Kidney Damage in Diabetics Exposed to Cadmium and Lead: The Emergent Role for β2-Microglobulin

1. Introduction

Hypertension, defined as systolic blood pressure (SBP) and/or diastolic blood pressure (DBP) ≥ 140/90 mm Hg, affects approximately one third of the adult population in economically developed countries [1,2]. Resistance hypertension in whom a regular antihypertensive medication formula fails to achieve target blood pressure control can be found in 10-15% of those with primary or essential hypertension [3,4].

Epidemiological data linking risk for kidney injury and hypertension to the environmental pollutants cadmium (Cd) and lead (Pb) are abundant [5,6,7,8], given that hypertension is known to be both a cause and a consequence of kidney damage [9,10,11,12,13,14]. A dose-response relationship between Cd exposure and hypertension risk has been reported in in a meta-analysis by Verzelloni et al. (2024) [15].

Concerningly, hypertension and diabetes are major causes of kidney failure, the estimated glomerular filtration rate (eGFR) falls below 15 mL/min/1.73 m²), when dialysis or a kidney transplant is required for survival [16,17,18]. Surprisingly, however, studies into the effects of Cd and Pb exposure on diabetic kidneys are limited [19,20,21]. In a Dutch prospective cohort study, a high rate of eGFR deterioration was noted in patients with diabetes, exposed to Cd [19]. A cross-sectional study on U.S. population observed that Pb exposure may have increased risk of CKD, especially in non-smoking diabetic women with body mass index (BMI) higher than 25 kg/m² [20]. In a cohort of Swedish women aged 64 years, an increased risk of elevated albumin excretion (15 mg/12 h) was found only in those with diabetes who had blood Cd within the top quartile [21].

This study was to investigate, within a case-control design, hypertension risk, fasting plasma glucose concentration ([Glc]_p), eGFR, SBP, DBP, and albumin excretion in Cd and Pb exposed diabetics and matched controls. Also, it was to explore association of serum β₂-microglobulin (β₂M) with risk of hypertension and/or kidney damage in Cd- and Pb-exposed people with and without diabetes. We focused on potential role of circulating β₂M because no prior knowledge to link hypertension in diabetics to the SH2B3-β₂M axis of blood pressure regulation, which has been unveiled recently [22,23,24].

2. Results

2.1. Description of Participants

The characteristics of participants can be found in Table 1, where they were assigned according to the tertile of serum concentration of β₂M ([β₂M]_s).

As data in Table 1 indicate, this cohort participants were predominantly women (78.1%), which distributed equally across the [β₂M]_s tertiles. The highest proportion of participants with diagnosed diabetes (72%), hypertension (69%), albuminuria (39%), fasting plasma glucose (FPG) ≥ 110 mg/dL (69.6%) and FPG ≥ 126 mg/dL (58.7%) were found in the [β₂M]_s top tertile. The parameters showing significant variations across the [β₂M]_s tertiles were SBP, eGFR, FPG, blood Cd and blood Pb concentrations. The variations in age, BMI, DBP, E_Cd/E_cr, E_Cd/C_cr, E_alb/E_cr (ACR) and E_alb/C_cr across the [β₂M]_s tertiles did not differ statistically as did the distribution of those with low eGFR.

2.2. Bivariate Correlation Analysis

The relationships of serum β₂M with other variables were assessed with the Spearman’s rank correlation analysis. Nine variables tested were age, BMI, FPG, SBP/DBP, eGFR (a clinical kidney function measure), E_alb/C_cr (a sign of kidney damage and diabetic kidney disease), and toxic metal exposure, reflected by E_Cd/C_cr and blood Cd/Pb levels. Results are tabulated (Table 2).

Serum β₂M concentration ([β₂M]_s) varied directly with age (r = 0.200), FPG (r = 0.210) and SBP (r = 0.229), while showing an inverse correlation with eGFR (r = −0.265). The strength of all four correlations were moderate. The correlation between [β₂M]_s and BMI, DBP, E_alb/C_cr, E_Cd/C_cr, and Cd/Pb exposure levels were statistically insignificant.

Interestingly, FPG varied directly with SBP (r = 0.250), E_alb/C_cr (r = 0.273) and Cd/Pb exposure (r = 0.181). Other notable correlations were SBP vs. E_alb/C_cr (r = 0.372), eGFR vs. E_Cd/C_cr (r = −0.227) and E_Cd/C_cr vs. Cd/Pb exposure categories (r = 0.301).

Because a reduction of eGFR to 60 mL/min/1.73 m² or below signifies chronic kidney disease (CKD), and eGFR falls as blood pressure rises, the relationships of [β₂M]_s with SBP were examined further using scatterplots and regression analysis. Results are presented in Figure 1.

An association of SBP with [β₂M]_s was significant in both eGFR subgroups (Figure 1a). The SBP and [β₂M]_s association was especially strong in those with eGFR levels commensurate to CKD (R² = 0.252). The association of SBP and [β₂M]_s in the normal eGFR group was also statistically significant although R² value was small (0.039). However, unlike SBP, [β₂M]_s was associated in DBP only in the low eGFR group (R² = 0.312) (Figure 1b).

Figure 2 provides additional subgroup analyses for E_alb/C_cr vs. blood pressure, given the moderate correlation between E_alb/C_cr and SBP (r = 0.372) evident from the correlation analysis and the fact that albuminuria is indicative of diabetic kidney disease.

An association of E_alb/C_cr with SBP was found only in diagnosed diabetics (Figure 1a). Similarly, E_alb/C_cr was associated with SBP only in those with FPG levels ≥ 126 mg/dL (Figure 1b). In comparison, E_alb/C_cr and DBP association was insignificant all subgroups (Figure 2c,d).

2.3. Logistic Regresssion Model for Serum β₂M Higher than the Median 5 mg/L

Logistic regression modeling was employed to identify determinants of high serum β₂M concentrations, and the modelling results are presented in Table 3.

The prevalence odds ratio (POR) for a high serum β₂M was influenced minimally by age, BMI, E_Cd/C_cr, gender, smoking, and hypertension. It was affected by two variables, diagnosed diabetes and eGFR. The POR for a high serum β₂M rose 4-fold (p = 0.001) in people diagnosed with diabetes. A 4% decrease in the POR for the high serum β₂M was associated with 1 mL/min/1.73 m² higher eGFR (p = 0.005).

2.4. Logistic Regresssion Model for Hyperglycemia

To ascertain an association between [β₂M]_s ≥ 5 mg/L and diagnosed diabetes, logistic regression analyses of measured FPG was conducted. Two logistic regression models were undertaken, one of which was for FPG ≥ 110 mg/dL, and the other was for FPG ≥ 126 mg/dL (Table 4).

In those with a high serum β₂M, the POR for FPG concentrations ≥110 mg/dL rose 3.4-fold (p = 0.002), and 2.8-fold (p = 0.004) in those with Cd/Pb exposure category 3. In comparison, the POR for FPG ≥ 126 mg/dL rose with age, a high serum β₂M (POR 3.8, p = 0.002), Cd/Pb exposure category 2 (POR 3.1, p = 0.021), and category 3 (POR 3.7, p = 0.014). The POR for FPG ≥ 126 mg/dL increased 7.7% per every 1-year increase in age (p = 0.004).

2.5. Logistic Regresssion Model for Hypertension and Albuminuria

In the bivariate correlation analysis, FPG varied directly with SBP (r = 0.250), E_alb/C_cr (r = 0.273) and Cd/Pb exposure categories (r = 0.181). To further explore the interrelationships of these variables, additional logistic regressions were conducted for hypertension and albuminuria, and results are presented in (Table 5 and Table 6).

As data in Table 5 indicate, age, BMI, and gender had little effects on the POR for hypertension and albuminuria. The POR for hypertension was increased 7-fold in non-smokers (p = 0.020), 3.7-fold in those with FPG ≥ 110 mg/dL (p = 0.001), 3.1-fold and 4.4-fold in those with the Cd/Pb exposure category 2 (p = 0.046), and category 3 (p = 0.005). In comparison, the POR for albuminuria was increased 2.95-fold in those with FPG ≥ 110 mg/dL (p = 0.013), but was not affected by other six variables.

Data in Table 6 indicate that the POR for hypertension rose 8-fold in non-smokers (p = 0.018), while it rose 2.9-fold, 3-fold, and 4-fold in those with FPG ≥ 126 mg/dL (p = 0.011), Cd’Pb exposure category 1 (p = 0.050) and category (p = 0.008). The POR for albumin rose 3.5-fold in those with FPG ≥ 126 mg/dL (p = 0.005).

The scatterplots and regression analysis for E_alb/C_cr vs. FPG and E_alb/C_cr vs. blood pressure are presented in Figure 3.

E_alb/C_cr was associated with FPG only in participants who had hypertension (Figure 3a). The E_alb/C_cr and FPG association was much tighter in the low eGFR group (R² = 0.303), compared to the normal eGFR group (R² = 0.065) (Figure 3b). E_alb/C_cr varied more closely with SBP in the high serum β₂M (R² = 0.155), compared to the low β2M group (R² = 0.099) (Figure 3c). E_alb/C_cr was associated with DBP only in the high serum β₂M group (R² = 0.103) (Figure 3d).

3. Discussion

3.1. Hypertension Associated with Hyperglycemia and Environmental Cd and Pb

Consistent with numerous literature reports, risk of hypertension among participants was increased by exposure to low doses of Cd and Pb (Table 5). Such low environmental exposure to Cd and Pb was suggested by mean values for blood Pb (4.49 mg/dL), blood Cd (0.57 µg/L) and the urinary Cd excretion rate (0.98 µg/g creatinine) (Table 1). These low exposure levels were comparable to those found in most environmentally exposed populations, reported in a recent dose-response meta-analysis [15]. Notably, risk of resistance hypertension in the representative U.S. population was increased 30-35% by Cd exposure, assessed with blood Cd levels [9]. Risk of hypertension in Korean population was increased 29, 47, and 78% by exposure to Pb and Cd only and Cd plus Pb, respectively [13].

Previously, rising SBP after Cd exposure has been causally linked to a fall of eGFR due to Cd-induced nephron destruction [25]. In the same study, a two-fold increase in risk of hypertension was associated with urinary Cd excretion of 1 µg/g creatinine, and a blood Cd of 0.61 µg/L. In line with such finding, herein, we observed an inverse correlation between SBP and C_cr-normalized Cd excretion rate (E_Cd/C_cr) (r = −0.227) (Table 2). A rise in SBP as eGFR falls, thus, explains a universally high prevalence of hypertension among those with low eGFR. (eGFR ≤ 60 mL/min/1.73 m²). Interestingly, a rapid fall in eGFR (≥ 3 mL/min/1.73 m² per year) has been causally linked to Cd excretion in a prospective cohort study from Switzerland [26], but an effect of the eGFR decline on blood pressure was not investigated.

Another notable result was that risk of hypertension among participants was influenced also by hyperglycemia (FPG ≥ 110 and ≥ 126 mg/dL) (Table 5 and Table 6). In a bivariate analysis, FPG correlated positively with SBP (r = 0.250), albumin excretion (r = 0.273) and Cd/Pb exposure (r = 0.181) (Table 2). The increased risk of hypertension among those with hyperglycemia may be a consequence of kidney damage, assessed with elevated levels of albumin excretion. A nonlinear relationship was observed between FBG and ACR in a representative U.S. population [27]. In a Chinese prospective cohort of non-diabetics, the risk of incident albuminuria rose 71% per every 18 mg/dL increment of FPG level [28]. A higher risk of hypertension was associated with an elevation in ACR within the normal range in a meta-analysis, and ACR was suggested to be a predictor of incident hypertension in the general population [29].

3.2. The SH3B-β₂M Axis: A Novel Blood Pressure Regulator

The protein β₂M is a non-polymorphic and non-glycosylated low-molecular-weight protein, forming an extracellular domain of the class I human leukocyte antigens or class I major histocompatibility complex, which is shed into the blood stream [30,31]. Its involvement in blood pressure control and hypertension development were deduced from the genome-wide association [22], the single nucleotide polymorphism in the SH2B3 locus, which encodes for the regulator of cytokine signaling and cell proliferation [23], and a human longitudinal study [24]. Data from knockout mouse models have provided additional support to the involvement of SH3B-β₂M axis in hypertension development plus kidney damage [23,24]. Comparing participants in top plasma β₂M to the bottom quartile, the prevalence and incidence of hypertension among the participants in the Framingham Heart Study rose 29% and 59%, respectively [24].

In the present study, we found that serum β₂M correlated with both FPG and SBP (Table 2). The SBP vs. [β₂M]_s was particular strong in those with low eGFR (Figure 2), and the odds of having high serum β₂M fell 4% per 1 mL/min/1.73 m² higher eGFR in the regression analysis (Table 3). A high serum β₂M was 4 times more prevalent in diagnosed diabetics group (Table 3). FPG ≥110 and ≥126 mg/dL both were more prevalent in those with a high serum β₂M (Table 4) and those with albuminuria (Table 5 and Table 6).

Subgroup analysis indicated that E_alb/C_cr vs. SBP was tighter in the high serum β₂M (R² = 0.155) than the low serum β₂M group (R² = 0.099) (Figure 3c). These may reflect the independent effect of β₂M (SH3B-β₂M axis) on SBP. It may also reflect more extensive kidney damage in those with [β₂M]_s ≥ 5 mg/L. In agreement with our study, an investigation from China observed elevated serum β₂M levels in patients with diabetes together with a 17% increase in prevalence of left ventricular hypertrophy per one standard deviation increase in serum β₂M [32].

To the best of our knowledge, the present study was the first to evaluate the potential functionality of the SH3B-β₂M axis of blood pressure control in people with diabetics, who were also exposed to Cd and Pb.

4. Materials and Methods

4.1. Selection of Study Subjects

We used a purposive sampling technique to recruit type 2 diabetics and non-diabetic controls during annual checkups at a health promoting center in Pakpoon Municipality, Nakhon Si Thammarat Province, Thailand. Diabetics and controls were matched for age, gender, and residential locality, and all subjects were at least 40 years old. Diabetes was defined as fasting plasma glucose [Glc]_p levels ≥ 126 mg/dL or a physician’s diagnosis.

Exclusion criteria were non-resident status, pregnancy and/or breastfeeding, and hospital records or a physician’s diagnosis of an advanced chronic disease, including heart disease, stroke, and cancer. Non-diabetic controls were apparently healthy and met all exclusion criteria.

We applied the structured interview questionnaires to collect sociodemographic data, educational attainment, occupation, health status, family history of diabetes, use of dietary supplements, alcohol consumption, and smoking status.

4.2. Collection of Blood and Urine Samples

Subjects were requested to fast overnight, and collection of blood and urine samples was undertaken at the Pakpoon health center on the morning of the following day. Blood samples for the glucose assay were collected in tubes containing fluoride that inhibited glycolysis. Blood samples for Cd and Pb analysis were collected in separate tubes containing ethylene diamine tetra-acetic acid (EDTA) as an anticoagulant.

Blood and urine samples were kept on ice and transported within one hour to the laboratory of Walailak University, where samples of plasma and serum were prepared. Aliquots of urine, whole blood, serum, and plasma were stored at −80 °C for later analysis.

4.3. Quantification of Exposure to Cd, Pb and Biomarkers of Kidney Effects

We used the human beta-2 microglobulin/β₂M ELISA pair set (Sino Biological Inc., Wayne, PA, USA) to determine serum concentration of β₂M ([β₂M_]s), with a lower limit of detection of 3.13 pg/mL. The plasma glucose assay was based on the oxidase-peroxidase method (Glu Colorimetric Assay Kit, Elabscience, Houston, TX, USA) [33]. Assays of creatinine in urine and plasma were based on Jaffe’s alkaline picrate method, as described previously [34]. The urinary albumin assay was based on the immunoturbidimetric method [35,36]

Urinary and whole blood Cd and Pb concentrations were determined with GBC System 5000 graphite furnace atomic absorption spectrometry (AAS) (GBC Scientific Equipment, Hampshire, IL, USA) [37]. Standards with As, Be, Cd, Cr (VI), Hg, Ni, Pb, Se, and Tl were used to calibrate the instrument (Merck KGaA, Darmstadt, Germany). Reference urine metal levels 1, 2, and 3 (Lyphocheck, Bio-Rad, Hercules, CA, USA) were used for quality control, analytical accuracy, and precision assurance. When urinary and blood concentrations of Cd and Pb were less than their detection limits, the concentration assigned were the detection limit values divided by the square root of 2 [38].

4.4. Calculation and Cut-Off Values for Albuminuria

Estimated GFR (eGFR) was computed with Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations [39]. CKD stages 1, 2, 3, 4, and 5 corresponded to eGFR of 90–119, 60–89, 30–59, 15–29, and < 15 mL/min/1.73 m², respectively.

In the present study, urine samples were collected at a single time point (voided urine). This necessitated a correction for interindividual differences in urine volume (dilution). To achieved this, we normalized excretion of Cd (E_Cd) and albumin (E_alb) to creatinine excretion (E_cr) and creatinine clearance (C_cr), using below equations.

E_x/E_cr = [x]_u/[cr]_u, where x = Cd or alb; [x]_u = urine concentration of x (mass/volume) and [cr]_u = urine creatinine concentration (mg/dL). E_x/E_cr was expressed as an amount of x excreted per g of creatinine. Albumin-to-creatinine ratio (ACR) is a well-known expression of E_alb/E_cr.

E_x/C_cr = [Cd]_u[cr]_p/[cr]^u, where x = Cd or alb; [x]_u = urine concentration of x (mass/volume); [cr]_p = plasma creatinine concentration (mg/dL); and [cr]_u = urine creatinine concentration (mg/dL). E_x/C_cr was expressed as an amount of x excreted per volume of the glomerular filtrate [40].

For E_alb/E_cr (ACR) data, albuminuria is defined as ACR values ≥ 20 and 30 mg/g creatinine in men and women, respectively [1,2,3]. A higher cut-off value for ACR in women is to compensate for their universally lower E_cr values, due to smaller muscle mass than men. In comparison, C_cr-normalization is not affected by muscle mass. Hence, for E_alb/C_cr data, albuminuria is defined as E_alb/C_cr values ≥ 0.2 mg/L filtrate in both men and women.

4.5. Statistical Analysis

Data were analyzed with IBM SPSS Statistics 21 (IBM Inc., New York, NY, USA). The variation of any continuous variable and differences in percentages across the tertile of serum β₂M concentrations ([β₂M]_s) (Table 1) were assessed by the Kruskal–Wallis’s test, and the Pearson chi-squared test, respectively. Spearman’s rank correlation analysis was employed to produce the correlation matrices of nine variables; [β₂M]_s, age, BMI, [Glc]_p, SBP, DBP, eGFR, E_alb/C_cr, E_Cd/C_cr, and Cd/Pb exposure category (Table 2). The one-sample Kolmogorov–Smirnov test was used to assess a deviation from a normal distribution of any continuous variable. Logarithmic transformation was applied to fasting the concentration of plasma glucose ([Glc]p), and the excretion rate of albumin and Cd (E_alb/E_cr and E_Cd/C_cr) that showed rightward skewing before they were subjected to parametric statistics analysis, scatterplots, and linear regressions (Figure 1, Figure 2 and Figure 3).

The Prevalence Odds Ratio (POR) values for [β₂M]_s ≥ 5 mg/L, [Glc]_p ≥ 110 and ≥ 126 mg/dL, hypertension, and albuminuria were determined by multivariable logistic regression modeling with adjustment for covariates (Table 3, Table 4, Table 5 and Table 6.)

5. Conclusions

Hyperglycemia, a falling eGFR, and rising SBP could be toxic manifestation of chronic exposure to low-level Cd and Pb, leading to hypertension, kidney damage and albuminuria. These findings underscore the metabolic and kidney disease coexistence, recognized by the American Heart Association as the cardiovascular–kidney–metabolic (CKM) syndrome. For the first time, evidence is presented herein to link circulating β₂M to rising SBP and kidney damage independently of Cd and Pb.