Abstract
Background:
Vascular calcification is a major complication in chronic kidney disease (CKD) that increases the risk of adverse clinical outcomes. Geriatric nutritional risk index (GNRI) is a simple nutritional assessment tool that predicts poor prognosis in elderly subjects. The purpose of the present study was to evaluate the correlation between GNRI and severity of vascular calcification in non-dialyzed CKD patients.
Methods and Results:
We enrolled 323 asymptomatic CKD patients. To evaluate abdominal aortic calcification (AAC), we used aortic calcification index (ACI) determined on non-contrast computed tomography. The patients were divided into three groups according to GNRI tertile. Median ACI significantly decreased with increasing GNRI tertile (15.5%, 13.6%, and 7.9%, respectively; P=0.001). On multivariate regression analysis GNRI was significantly correlated with ACI (β=−0.15, P=0.009). We also investigated the combination of GNRI and C-reactive-protein (CRP) for predicting the severity of AAC. Low GNRI and high CRP were significantly associated with severe AAC, compared with high GNRI and low CRP (OR, 4.07; P=0.004).
Conclusions:
GNRI was significantly associated with AAC in non-dialyzed CKD patients.
Vascular calcification is a major complication in chronic kidney disease (CKD) that increases the risk of adverse clinical outcomes.1–6
Moreover, CKD patients frequently experience cardiovascular events associated with atherosclerosis and vascular calcification before hemodialysis is initiated.7,8
Thus, risk stratification is important from the early CKD phase to detect patients at high risk of cardiovascular events as a comorbidity of severe calcification.
It is widely accepted that CKD, diabetes, hypertension, and aging are well-established risk factors for vascular calcification. Growing interest has recently been focused on malnutrition, which has been recognized as a new prognostic factor for cardiovascular diseases in CKD patients.9
The geriatric nutritional risk index (GNRI), calculated based on both serum albumin and body mass index (BMI), has been introduced as a simple and valuable screening tool to assess the nutrition status.10
Little is known, however, about the correlation between nutrition status and severity of vascular calcification in non-dialyzed CKD patients. The aim of the present study was therefore to evaluate the correlation between GNRI and severity of vascular calcification in non-dialyzed CKD patients.
Methods
Subjects
This observational study included 392 Japanese non-dialyzed asymptomatic CKD patients referred to the outpatient clinic at the Department of Nephrology, Nagoya University Hospital from November 2008 to October 2012. Exclusion criteria were lack of abdominal computed tomography (CT; n=34), active malignancy (n=7), lack of height and/or weight data needed to calculate GNRI (n=24), and previous abdominal aortic artery repair or stenting (n=4). After exclusion of these patients, a total of 323 patients were evaluated in the present study. CKD was defined as estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2
and proteinuria or renal disease on admission.11
Hypertension was defined as systolic blood pressure (SBP) ≥140 mmHg, with diastolic blood pressure ≥90 mmHg, and/or receiving treatment for hypertension. Diabetes mellitus (DM) was defined as use of any anti-hyperglycemic medication and/or fasting plasma glucose concentration >126 mg/dL and/or glycosylated hemoglobin concentration ≥6.5% (National Glycohemoglobin Standardization Program). Dyslipidemia was defined as low-density lipoprotein cholesterol (LDL-C) ≥140 mg/dL, with high-density lipoprotein cholesterol (HDL-C) ≤40 mg/dL, and triglyceride ≥150 mg/dL, and/or receiving treatment for hyperlipidemia. Current smoking was defined as active smoking. This study was approved by the local ethics committee (approval number 101, 2013) and was conducted in accordance with the ethical principles stated by the Declaration of Helsinki. Written informed consent was obtained from all patients.
Date Collection
Blood samples were obtained from all patients after fasting for 12 h. eGFR was calculated using the equation for Japanese subjects recommended by the Japanese Society of Nephrology: eGFR (mL/min/1.73 m2)=194×SCr−1.094×age−0.287×0.793 (for female subjects).12
Proteinuria was assessed using the dipstick test for spot urine (Uropaper αIII; Eikenkagaku, Japan) and defined as present if the dipstick result was ≥1+. Serum calcium level was corrected for albumin using the following formula: corrected calcium=total calcium+(4.0−albumin)×0.8, if albumin was <4.0 g/dL.
Abdominal Aortic Calcification Index
To evaluate the renal morphology and degree of abdominal aortic calcification (AAC), we used 64-slice non-contrast CT (Siemens Medical Solutions, Forchheim, Germany). The images were obtained in the supine position and craniocaudal direction, with a 5-mm slice thickness. Calcification was defined as density ≥130 Hounsfield units for an area ≥1 mm2. The AAC score was calculated from the takeoff of the renal artery to the bifurcation of the aorta into the common iliac arteries. The cross-section of the abdominal aorta on each slice was radially divided into 12 segments. Aortic calcification index (ACI) was calculated as follows: ACI=(total score for calcification on all slices)/12×1/(no. slices)×100 (%).13,14
Semi-quantitative measurement of AAC was conducted independently by two physicians blinded to the patient clinical characteristics. Severe AAC was defined as ACI >20%, as previously reported.5
Representative cross-sectional CT of severe AAC is shown in
Figure 1. The inter- and intra-observer variabilities of ACI were well correlated (r=0.97, P<0.001, and r=0.96, P<0.001, respectively).
The GNRI scoring system was developed by modifying the nutritional risk index for elderly subjects.10
This index is calculated from serum albumin and body weight, as follows: GNRI=[14.89×albumin (g/dL)]+[41.7×(body weight/ideal body weight)]. Body weight/ideal body weight was set to 1 when the patient’s body weight exceeded the ideal body weight. The ideal body weight in this study was calculated using height and a BMI of 22, as previously reported,15
instead of the Lorentz formula used in the original GNRI equation. BMI was calculated using height and body weight.
Statistical Analysis
All statistical analysis was performed using SPSS version 23 for Windows (SPSS, Chicago, IL, USA). Continous data are expressed as mean±SD for normally distributed variables or as median (Interquartile Range: IQR) for asymmetrically distributed variables. Categorical variables are expressed as n (%). Differences between continuous variables were assessed using one-way analysis of variance (ANOVA) for normally distributed data, and the Kruskal-Wallis test for non-normal distribution. Differences in categorical variables were assessed using the chi-squared or Fisher’s exact test. Spearman’s correlation coefficient and multivariate regression analysis were calculated to assess the association between ACI and various clinical parameters. To obtain an independent predictor for severe AAC, multivariate logistic regression analysis was performed for each parameter as a dependent variable. P<0.05 was considered statistically significant.
Results
A total of 323 patients were enrolled in this study. Mean age was 67.6±12.2 years, mean eGFR was 45.3±22.3 mL/min/1.73 m2, and median GNRI was 98.3. Of the subjects, 85.8% had hypertension, and 35.6% had DM. Patients were divided into three groups according to GNRI tertile: tertile 1 (T1), GNRI <95.2; tertile 2 (T2), 95.2≤GNRI<100.2; and tertile 3 (T3), GNRI ≥100.2.
Table 1
summarizes the baseline clinical characteristics. There were significant differences in age, number of male subjects, eGFR, triglyceride, C-reactive protein (CRP), prevalence of proteinuria, prevalence of diabetes, and prevalence of dyslipidemia between the three groups. The prevalence of hypertension and the level of serum phosphorus tended to increase with decreasing GNRI tertile. Other variables such as current smoker status were similar between the three groups. As shown in
Figure 2, median ACI significantly decreased with increasing GNRI tertile (15.5%, IQR, 2.0–37.5%; 13.6%, IQR, 4.4–30.4%; and 7.9%, IQR, 0–21.5%, for GNRI T1, T2, and T3, respectively; P for trend=0.001). On receiver operating characteristic analysis, the optimal cut-off of GNRI for predicting severe AAC was 99.6 (area under the curve, 0.62; 95% CI: 0.55–0.68, P<0.001). Moreover, we investigated 141 patients with second CT for any reason (mean duration from first to second CT, 3.9 years), and calculated ∆ACI/year [(second ACI−first ACI)/between-evaluation duration] according to GNRI tertile. A similar trend between median ACI progression and nutritional status was observed (1.40%; IQR, 0.31–3.20%; 1.31%, IQR, 0.40–3.46%; and 0.73%, IQR, 0.24–1.76% for GNRI T1, T2, and T3, respectively; P for trend=0.15), but this was not statistically significant.
Table 1.
Baseline Subject Characteristics
| Variables |
All
(n=323) |
GNRI |
P-value |
| T1 (<95.2) (n=108) |
T2 (95.2–<100.2) (n=107) |
T3 (≥100.2) (n=108) |
| Demographics |
| Age (years) |
67.6±12.2 |
69.8±12.6 |
68.6±10.7 |
64.5±12.6 |
0.003 |
| Male |
226 (70.0) |
69 (63.9) |
72 (67.3) |
85 (78.7) |
0.045 |
| GNRI |
98.3 (93.4–102.5) |
90.6 (86.5–93.3) |
98.3 (96.8–99.5) |
103 (102.5–105.7) |
<0.001 |
| Hypertension |
277 (85.8) |
94 (87.0) |
97 (90.7) |
86 (79.6) |
0.062 |
| Diabetes |
115 (35.6) |
46 (42.6) |
40 (37.4) |
29 (26.9) |
0.048 |
| Dyslipidemia |
245 (75.9) |
71 (65.7) |
86 (80.4) |
88 (81.5) |
0.011 |
| Current smoking |
34 (10.5) |
13 (12.0) |
8 (7.5) |
13 (12.0) |
0.439 |
| Laboratory data |
| Serum creatinine (mg/dL) |
1.31
(0.92–1.73) |
1.46
(0.98–1.91) |
1.41
(0.96–1.82) |
1.38
(0.88–1.59) |
0.138 |
| eGFR (mL/min/1.73 m2) |
41.3
(28.8–57.1) |
36.5
(25.5–50.7) |
41.5
(26.8–56.4) |
44.6
(34.9–59.9) |
0.012 |
| LDL-C (mg/dL) |
105
(87–124) |
99.5
(85.5–129.5) |
105
(90–120) |
101.5
(85.5–125.8) |
0.919 |
| HDL-C (mg/dL) |
45
(38–58) |
49
(38.3–62.8) |
44
(36–56) |
44
(39.3–56.8) |
0.228 |
| Triglycerides (mg/dL) |
133
(99–182.5) |
110
(86.3–150.8) |
139
(100–197) |
138.5
(109.3–194.5) |
<0.001 |
| Hemoglobin A1c (%) |
5.7 (5.4–6.3) |
5.8 (5.5–6.6) |
5.8 (5.4–6.3) |
5.7 (5.4–6.1) |
0.042 |
| Serum albumin (mg/dL) |
3.8 (3.6–4.1) |
3.5 (3.6–3.2) |
3.8 (3.7–3.9) |
4.2 (4.3–4.1) |
<0.001 |
| Corrected calcium (mg/dL) |
4.8±0.2 |
4.8±0.2 |
4.8±0.3 |
4.8±0.2 |
0.63 |
| Phosphorus (mg/dL) |
3.4 (3.0–3.8) |
3.4 (3.1–3.8) |
3.4 (3.1–3.8) |
3.3 (2.9–3.6) |
0.062 |
| CRP (mg/L) |
0.63
(0.28–1.50) |
0.75
(0.30–2.00) |
0.69
(0.31–1.60) |
0.52
(0.25–1.11) |
0.039 |
| Proteinuria |
139 (43.0) |
53 (49.1) |
51 (47.7) |
35 (32.4) |
0.023 |
| Medication |
| Antiplatelet agents |
103 (31.9) |
36 (33.3) |
37 (34.6) |
30 (27.8) |
0.522 |
| ACEI or ARB |
216 (66.9) |
70 (64.8) |
82 (76.6) |
64 (59.3) |
0.022 |
| β-blockers |
42 (13) |
15 (13.9) |
16 (15.0) |
11 (10.2) |
0.551 |
| Statin |
129 (39.9) |
39 (36.1) |
51 (47.7) |
39 (36.1) |
0.137 |
| Calcium carbonate |
8 (2.5) |
7 (6.5) |
1 (0.9) |
0 (0) |
0.004 |
| Vitamin D (active form) |
13 (4) |
7 (6.5) |
3 (2.8) |
3 (2.8) |
0.282 |
Data given as mean±SD, median (IQR), or n (%). ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; GNRI, geriatric nutritional risk index; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol.
On univariate regression analysis, ACI was significantly correlated with age, SBP, eGFR, LDL-C, HDL-C, CRP, and GNRI (Table 2A), while on ultivariate regression analysis, GNRI (β=−0.15, P=0.009), age (β=0.43, P<0.001), dyslipidemia (β=0.12, P=0.026) and current smoking (β=0.17, P=0.001) were significantly correlated with ACI (Table 2B). We also evaluated the combined effects of baseline GNRI (according to GNRI tertiles: <95.2, ≥95.2–<100.2, and ≥100.2) and CRP (defined according to the median, 0.625 mg/L) for predicting severe AAC (Figure 3A). After adjusting for conventional calcification risk factors, compared with high GNRI and low CRP, the OR of severe AAC was 4.07 (95% CI: 1.56–10.62) for low GNRI and high CRP; 3.60 (95% CI: 1.32–9.77) for low GNRI and low CRP; and 2.93 (95% CI: 1.15–7.46) for middle GNRI and high CRP. Moreover, on analysis of the combined effects of GNRI tertile and age grade for predicting severe AAC, even in patients <65 years old, low GNRI was significantly associated with severe AAC compared with the high GNRI group (OR, 7.18; 95% CI: 1.92–26.82;
Figure 3B).
Table 2.
(A) Univariate Indicators of ACI, (B) Multivariate Indicators of ACI
| A |
|
|
|
| Variables |
Spearman’s correlation |
|
| R |
P-value |
|
| Age (years) |
0.51 |
<0.001 |
|
| SBP (mmHg) |
0.18 |
0.001 |
|
| eGFR (mL/min/1.73 m2) |
−0.26 |
<0.001 |
|
| LDL-C (mg/dL) |
−0.23 |
<0.001 |
|
| HDL-C (mg/dL) |
−0.12 |
0.04 |
|
| Triglycerides (mg/dL) |
−0.029 |
0.60 |
|
| Hemoglobin A1c (%) |
0.11 |
0.054 |
|
| Corrected calcium (mg/dL) |
−0.037 |
0.52 |
|
| Phosphorus (mg/dL) |
0.061 |
0.29 |
|
| Log CRP (mg/dL) |
0.112 |
0.044 |
|
| GNRI |
−0.21 |
<0.001 |
|
| B |
|
|
|
| Variables |
Multiple regression |
| B (95% CI) |
β |
P-value |
| Age (years) |
0.43 (0.54 to 0.92) |
0.43 |
<0.001 |
| Gender, male |
3.99 (−0.39 to 8.38) |
0.094 |
0.074 |
| SBP (mmHg) |
0.056 (−0.061 to 0.17) |
0.052 |
0.35 |
| Hemoglobin A1c (%) |
0.23 (−2.33 to 2.79) |
0.010 |
0.86 |
| Dyslipidemia |
5.58 (0.66 to 10.50) |
0.12 |
0.026 |
| Current smoking |
10.95 (4.27 to 17.63) |
0.17 |
0.001 |
| eGFR (mL/min/1.73 m2) |
−0.012 (−0.12 to 0.09) |
−0.013 |
0.82 |
| Log CRP (mg/dL) |
1.42 (−0.36 to 3.19) |
0.12 |
0.12 |
| Proteinuria |
1.12 (−3.21 to 5.45) |
0.028 |
0.61 |
| GNRI |
−0.39 (−0.68 to −0.096) |
−0.15 |
0.009 |
P<0.05 indicates a significant correlation. ACI, aortic calcification index; SBP, systolic blood pressure. Other abbreviations as in Table 1.
Discussion
The main finding of the present study is that GNRI was inversely associated with AAC in non-dialyzed CKD patients. From the viewpoint of risk stratification in clinical practice, the present findings are considerably significant for non-dialyzed asymptomatic CKD patients because the assessment of nutritional status is a non-invasive and useful screening tool for predicting severity of vascular calcification.
Malnutrition is a common and major problem in end-stage renal disease.16
Thus, the growing interest in evaluating nutritional status may contribute to an improvement in the clinical outcome of these patients. Ko et al showed that low muscle mass is an independent risk factor of severe coronary artery calcification.17
Several recent studies have shown that hemodialysis patients with low GNRI have poor cardiovascular outcome,18
but little is known about nutrition status and its correlation with the severity of arteriosclerosis in non-dialyzed CKD patients. In the present cohort, low GNRI was significantly associated with severe AAC. Moreover, low GNRI was significantly associated with severe vascular calcification, even after excluding the aged patients. This suggests that GNRI could represent a simple and low-cost screening tool to predict the severity of arteriosclerosis and improve the identification of non-dialyzed asymptomatic CKD patients at high risk of future cardiovascular events.
Chronic systemic inflammation is common in CKD.19
Inflammation in CKD accelerates malnutrition and arteriosclerosis. Moreover, malnutrition and inflammation form a vicious cycle, leading to the development of vascular calcification and a subsequent increase in cardiovascular mortality in this population.20,21
From these observations, the concept of malnutrition inflammation complex syndrome was proposed.22
In the present study, the combination of low GNRI and high CRP had a 4.07-fold higher risk of severe AAC. This suggests that nutrition and inflammation status need to be assessed in order to better identify patients at high risk of vascular calcification and improve the ability to predict future CV events in asymptomatic CKD patients.
Vascular calcification is an important risk factor influencing morbidity and mortality in CKD patients.23–25
For this reason, current guidelines recommend screening for the presence of vascular calcification in CKD patients at stage 3–5 of the disease.11
Advanced CKD stage, diabetes, hypertension, and aging are traditional risk factors for arterial medial calcification. Moreover, non-traditional factors related to vascular calcification in CKD should be taken into account. Price et al reported that low dietary protein intake markedly increased the frequency and extent of medial artery calcification in an adenine-induced uremic rat model.26
The present findings are in line with this theory, and showed that GNRI, as a marker of nutritional status, is inversely correlated with the severity of AAC. Recent basic research has shown that dietary supplementation of L-lysine, an essential amino acid, decreased vascular calcification by modifying key pathways that exacerbate medial artery calcification.27
Human studies aimed at attenuating vascular calcification progression, however, have been limited. To establish a treatment strategy for preventing vascular calcification and improve the prognosis of these patients, further investigation is needed. We considered that assessment of nutrition status is very important, and that nutrition intervention might be more efficient and cost-effective in producing beneficial results than medical therapy alone.
Several limitations need to be considered in the present study. First, this study was conducted in a single center; it was an observational study involving a relatively small sample. Second, use of multi-slice CT did not allow distinction between intimal and medial vascular calcification. Third, because we conducted a medical record survey, we had no access to complete information on the duration of DM or hypertension. Fourth, the measured nutrition status could have been an effect of vascular calcification rather than a cause. Fifth, we did not measure objective parameters of fraility such as hand grip strength or gait speed in the present study. Finally, other CKD-mineral-bone-disorder factors that may be associated with vascular calcification in CKD patients, including serum fibroblast growth factor-23 and Fetuin-A, were not measured. These limitations should be taken into account when considering the results, and a large-scale study carried out to confirm them.
Conclusions
GNRI was inversely associated with severity of AAC in non-dialyzed CKD patients. Moreover, the addition of CRP to GNRI data significantly improved the ability to predict the severity of AAC. This suggests that the assessment of both nutrition and inflammation status is useful and necessary for predicting the severity of vascular calcification in clinical practice.
Acknowledgments
We thank Junko Imai, Misao Niwa, and Kanako Shibata for excellent assistance with the manuscript.
Disclosures
Y.Y. and R.M. belong to a development endowed by Chugai, Dainippon Sumitomo, Kowa, Kyowa Hakko Kirin, MSD, Nihon Medi-Physics, and Nippon Boehringer Ingelheim, and have received a research grant from Shionogi. H.I. has received lecture fees from Astellas, and Otsuka. T.M. has received lecture fees from Bayer, Daiichi Sankyo, Dainippon Sumitomo, Kowa, MSD, Mitsubishi Tanabe, Nippon Boehringer Ingelheim, Novartis, Pfizer Japan, Sanofi-Aventis, and Takeda, and has received an unrestricted research grant from Astellas, Daiichi Sankyo, Dainippon Sumitomo, Kowa, MSD, Mitsubishi Tanabe, Nippon Boehringer Ingelheim, Novartis, Otsuka, Pfizer Japan, Sanofi-Aventis, Takeda, and Teijin. The other authors declare no conflicts of interest.
References
- 1.
Russo D, Palmiero G, De Blasio AP, Balletta MM, Andreucci VE. Coronary artery calcification in patients with CRF not undergoing dialysis. Am J Kidney Dis 2004; 44: 1024–1030.
- 2.
Tomiyama C, Higa A, Dalboni MA, Cendoroglo M, Draibe SA, Cuppari L, et al. The impact of traditional and non-traditional risk factors on coronary calcification in pre-dialysis patients. Nephrol Dial Transplant 2006; 21: 2464–2471.
- 3.
Wu M, Rementer C, Giachelli CM. Vascular calcification: An update on mechanisms and challenges in treatment. Calcif Tissue Int 2013; 93: 365–373.
- 4.
Tatami Y, Yasuda Y, Suzuki S, Ishii H, Sawai A, Shibata Y, et al. Impact of abdominal aortic calcification on long-term cardiovascular outcomes in patients with chronic kidney disease. Atherosclerosis 2015; 243: 349–355.
- 5.
Hanada S, Ando R, Naito S, Kobayashi N, Wakabayashi M, Hata T, et al. Assessment and significance of abdominal aortic calcification in chronic kidney disease. Nephrol Dial Transplant 2010; 25: 1888–1895.
- 6.
Nishimura S, Izumi C, Nishiga M, Amano M, Imamura S, Onishi N, et al. Predictors of rapid progression and clinical outcome of asymptomatic severe aortic stenosis. Circ J 2016; 80: 1863–1869.
- 7.
Schiffrin EL, Lipman ML, Mann JF. Chronic kidney disease: Effects on the cardiovascular system. Circulation 2007; 116: 85–97.
- 8.
Russo D, Palmiero G, De Blasio AP, Balletta MM, Andreucci VE. Coronary artery calcification in patients with CRF not undergoing dialysis. Am J Kidney Dis 2004; 44: 1024–1030.
- 9.
Kovesdy CP, Kalantar-Zadeh K. Why is protein-energy wasting associated with mortality in chronic kidney disease? Semin Nephrol 2009; 29: 3–14.
- 10.
Bouillanne O, Morineau G, Dupont C, Coulombel I, Vincent JP, Nicolis I, et al. Geriatric nutritional risk index: A new index for evaluating at-risk elderly medical patients. Am J Clin Nutr 2005; 82: 777–783.
- 11.
Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl 2013; 3: 1–150.
- 12.
Matsuo S, Imai E, Horio M, Yasuda Y, Tomita K, Nitta K, et al; Collaborators developing the Japanese equation for estimated GER. Revised equations for estimated GFR from serum creatinine in Japan. Am J Kidney Dis 2009; 53: 982–992.
- 13.
Taniwaki H, Ishimura E, Tabata T, Tsujimoto Y, Shioi A, Shoji T, et al. Aortic calcification in haemodialysis patients with diabetes mellitus. Nephrol Dial Transplant 2005; 20: 2472–2478.
- 14.
Yamada K, Fujimoto S, Nishiura R, Komatsu H, Tatsumoto M, Sato Y, et al. Risk factors of the progression of abdominal aortic calcification in patients on chronic haemodialysis. Nephrol Dial Transplant 2007; 22: 2032–2037.
- 15.
Yamada K, Furuya R, Takita T, Maruyama Y, Yamaguchi Y, Ohkawa S, et al. Simplified nutritional screening tools for patients on maintenance hemodialysis. Am J Clin Nutr 2008; 87: 106–113.
- 16.
Kobayashi I, Ishimura E, Kato Y, Okuno S, Yamamoto T, Yamakawa T, et al. Geriatric Nutritional Risk Index, a simplified nutritional screening index, is a significant predictor of mortality in chronic dialysis patients. Nephrol Dial Transplant 2010; 25: 3361–3365.
- 17.
Ko BJ, Chang Y, Jung HS, Yun KE, Kim CW, Park HS, et al. Relationship between low relative muscle mass and coronary artery calcification in healthy adults. Arterioscler Thromb Vasc Biol 2016; 36: 1016–1021.
- 18.
Takahashi H, Ito Y, Ishii H, Aoyama T, Kamoi D, Kasuga H, et al. Geriatric nutritional risk index accurately predicts cardiovascular mortality in incident hemodialysis patients. J Cardiol 2014; 64: 32–36.
- 19.
Hwang IC, Park HE, Kim HL, Kim HM, Park JB, Yoon YE, et al. Systemic inflammation is associated with coronary artery calcification and all-cause mortality in chronic kidney disease. Circ J 2016; 80: 1644–1652.
- 20.
Hwang JH, Ryu J, An JN, Kim CT, Kim H, Yang J, et al. Pretransplant malnutrition, inflammation, and atherosclerosis affect cardiovascular outcomes after kidney transplantation. BMC Nephrol 2015; 16: 109.
- 21.
Yamada S, Tokumoto M, Tatsumoto N, Taniguchi M, Noguchi H, Nakano T, et al. Phosphate overload directly induces systemic inflammation and malnutrition as well as vascular calcification in uremia. Am J Physiol Renal Physiol 2014; 306: 1418–1428.
- 22.
Anand N, Alam MN. The malnutrition inflammation complex syndrome: The missing factor in the perio-chronic kidney disease interlink. J Clin Diagn Res 2013; 7: 763–767.
- 23.
Hruska K, Mathew S, Lund R, Fang Y, Sugatani T. Cardiovascular risk factors in chronic kidney disease: Does phosphate qualify? Kidney Int 2011; 79: 9–13.
- 24.
Floege J, Kim J, Ireland E, Chazot C, Drueke T, de Francisco A, et al. Serum iPTH, calcium and phosphate, and the risk of mortality in a European haemodialysis population. Nephrol Dial Transplant 2011; 26: 1948–1955.
- 25.
Palmer SC, Hayen A, Macaskill P, Pellegrini F, Craig JC, Elder GJ, et al. Serum levels of phosphorus, parathyroid hormone, and calcium and risks of death and cardiovascular disease in individuals with chronic kidney disease: A systematic review and meta-analysis. JAMA 2011; 305: 1119–1127.
- 26.
Price PA, Roublick AM, Williamson MK. Artery calcification in uremic rats is increased by a low protein diet and prevented by treatment with ibandronate. Kidney Int 2006; 70: 1577–1583.
- 27.
Shimomura A, Matsui I, Hamano T, Ishimoto T, Katou Y, Takehana K, et al. Dietary L-lysine prevents arterial calcification in adenine-induced uremic rats. J Am Soc Nephrol 2014; 25: 1954–1965.
