression results\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4593241/v1/56df405c3e9e6a46022162dc.jpg"},{"id":73093179,"identity":"a173f8ca-68c5-46f7-bd60-f9206f640552","added_by":"auto","created_at":"2025-01-06 16:09:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1480510,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4593241/v1/deeb132d-0804-4c18-b07d-eef12ecbc098.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Systemic Immune-Inflammation Index is Linked to Cataracts: Insights from NHANES 2005-2008","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe lens, positioned behind the iris yet ahead of the vitreous body and retina, is a key player in directing light onto the retina. Originating from ectodermal tissue, it contains epithelial cells that produce lens fibers, leading to the lens's thickening over time. Cataracts, marked by diminished lens clarity, impair vision, reduce contrast sensitivity, affect color perception, and cause glare(Asbell et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Leffler et al., 2020, Tewari et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The World Health Organization reports that cataracts account for approximately 46% of the nearly 180\u0026nbsp;million global cases of visual impairments(Demmin and Silverstein, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Despite being treatable, cataracts are a leading cause of vision loss worldwide, posing significant public health challenges(Cedrone et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1999\u003c/span\u003e, Hashemi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Factors such as aging, smoking, diabetes, and exposure to ultraviolet light are linked to the development of age-related cataracts(Liu et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). While cataract surgery can markedly improve vision, its affordability and the limited availability of surgeons in some regions remain obstacles(Yan et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe SII, a marker for systemic inflammatory response, is linked to the prognosis in elderly patients with tumors of the digestive system(Tian et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Hu et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This index is calculated using a specific formula: platelets \u0026times; neutrophils\u0026thinsp;\u0026divide;\u0026thinsp;lymphocytes(Hu et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). SII serves as a mortality predictor in tumor patients, with higher SII values indicating a greater mortality risk(Tian et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, He et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Li et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Such correlations might stem from the disequilibrium between pro-tumor and anti-tumor agents within the body during tumor states, characterized by increases in neutrophils and platelets, a decrease in lymphocytes, and consequently, a rise in SII levels. The utility of SII has been broadening, demonstrating its potential in predicting disease severity and assessing treatment efficacy in various studies(Wu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Xie et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Wang et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Tang et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Qin et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Importantly, recent research in ophthalmology has underscored a notable link between SII and primary open-angle glaucoma(Tang et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNHANES, a cross-sectional study, aims to gather detailed information on the health and nutritional conditions of U.S. households. Utilizing a complex, stratified, multistage probability cluster sampling approach, it aims to accurately represent the U.S. population(Hartwell et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Despite its extensive use in various research domains, investigations into the relationship between the SII and cataracts using the NHANES database remain unexplored.\u003c/p\u003e \u003cp\u003eTherefore, leveraging NHANES data, our team undertook a population-based cross-sectional analysis to explore the possible link between SII and adult cataract incidence.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source and subject selection\u003c/h2\u003e \u003cp\u003eThe NHANES systematically tracks the health and nutritional trends within the U.S. populace, utilizing an intricate, multistage probabilistic sampling technique. For further insights into NHANES's detailed methodology, refer to their website at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cdc.gov/nchs/nhanes/index.htm\u003c/span\u003e\u003cspan address=\"http://www.cdc.gov/nchs/nhanes/index.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Ethical clearance for the study was granted by the National Center for Health Statistics\u0026rsquo;ethics committee, with all subjects providing written consent(Mahemuti et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Our study excluded individuals lacking SII data, cataract information, and other necessary covariate details, resulting in a study population of 9665. The selection process for the sample is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e[Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e]\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eCataract identification criteria\u003c/h2\u003e \u003cp\u003eThe National Health and Nutrition Examination Survey queried adults aged 20 and above on their eye surgery history for cataracts prior to their eye examination(Park et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Individuals confirming such surgeries were identified as cataract cases in this study. Those who did not respond or provided ambiguous answers were omitted. Considering the rising occurrence and the expanded criteria for cataract surgery within the U.S.(Lundstr\u0026ouml;m et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), self-reported surgeries are presumed to signify clinically significant cataracts. This approach for cataract identification aligns with methodologies used in previous studies(Zhang et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Wang et al., 2016).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eThe definition of systemic immune-inflammation index\u003c/h2\u003e \u003cp\u003eComplete Blood Count (CBC) metrics utilize the Beckman Coulter methodology, combining counting and sizing techniques, an automated system for diluting and mixing samples, and a single beam photometer to measure hemoglobin levels. White Blood Cell (WBC) differentials apply VCS technology. The Beckman Coulter DxH 800 instrument, positioned within the NHANES mobile examination center (MEC), performs CBC analyses on participant blood samples, offering a comprehensive cell distribution profile. In alignment with prior research, SII is determined by multiplying the platelet and neutrophil counts, then dividing by the lymphocyte count(Hu et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Qin et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eCovariates assessment\u003c/h2\u003e \u003cp\u003eDemographic variables, including age, race/ethnicity, gender, education level, marital status, and Body Mass Index (BMI), were selected as covariates in our study. This demographic information was collected via computer-assisted personal interviews(Xing et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Given the known correlations between socio-economic status, living conditions, and health, these demographic factors were used to infer the social and living circumstances of the participants. Additionally, diabetes mellitus, a recognized risk factor for cataracts(Thompson and Lakhani, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), was incorporated as a covariate, with diabetes status determined through participants' self-reports(Zhang et al., 2023).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical methods\u003c/h2\u003e \u003cp\u003eData analysis was conducted using R2 and EmpowerStats software from X\u0026amp;Y Solutions, Inc., based in Boston, MA, accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.empowerstats.com\u003c/span\u003e\u003cspan address=\"http://www.empowerstats.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. This analysis took into account the NHANES' complex sampling framework by integrating sampling weights, strata, and primary sampling units. Continuous variables were reported as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard errors, and categorical variables as percentages\u0026thinsp;\u0026plusmn;\u0026thinsp;SE. Chi-square or T-tests were applied to explore demographic differences.\u003c/p\u003e \u003cp\u003eThe SII data exhibited a right-skewed distribution, leading to the application of a natural logarithm transformation for statistical analysis. Logistic regression models were employed to evaluate the association between SII levels and cataract risk, with Model 1 being unadjusted; Model 2 adjusted for age, race, gender, education, and marital status; and Model 3 additionally adjusted for BMI and diabetes mellitus. These analyses identified significant relationships between SII levels and the likelihood of cataracts. Quantile regression analyses further investigated these associations. Forest plots visually depicted the logistic regression outcomes, while smooth curve fitting explored the potential linear connections between SII levels and cataract occurrence, considering p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as indicating statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDescription of baseline information of the study sample\u003c/h2\u003e \u003cp\u003eOur study leveraged the NHANES dataset, initially encompassing 20,497 participants. Following the removal of cases with incomplete SII or eye data, 9,665 individuals were retained, and 10,832 were excluded. This screening process is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Of the participants analyzed, 48.39% were male, with an average age of 49.44\u0026thinsp;\u0026plusmn;\u0026thinsp;18.24 years. The mean SII value stood at 603.24\u0026thinsp;\u0026plusmn;\u0026thinsp;368.46. Cataracts were identified in 9.29% of the cohort.\u003c/p\u003e \u003cp\u003eParticipants were categorized based on their hyperlipidemia status, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The occurrence of cataracts showed significant correlations with several factors, including age, gender, race, education level, marital status, BMI, diabetes presence, and SII values, all with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Individuals diagnosed with cataracts typically were older, female, of non-Hispanic white ethnicity, had achieved a high school education level, were married or cohabiting, exhibited higher BMI, had diabetes, and presented elevated SII levels compared to their counterparts without cataracts.\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\u003eCharacteristics of participants stratified by cataract from NHANES 2005\u0026ndash;2008.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Cataract\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCataract\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.88\u0026thinsp;\u0026plusmn;\u0026thinsp;16.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.44\u0026thinsp;\u0026plusmn;\u0026thinsp;9.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.95\u0026thinsp;\u0026plusmn;\u0026thinsp;6.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.22\u0026thinsp;\u0026plusmn;\u0026thinsp;5.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII(mm\u0026sup3;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e596.92\u0026thinsp;\u0026plusmn;\u0026thinsp;359.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e665.00\u0026thinsp;\u0026plusmn;\u0026thinsp;440.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender,n(%)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4275 (48.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e402 (44.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4492 (51.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e496 (55.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1758 (20.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (7.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e671 (7.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (5.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic white\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4105 (46.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e637 (70.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1869 (21.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112 (12.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e364 (4.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (3.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 9th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1040 (11.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e185 (20.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u0026ndash;11Th grade (includes 12th grade with no diploma)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1435 (16.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146 (16.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school grad/ged or equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2106 (24.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e238 (26.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome college or AA degre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2418 (27.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e197 (21.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege graduate or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1768 (20.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132 (14.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried or living with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5529 (63.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e458 (51.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried or other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3238 (36.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e440 (49.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDIABETES MELLITUS\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e853 (9.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e232 (25.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7914 (90.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e666 (74.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for continuous variables: the p-value was calculated by a weighted linear regression model. % for categorical variables: the p-value was calculated by a weighted chi-square test. BMI, body mass index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e]\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eAssociation between SII and Cataract\u003c/h2\u003e \u003cp\u003eObserving modest effect sizes, the study applied a logarithmic transformation to the SII, converting it into lnSII. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the outcomes of the multivariable regression analysis, which elucidate the lnSII's relationship with cataracts. A consistent negative link between SII and cataracts manifested across all models (model 1: 1.29 [1.14, 1.47]; model 2: 1.20 [1.04, 1.39]; model 3: 1.20 [1.04, 1.39]).Following this, smooth curve fitting illustrated the linear association between SII levels and the incidence of cataracts, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, with adjustments for age, gender, race, education, marital status, BMI, and diabetes.\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\u003eAssociation between SII and cataract.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e OR\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e OR\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 3\u003csup\u003ec\u003c/sup\u003eOR\u003c/p\u003e \u003cp\u003e(95%Cl)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.29 (1.14, 1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.20 (1.04, 1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.20 (1.04, 1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003eModel 1:a no adjusted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003eb\u003c/sup\u003eModel 2:adjusted for age, race, gender, educational level and marital status.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ec\u003c/sup\u003eModel 3:further adjusted for BMI and diabetes mellitus.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e]\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e[Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e]\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section4\"\u003e \u003ch2\u003e[Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e]\u003c/h2\u003e \u003cp\u003e \u003cem\u003eRelationship of different quartiles of SII with the presence of cataract\u003c/em\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e analyze the relationship between different SII quartiles and cataract prevalence. In the highest quartile (Q4), all models demonstrated a negative correlation between SII levels and cataract likelihood. The odds ratios (ORs) indicated that increased SII values correlate with a higher cataract risk: Model 1 had an OR of 1.49 (95% CI\u0026thinsp;=\u0026thinsp;1.23\u0026ndash;1.81, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), Model 2 an OR of 1.33 (95% CI\u0026thinsp;=\u0026thinsp;1.05\u0026ndash;1.69, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and Model 3 an OR of 1.34 (95% CI\u0026thinsp;=\u0026thinsp;1.06\u0026ndash;1.70, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\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\u003eAssociation between lnSII and cataract in different quartiles\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e OR\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e OR\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 3\u003csup\u003ec\u003c/sup\u003eOR\u003c/p\u003e \u003cp\u003e(95%Cl)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnSII quartiles\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.82, 1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97 (0.76, 1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98 (0.77, 1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8752\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.16 (0.95, 1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17 (0.92, 1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.18 (0.92, 1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1854\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.49 (1.23, 1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.33 (1.05, 1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.34 (1.06, 1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003eModel 1: no adjusted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003eb\u003c/sup\u003eModel 2:adjusted for age, race, gender, educational level and marital status.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ec\u003c/sup\u003eModel 3:further adjusted for BMI and diabetes mellitus.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e]\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e[Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e]\u003c/h2\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this research, we employed the NHANES database to examine the potential link between the SII and cataract development. Our findings from this nationally representative cross-sectional analysis reveal a linear association between SII levels and cataract risk, highlighting that elevated SII values correlate with a higher likelihood of cataract formation. According to our understanding, this research pioneers the examination of the link between SII and cataracts.\u003c/p\u003e \u003cp\u003eSystemic inflammation is measurable through various biochemical or hematological markers commonly assessed in routine blood analyses, or via ratios derived from these markers(Sylman et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The SII, a novel and consistent marker of inflammation, is computed using the formula: platelet count \u0026times; neutrophil count / lymphocyte count(Hu et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Tong et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA heightened SII level signifies an inflammatory environment characterized by an increase in neutrophils and a decrease in lymphocytes, which may contribute to the initiation and progression of diverse diseases. The elevation in SII not only mirrors an inflammatory state but could also suggest a disruption in immune regulation. Cells such as neutrophils and lymphocytes are pivotal in controlling inflammation and immune reactions. Therefore, a high SII might reflect a condition of persistent immune activation, commonly seen in the etiology of various chronic diseases, including age-associated ailments like cataracts(Ferrucci and Fabbri, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe connection between inflammatory states and the formation of cataracts may also involve the action of inflammatory mediators and cytokines. For instance, cytokines released during inflammation, such as tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6), can directly affect the intraocular environment, promoting oxidative damage and apoptosis in lens epithelial cells, thereby accelerating cataract formation(Loukovaara et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe inflammatory changes in the lens microenvironment serve as a key intermediary linking SII to cataract development. When SII is elevated, there is an increase in inflammatory factors in the blood, which can enter the eye through ocular circulation, altering the lens microenvironment. Inflammatory factors like TNF-α and IL-6 can not only directly damage lens cells but also activate inflammatory pathways within the lens, inducing further cellular responses, including the production and release of cytokines, thereby exacerbating oxidative stress and cellular damage in the lens(Rajashekhar et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChanges in SII may also reflect the infiltration status of immune cells, especially neutrophils and lymphocytes, in the eye. The activation and infiltration of immune cells can cause direct physical damage to the lens or lead to lens cell injury and death indirectly through the release of inflammatory mediators and enzymes, thus accelerating cataract development.\u003c/p\u003e \u003cp\u003eOxidative stress is closely related to mitochondrial function. In conditions of inflammation and high SII, increased oxidative stress can lead to mitochondrial dysfunction, playing a crucial role in cataract formation. Mitochondrial dysfunction can result in reduced ATP production and metabolic imbalance within cells, affecting the normal function and survival of lens cells. Moreover, mitochondrial dysfunction can increase the production of reactive oxygen species (ROS) within cells, intensifying oxidative stress(Nunnari and Suomalainen, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, prolonged inflammatory responses and oxidative stress may lead to the depletion of the body's antioxidant defense mechanisms. For example, the activity of antioxidant enzymes such as super oxide dismutase (SOD) and glutathione peroxidase (GP) may decrease, along with reduced levels of antioxidants, diminishing the eye's ability to resist ROS and further exacerbating oxidative damage to the lens.\u003c/p\u003e \u003cp\u003eGiven the correlation between SII and cataracts, future research could explore therapeutic strategies targeting inflammation and oxidative stress. This may include developing new drugs or nutritional supplements aimed at mitigating inflammatory responses, enhancing antioxidant defenses, or directly scavenging ROS. Further studies should assess the potential value of SII in clinically evaluating cataract risk and monitoring disease progression. With a deeper understanding of the specific connection between SII and cataract development, SII could become an important marker for predicting cataract risk and guiding intervention measures.\u003c/p\u003e \u003cp\u003eOur research benefits from using a large, nationally representative sample and adjusting for key demographic, examinational, and laboratory factors, enhancing the credibility and applicability of our findings. Nonetheless, it has limitations, primarily due to its cross-sectional nature, which precludes establishing causality. Future prospective studies with substantial sample sizes are essential to clarify causal links.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eUtilizing the NHANES database, this study unveils for the first time a linear relationship between the SII and the occurrence of cataracts. Our findings indicate that higher levels of SII correlate with an increased risk of developing cataracts. This discovery provides a new biomarker reference for the risk assessment and early prevention of cataracts, highlighting the significant role of systemic inflammation in the development of cataracts.\u003c/p\u003e"},{"header":"Abbreviation","content":"\u003cp\u003eSII: Systemic Immune-Inflammation Index; NHANES: National Health and Nutrition Examination Survey; CBC: Complete Blood Count; WBC: White Blood Cell; MEC: mobile examination center; BMI: Body Mass Index; SE: standard errors; OR: odds ratio; TNF-\u0026alpha;: tumor necrosis factor-alpha; IL-6: interleukin-6; ROS: reactive oxygen species; SOD: super oxide dismutase; GP:glutathione peroxidase;\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eSupportive foundations\u003c/h2\u003e \u003cp\u003eThe study described was supported by grants from a Key Project and a Lab Project at Chongqing Three Gorges Medical College, China (SYS20210021), and a project supported by the Chongqing Education Commission Science and Technology Research Program (KJQN202302715).\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthical considerations and informed consent\u003c/h2\u003e \u003cp\u003eConsidering that the NHANES database is publicly accessible, and patient records are anonymous and de-identified, it does not involve informed consent or ethical approval.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eX L conceived the research idea. X L, GL Du, SN W, SZ and ZZ conducted data cleaning and literature review. XL, GLD, SN W, and SQ Z, contributed to drafting and critically revising the work for intellectual content. X L, GL D, SN W, and SQ Z, conducted the analysis and created the figures and tables. JF T provided a critical review of the manuscript. All authors have read and approved the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003e The authors would like to thank all reviewers for their valuable comments.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003ePublicly available datasets were analyzed in this study. This data can be\u0026nbsp;found at: https://www.cdc.gov/nchs/nhanes/index.htm.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eASBELL, P. A., DUALAN, I., MINDEL, J., BROCKS, D., AHMAD, M. \u0026amp; EPSTEIN, S. 2005. Age-related cataract. \u003cem\u003eLancet\u003c/em\u003e, 365, 599\u0026ndash;609.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCEDRONE, C., CULASSO, F., CESAREO, M., MANCINO, R., RICCI, F., CUPO, G. \u0026amp; CERULLI, L. 1999. Prevalence and incidence of age-related cataract in a population sample from Priverno, Italy. Ophthalmic Epidemiol, 6, 95\u0026ndash;103.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDEMMIN, D. L. \u0026amp; SILVERSTEIN, S. M. 2020. Visual Impairment and Mental Health: Unmet Needs and Treatment Options. Clin Ophthalmol, 14, 4229\u0026ndash;4251.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFERRUCCI, L. \u0026amp; FABBRI, E. 2018. Inflammageing: chronic inflammation in ageing, cardiovascular disease, and frailty. Nat Rev Cardiol, 15, 505\u0026ndash;522.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHARTWELL, M. L., KHOJASTEH, J., WETHERILL, M. S., CROFF, J. M. \u0026amp; WHEELER, D. 2019. 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Front Immunol, 13, 863640.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRAJASHEKHAR, G., RAMADAN, A., ABBURI, C., CALLAGHAN, B., TRAKTUEV, D. O., EVANS-MOLINA, C., MATURI, R., HARRIS, A., KERN, T. S. \u0026amp; MARCH, K. L. 2014. Regenerative therapeutic potential of adipose stromal cells in early stage diabetic retinopathy. PLoS One, 9, e84671.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSYLMAN, J. L., MITRUGNO, A., ATALLAH, M., TORMOEN, G. W., SHATZEL, J. J., TASSI YUNGA, S., WAGNER, T. H., LEPPERT, J. T., MALLICK, P. \u0026amp; MCCARTY, O. J. T. 2018. The Predictive Value of Inflammation-Related Peripheral Blood Measurements in Cancer Staging and Prognosis. Front Oncol, 8, 78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTANG, B., LI, S., HAN, J., CAO, W. \u0026amp; SUN, X. 2020. Associations between Blood Cell Profiles and Primary Open-Angle Glaucoma: A Retrospective Case-Control Study. Ophthalmic Res, 63, 413\u0026ndash;422.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTANG, Y., PENG, B., LIU, J., LIU, Z., XIA, Y. \u0026amp; GENG, B. 2022. Systemic immune-inflammation index and bone mineral density in postmenopausal women: A cross-sectional study of the national health and nutrition examination survey (NHANES) 2007\u0026ndash;2018. Front Immunol, 13, 975400.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTEWARI, D., SAMOILĂ, O., GOCAN, D., MOCAN, A., MOLDOVAN, C., DEVKOTA, H. P., ATANASOV, A. G., ZENGIN, G., ECHEVERR\u0026iacute;A, J., VODNAR, D., SZABO, B. \u0026amp; CRIŞAN, G. 2019. Medicinal Plants and Natural Products Used in Cataract Management. Front Pharmacol, 10, 466.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTHOMPSON, J. \u0026amp; LAKHANI, N. 2015. Cataracts. Prim Care, 42, 409\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTIAN, B. W., YANG, Y. F., YANG, C. C., YAN, L. 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Cadmium and lead exposure and risk of cataract surgery in U.S. adults. Int J Hyg Environ Health, 219, 850\u0026ndash;856.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWU, D., GAO, X., SHI, Y., WANG, H., WANG, W., LI, Y. \u0026amp; ZHENG, Z. 2022. Association between Handgrip Strength and the Systemic Immune-Inflammation Index: A Nationwide Study, NHANES 2011\u0026ndash;2014. Int J Environ Res Public Health, 19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXIE, R., XIAO, M., LI, L., MA, N., LIU, M., HUANG, X., LIU, Q. \u0026amp; ZHANG, Y. 2022. Association between SII and hepatic steatosis and liver fibrosis: A population-based study. Front Immunol, 13, 925690.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXING, W., GAO, W., ZHAO, Z., XU, X., BU, H., SU, H., MAO, G. \u0026amp; CHEN, J. 2023. Dietary flavonoids intake contributes to delay biological aging process: analysis from NHANES dataset. J Transl Med, 21, 492.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYAN, W., WANG, W., VAN WIJNGAARDEN, P., MUELLER, A. \u0026amp; HE, M. 2019. Longitudinal changes in global cataract surgery rate inequality and associations with socioeconomic indices. Clin Exp Ophthalmol, 47, 453\u0026ndash;460.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZHANG, Q., XIAO, S., JIAO, X. \u0026amp; SHEN, Y. 2023. The triglyceride-glucose index is a predictor for cardiovascular and all-cause mortality in CVD patients with diabetes or pre-diabetes: evidence from NHANES 2001\u0026ndash;2018. Cardiovasc Diabetol, 22, 279.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZHANG, X., COTCH, M. F., RYSKULOVA, A., PRIMO, S. A., NAIR, P., CHOU, C. F., GEISS, L. S., BARKER, L. E., ELLIOTT, A. F., CREWS, J. E. \u0026amp; SAADDINE, J. B. 2012. Vision health disparities in the United States by race/ethnicity, education, and economic status: findings from two nationally representative surveys. \u003cem\u003eAm J Ophthalmol\u003c/em\u003e, 154, S53-62.e1.\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Systemic Immune-Inflammation Index, cataracts, NHANES, systemic inflammation, risk assessment","lastPublishedDoi":"10.21203/rs.3.rs-4593241/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4593241/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo investigate the association between the Systemic Immune-Inflammation Index (SII) and cataract occurrence using the National Health and Nutrition Examination Survey (NHANES) 2005\u0026ndash;2008 data.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this population-based cross-sectional study, data from 9,665 NHANES participants were analyzed. The study included adults with complete data on SII, cataract status, and relevant covariates. Logistic regression models adjusted for demographic, clinical, and laboratory variables were used to evaluate the association between SII levels and the risk of cataracts.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results reveal a notable link between higher SII levels and a heightened risk of cataracts. Individuals in the top SII quartile exhibited a higher incidence of cataracts compared to those in the bottom quartile, a trend that remained consistent after adjustments in various models. Quantile regression analyses further supported the connection between increased SII levels and the likelihood of cataracts.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur analysis establishes a linear association between raised SII levels and an elevated risk of cataracts, underscoring systemic inflammation as a crucial element in cataract formation. These findings propose that SII could be an effective biomarker for cataract risk assessment and underline the significance of managing systemic inflammation to prevent cataracts.\u003c/p\u003e","manuscriptTitle":"The Systemic Immune-Inflammation Index is Linked to Cataracts: Insights from NHANES 2005-2008","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-18 16:02:06","doi":"10.21203/rs.3.rs-4593241/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-07T05:19:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-04T12:13:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"243201307588378856506572307413289376638","date":"2024-09-10T10:36:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-28T09:38:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"12086596492546157492681195070919266856","date":"2024-06-24T17:41:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-24T08:10:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-24T08:10:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-06-22T08:05:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-21T04:41:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-06-17T09:39:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ced4a07f-e79e-4e86-947d-7df57a130620","owner":[],"postedDate":"July 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":33890139,"name":"Health sciences/Diseases/Eye diseases"},{"id":33890140,"name":"Health sciences/Health care/Public health"}],"tags":[],"updatedAt":"2025-01-06T15:59:43+00:00","versionOfRecord":{"articleIdentity":"rs-4593241","link":"https://doi.org/10.1038/s41598-024-84204-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-01-02 15:57:08","publishedOnDateReadable":"January 2nd, 2025"},"versionCreatedAt":"2024-07-18 16:02:06","video":"","vorDoi":"10.1038/s41598-024-84204-7","vorDoiUrl":"https://doi.org/10.1038/s41598-024-84204-7","workflowStages":[]},"version":"v1","identity":"rs-4593241","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4593241","identity":"rs-4593241","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}