Non-Linear Relationship Between Serum Vitamin A and Cardiovascular Diseases: A Cross-Sectional Study Based on NHANES 1999-2002

Non-Linear Relationship Between Serum Vitamin A and Cardiovascular Diseases: A Cross-Sectional Study Based on NHANES 1999-2002 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Non-Linear Relationship Between Serum Vitamin A and Cardiovascular Diseases: A Cross-Sectional Study Based on NHANES 1999-2002 Jian Li, Lichun Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5712172/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective To investigate the association between serum vitamin A levels and cerebrovascular disease (CVD), given the unclear relationship between vitamin A and cardiovascular health despite CVD being a leading global cause of death. Methods This cross-sectional study analyzed data from 3,552 participants in the National Health and Nutrition Examination Survey (NHANES) 1999–2002. Serum vitamin A levels were measured as the exposure variable, with cerebrovascular disease as the outcome. Multiple confounders including demographic, lifestyle, and clinical factors were adjusted in the analysis. Results A nonlinear relationship was identified with a threshold at 1.15 µmol/L. Below this threshold, serum vitamin A showed a protective effect against cerebrovascular disease (OR = 0.083, 95% CI: 0.011–0.662, P = 0.0187), while above it, a trend toward increased risk was observed but not statistically significant (OR = 1.226, 95% CI: 0.973–1.546, P = 0.0842). Conclusion Serum vitamin A levels demonstrate a threshold effect on cerebrovascular disease risk, suggesting different roles at varying concentrations. Further longitudinal studies are needed to confirm these findings and establish optimal vitamin A ranges for cardiovascular health. Vitamin A Cerebrovascular Disease NHANES Cross-sectional Study Threshold Effect Figures Figure 1 Figure 2 Introduction Cardiovascular disease (CVD) is a major cause of mortality worldwide, accounting for 31% of total deaths globally. Coronary heart disease and stroke are the most prevalent types of CVD[ 1 , 2 ] [ 3 ]. In the United States, the Centers for Disease Control and Prevention (CDC) reports that over 860,000 deaths per year are attributable to CVD, making it among the most prevalent chronic diseases in adults [ 4 ]. Furthermore, data from the National Health and Nutrition Examination Survey (NHANES) Reports underscore a mounting burden of CVD, particularly among older adults and those afflicted with chronic conditions[ 5 ]. Vitamin A is a vital fat-soluble nutrient, primarily in the forms of retinol and retinoic acid, playing critical roles in immune regulation, cellular differentiation, and antioxidant defense[ 6 ]. Studies suggest that abnormal vitamin A levels are linked to various health risks, including infectious diseases, liver damage, and osteoporosis [ 7 ]. Its influence on CVD remains unclear but may involve mechanisms such as reducing oxidative stress and regulating inflammatory pathways[ 8 , 9 ]. Moreover, vitamin A levels have been associated with chronic conditions like diabetes, cancer, and nonalcoholic fatty liver disease, indicating its possible importance in metabolic health[ 10 ]. The relationship between serum vitamin A levels and CVD remains inconclusive. Some studies suggest that higher vitamin A levels may reduce oxidative stress and inflammation, potentially benefiting cardiovascular health[ 11 , 12 ]. Conversely, other research has found that excessive serum vitamin A level might increase the risks of vascular calcification and atherosclerosis[ 13 ]. These discrepancies likely stem from variability in study designs, population characteristics, and statistical methods. While previous studies have explored this association, the lack of large-scale population research emphasizes the need for further investigation[ 14 ]. This study uses data from the NHANES 1999–2000 and 2001–2002 cycles to evaluate the cross-sectional association between serum vitamin A levels and CVD. NHANES provides a nationally representative sample and comprehensive health data, including biomarker measurements and detailed dietary assessments, making it an ideal resource for such analyses[ 1 , 15 – 17 ]. This research aims to provide evidence on the potential relationship between serum vitamin A levels and cardiovascular health, contributing to both public health strategies and clinical applications while advancing the understanding of nutrition’s role in cardiovascular disease. Methods Study Population This cross-sectional study analyzed data from the NHANES 1999-2000 and 2001-2002 cycles. NHANES employs a complex, multistage probability sampling design to assess the health and nutritional status of the US population. The final analysis included 3,552 participants (378 with cerebrovascular disease and 3,174 controls). Inclusion criteria: NHANES participants aged ≥20 years. Exclusion criteria: (1) missing serum vitamin A data; (2) missing cardiovascular/cerebrovascular disease information; (3) missing key covariate data. All data were collected through standardized NHANES questionnaires, physical examinations, and laboratory tests. Variables The exposure variable was serum vitamin A level, measured using high-performance liquid chromatography (HPLC) in NHANES-designated laboratories following standardized protocols. Fasting blood samples were collected during morning examination sessions and stored at -70°C until analysis. Vitamin A levels were recorded in μmol/L and categorized into quartiles (Q1≤1.6, Q2 1.6-1.9, Q3 1.9-2.3, Q4 >2.3 μmol/L) for analysis. The outcome variable was cardiovascular and cerebrovascular disease, determined by self-reported physician diagnosis from the NHANES standardized questionnaire. Covariates included: (1) demographic characteristics: age, gender, race/ethnicity, poverty-income ratio; (2) lifestyle factors: smoking status, alcohol consumption; (3) clinical and biochemical indicators: diabetes history, serum vitamin E, total cholesterol, triglycerides, uric acid, and creatinine levels. These covariates were selected based on previously reported potential confounding factors and their biological relevance to exposure and outcome. Missing data (<5% of total sample) were handled using complete case analysis. Ethics Statement This study utilized publicly available de-identified data from NHANES. The NHANES program was approved by the Research Ethics Review Board of the National Center for Health Statistics (Protocol #98-12). As this was a retrospective analysis using anonymized data, informed consent was waived in accordance with the Declaration of Helsinki. The study protocol adhered to ethical guidelines for medical research, ensuring participant privacy and data security were adequately protected. Statistical analyses Continuous variables were presented as mean ± standard deviation and compared between groups using Student's t-test or Mann-Whitney U test as appropriate. Categorical variables were expressed as frequencies (percentages) and compared using chi-square test or Fisher's exact test. The association between serum vitamin A levels and cerebrovascular disease was examined using multiple logistic regression models. Serum vitamin A levels were analyzed both as a continuous variable and as quartiles to assess dose-response relationships. The potential nonlinear relationship between serum vitamin A and cerebrovascular disease was evaluated using generalized additive models with adjustment for confounding factors. A two-piecewise linear regression model was applied to examine the threshold effect of serum vitamin A on cerebrovascular disease risk, with the threshold level determined using a likelihood ratio test. The optimal threshold was identified by choosing the inflection point that provided the maximum model likelihood. The difference in slopes before and after the threshold was tested using a likelihood ratio test. All statistical analyses were performed using R software (version 4.1.0, R Foundation for Statistical Computing, Vienna, Austria) and SAS software (version 9.4, SAS Institute Inc., Cary, NC, USA). The "mgcv" package was used for GAM analysis, and the "segmented" package was used for threshold effect analysis. Two-sided P-values < 0.05 were considered statistically significant. The strength of associations was expressed as odds ratios (ORs) with 95% confidence intervals (CIs). Results Baseline characteristics of study participants Table 1 presents the baseline characteristics of the study population. Among 3,552 participants enrolled (378 with cerebrovascular disease and 3,174 controls), baseline analysis revealed that the cerebrovascular disease group was significantly older (67.204 ± 13.117 vs. 47.825 ± 18.285 years, P < 0.001) and had a lower Poverty-to-Income Ratio (2.253 ± 1.456 vs. 2.611 ± 1.613, P < 0.001). Laboratory measurements showed significantly elevated levels of serum vitamin A, vitamin E, uric acid, and creatinine in the cerebrovascular disease group (all P < 0.001). Demographic data demonstrated higher proportions of males (59.26% vs. 46.60%), individuals with lower education levels (47.75% vs. 37.05%), and diabetes mellitus prevalence (22.22% vs. 8.10%) in the cerebrovascular disease group (all P < 0.001). Regarding lifestyle characteristics, the cerebrovascular disease group showed significantly higher proportions of current smokers (23.28% vs. 18.05%) and former smokers (38.36% vs. 21.49%) (P < 0.001), and there was a marginally significant difference in regular alcohol consumption between the two groups (32.54% vs. 45.37%, P = 0.049). Table 2 shows the association between serum vitamin A concentration and the risk of cardiovascular and cerebrovascular disease. In the unadjusted model, each unit increase in serum vitamin A level was associated with an 88.7% higher risk of cardiovascular and cerebrovascular disease (OR=1.887, 95%CI: 1.620-2.197, P<0.001). After adjusting for age, gender, and race/ethnicity (Model I), the increased risk decreased to 33.7% (OR=1.337, 95%CI: 1.130-1.582, P<0.001). After further adjustment for income level, alcohol consumption, diabetes history, smoking status, and other serum indicators (Model II), the association remained significant with a 33.6% increased risk (OR=1.336, 95%CI: 1.076-1.658, P<0.01). When serum vitamin A concentration was categorized into quartiles, compared with the lowest quartile (Q1), the highest quartile (Q4) showed a significantly increased risk of 55.7% (OR=1.557, 95%CI: 1.030-2.354, P<0.05) in the fully adjusted model. Trend analysis revealed a significant dose-dependent increase in cardiovascular and cerebrovascular disease risk with increasing serum vitamin A levels (P trend<0.05). The relationship between serum vitamin A levels and cerebrovascular disease risk exhibited a significant nonlinear pattern, as illustrated in Figure 2. This nonlinear relationship was further confirmed by generalized additive models with adjustment for potential confounders, including age, sex, race/ethnicity, education level, family poverty-income ratio, smoking status, alcohol consumption, diabetes history, total cholesterol, triglycerides, uric acid, and serum creatinine (Figure 2). As shown in Table 3, using a segmented linear model, we identified an inflection point at 1.15 μmol/L. Below this threshold, serum vitamin A showed a significant protective association with cerebrovascular disease risk (OR=0.083, 95%CI: 0.011, 0.662, P=0.0187). However, above this threshold, although higher vitamin A levels demonstrated a tendency toward increased disease risk, this association did not reach statistical significance (OR=1.2226, 95%CI: 0.973, 1.546, P=0.0842). The difference in effects between the two segments remained statistically significant (P=0.020). Discussion The relationship between serum vitamin A levels and cerebrovascular disease risk exhibited a significant nonlinear pattern, with an identified threshold at 1.15 μmol/L. Using a segmented linear model, we found that serum vitamin A levels below this threshold were significantly associated with a protective effect against cerebrovascular disease (OR=0.083, 95% CI: 0.011–0.662, P=0.0187). However, above this threshold, higher serum vitamin A levels showed a tendency toward increased disease risk, though this association did not reach statistical significance (OR=1.226, 95% CI: 0.973–1.546, P=0.0842). The difference in effects between the two segments was statistically significant (P=0.020), indicating a distinct shift in the relationship at the threshold. These findings suggest that serum vitamin A levels may have a dual effect on cerebrovascular disease risk, with a protective role at lower levels and a potential risk-enhancing role at higher levels. Nicoll et al.[13] partially supported our findings of the nonlinear effect of vitamin A in their review on diet's impact on cardiovascular calcification. Based on a systematic literature review with a large sample size, they found that variations in vitamin A levels might affect cardiovascular health through different mechanisms. Similar to our study, they observed that vitamin A might exhibit different biological effects at varying concentrations. However, our study, using 3,552 participants from the NHANES database and employing a segmented linear regression model, more precisely identified the threshold effect at 1.15 μmol/L, which was not clearly revealed in their study. Yamaguchi et al.'s[11] research further supported our findings, discovering complex associations between circulating carotenoids, retinol, and metabolism-related proteins in older adults' plasma proteome. Park et al.'s[18] study confirmed the complex role of vitamin A in chronic diseases, particularly in non-alcoholic fatty liver disease. Compared to these studies, our research's advantage lies in using more rigorous statistical methods, generating a visualized nonlinear association graph (as shown in Figure 2) that more intuitively demonstrates the complex relationship between vitamin A levels and cerebrovascular disease risk. Eggersdorfer and Wyss's[8] research provided potential biological mechanism explanations for our results, indicating that vitamin A might affect vascular health by regulating oxidative stress and inflammatory pathways. These studies collectively support the nonlinear and multidimensional regulatory role of vitamin A in cardiovascular diseases, emphasizing the importance of personalized nutritional interventions. This study provides possible insights into the potential clinical implications of serum vitamin A levels in cerebrovascular disease risk, suggesting its relevance as a biomarker for vascular health. Unlike previous studies, which often lacked population representativeness or failed to explore nonlinear relationships, our research utilized a nationally representative sample and identified a threshold effect, offering a more nuanced understanding of vitamin A's dual role in vascular health. These findings indicate that maintaining serum vitamin A levels within an optimal range may help reduce cerebrovascular disease risk, emphasizing the importance of monitoring vitamin A levels. From a public health perspective, this study highlights the need for balanced dietary recommendations and supplementation policies to avoid both deficiency and excess. Future clinical guidelines could consider incorporating serum vitamin A screening into cardiovascular risk assessments, particularly in populations with a high prevalence of cerebrovascular diseases. Further research, including longitudinal studies and randomized controlled trials, is needed to confirm these findings and explore the mechanisms underlying the observed nonlinear relationship, such as the roles of oxidative stress, inflammation, and vascular calcification. This study has certain strengths that enhance its validity and contribute to the understanding of the relationship between serum vitamin A levels and cerebrovascular disease risk. The use of a nationally representative sample from NHANES increases the generalizability of our findings by reflecting diverse demographic and health-related characteristics. Additionally, the application of segmented regression analysis allowed for a more detailed exploration of the nonlinear relationship between vitamin A levels and disease risk, addressing a gap in the current literature. By adjusting for a wide range of potential confounders, including demographic and metabolic factors, the study reduces bias and increases the reliability of its results. Although observational in nature, the study design and rigorous statistical modeling provide meaningful insights that could inform future public health strategies and clinical assessments. This study has several limitations that should be considered when interpreting the findings. First, due to the inclusion criteria, individuals with missing data on serum vitamin A levels, cardiovascular or cerebrovascular diseases, or key covariates were excluded, which may limit the generalizability of the findings to these specific subgroups. Second, as the study focused on a nationally representative sample of the U.S. population, the findings may not be directly applicable to populations in other countries or regions with different dietary habits, genetic backgrounds, or healthcare systems. Third, this is an observational study, which allows for the identification of associations but cannot establish causal relationships. As such, it remains unclear whether the observed relationship between serum vitamin A levels and cerebrovascular disease risk is causal. Fourth, while we adjusted for a wide range of covariates to minimize bias, residual confounding from unmeasured or unknown variables cannot be ruled out. Finally, the cross-sectional design of the study restricts the ability to assess temporal relationships, and further longitudinal studies are necessary to confirm and expand upon these findings. Taken together, these limitations emphasize the need for additional studies to validate and extend our observations in other settings and populations. Conclusion This NHANES study demonstrated a nonlinear association between serum vitamin A levels and cerebrovascular disease risk, with evidence of threshold effects. Given the cross-sectional design, causality cannot be established. Longitudinal studies are warranted to verify these threshold effects and determine optimal serum vitamin A ranges. Declarations Data Availability Statement The datasets analyzed in this study are publicly accessible through the NHANES website: https://www.cdc.gov/nchs/nhanes. Ethics Statement This study was conducted under the approval of the National Center for Health Statistics (NCHS) Research Ethics Review Board, adhering to institutional guidelines and local regulations. The NHANES protocol, which provided the human samples for this study, received separate ethical approval from the same board. Written informed consent was waived under national legislation and institutional requirements. Author Contributions JL conceptualized the study design, conducted data extraction and statistical analyses, and prepared the initial manuscript draft. LW prepared the tables and figures; LT revised the whole manuscript and made necessary modifications for better logicality and readability; LCZ contributed to manuscript revision and provided critical intellectual input. Both authors contributed to manuscript revision and approved the submitted version. Acknowledgments We extend our gratitude to XL Chen from Yi-er College for her expertise in NHANES database management and her encouraging support throughout this research endeavor. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Conflict of Interest The authors declare no competing interests that could have influenced the work reported in this paper. 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Baseline Characteristics of the Study Population Stratified by Cerebrovascular Disease Status Variable No Cerebrovascular Disease (N = 3174) Cerebrovascular Disease (N = 378) P-value Age (years) 47.825 ± 18.285 67.204 ± 13.117 <0.001 PIR 2.611 ± 1.613 2.253 ± 1.456 <0.001 Vit A (μmol/L) 1.971 ± 0.595 2.264 ± 0.833 <0.001 Vit E (μmol/L) 29.774 ± 13.789 35.546 ± 18.028 <0.001 Chol (mmol/L) 5.146 ± 1.038 5.143 ± 1.046 0.958 TG (mmol/L) 1.617 ± 1.138 1.877 ± 1.144 <0.001 UA (μmol/L) 312.292 ± 89.038 353.857 ± 103.767 <0.001 Cr (μmol/L) 64.308 ± 42.621 86.481 ± 84.384 <0.001 Gender, male, n (%) 1479 (46.60%) 224 (59.26%) <0.001 Ethnicity, n (%) 0.001 - NHW 1460 (45.99%) 212 (56.08%) - NHB 534 (16.82%) 58 (15.34%) - MA 879 (27.69%) 87 (23.02%) - Other 301 (9.48%) 21 (5.56%) Education Level, n (%) <0.001 - Low 1174 (37.05%) 180 (47.75%) - Middle 711 (22.44%) 88 (23.34%) - High 1283 (40.49%) 109 (28.91%) Smoking Status, n (%) <0.001 - Never 1676 (52.93%) 166 (43.91%) - Former 814 (25.71%) 157 (41.53%) - Current 676 (21.35%) 55 (14.55%) Alcohol Use, n (%) 0.049 - Non-Drinker 971 (32.22%) 136 (37.36%) - Drinker 2042 (67.77%) 228 (62.63%) DM, n (%) 257 (8.10%) 84 (22.22%) <0.001 Abbreviations : PIR, Poverty-to-Income Ratio; Vit A, Serum Vitamin A; Vit E, Serum Vitamin E; Chol, Total Cholesterol; TG, Triglycerides; UA, Serum Uric Acid; Cr, Creatinine; NHW, Non-Hispanic White; NHB, Non-Hispanic Black; MA, Mexican American; DM, Diabetes Mellitus. Note : Continuous variables are presented as mean ± standard deviation. Categorical variables are presented as n (%). Table 2. OR (95% CI) for Cardiovascular Disease According to Serum Vitamin A Levels in Different Adjustment Models Variables Non-adjusted Model Adjusted Model I Adjusted Model II Serum vitamin A (μmol/L) 1.887 (1.620, 2.197) *** 1.337 (1.130, 1.582) *** 1.336 (1.076, 1.658) ** Quartiles of serum vitamin A Q1 (≤1.6) a 1.0 (Reference) 1.0 (Reference) 1.0 (Reference) Q2 (1.6-1.9) 1.356 (0.954, 1.926) 0.927 (0.636, 1.352) 1.097 (0.730, 1.649) Q3 (1.9-2.3) 1.692 (1.206, 2.372) ** 1.008 (0.696, 1.459) 1.217 (0.809, 1.831) Q4 (>2.3) 2.619 (1.904, 3.603) *** 1.212 (0.848, 1.731) 1.557 (1.030, 2.354) * P trend b <0.001 0.151 0.023 Abbreviations: OR, odds ratio; CI, confidence interval. Model I: Adjusted for age, gender, race/ethnicity Model II: Adjusted for age, gender, race/ethnicity, poverty income ratio, education level, alcohol use, smoking status, diabetes mellitus, vitamin E, total cholesterol, triglycerides, uric acid, creatinine Notes: a Q1-Q4: quartiles, b Trend test across quartiles, *P < 0.05, **P < 0.01, ***P < 0.001 Table 3. Threshold Effect Analysis of Serum Vitamin A on Cerebrovascular Disease Risk Models and Parameters Effect Estimate (95% CI) P value Model I (Linear) Linear effect 1.141 (0.911, 1.429) 0.2504 Model II (Segmented) Threshold (μmol/L) 1.15 Effect below threshold 0.083 (0.011, 0.662) 0.0187 Effect above threshold 1.226 (0.973, 1.546) 0.0842 Effect difference (above vs. below) 14.708 (1.752, 123.439) 0.0133 Predicted value at threshold -2.889 (-3.116, -2.663) Likelihood ratio test* 0.020 Abbreviations: CI, confidence interval; OR, odds ratio. Notes: Effect estimates are presented as odds ratios (OR) with 95% confidence intervals (CI). *Likelihood ratio test compares the fit of the segmented model (Model II) versus the linear model (Model I). Adjusted for age, gender, poverty income ratio, race/ethnicity, smoking status, alcohol use, serum vitamin E, diabetes mellitus, total cholesterol, triglycerides, uric acid, and creatinine levels. Additional Declarations No competing interests reported. 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. 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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-5712172","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":398362859,"identity":"272af583-3f98-4d91-9fcc-e77a1abb7cb6","order_by":0,"name":"Jian Li","email":"","orcid":"","institution":"Beijing Chao-Yang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Li","suffix":""},{"id":398362860,"identity":"7010d330-d89a-4fa8-abd4-c7ea08e5470f","order_by":1,"name":"Lichun Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYDCCAzAGM/PBxz8qJOTkidRiwMDAzpZszHDGwtiwgWgt/Dxm0oxtFYkIe3EAvuPNxx7z1PyRM2/mMTYunCeRwNjA/PDRDTxaJM8cSzfmOWZgLHOYrfDxzG0SeewMbMbGOXi0GNzIMZPOYTNInMHMvNmAd5tEMWMDD5s0Xi3333+TzvlnUD+DmcFMgneORGLDAUJabgAV5LYZJEgws5hJ8zYQoUXyTJq58d8+Y8MZzGzJhjOOSRgbNhPwC9/xw88ezvgmJy/Bf/jggw81dXLy7M0PH+PTAgRsaHxm/MqxaRkFo2AUjIJRgAYARzlIev/bcksAAAAASUVORK5CYII=","orcid":"","institution":"Beijing Chao-Yang Hospital","correspondingAuthor":true,"prefix":"","firstName":"Lichun","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2024-12-25 16:38:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5712172/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5712172/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73338721,"identity":"d1661c41-2177-4e66-a6fb-0326a4e5c94c","added_by":"auto","created_at":"2025-01-09 04:53:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":147874,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Fig1flowchart.png","url":"https://assets-eu.researchsquare.com/files/rs-5712172/v1/727a120b7504f23cb9bb31d4.png"},{"id":73338720,"identity":"1559caf4-206b-4209-ab8f-d164d6540aeb","added_by":"auto","created_at":"2025-01-09 04:53:43","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105171,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figure2amendments.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5712172/v1/284fe8e1356297f33b66022c.jpg"},{"id":73339919,"identity":"3d1abc9d-c94d-4aab-982f-a00500760467","added_by":"auto","created_at":"2025-01-09 05:09:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1606753,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5712172/v1/7365885a-9f2c-425f-b2ca-0d984a1bfcde.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Non-Linear Relationship Between Serum Vitamin A and Cardiovascular Diseases: A Cross-Sectional Study Based on NHANES 1999-2002","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular disease (CVD) is a major cause of mortality worldwide, accounting for 31% of total deaths globally. Coronary heart disease and stroke are the most prevalent types of CVD[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In the United States, the Centers for Disease Control and Prevention (CDC) reports that over 860,000 deaths per year are attributable to CVD, making it among the most prevalent chronic diseases in adults [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Furthermore, data from the National Health and Nutrition Examination Survey (NHANES) Reports underscore a mounting burden of CVD, particularly among older adults and those afflicted with chronic conditions[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVitamin A is a vital fat-soluble nutrient, primarily in the forms of retinol and retinoic acid, playing critical roles in immune regulation, cellular differentiation, and antioxidant defense[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Studies suggest that abnormal vitamin A levels are linked to various health risks, including infectious diseases, liver damage, and osteoporosis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Its influence on CVD remains unclear but may involve mechanisms such as reducing oxidative stress and regulating inflammatory pathways[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Moreover, vitamin A levels have been associated with chronic conditions like diabetes, cancer, and nonalcoholic fatty liver disease, indicating its possible importance in metabolic health[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe relationship between serum vitamin A levels and CVD remains inconclusive. Some studies suggest that higher vitamin A levels may reduce oxidative stress and inflammation, potentially benefiting cardiovascular health[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Conversely, other research has found that excessive serum vitamin A level might increase the risks of vascular calcification and atherosclerosis[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These discrepancies likely stem from variability in study designs, population characteristics, and statistical methods. While previous studies have explored this association, the lack of large-scale population research emphasizes the need for further investigation[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study uses data from the NHANES 1999\u0026ndash;2000 and 2001\u0026ndash;2002 cycles to evaluate the cross-sectional association between serum vitamin A levels and CVD. NHANES provides a nationally representative sample and comprehensive health data, including biomarker measurements and detailed dietary assessments, making it an ideal resource for such analyses[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This research aims to provide evidence on the potential relationship between serum vitamin A levels and cardiovascular health, contributing to both public health strategies and clinical applications while advancing the understanding of nutrition\u0026rsquo;s role in cardiovascular disease.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cross-sectional study analyzed data from the NHANES 1999-2000 and 2001-2002 cycles. NHANES employs a complex, multistage probability sampling design to assess the health and nutritional status of the US population. The final analysis included 3,552 participants (378 with cerebrovascular disease and 3,174 controls). Inclusion criteria: NHANES participants aged \u0026ge;20 years. Exclusion criteria: (1) missing serum vitamin A data; (2) missing cardiovascular/cerebrovascular disease information; (3) missing key covariate data. All data were collected through standardized NHANES questionnaires, physical examinations, and laboratory tests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe exposure variable was serum vitamin A level, measured using high-performance liquid chromatography (HPLC) in NHANES-designated laboratories following standardized protocols. Fasting blood samples were collected during morning examination sessions and stored at -70\u0026deg;C until analysis. Vitamin A levels were recorded in \u0026mu;mol/L and categorized into quartiles (Q1\u0026le;1.6, Q2 1.6-1.9, Q3 1.9-2.3, Q4 \u0026gt;2.3 \u0026mu;mol/L) for analysis. The outcome variable was cardiovascular and cerebrovascular disease, determined by self-reported physician diagnosis from the NHANES standardized questionnaire. Covariates included: (1) demographic characteristics: age, gender, race/ethnicity, poverty-income ratio; (2) lifestyle factors: smoking status, alcohol consumption; (3) clinical and biochemical indicators: diabetes history, serum vitamin E, total cholesterol, triglycerides, uric acid, and creatinine levels. These covariates were selected based on previously reported potential confounding factors and their biological relevance to exposure and outcome. Missing data (\u0026lt;5% of total sample) were handled using complete case analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized publicly available de-identified data from NHANES. The NHANES program was approved by the Research Ethics Review Board of the National Center for Health Statistics (Protocol #98-12). As this was a retrospective analysis using anonymized data, informed consent was waived in accordance with the Declaration of Helsinki. The study protocol adhered to ethical guidelines for medical research, ensuring participant privacy and data security were adequately protected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContinuous variables were presented as mean \u0026plusmn; standard deviation and compared between groups using Student\u0026apos;s t-test or Mann-Whitney U test as appropriate. Categorical variables were expressed as frequencies (percentages) and compared using chi-square test or Fisher\u0026apos;s exact test. The association between serum vitamin A levels and cerebrovascular disease was examined using multiple logistic regression models. Serum vitamin A levels were analyzed both as a continuous variable and as quartiles to assess dose-response relationships.\u003c/p\u003e\n\u003cp\u003eThe potential nonlinear relationship between serum vitamin A and cerebrovascular disease was evaluated using generalized additive models with adjustment for confounding factors. A two-piecewise linear regression model was applied to examine the threshold effect of serum vitamin A on cerebrovascular disease risk, with the threshold level determined using a likelihood ratio test. The optimal threshold was identified by choosing the inflection point that provided the maximum model likelihood. The difference in slopes before and after the threshold was tested using a likelihood ratio test.\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using R software (version 4.1.0, R Foundation for Statistical Computing, Vienna, Austria) and SAS software (version 9.4, SAS Institute Inc., Cary, NC, USA). The \u0026quot;mgcv\u0026quot; package was used for GAM analysis, and the \u0026quot;segmented\u0026quot; package was used for threshold effect analysis. Two-sided P-values \u0026lt; 0.05 were considered statistically significant. The strength of associations was expressed as odds ratios (ORs) with 95% confidence intervals (CIs).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics of study participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 presents the baseline characteristics of the study population. Among 3,552 participants enrolled (378 with cerebrovascular disease and 3,174 controls), baseline analysis revealed that the cerebrovascular disease group was significantly older (67.204 \u0026plusmn; 13.117 vs. 47.825 \u0026plusmn; 18.285 years, P \u0026lt; 0.001) and had a lower Poverty-to-Income Ratio (2.253 \u0026plusmn; 1.456 vs. 2.611 \u0026plusmn; 1.613, P \u0026lt; 0.001). Laboratory measurements showed significantly elevated levels of serum vitamin A, vitamin E, uric acid, and creatinine in the cerebrovascular disease group (all P \u0026lt; 0.001). Demographic data demonstrated higher proportions of males (59.26% vs. 46.60%), individuals with lower education levels (47.75% vs. 37.05%), and diabetes mellitus prevalence (22.22% vs. 8.10%) in the cerebrovascular disease group (all P \u0026lt; 0.001). Regarding lifestyle characteristics, the cerebrovascular disease group showed significantly higher proportions of current smokers (23.28% vs. 18.05%) and former smokers (38.36% vs. 21.49%) (P \u0026lt; 0.001), and there was a marginally significant difference in regular alcohol consumption between the two groups (32.54% vs. 45.37%, P = 0.049).\u003c/p\u003e\n\u003cp\u003eTable 2 shows the association between serum vitamin A concentration and the risk of cardiovascular and cerebrovascular disease. In the unadjusted model, each unit increase in serum vitamin A level was associated with an 88.7% higher risk of cardiovascular and cerebrovascular disease (OR=1.887, 95%CI: 1.620-2.197, P\u0026lt;0.001). After adjusting for age, gender, and race/ethnicity (Model I), the increased risk decreased to 33.7% (OR=1.337, 95%CI: 1.130-1.582, P\u0026lt;0.001). After further adjustment for income level, alcohol consumption, diabetes history, smoking status, and other serum indicators (Model II), the association remained significant with a 33.6% increased risk (OR=1.336, 95%CI: 1.076-1.658, P\u0026lt;0.01).\u003c/p\u003e\n\u003cp\u003eWhen serum vitamin A concentration was categorized into quartiles, compared with the lowest quartile (Q1), the highest quartile (Q4) showed a significantly increased risk of 55.7% (OR=1.557, 95%CI: 1.030-2.354, P\u0026lt;0.05) in the fully adjusted model. Trend analysis revealed a significant dose-dependent increase in cardiovascular and cerebrovascular disease risk with increasing serum vitamin A levels (P trend\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003eThe relationship between serum vitamin A levels and cerebrovascular disease risk exhibited a significant nonlinear pattern, as illustrated in Figure 2. This nonlinear relationship was further confirmed by generalized additive models with adjustment for potential confounders, including age, sex, race/ethnicity, education level, family poverty-income ratio, smoking status, alcohol consumption, diabetes history, total cholesterol, triglycerides, uric acid, and serum creatinine (Figure 2).\u003c/p\u003e\n\u003cp\u003eAs shown in Table 3, using a segmented linear model, we identified an inflection point at 1.15 \u0026mu;mol/L. Below this threshold, serum vitamin A showed a significant protective association with cerebrovascular disease risk (OR=0.083, 95%CI: 0.011, 0.662, P=0.0187). However, above this threshold, although higher vitamin A levels demonstrated a tendency toward increased disease risk, this association did not reach statistical significance (OR=1.2226, 95%CI: 0.973, 1.546, P=0.0842). The difference in effects between the two segments remained statistically significant (P=0.020).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe relationship between serum vitamin A levels and cerebrovascular disease risk exhibited a significant nonlinear pattern, with an identified threshold at 1.15 \u0026mu;mol/L. Using a segmented linear model, we found that serum vitamin A levels below this threshold were significantly associated with a protective effect against cerebrovascular disease (OR=0.083, 95% CI: 0.011\u0026ndash;0.662, P=0.0187). However, above this threshold, higher serum vitamin A levels showed a tendency toward increased disease risk, though this association did not reach statistical significance (OR=1.226, 95% CI: 0.973\u0026ndash;1.546, P=0.0842). The difference in effects between the two segments was statistically significant (P=0.020), indicating a distinct shift in the relationship at the threshold. These findings suggest that serum vitamin A levels may have a dual effect on cerebrovascular disease risk, with a protective role at lower levels and a potential risk-enhancing role at higher levels.\u003c/p\u003e\n\u003cp\u003eNicoll et al.[13] partially supported our findings of the nonlinear effect of vitamin A in their review on diet\u0026apos;s impact on cardiovascular calcification. Based on a systematic literature review with a large sample size, they found that variations in vitamin A levels might affect cardiovascular health through different mechanisms. Similar to our study, they observed that vitamin A might exhibit different biological effects at varying concentrations. However, our study, using 3,552 participants from the NHANES database and employing a segmented linear regression model, more precisely identified the threshold effect at 1.15 \u0026mu;mol/L, which was not clearly revealed in their study. Yamaguchi et al.\u0026apos;s[11] research further supported our findings, discovering complex associations between circulating carotenoids, retinol, and metabolism-related proteins in older adults\u0026apos; plasma proteome. Park et al.\u0026apos;s[18] study confirmed the complex role of vitamin A in chronic diseases, particularly in non-alcoholic fatty liver disease. Compared to these studies, our research\u0026apos;s advantage lies in using more rigorous statistical methods, generating a visualized nonlinear association graph (as shown in Figure 2) that more intuitively demonstrates the complex relationship between vitamin A levels and cerebrovascular disease risk. Eggersdorfer and Wyss\u0026apos;s[8] research provided potential biological mechanism explanations for our results, indicating that vitamin A might affect vascular health by regulating oxidative stress and inflammatory pathways. These studies collectively support the nonlinear and multidimensional regulatory role of vitamin A in cardiovascular diseases, emphasizing the importance of personalized nutritional interventions.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; This study provides possible insights into the potential clinical implications of serum vitamin A levels in cerebrovascular disease risk, suggesting its relevance as a biomarker for vascular health. Unlike previous studies, which often lacked population representativeness or failed to explore nonlinear relationships, our research utilized a nationally representative sample and identified a threshold effect, offering a more nuanced understanding of vitamin A\u0026apos;s dual role in vascular health. These findings indicate that maintaining serum vitamin A levels within an optimal range may help reduce cerebrovascular disease risk, emphasizing the importance of monitoring vitamin A levels. From a public health perspective, this study highlights the need for balanced dietary recommendations and supplementation policies to avoid both deficiency and excess. Future clinical guidelines could consider incorporating serum vitamin A screening into cardiovascular risk assessments, particularly in populations with a high prevalence of cerebrovascular diseases. Further research, including longitudinal studies and randomized controlled trials, is needed to confirm these findings and explore the mechanisms underlying the observed nonlinear relationship, such as the roles of oxidative stress, inflammation, and vascular calcification.\u003c/p\u003e\n\u003cp\u003eThis study has certain strengths that enhance its validity and contribute to the understanding of the relationship between serum vitamin A levels and cerebrovascular disease risk. The use of a nationally representative sample from NHANES increases the generalizability of our findings by reflecting diverse demographic and health-related characteristics. Additionally, the application of segmented regression analysis allowed for a more detailed exploration of the nonlinear relationship between vitamin A levels and disease risk, addressing a gap in the current literature. By adjusting for a wide range of potential confounders, including demographic and metabolic factors, the study reduces bias and increases the reliability of its results. Although observational in nature, the study design and rigorous statistical modeling provide meaningful insights that could inform future public health strategies and clinical assessments.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; This study has several limitations that should be considered when interpreting the findings. First, due to the inclusion criteria, individuals with missing data on serum vitamin A levels, cardiovascular or cerebrovascular diseases, or key covariates were excluded, which may limit the generalizability of the findings to these specific subgroups. Second, as the study focused on a nationally representative sample of the U.S. population, the findings may not be directly applicable to populations in other countries or regions with different dietary habits, genetic backgrounds, or healthcare systems. Third, this is an observational study, which allows for the identification of associations but cannot establish causal relationships. As such, it remains unclear whether the observed relationship between serum vitamin A levels and cerebrovascular disease risk is causal. Fourth, while we adjusted for a wide range of covariates to minimize bias, residual confounding from unmeasured or unknown variables cannot be ruled out. Finally, the cross-sectional design of the study restricts the ability to assess temporal relationships, and further longitudinal studies are necessary to confirm and expand upon these findings. Taken together, these limitations emphasize the need for additional studies to validate and extend our observations in other settings and populations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis NHANES study demonstrated a nonlinear association between serum vitamin A levels and cerebrovascular disease risk, with evidence of threshold effects. Given the cross-sectional design, causality cannot be established. Longitudinal studies are warranted to verify these threshold effects and determine optimal serum vitamin A ranges.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed in this study are publicly accessible through the NHANES website: https://www.cdc.gov/nchs/nhanes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted under the approval of the National Center for Health Statistics (NCHS) Research Ethics Review Board, adhering to institutional guidelines and local regulations. The NHANES protocol, which provided the human samples for this study, received separate ethical approval from the same board. Written informed consent was waived under national legislation and institutional requirements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJL conceptualized the study design, conducted data extraction and statistical analyses, and prepared the initial manuscript draft. LW prepared the tables and figures; LT revised the whole manuscript and made necessary modifications for better logicality and readability; LCZ contributed to manuscript revision and provided critical intellectual input. Both authors contributed to manuscript revision and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our gratitude to XL Chen from Yi-er College for her expertise in NHANES database management and her encouraging support throughout this research endeavor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests that could have influenced the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher\u0026apos;s Note\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe views and statements expressed in this article reflect solely the authors\u0026apos; perspectives and do not necessarily represent their affiliated institutions or the views of the publisher, editors, and reviewers. The publisher makes no warranty or endorsement regarding any products or claims discussed in this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdedokun TA, Kwaghe VG, Adedokun O, Badru T, Odili AN, Alfa J, Kolade-Yunusa HO, Ojji DB: \u003cstrong\u003ePrevalence and risk factors for subclinical atherosclerosis amongst adults living with HIV in University of Abuja Teaching Hospital, Gwagwalada\u003c/strong\u003e. \u003cem\u003eFrontiers in reproductive health \u003c/em\u003e2023, \u003cstrong\u003e5\u003c/strong\u003e:1092211.\u003c/li\u003e\n\u003cli\u003eMohammadifard N, Gotay C, Humphries KH, Ignaszewski A, Esmaillzadeh A, Sarrafzadegan N: \u003cstrong\u003eElectrolyte minerals intake and cardiovascular health\u003c/strong\u003e. \u003cem\u003eCritical reviews in food science and nutrition \u003c/em\u003e2019, \u003cstrong\u003e59\u003c/strong\u003e(15):2375-2385.\u003c/li\u003e\n\u003cli\u003e(World Health Organization, Cardiovascular 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Forum-Addressing the Safety and Effectiveness of Vitamin A Supplementation\u003c/strong\u003e. \u003cem\u003eAdvances in nutrition (Bethesda, Md) \u003c/em\u003e2020, \u003cstrong\u003e11\u003c/strong\u003e(2):185-199.\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Connor C, Varshosaz P, Moise AR: \u003cstrong\u003eMechanisms of Feedback Regulation of Vitamin A Metabolism\u003c/strong\u003e. \u003cem\u003eNutrients \u003c/em\u003e2022, \u003cstrong\u003e14\u003c/strong\u003e(6).\u003c/li\u003e\n\u003cli\u003eEggersdorfer M, Wyss A: \u003cstrong\u003eCarotenoids in human nutrition and health\u003c/strong\u003e. \u003cem\u003eArchives of biochemistry and biophysics \u003c/em\u003e2018, \u003cstrong\u003e652\u003c/strong\u003e:18-26.\u003c/li\u003e\n\u003cli\u003eFredenburgh LE, Merz AA, Cheng S: \u003cstrong\u003eHaeme oxygenase signalling pathway: implications for cardiovascular disease\u003c/strong\u003e. \u003cem\u003eEuropean heart journal \u003c/em\u003e2015, \u003cstrong\u003e36\u003c/strong\u003e(24):1512-1518.\u003c/li\u003e\n\u003cli\u003eLiu C, Sun X, Peng J, Yu H, Lu J, Feng Y: \u003cstrong\u003eAssociation between dietary vitamin A intake from different sources and non-alcoholic fatty liver disease among adults\u003c/strong\u003e. \u003cem\u003eScientific reports \u003c/em\u003e2024, \u003cstrong\u003e14\u003c/strong\u003e(1):1851.\u003c/li\u003e\n\u003cli\u003eYamaguchi Y, Zampino M, Tanaka T, Bandinelli S, Moaddel R, Fantoni G, Candia J, Ferrucci L, Semba RD: \u003cstrong\u003eThe Plasma Proteome Fingerprint Associated with Circulating Carotenoids and Retinol in Older Adults\u003c/strong\u003e. \u003cem\u003eThe Journal of nutrition \u003c/em\u003e2022, \u003cstrong\u003e152\u003c/strong\u003e(1):40-48.\u003c/li\u003e\n\u003cli\u003eNikrad N, Shakarami A, Tousi AZ, Farhangi MA, Ardekani AM, Jafarzadeh F: \u003cstrong\u003eDietary Antioxidant Quality Score (DAQS), serum lipids, markers of glucose homeostasis, blood pressure and anthropometric features among apparently metabolically healthy obese adults in two metropolises of Iran (Tabriz and Tehran): a cross-sectional study\u003c/strong\u003e. \u003cem\u003eBMC endocrine disorders \u003c/em\u003e2023, \u003cstrong\u003e23\u003c/strong\u003e(1):157.\u003c/li\u003e\n\u003cli\u003eNicoll R, Howard JM, Henein MY: \u003cstrong\u003eA review of the effect of diet on cardiovascular calcification\u003c/strong\u003e. \u003cem\u003eInternational journal of molecular sciences \u003c/em\u003e2015, \u003cstrong\u003e16\u003c/strong\u003e(4):8861-8883.\u003c/li\u003e\n\u003cli\u003eEndres D, Perlov E, Maier S, Feige B, Nickel K, Goll P, Bubl E, Lange T, Glauche V, Graf E\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eNormal Neurochemistry in the Prefrontal and Cerebellar Brain of Adults with Attention-Deficit Hyperactivity Disorder\u003c/strong\u003e. \u003cem\u003eFrontiers in behavioral neuroscience \u003c/em\u003e2015, \u003cstrong\u003e9\u003c/strong\u003e:242.\u003c/li\u003e\n\u003cli\u003eAkbar Z, Shi Z: \u003cstrong\u003eDietary Patterns and Circadian Syndrome among Adults Attending NHANES 2005-2016\u003c/strong\u003e. \u003cem\u003eNutrients \u003c/em\u003e2023, \u003cstrong\u003e15\u003c/strong\u003e(15).\u003c/li\u003e\n\u003cli\u003eDeng MG, Liu F, Wang K, Liang Y, Nie JQ, Liu J: \u003cstrong\u003eRelationship between dietary carotenoid intake and sleep duration in American adults: a population-based study\u003c/strong\u003e. \u003cem\u003eNutrition journal \u003c/em\u003e2023, \u003cstrong\u003e22\u003c/strong\u003e(1):68.\u003c/li\u003e\n\u003cli\u003eLiu H, Zhi J, Zhang C, Huang S, Ma Y, Luo D, Shi L: \u003cstrong\u003eAssociation between Weight-Adjusted Waist Index and depressive symptoms: A nationally representative cross-sectional study from NHANES 2005 to 2018\u003c/strong\u003e. \u003cem\u003eJournal of affective disorders \u003c/em\u003e2024, \u003cstrong\u003e350\u003c/strong\u003e:49-57.\u003c/li\u003e\n\u003cli\u003ePark Y, Smith-Warner SA, Zhang X, Park YJ, Kim H, Park H, Lee HA, Jung S: \u003cstrong\u003eAssociation between use of vitamin and mineral supplement and non-alcoholic fatty liver disease in hypertensive adults\u003c/strong\u003e. \u003cem\u003eScientific reports \u003c/em\u003e2023, \u003cstrong\u003e13\u003c/strong\u003e(1):13670.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Baseline Characteristics of the Study Population Stratified by Cerebrovascular Disease Status\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" width=\"680\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Cerebrovascular Disease (N = 3174)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCerebrovascular Disease (N = 378)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e47.825 \u0026plusmn; 18.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e67.204 \u0026plusmn; 13.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003ePIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e2.611 \u0026plusmn; 1.613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e2.253 \u0026plusmn; 1.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eVit A (\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e1.971 \u0026plusmn; 0.595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e2.264 \u0026plusmn; 0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eVit E (\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e29.774 \u0026plusmn; 13.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e35.546 \u0026plusmn; 18.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eChol (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e5.146 \u0026plusmn; 1.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e5.143 \u0026plusmn; 1.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e1.617 \u0026plusmn; 1.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e1.877 \u0026plusmn; 1.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eUA (\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e312.292 \u0026plusmn; 89.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e353.857 \u0026plusmn; 103.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eCr (\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e64.308 \u0026plusmn; 42.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e86.481 \u0026plusmn; 84.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eGender, male, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e1479 (46.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e224 (59.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eEthnicity, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e- NHW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e1460 (45.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e212 (56.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e- NHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e534 (16.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e58 (15.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e- MA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e879 (27.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e87 (23.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e- Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e301 (9.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e21 (5.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eEducation Level, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e- Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e1174 (37.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e180 (47.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e- Middle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e711 (22.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e88 (23.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e- High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e1283 (40.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e109 (28.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eSmoking Status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e- Never\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e1676 (52.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e166 (43.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e- Former\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e814 (25.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e157 (41.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e- Current\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e676 (21.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e55 (14.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eAlcohol Use, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e- Non-Drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e971 (32.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e136 (37.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e- Drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e2042 (67.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e228 (62.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eDM, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e257 (8.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e84 (22.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: PIR, Poverty-to-Income Ratio; Vit A, Serum Vitamin A; Vit E, Serum Vitamin E; Chol, Total Cholesterol; TG, Triglycerides; UA, Serum Uric Acid; Cr, Creatinine; NHW, Non-Hispanic White; NHB, Non-Hispanic Black; MA, Mexican American; DM, Diabetes Mellitus.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Continuous variables are presented as mean \u0026plusmn; standard deviation. Categorical variables are presented as n (%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. OR (95% CI) for Cardiovascular Disease According to Serum Vitamin A Levels in Different Adjustment Models\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" width=\"644\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-adjusted Model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted Model I\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted Model II\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eSerum vitamin A (\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e1.887 (1.620, 2.197) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e1.337 (1.130, 1.582) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e1.336 (1.076, 1.658) **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eQuartiles of serum vitamin A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eQ1 (\u0026le;1.6)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e1.0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e1.0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e1.0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eQ2 (1.6-1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e1.356 (0.954, 1.926)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e0.927 (0.636, 1.352)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e1.097 (0.730, 1.649)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eQ3 (1.9-2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e1.692 (1.206, 2.372) **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e1.008 (0.696, 1.459)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e1.217 (0.809, 1.831)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eQ4 (\u0026gt;2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e2.619 (1.904, 3.603) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e1.212 (0.848, 1.731)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e1.557 (1.030, 2.354) *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eP trend\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e OR, odds ratio; CI, confidence interval.\u003c/p\u003e\n\u003cp\u003eModel I: Adjusted for age, gender, race/ethnicity\u003c/p\u003e\n\u003cp\u003eModel II: Adjusted for age, gender, race/ethnicity, poverty income ratio, education level, alcohol use, smoking status, diabetes mellitus, vitamin E, total cholesterol, triglycerides, uric acid, creatinine\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e \u003csup\u003ea\u003c/sup\u003eQ1-Q4: quartiles, \u003csup\u003eb\u003c/sup\u003eTrend test across quartiles, *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. Threshold Effect Analysis of Serum Vitamin A on Cerebrovascular Disease Risk\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eModels and Parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEffect Estimate (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eModel I (Linear)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLinear effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.141 (0.911, 1.429)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2504\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eModel II (Segmented)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eThreshold (\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEffect below threshold\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.083 (0.011, 0.662)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEffect above threshold\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.226 (0.973, 1.546)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0842\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEffect difference (above vs. below)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.708 (1.752, 123.439)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePredicted value at threshold\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.889 (-3.116, -2.663)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLikelihood ratio test*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e CI, confidence interval; OR, odds ratio.\u003cstrong\u003eNotes:\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eEffect estimates are presented as odds ratios (OR) with 95% confidence intervals (CI).\u003c/li\u003e\n \u003cli\u003e*Likelihood ratio test compares the fit of the segmented model (Model II) versus the linear model (Model I).\u003c/li\u003e\n \u003cli\u003eAdjusted for age, gender, poverty income ratio, race/ethnicity, smoking status, alcohol use, serum vitamin E, diabetes mellitus, total cholesterol, triglycerides, uric acid, and creatinine levels.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Vitamin A, Cerebrovascular Disease, NHANES, Cross-sectional Study, Threshold Effect","lastPublishedDoi":"10.21203/rs.3.rs-5712172/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5712172/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 serum vitamin A levels and cerebrovascular disease (CVD), given the unclear relationship between vitamin A and cardiovascular health despite CVD being a leading global cause of death.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional study analyzed data from 3,552 participants in the National Health and Nutrition Examination Survey (NHANES) 1999\u0026ndash;2002. Serum vitamin A levels were measured as the exposure variable, with cerebrovascular disease as the outcome. Multiple confounders including demographic, lifestyle, and clinical factors were adjusted in the analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA nonlinear relationship was identified with a threshold at 1.15 \u0026micro;mol/L. Below this threshold, serum vitamin A showed a protective effect against cerebrovascular disease (OR\u0026thinsp;=\u0026thinsp;0.083, 95% CI: 0.011\u0026ndash;0.662, P\u0026thinsp;=\u0026thinsp;0.0187), while above it, a trend toward increased risk was observed but not statistically significant (OR\u0026thinsp;=\u0026thinsp;1.226, 95% CI: 0.973\u0026ndash;1.546, P\u0026thinsp;=\u0026thinsp;0.0842).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSerum vitamin A levels demonstrate a threshold effect on cerebrovascular disease risk, suggesting different roles at varying concentrations. Further longitudinal studies are needed to confirm these findings and establish optimal vitamin A ranges for cardiovascular health.\u003c/p\u003e","manuscriptTitle":"Non-Linear Relationship Between Serum Vitamin A and Cardiovascular Diseases: A Cross-Sectional Study Based on NHANES 1999-2002","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-09 04:53:19","doi":"10.21203/rs.3.rs-5712172/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":"71181f09-2a14-49b5-8ae9-82ad99c77fe4","owner":[],"postedDate":"January 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-09T04:53:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-09 04:53:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5712172","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5712172","identity":"rs-5712172","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}