Restricted accessResearch articleFirst published online 2020-7
Morphological Carotid Plaque Area Is Associated With Glomerular Filtration Rate: A Study of South Asian Indian Patients With Diabetes and Chronic Kidney Disease
We evaluated the association between automatically measured carotid total plaque area (TPA) and the estimated glomerular filtration rate (eGFR), a biomarker of chronic kidney disease (CKD). Automated average carotid intima–media thickness (cIMTave) and TPA measurements in carotid ultrasound (CUS) were performed using AtheroEdge (AtheroPoint). Pearson correlation coefficient (CC) was then computed between the TPA and eGFR for (1) males versus females, (2) diabetic versus nondiabetic patients, and (3) between the left and right carotid artery. Overall, 339 South Asian Indian patients with either type 2 diabetes mellitus (T2DM) or CKD, or hypertension (stage 1 or stage 2) were retrospectively analyzed by acquiring cIMTave and TPA measurements of their left and right common carotid arteries (CCA; total CUS: 678, mean age: 54.2 ± 9.8 years; 75.2% males; 93.5% with T2DM). The CC between TPA and eGFR for different scenarios were (1) for males and females −0.25 (P < .001) and −0.35 (P < .001), respectively; (2) for T2DM and non-T2DM −0.26 (P < .001) and −0.49 (P = .02), respectively, and (3) for left and right CCA −0.25 (P < .001) and −0.23 (P < .001), respectively. Automated TPA is an equally reliable biomarker compared with cIMTave for patients with CKD (with or without T2DM) with subclinical atherosclerosis.
GoASChertowGMFanDMcCullochCEHsuCY. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med. 2004;351:1296–305.
3.
LeeMSaverJLChangKHLiaoHWChangSCOvbiageleB. Low glomerular filtration rate and risk of stroke: meta-analysis. BMJ. 2010;341:c4249.
4.
SarnakMJLeveyASSchoolwerthAC, et al.Kidney disease as a risk factor for development of cardiovascular disease: a statement from the American Heart Association Councils on Kidney in Cardiovascular Disease, High Blood Pressure Research, Clinical Cardiology, and Epidemiology and Prevention. Circulation. 2003;108:2154–69.
5.
EknoyanGLameireNEckardtK, et al.KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int. 2013;3:5–14.
6.
CollinsAJLiSGilbertsonDTLiuJChenSCHerzogCA. Chronic kidney disease and cardiovascular disease in the Medicare population. Kidney Int. 2003:S24–31.
7.
RoumeliotisARoumeliotisSPanagoutsosS, et al.Carotid intima-media thickness is an independent predictor of all-cause mortality and cardiovascular morbidity in patients with diabetes mellitus type 2 and chronic kidney disease. Ren Fail. 2019;41:131–8.
8.
PolakJFPencinaMJPencinaKMO’DonnellCJWolfPAD’AgostinoRBSr. Carotid-wall intima-media thickness and cardiovascular events. N Engl J Med. 2011;365:213–21.
9.
Mathiesen EllisivBJohnsen SteinHWilsgaardTBønaa KaareHLøchenMLNjølstadI. Carotid plaque area and intima-media thickness in prediction of first-ever ischemic stroke. Stroke. 2011;42:972–8.
10.
LahotiSKumarSAgrawalS. Study of carotid intimal medial thickness in chronic kidney disease at rural teaching hospital. Ann Med Health Sci Res. 2017;7.
11.
SzetoCCChowKMWooKS, et al.Carotid intima media thickness predicts cardiovascular diseases in Chinese predialysis patients with chronic kidney disease. J Am Soc Nephrol. 2007;18:1966–72.
12.
LeeKSungJLeeSC, et al.Phenotypic and genetic relationships between kidney function and carotid intima-media thickness in Koreans: the Healthy Twin Study. Kidney Blood Press Res. 2012;35:259–64.
13.
TanakaMAbeYFurukadoS, et al.Chronic kidney disease and carotid atherosclerosis. J Stroke Cerebrovasc Dis. 2012;21:47–51.
14.
HinderliterAPadillaRLGillespieBW, et al.Association of carotid intima-media thickness with cardiovascular risk factors and patient outcomes in advanced chronic kidney disease: the RRI-CKD study. Clin Nephrol. 2015;84:10–20.
15.
SabaLBanchhorSKArakiT, et al.Intra-and inter-operator reproducibility analysis of automated cloud-based carotid intima media thickness ultrasound measurement. J Clin Diag Res. 2018;12:KC01–11.
16.
SteinJHTattersallMC. Carotid intima-media thickness and cardiovascular disease risk prediction. J Am Coll Cardiol. 2014;63:2301–2.
17.
GoffDCLloyd-JonesDM, Bennett G, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014;63:2935–59.
18.
Den RuijterHMPetersSAAndersonTJ, et al.Common carotid intima-media thickness measurements in cardiovascular risk prediction: a meta-analysis. JAMA. 2012;308:796–803.
19.
MolinariFPattichisCSZengG, et al.Completely automated multiresolution edge snapper—a new technique for an accurate carotid ultrasound IMT measurement: clinical validation and benchmarking on a multi-institutional database. IEEE Trans Image Process. 2012;21:1211–22.
20.
MolinariFZengGSuriJS. Intima-media thickness: setting a standard for a completely automated method of ultrasound measurement. IEEE Trans Ultrason Ferroelectr Freq Control. 2010;57:1112–24.
21.
MolinariFMeiburgerKMZengG, et al.Automated carotid IMT measurement and its validation in low contrast ultrasound database of 885 patient Indian population epidemiological study: results of atheroedge™ software. Int Angiol. 2012;31:42–53.
22.
SabaLMallariniGSanfilippoRZengGMontisciRSuriJ. Intima media thickness variability (IMTV) and its association with cerebrovascular events: a novel marker of carotid therosclerosis?Cardiovasc Diagn Ther. 2012;2:10.
23.
SpenceJDEliasziwMDiCiccoMHackamDGGalilRLohmannT. Carotid plaque area: a tool for targeting and evaluating vascular preventive therapy. Stroke. 2002;33:2916–22.
24.
JohnsenSHMathiesenEB. Carotid plaque compared with intima-media thickness as a predictor of coronary and cerebrovascular disease. Curr Cardiol Rep. 2009;11:2–7.
25.
InabaYChenJABergmannSR. Carotid plaque, compared with carotid intima-media thickness, more accurately predicts coronary artery disease events: a meta-analysis. Atherosclerosis. 2012;220:128–33.
26.
CachofeiroVGoicocheaMDe VinuesaSGOubiñaPLaheraVLuñoJ.Oxidative stress and inflammation, a link between chronic kidney disease and cardiovascular disease: new strategies to prevent cardiovascular risk in chronic kidney disease. Kidney Int. 2008;74:S4–9.
27.
ZalbaGFortuñoADíezJ. Oxidative stress and atherosclerosis in early chronic kidney disease. Nephrol Dial Transplant. 2006;21:2680–90.
28.
MunzelTHeitzerTHarrisonDG. The physiology and pathophysiology of the nitric oxide/superoxide system. Herz. 1997;22:158–72.
29.
Recio-MayoralABanerjeeDStreatherCKaskiJC. Endothelial dysfunction, inflammation and atherosclerosis in chronic kidney disease—a cross-sectional study of predialysis, dialysis and kidney-transplantation patients. Atherosclerosis. 2011;216:446–51.
30.
LibbyPRidkerPMMaseriA. Inflammation and atherosclerosis. Circulation. 2002;105:1135–43.
31.
LibbyP. Vascular biology of atherosclerosis: overview and state of the art. Am J Cardiol. 2003;91:3–6.
32.
LibbyPRidkerPMHanssonGK. Progress and challenges in translating the biology of atherosclerosis. Nature. 2011;473:317–25.
33.
LibbyP.History of discovery: inflammation in atherosclerosis. Arterioscler Thromb Vasc Biol. 2012;32:2045–51.
34.
KonVLintonMFFazioS. Atherosclerosis in chronic kidney disease: the role of macrophages. Nat Rev Nephrol. 2011;7:45.
35.
Cuadrado-GodiaEManiruzzamanMArakiT, et al.Morphologic TPA (mTPA) and composite risk score for moderate carotid atherosclerotic plaque is strongly associated with HbA1c in diabetes cohort. Comput Biol Med. 2018;101:128–45.
36.
SpenceJDUrquhartBLBangH. Effect of renal impairment on atherosclerosis: only partially mediated by homocysteine. Nephrol Dial Transplant. 2015;31:937–44.
37.
BogiatziCGloorGAllen-VercoeE, et al.Metabolic products of the intestinal microbiome and extremes of atherosclerosis. Atherosclerosis. 2018;273:91–7.
38.
PignanelliMBogiatziCGloorG, et al.Moderate renal impairment and toxic metabolites produced by the intestinal microbiome: dietary implications. J Ren Nutr. 2019;29:55–64.
39.
SteinJHKorcarzCEHurstRT, et al.Use of carotid ultrasound to identify subclinical vascular disease and evaluate cardiovascular disease risk: a consensus statement from the American Society of Echocardiography Carotid Intima-Media Thickness Task Force endorsed by the Society for Vascular Medicine. J Am Soc Echocardiogr. 2008;21:93–111.
40.
Cuadrado-GodiaESrivastavaSKSabaL, et al.Geometric total plaque area is an equally powerful phenotype compared with carotid intima-media thickness for stroke risk assessment: a deep learning approach. J Vasc Ultrasound. 2018:1544316718806421.
41.
SpenceJDSoloK.Resistant atherosclerosis: the need for monitoring of plaque burden. Stroke. 2017;48:1624–9.
42.
AlsulaimaniSGardenerHElkindMSCheungKSaccoRLRundekT. Elevated homocysteine and carotid plaque area and densitometry in the Northern Manhattan Study. Stroke. 2013;44:457–61.
43.
SabaLBanchhorSKSuriHS, et al.Accurate cloud-based smart IMT measurement, its validation and stroke risk stratification in carotid ultrasound: a web-based point-of-care tool for multicenter clinical trial. Comput Biol Med. 2016;75:217–34.
44.
SabaLMontisciRMolinariFTallapallyNZengGMallariniGSuriJS. Comparison between manual and automated analysis for the quantification of carotid wall by using sonography. A validation study with CT. Eur J Radiol. 2012;81:911–8.
45.
BaradaranHNgCRGuptaA, et al.Extracranial internal carotid artery calcium volume measurement using computer tomography. Int Angiol. 2017;36:445–61.
46.
MolinariFMeiburgerKMSabaLZengGAcharyaURLeddaMNicolaidesASuriJS. Fully automated dual-snake formulation for carotid intima-media thickness measurement: a new approach. J Ultrasound Med. 2012;31:1123–36.
47.
KumarPKArakiTRajanJ, et al.Accurate lumen diameter measurement in curved vessels in carotid ultrasound: an iterative scale-space and spatial transformation approach. Med Biol Eng Comput. 2017;55:1415–34.
48.
BanchhorSKArakiTLondheND, et al.Five multiresolution-based calcium volume measurement techniques from coronary IVUS videos: a comparative approach. Comput Methods Programs Biomed. 2016;134:237–58.
49.
SabaLBanchhorSKLondheND, et al.Web-based accurate measurements of carotid lumen diameter and stenosis severity: an ultrasound-based clinical tool for stroke risk assessment during multicenter clinical trials. Comput Biol Med. 2017;91:306–17.
50.
MolinariFMeiburgerKMSabaL, et al.Automated carotid IMT measurement and its validation in low contrast ultrasound database of 885 patient Indian population epidemiological study: results of Atheroedge® software. In: LucaSJasjitSSuriLMendesP, ed. Multi-Modality Atherosclerosis Imaging and Diagnosis. Berlin, Germany: Springer; 2014:209–19.
51.
MolinariFZengGSuriJS. A state of the art review on intima–media thickness (IMT) measurement and wall segmentation techniques for carotid ultrasound. Comput Methods Programs Biomed. 2010;100:201–21.
52.
ArakiTIkedaNShuklaD, et al.A new method for IVUS-based coronary artery disease risk stratification: a link between coronary & carotid ultrasound plaque burdens. Comput Methods Programs Biomed. 2016;124:161–79.
53.
ArakiTIkedaNShuklaD, et al.PCA-based polling strategy in machine learning framework for coronary artery disease risk assessment in intravascular ultrasound: a link between carotid and coronary grayscale plaque morphology. Comput Methods Programs Biomed. 2016;128:137–58.
54.
SabaLMeiburgerKMMolinariFAguilarLGalloDSteinmanDAMorbiducciU. Carotid IMT variability (IMTV) and its validation in symptomatic versus asymptomatic Italian population: can this be a useful index for studying symptomaticity?Echocardiography. 2012;29:1111–9.
55.
AcharyaUSreeSVMookiahMSabaLGaoHMallariniGSuriJS. Computed tomography carotid wall plaque characterization using a combination of discrete wavelet transform and texture features: a pilot study. Proc Inst Mech Eng H. 2013;227:643–54.
56.
SabaLBanchhorSKArakiT, et al.Intra-and inter-operator reproducibility of automated cloud-based carotid lumen diameter ultrasound measurement. Indian Heart J. 2018;70:649–64.
57.
NoorNMThanJCRijalOM, et al.Automatic lung segmentation using control feedback system: morphology and texture paradigm. J Med Syst. 2015;39:22.
58.
ThanJCSabaLNoorNM, et al.Lung disease stratification using amalgamation of Riesz and Gabor transforms in machine learning framework. Comput Biol Med. 2017;89:197–211.
59.
SabaLMolinariFMeiburgerK, et al.What is the correct distance measurement metric when measuring carotid ultrasound intima-media thickness automatically?Int Angiol. 2012;31:483–9.
SabaLGaoHRazE, et al.Semiautomated analysis of carotid artery wall thickness in MRI. J Magn Reson Imaging. 2014;39:1457–67.
62.
ShrivastavaVKLondheNDSonawaneRSSuriJS. Computer-aided diagnosis of psoriasis skin images with HOS, texture and color features: a first comparative study of its kind. Comput Methods Programs Biomed. 2016;126:98–109.
63.
ShrivastavaVKLondheNDSonawaneRSSuriJS. A novel and robust Bayesian approach for segmentation of psoriasis lesions and its risk stratification. Comput Methods Programs Biomed. 2017;150:9–22.
64.
BanchhorSKLondheNDSabaLRadevaPLairdJRSuriJS. Relationship between automated coronary calcium volumes and a set of manual coronary lumen volume, vessel volume and atheroma volume in Japanese diabetic cohort. J Clin Diagn Res. 2017;11:TC09.
65.
BanchhorSKLondheNDArakiTSabaLRadevaPLairdJRSuriJS. Wall-based measurement features provides an improved IVUS coronary artery risk assessment when fused with plaque texture-based features during machine learning paradigm. Comput Biol Med. 2017;91:198–212.
66.
BanchhorSKLondheNDArakiTSabaLRadevaPKhannaNNSuriJS. Calcium detection, its quantification, and grayscale morphology-based risk stratification using machine learning in multimodality big data coronary and carotid scans: a review. Comput Biol Med. 2018;101:184–98.
67.
MolinariFZengGSuriJS. Greedy technique and its validation for fusion of two segmentation paradigms leads to an accurate intima–media thickness measure in plaque carotid arterial ultrasound. J Vasc Ultrasound. 2010;34:63–73.
68.
MeiburgerKMMolinariFZengGSabaLSuriJS. Carotid automated ultrasound double line extraction system (CADLES) via Edge-Flow. Conf Proc IEEE Eng Med Biol Soc. 2011;2011:575–8.
69.
MolinariFMeiburgerKMZengG, et al.Carotid artery recognition system: a comparison of three automated paradigms for ultrasound images. Med Phys. 2012;39:378–91.
70.
MolinariFMeiburgerKMSabaLSabaLAcharyaURSanfilippoRNicolaidesASuriJS. Ultrasound IMT measurement on a multi-ethnic and multi-institutional database: our review and experience using four fully automated and one semi-automated methods. Comput Methods Programs Biomed. 2012;108:946–60.
71.
MolinariFMeiburgerKMSabaL, et al.Constrained snake vs. conventional snake for carotid ultrasound automated IMT measurements on multi-center data sets. Ultrasonics. 2012;52:949–61.
72.
SabaLMolinariFMeiburgerKMAcharyaURNicolaidesASuriJS. Inter- and intra-observer variability analysis of completely automated cIMT measurement software (AtheroEdge) and its benchmarking against commercial ultrasound scanner and expert readers. Comput Biol Med. 2013;43:1261–72.
73.
SabaLJamthikarAGuptaD, et al.Global perspective on carotid intima-media thickness and plaque: should the current measurement guidelines be revisited?Int Angiol. 2019;38:451–65.
KhannaNNJamthikarADArakiT, et al.Nonlinear model for the carotid artery disease 10-year risk prediction by fusing conventional cardiovascular factors to carotid ultrasound image phenotypes: a Japanese diabetes cohort study. Echocardiography. 2019;36:345–61.
DhingraRGazianoJMDjousséL. Chronic kidney disease and the risk of heart failure in men. Circ Heart Fail. 2011;4:138–44.
81.
WangYKatzmarzykPTHorswellRZhaoWJohnsonJHuG. Kidney function and the risk of cardiovascular disease in patients with type 2 diabetes. Kidney Int. 2014;85:1192–9.
82.
KhannaNNJamthikarADGuptaD, et al.Performance evaluation of 10-year ultrasound image-based stroke/cardiovascular (CV) risk calculator by comparing against ten conventional CV risk calculators: a diabetic study. Comput Biol Med. 2019;105:125–43.
83.
KhannaNNJamthikarADArakiT, et al.Effect of carotid image-based phenotypes on cardiovascular risk calculator: AECRS1.0. Med Biol Eng Comput. 2019;57:1553–66.
84.
PelisekJHahntowINEcksteinHH, et al.Impact of chronic kidney disease on carotid plaque vulnerability. J Vasc Surg. 2011;54:1643–9.
85.
SeoDHKimSHSongJH, et al.Presence of carotid plaque is associated with rapid renal function decline in patients with type 2 diabetes mellitus and normal renal function. Diabetes Metab J. 2019;43.
86.
RundekTGardenerHDella-MorteD, et al.The relationship between carotid intima-media thickness and carotid plaque in the Northern Manhattan Study. Atherosclerosis. 2015;241:364–70.
87.
GlagovSWeisenbergEZarinsCKStankunaviciusRKolettisGJ. Compensatory enlargement of human atherosclerotic coronary arteries. N Engl J Med. 1987;316:1371–5.
JeevarethinamAVenurajuSDumoA, et al.Usefulness of carotid plaques as predictors of obstructive coronary artery disease and cardiovascular events in asymptomatic individuals with diabetes mellitus. Am J Cardiol. 2018;121:910–6.
90.
MathiesenEBJohnsenSHWilsgaardTBønaaKHLøchenMLNjølstadI. Carotid plaque area and intima-media thickness in prediction of first-ever ischemic stroke: a 10-year follow-up of 6584 men and women: The Tromsø Study. Stroke. 2011;42:972–8.
91.
SillesenH. Carotid intima-media thickness and/or carotid plaque: what is relevant?European J Vasc Endovasc Surg. 2014;48:115–7.
92.
JohnsenSHMathiesenEB. Carotid plaque compared with intima-media thickness as a predictor of coronary and cerebrovascular disease. Curr Cardiol Rep. 2009;11:21–7.
93.
SpenceJD. Measurement of intima-media thickness vs. carotid plaque: uses in patient care, genetic research and evaluation of new therapies. Int J Stroke. 2006;1:216–21.
94.
SabaLSanfilippoRdi MartinoM, et al.Volumetric analysis of carotid plaque components and cerebral microbleeds: a correlative study. J Stroke Cerebrovasc Dis. 2017;26:552–8.
95.
SabaLSanfilippoRSanniaSAnzideiMMontisciRMallariniGSuriJS. Association between carotid artery plaque volume, composition, and ulceration: a retrospective assessment with MDCT. Am J Roentgenol. 2012;199:151–6.
96.
SharmaAMGuptaAKumarPK, et al.A review on carotid ultrasound atherosclerotic tissue characterization and stroke risk stratification in machine learning framework. Curr Atheroscler Rep. 2015;17:55.
97.
ArakiTKumarPKSuriHS, et al.Two automated techniques for carotid lumen diameter measurement: regional versus boundary approaches. J Med Syst. 2016;40:182.
98.
SabaLArakiTKrishna KumarP, et al.Carotid inter-adventitial diameter is more strongly related to plaque score than lumen diameter: an automated tool for stroke analysis. J Clin Ultrasound. 2016;44:210–20.
99.
KumarPKArakiTRajanJLairdJRNicolaidesASuriJS. State-of-the-art review on automated lumen and adventitial border delineation and its measurements in carotid ultrasound. Comput Methods Programs Biomed. 2018;163:155–68.
100.
IkedaNDeyNSharmaA, et al.Automated segmental-IMT measurement in thin/thick plaque with bulb presence in carotid ultrasound from multiple scanners: stroke risk assessment. Comput Methods Programs Biomed. 2017;141:73–81.
101.
JamthikarAGuptaDKhannaNN, et al.A low-cost machine learning-based cardiovascular/stroke risk assessment system: integration of conventional factors with image phenotypes. Cardiovasc Diagn Ther. 2019;9:420–30.
102.
Cuadrado-GodiaEJamthikarADGuptaD, et al.Ranking of stroke and cardiovascular risk factors for an optimal risk calculator design: logistic regression approach. Comput Biol Med. 2019;108:182–95.
103.
AcharyaURMookiahMRKSreeSV, et al.Atherosclerotic plaque tissue characterization in 2D ultrasound longitudinal carotid scans for automated classification: a paradigm for stroke risk assessment. Med Biol Eng Comput. 2013;51:513–23.
104.
SabaLJainPKSuriHS, et al.Plaque tissue morphology-based stroke risk stratification using carotid ultrasound: a polling-based PCA learning paradigm. J Med Syst. 2017;41:98.
105.
AcharyaUVinitha SreeSMookiahM, et al.Diagnosis of Hashimoto’s thyroiditis in ultrasound using tissue characterization and pixel classification. Proc Inst Mech Eng H. 2013;227:788–98.
106.
BoiAJamthikarADSabaL, et al.A survey on coronary atherosclerotic plaque tissue characterization in intravascular optical coherence tomography. Curr Atheroscler Rep. 2018;20:33.
107.
BiswasMKuppiliVEdlaDR, et al.Symtosis: a liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm. Comput Methods Programs Biomed. 2017;155:165–77.
108.
KuppiliVBiswasMSreekumarA, et al.Extreme learning machine framework for risk stratification of fatty liver disease using ultrasound tissue characterization. J Med Syst. 2017;41:152.
109.
JamthikarAGuptaDSabaL, et al.Cardiovascular/stroke risk predictive calculators: a comparison between statistical and machine learning models. Cardiovasc Diagn Ther. 2020;9:420.
110.
SabaLDeyNAshourAS, et al.Automated stratification of liver disease in ultrasound: an online accurate feature classification paradigm. Comput Methods Programs Biomed. 2016;130:118–34.
111.
SuriJS. Imaging based symptomatic classification and cardiovascular stroke risk score estimation. Google Patents. 2011.
112.
MartisRJAcharyaURPrasadHChuaCKLimCMSuriJS. Application of higher order statistics for atrial arrhythmia classification. Biomed Sign Process Control. 2013;8:888–900.
113.
NarayananRKurhanewiczJShinoharaKCrawfordEDSimoneauASuriJS. MRI-ultrasound registration for targeted prostate biopsy. Paper presented at: Proceedings of the IEEE International Symposium on Biomed Imaging: From Nano to Macro, Boston, MA, USA; 2009.
114.
El-BazASuriJS. Big Data in Multimodal Medical Imaging. Boca Raton, FL: CRC Press; 2019.
115.
El-BazASAcharyaRMirmehdiMSuriJS. Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies. Berlin, Germany: Springer Science & Business Media; 2011.
116.
Rikin TrivediLSJasjitSSuriT, eds. 3d Imaging Technologies in Atherosclerosis. Berlin, Germany: Springer; 2015.
ZhaoSSuriJ. Improved 3D reconstruction algorithm for ultrasound B-scan image with freehand tracker. Paper presented at: Medical Imaging 2010: Ultrasonic Imaging, Tomography, and Therapy; 2010.
119.
ZhangLZhaoFYangY, et al.Association between carotid artery intima-media thickness and early-stage CKD in a Chinese population. Am J Kidney Dis. 2007;49:786–92.
120.
KastarinenHUkkolaOKesäniemiYA. Glomerular filtration rate is related to carotid intima–media thickness in middle-aged adults. Nephrol Dial Transplant. 2009;24:2767–72.
121.
ChoDH. Response: increased carotid intima-media thickness is associated with progression of diabetic nephropathy in patients with type 2 diabetes. Endocrinol Metab. 2012;27:171–2.
122.
ChhajedNChandraBSShettyMSShettyC. Correlation of carotid intimal-medial thickness with estimated glomerular filtration rate and cardiovascular risk factors in chronic kidney disease. Saudi J Kidney Dis Transpl. 2014;25:572.
123.
Dehdashti ShahrokhHZakerkishMJenabiA, et al.Association of microalbuminuria and estimated glomerular filtration rate with carotid intima-media thickness in patients with type 2 diabetes mellitus. Jentashapir J Health Res. 2015;6:e27584.
124.
KajitaniNUchidaHASuminoeIKakioYKitagawaMSatoHWadaJ. Chronic kidney disease is associated with carotid atherosclerosis and symptomatic ischaemic stroke. J Int Med Res. 2018;46:3873–83.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
