Association between Controlling Nutritional Status Score and Severity of Coronary artery stenosis in elderly patients

doi:10.21203/rs.3.rs-2434275/v1

Background

The Controlling Nutritional Status (CONUT) Score is a tool to evaluate the body nutritional state. We investigated whether the CONUT score was associated with the incidence of coronary artery disease (CAD) and the severity of coronary artery stenosis in elderly patients.

Methods

This study involved 3296 elderly inpatients undergone coronary angiography. A demographic and clinical profile, as well as a Gensini score, were collected. The CONUT score were evaluated by the score system. There was a correlation analysis, a multivariable logistic regression analysis, and an analysis of the receiver operating characteristic curve (ROC curve).

Results

Based on the quartiles of CONUT scores, 3296 elderly inpatients were divided into four groups. There were significant differences in the vessel of coronary artery stenosis among the four groups (P < 0.05). The CONUT score was significantly positively correlated with Gensini score (r = 0.416, p < 0.001). After we divided enrolled patients into four groups by Gensini score and degree of coronary artery stenosis, the CONUT score was closely associated with the severity of coronary artery stenosis. For multivariable logistic regression, the CONUT score was risk factors of incidence of CAD and process of atherosclerosis progression after adjustment for potential confounding factors. The AUCs were 0.807 (95% CI: 0.790–0.824) for CAD, 0.717 (95% CI: 0.692–0.742) for mild stenosis, 0.842 (95% CI: 0.824–0.861) for moderate stenosis and 0.887 (95% CI: 0.867–0.908) for Severe stenosis by receiver operating characteristic analysis.

Conclusions

The CONUT score confers a predict factor of CAD in elderly patients, which also indicated the prognosis value in atherosclerosis progression.

Coronary artery disease

the CONUT score

coronary artery stenosis

Gensini score

older people

Cardiovascular diseases (CVDs) are the leading cause of mortality over the world. As the main component of CVDs, Coronary artery disease (CAD) is characterized by the occurrence and development of atherosclerosis, which leads to coronary artery stenosis or occlusion [1]. Age is the critical prognosis factor related to the development of CAD, as well as mortality[2]. Compared with young patients, the incidence of left main coronary artery disease, triple vessel disease and left ventricular dysfunction are higher in older CAD patients, contributing to poor outcome[3]. In general, older persons experience higher rates of undernutrition and malnutrition than the general population[4]. Previous research found that nutritional state was the independent prognosis factor of poor outcome in older patients with CAD [5].

The Controlling Nutritional Status (CONUT) Score is comprised of three main components: albumin levels, total cholesterol levels and lymphocyte count, which assessed the nutritional status of patients originally [6]. It reflects the protein reserves, calories consumed and immune defense states. Several studies have proved that CONUT score was associated with the poor outcome of CAD patients,which indicated malnutrition is an independent predictor of major adverse cardiovascular events in CAD patients[7,8]. However, the relationship between CONUT score and the severity of coronary artery stenosis has not yet been fully elucidated. we investigated the association of the CONUT score with the severity of coronary artery stenosis as well as plaque burden and stability in elder patients. Using the CONUT score as a diagnostic and risk assessment tool for high-risk elder patients, it was concluded that the score can be helpful in clinical practice.

Study design

In total, 3296 inpatients with chest pain and suspected CAD who had undergone coronary angiography were consecutively enrolled in this study between January 2015 and December 2021. A coronary angiography was performed on 2518 patients and 778 controls. An individual with CAD has stenosis of at least 50% of at least one major coronary artery, while the individual in the control group does not have spectacular stenosis of any coronary artery (stenosis of over 50%). Infectious diseases, malignant tumors, immune system diseases, and severe diseases of other systems were excluded. The study was approved by the institutional review board of Shenzhen Traditional Chinese Medicine Hospital(K2022-186-01) and informed consent was obtained from all participants. Figure 1 shows the study flowchart.

Data Collection

The patients’ sex, age, the history of smoker, body mass index(BMI), heart rate (HR), blood pressure(BP),left ventricular ejection fraction (LVEF), blood routine, liver and kidney function,blood lipid indexes, and medical history were obtained and collected for all participants. The day of admission,Blood samples were collected from enrolled patients participant on the day of admission.

Definitions

According to the World Health Organization, Aged 65 and older was defined as elderly[9].The CONUT score calculated from the level of albumin, lymphocyte count, and total cholesterol, according to the relevant diagnostic criteria[10]. The diagnostic criteria of hypertension (HT) [11] and diabetes [12] were based on authoritative international guidelines. Using the Gensini scoring criteria, the severity of coronary artery stenosis was quantified [13]. According to Gensini score, CAD classified into mild stenosis(50%-75%), moderate stenosis(76–90%), severe stenosis (91–100%). Based on the most severity of coronary artery, the enrolled patients were divided into mild stenosis group, moderate stenosis group and severe stenosis group.

Procedure

First, the enrolled patients were divided into four groups by quartile of CONUT score. Then, we explored the correlation among the CONUT score and clinical data. According to the severity of coronary artery and Gensini, patients were divided into four groups and the level of CONUT score were compared among the four groups. We performed multivariate logistic regression and ROC curve to explore the prognosis value of CONUT score for the severity of coronary artery.

Statistical Analyses

IBM Corporation, Armonk, NY, USA, provided SPSS software, version 26.0, for the statistical analysis. To test for normality of continuous variables with a normal distribution, the Kolmogorov-Smirnov test was used. Means and standard deviations were reported for continuous variables with a normal distribution; When non-normally distributed data was reported, the median and range were compared using Student's t test or Mann-Whitney U test.Chi-square analysis was conducted on the enumeration data presented as frequencies and percentages. The correlations between the CONUT score and clinical parameters were assessed using Spearman’s rank order test. Our study investigated the relationship between CONUT score and coronary artery severity using multivariable logistic regression analyses. There were four mixed regression models, which were adjusted for male, age,SBP,DBP,HR,BMI,LVEF,Hypertension,Diabetes,Stroke,History of CAD, Smoker, WBC, RBC, Platelet, HGB, LDL cholesterol,HDL cholesterol, Triglycerides, creatinine, Uric acid, Fasting blood glucose, K, Antiplatelet, Statins, ACE inhibitors/ARBs,β-blockers,CCBs,hypoglycemic agents,gensis. ROC curves were constructed, and the area under the curve (AUC) was calculated to determine the cutoff values for coronary artery severity prognosis. A two-sided p-value < 0.05 was considered statistically significant.

Baseline characteristics of four group based on quartile of CONUT score

According to the quartile of CONUT score, the enrolled patients were divided into four group, Q1(0,n = 1035), Q2(1,n = 689), Q3(2–3,n-890) and Q4(4–12,n = 682). The age of four group were 71.8 ± 4.3, 71.7 ± 4.2, 72.0 ± 4.3 and 72.3 ± 4.8 (P = 0.064). There were significant differences in hypertension, diabetes, smoker and stroke among four groups (all P < 0.05). Compared to Q1,Q2 and Q3, the level of lymphocyte, albumin, Total cholesterol, HGB were the lowest in Q4 group (P < 0.05). For Gensini score, HDL cholesterol,LVEF, there were significant differences in the variables among the four groups (all P < 0.05). For coronary artery, the present of 3-vessel were 4.9%, 9.6%, 13.9% and 26.1%, respectively. There were significant differences in the variables among the four groups (all P < 0.05, Table 1).

Table 1

The baseline clinical characteristics of four groups based on quartile of CONUT score
Factors	CONUT score				P value	P*<0.05
	Q1 (0)	Q2 (1)	Q3 (2–3)	Q4 (4–12)
	(n = 1035)	(n = 689)	(n = 890)	(n = 682)
Male,n(%)	649(62.7)	449(65.2)	592(66.5)	414(60.7)	0.080
Age,years	71.8 ± 4.3	71.7 ± 4.2	72.0 ± 4.3	72.3 ± 4.8	0.064	c,e
Hypertension,n(%)	452(43.7)	339(49.2)	449(50.4)	343(50.3)	0.008	b,c
Diabetes,n(%)	243(23.5)	188(27.3)	271(30.4)	212(31.1)	0.001	b,c
Smoker,n(%)	197(19.0)	164(23.8)	251(28.2)	182(26.7)	< 0.001	b
Stroke,n(%)	112(10.8)	101(14.7)	138(15.5)	102(15.0)	0.011	b,c
History of CAD,n(%)	36(3.5)	23(3.3)	21(2.4)	27(4.0)	0.317
BMI,Kg/m²	23.0 ± 2.0	23.1 ± 1.9	22.9 ± 2.0	22.9 ± 1.9	0.147	d
SBP,mmHg	138.0 ± 10.2	138.3 ± 10.0	137.9 ± 10.0	137.3 ± 10.3	0.324
DBP,mmHg	75.7 ± 5.2	76.2 ± 5.1	75.8 ± 5.1	76.2 ± 5.1	0.092	c
HR,bpm	69.8 ± 5.0	69.8 ± 4.8	69.9 ± 4.6	69.8 ± 4.8	0.922
lymphocyte,10^*9/L	2.2 ± 0.5	2.1 ± 0.6	1.9 ± 0.7	1.6 ± 0.7	< 0.001	a,b,c,d,e,f
albumin,g/L	41.6 ± 3.5	41.8 ± 3.4	37.9 ± 4.7	30.5 ± 5.0	< 0.001	b,c,d,e,f
Total cholesterol,mmol/L	5.3 ± 0.7	4.4 ± 0.7	4.4 ± 1.1	4.3 ± 1.2	< 0.001	a,b,c,e
Gensini score	7(0,9)	7(3,18)	28(8,40)	25(38,42)	< 0.001	b,c,d,e
Fasting blood glucose,mmol/L	6.16 ± 2.11	5.96 ± 1.82	6.20 ± 2.15	6.25 ± 2.45	0.060	d,e
Triglycerides, mmol/L	1.72 ± 0.21	1.61 ± 0.13	1.67 ± 0.11	1.68 ± 0.23	0.311
K,mmol/L	4.12 ± 0.37	4.12 ± 0.36	4.10 ± 0.37	4.12 ± 0.40	0.856
LDL cholesterol,mmol/L	2.52 ± 0.62	2.51 ± 0.63	2.49 ± 0.68	2.64 ± 0.66	0.376
HDL cholesterol,mmol/L	1.29 ± 0.45	1.21 ± 0.40	1.18 ± 0.44	1.19 ± 0.42	< 0.001	a,b,c
WBC,10¹²/L	6.5 ± 1.9	6.4 ± 1.7	6.4 ± 2.0	6.2 ± 2.7	0.052	c,e,f
RBC,10¹²/L	4.6 ± 0.5	4.6 ± 0.5	4.5 ± 0.6	4.5 ± 0.5	0.145
HGB, g/L	139.3 ± 19.8	140.0 ± 18.1	137.0 ± 20.4	133.6 ± 19.6	< 0.001	b,c,d,e,f
Platelet,10⁹/L	209.6 ± 60.4	208.9 ± 55.8	208.4 ± 67.0	205.5 ± 65.9	0.592
creatinine, µmol/L	74.9 ± 40.9	74.1 ± 36.9	74.5 ± 36.8	72.9 ± 38.3	0.757
Uric acid, µmol/L	362.4 ± 99.5	358.6 ± 99.8	358.7 ± 96.4	359.0 ± 94.3	0.518
LVEF,%	53.9 ± 7.6	53.7 ± 7.6	52.8 ± 7.9	52.3 ± 7.6	< 0.001	b,c,d,e
Medication,n (%)
Antiplatelet	486(47.0)	531(77.1)	851(95.6)	650(95.3)	< 0.001	a,b,c,d,e
Statins	698(67.4)	557(80.8)	857(96.3)	666(97.7)	< 0.001	a,b,c,d,e
Diuretics	46(12.4)	167(12.5)	0.984
ACE inhibitors/ARBs	388(37.5)	271(39.3)	367(41.2)	246(36.1)	0.161
β-blockers	386(37.3)	295(42.8)	385(43.3)	265(38.9)	0.023
CCBs	389(37.6)	271(39.3)	366(41.1)	239(35.0)	0.086
hypoglycemic agents	222(21.4)	169(24.5)	248(27.9)	165(28.6)	0.002	b,c
vessel,n (%)
None	549(53.0)	158(22.9)	39(4.4)	32(4.7)	< 0.001	a,b,c,d,e
1-vessel	263(25.4)	273(39.6)	324(36.4)	155(22.7)	< 0.001	a,b,e,f
2-vessel	174(16.8)	192(27.9)	403(45.3)	317(46.5)	< 0.001	a,b,c,d,e
3-vessel	49(4.9)	66(9.6)	124(13.9)	178(26.1)	< 0.001	b,c,e,f
Q1 vs Q2 P < 0.05:a ;Q1 vs Q3 P < 0.05:b ;Q1 vs Q4 P < 0.05:c ;Q2 vs Q3 P < 0.05:d ;Q2 vs Q4 P < 0.05:e ;Q3 vs Q4 P < 0.05:f;

Correlations Of The Conut Score With Clinical Parameters

The CONUT score had a slight positive association with age (r = 0.026, p = 0.049) (Table 2).In addition, the CONUT score was significantly positively correlated with the gensini score (r = 0.416, p < 0.001). Furthermore, the CONUT score showed a significant negative correlation with HDL cholesterol (r=-0.080, p < 0.001), WBC (r=-0.058, p < 0.001), RBC (r=-0.081, p < 0.001), HGB (r=-0.091, p < 0.001), Platelet (r=-0.032, p = 0.011), Uric acid (r=-0.030, p = 0.017) and LVEF (r=-0.056, p < 0.001) (Table 2).

Table 2

Correlations of the CONUT score with clinical parameters
Factors	The CONUT score
Factors	r	P
Age,years	0.026	0.049
BMI,Kg/m2	-0.017	0.183
SBP,mmHg	-0.019	0.133
DBP,mmHg	0.017	0.172
HR,bpm	-0.002	0.903
Gensini	0.416	< 0.001
Fasting blood glucose,mmol/L	0.024	0.061
Triglycerides, mmol/L	-0.013	0.318
K,mmol/L	-0.01	0.430
LDL cholesterol,mmol/L	0.011	0.342
HDL cholesterol,mmol/L	-0.080	< 0.001
WBC,1012/L	-0.058	< 0.001
RBC,1012/L	-0.081	< 0.001
HGB, g/L	-0.091	< 0.001
Platelet,109/L	-0.032	0.011
creatinine, µmol/L	-0.018	0.145
Uric acid, µmol/L	-0.030	0.017
LVEF,%	-0.056	< 0.001

The Conut Score In Patients With Different Coronary Artery Severity

We divided the enrolled patients into the control group(n = 778), mild stenosis group (n = 881), moderate stenosis group(n = 1152) and severe stenosis group(n = 485). The level of CONUT score were 0(0,1), 1(0,2), 2(1,4) and 4(2,6), which were significant differences among four groups (all p < 0.05). Compared to control group, mild stenosis group, moderate stenosis, the incidence of hypertension,diabetes and smoker were the highest in the severe stenosis group (all P < 0.05). The severe stenosis group showed significantly higher age, Gensini score, fasting blood glucose, triglycerides and LDL cholesterol than other three groups (all p < 0.05, Table 3).

Table 3

The baseline clinical characteristics of Control vs Mild stenosis vs Moderate stenosis vs. Severe stenosis in the study
Factors	Control	Mild stenosis	Moderate stenosis	Severe stenosis	P value	P^*<0.05
Factors	(n = 778)	(n = 881)	(n = 1152)	(n = 485)	P value	P^*<0.05
Male,n(%)	502(64.5)	569(64.6)	726(63.0)	307(63.3)	0.857
Age^,years	71.2 ± 4.3	71.6 ± 4.2	71.7 ± 4.5	72.7 ± 4.5	< 0.001	a,b,c,e,f
Hypertension,n(%)	337(43.3)	417(47.3)	576(50.0)	253(52.2)	0.007	b,c
Diabetes,n(%)	152(19.5)	229(26.0)	346(30.0)	187(38.6)	< 0.001	a,b,c,e
Smoker,n(%)	131(16.8)	187(21.2)	303(26.3)	173(35.7)	< 0.001	b,c,e
Strok^e,n(%)	83(10.7)	139(15.8)	163(14.1)	68(14.0)	0.024
History of CAD,n(%)	22(2.8)	31(3.5)	30(2.6)	24(4.9)	0.085
CONUT score	0(0,1)	1(0,2)	2(1,4)	4(2,6)	< 0.001	b,c,e,f
BMI,Kg/m²	23.1 ± 2.1	23.1 ± 1.9	23.0 ± 2.0	22.8 ± 1.7	0.033	c,e
SBP,mmHg	137.7 ± 10.3	138.4 ± 10.1	138.0 ± 10.0	137.1 ± 10.1	0.143	e
DBP,mmHg	75.6 ± 5.2	76.1 ± 5.2	76.0 ± 4.9	76.0 ± 5.2	0.181	a
HR,bpm	69.2 ± 4.7	70.0 ± 4.9	70.1 ± 4.7	70.0 ± 5.0	< 0.001	a,b,c
lymphocyte,10^*9/L	2.1 ± 0.5	2.0 ± 0.5	2.0 ± 0.8	1.8 ± 0.8	< 0.001	b,c,d,e,f
albumin,g/L	40.6 ± 4.5	38.4 ± 4.9	37.8 ± 6.3	36.0 ± 7.7	< 0.001	a,b,c,d,e,f
Total cholesterol,mmol/L	4.8 ± 0.8	4.9 ± 0.8	4.5 ± 1.1	4.4 ± 1.2	< 0.001	a,b,c,d,e,f
Gensini score	0(0,0)	7(6,9)	34(26,42)	58(44,80)	< 0.001	a,b,c,d,e,f
Fasting blood glucose,mmol/L	6.04 ± 2.14	6.23 ± 2.46	6.13 ± 1.93	6.50 ± 1.96	< 0.001	c,d,e,f
Triglycerides, mmol/L	1.60 ± 0.23	1.60 ± 0.22	1.73 ± 0.19	1.83 ± 0.12	< 0.001	b,c,d,e
K,mmol/L	4.12 ± 0.39	4.10 ± 0.37	4.12 ± 0.36	4.11 ± 0.40	0.611
LDL cholesterol,mmol/L	2.55 ± 0.90	2.62 ± 0.85	2.69 ± 0.91	2.73 ± 0.92	0.001	b,c,e
HDL cholesterol,mmol/L	1.31 ± 0.50	1.24 ± 0.43	1.20 ± 0.44	1.13 ± 0.30	< 0.001	a,b,c,e,f
WBC,10¹²/L	6.4 ± 1.9	6.4 ± 1.9	6.3 ± 1.9	6.5 ± 1.8	0.548
RBC,10¹²/L	4.6 ± 0.5	4.6 ± 0.5	4.6 ± 0.6	4.6 ± 0.5	0.643
HGB, g/L	138.0 ± 18.4	137.4 ± 19.5	137.5 ± 20.6	137.8 ± 19.5	0.938
P^latelet,10⁹/L	207.8 ± 60.4	208.7 ± 64.6	207.3 ± 62.9	210.6 ± 61.3	0.786
creatinine, µmol/L	75.2 ± 36.5	75.3 ± 45.3	72.8 ± 23.6	74.0 ± 53.3	0.431
Uric acid, µmol/L	362.2 ± 99.2	356.0 ± 98.8	356.6 ± 97.4	355.8 ± 98.3	0.592
LVEF,%	54.4 ± 7.7	53.5 ± 7.5	53.0 ± 7.7	51.3 ± 7.5	< 0.001	a,b,c,e,f
Medication,n (%)
Antiplatelet	0(0)	881(100)	1152(100)	485(100)	< 0.001	a,b,c
Statins	260(33.4)	881(100)	1152(100)	485(100)	< 0.001	a,b,c
ACE inhibitors/ARBs	283(36.4)	351(39.8)	440(38.2)	198(40.8)	0.349
β-blockers	286(36.8)	360(40.9)	486(42.2)	199(41.0)	0.113
CCBs	292(37.5)	348(39.5)	437(37.9)	188(38.8)	0.842
hypoglycemic agents	133(17.1)	213(24.2)	317(27.5)	171(35.3)	< 0.001	b,c,e
vessel,n (%)
None	778(100.0)	0(0)	0(0)	0(0)	< 0.001	a,b,c
1-vessel	0(0)	721(81.8)	142(16.2)	18(2.0)	< 0.001	a,b,d,e,f
2-vessel	0(0)	196(17.0)	869(75.4)	87(7.6)	< 0.001	a,b,d,f
3-vessel	0(0)	98(20.2)	75(15.5)	312(64.3)	< 0.001	a,b,c,e,f

The Conut Score And The Gensini Score

To explore the relationship between the level of Count score and the severity of coronary artery,we divided the patients into four groups by the quartile of gensini score,Q1(< 6, n = 863), Q2(6–12,n = 801), Q3(14–38,n = 859) and Q4(> 38,n = 773).The level of CONUT score were 0(0,1), 1(0,2), 2(1,4) and 3(1,5), which were significant differences among four groups (all p < 0.05). There were significant differences in hypertension, diabetes, smoke stroke, SBP and HR among the groups (all p < 0.05). Similarly, the Q4 group showed the lowest in albumin and total cholesterol than the other groups (all p < 0.05). For the medical history, there were significant differences in antiplatelet, statins and hypoglycemic agents among four groups(all p < 0.05,Table 4).

Table 4

The baseline clinical characteristics of four groups based on quartile of Gensini score.
Factors	Gensini score				P value	P^*<0.05
	Q1 (< 6)	Q2 (6–12)	Q3 (14–38)	Q4 (> 38)
	(n = 863)	(n = 801)	(n = 859)	(n = 773)
Male,n(%)	554(64.2)	531(66.3)	522(60.8)	497(64.3)	0.126
Age^,years	71.2 ± 4.4	71.7 ± 4.3	71.7 ± 4.4	72.2 ± 4.5	0.038	c
Hypertension,n(%)	384(44.5)	368(45.9)	427(49.7)	404(52.3)	0.007	c,d
Diabetes,n(%)	175(20.3)	215(26.8)	226(26.3)	298(38.6)	< 0.001	c,e,f
Smoker,n(%)	147(17.0)	181(22.6)	223(26.0)	243(31.4)	< 0.001	b,c
Strok^e,n(%)	98(11.4)	129(16.1)	109(12.7)	117(15.1)	0.019
History of CADn(%)	23(2.7)	30(3.7)	24(2.9)	30(3.9)	0.374
Conut score	0(0,1)	1(0,2)	2(1,4)	3(1,5)	< 0.001	b,c,d,e
BMI,Kg/m²	23.1 ± 2.1	23.1 ± 1.9	22.9 ± 2.0	23.0 ± 1.9	0.117
SBP,mmHg	137.7 ± 10.4	138.4 ± 10.0	138.3 ± 10.3	137.1 ± 9.8	0.039	e,f
DBP,mmHg	75.7 ± 5.2	76.0 ± 5.1	76.1 ± 4.9	76.1 ± 5.2	0.298	e,f
HR,bpm	69.3 ± 4.8	70.0 ± 4.9	70.3 ± 4.6	69.7 ± 4.9	< 0.001	a,b,f
lymphocyte,10^*9/L	2.1 ± 0.5	2.0 ± 0.6	1.9 ± 0.7	1.9 ± 0.8	< 0.001	b,c,d,e
albumin,g/L	40.4 ± 4.5	38.4 ± 5.1	37.5 ± 6.2	37.0 ± 7.3	< 0.001	a,b,c,d,e
Total cholesterol,mmol/L	4.8 ± 0.8	4.9 ± 0.9	4.6 ± 1.2	4.4 ± 1.1	< 0.001	b,c,d,e,f
Fasting blood glucose,mmol/L	6.06 ± 2.22	6.20 ± 2.37	6.04 ± 1.94	6.31 ± 2.01	0.032	c,f
Triglycerides, mmol/L	1.61 ± 0.21	1.62 ± 0.19	1.73 ± 0.28	1.77 ± 0.22	0.009	b,c,d,e
K,mmol/L	4.12 ± 0.39	4.10 ± 0.37	4.13 ± 0.38	4.11 ± 0.37	0.167
LDL cholesterol,mmol/L	2.55 ± 0.89	2.63 ± 0.87	2.70 ± 0.94	2.70 ± 0.86	0.002	b,c
HDL cholesterol,mmol/L	1.30 ± 0.49	1.28 ± 0.52	1.16 ± 0.36	1.15 ± 0.31	< 0.001	b,c,d,e
WBC,10¹²/L	6.4 ± 1.9	6.4 ± 1.9	6.3 ± 1.9	6.5 ± 2.6	0.527
RBC,10¹²/L	4.6 ± 0.5	4.6 ± 0.5	4.6 ± 0.6	4.6 ± 0.5	0.865
HGB, g/L	138.0 ± 18.8	137.6 ± 19.2	137.3 ± 19.7	137.7 ± 20.9	0.897
P^latelet,10⁹/L	207.5 ± 61.5	208.6 ± 64.3	208.3 ± 61.1	208.9 ± 60.1	0.970
creatinine, µmol/L	75.3 ± 35.1	72.9 ± 21.2	76.0 ± 25.2	72.4 ± 30.7	0.155
Uric acid, µmol/L	361.4 ± 97.6	358.1 ± 96.5	355.4 ± 98.5	355.4 ± 98.7	0.614
LVEF,%	54.2 ± 7.7	53.6 ± 7.6	52.9 ± 7.7	52.0 ± 7.6	< 0.001	b,c,e,f
Medication,n (%)
Antiplatelet	87(10.1)	799(99.8)	859(100)	773(100)	< 0.001	a,b,c
Statins	345(40.0)	801(100)	1152(100)	485(100)	< 0.001	a,b,c
ACE inhibitors/ARBs	325(37.7)	298(37.2)	332(38.6)	317(41.0)	0.411
β-blockers	330(38.2)	315(39.3)	348(40.5)	338(43.7)	0.132
CCBs	331(38.4)	300(37.5)	329(38.3)	305(39.5)	0.880
hypoglycemic agents	154(17.8)	200(25.0)	201(23.4)	279(36.1)	< 0.001	c,e,f
vessel,n (%)
None	776(89.9)	2(0.2)	0(0)	0(0)	< 0.001	a,b,c
1-vessel	87(10.1)	739(92.3)	117(13.6)	72(9.3)	< 0.001	a,d,e
2-vessel	0(0)	59(7.4)	694(80.8)	333(43.1)	< 0.001	b,c,d,e,f
3-vessel	0(0)	1(0.1)	48(5.6)	368(47.6)	< 0.001	c,e,f

The Conut Score And Coronary Artery Severity

The CONUT score and coronary artery disease are associated based on multivariate logistic regression in Table 5. There were four models after adjusting for male, age, SBP, DBP, HR, BMI, LVEF, Hypertension, Diabetes, Stroke, History of CAD, Smoker, WBC, RBC, Platelet, HGB, LDL cholesterol, HDL cholesterol, Triglycerides, creatinine, Uric acid, Fasting blood glucose, K, Antiplatelet, Statins, ACE inhibitors/ARBs,β-blockers, CCBs, hypoglycemic agents,gensini.The quartiles of CONUT score were independent factors of coronary artery disease in four models (model1: OR = 3.775, 95% CI 3.375–4.223, P < 0.001; model2: OR = 3.699, 95% CI 3.303–4.142, P < 0.001; model3: OR = 4.047, 95% CI 3.587–4.566; model4: OR = 4.050, 95% CI 3.589–4.569). Besides, the higher CONUT score quartile was associated with the incidence of coronary artery disease. The multivariate logistic regression also showed that the CONUT score remained independent predictive factors of the incidence of mild stenosis, moderate stenosis and severe stenosis (all P < 0.05, Table 5).

Table 5

The multivariate logistic regression of coronary artery severity
Models	Conut score
		Coronary artery disease			Mild stenosis			Moderate stenosis			Severe stenosis
		OR	95%CI	P Value	OR	95%CI	P Value	OR	95%CI	P Value	OR	95%CI	P Value
Model 1	continuous variable	2.350	2.149–2.569	< 0.001	1.629	1.475–1.798	< 0.001	2.510	2.280–2.763	< 0.001	2.253	2.046–2.480	< 0.001
	Per quartile	3.775	3.375–4.223	< 0.001	2.654	2.326–3.030	< 0.001	4.472	3.929–5.090	< 0.001	5.109	4.356–5.992	< 0.001
	Q1	-	-
	Q2	3.860	3.103-4.800	< 0.001	3.714	2.905–4.747	< 0.001	3.884	2.935–5.140	< 0.001	4.958	3.228–7.616	< 0.001
	Q3	25.780	18.243–36.424	< 0.001	14.590	10.069–21.140	< 0.001	40.619	27.881–59.175	< 0.001	44.085	27.363–71.026	< 0.001
	Q4	24.396	16.709–35.620	< 0.001	3.686	2.312–5.877	< 0.001	39.244	26.034–59.156	< 0.001	98.291	60.452-159.814	< 0.001
Model 2	continuous variable	2.299	2.102–2.514	< 0.001	1.614	1.462–1.782	< 0.001	2.468	2.240–2.719	< 0.001	2.170	1.970–2.389	< 0.001
	Per quartile	3.699	3.303–4.142	< 0.001	2.630	2.303–3.003	< 0.001	4.445	3.896–5.071	< 0.001	4.972	4.215–5.863	< 0.001
	Q1	-	-
	Q2	3.840	3.078–4.790	< 0.001	3.677	2.872–4.709	< 0.001	4.018	3.017–5.351	< 0.001	4.366	2.785–6.844	< 0.001
	Q3	24.674	17.434–34.919	< 0.001	14.409	9.930-20.907	< 0.001	39.854	27.215–58.364	< 0.001	38.671	23.564–64.464	< 0.001
	Q4	22.953	15.691–33.574	< 0.001	3.592	2.245–5.746	< 0.001	39.444	26.038–59.754	< 0.001	94.259	56.838-156.316	< 0.001
Model 3	continuous variable	2.405	2.190–2.642	< 0.001	1.629	1.473–1.801	< 0.001	2.864	2.561–3.202	< 0.001	2.303	2.068–2.566	< 0.001
	Per quartile	4.047	3.587–4.566	< 0.001	2.708	2.364–3.101	< 0.001	6.368	5.378–7.539	< 0.001	6.405	5.200–7.890	< 0.001
	Q1	-	-
	Q2	4.493	3.568–5.658	< 0.001	3.980	3.086–5.133	< 0.001	6.986	4.995–9.773	< 0.001	7.584	4.485–12.823	< 0.001
	Q3	30.542	21.345–43.701	< 0.001	15.590	10.669–22.781	< 0.001	99.265	62.603-157.398	< 0.001	84.050	45.502-155.256	< 0.001
	Q4	27.475	18.579–40.629	< 0.001	3.741	2.319–6.034	< 0.001	103.375	62.744-170.319	< 0.001	199.602	105.849-376.395	< 0.001
Model 4	continuous variable	2.408	2.192–2.645	< 0.001	1.621	1.466–1.792	< 0.001	2.950	2.632–3.307	< 0.001	2.372	2.122–2.651	< 0.001
	Per quartile	4.050	3.589–4.569	< 0.001	2.687	2.346–3.079	< 0.001	6.776	5.690–8.070	< 0.001	6.748	5.438–8.373	< 0.001
	Q1	-	-
	Q2	4.486	3.561–5.652	< 0.001	4.065	3.147–5.249	< 0.001	6.896	4.923–9.660	< 0.001	7.649	4.493–13.022	< 0.001
	Q3	30.633	21.407–43.835	< 0.001	15.484	10.595–22.629	< 0.001	113.299	70.723-181.507	< 0.001	90.924	48.632-169.993	< 0.001
	Q4	27.281	18.422–40.400	< 0.001	3.527	2.193–5.674	< 0.001	123.833	73.738-207.959	< 0.001	232.514	120.691-447.944	< 0.001
Moldel 1: adjusted for male, age,SBP,DBP,HR;
Moldel 2: adjusted for male, age,SBP,DBP,HR,BMI,LVEF,Hypertension,Diabetes,Stroke,History of CAD,Smoker;
Moldel 3: adjusted for male, age,SBP,DBP,HR,BMI,LVEF,Hypertension,Diabetes,Stroke,History of CAD, Smoker, WBC, RBC, Platelet, HGB, LDL cholesterol, HDL cholesterol, Triglycerides, creatinine, Uric acid;
Moldel 4: adjusted for male, age,SBP,DBP,HR,BMI,LVEF,Hypertension,Diabetes,Stroke,History of CAD, Smoker, WBC, RBC, Platelet, HGB, LDL cholesterol, HDL cholesterol, Triglycerides, creatinine, Uric acid, Fasting blood glucose, K, Antiplatelet, Statins, ACE inhibitors/ARBs,β-blockers, CCBs, hypoglycemic agents,gensis.

Roc Analysis

The Area under the curve (AUC) value of ROC curve reflects the predict ability of coronary artery severity. For coronary artery disease, the AUCs were 0.807 (95% CI: 0.790–0.824) of CONUT score. Tables 6 and Fig- also illustrated the ROC of the CONUT score for the diagnosis of mild stenosis (AUC:0.717, 95% CI: 0.692–0.742, P < 0.05) and the cut-off was 1. For moderate stenosis and severe stenosis, the the cut-off were 2 and 3. The ROC analysis showed the CONUT score had predict ability of coronary artery disease(Fig. 2).

Table 6

The ROC Curve analysis of the MetS with poor collateralization
Factors	AUC	P	95%CI	Se(%)	Sp(%)	Cut off point
Coronary artery disease	0.807	＜0.001	0.790–0.824	80.7	70.6	1
Mild stenosis	0.717	0.001	0.692–0.742	69.2	70.6	1
Moderate stenosis	0.842	＜0.001	0.824–0.861	69.5	90.9	2
Severe stenosis	0.887	＜0.001	0.867–0.908	62.9	95.8	3
Se:sensitive,SP:specifity.

A multivariable and ROC analysis revealed significant CONUT scores in elderly patients with severe coronary stenosis. In addition to its independent predictive value, the CONUT score also contributed to the severity of coronary arteries after adjusting for confounding factors. Therefore, the CONUT score may be useful for diagnosing, evaluating risk, and assessing prognosis in elderly CAD patients. Furthermore, the CONUT score, as a nutritional status score, alos showed diagnostic value for the process of atherosclerotic plaques.

Epidemiology studies have showed older age was cardiovascular disease (CVD) risk factor,especially for CAD[14]. One the one hand, with the increase of age, there were genomic instability、cellular senescence and epigenetic changes in molecular hallmarks [15]. On the other hand, the comorbidities, such as hypertension, diabetes, may also predispose elderly patients to an increased risk of the development and process of atherosclerotic plaques [16]. Compared to the young person, the older persons more likely have poor nutrition status [17]. Previous studies found that malnutrition was associated with the atherosclerosis, especially in dialysis and diabetes patients. Malnutrition had a critical role in the inflammation, endothelial dysfunction, oxidative stress and smooth muscle cell proliferation processes [18]. A previous study enrolled 461 type 2 diabetes patients and divided into malnutrition group and control group by the CONUT score.Multivariate regression analyses demonstrated that the CONUT score was associated with carotid atherosclerosis,especially in old patients [19].

The CONUT score was one of tools to measure nutritional status, which was first reported by Ignacio de in 2005. It consisted of three components: lymphocyte count, albumin levels and total cholesterol levels [20]. Previous studies have found that the CONUT score has predictive value of the incidence of poor outcome in CAD. In clinical practice, Chen SC et al enrolled 3118 CAD patients with percutaneous coronary intervention. After follow-up of 58.5 ± 35.8 months, the CONUT score was independently associated with the incidence of myocardial infarction, cardiovascular death, congestive heart failure and major adverse cardiovascular event (MACE) [21]. A meta analysis analysis nine observational studies with 81,257 CAD patients.they found that per point increase in the CONUT score was correlated with 20 and 23% additional risk of all-cause mortality and MACE, respectively [7].

In this study, we compared the the CONUT score among elderly patients with and without CAD and discovered that the the CONUT score was associated with incidence of CAD by ROC curve. According to the Gensini score and the severity of coronary stenosis, the CONUT score ccould be used to predict the degrees of coronary artery stenosis in old patients. Lymphocytes regulated inflammation and inhibited atherosclerosis. The decrease of lymphocytes indicated that the atherosclerotic burden was increased [22]. A recent study has indicated that a high neutrophil to lymphocyte ratio was associated with carotid intimal and media thickness of atherosclerosis[23]. Albumin levels were not only reflect nutritional status but also related to the acute phase reaction. Lower albumin levels had predictive value of coronary artery persistent injury, which was also indicated the progression of atherosclerosis and thrombosis[24]. For the Atherosclerosis Risk in Communities study, lower serum albumin was an independent risk factors of all-cause death.Besides, older adults with low serum albumin were considered a high-risk group[25]. Cholesterol homeostasis was play a critical role in the atherosclerosis and disruption could exacerbate the inflammatory response. The negative relationship between total cholesterol levels and atherosclerosis cause of the lipid paradox[26]. The reason of lipid paradox is still unclear. We speculated that low total cholesterol level also induced the process of atherosclerosis in hyper-inflammatory and cachexia state. Three central features are present in the CONUT score, including inflammatory, immune, and dyslipidaemia factors. These factors are interrelated as well as independent that play a role in atherosclerosis.

There are several nutritional assessment tools, such as the CONUT score, Prognostic Nutritional Index (PNI), geriatric nutritional risk index (GNRI). we selected the CONUT score in our study that might be attributed to the following aspects. First, three clinical indices evaluate the state of the body accurately,which not only as a tool of malnutrition but also reflect other states. Besides, Several studies indicated that the CONUT score may be more suitable for the assessment of malnutrition in old patients. Wang A et al enrolled 94 Hilar cholangiocarcinoma patients and found that the predictive power of CONUT score was higher than PNI after ROC analysis [27]. In peritoneal dialysis patients, the largest AUC to predict all-cause mortality was the CONUT score (AUC: 0.733; 95% CI, 0.674–0.787),compared to PNI and GNRI [28].

Limitations

In the study, there are some limitations. First, the cross-sectional and single-center study may have introduced selection bias. Second, the coronary artery with moderate-to-severe stenosis based on judgments of the operators. The result might be more convincing under intravascular ultrasound guidance. Furthermore, the use of statins affect the level of total Cholesterol.We adjusted the factor of statin in logistic regression to eliminate the influence of the results.

In elderly patients, CAD is tightly associated with the CONUT score. After adjusting for potential risks and ROC analysis, we found that the CONUT score is an independent predictive factor of atherosclerosis progression in elderly CAD patients.

AUC

area under the curve

BP

blood pressure

BMI

body mass index

CAD

coronary artery disease

CONUT

Controlling Nutritional Status

CVD

cardiovascular disease

GNRI

geriatric nutritional risk index

HR

heart rate

LVEF

left ventricular ejection fraction

MACE

major adverse cardiovascular event

PNI

Prognostic Nutritional Index

ROC curve

receiver operating characteristic curve.

Ethics approval and consent to participate

All the study procedures were conducted following the Declaration of Helsinki.The study was approved by the Ethics Committee of Shenzhen Traditional Chinese Medicine Hospital(K2022-186-01).Informed consent from participants was agreed by Shenzhen Traditional Chinese Medicine Hospital Human Research Ethics Committee.
Consent for publication
Not applicable
Availability of data and materials

The datasets generated and analyzed during the current study are not publicly available owing to data security issues, but are available from the corresponding author on reasonable request.

Competing interests
The authors declare that they have no competing interests.
Funding

The project has been approved by Shenzhen Traditional Chinese Medicine Hospital“3030 Program”Chinese Medicine Clinical Research Project.(G3030202128).

Authors' contributions

Manuscript drafting: Guofu Zhong; conception: Mingtai Chen & Junteng Chen; study design: Mujuan Xu & Yue Han; interpretation of data: Guofu Zhong, Xiao Chang, & Ling Men; analysis: Guofu Zhong & Ling Wang; data acquisition: Ling Wang; supervision: Ling Wang. All authors read and approved the final manuscript.

Acknowledgements
None

Engelbertz C, Pinnschmidt HO, Freisinger E, Reinecke H, Schmitz B, Fobker M,et al. Sex-specific differences and long-term outcome of patients with coronary artery disease and chronic kidney disease: the Coronary Artery Disease and Renal Failure (CAD-REF) Registry.Clin Res Cardiol. 2021; 110:1625-1636 .
Yu YT, Hou ZH, Lu B, An YQ, Gao Y, Yin WH,et al.Prevalence of coronary artery disease in symptomatic patients with zero coronary artery calcium score in different age population.Int J Cardiovasc Imaging.2021; 37:723-729.
Capranzano P, Angiolillo DJ.Antithrombotic Management of Elderly Patients With Coronary Artery Disease.JACC Cardiovasc Interv. 2021; 14:723-738.
Azzolina D, Vedovelli L, Gallipoli S, French M, Ghidina M, Lamprecht M,et al. Nutrients and Caloric Intake Associated with Fruits, Vegetables, and Legumes in the Elderly European Population.Nutrients. 2020; 12:2746.
Wada H, Dohi T, Miyauchi K, Endo H, Tsuboi S, Ogita M, et al. Combined effect of nutritional status on long-term outcomes in patients with coronary artery disease undergoing percutaneous coronary intervention.Heart Vessels. 2018; 33:1445-1452.
Hudzik B, Nowak J, Szkodziński J, Zubelewicz-Szkodzińska B. Visceral Adiposity in Relation to Body Adiposity and Nutritional Status in Elderly Patients with Stable Coronary Artery Disease.Nutrients. 2021; 13:2351.
Arero G, Arero AG, Mohammed SH, Vasheghani-Farahani A. Prognostic Potential of the Controlling Nutritional Status (CONUT) Score in Predicting All-Cause Mortality and Major Adverse Cardiovascular Events in Patients With Coronary Artery Disease: A Meta-Analysis.Front Nutr. 2022; 9:850641.
Wang H, Liu F, Ma H, Yin H, Wang P, Bai B,et al.Associations between depression, nutrition, and outcomes among individuals with coronary artery disease.Nutrition. 2021; 86:111157.
Tee JW, Rutges J, Marion T, Street J, Paquette S, Ailon T, et al. Factors predictive of topographical accuracy in spine level localization.J Spine Surg. 2017; 3:23-30.
Zhang G, Zhang Y, He F, Wu H, Wang C, Fu C. Preoperative controlling nutritional status (CONUT) score is a prognostic factor for early-stage cervical cancer patients with high-risk factors.Gynecol Oncol. 2021; 162:763-769.
Williams B, Mancia G, Spiering W, Rosei EA, Azizi M, Burnier M, et al. 2018 ESC/ESH Guidelines for the management of arterial hypertension. Eur Heart J.2018; 39(33):3021-3104.
Garber AJ, Handelsman Y, Grunberger G, Einhorn D, Abrahamson MJ, Barzilay JI,et al. Consensus statement by the American association of clinical endocrinologists and American college of endocrinology on the comprehensive type 2 diabetes management algorithm - 2020 executive summary. Endocr Pract. 2020;26:107-139.
Sinning C, Lillpopp L, Appelbaum S, Ojeda F, Zeller T, Schnabel R,et al. Angiographic score assessment improves cardiovascular risk prediction: the clinical value of SYNTAX and Gensini application.Clin Res Cardiol. 2013;102:495-503.
Hsu B, Korda R, Naganathan V, Lewis P, Ooi SY, Brieger D,et al.Burden of cardiovascular diseases in older adults using aged care services.Age Ageing.2021; 50:1845-1849.
Shah AI, Alabaster A, Dontsi M, Rana JS, Solomon MD, Krishnaswami A. Comparison of coronary revascularization strategies in older adults presenting with acute coronary syndromes.J Am Geriatr Soc. 2022;70:2235-2245.
Duggan JP, Peters AS, Trachiotis GD, Antevil JL.Epidemiology of Coronary Artery Disease.Surg Clin North Am. 2022;102:499-516.
Jardine M, Miles A, Allen J. Self-reported Swallowing and Nutrition Status in Community-Living Older Adults.Dysphagia. 2021;36:198-206.
Huang Z, Fang J, Song A, Tong Y, Deng H, Wei S,et al. The association between self-management ability and malnutrition-inflammation-atherosclerosis syndrome in peritoneal dialysis patients: a cross-sectional study.BMC Nephrol. 2021;22:13.
Mineoka Y, Ishii M, Hashimoto Y, Nakamura N, Fukui M.Malnutrition assessed by controlling nutritional status is correlated to carotid atherosclerosis in patients with type 2 diabetes.Endocr J. 2019;66:1073-1082.
Ignacio de Ulíbarri J, González-Madroño A, de Villar NG, González P, González B, Mancha A, et al.CONUT: a tool for controlling nutritional status. First validation in a hospital population.Nutr Hosp. 2005;20:38-45.
Chen SC, Yang YL, Wu CH, Huang SS, Chan WL, Lin SJ,et al. Association between Preoperative Nutritional Status and Clinical Outcomes of Patients with Coronary Artery Disease Undergoing Percutaneous Coronary Intervention.Nutrients. 2020;12:1295.
Lin J, Tang B, Feng Z, Hao W, Hu W. Decreased B lymphocytes subpopulations are associated with higher atherosclerotic risk in elderly patients with moderate-to-severe chronic kidney diseases.BMC Nephrol. 2021;22:396.
Meng LB, Yu ZM, Guo P, Wang QQ, Qi RM, Shan MJ, et al.Neutrophils and neutrophil-lymphocyte ratio: Inflammatory markers associated with intimal-media thickness of atherosclerosis.Thromb Res.2018;170:45-52.
Liu S, Niu J, Wu S, Xin Z, Zhao Z, Xu M,et al.Urinary albumin-to-creatinine ratio levels are associated with subclinical atherosclerosis and predict CVD events and all-cause deaths: a prospective analysis.BMJ Open. 2021;11:e040890.
Shannon CM, Ballew SH, Daya N, Zhou L, Chang AR, Sang Y,et al.Serum albumin and risks of hospitalization and death: Findings from the Atherosclerosis Risk in Communities study.J Am Geriatr Soc. 2021;69:2865-2876.
Abe RJ, Abe JI, Nguyen MTH, Olmsted-Davis EA, Mamun A, Banerjee P, et al.Free Cholesterol Bioavailability and Atherosclerosis. Curr Atheroscler Rep.2022; 24:323-336.
Wang A, He Z, Cong P, Qu Y, Hu T, Cai Y, et al. Controlling Nutritional Status (CONUT) Score as a New Indicator of Prognosis in Patients With Hilar Cholangiocarcinoma Is Superior to NLR and PNI: A Single-Center Retrospective Study.Front Oncol.2020;10:593452.
Yang Y, Zhou H, Zhang P, Chao W, Zou Y, Yang M. Evaluation of objective nutritional indexes as predictors of worse outcomes in peritoneal dialysis patients.Nutrition.2020;79-80:110963.

No competing interests reported.

Association between Controlling Nutritional Status Score and Severity of Coronary artery stenosis in elderly patients

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Materials And Methods

Study design

Data Collection

Definitions

Procedure

Statistical Analyses

Results

Baseline characteristics of four group based on quartile of CONUT score

Correlations Of The Conut Score With Clinical Parameters

The Conut Score In Patients With Different Coronary Artery Severity

The Conut Score And The Gensini Score

The Conut Score And Coronary Artery Severity

Roc Analysis

Discussion

Limitations

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1