A total of 117 patients with T2DM were enrolled in this retrospective cross-sectional investigation. All participants received care in the endocrinology outpatient or inpatient departments of the Department of Endocrinology, the First Hospital of Putian City, between January 1, 2025, and June 1, 2025. The study protocol received approval from the Ethics Committee of the First Hospital of Putian City, Fujian Province, China, and all procedures adhered to the tenets of the Declaration of Helsinki. Participants aged between 30 and 80 years were included in the study to focus on the typical adult T2DM population and to reduce potential confounding related to early-onset diabetes and advanced age–related comorbidities. The diagnosis of T2DM was based on the American Diabetes Association (ADA) diagnostic criteria (American Diabetes Association, 2024), requiring a fasting plasma glucose (FPG) ≥ 7.0 mmol/L and/or HbA1c ≥6.5% (39). All diagnoses were verified through a review of the electronic medical record system.

The study excluded individuals with: 1) severe infections or acute diabetic hyperglycemic states (such as ketoacidosis or hyperosmolar state); 2) autoimmune diseases; 3) severe dysfunction or insufficiency of vital organs, including the heart, liver, kidneys, or brain. Specifically, individuals with an estimated glomerular filtration rate (eGFR) ≤ 30 mL/min/1.73 m² were excluded. Informed consent was acquired from all subjects before their inclusion in the investigation. As this was a retrospective cross-sectional study, no longitudinal follow-up was performed. To minimize selection bias, all consecutive eligible patients meeting the inclusion and exclusion criteria during the study period were included.

Data on demographic characteristics, clinical diagnoses, diabetes duration, use of antihypertensive drugs, and laboratory measurements were retrospectively extracted from the hospital electronic medical record system in July 2025, after completion of the study period. Lifestyle information, including smoking status and alcohol consumption, was obtained using structured questionnaires administered once at the time of the clinical visit by trained research staff, with relevant information cross-checked against electronic medical records. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared (kg/m²). Although questionnaires were administered during routine clinical care, data extraction and analysis were performed retrospectively. Participants with missing data in key variables were excluded at the screening stage; therefore, the final analytical sample comprised 117 individuals with complete data on all variables included in the primary analyses.

Information on lipid-lowering therapy was extracted from electronic medical records when available. Patients receiving lipid-lowering treatment were included in the study to reflect real-world clinical conditions. Lipid parameters, including apoB and triglycerides, were analyzed using the measured values obtained at the time of assessment, reflecting the participants’ actual lipid status under routine clinical management. Data on dietary interventions and antioxidant supplementation were not systematically available and therefore were not included in the analysis.

Measurement of whole blood copper concentration was performed using inductively coupled plasma mass spectrometry (ICP-MS; Agilent 7850, USA) at a certified third-party clinical laboratory (ADICON Clinical Laboratory, China). Fasting venous whole blood samples were collected in trace-element–free EDTA anticoagulant tubes. Sample preparation, digestion, and ICP-MS measurements were conducted according to validated standard operating procedures routinely used for clinical trace element assessment. Calibration, internal standardization, and quality control procedures were implemented in accordance with established clinical laboratory quality assurance standards. Samples with visible hemolysis were excluded as part of routine laboratory quality control.

ICP-MS is a well-established analytical technique for trace element quantification in biological specimens and has been widely applied for measuring copper in whole blood and plasma (40, 41). The analytical performance of the ICP-MS method, including limits of detection, precision, and accuracy, has been previously described in the literature for clinical copper determination. In the present study, all reported copper concentrations provided by the laboratory were within the detectable range of the assay, and no values were reported as below the limit of detection.

FPG and fasting lipid parameters, including TC, TG, HDL-C, LDL-C, apoA, and apoB were measured in serum samples obtained from fasting venous blood using a Polaris c2000 automated biochemical analyzer (KHB, Shanghai, China). Glycated hemoglobin (HbA1c) was assessed in whole blood by high-performance liquid chromatography (HPLC) using a YH-60 analyzer (Hunan Yonghe-Sun Biotechnology Co., Ltd., China).

Statistical analysis was performed using SPSS Statistics (version 27) and R software (version 4.5.1). Normality of continuous variables was assessed using the Shapiro–Wilk test in combination with visual inspection of histograms and Q–Q plots. Continuous variables were summarized as mean ± standard deviation for normally distributed data or median (25th and 75th percentiles) for non-normally distributed data, while categorical variables were presented as number (percentage).

Baseline characteristics were compared between apoB-stratified groups using the independent-samples t-test for normally distributed continuous variables, the Mann–Whitney U test for non-normally distributed variables, and the chi-square test or Fisher’s exact test for categorical variables, as appropriate.

For stratified analyses, apoB was dichotomized at the median value (0.93 g/L). This approach was adopted to ensure balanced subgroup sizes and stable estimation of potential effect modification, given the skewed distribution of apoB and the absence of a universally accepted clinical cut-off specifically applicable to effect modification analyses in a cross-sectional context.

Spearman correlation analysis was initially conducted to explore pairwise associations among key variables. Multivariable linear regression models were then used to examine the association between whole blood copper concentration and HbA1c and to assess potential effect modification by lipid-related parameters and other factors. Two models were constructed: Model 1 was unadjusted, and Model 2 was adjusted for age, sex, BMI, patient type (outpatient/inpatient), and diabetes duration.

Whole blood copper concentration was treated as a continuous variable in all regression models to preserve statistical power and avoid information loss due to categorization. Effect modification by lipid parameters, age, sex, and BMI was evaluated using interaction tests and stratified analyses.

Potential nonlinear dose–response relationships between whole blood copper and HbA1c were assessed using restricted cubic spline (RCS) models with knots placed at the 10th, 50th, and 90th percentiles. The underlying assumptions of linear regression, including the normality of residuals, homoscedasticity, independence, and the absence of influential outliers, were verified. A two-sided P value< 0.05 was considered statistically significant for all analyses.

Baseline comparisons were descriptive in nature and intended to provide an overview of participant characteristics across apoB strata. Differences between apoB groups reflect characteristics associated with apoB stratification and were not interpreted for causal inference.

This study comprised a total of 117 participants. As shown in Table 1, the mean age of all participants was (55.15 ± 10.70) years, 62.4% were male, the mean BMI was (24.79 ± 3.11) kg/m², and 50.4% were inpatients. The concentrations of whole blood copper, HbA1c, FPG, and apoB showed non-normal distributions, with median (25th percentile, 75th percentile) values of 85.79 (78.17, 93.42) μg/dL, 8.00 (6.80, 10.65) %, 7.65 (6.52, 12.45) mmol/L, and 0.93 (0.76, 1.08) g/L, respectively.

Compared with the low apoB group, the high apoB group showed significantly higher blood glucose and lipid indicators (P< 0.05), while no statistically significant differences were observed between the two groups in baseline characteristics such as age, sex, body mass index, diabetes duration, and whole blood copper concentration (P > 0.05).

Preliminary Spearman correlation demonstrated a weak but statistically significant positive association between whole blood copper concentration and HbA1c levels (r = 0.280, p = 0.002). To further investigate the independent effect of copper on HbA1c, we constructed multiple linear regression models (Table 2). In Model 1, which was unadjusted for any covariates, whole blood copper concentration showed a significant positive association with HbA1c levels (B = 0.057, 95% CI: 0.021 to 0.093, p = 0.002). However, after further adjustment for potential confounders including sex, age, BMI, diabetes duration, and patient type in Model 2, this association was no longer statistically significant (B = 0.023, 95% CI: -0.008 to 0.054, p = 0.148). In exploratory analyses, no significant association was observed between whole blood copper and FPG in either Model 1 or Model 2 (see Table 2, FPG section).

A significant interaction was observed for serum apoB concentration, modifying the relationship of whole blood copper with HbA1c levels (P for interaction< 0.001; Table 3). Stratified analysis demonstrated a significant positive link between blood copper and HbA1c in individuals with low apoB levels (< 0.93 g/L) (B = 0.051; 95% CI: 0.011, 0.090; p = 0.013), conversely, no such significant association was detected in the high apoB subgroup (≥0.93 g/L) (B = -0.011; 95% CI: -0.056, 0.035; p = 0.639) (as shown in Figure 1). Furthermore, we systematically evaluated potential effect modification by a range of demographic, clinical, and metabolic parameters, including age, sex, BMI, patient type (inpatient or outpatient), diabetes duration, TG, TC, HDL-C, LDL-C, apoA, and apoB. Among all variables tested, only the interaction between TG and blood copper reached statistical significance (P for interaction = 0.020). The stratified pattern for TG was consistent with that of apoB: a positive association was observed in the low TG group (< 1.39 mmol/L) (B = 0.064; 95% CI: 0.010, 0.118; p = 0.021), in contrast, the high TG group showed no significant link (≥1.39 mmol/L) (B = -0.014; 95% CI: -0.050, 0.022; p = 0.442) (Figure 1). This implies that TG levels also moderate the relationship between copper and HbA1c to some extent. Detailed results are presented in Table 3.

Although the main effect of whole blood copper on FPG was not significant in either the unadjusted or adjusted models (Model 1: B = 0.057; 95% CI: -0.002, 0.116; p = 0.058; Model 2: B = 0.006; 95% CI: -0.046, 0.058; p = 0.820) (see Table 2, FPG section), we proceeded with pre-specified interaction analyses to further explore potential effect specificity. Interestingly, we found that apoB levels also significantly modified the association between whole blood copper and FPG (P for interaction = 0.012). Although the stratified analysis, likely owing to reduced statistical power, yielded non-significant p-values for individual subgroups, the direction and pattern of effects were consistent with the primary analysis using HbA1c as the outcome: a positive effect was observed in the low apoB group (< 0.93 g/L) (B = 0.038, p = 0.260), while a negative effect was found in the high apoB group (≥ 0.93 g/L) (B = -0.050, p = 0.165). This finding further supports apoB as a key effect modifier for the copper-glucose metabolism relationship.

The relationship between whole blood copper concentration and HbA1c was analyzed using restricted cubic splines. Upon adjustment for key covariates including sex, age, BMI, patient type, and diabetes duration, the ANOVA model identified no significant departure from linearity in the association of whole blood copper with HbA1c (P-overall = 0.332, P-nonlinear = 0.737) (see Figure 2).