Abstract
Aim: The Controlling Nutritional Status (CONUT) score and the Prognostic Nutritional Index (PNI) reflect the immunonutritional status of patients. However, the associations of these two indices with cardiovascular disease (CVD) have not been characterized in patients with chronic kidney disease (CKD). Therefore, the current study aimed to determine whether the CONUT score or PNI was associated with prior CVD in patients with CKD.
Methods: A cross-sectional study of 2,751 patients with CKD who were not on dialysis was performed. The patients were grouped into tertiles (T1–T3) of PNI and placed into three groups following their CONUT score: low- (CONUT score, 0), mild- (CONUT score, 1–2), and moderate-to-high- (CONUT score, ≥ 3) risk groups.
Results: Prior CVD was present in 655 (24%) of the participants. Multivariable logistic regression analyses, with adjustment for potential confounders, showed that high CONUT score was associated with prior CVD than the low score (mild-risk group: odds ratio [OR]=1.35, 95% confidence interval [CI]=1.04–1.76; moderate-to-high-risk group: OR=1.66, 95% CI=1.19–2.30). In addition, the lower PNI tertiles were independently associated with prior CVD compared with T3 of PNI (T1: OR=1.45, 95% CI=1.09–1.92; T2: OR=1.32, 95% CI=1.01–1.72).
Conclusions: Both CONUT score and PNI were found to be independently associated with prior CVD in patients with CKD in the present cross-sectional study. A longitudinal study is needed to elucidate whether these two indices are associated with subsequent cardiovascular events.
Introduction
Patients with chronic kidney disease (CKD) are considered to be at high risk of cardiovascular (CV) morbidity and mortality1). A previous epidemiological study showed graded associations between low estimated glomerular filtration rate (eGFR) and the risks of mortality, CV events, and hospitalization2). The National Health and Nutrition Examination Survey of data collected between 2015 and 2018 showed that the prevalence of prior CV diseases (CVD), e.g., coronary heart disease, heart failure, and stroke, was 9.3%3). Studies have also reported that CVD comorbidities are more prevalent in patients with CKD. In the Chronic Renal Insufficiency Cohort Study, conducted in patients with CKD in the USA, the prevalence of prior CVD was 33.3%4), whereas in Japanese patients with CKD, the prevalence of prior CVD was 25.6% and CVD comorbidities became more prevalent as eGFR declined5). The prevalence of prior CVD significantly increased with advancing CKD stage as shown in a previous cross-sectional study6). In the World Health Organization (WHO) statement, malnutrition, in all its forms, includes undernutrition, inadequate vitamins or minerals, overweight, obesity, and resulting diet-related noncommunicable diseases7). Patients with CKD also have a high malnutrition prevalence. CKD-associated malnutrition can be explained by several conditions, including undernutrition, systemic inflammation, comorbidities, hormonal abnormalities, and dialysis8, 9). In addition, undernourished signs have been reported to be more common in patients undergoing dialysis who have CVD than in those who do not10). Accordingly, malnutrition, prior CVD, and CKD have been suggested to may be related.
Several composite nutritional assessments have been currently reported, e.g., the Subjective Global Assessment (SGA)11), the Mini Nutritional Assessment (MNA)12), the Malnutrition Inflammation Score (MIS)13), the Geriatric Nutritional Risk Index (GNRI)14), the Controlling Nutritional Status (CONUT) score15), and the Prognostic Nutritional Index (PNI)16). The SGA, MNA, and MIS are evaluated by experienced clinicians based on the patient’s symptoms and physical examination. Conversely, objective nutritional indices, e.g., GNRI, CONUT score, and PNI, are calculated using widely used and inexpensive markers. The GNRI is calculated from serum albumin level and body mass index (BMI). GNRI may be underestimated in patients with normal or higher BMI levels because it considers low body weight to be a marker of undernutrition17, 18).
Nutritional indices, e.g., the CONUT score and PNI, are increasingly used to assess patients’ nutritional status. The CONUT score, which is calculated using the patient’s serum albumin concentration, total lymphocyte count, and total cholesterol concentration, was developed as a screening tool for the early detection of poor nutritional status15). PNI is calculated as 10×serum albumin (g/dL)＋0.005×lymphocyte count (/µL). These two indices are easy to use because they are calculated using routinely-measured laboratory parameters. Serum albumin is used as an indicator of protein reserve, total cholesterol is used as a marker of energy depletion, and total lymphocyte count reflects immune system status15). Therefore, both the CONUT score and PNI are considered to reflect not only nutritional status but also immune status.
Nutritional assessment tools, e.g., the CONUT score and PNI, are of prognostic value in patients with malignancy, heart failure, peripheral artery disease (PAD), stroke, and coronary artery disease17-26). Several studies have also demonstrated cross-sectional associations of the CONUT score or PNI with the prevalence of prior CVD. For example, the CONUT score is independently associated with carotid atherosclerosis in patients with chronic heart failure21). In addition, in patients with PAD who are undergoing endovascular therapy, those with a high CONUT score are more likely to have critical limb ischemia23). Furthermore, asymptomatic patients with cardiac disease with a high CONUT score have a higher prevalence of previous myocardial infarction or stroke22) and patients with coronary artery disease and a high CONUT score have more underlying comorbidities, including congestive heart failure and extensive coronary artery disease19). The prevalence of transient ischemic attack or previous stroke was higher in patients with low PNI than in those with high PNI in acute ischemic stroke patients with intravenous thrombolysis20). Low PNI was also shown to be associated with a higher prevalence of multiple vessel disease in patients with coronary artery disease17). In addition, one study has reported that the CONUT score is a reliable predictor of all-cause mortality, CVD, and technique failure defined as a permanent switch from peritoneal dialysis to hemodialysis in patients undergoing peritoneal dialysis27).
Several observational studies documented the effects of both the CONUT score and PNI on outcomes in various diseases. Information regarding the comparison of the effects of these two nutritional indices on outcomes is limited and inconsistent. The CONUT score was associated with an increased risk of all-cause mortality, which is in contrast to PNI, in patients with acute myocardial infarction28). However, both the CONUT score and PNI were better correlated with systemic lupus erythematosus disease activity in patients with lupus nephritis, and only PNI was associated with renal outcome29). Another report showed that the CONUT score and PNI were independently associated with technique failure defined as a permanent switch from peritoneal dialysis to hemodialysis in patients undergoing peritoneal dialysis30). Conversely, it is believed that no cross-sectional studies have explored the association of both indices with prior CVD and simultaneously compared the effects of the two indices on prior CVD in patients with CKD who are not undergoing dialysis.
Aim
The present study aimed to determine whether the CONUT score and PNI, which are surrogate markers of nutritional status, are associated with prior CVD and to simultaneously compare the effects of these two indices on prior CVD in a large cross-sectional study of patients with CKD who were not undergoing dialysis.
Methods
Participants
The participants were recruited from a prospective, multicenter, observational cohort study of Japanese patients with CKD who were not on dialysis: the Fukuoka Kidney disease Registry (FKR) study. The FKR study is conducted at a project management center at Kyushu University in Fukuoka and 20 clinical investigation sites in Fukuoka and Saga prefectures. The detailed design and methods for the FKR study have been previously published31). CKD was defined as either of the following that was present for ＞3 months per the Kidney Disease: Improving Global Outcomes clinical practice guidelines32): (1) decreased eGFR ＜60 mL/min/1.73 m2 or (2) eGFR ≥ 60 mL/min/1.73 m2 and one or more of the following markers of kidney damage: albuminuria ≥ 30 mg/g creatinine, urinary sediment abnormalities, electrolyte abnormalities due to tubular disorders, pathologic abnormalities detected by histology, and structural abnormalities (unilateral or bilateral kidney atrophy and unilateral on abdominal ultrasonography and nephrectomized state). The eGFR was calculated in patients ＜18 years old using the Schwartz formula and in patients ≥ 18 years old using the following formula33-35): eGFR (mL/min/1.73 m2)=194×serum creatinine−1.094×age−0.287 (×0.739 if female). The classification of CKD stages based on eGFR is as follows: G1 stage, eGFR ≥ 90 mL/min/1.73 m2; G2 stage, eGFR 60–89 mL/min/1.73 m2; G3a stage, eGFR 45–59 mL/min/1.73 m2; G3b stage, eGFR 30–44 mL/min/1.73 m2; G4 stage, eGFR 15–29 mL/min/1.73 m2; and G5 stage, eGFR ＜15 mL/min/1.73 m2. The study was approved by the Clinical Research Ethics Committee of the Institutional Review Board at Kyushu University (approval number 469-04) and the ethics committees at all the participating institutions. It was also registered in the UMIN Clinical Trials Registry (UMIN000007988). All the participants provided written informed consent.
Between January 2013 and March 2017, 4,476 patients were enrolled in the FKR study. Patients were excluded if information regarding their BMI (n=48), blood pressure (n=44), smoking history (n=501), laboratory test results (n=1,085), CVD history (n=4), or medication history (n=16) was not available or if they had a history of a hematological disorder, e.g., malignant lymphoma or leukemia (n=27). The remaining 2,751 patients were enrolled in the present cross-sectional study.
Data Collection
Serum albumin, serum creatinine, total cholesterol, triglycerides, high-density lipoprotein (HDL) cholesterol, and C-reactive protein (CRP) concentration measurements and the calculation of urinary protein-to-creatinine ratio (Up/Ucr) were performed by a central laboratory (LSI Medience Corp., Tokyo, Japan) using standard techniques. The participants’ white blood cell count, lymphocyte count, and hemoglobin level were extracted from their medical records. Low-density lipoprotein (LDL) cholesterol was determined using the Friedwald formula for patients with a triglyceride concentration of ＜400 mg/dL36). For 59 patients with a triglyceride concentration of ≥ 400 mg/dL, LDL cholesterol was directly measured using a homogeneous assay in an assay kit (MetaboLead LDL-C, Kyowa Medex Co., Ltd., Tokyo, Japan; Cholestest LDL, Sekisui Medical Co., Ltd., Tokyo, Japan; or others) that was used at each hospital that participated in the FKR study.
CONUT scores were made following the ranges of lymphocyte count, serum albumin concentration, and total cholesterol concentration. Specifically, serum albumin concentration ≥ 3.5, 3.0–3.49, 2.5–2.99, and ＜2.5 g/dL g/dL is equal to 0, 2, 4, and, 6 points, respectively; lymphocyte count ≥ 1,600/µL, 1,200–1,599/µL, 800–1,199/µL, and ＜800/µL is equivalent to 0, 1, 2, and 3 points, respectively; and total cholesterol ≥ 180, 140–179, 100–139, and ＜100 mg/dL is equivalent to 0, 1, 2, and 3 points, respectively15). The participants were classified as low- (CONUT score=0), mild- (CONUT score=1–2), moderate- (CONUT score=3–4), or high- (CONUT score ≥ 5) risk groups following previously published criteria23). Moreover, the moderate- and high-risk groups were combined to form a moderate-to-high-risk group before statistical analysis. PNI was calculated using the following formula: 10×serum albumin (g/dL)＋0.005×total lymphocyte count (/µL)37).
The enrolled patients were interviewed and clinically examined during enrollment. Their medical histories and outpatient records were evaluated in detail. Demographic information (age and sex), the use of medication, and the presence of atherosclerotic risk factors (hypertension and diabetes mellitus) at enrollment were recorded for each patient. Hypertension was defined as systolic blood pressure of ≥ 140 mmHg, diastolic blood pressure of ≥ 90 mmHg, or the current use of antihypertensive drugs. Diabetes mellitus was defined as an HbA1c level of ＞6.5%, a 2-h glucose concentration ＞200 mg/dL during 75-g oral glucose tolerance testing or the use of hypoglycemic agents. The renin–angiotensin–aldosterone system (RAAS) inhibitors that were used at enrollment were angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, renin inhibitors, and mineralocorticoid receptor blockers; the immunosuppressants were glucocorticoids, cyclosporine, cyclophosphamide, mizoribine, and tacrolimus; and the lipid-lowering agents were statins, ezetimibe, fibrate, and unsaturated fatty acids. The mean of two blood pressure measurements made on separate days near the time of enrollment was recorded for each participant. BMI was calculated as weight (in kilograms) divided by height (in meters) squared.
Definition of Prior CVD
Prior CVD was defined as a history of ischemic heart disease, congestive heart failure, stroke (brain infarction or hemorrhage), PAD, thoracic aortic aneurysm, or abdominal aortic aneurysm. Ischemic heart disease was defined as myocardial infarction, angina pectoris diagnosed using coronary angiography, or coronary heart disease with revascularization. Congestive heart failure was diagnosed by the attending physician, regardless of hospitalization. Stroke was defined as a permanent neurological deficit with evidence of intracranial lesions obtained using brain computed tomography or magnetic resonance imaging. PAD was defined using the presence of symptoms of Fontaine class II or higher, an ankle-brachial blood pressure index of ＜0.9, arterial stenosis diagnosed on imaging, or revascularization6).
Statistical Analyses
Continuous data are expressed as the median (interquartile range, IQR), and categorical data are expressed as the number (in percentage). Participants were assigned into three groups: low- (CONUT score=0), mild- (CONUT score=1–2), or moderate-to-high- (CONUT score ≥ 3) risk groups and were also categorized according to PNI tertiles. Two groups were compared using the Wilcoxon rank-sum test for nonparametric data. Moreover, logistic regression analyses were used to identify the factors associated with prior CVD. The current study selected traditional CV risk factors (age, sex, smoking, diabetes mellitus, systolic blood pressure, triglycerides, HDL cholesterol, LDL cholesterol, and BMI), nontraditional CV risk factors (hemoglobin, CRP, Up/Ucr, and eGFR), malignancy (other than malignant lymphoma and leukemia) and rheumatoid arthritis that affect nutritional status, use of immunosuppressants and lipid-lowering agents that affect the CONUT score and/or PNI, and use of RAAS inhibitors that are administered frequently as blood pressure-lowering agents in patients with CKD as covariates. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for each variable. In addition, receiver operating characteristic (ROC) curve analyses were used to evaluate the diagnostic performance of CONUT score and PNI for the prediction of prior CVD. The areas under the ROC curves (AUCs) were also calculated for the basic model, which consisted of traditional CV risk factors (age, sex, smoking, diabetes mellitus, systolic blood pressure, triglycerides, HDL cholesterol, LDL cholesterol, and BMI), and for models that also included the CONUT score, PNI, or serum albumin. Whether the addition of the CONUT score or PNI to the basic model improved the diagnostic value of the model for prior CVD versus that of the basic model was also determined. Subgroup analyses were performed according to the sex of the participants, the presence or absence of specific factors, and values of continuous variables below or above the median. The effects of interactions of the CONUT score or PNI and other variables on the risk of prior CVD were estimated by adding the interaction terms for the CONUT score or PNI and other variables to the relevant model. Data were analyzed using STATA version 14 (Stata Corp., College Station, TX, USA) and p＜0.05 was accepted as indicating statistical significance.
Results
Clinical Characteristics of the Entire Group of Participants
The median age of the participants was 67 years old (IQR, 56–76 years old; min–max, 16–97 years old). The median eGFR for the participants was 39.0 mL/min/1.73 m2 (IQR, 22.9–58.0 mL/min/1.73 m2; min–max, 3.8–176.1 mL/min/1.73 m2). Of the 2,751 participants, 634 (23%), 507 (18%), 575 (21%), 689 (25%), and 346 (13%) were categorized as having CKD stage G1–2, G3a, G3b, G4, and G5, respectively. Of the entire group, 655 (24%) had a history of CVD. The prevalence of CVD significantly increased with advancing CKD stage: 41 (6%), 70 (14%), 145 (25%), 270 (39%), and 129 (37%) had CKD stage G1–2, G3a, G3b, G4, and G5, respectively (P for trend ＜0.01). The numbers of participants that were placed into each category of serum albumin concentration, total lymphocyte count, and total cholesterol concentration were as follows: serum albumin concentration: ≥ 3.5 g/dL, n=2,484; 3.0–3.49 g/dL, n=194; 2.5–2.99 g/dL, n=56; and ＜2.5 g/dL, n=17; lymphocyte count: ≥ 1,600/µL, n=1,372; 1,200–1,599/µL, n=737; 800–1,199/µL, n=469; and ＜800/µL, n=173; and total cholesterol: ≥ 180 mg/dL, n=1,747; 140–179 mg/dL, n=795; 100–139 mg/dL, n=198; and ＜100 mg/dL, n=11. The CONUT scores recorded were between 0 and 11. The numbers of participants with low, mild, moderate, and high CONUT scores were 872 (32%), 1,288 (47%), 452 (16%), and 139 (5%), respectively. The median value of PNI was 48.7 (IQR, 45.0–52.4).
Clinical Characteristics of the Participants Based on CONUT Score and PNI
The clinical characteristics of participants based on the CONUT score are shown in Table 1. Of the participants, 591 had either moderate (n=452) or high (n=139) CONUT score and were combined to compose the moderate-to-high-risk group. Patients with high CONUT scores were more likely to be older, male, have diabetes mellitus, have hypertension, have malignancy other than malignant lymphoma and leukemia, have rheumatoid arthritis, and are smokers. High systolic blood pressure and low BMI were associated with a high CONUT score. CRP concentration tended to be high with higher CONUT scores, but this trend was not statistically significant. As the CONUT score was higher, Up/Ucr was higher and eGFR was lower. The prevalence of prior CVD was significantly higher as the CONUT score was higher.
Table 1.
Clinical characteristics of Japanese patients with chronic kidney disease, stratified according to CONUT score
| Variables |
All
(n = 2,751)
|
Low (n = 872)
|
Mild
(n = 1,288)
|
Moderate to high (n = 591)
|
P for trend
|
| Age (years) |
67 (56, 76) |
64 (52, 73) |
67 (55, 76) |
71 (62, 79) |
＜0.01 |
| Male, n (%)
|
1510 (55) |
425 (49) |
701 (54) |
384 (65) |
＜0.01 |
| Diabetes mellitus, n (%)
|
738 (27) |
168 (19) |
330 (26) |
240 (41) |
＜0.01 |
| Hypertension, n (%)
|
2324 (84) |
699 (80) |
1086 (84) |
539 (91) |
＜0.01 |
| Malignancy, n (%)
|
321 (12) |
84 (10) |
144 (11) |
93 (16) |
＜0.01 |
| Rheumatoid arthritis, n (%)
|
72 (3) |
14 (2) |
31 (2) |
27 (5) |
＜0.01 |
| Smoking, n (%)
|
1446 (53) |
418 (48) |
652 (51) |
376 (64) |
＜0.01 |
| Systolic blood pressure (mmHg) |
130 (120, 142) |
130 (119, 141) |
130 (119, 140) |
132 (121, 144) |
0.02 |
| Diastolic blood pressure (mmHg) |
74 (67, 81) |
76 (68, 82) |
74 (67, 81) |
71 (65, 80) |
＜0.01 |
| Body mass index (kg/m2)
|
22.8 (20.5, 25.4) |
23.5 (21.2, 26.1) |
22.8 (20.3, 25.5) |
22.1 (19.9, 24.1) |
＜0.01 |
| Lymphocyte count (×1000/μL) |
1.60 (1.22, 2.03) |
2.04 (1.81, 2.46) |
1.46 (1.25, 1.80) |
1.05 (0.77, 1.32) |
＜0.01 |
| Serum albumin (g/dL) |
4.1 (3.8, 4.3) |
4.2 (3.9, 4.4) |
4.1 (3.8, 4.3) |
3.7 (3.3, 4.1) |
＜0.01 |
| Total cholesterol (mg/dL) |
192 (168, 218) |
210 (195, 230) |
184 (164, 209) |
164 (140, 195) |
＜0.01 |
| Triglycerides (mg/dL) |
120 (88, 170) |
134 (98, 198) |
119 (87, 164) |
104 (80, 144) |
＜0.01 |
| HDL cholesterol (mg/dL) |
57 (46, 71) |
60 (48, 75) |
56 (45, 70) |
54 (43, 68) |
＜0.01 |
| LDL cholesterol (mg/dL) |
104 (84, 126) |
120 (105, 138) |
99 (84, 121) |
85 (68, 107) |
＜0.01 |
| Hemoglobin (g/dL) |
12.7 (11.3, 14.1) |
13.4 (12.1, 14.7) |
12.8 (11.4, 14.0) |
11.5 (10.4, 12.8) |
＜0.01 |
| C-reactive protein (mg/dL) |
0.05 (0.02, 0.13) |
0.05 (0.02, 0.11) |
0.05 (0.02, 0.11) |
0.06 (0.02, 0.19) |
0.06 |
| Up/Ucr (g/g・Creatinine) |
0.39 (0.11, 1.28) |
0.28 (0.10, 0.94) |
0.32 (0.10, 1.15) |
0.94 (0.25, 2.66) |
＜0.01 |
| eGFR (mL/min/1.73 m2)
|
39.0 (22.9, 58.0) |
47.2 (31.6, 65.4) |
39.8 (23.0, 57.9) |
25.0 (14.7, 41.5) |
＜0.01 |
| Use of RAAS inhibitors, n (%)
|
1988 (72) |
594 (68) |
945 (73) |
449 (76) |
＜0.01 |
| Use of immunosuppressants, n (%)
|
531 (19) |
183 (21) |
228 (18) |
120 (20) |
0.56 |
| Use of lipid-lowering agents, n (%)
|
1258 (46) |
365 (42) |
620 (48) |
273 (46) |
0.053 |
| CVD, n (%)
|
655 (24) |
123 (14) |
309 (24) |
223 (38) |
＜0.01 |
| IHD, n (%)
|
318 (12) |
57 (7) |
160 (12) |
101 (17) |
＜0.01 |
| CHF, n (%)
|
80 (3) |
10 (1) |
33 (3) |
37 (6) |
＜0.01 |
| Stroke, n (%)
|
289 (11) |
62 (7) |
134 (10) |
93 (16) |
＜0.01 |
| PAD, n (%)
|
94 (3) |
15 (2) |
41 (3) |
38 (6) |
＜0.01 |
| Thoracic aortic aneurysm, n (%)
|
25 (0.9) |
5 (0.6) |
13 (1) |
7 (1.2) |
0.21 |
| Abdominal aortic aneurysm, n (%)
|
66 (2.4) |
13 (1.5) |
27 (2.1) |
26 (4.4) |
＜0.01 |
Values are expressed as number (percent) or median (interquartile range). CONUT, controlling nutritional status; HDL, high-density lipoprotein; LDL, low-density lipoprotein; Up/Ucr, urinary protein-to-creatinine ratio; eGFR, estimated glomerular filtration rate; RAAS, renin–angiotensin– aldosterone system; CVD, cardiovascular disease; IHD, ischemic heart disease; CHF, congestive heart failure; PAD, peripheral artery disease.
Table 2 shows the clinical characteristics of the participants, according to PNI tertiles. Participants in the lower PNI tertiles were older and more likely to have diabetes mellitus, hypertension, malignancy, rheumatoid arthritis, and smoke. Higher systolic blood pressure and lower BMI were associated with lower PNI. The lower PNI tertiles were associated with higher CRP and Up/Ucr and lower eGFR. A significantly higher prevalence of prior CVD was noted in the lower PNI tertiles.
Table 2.
Clinical characteristics of Japanese patients with chronic kidney disease, stratified according to tertiles of PNI
| Variables |
Tertile 1 (n = 917)
(PNI, 15.62–46.47)
|
Tertile 2 (n = 917)
(PNI, 46.48–50.97)
|
Tertile 3 (n = 917)
(PNI, 50.98–78.47)
|
P for trend
|
| Age (years) |
72 (62, 79) |
67 (56, 75) |
63 (50, 71) |
＜0.01 |
| Male, n (%)
|
535 (58) |
477 (52) |
498 (54) |
0.08 |
| Diabetes mellitus, n (%)
|
329 (36) |
241 (26) |
168 (18) |
＜0.01 |
| Hypertension, n (%)
|
824 (90) |
782 (85) |
718 (78) |
＜0.01 |
| Malignancy, n (%)
|
137 (15) |
101 (11) |
83 (9) |
＜0.01 |
| Rheumatoid arthritis, n (%)
|
39 (4) |
25 (3) |
8 (1) |
＜0.01 |
| Smoking, n (%)
|
542 (59) |
456 (50) |
448 (49) |
＜0.01 |
| Systolic blood pressure (mmHg) |
132 (120, 144) |
130 (120, 141) |
129 (119, 140) |
＜0.01 |
| Diastolic blood pressure (mmHg) |
72 (65, 80) |
74 (67, 81) |
75 (68, 83) |
＜0.01 |
| Body mass index (kg/m2)
|
22.5 (20.2, 24.7) |
22.8 (20.4, 25.4) |
23.3 (20.9, 26.1) |
＜0.01 |
| Lymphocyte count (×1000/μL) |
1.22 (0.94, 1.50) |
1.57 (1.29, 1.88) |
2.07 (1.72, 2.56) |
＜0.01 |
| Serum albumin (g/dL) |
3.7 (3.4, 3.9) |
4.1 (3.9, 4.2) |
4.4 (4.2, 4.6) |
＜0.01 |
| Total cholesterol (mg/dL) |
183 (157, 214) |
190 (168, 215) |
199 (178, 222) |
＜0.01 |
| Triglycerides (mg/dL) |
114 (85, 159) |
121 (87, 169) |
125 (91, 181) |
＜0.01 |
| HDL cholesterol (mg/dL) |
56 (44, 70) |
57 (45, 71) |
57 (47, 72) |
0.08 |
| LDL cholesterol (mg/dL) |
99 (79, 123) |
104 (84, 126) |
110 (93, 130) |
＜0.01 |
| Hemoglobin (g/dL) |
11.7 (10.6, 13.1) |
12.7 (11.5, 13.9) |
13.7 (12.5, 14.9) |
＜0.01 |
| C-reactive protein (mg/dL) |
0.07 (0.02, 0.19) |
0.05 (0.02, 0.11) |
0.04 (0.02, 0.10) |
＜0.01 |
| Up/Ucr (g/g・Creatinine) |
0.93 (0.25, 2.70) |
0.36 (0.11, 1.12) |
0.18 (0.07, 0.62) |
＜0.01 |
| eGFR (mL/min/1.73 m2)
|
27.3 (15.7, 45.0) |
37.6 (22.5, 56.0) |
50.2 (34.3, 70.3) |
＜0.01 |
| Use of RAAS inhibitors, n (%)
|
690 (75) |
684 (76) |
614 (67) |
＜0.01 |
| Use of immunosuppressants, n (%)
|
203 (22) |
164 (18) |
164 (18) |
0.02 |
| Use of lipid-lowering agents, n (%)
|
409 (45) |
439 (48) |
410 (45) |
0.96 |
| CVD, n (%)
|
302 (33) |
222 (24) |
131 (14) |
＜0.01 |
| IHD, n (%)
|
144 (16) |
114 (12) |
60 (7) |
＜0.01 |
| CHF, n (%)
|
44 (5) |
24 (3) |
12 (1) |
＜0.01 |
| Stroke, n (%)
|
130 (14) |
97 (11) |
62 (7) |
＜0.01 |
| PAD, n (%)
|
42 (5) |
38 (4) |
14 (2) |
＜0.01 |
| Thoracic aortic aneurysm, n (%)
|
11 (1) |
10 (1) |
4 (0.4) |
0.09 |
| Abdominal aortic aneurysm, n (%)
|
32 (4) |
21 (2) |
13 (1) |
＜0.01 |
Values are expressed as number (percent) or median (interquartile range). PNI, prognostic nutritional index; HDL, high-density lipoprotein; LDL, low-density lipoprotein; Up/Ucr, urinary protein-to-creatinine ratio; eGFR, estimated glomerular filtration rate; RAAS, renin–angiotensin– aldosterone system; CVD, cardiovascular disease; IHD, ischemic heart disease; CHF, congestive heart failure; PAD, peripheral artery disease.
The ORs (95% CIs) for prior CVD associated with each parameter, established using univariable logistic regression analyses, was shown in Table 3. Prior CVD was significantly associated with older age, male sex, the presence of diabetes mellitus, and smoking. Low hemoglobin level and eGFR, and high CRP and Up/Ucr were associated with prior CVD. Significant associations on low lymphocyte count, low serum albumin concentration, and low total cholesterol were also identified with prior CVD. In addition, a high CONUT score and low PNI were associated with prior CVD.
Table 3.
Odds ratio for prior CVD of each variable in univariable logistic regression analyses
| Variables |
OR (95% CI) |
P
|
| Age (per 10-year higher) |
1.85 (1.71, 2.01) |
＜0.01 |
| Male |
2.71 (2.23, 3.28) |
＜0.01 |
| Diabetes mellitus |
2.48 (2.06, 2.99) |
＜0.01 |
| Malignancy |
1.63 (1.27, 2.10) |
＜0.01 |
| Rheumatoid arthritis |
1.24 (0.73, 2.09) |
0.42 |
| Smoking |
2.42 (2.01, 2.92) |
＜0.01 |
| Systolic blood pressure (per 10-mmHg higher) |
1.04 (0.99, 1.10) |
0.09 |
| Body mass index (per 1-kg/m2 higher)
|
0.999 (0.98, 1.02) |
0.93 |
| Hemoglobin (per 1-g/dL higher) |
0.77 (0.73, 0.81) |
＜0.01 |
| CRP (per 0.01-mg/dL higher) |
1.002 (1.001, 1.004) |
＜0.01 |
| Up/Ucr (per 1-g/g・Creatinine higher) |
1.09 (1.05, 1.14) |
＜0.01 |
| eGFR (per 1-mL/min/1.73 m2 higher)
|
0.964 (0.960, 0.969) |
＜0.01 |
| Use of RAAS inhibitors |
1.41 (1.15, 1.73) |
＜0.01 |
| Use of immunosuppressants |
0.52 (0.40, 0.67) |
＜0.01 |
| Use of lipid-lowering agents |
1.70 (1.42, 2.02) |
＜0.01 |
| Triglycerides (per 10-mg/dL higher) |
1.00 (0.99, 1.01) |
0.64 |
| HDL cholesterol (per 1-mg/dL higher) |
0.971 (0.966, 0.976) |
＜0.01 |
| LDL cholesterol (per 1-mg/dL higher) |
0.990 (0.987, 0.993) |
＜0.01 |
| Lymphocyte count (per 0.001-/μL higher) |
0.58 (0.50, 0.67) |
＜0.01 |
| Serum albumin (per 1-g/dL higher) |
0.51 (0.42, 0.61) |
＜0.01 |
| Total cholesterol (per 10-mg/dL higher) |
0.88 (0.86, 0.90) |
＜0.01 |
| Triglycerides (per 10-mg/dL higher) |
1.00 (0.99, 1.01) |
0.58 |
| CONUT score (per 1-unit higher) |
1.35 (1.27, 1.42) |
＜0.01 |
| PNI (per 1-unit higher) |
0.93 (0.92, 0.94) |
＜0.01 |
Abbreviations: CVD, cardiovascular disease; OR, odds ratio; CI, confidence interval; CRP, C-reactive protein; Up/Ucr, urinary protein-to-creatinine ratio; eGFR, estimated glomerular filtration rate; RAAS, renin– angiotensin–aldosterone system; HDL, high-density lipoprotein; LDL, low-density lipoprotein; CONUT, controlling nutritional status; PNI, prognostic nutritional index.
The adjusted ORs (95% CIs) for prior CVD associated with the CONUT score and PNI is shown in Table 4. The CONUT score (per 1 unit higher) was significantly positively associated with prior CVD in the fully adjusted model (model 3), and participants with mild and moderate-to-high CONUT scores had a significantly higher association with prior CVD than those with low CONUT scores. In contrast, multivariable-adjusted logistic regression analysis (model 3) demonstrated that PNI (per 1 unit higher) was significantly negatively associated with the prevalence of prior CVD, and being in the lower PNI tertiles (T1 and T2) was identified as being an independent risk factor for prior CVD, compared with being in T3 of PNI.
Table 4.
Adjusted odds ratios for prior CVD according to CONUT score and PNI
|
Model 1 |
Model 2 |
Model 3 |
| OR (95%CI) |
P
|
P for trend
|
OR (95% CI) |
P
|
P for trend
|
OR (95% CI) |
P
|
P for trend
|
| CONUT score |
|
|
|
|
|
|
|
|
|
| Low |
Reference |
|
＜0.01 |
Reference |
|
＜0.01 |
Reference |
|
＜0.01 |
| Mild |
1.71 (1.34, 2.17) |
＜0.01 |
|
1.51 (1.17, 1.96) |
＜0.01 |
|
1.34 (1.02, 1.75) |
0.03 |
|
| Moderate to high |
2.62 (2.00, 3.42) |
＜0.01 |
|
2.08 (1.53, 2.82) |
＜0.01 |
|
1.62 (1.15, 2.26) |
＜0.01 |
|
| Continuous (per 1-unit higher) |
1.24 (1.17, 1.31) |
＜0.01 |
– |
1.18 (1.10, 1.26) |
＜0.01 |
– |
1.12 (1.04, 1.21) |
＜0.01 |
– |
| PNI |
|
|
|
|
|
|
|
|
|
| Tertile 1 |
2.04 (1.59, 2.61) |
＜0.01 |
＜0.01 |
1.80 (1.39, 2.32) |
＜0.01 |
＜0.01 |
1.42 (1.06, 1.89) |
0.02 |
0.02 |
| Tertile 2 |
1.64 (1.27, 2.11) |
＜0.01 |
|
1.53 (1.18, 1.98) |
＜0.01 |
|
1.30 (0.997, 1.71) |
0.05 |
|
| Tertile 3 |
Reference |
|
|
Reference |
|
|
Reference |
|
|
| Continuous (per 1-unit higher) |
0.95 (0.94, 0.97) |
＜0.01 |
– |
0.96 (0.94, 0.98) |
＜0.01 |
– |
0.98 (0.96, 0.997) |
0.02 |
– |
Model 1: adjusted for age and sex.
Model 2: model 1, plus adjusted for smoking, diabetes mellitus, systolic blood pressure, triglycerides, HDL cholesterol, and LDL cholesterol.
Model 3: model 2, plus adjusted for malignancy, rheumatoid arthritis, use of immunosuppressants, use of lipid-lowering agents, use of RAAS inhibitors, body mass index, Up/Ucr, hemoglobin, CRP, and eGFR.
Abbreviations: CVD, cardiovascular disease; CONUT, controlling nutritional status; PNI, prognostic nutritional index; OR, odds ratio; CI, confidence interval; HDL, high-density lipoprotein; LDL, low-density lipoprotein; RAAS, renin–angiotensin–aldosterone system; Up/Ucr, urinary protein-to-creatinine ratio; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate.
Furthermore, all participants were divided into two groups based on the total cholesterol concentration (lower total cholesterol group [total cholesterol=71–192 mg/dL, n=1,394] and higher total cholesterol group [total cholesterol=193–417 mg/dL, n=1,357]). The median (IQR) CONUT scores in the lower and higher total cholesterol groups were 2 (1–3) and 0 (0–2), respectively. Additionally, the CONUT score in the higher total cholesterol group was significantly lower than that in the lower total cholesterol group (P＜0.01). The CONUT score (per 1 unit higher) was associated with prior CVD (OR 1.12, 95% CI 1.00–1.24, P=0.04) in the lower total cholesterol group, whereas a significant association between the CONUT score and prior CVD was not found in the higher total cholesterol group (OR 1.14, 95% CI 0.97–1.29, P=0.06).
Results of Subgroup Analyses
The adjusted ORs (95% CIs) for prior CVD associated with the CONUT score and PNI in participants categorized following demographic and clinical characteristics is summarized in Table 5. A significant interaction was noted between the CONUT score and CRP concerning the prevalence of prior CVD. Significant associations were noted between the CONUT score and prior CVD in participants with either low or high CRP concentrations, but the association was stronger in those with low CRP than in those with high CRP. In addition, a significant interaction exists between PNI and BMI concerning the prevalence of prior CVD. PNI was not associated with prior CVD in participants with low BMI, whereas a significant association was noted in participants with high BMI.
Table 5.
Adjusted odds ratio for prior CVD per 1.0-unit higher in CONUT score and PNI among subgroups of Japanese patients with chronic kidney disease
| variables |
Subgroups |
No. of patients |
CONUT score (per 1-unit higher) |
PNI (per 1-unit higher) |
| OR (95% CI) |
P
|
P for interaction
|
OR (95% CI) |
P
|
P for interaction
|
| Age |
Low (≤ 67 years) |
1,409 |
1.28 (1.13, 1.46) |
＜0.01 |
0.12 |
0.95 (0.91, 0.99) |
0.01 |
0.98 |
|
High (＞67 years) |
1,342 |
1.06 (0.96, 1.16) |
0.26 |
|
0.98 (0.96, 1.01) |
0.24 |
|
| Sex |
Male |
1,510 |
1.12 (1.02, 1.22) |
0.02 |
0.47 |
0.98 (0.95, 1.01) |
0.14 |
0.16 |
|
Female |
1,241 |
1.16 (1.01, 1.32) |
0.03 |
|
0.96 (0.93, 0.998) |
0.04 |
|
| Diabetes mellitus |
Absence |
2,013 |
1.17 (1.06, 1.29) |
＜0.01 |
0.12 |
0.98 (0.95, 1.00) |
0.10 |
0.60 |
|
Presence |
738 |
1.08 (0.96, 1.22) |
0.22 |
|
0.97 (0.93, 1.00) |
0.07 |
|
| C-reactive protein |
Low (≤ 0.05 mg/dL) |
1,384 |
1.17 (1.03, 1.32) |
0.01 |
0.04 |
0.98 (0.95, 1.01) |
0.26 |
0.08 |
|
High (＞0.05 mg/dL) |
1,367 |
1.11 (1.00, 1.22) |
0.04 |
|
0.98 (0.95, 1.00) |
0.08 |
|
| eGFR |
Low (≤ 39.04 mL/min/1.73 m2)
|
1,376 |
1.13 (1.03, 1.23) |
＜0.01 |
0.08 |
0.97 (0.95, 0.995) |
0.02 |
0.46 |
|
High (＞39.04 mL/min/1.73 m2)
|
1,375 |
1.13 (0.97, 1.31) |
0.12 |
|
0.99 (0.95, 1.03) |
0.53 |
|
| Use of lipid-lowering agents |
Absence |
1,493 |
1.14 (1.02, 1.27) |
0.02 |
0.26 |
0.97 (0.94, 1.01) |
0.10 |
0.70 |
|
Presence |
1,258 |
1.12 (1.01, 1.25) |
0.03 |
|
0.98 (0.95, 1.01) |
0.11 |
|
| Body mass index |
Low (≤ 22.813 kg/m2)
|
1,376 |
1.11 (0.99, 1.23) |
0.07 |
0.17 |
0.99 (0.96, 1.02) |
0.64 |
0.04 |
|
High (＞22.813 kg/m2)
|
1,375 |
1.15 (1.04, 1.28) |
＜0.01 |
|
0.96 (0.93, 0.99) |
＜0.01 |
|
Adjusted for age, sex, smoking, diabetes mellitus, systolic blood pressure, triglycerides, HDL cholesterol, LDL cholesterol, malignancy, rheumatoid arthritis, use of lipid-lowering agents, use of immunosuppressants, use of RAAS inhibitors, Up/Ucr, body mass index, hemoglobin, CRP, and eGFR.
Abbreviations: CVD, cardiovascular disease; CONUT, controlling nutritional status; PNI, prognostic nutritional index; OR, odds ratio; CI, confidence interval; eGFR, estimated glomerular filtration rate; HDL, high-density lipoprotein; LDL, low-density lipoprotein; RAAS, renin– angiotensin–aldosterone system; Up/Ucr, urinary protein-to-creatinine ratio; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate.
The diagnostic performance concerning prior CVD was evaluated based on the AUCs for the CONUT score alone, PNI alone, the basic model, the basic model＋CONUT score, the basic model＋PNI, and the basic model＋serum albumin (Table 6 and Fig.1). The AUCs for both the basic model＋CONUT score and the basic model＋PNI were significantly higher than that for the basic model. However, no significant difference was noted in the AUCs between the basic model＋CONUT score and the basic model＋PNI (P=0.13). In addition, no difference exists in the AUCs for prior CVD between the basic model＋serum albumin and the basic model. Furthermore, the AUCs of both the basic model＋CONUT score and the basic model＋PNI were significantly higher than those of the basic model＋serum albumin (basic model＋CONUT score vs. basic model＋serum albumin, P=0.0004; basic model＋PNI vs. basic model＋serum albumin, P=0.003).
Table 6.
Area under the ROC curve for prior CVD in each variable
| variables |
AUC |
95% CI |
| CONUT score |
0.6429 |
0.6193, 0.6666 |
| PNI |
0.6283 |
0.6041, 0.6525 |
| Serum albumin |
0.5989 |
0.5743, 0.6234 |
| Basic model |
0.7774 |
0.7580, 0.7967 |
| ＊Basic model＋CONUT score
|
0.7850 |
0.7666, 0.8040 |
| ＊＊Basic model＋PNI
|
0.7827 |
0.7636, 0.8018 |
| ＊＊＊Basic model＋serum albumin
|
0.7780 |
0.7588, 0.7973 |
Basic model: age, sex, smoking, diabetes mellitus, systolic blood pressure, triglycerides, HDL cholesterol, LDL cholesterol, and body mass index.
＊P = 0.002 vs. basic model; ＊＊P = 0.02 vs. basic model; ＊＊＊P = 0.48 vs. basic model
Abbreviations: ROC, receiver operating characteristic; CVD, cardiovascular disease; AUC, area under the ROC curve; CI, confidence interval; CONUT, controlling nutritional status; PNI, prognostic nutritional index; HDL, high-density lipoprotein; LDL, low-density lipoprotein.
Sensitivity analyses were also performed after the exclusion of 378 patients with malignancy as well as rheumatoid arthritis that affects nutritional status (Table 7). Higher CONUT scores and lower PNI were associated with prior CVD in the fully adjusted model (model 3). The results of these sensitivity analyses were similar to those shown in Table 4.
Table 7.
Adjusted odds ratios for prior CVD according to CONUT score and PNI in 2373 patients without malignancy or rheumatoid arthritis (sensitivity analyses)
|
Model 1 |
Model 2 |
Model 3 |
| OR (95%CI) |
P
|
P for trend
|
OR (95% CI) |
P
|
P for trend
|
OR (95% CI) |
P
|
P for trend
|
| CONUT score |
|
|
|
|
|
|
|
|
|
| Low |
Reference |
|
＜0.01 |
Reference |
|
＜0.01 |
Reference |
|
0.01 |
| Mild |
1.74 (1.34, 2.26) |
＜0.01 |
|
1.47 (1.11, 1.94) |
＜0.01 |
|
1.29 (0.96, 1.72) |
0.09 |
|
| Moderate to high |
2.72 (2.02, 3.66) |
＜0.01 |
|
2.01 (1.43, 2.82) |
＜0.01 |
|
1.59 (1.10, 2.31) |
0.01 |
|
| Continuous (per 1-unit higher) |
1.26 (1.18, 1.34) |
＜0.01 |
– |
1.18 (1.10, 1.27) |
＜0.01 |
– |
1.14 (1.04, 1.24) |
＜0.01 |
– |
| PNI |
|
|
|
|
|
|
|
|
|
| Tertile 1 |
2.10 (1.60, 2.75) |
＜0.01 |
＜0.01 |
1.78 (1.34, 2.36) |
＜0.01 |
＜0.01 |
1.44 (1.05, 1.97) |
0.02 |
0.03 |
| Tertile 2 |
1.58 (1.20, 2.09) |
＜0.01 |
|
1.48 (1.11, 1.97) |
＜0.01 |
|
1.26 (0.94, 1.70) |
0.12 |
|
| Tertile 3 |
Reference |
|
|
Reference |
|
|
Reference |
|
|
| Continuous (per 1-unit higher) |
0.95 (0.93, 0.97) |
＜0.01 |
– |
0.96 (0.94, 0.98) |
＜0.01 |
– |
0.97 (0.95, 0.997) |
0.03 |
– |
Model 1: adjusted for age and sex.
Model 2: model 1, plus adjusted for smoking, diabetes mellitus, systolic blood pressure, triglycerides, HDL cholesterol, and LDL cholesterol.
Model 3: model 2, plus adjusted for use of immunosuppressants, use of lipid-lowering agents, use of RAAS inhibitors, body mass index, Up/ Ucr, hemoglobin, CRP, and eGFR.
Abbreviations: CVD, cardiovascular disease; CONUT, controlling nutritional status; PNI, prognostic nutritional index; OR, odds ratio; CI, confidence interval; HDL, high-density lipoprotein; LDL, low-density lipoprotein; RAAS, renin–angiotensin–aldosterone system; Up/Ucr, urinary protein-to-creatinine ratio; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate.
Discussion
The present study was performed to determine whether the CONUT score or PNI was associated with prior CVD in patients with CKD who were not on dialysis. Prior CVD was associated with both high CONUT score and low PNI, independent of potential confounding factors. Furthermore, the diagnostic performance of the CONUT score and PNI was evaluated concerning prior CVD using ROC curve analyses. The addition of the CONUT score or PNI significantly improved diagnostic accuracy compared with the AUC of the basic model, which included a range of traditional CV risk factors. However, no significant difference in the AUCs for prior CVD was noted between the CONUT score and PNI. Furthermore, the findings that participants in the higher total cholesterol group who had a lower CONUT score compared with the lower total cholesterol group did not show a significant association between the CONUT score and prior CVD suggested that the association between the CONUT score and prior CVD may be attenuated if the CONUT score is lower.
Serum albumin has antiinflammatory, antioxidant, anticoagulant, and antiplatelet aggregation activity, and low serum albumin concentration is independently linked with incidents of ischemic heart disease, heart failure, atrial fibrillation, stroke, and venous thromboembolism38). Several previous studies have also demonstrated that low serum albumin concentration is cross-sectionally associated with CVD, including coronary artery disease, heart failure, stroke, and PAD, in patients with stage G5 CKD39) and is also associated with coronary artery disease in patients undergoing dialysis40). Both T and B lymphopenia is common in CKD41-43). In addition, T lymphopenia is caused by both a reduction in the number of T regulatory cells and an increase in the number of proinflammatory T helper 17 lymphocytes, and these changes in the proportions of regulatory cells lead to inflammation. In contrast, B lymphopenia is caused by decreases in the numbers of B1 and B2 cells. In general, the number of B1 lymphocytes declines more than that of B2 lymphocytes, leading to an imbalance in the B-lymphocyte population. These two changes contribute to the development of CKD-associated CVD44). Low serum albumin concentration and lymphocyte count were associated with CVD following univariable logistic analysis in the present study (Table 3). The use of a combination of these two components in the CONUT score or PNI may more accurately reflect immunonutritional status given these findings. Moreover, previous studies have shown a close link between nutrition and immunity45, 46). Adipose tissue volume changes in response to under- or overnutrition. These changes in adipose tissue affect hormone and cytokine secretion from adipose tissue, resulting in changes in the immune cell populations45). Additionally, changes in the overall nutritional status, e.g., undernutrition or obesity, contribute to altered T-cell metabolism and behavior46). However, it is believed that no reports exist showing a direct association of the CONUT score or PNI with immune status. Thus, further studies are needed to elucidate whether these indices directly reflect changes in immunometabolism.
Hypercholesterolemia is a well-known risk factor for CV morbidity and mortality in the general population47-49). In contrast, several observational studies have demonstrated associations between low total cholesterol concentration and high mortality in patients undergoing dialysis50-54) and in those with CKD who are not on dialysis55). This paradoxical association between low total cholesterol and mortality may be explained by the presence of malnutrition and/or inflammation52, 53, 55). However, the relationship between total cholesterol and CVD is inconsistent. Total cholesterol concentration is not associated with coronary heart disease, cerebrovascular disease, or PAD in patients undergoing hemodialysis, as shown in one previous cross-sectional study56). However, in another study, total cholesterol concentration was lower in patients who were undergoing dialysis and had coronary artery disease than in those who did not40). A significant association between low total cholesterol and prior CVD was found in the present study (Table 3). A low total cholesterol concentration increases the CONUT score, in contrast to PNI, because the CONUT score includes total cholesterol as one of its three components. The AUC for prior CVD of the CONUT score tended to be slightly higher than that of PNI in the ROC curve analyses, but the difference did not reach statistical significance. Thus, further studies are warranted to compare the longitudinal relationships of the CONUT score and PNI with adverse outcomes, e.g., CVD events and mortality.
The association between the prevalence of prior CVD and the number of patients who were taking lipid-lowering agents was also examined. The percentage of participants who were taking lipid-lowering agents was higher in participants with prior CVD than in those without prior CVD. Additionally, total cholesterol concentration in participants who were taking lipid-lowering agents was significantly lower compared with those who were not taking lipid-lowering agents (data not shown). The use of lipid-lowering agents was positively associated with prior CVD (Table 3). Thus, the use of lipid-lowering agents may influence the association between lower total cholesterol and prior CVD. Additionally, total cholesterol levels may reflect either the effect of lipid-lowering agents or nutritional status. Consequently, the causal relationships between total cholesterol, nutritional status, and the use of lipid-lowering agents remain uncertain. Furthermore, whether incorporating total cholesterol as a component of a nutritional index such as the CONUT score has a greater advantage in exploring the relationship between the nutritional status and prior CVD compared with the situation without incorporating total cholesterol is unknown considering that no significant difference was noted in the diagnostic performance for prior CVD between the CONUT score and PNI.
No interaction exists for prior CVD between the CONUT score and the use of lipid-lowering agents as shown in the subgroup analyses in Table 5. Furthermore, the CONUT score was independently associated with prior CVD in participants both with and without lipid-lowering agents. No significant interaction for prior CVD was also noted between PNI and the use of lipid-lowering agents. Therefore, the associations between the nutritional status and prior CVD in CKD may not be changed even if lipid-lowering agents were administered.
Previous studies have shown that the CONUT score is closely associated with the concentrations of inflammatory markers (e.g., tumor necrosis factor-α and CRP) and carotid atherosclerosis in patients with chronic heart failure21), and that patients with acute heart failure and high CONUT score are more likely to have a high CRP concentration than those with a low CONUT score26). In addition, patients with coronary artery disease in the lowest PNI tertiles were shown to have a higher prevalence of multivessel coronary disease and higher high-sensitivity CRP concentrations17). The present subgroup analyses identified a significant interaction between the CONUT score and CRP concerning the prevalence of prior CVD. The association between the CONUT score and prior CVD was stronger in participants with low CRP than in those with high CRP (Table 5). However, a significant association between low PNI and high CRP (Table 2) was also found. Participants with high CONUT scores tended to have high CRP concentrations, but no significant association was noted between these two variables (Table 1). In addition, univariable logistic regression analyses showed significant associations of CRP, the CONUT score, and PNI with prior CVD. Furthermore, malnutrition, which is defined as undernutrition with higher SGA scores, has been reported to be closely related to inflammation and atherosclerosis in patients with terminal kidney dysfunction who are not undergoing dialysis57). In this context, the inflammation that is associated with malnutrition may play a role in CVD coexistence.
The double burden of malnutrition is characterized by the coexistence of undernutrition (wasting, stunting, and micronutrient deficiency) along with being overweight and obesity, or diet-related noncommunicable diseases, within individuals, households, and populations and throughout life58). A recent systematic review and meta-analyses demonstrated that the dual burden of malnutrition in the Asia-Pacific region is alarmingly high and is tilted toward obesity59). Chien et al. also reported that obese undernourished individuals, characterized by high BMI and low serum albumin, have a higher prevalence of CVD than lean undernourished individuals (low BMI and low serum albumin) among asymptomatic Taiwanese adults60). A significant interaction between PNI and BMI concerning the prevalence of prior CVD was identified in the present study. Participants with low BMI did not show a significant association between PNI and prior CVD, whereas low PNI was significantly associated with prior CVD in those with high BMI (Table 5). These findings are consistent with those of Chien et al. 60).
According to the WHO statement, higher BMI is a major risk factor for noncommunicable diseases, e.g., CVD (mainly heart disease and stroke), diabetes, musculoskeletal disorders (especially osteoarthritis, which is a highly disabling degenerative disease of the joints), and some cancers61). In a large population-based study, compared with normal BMI, being overweight was also significantly associated with an increased risk of CVD development62). The CVD complications associated with obesity or being overweight are also driven by processes involving hormones and peptides, which include inflammation, insulin resistance, endothelial dysfunction, coronary calcification, and activation of the coagulation, renin–angiotensin, or sympathetic nervous systems63, 64). In this context, individuals with higher BMI are susceptible to having several risk factors for CVD complications. However, univariable logistic analyses in the current study showed that higher BMI was not associated with prior CVD (Table 3), while participants with higher BMI showed a significant association between lower PNI and prior CVD in the subgroup analyses (Table 5). Therefore, a higher BMI may not affect the relationship between lower PNI and the coexistence of CVD. Conversely, the GNRI, which consists of serum albumin and BMI, is a simple objective index of malnutrition to assess the nutritional status of older hospitalized patients. The GNRI is calculated using the following formula: 14.89×serum albumin (g/dL)＋41.7×(present body weight/ideal body weight)14). Lower GNRI was associated with CV events in patients who underwent echocardiographic evaluation65) in patients with CKD66) and in patients with PAD67). However, several studies demonstrated that lower GNRI was independently associated with adverse renal outcomes in patients with CKD68, 69), and the baseline characteristics in these studies did not show a significant association between the GNRI and the prevalence of prior CVD. Univariable logistic regression analysis showed a significant association between lower GNRI and prior CVD in the present study. However, no association was noted between the two variables in the fully adjusted model (data not shown). Lower GNRI indicates lower values of both serum albumin and BMI65-68). Lower PNI in participants with lower BMI did not show a significant association with prior CVD in the present subgroup analysis. This finding may be consistent with the results showing no association between lower GNRI related to lower BMI and prior CVD in previous studies68, 69). Furthermore, the association between PNI and prior CVD may be diminished if BMI was lower.
Data regarding interventions for malnutrition in CKD patients who are not on dialysis are limited. A previous randomized-controlled trial demonstrated that patients with stages 3–4 CKD receiving a nonprotein calorie oral nutritional supplement showed a significant reduction in urinary protein, but no improvement in kidney function was noted compared with the control group70). A recent large cohort study conducted in patients with CKD who were not on dialysis showed that a dietitian-led oral nutritional supplement improved nutritional status trajectories, as measured by the BMI, serum albumin, and the neutrophil-to-lymphocyte ratio71). However, the effect of oral nutritional supplementation on subsequent CVD in CKD remains unknown. Further investigations are warranted to determine whether oral nutritional supplementation is associated with favorable outcomes in CVD development.
The present study had some limitations. First, its cross-sectional nature prevents conclusions from being drawn about causal relationships. Therefore, a longitudinal study should be conducted in this population to determine whether the CONUT score and PNI are associated with subsequent CVD events. Second, nutritional status was only assessed using the CONUT score and PNI on one occasion. Longitudinal changes in these indices may have been more informative. Third, anthropometric (skinfold thickness, midarm muscle circumference, or muscle strength), body composition, or SGA, which are considered to be components of the standard nutritional assessment for patients with CKD, were not performed. The 7-point SGA is recommended as a valid and reliable tool for the assessment of nutritional status in adult patients who are undergoing dialysis. However, more studies are required to determine which composite nutritional indices are appropriate for the nutritional screening or assessment of patients with CKD who are not undergoing dialysis following the Kidney Disease Outcomes Quality Initiative (KDOQI) guidelines 72). Neither the CONUT score nor PNI is recommended by the KDOQI guidelines. Thus, very few studies have been currently conducted in patients with CKD regarding the relationships of the CONUT score or PNI with outcomes. Therefore, the longitudinal relationships between these two indices and adverse outcomes should be elucidated in further studies in such a population. Finally, many participants for whom data were not available were excluded from the present study. Differences between the enrolled and excluded patients were determined (Supplementary Table 1) and significant differences in age, BMI, triglycerides, HDL cholesterol, eGFR, and the prevalence of the use of renin–angiotensin–aldosterone inhibitors and immunosuppressants were found between the two groups. However, no significant difference in the prevalence of prior CVD was noted between the groups, which implies that the exclusion of this many patients may not have altered the associations of the CONUT score and PNI with prior CVD that were identified.
Supplementary Table 1.
Clinical characteristics of the enrolled and excluded patients
|
Enrolled patients (n = 2751)
|
Excluded patients (n = 1725)
|
P
|
| Age (years) |
67 (56, 76) |
67 (52, 73) |
＜0.01 |
| Male, n (%)
|
1510 (55) |
997 (58) |
0.06 |
| Diabetes mellitus, n (%)
|
738 (27) |
491 (28) |
0.23 |
| Hypertension, n (%)
|
2324 (84) |
1404 (82) |
0.01 |
| Smoking, n (%)
|
1446 (53) |
618 (52) |
0.79 |
| Systolic blood pressure (mmHg) |
130 (120, 142) |
129 (118, 142) |
0.14 |
| Diastolic blood pressure (mmHg) |
74 (67, 81) |
74 (67, 82) |
0.14 |
| Body mass index (kg/m2)
|
22.8 (20.5, 25.4) |
23.1 (20.8, 25.9) |
＜0.01 |
| Lymphocyte count (×1000/μL) |
1.60 (1.22, 2.03) |
1.60 (1.23, 2.08) |
0.27 |
| Serum albumin (g/dL) |
4.1 (3.8, 4.3) |
4.1 (3.8, 4.3) |
0.11 |
| Total cholesterol (mg/dL) |
192 (168, 218) |
189 (167, 216) |
0.34 |
| Triglycerides (mg/dL) |
120 (88, 169) |
121 (85, 172) |
＜0.01 |
| HDL cholesterol (mg/dL) |
57 (46, 71) |
56 (45, 69) |
＜0.01 |
| LDL cholesterol (mg/dL) |
104 (84, 126) |
104 (84.9, 124) |
0.96 |
| Hemoglobin (g/dL) |
12.7 (11.3, 14.1) |
12.8 (11.4, 14.2) |
0.29 |
| C-reactive protein (mg/dL) |
0.05 (0.02, 0.13) |
0.05 (0.02, 0.12) |
0.07 |
| Up/Ucr (g/g・Creatinine) |
0.39 (0.11, 1.28) |
0.4 (0.11, 1.35) |
0.65 |
| eGFR (mL/min/1.73 m2)
|
39.0 (22.9, 58.0) |
41.7 (24.2, 61.2) |
＜0.01 |
| Use of RAAS inhibitors, n (%)
|
1988 (72) |
1188 (69) |
0.04 |
| Use of immunosuppressants, n (%)
|
531 (19) |
251 (15) |
＜0.01 |
| Use of lipid-lowering agents, n (%)
|
1258 (46) |
734 (43) |
0.08 |
| CVD, n (%)
|
655 (24) |
386 (22) |
0.30 |
| IHD, n (%)
|
318 (12) |
163 (10) |
0.03 |
| CHF, n (%)
|
80 (3) |
52 (3) |
0.82 |
| Stroke, n (%)
|
289 (11) |
194 (11) |
0.41 |
| PAD, n (%)
|
94 (3) |
50 (3) |
0.34 |
| Thoracic aortic aneurysm, n (%)
|
25 (0.9) |
14 (0.8) |
0.73 |
| Abdominal aortic aneurysm, n (%)
|
66 (2.4) |
28 (2) |
0.08 |
Values are expressed as number (percent) or median (interquartile range).
Abbreviations: HDL, high-density lipoprotein; LDL, low-density lipoprotein; Up/Ucr, urinary protein-to-creatinine ratio; eGFR, estimated glomerular filtration rate; RAAS, renin–angiotensin–aldosterone system; CVD, cardiovascular disease; IHD, ischemic heart disease; CHF, congestive heart failure; PAD, peripheral artery disease.
Conclusion
The present cross-sectional study aimed to determine whether the CONUT score or PNI was associated with prior CVD in patients with CKD. Both of these nutritional tools were associated with prior CVD independent of potential confounding factors. Additionally, no significant difference was noted in the diagnostic performance for prior CVD between the CONUT score and PNI. However, further studies are needed to clarify whether these two indices may have significant value for the prediction of subsequent CV events.
Acknowledgements
The authors thank the participants in the FKR Study, the members of the FKR Study Group listed below, and all the personnel in the participating institutions who were involved in the study. We thank Mark Cleasby, PhD from Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript.
Conflict of Interest
The authors declare that they have no conflict of interest.
Steering Committee and Principal Collaborators of the FKR Study Group
Satoru Fujimi (Fukuoka Renal Clinic), Hideki Hirakata (Fukuoka Renal Clinic), Tadashi Hirano (Hakujyuji Hospital), Tetsuhiko Yoshida (Hamanomachi Hospital), Takashi Deguchi (Hamanomachi Hospital), Hideki Yotsueda (Harasanshin Hospital), Kiichiro Fujisaki (Iizuka Hospital), Keita Takae (Japanese Red Cross Fukuoka Hospital), Koji Mitsuiki (Harasanshin Hospital), Akinori Nagashima (Japanese Red Cross Karatsu Hospital), Ritsuko Katafuchi (Kano Hospital), Hidetoshi Kanai (Kokura Memorial Hospital), Kenji Harada (Kokura Memorial Hospital), Tohru Mizumasa (Kyushu Central Hospital), Takanari Kitazono (Kyushu University), Toshiaki Nakano (Kyushu University), Toshiharu Ninomiya (Kyushu University), Kumiko Torisu (Kyushu University), Akihiro Tsuchimoto (Kyushu University), Shunsuke Yamada (Kyushu University), Hiroto Hiyamuta (Fukuoka University), Shigeru Tanaka (Kyushu University), Dai Matsuo (Munakata Medical Association Hospital), Yusuke Kuroki (National Fukuoka-Higashi Medical Center), Hiroshi Nagae (National Fukuoka-Higashi Medical Center), Masaru Nakayama (National Kyushu Medical Center), Kazuhiko Tsuruya (Nara Medical University), Masaharu Nagata (Shin-eikai Hospital), Taihei Yanagida (Steel Memorial Yawata Hospital), Shotaro Ohnaka (Tagawa Municipal Hospital).
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