Kidney Res Clin Pract > Volume 43(3); 2024 > Article
Ko, Kim, Park, Han, Kang, Sung, Lee, Lee, Oh, Yoo, and on behalf of the KNOW-CKD investigators: Triglyceride-glucose index is an independent predictor of coronary artery calcification progression in patients with chronic kidney disease



Coronary artery calcification (CAC) is highly prevalent in patients with chronic kidney disease (CKD) and is associated with major adverse cardiovascular events and metabolic disturbances. The triglyceride-glucose index (TyGI), a novel surrogate marker of metabolic syndrome and insulin resistance, is associated with CAC in the general population and in patients with diabetes. This study investigated the association between the TyGI and CAC progression in patients with CKD, which is unknown.


A total of 1,154 patients with CKD (grades 1–5; age, 52.8 ± 11.9 years; male, 688 [59.6%]) were enrolled from the KNOW-CKD (KoreaN Cohort Study for Outcomes in Patients With Chronic Kidney Disease). The TyGI was calculated as follows: ln (fasting triglycerides × fasting glucose/2). Patients were classified into tertiles (low, intermediate, high) based on the TyGI. The primary outcome was annualized percentage change in CAC score [(percent change in CAC score + 1)12/follow-up months – 1] of ≥15%, defined as CAC progression.


During the 4-year follow-up, the percentage of patients with CAC progression increased across TyGI groups (28.6%, 37.5%, and 46.2% in low, intermediate, and high groups, respectively; p < 0.001). A high TyGI was associated with an increased risk of CAC progression (odds ratio [OR], 2.11; 95% confidence interval [CI], 1.14–3.88; p = 0.02) compared to the low group. Moreover, a 1-point increase in the TyGI was related to increased risk of CAC progression (OR, 1.55; 95% CI, 1.06–1.76; p = 0.02) after adjustment.


A high TyGI may be a useful predictor of CAC progression in CKD.

Graphical abstract


Coronary artery calcification (CAC) and its progression are known to be related to cardiovascular events [13]. Many studies have reported the association between CAC and metabolic disturbances such as central obesity, insulin resistance, and dyslipidemia [48]. The triglyceride-glucose index (TyGI) is a reliable surrogate marker of insulin resistance [9], and it is considered even more sensitive and specific than the Homeostatic Model Assessment for Insulin Resistance [10]. There have been several studies on insulin resistance and its relationship with cardiovascular events in the general population and in patients with diabetes mellitus (DM) [1114]. For patients with chronic kidney disease (CKD), cardiovascular disease (CVD) and cerebrovascular disease are major causes of mortality [15]. While CKD itself is a potential risk factor for CVD, the previous CRIC (Chronic Renal Insufficiency Cohort) study consisting of patients with estimated glomerular filtration rate (eGFR) ranging from 20 to 70 mL/min/1.73 m2 showed that CAC was significantly independently related to increased risk of CVD [16]. In addition to this, CAC is associated with both a higher risk of CVD and an increased risk of CKD progression [17]. Nevertheless, few studies have investigated the connection between the TyGI and CAC progression in patients with CKD. Therefore, this study aimed to investigate the association between the TyGI and CAC progression in patients with CKD.



The participants were enrolled from the nationwide multi-center prospective observational cohort of the KNOW-CKD (KoreaN Cohort Study for Outcomes in Patients With Chronic Kidney Disease) which consists of 2,238 Korean predialysis patients with grade 1 to 5 CKD aged between 20–75 years. This cohort study included patients with grade 1 to 2 CKD who had albuminuria or different early kidney damage markers even with a normal or slightly decreased glomerular filtration rate. Patients with previous chronic dialysis, kidney transplantation, or unavailable demographic information, lab data, or CAC score were excluded. Finally, 1,154 patients with grade 1 to 5 CKD were enrolled and classified into one of three groups based on the baseline TyGI: low (n = 382), intermediate (n = 387), or high (n = 385) (Fig. 1).
The study rationale, design, and methods are described in detail elsewhere [18]. The Institutional Review Board of each participating center approved the study protocol. All patients gave written informed consent at enrollment.

Data collection

Demographic and clinical data including age, sex, alcohol consumption, smoking history, comorbidities, and medications were collected at the time of enrollment. Patients stood barefoot to measure their height to the nearest 0.1 cm using a digital stadiometer. Bodyweight was measured to the nearest 0.1 kg in light clothes without shoes, using standard methods. Body mass index (BMI) was calculated as weight / height2. Blood pressure (BP) was measured after 5 minutes of resting in a sitting position at the office by a trained nurse using a mercury sphygmomanometer or calibrated oscillometer electronic sphygmomanometer at each participating center according to the American Heart Association standardized protocol [19]. The mean of three BP measurements from each visit was used as the BP value. The electronic device was validated by comparing the device readings alternating with five mercury sphygmomanometer values. Hypertension was defined as BP of ≥140/90 mmHg, self-reported hypertension, or current use of antihypertensive drugs such as calcium-channel blockers, angiotensin-converting enzyme inhibitors, angiotensin-receptor blockers, beta-blockers, and diuretics. DM was defined as self-reported DM, fasting plasma glucose of ≥126 mg/dL, or use of glucose-lowering drugs. Smoking status was binarized as current smoker or former/never smoker. Alcohol drinkers were defined as patients who drink alcohol more than twice a week. All data including baseline demographic information and laboratory data were gained from the electronic data management system (PhactaX). Laboratory data such as hemoglobin, blood urea nitrogen (BUN), creatinine, uric acid, total cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), calcium, phosphate, and parathyroid hormone (PTH) levels were measured from overnight fasting venous samples. Fibroblast growth factor 23 (FGF-23) was measured by enzyme-linked immunosorbent assay (Immutopics). The TyGI was calculated as follows: ln (fasting triglycerides × fasting glucose / 2).

Outcome measures

CAC scores were measured at enrollment and at the 4-year follow-up. CAC was assessed via electrocardiography-gated coronary 64-slice multidetector chest computed tomography. The CAC density score was calculated by dividing the Agatston score by the total area score by experienced cardiac radiologists from each participating center. The primary outcome was CAC progression, which was defined as an annualized percentage change in CAC scores ≥15% [2024]. As in previous studies [25], the percentage change of CAC score was defined as [(CAC score at follow-up) – (CAC score at enrollment)] / (CAC score at enrollment + 1). Adding 1 to the CAC score at enrollment in the denominator allows for the inclusion of patients with a baseline CAC score of 0. Furthermore, the annualized percentage change in CAC score was calculated as (percent change of CAC score + 1)1/4 – 1. The value “1/4” in this equation was derived from (12/follow-up months), where the number of follow-up months is 48. Secondary outcomes included major adverse cardiovascular events (MACE), such as myocardial infarction, nonfatal stroke, and all-cause mortality.

Statistical analysis

Continuous variables were stated as mean ± standard deviation and categorical variables as number (percentage). Baseline characteristics and changes in CAC scores were compared between groups using analysis of variance for continuous variables and the chi-square test for categorical variables. Multivariable logistic regression analyses were performed to evaluate the association between the TyGI and CAC progression in each group. Odds ratios (ORs) and 95% confidence intervals (CIs) were used to provide the relative risk for CAC progression. Statistical analyses in this study were performed using IBM SPSS version 26 (IBM Corp.), and R software version 4.3.2 (R Foundation for Statistical Computing). A p-value of <0.05 was considered significant.


Baseline characteristics

The mean age of study subjects was 52.8 ± 11.9 years and 688 (59.6%) were male. The mean TyGI was 8.8 ± 0.6 in all patients, 8.2 ± 0.3 in the low TyGI group, 8.8 ± 0.2 in the intermediate group, and 9.5 ± 0.4 in the high group. Compared to the low TyGI group, patients with a high TyGI tended to be older, have higher BMI and systolic BP (SBP), and more comorbidities such as hypertension and DM. The high TyGI group also had higher levels of calcium, BUN, creatinine, fasting plasma glucose, total cholesterol, and triglycerides but lower eGFR and HDL-C compared to the low TyGI group. There were no significant differences in bone mineral disease markers such as phosphate, alkaline phosphate, FGF-23, or total PTH other than calcium among different TyGI groups (Table 1).

Baseline and change in coronary artery calcification

During the 4-year follow-up, the median value of annualized percentage change of patients with CAC progression increased across TyGI groups (0%, 7.0%, and 13.6% in low, intermediate, and high TyGI groups, respectively; p < 0.001) (Table 2, Fig. 2). The percentage of patients with CAC progression increased across TyGI groups (28.6%, 37.5%, and 46.2% in low, intermediate, and high TyGI groups, respectively; p < 0.001) (Table 2).

Association between triglyceride-glucose index and coronary artery calcification progression in patients with chronic kidney disease

In multivariable logistic regression analysis, the high TyGI group was associated with increased risk of CAC progression (OR, 2.11; 95% CI, 1.14–3.88; p = 0.02) compared to the low TyGI group after adjusting for age, sex, BMI, waist circumference, smoking status, alcohol status, exercise, hypertension, DM, SBP, eGFR, urine protein creatinine ratio, LDL-C, and HDL-C. Moreover, a 1-point increase in the TyGI was related to increased risk of CAC progression (OR, 1.55; 95% CI, 1.06–1.76; p = 0.003) after adjusting for confounding factors (Table 3).

Association between the triglyceride-glucose index and major adverse cardiovascular events in patients with chronic kidney disease

To determine the clinical significance of the relationship between the TyGI and CAC progression, MACE was evaluated within each TyGI group using multivariable Cox analysis. Compared to the low TyGI group, the intermediate and high TyGI groups exhibited an increased risk of MACE (hazard ratio [HR], 1.50; 95% CI, 1.11–2.03; p = 0.009 and HR, 2.15; 95% CI, 1.59–2.91; p < 0.001) after adjusting for age, sex, BMI, smoking, alcohol consumption, hypertension, DM, SBP, eGFR, LDL-C, and HDL-C.

Subgroup analyses

To assess how different subgroups influence the relationship between the TyGI and CAC progression, patients were stratified by age, sex, BMI, eGFR, SPB, and DM (Fig. 3). These analyses revealed an interaction between age and TyGI (p for interaction = 0.02), between DM and TyGI (p for interaction = 0.003), and between eGFR and TyGI (p for interaction = 0.001). These findings suggest that the impact of the TyGI is influenced by age, kidney function, and DM.

Sensitivity analyses

To validate the findings in this study, sensitivity analyses were conducted using an alternative definition of CAC progression. This alternative definition involves the square-root transformation of the difference between baseline and follow-up CAC scores [√CAC score (follow-up) − √CAC score (baseline)], with a threshold set at greater than 2.5. This threshold was chosen to minimize the effect of interscan variability. Multivariable Cox analyses were carried out, adjusting for the same variables as previously described, and revealed that the highest tertile of the TyGI and a one-unit increase in TyGI were both associated with a significantly elevated risk of CAC progression (Table 4).


As mentioned in other traditional studies, the baseline characteristic analysis showed a significant relationship between a high TyGI and well-known CVD risk factors including comorbidities [1113,2629]. In addition, in the current study, the baseline and follow-up CAC score and their changes increased in a stepwise manner according to the TyGI. Furthermore, the proportion of patients with CAC progression during follow-up was higher in the high TyGI group. This study also demonstrated an independent association between the TyGI and CAC progression in patients with CKD. This relationship was consistent even after adjusting for widely known CVD risk factors including eGFR. In the continued exploration of the clinical significance of the relationship between the TyGI and CAC progression, our investigation revealed that elevated TyGI levels were associated with increased risks of MACE when compared to CKD patients with lower TyGI levels.
There are several cross-sectional and population-based studies supporting the positive correlation between the TyGI and CAC progression [1113]. The mechanism of association between a high TyGI and CAC progression is uncertain. While the TyGI is an important surrogate marker for insulin resistance, previous studies demonstrated that insulin resistance contributes to atherogenesis and progression of atherosclerosis by triggering macrophage apoptosis and endothelial and vascular smooth muscle cell damage [3032]. Hyperinsulinemia can induce oxidative stress and interfere with the proper functioning of endothelial cells. Also, consequent reduction in nitric oxide availability may contribute to both functional and structural injury to blood vessels [33]. Moreover, hyperinsulinemia can trigger osteogenic differentiation and the formation of calcifications in vascular cells [34]. Furthermore, insulin resistance can accelerate the accumulation of advanced glycosylation end-products, thereby promoting the progression of CAC [35]. Atherosclerosis and valvular calcification commonly manifest in individuals with CKD [36]. Although it is challenging to determine the causal relationship from this observational cohort study, there have been studies suggesting that CAC progression may be related to the deterioration of kidney function [17,37]. A plausible explanation may be that the process, as described above, wherein a high TyGI may lead to CAC progression, could further contribute to the development of CKD. This could represent an ongoing process in patients with CKD. Further prospective studies are needed to elucidate the mechanism underlying this association in patients with CKD.
There is a lack of a clinical causal relationship between the TyGI and CAC since there are no data or clinical trials on improved obesity and coronary calcium score, proven TyGI-lowering medications, or improvement in CAC. Therefore, further studies are needed to determine the precise causal relationship between the TyGI and CAC progression.
There were several limitations in this study. First, this was an observational study; therefore, it was difficult to control for all possible confounding factors. Second, the study was limited to one ethnicity, and so the results cannot be generalized to other populations with different ethnicities. Despite these limitations, this study included a large population of patients with CKD and CAC measurements; and to our knowledge, it is the first to determine a correlation between CAC progression and a high TyGI in patients with CKD using longitudinal data, unlike other cross-sectional studies. Another notable aspect of this study is the simplicity of obtaining the TyGI through routine blood tests. This index could be valuable as an alternative indicator, not only for predicting the presence of CAC but also for doing so without radiation exposure.
In conclusion, this study showed an independent association of a high TyGI with CAC progression regardless of other CVD risk factors in patients with CKD. A high TyGI may be a useful predictor of CAC progression implying CVD risk and kidney outcomes in patients with CKD.


Additional information

The approval numbers of each Institutional Review Board are as follows: Seoul National University Hospital (No. 1810-132-982), Seoul National University Bundang Hospital (No. B-1901-514-409), Severance Hospital (No. 4-2019-0152), Kangbuk Samsung Hospital (No. 2019-04-011), The Catholic University of Korea, Seoul St. Mary’s Hospital (No. KC19OEDI0263), Gachon University Gil Medical Center (No. GAIRB2019-154), Nowon Eulji Medical Center, Eulji University (No. 201904-01), Chonnam National University Hospital (No. 2021-07-111), Inje University Pusan Paik Hospital (No. 2018-01-203), Hallym University Dongtan Sacred Heart Hospital (No. 2019-11-006), National Health Insurance Service Ilsan Hospital (No. 2019-12-008), Seoul National University Boramae Medical Center (No. 20-2019-76), Pusan National University Hospital (No. 1912-019-086), and Chungnam National University Hospital (No. 2021-07-111).

Conflicts of interest

Tae-Hyun Yoo is the Editor-in-Chief of Kidney Research and Clinical Practice and was not involved in the review process of this article. All authors have no other potential conflicts of interest relevant to this article.


This work was supported by the Research Program funded by the Korea Disease Control and Prevention Agency (2011E3300300, 2012E3301100, 2013E3301600, 2013E3301601, 2013E3301602, 2016E3300200, 2016E3300201, 2016E3300202, 2019E320100, 2019E320101, 2019E320102, and 2022-11-007). The funding sources had no role in the design and conduct of the study, collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Data sharing statement

The data presented in this study are available from the corresponding author upon reasonable request.

Authors’ contributions

Conceptualization, Investigation: YEK, THY

Methodology: YEK, HWK, JTP, SHH, SWK, THY

Data curation: HWK, THY

Formal analysis: YEK, HWK, JTP, SHH, SWK, THY

Supervision: THY, HWK, JTP, SHH, SWK, SS, KBL, JL, KHO

Writing–original draft: YEK, THY

Writing–review & editing: YEK, THY

All authors critically reviewed and approved the final manuscript.


The authors thank the clinical research coordinators of each participating institution and the Medical Research Collaborating Center, Seoul National University Hospital for the data management and data quality control. This is a group study by multi-center collaboration by the KNOW-CKD Investigator Group.

Figure 1.

Flow diagram for the patients enrolled in this study.

CAC, coronary artery calcification.
Figure 2.

Percentage change in CAC among different TyGI groups.

Percentage change of patients with CAC progression increased across TyGI groups (with the mean values of 16.7%, 20.8%, and 24.9% in low, intermediate, and high TyGI groups, respectively; p < 0.001).
CAC, coronary artery calcification; TyGI, triglyceride-glucose index.
Figure 3.

Forest plot for modification effects of subgroups on the relationship between triglyceride-glucose index and CAC progression.

BMI, body mass index; CAC, coronary artery calcification; CI, confidence interval; DM, Diabetes mellitus; e-GFR, estimated glomerular filtration rate; OR, odds ratio; SBP, systolic blood pressure.
Table 1.
Baseline characteristics at enrollment among patients stratified by TyGI
Characteristic TyGI
Total Low Intermediate High
No. of subjects 1,154 (100) 382 (33.1) 387 (33.5) 385 (33.4)
Age (yr) 52.8 ± 11.9 50.5 ± 12.1 53.7 ± 11.9 54.1 ± 11.4 <0.001
Male sex 688 (59.6) 193 (50.5) 233 (60.2) 262 (68.1) <0.001
SBP (mmHg) 126.0 ± 14.7 123.7 ± 13.9 126.5 ± 14.4 127.8 ± 15.4 <0.001
DBP (mmHg) 76.8 ± 10.4 76.2 ± 10.3 76.9 ± 9.9 77.3 ± 10.8 0.36
Body mass index (kg/m2) 24.7 ± 3.3 23.2 ± 3.1 24.6 ± 2.9 26.1 ± 3.4 <0.001
 Hypertension 1,101 (95.4) 351 (91.9) 371 (95.9) 379 (98.4) <0.001
 Diabetes mellitus 307 (26.6) 49 (12.8) 102 (26.4) 156 (40.5) <0.001
Statin use 583 (50.5) 152 (39.8) 220 (56.8) 211 (54.8) <0.001
Alcohol 349 (30.2) 89 (23.3) 105 (27.1) 155 (40.3) <0.001
Smoking 382 (33.1) 238 (61.5) 42 (10.9) 102 (26.5) <0.001
Exercise 359 (31.1) 111 (29.1) 124 (32.0) 124 (32.2) 0.80
Laboratory value
 Calcium (mg/dL) 9.24 ± 0.43 9.16 ± 0.39 9.25 ± 0.44 9.29 ± 0.43 <0.001
 Phosphate (mg/dL) 3.54 ± 0.56 3.48 ± 0.53 3.56 ± 0.56 3.58 ± 0.58 0.053
 Alkaline phosphate (U/L) 85.4 ± 62.9 82.0 ± 59.0 87.0 ± 65.3 87.3 ± 64.1 0.42
 FGF-23 (RU/mL) 20.1 ± 29.4 17.9 ± 23.6 21.6 ± 38.4 20.7 ± 23.6 0.21
 Total PTH (pg/mL) 53.6 ± 39.0 53.3 ± 37.0 52.7 ± 40.5 55.0 ± 39.8 0.80
 Blood urea nitrogen (mg/dL) 23.1 ± 10.9 21.7 ± 10.4 24.2 ± 11.5 23.4 ± 10.6 0.005
 Creatinine (mg/dL) 1.38 ± 0.81 1.28 ± 0.60 1.41 ± 0.75 1.45 ± 1.03 0.004
 eGFR (mL/min/1.73 m2) 62.3 ± 28.8 67.0 ± 30.2 60.3 ± 28.6 59.7 ± 27.1 <0.001
 Fasting plasma glucose (mg/dL) 107.1 ± 31.1 92.7 ± 11.5 104.7 ± 20.4 123.9 ± 43.1 <0.001
 Total cholesterol (mg/dL) 174.9 ± 36.0 170.0 ± 33.2 173.5 ± 35.5 181.7 ± 38.0 <0.001
 Triglyceride (mg/dL) 152.2 ± 94.2 79.0 ± 20.7 129.8 ± 26.6 247.4 ± 102.9 <0.001
 LDL-C (mg/dL) 97.4 ± 30.1 94.9 ± 27.4 99.8 ± 30.3 97.3 ± 32.2 0.08
 HDL-C (mg/dL) 50.8 ± 15.0 58.0 ± 16.3 50.7 ± 13.5 43.7 ± 11.2 <0.001
 TyGI 8.8 ± 0.6 8.2 ± 0.3 8.8 ± 0.2 9.5 ± 0.4 <0.001

Data are expressed as number (%) or mean ± standard deviation.

DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; FGF-23, fibroblast growth factor 23; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; PTH, parathyroid hormone; SBP, systolic blood pressure; TyGI, triglyceride-glucose index.

Table 2.
Change in CAC scores stratified by TyGI
Group No. of subjects CAC score
Percent change in CAC scores CAC progression p-value
Baseline 4-yr follow-up
Low 382 0.0 (0.0–8.0) 0.0 (0.0–42.4) 0.0 (0.0–19.7) 109 (28.5) <0.001
Intermediate 387 0.5 (0.0–62.2) 8.3 (0.0–155.3) 7.0 (0.0–26.9) 145 (37.5)
High 385 5.3 (0.0–76.6) 28.5 (0.0–187.2) 13.6 (0.0–33.4) 178 (46.2)

Data are expressed as median (interquartile range) or number (%).

CAC, coronary artery calcification; TyGI, triglyceride-glucose index.

Table 3.
ORs for CAC progression according to the TyGI
Variable Model 1a
Model 2b
Model 3c
OR (95% CI) p-value OR (95% CI) p-value OR (95% CI) p-value
 Low Reference - Reference - Reference -
 Intermediate 1.50 (1.11–2.03) 0.009 1.22 (0.84–1.77) 0.31 1.49 (0.86–2.56) 0.16
 High 2.15 (1.59–2.91) <0.001 1.79 (1.22–2.63) 0.003 2.11 (1.14–3.88) 0.02
TyGI (per 1-point increase) 1.39 (1.08–1.79) 0.01 1.68 (1.30–2.17) <0.001 1.55 (1.06–1.76) 0.003

CAC, coronary artery calcification; CI, confidence interval; OR, odds ratio; TyGI, triglyceride-glucose index.

a Unadjusted.

b Adjusted for age, sex, body mass index, waist, smoking, alcohol, and exercise.

c Model 2 + hypertension, diabetes mellitus, systolic blood pressure, estimated glomerular filtration rate, urine protein to creatinine ratio, and low- and high-density lipoprotein cholesterol.

Table 4.
ORs for CAC progression with different definitions according to the TyGI
Variable Model 1a
Model 2b
Model 3c
OR (95% CI) p-value OR (95% CI) p-value OR (95% CI) p-value
 Low Reference - Reference - Reference -
 Intermediate 1.82 (1.34–2.48) <0.001 1.48 (1.00–2.21) 0.05 1.57 (0.86–2.90) 0.15
 High 2.29 (1.69–3.11) <0.001 1.67 (1.11–2.51) 0.01 1.85 (0.94–3.68) 0.08
TyGI (per 1-point increase) 1.82 (1.50–2.22) <0.001 1.53 (1.17–2.00) 0.002 1.61 (1.01–2.60) 0.048

CAC, coronary artery calcification; CI, confidence interval; OR, odds ratio; TyGI, triglyceride-glucose index.

a Unadjusted.

b Adjusted for age, sex, body mass index, waist, smoking, alcohol, and exercise.

c Model 2 + hypertension, diabetes mellitus, systolic blood pressure, estimated glomerular filtration rate, urine protein to creatinine ratio, and low- and high-density lipoprotein cholesterol.


1. Wong ND, Hsu JC, Detrano RC, Diamond G, Eisenberg H, Gardin JM. Coronary artery calcium evaluation by electron beam computed tomography and its relation to new cardiovascular events. Am J Cardiol 2000;86:495–498.
crossref pmid
2. Budoff MJ, Shaw LJ, Liu ST, et al. Long-term prognosis associated with coronary calcification: observations from a registry of 25,253 patients. J Am Coll Cardiol 2007;49:1860–1870.
3. Detrano R, Guerci AD, Carr JJ, et al. Coronary calcium as a predictor of coronary events in four racial or ethnic groups. N Engl J Med 2008;358:1336–1345.
crossref pmid
4. Ha KH, Kim DJ. Association of metabolic syndrome with coronary artery calcification. Korean J Intern Med 2015;30:29–31.
crossref pmid
5. Wong ND, Sciammarella MG, Polk D, et al. The metabolic syndrome, diabetes, and subclinical atherosclerosis assessed by coronary calcium. J Am Coll Cardiol 2003;41:1547–1553.
crossref pmid
6. Wong ND, Nelson JC, Granston T, et al. Metabolic syndrome, diabetes, and incidence and progression of coronary calcium: the Multiethnic Study of Atherosclerosis study. JACC Cardiovasc Imaging 2012;5:358–366.
pmid pmc
7. Malik S, Budoff MJ, Katz R, et al. Impact of subclinical atherosclerosis on cardiovascular disease events in individuals with metabolic syndrome and diabetes: the multi-ethnic study of atherosclerosis. Diabetes Care 2011;34:2285–2290.
pmid pmc
8. Seo MH, Rhee EJ, Park SE, et al. Metabolic syndrome criteria as predictors of subclinical atherosclerosis based on the coronary calcium score. Korean J Intern Med 2015;30:73–81.
crossref pmid
9. Guerrero-Romero F, Simental-Mendía LE, González-Ortiz M, et al. The product of triglycerides and glucose, a simple measure of insulin sensitivity: comparison with the euglycemic-hyperinsulinemic clamp. J Clin Endocrinol Metab 2010;95:3347–3351.
crossref pmid
10. Kim JK. Hyperinsulinemic-euglycemic clamp to assess insulin sensitivity in vivo. Methods Mol Biol 2009;560:221–238.
crossref pmid
11. Hanley AJ, Williams K, Stern MP, Haffner SM. Homeostasis model assessment of insulin resistance in relation to the incidence of cardiovascular disease: the San Antonio Heart Study. Diabetes Care 2002;25:1177–1184.
12. Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord 2008;6:299–304.
crossref pmid
13. Vasques AC, Novaes FS, de Oliveira Mda S, et al. TyG index performs better than HOMA in a Brazilian population: a hyperglycemic clamp validated study. Diabetes Res Clin Pract 2011;93:e98–e100.
crossref pmid
14. Guerrero-Romero F, Villalobos-Molina R, Jiménez-Flores JR, et al. Fasting triglycerides and glucose index as a diagnostic test for insulin resistance in young adults. Arch Med Res 2016;47:382–387.
crossref pmid
15. Go AS, Chertow GM, Fan D, McCulloch CE, Hsu CY. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med 2004;351:1296–1305.
crossref pmid
16. Chen J, Budoff MJ, Reilly MP, et al. Coronary artery calcification and risk of cardiovascular disease and death among patients with chronic kidney disease. JAMA Cardiol 2017;2:635–643.
crossref pmid pmc
17. Yun HR, Joo YS, Kim HW, et al. Coronary artery calcification score and the progression of chronic kidney disease. J Am Soc Nephrol 2022;33:1590–1601.
crossref pmid pmc
18. Oh KH, Park SK, Park HC, et al. KNOW-CKD (KoreaN cohort study for Outcome in patients With Chronic Kidney Disease): design and methods. BMC Nephrol 2014;15:80.
crossref pmid pmc pdf
19. Pickering TG, Hall JE, Appel LJ, et al. Recommendations for blood pressure measurement in humans and experimental animals: part 1: blood pressure measurement in humans: a statement for professionals from the Subcommittee of Professional and Public Education of the American Heart Association Council on High Blood Pressure Research. Circulation 2005;111:697–716.
crossref pmid
20. Raggi P, Chertow GM, Torres PU, et al. The ADVANCE study: a randomized study to evaluate the effects of cinacalcet plus low-dose vitamin D on vascular calcification in patients on hemodialysis. Nephrol Dial Transplant 2011;26:1327–1339.
crossref pmid
21. Raggi P, Callister TQ, Shaw LJ. Progression of coronary artery calcium and risk of first myocardial infarction in patients receiving cholesterol-lowering therapy. Arterioscler Thromb Vasc Biol 2004;24:1272–1277.
crossref pmid
22. Qunibi W, Moustafa M, Muenz LR, et al. A 1-year randomized trial of calcium acetate versus sevelamer on progression of coronary artery calcification in hemodialysis patients with comparable lipid control: the Calcium Acetate Renagel Evaluation-2 (CARE-2) study. Am J Kidney Dis 2008;51:952–965.
crossref pmid
23. Callister TQ, Cooil B, Raya SP, Lippolis NJ, Russo DJ, Raggi P. Coronary artery disease: improved reproducibility of calcium scoring with an electron-beam CT volumetric method. Radiology 1998;208:807–814.
crossref pmid
24. Block GA, Spiegel DM, Ehrlich J, et al. Effects of sevelamer and calcium on coronary artery calcification in patients new to hemodialysis. Kidney Int 2005;68:1815–1824.
crossref pmid
25. Sakaguchi Y, Hamano T, Obi Y, et al. A randomized trial of magnesium oxide and oral carbon adsorbent for coronary artery calcification in predialysis CKD. J Am Soc Nephrol 2019;30:1073–1085.
crossref pmid pmc
26. Jian S, Su-Mei N, Xue C, Jie Z, Xue-Sen W. Association and interaction between triglyceride-glucose index and obesity on risk of hypertension in middle-aged and elderly adults. Clin Exp Hypertens 2017;39:732–739.
crossref pmid
27. Lee DY, Lee ES, Kim JH, et al. Predictive value of triglyceride glucose index for the risk of incident diabetes: a 4-year retrospective longitudinal study. PLoS One 2016;11:e0163465.
crossref pmid pmc
28. Zheng R, Mao Y. Triglyceride and glucose (TyG) index as a predictor of incident hypertension: a 9-year longitudinal population-based study. Lipids Health Dis 2017;16:175.
crossref pmid pmc pdf
29. da Silva A, Caldas AP, Hermsdorff HH, et al. Triglyceride-glucose index is associated with symptomatic coronary artery disease in patients in secondary care. Cardiovasc Diabetol 2019;18:89.
crossref pmid pmc pdf
30. Reardon CA, Lingaraju A, Schoenfelt KQ, et al. Obesity and insulin resistance promote atherosclerosis through an IFNγ-regulated macrophage protein network. Cell Rep 2018;23:3021–3030.
crossref pmid pmc
31. Bornfeldt KE, Tabas I. Insulin resistance, hyperglycemia, and atherosclerosis. Cell Metab 2011;14:575–585.
crossref pmid pmc
32. Sung KC, Wild SH, Kwag HJ, Byrne CD. Fatty liver, insulin resistance, and features of metabolic syndrome: relationships with coronary artery calcium in 10,153 people. Diabetes Care 2012;35:2359–2364.
pmid pmc
33. Jia G, Aroor AR, DeMarco VG, Martinez-Lemus LA, Meininger GA, Sowers JR. Vascular stiffness in insulin resistance and obesity. Front Physiol 2015;6:231.
crossref pmid pmc
34. Chen Y, Zhao X, Wu H. Arterial stiffness: a focus on vascular calcification and its link to bone mineralization. Arterioscler Thromb Vasc Biol 2020;40:1078–1093.
pmid pmc
35. Llauradó G, Ceperuelo-Mallafré V, Vilardell C, et al. Advanced glycation end products are associated with arterial stiffness in type 1 diabetes. J Endocrinol 2014;221:405–413.
crossref pmid
36. Ketteler M, Schlieper G, Floege J. Calcification and cardiovascular health: new insights into an old phenomenon. Hypertension 2006;47:1027–1034.
crossref pmid
37. Lamarche MC, Hopman WM, Garland JS, White CA, Holden RM. Relationship of coronary artery calcification with renal function decline and mortality in predialysis chronic kidney disease patients. Nephrol Dial Transplant 2019;34:1715–1722.
crossref pmid

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