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Article

Enhanced Kidney Damage in Individuals with Diabetes Who Are Chronically Exposed to Cadmium and Lead: The Emergent Role for β2-Microglobulin

by
Soisungwan Satarug
1,*,
David A. Vesey
1,2,
Donrawee Waeyeng
3,
Tanaporn Khamphaya
4 and
Supabhorn Yimthiang
4
1
Centre for Kidney Disease Research, Translational Research Institute, Woolloongabba, Brisbane, QLD 4102, Australia
2
Department of Kidney and Transplant Services, Princess Alexandra Hospital, Brisbane, QLD 4102, Australia
3
Environmental Health and Technology, School of Public Health, Walailak University, Nakhon Si Thammarat 80160, Thailand
4
Occupational Health and Safety, School of Public Health, Walailak University, Nakhon Si Thammarat 80160, Thailand
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2025, 26(18), 9208; https://doi.org/10.3390/ijms26189208
Submission received: 25 August 2025 / Revised: 17 September 2025 / Accepted: 19 September 2025 / Published: 20 September 2025
(This article belongs to the Section Molecular Biology)
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Abstract

Elevated levels of circulating β2-microglobulin (β2M) are linked to an increased risk of hypertension and mortality from diabetes. The present study tests the hypothesis that the environmental pollutants, cadmium (Cd) and lead (Pb), by increasing plasma β2M levels, promote the development of hypertension and progression of diabetic kidney disease. Herein, we analyzed data from a Thai cohort of 72 individuals with diabetes and 65 controls without diabetes who were chronically exposed to low levels of Cd and Pb. In all subjects, serum concentrations of β2M inversely correlated with the estimated glomerular filtration rate (eGFR) (r = −0.265) and directly with age (r = 0.200), fasting plasma glucose (r = 0.210), and systolic blood pressure (r = 0.229). The prevalence odds ratio (POR) for hyperglycemia increased 7.7% for every 1-year increase in age and increased 3.9-fold, 3.1-fold, and 3.7-fold in those with serum β2M levels ≥ 5 mg/L, Cd/Pb exposure categories 2 and 3, respectively. The POR for hypertension increased 2.9-fold, 3-fold, and 4-fold by hyperglycemia (p = 0.011), Cd/Pb exposure categories 2 and 3. The POR for albuminuria increased 3.5-fold by hyperglycemia. In conclusion, kidney damage, evident from albuminuria, was particularly pronounced in participants with diabetes who had a serum β2M above 5 mg/L plus chronic exposure to low-dose Cd and Pb. For the first time, through a mediation analysis, we provide evidence that links Cd exposure to the SH2B3-β2M pathway of blood pressure homeostasis in people with and without diabetes.

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 most economically developed countries [1,2]. Resistance hypertension, where a regular anti-hypertensive medication formulation 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 the 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 a meta-analysis by Verzelloni et al. [15]. Hypertension and diabetes are leading causes of CKD [16,17,18]. Surprisingly, however, research studies into the kidney effects of Cd and Pb exposure in people with diabetes are limited [19,20,21], as summarized below.
In a Dutch prospective cohort study, patients with diabetes who were exposed to Cd had an estimated glomerular filtration rate (eGFR) falling at a high rate [19]. A cross-sectional study on the U.S. population observed that Pb exposure may have increased the risk of CKD, especially in women with a body mass index (BMI) higher than 25 kg/m2 plus diabetes and who were non-smokers [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]. A study from Taiwan observed that Cd-related kidney damage was more extensive in women than men [22].
Excretion of β2-microglobulin (β2M) has been widely used as a marker of kidney tubular dysfunction. Recent studies, however, have linked circulating β2M to the SH2B3-β2M axis of blood pressure regulation [23]. The present study aimed to investigate the mechanisms of kidney damage in people with diabetes who were chronically exposed to low-dose Cd and Pb, emphasizing the role of serum β2M. Also, it explored how Cd/Pb exposure induces kidney tubular cell toxicity and accelerates diabetic kidney disease (DKD). In our previous case–control study [24], we reported that people chronically exposed to Cd and Pb have enhanced risks of hyperglycemia, eGFR reduction, and albuminuria. It is likely that one or both metals cause these adverse outcomes.
Herein, we hypothesize that Cd/Pb exposure and/or diabetes (hyperglycemia) increase plasma β2M levels, which, in turn, raises blood pressure and induces kidney tubular cell damage.

2. Results

2.1. Description of Participants

Participants were assigned according to the tertile of serum β2M levels (Table 1).
As shown in Table 1, 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, ECd/Ecr, ECd/Ccr, Ealb/Ecr (ACR), and Ealb/Ccr across the [β2M]s tertiles did not differ statistically, as did the distribution of those with a low eGFR.

2.2. Bivariate Correlation Analysis

The relationships of serum β2M with other variables were assessed with Spearman’s rank correlation analysis. Ten variables tested were age, BMI, FPG, SBP/DBP, eGFR (a clinical kidney function measure), Ealb/Ccr (a sign of kidney damage and diabetic kidney disease), Eβ2M/Ccr (an indicator of tubular dysfunction), and toxic metal exposure, reflected by ECd/Ccr, and blood Cd and Pb levels. Results are tabulated (Table 2).
Serum β2M concentration ([β2M]s) varied directly with age (r = 0.200), FPG (r = 0.210), SBP (r = 0.229), and Eβ2M/Ccr (r = 0.390), while showing an inverse correlation with eGFR (r = −0.265). The strength of all five correlations was moderate. The correlation between [β2M]s with all other variables, BMI, DBP, Ealb/Ccr, ECd/Ccr, and Cd/Pb exposure levels, were statistically insignificant.
Interestingly, the FPG varied directly with SBP (r = 0.250), Ealb/Ccr (r = 0.273), Eβ2M/Ccr (r = 0.306), and Cd/Pb exposure levels (r = 0.181). Other notable correlations were SBP vs. Ealb/Ccr (r = 0.372), SBP vs. Eβ2M/Ccr (r = 0.237), eGFR vs. ECd/Ccr (r = −0.227), eGFR vs. Eβ2M/Ccr (r = −0.515), and ECd/Ccr vs. Cd/Pb exposure categories (r = 0.301).
Because a reduction in the eGFR to 60 mL/min/1.73 m2 or below signifies chronic kidney disease (CKD), and because the eGFR falls as blood pressure rises, the relationships of [β2M]s with SBP were examined further in subgroups using scatterplots and a regression analysis. Results are presented in Figure 1.
An association of SBP with [β2M]s was significant in the 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 the 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).
Based on bivariate correlation data (Table 2), a simple mediation model was used to examine the indirect effects of Cd on [β2M]s through kidney tubular toxicity, reflected by the excretion rate of β2M (Eβ2M/Ccr) (Figure 2).
The Sobel test of (a*b) indicated that the effect of Cd on serum β2M levels was through Eβ2M, while its direct effect did not reach a statistically significant level. The mediating effects of Cd on SBP/DBP were examined also, as shown in Figure 3.
Cd indirectly influenced the SBP (Figure 3a) but not DBP (Figure 3b). The direct effects of Cd on SBP and DBP were statistically insignificant.
Because Ealb/Ccr, like Eβ2M/Ccr, can reflect an impairment in tubular function, the correlation of this parameter with SBP was examined further in subgroups by scatterplots and a regression analysis (Figure 4).
Ealb/Ccr was associated with SBP only in those with diagnosed diabetes (Figure 4a). Similarly, Ealb/Ccr was associated with SBP only in those with FPG levels ≥ 126 mg/dL (Figure 4b). However, the Ealb/Ccr and DBP association was not significant in any subgroup (Figure 4c,d).

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

Variables that influenced serum β2M levels are listed in Table 3.
The prevalence odds ratio (POR) for a high serum β2M was minimally affected by age, BMI, ECd/Ccr, gender, smoking, and hypertension. It was affected by diagnosed diabetes and eGFR. The POR for a high serum β2M rose 4-fold (p = 0.001) in participants with diagnosed diabetes. A 4% decrease in the POR for the high serum β2M was associated with a 1 mL/min/1.73 m2 higher eGFR (p = 0.005).

2.4. Logistic Regresssion Model for Hyperglycemia

To examine the potential association between [β2M]s ≥ 5 mg/L and an elevation of FPG, logistic regression models were undertaken for FPG ≥ 110 mg/dL and ≥ 126 mg/dL (Table 4).
The POR for prediabetes (FPG ≥ 110 mg/dL) rose 3.4-fold (p = 0.002) and 2.8-fold (p = 0.004) in those with a high serum β2M and Cd/Pb exposure category 3, respectively. The POR for diabetes (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% for every 1-year increase in age (p = 0.004).

2.5. Logistic Regresssion Model for Hypertension and Albuminuria

In the bivariate analysis (Table 2), the FPG varied directly with SBP (r = 0.250), Ealb/Ccr (r = 0.273), Eβ2M/Ccr (r = 0.306), and Cd/Pb exposure categories (r = 0.181). To further explore the interrelationships, additional logistic regression models were used (Table 5 and Table 6).
As shown in Table 5, age, BMI, and gender had little effect 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 within the Cd/Pb exposure category 2 (p = 0.046) and category 3 (p = 0.005). In comparison, the POR for albuminuria increased 2.95-fold in those with FPG ≥ 110 mg/dL (p = 0.013) but was not affected by the other six variables.
As shown in Table 6, 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) and in the Cd/Pb exposure category 2 (p = 0.050) and category 3 (p = 0.008). The POR for albumin rose 3.5-fold in those with FPG ≥ 126 mg/dL (p = 0.005).
The scatterplots and the regression analysis for Ealb/Ccr vs. FPG and Ealb/Ccr vs. blood pressure are presented in Figure 3.
Ealb/Ccr was associated with FPG but only in participants with hypertension (Figure 5a). The Ealb/Ccr and FPG association in the low eGFR group was much tighter compared to the normal eGFR group (R2 0.303 vs. 0.065) (Figure 5b). Ealb/Ccr in those with high serum β2M varied more closely with SBP than those with low serum β2M (R2 0.155 vs. 0.099) (Figure 5c). Ealb/Ccr was associated with DBP but only in the high serum β2M group (R2 = 0.103) (Figure 5d).

3. Discussion

3.1. Hypertension Associated with Hyperglycemia and Environmental Cd and Pb

Consistent with numerous literature reports, the risk of hypertension among participants was increased by the 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), urinary Cd (0.65 µg/L), and the 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]. Concerningly, the risk of resistance hypertension was increased 30–35% by Cd exposure in the representative U.S. population, assessed with blood Cd levels [9]. In another U.S. population study (NHANES 2005–2016), urinary Cd ranging between 0.025 and 0.501 µg/L correlated with diabetes [25]. The risk of hypertension in a Korean population increased 29, 47, and 78% by exposure to Pb and Cd alone and Cd plus Pb, respectively, [13].
Previously, rising SBP after Cd exposure has been causally linked to a fall of eGFR following the nephron destruction induced by Cd [26]. In the same study, a 2-fold increase in the risk of hypertension was associated with the urinary Cd excretion of 1 µg/g creatinine and a blood Cd of 0.61 µg/L. In line with such findings, herein, we observed an inverse correlation between a SBP and Ccr-normalized Cd excretion rate (ECd/Ccr) (r = −0.227) (Table 2). A rise in SBP as the eGFR falls explains a universally high prevalence of hypertension among those with a 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 [27], but an effect of the eGFR decline on blood pressure was not investigated.
Another notable result of the present work was that the risk of hypertension among participants was also influenced by hyperglycemia (FPG ≥ 110 and ≥126 mg/dL) (Table 5 and Table 6). In a bivariate analysis, the 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 [28]. In a Chinese prospective cohort of non-diabetics, the risk of incident albuminuria rose 71% per every 18 mg/dL increment of FPG levels [29].
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 [30]. Earlier studies on a Japanese population observed an increased risk of hypertension and a large decrease in the eGFR among those with an elevated Eβ2M/Ecr, but environmental exposure to toxic metals was not investigated [31,32]. Increased blood pressure was associated with serum Cd in a recent cross-sectional study on the Japanese general population [33].

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 antigen or class I major histocompatibility complex, which is shed into the blood stream [34]. Its involvement in blood pressure control and hypertension development was deduced from a genome-wide association [23], single nucleotide polymorphism in the SH2B3 locus, which encodes for the regulator of cytokine signaling and cell proliferation, and a human longitudinal study [35]. Data from knockout mouse models have provided additional support to the involvement of SH3B-β2M axis in hypertension development plus kidney damage [35]. 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 [35].
In the present study, we found that serum β2M correlated with both FPG and SBP (Table 2). The SBP vs. [β2M]s was particularly strong in those with a low eGFR (Figure 2), and the odds of having a high serum β2M fell 4% for every 1 mL/min/1.73 m2 higher eGFR in the regression analysis (Table 3). A high serum β2M was four times more prevalent in the 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 Ealb/Ccr vs. SBP was tighter in the high serum β2M (R2 = 0.155) than the low serum β2M group (R2 = 0.099) (Figure 3a. This may reflect the independent effect of β2M (SH2B3-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 the prevalence of left ventricular hypertrophy per one standard deviation increase in serum β2M [36]. Furthermore, elevated serum β2M levels were associated with diabetes-related mortality and DKD [37,38]. Collectively, these findings suggest serum β2M as a potential biomarker for cardiovascular–kidney–metabolic (CKM) syndrome.

3.3. Mediating Effects of Cd on Serum β2M and SBP

We used the Baron and Kenny method to determine whether the effects of Cd on serum β2M and blood pressure (SBP/DBP) were direct/indirect or mediated by kidney tubular toxicity, assessed with the excretion of β2M (Eβ2M/Ccr). We found that a statistically significant effect of Cd on serum β2M was mediated by Eβ2M/Ccr with a tendency for a direct effect; the p-value for its direct effect (c’) was 0.051 (Figure 2). The direct effect of Cd on the levels of circulating β2M requires confirmation with a larger sample group.
We also found that an effect of Cd on SBP, not DBP, was totally mediated by Eβ2M/Ccr (Figure 3). Thus, tubular dysfunction (Eβ2M/Ccr) appeared to mediate the effects of Cd on both SBP and serum β2M concentrations.
To the best of our knowledge, the present study is the first to investigate a novel pathway of blood pressure control in Cd/Pb-exposed people with and without diabetes. Our findings are consistent with the role of serum β2M in the incidence and prevalence of hypertension recorded in the Framingham Heart Study and the functionality of the SH2B3-β2M axis for blood pressure homeostasis [23,35].

3.4. Strengths and Limitations

We used a cross-sectional design with a Thai cohort to explore how Cd and Pb induce kidney tubular cell damage and accelerate DKD, emphasizing β2M and hypertension. The use of multiple indicators of exposure and outcomes (Cd/Pb in blood, Cd in urine, eGFR, FPG, SBP/DBP, Eβ2M, and Ealb) is the major strength. The focus on women was considered as a strength because women are an at-risk group; an increased risk of hypertension was associated with blood Cd as little as 0.4 µg/L in white and Mexican–American women but not in black women or white, black, or Mexican–American men [39]. Thus, women form a Cd-sensitive group, suitable for mechanistic investigation with a modest sample size. The limitations include a one-time-only assessment of Cd/Pb exposure and its outcomes plus the limited sample size, non-representativeness, and inability to adequately adjust smoking effects.

4. Materials and Methods

4.1. Data Sourcing

Individuals with and without diabetes were selected from a pre-existing cohort of 88 diabetes and 88 non-diabetes controls, conducted from June to December 2021 (approval number WUEC-20-132-01, 28 May 2020). The selection criteria for both cases and controls, matching strategies, and findings have been reported previously [24]. The pre-existing cohort employed a purposive sampling technique to recruit 100 individuals with diagnosed diabetes and 100 potential non-diabetic controls from administrative records of a health promoting center in Pakpoon Municipality, Nakhon Si Thammarat Province, Thailand.
In brief, the inclusion criteria for cases were residents of the Pak Poon municipality, aged 40 years or older, who attended annual health checkups, and who were diagnosed with type 2 diabetes. For the control group, 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.
Participants were provided with the study objectives, study procedures, potential risks, and benefits, and they gave written informed consent prior to participation. Structured interview questionnaires were used to collect sociodemographic data, educational attainment, occupation, health status, family history of diabetes, use of dietary supplements, alcohol consumption, and smoking status. After individuals with missing data were excluded, a total of 137 individuals (72 with diabetes and 65 without diabetes) were analyzed in the present study.

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. Morning voided urine samples were collected in acid-washed polypropylene collection cups. Blood samples for the glucose assay were collected in tubes containing heparin as an anticoagulant and fluoride as an inhibitor of 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 at Walailak University, where samples of plasma and serum were prepared. To prevent the degradation of β2M in acidic conditions, an alkaline (NaOH) solution was added to adjust the pH of urine aliquots to >6 before storage. 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 urine and serum concentration of β2M, 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, Catalog No: E-BC-K234-M, Houston, TX, USA) [40]. Assays of creatinine in urine and plasma were based on Jaffe’s alkaline picrate method, as described previously [41]. The urinary albumin assay was based on the immunoturbidimetric method [42,43]. The coefficient of variation (CV) for all blood and urine assays was within acceptable clinical chemistry standards.
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) [44]. 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 was the detection limit value divided by the square root of 2 [45].

4.4. Assessment of Simultaneous Cd/Pb Exposure

To evaluate effects of simultaneous Cd and Pb exposures, subjects were grouped based on blood Cd and blood Pb levels. Accordingly, each subject was assigned to the Cd/Pb exposure category 1, 2, or 3 using her/his blood Cd and blood Pb levels. Respective Cd/Pb exposure categories 1, 2, and 3 were defined as blood Cd and blood Pb levels ≤ median, blood Cd or blood Pb levels ≥ median, and blood Cd plus blood Pb levels were above the median. The median for blood Cd was 0.3 µg/L and the median for blood Pb was 2.12 µg/dL. There were 44, 54, and 39 subjects with the Cd/Pb exposure categories 1, 2, and 3, respectively.

4.5. Calculation and Cut-Off Values for Albuminuria

Estimated GFR (eGFR) was computed with Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations [46]. 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 achieve this, we normalized excretion of Cd (ECd) and albumin (Ealb) to creatinine excretion (Ecr) and creatinine clearance (Ccr), using the below equations.
Ex/Ecr = [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). Ex/Ecr was expressed as an amount of x excreted per g of creatinine. Albumin-to-creatinine ratio (ACR) is a well-known expression of Ealb/Ecr.
Ex/Ccr = [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). Ex/Ccr was expressed as an amount of x excreted per volume of the glomerular filtrate [47].
For Ealb/Ecr (ACR) data, albuminuria is defined as ACR value ≥ 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 Ecr values due to having a smaller muscle mass than men. In comparison, Ccr-normalization is not affected by muscle mass. Hence, for Ealb/Ccr data, albuminuria is defined as Ealb/Ccr values ≥ 0.2 mg/L filtrate in both men and women.

4.6. The Causal Inference Analysis

The Baron and Kenny method [48,49,50] was employed to explore the causal connection between outcomes of Cd-induced kidney tubular dysfunction and blood pressure control by the SH2B3 pathway. The mediation models are depicted below (Scheme 1).
In theory, the variability in serum β2M levels depends on the rate of its production by nucleated cells in the body and its degradation within the proximal tubular (PT) cells of the kidneys [34]. An increase in the excretion of β2M (Eβ2M/Ccr) is a well-known consequence Cd toxicity in the PT cells.
Model A tests for the potential direct effect of ECd/Ccr on β2M production and the indirect effect of ECd/Ccr on β2M degradation by the kidneys. Model B tests for the potential direct and indirect effects of ECd/Ccr on SBP/DBP. Given a significant correlation between ECd/Ccr and Cd/Pb exposure levels (Table 2), the observed effects reported in Figure 2 and Figure 3 may have reflected a combined effect of Cd/Pb exposure. Due to the low Pb exposure levels among participants, their urinary Pb excretions were below detection limits. Excretion of Cd is an indicator of cumulative kidney burden. Bone Pb is a reliable marker to assess long-term Pb exposure.

4.7. Statistical Analysis

Data were analyzed with IBM SPSS Statistics 21 (IBM Inc., New York, NY, USA). The variation in any continuous variable and differences in percentages across the tertiles of [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 ten variables: [β2M]s, age, BMI, [Glc]p, SBP, DBP, eGFR, Ealb/Ccr, Eβ2M/Ccr, ECd/Ccr, and Cd/Pb exposure category (Table 2). The one-sample Kolmogorov–Smirnov test was used to assess deviation from a normal distribution of any continuous variable. Logarithmic transformation was applied to fasting plasma glucose concentration ([Glc]p) and the excretion rate of albumin and Cd (Ealb/Ecr and ECd/Ccr) that showed rightward skewing before they were subjected to parametric statistics analyses, scatterplots, and linear regressions (Figure 1, Figure 2 and Figure 3).
The prevalence odds ratio (POR) values for [β2M]s ≥ 5 mg/L, FPG ≥ 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). The common variables incorporated in models as covariates were age, BMI, and smoking, given that they all have an impact on kidney function. In addition, smoking could also be a source of Cd and Pb exposure.

5. Conclusions

Hyperglycemia, a falling eGFR, and rising SBP could be the toxic manifestation of chronic exposure to low-level Cd and Pb, leading to hypertension, kidney damage, and albuminuria. For the first time, we present evidence that causally links circulating β2M to rising SBP and kidney damage, associated with Cd/Pb exposure. These findings underscore the coexistence of metabolic and kidney disease, recognized by the American Heart Association as cardiovascular–kidney–metabolic (CKM) syndrome. Our work bridges Cd/Pb exposure, β2M, and the SH3B pathway—a novel framework for diabetic kidney disease pathogenesis. It expands on genomic studies and emphasizes β2M as a biomarker for CKM syndrome.

Author Contributions

Conceptualization, S.S., D.A.V. and S.Y.; methodology, S.Y., D.W. and T.K.; formal analysis, S.Y. and S.S.; investigation, S.Y., D.W. and T.K.; resources, S.Y. and D.A.V.; original draft preparation, S.S., D.A.V. and S.Y.; review and editing, S.S. and D.A.V. All authors have read and agreed to the published version of the manuscript.

Funding

This research project was funded by the Research Grant, WU-IRG-63-026, of Walailak University, Nakhon Si Thammarat Province, Thailand.

Institutional Review Board Statement

This study was adhered to the guidelines of the Declaration of Helsinki and approved by the Office of the Human Research Ethics Committee of Walailak University, Nakhon Si Thammarat Province, Thailand. Approval number WUEC-24-275-01 (7 August 2024).

Informed Consent Statement

Written informed consent was obtained from all subjects involved in the study.

Data Availability Statement

All data are contained within this article.

Acknowledgments

We thank the staff of a health promoting center in Pakpoon Municipality, Nakhon Si Thammarat Province, Thailand for their assistance with collection of biospecimens and data.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Unger, T.; Borghi, C.; Charchar, F.; Khan, N.A.; Poulter, N.R.; Prabhakaran, D.; Ramirez, A.; Schlaich, M.; Stergiou, G.S.; Tomaszewski, M.; et al. 2020 International Society of Hypertension Global Hyperten-sion Practice Guidelines. Hypertension 2020, 75, 1334–1357. [Google Scholar] [CrossRef]
  2. Murray, C.J.L.; Aravkin, A.Y.; Zheng, P.; Abbafati, C.; Abbas, K.M.; Abbasi-Kangevari, M.; Abd-Allah, F.; Abdelalim, A.; Abdollahi, M.; Abdollahpour, I.; et al. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet 2020, 396, 1223–1249. [Google Scholar] [CrossRef] [PubMed]
  3. Calhoun, D.A.; Jones, D.; Textor, S.; Goff, D.C.; Murphy, T.P.; Toto, R.D.; White, A.; Cushman, W.C.; White, W.; Sica, D.; et al. Resistant hypertension: Diagnosis, evaluation, and treatment. A scientific statement from the American Heart Association Professional Education Committee of the Council for High Blood Pressure Research. Hypertension 2008, 51, 1403–1419. [Google Scholar] [CrossRef] [PubMed]
  4. Park, S.; Shin, J.; Ihm, S.H.; Kim, K.I.; Kim, H.L.; Kim, H.C.; Lee, E.M.; Lee, J.H.; Ahn, S.Y.; Cho, E.J.; et al. Resistant hyper-tension: Consensus document from the Korean society of hypertension. Clin. Hypertens. 2023, 29, 30. [Google Scholar] [CrossRef] [PubMed]
  5. Guyton, A.C. Blood pressure control-special role of the kidneys and body fluids. Science 1991, 252, 1813–1816. [Google Scholar] [CrossRef]
  6. Leite, A.P.O.; Li, X.C.; Nwia, S.M.; Hassan, R.; Zhuo, J.L. Angiotensin II and AT1a Receptors in the Proximal Tubules of the Kidney: New Roles in Blood Pressure Control and Hypertension. Int. J. Mol. Sci. 2022, 23, 2402. [Google Scholar] [CrossRef]
  7. Bloch, M.J.; Basile, J.N. Review of recent literature in hypertension: Updated clinical practice guidelines for chronic kidney disease now include albuminuria in the classification system. J. Clin. Hypertens. 2013, 15, 865–867. [Google Scholar] [CrossRef]
  8. Qiu, L.; Wu, B. The relationship between the age of onset of hypertension and chronic kidney disease: A cross-sectional study of the American population. Front. Cardiovasc. Med. 2024, 11, 1426953. [Google Scholar] [CrossRef]
  9. Chen, H.; Zou, Y.; Leng, X.; Huang, F.; Huang, R.; Wijayabahu, A.; Chen, X.; Xu, Y. Associations of blood lead, cadmium, and mercury with resistant hypertension among adults in NHANES, 1999–2018. Environ. Health Prev. Med. 2023, 28, 66. [Google Scholar] [CrossRef]
  10. Zhang, J.; Wang, X.; Ma, Z.; Dang, Y.; Yang, Y.; Cao, S.; Ouyang, C.; Shi, X.; Pan, J.; Hu, X. Associations of urinary and blood cadmium concentrations with all-cause mortality in US adults with chronic kidney disease: A prospective cohort study. Environ. Sci. Pollut. Res. Int. 2023, 30, 61659–61671. [Google Scholar] [CrossRef]
  11. Yin, G.; Zhao, S.; Zhao, M.; Xu, J.; Ge, X.; Wu, J.; Zhou, Y.; Liu, X.; Wei, L.; Xu, Q. Complex interplay of heavy metals and renal injury: New perspectives from longitudinal epidemiological evidence. Ecotoxicol. Environ. Saf. 2024, 278, 116424. [Google Scholar] [CrossRef] [PubMed]
  12. Yin, G.; Xin, M.; Zhao, S.; Zhao, M.; Xu, J.; Chen, X.; Xu, Q. Heavy metals and elderly kidney health: A multidimensional study through Enviro-target Mendelian Randomization. Ecotoxicol. Environ. Saf. 2024, 281, 116659. [Google Scholar] [CrossRef] [PubMed]
  13. Kim, D.H.; Lee, S.; Jang, M.; Kim, K. Effect of combined exposure to lead, mercury, and cadmium on hypertension: The 2008-2013 Korean National Health and Nutrition Examination Surveys. Int. J. Occup. Med. Environ. Health 2025, 28, 264. [Google Scholar] [CrossRef]
  14. Yeon, J.; Kang, S.; Park, J.; Ahn, J.H.; Cho, E.A.; Lee, S.H.; Ryu, K.H.; Shim, J.G. Association between blood cadmium levels and the risk of chronic kidney disease in Korea, based on the Korea National Health and Nutrition Examination Survey 2016–2017. Asian Biomed. (Res. Rev. News) 2025, 19, 60–66. [Google Scholar] [CrossRef]
  15. Verzelloni, P.; Giuliano, V.; Wise, L.A.; Urbano, T.; Baraldi, C.; Vinceti, M.; Filippini, T. Cadmium exposure and risk of hy-pertension: A systematic review and dose-response meta-analysis. Environ. Res. 2024, 263 Pt 1, 120014. [Google Scholar] [CrossRef]
  16. Murton, M.; Goff-Leggett, D.; Bobrowska, A.; Garcia Sanchez, J.J.; James, G.; Wittbrodt, E.; Nolan, S.; Sörstadius, E.; Pecoits-Filho, R.; Tuttle, K. Burden of Chronic Kidney Disease by KDIGO Categories of Glomerular Filtration Rate and Albuminuria: A Systematic Review. Adv. Ther. 2021, 38, 180–200. [Google Scholar] [CrossRef]
  17. Kalantar-Zadeh, K.; Jafar, T.H.; Nitsch, D.; Neuen, B.L.; Perkovic, V. Chronic kidney disease. Lancet 2021, 398, 786–802. [Google Scholar] [CrossRef]
  18. Farrell, D.R.; Vassalotti, J.A. Screening, identifying, and treating chronic kidney disease: Why, who, when, how, and what? BMC Nephrol. 2024, 25, 34. [Google Scholar] [CrossRef] [PubMed]
  19. Oosterwijk, M.M.; Hagedoorn, I.J.M.; Maatman, R.G.H.J.; Bakker, S.J.L.; Navis, G.; Laverman, G.D. Cadmium, active smoking and renal function deterioration in patients with type 2 diabetes. Nephrol. Dial. Transplant. 2023, 38, 876–883. [Google Scholar] [CrossRef]
  20. Zhao, H.; Yin, R.; Wang, Y.; Wang, Z.; Zhang, L.; Xu, Y.; Wang, D.; Wu, J.; Wei, L.; Yang, L.; et al. Association between blood heavy metals and diabetic kidney disease among type 2 diabetic patients: A cross-sectional study. Sci. Rep. 2024, 14, 26823. [Google Scholar] [CrossRef]
  21. Barregard, L.; Bergström, G.; Fagerberg, B. Cadmium, type 2 diabetes, and kidney damage in a cohort of middle-aged women. Environ. Res. 2014, 135, 311–316. [Google Scholar] [CrossRef]
  22. Liao, K.W.; Chien, L.C.; Chen, Y.C.; Kao, H.C. Sex-specific differences in early renal impairment associated with arsenic, lead, and cadmium exposure among young adults in Taiwan. Environ. Sci. Pollut. Res. Int. 2022, 29, 52655–52664. [Google Scholar] [CrossRef]
  23. Huan, T.; Meng, Q.; Saleh, M.A.; Norlander, A.E.; Joehanes, R.; Zhu, J.; Chen, B.H.; Zhang, B.; Johnson, A.D.; Ying, S.; et al. Integrative network analysis reveals molecular mechanisms of blood pressure regulation. Mol. Syst. Biol. 2015, 11, 799. [Google Scholar] [CrossRef]
  24. Yimthiang, S.; Pouyfung, P.; Khamphaya, T.; Kuraeiad, S.; Wongrith, P.; Vesey, D.A.; Gobe, G.C.; Satarug, S. Effects of Environmental Exposure to Cadmium and Lead on the Risks of Diabetes and Kidney Dysfunction. Int. J. Environ. Res. Public Health 2022, 19, 2259. [Google Scholar] [CrossRef]
  25. He, J.; Zhou, W.; Zhang, H.; Shen, J. In vivo heavy metal and diabetes association: A cross-sectional interpretable machine learning analysis of NHANES. Int. J. Diabetes Develop. Ctries. 2025, 1–15. [Google Scholar] [CrossRef]
  26. Satarug, S.; Vesey, D.A.; Yimthiang, S.; Khamphaya, T.; Pouyfung, P.; Đorđević, A.B. Environmental Cadmium Exposure Induces an Increase in Systolic Blood Pressure by Its Effect on GFR. Stresses 2024, 4, 436–451. [Google Scholar] [CrossRef]
  27. Xie, S.; Perrais, M.; Golshayan, D.; Wuerzner, G.; Vaucher, J.; Thomas, A.; Marques-Vidal, P. Association between urinary heavy metal/trace element concentrations and kidney function: A prospective study. Clin. Kidney J. 2024, 18, sfae378. [Google Scholar] [CrossRef] [PubMed]
  28. Song, J.; Wang, P.; Li, H. U-shaped relationship between fasting blood glucose and urinary albumin-to-creatinine ratio in the general United States population. Front. Endocrinol. 2024, 15, 1334949. [Google Scholar] [CrossRef] [PubMed]
  29. Jiang, Y.; Jia, J.; Li, J.; Huo, Y.; Fan, F.; Zhang, Y. Impaired fasting blood glucose is associated with incident albuminuria: Data from a Chinese community-based cohort. J. Diabetes Complicat. 2022, 36, 108125. [Google Scholar] [CrossRef]
  30. Ren, F.; Li, M.; Xu, H.; Qin, X.; Teng, Y. Urine albumin-to-creatinine ratio within the normal range and risk of hypertension in the general population: A meta-analysis. J. Clin. Hypertens. 2021, 23, 1284–1290. [Google Scholar] [CrossRef]
  31. Mashima, Y.; Konta, T.; Kudo, K.; Takasaki, S.; Ichikawa, K.; Suzuki, K.; Shibata, Y.; Watanabe, T.; Kato, T.; Kawata, S.; et al. Increases in urinary albumin and beta2-microglobulin are independently associated with blood pressure in the Japanese general population: The Takahata Study. Hypertens. Res. 2011, 34, 831–835. [Google Scholar] [CrossRef] [PubMed]
  32. Kudo, K.; Konta, T.; Mashima, Y.; Ichikawa, K.; Takasaki, S.; Ikeda, A.; Hoshikawa, M.; Suzuki, K.; Shibata, Y.; Watanabe, T.; et al. The association between renal tubular damage and rapid renal deterioration in the Japanese population: The Takahata study. Clin. Exp. Nephrol. 2011, 15, 235–241. [Google Scholar] [CrossRef]
  33. Kaneda, M.; Wai, K.M.; Kanda, A.; Ando, M.; Murashita, K.; Nakaji, S.; Ihara, K. Low Level of Serum Cadmium in Relation to Blood Pressures Among Japanese General Population. Biol. Trace Elem. Res. 2022, 200, 67–75. [Google Scholar] [CrossRef]
  34. Argyropoulos, C.P.; Chen, S.S.; Ng, Y.-H.; Roumelioti, M.-E.; Shaffi, K.; Singh, P.P.; Tzamaloukas, A.H. Rediscovering Beta-2 Microglobulin As a Biomarker across the Spectrum of Kidney Diseases. Front. Med. 2017, 4, 73. [Google Scholar] [CrossRef]
  35. Keefe, J.A.; Hwang, S.J.; Huan, T.; Mendelson, M.; Yao, C.; Courchesne, P.; Saleh, M.A.; Madhur, M.S.; Levy, D. Evidence for a causal role of the SH2B3-β2M axis in blood pressure regulation. Hypertension 2019, 73, 497–503. [Google Scholar] [CrossRef]
  36. Zhang, Y.; Peng, G.; Leng, W.; Li, Y.; Li, H.; Zhou, L.; Ge, L.; Shao, J.; Li, X.; Long, M. Association between serum β2-microglobulin and left ventricular hypertrophy in patients with type 2 diabetes mellitus: A cross-sectional study. J. Diabetes 2024, 16, e13599. [Google Scholar] [CrossRef] [PubMed]
  37. Cheung, C.L.; Lam, K.S.; Cheung, B.M. Serum beta-2 microglobulin predicts mortality in people with diabetes. Eur. J. Endocrinol. 2013, 169, 1–7. [Google Scholar] [CrossRef]
  38. Kim, M.K.; Yun, K.J.; Chun, H.J.; Jang, E.H.; Han, K.D.; Park, Y.M.; Baek, K.H.; Song, K.H.; Cha, B.Y.; Park, C.S.; et al. Clinical utility of serum beta-2-microglobulin as a predictor of diabetic complications in patients with type 2 diabetes without renal impairment. Diabetes Metab. 2014, 40, 459–465. [Google Scholar] [CrossRef] [PubMed]
  39. Scinicariello, F.; Abadin, H.G.; Murray, H.E. Association of low-level blood lead and blood pressure in NHANES 1999–2006. Environ. Res. 2011, 111, 1249–1257. [Google Scholar] [CrossRef]
  40. Xu, R.; Tan, X.; Li, T.; Liu, S.; Li, Y.; Li, H. Norepinephrine-induced AuPd aerogels with peroxidase- and glucose oxidase-like activity for colorimetric determination of glucose. Mikrochim. Acta 2021, 188, 362. [Google Scholar] [CrossRef]
  41. Apple, F.; Bandt, C.; Prosch, A.; Erlandson, G.; Holmstrom, V.; Scholen, J.; Googins, M. Creatinine clearance: Enzymatic vs Jaffé determinations of creatinine in plasma and urine. Clin. Chem. 1986, 32, 388–390. [Google Scholar] [CrossRef] [PubMed]
  42. Bargnoux, A.S.; Barrot, A.; Fesler, P.; Kuster, N.; Badiou, S.; Dupuy, A.M.; Ribstein, J.; Cristol, J.P. Evaluation of five immunoturbidimetric assays for urinary albumin quantification and their impact on albuminuria categorization. Clin. Biochem. 2014, 47, 250–253. [Google Scholar] [CrossRef]
  43. Kok, M.B.; Tegelaers, F.P.; van Dam, B.; van Rijn, J.L.; van Pelt, J. Carbamylation of albumin is a cause for discrepancies between albumin assays. Clin. Chim. Acta 2014, 434, 6–10. [Google Scholar] [CrossRef] [PubMed]
  44. Trzcinka-Ochocka, M.; Brodzka, R.; Janasik, B. Useful and Fast Method for Blood Lead and Cadmium Determination Using ICP-MS and GF-AAS; Validation Parameters. J. Clin. Lab. Anal. 2016, 30, 130–139. [Google Scholar] [CrossRef] [PubMed]
  45. Hornung, R.W.; Reed, L.D. Estimation of average concentration in the presence of nondetectable values. Appl. Occup. Environ. Hyg. 1990, 5, 46–51. [Google Scholar] [CrossRef]
  46. Levey, A.S.; Becker, C.; Inker, L.A. Glomerular filtration rate and albuminuria for detection and staging of acute and chronic kidney disease in adults: A systematic review. JAMA 2015, 313, 837–846. [Google Scholar] [CrossRef]
  47. Phelps, K.R.; Gosmanova, E.O. A generic method for analysis of plasma concentrations. Clin. Nephrol. 2020, 94, 43–49. [Google Scholar] [CrossRef]
  48. Preacher, K.J. Advances in mediation analysis: A survey and synthesis of new developments. Annu. Rev. Psychol. 2015, 66, 825–852. [Google Scholar] [CrossRef]
  49. MacKinnon, D.P.; Warsi, G.; Dwyer, J.H. A simulation study of mediated effect measures. Multiv. Behav. Res. 1995, 30, 41–62. [Google Scholar] [CrossRef]
  50. Preacher, K.J.; Hayes, A.F. SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behav. Res. Meth. Instrum. Comput. 2004, 36, 717–731. [Google Scholar] [CrossRef]
Ijms 26 09208 g001
Figure 1. Blood pressure in relation to serum β2M levels. Scatterplots related serum β2M concentration to SBP (a) and DBP (b) in participants with normal eGFR and low eGFR. SBP, systolic blood pressure; DBP, diastolic blood pressure. A hexagon represents an individual participant. The normal and low eGFRs were defined as eGFR > 60 and ≤ 60 mL/min/1.73 m2, respectively.
Figure 1. Blood pressure in relation to serum β2M levels. Scatterplots related serum β2M concentration to SBP (a) and DBP (b) in participants with normal eGFR and low eGFR. SBP, systolic blood pressure; DBP, diastolic blood pressure. A hexagon represents an individual participant. The normal and low eGFRs were defined as eGFR > 60 and ≤ 60 mL/min/1.73 m2, respectively.
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Figure 2. Mediating effects of Cd on serum β2M levels. On the left, a model depicts ECd, Eβ2M, and [β2M]s as independent variable, mediator, and dependent variable, respectively. On the right, the Sobel test for the indirect (a*b) and direct (c’) effects of ECd on [β2M]s is presented. Data were from 137 subjects. a: unstandardized β coefficient for ECd and Eβ2M association; b: unstandardized β coefficient for Eβ2M and [β2M]s association.
Figure 2. Mediating effects of Cd on serum β2M levels. On the left, a model depicts ECd, Eβ2M, and [β2M]s as independent variable, mediator, and dependent variable, respectively. On the right, the Sobel test for the indirect (a*b) and direct (c’) effects of ECd on [β2M]s is presented. Data were from 137 subjects. a: unstandardized β coefficient for ECd and Eβ2M association; b: unstandardized β coefficient for Eβ2M and [β2M]s association.
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Figure 3. Mediating effects of Cd on blood pressure. Models for testing Cd effects on SBP (a) and DBP (b) through Eβ2M, and the Sobel test for the indirect (a*b) and direct (c’) effects of ECd. Data were from 135 and 134 subjects for the SBP and DBP models, respectively. a: unstandardized β coefficient for ECd and Eβ2M association; b: unstandardized β coefficient for Eβ2M and SBP/DBP.
Figure 3. Mediating effects of Cd on blood pressure. Models for testing Cd effects on SBP (a) and DBP (b) through Eβ2M, and the Sobel test for the indirect (a*b) and direct (c’) effects of ECd. Data were from 135 and 134 subjects for the SBP and DBP models, respectively. a: unstandardized β coefficient for ECd and Eβ2M association; b: unstandardized β coefficient for Eβ2M and SBP/DBP.
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Ijms 26 09208 g004aIjms 26 09208 g004b
Figure 4. Albumin excretion in relation to blood pressure increments. Scatterplots related Ealb/Ccr to SBP in participants with and without diabetes (a) and those with/without hyperglycemia (b). Scatterplots related Ealb/Ccr to DBP in subjects with and without diabetes (c) and those with/ without hyperglycemia (d).
Figure 4. Albumin excretion in relation to blood pressure increments. Scatterplots related Ealb/Ccr to SBP in participants with and without diabetes (a) and those with/without hyperglycemia (b). Scatterplots related Ealb/Ccr to DBP in subjects with and without diabetes (c) and those with/ without hyperglycemia (d).
Ijms 26 09208 g004aIjms 26 09208 g004b
Ijms 26 09208 g005aIjms 26 09208 g005b
Figure 5. Albumin excretion in relation to fasting plasma glucose concentration and blood pressure. Scatterplots related serum Ealb/Ccr to [Glc]p (a,b), SBP (c), and DBP (d) in participants with/without hypertension (a), normal/ low eGFR (b), and low/high serum β2M (c,d). [Glc]p = fasting plasma glucose concentration. The normal and low eGFR were defined as eGFR > 60 and ≤ 60 mL/min/1.73 m2, respectively. The low and high serum β2M were defined, respectively, as [β2M]s < 5 and ≥5 mg/L.
Figure 5. Albumin excretion in relation to fasting plasma glucose concentration and blood pressure. Scatterplots related serum Ealb/Ccr to [Glc]p (a,b), SBP (c), and DBP (d) in participants with/without hypertension (a), normal/ low eGFR (b), and low/high serum β2M (c,d). [Glc]p = fasting plasma glucose concentration. The normal and low eGFR were defined as eGFR > 60 and ≤ 60 mL/min/1.73 m2, respectively. The low and high serum β2M were defined, respectively, as [β2M]s < 5 and ≥5 mg/L.
Ijms 26 09208 g005aIjms 26 09208 g005b
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Scheme 1. The models for analysis of β2M as the mediator of Cd effects. In models A and B, Eβ2M was a single mediator (M), whilst a, b, and c’ are unstandardized β coefficients, describing Cd effects on the independent variable [β2M]s (model A) and SBP/DBP (model B). Eβ2M, excretion of β2M; [β2M]s, serum concentration of β2M; and SBP/DBP, systolic/diastolic blood pressure.
Scheme 1. The models for analysis of β2M as the mediator of Cd effects. In models A and B, Eβ2M was a single mediator (M), whilst a, b, and c’ are unstandardized β coefficients, describing Cd effects on the independent variable [β2M]s (model A) and SBP/DBP (model B). Eβ2M, excretion of β2M; [β2M]s, serum concentration of β2M; and SBP/DBP, systolic/diastolic blood pressure.
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Table 1. Participant characteristics according to tertile of serum β2-microglobulin.
Table 1. Participant characteristics according to tertile of serum β2-microglobulin.
VariablesAll Subjects
N = 137		Tertile of Serum β2M Concentration, mg/Lp
T1: 1–3.9
n = 45		T2: 4.0–5.9
n = 46		T3: 6.0–16
n = 46
Women, %78.180.082.671.10.421
Smoking, %10.24.48.717,40.115
Diagnosed diabetes47.442.228.371.7<0.001
Age, years59.7 (9.1)57.2 (9.7)60.4 (8.3)61.4 (8.9)0.060
BMI, kg/m225.6 (4.8)26.3 (5.7)25.1 (3.9)25.4 (4.6)0.751
SBP, mm Hg138 (18)136 (17)133 (15)145 (19)0.004
DBP, mm Hg85 (9)84 (10)83 (8)87 (9)0.129
Hypertension, %54.554.540.068.90.023
eGFR, mL/min/1.73 m279 (16)86 (16)75 (15)77 (15)0.002
Low eGFR a, %12.76.715.215.20.326
FPG, mg/dL129 (61)135 (83)119 (51)134 (41)0.007
[Cd]b, µg/L0.57 (0.70)0.40 (0.56)0.59 (0.69)0.70 (0.81)0.041
[Pb]b, mg/dL4.49 (4.78)4.73 (5.11)3.07 (2.13)5.68 (5.96)0.002
ECd/Ecr, µg/g creatinine0.98 (1.86)1.05 (1.87)1.09 (2.14)0.79 (1.53)0.567
ECd/Ccr, (µg/L filtrate) × 1000.86 (1.68)0.86 (1.56)1.01 (2.00)0.69 (1.45)0.796
Eβ2M/Ccr, (µg/L filtrate) × 10099 (114)63 (77)78 (110)155 (129)<0.001
ACR (Ealb/Ecr), mg/g creatinine40 (102)26 (70)43 (134)50 (90)0.463
Ealb/Ccr, (mg/L filtrate) × 10037 (106)20 (49)44 (155)47 (84)0.366
Ealb/Ccr ≥ 0.2 mg/L filtrate, %26.317.821.739.10.048
FPG ≥ 110 mg/dL, %48.942.234.869.60.002
FPG ≥ 126 mg/dL, %39.435.623.958.70.002
eGFR, estimated glomerular filtration rate; a low eGFR was defined as eGFR values ≤ 60 mL/min/1.73 m2; FPG, fasting plasma glucose; β2M, β2-microglobulin; Cd, cadmium; Pb, lead; [x]b, blood concentration of x; [x]p, plasma concentration of x; [x]u, urine concentration of x; Ecr, creatinine excretion; Ccr, creatinine clearance; and ACR, albumin-to-creatinine ratio. Values for continuous variables are the mean (standard deviation, SD). Mean (SD) values for [β2M]s in all subjects and the serum β2M tertiles 1, 2, and 3 are 5.98 (3.29), 3.03 (0.81), 4.95 (0.50), and 9.90 (2.53) mg/L, respectively. The variability of continuous variables across [β2M]s tertiles was assessed with the Kruskal–Wallis test. The distribution of categorial variables was assessed with chi-square test. For all tests, p-values ≤ 0.05 indicate statistical significance.
Table 2. Bivariate correlation analysis of the serum concentration of β2-microglobulin.
Table 2. Bivariate correlation analysis of the serum concentration of β2-microglobulin.
VariablesSpearman’s Correlation Coefficient
[β2M]sAgeBMIFPGSBPDBPeGFREalb/CcrEβ2M/CcrECd/Ccr
Age0.200 *
BMI−0.061−0.262 **
FPG0.210 *−0.222 **0.184 *
SBP0.229 **0.224 **0.0720.250 **
DBP0.117−0.1230.0430.1680.552 **
eGFR−0.265 **−0.356 **0.1610.089−0.0480.042
Ealb/Ccr0.1380.0850.0760.273 **0.372 **0.232 **−0.136
Eβ2M/Ccr0.390 **0.170 *−0.0660.306 **0.237 **0.051−0.515 **0.265 **
ECd/Ccr0.0210.078−0.0830.1660.1330.123−0.227 **0.1060.496 **
Cd/Pb exposure a 0.1580.009−0.0120.181 *0.1140.194 *−0.0130.0950.309 **0.301 **
β2M, β2-microglobulin; [β2M]s, serum concentration of β2M; BMI, body mass index; FPG, fasting plasma glucose concentration; SBP, systolic blood pressure; DBP, diastolic blood pressure; and eGFR, estimated glomerular filtration rate. a Median blood Cd of 0.3 µg/L and median blood Pb of 2.12 µg/dL were used to assign Cd/Pb exposure levels. The Cd/Pb exposure category 1, 2, or 3 was assigned, respectively, when blood Cd or blood Pb is below its median, blood Cd or blood Pb above its median, or blood Cd and blood Pb were both above the medians. * Significant correlation at the 0.05 level. ** Significant correlation at the 0.01 level. Data for SBP and DBP were from 135 and 134 subjects, respectively. Data for all other variables were from 137 subjects.
Table 3. Determinants of the prevalence odds ratios for an elevated serum β2M concentration.
Table 3. Determinants of the prevalence odds ratios for an elevated serum β2M concentration.
Independent
Variables/Factors		[β2M]s ≥ 5 mg/L
β CoefficientsPOR95% CIp
(SE)LowerUpper
Age, years0.025 (0.025)1.0250.9761.0760.320
BMI, kg/m2−0.026 (0.046)0.9740.8901.0670.574
eGFR, mL/min/1.73 m2−0.041 (0.014)0.9600.9330.9880.005
Log10[(ECd/Ccr) × 105], µg/ L filtrate0.257 (0.281)1.2930.7462.2400.360
Gender0.136 (0.607)1.1460.3493.7630.822
Smoking1.410 (0.856)4.0980.76521.950.100
Diagnosed diabetes1.411 (0.425)4.0991.7839.4210.001
Hypertension0.436 (0.406)1.5470.6993.4250.282
[β2M]s, serum β2-microglobulin concentration; POR, prevalence odds ratio; CI, confidence interval; BMI, body mass index; and eGFR, estimated glomerular filtration rate. For all tests, p-values ≤ 0.05 indicate statistical significance. Data were from 134 subjects.
Table 4. Independent association of serum β2M and the prevalence odds of hyperglycemia.
Table 4. Independent association of serum β2M and the prevalence odds of hyperglycemia.
Independent VariablesFPG ≥ 110 mg/dLFPG ≥ 126 mg/dL
POR (95% CI)pPOR (95% CI)p
Age, years1.046 (0.999, 1.096)0.0561.077 (1.023, 1.133)0.004
BMI, kg/m20.929 (0.855, 1.011)0.0880.942 (0.865, 1.027)0.177
Gender1.574 (0.505, 4.910)0.4341.338 (0.424, 4.225)0.620
Smoking3.087 (0.628, 15.18)0.1652.881 (0.538, 15.42)0.216
[β2M]s ≥ 5 mg/dL3.392 (1.554, 7.406)0.0023.875 (1.673, 8.977)0.002
Cd/Pb exposure category a
1Referent Referent
22.107 (0.825, 5.378)0.1193.141 (1.185, 8.328)0.021
32.802 (1.026, 7.651)0.0443.702 (1.299, 10.54)0.014
FPG, fasting plasma glucose; POR, prevalence odds ratio; CI, confidence interval; BMI, body mass index; and [β2M]s, serum concentration of β2M. a Median blood Cd (0.3 µg/L) and median blood Pb (2.12 µg/dL) were used to describe Cd/Pb exposure levels. The Cd/Pb exposure category 1, 2, or 3 was assigned, respectively, when blood Cd or blood Pb was below its median, blood Cd or blood Pb was above its median, or blood Cd and blood Pb were both above their medians. For all tests, p-values ≤ 0.05 indicate statistical significance. Data were from 137 subjects.
Table 5. Association of the prevalence odds for hypertension and albuminuria with hyperglycemia in the pre-diabetes range.
Table 5. Association of the prevalence odds for hypertension and albuminuria with hyperglycemia in the pre-diabetes range.
Independent VariablesHypertension aAlbuminuria b
POR (95% CI)pPOR (95% CI)p
Age, years0.964 (0.920, 1.009)0.1140.974 (0.927, 1.023)0.295
BMI, kg/m20.975 (0.894, 1.063)0.5610.980 (0.898, 1.070)0.653
Gender2.299 (0.690, 7.662)0.1752.490 (0.763, 8.122)0.130
Non-smoker7.920 (1.381, 45.42)0.0203.187 (0.559, 18.18)0.192
FPG ≥ 110 mg/dL3.664 (1.658, 8.097)0.0012.955 (1.254, 6.965)0.013
Cd/Pb exposure category c
1Referent Referent
23.063 (1.022, 9.186)0.0461.369 (0.513, 3.650)0.530
34.413 (1.555, 12.53)0.0051.993 (0.664, 5.980)0.219
a Hypertension was based on physician diagnosis and/or use of anti-hypertensive medication. b Albuminuria was defined as Ealb/Ccr ≥ 0.2 mg/ L filtrate. FPG, fasting plasma glucose; POR, prevalence odds ratio; CI, confidence interval; and BMI, body mass index. c Median blood Cd (0.3 µg/L) and median blood Pb (2.12 µg/dL) were used to describe Cd/Pb exposure levels. The Cd/Pb exposure category 1, 2, or 3 was assigned, respectively, when blood Cd or blood Pb was below its median, blood Cd or blood Pb was above its median, or blood Cd and blood Pb were both above their medians. For all tests, p-values ≤ 0.05 indicate statistical significance. Data for hypertension and albuminuria outcomes were from 134 and 137 subjects, respectively.
Table 6. Association of the prevalence odds for hypertension and albuminuria with hyperglycemia in diabetes range.
Table 6. Association of the prevalence odds for hypertension and albuminuria with hyperglycemia in diabetes range.
Independent VariablesHypertension aAlbuminuria b
POR (95% CI)pPOR (95% CI)p
Age, years0.963 (0.920, 1.008)0.1010.965 (0.917, 1.016)0.172
BMI, kg/m20.969 (0.880, 1.056)0.4690.976 (0/895, 1.065)0.586
Gender2.356 (0.732, 7.577)0.1512.671 (0.817, 8.737)0.104
Non-smoker8.030 (1.429, 45.11)0.0183.275 (0.572, 18.76)0.183
FPG ≥ 126 mg/dL2.905 (1.275, 6.622)0.0113.482 (1.458, 8.312)0.005
Cd/Pb exposure category c
1Referent Referent
22.966 (0.998, 8.811)0.0501.196 (0.439, 3.260)0.726
34.053 (1.445, 11.36)0.0081.846 (0.603, 5.449)0.283
a Hypertension was based on physician diagnosis and/or use of anti-hypertensive medication. b Albuminuria was defined as Ealb/Ccr ≥ 0.2 mg/ L filtrate. FPG, fasting plasma glucose concentration; POR, prevalence odds ratio; CI, confidence interval; and BMI, body mass index. c Median blood Cd of 0.3 µg/L and median blood Pb of 2.12 µg/dL were used to describe Cd/Pb exposure levels. The Cd/Pb exposure category 1, 2, or 3 was assigned, respectively, when blood Cd or blood Pb was below its median, blood Cd or blood Pb was above its median, or blood Cd and blood Pb were both above their medians. For all tests, p-values ≤ 0.05 indicate statistical significance. Data for hypertension and albuminuria outcomes were from 134 and 137 subjects, respectively.
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Satarug, S.; Vesey, D.A.; Waeyeng, D.; Khamphaya, T.; Yimthiang, S. Enhanced Kidney Damage in Individuals with Diabetes Who Are Chronically Exposed to Cadmium and Lead: The Emergent Role for β2-Microglobulin. Int. J. Mol. Sci. 2025, 26, 9208. https://doi.org/10.3390/ijms26189208

AMA Style

Satarug S, Vesey DA, Waeyeng D, Khamphaya T, Yimthiang S. Enhanced Kidney Damage in Individuals with Diabetes Who Are Chronically Exposed to Cadmium and Lead: The Emergent Role for β2-Microglobulin. International Journal of Molecular Sciences. 2025; 26(18):9208. https://doi.org/10.3390/ijms26189208

Chicago/Turabian Style

Satarug, Soisungwan, David A. Vesey, Donrawee Waeyeng, Tanaporn Khamphaya, and Supabhorn Yimthiang. 2025. "Enhanced Kidney Damage in Individuals with Diabetes Who Are Chronically Exposed to Cadmium and Lead: The Emergent Role for β2-Microglobulin" International Journal of Molecular Sciences 26, no. 18: 9208. https://doi.org/10.3390/ijms26189208

APA Style

Satarug, S., Vesey, D. A., Waeyeng, D., Khamphaya, T., & Yimthiang, S. (2025). Enhanced Kidney Damage in Individuals with Diabetes Who Are Chronically Exposed to Cadmium and Lead: The Emergent Role for β2-Microglobulin. International Journal of Molecular Sciences, 26(18), 9208. https://doi.org/10.3390/ijms26189208

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