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The relationship between heavy metal exposure and type 2 diabetes: a large-scale retrospective cohort study using occupational health examinations


Oh, Sung Yong et al. (2021), The relationship between heavy metal exposure and type 2 diabetes: a large-scale retrospective cohort study using occupational health examinations, Dryad, Dataset,


Objectives: To investigate the associations between heavy metal exposure and serum ferritin levels, physical measurements, and type 2 diabetes mellitus (DM).

Design: A retrospective cohort study.

Setting: Changwon, the location of this study, is a Korean representative industrial city. Data were obtained from medical check-ups between 2002 and 2018.

Participants: A total of 34,814 male subjects were included. Of them, 1,035 subjects with lead exposure, 200 subjects with cadmium exposure, and the 33,579 remaining were assigned to cohort A, cohort B, and the control cohort, respectively. Data including personal history of alcohol and smoking, age, height, weight, the follow-up duration, HbA1c, fasting blood sugar (FBS), ferritin levels, and lead and cadmium levels within one year after exposure were collected. 

Primary Outcome Measure: In subjects without diabetes, changes in FBS and HbA1c were analyzed through repeated tests at intervals of one year or longer after the occupational exposure to heavy metals.

Results: In cohort A, DM was diagnosed in 33 subjects. There was a significant difference in lead concentrations between the subjects diagnosed with DM and those without DM during the follow-up period (3.94 ± 2.92 mg/dL versus 2.81 ± 2.03 mg/dL, p = 0.002). Simple exposure to heavy metals (lead and cadmium) was not associated with DM in Cox regression models (lead exposure hazard ratio [HR] 1.01, 95% CI 0.58 – 1.77, p 0.971; cadmium exposure HR 1.48, 95% CI: 0.61 – 3.55, p = 0.385). Annual changes in FBS according to lead concentration at the beginning of exposure showed a positive correlation (r = 0.072, p = 0.032).

Conclusion: Our findings demonstrated that simple occupational exposure to heavy metals lead and cadmium was not associated with the incidence of DM. However, lead concentrations at the beginning of the exposure might be an indicator of DM and glucose elevations.


Study population

Changwon, the location of this study, is a representative industrial city in Korea. Many occupations involve heavy metal exposure, including employees of battery-manufacturing plants. This cohort study was based on the data from occupational health examinations (n = 403,253) conducted from 2002 to 2018 in subjects with jobs related to heavy metals. A schematic flow chart for the selection of subjects is shown in Figure 1. All participants underwent a physical examination with a blood sample taken in the morning following an overnight fast. They also filled out a questionnaire. Among these 403,253 subjects, 89,826 who had ferritin blood levels measured were included and 38,039 women were excluded. In occupational screening, most women were fertile. The ferritin results might be low because of menstruation. A total of 269 subjects were excluded because of the unavailability of HbA1c or FBS data. Furthermore, 2709 subjects who were already diagnosed with DM were excluded (DM was defined as FBS ≥ 126 mg/dl, HbA1c ≥ 6.5%, or a history of DM reported in the questionnaire). Additionally, 28,151 subjects were excluded because they only had one screening result without follow-up data. Finally, 34,814 subjects were included in the analysis. Of these, 1,035 subjects with lead exposure, 200 subjects with cadmium exposure, and the 33,579 remaining subjects were assigned to cohort A, cohort B, and the control cohort, respectively. This study collected subject data including age, HbA1c, FBS, ferritin levels, height, body weight, the follow-up duration, and the concentrations of heavy metals (lead and cadmium). The study protocol was approved by the Institutional Review Board (IRB) of Samsung Changwon Medical Center (SCMC-2019-04-014). All participants provided written informed consent for the use of their data.   

Data collection

This study was based on data from occupational health examinations already conducted. The health check-up data included objective numerical data such as blood tests, imaging tests, and physical examinations, as well as the questionnaire responses of the subjects. The questionnaire included items on personal history, physical activity, systemic symptoms, sleep patterns, stress, anxiety, depression, gambling, and job stress. All data were computerized. The authors analysed the demographic information, physical examination results, past history, and laboratory results (HbA1c, blood glucose, ferritin, lead, and cadmium levels). After obtaining IRB approval, two authors (JHJ and MHJ) independently analysed the data.

Measuring blood levels of lead and cadmium

To measure the blood levels of lead and cadmium, 3 ml of blood was collected from each subject into vacuum bottles using heparin as an anticoagulant in the morning following an overnight fast. Blood samples were diluted 1:15 and 1:10 to measure the lead and cadmium concentrations, respectively, with 2.5 ml of 10% Triton X-100, 0.1 ml of concentrated nitric acid, and 1 ml of 10% ammonium di-hydrogen phosphate as a modifier. Graphite-furnace atomic absorption spectrometry with Zeeman background correction (PinAAcle 9i00z Atomic absorption spectrometer, PerkinElmer, Norwalk, Connecticut, USA) was used to measure the lead and cadmium levels in all subjects within the first year of heavy metal exposure. The minimum detectable limits of lead and cadmium were measured to the third decimal place (0.001mg/dl), and concentrations below that were considered to be zero.

Statistical analyses

The continuous variables are presented as means ± standard deviation. The categorical variables are presented as the number of cases and percentages. An independent t-test was used to evaluate the significance of the mean differences between the continuous variables for demographical factors such as age and body mass index (BMI). The Cox proportional hazard model was used to identify potential predictors in the baseline characteristics for type 2 DM in subjects who were not diagnosed with DM. In the Cox hazard model, the development of type 2 DM was considered a dependent variable and as independent variables were set to the exposure levels of lead and cadmium and the known risk factors (age, BMI, smoking, drinking, HbA1c, FBS, and ferritin). A mixed model was used to assess the effects of heavy metal exposure and ferritin on FBS and HbA1c, respectively. The annual changes in FBS and HbA1c with lead concentrations are shown in a scatter plot. Stata 14.0 software (Stata Corporation, College Station, TX, USA) was used for all statistical analyses.

Operational definitions

1. Type 2 DM was defined in patients with a diabetes diagnosis history taking anti-diabetic medication or satisfying the American Diabetes Association (ADA) criteria of HbA1c ≥ 6.5% or FBS ≥ 126 mg/dl in a blood test after an 8-hour fast.

2. Newly diagnosed diabetes was defined in subjects without a history of diabetes who had an HbA1c of < 6.5% and an FBS of < 100 mg/dl in the first health check-up after joining the company and were newly diagnosed with diabetes (HbA1c ≥ 6.5% or FBS ≥ 126 mg/dl) in a follow-up health check-up conducted at least one year later.

3. The heavy metal exposure subjects were those who worked in the lead industry, those who were in charge of lead welding and mounting in shipyards, and subjects who worked in Ni-Cd battery manufacturing factories.

4. Simple occupational exposure to lead or cadmium, called simple exposure, referred to subjects who worked on-site at the workplace regardless of the intensity of the exposure.

5. The beginning of exposure referred to the first occupational health examination conducted within a year of working in the workplace related to heavy metal exposure.

Patient and public involvement

The patients and the public were not involved in the development of the research question or the design of the study. No patients or public members were involved in the recruitment or conduct of the study. Since this study used de-identified results, the authors do not plan to disseminate the study results to the study participants individually but plan to publish the paper with open access.


Dong-A University, Award: 2020