Dietary iron intake is nonlinearly associated with the risk of diabetic retinopathy in adults with type 2 diabetes

Dietary iron intake is nonlinearly associated with the risk of diabetic retinopathy in adults with type 2 diabetes | 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 Dietary iron intake is nonlinearly associated with the risk of diabetic retinopathy in adults with type 2 diabetes Xiaoyun Chen, Yihang Fu, Hongyu Si, Wenfei Li, Weimin Yang, Wei Xiao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5184395/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Apr, 2025 Read the published version in BMC Endocrine Disorders → Version 1 posted 9 You are reading this latest preprint version Abstract Objective: To elucidate the association between dietary iron intake and diabetic retinopathy (DR) in type 2 diabetes (T2D) patients. Methods: Participants from the National Health and Nutrition Examination Survey (NHANES) 2005-2008 aged over 40 years with T2D were included. Dietary iron intake was estimated from standardised questionnaires. The presence of DR and vision-threatening DR (VTDR) was determined through retinal imaging. We used logistic regression to assess the relationship between iron intake and DR, and restricted cubic splines to reveal nonlinear links. Results: The study enrolled 1172 T2D adults. We found significant nonlinear associations between dietary iron intake and DR among females ( P = 0.023), but not in males ( P = 0.490). Compared with the lowest quartile of iron intake, the third quartile (13.2-18.1 mg/d) yielded significantly lower odds of developing DR (odds ratio [OR], 0.59; 95% CI, 0.39-0.90) and VTDR (OR, 0.42; 95% CI, 0.19-0.94). Stratified logistic analyses showed that medium-high iron intake was associated with lower risks of DR in females (OR, 0.44; 95% CI, 0.24-0.81), non-Hispanic Blacks (OR, 0.38; 95% CI, 0.17-0.85), and individuals with obesity (OR, 0.45; 95% CI, 0.25-0.82), high HbA1c (OR, 0.56; 95% CI, 0.34-0.93), long diabetes duration (OR, 0.40; 95% CI, 0.21-0.76) or low blood haemoglobin (OR, 0.17; 95% CI, 0.05-0.60). Conclusion: Dietary iron intake was nonlinearly negatively associated with the prevalence of DR and VTDR, showing protective effect against retinopathy of medium-high iron intake in T2D patients. Such associations significantly vary by multiple factors such as age, ethnicity, obesity and glycaemic control. diabetic retinopathy vision-threatening diabetic retinopathy dietary iron intake NHANES Figures Figure 1 Figure 2 Introduction Diabetic retinopathy (DR) is one of the major causes of vision impairment globally[ 1 ]. According to the Global Burden of Disease Study 2019, DR has been the fifth leading cause of blindness and moderate-to-worse visual impairment among individuals aged 50 years and greater[ 2 ]. Of note, it was the only cause of blindness with a globally increasing trend in age-standardised prevalence from 1990 to 2020[ 2 ]. Among individuals with diabetes, approximately a third have any sign of DR, and a third of them might have vision-threatening diabetic retinopathy (VTDR)[ 3 ]. With the estimated global diabetes prevalence to be rising from 9.3% (463 million) in 2019 to 10.9% (700 million) by 2045[ 4 ], along with longer life expectancy and lifestyle changes, the global burden of DR is expected to grow rapidly. Although hyperglycaemia, diabetes duration, and hypertension are considered as major risk factors for DR, they merely account for a small amount of the variation in the DR risk[ 5 ]. Several novel pathogeneses have raised that the abnormal homeostasis of trace elements including iron may associate with the progression of DR[ 6 ]. However, little is known about the association between dietary iron intake and DR. Iron plays a critical role in dynamic redox balance, disruption of which can lead to oxidative stress and damage to target organs including the retina[ 7 , 8 ]. Nevertheless, previous studies showed contradictory results regarding whether iron could protect the retina from harmful stimuli. On one hand, iron overload can exacerbate the development of retinopathy in mice, implying that iron depletion strategies may ameliorate diabetic microvascular complications[ 9 , 10 ]. On the other hand, epidemiologic and clinical studies showed that iron deficiency anaemia (IDA) was related to increased risk of DR[ 11 , 12 ], indicating the efficacy of anaemia treatment in decreasing retinopathy risk in type 2 diabetes (T2D). Such findings were supported by a recent study[ 13 ], in which serum iron was negatively correlated with the occurrence of DR in diabetic adults. Notably, all previous studies concentrated on the relationship of body iron status rather than dietary intake of iron and DR. The current assessment of iron status is derived from a battery of hemaetological indicators, including serum ferritin, transferrin saturation, and mean corpuscular volume, etc. However, measuring these hemaetological indicators is invasive and might be infeasible as for cost and complexity of procedure in certain settings. Thus, there is a need for a simpler and more accessible method to evaluate iron status. A complementary and easy-to-use option is to consider iron intake from diet and dietary supplements. When associations between DR risks and iron intake through nutritional assessment could be accurately estimated, it is promising to establish recommendations for appropriate dietary iron intake as to prevent DR in T2D patients. Nutritional interventions with optimal combinations of iron and other nutrients that support conventional therapies could be developed to reduce disease risk and severity in T2D patients. Herein, we investigated the association between the level of dietary iron intake, measured by a standardised questionnaire, and the risk of DR and VTDR among patients with T2D in a series of national representative samples. We further assessed whether such associations varied in specific subgroups of population defined by major stratification factors. Materials and Method Study design and population This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting statement (Supplemental Material 1). We conducted this cross-sectional study using the data from the National Health and Nutrition Examination Survey (NHANES) 2005 to 2008. NHANES is a population-based, multipurpose survey to evaluate the health and nutritional status of the US population. The National Centre for Health Statistics (NCHS) ethics review board reviewed and approved the study protocol. The study was conducted adhered to the principles of the Declaration of Helsinki, and written informed consent was obtained from each participant. Detailed data and methodology files are accessible online[ 14 ]. All potentially identifiable information has been removed to ensure the confidentiality of participants and their households. Briefly, every 2-year cycle of survey comprises of questionnaires administered at home, and a standardised physical examination in a mobile examination centre (MEC) including physical measurements and collection of biospecimens for laboratory tests. For 2005–2006 and 2007–2008 cycles, retinal photography and dietary interview were conducted to subjects older than 40 years, making this analysis available. Herein, T2D was defined according to participants’ meeting one or more of the following criteria: (1) self-reported physician diagnosis of diabetes; (2) using oral glucose-lowering medicines or insulin; and (3) fasting plasma glucose level of at least 126mg/dL, or haemoglobin A1c (HbA1c) level of at least 6.5%[ 15 ]. In this study, only those with complete data of dietary iron intake, diabetes and retinopathy were eligible. Patient and public involvement Patients or the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research. Assessment of dietary iron intake All NHANES examinees were eligible for two 24-hour dietary recall interviews. The first was performed in-person in the MECs, and the second by telephone 3 to 10 days later. Intakes of energy, nutrients, and other components from foods and beverages were estimated from the two detailed dietary interview. Detailed questionnaire and methodology of dietary intake calculation is publicly accessible online[ 14 ]. In this study, dietary intake of iron was calculated by averaging data of two 24-hour recalls if available, otherwise the single reliable dietary recall data was used. The continuous iron intake data were further dichotomised as adequate and inadequate categories according to the Recommended Dietary Allowance (RDA) issued by the Food and Nutrition Board of the National Academies[ 16 ]. Ascertainment of diabetic retinopathy During 2005–2008 NHANES survey, the presence of major retinal diseases including DR was tested for participants aged over 40 years by the Retinal Imaging. Two 45-degree digital retinal images for each eye were captured utilising the Canon Non-Mydriatic Retinal Camera CR6-45NM (Canon, Tokyo, Japan), one focused on the optic nerve and the other on the macula. The digital images were transferred to the University of Wisconsin Ocular Epidemiologic Reading Centre, Madison for grading retinopathies according to the standardised protocol. At least two raters graded a same set of images. If the first two graders disagreed, the third graded the image. If two of three disagreed, an adjudicator would make a final decision. DR severity was broadly classified into 4 levels: no DR, mild non-proliferative diabetic retinopathy (NPDR), moderate/severe NPDR, and proliferative diabetic retinopathy (PDR) according to the Early Treatment Diabetic Retinopathy Study (ETDRS) classification standards[ 17 ]. In this study, we aggregated these 4 levels as no DR versus any DR (incorporating mild NPDR, moderate/severe NPDR, and PDR). We also defined VTDR as the presence of severe NPDR, PDR, or clinically significant macular oedema (CSME). Herein CSME was defined when (1) the oedema involved the fovea or within 500 microns of the fovea, and/or (2) a 1 + disc area of oedema present with at least a portion of it involving the macula. Outcomes of our study were defined according to the worse one of two eyes. Assessment of covariates Sociodemographic variables, including age, sex, race/ethnicity, education attainment, and poverty income ratio (PIR) was collected through a questionnaire. Race/ethnicity was self-reported according to NCHS categories (Mexican American, non-Hispanic Black, non-Hispanic White, other Hispanic, or other). We combined other Hispanic and other race/ethnicity groups as the broader “other group”. Education was grouped into three categories: 1) less than high school, 2) high school or equivalent, and 3) greater than high school. PIR was classified into 3 categories: less than 1.30, 1.30 to 3.49, and 3.5 or higher. Body mass index (BMI) was calculated as weight in kilograms divided by height in metres squared, and was categorised as three groups: normal or underweight (less than 25.0 kg/m 2 ), overweight (25.0 to 30.0 kg/m 2 ), and obese (greater than 30.0 kg/m 2 ). Smoking status was classified into 3 categories: never (less than 100 cigarettes in lifetime), former (greater than 100 cigarettes in lifetime, but had given up at the time of interview), and current smoker (greater than 100 cigarettes in lifetime and currently smoking). Alcohol consumption was classified into 3 categories: never (never drank alcohol in lifetime and the past 12 months), former (had ever drunk in lifetime, but not in the past 12 months), and current drinker (drunk at least 12 alcoholic drinks during the past year or lifetime and consumed alcohol at least 1 day during the past year). Duration of diabetes was self-reported by interviewees, and was dichotomised as < 10 and ≥ 10 years. Haemoglobin A1c (HbA1c) raw data were continuous, and were further grouped into 12 g/dL) and low (< 12 g/dL) based on the standard of World Health Organization[ 18 ]. Information about medical comorbidities was obtained from physical examination and questionnaire. We defined hypertension if participants with (1) diastolic blood pressure ≥ 80 mmHg, or systolic blood pressure ≥ 130 mmHg according to the mean value of 3 measurements, or (2) self-reported hypertension history, or (3) taking blood pressure medications[ 19 ]. Individuals whose total cholesterol ≥ 240 mg/dL (6.2mmol/L), or taking lipid-lowering medications were considered as having hypercholesteraemia. Congestive heart failure, coronary heart disease, heart attack, angina/angina pectoris, and stroke were confirmed based on self-reported physician diagnosis. The full questionnaire can be found in the Supplemental Material 2. Statistical analysis In this study, we reported descriptive statistics as numbers (percentages) for categorical variables, and means (standard deviations, SDs) for continuous variables. We used χ2 and unpaired t -tests to assess differences in sociodemographic, clinical and dietary characteristics between groups. We established logistic regression models to evaluate odds ratios (ORs) of DR and VTDR based on iron intake quartiles after adjustment for demographic variables, lifestyle variables, DM related variables, and medical comorbidities. To explore nonlinear association between DR and iron intake, we used restricted cubic spline (RCS) logistic regression analyses after adjusting the abovementioned confounders. We set four knots (5th, 35th, 65th, and 95th percentiles of iron intake) according to Harrell’s recommendation that four knots can offer an adequate fit of the model and well balance flexibility and imprecision caused by overfitting[ 20 ]. The R package plotRCS (version 0.1.4) was used to visualise splines[ 21 ]. Logistic regression and RCS analysis were stratified by sex, race/ethnicity, weight status, HbA1c level (< 7.0% or ≥ 7.0%), duration of diabetes (< 10 years or ≥ 10 years), and blood haemoglobin level (normal/high or low). We performed sensitivity analyses to evaluate the robustness of major results by applying the following strategies: (1) excluding participants with outlying iron intake data (≥ 3SD from the mean value), (2) reconstructing logistic regression and RCS models through ignoring missing data rather than multiple imputation (i.e., complete case analysis). All data analyses were performed by R statistical package (R Core Team, Vienna, Austria, version 4.2.3). Variables with missing values were imputed through multiple imputation approach using mice package (version 3.15.0)[ 22 ]. P value less than 0.05 was considered to be statistically significant. Results Participant characteristics Of all participants from the NHANES 2005–2006 (n = 10348) and 2007–2008 (n = 10149), 18923 participants younger than 40 years (n = 13416) or without diabetes (n = 5507) were excluded. Participants with ungradable images (n = 374) or without dietary iron intake data (n = 28) were excluded as well, leaving 1172 participants ultimately included (Fig. 1 ). Nine covariates of the dataset had missing values over 1% of observations (Supplemental Figure S1 ), and PIR had the highest proportion (8%). To take into account the effect of missing data, we handled missing data with multiple imputation technique for the subsequent analyses. The general characteristics of study population by DR status were presented in Table 1. There were 358 participants (30.5%) with any DR, among whom 188 were males (52.5%). Compared with disease-free individuals, DR patients had comparable average age, sex, education, marriage status, PIR, smoking status, and alcohol consumption, but were more likely to be non-Hispanic black, Mexican American, and have higher BMI. DR population showed poorer general health condition, longer diabetes duration, higher levels of HbA1c, lower levels of blood haemoglobin, as well as higher prevalence of systemic comorbidities including congestive heart failure, heart attack and stroke (all P < 0.05, Table 1). The mean dietary iron intake of subjects with DR was 13.2 ± 6.24 mg/d, which was significantly lower than that of subjects without DR (14.3 ± 6.76 mg/d, P = 0.008). However, the proportion of participants meeting the sufficient daily iron intake was comparable between two groups (23.5% vs. 19.8%, P = 0.176, Table 1). When analysed according to the severity of DR, the iron intake in the VTDR subgroup was significantly lower than that in the groups without DR and without VTDR ( P = 0.007, Supplemental Figure S2 ). Table 1. Characteristics of the study population according to status of diabetic retinopathy Characteristic Total Participants (n = 1172) Without DR (n = 814) With DR (n = 358) P value c Sex, n (%) 0.442 Male 594 (50.7%) 406 (49.9%) 188 (52.5%) Female 578 (49.3%) 408 (50.1%) 170 (47.5%) Age, mean (SD), y 63.2 (10.8) 63.0 (10.8) 63.7 (10.8) 0.250 Race/ethnicity, n (%) 0.002 Non-Hispanic White 485 (41.4%) 360 (44.2%) 125 (34.9%) Non-Hispanic Black 337 (28.8%) 210 (25.8%) 127 (35.5%) Mexican American 226 (19.3%) 153 (18.8%) 73 (20.4%) Other 124 (10.6%) 91 (11.2%) 33 (9.2%) Education, n (%) 0.056 Less than high school 467 (39.8%) 306 (37.6%) 161 (45.0%) High school 308 (26.3%) 224 (27.5%) 84 (23.5%) College or higher 397 (33.9%) 284 (34.9%) 113 (31.6%) Marital status, n (%) 0.578 Married or living with partner 726 (61.9%) 509 (62.5%) 217 (60.6%) Not married 446 (38.1%) 305 (37.5%) 141 (39.4%) Poverty income ratio, n (%) 0.455 3.50 295 (25.2%) 213 (26.2%) 82 (22.9%) Weight status by BMI, n (%) 0.030 Normal or underweight ( 30.0) 688 (58.7%) 495 (60.8%) 193 (53.9%) Smoking status, n (%) 0.052 Never smoker 532 (45.4%) 351 (43.1%) 181 (50.6%) Former smoker 442 (37.7%) 323 (39.7%) 119 (33.2%) Current smoker 198 (16.9%) 140 (17.2%) 58 (16.2%) Alcohol consumption, n (%) 0.229 Never drinker 213 (18.2%) 147 (18.1%) 66 (18.4%) Former drinker 179 (15.3%) 115 (14.1%) 64 (17.9%) Current drinker 780 (66.6%) 552 (67.8%) 228 (63.7%) General health status, n (%) 0.003 Excellent to good 640 (54.6%) 468 (57.5%) 172 (48.0%) Fair or poor 532 (45.4%) 346 (42.5%) 186 (52.0%) Duration of diabetes, n (%) <0.001 10 years 386 (32.9%) 159 (19.5%) 227 (63.4%) HbA1c, mean (SD) 7.25 (1.68) 6.94 (1.49) 7.98 (1.86) <0.001 HbA1c level, n (%) <0.001 6.5 761 (64.9%) 474 (58.2%) 287 (80.2%) Blood haemoglobin level, n (%) a 0.004 Normal/High 997 (85.1%) 709 (87.1%) 288 (80.4%) Low 175 (14.9%) 105 (12.9%) 70 (19.6%) History of comorbidities, n (%) Hypertension 965 (82.3%) 660 (81.1%) 305 (85.2%) 0.105 Hypercholesteraemia 643 (54.9%) 432 (53.1%) 212 (59.2%) 0.059 Congestive heart failure 123 (10.5%) 64 (7.9%) 59 (16.5%) <0.001 Coronary heart disease 138 (11.8%) 88 (10.8%) 50 (14.0%) 0.148 Angina/angina pectoris 97 (8.3%) 64 (7.9%) 33 (9.2%) 0.509 Heart attack 139 (11.9%) 82 (10.1%) 57 (15.9%) 0.006 Stroke 127 (10.8%) 74 (9.1%) 53 (14.8%) 0.005 Dietary iron intake, mg/d, Mean (SD) 13.9 (6.62) 14.3 (6.76) 13.2 (6.24) 0.008 Adequate intake of iron by RDA, n (%) b 0.176 Adequate 245 (20.9%) 161 (19.8%) 84 (23.5%) Inadequate 927 (79.1%) 653 (80.2%) 274 (76.5%) Abbreviations: BMI, body mass index (calculated as weight in kilograms divided by height in metres squared); DR, diabetic retinopathy; RDA, recommended dietary allowance; SD, standard deviation. a Blood haemoglobin level was classified according to haemoglobin concentrations for the diagnosis of anaemia and assessment of severity released by World Health Organization in 2011. b Intake recommendations for iron were developed by the Food and Nutrition Board (FNB) at the Institute of Medicine (IOM) of the National Academies (formerly National Academy of Sciences). c Comparisons were made by the use of the chi-square for categorical variables and the 2-sample t test for continuous variables. Association of dietary iron intake and DR and VTDR Table 2 presents the results of multivariable logistic regression analyses. In crude models, participants in the third and fourth quartiles showed significantly lower risk of DR as compared with the first (reference) group (quartile 3: OR = 0.57; 95%CI, 0.40-0.82; quartile 4: OR = 0.68; 95%CI, 0.48-0.96). After adjustment for multiple covariates, significant associations remained for the third quartile in all logistic regression models (Table 2; for the saturated model: OR=0.59, 95%CI, 0.39-0.90). Besides, no significant relationship was shown between the second quartile and the reference group in any models (all P > 0.05). When dietary iron intake was dichotomised as adequate versus inadequate by RDA, no significant relation was found between iron intake and DR (Table 2, all P > 0.05). Compared with the first quartile, all other quartiles yielded significantly lower odds of VTDR in crude models (quartile 2: OR = 0.47; 95%CI, 0.25-0.89; quartile 3: OR = 0.43; 95%CI, 0.22-0.83; quartile 4: OR = 0.44; 95%CI, 0.22-0.86). With adjustment of covariates, this relationship remained significant for the third quartile in all models (Table 2; for the saturated model: OR=0.42, 95%CI, 0.19-0.94). Nonetheless, correlation between VTDR risk and the fourth quartile of iron intake became insignificant after multiple adjustments (all P > 0.05). Inadequate intake of iron according to RDA showed no added risk of VTDR in either crude or multi-adjusted models (all P > 0.05). Table 2. Association of dietary iron intake with diabetic retinopathy and vision-threatening diabetic retinopathy Abbreviations: CI, confidence interval; OR, odds ratio. a Number of participants with T2D in NHANES 2005-2008. b Number of participants with T2D and diabetic retinopathy or vision-threatening diabetic retinopathy in NHANES 2005-2008. c Model 1: Adjusted for demographic variables (age, sex, race/ethnicity, education, marital status, poverty income ratio). d Model 2: Adjusted for demographic and lifestyle variables (smoking, drinking, body mass index). e Model 3: Adjusted for demographic, lifestyle, diabetes related variables (duration of diabetes, HbA1c level), and medical comorbidity variables (general health condition, history of angina/angina pectoris, congestive heart failure, coronary heart disease, heart attack, hypercholesteraemia, hypertension, and stroke). f Adequate dietary iron intake is defined according to Recommended Dietary Allowance (RDA) developed by the Food and Nutrition Board (FNB) at the Institute of Medicine (IOM) of the National Academies (formerly National Academy of Sciences). Stratified analyses for association of dietary iron intake and DR Table 3 demonstrated the associations between dietary iron and DR by various stratifying factors, including sex, race/ethnicity, weight status, HbA1c level, duration of diabetes, and blood haemoglobin level. Compared with the first quartile, subjects within the third quartile yielded significantly lower odds of DR in female, non-Hispanic Black, obese, HbA1c ≥ 6.5%, and DM duration ≥10 years groups. Remarkably, for individuals with low blood haemoglobin, both the third and the fourth quartiles showed decreased risks of DR (quartile 3: OR = 0.17; 95%CI, 0.05-0.60; quartile 4: OR = 0.23; 95%CI, 0.06-0.90), but were insignificant in those with normal or high blood haemoglobin. No significant interaction between any stratification factors and dietary iron intake was found (Table 3, all P > 0.05). Table 3. Stratified analyses for association of dietary iron with diabetic retinopathy * Abbreviations: BMI, body mass index (calculated as weight in kilograms divided by height in metres squared); OR, odds ratio. * Analyses were adjusted for age, sex (except for sex stratification), race/ethnicity (except for race/ethnicity stratification), body weight status (except for weight status stratification), duration of diabetes (except for diabetes duration stratification) and medical comorbidity variables (general health condition, history of angina/angina pectoris, congestive heart failure, coronary heart disease, heart attack, hypercholesteraemia, hypertension, and stroke). † Blood haemoglobin level was classified according to haemoglobin concentrations for the diagnosis of anaemia and assessment of severity released by World Health Organization in 2011. Nonlinear relationship between dietary iron intake and DR The non-monotonic relationship revealed by logistic regression analysis promoted us to further investigate the nonlinear relationship of DR and dietary iron intake. RCS analyses showed nonlinear associations between DR and dietary iron intake varied by sex (Figure 2). In males, no significant relationship was found between dietary iron and DR risk ( P overall =0.230, P nonlinear =0.490) (Figure 2A). In females, however, an approximately U-shaped relationship was revealed; only moderate level of dietary iron intake was associated with decreased risk of DR ( P overall =0.022, P nonlinear =0.023) (Figure 2B). No significant nonlinear association was found in subgroups by stratification factors other than sex (Supplemental Figure S3). Sensitivity analyses Sensitivity analyses showed the robustness of our major results. The association between DR and quartiles of iron intake did not substantially change when participants with outlying data were excluded (Supplemental Table S1 for any DR, Supplemental Table S2 for VTDR). Such associations remained significant when we removed missing data instead of adopting multiple imputation (Supplemental Table S3 for any DR, Supplemental Table S4 for VTDR). This indicated that the outliers did not significantly deflate or inflate the mean of the sample, and had minimal influence on the association derived from the mean. Similarly, when excluding the participants with outlying data (Supplemental Figure S4) or ignoring missing data (Supplemental Figure S5), the RCS analysis results did not substantially change, indicating that missing data caused little noise or bias to estimation. Discussion In this large-scale, nationally representative T2D cohort, we found significantly lower daily dietary iron intake in individuals with DR, particularly those with VTDR. After adjustment for major confounding factors, medium-high level of dietary iron intake (13.2-18.1 mg/d) was associated with 59% risk reduction for DR, and 42% risk reduction for VTDR. Of note, beneficial effects of adequate iron intake were more profound in several specific subpopulations, including females, non-Hispanic Blacks, individuals with longer diabetes duration, higher level of HbA1c, concurrent obesity, or anaemia. Spline regression analysis demonstrated that there was a nonlinear U-relationship between the daily iron intake amount and DR risk in females, but not in males. Sensitivity analyses confirmed the robustness of the above findings. As one of the essential minerals, iron is vital for maintaining the normal structures and functions of a number of macromolecules in cells. Dysregulation of iron homeostasis, either excess or deficiency, might lead to a variety of chronic diseases including diabetes. In patients with pre-existing diabetes, iron deficiency anaemia (IDA) can exacerbate retinopathy through inducing long-term hypoxia in the retina[23]. Additionally, increased lipid peroxidation induced by IDA could elevate HbA1c level[24], which is a strong indicator to predict the onset and progression of DR. Luckily, elevated HbA1c in T2D patients with IDA could be ameliorated by 3-month iron supplementation therapy[25]. Iron overload, however, generates various oxygen and nitrogen species via Fenton reaction, which is one of the major causative factors for diabetes and its complications[26]. Recently, an animal study demonstrated that excessive iron can exacerbate the development of DR by increasing retinal renin expression in mice[9]. Another study also verified that iron accumulation induced the diabetic-related pericyte loss in eyes and aggravated diabetic microvascular complications[10]. Collectively, ensuring a balanced iron status in the body is critical for preventing the occurrence and progression of diabetic ocular complications. Our findings in this study were well consistent with this concept, and for the first time we revealed possible beneficial effects of medium-high dietary iron intake on preventing DR and VTDR; neither higher nor lower amount was significantly associated with the occurrence of DR. Another interesting finding of this study is that sex can potentially modify the relationship between dietary iron intake and DR. Both multivariate logistic regression and spline analysis models demonstrated that medium-high daily iron intake was associated with a reduced risk of DR in females but not in males. The underlying mechanism is difficult to interpret but may be related to sex differences in iron homeostasis under the influences of hormonal, genetic, and dietary factors. Females have lower iron storages than males because of menstruation. On average, females lose about 20-60 mg of iron per menstrual cycle, an amount comparable to daily dietary iron intake requirements for males. Besides, differences in dietary habit by sex may also affect iron absorption: men consume more haem iron, while women consume more non-haem iron. Haem iron is more available for absorption from the diet than non-haem iron, which may substantially affect iron status and health outcomes[27]. The stratified analysis in this study found that factors other than sex may also modify the relation between dietary iron intake and DR. Participants who are non-Hispanic Black, obese, with low haemoglobin levels, with poorer glycaemic control (HbA1c≥6.5%) and having a longer duration of diabetes (≥ 10 years) may benefit more from sufficient iron intake than others. A possible reason is that T2D patients with obesity, of non-Hispanic Black ethnicity, and unsatisfactory glycaemic control are more likely to have concurrent iron deficiency or IDA[28-30]. In response, our study also found a significant inverse association between dietary iron intake and the risk of DR in T2D patients with anaemia. Given that anaemia is an established risk factor for DR[11, 12], it is particularly important for the abovementioned at-risk populations to routinely evaluate and maintain sufficient iron intakes. The findings of our study are relevant for clinical practice, and provide implications for future research as well. To help prevent DR, we encourage adults with T2D to consume a diet with sufficient iron. The optimal amount of daily iron intake showing protective effects on DR (13.2-18.1 mg/d) is higher than the RDA for male adults (8 mg/d) and close to that for premenopausal women (18 mg/d). As this range of daily iron intake is far below the upper limits established by the Food and Nutrition Board (FNB) for adults (< 45 mg/d), it poses very little risk of iron overload and toxicity. Higher iron intake than 18.2 mg/d may not be recommended because excessive iron intake is not associated with further reduced DR risk, but may cause other health problems like neurological disorders. Moreover, due to the nonlinear relationship between dietary iron and DR, our study also indicated that dichotomised cut-off values of RDA may not be capable of guiding iron intake for diabetes patients in terms of minimising retinopathy risk. In the future, a more sophisticated “protective dietary pattern” incorporating iron intake against diabetic retinopathy is to be developed and validated. There are several strengths of our study. Compared with previous studies (Supplemental Table S5), this is the first study to illustrate the nonlinear relationships between dietary iron intake and DR using spline analyses, a powerful technique to delineate the nonlinear nature of many phenomena in clinical research. Moreover, the NHANES data are high-quality and of great representativeness which ensured the generalisability of our findings and conclusions. The comprehensive survey data allowed us to adjust potential confounders in statistical models as well. Nevertheless, this study has several limitations. Firstly, due to the cross-sectional design, we only identified correlation rather than causation between dietary iron intake and DR. Further dietary trials are needed to establish causality and to test the efficacy of dietary iron interventions. Secondly, the dietary data in NHANES were acquired by two 24-hour recalls, largely depending on participants’ memory. Recall bias could not be excluded, and the actual daily nutrient intake level might slightly differ from the self-reported data. Thirdly, due to the unavailability of data on supplement intake of iron, our study only included dietary iron intake but not on metal supplements. Fourthly, although we adjusted for a comprehensive range of confounding factors, residual or unknown confounding cannot be entirely excluded. Fifthly, cultural and dietary differences across the population have the potential to impact the findings. Sixthly, although the sample population is highly representative and with large size, the prevalence of VTDR was relatively low which may have contributed to the non-statistically significant results. In summary, our study found a nonlinear association between dietary iron intake and DR risk. Medium-high level of iron intake was associated with a reduced risk of DR and VTDR, especially for females, non-Hispanic Blacks, obese people, those with HbA1c > 6.5%, and diabetes duration > 10 years. High or low levels of iron intake may not be conducive to preventing the development of DR. A precise efficacy of dietary iron intervention strategy and its impact on DR and VTDR are to be determined in longitudinal studies and controlled trials. Declarations Clinical trial number: Not applicable. Acknowledgement: We thank all the participants and staff involved in the National Health and Nutrition Examination Survey for their invaluable contributions. Author contributions: X.C. and W.X. was responsible for the concept, design, and supervision. Y.F. and H.S. collected the data from the database. W.L. and W.Y. conducted data analyses and visualisation. Y.F. and H.S. wrote the original draft. X.C. and W.X. revised the manuscript critically. All authors reviewed the manuscript. Competing interests: None declared. Funding: This study was supported by the Guangdong Basic and Applied Basic Research Foundation (2023A1515012192, 2023A1515030108). Role of the funder/sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Ethical approval: The NHANES protocol was approved by the National Center for Health Statistic (NCHS) Research Ethics Review Board (Protocol #2005-06 for NHANES 2005-2006; Continuation of Protocol #2005-06 for NHANES 2007-2008). The Ethics Review Board approval information is accessible at: https://www.cdc.gov/nchs/nhanes/irba98.htm (accessed on 8 August, 2024). Patient consent for publication: Not applicable Additional contributions: None reported. Data availability statement: Data are available in a public, open access repository. References Wong TY, Cheung CM, Larsen M, Sharma S, Simó R: Diabetic retinopathy . Nat Rev Dis Primers 2016, 2 :16012. Collaborators GBaVI: Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study . Lancet Glob Health 2021, 9 (2):e144-e160. Yau JW, Rogers SL, Kawasaki R, Lamoureux EL, Kowalski JW, Bek T, Chen SJ, Dekker JM, Fletcher A, Grauslund J et al : Global prevalence and major risk factors of diabetic retinopathy . Diabetes Care 2012, 35 (3):556-564. Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, Colagiuri S, Guariguata L, Motala AA, Ogurtsova K et al : Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9(th) edition . Diabetes Res Clin Pract 2019, 157 :107843. Tan GS, Ikram MK, Wong TY: Traditional and novel risk factors of diabetic retinopathy and research challenges . Curr Med Chem 2013, 20 (26):3189-3199. Harrison AV, Lorenzo FR, McClain DA: Iron and the Pathophysiology of Diabetes . Annu Rev Physiol 2023, 85 :339-362. Kang Q, Yang C: Oxidative stress and diabetic retinopathy: Molecular mechanisms, pathogenetic role and therapeutic implications . Redox Biol 2020, 37 :101799. Galaris D, Barbouti A, Pantopoulos K: Iron homeostasis and oxidative stress: An intimate relationship . Biochim Biophys Acta Mol Cell Res 2019, 1866 (12):118535. Chaudhary K, Promsote W, Ananth S, Veeranan-Karmegam R, Tawfik A, Arjunan P, Martin P, Smith SB, Thangaraju M, Kisselev O et al : Iron Overload Accelerates the Progression of Diabetic Retinopathy in Association with Increased Retinal Renin Expression . Sci Rep 2018, 8 (1):3025. Altamura S, Müdder K, Schlotterer A, Fleming T, Heidenreich E, Qiu R, Hammes HP, Nawroth P, Muckenthaler MU: Iron aggravates hepatic insulin resistance in the absence of inflammation in a novel db/db mouse model with iron overload . Mol Metab 2021, 51 :101235. Wang J, Xin X, Luo W, Wang R, Wang X, Si S, Mo M, Shao B, Wang S, Shen Y et al : Anemia and Diabetic Kidney Disease Had Joint Effect on Diabetic Retinopathy Among Patients With Type 2 Diabetes . Invest Ophthalmol Vis Sci 2020, 61 (14):25. Chung JO, Park SY, Chung DJ, Chung MY: Relationship between anemia, serum bilirubin concentrations, and diabetic retinopathy in individuals with type 2 diabetes . Medicine (Baltimore) 2019, 98 (43):e17693. Chen YJ, Chen JT, Tai MC, Liang CM, Chen YY, Chen WL: Serum Iron and Risk of Diabetic Retinopathy . Nutrients 2020, 12 (8). NHANES Survey Methods and Analytic Guidelines. [https://wwwn.cdc.gov/nchs/nhanes/analyticguidelines.aspx] Association AD: 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2020 . Diabetes Care 2020, 43 (Suppl 1):S14-s31. Iron - Health Professional Fact Sheet [https://ods.od.nih.gov/factsheets/Iron-HealthProfessional/] Zhang X, Saaddine JB, Chou CF, Cotch MF, Cheng YJ, Geiss LS, Gregg EW, Albright AL, Klein BE, Klein R: Prevalence of diabetic retinopathy in the United States, 2005-2008 . JAMA 2010, 304 (6):649-656. Anaemia - World Health Organization (WHO) [https://www.who.int/health-topics/anaemia] Whelton PK, Carey RM, Aronow WS, Casey DE, Jr., Collins KJ, Dennison Himmelfarb C, DePalma SM, Gidding S, Jamerson KA, Jones DW et al : 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines . Circulation 2018, 138 (17):e426-e483. Harrell FE: Regression Modeling Strategies: with applications to linear models, logistic regression, and survival analysis : Springer-Verlag New York; 2010. rcsplot: Plot restricted cubic splines curves [https://cran.r-project.org/web/packages/plotRCS/index.html] van Buuren S, Groothuis-Oudshoorn K: mice: Multivariate Imputation by Chained Equations in R . Journal of Statistical Software 2011, 45 (3):1 - 67. Lee MK, Han KD, Lee JH, Sohn SY, Jeong JS, Kim MK, Baek KH, Song KH, Kwon HS: High hemoglobin levels are associated with decreased risk of diabetic retinopathy in Korean type 2 diabetes . Sci Rep 2018, 8 (1):5538. Balamurugan R, Selvaraj N, Bobby Z, Sathiyapriya V: Increased glycated hemoglobin level in non-diabetic nephrotic children is associated with oxidative stress . Indian J Physiol Pharmacol 2007, 51 (2):153-159. Tarim O, Küçükerdoğan A, Günay U, Eralp O, Ercan I: Effects of iron deficiency anemia on hemoglobin A1c in type 1 diabetes mellitus . Pediatr Int 1999, 41 (4):357-362. Halliwell B, Gutteridge JM: Oxygen toxicity, oxygen radicals, transition metals and disease . Biochem J 1984, 219 (1):1-14. Abbaspour N, Hurrell R, Kelishadi R: Review on iron and its importance for human health . J Res Med Sci 2014, 19 (2):164-174. Qiu F, Wu L, Yang G, Zhang C, Liu X, Sun X, Chen X, Wang N: The role of iron metabolism in chronic diseases related to obesity . Mol Med 2022, 28 (1):130. Le CH: The Prevalence of Anemia and Moderate-Severe Anemia in the US Population (NHANES 2003-2012) . PLoS One 2016, 11 (11):e0166635. Guo W, Zhou Q, Jia Y, Xu J: Increased Levels of Glycated Hemoglobin A1c and Iron Deficiency Anemia: A Review . Med Sci Monit 2019, 25 :8371-8378. Additional Declarations No competing interests reported. Supplementary Files SupplementalMateriallegends.docx SupplementalFigureS1.pdf SupplementalFigureS2.pdf SupplementalFigureS3.pdf SupplementalFigureS4.pdf SupplementalFigureS5.pdf SupplementalMaterial1STROBEchecklist.docx SupplementalMaterial2NHANESquestionnaire20052008.pdf SupplementalTableS1.docx SupplementalTableS2.docx SupplementalTableS3.docx SupplementalTableS4.docx SupplementalTableS5.docx Cite Share Download PDF Status: Published Journal Publication published 18 Apr, 2025 Read the published version in BMC Endocrine Disorders → Version 1 posted Editorial decision: Revision requested 02 Apr, 2025 Editor assigned by journal 02 Apr, 2025 Reviews received at journal 01 Apr, 2025 Reviewers agreed at journal 26 Mar, 2025 Reviews received at journal 25 Mar, 2025 Reviewers agreed at journal 25 Mar, 2025 Reviewers invited by journal 25 Mar, 2025 Submission checks completed at journal 25 Mar, 2025 First submitted to journal 23 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5184395","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":433927493,"identity":"2258b8ab-698a-4bc5-b135-3c463d61783c","order_by":0,"name":"Xiaoyun Chen","email":"","orcid":"","institution":"Zhongshan Ophthalmic Centre","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyun","middleName":"","lastName":"Chen","suffix":""},{"id":433927494,"identity":"150b18e0-b703-41b6-8356-207dbd630cdd","order_by":1,"name":"Yihang Fu","email":"","orcid":"","institution":"Zhongshan Ophthalmic Centre","correspondingAuthor":false,"prefix":"","firstName":"Yihang","middleName":"","lastName":"Fu","suffix":""},{"id":433927495,"identity":"b6c988ba-0912-4559-97d0-ae86e4d2d320","order_by":2,"name":"Hongyu Si","email":"","orcid":"","institution":"Zhongshan Ophthalmic Centre","correspondingAuthor":false,"prefix":"","firstName":"Hongyu","middleName":"","lastName":"Si","suffix":""},{"id":433927496,"identity":"5505f4ab-46db-48f3-9b75-04b9e6e47154","order_by":3,"name":"Wenfei Li","email":"","orcid":"","institution":"Zhongshan Ophthalmic Centre","correspondingAuthor":false,"prefix":"","firstName":"Wenfei","middleName":"","lastName":"Li","suffix":""},{"id":433927497,"identity":"4307cfad-70b2-4c87-9a96-8c3293f28a86","order_by":4,"name":"Weimin Yang","email":"","orcid":"","institution":"Zhongshan Ophthalmic Centre","correspondingAuthor":false,"prefix":"","firstName":"Weimin","middleName":"","lastName":"Yang","suffix":""},{"id":433927498,"identity":"fc20d2c5-7db3-403f-b1ed-31269f30bcf9","order_by":5,"name":"Wei Xiao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYDACZhBR8V+OgYGx8QBMUIKwljPMxkAtDURqAQHGNubEBiBNnBaD48wPH/OwsaWvbT8MtOXPYXuDA8wHb/Mw2OXh0iLZzGZszMPDk7vtTGLDAca2w4kbDrAlW/MwJBfj0sLPzGAmnSMhkbvtAEhLw+EEgwM8ZtI8DAfATsUG2JjZv0nnGBikm51/CHMY/ze8WviZgWbmJCQkmN0A2sLAdphxwwEeNrxaJJt5io3/HDhguO0G0JbEtvTEmYfZjC3nGCTj1GJw/vjGhzP/HZA3O5/+8MGHP9b2fMebH954U2GHUwsqSGBohkauAVHqwaCOeKWjYBSMglEwYgAAMFBZflWHD3EAAAAASUVORK5CYII=","orcid":"","institution":"Zhongshan Ophthalmic Centre","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Xiao","suffix":""}],"badges":[],"createdAt":"2024-10-01 04:08:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5184395/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5184395/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12902-025-01926-z","type":"published","date":"2025-04-18T15:57:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79321096,"identity":"5477dfd0-1f4b-4d95-be66-4fbb455d2e7c","added_by":"auto","created_at":"2025-03-27 04:29:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":175817,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of participant enrolment of this study.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5184395/v1/4d3c90f29f9b25da73e347f4.png"},{"id":79321095,"identity":"41b8f7ff-00dc-45e6-89a8-ac98b8e6d21f","added_by":"auto","created_at":"2025-03-27 04:29:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":118084,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between dietary iron intake and diabetic retinopathy by sex using restricted cubic spline model. A. Male; B. Female. Graphs show odds ratio (OR) for any DR according to dietary iron level adjusted for age, race, education, marital status, poverty income ratio, body mass index, smoking status, alcohol consumption, duration of diabetes, HbA1c level, hypertension, hypercholesterolaemia, history of comorbidities, including congestive heart failure, heart attack and stroke. Data were fitted by a logistic regression model, and the model was conducted with 4 knots at the 5\u003csup\u003eth\u003c/sup\u003e, 35\u003csup\u003eth\u003c/sup\u003e, 65\u003csup\u003eth\u003c/sup\u003e, 95\u003csup\u003eth\u003c/sup\u003e percentiles of iron intake (reference is the 5\u003csup\u003eth\u003c/sup\u003e percentile). Solid lines indicate ORs, and shadow shape indicate 95% CIs. OR, odds ratio; CI, confidence interval.\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5184395/v1/376b93c18b31e5d821cb9ee9.png"},{"id":81050974,"identity":"54031ac3-59b0-4c9a-9352-d720960a2d16","added_by":"auto","created_at":"2025-04-21 16:09:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2555259,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5184395/v1/327f9fce-3c01-41b8-af5f-00086b1900e4.pdf"},{"id":79322011,"identity":"fbb0df3a-6cb5-4ae5-89cb-65501d40e83f","added_by":"auto","created_at":"2025-03-27 04:37:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":40135,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMateriallegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-5184395/v1/b6c4e5fef79e8ee9ba94b2c4.docx"},{"id":79322016,"identity":"5d970165-8c19-4094-b3f1-b0051393408a","added_by":"auto","created_at":"2025-03-27 04:37:39","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":107561,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5184395/v1/7b428cfbddc5e32cf65267ee.pdf"},{"id":79321099,"identity":"3fd85da8-6cd3-4567-99e3-52667c590c89","added_by":"auto","created_at":"2025-03-27 04:29:38","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":247686,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFigureS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5184395/v1/c861e13a3d64f1e5ee7a3424.pdf"},{"id":79321116,"identity":"188244fa-c00e-4363-9340-29c3ac4dd337","added_by":"auto","created_at":"2025-03-27 04:29:39","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":392003,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFigureS3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5184395/v1/aaa75193f7bd72191ed73e7b.pdf"},{"id":79322014,"identity":"6e32dae3-45c7-4900-bc0a-c1d89770218b","added_by":"auto","created_at":"2025-03-27 04:37:38","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":12607,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFigureS4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5184395/v1/4ccdc13e85108cc89307577a.pdf"},{"id":79321107,"identity":"850b6867-ccd7-4c2f-955a-fdbebd04101c","added_by":"auto","created_at":"2025-03-27 04:29:38","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":12686,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFigureS5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5184395/v1/911e25cc7c230b8208835069.pdf"},{"id":79321101,"identity":"973d6866-bc15-48a1-b450-3a04c90a5d4a","added_by":"auto","created_at":"2025-03-27 04:29:38","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":33002,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterial1STROBEchecklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-5184395/v1/ab6ef69d7fa5073cfdd6e96d.docx"},{"id":79321105,"identity":"21cdcdbc-b5ce-4520-b6d6-c048fe42b6cc","added_by":"auto","created_at":"2025-03-27 04:29:38","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":1013061,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterial2NHANESquestionnaire20052008.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5184395/v1/67541a7fa3fbf512104a6c38.pdf"},{"id":79321109,"identity":"eb4d5477-16b7-4a6a-ac98-763a3d19611c","added_by":"auto","created_at":"2025-03-27 04:29:39","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":20959,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5184395/v1/78479dc5dbd52de8067db21a.docx"},{"id":79322017,"identity":"4f28d2e6-400a-4ba4-92bd-7dc275dee397","added_by":"auto","created_at":"2025-03-27 04:37:39","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":20662,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5184395/v1/e725e96a48a7e14dfd3dcdb4.docx"},{"id":79321106,"identity":"11dfb119-d019-463f-9aa1-384bba7f0b88","added_by":"auto","created_at":"2025-03-27 04:29:38","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":20364,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTableS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-5184395/v1/af7da88e9781d0d556e4823d.docx"},{"id":79321108,"identity":"c9a70e4e-1817-486f-968d-7168807c3e63","added_by":"auto","created_at":"2025-03-27 04:29:38","extension":"docx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":20594,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTableS4.docx","url":"https://assets-eu.researchsquare.com/files/rs-5184395/v1/6ef6991b248d6005a8cb7dcb.docx"},{"id":79321119,"identity":"90b38bbb-de4d-4ff7-ade4-1c65fd97f5fa","added_by":"auto","created_at":"2025-03-27 04:29:39","extension":"docx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":17982,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTableS5.docx","url":"https://assets-eu.researchsquare.com/files/rs-5184395/v1/92f1471aa8704829b3082123.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dietary iron intake is nonlinearly associated with the risk of diabetic retinopathy in adults with type 2 diabetes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiabetic retinopathy (DR) is one of the major causes of vision impairment globally[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to the Global Burden of Disease Study 2019, DR has been the fifth leading cause of blindness and moderate-to-worse visual impairment among individuals aged 50 years and greater[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Of note, it was the only cause of blindness with a globally increasing trend in age-standardised prevalence from 1990 to 2020[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Among individuals with diabetes, approximately a third have any sign of DR, and a third of them might have vision-threatening diabetic retinopathy (VTDR)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. With the estimated global diabetes prevalence to be rising from 9.3% (463\u0026nbsp;million) in 2019 to 10.9% (700\u0026nbsp;million) by 2045[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], along with longer life expectancy and lifestyle changes, the global burden of DR is expected to grow rapidly. Although hyperglycaemia, diabetes duration, and hypertension are considered as major risk factors for DR, they merely account for a small amount of the variation in the DR risk[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Several novel pathogeneses have raised that the abnormal homeostasis of trace elements including iron may associate with the progression of DR[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, little is known about the association between dietary iron intake and DR.\u003c/p\u003e \u003cp\u003eIron plays a critical role in dynamic redox balance, disruption of which can lead to oxidative stress and damage to target organs including the retina[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Nevertheless, previous studies showed contradictory results regarding whether iron could protect the retina from harmful stimuli. On one hand, iron overload can exacerbate the development of retinopathy in mice, implying that iron depletion strategies may ameliorate diabetic microvascular complications[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. On the other hand, epidemiologic and clinical studies showed that iron deficiency anaemia (IDA) was related to increased risk of DR[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], indicating the efficacy of anaemia treatment in decreasing retinopathy risk in type 2 diabetes (T2D). Such findings were supported by a recent study[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], in which serum iron was negatively correlated with the occurrence of DR in diabetic adults. Notably, all previous studies concentrated on the relationship of body iron status rather than dietary intake of iron and DR. The current assessment of iron status is derived from a battery of hemaetological indicators, including serum ferritin, transferrin saturation, and mean corpuscular volume, etc. However, measuring these hemaetological indicators is invasive and might be infeasible as for cost and complexity of procedure in certain settings. Thus, there is a need for a simpler and more accessible method to evaluate iron status. A complementary and easy-to-use option is to consider iron intake from diet and dietary supplements. When associations between DR risks and iron intake through nutritional assessment could be accurately estimated, it is promising to establish recommendations for appropriate dietary iron intake as to prevent DR in T2D patients. Nutritional interventions with optimal combinations of iron and other nutrients that support conventional therapies could be developed to reduce disease risk and severity in T2D patients.\u003c/p\u003e \u003cp\u003eHerein, we investigated the association between the level of dietary iron intake, measured by a standardised questionnaire, and the risk of DR and VTDR among patients with T2D in a series of national representative samples. We further assessed whether such associations varied in specific subgroups of population defined by major stratification factors.\u003c/p\u003e"},{"header":"Materials and Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003e This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting statement (Supplemental Material 1). We conducted this cross-sectional study using the data from the National Health and Nutrition Examination Survey (NHANES) 2005 to 2008. NHANES is a population-based, multipurpose survey to evaluate the health and nutritional status of the US population. The National Centre for Health Statistics (NCHS) ethics review board reviewed and approved the study protocol. The study was conducted adhered to the principles of the Declaration of Helsinki, and written informed consent was obtained from each participant. Detailed data and methodology files are accessible online[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. All potentially identifiable information has been removed to ensure the confidentiality of participants and their households. Briefly, every 2-year cycle of survey comprises of questionnaires administered at home, and a standardised physical examination in a mobile examination centre (MEC) including physical measurements and collection of biospecimens for laboratory tests. For 2005\u0026ndash;2006 and 2007\u0026ndash;2008 cycles, retinal photography and dietary interview were conducted to subjects older than 40 years, making this analysis available. Herein, T2D was defined according to participants\u0026rsquo; meeting one or more of the following criteria: (1) self-reported physician diagnosis of diabetes; (2) using oral glucose-lowering medicines or insulin; and (3) fasting plasma glucose level of at least 126mg/dL, or haemoglobin A1c (HbA1c) level of at least 6.5%[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In this study, only those with complete data of dietary iron intake, diabetes and retinopathy were eligible.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePatient and public involvement\u003c/h3\u003e\n\u003cp\u003ePatients or the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research.\u003c/p\u003e\n\u003ch3\u003eAssessment of dietary iron intake\u003c/h3\u003e\n\u003cp\u003eAll NHANES examinees were eligible for two 24-hour dietary recall interviews. The first was performed in-person in the MECs, and the second by telephone 3 to 10 days later. Intakes of energy, nutrients, and other components from foods and beverages were estimated from the two detailed dietary interview. Detailed questionnaire and methodology of dietary intake calculation is publicly accessible online[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In this study, dietary intake of iron was calculated by averaging data of two 24-hour recalls if available, otherwise the single reliable dietary recall data was used. The continuous iron intake data were further dichotomised as adequate and inadequate categories according to the Recommended Dietary Allowance (RDA) issued by the Food and Nutrition Board of the National Academies[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eAscertainment of diabetic retinopathy\u003c/h3\u003e\n\u003cp\u003eDuring 2005\u0026ndash;2008 NHANES survey, the presence of major retinal diseases including DR was tested for participants aged over 40 years by the Retinal Imaging. Two 45-degree digital retinal images for each eye were captured utilising the Canon Non-Mydriatic Retinal Camera CR6-45NM (Canon, Tokyo, Japan), one focused on the optic nerve and the other on the macula. The digital images were transferred to the University of Wisconsin Ocular Epidemiologic Reading Centre, Madison for grading retinopathies according to the standardised protocol. At least two raters graded a same set of images. If the first two graders disagreed, the third graded the image. If two of three disagreed, an adjudicator would make a final decision.\u003c/p\u003e \u003cp\u003eDR severity was broadly classified into 4 levels: no DR, mild non-proliferative diabetic retinopathy (NPDR), moderate/severe NPDR, and proliferative diabetic retinopathy (PDR) according to the Early Treatment Diabetic Retinopathy Study (ETDRS) classification standards[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In this study, we aggregated these 4 levels as no DR versus any DR (incorporating mild NPDR, moderate/severe NPDR, and PDR). We also defined VTDR as the presence of severe NPDR, PDR, or clinically significant macular oedema (CSME). Herein CSME was defined when (1) the oedema involved the fovea or within 500 microns of the fovea, and/or (2) a 1\u0026thinsp;+\u0026thinsp;disc area of oedema present with at least a portion of it involving the macula. Outcomes of our study were defined according to the worse one of two eyes.\u003c/p\u003e\n\u003ch3\u003eAssessment of covariates\u003c/h3\u003e\n\u003cp\u003eSociodemographic variables, including age, sex, race/ethnicity, education attainment, and poverty income ratio (PIR) was collected through a questionnaire. Race/ethnicity was self-reported according to NCHS categories (Mexican American, non-Hispanic Black, non-Hispanic White, other Hispanic, or other). We combined other Hispanic and other race/ethnicity groups as the broader \u0026ldquo;other group\u0026rdquo;. Education was grouped into three categories: 1) less than high school, 2) high school or equivalent, and 3) greater than high school. PIR was classified into 3 categories: less than 1.30, 1.30 to 3.49, and 3.5 or higher. Body mass index (BMI) was calculated as weight in kilograms divided by height in metres squared, and was categorised as three groups: normal or underweight (less than 25.0 kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (25.0 to 30.0 kg/m\u003csup\u003e2\u003c/sup\u003e), and obese (greater than 30.0 kg/m\u003csup\u003e2\u003c/sup\u003e). Smoking status was classified into 3 categories: never (less than 100 cigarettes in lifetime), former (greater than 100 cigarettes in lifetime, but had given up at the time of interview), and current smoker (greater than 100 cigarettes in lifetime and currently smoking). Alcohol consumption was classified into 3 categories: never (never drank alcohol in lifetime and the past 12 months), former (had ever drunk in lifetime, but not in the past 12 months), and current drinker (drunk at least 12 alcoholic drinks during the past year or lifetime and consumed alcohol at least 1 day during the past year).\u003c/p\u003e \u003cp\u003eDuration of diabetes was self-reported by interviewees, and was dichotomised as \u0026lt;\u0026thinsp;10 and \u0026ge;\u0026thinsp;10 years. Haemoglobin A1c (HbA1c) raw data were continuous, and were further grouped into \u0026lt;\u0026thinsp;7.0% versus \u0026ge;\u0026thinsp;7.0%. Blood haemoglobin level was dichotomised as normal/high (\u0026gt;\u0026thinsp;12 g/dL) and low (\u0026lt;\u0026thinsp;12 g/dL) based on the standard of World Health Organization[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInformation about medical comorbidities was obtained from physical examination and questionnaire. We defined hypertension if participants with (1) diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;80 mmHg, or systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;130 mmHg according to the mean value of 3 measurements, or (2) self-reported hypertension history, or (3) taking blood pressure medications[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Individuals whose total cholesterol\u0026thinsp;\u0026ge;\u0026thinsp;240 mg/dL (6.2mmol/L), or taking lipid-lowering medications were considered as having hypercholesteraemia. Congestive heart failure, coronary heart disease, heart attack, angina/angina pectoris, and stroke were confirmed based on self-reported physician diagnosis. The full questionnaire can be found in the Supplemental Material 2.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eIn this study, we reported descriptive statistics as numbers (percentages) for categorical variables, and means (standard deviations, SDs) for continuous variables. We used \u003cem\u003eχ2\u003c/em\u003e and unpaired \u003cem\u003et\u003c/em\u003e-tests to assess differences in sociodemographic, clinical and dietary characteristics between groups. We established logistic regression models to evaluate odds ratios (ORs) of DR and VTDR based on iron intake quartiles after adjustment for demographic variables, lifestyle variables, DM related variables, and medical comorbidities.\u003c/p\u003e \u003cp\u003eTo explore nonlinear association between DR and iron intake, we used restricted cubic spline (RCS) logistic regression analyses after adjusting the abovementioned confounders. We set four knots (5th, 35th, 65th, and 95th percentiles of iron intake) according to Harrell\u0026rsquo;s recommendation that four knots can offer an adequate fit of the model and well balance flexibility and imprecision caused by overfitting[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The R package plotRCS (version 0.1.4) was used to visualise splines[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Logistic regression and RCS analysis were stratified by sex, race/ethnicity, weight status, HbA1c level (\u0026lt;\u0026thinsp;7.0% or \u0026ge;\u0026thinsp;7.0%), duration of diabetes (\u0026lt;\u0026thinsp;10 years or \u0026ge;\u0026thinsp;10 years), and blood haemoglobin level (normal/high or low). We performed sensitivity analyses to evaluate the robustness of major results by applying the following strategies: (1) excluding participants with outlying iron intake data (\u0026ge;\u0026thinsp;3SD from the mean value), (2) reconstructing logistic regression and RCS models through ignoring missing data rather than multiple imputation (i.e., complete case analysis). All data analyses were performed by R statistical package (R Core Team, Vienna, Austria, version 4.2.3). Variables with missing values were imputed through multiple imputation approach using mice package (version 3.15.0)[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. \u003cem\u003eP\u003c/em\u003e value less than 0.05 was considered to be statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eParticipant characteristics\u003c/h2\u003e \u003cp\u003eOf all participants from the NHANES 2005\u0026ndash;2006 (n\u0026thinsp;=\u0026thinsp;10348) and 2007\u0026ndash;2008 (n\u0026thinsp;=\u0026thinsp;10149), 18923 participants younger than 40 years (n\u0026thinsp;=\u0026thinsp;13416) or without diabetes (n\u0026thinsp;=\u0026thinsp;5507) were excluded. Participants with ungradable images (n\u0026thinsp;=\u0026thinsp;374) or without dietary iron intake data (n\u0026thinsp;=\u0026thinsp;28) were excluded as well, leaving 1172 participants ultimately included (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Nine covariates of the dataset had missing values over 1% of observations (Supplemental Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), and PIR had the highest proportion (8%). To take into account the effect of missing data, we handled missing data with multiple imputation technique for the subsequent analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe general characteristics of study population by DR status were presented in Table\u0026nbsp;1. There were 358 participants (30.5%) with any DR, among whom 188 were males (52.5%). Compared with disease-free individuals, DR patients had comparable average age, sex, education, marriage status, PIR, smoking status, and alcohol consumption, but were more likely to be non-Hispanic black, Mexican American, and have higher BMI. DR population showed poorer general health condition, longer diabetes duration, higher levels of HbA1c, lower levels of blood haemoglobin, as well as higher prevalence of systemic comorbidities including congestive heart failure, heart attack and stroke (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table\u0026nbsp;1). The mean dietary iron intake of subjects with DR was 13.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.24 mg/d, which was significantly lower than that of subjects without DR (14.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.76 mg/d, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008). However, the proportion of participants meeting the sufficient daily iron intake was comparable between two groups (23.5% vs. 19.8%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.176, Table\u0026nbsp;1). When analysed according to the severity of DR, the iron intake in the VTDR subgroup was significantly lower than that in the groups without DR and without VTDR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007, Supplemental Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable\u0026nbsp;1. Characteristics of the study population according to status of diabetic retinopathy\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"680\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal Participants\u003c/p\u003e\n \u003cp\u003e(n = 1172)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWithout DR\u003c/p\u003e\n \u003cp\u003e(n = 814)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWith DR\u003c/p\u003e\n \u003cp\u003e(n = 358)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e594 (50.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e406 (49.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e188 (52.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e578 (49.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e408 (50.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e170 (47.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge, mean (SD), y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e63.2 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e63.0 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e63.7 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRace/ethnicity, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e485 (41.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e360 (44.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e125 (34.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e337 (28.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e210 (25.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e127 (35.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e226 (19.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e153 (18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e73 (20.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e124 (10.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e91 (11.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33 (9.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEducation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLess than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e467 (39.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e306 (37.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e161 (45.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e308 (26.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e224 (27.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e84 (23.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCollege or higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e397 (33.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e284 (34.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e113 (31.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMarital status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMarried or living with partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e726 (61.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e509 (62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e217 (60.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNot married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e446 (38.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e305 (37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e141 (39.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePoverty income ratio, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e573 (48.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e390 (47.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e183 (51.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e1.85 to 3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e304 (25.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e211 (25.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93 (26.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cu\u003e\u0026gt;\u003c/u\u003e 3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e295 (25.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e213 (26.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82 (22.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWeight status by BMI, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNormal or underweight (\u0026lt;25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e152 (13.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e107 (13.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45 (12.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOverweight (25.0 to 30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e332 (28.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e212 (26.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e120 (33.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eObese (\u003cu\u003e\u0026gt;\u003c/u\u003e 30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e688 (58.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e495 (60.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e193 (53.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSmoking status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNever smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e532 (45.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e351 (43.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e181 (50.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFormer smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e442 (37.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e323 (39.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e119 (33.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e198 (16.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e140 (17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58 (16.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAlcohol consumption, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNever drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e213 (18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e147 (18.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66 (18.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFormer drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e179 (15.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e115 (14.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64 (17.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e780 (66.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e552 (67.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e228 (63.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGeneral health status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eExcellent to good\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e640 (54.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e468 (57.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e172 (48.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFair or poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e532 (45.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e346 (42.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e186 (52.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDuration of diabetes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e786 (67.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e655 (80.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e131 (36.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cu\u003e\u0026gt;\u003c/u\u003e 10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e386 (32.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e159 (19.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e227 (63.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHbA1c, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.25 (1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.94 (1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.98 (1.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHbA1c level, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e411 (35.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e340 (41.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e71 (19.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cu\u003e\u0026gt;\u003c/u\u003e 6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e761 (64.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e474 (58.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e287 (80.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBlood haemoglobin level, n (%)\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNormal/High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e997 (85.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e709 (87.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e288 (80.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e175 (14.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e105 (12.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e70 (19.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHistory of comorbidities, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e965 (82.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e660 (81.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e305 (85.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHypercholesteraemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e643 (54.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e432 (53.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e212 (59.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCongestive heart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e123 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64 (7.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e59 (16.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCoronary heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e138 (11.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e88 (10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50 (14.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngina/angina pectoris\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e97 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64 (7.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33 (9.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHeart attack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e139 (11.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82 (10.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e57 (15.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eStroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e127 (10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74 (9.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53 (14.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDietary iron intake, mg/d, Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.9 (6.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.3 (6.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.2 (6.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAdequate intake of iron by RDA, n (%) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAdequate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e245 (20.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e161 (19.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e84 (23.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInadequate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e927 (79.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e653 (80.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e274 (76.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e BMI, body mass index (calculated as weight in kilograms divided by height in metres squared); DR, diabetic retinopathy; RDA, recommended dietary allowance; SD, standard deviation.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eBlood haemoglobin level was classified according to haemoglobin concentrations for the diagnosis of anaemia and assessment of severity released by World Health Organization in 2011.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u0026nbsp;\u003c/sup\u003eIntake recommendations for iron were developed by the Food and Nutrition Board (FNB) at the Institute of Medicine (IOM) of the National Academies (formerly National Academy of Sciences).\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003e Comparisons were made by the use of the chi-square for categorical variables and the\u003c/p\u003e\n\u003cp\u003e2-sample \u003cem\u003et\u003c/em\u003e test for continuous variables.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAssociation of dietary iron intake and DR and VTDR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 presents the results of multivariable logistic regression analyses. In crude models, participants in the third and fourth quartiles showed significantly lower risk of DR as compared with the first (reference) group (quartile 3: OR = 0.57; 95%CI, 0.40-0.82; quartile 4: OR = 0.68; 95%CI, 0.48-0.96). After adjustment for multiple covariates, significant associations remained for the third quartile in all logistic regression models (Table 2; for the saturated model: OR=0.59, 95%CI, 0.39-0.90). Besides, no significant relationship was shown between the second quartile and the reference group in any models (all \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). When dietary iron intake was dichotomised as adequate versus inadequate by RDA, no significant relation was found between iron intake and DR (Table 2, all \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompared with the first quartile, all other quartiles yielded significantly lower odds of VTDR in crude models (quartile 2: OR = 0.47; 95%CI, 0.25-0.89; quartile 3: OR = 0.43; 95%CI, 0.22-0.83; quartile 4: OR = 0.44; 95%CI, 0.22-0.86). With adjustment of covariates, this relationship remained significant for the third quartile in all models (Table 2; for the saturated model: OR=0.42, 95%CI, 0.19-0.94). Nonetheless, correlation between VTDR risk and the fourth quartile of iron intake became insignificant after multiple adjustments (all \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). Inadequate intake of iron according to RDA showed no added risk of VTDR in either crude or multi-adjusted models (all \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Association of dietary iron intake with diabetic retinopathy and vision-threatening diabetic retinopathy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eCI, confidence interval; OR, odds ratio.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eNumber of participants with T2D in NHANES 2005-2008.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/sup\u003eNumber of participants with T2D and diabetic retinopathy or vision-threatening diabetic retinopathy in NHANES 2005-2008.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eModel 1: Adjusted for demographic variables (age, sex, race/ethnicity, education, marital status, poverty income ratio).\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ed\u003c/sup\u003e Model 2: Adjusted for demographic and lifestyle variables (smoking, drinking, body mass index).\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ee\u003c/sup\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eModel 3: Adjusted for demographic, lifestyle, diabetes related variables (duration of diabetes, HbA1c level), and medical comorbidity variables (general health condition, history of angina/angina pectoris, congestive heart failure, coronary heart disease, heart attack, hypercholesteraemia, hypertension, and stroke).\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ef\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eAdequate dietary iron intake is defined according to Recommended Dietary Allowance (RDA) developed by the Food and Nutrition Board (FNB) at the Institute of Medicine (IOM) of the National Academies (formerly National Academy of Sciences).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStratified analyses for association of dietary iron intake and DR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 demonstrated the associations between dietary iron and DR by various stratifying factors, including sex, race/ethnicity, weight status, HbA1c level, duration of diabetes, and blood haemoglobin level. Compared with the first quartile, subjects within the third quartile yielded significantly lower odds of DR in female, non-Hispanic Black, obese, HbA1c \u0026ge; 6.5%, and DM duration \u0026ge;10 years groups. Remarkably, for individuals with low blood haemoglobin, both the third and the fourth quartiles showed decreased risks of DR (quartile 3: OR = 0.17; 95%CI, 0.05-0.60; quartile 4: OR = 0.23; 95%CI, 0.06-0.90), but were insignificant in those with normal or high blood haemoglobin. No significant interaction between any stratification factors and dietary iron intake was found (Table 3, all \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Stratified analyses for association of dietary iron with diabetic retinopathy\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e BMI, body mass index (calculated as weight in kilograms divided by height in metres squared); OR, odds ratio.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e Analyses were adjusted for age, sex (except for sex stratification), race/ethnicity (except for race/ethnicity stratification), body weight status (except for weight status stratification), duration of diabetes (except for diabetes duration stratification) and medical comorbidity variables (general health condition, history of angina/angina pectoris, congestive heart failure, coronary heart disease, heart attack, hypercholesteraemia, hypertension, and stroke).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/strong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eBlood haemoglobin level was classified according to haemoglobin concentrations for the diagnosis of anaemia and assessment of severity released by World Health Organization in 2011.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eNonlinear relationship between dietary iron intake and DR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe non-monotonic relationship revealed by logistic regression analysis promoted us to further investigate the nonlinear relationship of DR and dietary iron intake. RCS analyses showed nonlinear associations between DR and dietary iron intake varied by sex (Figure 2). In males, no significant relationship was found between dietary iron and DR risk (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eoverall\u003c/sub\u003e=0.230, \u003cem\u003eP\u003c/em\u003e\u003csub\u003enonlinear\u003c/sub\u003e=0.490) (Figure 2A). In females, however, an approximately U-shaped relationship was revealed; only moderate level of dietary iron intake was associated with decreased risk of DR (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eoverall\u003c/sub\u003e=0.022, \u003cem\u003eP\u003c/em\u003e\u003csub\u003enonlinear\u003c/sub\u003e=0.023) (Figure 2B). No significant nonlinear association was found in subgroups by stratification factors other than sex (Supplemental Figure S3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSensitivity analyses showed the robustness of our major results. The association between DR and quartiles of iron intake did not substantially change when participants with outlying data were excluded (Supplemental Table S1 for any DR, Supplemental Table S2 for VTDR). Such associations remained significant when we removed missing data instead of adopting multiple imputation (Supplemental Table S3 for any DR, Supplemental Table S4 for VTDR). This indicated that the outliers did not significantly deflate or inflate the mean of the sample, and had minimal influence on the association derived from the mean. Similarly, when excluding the participants with outlying data (Supplemental Figure S4) or ignoring missing data (Supplemental Figure S5), the RCS analysis results did not substantially change, indicating that missing data caused little noise or bias to estimation.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large-scale, nationally representative T2D cohort,\u0026nbsp;we found significantly lower daily dietary iron intake in individuals with DR, particularly those with VTDR. After adjustment for major confounding factors, medium-high level of dietary iron intake (13.2-18.1 mg/d) was associated with 59% risk reduction for DR, and 42% risk reduction for VTDR. Of note, beneficial effects of adequate iron intake were more profound in several specific subpopulations, including females, non-Hispanic Blacks, individuals with longer diabetes duration, higher level of HbA1c, concurrent obesity, or anaemia. Spline regression analysis demonstrated that there was a nonlinear U-relationship between the daily iron intake amount and DR risk in females, but not in males. Sensitivity analyses confirmed the robustness of the above findings.\u003c/p\u003e\n\u003cp\u003eAs one of the essential minerals, iron is vital for maintaining the normal structures and functions of a number of macromolecules in cells. Dysregulation of iron homeostasis, either excess or deficiency, might lead to a variety of chronic diseases including diabetes. In patients with pre-existing diabetes, iron deficiency anaemia (IDA) can exacerbate retinopathy through inducing long-term hypoxia in the retina[23]. Additionally, increased lipid peroxidation induced by IDA could elevate HbA1c level[24], which is a strong indicator to predict the onset and progression of DR. Luckily, elevated HbA1c in T2D patients with IDA could be ameliorated by 3-month iron supplementation therapy[25]. Iron overload, however, generates various oxygen and nitrogen species via Fenton reaction, which is one of the major causative factors for diabetes and its complications[26]. Recently, an animal study demonstrated that excessive iron can exacerbate the development of DR by increasing retinal renin expression in mice[9]. Another study also verified that iron accumulation induced the diabetic-related pericyte loss in eyes and aggravated diabetic microvascular complications[10]. Collectively, ensuring a balanced iron status in the body is critical for preventing the occurrence and progression of diabetic ocular complications. Our findings in this study were well consistent with this concept, and for the first time we revealed possible beneficial effects of medium-high dietary iron intake on preventing DR and VTDR; neither higher nor lower amount was significantly associated with the occurrence of DR.\u003c/p\u003e\n\u003cp\u003eAnother interesting finding of this study is that sex can potentially modify the relationship between dietary iron intake and DR.\u0026nbsp;Both multivariate logistic regression and spline analysis models demonstrated that medium-high daily iron intake was associated with a reduced risk of DR in females but not in males. The underlying mechanism is difficult to interpret but may be related to sex differences in iron homeostasis under the influences of hormonal, genetic, and dietary factors. Females have lower iron storages than males because of menstruation. On average, females lose about 20-60 mg of iron per menstrual cycle, an amount comparable to daily dietary iron intake requirements for males. Besides, differences in dietary habit by sex may also affect iron absorption: men consume more haem iron, while women consume more non-haem iron. Haem iron is more available for absorption from the diet than non-haem iron, which may substantially affect iron status and health outcomes[27].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe stratified analysis in this study found that factors other than sex may also modify the relation between dietary iron intake and DR. Participants who are non-Hispanic Black, obese, with low haemoglobin levels, with poorer glycaemic control (HbA1c\u0026ge;6.5%) and having a longer duration of diabetes (\u0026ge; 10 years) may benefit more from sufficient iron intake than others. A possible reason is that T2D patients with obesity, of non-Hispanic Black ethnicity, and unsatisfactory glycaemic control are more likely to have concurrent iron deficiency or IDA[28-30]. In response, our study also found a significant inverse association between dietary iron intake and the risk of DR in T2D patients with anaemia. Given that anaemia is an established risk factor for DR[11, 12], it is particularly important for the abovementioned at-risk populations to routinely evaluate and maintain sufficient iron intakes.\u003c/p\u003e\n\u003cp\u003eThe findings of our study are relevant for clinical practice, and provide implications for future research as well. To help prevent DR, we encourage adults with T2D to consume a diet with sufficient iron. The optimal amount of daily iron intake showing protective effects on DR (13.2-18.1 mg/d) is higher than the RDA for male adults (8 mg/d) and close to that for premenopausal women (18 mg/d). As this range of daily iron intake is far below the upper limits established by the Food and Nutrition Board (FNB) for adults (\u0026lt; 45 mg/d), it poses very little risk of iron overload and toxicity. Higher iron intake than 18.2 mg/d may not be recommended because excessive iron intake is not associated with further reduced DR risk, but may cause other health problems like neurological disorders. Moreover, due to the nonlinear relationship between dietary iron and DR, our study also indicated that dichotomised cut-off values of RDA may not be capable of guiding iron intake for diabetes patients in terms of minimising retinopathy risk. In the future, a more sophisticated \u0026ldquo;protective dietary pattern\u0026rdquo; incorporating iron intake against diabetic retinopathy is to be developed and validated.\u003c/p\u003e\n\u003cp\u003eThere are several strengths of our study. Compared with previous studies (Supplemental Table S5), this is the first study to illustrate the nonlinear relationships between dietary iron intake and DR using spline analyses, a powerful technique to delineate the nonlinear nature of many phenomena in clinical research. Moreover, the NHANES data are high-quality and of great representativeness which ensured the generalisability of our findings and conclusions. The comprehensive survey data allowed us to adjust potential confounders in statistical models as well.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNevertheless, this study has several limitations. Firstly, due to the cross-sectional design, we only identified correlation rather than causation between dietary iron intake and DR. Further dietary trials are needed to establish causality and to test the efficacy of dietary iron interventions. Secondly, the dietary data in NHANES were acquired by two 24-hour recalls, largely depending on participants\u0026rsquo; memory. Recall bias could not be excluded, and the actual daily nutrient intake level might slightly differ from the self-reported data. Thirdly, due to the unavailability of data on supplement intake of iron, our study only included dietary iron intake but not on metal supplements. Fourthly, although we adjusted for a comprehensive range of confounding factors, residual or unknown confounding cannot be entirely excluded. Fifthly, cultural and dietary differences across the population have the potential to impact the findings. Sixthly, although the sample population is highly representative and with large size, the prevalence of VTDR was relatively low which may have contributed to the non-statistically significant results.\u003c/p\u003e\n\u003cp\u003eIn summary, our study found a nonlinear association between dietary iron intake and DR risk. Medium-high level of iron intake was associated with a reduced risk of DR and VTDR, especially for\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003efemales, non-Hispanic Blacks, obese people, those with HbA1c \u003cu\u003e\u0026gt;\u003c/u\u003e 6.5%, and diabetes duration \u003cu\u003e\u0026gt;\u003c/u\u003e 10 years. High or low levels of iron intake may not be conducive to preventing the development of DR. A precise efficacy of dietary iron intervention strategy and its impact on DR and VTDR are to be determined in longitudinal studies and controlled trials.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u0026nbsp;\u003c/strong\u003eWe thank all the participants and staff involved in the National Health and Nutrition Examination Survey for their invaluable contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003eX.C. and W.X. was responsible for the concept, design, and supervision. Y.F. and H.S. collected the data from the database. W.L. and W.Y. conducted data analyses and visualisation. Y.F. and H.S. wrote the original draft. X.C. and W.X. revised the manuscript critically. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eNone declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis study was supported by the Guangdong Basic and Applied Basic Research Foundation (2023A1515012192, 2023A1515030108).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRole of the funder/sponsor:\u003c/strong\u003e The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u003c/strong\u003e The NHANES protocol was approved by the National Center for Health Statistic (NCHS) Research Ethics Review Board (Protocol #2005-06 for NHANES 2005-2006; Continuation of Protocol #2005-06 for NHANES 2007-2008). The Ethics Review Board approval information is accessible at: https://www.cdc.gov/nchs/nhanes/irba98.htm (accessed on 8 August, 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent for publication:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional contributions:\u003c/strong\u003e None reported.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u003c/strong\u003e Data are available in a public, open access repository.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWong TY, Cheung CM, Larsen M, Sharma S, Sim\u0026oacute; R: \u003cstrong\u003eDiabetic retinopathy\u003c/strong\u003e. \u003cem\u003eNat Rev Dis Primers \u003c/em\u003e2016, \u003cstrong\u003e2\u003c/strong\u003e:16012.\u003c/li\u003e\n\u003cli\u003eCollaborators GBaVI: \u003cstrong\u003eCauses of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study\u003c/strong\u003e. \u003cem\u003eLancet Glob Health \u003c/em\u003e2021, \u003cstrong\u003e9\u003c/strong\u003e(2):e144-e160.\u003c/li\u003e\n\u003cli\u003eYau JW, Rogers SL, Kawasaki R, Lamoureux EL, Kowalski JW, Bek T, Chen SJ, Dekker JM, Fletcher A, Grauslund J\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGlobal prevalence and major risk factors of diabetic retinopathy\u003c/strong\u003e. \u003cem\u003eDiabetes Care \u003c/em\u003e2012, \u003cstrong\u003e35\u003c/strong\u003e(3):556-564.\u003c/li\u003e\n\u003cli\u003eSaeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, Colagiuri S, Guariguata L, Motala AA, Ogurtsova K\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGlobal and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9(th) edition\u003c/strong\u003e. \u003cem\u003eDiabetes Res Clin Pract \u003c/em\u003e2019, \u003cstrong\u003e157\u003c/strong\u003e:107843.\u003c/li\u003e\n\u003cli\u003eTan GS, Ikram MK, Wong TY: \u003cstrong\u003eTraditional and novel risk factors of diabetic retinopathy and research challenges\u003c/strong\u003e. \u003cem\u003eCurr Med Chem \u003c/em\u003e2013, \u003cstrong\u003e20\u003c/strong\u003e(26):3189-3199.\u003c/li\u003e\n\u003cli\u003eHarrison AV, Lorenzo FR, McClain DA: \u003cstrong\u003eIron and the Pathophysiology of Diabetes\u003c/strong\u003e. \u003cem\u003eAnnu Rev Physiol \u003c/em\u003e2023, \u003cstrong\u003e85\u003c/strong\u003e:339-362.\u003c/li\u003e\n\u003cli\u003eKang Q, Yang C: \u003cstrong\u003eOxidative stress and diabetic retinopathy: Molecular mechanisms, pathogenetic role and therapeutic implications\u003c/strong\u003e. \u003cem\u003eRedox Biol \u003c/em\u003e2020, \u003cstrong\u003e37\u003c/strong\u003e:101799.\u003c/li\u003e\n\u003cli\u003eGalaris D, Barbouti A, Pantopoulos K: \u003cstrong\u003eIron homeostasis and oxidative stress: An intimate relationship\u003c/strong\u003e. \u003cem\u003eBiochim Biophys Acta Mol Cell Res \u003c/em\u003e2019, \u003cstrong\u003e1866\u003c/strong\u003e(12):118535.\u003c/li\u003e\n\u003cli\u003eChaudhary K, Promsote W, Ananth S, Veeranan-Karmegam R, Tawfik A, Arjunan P, Martin P, Smith SB, Thangaraju M, Kisselev O\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eIron Overload Accelerates the Progression of Diabetic Retinopathy in Association with Increased Retinal Renin Expression\u003c/strong\u003e. \u003cem\u003eSci Rep \u003c/em\u003e2018, \u003cstrong\u003e8\u003c/strong\u003e(1):3025.\u003c/li\u003e\n\u003cli\u003eAltamura S, M\u0026uuml;dder K, Schlotterer A, Fleming T, Heidenreich E, Qiu R, Hammes HP, Nawroth P, Muckenthaler MU: \u003cstrong\u003eIron aggravates hepatic insulin resistance in the absence of inflammation in a novel db/db mouse model with iron overload\u003c/strong\u003e. \u003cem\u003eMol Metab \u003c/em\u003e2021, \u003cstrong\u003e51\u003c/strong\u003e:101235.\u003c/li\u003e\n\u003cli\u003eWang J, Xin X, Luo W, Wang R, Wang X, Si S, Mo M, Shao B, Wang S, Shen Y\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eAnemia and Diabetic Kidney Disease Had Joint Effect on Diabetic Retinopathy Among Patients With Type 2 Diabetes\u003c/strong\u003e. \u003cem\u003eInvest Ophthalmol Vis Sci \u003c/em\u003e2020, \u003cstrong\u003e61\u003c/strong\u003e(14):25.\u003c/li\u003e\n\u003cli\u003eChung JO, Park SY, Chung DJ, Chung MY: \u003cstrong\u003eRelationship between anemia, serum bilirubin concentrations, and diabetic retinopathy in individuals with type 2 diabetes\u003c/strong\u003e. \u003cem\u003eMedicine (Baltimore) \u003c/em\u003e2019, \u003cstrong\u003e98\u003c/strong\u003e(43):e17693.\u003c/li\u003e\n\u003cli\u003eChen YJ, Chen JT, Tai MC, Liang CM, Chen YY, Chen WL: \u003cstrong\u003eSerum Iron and Risk of Diabetic Retinopathy\u003c/strong\u003e. \u003cem\u003eNutrients \u003c/em\u003e2020, \u003cstrong\u003e12\u003c/strong\u003e(8).\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eNHANES Survey Methods and Analytic Guidelines. \u003c/strong\u003e[https://wwwn.cdc.gov/nchs/nhanes/analyticguidelines.aspx]\u003c/li\u003e\n\u003cli\u003eAssociation AD: \u003cstrong\u003e2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2020\u003c/strong\u003e. \u003cem\u003eDiabetes Care \u003c/em\u003e2020, \u003cstrong\u003e43\u003c/strong\u003e(Suppl 1):S14-s31.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eIron - Health Professional Fact Sheet \u003c/strong\u003e[https://ods.od.nih.gov/factsheets/Iron-HealthProfessional/]\u003c/li\u003e\n\u003cli\u003eZhang X, Saaddine JB, Chou CF, Cotch MF, Cheng YJ, Geiss LS, Gregg EW, Albright AL, Klein BE, Klein R: \u003cstrong\u003ePrevalence of diabetic retinopathy in the United States, 2005-2008\u003c/strong\u003e. \u003cem\u003eJAMA \u003c/em\u003e2010, \u003cstrong\u003e304\u003c/strong\u003e(6):649-656.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eAnaemia - World Health Organization (WHO) \u003c/strong\u003e[https://www.who.int/health-topics/anaemia]\u003c/li\u003e\n\u003cli\u003eWhelton PK, Carey RM, Aronow WS, Casey DE, Jr., Collins KJ, Dennison Himmelfarb C, DePalma SM, Gidding S, Jamerson KA, Jones DW\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003e2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines\u003c/strong\u003e. \u003cem\u003eCirculation \u003c/em\u003e2018, \u003cstrong\u003e138\u003c/strong\u003e(17):e426-e483.\u003c/li\u003e\n\u003cli\u003eHarrell FE: \u003cstrong\u003eRegression Modeling Strategies: with applications to linear models, logistic regression, and survival analysis\u003c/strong\u003e: Springer-Verlag New York; 2010.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ercsplot: Plot restricted cubic splines curves \u003c/strong\u003e[https://cran.r-project.org/web/packages/plotRCS/index.html]\u003c/li\u003e\n\u003cli\u003evan Buuren S, Groothuis-Oudshoorn K: \u003cstrong\u003emice: Multivariate Imputation by Chained Equations in R\u003c/strong\u003e. \u003cem\u003eJournal of Statistical Software \u003c/em\u003e2011, \u003cstrong\u003e45\u003c/strong\u003e(3):1 - 67.\u003c/li\u003e\n\u003cli\u003eLee MK, Han KD, Lee JH, Sohn SY, Jeong JS, Kim MK, Baek KH, Song KH, Kwon HS: \u003cstrong\u003eHigh hemoglobin levels are associated with decreased risk of diabetic retinopathy in Korean type 2 diabetes\u003c/strong\u003e. \u003cem\u003eSci Rep \u003c/em\u003e2018, \u003cstrong\u003e8\u003c/strong\u003e(1):5538.\u003c/li\u003e\n\u003cli\u003eBalamurugan R, Selvaraj N, Bobby Z, Sathiyapriya V: \u003cstrong\u003eIncreased glycated hemoglobin level in non-diabetic nephrotic children is associated with oxidative stress\u003c/strong\u003e. \u003cem\u003eIndian J Physiol Pharmacol \u003c/em\u003e2007, \u003cstrong\u003e51\u003c/strong\u003e(2):153-159.\u003c/li\u003e\n\u003cli\u003eTarim O, K\u0026uuml;\u0026ccedil;\u0026uuml;kerdoğan A, G\u0026uuml;nay U, Eralp O, Ercan I: \u003cstrong\u003eEffects of iron deficiency anemia on hemoglobin A1c in type 1 diabetes mellitus\u003c/strong\u003e. \u003cem\u003ePediatr Int \u003c/em\u003e1999, \u003cstrong\u003e41\u003c/strong\u003e(4):357-362.\u003c/li\u003e\n\u003cli\u003eHalliwell B, Gutteridge JM: \u003cstrong\u003eOxygen toxicity, oxygen radicals, transition metals and disease\u003c/strong\u003e. \u003cem\u003eBiochem J \u003c/em\u003e1984, \u003cstrong\u003e219\u003c/strong\u003e(1):1-14.\u003c/li\u003e\n\u003cli\u003eAbbaspour N, Hurrell R, Kelishadi R: \u003cstrong\u003eReview on iron and its importance for human health\u003c/strong\u003e. \u003cem\u003eJ Res Med Sci \u003c/em\u003e2014, \u003cstrong\u003e19\u003c/strong\u003e(2):164-174.\u003c/li\u003e\n\u003cli\u003eQiu F, Wu L, Yang G, Zhang C, Liu X, Sun X, Chen X, Wang N: \u003cstrong\u003eThe role of iron metabolism in chronic diseases related to obesity\u003c/strong\u003e. \u003cem\u003eMol Med \u003c/em\u003e2022, \u003cstrong\u003e28\u003c/strong\u003e(1):130.\u003c/li\u003e\n\u003cli\u003eLe CH: \u003cstrong\u003eThe Prevalence of Anemia and Moderate-Severe Anemia in the US Population (NHANES 2003-2012)\u003c/strong\u003e. \u003cem\u003ePLoS One \u003c/em\u003e2016, \u003cstrong\u003e11\u003c/strong\u003e(11):e0166635.\u003c/li\u003e\n\u003cli\u003eGuo W, Zhou Q, Jia Y, Xu J: \u003cstrong\u003eIncreased Levels of Glycated Hemoglobin A1c and Iron Deficiency Anemia: A Review\u003c/strong\u003e. \u003cem\u003eMed Sci Monit \u003c/em\u003e2019, \u003cstrong\u003e25\u003c/strong\u003e:8371-8378.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"diabetic retinopathy, vision-threatening diabetic retinopathy, dietary iron intake, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-5184395/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5184395/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To elucidate the association between dietary iron intake and diabetic retinopathy (DR) in type 2 diabetes (T2D) patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Participants from the National Health and Nutrition Examination Survey (NHANES) 2005-2008 aged over 40 years with T2D were included. Dietary iron intake was estimated from standardised questionnaires. The presence of DR and vision-threatening DR (VTDR) was determined through retinal imaging. We used logistic regression to assess the relationship between iron intake and DR, and restricted cubic splines to reveal nonlinear links.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The study enrolled 1172 T2D adults. We found significant nonlinear associations between dietary iron intake and DR among females (\u003cem\u003eP\u003c/em\u003e = 0.023), but not in males (\u003cem\u003eP\u003c/em\u003e = 0.490). Compared with the lowest quartile of iron intake, the third quartile (13.2-18.1 mg/d) yielded significantly lower odds of developing DR (odds ratio [OR], 0.59; 95% CI, 0.39-0.90) and VTDR (OR, 0.42; 95% CI, 0.19-0.94). Stratified logistic analyses showed that medium-high iron intake was associated with lower risks of DR in females (OR, 0.44; 95% CI, 0.24-0.81), non-Hispanic Blacks (OR, 0.38; 95% CI, 0.17-0.85), and individuals with obesity (OR, 0.45; 95% CI, 0.25-0.82), high HbA1c (OR, 0.56; 95% CI, 0.34-0.93), long diabetes duration (OR, 0.40; 95% CI, 0.21-0.76) or low blood haemoglobin (OR, 0.17; 95% CI, 0.05-0.60).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Dietary iron intake was nonlinearly negatively associated with the prevalence of DR and VTDR, showing protective effect against retinopathy of medium-high iron intake in T2D patients. Such associations significantly vary by multiple factors such as age, ethnicity, obesity and glycaemic control.\u003c/p\u003e","manuscriptTitle":"Dietary iron intake is nonlinearly associated with the risk of diabetic retinopathy in adults with type 2 diabetes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-27 04:29:33","doi":"10.21203/rs.3.rs-5184395/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-02T07:12:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-02T07:09:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-01T13:23:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179347177571538859376510271964343601027","date":"2025-03-26T23:56:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-25T18:45:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"65112381703914499798667517022493165659","date":"2025-03-25T18:44:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-25T16:14:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-25T12:42:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Endocrine Disorders","date":"2025-03-23T16:45:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c1a4f995-91e5-43e2-acab-7d4dad53bb89","owner":[],"postedDate":"March 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-21T16:03:18+00:00","versionOfRecord":{"articleIdentity":"rs-5184395","link":"https://doi.org/10.1186/s12902-025-01926-z","journal":{"identity":"bmc-endocrine-disorders","isVorOnly":false,"title":"BMC Endocrine Disorders"},"publishedOn":"2025-04-18 15:57:27","publishedOnDateReadable":"April 18th, 2025"},"versionCreatedAt":"2025-03-27 04:29:33","video":"","vorDoi":"10.1186/s12902-025-01926-z","vorDoiUrl":"https://doi.org/10.1186/s12902-025-01926-z","workflowStages":[]},"version":"v1","identity":"rs-5184395","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5184395","identity":"rs-5184395","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}