Introduction

Diabetes mellitus (DM) is a major public health problem worldwide and one of the major diseases threatening human health and survival. Diabetic retinopathy (DR) is the most common microvascular complication of diabetes and one of the leading causes of blindness in people with diabetes in clinical practice, affecting people’s quality of life1,2,3. The biochemical mechanisms underpinning the development of DR are poorly understood and may be related to metabolic abnormalities, cellular signaling pathway transduction, glucotoxicity, and oxidative stress4. Although accumulating epidemiological evidence has linked serum metal exposure to diabetes, the association between heavy metals and DR remains elusive.

Due to the rapid development of industrialization and urbanization, the pollution concentration of lead (Pb), cadmium (Cd), mercury (Hg), selenium (Se), and manganese (Mn) in the environment is higher than that of other heavy metals5,6. Epidemiologic studies that have evaluated the relationship between these heavy metals and the risk of type 2 diabetes mellitus (T2DM) and its complications. An observational study showed that higher Mn intake was directly associated with a lower risk of type 2 diabetes in a population of postmenopausal women7. Previous study found a negative association between typical levels of serum Cd and diabetes risk in a United States of America population aged 20 years or older8. Kornhauser et al. reported that plasma Se was negatively associated with the severity of diabetic nephropathy in patients with type 2 diabetes9. There exists scientific evidence indicating that low levels of Cd exposure exacerbate the progression of kidney disease in diabetics10. However, the relationship between these heavy metals and diabetic retinopathy is poorly studied. Serum albumin (ALB) is a crucial physiological transporter of vital metal ions Pb2+, Cd2+, Hg2+, Se2+, and Mn2+ in the bloodstream11,12. Nonetheless, the evidence as to whether heavy metals influence the incidence of DR by binding to ALB and thereby affecting the incidence of DR evidence on the relationship between serum ALB and heavy metals and DR is unclear.

Therefore, we hypothesized that the relationship between blood levels of these five heavy metals and DR is nonlinear. The present sought to explore the association between blood levels of Pb, Cd, Hg, Se, Mn, and DR using data from a representative sample of people with T2DM who participated in the National Health and Nutrition Examination Survey (NHANES) between 2011 and 2020.

Methods

Sample and overall

The National Center for Health Statistics (NCHS) conducted a population-based cross-sectional NHANES to collect data on the nutritional and health conditions of children and adults in the United States of America via interviews, physical examinations, and laboratory tests. NHANES employs a categorized, multistage probability sampling design to select a sample of an unstructured population to evaluate health and nutritional status. NHANES procedures and protocols were approved by the NCHS Research Ethics Review Committee and written consent was obtained.

Inclusion and exclusion criteria

The study population was mainly over 30 years of age. This cross-sectional analysis used data from five NHANES survey cycles (each lasting two years) between 2011 and 2020. Of the 45,462 participants in this study, participants aged < 30 years (n = 23,484), those with unknown diabetes-related conditions, and those lacking information on the glycated hemoglobin (HbA1c) state or the HbA1c level was less than 6.5% (n = 18,955) were eliminated. Diabetic patients aged < 30 years (n = 910) were eliminated to lower the likelihood of having type 1 diabetes. Besides, participants lacking information on serum levels of heavy metals (n = 517) and DR (n = 13) were disqualified. We filled in the remaining missing values in using the average of the entire sequence in the spss software. Finally, 1583 participants were included in the analysis. The study design flow is depicted in Fig. 1.

Figure 1
figure 1

Flowchart of study target population NHANES (2011–2020).

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Diagnosis of DR

The following criteria were used to identify diabetes: (1) a prior diagnosis from a medical expert, (2) fasting blood glucose (FBG) levels of > 7.0 mmol/L, (3) HbA1c levels of > 6.5%, or (4) receiving diabetes medication13. DR was self-reported using a dichotomous classification, whereby the respondent had been informed by their physician that diabetes was affecting their eyes.

Determination of heavy metals

Whole blood specimens were collected by physicians at the NHANES Mobile Examination Center (MEC) via venipuncture in EDTA-coated tubes, centrifuged on-site, and stored frozen at − 30 °C for transportation to the Centers for Disease Control and Prevention (CDC) laboratory in California, where they were stored in a frozen state until further analysis. First, in the sample dilution step, a small amount of whole blood is taken from a larger sample and mixed to distribute the cellular components evenly. This mixing is critical because certain metals (e.g., lead) are primarily associated with red blood cells. Samples with clots or microclots are identified and excluded from analysis due to concerns about sample inhomogeneity. Diluted blood samples are prepared by mixing 1 part of the sample, 1 part of water, and 48 parts of diluent. The diluent contains chemicals that release metals from red blood cells, reduce ionization inhibition, prevent clogging, and enable the use of internal standards. The diluted sample is then passed through an inductively coupled plasma (ICP) ion source into the mass spectrometer. The liquid blood sample is converted into aerosol droplets that are vaporized, atomized, and ionized in the plasma region. The resulting ions enter the mass spectrometer with argon gas for analysis. The Dynamic Reaction Cell (DRC) plays a vital role in the selective reaction by removing interferences or enhancing the ion signal of specific elements. The ions passing through the DRC are electrically selected and directed to the analytical quadrupole. The electrical signal generated by the ion impact detector is processed into digital information to determine the elemental concentration. For the values under LODs, an imputed fill value (LOD/√2) was adopted to fill up the vacancy in data preprocessing. Quality assessment and quality control (QA/QC) processes for data collection from blood samples complied with the Therapeutic Laboratory Development Act of 198814. Detail information of the NHANES laboratory procedure is available at https://www.cdc.gov/nchs/nhanes/index.htm.

Covariates

Data on demographics (age, gender, and race/ethnicity), academic achievement, poverty-to-income proportion, smoking, body mass index (BMI), waist circumference, serum ALB, FBG, HbA1c, triglycerides, and total cholesterol were collected using standardized questionnaires. Participants were divided into Caucasian, Black, Hispanic, Mexican American, and other race categories. Height (H, m) and weight (W, kg) were measured according to norms, and BMI was calculated as W/H2 (kg/m2). Smokers were defined as individuals who smoked ≥ 100 cigarettes15. Smoking status was divided into three categories: never smoked (< 100 cigarettes in their lifetime), gave up smoking (previously smoked > 100 cigarettes but no longer smoke), and still smoking (previously smoked > 100 cigarettes and still smoking). Hypertension was defined as systolic blood pressure > 140 mmHg or diastolic blood pressure > 90 mmHg, use of blood pressure drugs, or self-reported high blood pressure16.

Statistical analysis

Categorical variables were expressed as proportions and frequencies and were statistically analyzed using Chi-square analyses. Continuous variables were expressed as mean and standard errors and were analyzed using linear regression models. Multifactorial logistic regression analysis was used to examine the relationship between heavy metal exposure and DR. Additionally, subgroup analysis of heavy metal concentration and DR was performed to test whether the effect of serum levels of heavy metal on DR could be altered by age, sex, and BMI. All data analyses were performed using R (version 4.0.2) and SPSS software (version 24.0). p < 0.05 was considered statistically significant.

Institutional review board statement

Our study used five cycles of open NHANES database (2011–2020), National Center for Health Statistics granted the study procedures of Ethics Review Board. These data could be accessible at the following URL: https://www.cdc.gov/nchs/nhanes/irba98.htm#print.

Ethics statement

All survey participants signed a declaration of consent form after being made aware of the nature of the poll. The informed consent was approved after evaluation by the Committee of the National Center for Statistics on Health Ethics Assessment Board. To make the best use of these resources, all of the data is made publicly accessible after formal anonymization. These data could be accessible at the following URL: https://www.cdc.gov/nchs/nhanes/irba98.htm#print.

Results

Participant characteristics

This study included 1583 T2DM patients, of which 331 had concomitant DR and 1252 had no concomitant DR. The demographic details of participants with or without DR are shown in Table 1. DR participants were older and had higher HbA1c levels and lower waist circumference, BMI, serum ALB, and serum Mn levels than non-DR participants. No statistically significant difference was observed between DR and non-DR groups in terms of race, gender, education, household income-to-poverty ratio, triglycerides, total cholesterol, smoking status, blood Pb, Cd, Hg, and Se, and hypertension status (p > 0.05).

Table 1 Characteristics of study sample with and without diabetic retinopathy.
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Relationship between serum level of heavy metal and DR

Multifactorial logistic regression analysis was performed to evaluate the relationship between serum heavy metal exposure and DR, as shown in Table 2. The data from Model 1 was left unadjusted, Model 2 was adjusted for gender, age, and race, and Model 3 was adjusted for education, household income-to-poverty ratio, smoking, hypertension, and BMI based on Model 2. The blood Mn exposure in Model 1 decreased the incidence of DR (p = 0.002). The blood Mn exposure was significantly negatively associated with DR in Models 2 and 3. However, no association was observed between blood Cd, Se, Hg, or Pb exposure and DR in all models (p > 0.05).

Table 2 Association between blood metal levels and DR.
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Subgroup analysis

A significant negative correlation between blood Mn content and DR was observed after adjusting for age, sex, ethnicity, and other factors. The subgroups were further stratified by age, sex, and BMI, as shown in Table 3. In the age ≥ 60 years group, blood Mn was significantly negatively associated with DR prevalence without adjusting for confounders (p < 0.001) and even after adjusting for all potential variables. However, no significant association between blood Mn and DR in the 30–44 and 45–60 age categories (p > 0.05). After adjusting for all variables, the data were stratified by gender, and the results showed there was a significant negative association between blood Mn concentration and DR prevalence in the male population compared with the female population (p = 0.013). When stratified by BMI, no significant correlations were observed between blood Mn and DR in 0–25 and 25–30 kg/m2 groups after adjusting for confounders. Additionally, a significant inverse correlation was found between blood Mn and DR in the BMI ≥ 30 kg/m2 group (p = 0.014).

Table 3 Association between serum manganese and stratified by age, sex, and BMI.
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Effect of mediating factors on the association between serum Mn and DR

The Baron and Kenny causal step method combined with multiple linear regression models was utilized to analyze the mediating role of serum ALB in the association between serum Mn and DR. As shown in Fig. 2, after adjusting for Model 3, serum Mn had a direct impact on the prevalence of DR (p = 0.010), which was partially mediated by ALB (p = 0.008). Serum ALB mediated 12.80% of the association between blood Mn and DR.

Figure 2
figure 2

Effect of the serum albumin (mediator) on the relationship between blood manganese (exposure) and diabetic retinopathy (DR outcome); DE direct effect, IE indirect effect, TE total effect.

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Discussion

The present study aimed to elucidate whether there was an association between serum levels of heavy metals and DR among individuals with T2DM in the United States of America. We found a substantial negative relationship between blood Mn and DR, which was partially mediated by serum ALB. Only individuals aged 60 years exhibited a significant inverse association between blood Mn levels and DR after age stratification. Under sex stratification, a significant negative association between blood Mn levels and DR prevalence was observed in the male population compared with the female population. Moreover, a statistically significant association between blood Mn and DR was observed in the BMI ≥ 30 kg/m2 group under BMI stratification.

DR, a common microvascular complication of diabetes with a global prevalence of 34.6%, is a major cause of blindness17. The onset and evolution of DR are influenced by various mechanisms, such as inflammatory processes, high blood pressure, abnormal lipid metabolism, and insulin resistance18,19,20. Only a few studies have examined the association between exposure to heavy metals and DR, and the findings are controversial. Pb, Hg, Se, and Cd are widespread environmental heavy metal toxicants. Handan et al. found that Se exerts a protective effect on retinal pigment epithelium (ARPE-19) and primary human retinal microvascular endothelial (ACBRI 181) cells against high glucose (HG)-induced oxidative stress and apoptosis21. A previous retrospective study found that blood Cd levels were higher in the DR group than in the DM group22. These findings are inconsistent with our results, perhaps due to different populations, races, disease states, and health conditions that affect the absorption, transport, and metabolism of Se and Cd in the body, affecting the association between Se and Cd and the disease.

Mn is one of the essential components of the Mn super antioxidant dismutase (MnSOD), which scavenges reactive oxygen species (ROS) under mitochondrial oxidative stress. MnSOD genes and Mn levels may impact MnSOD activity23. It was previously demonstrated that Mn administration improved MnSOD activation and shielded people from T2DM and related consequences24. Kowluru et al. demonstrated that Mn-SOD overexpression prevented glucose-induced increased oxidative stress and apoptosis of retinal endothelial cells, suggesting a protective role for Mn-SOD in the pathogenesis of diabetic retinopathy25. On the other hand, previous studies have found that insulin synthesis and secretion in the pancreas are impaired, and insulin degradation and glucagon release are accelerated in Mn-deficient mice, whereas other studies have found that Mn reduces oxidative stress through antioxidant enzyme systems and non-enzymatic pathways to protect pancreatic islet cells from reactive oxygen species (ROS)26,27. The current study suggested that blood Mn may act as a barrier against the onset of DR. This is consistent with our findings. However, whether Mn can influence the development of DR through other metabolic pathways has yet to be investigated.

In plasma, approximately 80% of the oxidized state of Mn2+ is bound to ALB and globulin and transported to the liver, kidney, small intestine, endocrine glands, pancreas, brain, bone, muscle, and hair12,28. ALB is a glycosylated protein synthesized and secreted by hepatocytes, which has anti-oxidative stress and anti-inflammatory29. Previous studies have shown reduced serum ALB levels are associated with DR risk30,31. The present study found that serum ALB significantly mediated the association between blood Mn and DR. This implies that blood manganese combined with serum albumin may reduce the development of DR through its antioxidant and anti-inflammatory effects.

In this study, we found a negative correlation between blood Mn and DR only in people aged ≥ 60 years. This may be because aging is linked to a decline in Mn levels. This generally agrees with a large cross-sectional study32, which found that serum Mn levels in people aged ≥ 60 years may contribute to preventing and controlling prediabetes and diabetes. Compared with females, males in the current study exhibited an adverse correlation between serum Mn levels and DR. According to a survey of dietary intake of Mn, females received considerably more Mn from their diets than males33. The survey also showed that the biological half-life of Mn was substantially lower in women than in men. These results imply that Mn absorption and metabolism differ between males and females, which may help to explain some of the observed gender disparities in our study.

Our study is the first study to explore the relationship between blood levels of heavy metals and DR in T2DM patients older than 30 in the United States of America. Nonetheless, the current study has some limitations. First, we could not demonstrate the causal link between specific molecular mechanisms in blood Mn and DR due to the cross-sectional design of the study, which should be further validated in a prospective cohort study and fundamental research. Second, the outcome variable was based on self-reported history of DR, which may not be entirely accurate. Third, although some of the confounders were adjusted in this study, there were still many confounders affecting DR, such as drug use and family history of dyslipidemia, that we were unable to obtain. Finally, because the NHANES-based study population was used, it was difficult to assess the validity of the findings from various ethnic studies.

Conclusions

In summary, the present study found a significant negative association between blood Mn levels and DR, suggesting that an appropriate increase in Mn intake may delay the onset and development of DR.