Abstract
Aim: T50 is the time required for primary calciprotein particles (CPPs) to transform into secondary CPPsin vitro, reflecting serum calcification propensity, and used as a biomarker for calcification stress. Since secondary CPPs induce inflammation and oxidative stress, they may promote atherosclerosis. We investigated whether or not T50 was associated with carotid artery intima-media thickness (IMT).
Methods: This was a cross-sectional study of 202 health examinees. T50 was measured by the established nephelometric method. Carotid artery IMT was measured by high-resolution ultrasonography. The association between T50 and IMT was evaluated by a multivariable-adjusted linear regression analysis.
Results: In a univariate analysis, IMT was not significantly correlated with T50. A multivariable-adjusted linear regression analysis showed that IMT was independently associated with age, sex, diabetes mellitus, dyslipidemia, and fetuin-A but not with T50 in the total subjects. However, when stratified by the estimated glomerular filtration rate (eGFR), T50 was independently and inversely associated with IMT in the subgroup with an eGFR ＜60 mL/min/1.73 m2 (β = −0.418, P = 0.013), whereas it was not in the subgroup with an eGFR ≥ 60 mL/min/1.73 m2.
Conclusion: T50 was independently and inversely associated with IMT in health examinees with a reduced kidney function, suggesting a novel link between calcification stress and atherosclerosis, particularly in those with chronic kidney disease.
Keywords: Atherosclerosis, Calcification stress, Intima-media thickness, Serum calcification propensity, Subclinical atherosclerosis
Introduction
Atherosclerosis is characterized by the accumulation of cholesterol, macrophages, and other inflammatory cells in the intima of arteries 1) . Plasma low-density lipoproteins (LDLs) enter the intima, become oxidized, and are phagocytosed by macrophages. The intimal thickness of the affected artery is increased, whereas the lumen is narrowed when the thickening exceeds the positive remodeling of the arterial wall, resulting in atherosclerotic cardiovascular disease (CVD), such as ischemic heart disease, ischemic stroke, and peripheral artery disease (PAD) 2 , 3) . Oxidative stress and inflammation play important roles in the pathogenesis of atherosclerosis 4) .
The risk factors for atherosclerosis and atherosclerotic CVD include older age, male sex, hypertension, dyslipidemia, diabetes mellitus, smoking, and chronic kidney disease (CKD). Intima-media thickness (IMT) has been used as a quantitative measure of atherosclerosis that can be determined non-invasively by high-resolution ultrasonography 5) , and IMT is increased in patients with CVD 6) as well as in those at an increased risk for CVD 7 , 8) . Patients with CKD have an elevated risk for CVD 9) , and vascular calcification is prevalent in those with a reduced kidney function particularly in those treated with hemodialysis 10) . The elevated CVD risk in CKD is explained by both traditional risk factors and non-traditional risk factors, such as inflammation, oxidative stress, and calcification stress 11 , 12) .
Regarding calcification stress or mineral stress, plasma is hyper-saturated with calcium and phosphate, but the crystallization of calcium-phosphate are inhibited by the action of a liver-derived glycoprotein fetuin-A 13) , which forms nano-sized particles called calciprotein monomers (CPMs) 14) . CPMs spontaneously aggregate and transform into small and spherical calciprotein particles (primary CPPs) containing amorphous calcium-phosphate clusters, and further into larger and irregular-shaped ones (secondary CPPs) containing calcium-phosphate crystalline. Since secondary CPPs can induce inflammation and oxidative stress, they can promote not only vascular calcification but atherosclerosis as well 15) .
The T50 calciprotein crystallization test 16) , also called serum calcification propensity 17) , is a biomarker of calcification stress or mineral stress developed by Pasch et al. 18) . T50 is the time required to transform from primary to secondary CPPs in vitro 17) . A shorter T50 reflects a lower protective function of the serum against calciprotein crystallization or a higher serum calcification propensity 17) . Clinical studies in CKD and other populations have established the T50 test as a serum biomarker for calcification stress 18) by showing the associations of T50 with vascular calcification and an increased risk of cardiovascular events and all-cause mortality 19 - 24) . Thus far, however, the relationship between T50 and IMT has not been studied.
Aim
The present study explored the possible association between T50 and IMT in health examinees.
Methods
Study Design and Participants
This study was a post-hoc analysis of our previous study, which was a cross-sectional study in health examinees 25) . Namely, the study was conducted in 202 individuals selected from the MedCity21 health examination registry, Osaka Metropolitan University, Osaka, Japan. The inclusion criteria and exclusion criteria of selection of the study subjects has been described in detail elsewhere 25) . Data were extracted from the registry database. Because T50, serum fetuin-A, and serum magnesium values were not available from the database, we measured them using freshly frozen serum samples collected at the same time as the health examination and stored at −80℃.
Ethical Considerations
This study was conducted in accordance with the latest version of the Declaration of Helsinki and the Ethical Guidelines for Clinical Studies by the Ministry of Health, Labor and Welfare, Japan. The protocol of the MedCity21 health examination registry was reviewed and approved by the ethics committee of Osaka City University Graduate School of Medicine (Approval No. 2927, September 01, 2014). However, the current name is Osaka Metropolitan University. All participants of this registry provided their written informed consent prior to participation. The protocol of the present analysis was also reviewed and approved by the ethics committee of Osaka City University Graduate School of Medicine (Approval No. 2021-197, December 15, 2021). Because of the observational nature of this study, the requirement for written informed consent was waived, as participants were provided with the opportunity to withdraw their consent.
Measurement of IMT
Carotid artery IMT was measured by ultrasonographic B-mode imaging using high-resolution real-time ultrasonography (Aplio 500® and Aplio a550®; Canon Medical Systems Co., Ltd., Tochigi, Japan) with a 7.5-MHz probe as a morphological index for arterial wall thickening 7 , 26) . Ultrasonographic examinations were performed for both sides with the examinee in a supine position. We recorded the maximum thickness in each area of the common carotid artery, carotid bulb, and internal carotid artery on both sides. We used the maximum value among these measurements for the analysis. In this paper, “IMT” means the maximum IMT thus determined.
Measurements of T50
As previously described 25) , we measured T50 in our laboratory 27 , 28) according to the method by Pasch et al. 17) . In short, test serum samples were mixed with NaCl solution in a 96-well microplate placed in a nephelometric apparatus (Nephelostar Plus®, BGM Labtech, Ortenberg, Germany) equipped with a thermo-constant room (36–37℃). The measurement was started by adding pre-warmed phosphate-containing solution and calcium-containing solution using an automated dispenser. The change in signals caused by the transformation from primary CPPs to secondary CPPs was detected by continuous nephelometric monitoring, and T50 was determined by using the MARS® analysis software program (BGM Labtech, Ortenberg, Germany). The intra- and inter-assay coefficients of variation were less than 4.5% 28) . We used the average of the duplicated measurements for statistical analyses. The T50 assay in our laboratory was validated against the gold-standard measurement at Calciscon AG (Nidau, Switzerland) 28) .
Assays for Fetuin-A and Magnesium
Serum fetuin-A was determined by enzyme-linked immunoassay using a commercial assay kit (Human fetuin-A ELISA kit; BioVender, Brno, Czech Republic) 25 , 29 , 30) . Serum magnesium was measured by an enzymatic assay (L-type Wako Mg·N; FUJIFILM Wako Pure Chemical Co., Ltd., Osaka, Japan) using an automated analyzer 25) .
Other Variables
Data for other variables were extracted from the database of the MediCity21 health examination registry 25) . Prior CVD included coronary artery disease, stroke, and peripheral artery disease based on questionnaire. Diabetes mellitus was defined by a fasting glucose level of ≥ 126 mg/dL, HbA1c of ≥ 6.5%, and/or current use of any medication for diabetes mellitus 31) . Since we excluded patients with type 1 diabetes, gestational diabetes, and/or specific types of diabetes due to other causes, the patients with diabetes in this analysis were those with type 2 diabetes. Hypertension was defined by systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, and/or current use of any medication for hypertension 32) . Dyslipidemia was defined by low-density lipoprotein cholesterol ≥ 140 mg/dL, triglycerides ≥ 150 mg/dL, high-density lipoprotein cholesterol ＜40 mg/dL at fasting state, and/or current use of any medication for dyslipidemia 5) . The body mass index (BMI) was calculated as the body weight (kg) divided by the height squared (m2). The estimated glomerular filtration rate (eGFR) was calculated from the age, sex, and serum creatinine concentration using a formula for Japanese individuals 33) . A reduced eGFR was defined as an eGFR ＜60 mL/min/1.73 m². Proteinuria was tested by the dipstick method.
Statistical Analyses
Clinical characteristics of the total study participants and the subgroups by the kidney function were summarized as medians (interquartile ranges [IQRs]) for continuous variables and numbers (percentages) for categorical variables. Comparisons between subgroups were done by the Mann-Whitney U-test and χ2 test as appropriate.
To explore whether or not T50 was associated with IMT, we first examined the correlation between T50 and IMT by Spearman’s rank correlation test in the total participants. Because a preliminary analysis suggested an effect modification by the kidney function, the correlation was examined in those with an eGFR ≥ 60 mL/min/1.73 m2 and those with an eGFR ＜60 mL/min/1.73 m2. The association between T50 and IMT was then further examined using a multivariable-adjusted linear regression model in which IMT was included after logarithmical transformation to meet the assumption of normal distribution of residual. Adjustment was performed for age, sex, prior CVD, diabetes mellitus, dyslipidemia, hypertension, smoking habit, kidney function, proteinuria, and C-reactive protein (CRP) level. Further adjustment was done for fetuin-A, phosphate, and magnesium levels, as these variables were deemed possible confounders.
We examined the possible effect modification by major vascular risk factors on the relationship between T50 and IMT by inserting the interaction term between T50 and the risk factor in the above-mentioned regression model, and if a potential effect modification was found, the analysis was stratified by the factor. In the subgroup analyses, considering the smaller sample size, covariates were sequentially included in multivariable regression models in a stepwise manner.
These statistical calculations were conducted using the JMP Pro software program, version 18.0.2 (SAS Institute Japan, Tokyo, Japan), and the free software program EZR (with R version 4.1.2), which was developed by Professor Kanda at Jichi Medical University Saitama Medical Center 34) . A two-sided P value of ＜0.05 was considered statistically significant.
Results
Clinical Characteristics of the Participants
Table 1 shows the characteristics of the participants of this study. The median age was 63 years old, and 69.8% were men. A certain proportion of the participants had diabetes mellitus (50.0%), dyslipidemia (55.4%), hypertension (49.5%), a current smoking habit (23.3%), a reduced kidney function (eGFR ＜60 mL/min/1.73 m2; 19.3%), and positive proteinuria (1.5%).
Table 1. Characteristics of the study subjects.
| variable | unit | Total participants (N = 202) | eGFR ＜60 mL/ min/1.73 m2 (N = 39) | eGFR ≥ 60 mL/ min/1.73 m2 (N = 163) | P-value for comparison between group |
|---|---|---|---|---|---|
| Age | years | 63 (55–71) | 68 (63–74) | 62 (53–69) | ＜0.001 |
| Sex (male) | N (%) | 141 (69.8%) | 31 (79.5%) | 110 (67.5%) | 0.176 |
| BMI | kg/m2 | 24.4 (22.5–26.6) | 24.7 (22.3–26.2) | 24.4 (22.5–26.7) | 0.839 |
| Prior CVD | N (%) | 11 (5.4%) | 6 (15.4%) | 5 (3.1%) | 0.002 |
| Diabetes mellitus | N (%) | 101 (50.0%) | 21 (53.8%) | 80 (49.1%) | 0.595 |
| Fasting plasma glucose | mg/dL | 114 (102–134) | 118 (101–134) | 113 (102–134) | 0.820 |
| HbA1c | % | 6.1 (5.7–6.7) | 6.0 (5.7–6.7) | 6.1 (5.7–6.7) | 0.961 |
| Use of medication for diabetes | N (%) | 48 (23.8%) | 8 (20.5%) | 40 (24.5%) | 0.598 |
| Dyslipidemia | N (%) | 112 (55.4%) | 25 (64.1%) | 87 (53.3%) | 0.228 |
| Total cholesterol | mg/dL | 195 (174–219) | 187 (165–219) | 195 (176–219) | 0.200 |
| Triglycerides | mg/dL | 97 (72–142) | 103 (80–164) | 96 (72–140) | 0.204 |
| HDL-C | mg/dL | 55 (46–66) | 52 (43–61) | 55 (47–67) | 0.070 |
| LDL-C | mg/dL | 113 (94–128) | 103 (83–132) | 113 (95–128) | 0.231 |
| Use of medication for dyslipidemia | N (%) | 62 (30.7%) | 16 (41.0%) | 46 (28.2%) | 0.121 |
| Hypertension | N (%) | 100 (49.5%) | 28 (71.8%) | 72 (44.2%) | 0.002 |
| Systolic blood pressure | mmHg | 126 (115–134) | 126 (113–138) | 126 (115–133) | 0.701 |
| Diastolic blood pressure | mmHg | 76 (70–82) | 74 (65–82) | 76 (70–82) | 0.244 |
| Use of medication for hypertension | N (%) | 77 (38.1%) | 23 (59.0%) | 54 (33.1%) | 0.003 |
| Current smoking | N (%) | 47 (23.3%) | 6 (15.4%) | 41 (25.2%) | 0.196 |
| eGFR | mL/min/1.73 m2 | 72.5 (62.0–83.1) | 55.7 (51.5–57.8) | 76.1 (69.0–85.6) | ＜0.001 |
| eGFR ＜60 mL/min/1.73 m2 | N (%) | 39 (19.3%) | 39 (100.0%) | 0 (0%) | ＜0.001 |
| Proteinuria (positive) | N (%) | 3 (1.5%) | 1 (2.6%) | 2 (1.2%) | 0.541 |
| Serum calcium | mg/dL | 9.4 (9.2–9.6) | 9.5 (9.1–9.7) | 9.3 (9.2–9.5) | 0.268 |
| Serum phosphate | mg/dL | 3.3 (3.1–3.6) | 3.3 (2.9–3.6) | 3.4 (3.1–3.6) | 0.201 |
| Serum magnesium | mg/dL | 2.0 (1.9–2.1) | 2.0 (1.9–2.2) | 2.0 (2.0–2.1) | 0.915 |
| Serum fetuin-A | μg/mL | 229 (202–263) | 251 (221–272) | 225 (200–257) | 0.014 |
| Serum albumin | g/dL | 4.2 (4.1–4.4) | 4.2 (4.0–4.4) | 4.2 (4.1–4.4) | 0.616 |
| T50 | min | 188 (164–211) | 200 (174–218) | 187 (160–208) | 0.049 |
| IMT | mm | 1.5 (1.1-2.2) | 1.8 (1.3-2.8) | 1.4 (1.0-2.1) | 0.003 |
The table gives medians (interquartile ranges) for continuous variables and numbers (percentages) for categorical variables.
BMI, body mass index; CVD, cardiovascular disease; HDL-C, high-density lipoprotein cholesterol; IMT, intima-media thickness; LDL-C, low-density lipoprotein cholesterol; HbA1c, hemoglobin A1c; and eGFR, estimated glomerular filtration rate.
Correlation between T50 and IMT
As shown in Fig.1 , T50 was not significantly correlated with IMT in the total participants (Spearman’s r = −0.010, P = 0.892). When the participants were divided into 2 subgroups by eGFR, a significant and inverse association was noted in the subgroup with a reduced eGFR ＜60 mL/min/1.73 m2 (Spearman’s r = −0.414, P = 0.009), whereas no significant correlation was found in the subgroup without a reduced eGFR (Spearman’s r = 0.023, P = 0.769).
Fig.1.Correlation between T50 and IMT.
Correlation was evaluated by Spearman’s rank correlation test in the total population (N = 202), those with an eGFR ≥ 60 mL/min/1.73 m2, and those with an eGFR ＜60 mL/min/1.73 m2. IMT, intima-media thickness; T50, calciprotein crystallization (serum calcification propensity); rs, Spearman’s correlation coefficient; eGFR, estimated glomerular filtration rate.
Independent association between T50 and IMT
The association of T50 with IMT was examined with a multivariable-adjusted regression model adjusted for the major vascular risk factors ( Table 2 , model 1). Although age, male sex, the presence of diabetes mellitus, and the presence of dyslipidemia were significant factors associated with a higher IMT, the association of T50 with IMT was again not significant in the total population (β = −0.009, P = 0.876). Further adjustment was then done for fetuin-A (model 2), serum phosphate (model 3), and serum magnesium (model 4), as these factors were possible confounders. Although fetuin-A was found to be positively associated with IMT (β = 0.156, P = 0.019), the association between T50 and IMT was not significant in any model.
Table 2. Results of a multivariable-adjusted regression analysis of factors independently associated with IMT in the total study participants.
| Exposure variables | Model 1 | Model 2 | Model 3 | Model 4 | ||||
|---|---|---|---|---|---|---|---|---|
| Std. coeff. (β) [95% CI] | P value | Std. coeff. (β) [95% CI] | P value | Std. coeff. (β) [95% CI] | P value | Std. coeff. (β) [95% CI] | P value | |
| Age | 0.451 [0.320, 0.583] | ＜0.001 | 0.454 [0.324, 0.584] | ＜0.001 | 0.452 [0.320, 0.584] | ＜0.001 | 0.451 [0.319, 0.583] | ＜0.001 |
| Sex | ||||||||
| (male =1, female = 0) | 0.239 [0.125, 0.353] | ＜0.001 | 0.247 [0.134, 0.360] | ＜0.001 | 0.262 [0.139, 0.385] | ＜0.001 | 0.239 [0.125, 0.354] | ＜0.001 |
| BMI |
−0.052 [−0.187, 0.083] |
0.451 |
−0.058 [−0.191, 0.076] |
0.395 |
−0.051 [−0.186, 0.085] |
0.463 |
−0.048 [−0.184, 0.089] |
0.493 |
| Log10 CRP |
0.023 [−0.207, 0.297] |
0.724 |
0.028 [−0.193, 0.306] |
0.657 |
0.027 [−0.201, 0.304] |
0.687 |
0.023 [−0.208, 0.297] |
0.729 |
| Prior CVD | ||||||||
| (yes = 1, no = 0) |
0.025 [−0.092, 0.142] |
0.676 |
0.024 [−0.091, 0.140] |
0.685 | 0.022 [0.096, 0.139] | 0.717 |
0.025 [−0.093, 0.142] |
0.679 |
| Diabetes mellitus | ||||||||
| (yes = 1, no = 0) | 0.210 [0.090, 0.331] | ＜0.001 | 0.183 [0.062, 0.304] | 0.003 | 0.203 [0.082, 0.324] | 0.001 | 0.210 [0.090, 0.332] | ＜0.001 |
| Dyslipidemia | ||||||||
| (yes = 1, no = 0) | 0.126 [0.003, 0.250] | 0.044 | 0.124 [0.003, 0.247] | 0.045 | 0.125 [0.002, 0.248] | 0.047 | 0.127 [0.004, 0.251] | 0.043 |
| Hypertension | ||||||||
| (yes = 1, no = 0) |
0.077 [−0.054, 0.209] |
0.248 |
0.069 [−0.062, 0.199] |
0.300 |
0.083 [−0.049, 0.216] |
0.215 |
0.076 [−0.055, 0.208] |
0.255 |
| Current smoking | ||||||||
| (yes = 1, no = 0) |
0.087 [−0.030, 0.204] |
0.145 |
0.074 [−0.042, 0.190] |
0.212 |
0.089 [−0.027, 0.207] |
0.134 |
0.092 [−0.027, 0.212] |
0.129 |
| Proteinuria | ||||||||
| (yes = 1, no = 0) |
0.010 [−0.103, 0.122] |
0.868 |
0.005 [−0.106, 0.116] |
0.934 |
0.006 [−0.106, 0.118] |
0.917 |
0.011 [−0.101, 0.124] |
0.843 |
| Reduced eGFR | ||||||||
| (yes = 1, no = 0) |
0.027 [−0.092, 0.148] |
0.650 |
0.014 [−0.105, 0.133] |
0.813 |
0.031 [−0.089, 0.151] |
0.613 |
0.028 [−0.092, 0.149] |
0.643 |
| T50 |
−0.009 [−0.124, 0.106] |
0.876 |
−0.085 [−0.215, 0.045] |
0.199 |
−0.006 [−0.121, 0.109] |
0.914 |
−0.011 [−0.126, 0.105] |
0.853 |
| Serum fetuin-A | --- | --- | 0.156 [0.026, 0.286] | 0.019 | --- | --- | --- | --- |
| Serum phosphate | --- | --- | --- | --- |
0.061 [−0.059, 0.183] |
0.316 | --- | --- |
| Serum magnesium | --- | --- | --- | --- | --- | --- |
0.028 [−0.086, 0.142] |
0.626 |
| Model summary | R2 = 0.422 (P＜0.001) | R2 = 0.439 (P＜0.001) | R2 = 0.425 (P＜0.001) | R2 = 0.423 (P＜0.001) | ||||
The table shows the results of multivariable-adjusted linear regression analyses examining factors associated with IMT in the total study population without (model 1) and with further adjustment for serum fetuin-A (model 2), serum phosphate (model 3), and serum magnesium (model 4). IMT was included in the model after logarithmical transformation to meet the assumption of normal distribution of residual. Prior CVD, diabetes mellitus, dyslipidemia, hypertension, current smoking, proteinuria, and reduced eGFR were entered as binary variables. The definitions of prior CVD, diabetes mellitus, dyslipidemia, hypertension, and a reduced eGFR are described in the Methods section. The interaction term between T50 and a reduced eGFR was nearly significant when inserted into models 1 through 4 (P = 0.063, 0.058, 0.058, and 0.064, respectively).
T50, serum calcification propensity; CI, confidence interval; IMT, intima-media thickness; eGFR, estimated glomerular filtration rate; Std. coeff., standardized coefficient of regression; BMI, body mass index; CVD, cardiovascular disease; CRP, C-reactive protein; R2, coefficient of determination.
Subgroup Analyses Stratified by the Kidney Function
In all of the above models, the interaction term between T50 and reduced eGFR was nearly significant (P = 0.063, 0.058, 0.058, and 0.064 for models 1 through 4, respectively), suggesting a potential effect modification by the eGFR. We therefore conducted a stratified analysis using the same models in the subgroups with and without a reduced eGFR ( Table 3 ) . In these analyses, adjustment was done in a stepwise manner, as the sample size in the subgroups was smaller than the total population. In any model, T50 consistently showed a significant and inverse association with IMT in the subgroup with an eGFR ＜60 mL/min/1.73 m2, whereas it was not significant in the subgroup with an eGFR ≥ 60 mL/min/1.73 m2. In contrast, although not shown in Table 3 , in model 5, fetuin-A showed a significant and positive association with IMT (β = 0.170, P = 0.024) in the subgroup with an eGFR ≥ 60 mL/min/1.73 m2, whereas it was not significant (β = 0.132, P = 0.436) in the subgroup with an eGFR ＜60 mL/min/1.73 m2.
Table 3. Independent association between T50 and IMT in subgroups stratified by kidney function.
| Model | Adjustment | eGFR ≥ 60 mL/min/1.73 m2 (N = 163) | eGFR ＜60 mL/min/1.73 m2 (N = 39) | ||
|---|---|---|---|---|---|
| Std. coeff. (β) [95% CI] (P-value) | R2 (P-value) | Std. coeff. (β) [95% CI] (P-value) | R2 (P-value) | ||
| 1 | unadjusted |
0.025 [−0.129, 0.178] (P = 0.753) |
0.001 (P = 0.753) |
−0.402 [−0.682, −0.094] (P = 0.011) |
0.162 (P = 0.011) |
| 2 | Age, sex, and eGFR |
0.072 [−0.061, 0.203] (P = 0.292) |
0.279 (P＜0.001) |
−0.303 [−0.539, −0.045] (P = 0.022) |
0.504 (P＜0.001) |
| 3 | Model 2 + diabetes mellitus, dyslipidemia, and hypertension |
0.022 [−0.104, 0.148] (P =0.732) |
0.387 (P＜0.001) |
−0.333 [−0.556, −0.086] (P = 0.009) |
0.615 (P＜0.001) |
| 4 | Model 3 + prior CVD, BMI, current smoking, CRP (log−transformed), and proteinuria |
0.035 [−0.095, 0.165] (P = 0.596) |
0.401 (P＜0.001) |
−0.346 [−0.579, −0.087] (P = 0.010) |
0.681 (P＜0.001) |
| 5 | Model 4 + fetuin−A |
−0.045 [−0.189, 0.101] (P = 0.552) |
0.421 (P＜0.001) |
−0.418 [−0.710, −0.095] (P = 0.013) |
0.689 (P = 0.001) |
| 6 | Model 4 + phosphate |
0.038 [−0.093, 0.167] (P = 0.573) |
0.402 (P＜0.001) |
−0.339 [−0.562, −0.091] (P = 0.009) |
0.720 (P＜0.001) |
| 7 | Model 4 + magnesium |
0.037 [−0.094, 0.166] (P = 0.586) |
0.401 (P＜0.001) |
−0.344 [−0.582, −0.080] (P = 0.012) |
0.682 (P = 0.002) |
This table summarizes the association between T50 and IMT by multivariable-adjusted linear regression analyses in subgroups stratified by the kidney function. Model 1 was unadjusted. Model 2 was adjusted for the age, sex, and eGFR. Model 3 was adjusted for the variables in model 2 plus diabetes mellitus, dyslipidemia, and hypertension. Model 4 was adjusted for the variables in model 3 plus prior CVD, the BMI, smoking habit, CRP levels, and proteinuria. Models 5, 6, and 7 were adjusted for the variables in model 4 plus fetuin-A (model 5), phosphate (model 6), and magnesium (model 7). The definitions of prior CVD, diabetes mellitus, dyslipidemia, hypertension, and a reduced eGFR are described in methods section. Although not shown in the table, in model 5, fetuin-A was independently and positively associated with IMT in the subgroup with an eGFR ≥ 60 mL/min/1.73 m2 (β = 0.170, P = 0.024) but not in the subgroup with an eGFR ＜60 mL/min/1.73 m2 (β = 0.132, P = 0.436).
eGFR, estimated glomerular filtration rate; CI, confidence interval; CVD, cardiovascular disease; BMI, body mass index; CRP, C−reactive protein;
β, standardized regression coefficient; R2, coefficient of determination for the model.
Factors Independently Associated with IMT in Subgroups Stratified by the Kidney Function
Table 4 shows the associations between IMT and individual variables in the subgroups by eGFR using the same model as model 5 in Table 3 . In the subgroup with an eGFR ≥ 60 mL/min/1.73 m2, diabetes mellitus, dyslipidemia, and fetuin-A were independently associated with IMT. In contrast, these factors were not significantly associated with IMT in the subgroup with an eGFR ＜60 mL/min/1.73 m2, whereas hypertension and T50 were independent factors associated with IMT. When hypertension (binary variable) was replaced by systolic blood pressure (continuous variable), hypertension and systolic blood pressure showed similar associations with IMT, and the association between T50 and IMT was not changed (data not shown).
Table 4. Factors independently associated with IMT in subgroups by the kidney function.
| Exposure variables | eGFR ≥ 60 mL/min/1.73 m2 (N = 163) | eGFR ＜60 mL/min/1.73 m2 (N = 39) | ||
|---|---|---|---|---|
| Std. coeff. (β) [95% CI] | P value | Std. coeff. (β) [95% CI] | P value | |
| T50 | −0.045 [−0.189, 0.101] | 0.552 | −0.418 [−0.710, −0.095] | 0.013 |
| Age | 0.437 [0.292, 0.592] | ＜0.001 | 0.382 [0.093, 0.709] | 0.013 |
| Sex (male =1, female = 0) | 0.289 [0.158, 0.414] | ＜0.001 | 0.015 [−0.229, 0.258] | 0.903 |
| eGFR | −0.046 [−0.184, 0.092] | 0.513 | 0.043 [−0.290, 0.362] | 0.822 |
| Diabetes mellitus (yes = 1, no = 0) | 0.214 [0.073, 0.359] | 0.003 | 0.086 [−0.351, 0.167] | 0.550 |
| Dyslipidemia (yes = 1, no = 0) | 0.143 [0.002, 0.285] | 0.047 | −0.105 [−0.351, 0.167] | 0.470 |
| Hypertension (yes = 1, no = 0) | 0.024 [−0.124, 0.173] | 0.745 | 0.386 [0.059, 0.655] | 0.021 |
| Prior CVD (yes = 1, no = 0) | −0.004 [−0.178, 0.168] | 0.951 | 0.057 [−0.139, 0.198] | 0.721 |
| BMI | −0.012 [−0.166, 0.144] | 0.886 | −0.197 [−0.506, 0.099] | 0.177 |
| Current smoking (yes = 1, no = 0) | 0.086 [−0.049, 0.217] | 0.214 | 0.054 [−0.211, 0.316] | 0.683 |
| Log10 CRP | −0.016 [−0.324, 0.260] | 0.829 | 0.123 [−0.248, 0.676] | 0.348 |
| Proteinuria (yes = 1, no = 0) | −0.068 [−0.217, 0.065] | 0.291 | 0.224 [−0.097, 0.381] | 0.231 |
| Serum fetuin−A | 0.170 [0.022, 0.308] | 0.024 | 0.132 [−0.237, 0.532] | 0.436 |
| Model summary | R2 = 0.421, P ＜0.001 | R2 = 0.690, P ＜0.001 | ||
This table shows the associations between IMT and individual variables in the subgroups by the eGFR using the same model as model 5 in Table 3. T50, calciprotein crystallization test (serum calcification propensity); eGFR, estimated glomerular filtration rate; CI, confidence interval; CVD, cardiovascular disease; BMI, body mass index; CRP, C−reactive protein; β, standardized regression coefficient; R2, coefficient of determination for the model.
Discussion
We addressed whether T50 was associated with IMT in this cross-sectional study of participants in our health examination registry. Although T50 was not significantly associated with IMT in the total population, we noticed a potential effect modification by the kidney function on the association between T50 and IMT. The analysis stratified by the eGFR showed that T50 was significantly and inversely associated with IMT in the subgroup with an eGFR ＜60 ml/min/1.73 m2, whereas this association was not significant in the subgroup with an eGFR ≥ 60 ml/min/1.73 m2. To our knowledge, this is the first study to examine the link between T50 and IMT.
Previous studies showed that T50 was associated with risk for all-cause mortality 19 - 22 , 35) and CVD 22 , 23 , 35) . Regarding the possible association of T50 with arterial wall properties, Bundy et al. revealed the association with vascular calcification 35) , and one study showed an inverse association between T50 and pulse wave velocity, a measure of arterial stiffness, in patients with CKD 36) . Thus, the present study provides a novel finding that T50 is inversely associated with IMT, an established surrogate marker of atherosclerosis, in those with a reduced kidney function.
Previous studies showed that T50 does not necessarily parallel the traditional atherosclerotic risk factors. We previously 25) showed that the age, sex, body mass index, smoking habit, presence of hypertension, presence of diabetes mellitus, and eGFR were not significantly associated with T50, and that the presence of dyslipidemia was positively (not inversely) associated with T50. The positive associations of T50 with serum lipid levels have been reported also by other studies 37 - 39) . In addition, the present study showed that the association between IMT and T50 was independent of these traditional risk factors. Thus, the observed inverse association between T50 and IMT may be explained by the oxidative stress and inflammation which are induced by secondary CPP, not by the confounding of traditional risk factors.
According to the experiments with cultured human vascular smooth muscle cells (VSMCs) by Aghagolzadeh et al. 15) , secondary CPP, not primary CPP, in the culture medium promoted calcification in a dose-dependent manner. They also found that secondary CPP induced oxidative stress and up-regulation of tissue necrosis factor (TNF)-α which enhanced the calcification via its receptor TNF receptor type 1. Therefore, it is likely that secondary CPP can contribute to atherosclerosis by inducing oxidative stress and inflammation, although evidence for the direct effect of secondary CPP on atherosclerosis is lacking. Although a shorter T50 does not directly translate into higher levels of secondary CPP, we interpret the result of this study to suggest the link between secondary CPP and atherosclerosis as assessed by IMT.
Of interest, the inverse association between T50 and IMT was significant in the subgroup with a reduced kidney function but not in the subgroup with a preserved kidney function. Conversely, the association of dyslipidemia with IMT was significant in the subgroup with a preserved kidney function whereas it was not significant in the subgroup with a reduced kidney function. These findings suggest that the contributions of risk factors to atherosclerosis vary depending on the kidney function. In line with our results, a large cohort study 40) showed that the association between LDL-C and risk for myocardial infarction was shown to be weaker in those with a lower eGFR, and the association was no longer significant in those with an eGFR of 15 mL/min/1.73 m2. In addition, the intervention with statin failed to reduce the CVD risk in patients with kidney failure undergoing hemodialysis 41) . T50 showed positive associations with serum triglyceride, total cholesterol, LDL-C, and the presence of dyslipidemia 25 , 38) in various populations, suggesting that serum calcification propensity is lower when dyslipidemia is present. We speculate that the contribution of T50 or secondary CPP in atherosclerosis is more apparent when the contribution of dyslipidemia is less apparent.
Furthermore, we noticed contrasting associations between T50 and IMT versus between fetuin-A and IMT, although T50 and fetuin-A showed a close positive correlation in the total participants (Spearman’s r = 0.680, P＜0.001). Consistent with previous reports 42 , 43) , IMT showed a positive association with fetuin-A in the total participants as well as in the subgroup with a preserved kidney function. In contrast, IMT showed an inverse association with T50 in the subgroup with a reduced kidney function. We do not know the exact mechanisms underlying these observations, although it may be explained by the two distinct functions of fetuin-A 44) . Namely, fetuin-A is known to induce insulin resistance that can promote atherosclerosis 45 , 46) , whereas fetuin-A inhibits vascular calcification by suppressing calciprotein crystallization 47) . Thus, the relative roles of fetuin-A in atherosclerosis and vascular calcification may vary depending on the kidney function.
In the present study, T50 was higher in the subgroup with a reduced kidney function than in the subgroup without a reduced kidney function. This finding may appear strange since T50 was reported to be lower in patients with kidney failure on hemodialysis than in control subjects with a normal kidney function 48) . However, the association between T50 and the kidney function is not consistent among studies 27 , 35 , 37 , 38) . In the study by Bernhard et al. 49) , which included CKD stages 1 to 5, the T50 was determined not by the kidney function but by the concentration of promoters and inhibitors of calcification in serum. We also confirmed that the eGFR was not a significant determinant of T50 25) , instead being determined by other factors, such as serum phosphate, magnesium, and fetuin-A. In the present study, the subgroup with a reduced kidney function showed a significantly higher fetuin-A level than those with a preserved kidney function. We speculate that the higher T50 in the subgroup with a reduced kidney function was due not to the kidney function but to the elevated fetuin-A level in this subgroup.
Several limitations associated with the present study warrant mention. First, because the participants of this study were health examinees in Japan, the generalizability of the results needs confirmation in other clinical settings. Second, because the sample size was relatively small, the results may have over- or underestimated the true association. Third, although we used carotid artery IMT, other measures of atherosclerosis may give different results. Fourth, because of the cross-sectional design of the study, we cannot discuss the causality of the observed association.
Conclusion
T50 was independently and inversely associated with IMT in health examinees with a reduced kidney function, suggesting a novel link between mineral stress and atherosclerosis in those with a reduced kidney function. Further studies are needed to establish the observation in other clinical settings and to clarify the precise mechanisms behind the observed association.
Conflicts of Interest
All authors report that they have nothing to disclose for this study.
Acknowledgement
We would like to convey our heartfelt appreciation to Mayumi Taniguchi-Seko and Chiaki
Murakami at the Department of Metabolism, Endocrinology, and Metabolism, Osaka Metropolitan University, for their excellent technical assistance. We would also like to express our gratitude to Yasuko Okui and Wataru Yamashita at the Department of Central Clinical Laboratory, Osaka Metropolitan University Hospital. A part of this study was presented in a preliminary form at the 56th Annual Meeting of Japan Atherosclerosis Society (July 7, 2024, Kobe, Japan).
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