Association Between Metallic Elements in Diet and Diabetic Retinopathy: A Cross-Sectional Study

Association Between Metallic Elements in Diet and Diabetic Retinopathy: A Cross-Sectional Study | 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 Association Between Metallic Elements in Diet and Diabetic Retinopathy: A Cross-Sectional Study Chenglin Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6225317/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Mar, 2026 Read the published version in Delhi Journal of Ophthalmology → Version 1 posted You are reading this latest preprint version Abstract Purpose: To evaluate the association between consumption of Zn, Cu, Fe, Na, K, Ca, Mg and DR in America through NHANES 2005-2008. Methods: Most representative 163 participants were consisted of 89 with DR and 74 without DR. The multivariate logistic regression was applied to estimate the correlation between consumption of these metallic elements and DR incidence rate in 3 models. And Weighted Quantile Sum (WQS) regression was used to compare the most obvious metal which influence DR risk. Finally, Restricted cubic splines (RCS) were used to investigate the potential nonlinear relationship between DR risk and zinc. Results: Participants with DR showed significantly higher zinc intake level compared to without DR (DR: 13.66±7.41, non-DR: 9.40±5.61, p=0.005). Logistic regression analysis showed a marked positive linear relationship between zinc and the incidence of DR among 7 metallic elements (OR: 1.13, 95% CI: 1.07-1.20, p < 0.001). The significant association was consistent across different adjustment models. But there were no statistically significant associations between DR and the other 6 elements. WQS regression noted that zinc take up the highest estimated weight 0.4236. RCS revealed that there is no nonlinear relationship between zinc and DR risk with p for nonlinear 0.609. Conclusion: In NHANES, participants with type 2 diabetes mellitus (T2DM) exhibiting higher dietary zinc intake demonstrated significantly increased risk of DR. And the relationship between zinc intake and DR is positive and linear. retina risk factor diabetic retinopathy diet Figures Figure 1 Figure 2 Figure 3 1. INTRODUCTION Diabetes has been on the rise globally since the 1990s, with its prevalence closely linked to economic development[ 1 ]. The Lancet has labeled diabetes as a defining disease of the 21st century, predicting that the number of cases will reach 1.31 million by 2050[ 2 ]. The majority of deaths related to diabetes are caused by conditions such as heart disease, cancer, and renal failure. This paper centers on diabetic retinopathy, the prevalent microvascular complication associated with diabetes. In the early stages of diabetic retinopathy (DR), microaneurysms form in the capillary walls, a phase commonly referred to as non-proliferative DR (NPDR)[ 3 ]. As the disease progresses, hard exudates, bolt hemorrhages, and fluctuations in veins caliber become evident[ 4 ]. In severe non-proliferative stages, ischemia and hypoxia develop in the retina due to microaneurysm induced occlusion and the hyperviscosity of blood caused by hyperglycemia. A buildup of angiogenic factors leads to the compensatory growth of blood vessels in the optic disc or other areas of the retina, marking the transition to proliferative DR (PDR) [ 3 ]. However, these newly formed vessels fail to restore oxygen and nutrients to the damaged tissue, instead contributing to adverse outcomes such as retinal detachment, macular edema, neovascular glaucoma, and vitreous hemorrhage. This accounts for why diabetic retinopathy is among the primary contributors to vision loss across the globe[ 5 ]. Currently, treatments for diabetic retinopathy (DR) have become increasingly advanced, including laser photocoagulation, the use of VEGF inhibitors as the preferred medications, and surgical interventions[ 3 ]. As research deepens, oxidative stress has emerged as a key focus of study. Several metal elements have been examined for their role in DR. Zinc deficiency is observed in DR patients, while iron can trigger ferroptosis in retinal cells, exacerbating the condition[ 6 , 7 ]. Additionally, copper overload has been observed in both PDR and NPDR compared to controls[ 8 ]. An increasing number of studies have reported changes in metals levels associated with DR. But research on dietary mineral intake remains insufficiently explored. Existing studies on dietary factors have highlighted the protective roles of vitamins A, C, and E against DR[ 9 ]. This research seeks to identify the risk factors for diabetic retinopathy and offer evidence based dietary guidelines for individuals with diabetes. It examines the relationship between the intake of zinc, iron, copper, sodium, potassium, and calcium and the development of DR, using data from a representative cohort of type 2 diabetes patients from NHANES 2005–2008. 2. METHODS AND MATERIALS 2.1 Database Introduction Conducted by the National Center for Health Statistics (NCHS), NHANES is designed to evaluate the health condition of the U.S. population via biennial assessments. These evaluations cover a wide range of topics, including lifestyle and medical history. The NHANES database consists of five main modules: demographics, dietary, questionnaire, physical examination, and laboratory tests. The data used in this study were sourced from all five of these modules. The independent variable, metal intake from the diet, was obtained from the dietary module, while the dependent variable, the presence of diabetic retinopathy, was identified through retinal screening in the physical examination module. Given the multistage, complex sampling design of NHANES, weighted statistical methods were applied in this study's analyses. 2.2 Inclusion and Exclusion Criteria This study included participants aged 40–70 years with a diabetes onset age > 10 years, who underwent comprehensive retinopathy examinations and completed dietary interviews (n = 610). Drawing on prior research, the length of time a person has had diabetes is a significant risk factor for the development of DR. Epidemiological studies on DR have recognized the length of time since diabetes diagnosis as a significant risk factor for DR. With an increased duration of diabetes, the likelihood of developing DR also increases, with individuals having diabetes for over 10 years having a greater than 60% chance of retinal complications[ 10 ]. In this study, the definition of DR as the outcome variable also includes diabetes duration as a key factor. Participants with diabetes duration < 11 years and diagnosed with PDR or NPDR were classified as the rapidly DR group (n = 89). Participants with diabetes duration ≥ 11 years and no DR diagnosis were assigned to the non-DR group (n = 74). The remaining participants (n = 447) were not enrolled. Including DR patients with longer disease durations theoretically exposes them to higher levels of risk factors compared to those with a later onset of the condition. The detailed study workflow is illustrated in Fig. 1 . 2.3 Assessment of DM and DR The criteria of diagnosing diabetes are an existed diagnosis by a healthcare professional, FBG levels exceeded 7.0 mmol/L, glycated hemoglobin (HbA1c) levels were above 6.5%, or current use of diabetes pills[ 11 ]. DR was diagnosed through retinopathy examination and classified into four stages: no retinopathy, mild NPDR, moderate/severe NPDR, and PDR. 2.4 Diet Metallic Elements Data Collection Dietary metallic elements (Zn, Fe, Cu, Na, K, and Ca) were derived from the total nutrient intake data collected on the first day (DR1TOT). DR1TOT, a dietary dataset from NHANES, was compiled through structured dietary interviews. The data on dietary intake was used to evaluate the types and quantities of food and drinks consumed during the 24-hour window before the interview (covering the full day from 00:00 to 00:00). Additionally, it was used to determine the consumption of calories, vitamins and minerals, and dietary fiber and antioxidants derived from these food items, in accordance with the dietary interview protocol. DR1TOT exclusively reflected nutrients obtained from foods, beverages, and water. This study did not include nutrients from non-food sources, such as dietary supplements or medications. 2.5 Covariates A household interview was conducted to collect age, gender, race, income-to-poverty ratio (PIR), and education level. Participants were categorized into 4 races: Mexican American, Non-Hispanic Black, Non-Hispanic White, and Other. Smoking history was classified based on questionnaire responses, which was categorized into three statuses: (1) never smokers (participants who had smoked fewer than 100 cigarettes in their lifetime), (2) former smokers (participants who had smoked more than 100 cigarettes but had quit smoking), and (3) current smokers (participants who were actively smoking). The PIR was utilized as a metric to assess household socioeconomic status. Households with a PIR 4.9 as high income. Research has shown that maintaining blood pressure below 150/85 mm Hg can significantly delay the progression of DR[ 12 ]. Therefore, blood pressure was included as a covariate in the analysis. Hypertension was diagnosed by blood pressure ≥ 140/90 mm Hg, based on four blood pressure measurements from the Mobile Examination Center (MEC), current use of antihypertensive medications, or self-reported hypertension[ 13 ]. The unit of BMI is kg/m². Hyperviscosity of the blood is considered a risk factor for DR, with its characteristic features being low serum protein levels and hyperlipidemia[ 3 ]. Therefore, serum triglycerides, total cholesterol, and albumin were included as covariates in the analysis. The blood samples used were collected from the MEC. Specimens were stored at − 30°C and transported to designated laboratories for analysis. Total cholesterol levels were quantified enzymatically in serum samples. Serum albumin concentrations were determined using a bichromatic digital endpoint methodology. More detailed measurement methods can be found in NHANES 2005–2006 and 2007–2008 laboratory methods. 2.6 Baseline description R package ‘survey’ was utilized to perform the complex survey sampling. Categorical variables are described by the original frequency and the weighted percentage. Statistical difference analysis was performed by weighted chi-square test. Continuous variables were presented as weighted means ± standard deviations and analyzed using weighted linear regression models. Missing categorical data was imputed as "Unknown". Missing continuous data were imputed using mean values. The statistical analyses were conducted using R (version 4.3.3). Statistical significance was set at a two-tailed p-value < 0.05. 2.7 Association between DR and metallic elements The correlation between metals and DR was tested by multivariate adjusted logistic regression. 3 models were constructed to capture the cogent relationship. Model 1 is crude model with no adjusted covariates. Demographic variable poverty-to-income ratio (PIR), gender, age, education, marital and race were adjusted in model 2. In basis of model 2, BMI, hypertension, smoking and drinking statues were adjusted in model 3. 2.8 WQS regression in metallic elements and DR R package WQS was used in this analysis. WQS regression was utilized to compare the effect of different metallic elements for DR risk. Due to the results of correlation examination, the probable relationship is positive and WQS only provide one-way assessment of exposures. So, the positive weighted index was estimated by setting the function (b1_pos = TRUE). 2.9 Tendency analysis between DR and metallic elements Metallic elements intake levels were categorized into quartiles (Q1–Q4) based on weighted 25th, 50th, and 75th percentiles. Q1, Q2, Q3 and Q4 were converged to the categorical variable for analysis. Q1 is regard as the reference group. Then the medians of each quartile were extracted as continuous variable for tendency analysis. 2.10 RCS for nonlinear analysis R package ‘rms’ was used in analysis. Restriction cubic splines were used to detect whether daily metallic elements intake was nonlinear with the risk of DR. The number of metallic elements performed log transformation in construct the model of RCS. Knots were set as 3 and the median of metallic elements were set as reference, which means that the RCS plot showed the OR (95%CI) changes based on the median. 3. RESULTS 3.1 Overall description A total of 163 participants were included in this analysis, of whom 74 had no concurrent DR over 11 years of diabetes and 89 had concurrent DR less than 11 years. Participant demographic characteristics are summarized in Table 1 . Participants belonging to rapid DR group exhibited a higher proportion of males, elevated zinc intake levels, lower BMI, and reduced educational attainment compared to non-DR participants (p < 0.05). No notable differences were found between the two groups in terms of race, PIR, triglyceride levels, total cholesterol, serum albumin, smoking status, or hypertension history. Table 1 Participant characteristics with rapid DR and those without DR Variable Rapid Diabetic retinopathy (n = 89) Non-Diabetic retinopathy (n = 74) p-value Age, years 54.93 ± 8.08 56.32 ± 7.13 0.437 Gender, n (%) 0.005** Male 56(65.1%) 37(43.2%) Female 33(34.9%) 37(56.8%) Race, n (%) 0.304 Mexican American 25(12.5%) 16(9.8%) Non-Hispanic black 33(24.3%) 30(23.5%) Non-Hispanic white 25(52.5%) 22(63.9%) Other races 6(10.7%) 6(2.8%) PIR, n (%) 0.165 High 11(21.4%) 14(33.0%) Medium 45(51.0%) 26(33.6%) Low 27(20.3%) 29(29.9%) Unknown 6(7.4%) 5(3.5%) Education, n (%) 0.049* High school or above 45(66.5%) 50(80.0%) Below high school 44(33.5%) 24(20.0%) Smoking status, n (%) 0.057 Current Smoker 23(28.4%) 17(22.1%) Former Smoker 24(18.9%) 28(41.0%) Never Smoke 42(52.7%) 29(36.9%) Hypertension, n (%) 0.352 Yes 76(87.6%) 63(81.0%) No 13(12.4%) 11(19.0%) BMI (kg/m2) 32.56 ± 6.54 35.71 ± 7.39 0.014* Ca (mg/day) 898.66 ± 492.07 727.98 ± 463.43 0.149 Mg (mg/day) 322.48 ± 161.61 264.12 ± 134.36 0.139 Zn (mg/day) 13.66 ± 7.41 9.40 ± 5.61 0.005** Cu (mg/day) 1.50 ± 0.74 1.27 ± 0.73 0.31 K (mg/day) 2844.37 ± 1095.67 2386.90 ± 1191.79 0.144 Na (mg/day) 3859.26 ± 1842.86 3312.11 ± 1905.72 0.291 Fe (mg/day) 16.13 ± 7.12 13.68 ± 8.44 0.072 Serum Albumin (g/dL) 4.12 ± 0.30 4.00 ± 0.34 0.121 Triglycerides (mg/dL) 196.58 ± 162.68 245.57 ± 278.90 0.466 Total-Cholesterol (mg/dL) 187.68 ± 49.10 186.67 ± 53.61 0.935 3.2 Correlation analysis in metallic elements and DR Logistic regression was utilized to assess the correlation between the metallic elements and diabetic retinopathy, as shown in Table 2 . In Model 1, zinc intake was positively associated with the incidence of DR (OR: 1.11, 95% CI: 1.05–1.17, p < 0.001). The significant association between zinc and DR was maintained in the models with more variable adjustments (Model2 OR: 1.10, 95% CI: 1.02–1.18, p = 0.014 and Model3 OR: 1.11, 95% CI: 1.00-1.23, p = 0.048). But dietary intake of other metals did not show a clear association with DR in any of the touch patterns. Table 2 Association between mineral intake and DR. Model1, OR [95% CI] p Model2, OR [95% CI] p Model3, OR [95% CI] p DR Ca(mg/day) 1.00 [1.00, 1.00] 0.143 1.00 [1.00, 1.00] 0.386 1.00 [1.00, 1.00] 0.573 Mg(mg/day) 1.00 [1.00, 1.01] 0.093 1.00 [1.00, 1.01] 0.214 1.00 [1.00, 1.01] 0.243 Zn(mg/day) 1.11 [1.05, 1.17] < 0.001 1.10 [1.02, 1.18] 0.014 1.11 [1.00, 1.23] 0.048 Cu(mg/day) 1.56 [0.60, 4.08] 0.343 1.57 [0.64, 3.83] 0.298 1.66 [0.63, 4.36] 0.262 K(mg/day) 1.00 [1.00, 1.00] 0.144 1.00 [1.00, 1.00] 0.267 1.00 [1.00, 1.00] 0.195 Na(mg/day) 1.00 [1.00, 1.00] 0.286 1.00 [1.00, 1.00] 0.444 1.00 [1.00, 1.00] 0.416 Fe(mg/day) 1.04 [0.99, 1.11] 0.134 1.02 [0.95, 1.10] 0.522 1.02 [0.93, 1.11] 0.670 Model 1 includes no covariates; Model 2 is adjusted for age, gender, PIR, education and race; Model 3: adjusted smoking status, drinking status, hypertension and BMI based on model2. OR: odds ratio; CI: confidence interval. 3.3 WQS regression of metallic elements Estimated weight for WQS index were shown in Fig. 2 . Zn and K is the top 2 metallic elements showing their positive relationship in DR risk. The estimated weight of Zn and K were 0.4236 and 0.3883 respectively. That’s to say, the effect of Zn and K is more powerful than other elements for DR risk. 3.4 Tendency examination of metallic elements The results of logistic regression are given in Table 3 . Among 7 metallic elements, zinc is the only elements showed obvious tendency in 3 models. In modal 1, Zn was analyzed using the median of each quartile as a continuous variable and showed that zinc intake significantly increased the incidence of DR (p < 0.001). This positive relationship was maintained respectively in model 2 with p for trend = 0.008 and model 3 with p for trend = 0.023 (detailed OR were shown in Supplement Table 1 ). And the OR value is increased in Q2 to Q4. These results suggest that higher zinc intake is associated with increased risk of DR. But the other 6 metallic elements didn’t show significant tendency. Although the Ca showed significant association with DR risk with p for trend = 0.049 in crude model, but the OR value equaled 1 and significance couldn’t maintain in adjusted model 2 and model 3. Table 3 Association of metal intake and DR. The model adjustment strategies are consistent with those in Table 2 . Metal elements Quartile Interval(mg/d) Median Model 1 Model 2 Model 3 OR [95% CI] P value for Trend OR [95% CI] P value for Trend OR [95% CI] P value for Trend Zn Q1 Zn < 6.52 4.86 Ref < 0.001 Ref 0.005 Ref 0.046 Q2 6.52 ≤ Zn<10.10 8.21 0.90 [0.21, 3.81] 0.67 [0.15, 2.93] 0.77 [0.07, 8.02] Q3 10.10 ≤ Zn < 16.04 13.1 2.93 [0.89, 9.64] 2.62 [0.72, 9.56] 2.86 [0.37, 21.95] Q4 Zn ≥ 16.04 19.79 5.07 [1.84, 13.97] 6.20 [1.32, 29.05] 4.92 [0.37, 65.40] Cu Q1 Cu < 0.844 0.6315 Ref 0.253 Ref 0.156 Ref 0.401 Q2 0.844 ≤ Cu<1.225 1.002 1.90 [0.70, 5.16] 1.00 [0.27, 3.71] 1.48 [0.22, 9.90] Q3 1.225 ≤ Cu < 1.799 1.487 4.11 [0.82, 20.63] 2.88 [0.50, 16.53] 2.72 [0.19, 39.15] Q4 Cu ≥ 1.799 2.192 2.37 [0.46, 12.30] 2.41 [0.41, 14.06] 1.86 [0.16, 21.99] Fe Q1 Fe < 9.09 7.27 Ref 0.269 Ref 0.251 Ref 0.548 Q2 9.09 ≤ Fe<13.00 11.085 2.28 [0.56, 9.29] 2.33 [0.69, 7.84] 3.66 [0.46, 29.06] Q3 13.00 ≤ Fe < 19.15 16.14 4.99 [1.13, 22.03] 5.25 [0.78, 35.29] 5.25 [0.35, 79.16] Q4 Fe ≥ 19.15 25.75 3.09 [1.10, 8.66] 2.67 [0.60, 11.92] 2.00 [0.19, 20.74] Na Q1 Na < 2242 1677 Ref 0.454 Ref 0.073 Ref 0.12 Q2 2242 ≤ Na<3061 2561.5 1.37 [0.38, 4.95] 1.25 [0.29, 5.43] 1.16 [0.15, 8.94] Q3 3061 ≤ Na < 4812 3789.5 3.59 [1.41, 9.15] 3.16 [1.09, 9.17] 4.10 [0.43, 38.75] Q4 Na ≥ 4812 6184 2.56 [0.66, 10.01] 3.29 [0.73, 14.85] 3.42 [0.39, 30.08] K Q1 K < 1816 1244.5 Ref 0.105 Ref 0.132 Ref 0.22 Q2 1816 ≤ K<2525 2162 2.61 [0.62, 10.95] 2.28 [0.59, 8.90] 3.17 [0.36, 27.89] Q3 2525 ≤ K < 3418 2871 4.59 [1.06, 19.83] 3.50 [0.51, 23.95] 3.77 [0.21, 66.37] Q4 K ≥ 3418 4094 3.26 [0.88, 12.05] 3.33 [0.70, 15.91] 3.77 [0.28, 51.65] Ca Q1 Ca < 479 1244.5 Ref 0.049 Ref 0.165 Ref 0.299 Q2 479 ≤ Ca<682 2162 0.54 [0.15, 1.96] 0.57 [0.16, 1.99] 0.77 [0.11, 5.16] Q3 682 ≤ Ca < 1147 2871 1.31 [0.38, 4.45] 0.81 [0.19, 3.46] 0.91 [0.11, 7.59] Q4 Ca ≥ 1147 4094 2.75 [0.61, 12.43] 2.69 [0.36, 20.19] 2.69 [0.11, 63.89] Mg Q1 Mg < 184 126.5 Ref 0.052 Ref 0.111 Ref 0.249 Q2 184 ≤ Mg < 286 232.5 2.57 [0.60, 11.06] 1.99 [0.43, 9.21] 3.10 [0.28, 33.87] Q3 286 ≤ Mg < 358 317 2.63 [0.51, 13.53] 2.46 [0.46, 13.31] 2.33 [0.18, 30.25] Q4 Mg ≥ 358 454 3.81 [1.09, 13.36] 3.82 [0.72, 20.17] 4.13 [0.24, 71.98] 3.5 Examination of Non-linear of metallic elements and DR To verify whether the incidence of DR increases with the increase of metallic elements intake, the RCS plots were drawn. The RCS model is also adjusted the variables in adjusted model 3. Shown in Fig. 3 the p for overall is 0.015 and the p-value of the nonlinear relationship showed 0.609, indicating that there was a significant linear relationship between the zinc and DR risk. The curve showed an overall upward trend, indicating that increased zinc intake gradually increased the risk of DR. The RCS plots of other metallic elements is saved in supplement Fig. 1 . The rest of 6 metallic elements didn’t show obvious significant association in DR risk. 4. DISSCUSSION The present study aimed to reveal the correlation between metallic elements intake and diabetic retinopathy in individuals from United States. Among the 7 metallic elements, zinc showed the most powerful impact in DR risk. The multivariate logistic regression, WQS regression and tendency analysis showed the consistent results. For people with diabetes, an increased dietary intake of zinc increases the risk of developing DR. Furthermore, the relationship was found to be linear, suggesting that higher zinc intake may increase the incidence of DR. The WQS regression indicated that K was the second highest weight index. But the other analysis declared that K own no significant impact in DR risk. The potential mechanism underlying DR is related to oxidative stress[ 14 ]. Hyperglycemia speeds up the formation of advanced glycation end products (AGEs), which trigger the generation of reactive oxygen species (ROS) in retinal pericytes, mainly by activating NADPH oxidase[ 15 , 16 ]. This oxidative stress leads to retinal pericyte apoptosis. Zinc commonly serves as an antioxidant in the body. This is because zinc is the cofactor for the isomers of superoxide dismutase (SOD) [ 17 ]. SOD suppresses apoptosis in retinal pericytes through the inhibition of NADPH oxidase in DR [ 18 ]. Existing research has shown that zinc acts as a protective factor in DR due to its antioxidant properties[ 19 ]. But as a metal ion, excessive zinc may negate its original antioxidant properties and instead exacerbate oxidative stress. Yang et al. demonstrated that zinc overload induces ROS, which mediates mitophagy and mitochondrial dysfunction in cardiomyocytes. This cytotoxicity is exacerbated in the absence of mitofusin 2 (Mfn2) [ 20 ]. Mfn2 plays a critical role in the regulation of mitochondrial energy processes, encompassing nutrient metabolism, the maintenance of membrane voltage, and the generation of ATP through oxidative pathways [ 21 ]. Similarly, reduced expression of Mfn2 (both gene and protein) has been observed in the retina in diabetes[ 22 ]. Nevertheless, the function of Mfn2 in DR is still being studied. There may be a potential link between zinc overload and Mfn2 in DR. Zinc overload may induce ROS in diabetic retinas, exacerbated by a deficiency in Mfn2. There may be an interplay between SOD and Zn-induced ROS in the progression of DR. The classic clinical manifestation of DR is ocular neovascularization[ 23 ]. Numerous studies have shown that hypoxia-inducible factor-1 and VEGF are involved in ocular neovascularization triggered by ischemia and hypoxia[ 24 ]. Some cancer studies have shown that VEGF expression can be lowered by zinc through inhibiting NF-𝜅B[ 25 ]. Based on this, the hypothesis that zinc supplementation could lower VEGF levels and help rescue the retina from DR was proposed[ 19 ]. However, Kheirouri et al. demonstrated through a randomized controlled trial that zinc supplementation did not alter VEGF levels in DR patients[ 26 ]. Traditionally, most DR patients have been recommended zinc supplementation. It is clear that zinc plays a crucial role in the production, preservation, and release of insulin within pancreatic β cells[ 27 ]. Zinc is thought to engage in interaction with taurine and vitamin A, alter photoreceptor plasma membranes, and modulate the light-opsin reaction in the retina and retinal pigment epithelium[ 28 ]. It should be emphasized that the role of zinc in rescuing DR is not as direct as its role in diabetes therapy. Zinc is originally abounded in ocular tissue compared to other tissues[ 29 ]. Continuous, uninterrupted zinc supplementation is likely to lead to high retinal zinc concentrations, even if serum zinc levels are lower than normal. In other words, high levels of zinc intake may not rescue the retina from DR by lowering VEGF levels or increasing antioxidants in the body. On the contrary, these abnormal zinc concentrations could harm the retina by increasing ROS. In summary, a high intake of zinc is linked to a higher occurrence of DR. Zinc overload-induced ROS may damage the retina. Further investigation is needed to determine whether zinc supplementation or high levels of zinc are suitable for DR therapy. Declarations Ethics approval and consent to participate Participation was voluntary and totally understand NHANES process and examination. Ethics approval ID are Protocol #2005-06 and Continuation of Protocol #2005-06. Declaration of Publication Consent The author affirms that ①the work described has not been previously published, ②it is not being considered for publication in other places, ③the author has given consent for its publication, and ④the relevant authorities have sanctioned its publication at the institution where the research was conducted. Contributions Study's conception and design, data collecting, statistical analysis, writing draft and review editing were all finished by Chenglin Liu. The author revised the article and approved the final version. Declaration of Competing Interests The author states that there are no conflicts of interest regarding the manuscript. Funding No financial support was provided for the preparation of this manuscript. Acknowledgments Not applicable. Data Availability Data and supplementary materials are available under request. References Xu Y, Lu J, Li M, et al. Diabetes in China part 1: epidemiology and risk factors. The Lancet Public health. 2024;9(12):e1089-e97. doi: 10.1016/s2468-2667(24)00250-0. The L. Diabetes: a defining disease of the 21st century. Lancet (London, England). 2023;401(10394):2087. doi: 10.1016/s0140-6736(23)01296-5. Kollias AN, Ulbig MW. Diabetic retinopathy: Early diagnosis and effective treatment. Deutsches Arzteblatt international. 2010;107(5):75-83; quiz 4. doi: 10.3238/arztebl.2010.0075. Early photocoagulation for diabetic retinopathy. ETDRS report number 9. Early Treatment Diabetic Retinopathy Study Research Group. Ophthalmology. 1991;98(5 Suppl):766-85. 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The Wisconsin Epidemiologic Study of Diabetic Retinopathy: XXII the twenty-five-year progression of retinopathy in persons with type 1 diabetes. Ophthalmology. 2008;115(11):1859-68. doi: 10.1016/j.ophtha.2008.08.023. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2018. Diabetes care. 2018;41(Suppl 1):S13-s27. doi: 10.2337/dc18-S002. King P, Peacock I, Donnelly R. The UK prospective diabetes study (UKPDS): clinical and therapeutic implications for type 2 diabetes. British journal of clinical pharmacology. 1999;48(5):643-8. doi: 10.1046/j.1365-2125.1999.00092.x. Unger T, Borghi C, Charchar F, et al. 2020 International Society of Hypertension global hypertension practice guidelines. Journal of hypertension. 2020;38(6):982-1004. doi: 10.1097/hjh.0000000000002453. Kang Q, Yang C. Oxidative stress and diabetic retinopathy: Molecular mechanisms, pathogenetic role and therapeutic implications. Redox biology. 2020;37:101799. doi: 10.1016/j.redox.2020.101799. Kim J, Kim KM, Kim CS, et al. Puerarin inhibits the retinal pericyte apoptosis induced by advanced glycation end products in vitro and in vivo by inhibiting NADPH oxidase-related oxidative stress. Free radical biology & medicine. 2012;53(2):357-65. doi: 10.1016/j.freeradbiomed.2012.04.030. Yamagishi S, Amano S, Inagaki Y, et al. Advanced glycation end products-induced apoptosis and overexpression of vascular endothelial growth factor in bovine retinal pericytes. Biochemical and biophysical research communications. 2002;290(3):973-8. doi: 10.1006/bbrc.2001.6312. Doddigarla Z, Parwez I, Ahmad J. Correlation of serum chromium, zinc, magnesium and SOD levels with HbA1c in type 2 diabetes: A cross sectional analysis. Diabetes & metabolic syndrome. 2016;10(1 Suppl 1):S126-9. doi: 10.1016/j.dsx.2015.10.008. Prasad AS. Zinc: role in immunity, oxidative stress and chronic inflammation. Current opinion in clinical nutrition and metabolic care. 2009;12(6):646-52. doi: 10.1097/MCO.0b013e3283312956. Dascalu AM, Anghelache A, Stana D, et al. Serum levels of copper and zinc in diabetic retinopathy: Potential new therapeutic targets (Review). Experimental and therapeutic medicine. 2022;23(5):324. doi: 10.3892/etm.2022.11253. Yang Y, Wang P, Guo J, et al. Zinc Overload Induces Damage to H9c2 Cardiomyocyte Through Mitochondrial Dysfunction and ROS-Mediated Mitophagy. Cardiovascular toxicology. 2023;23(11-12):388-405. doi: 10.1007/s12012-023-09811-8. Stuppia G, Rizzo F, Riboldi G, et al. MFN2-related neuropathies: Clinical features, molecular pathogenesis and therapeutic perspectives. Journal of the neurological sciences. 2015;356(1-2):7-18. doi: 10.1016/j.jns.2015.05.033. Zhong Q, Kowluru RA. Diabetic retinopathy and damage to mitochondrial structure and transport machinery. Investigative ophthalmology & visual science. 2011;52(12):8739-46. doi: 10.1167/iovs.11-8045. Clermont AC, Aiello LP, Mori F, et al. Vascular endothelial growth factor and severity of nonproliferative diabetic retinopathy mediate retinal hemodynamics in vivo: a potential role for vascular endothelial growth factor in the progression of nonproliferative diabetic retinopathy. American journal of ophthalmology. 1997;124(4):433-46. doi: 10.1016/s0002-9394(14)70860-8. Adamis AP, Miller JW, Bernal MT, et al. Increased vascular endothelial growth factor levels in the vitreous of eyes with proliferative diabetic retinopathy. American journal of ophthalmology. 1994;118(4):445-50. doi: 10.1016/s0002-9394(14)75794-0. Uzzo RG, Crispen PL, Golovine K, et al. Diverse effects of zinc on NF-kappaB and AP-1 transcription factors: implications for prostate cancer progression. Carcinogenesis. 2006;27(10):1980-90. doi: 10.1093/carcin/bgl034. Kheirouri S, Naghizadeh S, Alizadeh M. Zinc supplementation does not influence serum levels of VEGF, BDNF, and NGF in diabetic retinopathy patients: a randomized controlled clinical trial. Nutritional neuroscience. 2019;22(10):718-24. doi: 10.1080/1028415x.2018.1436236. Khan AR, Awan FR. Metals in the pathogenesis of type 2 diabetes. Journal of diabetes and metabolic disorders. 2014;13(1):16. doi: 10.1186/2251-6581-13-16. Ugarte M, Osborne NN. Zinc in the retina. Progress in neurobiology. 2001;64(3):219-49. doi: 10.1016/s0301-0082(00)00057-5. Galin MA, Nano HD, Hall T. Ocular zinc concentration. Investigative ophthalmology. 1962;1:142-8. Additional Declarations No competing interests reported. Supplementary Files SupplementFigureandTable.docx Cite Share Download PDF Status: Published Journal Publication published 12 Mar, 2026 Read the published version in Delhi Journal of Ophthalmology → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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The red line indicates the odds ratio (OR), while the blue area denotes the 95% confidence interval.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6225317/v1/f02a96f271d3e6fe582e4d91.png"},{"id":104574490,"identity":"be505ef3-7ba7-4a51-8148-41f6564b0d78","added_by":"auto","created_at":"2026-03-13 13:36:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1227357,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6225317/v1/0ed9eb54-a844-4921-89aa-ca051023ad86.pdf"},{"id":79216945,"identity":"1461e4a7-5085-4582-9673-bf449feee1c7","added_by":"auto","created_at":"2025-03-25 19:01:22","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":233075,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementFigureandTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-6225317/v1/e8d945b81b5dc66f27a1d765.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between Metallic Elements in Diet and Diabetic Retinopathy: A Cross-Sectional Study","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eDiabetes has been on the rise globally since the 1990s, with its prevalence closely linked to economic development[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The Lancet has labeled diabetes as a defining disease of the 21st century, predicting that the number of cases will reach 1.31\u0026nbsp;million by 2050[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The majority of deaths related to diabetes are caused by conditions such as heart disease, cancer, and renal failure. This paper centers on diabetic retinopathy, the prevalent microvascular complication associated with diabetes.\u003c/p\u003e \u003cp\u003eIn the early stages of diabetic retinopathy (DR), microaneurysms form in the capillary walls, a phase commonly referred to as non-proliferative DR (NPDR)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As the disease progresses, hard exudates, bolt hemorrhages, and fluctuations in veins caliber become evident[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In severe non-proliferative stages, ischemia and hypoxia develop in the retina due to microaneurysm induced occlusion and the hyperviscosity of blood caused by hyperglycemia. A buildup of angiogenic factors leads to the compensatory growth of blood vessels in the optic disc or other areas of the retina, marking the transition to proliferative DR (PDR) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, these newly formed vessels fail to restore oxygen and nutrients to the damaged tissue, instead contributing to adverse outcomes such as retinal detachment, macular edema, neovascular glaucoma, and vitreous hemorrhage. This accounts for why diabetic retinopathy is among the primary contributors to vision loss across the globe[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrently, treatments for diabetic retinopathy (DR) have become increasingly advanced, including laser photocoagulation, the use of VEGF inhibitors as the preferred medications, and surgical interventions[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As research deepens, oxidative stress has emerged as a key focus of study. Several metal elements have been examined for their role in DR. Zinc deficiency is observed in DR patients, while iron can trigger ferroptosis in retinal cells, exacerbating the condition[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Additionally, copper overload has been observed in both PDR and NPDR compared to controls[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. An increasing number of studies have reported changes in metals levels associated with DR. But research on dietary mineral intake remains insufficiently explored. Existing studies on dietary factors have highlighted the protective roles of vitamins A, C, and E against DR[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This research seeks to identify the risk factors for diabetic retinopathy and offer evidence based dietary guidelines for individuals with diabetes. It examines the relationship between the intake of zinc, iron, copper, sodium, potassium, and calcium and the development of DR, using data from a representative cohort of type 2 diabetes patients from NHANES 2005\u0026ndash;2008.\u003c/p\u003e"},{"header":"2. METHODS AND MATERIALS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Database Introduction\u003c/h2\u003e \u003cp\u003eConducted by the National Center for Health Statistics (NCHS), NHANES is designed to evaluate the health condition of the U.S. population via biennial assessments. These evaluations cover a wide range of topics, including lifestyle and medical history. The NHANES database consists of five main modules: demographics, dietary, questionnaire, physical examination, and laboratory tests. The data used in this study were sourced from all five of these modules. The independent variable, metal intake from the diet, was obtained from the dietary module, while the dependent variable, the presence of diabetic retinopathy, was identified through retinal screening in the physical examination module. Given the multistage, complex sampling design of NHANES, weighted statistical methods were applied in this study's analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Inclusion and Exclusion Criteria\u003c/h2\u003e \u003cp\u003eThis study included participants aged 40\u0026ndash;70 years with a diabetes onset age\u0026thinsp;\u0026gt;\u0026thinsp;10 years, who underwent comprehensive retinopathy examinations and completed dietary interviews (n\u0026thinsp;=\u0026thinsp;610). Drawing on prior research, the length of time a person has had diabetes is a significant risk factor for the development of DR. Epidemiological studies on DR have recognized the length of time since diabetes diagnosis as a significant risk factor for DR. With an increased duration of diabetes, the likelihood of developing DR also increases, with individuals having diabetes for over 10 years having a greater than 60% chance of retinal complications[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In this study, the definition of DR as the outcome variable also includes diabetes duration as a key factor. Participants with diabetes duration\u0026thinsp;\u0026lt;\u0026thinsp;11 years and diagnosed with PDR or NPDR were classified as the rapidly DR group (n\u0026thinsp;=\u0026thinsp;89). Participants with diabetes duration\u0026thinsp;\u0026ge;\u0026thinsp;11 years and no DR diagnosis were assigned to the non-DR group (n\u0026thinsp;=\u0026thinsp;74). The remaining participants (n\u0026thinsp;=\u0026thinsp;447) were not enrolled. Including DR patients with longer disease durations theoretically exposes them to higher levels of risk factors compared to those with a later onset of the condition. The detailed study workflow is illustrated 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=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Assessment of DM and DR\u003c/h2\u003e \u003cp\u003eThe criteria of diagnosing diabetes are an existed diagnosis by a healthcare professional, FBG levels exceeded 7.0 mmol/L, glycated hemoglobin (HbA1c) levels were above 6.5%, or current use of diabetes pills[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. DR was diagnosed through retinopathy examination and classified into four stages: no retinopathy, mild NPDR, moderate/severe NPDR, and PDR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Diet Metallic Elements Data Collection\u003c/h2\u003e \u003cp\u003eDietary metallic elements (Zn, Fe, Cu, Na, K, and Ca) were derived from the total nutrient intake data collected on the first day (DR1TOT). DR1TOT, a dietary dataset from NHANES, was compiled through structured dietary interviews. The data on dietary intake was used to evaluate the types and quantities of food and drinks consumed during the 24-hour window before the interview (covering the full day from 00:00 to 00:00). Additionally, it was used to determine the consumption of calories, vitamins and minerals, and dietary fiber and antioxidants derived from these food items, in accordance with the dietary interview protocol. DR1TOT exclusively reflected nutrients obtained from foods, beverages, and water. This study did not include nutrients from non-food sources, such as dietary supplements or medications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Covariates\u003c/h2\u003e \u003cp\u003eA household interview was conducted to collect age, gender, race, income-to-poverty ratio (PIR), and education level. Participants were categorized into 4 races: Mexican American, Non-Hispanic Black, Non-Hispanic White, and Other. Smoking history was classified based on questionnaire responses, which was categorized into three statuses: (1) never smokers (participants who had smoked fewer than 100 cigarettes in their lifetime), (2) former smokers (participants who had smoked more than 100 cigarettes but had quit smoking), and (3) current smokers (participants who were actively smoking). The PIR was utilized as a metric to assess household socioeconomic status. Households with a PIR\u0026thinsp;\u0026lt;\u0026thinsp;1.3 were classified as low income, those with a PIR between 1.3 and 4.9 as medium income, and those with a PIR\u0026thinsp;\u0026gt;\u0026thinsp;4.9 as high income. Research has shown that maintaining blood pressure below 150/85 mm Hg can significantly delay the progression of DR[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Therefore, blood pressure was included as a covariate in the analysis. Hypertension was diagnosed by blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140/90 mm Hg, based on four blood pressure measurements from the Mobile Examination Center (MEC), current use of antihypertensive medications, or self-reported hypertension[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The unit of BMI is kg/m\u0026sup2;.\u003c/p\u003e \u003cp\u003eHyperviscosity of the blood is considered a risk factor for DR, with its characteristic features being low serum protein levels and hyperlipidemia[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, serum triglycerides, total cholesterol, and albumin were included as covariates in the analysis. The blood samples used were collected from the MEC. Specimens were stored at \u0026minus;\u0026thinsp;30\u0026deg;C and transported to designated laboratories for analysis. Total cholesterol levels were quantified enzymatically in serum samples. Serum albumin concentrations were determined using a bichromatic digital endpoint methodology. More detailed measurement methods can be found in NHANES 2005\u0026ndash;2006 and 2007\u0026ndash;2008 laboratory methods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Baseline description\u003c/h2\u003e \u003cp\u003eR package \u0026lsquo;survey\u0026rsquo; was utilized to perform the complex survey sampling. Categorical variables are described by the original frequency and the weighted percentage. Statistical difference analysis was performed by weighted chi-square test. Continuous variables were presented as weighted means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations and analyzed using weighted linear regression models. Missing categorical data was imputed as \"Unknown\". Missing continuous data were imputed using mean values. The statistical analyses were conducted using R (version 4.3.3). Statistical significance was set at a two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Association between DR and metallic elements\u003c/h2\u003e \u003cp\u003eThe correlation between metals and DR was tested by multivariate adjusted logistic regression. 3 models were constructed to capture the cogent relationship. Model 1 is crude model with no adjusted covariates. Demographic variable poverty-to-income ratio (PIR), gender, age, education, marital and race were adjusted in model 2. In basis of model 2, BMI, hypertension, smoking and drinking statues were adjusted in model 3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 WQS regression in metallic elements and DR\u003c/h2\u003e \u003cp\u003eR package WQS was used in this analysis. WQS regression was utilized to compare the effect of different metallic elements for DR risk. Due to the results of correlation examination, the probable relationship is positive and WQS only provide one-way assessment of exposures. So, the positive weighted index was estimated by setting the function (b1_pos\u0026thinsp;=\u0026thinsp;TRUE).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Tendency analysis between DR and metallic elements\u003c/h2\u003e \u003cp\u003eMetallic elements intake levels were categorized into quartiles (Q1\u0026ndash;Q4) based on weighted 25th, 50th, and 75th percentiles. Q1, Q2, Q3 and Q4 were converged to the categorical variable for analysis. Q1 is regard as the reference group. Then the medians of each quartile were extracted as continuous variable for tendency analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 RCS for nonlinear analysis\u003c/h2\u003e \u003cp\u003eR package \u0026lsquo;rms\u0026rsquo; was used in analysis. Restriction cubic splines were used to detect whether daily metallic elements intake was nonlinear with the risk of DR. The number of metallic elements performed log transformation in construct the model of RCS. Knots were set as 3 and the median of metallic elements were set as reference, which means that the RCS plot showed the OR (95%CI) changes based on the median.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Overall description\u003c/h2\u003e \u003cp\u003eA total of 163 participants were included in this analysis, of whom 74 had no concurrent DR over 11 years of diabetes and 89 had concurrent DR less than 11 years. Participant demographic characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Participants belonging to rapid DR group exhibited a higher proportion of males, elevated zinc intake levels, lower BMI, and reduced educational attainment compared to non-DR participants (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No notable differences were found between the two groups in terms of race, PIR, triglyceride levels, total cholesterol, serum albumin, smoking status, or hypertension history.\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\u003eParticipant characteristics with rapid DR and those without DR\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRapid Diabetic retinopathy (n\u0026thinsp;=\u0026thinsp;89)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-Diabetic retinopathy (n\u0026thinsp;=\u0026thinsp;74)\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\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.93\u0026thinsp;\u0026plusmn;\u0026thinsp;8.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.32\u0026thinsp;\u0026plusmn;\u0026thinsp;7.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eGender, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005**\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\u003e56(65.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37(43.2%)\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\u003e33(34.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37(56.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eRace, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(9.8%)\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\u003e33(24.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(23.5%)\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\u003e25(52.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(63.9%)\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 races\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(10.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePIR, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(21.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(33.0%)\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\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45(51.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(33.6%)\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\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27(20.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(29.9%)\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\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eEducation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.049*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45(66.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50(80.0%)\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\u003eBelow high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44(33.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSmoking status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(28.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(22.1%)\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\u003eFormer Smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(18.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(41.0%)\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\u003eNever Smoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42(52.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(36.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.352\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\u003e76(87.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63(81.0%)\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\u003e13(12.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(19.0%)\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\u003eBMI (kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.56\u0026thinsp;\u0026plusmn;\u0026thinsp;6.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.71\u0026thinsp;\u0026plusmn;\u0026thinsp;7.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa (mg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e898.66\u0026thinsp;\u0026plusmn;\u0026thinsp;492.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e727.98\u0026thinsp;\u0026plusmn;\u0026thinsp;463.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg (mg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e322.48\u0026thinsp;\u0026plusmn;\u0026thinsp;161.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e264.12\u0026thinsp;\u0026plusmn;\u0026thinsp;134.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn (mg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.66\u0026thinsp;\u0026plusmn;\u0026thinsp;7.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.40\u0026thinsp;\u0026plusmn;\u0026thinsp;5.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCu (mg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK (mg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2844.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1095.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2386.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1191.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa (mg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3859.26\u0026thinsp;\u0026plusmn;\u0026thinsp;1842.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3312.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1905.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFe (mg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.13\u0026thinsp;\u0026plusmn;\u0026thinsp;7.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.68\u0026thinsp;\u0026plusmn;\u0026thinsp;8.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum Albumin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e196.58\u0026thinsp;\u0026plusmn;\u0026thinsp;162.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e245.57\u0026thinsp;\u0026plusmn;\u0026thinsp;278.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal-Cholesterol (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e187.68\u0026thinsp;\u0026plusmn;\u0026thinsp;49.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e186.67\u0026thinsp;\u0026plusmn;\u0026thinsp;53.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Correlation analysis in metallic elements and DR\u003c/h2\u003e \u003cp\u003eLogistic regression was utilized to assess the correlation between the metallic elements and diabetic retinopathy, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In Model 1, zinc intake was positively associated with the incidence of DR (OR: 1.11, 95% CI: 1.05\u0026ndash;1.17, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The significant association between zinc and DR was maintained in the models with more variable adjustments (Model2 OR: 1.10, 95% CI: 1.02\u0026ndash;1.18, p\u0026thinsp;=\u0026thinsp;0.014 and Model3 OR: 1.11, 95% CI: 1.00-1.23, p\u0026thinsp;=\u0026thinsp;0.048). But dietary intake of other metals did not show a clear association with DR in any of the touch patterns.\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 mineral intake and DR.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel1, OR [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel2, OR [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel3, OR [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa(mg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 [1.00, 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00 [1.00, 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00 [1.00, 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg(mg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 [1.00, 1.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00 [1.00, 1.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00 [1.00, 1.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn(mg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11 [1.05, 1.17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.10 [1.02, 1.18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.11 [1.00, 1.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCu(mg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.56 [0.60, 4.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.57 [0.64, 3.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.66 [0.63, 4.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK(mg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 [1.00, 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00 [1.00, 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00 [1.00, 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa(mg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 [1.00, 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00 [1.00, 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00 [1.00, 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFe(mg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.04 [0.99, 1.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02 [0.95, 1.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.02 [0.93, 1.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eModel 1 includes no covariates; Model 2 is adjusted for age, gender, PIR, education and race; Model 3: adjusted smoking status, drinking status, hypertension and BMI based on model2. OR: odds ratio; CI: confidence interval.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 WQS regression of metallic elements\u003c/h2\u003e \u003cp\u003eEstimated weight for WQS index were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Zn and K is the top 2 metallic elements showing their positive relationship in DR risk. The estimated weight of Zn and K were 0.4236 and 0.3883 respectively. That\u0026rsquo;s to say, the effect of Zn and K is more powerful than other elements for DR risk.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Tendency examination of metallic elements\u003c/h2\u003e \u003cp\u003eThe results of logistic regression are given in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Among 7 metallic elements, zinc is the only elements showed obvious tendency in 3 models. In modal 1, Zn was analyzed using the median of each quartile as a continuous variable and showed that zinc intake significantly increased the incidence of DR (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This positive relationship was maintained respectively in model 2 with p for trend\u0026thinsp;=\u0026thinsp;0.008 and model 3 with p for trend\u0026thinsp;=\u0026thinsp;0.023 (detailed OR were shown in Supplement Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). And the OR value is increased in Q2 to Q4. These results suggest that higher zinc intake is associated with increased risk of DR. But the other 6 metallic elements didn\u0026rsquo;t show significant tendency. Although the Ca showed significant association with DR risk with p for trend\u0026thinsp;=\u0026thinsp;0.049 in crude model, but the OR value equaled 1 and significance couldn\u0026rsquo;t maintain in adjusted model 2 and model 3.\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 of metal intake and DR. The model adjustment strategies are consistent with those in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eMetal elements\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eQuartile\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eInterval(mg/d)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eMedian\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eOR [95% CI]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eP value for Trend\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eOR [95% CI]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eP value for Trend\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eOR [95% CI]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eP value for Trend\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZn\u0026thinsp;\u0026lt;\u0026thinsp;6.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.52\u0026thinsp;\u0026le;\u0026thinsp;Zn\u0026lt;10.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90 [0.21, 3.81]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.67 [0.15, 2.93]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.77 [0.07, 8.02]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.10\u0026thinsp;\u0026le;\u0026thinsp;Zn\u0026thinsp;\u0026lt;\u0026thinsp;16.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.93 [0.89, 9.64]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.62 [0.72, 9.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.86 [0.37, 21.95]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZn\u0026thinsp;\u0026ge;\u0026thinsp;16.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.07 [1.84, 13.97]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.20 [1.32, 29.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.92 [0.37, 65.40]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCu\u0026thinsp;\u0026lt;\u0026thinsp;0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.401\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.844\u0026thinsp;\u0026le;\u0026thinsp;Cu\u0026lt;1.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.90 [0.70, 5.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00 [0.27, 3.71]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.48 [0.22, 9.90]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.225\u0026thinsp;\u0026le;\u0026thinsp;Cu\u0026thinsp;\u0026lt;\u0026thinsp;1.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.11 [0.82, 20.63]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.88 [0.50, 16.53]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.72 [0.19, 39.15]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCu\u0026thinsp;\u0026ge;\u0026thinsp;1.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.37 [0.46, 12.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.41 [0.41, 14.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.86 [0.16, 21.99]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFe\u0026thinsp;\u0026lt;\u0026thinsp;9.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.09\u0026thinsp;\u0026le;\u0026thinsp;Fe\u0026lt;13.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.28 [0.56, 9.29]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.33 [0.69, 7.84]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.66 [0.46, 29.06]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.00\u0026thinsp;\u0026le;\u0026thinsp;Fe\u0026thinsp;\u0026lt;\u0026thinsp;19.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.99 [1.13, 22.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.25 [0.78, 35.29]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.25 [0.35, 79.16]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFe\u0026thinsp;\u0026ge;\u0026thinsp;19.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.09 [1.10, 8.66]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.67 [0.60, 11.92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.00 [0.19, 20.74]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eNa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNa\u0026thinsp;\u0026lt;\u0026thinsp;2242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2242\u0026thinsp;\u0026le;\u0026thinsp;Na\u0026lt;3061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2561.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.37 [0.38, 4.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.25 [0.29, 5.43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.16 [0.15, 8.94]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3061\u0026thinsp;\u0026le;\u0026thinsp;Na\u0026thinsp;\u0026lt;\u0026thinsp;4812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3789.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.59 [1.41, 9.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.16 [1.09, 9.17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.10 [0.43, 38.75]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNa\u0026thinsp;\u0026ge;\u0026thinsp;4812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.56 [0.66, 10.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.29 [0.73, 14.85]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.42 [0.39, 30.08]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eK\u0026thinsp;\u0026lt;\u0026thinsp;1816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1244.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1816\u0026thinsp;\u0026le;\u0026thinsp;K\u0026lt;2525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.61 [0.62, 10.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.28 [0.59, 8.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.17 [0.36, 27.89]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2525\u0026thinsp;\u0026le;\u0026thinsp;K\u0026thinsp;\u0026lt;\u0026thinsp;3418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.59 [1.06, 19.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.50 [0.51, 23.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.77 [0.21, 66.37]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eK\u0026thinsp;\u0026ge;\u0026thinsp;3418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.26 [0.88, 12.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.33 [0.70, 15.91]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.77 [0.28, 51.65]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eCa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCa\u0026thinsp;\u0026lt;\u0026thinsp;479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1244.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e479\u0026thinsp;\u0026le;\u0026thinsp;Ca\u0026lt;682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.54 [0.15, 1.96]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.57 [0.16, 1.99]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.77 [0.11, 5.16]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e682\u0026thinsp;\u0026le;\u0026thinsp;Ca\u0026thinsp;\u0026lt;\u0026thinsp;1147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.31 [0.38, 4.45]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.81 [0.19, 3.46]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.91 [0.11, 7.59]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCa\u0026thinsp;\u0026ge;\u0026thinsp;1147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.75 [0.61, 12.43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.69 [0.36, 20.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.69 [0.11, 63.89]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMg\u0026thinsp;\u0026lt;\u0026thinsp;184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e184\u0026thinsp;\u0026le;\u0026thinsp;Mg\u0026thinsp;\u0026lt;\u0026thinsp;286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e232.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.57 [0.60, 11.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.99 [0.43, 9.21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.10 [0.28, 33.87]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e286\u0026thinsp;\u0026le;\u0026thinsp;Mg\u0026thinsp;\u0026lt;\u0026thinsp;358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.63 [0.51, 13.53]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.46 [0.46, 13.31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.33 [0.18, 30.25]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMg\u0026thinsp;\u0026ge;\u0026thinsp;358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.81 [1.09, 13.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.82 [0.72, 20.17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.13 [0.24, 71.98]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Examination of Non-linear of metallic elements and DR\u003c/h2\u003e \u003cp\u003eTo verify whether the incidence of DR increases with the increase of metallic elements intake, the RCS plots were drawn. The RCS model is also adjusted the variables in adjusted model 3. Shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e the p for overall is 0.015 and the p-value of the nonlinear relationship showed 0.609, indicating that there was a significant linear relationship between the zinc and DR risk. The curve showed an overall upward trend, indicating that increased zinc intake gradually increased the risk of DR. The RCS plots of other metallic elements is saved in supplement Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The rest of 6 metallic elements didn\u0026rsquo;t show obvious significant association in DR risk.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISSCUSSION","content":"\u003cp\u003eThe present study aimed to reveal the correlation between metallic elements intake and diabetic retinopathy in individuals from United States. Among the 7 metallic elements, zinc showed the most powerful impact in DR risk. The multivariate logistic regression, WQS regression and tendency analysis showed the consistent results. For people with diabetes, an increased dietary intake of zinc increases the risk of developing DR. Furthermore, the relationship was found to be linear, suggesting that higher zinc intake may increase the incidence of DR. The WQS regression indicated that K was the second highest weight index. But the other analysis declared that K own no significant impact in DR risk.\u003c/p\u003e \u003cp\u003eThe potential mechanism underlying DR is related to oxidative stress[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Hyperglycemia speeds up the formation of advanced glycation end products (AGEs), which trigger the generation of reactive oxygen species (ROS) in retinal pericytes, mainly by activating NADPH oxidase[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This oxidative stress leads to retinal pericyte apoptosis. Zinc commonly serves as an antioxidant in the body. This is because zinc is the cofactor for the isomers of superoxide dismutase (SOD) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. SOD suppresses apoptosis in retinal pericytes through the inhibition of NADPH oxidase in DR [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Existing research has shown that zinc acts as a protective factor in DR due to its antioxidant properties[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. But as a metal ion, excessive zinc may negate its original antioxidant properties and instead exacerbate oxidative stress. Yang et al. demonstrated that zinc overload induces ROS, which mediates mitophagy and mitochondrial dysfunction in cardiomyocytes. This cytotoxicity is exacerbated in the absence of mitofusin 2 (Mfn2) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Mfn2 plays a critical role in the regulation of mitochondrial energy processes, encompassing nutrient metabolism, the maintenance of membrane voltage, and the generation of ATP through oxidative pathways [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Similarly, reduced expression of Mfn2 (both gene and protein) has been observed in the retina in diabetes[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Nevertheless, the function of Mfn2 in DR is still being studied. There may be a potential link between zinc overload and Mfn2 in DR. Zinc overload may induce ROS in diabetic retinas, exacerbated by a deficiency in Mfn2. There may be an interplay between SOD and Zn-induced ROS in the progression of DR.\u003c/p\u003e \u003cp\u003eThe classic clinical manifestation of DR is ocular neovascularization[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Numerous studies have shown that hypoxia-inducible factor-1 and VEGF are involved in ocular neovascularization triggered by ischemia and hypoxia[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Some cancer studies have shown that VEGF expression can be lowered by zinc through inhibiting NF-\u0026#120581;B[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Based on this, the hypothesis that zinc supplementation could lower VEGF levels and help rescue the retina from DR was proposed[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, Kheirouri et al. demonstrated through a randomized controlled trial that zinc supplementation did not alter VEGF levels in DR patients[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTraditionally, most DR patients have been recommended zinc supplementation. It is clear that zinc plays a crucial role in the production, preservation, and release of insulin within pancreatic β cells[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Zinc is thought to engage in interaction with taurine and vitamin A, alter photoreceptor plasma membranes, and modulate the light-opsin reaction in the retina and retinal pigment epithelium[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. It should be emphasized that the role of zinc in rescuing DR is not as direct as its role in diabetes therapy. Zinc is originally abounded in ocular tissue compared to other tissues[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Continuous, uninterrupted zinc supplementation is likely to lead to high retinal zinc concentrations, even if serum zinc levels are lower than normal. In other words, high levels of zinc intake may not rescue the retina from DR by lowering VEGF levels or increasing antioxidants in the body. On the contrary, these abnormal zinc concentrations could harm the retina by increasing ROS.\u003c/p\u003e \u003cp\u003eIn summary, a high intake of zinc is linked to a higher occurrence of DR. Zinc overload-induced ROS may damage the retina. Further investigation is needed to determine whether zinc supplementation or high levels of zinc are suitable for DR therapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipation was voluntary and totally understand NHANES process and examination. Ethics approval ID are Protocol #2005-06 and Continuation of Protocol #2005-06.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDeclaration of Publication Consent\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author affirms that\u0026nbsp;①the work described has not been previously published,\u0026nbsp;②it is not being considered for publication in other places,\u0026nbsp;③the author has given consent for its publication, and\u0026nbsp;④the relevant authorities have sanctioned its publication at the institution where the research was conducted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eContributions\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy's conception and design, data collecting, statistical analysis, writing draft and review editing were all finished by Chenglin Liu. The author revised the article and approved the final version.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDeclaration of Competing Interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author states that there are no conflicts of interest regarding the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo financial support was provided for the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData and supplementary materials are available under request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eXu Y, Lu J, Li M, et al. Diabetes in China part 1: epidemiology and risk factors. 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Journal of the neurological sciences. 2015;356(1-2):7-18. doi: 10.1016/j.jns.2015.05.033.\u003c/li\u003e\n\u003cli\u003eZhong Q, Kowluru RA. Diabetic retinopathy and damage to mitochondrial structure and transport machinery. Investigative ophthalmology \u0026amp; visual science. 2011;52(12):8739-46. doi: 10.1167/iovs.11-8045.\u003c/li\u003e\n\u003cli\u003eClermont AC, Aiello LP, Mori F, et al. Vascular endothelial growth factor and severity of nonproliferative diabetic retinopathy mediate retinal hemodynamics in vivo: a potential role for vascular endothelial growth factor in the progression of nonproliferative diabetic retinopathy. American journal of ophthalmology. 1997;124(4):433-46. doi: 10.1016/s0002-9394(14)70860-8.\u003c/li\u003e\n\u003cli\u003eAdamis AP, Miller JW, Bernal MT, et al. Increased vascular endothelial growth factor levels in the vitreous of eyes with proliferative diabetic retinopathy. American journal of ophthalmology. 1994;118(4):445-50. doi: 10.1016/s0002-9394(14)75794-0.\u003c/li\u003e\n\u003cli\u003eUzzo RG, Crispen PL, Golovine K, et al. Diverse effects of zinc on NF-kappaB and AP-1 transcription factors: implications for prostate cancer progression. Carcinogenesis. 2006;27(10):1980-90. doi: 10.1093/carcin/bgl034.\u003c/li\u003e\n\u003cli\u003eKheirouri S, Naghizadeh S, Alizadeh M. Zinc supplementation does not influence serum levels of VEGF, BDNF, and NGF in diabetic retinopathy patients: a randomized controlled clinical trial. Nutritional neuroscience. 2019;22(10):718-24. doi: 10.1080/1028415x.2018.1436236.\u003c/li\u003e\n\u003cli\u003eKhan AR, Awan FR. Metals in the pathogenesis of type 2 diabetes. Journal of diabetes and metabolic disorders. 2014;13(1):16. doi: 10.1186/2251-6581-13-16.\u003c/li\u003e\n\u003cli\u003eUgarte M, Osborne NN. Zinc in the retina. Progress in neurobiology. 2001;64(3):219-49. doi: 10.1016/s0301-0082(00)00057-5.\u003c/li\u003e\n\u003cli\u003eGalin MA, Nano HD, Hall T. Ocular zinc concentration. Investigative ophthalmology. 1962;1:142-8. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"retina, risk factor, diabetic retinopathy, diet","lastPublishedDoi":"10.21203/rs.3.rs-6225317/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6225317/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e To evaluate the association between consumption of Zn, Cu, Fe, Na, K, Ca, Mg and DR in America through NHANES 2005-2008.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Most representative 163 participants were consisted of 89 with DR and 74 without DR. The multivariate logistic regression was applied to estimate the correlation between consumption of these metallic elements and DR incidence rate in 3 models. And Weighted Quantile Sum (WQS) regression was used to compare the most obvious metal which influence DR risk. Finally, Restricted cubic splines (RCS) were used to investigate the potential nonlinear relationship between DR risk and zinc.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Participants with DR showed significantly higher zinc intake level compared to without DR (DR: 13.66±7.41, non-DR: 9.40±5.61, p=0.005). Logistic regression analysis showed a marked positive linear relationship between zinc and the incidence of DR among 7 metallic elements (OR: 1.13, 95% CI: 1.07-1.20, p \u0026lt; 0.001). The significant association was consistent across different adjustment models. But there were no statistically significant associations between DR and the other 6 elements. WQS regression noted that zinc take up the highest estimated weight 0.4236. RCS revealed that there is no nonlinear relationship between zinc and DR risk with p for nonlinear 0.609.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e In NHANES, participants with type 2 diabetes mellitus (T2DM) exhibiting higher dietary zinc intake demonstrated significantly increased risk of DR. And the relationship between zinc intake and DR is positive and linear.\u003c/p\u003e","manuscriptTitle":"Association Between Metallic Elements in Diet and Diabetic Retinopathy: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-25 19:01:17","doi":"10.21203/rs.3.rs-6225317/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":"60369424-6a6e-4519-95ac-941ed86c00e5","owner":[],"postedDate":"March 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-13T13:36:41+00:00","versionOfRecord":{"articleIdentity":"rs-6225317","link":"https://doi.org/10.4103/DLJO.DLJO_81_25","journal":{"identity":"delhi-journal-of-ophthalmology","isVorOnly":true,"title":"Delhi Journal of Ophthalmology"},"publishedOn":"2026-03-13 00:00:00","publishedOnDateReadable":"March 13th, 2026"},"versionCreatedAt":"2025-03-25 19:01:17","video":"","vorDoi":"10.4103/DLJO.DLJO_81_25","vorDoiUrl":"https://doi.org/10.4103/DLJO.DLJO_81_25","workflowStages":[]},"version":"v1","identity":"rs-6225317","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6225317","identity":"rs-6225317","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}