Abstract
Background:
Peripheral artery disease (PAD) is an athero-occlusive disease and a known risk factor for cardiovascular events. The controlling nutritional status (CONUT) score and geriatric nutritional risk index (GNRI) are objective tools for evaluating malnutrition and are reportedly associated with poor clinical outcomes in patients with fatal diseases. However, the effect of malnutrition on the clinical outcomes in patients with PAD remains unclear.
Methods and Results:
We enrolled 357 patients with PAD who underwent endovascular therapy. Malnutrition was diagnosed by CONUT score and GNRI as in previous reports. During a median follow-up period of 1,071 days, there were 67 major adverse cardiovascular and leg events (MACLEs). The CONUT score- and GNRI-based malnutrition statuses were identified in 56% and 46% of the patients, respectively. Proportion of malnutrition increased with advancing Fontaine class. The multivariate Cox proportional hazard regression analysis demonstrated that both the CONUT score- and GNRI-based malnutrition status was an independent predictor of MACLEs. The Kaplan-Meier analysis demonstrated that the MACLE ratio increased with deteriorating malnutrition. Finally, the addition of the CONUT score or GNRI to the known risk factors significantly improved the net reclassification index and integrated discrimination index.
Conclusions:
Malnutrition was common and closely associated with the clinical outcomes in patients with PAD, indicating that it is a novel therapeutic target in the management of these patients.
Peripheral artery disease (PAD) is a part of the systemic atherothrombosis that is highly concomitant with cerebrocardiovascular diseases.1
Despite advancing medical therapy and endovascular therapy (EVT), PAD remains an important condition resulting in poor patient quality of life and high all-cause and cardiovascular mortality rates.2
PAD is associated with muscle atrophy of the lower extremity and malnutrition.3,4
Malnutrition is reported to be associated with a poor prognosis in various diseases,5,6
and is being increasingly recognized as a therapeutic target. However, the clinical significance of malnutrition in patients with PAD has not yet been fully elucidated.
The objective nutritional status of patients is noted in a broad spectrum of fatal diseases. Two objective indexes, the controlling nutritional status (CONUT) score and geriatric nutritional risk index (GNRI), are widely used for malnutrition. The CONUT score involves protein reserve depletion, caloric depletion, and impaired immune defenses. Conversely, GNRI is calculated using the serum albumin level and body weight. These are reportedly associated with poor clinical outcomes in patients with chronic heart failure and cancer.7,8
Because nutrition contributes to immune defense, we hypothesized that the CONUT score is potentially the preferred tool for assessing the nutritional status of patients with PAD who are at a high risk for lower limb infection.
The reduction of skeletal muscle (i.e., sarcopenia) is known to be a prognostic factor in various diseases.9,10
A previous report indicated a close relationship between nutrition and sarcopenia,11
but to date there has not been a report on the association between malnutrition and sarcopenia in patients with PAD.
The aim of the present study was to reveal the clinical significance of malnutrition in patients with PAD. To clarify this, we focused on the CONUT score and examined (1) the prevalence of malnutrition, (2) effect of malnutrition on the clinical outcome, and (3) association between malnutrition and sarcopenia in patients with PAD.
Methods
Study Population
This was a prospective study of 357 patients who were admitted to hospital for first PAD treatment. PAD was diagnosed in accordance with the ankle-brachial index (ABI) and computed tomographic angiography findings. EVT was performed by experienced cardiologists according to the recommendations of the Trans-Atlantic Inter-Society Consensus II (TASC II) guideline.12
Optimized medical therapy was independently administered by the physicians on the basis of symptom improvement. The exclusion criteria were acute coronary syndrome within 3 months preceding admission and malignant disease. Demographic and clinical data, including age, sex, smoking history, cardiovascular risk factors, ABI, and medications, were collected from the patients’ medical records and interviews.
Ethics Statement
The study protocol was approved by the Ethics Committee of Yamagata University School of Medicine (no. 395), and all participants provided written informed consent. All procedures were performed in accordance with the Helsinki Declaration principles.
Measurements
Hypertension was defined as a systolic blood pressure ≥140 mmHg, diastolic blood pressure ≥90 mmHg, or antihypertensive medication use. Hyperlipidemia was defined as a total cholesterol level ≥220 mg/dL, triglyceride level ≥150 mg/dL, or antihyperlipidemic medication use. Diabetes mellitus was defined as a fasting blood sugar level ≥126 mg/dL and glycosylated hemoglobin A1c (HbA1c) level ≥6.5% (National Glycohemoglobin Standardization Program) or antidiabetic medication use.
Biochemical Markers
Blood samples were obtained early in the morning before the first EVT. Serum albumin, total cholesterol, high-sensitivity C-reactive protein (hsCRP), blood lymphocytes, HbA1c, and fasting blood sugar levels were measured.
Because patients with PAD have reduced muscles from both decreased physical activity and amputation, their serum creatinine levels tend to be underestimated. Therefore, we measured the cystatin C-based estimated glomerular filtration rate (eGFRcys) and creatinine-based eGFR (eGFRcr).13–15
eGFRcr and eGFRcys were calculated as follows: eGFRcr in men=194×sCr−1.094×age−0.287; eGFRcr in women=194×sCr−1.094×age−0.287×0.739.
eGFRcys in men=104×cystatin C−1.019×0.996age−8; eGFRcys in women=(104×cystatin C−1.019×0.996age×0.929)−8.
Severity of PAD
The severity of PAD was determined by Fontaine class according to the TASC II guideline.16
Briefly, Fontaine classes II, III, and IV were defined as intermittent claudication, rest pain, and critical limb ischemia (CLI), respectively.
Nutritional Status
The CONUT score includes the serum albumin and total cholesterol levels and total lymphocyte count.17
The serum albumin level is used as an indicator of protein reserves, the serum total cholesterol level is used as a parameter of caloric depletion and the total lymphocyte count is used as an indicator of impaired immune defense because of malnutrition. Patients with CONUT scores of 0–1 have a normal nutritional status; those with a score of 2–4 are at a mild risk; a score of 5–8 indicates moderate risk; and a score of 9–12 show a severe risk of malnutrition. GNRI was also calculated using the following equation: GNRI=14.89×serum albumin level in g/dL+41.7×(body weight in kg/ideal body weight). Patients with a GNRI >98 have a normal nutritional status; GNRI of 92–98 is a mild risk; and GNRI <92 indicates moderate to severe risk of malnutrition.18
Evaluation of Muscle Sarcopenia
Fat-free mass (FFM), a marker for sarcopenia, was estimated using the following equation: FFM (kg)=7.38+0.02908×urinary creatinine level. FFM was normalized by the square of the patient’s height in meters to obtain the FFM index (FFMI).9
Sarcopenia was defined as an FFMI ≤17 kg/m2
in men and ≤15 kg/m2
in women as previously described.19
Endpoint and Follow-up
All participants were prospectively followed up using telephone interviews or medical records twice yearly for a median period of 1,071 days (interquartile range (IQR), 367–1,368 days; longest follow-up, 1,600 days). The endpoint was major adverse cardiovascular and leg events (MACLEs), including all-cause and cardiovascular deaths; rehospitalization for cardiovascular diseases, such as non-fatal acute myocardial infarction, unstable angina, heart failure, cardioembolic stroke, or for re-intervention; and development of CLI and subsequent amputation.
Statistical Analysis
Continuous data are expressed as mean±standard deviation, and skewed data are expressed as median with IQR. Continuous and categorical variables were compared using t-tests and chi-square tests, respectively. Data that were not normally distributed were compared using the Mann-Whitney U-test. The differences among the 3 groups were analyzed using analysis of variance (ANOVA) with Tukey’s post hoc test. A Cox proportional hazard analysis was performed to identify the independent predictors for MACLEs. A multivariate analysis using a forward stepwise Cox proportional hazards model was performed to evaluate the independent predictors for combined MACLEs. Survival curves were constructed using the Kaplan-Meier method and compared using the Wilcoxon test. P<0.05 was considered statistically significant. We calculated the net reclassification index (NRI) and integrated discrimination index (IDI) to measure the quality of improvement for correct reclassification following the addition of the CONUT score or GNRI to the model. All statistical analyses were performed using a standard program package (JMP version 12; SAS Institute Inc., Cary, NC, USA and R 3.2.4 with additional packages, including Rcmdr, Epi, pROC, and PredictABEL).
Results
Baseline Characteristics and Comparison of the Clinical Characteristics Between the Patients With PAD With and Without MACLEs
The patients’ baseline characteristics are shown in
Table 1. There were 261 (73%) patients classified as Fontaine class II, 42 (12%) as Fontaine class III, and 54 (15%) as Fontaine class IV. Hypertension, diabetes mellitus, and hyperlipidemia were identified in 280 (78%), 174 (49%), and 194 (53%) patients, respectively. There were 95 (27%) current smokers and 68 (19%) hemodialysis patients. CONUT score-based malnutrition and GNRI-based malnutrition were identified in 199 (56%) and 162 (46%) patients, respectively.
Table 1.
Comparison of Clinical Characteristics of Patients With and Without MACLE
| Variable |
All patients
(n=357) |
MACLE (+)
(n=67) |
MACLE (−)
(n=290) |
P value |
| Age |
74±9 |
77±8 |
73±9 |
0.007 |
| M/F |
287/70 |
52/15 |
235/55 |
0.525 |
| BMI, kg/m2 |
22.2±3.4 |
21.6±3.7 |
22.3±3.3 |
0.140 |
| FFMI, kg/m2 |
17.9±1.7 |
17.4±2.1 |
18.0±1.5 |
0.021 |
| Sarcopenia, n (%) |
77 (22) |
22 (33) |
55 (19) |
0.013 |
| Hypertension, n (%) |
280 (78) |
50 (75) |
230 (79) |
0.401 |
| Hyperlipidemia, n (%) |
194 (54) |
27 (40) |
167 (58) |
0.011 |
| DM, n (%) |
174 (49) |
42 (63) |
132 (46) |
0.011 |
| Current smoking, n (%) |
95 (27) |
20 (29) |
75 (26) |
0.506 |
| Previous IHD, n (%) |
116 (32) |
31 (46) |
85 (29) |
0.008 |
| Previous cerebrovascular disease |
68 (19) |
13 (19) |
52 (18) |
0.778 |
| HD, n (%) |
68 (19) |
20 (29) |
48 (17) |
0.013 |
| Fontaine II/III/IV, n (%) |
261/42/54 (73/12/15) |
33/10/24 (49/15/35) |
228/32/30 (79/11/10) |
<0.001 |
| EVT data |
| Iliac artery, n (%) |
223 (62) |
28 (42) |
195 (67) |
<0.001 |
| Femoropopliteal artery, n (%) |
205 (57) |
42 (63) |
163 (56) |
0.334 |
| Tibial or peroneal artery, n (%) |
67 (19) |
24 (36) |
43 (15) |
<0.001 |
| Stent, n (%) |
316 (89) |
52 (78) |
264 (91) |
0.002 |
| Pre-ABI |
0.59±0.16 |
0.55±0.18 |
0.60±0.16 |
0.097 |
| Post-ABI |
0.88±0.18 |
0.82±0.18 |
0.89±0.18 |
0.038 |
| Biochemical data |
| Serum albumin (mg/dL) |
3.7±0.5 |
3.3±0.6 |
3.8±0.5 |
<0.001 |
| Total cholesterol (mg/dL) |
172±40 |
172±40 |
159±35 |
0.003 |
| Lymphocyte count (103/mL) |
1.6±0.6 |
1.5±0.6 |
1.7±0.6 |
0.004 |
| Creatinine (mg/dL) |
2.1±2.8 |
2.8±3.2 |
2.0±2.6 |
0.027 |
| Cystatin C (mg/L) |
2.2±2.1 |
2.7±2.2 |
2.0±2.0 |
0.023 |
| eGFRcr (mL/min/1.73 m2) |
57.8±36.5 |
52.4±53.8 |
59.1±31.2 |
0.177 |
| eGFRcys (mL/min/1.73 m2) |
50.8±28.9 |
38.1±25.3 |
53.8±28.9 |
<0.001 |
| hsCRP (pg/mL) |
0.108 (0.043–0.427) |
0.528 (0.089–1.030) |
0.095 (0.041–0.248) |
<0.001 |
| HbA1c (%) |
6.1±1.0 |
6.1±1.2 |
6.1±1.0 |
0.650 |
| Fasting blood sugar (mg/dL) |
120±42 |
127±50 |
118±39 |
0.101 |
CONUT score: normal/mild/moderate to
severe, n (%) |
158/155/44 (44/44/12) |
15/29/23 (23/43/34) |
143/126/21 (49/44/7) |
<0.001 |
GNRI: normal/mild/moderate to severe,
n (%) |
193/71/93 (54/20/26) |
18/14/35 (27/21/52) |
175/57/58 (60/20/20) |
<0.001 |
| Medications |
| Aspirin, n (%) |
251 (70) |
50 (75) |
201 (69) |
0.391 |
| Clopidogrel, n (%) |
225 (63) |
37 (55) |
188 (65) |
0.142 |
| Cilostazol, n (%) |
114 (32) |
25 (37) |
89 (31) |
0.295 |
| Other APDs, n (%) |
62 (17) |
14 (21) |
48 (17) |
0.398 |
| ACEIs and/or ARBs, n (%) |
212 (59) |
40 (59) |
172 (59) |
0.953 |
| CCBs, n (%) |
197 (55) |
32 (47) |
165 (57) |
0.176 |
| Statins, n (%) |
179 (50) |
25 (37) |
154 (53) |
0.020 |
Data are expressed as mean±SD, number (percentage), or median (interquartile range). ABI, ankle-brachial index; ACEI, angiotensin-converting enzyme inhibitor; APD, antiplatelet drug; ARB, angiotensin II recepter blocker; BMI, body mass index; CCB, calcium channel blocker; CONUT, controlling nutritional status; DM, diabetes mellitus; eGFR, estimated glomerular filtration; EVT, endovascular therapy; FFMI, fat-free mass index; GNRI, geriatric nutritional risk index; HD, hemodialysis; hsCRP, high-sensitivity C-reactive protein; IHD, ischemic heart disease.
During the follow-up period, there were 67 MACLEs, including 10 all-cause deaths, 5 patients rehospitalized for stroke, 17 heart failure cases, 12 acute coronary syndrome cases, and 23 cases of CLI and amputation. Although the success rate of 1st EVT was 95%, it was 89% even in CLI patients. There were 6 cases of failure among the CLI patients. Second EVT was performed in 2 patients; 2 patients died before 2nd EVT and remaining 2 patients underwent amputation. The patients with MACLEs had a higher prevalence rate of CONUT score- and GNRI-based malnutrition than those without. Further, the patients with MACLEs were older and had lower body mass index (BMI), FFMI, and pre- and post-ABI and more severe Fontaine class than those without. The patients with MACLEs also had a higher prevalence of sarcopenia, diabetes mellitus, previous ischemic heart disease (IHD), hemodialysis, and tibial or peroneal artery occlusion and lower prevalence of iliac artery occlusion than those without. The serum levels of cystatin C and hsCRP were higher, and the serum albumin and total cholesterol levels, total lymphocyte count, and eGFRcys were lower in the patients with MACLEs than in those without. There were no significant differences in sex, BMI, prevalence of hypertension, hyperlipidemia, current smoking, previous cerebrovascular disease, and femoropopliteal artery occlusion, or serum levels of creatinine, HbA1c, or fasting blood sugar between the patients with and without MACLEs.
Comparisons of the Clinical Characteristics Between the Patients With Normal Nutrition and Mild and Moderate to Severe Malnutrition Based on the CONUT Score
We divided all patients into 3 groups according to their nutritional status according to the CONUT score. The patients with moderate to severe malnutrition were older and had lower BMI and FFMI and more severe Fontaine class than those with normal nutrition or mild malnutrition (Table 2). The patients with moderate to severe malnutrition had a higher prevalence of sarcopenia, hemodialysis, and tibial or peroneal artery occlusion and lower prevalence of current smoking, iliac artery occlusion, and GNRI-based moderate to severe malnutrition than those with other malnutrition statuses. The serum levels of creatinine, cystatin C, and hsCRP increased, and the eGFRcys decreased with deteriorating malnutrition. There were no significant differences in sex, prevalence of hypertension, hyperlipidemia, diabetes mellitus, previous IHD, previous cerebrovascular disease, and femoropopliteal artery occlusion, pre- and post- ABI, serum levels of HbA1c or fasting blood sugar among the 3 groups.
Table 2.
Comparison of Clinical Characteristics of Patients With Normal Nutrition or Mild and Moderate to Severe Malnutrition Based on the CONUT Score
| Variable |
Normal
(n=158) |
Mild
(n=155) |
Moderate to severe
(n=44) |
| Age |
73±9 |
74±9 |
77±10* |
| M/F |
130/28 |
124/31 |
33/11 |
| BMI, kg/m2 |
22.2±3.2 |
22.6±3.5 |
20.9±3.3*,# |
| FFMI, kg/m2 |
17.9±1.5 |
17.9±1.8 |
17.2±1.7*,# |
| Sarcopenia, n (%) |
29 (18) |
31 (20) |
18 (41)*,# |
| Hypertension, n (%) |
133 (84) |
116 (75) |
31 (71) |
| Hyperlipidemia, n (%) |
99 (63) |
79 (51) |
16 (36) |
| DM, n (%) |
69 (44) |
83 (54) |
22 (50) |
| Current smoking, n (%) |
79 (50) |
8 (5)* |
8 (18)† |
| Previous IHD, n (%) |
44 (28) |
57 (37) |
15 (34) |
| Previous cerebrovascular disease |
30 (19) |
28 (18) |
7 (16) |
| HD, n (%) |
13 (8) |
41 (26) |
14 (32)† |
| Fontaine II/III/IV |
123/26/9 |
122/14/19 |
16/2/26† |
| EVT data |
| Iliac artery, n (%) |
109 (69) |
95 (61) |
19 (43)† |
| Femoropopliteal artery, n (%) |
88 (56) |
93 (60) |
24 (55) |
| Tibial or peroneal artery, n (%) |
23 (15) |
25 (16) |
19 (43)† |
| Stent, n (%) |
146 (92) |
140 (90) |
30 (68) |
| Pre-ABI |
0.58±0.15 |
0.61±0.17 |
0.56±0.19 |
| Post-ABI |
0.90±0.18 |
0.87±0.18 |
0.81±0.15 |
| Biochemical data |
| Serum albumin (mg/dL) |
4.0±0.3 |
3.7±0.4* |
2.8±0.4*,# |
| Total cholesterol (mg/dL) |
191±38 |
161±31* |
145±48*,# |
| Lymphocyte count (103/mL) |
2.1±0.6 |
1.4±0.5* |
1.2±0.5* |
| Creatinine (mg/dL) |
1.5±1.9 |
2.7±3.4* |
2.5±2.6* |
| Cystatin C (mg/L) |
1.6±1.4 |
2.5±2.4* |
2.9±2.3* |
| eGFRcr (mL/min/1.73 m2) |
58.1±64.0 |
54.3±35.0 |
61.2±26.0 |
| eGFRcys (mL/min/1.73 m2) |
56.3±24.5 |
49.0±31.7* |
37.6±28.6*,# |
| hsCRP (pg/mL) |
0.090 (0.039–0.220) |
0.101 (0.040–0.346)* |
1.020 (0.302–1.03)*,# |
| HbA1c (%) |
6.1±1.1 |
6.1±1.0 |
6.1±1.2 |
| Fasting blood sugar (mg/dL) |
114±35 |
124±48 |
122±36 |
| GNRI: normal/mild/moderate to severe, n (%) |
114/31/13 (72/20/8) |
78/37/40 (50/24/26) |
1/3/40 (2/7/91)† |
| MACLE |
15 (9) |
29 (19) |
23 (51)† |
| Medications |
| Aspirin, n (%) |
113 (72) |
110 (71) |
28 (64) |
| Clopidogrel, n (%) |
99 (63) |
99 (64) |
27 (61) |
| Cilostazol, n (%) |
48 (30) |
53 (34) |
13 (30) |
| Other APDs, n (%) |
26 (16) |
27 (17) |
9 (20) |
| ACEIs and/or ARBs, n (%) |
99 (63) |
93 (60) |
20 (45) |
| CCBs, n (%) |
91 (58) |
90 (58) |
16 (36)† |
| Statins, n (%) |
79 (55) |
87 (51) |
13 (30)† |
Data are expressed as mean±SD, number (percentage), or median (interquartile range). *P<0.05 vs. normal group, #P<0.05 vs. mild group, †P<0.05 by chi-square test. MACLE, major adverse cardiovascular and leg event. Other abbreviaions as in Table 1.
As shown in
Figure 1, almost all patients with CONUT score-based moderate to severe malnutrition had GNRI-based moderate to severe malnutrition. However, the patients with GNRI-based moderate to severe malnutrition did not necessarily have CONUT score-based malnutrition. The MACLE ratio in the patients with CONUT score-based moderate to severe malnutrition was 51% (Table 2). Conversely, the MACLE ratio in the patients with GNRI-based moderate to severe malnutrition was 38% (unpublished data).
Association of the CONUT Score With PAD Severity
As shown in
Figure 2, the serum albumin level, total cholesterol level, blood lymphocyte count, and FFMI decreased with advancing Fontaine class.
Figure 3
demonstrates that the proportions of moderate to severe malnutrition based on both the CONUT score and GNRI were significantly higher in Fontaine class IV than in Fontaine classes II and III.
Malnutrition and Clinical Outcomes
To examine the effect of malnutrition on the clinical outcomes in patients with PAD, we performed univariate and multivariate Cox proportional hazard regression analyses. The univariate Cox proportional hazard regression analysis demonstrated that age, BMI, FFMI, hypertension, hyperlipidemia, diabetes mellitus, previous IHD, hemodialysis, CLI, eGFRcys, hsCRP level, CONUT score, and GNRI were significantly associated with MACLEs (Table 3). The multivariate Cox proportional hazard regression analysis demonstrated that the CONUT score was an independent predictor for MACLEs after adjusting for age, FFMI, hyperlipidemia, previous IHD, CLI, eGFRcys, and hsCRP level (hazard ratio, 4.351; 95% confidence interval [CI], 1.563–12.113; P=0.005;
Table 3). When the CONUT score was substituted for the GNRI, GNRI also became an independent predictor for MACLEs in the patients with PAD after adjusting for the same factors (hazard ratio, 2.855; 95% CI, 1.145–7.15; P=0.024;
Table 3). Similarly, both CONUT score and GNRI independently predicted MACEs in the multivariate analyses (Table 3).
Table 3.
Univariate and Multivariate Cox Proportional Hazard Analysis of Predicting MACLE and Multivariate Cox Proportional Hazard Analysis of Predicting MACE in Patients With PAD
| Variable |
Hazard ratio |
95% CI |
P value |
| Univariate analysis |
| Age |
1.037 |
1.005–1.071 |
0.023 |
| Sex |
1.176 |
0.618–2.237 |
0.620 |
| BMI* |
0.718 |
0.546–0.947 |
0.019 |
| FFMI* |
0.777 |
0.655–0.920 |
0.004 |
| Hypertension |
0.552 |
0.306–0.995 |
0.048 |
| Hyperlipidemia |
0.466 |
0.272–0.797 |
0.005 |
| DM |
2.311 |
1.337–3.994 |
0.003 |
| Previous IHD |
2.100 |
1.228–3.594 |
0.007 |
| Previous cerebrovascular disease |
0.933 |
0.468–1.862 |
0.845 |
| HD |
2.041 |
1.114–3.739 |
0.021 |
| CLI |
5.074 |
2.721–9.463 |
<0.001 |
| eGFRcys* |
0.592 |
0.453–0.770 |
0.002 |
| hsCRP* |
2.261 |
1.752–2.900 |
<0.001 |
| CONUT |
| Moderate to severe vs. normal |
12.819 |
5.802–28.324 |
<0.001 |
| Mild vs. normal |
1.872 |
0.965–3.633 |
0.637 |
| GNRI |
| Moderate to severe vs. normal |
5.463 |
2.901–10.287 |
<0.001 |
| Mild vs. normal |
1.940 |
0.886–4.245 |
0.097 |
| Multivariate analysis for MACLE |
| CONUT |
| Moderate to severe vs. normal# |
4.351 |
1.563–12.113 |
0.005 |
| Mild vs. normal |
1.120 |
0.528–2.377 |
0.767 |
| GNRI |
| Moderate to severe vs. normal# |
2.855 |
1.145–7.115 |
0.024 |
| Mild vs. normal |
1.775 |
0.728–4.329 |
0.207 |
| Multivariate analysis for MACE |
| CONUT |
| Moderate to severe vs. normal$ |
3.084 |
1.020–9.320 |
0.046 |
| Mild vs. normal |
1.140 |
0.516–2.521 |
0.746 |
| GNRI |
| Moderate to severe vs. normal$ |
2.757 |
1.628–6.653 |
0.024 |
| Mild vs. normal |
1.628 |
0.630–4.206 |
0.314 |
*Per 1-SD increase. #Hazard ratios were adjusted for age, FFMI, hyperlipidemia, previous IHD, CLI, eGFRcys, and hsCRP. $Hazard ratios were adjusted for age, previous IHD, CLI, eGFRcys, and hsCRP. CI, confidence interval; CLI, critical limb ischemia; MACE, major adverse cardiac event; PAD, peripheral artery disease. Other abbreviaions as in Tables 1,2.
Importantly, the hazard ratio for the CONUT score was higher than that for the GNRI of the patients with PAD. The Kaplan-Meier analysis showed that the moderate to severe malnutrition group had the highest MACLE and MACE ratio among the groups (Figure 4).
Improvement of Reclassification by Addition of the CONUT Score or GNRI to Predict MACLEs
We evaluated the improvement in the NRI and IDI to examine whether the model fit and discrimination improved with the addition of the CONUT score or GNRI to the basic predictors such as age, BMI, diabetes mellitus, previous IHD, hemodialysis, and Fontaine class. The NRI and IDI significantly improved following the addition of the CONUT score or GNRI to the basic predictors (Table 4).
Table 4.
Statistics for Model Fit and Improvement With the Addition of Malnutrition on the Prediction of MACLE
| |
NRI (95% CI, P value) |
IDI (95% CI, P value) |
| Baseline model |
Reference |
Reference |
| +CONUT score |
0.4539 (0.1928–0.7150, P=0.0007) |
0.0398 (0.0133–0.0663, P=0.0032) |
| +GNRI |
0.5433 (0.2825–0.8040, P<0.0001) |
0.0628 (0.0323–0.0933, P<0.0001) |
Baseline model included age, FFMI, DM, previous IHD, HD, Fontaine class. IDI, integrated discrimination index; NRI, net reclassification index. Other abbreviaions as in Tables 1–3.
To examine the association between malnutrition and sarcopenia, we divided the patients into 4 groups according to the CONUT score-based malnutrition and sarcopenia. There were 49 (14%) patients with both malnutrition and sarcopenia and they had the highest hazard ratio after adjusting for age, FFMI, hyperlipidemia, previous IHD, CLI, eGFRcys, and hsCRP level (hazard ratio, 3.257; 95% CI, 1.031–10.292;
Figure S1). On the other hand, multivariate logistic analysis demonstrated that sarcopenia was not significantly associated with MACLEs after adjustment for malnutrition, suggesting that comorbid sarcopenia and malnutrition, but not sarcopenia itself, led to the poor clinical outcome.
Discussion
The present study elucidated the following points: (1) almost half of the patients with PAD had malnutrition; (2) the proportion of moderate to severe malnutrition was significantly higher in the patients classified as Fontaine class IV than in those in Fontaine classes II and III; (3) malnutrition, but not sarcopenia, was an independent predictor for MACLEs in patients with PAD after adjusting for confounding factors; (4) the hazard ratio for the CONUT score was high compared with that for the GNRI; (5) the prediction model with malnutrition improved the prognostic capacity for patients with PAD; and (6) there were 14% of patients with PAD with malnutrition and sarcopenia, who had the highest risk for MACLEs.
The prevalence rate of malnutrition in patients with heart failure and cancer has been reported as 60–70% and 42%, respectively.7,20
Similarly, approximately half of the present patients with PAD had a nutritional disorder, based on the CONUT score and GNRI, indicating that malnutrition is a common complication in patients with PAD. The prevalence of moderate to severe malnutrition increased with advancing Fontaine class. Notably, all components of the CONUT score decreased with advancing Fontaine class, indicating the association of malnutrition with PAD severity.
It is known that atherosclerosis is a systemic inflammatory disease.21
Inflammatory markers, such as hsCRP, are useful for predicting cardiovascular risks in patients with coronary artery disease. Rein et al22
reported that the incidence of systemic inflammation is higher in patients with PAD than in those with coronary artery disease. It was thought that PAD is not a simple atherosclerotic disease of the peripheral arteries but a part of a polyvascular disease.3
Systemic inflammation via increased cytokines, oxidative stress, and inflammatory cell accumulation causes malnutrition by affecting appetite and resting energy expenditure and increasing protein hydrolysis and muscle protein breakdown.21,23,24
Conversely, malnutrition reportedly promotes atherosclerosis.25
This vicious cycle is called the malnutrition-inflammation-atherosclerosis syndrome.23
In the present study, malnutrition was closely associated with systemic inflammation and PAD severity. Because malnutrition is associated with delayed wound healing and susceptibility to infection,26
patients with PAD and malnutrition are likely to experience amputation and sepsis.
A retrospective study showed that GNRI at admission was closely associated with death and amputation in patients with CLI.27
In accordance with that report, GNRI was also independent predictor for MACLE in the present study. Additionally, the NRI and IDI were increased by the addition of CONUT or GNRI, so malnutrition improved the prognostic capacity to predict MACLEs. However, GNRI-based moderate to severe malnutrition showed a relatively low MACLE rate and hazard ratio compared with CONUT score-based malnutrition. These malnutrition statuses did not necessarily overlap in the patients with PAD. In contrast to the GNRI, the CONUT score includes the lymphocyte count and total cholesterol level, which reflect immune function and caloric depletion, respectively. It is postulated that these are crucial in repairing CLI and for energy production in the lower limb, which may explain the difference in prognostic capacity in patients with PAD. Taking these facts into consideration, the CONUT score is the preferred tool for assessing the nutritional status of patients with PAD.
It has been reported that malnutrition and sarcopenia are often present in parallel in individuals,28
because they have common factors, such as aging and decreased nutrient intake, body weight, muscle mass, and physical function.29
Only 14% of the patients in the present study had malnutrition and sarcopenia. A previous report demonstrated that sarcopenia was a risk factor for poor clinical outcomes in patients with PAD and CLI.30
Unexpectedly, sarcopenia was not associated with MACLEs in our multivariate Cox proportional hazard regression analysis. Although patients with comorbid malnutrition and sarcopenia had poor clinical outcomes, we could not reveal the usefulness of the combined measurement in the patients with PAD. One explanation may be that sarcopenia develops as a sequel to malnutrition.
Patients with PAD are reported to have less dietary intake of antioxidants such as vitamins A, C, and E and omega-3 fatty acid.4,31
Eicosapentaenoic acid, one of the omega-3 fatty acids, improves rest pain and CLI in patients with PAD. On the other hand, the relationship between statin use and malnutrition is under discussion in elderly patients with acute myocardial infarction.32
In the present study, patients who took statins had better nutritional status, excluding total cholesterol and favorable clinical outcome, than those who did not (Figure S2). Recent study demonstrated the protective role of statins in patients with PAD.33,34
Although similar results were found in the present study, statins may be prescribed for patients who had a favorable nutritional status. Further study is needed to reveal the beneficial role of statins on the nutritional status.
A limitation of the present study was the lack of the data regarding the serial changes in nutritional status, so we could not determine any effect of this on the clinical outcome in patients with PAD.
Although the effectiveness of nutrition therapy for patients with PAD remains unclear, we showed that malnutrition is common and a risk factor for poor clinical outcomes in patients with PAD, suggesting that it could be added as a prognostic factor to the established risk factors and be a novel therapeutic target in the management of patients with PAD.
Conclusions
Malnutrition was observed in half of a cohort of PAD patients and its prevalence reached greater than 80% in patients with CLI. The comorbidity of malnutrition and sarcopenia was higher in CLI patients than in those with claudication, which led to a poor prognosis in patients with PAD. However, sarcopenia could not fulfill the criteria for additional risk to malnutrition in the present study. Finally, malnutrition was closely associated with severity and clinical outcome in patients with PAD, indicating that it is a common complication requiring management and treatment in patients with PAD.
Conflict of Interest / Acknowledgements
None.
Supplementary Files
Supplementary File 1
Figure S1.
Hazard ratio for MACLEs among the 4 groups based on the presence of malnutrition and sarcopenia.
Figure S2.
Comparisons of serum albumin (A), total lymphocyte (B), total cholesterol (C), and geriatric nutritional risk index (GNRI,
D) between patients with and without statin treatment.
Please find supplementary file(s);
http://dx.doi.org/10.1253/circj.CJ-17-0731
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