Controlled Nutritional Status Scores and Bleeding Events in Lower Extremity Arterial Disease　― 3-Year Clinical Outcome Study ―

Abstract

Background: Malnutrition has been associated with poor prognosis in patients with lower extremity arterial disease (LEAD). Malnutrition can influence adverse bleeding events in patients with coronary artery disease. However, the association between bleeding events and malnutrition in patients with LEAD remains unclear. Therefore, this study evaluated the association between nutritional screening with controlled nutritional status (CONUT) score and bleeding events in patients with LEAD.

Methods and Results: This single-center retrospective observational study included 297 patients with LEAD who underwent endovascular therapy between January 2016 and December 2021. The high CONUT group was compared with the low CONUT group. The primary outcome was freedom from bleeding events, which was defined as Bleeding Academic Research Consortium type 3 or type 5. The high CONUT group had significantly higher cumulative bleeding events than the low CONUT group (log-rank P value <0.0001). Univariate analysis revealed that patients with a high CONUT score (hazard ratio [HR] 4.48; 95% confidence Interval [CI] 1.92–10.4; P=0.0005), chronic limb-threatening ischemia (HR 5.30; 95% CI 2.07–13.5; P=0.0005), non-ambulatory (HR 3.12; 95% CI 1.11–8.77; P= 0.03), and chronic kidney disease on hemodialysis (HR 4.01; 95% CI 1.72–9.33; P=0.001) had significantly more bleeding events than those with low CONUT scores.

Conclusions: A high CONUT score at admission is closely associated with bleeding events in patients with LEAD.

Malnutrition is a factor that exacerbates limb salvage and life expectancy among patients with lower extremity arterial disease (LEAD).^1,2 Frailty and low body mass index, which are influenced by malnutrition, have been associated with poor prognosis in patients with chronic limb-threatening ischemia (CLTI).^3,4 Stenvinkel et al. reported that inflammation and malnutrition are closely associated with arteriosclerosis, referencing malnutrition-inflammation-atherosclerosis syndrome.⁵ Chronic inflammation causes malnutrition by affecting appetite and resting energy expenditure, increasing protein hydrolysis and muscle protein breakdown, leading to sarcopenia and frailty.^6,7 Therefore, assessment of nutritional status is important for estimating limb salvage and life expectancy in patients with LEAD, especially patients with CLTI. Traditionally, hypoalbuminemia and low body mass index have been used as objective indicators of nutritional status.^4,8 The controlled nutritional status (CONUT) score is an objective indicator of malnutrition.⁹ A previous assessment of CONUT scores revealed that it could be a predictor of wound healing in patients diagnosed with CLTI.¹⁰ Moreover, the objective malnutrition status of patients with LEAD affects clinical outcomes, such as major adverse cardiovascular events or major adverse limb events.^11–13 Peripheral artery disease (PAD) is a high bleeding risk factor, and CLTI is a strong predictor of bleeding events among patients with LEAD.^14,15 The objective indicators of malnutrition are associated with bleeding events among patients with coronary artery disease; however, it has not been reported among patients with LEAD.¹⁶ Therefore, this study aimed to evaluate the association between the CONUT score and bleeding events in patients with LEAD.

Methods

Study Population

Figure 1 illustrates the study protocol. This single-center, retrospective, observational study included 314 patients who underwent endovascular therapy (EVT) for symptomatic atherosclerotic LEAD between January 2016 and December 2021. The inclusion criteria involved patients aged ≥20 years with an ischemic symptom caused by atherosclerotic LEAD. Eleven patients diagnosed with LEAD without atherosclerosis and were excluded. Six patients who did not have serum albumin, total cholesterol levels, or lymphocyte counts measured at admission were excluded. Overall, 297 cases were included in this study, and the median follow-up period was 1,080 days (interquartile range 415–1,716 days). The patients were divided into a high CONUT group (n=65) and a low CONUT group (n=232).

Figure 1.

Flowchart of the patient enrollment process. Three hundred and fourteen cases were enrolled in this study. Eleven cases were diagnosed as lower extremity artery disease (LEAD) without atherosclerosis and were excluded. Six patients who did not have serum albumin, total cholesterol levels, or lymphocyte counts measured at admission were excluded. Analysis was performed on the remaining 297 cases. EVT, endovascular therapy; CONUT, controlled nutritional status.

Ethics Approval and Informed Consent Statements

This study was approved by the Fukuoka University Institutional Ethics Committee (approval no. 2018M087). The study was conducted in accordance with the principles of the Declaration of Helsinki. Informed consent for the information published in this article was not obtained because this research was a retrospective observational study that did not involve invasion, intervention, or the use of samples obtained from the human body.

Definitions

Bleeding events were defined as Bleeding Academic Research Consortium type 3 or type 5.¹⁷ LEAD is a symptomatic PAD caused by atherosclerotic stenosis or occlusion. It occurs in lower extremity claudication, other ischemia-related exertional leg symptoms, and CLTI. CLTI was defined as chronic ischemic pain at rest, ulceration, or gangrene resulting from an objectively proven arterial occlusive disease. The target lesions of EVT included aortoiliac, femoropopliteal, and below-knee lesions. Regardless of the target lesions and devices used in the procedure, patients were enrolled in this study. The duration of antiplatelet therapy after the procedure was at the physician’s discretion.

The CONUT score was calculated from the serum albumin, total cholesterol levels, and total lymphocyte counts.⁹ The scores, ranging from 0 to 12 points, were categorized as normal (0–1 point), mild risk (2–4 points), moderate risk (5–8 points), or severe risk (9–12 points). Eligible patients were classified into 2 groups based on their CONUT scores: low CONUT group (0–4 points), and high CONUT group (5–12 points), based on previous studies.^10,11,18 The objective status of malnutrition was evaluated using the CONUT score.

Outcome Measurement

The primary outcome was freedom from bleeding events at 3 years, which was defined as Bleeding Academic Research Consortium type 3 or type 5 between the high CONUT and low CONUT groups. Details of bleeding events were collected and evaluated.

The secondary outcome was freedom from major adverse cardiovascular and limb events (MACLEs) between the high CONUT and low CONUT groups. MACLEs were defined as all-cause death, acute coronary syndrome, stroke, major reintervention (EVT or surgical bypass), major amputation, and acute limb ischemia. Major amputation was defined as an above-ankle amputation.

Moreover, freedom from bleeding events between the high CONUT and low CONUT groups in patients with CLTI was analyzed by a post-hoc analysis.

Follow up

The patients were followed up every 3–6 months on an outpatient basis after revascularization. Cardiovascular and bleeding events were confirmed at each visit.

Statistical Analysis

Data on baseline characteristics were used to assess the distribution of variables. Continuous variables were summarized as mean values with standard deviations (SD), and categorical variables were summarized as counts and percentages. The baseline characteristics and number of bleeding events between the high CONUT and low CONUT groups were compared using the P value calculated using the Student’s t-test and Fisher’s exact test. The primary and secondary outcomes of the 2 groups were analyzed using Kaplan-Meier survival curves for the visual presentation of the first time-to-event. The P values were analyzed using log-rank analysis. Hazard ratios (HRs), 95% confidence intervals (CI), and P values were derived from the COX regression model to estimate adverse bleeding events. The variables with significant differences in univariate analysis and those with an important association with bleeding risk in previous studies were entered into the multivariate model as follows: chronic kidney disease (CKD) on hemodialysis, CLTI, and higher CONUT score (per 1.0 point). Moreover, the bleeding events between the high CONUT and low CONUT groups in patients with CLTI were evaluated as a post-hoc analysis. The patients were categorized into the high CONUT (n=54) and low CONUT (n=77) groups. Freedom from bleeding events was visualized using Kaplan–Meier survival curves.

Statistical significance was set at P<0.05. All analyses were performed using JMP software (version 17.2).

Results

Table 1 presents the baseline characteristics of the patients. The mean (±SD) age was 73.5±8.3 years, and 23.9% were women. Most patients were ambulatory without a wheelchair (81.8%). The mean (±SD) of the clinical frailty scale was 2.3±1.8, and the high CONUT group had a significantly higher clinical frailty scale than the low CONUT group (P<0.0001). The prevalence rates of type 2 diabetes, CKD on hemodialysis, chronic heart failure (CHF), cerebral vascular disease (CVD), and dyslipidemia were 65.9%, 29.2%, 52.8%, 26.9%, and 70.7%, respectively. The proportions of patients with type 2 diabetes, CKD on hemodialysis, CHF, CVD, and dyslipidemia were significantly higher in the high CONUT group than in the low CONUT group. Regarding limb conditions, 55.8% presented with a history of intermittent claudication (Rutherford classification 1–3), and rest pain, gangrene, and refractory ulcers were observed in 44.1% of patients. The prevalence of CLTI was significantly higher in the high CONUT group than in the low CONUT group (P<0.0001).

Table 1.

Baseline Patient and Lesion Characteristics

Variable	Overall (n=297)	Low CONUT group (n=232)	High CONUT group (n=65)	P value
Age (years)	73.5±8.3	73.5±8.0	73.6±9.5	0.93
Sex, female	71 (23.9)	56 (24.1)	15 (23.0)	1.00
BMI (kg/m2)	22.7±3.6	22.9±3.5	22.0±4.1	0.08
Current smoker	59 (19.8)	55 (23.7)	4 (6.1)	0.001
Fully ambulant	243 (81.8)	207 (89.2)	36 (55.3)	<0.0001
Clinical frailty scale	2.3±1.8	1.9±1.6	3.7±2.0	<0.0001
Hypertension	251 (84.5)	200 (86.2)	51 (78.4)	0.17
Dyslipidemia	210 (70.7)	172 (74.1)	38 (58.4)	0.02
Diabetes	196 (65.9)	146 (62.9)	50 (76.9)	0.03
CKD on hemodialysis	87 (29.2)	48 (20.6)	39 (60.0)	<0.0001
Chronic heart failure	157 (52.8)	106 (45.6)	51 (78.4)	<0.0001
Coronary artery disease	134 (45.1)	95 (40.9)	39 (60.0)	0.007
Atrial fibrillation	55 (14.8)	25 (10.7)	19 (29.2)	0.0006
Cerebral vascular disorder	80 (26.9)	55 (23.7)	25 (38.4)	0.02
Rutherford classification
Categories 1–3	166 (55.8)	155 (66.8)	11 (16.9)	–
Category 4	17 (5.7)	15 (6.4)	2 (3.0)	–
Category 5	101 (34.0)	54 (23.2)	47 (72.3)	–
Category 6	13 (4.3)	8 (3.4)	5 (7.6)	–
CLTI	131 (44.1)	77 (33.1)	54 (83.0)	<0.0001

Data for the items of ‘age’, ‘BMI’, and ‘clinical frailty scale’ are presented as mean±SD; other data are presented as n (%). BMI, body mass index; CKD, chronic kidney disease; CLTI, chronic limb-threatening ischemia; CONUT, controlled nutritional status.

The laboratory data and proportion of drugs administered at admission are presented in Table 2. Serum hemoglobin levels and all 3 variables comprising the CONUT score were significantly lower in the high CONUT group than in the low CONUT group. Moreover, serum C-reactive protein (CRP) levels were higher in the high CONUT group. However, platelet counts and lipoprotein (a) levels were not significantly different between the 2 groups. The proportions of cilostazol and statin use were higher in the low CONUT group than in the high CONUT group. Moreover, the proportion of proton pump inhibitors (PPIs) was higher in the high CONUT group. However, those of aspirin, P2Y12-inhibitor, and oral anticoagulants, including warfarin and lipid-lowering therapy, except for statins, did not differ between the groups.

Table 2.

Baseline Laboratory Data and Proportion of Drugs Administered at Admission

Variable	Overall (n=297)	Low CONUT group (n=232)	High CONUT group (n=65)	P value
Hemoglobin (g/dL)	12.4±5.5	12.8±6.0	11.2±2.0	0.04
Creatinine (mg/dL)	1.06±0.50	1.04±0.47	1.20±0.68	0.14
eGFR (mL/min/1.73 m2)	56.7±19.6	57.8±19.9	49.6±15.9	0.04
Albumin (g/dL)	3.7±0.5	3.89±0.38	3.07±0.49	<0.0001
CRP (g/dL)	1.1±2.6	0.7±2.4	2.3±3.2	<0.0001
Total cholesterol (mg/dL)	157.1±36.7	164.2±35.7	132.0±28.8	<0.0001
HDL cholesterol (mg/dL)	43.6±14.0	45.4±14.5	37.2±1.2	<0.0001
LDL cholesterol (mg/dL)	86.1±29.6	90.6±29.3	69.8±24.6	<0.0001
Lipoprotein (a) (mg/dL)	24.3±24.6	24.5±25.8	23.6±19.5	0.80
Triglyceride (mg/dL)	116.8±61.5	121.8±65.6	98.7±38.5	0.007
Platelet (103/μL)	22.6±8.4	22.8±8.2	22.0±8.9	0.47
HbA1c (%)	6.5±1.0	6.5±1.0	6.6±1.1	0.32
Lymphocytes (/μL)	1,471.5±673.1	1,606.2±661.3	990.6±465.7	<0.0001
Aspirin	221 (74.4)	175 (75.4)	46 (70.7)	0.52
P2Y12 inhibitor	210 (70.7)	168 (72.4)	42 (64.6)	0.22
Cilostazol	81 (27.2)	73 (31.4)	8 (12.3)	0.008
Warfarin	24 (8.0)	15 (6.4)	9 (13.8)	0.06
OAC	52 (17.5)	36 (15.5)	16 (24.6)	0.09
Proton-pump inhibitor	212 (71.3)	158 (68.1)	54 (83.0)	0.01
H2-blocker	22 (7.4)	19 (8.1)	3 (4.6)	0.42
Statin	196 (65.9)	161 (69.4)	35 (53.8)	0.02
Ezetimibe	32 (10.7)	27 (11.6)	5 (7.6)	0.49
EPA	26 (6.1)	22 (9.4)	4 (6.1)	0.61
PCSK9 inhibitor	1 (0.3)	0 (0)	1 (1.5)	0.21

Data for laboratory items are presented as mean±SD; other data are presented as n (%). CONUT, controlled nutritional status; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; EPA, eicosapentaenoic acid; HbA1c, hemoglobin A1c; HDL, high-density lipoprotein; LDL, low-density lipoprotein; OAC, oral anticoagulant; PCSK9, proprotein convertase subtilisin/kexin type 9.

Figure 2 shows the primary outcomes. Freedom from cumulative bleeding events were significantly lower in the low CONUT group than the high CONUT group (96.1±1.4% vs. 76.8±7.4%; log-rank P value <0.0001). Univariate analysis revealed that patients with a higher CONUT score per 1.0 point (HR 1.39; 95% CI 1.18–1.63; P=0.0001), CLTI (HR 5.30; 95% CI 2.07–13.5; P=0.0005), non-ambulatory (HR 3.12; 95% CI 1.11–8.77; P=0.03), and CKD on hemodialysis (HR 4.01; 95% CI 1.72–9.33; P=0.001) had significantly more bleeding events (Table 3). Moreover, patients with low body mass index (per −1.0 kg/m²), atrial fibrillation, CHF, and low serum hemoglobin (<11.0 g/dL) did not exhibit significant bleeding events. CLTI (HR 2.86; 95% CI 0.99–8.23; P=0.04) was a stronger predictor than other bleeding factors (higher CONUT score, CKD on hemodialysis) in multivariate analysis.

Figure 2.

Cumulative bleeding events between the high and low controlled nutritional status (CONUT) groups using the Kaplan-Meier curve. EVT, endovascular therapy; SE, standard error.

Table 3.

Association Between Baseline Characteristics and Bleeding Events Calculated Using COX Hazards Analysis

Variable	Unadjusted HR (95% CI); P value	Adjusted HR (95% CI); P value
Sex, female	1.05 (0.39–2.87); 0.90	NI
Age (per 10)	0.98 (0.59–1.64); 0.94	NI
BMI (per −1.0)	0.98 (0.88–1.10); 0.72	NI
Non-ambulatory	3.12 (1.11–8.77); 0.03	NI
Hypertension	1.03 (0.30–3.48); 0.96	NI
Diabetes	1.45 (0.56–3.72); 0.43	NI
Coronary artery disease	1.14 (0.50–2.58); 0.75	NI
CKD on hemodialysis	4.01 (1.72–9.33); 0.001	2.38 (0.88–5.37); 0.08
Chronic heart failure	2.10 (0.90–5.36); 0.08	NI
Atrial fibrillation	1.97 (0.69–5.20); 0.20	NI
CLTI	5.30 (2.07–13.5); 0.0005	2.86 (0.99–8.23); 0.04
Serum albumin (per −0.5)	1.92 (1.27–2.86); 0.001	NI
Serum CRP (per 1.0)	1.11 (0.98–1.21); 0.08	NI
CONUT (per 1.0)	1.39 (1.18–1.63); 0.0001	1.20 (0.98–1.46); 0.06
Anemia (hemoglobin <11 g/dL)	2.21 (0.97–5.06); 0.058	NI
Aspirin use	1.72 (0.50–5.82); 0.38	NI
P2Y12 inhibitor use	1.14 (0.46–2.79); 0.76	NI
Cilostazol use	0.71 (0.26–1.94); 0.51	NI
OAC use	1.21 (0.44–3.34); 0.69	NI
Warfarin use	0.77 (0.17–3.50); 0.74	NI
Gastric acid inhibitor use	1.57 (0.53–4.65); 0.41	NI

CI, confidence interval; HR, hazard ratio; NI, not included.

Table 4 presents the details of the bleeding events observed between the 2 groups. The rate of intracerebral hemorrhage and subdural hematoma was significantly higher in the high CONUT group than in the low CONUT group. However, the rate of gastrointestinal hemorrhage was insignificantly different between the 2 groups.

Table 4.

Details of Bleeding Events Between the 2 Groups

Variable	Overall (n=297)	Low CONUT group (n=232)	High CONUT group (n=65)	P value
Subarachnoid hemorrhage	2 (0.6)	1 (0.4)	1 (1.5)	0.39
Intracerebral hemorrhage	5 (1.6)	1 (0.4)	4 (6.1)	0.008
Subdural hematoma	2 (0.6)	0 (0)	2 (3.0)	0.04
Gastrointestinal hemorrhage	9 (3.0)	7 (3.0)	2 (3.0)	1.00
Other	4 (1.3)	3 (1.2)	1 (1.5)	1.00

Data are presented as n (%). CONUT, controlled nutritional status.

Figure 3 shows the Kaplan-Meier curve of freedom from MACLEs between the 2 groups. Cumulative MACLEs were significantly higher in the high CONUT group than in the low CONUT group (log-rank P value <0.0001).

Figure 3.

Freedom from cumulative major adverse cardiovascular and limb events (MACLEs) between the high and low controlled nutritional status (CONUT) groups using the Kaplan-Meier curve. EVT, endovascular therapy; SE, standard error.

We compared the high CONUT score group with the low CONUT score group in patients with CLTI (Supplementary Table). The proportions of patients with non-ambulatory CKD on hemodialysis, CHF, and atrial fibrillation were significantly higher in the high CONUT group than in the low CONUT group. However, the proportions of patients with dyslipidemia and diabetes did not differ. Supplementary Figure revealed that the CONUT score was associated with bleeding events in patients with CLTI (log-rank P value=0.04).

Discussion

This study newly revealed that a high CONUT score was associated with bleeding events among patients with LEAD. This finding underscores the importance of careful bleeding risk assessment in the management of malnutrition in patients with PAD.

The present study showed that the high CONUT group included more patients with high frailty, CLTI, CHF, CVD, CKD on hemodialysis, and higher CRP levels. Serum CRP levels, which are an indicator of acute and chronic inflammation, can be a predictor of future cardiovascular events.¹⁹ Tokuda et al. reported that serum CRP levels could affect long-term cardiac events in a Japanese population with coronary artery disease.²⁰ Moreover, serum CRP levels are closely associated with all-cause mortality and the nutritional status of patients with CLTI.²¹ Previous studies have reported significantly higher MACLEs in the high CONUT group than in the low CONUT group.^10,11,21 In this study, the outcome of MACLEs was similar to previous studies. Moreover, the concept of oral frailty has been in the spotlight in recent years. Ogawa et al. reported that poor oral function was associated with less favorable outcomes in patients with cardiovascular diseases.²² Oral frailty may contribute to malnutrition, leading to physical frailty and muscle weakness. Therefore, this study revealed that malnutrition was closely associated with limb severity, high frailty, severe comorbidities such as CHF, CVD and CKD on hemodialysis, and prognosis of the patients with LEAD.

This study revealed the relationship between objective indicators of malnutrition and bleeding events in patients with LEAD. The nutritional assessment of patients in hospitalization is important to assess the prognosis and future fatal events,^2–4,11,12 and the Global Leadership Initiative on Malnutrition is proposed as a nutritional assessment for diagnosis and grading the severity of malnutrition.²³ However, nutrition screening is the first step in identifying patients with nutritional disorders, especially those who are at risk of malnutrition or suspected to be at risk of malnutrition. The CONUT score is feasible for evaluating the nutrition status of patients easily, and the system seems to be an efficient tool for early detection of malnutrition.⁹ Therefore, we evaluated nutritional status using CONUT scores. The CONUT score is calculated using serum albumin, total cholesterol levels, and lymphocyte counts. The serum albumin level is an important indicator of the nutritional status of patients. Tatami et al. reported that a decreased serum albumin level predicted bleeding events in patients with coronary artery disease after percutaneous coronary intervention.²⁴ However, systemic inflammation, decreased albumin synthesis in the liver, and loss of albumin to the extravascular space can occur in hypoalbuminemia.^25,26 The levels of total cholesterol and lymphocyte counts are used to assess nutritional status, but 1 of these cannot accurately indicate a patient’s nutritional status. Moreover, it has been reported that PAD is a part of polyvascular disease.²⁷ Most patients with PAD, especially CLTI in the Japanese population, have many comorbidities, frailty, and sarcopenia.²⁸ As mentioned above, inflammation is closely associated with atherosclerosis. Evaluation using data from a single laboratory data is not suitable for the assessment of nutritional status. Conversely, the CONUT scoring system can be useful for nutritional screening among patients with PAD. The mechanism is unclear that malnutrition is closely associated with bleeding events. Previous reports have described that malnutrition can cause vitamin deficiency and a decreased immune system.^29,30 These mechanisms may lead to coagulopathy and bleeding.

Univariate analysis revealed that patients with a high CONUT score, CLTI, non-ambulatory, and CKD on hemodialysis had significantly more bleeding events. Tokuda et al. reported that CLTI is a residual factor for hemorrhage in patients with LEAD.¹⁵ In Japan, many patients on hemodialysis also have CLTI. In patients on hemodialysis, vascular calcification can be accelerated because of abnormalities in calcium-phosphate metabolism and dysregulated microcirculatory function.^31,32 Additionally, Ohtake et al. reported that the progression of vascular calcification in the lower limbs was strongly associated with long-term prognosis in patients undergoing hemodialysis.³³ Vascular calcification may contribute to vascular vulnerability, which is a risk factor for bleeding. Therefore, the pathological conditions are different between CLTI and intermittent claudication, and CLTI is the most advanced stage of LEAD. As previously mentioned, inflammation and malnutrition are closely associated with arteriosclerosis, leading to frailty, especially in patients with advanced CKD. Consequently, CLTI had a strong association with bleeding events in the multivariate analysis. Supplementary Figure shows that the outcome of bleeding events in the CLTI group was similar to the primary outcome. The CONUT score may be a useful indicator of hemorrhage in patients with CLTI, and further study is needed.

The rate of intracerebral hemorrhage and subdural hematoma was significantly higher in the high CONUT group than in the low CONUT group. In contrast, Tokuda et al. reported a higher incidence of gastrointestinal bleeding events in patients with CLTI than in those with intermittent claudication.¹⁵ This study revealed that the proportion of PPIs was higher in the high CONUT group, while gastrointestinal hemorrhage was insignificantly different between the groups. Yamamoto et al. reported that the proportion of PPIs was associated with a lower incidence of gastrointestinal bleeding events in patients with coronary artery disease, especially those at high bleeding risk.³⁴ Therefore, it may lead to a reduced risk of fatal gastrointestinal bleeding events in the high CONUT group. Moreover, total cholesterol levels can be influenced by the status of immune deficiency, systemic inflammation, and lipid-lowering therapy. The prevalence of dyslipidemia and administration of statins were higher in the low CONUT group than in the high CONUT group. However, the total cholesterol and low-density lipoprotein levels were significantly lower in the high CONUT group. Previous reports have described that genetically low serum cholesterol levels are associated with an increased risk of intracerebral hemorrhage, regardless of lipid-lowering therapy.^35,36 The underlying pathological mechanism remains unclear. In the present study, high CONUT scores were associated with intracerebral hemorrhage, and CONUT scores may predict the occurrence of intracerebral hemorrhage in patients with LEAD.

In this study, the duration of dual antiplatelet therapy (DAPT) was not considered because the regimen of DAPT is different for each target lesion and final device in terms of peripheral intervention. Moreover, it was a retrospective study, and we were not able to follow up the duration of DAPT. The number of antithrombotic drugs was closely associated with bleeding events among patients with LEAD.¹⁵ Therefore, it was a major limitation in this study.

The CONUT score is associated with future cardiovascular events and wound healing rate, and this study revealed the relationship between the CONUT score and fatal bleeding events. Although it is unclear whether the intervention on malnutrition can influence the prognosis in patients with PAD, a previous study reported that the nutritional intervention was able to improve long-term prognosis among patients with heart failure.³⁷ The high CONUT group had more patients with CHF, respectively. Therefore, nutritional intervention should be considered in patients with LEAD, especially for those with poor nutritional status.

Study Limitations

This study had some limitations. First, this was a single-center, retrospective observational study. The sample size was small, which may have caused a selection bias. Second, the effects of the duration and number of antithrombotic drugs were not considered. As mentioned above, we did not evaluate the association between bleeding events and drug administration. Third, comorbidities, such as systemic inflammatory diseases and cancer, were not considered. These factors may affect the objective indicators of nutrition when assessing the nutrition and inflammation status.

Conclusions

In patients with LEAD, high CONUT scores are associated with limb severity and clinical outcomes, including fatal bleeding events.

Acknowledgments

The authors thank the medical staff at the cardiac catheterization laboratory at Fukuoka University Hospital, and Editage (www.editage.jp) for editing the English language.

Sources of Funding

The authors received no financial support for this research, authorship, or publication.

Disclosures

S.M. is a member of Circulation Reports’ Editorial Team. The authors declare no potential conflicts of interest regarding this research, authorship, or publication. We have no other identifying information related to the authors and their institutions, such as funders and approval committees, that might compromise our anonymity.

IRB Information

This study was approved by the Fukuoka University Institutional Ethics Committee (approval no. 2018M087). The study was conducted in accordance with the principles of the Declaration of Helsinki. Informed consent for the information published in this article was not obtained because this research was a retrospective observational study that did not involve invasion, intervention, or the use of samples obtained from the human body.

Data Availability

Statistical analysis plans and the data presented in this article will be shared on reasonable request to the corresponding author. The data will be shared in an Excel file via email. The data will be available for 1 year from publication.

Supplementary Files

Please find supplementary file(s);

https://doi.org/10.1253/circrep.CR-25-0076

References

• 1.   Lin TY, Chou HH, Hsieh TH, Huang HL, Hung SC; TRENDPAD Study Group. Malnutrition is associated with increased morbidity and death in dialysis patients undergoing endovascular therapy for peripheral artery disease. Eur J Vasc Endovasc Surg 2022; 64: 225–233, doi:10.1016/j.ejvs.2022.03.045.
• 2.   Mizobuchi K, Jujo K, Minami Y, Ishida I, Nakao M, Hagiwara N. The baseline nutritional status predicts long-term mortality in patients undergoing endovascular therapy. Nutrients 2019; 11: 1745, doi:10.3390/nu11081745.
• 3.   Soga Y, Iida O, Takahara M, Hirano K, Suzuki K, Kawasaki D, et al. Two-year life expectancy in patients with critical limb ischemia. JACC Cardiovasc Interv 2014; 7: 1444–1449, doi:10.1016/j.jcin.2014.06.018.
• 4.   Murata N, Soga Y, Iida O, Yamauchi Y, Hirano K, Kawasaki D, et al. Complex relationship of body mass index with mortality in patients with critical limb ischemia undergoing endovascular treatment. Eur J Vasc Endovasc Surg 2015; 49: 297–305, doi:10.1016/j.ejvs.2014.10.014.
• 5.   Stenvinkel P, Heimbürger O, Lindholm B, Kaysen GA, Bergström J. Are there two types of malnutrition in chronic renal failure? Evidence for relationships between malnutrition, inflammation and atherosclerosis (MIA syndrome). Nephrol Dial Transplant 2000; 15: 953–960, doi:10.1093/ndt/15.7.953.
• 6.   Piccoli GB, Cederholm T, Avesani CM, Bakker SJL, Bellizzi V, Cuerda C, et al. Nutritional status and the risk of malnutrition in older adults with chronic kidney disease – Implications for low protein intake and nutritional care: A critical review endorsed by ERN-ERA and ESPEN. Clin Nutr 2023; 42: 443–457, doi:10.1016/j.clnu.2023.01.018.
• 7.   Molinari P, Caldiroli L, Abinti M, Nardelli L, Armelloni S, Cesari M, et al. Frailty is associated with malnutrition-inflammation syndrome in older CKD patients. Nutrients 2024; 16: 2626, doi:10.3390/nu16162626.
• 8.   Peacock MR, Farber A, Eslami MH, Kalish JA, Rybin D, Doros G, et al. Hypoalbuminemia predicts perioperative morbidity and mortality after infrainguinal lower extremity bypass for critical limb ischemia. Ann Vasc Surg 2017; 41: 169–175.e4, doi:10.1016/j.avsg.2016.08.043.
• 9.   Ulíbarri JI, González-Madroño A, Villar NGP, 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.
• 10.   Mine K, Sugihara M, Fujita T, Kato Y, Gondou K, Arimura T, et al. Impact of controlling a nutritional status score on wound healing in patients with chronic limb-threatening ischemia after endovascular treatment. Nutrients 2021; 13: 3710, doi:10.3390/nu13113710.
• 11.   Yokoyama M, Watanabe T, Otaki Y, Watanabe K, Toshima T, Sugai T, et al. Impact of objective malnutrition status on the clinical outcomes in patients with peripheral artery disease following endovascular therapy. Circ J 2018; 82: 847–856, doi:10.1253/circj.CJ-17-0731.
• 12.   Fukino K, Ueshima D, Yamaguchi T, Mizuno A, Tobita K, Suzuki K, et al. Prognostic impact of reduced left ventricular ejection fraction after endovascular therapy for lower extremities. Circ J 2024; 88: 341–350, doi:10.1253/circj.CJ-23-0215.
• 13.   Soga Y, Takahara M, Yamauchi Y, Iida O, Fujihara M, Kawasaki D, et al. Efficacy of rheocarna, a novel apheresis device, in patients with no- or poor-option chronic limb-threatening ischemia. Circ J 2025; 89: 557–565, doi:10.1253/circj.CJ-24-0830.
• 14.   Natsuaki M, Morimoto T, Yamaji K, Watanabe H, Yoshikawa Y, Shiomi H, et al. Prediction of thrombotic and bleeding events after percutaneous coronary intervention: CREDO-Kyoto thrombotic and bleeding risk scores. J Am Heart Assoc 2018; 7: e008708, doi:10.1161/JAHA.118.008708.
• 15.   Tokuda T, Yoshioka N, Koyama A, Yamada T, Shimamura K, Nishikawa R. Chronic limb-threatening ischemia is a residual bleeding risk factor among patients with lower extremity artery disease. J Atheroscler Thromb 2024; 31: 100–108, doi:10.5551/jat.6424.
• 16.   Nakanishi N, Kaikita K, Ishii M, Kuyama N, Tabata N, Ito M, et al. Malnutrition-associated high bleeding risk with low thrombogenicity in patients undergoing percutaneous coronary intervention. Nutr Metab Cardiovasc Dis 2022; 32: 1227–1235, doi:10.1016/j.numecd.2022.01.016.
• 17.   Mehran R, Rao SV, Bhatt DL, Gibson CM, Caixeta A, Eikelboom J, et al. Standardized bleeding definitions for cardiovascular clinical trials: A consensus report from the Bleeding Academic Research Consortium. Circulation 2011; 123: 2736–2747, doi:10.1161/CIRCULATIONAHA.110.009449.
• 18.   Noda A, Doi S, Kuwata S, Shiokawa N, Suzuki N, Kanamitsu Y, et al. Preprocedural controlling nutritional status score as a predictor of mortality in patients undergoing transcatheter mitral valve repair: A single center experience in Japan. Circ Rep 2023; 5: 442–449, doi:10.1253/circrep.CR-23-0055.
• 19.   Ridker PM, Cushman M, Stampfer MJ, Tracy RP, Hennekens CH. Inflammation, aspirin, and the risk of cardiovascular disease in apparently healthy men. N Engl J Med 1997; 336: 973–979, doi:10.1056/NEJM199704033361401.
• 20.   Tokuda K, Tanaka A, Tobe A, Shirai Y, Kurobe M, Kubota Y, et al. Impact of C-reactive protein on long-term cardiac events in stable coronary artery disease patients with chronic kidney disease. J Atheroscler Thromb 2023; 30: 1635–1643, doi:10.5551/jat.64047.
• 21.   Takahara M, Iida O, Soga Y, Azuma N, Nanto S. clinical impact of measures for frailty severity in poor-risk patients undergoing revascularization for chronic limb-threatening ischemia. J Atheroscler Thromb 2022; 29: 221–228, doi:10.5551/jat.61481.
• 22.   Ogawa M, Okamura M, Yagi T, Maekawa K, Amakasu K, Inoue T, et al. Oral health and cardiovascular disease: A scoping review of assessment methods, risk factors, and prognosis. Circ Rep 2025; 7: 223–230, doi:10.1253/circrep.CR-24-0187.
• 23.   Cederholm T, Jensen GL, Correia MITD, Gonzalez MC, Fukushima R, Higashiguchi T, et al. GLIM criteria for the diagnosis of malnutrition: A consensus report from the global clinical nutrition community. Clin Nutr 2019; 38: 1–9, doi:10.1016/j.clnu.2018.08.002.
• 24.   Tatami Y, Ishii H, Aoki T, Harada K, Hirayama K, Shibata Y, et al. Decreased serum albumin predicts bleeding events in patients on antiplatelet therapy after percutaneous coronary intervention. Circ J 2017; 81: 999–1005, doi:10.1253/circj.CJ-17-0015.
• 25.   Kaysen GA, Don BR. Factors that affect albumin concentration in dialysis patients and their relationship to vascular disease. Kidney Int Suppl 2003; 63: S94–S97, doi:10.1046/j.1523-1755.63.s84.20.x.
• 26.   Akirov A, Masri-Iraqi H, Atamna A, Shimon I. Low albumin levels are associated with mortality risk in hospitalized patients. Am J Med 2017; 130: 1465.e11–1465.e19, doi:10.1016/j.amjmed.2017.07.020.
• 27.   Yamazaki T, Goto S, Shigematsu H, Shimada K, Uchiyama S, Nagai R, et al. Prevalence, awareness and treatment of cardiovascular risk factors in patients at high risk of atherothrombosis in Japan. Circ J 2007; 71: 995–1003, doi:10.1253/circj.71.995.
• 28.   Takahara M, Iida O, Soga Y, Azuma N, Nanto S. Clinical impact of measures for frailty severity in poor-risk patients undergoing revascularization for chronic limb threatening ischemia. J Atheroscler Thromb 2022; 29: 221–228, doi:10.5551/jat.61481.
• 29.   Carr AC, Maggini S. Vitamin C and immune function. Nutrients 2017; 9: 1211, doi:10.3390/nu9111211.
• 30.   Shearer MJ, Fu X, Booth SL. Vitamin K nutrition, metabolism, and requirements: Current concepts and future research. Adv Nutr 2012; 3: 182–195, doi:10.3945/an.111.001800.
• 31.   Kobayashi S. Cardiovascular events in chronic kidney disease (CKD): An importance of vascular calcification and microcirculatory impairment. Ren Replace Ther 2016; 2: 55, doi:10.1186/s41100-016-0062-y.
• 32.   Sigrist MK, McIntyre CW. Vascular calcification is associated with impaired microcirculatory function in chronic haemodialysis patients. Nephron Clin Pract 2008; 108: c121–c126, doi:10.1159/000114202.
• 33.   Ohtake T, Mitomo A, Yamano M, Shimizu T, Mochida Y, Ishioka K, et al. Impact of arterial calcification of the lower limbs on long-term clinical outcomes in patients on hemodialysis. J Clin Med 2023; 12: 1299, doi:10.3390/jcm12041299.
• 34.   Yamamoto K, Yamamoto E, Morimoto T, Shiomi H, Domei T, Taniguchi R, et al. Long-term effects of proton pump inhibitors in patients undergoing percutaneous coronary intervention in high-risk subgroups. Circ J 2024; 88: 1778–1787, doi:10.1253/circj.CJ-24-0371.
• 35.   Sun L, Clarke R, Bennett D, Guo Y, Walters RG, Hill M, et al. Causal associations of blood lipids with risk of ischemic stroke and intracerebral hemorrhage in Chinese adults. Nat Med 2019; 25: 569–574, doi:101038/s41591-019-0366-x.
• 36.   Falcone GJ, Kirsch E, Acosta JN, Noche RB, Leasure A, Marini S, et al. Genetically elevated LDL associates with lower risk of intracerebral hemorrhage. Ann Neurol 2020; 88: 56–66, doi:10.1002/ana.25740.
• 37.   Miyata H, Matsumura K, Takase T, Sugimoto K, Funauchi Y, Yagi E, et al. Clinical importance of protein intake in hospitalized elderly patients with heart failure. Circ Rep 2024; 7: 47–54, doi:10.1253/circrep.CR-24-0067.